hypothesis on air pollution

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• v.110(1); 2002 Jan

Air pollution and daily mortality: a hypothesis concerning the role of impaired homeostasis.

We propose a hypothesis to explain the association between daily fluctuations in ambient air pollution, especially airborne particles, and death rates that can be tested in an experimental model. The association between airborne particulates and mortality has been observed internationally across cities with differing sources of pollution, climates, and demographies and has involved chiefly individuals with advanced chronic illnesses and the elderly. As these individuals lose the capacity to maintain stable, optimal internal environments (i.e., as their homeostatic capacity declines), they become increasingly vulnerable to external stress. To model homeostatic capacity for predicting this vulnerability, a variety of regulated physiologic variables may be monitored prospectively. They include the maintenance of deep body temperature and heart rate, as well as the circadian oscillations around these set-points. Examples are provided of the disruptive changes shown by these variables in inbred mice as the animals approach death. We consider briefly the implications that the hypothesis may hold for several epidemiologic issues, including the degree of prematurity of the deaths, the unlikelihood of a threshold effect, and the role that coarse, noncombustive particles may play in the association.

The Full Text of this article is available as a PDF (719K).

Selected References

These references are in PubMed. This may not be the complete list of references from this article.

• HILL AB. THE ENVIRONMENT AND DISEASE: ASSOCIATION OR CAUSATION? Proc R Soc Med. 1965 May; 58 :295–300. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Susser M. What is a cause and how do we know one? A grammar for pragmatic epidemiology. Am J Epidemiol. 1991 Apr 1; 133 (7):635–648. [ PubMed ] [ Google Scholar ]
• Vedal S. Ambient particles and health: lines that divide. J Air Waste Manag Assoc. 1997 May; 47 (5):551–581. [ PubMed ] [ Google Scholar ]
• Kunst AE, Looman CW, Mackenbach JP. Outdoor air temperature and mortality in The Netherlands: a time-series analysis. Am J Epidemiol. 1993 Feb 1; 137 (3):331–341. [ PubMed ] [ Google Scholar ]
• Spix C, Heinrich J, Dockery D, Schwartz J, Völksch G, Schwinkowski K, Cöllen C, Wichmann HE. Air pollution and daily mortality in Erfurt, east Germany, 1980-1989. Environ Health Perspect. 1993 Nov; 101 (6):518–526. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Khaw KT. Temperature and cardiovascular mortality. Lancet. 1995 Feb 11; 345 (8946):337–338. [ PubMed ] [ Google Scholar ]
• Chamblee RF, Evans MC. New dimensions in cause of death statistics. Am J Public Health. 1982 Nov; 72 (11):1265–1270. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kircher T, Nelson J, Burdo H. The autopsy as a measure of accuracy of the death certificate. N Engl J Med. 1985 Nov 14; 313 (20):1263–1269. [ PubMed ] [ Google Scholar ]
• Schwartz J. Air pollution and daily mortality: a review and meta analysis. Environ Res. 1994 Jan; 64 (1):36–52. [ PubMed ] [ Google Scholar ]
• Lipfert FW, Wyzga RE. Air pollution and mortality: issues and uncertainties. J Air Waste Manag Assoc. 1995 Dec; 45 (12):949–966. [ PubMed ] [ Google Scholar ]
• Thurston GD. A critical review of PM10-mortality time-series studies. J Expo Anal Environ Epidemiol. 1996 Jan-Mar; 6 (1):3–21. [ PubMed ] [ Google Scholar ]
• Egolf DA. Equilibrium regained: from nonequilibrium chaos to statistical mechanics. Science. 2000 Jan 7; 287 (5450):101–104. [ PubMed ] [ Google Scholar ]
• Finch CE, Pike MC, Witten M. Slow mortality rate accelerations during aging in some animals approximate that of humans. Science. 1990 Aug 24; 249 (4971):902–905. [ PubMed ] [ Google Scholar ]
• Vaupel JW, Carey JR. Compositional interpretations of medfly mortality. Science. 1993 Jun 11; 260 (5114):1666–1667. [ PubMed ] [ Google Scholar ]
• STREHLER BL, MILDVAN AS. General theory of mortality and aging. Science. 1960 Jul 1; 132 (3418):14–21. [ PubMed ] [ Google Scholar ]
• Ware JH, Dockery DW, Louis TA, Xu XP, Ferris BG, Jr, Speizer FE. Longitudinal and cross-sectional estimates of pulmonary function decline in never-smoking adults. Am J Epidemiol. 1990 Oct; 132 (4):685–700. [ PubMed ] [ Google Scholar ]
• Piantanelli L, Rossolini G, Nisbet R. Modelling survivorship kinetics: a two-parameter model. Gerontology. 1992; 38 (1-2):30–40. [ PubMed ] [ Google Scholar ]
• Eakin T, Witten M. How square is the survival curve of a given species? Exp Gerontol. 1995 Jan-Feb; 30 (1):33–64. [ PubMed ] [ Google Scholar ]
• Gelman R, Watson A, Bronson R, Yunis E. Murine chromosomal regions correlated with longevity. Genetics. 1988 Apr; 118 (4):693–704. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Carey JR, Liedo P, Orozco D, Vaupel JW. Slowing of mortality rates at older ages in large medfly cohorts. Science. 1992 Oct 16; 258 (5081):457–461. [ PubMed ] [ Google Scholar ]
• Finch CE, Tanzi RE. Genetics of aging. Science. 1997 Oct 17; 278 (5337):407–411. [ PubMed ] [ Google Scholar ]
• Goldenfeld N, Kadanoff LP. Simple lessons from complexity . Science. 1999 Apr 2; 284 (5411):87–89. [ PubMed ] [ Google Scholar ]
• Goldberger AL, Rigney DR, West BJ. Chaos and fractals in human physiology. Sci Am. 1990 Feb; 262 (2):42–49. [ PubMed ] [ Google Scholar ]
• Lipsitz LA, Goldberger AL. Loss of 'complexity' and aging. Potential applications of fractals and chaos theory to senescence. JAMA. 1992 Apr 1; 267 (13):1806–1809. [ PubMed ] [ Google Scholar ]
• Lipfert FW. Air pollution and human health: perspectives for the '90s and beyond. Risk Anal. 1997 Apr; 17 (2):137–146. [ PubMed ] [ Google Scholar ]
• Daniels MJ, Dominici F, Samet JM, Zeger SL. Estimating particulate matter-mortality dose-response curves and threshold levels: an analysis of daily time-series for the 20 largest US cities. Am J Epidemiol. 2000 Sep 1; 152 (5):397–406. [ PubMed ] [ Google Scholar ]
• Houk JC. Control strategies in physiological systems. FASEB J. 1988 Feb; 2 (2):97–107. [ PubMed ] [ Google Scholar ]
• Harris RB. Role of set-point theory in regulation of body weight. FASEB J. 1990 Dec; 4 (15):3310–3318. [ PubMed ] [ Google Scholar ]
• Refinetti R. Homeostasis and circadian rhythmicity in the control of body temperature. Ann N Y Acad Sci. 1997 Mar 15; 813 :63–70. [ PubMed ] [ Google Scholar ]
• Czeisler CA, Duffy JF, Shanahan TL, Brown EN, Mitchell JF, Rimmer DW, Ronda JM, Silva EJ, Allan JS, Emens JS, et al. Stability, precision, and near-24-hour period of the human circadian pacemaker. Science. 1999 Jun 25; 284 (5423):2177–2181. [ PubMed ] [ Google Scholar ]
• Muller JE, Ludmer PL, Willich SN, Tofler GH, Aylmer G, Klangos I, Stone PH. Circadian variation in the frequency of sudden cardiac death. Circulation. 1987 Jan; 75 (1):131–138. [ PubMed ] [ Google Scholar ]
• Cripps T, Rocker G, Stradling J. Nocturnal hypoxia and arrhythmias in patients with impaired left ventricular function. Br Heart J. 1992 Oct; 68 (4):382–386. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Scales WE, Vander AJ, Brown MB, Kluger MJ. Human circadian rhythms in temperature, trace metals, and blood variables. J Appl Physiol (1985) 1988 Oct; 65 (4):1840–1846. [ PubMed ] [ Google Scholar ]
• Reuler JB. Hypothermia: pathophysiology, clinical settings, and management. Ann Intern Med. 1978 Oct; 89 (4):519–527. [ PubMed ] [ Google Scholar ]
• MacKenzie MA. Pathophysiology and clinical implications of human poikilothermia. Ann N Y Acad Sci. 1997 Mar 15; 813 :738–740. [ PubMed ] [ Google Scholar ]
• Rowe JW, Kahn RL. Successful aging. Gerontologist. 1997 Aug; 37 (4):433–440. [ PubMed ] [ Google Scholar ]
• Myerburg RJ, Interian A, Jr, Mitrani RM, Kessler KM, Castellanos A. Frequency of sudden cardiac death and profiles of risk. Am J Cardiol. 1997 Sep 11; 80 (5B):10F–19F. [ PubMed ] [ Google Scholar ]
• Murray CJ, Nelson CR. State-space modeling of the relationship between air quality and mortality. J Air Waste Manag Assoc. 2000 Jul; 50 (7):1075–1080. [ PubMed ] [ Google Scholar ]
• Thurston GD, Ito K, Lippmann M, Hayes C. Reexamination of London, England, mortality in relation to exposure to acidic aerosols during 1963-1972 winters. Environ Health Perspect. 1989 Feb; 79 :73–82. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gerrity TR, Lee PS, Hass FJ, Marinelli A, Werner P, Lourenço RV. Calculated deposition of inhaled particles in the airway generations of normal subjects. J Appl Physiol Respir Environ Exerc Physiol. 1979 Oct; 47 (4):867–873. [ PubMed ] [ Google Scholar ]
• Bowes SM, 3rd, Laube BL, Links JM, Frank R. Regional deposition of inhaled fog droplets: preliminary observations. Environ Health Perspect. 1989 Feb; 79 :151–157. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Karlsson JA, Sant'Ambrogio G, Widdicombe J. Afferent neural pathways in cough and reflex bronchoconstriction. J Appl Physiol (1985) 1988 Sep; 65 (3):1007–1023. [ PubMed ] [ Google Scholar ]
• Sekizawa K, Yanai M, Shimizu Y, Sasaki H, Takishima T. Serial distribution of bronchoconstriction in normal subjects. Methacholine versus histamine. Am Rev Respir Dis. 1988 Jun; 137 (6):1312–1316. [ PubMed ] [ Google Scholar ]
• Wilson WE, Suh HH. Fine particles and coarse particles: concentration relationships relevant to epidemiologic studies. J Air Waste Manag Assoc. 1997 Dec; 47 (12):1238–1249. [ PubMed ] [ Google Scholar ]
• Zeger SL, Dominici F, Samet J. Harvesting-resistant estimates of air pollution effects on mortality. Epidemiology. 1999 Mar; 10 (2):171–175. [ PubMed ] [ Google Scholar ]
• Dockery DW, Pope CA, 3rd, Xu X, Spengler JD, Ware JH, Fay ME, Ferris BG, Jr, Speizer FE. An association between air pollution and mortality in six U.S. cities. N Engl J Med. 1993 Dec 9; 329 (24):1753–1759. [ PubMed ] [ Google Scholar ]
• Pope CA, 3rd, Thun MJ, Namboodiri MM, Dockery DW, Evans JS, Speizer FE, Heath CW., Jr Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults. Am J Respir Crit Care Med. 1995 Mar; 151 (3 Pt 1):669–674. [ PubMed ] [ Google Scholar ]

Air Pollution

Our overview of indoor and outdoor air pollution.

By: Hannah Ritchie and Max Roser

This article was first published in October 2017 and last revised in February 2024.

Air pollution is one of the world's largest health and environmental problems. It develops in two contexts: indoor (household) air pollution and outdoor air pollution.

In this topic page, we look at the aggregate picture of air pollution – both indoor and outdoor. We also have dedicated topic pages that look in more depth at these subjects:

Indoor Air Pollution

Look in detail at the data and research on the health impacts of Indoor Air Pollution, attributed deaths, and its causes across the world

Outdoor Air Pollution

Look in detail at the data and research on exposure to Outdoor Air Pollution, its health impacts, and attributed deaths across the world

Look in detail at the data and research on energy consumption, its impacts around the world today, and how this has changed over time

See all interactive charts on Air Pollution ↓

Other research and writing on air pollution on Our World in Data:

• Air pollution: does it get worse before it gets better?
• Data Review: How many people die from air pollution?
• Energy poverty and indoor air pollution: a problem as old as humanity that we can end within our lifetime
• How many people do not have access to clean fuels for cooking?
• What are the safest and cleanest sources of energy?
• What the history of London’s air pollution can tell us about the future of today’s growing megacities
• When will countries phase out coal power?

Air pollution is one of the world's leading risk factors for death

Air pollution is responsible for millions of deaths each year.

Air pollution – the combination of outdoor and indoor particulate matter and ozone – is a risk factor for many of the leading causes of death, including heart disease, stroke, lower respiratory infections, lung cancer, diabetes, and chronic obstructive pulmonary disease (COPD).

The Institute for Health Metrics and Evaluation (IHME), in its Global Burden of Disease study, provides estimates of the number of deaths attributed to the range of risk factors for disease. 1

In the visualization, we see the number of deaths per year attributed to each risk factor. This chart shows the global total but can be explored for any country or region using the "change country" toggle.

Air pollution is one of the leading risk factors for death. In low-income countries, it is often very near the top of the list (or is the leading risk factor).

Air pollution contributes to one in ten deaths globally

In recent years, air pollution has contributed to one in ten deaths globally. 2

In the map shown here, we see the share of deaths attributed to air pollution across the world.

Air pollution is one of the leading risk factors for disease burden

Air pollution is one of the leading risk factors for death. But its impacts go even further; it is also one of the main contributors to the global disease burden.

Global disease burden takes into account not only years of life lost to early death but also the number of years lived in poor health.

In the visualization, we see risk factors ranked in order of DALYs – disability-adjusted life years – the metric used to assess disease burden. Again, air pollution is near the top of the list, making it one of the leading risk factors for poor health across the world.

Air pollution not only takes years from people's lives but also has a large effect on the quality of life while they're still living.

Who is most affected by air pollution?

Death rates from air pollution are highest in low-to-middle-income countries.

Air pollution is a health and environmental issue across all countries of the world but with large differences in severity.

In the interactive map, we show death rates from air pollution across the world, measured as the number of deaths per 100,000 people in a given country or region.

The burden of air pollution tends to be greater across both low and middle-income countries for two reasons: indoor pollution rates tend to be high in low-income countries due to a reliance on solid fuels for cooking, and outdoor air pollution tends to increase as countries industrialize and shift from low to middle incomes.

A map of the number of deaths from air pollution by country can be found here .

How are death rates from air pollution changing?

Death rates from air pollution are falling – mainly due to improvements in indoor pollution.

In the visualization, we show global death rates from air pollution over time – shown as the total air pollution – in addition to the individual contributions from outdoor and indoor pollution.

Globally, we see that in recent decades, the death rates from total air pollution have declined: since 1990, death rates have nearly halved. But, as we see from the breakdown, this decline has been primarily driven by improvements in indoor air pollution.

Death rates from indoor air pollution have seen an impressive decline, while improvements in outdoor pollution have been much more modest.

You can explore this data for any country or region using the "change country" toggle on the interactive chart.

Interactive charts on air pollution

Murray, C. J., Aravkin, A. Y., Zheng, P., Abbafati, C., Abbas, K. M., Abbasi-Kangevari, M., ... & Borzouei, S. (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019 .  The Lancet ,  396 (10258), 1223-1249.

Here, we use the term 'contributes,' meaning it was one of the attributed risk factors for a given disease or cause of death. There can be multiple risk factors for a given disease that can amplify one another. This means that in some cases, air pollution was not the only risk factor but one of several.

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Open Access

Peer-reviewed

Research Article

Air pollution, respiratory illness and behavioral adaptation: Evidence from South Korea

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Economics, Kyung Hee University, Seoul, South Korea, Department of Economics, Barnard College, Columbia University, New York, New York, United States of America

Roles Data curation, Formal analysis, Investigation, Methodology, Software

Affiliation Department of Economics, Kyung Hee University, Seoul, South Korea

• Tackseung Jun, 

• Published: August 13, 2019
• https://doi.org/10.1371/journal.pone.0221098
• Reader Comments

Air pollution is closely associated with the development of respiratory illness. Behavioral adaptations of people to air pollution may influence its impact, yet this has not been investigated in the literature. Our hypothesis is that people experience and learn the underlying air quality to decide their adaptation, and they have a stronger incentive to behaviorally adapt to the air quality as it deteriorates. We tested our hypothesis on a sample of approximately 25,700 individuals from South Korea from 2002 to 2013 that contained information on daily doctor’s visits due to respiratory disease. We matched individuals to the mean of the past seven-day concentration of the particulate matter of size between 2.5 and 10 micrometers (PM 10 ) in their county of residence. We examined whether people living in counties with greater air pollution suffer less from respiratory disease when the concentration increases. For the analysis, we separated counties into quintiles based on their mean seven-day PM 10 , and regressed the binary indicator of a daily doctor’s visit with a resulting diagnosis of respiratory disease on the seven-day PM 10 concentration of the county of residence interacted with the quintile dummies. The key findings are that a 1-standard-deviation increase in the seven-day PM 10 concentration in the two lowest quintiles is associated with an increase of 0.054 percentage points in the likelihood of a doctor’s visit with a resulting diagnosis of respiratory disease, which is about 40% larger than the effect in higher quintiles, and the size of 1-standard-deviation gradually increases from 0.037 percentage points in the third quintile to 0.040 percentage points in the fifth quintile. The smaller increase in the likelihood of respiratory disease in more polluted locations can be explained by the behavioral adaptation to the environment, but the effectiveness of the adaptation seems limited among the highly polluted locations.

Citation: Jun T, Min I-s (2019) Air pollution, respiratory illness and behavioral adaptation: Evidence from South Korea. PLoS ONE 14(8): e0221098. https://doi.org/10.1371/journal.pone.0221098

Editor: Stephania A. Cormier, Louisiana State University System, UNITED STATES

Received: January 29, 2019; Accepted: July 30, 2019; Published: August 13, 2019

Copyright: © 2019 Jun, Min. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data set used for this study are available from the Figshare database: https://figshare.com/s/9f022040bbf3e4444d02 .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Air pollution has significant adverse health effects on people. In 2013, 5.5 million premature deaths worldwide, or 1 in every 10 total deaths, were attributable to air pollution. These deaths cost the global economy about $225 billion USD in labor income lost [ 1 ]. Air pollution is especially severe in some of the world’s fastest-growing urban regions in low and middle income countries. This raises further concern that people may myopically endure the depreciation of air quality as the price to pay for more income, while dirty air can have serious long-term consequences [ 2 – 4 ].

The science is clear in linking breathing polluted air to the deterioration of respiratory function and the subsequent development of respiratory illness. Particles deposited in the respiratory tract in sufficient amounts can induce inflammation, and airway inflammation increases airway responsiveness to irritants and may reduce lung function [ 5 – 10 ]. Much empirical evidence supports the adverse impact of air pollution. As an example, living close to streets with a high traffic density is found to be a risk factor for the occurrence of respiratory disease [ 11 – 17 ]. A meta-analysis of birth cohorts found a clear association between air pollution and respiratory infections, such as pneumonia [ 18 ]. However, the degree to which respiratory illness is influenced by air pollution differs greatly among these studies. Some studies reported no significant relationship between exposure to polluted air and the occurrence of respiratory disease [ 19 – 22 ].

Here, we hypothesize that (i) people experience and learn the underlying air quality to adapt themselves to reduce the adverse health effects of air pollution, and (ii) they have a stronger incentive for behavioral adaptation as the air quality deteriorates. According to this hypothesis, the impact of polluted air on the likelihood of respiratory disease should be modulated by human behaviors. Studies in this line of research [ 23 – 27 ] found a lower hospital admission rate due to respiratory illness on days with active pollution alert. This finding is consistent with the hypothesis that people attempted to avoid exposure to pollution.

The behavioral response to the daily pollution alert is only one part of the greater underlying behavioral adaptations to living with air pollution. Many other behavioral adaptations may require structural changes in one’s life. For example, people build indoor facilities for outdoor activities, install air-purification system in buildings, and enforce law to reduce emissions from vehicles and factories, and so on. Therefore, behavioral adaptations to polluted air are numerous and diversified, yet many of them are unobserved, which makes encompassing all the dimensions of behavioral adaptation infeasible.

How do we assess the impact of diverse adaptations on respiratory disease, without listing all possible behavioral adaptations? Our empirical strategy is based on the observation that many of these behavioral adaptations stem from one’s learning and experience of the environment. A classic example is a behavioral adaptation to local climate conditions. The studies [ 28 – 30 ] found that mortality from extremely hot temperature is smaller in regions with more frequent hot temperature events, suggesting that people adapted themselves to local temperature conditions, for example by installing air-conditioning systems.

In this paper, we assume that people are accustomed to the underlying air quality of the place they live in and base their adaptation decision on it. As long as the factors that could influence the air quality of the county remain stable, the concentration of a major pollutant in a county could represent the underlying air quality of the county, and can be a proxy for the degree of behavioral adaptation of the people in the county. Our regression model tests whether people who live in a place of higher concentration are better adapted to pollution and thus suffer less from respiratory disease due to an increase in the ambient pollutant concentration.

2. Materials and methods

2.1 data processing and description.

The data on individual patients was based on an extraction from the National Health Insurance System (NHIS) of South Korea. We randomly extracted 30,000 individuals who existed in the database of the NHIS at January 1st, 2002, and kept track of them until December 31st, 2013. For each person, we extracted gender, age category, county of residence, and the daily information on doctor’s visits with the classification of diagnosed diseases according to the Korea Classification of Disease (KCD-6). The information on doctor’s visits is used to construct a daily-level binary indicator of a doctor’s visit with a resulting diagnosis of respiratory disease, which is classified as codes J00 to J99 and R00 to R09 according to the KCD-6 (The details of disease for each code are listed in S1 Table ). Acute respiratory infections in the upper and lower tracts, which are closely related to ambient pollution [ 31 , 32 ], explain about 68% of the diagnoses of respiratory disease ( S1 Table ).

The daily mean concentration of particulate matter of size between 2.5 and 10 micrometers (PM 10 ) at station level between January 1st, 2002 and December 31st, 2013 was used to represent the degree of air pollution. A network of 333 air-monitoring stations throughout South Korea collects hourly samples of PM 10 . These pollutants are inhalable particles and small enough to penetrate the thoracic region of the respiratory system, potentially affecting the likelihood of respiratory disease [ 33 , 34 ].

We matched the individual data with the seven-day PM 10 concentration, if available, of the county the individual lived in. If the county had more than one station, then the mean of the PM 10 concentrations from the stations was assigned to individuals in the county. People living in counties with no air monitoring system were excluded from the sample.

Moreover, the factors that may influence exposure to air pollution should be taken into account in the regression to estimate the effect of the PM 10 concentration on respiratory disease. For example, comfortable outdoor temperature will favor outdoor activities, which will increase exposure to ambient pollution. In this paper, the station-level daily maximum temperatures, obtained from weather-monitoring stations operated by the Korea National Weather Service, are used to construct a county-level daily maximum temperature series, and to define the comfortable temperature ranges (maximum temperature between 20°C and 26°C) for outdoor activities.

The resulting sample for the analysis, after matching and merging data on patients, ambient pollution and temperature, runs from January 1st 2002 to December 31st 2013 and has about 25,700 individuals per year from 236 counties ( S2 Table ). The changes in population over the sample periods are due to changes in the availability of the pollutant information, and a natural attrition of the sample as people decease. The population of ages 1–9 shrinks over time as people in this age category get older to move into the higher age group, but no one is newly recruited to the sample. Approximately 50.1% of population are women. People of ages 20–39 have the largest share (36.2%) in the sample, and the oldest (ages 60–89) have the lowest (10.7%) ( S2 Table ).

Regarding the frequency of respiratory disease, approximately 0.57% of observations have had a doctor’s visit with a resulting diagnosis of respiratory disease ( S3 Table ). This can be interpreted as the average likelihood of respiratory disease. The likelihood is the highest among people of ages 10–19, with 1.6%, and the lowest among people of ages 20–39, with 0.42%. Females (0.64%) have a greater chance of respiratory disease diagnosis than males (0.49%).

The time trend of the likelihood of respiratory disease exhibits an abrupt increase in 2007 during the period of 2002–2013. In Fig 1A , the indicators of a doctor’s visit are aggregated to create the monthly mean time series of likelihood of respiratory disease. It shows no significant time trend between 2002 and 2006, but abruptly increases in 2007 and has maintained its overall level since then. This jump is due to a major healthcare reform in South Korea that lowered the patient’s share of medical expenses, which resulted in more frequent visits to hospitals.

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( A ) The monthly time series of the average likelihood of respiratory disease are represented by the pink line, accompanied by a smoothed fit of the mean likelihood according to a generalized additive model (red line). ( B ) The same as ( A ) but the mean of the PM 10 concentrations (light blue for the un-smoothed and blue for the smoothed series) are displayed.

https://doi.org/10.1371/journal.pone.0221098.g001

The time trend of the monthly mean PM 10 concentration shows a gradually decreasing trend between 2002 and 2013, possibly due to stricter enforcement of air quality controls ( Fig 1B ).

The temporal relationship between the pollution and respiratory disease is clear on monthly means. Fig 2 shows the monthly means of the likelihood of respiratory disease and the PM 10 concentration co-move, and exhibit a clear seasonal cycle: the likelihood of respiratory disease is highest in winter and lowest in summer. The monthly mean PM 10 concentration follows a similar seasonal pattern: the concentration is highest during the winter and early spring, and lowest during the summer and early fall. This is due to the fact that more coal and other fossil fuels are burned during the colder seasons, and cold air near the ground, trapped by a layer of warm air due to temperature inversion, holds air pollutants, while warm air can rise easily and carry away pollutants during the warmer months [ 35 ].

The averages of the likelihood of respiratory disease by month (multiplied by 10 4 ) are represented in black line (values are denoted on vertical axis on the left). The averages of the PM 10 concentration by month are displayed in red dotted lines (values are denoted on vertical axis on the right).

https://doi.org/10.1371/journal.pone.0221098.g002

2.2 Empirical strategy

Since the likelihood of respiratory disease was based on doctor’s visits, there can be a delay in time between exposure to air pollution and the actual visit. This temporal misalignment may be due to an individual’s delay in visiting a doctor, or a delay in the development of respiratory illness [ 36 ], which is related to different biological reactions to the particles [ 37 ]. The delay is different across types of symptoms, and typically lasts at most 6 days [ 38 – 41 ]. To incorporate possible effects of past exposure to pollution to doctors’ visits, we used the mean of the PM 10 concentrations during the previous seven days, and called it the seven-day PM 10 concentration.

In order to represent the difference in the underlying air quality of counties, we separated counties into the population-weighted quintiles according to their seven-day PM 10 concentration such that (ii) the total population by quintile is comparable, and (ii) counties belonging to higher quintiles have higher seven-day PM 10 concentration. The population of a county is defined as the mean of the actual population in the county, rather than the number of individuals in the sample, during the sample period. Since the seven-day PM 10 concentration is supposed to represent the underlying air quality, counties that potentially have undergone large environmental changes are dropped from the analysis, i.e. the ones that have advanced to an urban city or become a hosting county for a major industrial complex during the sample period. S4 Table shows that the seven-day PM 10 concentration was, by construction, lowest in the first quintile with 44.2 μg / m 3 or microgram per cubic meter, and was highest in the fifth quintile with 63.6 μg / m 3 , while the mean is 54.8 μg / m 3 . The size of the population of a county is typically smaller among counties in the lower quintiles.

Fig 3A shows the spatial distribution of the counties’ quintiles based on the seven-day PM 10 concentration. Notice that the air quality in the neighboring counties seems to be correlated, especially in the large metropolitan area. For example, the capital city of South Korea, and its neighboring county in the upper left corner of Fig 3A mostly belong to the fifth quintile. Similarly, counties in the fifth quintile at the bottom right corner of the figure are the ones in the second largest city of the country. This spatial correlation may be due to the small size of the PM 10 which makes it stay in the air for a long time, and can be scattered easily from a source to a neighboring region [ 42 ]. It may also be due to the similarity of economic activities and infrastructure among the neighboring counties.

The bordered areas of the map represent counties, and are shaded according to the quintile they belong to. The quintiles in (A) and (B) are created based on the seven-day PM 10 concentration and mean likelihood of respiratory disease, respectively. The counties with no seven-day PM 10 concentration observations or no individuals in the sample are labeled as missing.

https://doi.org/10.1371/journal.pone.0221098.g003

On the other hand, the likelihood of respiratory disease is spatially mixed. To compare the distribution of seven-day PM 10 concentration and the mean likelihood of respiratory disease, counties are separated into quintiles according to their mean likelihood of respiratory disease, such that the higher quintiles have a higher likelihood of respiratory disease. Fig 3B shows that compared to the distribution of the seven-day PM 10 concentration, the likelihood of respiratory disease is more equally distributed across the country. It is also noticeable from Fig 3 that counties of high concentration are not necessarily associated with high probability of respiratory disease. According to our hypothesis, a part of this discrepancy is due to the behavioral adaptation by people to ambient air pollution.

If the cross sectional unit of analysis were a county, then the spatial regression would be an appropriate empirical strategy when it is suspected that the error terms are contemporaneously correlated, and so the likelihood of respiratory disease of neighboring counties are correlated. However, the unit of our analysis is a person, and no information about the exact location of people are available to compute the spatial correlation based on the Moran’s I [ 43 ]. Hence, the spatial regression is infeasible for our sample. Instead, we included the county dummies in the regression, as people from the same county face the identical degree of pollution. Furthermore, the heterogeneity among individuals is taken into account by including the individual dummies in the regression. Any remaining correlations in the error term will be removed by estimating the robust standard errors.

The key empirical strategy was to regress the daily indicator of a doctor’s visit with a resulting diagnosis of respiratory disease on the seven-day PM 10 concentration across different quintiles by interacting the quintile dummies with the seven-day PM 10 concentration. This specification is used to identify the potential difference in the impact of the seven-day PM 10 concentration on the respiratory disease across counties in different quintiles. Our adaptation hypothesis would be supported if the estimated coefficients of the seven-day PM 10 concentration in the regression were smaller in higher quintiles.

2.3 Regression model

The rest of the independent variables included factors that may influence the occurrence of respiratory disease and doctor’s visits: R i , j , t −1 is a one-day lagged dependent variable that reflects dynamics of change in the likelihood of respiratory disease. A dummy variable T j , t represents a binary indicator of the comfortable temperature for outdoor activities and was equal to 1 if the daily maximum temperature was between 20°C and 26°C, and 0 if otherwise. D j × m is a dummy for the interactions between county and month, which absorb differences in seasonal variations in respiratory patients across counties, so that the availability of hospitals and income differences will not confound the coefficient estimates. D y × m is a dummy for interactions between year and month, which control factors that are common across counties, such as changes in national health insurance (the one in year 2007 in Fig 1A ) and the environmental policy. H t is a dummy equal to 1 if day t is a holiday or weekend, and 0 if otherwise. This holiday dummy captures the reality that many hospitals are closed during holidays and weekends, which restricts patients from visiting a doctor. The individual dummy c i is included to control for the individual heterogeneity, such as the allergic predisposition and propensity for respiratory disease which can influence the likelihood of respiratory disease [ 44 ]. The term μ i , j , t is a random error term. Finally, the estimated coefficients of γ q in specification (1) represent the mean of within-individual variation of the seven-day PM 10 concentration’s impact on the likelihood of respiratory disease, controlling for various confounding factors.

We estimated the regression specified in (1) using a linear likelihood model with fixed effects. However, since the dependent variable is binary, a nonlinear model such as a logistic regression may be more appropriate if it fits the data better than a linear model. However, a nonlinear model demands great computational resources to achieve the convergence of an iterative process of maximum likelihood when the sample size is large, with a large number of variables used in the estimation. Therefore, we restrict our analysis to the linear likelihood model.

The estimated effects of the seven-day PM 10 concentration on the likelihood of respiratory disease supported both the behavioral adaptation hypothesis and its limitations. Fig 4 shows that the first two quintiles have the higher coefficients of the seven-day PM 10 concentration than the last three quintiles. A 1-standard-devivation increase in the seven-day PM 10 concentration in the first two quintiles was associated with an increase of 0.054 percentage point (PP) in the likelihood of respiratory disease. The size of this effect is not trivial as it amounts to about 10% of the mean likelihood of respiratory disease in the whole sample. Moreover, these coefficients were statistically distinguishable from the coefficients for the higher quintiles, as the 95% confidence interval of estimated coefficient in the first two quintiles did not overlap with the others. This result implies that people who lived under greater air pollution (those in high quintiles) seem to adapt to their environment to suffer less from respiratory illness, compared to people who lived with cleaner air (those in lower quintiles).

The estimated coefficients of the seven-day concentration of PM 10 on the likelihood of respiratory disease using the whole sample are shown with the 95% error bars for each quintile. The errors are based on the robust standard errors, clustered by individual.

https://doi.org/10.1371/journal.pone.0221098.g004

Notice, however, that the effect of a 1-standard-deviation increase in the seven-day PM 10 concentration gradually increased from 0.037 PP in the third quintile to 0.040 PP in the fifth quintile. This result can be interpreted as the limitation of behavioral adaptation: if the PM 10 concentration was sufficiently high, the efficiency of adaptation was limited such that a greater concentration would lead to a higher chance of respiratory disease.

To examine whether the estimated coefficients of the seven-day PM 10 concentration are heterogeneous across the population, we separately estimated the regression in (1) by age group. The five age groups were: ages 1–9, 10–19, 20–39, 40–59, and 60–89. The regression results by age group are summarized in Fig 5 .

For each age group, the coefficients of the seven-day PM 10 concentration are estimated for all people (black line), and separately by gender (blue line for male and red line for female). The dotted horizontal lines denote the mean of the estimated coefficients based on all genders over quintiles.

https://doi.org/10.1371/journal.pone.0221098.g005

Fig 5 reveals that there was a clear disparity by age group in the magnitude of the estimated coefficients, suggesting a difference in vulnerability to pollution by age. Specifically, the estimated coefficient for the young (ages 1–9 and 10–19) and the oldest (ages 60–89) is around 0.26×10 −4 , while it is only about 0.09×10 −4 and 0.18×10 −4 for people of ages 20–39 and 40–59, respectively (as shown by the dotted lines in Fig 5 ). This difference by age group is explained by the findings from previous studies that young people are more susceptible to the adverse effects of pollution, due to their immature immune systems, continuing development of their lungs during the early post-neonatal period [ 45 – 47 ], more frequent outdoor activities [ 48 ], and the elderly have weak respiratory functionality [ 49 ]. In other words, these factors could limit the efficiency of behavioral adaptation.

It is noticeable that the estimated coefficient for the people of age 1–9 increased the most from the third to the fifth quintile ( Fig 5A ). Recall that the estimated coefficient based on the whole population ( Fig 4 ) also increased from the third quintile, which was related to the limitation to adaptation. Hence, the limitation may be due to the age-specific difference in degrees of vulnerability to pollution, and so is evident among the most vulnerable population of the youngest (aged 1–9).

We further investigated the possible differences by gender in the behavioral adaptation. Fig 5 reports the estimated coefficients of the seven-day PM 10 concentration by gender for each age group. It shows that females generally suffer more from respiratory disease due to an increase in the seven-day PM 10 concentration. Regarding behavioral adaptation, higher coefficients are generally associated with lower quintiles among females than among males, which suggests that females are more concerned with behavioral adaptation so that females in higher pollution regions suffer less from respiratory disease due to an increase in the PM 10 concentration.

The limitation of the behavioral adaptation also differs by gender. Fig 5 shows a consistent increase in the coefficient of the seven-day PM 10 concentration from the third to the fifth quintile among young males (age 1–9 and 10–19). In contrast, such an increase is only visible, with a smaller magnitude, among the youngest population (age 1–9) of females. This difference can be explained by the gender difference in lung development: Males have less mature lungs and narrower airways during childhood, which could make them more vulnerable to air pollution [ 50 , 51 ]. However, the verdict on the gender difference in the relationship between air pollution and respiratory disease is far from unanimous in the literature [ 52 – 54 ]. Hence, further research is required to uncover the different mechanisms by which females and males respond to air pollution.

4. Discussion

In this paper, we did not explicitly measure the impact of specific behavioral adaptations. The weakness of this approach is that we could not attribute heterogeneity across quintiles to a specific adaptation measure. Instead, we claimed that the estimation results are consistent with predictions based on the behavioral adaptation hypothesis.

However, we can validate our empirical specification that a difference in mean pollution concentration is a likely cause of a difference in daily changes in the likelihood of respiratory disease. For this purpose, we performed a “false experiment,” where we took advantage of data on patients diagnosed with non-respiratory diseases that were unlikely to be caused by exposure to ambient pollution. These diseases included burns/injuries and digestive diseases (see S5 Table for a complete list of these diseases). The goal of the experiment is to confirm that only the occurrence of respiratory illness was influenced by changes in the seven-day PM 10 concentration. For the experiment, we estimated the same regression as in specification (1), but the dependent variable in the regression was replaced by a binary indicator of a doctor’s visit with a resulting diagnosis of non-respiratory disease. Fig 6 reports the estimated coefficients by quintile for non-respiratory disease. It showed that most of the estimated effects of the seven-day PM 10 concentration on the likelihood of non-respiratory disease were statistically insignificant and did not exhibit systematic patterns across quintiles. These results suggested that the estimated difference in the effect of PM 10 concentration on respiratory illness across quintiles was likely to be caused by a difference in the seven-day PM 10 concentration across quintiles.

The estimated coefficients (multiplied by 10 4 ) of the seven-day concentration on the likelihood of non-respiratory disease, listed in S5 Table , are presented with 95% error bars for each quintile: ( A ) Infectious and parasitic disease; ( B ) Nutritional and metabolic diseases; ( C ) Diseases of the eye and adnexa; ( D ) Diseases of the circulatory system; ( E ) Diseases of the digestive system; ( F ) Diseases of the skin and subcutaneous tissue; ( G ) Diseases of the musculoskeletal system and connective tissue; and ( H ) Injury, poisoning, and certain consequences of external causes.

https://doi.org/10.1371/journal.pone.0221098.g006

Another weakness of our estimation is that the estimated effect of the seven-day PM 10 concentration ignored any long-term consequences (e.g. costs from complications and hospitalization) and indirect costs (e.g. loss in labor income) associated with the disease. The further research on long-term consequences associated with the development of respiratory disease due to increase in the PM 10 concentration is required with more detailed information.

5. Conclusions

We examined the relationship between air pollution and the occurrence of respiratory disease using multiple dimensions of patient and pollution data. We found that people living in counties with lower mean pollution suffered more from respiratory disease due to an increase in ambient pollution. The result is consistent with the behavioral adaptation hypothesis. However, our results also indicate that when the mean pollution was beyond a critical level, behavioral adaptations seemed to be less efficient. This limitation was most severe among people who were most vulnerable to ambient pollution.

From the perspective of policy design, our results highlight the significance of behavioral adaptation in determining the actual impact of pollution in cases of respiratory disease. According to our analysis, counties in low quintiles or having low pollution were typically smaller in population, and thus were traditionally not at the center of environmental and health policy. However, according to our results, people living in these counties may suffer the most when quality of air depreciated quickly before they could adapt to it. Therefore, in contrast to conventional wisdom, preventative public health policy against air pollution should also be directed to population in these regions, as much as to people in high pollution regions. More effective policy design must consider both behavioral adaptation to air pollution, as well as its limitations.

Supporting information

S1 table. a list of categories of respiratory disease by its frequency..

https://doi.org/10.1371/journal.pone.0221098.s001

S2 Table. The sample size by year and age group.

https://doi.org/10.1371/journal.pone.0221098.s002

S3 Table. Summary statistics.

https://doi.org/10.1371/journal.pone.0221098.s003

S4 Table. Summary statistics by quintile.

https://doi.org/10.1371/journal.pone.0221098.s004

S5 Table. Description of non-respiratory diseases categories.

https://doi.org/10.1371/journal.pone.0221098.s005

Acknowledgments

The authors thank the National Health Insurance System (NHIS) of South Korea for providing the data used in this research.

• 1. World Bank. The cost of air pollution strengthening the economic case for action. The World Bank and Institute for Health Metrics and Evaluation; 2016.
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• 30. Heutel G, Miller NH, Molitor D. Adaptation and the Mortality Effects of Temperature Across U.S. Climate Regions. NBER Working Paper. 2018: 23271.

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Q&a: scientists analyze how the pandemic affected air quality.

Sofie Bates

What is nitrogen dioxide and how is it related to air quality, what happened with no2 levels when the covid-19 pandemic triggered lockdowns in march 2020, how did you determine no2 levels for different neighborhoods, what trends did you see when you looked at how the drop in no2 differed between neighborhoods, your team analyzed no2 levels in the 15 largest metropolitan areas in the united states. did you see this result in all of them, so, even with the improvements in air quality during the pandemic, no2 levels for communities of color were still higher than the pre-pandemic no2 levels for the whitest communities. what might be driving that discrepancy.

The coronavirus pandemic has changed the way we live, and several studies have documented how those widespread changes in human behavior have impacted the environment. NASA scientists and others using data from NASA and our partner satellites have shown that air pollution levels dropped significantly during COVID-19. A new, NASA-funded study, conducted by scientists at The George Washington University (GW) in Washington, D.C., zoomed in on the 15 largest metropolitan areas in the United States to see how the drop in air pollution differed from neighborhood to neighborhood . The paper was published July 20 in the journal Proceedings of the National Academy of Sciences.

We talked to the lead author on the study, Gaige Kerr, about how the COVID-19 pandemic led to better air quality – and how those improvements were unequal for people of different races, ethnicities and socioeconomic levels. Kerr is a research scientist at GW. The interview has been lightly edited for clarity.

Nitrogen dioxide, or NO2, is a trace gas in the atmosphere. It’s one of the six air pollutants regulated by the U.S. Environmental Protection Agency (EPA) under the Clean Air Act . Even though NO2 is only present in small amounts, it’s very harmful for human health and can trigger respiratory illnesses like asthma. NO2 also leads to the formation of ozone near Earth’s surface, another harmful air pollutant.

NO2 can come from natural things like lightning or microbes in the soil. But in cities, the majority of NO2 stems from human activity and fossil fuel combustion. Roughly 50% of the ambient NO2 comes from traffic emissions. The other large sources come from power plants, incinerators and factories.

During COVID, we had the opportunity to see how taking many cars off of the road and planes out of the skies affected air pollution in the real world in this unintended experiment. We know from past research that there are NO2 pollution disparities based on several factors – most notably race, ethnicity and income – and that communities of color and lower socioeconomic status face much higher concentrations of NO2. So, we wanted to understand how this unprecedented, extraordinary drop in human activity and emissions impacted NO2 disparities.

In cities, NO2 levels plummeted at first. That had a lot to do with the drop in traffic and travel, since vehicle traffic is the largest contributor to NO2 in cities. We saw widespread decreases in NO2 across urban areas in the United States during the pandemic, but the magnitude varied. NO2 levels dropped by about 10% to 35% on average, depending on the city. New York City and Los Angeles had very large drops, but NO2 disparities across different racial, ethnic and socioeconomic groups were very large in these cities.

We used data from the TROPOspheric Monitoring Instrument ( TROPOMI ) on the European Commission’s Copernicus Sentinel-5P satellite. With TROPOMI, in near real time, we can look at NO2 levels in the atmosphere at a very high resolution – like neighborhood by neighborhood. We compared TROPOMI’s NO2 measurements with data from the U.S. Census Bureau from the American Community Survey . We collected all of the satellite measurements within a census tract – which we can think of as the size of a small neighborhood – and paired that with the demographic data like race and ethnicity, median household income, education level and vehicle ownership.

The largest decreases in NO2 level occurred in the metropolitan area neighborhoods with a larger non-white population. But even with these large reductions, the levels of NO2 in those areas during the pandemic were still higher than the pre-pandemic levels of NO2 in the neighborhoods with a higher percentage of white residents. In many of the cities we examined, there was no change in the size of NO2 disparities between the most and least white or the highest or lowest income neighborhoods, despite an overall decrease in NO2.

Yes, we saw widespread decreases in NO2 across urban areas in the United States during the pandemic, but the magnitude varied. NO2 levels dropped by roughly 10% to 35% depending on the city.

We also did a deep dive into Detroit, New York, and Atlanta by looking at maps of NO2 data and overlaying that with information from the U.S. Census Bureau. For example, in Detroit, we found the largest NO2 drops were along the Detroit River. When we looked at what pollution sources lie around these neighborhoods, we saw that they were boxed in by a very busy interstate on one side and, on the other side, one of the busiest border crossings in North America, from Detroit to Windsor, Ontario, in Canada. There’s usually a lot of heavy-duty trucks idling at the border crossing, waiting for their turn to get through customs and border patrol. So, it makes sense why we saw some of the largest NO2 drops in those neighborhoods in Detroit.

In New York City, we found that the largest decrease in NO2 pollution occurred in Harlem and the Bronx. This part of the city has been referred to as “asthma alley” due to asthma rates well above the national and state average. These neighborhoods and their citizens, who are primarily Black and Hispanic, face a barrage of pollution from heavy-duty trucking and industry.

In Atlanta, the largest NO2 drops were in the southwest part of the city where there’s a large international airport. That part of the city is also home to a majority black and low-income population. In addition to all of the highways and interstates in that part of the city, it’s likely that emissions related to aviation are contributing to the NO2 drops. The Guardian reported that in March of 2020 around 50% of flights had been cancelled from Atlanta . So, there was not only a lot less vehicle traffic to the airport but also fewer planes flying in and out of the airport.

When we looked at the distribution of major roads in relation to each census tract, we found that the areas with the biggest drops in NO2 had about nine to 10 times more highways and interstates nearby than the census tracts with the smallest drops. The neighborhoods with a larger non-white population have about five or six times more highways and interstates nearby than the most-white neighborhoods.

Something else I thought was really interesting is that we found that census tracts with very low vehicle ownership in low-income communities and communities of color had some of the largest NO2 drops during the lockdown. This suggests that some of the air pollution experienced by people living in these areas is not caused by their own consumption of fossil fuels that produce air pollutants like NO2.

Download the full visuals from NASA Goddard’s Scientific Visualization Studio .

By  Sofie Bates

NASA’s Earth Science News Team

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• Published: 04 October 2021

A conversation on the impacts and mitigation of air pollution

Nature Communications volume  12 , Article number:  5823 ( 2021 ) Cite this article

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Air pollution and the associated health impacts affect millions of people around the world. In this Q&A, Professor Haikun Wang, an expert on the health risks of air pollution and climate change at Nanjing University, shares with Nature Communications their thoughts on the impacts of air pollution and the policies needed to tackle emissions.

1. What aspect of air pollution concerns you the most?

My primary concern is the impacts, especially the health impacts of air pollution and their socioeconomic drivers, such as trade, population aging, income and etc. Air pollution has become a leading environmental risk factor affecting urban and rural populations around the world. The Global Burden of Diseases Study estimates that ambient (outdoor) air pollution of particulate matter and ozone is responsible for nearly 6.7 million premature deaths worldwide in 2019. And the majority of these deaths occurred in developing countries with large populations and serious air pollution such as India and China. The health impacts of air pollution are not only affected by the exposure level but are also closely related to the exposed population, social and economic factors, which therefore must be considered in order to formulate effective air pollution control policies to protect human health.

2. What are your thoughts on current policy enforcement, and how well or not this is being achieved?

Substantial health benefits have been achieved around the world through implementing air pollution control policies during the last several decades. For example, the USA Clean Air Act was implemented in the 1970s, and the resulting cleaner air in 2020 has been estimated to prevent ~230,000 deaths. The historical control policies on vehicle emissions in China from 1998 to 2015 have also led to substantial reductions in air pollution impacts - the number of deaths attributable to air pollution in 2015 would have been around 510,000 higher than without those controls. More recently, since the promulgation in 2013 of China’s toughest-ever Air Pollution Prevention and Control Action Plan, the PM 2.5 concentration has substantially declined, by around 30% in 2017.

However, there are still major challenges ahead. Developed countries with cleaner air should continue to reduce pollution, because recent epidemiological studies have found that reducing PM 2.5 concentrations further from an already low level would bring much greater health benefits that might cover their cost of pollution control policies. For developing countries, they might bear the impact of pollution transfer from developed countries via outsourcing and international trade, but equally their pollution might also affect other regions through long-range transboundary transportation. The capacity of supporting scientific decision-making, supervision and management also needs to be strengthened to ensure the full implementation of air pollution control policies in developing countries. This highlights the requirement for successful collaboration between scientists, engineers, and policy makers from regional to global scales to develop cost-effective technologies and policies to address these challenges.

3. How effective is voluntary action vs government mandated policy in reducing air pollution?

Scholars and policy makers have debated the effectiveness of voluntary action and government mandated policies in mitigating environmental pollution for many years. I personally think that mandatory powers of government are currently more effective in reducing air pollution. Firms and citizens usually maximize their self-interest but lack the motivation to pay extra money to mitigate air pollution, because the costs of environmental pollution (e.g., negative health impacts of air pollution) are usually not fully evaluated or included in their cost (a.k.a. indirect cost). It could be problematic to expect too much from voluntary actions to reduce air pollution. Of course, mandated policies also have limitations. For example, they might have negative implications on the competitiveness of firms such as causing reduced profits margins. Mandated policies might also be easily influenced by the decisions of individual governments, such as President Trump’s withdrawal from the Paris Agreement. From this perspective, we need to study how to combine the voluntary action with mandated policy more effectively in the future, especially as the public awareness for environmental protection is generally increasing with social-economic development.

4. Socioeconomic factors such as income, education and wealth have been shown to play a key role in public health air pollution impacts. What needs to be done to ensure that policies developed are equitable and just?

Socioeconomic factors could affect air pollution and related health burdens, not only within a country, but also across various countries through trade. Recently, air pollutant emission levels have grown rapidly in some developing countries but stabilized or even decreased in many developed countries. This is partly because developing countries produce and export emission-intensive products to support the consumption in developed countries. Of course, such trade would increase economic efficiency and benefit both developed and developing countries. However, the environmental costs of developing countries are much higher than that of developed countries relative to their respective economic gains. Developing countries experience air pollution exposure inequality through global trade. Similar problems might also exist in domestic trade between developed and developing regions within a country like China.

Such environmental inequalities reflect the different economic development stages of trading partners. Developing countries or regions are often not able to afford technological innovations needed to mitigate pollution. Thus, establishing an effective collaborative framework between developed and developing countries to technologically and financially support pollution control and R&D efforts in developing countries would help to improve the overall quality of their exports while mitigating regional inequality. Additionally, a compensation scheme based on the principle “who benefits, who compensates” may be a practical solution to allocate the ecological burdens equally among countries or regions.

5. Technological advances to mitigate air pollution such as retrofitting coal-fired plants are touted as potential cost-effective solutions. What are the most promising recent advances to mitigate against pollutants?

As fossil fuels are still the major energy source supporting the global economic development, existing coal-fired power plants and fuel vehicles can not be replaced instantly. The traditional end-of-pipe pollution control technologies, or process control technologies, are still very important for air pollution control, especially in some developing countries such as China and India. In the short term, technological advances such as retrofitting coal-fired plants might be cost-effective solutions. However, as more aggressive pollution controls tend to cost more money, the marginal abatement cost usually go up rapidly with emission levels getting lower. This has already happened in some developed countries and also in China’s developed regions like Shanghai. From a technical point of view, clean and low-carbon energy, such as wind and solar energy, should be the ultimate solution to the energy needs and air pollution in the future. Especially in the context of addressing global climate change, application of the low-carbon energies would bring the synergistic effect of reducing CO 2 , CH 4 and other greenhouse gas emissions. Researches have illustrated that renewable energies are more cost-effective compared to traditional fossil fuels if we consider the cost of their impacts (e.g., health impacts).

6. Do you hold out more hope for technological solutions, or political action, as a means to reduce air pollution?

In my opinion, they are equally important. On the one hand, technological solutions are the foundation, which provides us with the basic tools for air pollution control. Good policies and management, on the other hand, can not only accelerate the application of advanced technologies but also make these tools work more efficiently. Developed countries usually have relatively higher social and economic management efficiency. They should focus more on the development of new technologies to achieve further reductions in air pollution, and provide technological support for developing countries. Policy measures should also be strengthened to mitigate direct and indirect pollution transfer accompanied by the outsourcing of emission-intensive industries from developed countries to developing ones. For developing countries, they need to strengthen their political determination to protect the environment and public health, and minimize air pollution while the economy is growing. At the same time, they should learn and adopt advanced technologies and management experiences from developed countries, and maximize the effect of their existing technological solutions.

7. Finally, how would you like collaboration between physical, health and policy scientists working on air pollution to improve?

Air pollution, by definition, includes the emission, transformation, impact, and mitigation of multiple air pollutants, which involves physical, health, and policy sciences. Therefore, we should, at first, understand that a robust collaboration between scientists from these fields is crucial to successfully address air pollution issues. For example, if one has a research issue/objective to evaluate the health effects of a specific air pollution policy, one might need help from other natural and/or social scientists. A comprehensive study on air pollution is usually impossible to be completed by one individual scientist or the scientific community only, and cooperation with the public, policy makers, and even private corporations are sometimes necessary.

Second, the effective cooperation between scientists from different fields (often with different ideologies, methods and tools) is challenging. Sufficient communication with colleagues of different knowledge backgrounds in atmospheric science, public health, and policy analysis is essential. It would help you to understand the tools and data each scientist can bring, and to connect these data and tools effectively across disciplines. Only with this, the technical roadmap and detailed approaches (e.g., including policy scenario analysis–emissions–atmospheric transport–health effects–cost/benefit) can be determined for a study on air pollution policy.

Third, we should also keep in mind that such research that integrates physical, medical, and social sciences on air pollution might never be perfect. But they are the effective (if not the only) solutions to comprehensive issues like air pollution, and the results would become more reliable with the improvement in the individual fields and the collaboration among physical, health and policy scientists.

Finally, data and results should be interpreted and shared by collaborators to keep transparency. Clear communication of research results and their uncertainty with the public and policy makers is also a must. I believe that, with robust scientific results and transparent policy-making process, we can ultimately design and implement more efficient air pollution policies.

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A conversation on the impacts and mitigation of air pollution. Nat Commun 12 , 5823 (2021). https://doi.org/10.1038/s41467-021-25401-0

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NO 2 indicates nitrogen dioxide; PM 2.5 , particulate matter under 2.5 μm; and WHO, World Health Organization. Shading in panel A represents IQRs.

Results are from model 3, which is adjusted for ethnicity, family psychiatric history, maternal social class, maternal education, house tenure, population density, neighborhood deprivation, social fragmentation, and greenspace. Sample sizes of imputed data sets range from 2952 (adolescence noise pollution and psychotic experiences) to 6154 (pregnancy air pollution and anxiety). NO 2 indicates nitrogen dioxide; OR, odds ratio; and PM 2.5 , particulate matter less than 2.5 μm.

eMethods. Participants, pollution data, covariates, and multiple imputation

eResults. Findings from sensitivity analyses

eDiscussion. Interpretation of sensitivity analyses

eFigure 1. Correlations between NO2, PM2.5, and noise pollution across pregnancy, childhood, and adolescence

eFigure 2. Directed acyclic graph (DAG)

eTable 1. Association of early-life noise pollution exposure with youth mental health problems, treating noise pollution as a categorical variable

eTable 2. Comparison between e-value and covariate point estimates: pregnancy PM2.5 and psychotic experiences

eTable 3. Comparison between e-value and covariate point estimates: adolescent noise pollution and anxiety

eTable 4. Adjusting pollutants for one another: associations of early-life air and noise pollution exposure with youth mental health problems

eTable 5. Restricting to non-movers (~30% of participants): associations of early-life air and noise pollution exposure with youth mental health problems

eTable 6. Complete case analysis: associations of early-life air and noise pollution exposure with youth mental health problems

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Newbury JB , Heron J , Kirkbride JB, et al. Air and Noise Pollution Exposure in Early Life and Mental Health From Adolescence to Young Adulthood. JAMA Netw Open. 2024;7(5):e2412169. doi:10.1001/jamanetworkopen.2024.12169

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Air and Noise Pollution Exposure in Early Life and Mental Health From Adolescence to Young Adulthood

• 1 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
• 2 Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
• 3 PsyLife Group, Division of Psychiatry, University College London, London, United Kingdom
• 4 ESRC Centre for Society and Mental Health, King’s College London, London, United Kingdom
• 5 Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
• 6 Centre for Implementation Science, Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
• 7 UK Longitudinal Linkage Collaboration, University of Bristol, Bristol, United Kingdom
• 8 MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom

Question   Is exposure to air and noise pollution in pregnancy, childhood, and adolescence associated with the development of psychotic experiences, depression, and anxiety between 13 and 24 years of age?

Findings   In this longitudinal birth cohort study followed up into adulthood that included 9065 participants with mental health data, higher exposure to fine particulate matter (PM 2.5 ) in pregnancy and childhood was associated with increased psychotic experiences and in pregnancy was associated with higher rates of depression. Higher noise pollution exposure in childhood and adolescence was associated with increased anxiety.

Meaning   These findings build on evidence associating air and noise pollution with mental health, highlighting a role of early-life pollution exposure in youth mental health problems.

Importance   Growing evidence associates air pollution exposure with various psychiatric disorders. However, the importance of early-life (eg, prenatal) air pollution exposure to mental health during youth is poorly understood, and few longitudinal studies have investigated the association of noise pollution with youth mental health.

Objectives   To examine the longitudinal associations of air and noise pollution exposure in pregnancy, childhood, and adolescence with psychotic experiences, depression, and anxiety in youths from ages 13 to 24 years.

Design, Setting, and Participants   This cohort study used data from the Avon Longitudinal Study of Parents and Children, an ongoing longitudinal birth cohort founded in 1991 through 1993 in Southwest England, United Kingdom. The cohort includes over 14 000 infants with due dates between April 1, 1991, and December 31, 1992, who were subsequently followed up into adulthood. Data were analyzed October 29, 2021, to March 11, 2024.

Exposures   A novel linkage (completed in 2020) was performed to link high-resolution (100 m 2 ) estimates of nitrogen dioxide (NO 2 ), fine particulate matter under 2.5 μm (PM 2.5 ), and noise pollution to home addresses from pregnancy to 12 years of age.

Main outcomes and measures   Psychotic experiences, depression, and anxiety were measured at ages 13, 18, and 24 years. Logistic regression models controlled for key individual-, family-, and area-level confounders.

Results   This cohort study included 9065 participants who had any mental health data, of whom (with sample size varying by parameter) 51.4% (4657 of 9051) were female, 19.5% (1544 of 7910) reported psychotic experiences, 11.4% (947 of 8344) reported depression, and 9.7% (811 of 8398) reported anxiety. Mean (SD) age at follow-up was 24.5 (0.8) years. After covariate adjustment, IQR increases (0.72 μg/m 3 ) in PM 2.5 levels during pregnancy (adjusted odds ratio [AOR], 1.11 [95% CI, 1.04-1.19]; P  = .002) and during childhood (AOR, 1.09 [95% CI, 1.00-1.10]; P  = .04) were associated with elevated odds for psychotic experiences. Pregnancy PM 2.5 exposure was also associated with depression (AOR, 1.10 [95% CI, 1.02-1.18]; P  = .01). Higher noise pollution exposure in childhood (AOR, 1.19 [95% CI, 1.03-1.38]; P  = .02) and adolescence (AOR, 1.22 [95% CI, 1.02-1.45]; P  = .03) was associated with elevated odds for anxiety.

Conclusions and Relevance   In this longitudinal cohort study, early-life air and noise pollution exposure were prospectively associated with 3 common mental health problems from adolescence to young adulthood. There was a degree of specificity in terms of pollutant-timing-outcome associations. Interventions to reduce air and noise pollution exposure (eg, clean air zones) could potentially improve population mental health. Replication using quasi-experimental designs is now needed to shed further light on the underlying causes of these associations.

Childhood, adolescence, and early adulthood are critical periods for the development of psychiatric disorders: worldwide, nearly two-thirds of individuals affected become unwell by 25 years of age. 1 Identifying early-life risk factors is a crucial research challenge in developing preventative interventions and improving lifelong mental health trajectories.

Growing evidence suggests that air pollution exposure may be associated with the onset of psychiatric problems, including mood, affective, and psychotic disorders. 2 - 6 Air pollution comprises toxic gases and particulate matter (ie, organic and inorganic solid and liquid aerosols) of mostly anthropogenic origin. 7 Understanding the potential effect of air pollution on mental health is increasingly crucial, given the human and societal cost of poor mental health, 8 the global shift toward urban living, 9 , 10 and the backdrop of emissions-induced climate change. 11 Air pollution could negatively affect mental health via numerous pathways, including by compromising the blood-brain barrier, promoting neuroinflammation and oxidative stress, and directly entering the brain and damaging tissue therein. 12 , 13 However, key research gaps remain. First, the relative importance of early-life exposure, including prenatal exposure, is uncertain. Infants and children are thought to be especially vulnerable to air pollution, 14 , 15 but longitudinal, high-resolution pollution data spanning the early years of human life are scarce. Second, relatively few studies have examined the association of air pollution with youth mental health problems, 16 despite youth being a critical period for intervention. Third, few longitudinal studies have investigated the role of noise pollution in mental health, 17 despite the correlation between noise and air pollution. 18 Finally, studies have often used crude pollution data and lacked adequate controls for potential confounders.

We aimed to advance understanding on this topic by capitalizing on a novel linkage between high-resolution outdoor air and noise pollution data and a cohort of over 14 000 infants born in Southwest England in 1991 through 1993 and followed up into adulthood. We examined the association of air and noise pollution exposure from pregnancy to 12 years of age with mental health problems from ages 13 to 24 years. Based on previous evidence, we focused on psychotic experiences (eg, subclinical hallucinations and delusions), depression, and anxiety. These problems are common 1 , 19 - 21 and increasing 22 among youth and strongly predict future psychopathology, 23 , 24 making them useful and important targets. We hypothesized that participants exposed to higher air and noise pollution would subsequently experience worse mental health.

The Avon Longitudinal Study of Parents and Children (ALSPAC) is a UK birth cohort, 25 - 28 described further in the eMethods in Supplement 1 . Briefly, pregnant women residing in and around the City of Bristol (population approximately 714 000 in 2024) in Southwest England with due dates between April 1, 1991, and December 31, 1992, were approached to take part in the study. The initial number of pregnancies enrolled was 14 551, resulting in 13 988 children alive at 1 year of age. At age 7 years, the initial sample was bolstered with additional eligible cases, resulting in 14 901 infants alive at 1 year of age. The catchment area has a mix of urban, suburban, and rural environments. 29 The study website contains details of all the data and a fully searchable data dictionary and variable search tool. 30 Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. The present study is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline. 31

Psychotic experiences were measured at ages 13, 18, and 24 years using a semi-structured interview 32 that consisted of 12 core items about hallucinations, delusions, and thought interference, rated against the Schedule for Clinical Assessment in Neuropsychiatry version 2.0 (SCAN 2.0). 33 Consistent with previous ALSPAC studies, 34 , 35 psychotic experiences were defined such that 0 represented none, and 1 represented suspected or definite. The reporting period at each phase was since the participant’s 12th birthday. At 13 years of age, 13.6% (926 of 6788) of participants reported psychotic experiences, at 18 years of age 9.2% (432 of 4715) reported psychotic experiences, and at 24 years of age, 12.6% (491 of 3888) reported psychotic experiences. We summed psychotic experiences across time points and dichotomized the variable for analyses such that participants received a score of 1 for suspected or definite psychotic experiences if they reported psychotic experiences at any age.

Depression and anxiety were measured at age 13 years via parent-completed Development and Well-being Assessments. 36 Responses were classified into probabilistic bands according to Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) criteria for major depressive disorder and generalized anxiety disorder, and dichotomized for analysis (bands 0-2, 0; bands 3-5, 1). At ages 18 and 24 years, depression and anxiety were measured using the Clinical Interview Schedule Revised, 37 a self-administered computerized interview that gave International Statistical Classification of Diseases, Tenth Revision , diagnoses of moderate to severe depression and generalized anxiety disorder. The reporting period at each phase was the past month, although a 6-month reporting period was used for anxiety at 13 years of age. At 13 years of age, 5.6% (386 of 6944 of participants) reported depression and 3.6% (254 of 7044) reported anxiety. At 18 years of age, 7.9% (359 of 4560) reported depression and 5.7% (262 of 4560) reported anxiety. At 24 years of age, 7.7% (304 of 3965) reported depression and 9.8% (386 of 3956) reported anxiety. We summed depression and anxiety across time points and dichotomized the variables for analysis such that participants received a score of 1 if they had depression or anxiety at any age.

Air pollutants included nitrogen dioxide (NO 2 ) and fine particulate matter with a diameter smaller than 2.5 μm (PM 2.5 ). Both pollutants have well-established health impacts 10 and more recent associations with psychiatric disorders. 5 These air pollutants were estimated as part of the LifeCycle project 38 using the Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) model, which is described elsewhere and further in the eMethods in Supplement 1 . 39 Briefly, the ELAPSE model is a hybrid land-use regression model for Europe that derived concentrations of NO 2 and PM 2.5 in 2010. The model produces annualized estimates at 100 m 2 resolution, explaining 59% and 71% of measured spatial variability for NO 2 and PM 2.5 , respectively. 39 Estimates were linked to residential geocodes from pregnancy to age 12 years for participants who had lived in the original ALSPAC catchment area 29 up to 12 years of age and provided permission for geospatial linkage. Linkage was completed in 2020.

Residential noise pollution exposure was also estimated as part of the LifeCycle project 38 based on the UK Government’s Department for Environment, Food and Rural Affairs 2006 road traffic noise map. Data represent an annualized mean of day and night noise pollution, categorized according to low to medium (<55 dB: the European Environment Agency’s threshold 40 ), high (55-60 dB), and very high (>60 dB) noise. eFigure 1 in Supplement 1 shows the correlation between noise pollution, NO 2 , and PM 2.5 across time points.

Potential confounders were informed by the literature and formally selected using a directed acyclic graph (eFigure 2 in Supplement 1 ). We considered individual- and family-level covariates that could be associated with mental health problems and with downward mobility into more polluted neighborhoods. These included ethnicity self-reported by mothers during pregnancy, family psychiatric history, maternal social class, maternal education, and housing tenure. Area-level covariates included population density, neighborhood deprivation, social fragmentation, and greenspace and were time varying, corresponding to the timing of pollution exposure. Covariates are described fully in the eMethods in Supplement 1 and briefly below.

Race and ethnic group was reported by mothers during pregnancy, with specific categories to select including Bangladeshi, Black/African, Black/Caribbean, Black/other, Chinese, Indian, Pakistani, White, and any other ethnic group. Family psychiatric problems were reported by mothers and fathers during pregnancy and defined as the presence of any psychiatric problem affecting the mother, father, or any biological grandparent. Maternal social class based on occupation was reported by mothers during pregnancy. Maternal education was reported by mothers when infants were around 8 months. Home ownership was reported by mothers during pregnancy.

Population density was derived from 1991 and 2001 census data. 35 Area-level deprivation was based on the Index of Multiple Deprivation 2000. 41 Social fragmentation was based on a z-scored sum of census data on residential mobility, marital status, single-person households, and home ownership. 35 Greenspace was assessed based on the Normalized Difference Vegetation Index. 42

Analyses were performed from October 29, 2021, to March 11, 2024, in Stata, version 18.0 (StataCorp LLC). The code can be found at GitHub. 43 The characteristics of the sample with vs without mental health data were described according to percentages, means, and standard deviations. Group differences were explored using χ 2 and t tests. To explore the importance of different exposure periods, we derived exposure estimates for 3 developmental stages, pregnancy, childhood (birth to age 9 years), and adolescence (ages 10-12 years), 44 which were calculated using mean exposure values for NO 2 , PM 2.5 , and noise pollution during these age windows. Given that NO 2 and PM 2.5 had very different absolute ranges, scores were standardized by dividing by the IQR. To aid comparison between air and noise pollution, we treated noise pollution as a continuous variable, assuming a normal distribution underlying the categorical variable. Results treating noise as categorical are reported in eTable 1 in Supplement 1 .

For main analyses, logistic regression was used to examine the associations of NO 2 , PM 2.5 , and noise pollution in pregnancy, childhood, and adolescence with the mental health outcomes. We conducted an unadjusted model (model 1), then adjusted for individual- and family-level covariates (model 2), and then additionally adjusted for area-level covariates (model 3). To better understand the independent associations from different exposure periods, we then adjusted childhood and adolescent exposure for previous exposure (model 4). However, given that the high correlation between pollutants over time (eFigure 1 in Supplement 1 ) could introduce multicollinearity, we interpreted model 4 with caution. To estimate residual confounding, we also calculated E values 45 for models 3 and 4, which indicate the strength of association that an unmeasured confounder would require to nullify associations. All models accounted for potential hierarchy in the data by clustering around the lower layer super output area (containing a mean of about 1500 residents) using the cluster command, which provides robust SEs adjusted for within cluster correlated data. 46 All analyses were conducted following multiple imputation by chained equations, 47 described in the eMethods in Supplement 1 . A 2-sided value of P  < .05 was considered statistically significant.

We conducted 3 sensitivity analyses. First, we analyzed NO 2 , PM 2.5 , and noise pollution simultaneously, to control each for the others and address potential copollutant confounding. Second, we restricted analyses to participants who did not move house from pregnancy to age 12 years (29.8%) to keep pollution levels as consistent as possible over time. Third, we repeated main analyses for individuals with complete data.

The study included 9065 participants (mean [SD] age at follow-up, 24.5 [0.8] years) who had any mental health data, of whom (with sample sizes varying by parameter) 51.4% (4657 of 9051) were female, 48.6% (4394 of 9051) were male, 95.8% (7616 of 7954) were ethnically White, and 4.2% (338 of 7954) were of other ethnicity (which included Bangladeshi, Black African, Black Caribbean, Chinese, Indian, Pakistani, and others; these categories were collapsed into one because numbers in some categories were small enough to increase the risk of identification). In addition, 19.5% (1544 of 7910) reported psychotic experiences, 11.4% (947 of 8344) reported depression, and 9.7% (811 of 8398) reported anxiety ( Table 1 ). Over half of participants (60.8% [4793 of 7886]) had a family psychiatric history; 21.8% (1583 of 7248) had mothers who worked in manual occupations; 15.7% (1274 of 8093) had mothers with degrees; and 81.6% (6670 of 8176) lived in homes owned by their parent (or parents). Mean (SD) population density was 33 (21) persons per hectare, and 19.3% (933 of 4831) of participants lived in the most deprived neighborhoods. The sample with vs without mental health data differed for most variables: participants with mental health data were more likely to be female, be White, have a family psychiatric history, and have more advantaged characteristics across the other variables. These differences should be borne in mind when interpreting the results.

Figure 1 A shows estimated levels of NO 2 and PM 2.5 for the sample, alongside the World Health Organization’s (WHO) 2021 exposure thresholds. 48 Mean (SD) levels of NO 2 (eg, 26.9 [4.2] μg/m 3 in pregnancy vs 21.1 [3.5] μg/m 3 at 12 years of age) and PM 2.5 (eg, 13.3 [0.9] μg/m 3 in pregnancy vs 10.7 [0.8] μg/m 3 at 12 years of age) decreased slightly over time. However, the mean exposure at age 12 years remained above the WHO’s thresholds for both pollutants (NO 2 , 10.0 μg/m 3 ; PM 2.5 , 5.0 μg/m 3 ). Additionally, over two-thirds of participants were exposed to high or very high noise pollution, 40 which changed little over time (eg, 22.7% in pregnancy vs 22.2% at year 12 for high noise pollution) ( Figure 1 B).

Associations of levels of NO 2, PM 2.5 , and noise pollution with psychotic experiences, depression, and anxiety are given in Table 2 , which shows unadjusted and adjusted results alongside E values, and Figure 2 , which shows model 3 results. Before covariate adjustment, IQR (4.47 μg/m 3 ) increases in NO 2 levels during pregnancy were associated with elevated odds for psychotic experiences (odds ratio [OR], 1.08, [95% CI, 1.00-1.17]; P  = .04). However, there was no association after adjusting for area-level covariates. In contrast, following covariate adjustment, IQR (0.72 μg/m 3 ) increases in PM 2.5 during pregnancy (adjusted [A]OR, 1.11 [95% CI, 1.04-1.19]; P  = .002) and childhood (AOR, 1.09 [95% CI, 1.00-1.19]; P  = .04) were associated with elevated odds for psychotic experiences, although for childhood exposure (model 4), there was no association after adjusting for pregnancy exposure. There was no association between noise pollution and psychotic experiences (eg, AOR, 1.04 [95% CI, 0.92-1.18]; P  = .50 during pregnancy).

Following covariate adjustment, IQR increases in PM 2.5 during pregnancy were associated with elevated odds for depression (eg, AOR, 1.10 [95% CI, 1.02-1.18]; P  = .01 during pregnancy). There were no associations between NO 2 (eg, AOR, 1.10 [95% CI, 0.98-1.24]; P  = .10 during pregnancy) or noise pollution (eg, AOR, 1.02 [95% CI, 0.89-1.18]; P  = .74 during pregnancy) and depression.

Before covariate adjustment, IQR increases in NO 2 in pregnancy (OR, 1.14 [95% CI, 1.04-1.26]; P  = .006) and childhood (OR, 1.15 [95% CI, 1.03-1.27]; P  = .009) were associated with elevated odds for anxiety, but associations were attenuated to the null after adjusting for area-level covariates. There were no associations between PM 2.5 exposure during childhood and anxiety (AOR, 1.10 [95% CI, 0.97-1.25]; P = .58 for model 3). In contrast, participants exposed to higher noise pollution in childhood (AOR, 1.19 [95% CI, 1.03-1.38]; P  = .02) and in adolescence (AOR, 1.22 [95% CI, 1.02-1.45]; P  = .03) had elevated odds for anxiety; however, adolescent exposure was attenuated to the null after controlling for pregnancy and childhood exposure (model 4). eTable 1 in Supplement 1 gives results when noise pollution was treated as categorical. This analysis highlighted several dose-response associations, although no difference in model fit was observed compared with the main results.

In eTables 2 and 3 in Supplement 1 , we take as examples the associations of pregnancy PM 2.5 with psychotic experiences and adolescent noise pollution with anxiety from model 3 and compare the E values to the associations from included covariates. The E value ORs were 1.46 (lower confidence limit, 1.24) for pregnancy PM 2.5 with psychotic experiences and 1.74 (lower confidence limit, 1.16) for adolescent noise pollution with anxiety. These E value ORs were larger in magnitude than the ORs for associations of the covariates with the exposures and outcomes, indicating that an unmeasured confounder would require a relatively strong confounding influence to nullify associations.

Results from sensitivity analyses are described in the eResults in Supplement 1 , presented in eTables 4 to 6 in Supplement 1 , and addressed in the eDiscussion in Supplement 1 . Briefly, point estimates were generally similar after adjusting pollutants for each other, similar (and often higher) for participants who did not move house, and similar for complete cases, although CIs were often less precise.

In this longitudinal birth cohort study with a follow-up of approximately 25 years, participants exposed to higher PM 2.5 during pregnancy and childhood subsequently experienced more psychotic experiences and (for pregnancy exposure only) depression. In contrast, higher noise pollution in childhood and adolescence were associated subsequently with more anxiety. These associations were not explained by numerous potential individual-, family-, and area-level confounders.

Our findings suggest an important role of early-life (including prenatal) exposure to air pollution in the development of youth mental health problems. Early-life exposure could be detrimental to mental health given the extensive brain development and epigenetic processes that occur in utero and during infancy. 13 , 15 , 49 , 50 Air pollution exposure could also lead to restricted fetal growth 51 and preterm birth, 52 which are both risk factors for psychopathology. Notably, the point estimate for pregnancy PM 2.5 and depression (10% elevated odds for every 0.72 μg/m 3 increase) was considerably greater than a previous meta-analytic estimate based on exposure in adulthood (10% elevated odds for every 10 μg/m 3 increase). 2 These contrasting findings are in keeping with a particularly detrimental role of early-life air pollution exposure. However, our findings could also have arisen if early-life exposure data provide a proxy for cumulative exposure over a longer period, given that families often settle when children are young.

For noise pollution, evidence was strongest for childhood and adolescent exposure. Childhood and adolescent noise pollution exposure could increase anxiety by increasing stress and disrupting sleep, with high noise potentially leading to chronic physiological arousal and disruption to endocrinology. 53 Noise pollution could also impact cognition, 54 which could increase anxiety by impacting concentration during school years. It was interesting that noise pollution was associated with anxiety but not with psychotic experiences or depression. However, our measure of noise pollution estimated only decibels (ie, intensity) from road sources. Other qualities of noise, such as pitch, could be relevant to mental health.

We acknowledge several limitations. First, the causality of the findings is uncertain given that data were observational. Despite comprehensive covariate adjustment, residual confounding is inevitable given imperfect selection and measurement of covariates. The relatively large E values strengthened our confidence in the findings, but future studies should consider other methods to address confounding, such as quasi-experimental designs. Second, ALSPAC families are more affluent and less diverse than the UK population. 55 The extent to which our findings generalize to other populations and locations is uncertain. Our findings likely generalize to cities and surrounds in other high-income countries, but may be less generalizable to urban settings in lower-income countries, which can have more extreme pollution concentrations. 56 Third, modeled pollution data are subject to various sources of measurement error, 39 particularly Berkson-like error whereby estimates are smoother (less variable) than reality, leading to less precise, although unbiased, exposure-outcome estimates. 57 , 58 For instance, the 100 m 2 resolution, although an improvement over many previous studies, 59 - 61 would have masked hyperlocal variation (eg, differences between participants living on adjacent streets), to which NO 2 is especially prone due to its short decay function. 62 Additionally, the model estimated residential exposure, which would have masked variation due to behavior and time spent away from home. Finer-resolution data, including personal exposure estimates, would enable more precise exposure-outcome estimates, particularly for NO 2 . Fourth, we could not apply life-course models to investigate sensitive periods vs cumulative effects, as there was limited within-person variation in exposure over time. Larger data sets (eg, national registries) and quasi-experimental designs would be required to further tease out this question.

The results of this cohort study provide novel evidence that early-life exposure to particulate matter is prospectively associated with the development of psychotic experiences and depression in youth. This study, which is among only a handful of longitudinal studies to investigate the association between noise pollution and mental health, also finds an association with anxiety. The findings suggest a degree of specificity in terms of pollutant-timing-outcome pathways. The opportunity for intervention is potentially enormous. However, although our this study addressed various biases affecting observational research, the causality of the findings remains uncertain. There is now a pressing need for further longitudinal research using more precise measures of air and noise pollution and for replication using quasi-experimental designs.

Accepted for Publication: March 15, 2023.

Published: May 28, 2024. doi:10.1001/jamanetworkopen.2024.12169

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Newbury JB et al. JAMA Network Open .

Corresponding Author: Joanne B. Newbury, PhD, Population Health Sciences, Bristol Medical School, Oakfield House, Bristol, BS8 2BN, United Kingdom ( [email protected] ).

Author Contributions: Dr Newbury had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Newbury, Kirkbride, Fisher, Bakolis.

Acquisition, analysis, or interpretation of data: Newbury, Heron, Kirkbride, Boyd, Thomas, Zammit.

Drafting of the manuscript: Newbury.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Newbury, Heron, Bakolis.

Obtained funding: Newbury, Zammit.

Administrative, technical, or material support: Boyd, Thomas.

Supervision: Heron, Kirkbride, Fisher, Bakolis, Zammit.

Conflict of Interest Disclosures: Prof Fisher reported receiving grants from the Economic and Social Research Council (ESRC) during the conduct of the study. Dr Heron and Prof Zammit are supported by a grant from the National Institute for Health and Care Research (NIHR) Biomedical Research Centre. Prof Fisher is supported by the ESRC Centre for Society and Mental Health at King’s College London. Dr Bakolis is supported in part by the NIHR Biomedical Research Centre at South London and Maudsley National Health Service (NHS) Foundation Trust and King’s College London and by the NIHR Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust. Messrs Boyd and Thomas are funded by the UK Medical Research Council (MRC) and ESRC to develop centralized record linkage services via the UK Longitudinal Linkage Collaboration and by Health Data Research UK to support the development of social and environmental epidemiology in longitudinal studies. No other disclosures were reported.

Funding/Support: The UK MRC and Wellcome Trust (grant 217065/Z/19/Z) and the University of Bristol provide core support for the Avon Longitudinal Study of Parents and Children (ALSPAC). This research was funded in whole, or in part, by grant 218632/Z/19/Z from the Wellcome Trust. This research was specifically funded by grants from the UK MRC to collect data on psychotic experiences, depression, and anxiety (MR/M006727/1 and G0701503/85179 to Prof Zammit); and a grant from the Natural Environment Research Council to facilitate linkage to geospatial and natural environment data (R8/H12/83/NE/P01830/1 to Mr Boyd). Dr Newbury is funded by Sir Henry Wellcome Postdoctoral Fellowship 218632/Z/19/Z from the Wellcome Trust and grant COV19/200057 from the British Academy.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: This publication is the work of the authors, and they serve as guarantors for the contents of this paper. The views expressed are those of the authors and not necessarily those of the ESRC or King’s College London.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We are extremely grateful to all the families who took part in this study; the midwives for their help in recruiting them; and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. We are also extremely grateful to ISGlobal, Barcelona, for conducting the LifeCycle project and generating the air and noise pollution data.

Additional Information: A comprehensive list of grant funding is available on the ALSPAC website ( http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgments.pdf ).

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Air Pollution: Current and Future Challenges

Despite dramatic progress cleaning the air since 1970, air pollution in the United States continues to harm people’s health and the environment. Under the Clean Air Act, EPA continues to work with state, local and tribal governments, other federal agencies, and stakeholders to reduce air pollution and the damage that it causes.

• Learn about more about air pollution, air pollution programs, and what you can do.

Outdoor air pollution challenges facing the United States today include:

• Meeting health-based standards for common air pollutants
• Limiting climate change
• Reducing risks from toxic air pollutants
• Protecting the stratospheric ozone layer against degradation

Indoor air pollution, which arises from a variety of causes, also can cause health problems. For more information on indoor air pollution, which is not regulated under the Clean Air Act, see EPA’s indoor air web site .

Air Pollution Challenges: Common Pollutants

Great progress has been made in achieving national air quality standards, which EPA originally established in 1971 and updates periodically based on the latest science. One sign of this progress is that visible air pollution is less frequent and widespread than it was in the 1970s.

However, air pollution can be harmful even when it is not visible. Newer scientific studies have shown that some pollutants can harm public health and welfare even at very low levels. EPA in recent years revised standards for five of the six common pollutants subject to national air quality standards. EPA made the standards more protective because new, peer-reviewed scientific studies showed that existing standards were not adequate to protect public health and the environment.

Status of common pollutant problems in brief

Today, pollution levels in many areas of the United States exceed national air quality standards for at least one of the six common pollutants:

• Although levels of particle pollution and ground-level ozone pollution are substantially lower than in the past, levels are unhealthy in numerous areas of the country. Both pollutants are the result of emissions from diverse sources, and travel long distances and across state lines. An extensive body of scientific evidence shows that long- and short-term exposures to fine particle pollution, also known as fine particulate matter (PM 2.5 ), can cause premature death and harmful effects on the cardiovascular system, including increased hospital admissions and emergency department visits for heart attacks and strokes. Scientific evidence also links PM to harmful respiratory effects, including asthma attacks. Ozone can increase the frequency of asthma attacks, cause shortness of breath, aggravate lung diseases, and cause permanent damage to lungs through long-term exposure. Elevated ozone levels are linked to increases in hospitalizations, emergency room visits and premature death. Both pollutants cause environmental damage, and fine particles impair visibility. Fine particles can be emitted directly or formed from gaseous emissions including sulfur dioxide or nitrogen oxides. Ozone, a colorless gas, is created when emissions of nitrogen oxides and volatile organic compounds react.  
• For unhealthy peak levels of sulfur dioxide and nitrogen dioxide , EPA is working with states and others on ways to determine where and how often unhealthy peaks occur. Both pollutants cause multiple adverse respiratory effects including increased asthma symptoms, and are associated with increased emergency department visits and hospital admissions for respiratory illness. Both pollutants cause environmental damage, and are byproducts of fossil fuel combustion.  
• Airborne lead pollution, a nationwide health concern before EPA phased out lead in motor vehicle gasoline under Clean Air Act authority, now meets national air quality standards except in areas near certain large lead-emitting industrial facilities. Lead is associated with neurological effects in children, such as behavioral problems, learning deficits and lowered IQ, and high blood pressure and heart disease in adults.  
• The entire nation meets the carbon monoxide air quality standards, largely because of emissions standards for new motor vehicles under the Clean Air Act.

In Brief: How EPA is working with states and tribes to limit common air pollutants

• EPA's air research provides the critical science to develop and implement outdoor air regulations under the Clean Air Act and puts new tools and information in the hands of air quality managers and regulators to protect the air we breathe.  
• To reflect new scientific studies, EPA revised the national air quality standards for fine particles (2006, 2012), ground-level ozone (2008, 2015), sulfur dioxide (2010), nitrogen dioxide (2010), and lead (2008). After the scientific review, EPA decided to retain the existing standards for carbon monoxide.  EPA strengthened the air quality standards for ground-level ozone in October 2015 based on extensive scientific evidence about ozone’s effects.

EPA has designated areas meeting and not meeting the air quality standards for the 2006 and 2012 PM standards and the 2008 ozone standard, and has completed an initial round of area designations for the 2010 sulfur dioxide standard. The agency also issues rules or guidance for state implementation of the various ambient air quality standards – for example, in March 2015, proposing requirements for implementation of current and future fine particle standards. EPA is working with states to improve data to support implementation of the 2010 sulfur dioxide and nitrogen dioxide standards.

For areas not meeting the national air quality standards, states are required to adopt state implementation plan revisions containing measures needed to meet the standards as expeditiously as practicable and within time periods specified in the Clean Air Act (except that plans are not required for areas with “marginal” ozone levels).

• EPA is helping states to meet standards for common pollutants by issuing federal emissions standards for new motor vehicles and non-road engines, national emissions standards for categories of new industrial equipment (e.g., power plants, industrial boilers, cement manufacturing, secondary lead smelting), and technical and policy guidance for state implementation plans. EPA and state rules already on the books are projected to help 99 percent of counties with monitors meet the revised fine particle standards by 2020. The Mercury and Air Toxics Standards for new and existing power plants issued in December 2011 are achieving reductions in fine particles and sulfur dioxide as a byproduct of controls required to cut toxic emissions.  
• Vehicles and their fuels continue to be an important contributor to air pollution. EPA in 2014 issued standards commonly known as Tier 3, which consider the vehicle and its fuel as an integrated system, setting new vehicle emissions standards and a new gasoline sulfur standard beginning in 2017. The vehicle emissions standards will reduce both tailpipe and evaporative emissions from passenger cars, light-duty trucks, medium-duty passenger vehicles, and some heavy-duty vehicles. The gasoline sulfur standard will enable more stringent vehicle emissions standards and will make emissions control systems more effective. These rules further cut the sulfur content of gasoline. Cleaner fuel makes possible the use of new vehicle emission control technologies and cuts harmful emissions in existing vehicles. The standards will reduce atmospheric levels of ozone, fine particles, nitrogen dioxide, and toxic pollution.

Learn more about common pollutants, health effects, standards and implementation:

• fine particles
• ground-level ozone
• sulfur dioxide
• nitrogen dioxide
• carbon monoxide

Air Pollution Challenges: Climate Change

EPA determined in 2009 that emissions of carbon dioxide and other long-lived greenhouse gases that build up in the atmosphere endanger the health and welfare of current and future generations by causing climate change and ocean acidification. Long-lived greenhouse gases , which trap heat in the atmosphere, include carbon dioxide, methane, nitrous oxide, and fluorinated gases. These gases are produced by a numerous and diverse human activities.

In May 2010, the National Research Council, the operating arm of the National Academy of Sciences, published an assessment which concluded that “climate change is occurring, is caused largely by human activities, and poses significant risks for - and in many cases is already affecting - a broad range of human and natural systems.” 1 The NRC stated that this conclusion is based on findings that are consistent with several other major assessments of the state of scientific knowledge on climate change. 2

Climate change impacts on public health and welfare

The risks to public health and the environment from climate change are substantial and far-reaching. Scientists warn that carbon pollution and resulting climate change are expected to lead to more intense hurricanes and storms, heavier and more frequent flooding, increased drought, and more severe wildfires - events that can cause deaths, injuries, and billions of dollars of damage to property and the nation’s infrastructure.

Carbon dioxide and other greenhouse gas pollution leads to more frequent and intense heat waves that increase mortality, especially among the poor and elderly. 3 Other climate change public health concerns raised in the scientific literature include anticipated increases in ground-level ozone pollution 4 , the potential for enhanced spread of some waterborne and pest-related diseases 5 , and evidence for increased production or dispersion of airborne allergens. 6

Other effects of greenhouse gas pollution noted in the scientific literature include ocean acidification, sea level rise and increased storm surge, harm to agriculture and forests, species extinctions and ecosystem damage. 7 Climate change impacts in certain regions of the world (potentially leading, for example, to food scarcity, conflicts or mass migration) may exacerbate problems that raise humanitarian, trade and national security issues for the United States. 8

The U.S. government's May 2014 National Climate Assessment concluded that climate change impacts are already manifesting themselves and imposing losses and costs. 9 The report documents increases in extreme weather and climate events in recent decades, with resulting damage and disruption to human well-being, infrastructure, ecosystems, and agriculture, and projects continued increases in impacts across a wide range of communities, sectors, and ecosystems.

Those most vulnerable to climate related health effects - such as children, the elderly, the poor, and future generations - face disproportionate risks. 10 Recent studies also find that certain communities, including low-income communities and some communities of color (more specifically, populations defined jointly by ethnic/racial characteristics and geographic location), are disproportionately affected by certain climate-change-related impacts - including heat waves, degraded air quality, and extreme weather events - which are associated with increased deaths, illnesses, and economic challenges. Studies also find that climate change poses particular threats to the health, well-being, and ways of life of indigenous peoples in the U.S.

The National Research Council (NRC) and other scientific bodies have emphasized that it is important to take initial steps to reduce greenhouse gases without delay because, once emitted, greenhouse gases persist in the atmosphere for long time periods. As the NRC explained in a recent report, “The sooner that serious efforts to reduce greenhouse gas emissions proceed, the lower the risks posed by climate change, and the less pressure there will be to make larger, more rapid, and potentially more expensive reductions later.” 11

In brief: What EPA is doing about climate change

Under the Clean Air Act, EPA is taking initial common sense steps to limit greenhouse gas pollution from large sources:

EPA and the National Highway and Traffic Safety Administration between 2010 and 2012 issued the first national greenhouse gas emission standards and fuel economy standards for cars and light trucks for model years 2012-2025, and for medium- and heavy-duty trucks for 2014-2018.  Proposed truck standards for 2018 and beyond were announced in June 2015.  EPA is also responsible for developing and implementing regulations to ensure that transportation fuel sold in the United States contains a minimum volume of renewable fuel. Learn more about clean vehicles

EPA and states in 2011 began requiring preconstruction permits that limit greenhouse gas emissions from large new stationary sources - such as power plants, refineries, cement plants, and steel mills - when they are built or undergo major modification. Learn more about GHG permitting

• On August 3, 2015, President Obama and EPA announced the Clean Power Plan – a historic and important step in reducing carbon pollution from power plants that takes real action on climate change. Shaped by years of unprecedented outreach and public engagement, the final Clean Power Plan is fair, flexible and designed to strengthen the fast-growing trend toward cleaner and lower-polluting American energy. With strong but achievable standards for power plants, and customized goals for states to cut the carbon pollution that is driving climate change, the Clean Power Plan provides national consistency, accountability and a level playing field while reflecting each state’s energy mix. It also shows the world that the United States is committed to leading global efforts to address climate change. Learn more about the Clean Power Plan, the Carbon Pollution Standards, the Federal Plan, and model rule for states

The Clean Power Plan will reduce carbon pollution from existing power plants, the nation’s largest source, while maintaining energy reliability and affordability.  The Clean Air Act creates a partnership between EPA, states, tribes and U.S. territories – with EPA setting a goal, and states and tribes choosing how they will meet it.  This partnership is laid out in the Clean Power Plan.

Also on August 3, 2015, EPA issued final Carbon Pollution Standards for new, modified, and constructed power plants, and proposed a Federal Plan and model rules to assist states in implementing the Clean Power Plan.

On February 9, 2016, the Supreme Court stayed implementation of the Clean Power Plan pending judicial review. The Court’s decision was not on the merits of the rule. EPA firmly believes the Clean Power Plan will be upheld when the merits are considered because the rule rests on strong scientific and legal foundations.

On October 16, 2017, EPA  proposed to repeal the CPP and rescind the accompanying legal memorandum.

EPA is implementing its Strategy to Reduce Methane Emissions released in March 2014. In January 2015 EPA announced a new goal to cut methane emissions from the oil and gas sector by 40 – 45 percent from 2012 levels by 2025, and a set of actions by EPA and other agencies to put the U.S. on a path to achieve this ambitious goal. In August 2015, EPA proposed new common-sense measures to cut methane emissions, reduce smog-forming air pollution and provide certainty for industry through proposed rules for the oil and gas industry . The agency also proposed to further reduce emissions of methane-rich gas from municipal solid waste landfills . In March 2016 EPA launched the National Gas STAR Methane Challenge Program under which oil and gas companies can make, track and showcase ambitious commitments to reduce methane emissions.

EPA in July 2015 finalized a rule to prohibit certain uses of hydrofluorocarbons -- a class of potent greenhouse gases used in air conditioning, refrigeration and other equipment -- in favor of safer alternatives. The U.S. also has proposed amendments to the Montreal Protocol to achieve reductions in HFCs internationally.

Learn more about climate science, control efforts, and adaptation on EPA’s climate change web site

Air Pollution Challenges: Toxic Pollutants

While overall emissions of air toxics have declined significantly since 1990, substantial quantities of toxic pollutants continue to be released into the air. Elevated risks can occur in urban areas, near industrial facilities, and in areas with high transportation emissions.

Numerous toxic pollutants from diverse sources

Hazardous air pollutants, also called air toxics, include 187 pollutants listed in the Clean Air Act. EPA can add pollutants that are known or suspected to cause cancer or other serious health effects, such as reproductive effects or birth defects, or to cause adverse environmental effects.

Examples of air toxics include benzene, which is found in gasoline; perchloroethylene, which is emitted from some dry cleaning facilities; and methylene chloride, which is used as a solvent and paint stripper by a number of industries. Other examples of air toxics include dioxin, asbestos, and metals such as cadmium, mercury, chromium, and lead compounds.

Most air toxics originate from manmade sources, including mobile sources such as motor vehicles, industrial facilities and small “area” sources. Numerous categories of stationary sources emit air toxics, including power plants, chemical manufacturing, aerospace manufacturing and steel mills. Some air toxics are released in large amounts from natural sources such as forest fires.

Health risks from air toxics

EPA’s most recent national assessment of inhalation risks from air toxics 12 estimated that the whole nation experiences lifetime cancer risks above ten in a million, and that almost 14 million people in more than 60 urban locations have lifetime cancer risks greater than 100 in a million. Since that 2005 assessment, EPA standards have required significant further reductions in toxic emissions.

Elevated risks are often found in the largest urban areas where there are multiple emission sources, communities near industrial facilities, and/or areas near large roadways or transportation facilities. Benzene and formaldehyde are two of the biggest cancer risk drivers, and acrolein tends to dominate non-cancer risks.

In brief: How EPA is working with states and communities to reduce toxic air pollution

EPA standards based on technology performance have been successful in achieving large reductions in national emissions of air toxics. As directed by Congress, EPA has completed emissions standards for all 174 major source categories, and 68 categories of small area sources representing 90 percent of emissions of 30 priority pollutants for urban areas. In addition, EPA has reduced the benzene content in gasoline, and has established stringent emission standards for on-road and nonroad diesel and gasoline engine emissions that significantly reduce emissions of mobile source air toxics. As required by the Act, EPA has completed residual risk assessments and technology reviews covering numerous regulated source categories to assess whether more protective air toxics standards are warranted. EPA has updated standards as appropriate. Additional residual risk assessments and technology reviews are currently underway.

EPA also encourages and supports area-wide air toxics strategies of state, tribal and local agencies through national, regional and community-based initiatives. Among these initiatives are the National Clean Diesel Campaign , which through partnerships and grants reduces diesel emissions for existing engines that EPA does not regulate; Clean School Bus USA , a national partnership to minimize pollution from school buses; the SmartWay Transport Partnership to promote efficient goods movement; wood smoke reduction initiatives; a collision repair campaign involving autobody shops; community-scale air toxics ambient monitoring grants ; and other programs including Community Action for a Renewed Environment (CARE). The CARE program helps communities develop broad-based local partnerships (that include business and local government) and conduct community-driven problem solving as they build capacity to understand and take effective actions on addressing environmental problems.

Learn more about air toxics, stationary sources of emissions, and control efforts Learn more about mobile source air toxics and control efforts

Air Pollution Challenges: Protecting the Stratospheric Ozone Layer

The  ozone (O 3 ) layer  in the stratosphere protects life on earth by filtering out harmful ultraviolet radiation (UV) from the sun. When chlorofluorocarbons (CFCs) and other ozone-degrading chemicals  are emitted, they mix with the atmosphere and eventually rise to the stratosphere. There, the chlorine and the bromine they contain initiate chemical reactions that destroy ozone. This destruction has occurred at a more rapid rate than ozone can be created through natural processes, depleting the ozone layer.

The toll on public health and the environment

Higher levels of  ultraviolet radiation  reaching Earth's surface lead to health and environmental effects such as a greater incidence of skin cancer, cataracts, and impaired immune systems. Higher levels of ultraviolet radiation also reduce crop yields, diminish the productivity of the oceans, and possibly contribute to the decline of amphibious populations that is occurring around the world.

In brief: What’s being done to protect the ozone layer

Countries around the world are phasing out the production of chemicals that destroy ozone in the Earth's upper atmosphere under an international treaty known as the Montreal Protocol . Using a flexible and innovative regulatory approach, the United States already has phased out production of those substances having the greatest potential to deplete the ozone layer under Clean Air Act provisions enacted to implement the Montreal Protocol. These chemicals include CFCs, halons, methyl chloroform and carbon tetrachloride. The United States and other countries are currently phasing out production of hydrochlorofluorocarbons (HCFCs), chemicals being used globally in refrigeration and air-conditioning equipment and in making foams. Phasing out CFCs and HCFCs is also beneficial in protecting the earth's climate, as these substances are also very damaging greenhouse gases.

Also under the Clean Air Act, EPA implements regulatory programs to:

Ensure that refrigerants and halon fire extinguishing agents are recycled properly.

Ensure that alternatives to ozone-depleting substances (ODS) are evaluated for their impacts on human health and the environment.

Ban the release of ozone-depleting refrigerants during the service, maintenance, and disposal of air conditioners and other refrigeration equipment.

Require that manufacturers label products either containing or made with the most harmful ODS.

These vital measures are helping to protect human health and the global environment.

The work of protecting the ozone layer is not finished. EPA plans to complete the phase-out of ozone-depleting substances that continue to be produced, and continue efforts to minimize releases of chemicals in use. Since ozone-depleting substances persist in the air for long periods of time, the past use of these substances continues to affect the ozone layer today. In our work to expedite the recovery of the ozone layer, EPA plans to augment CAA implementation by:

Continuing to provide forecasts of the expected risk of overexposure to UV radiation from the sun through the UV Index, and to educate the public on how to protect themselves from over exposure to UV radiation.

Continuing to foster domestic and international partnerships to protect the ozone layer.

Encouraging the development of products, technologies, and initiatives that reap co-benefits in climate change and energy efficiency.

Learn more About EPA’s Ozone Layer Protection Programs

Some of the following links exit the site

1 National Research Council (2010), Advancing the Science of Climate Change , National Academy Press, Washington, D.C., p. 3.

2 National Research Council (2010), Advancing the Science of Climate Change , National Academy Press, Washington, D.C., p. 286.

3 USGCRP (2009).  Global Climate Change Impacts in the United States . Karl, T.R., J.M. Melillo, and T.C. Peterson (eds.). United States Global Change Research Program. Cambridge University Press, New York, NY, USA.

4 CCSP (2008).  Analyses of the effects of global change on human health and welfare and human systems . A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. Gamble, J.L. (ed.), K.L. Ebi, F.G. Sussman, T.J. Wilbanks, (Authors). U.S. Environmental Protection Agency, Washington, DC, USA.

5 Confalonieri, U., B. Menne, R. Akhtar, K.L. Ebi, M. Hauengue, R.S. Kovats, B. Revich and A. Woodward (2007). Human health. In:  Climate Change 2007: Impacts, Adaptation and Vulnerability  .  Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change  Parry, M.L., O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, (eds.), Cambridge University Press, Cambridge, United Kingdom.

7 An explanation of observed and projected climate change and its associated impacts on health, society, and the environment is included in the EPA’s Endangerment Finding and associated technical support document (TSD). See EPA, “ Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act ,” 74 FR 66496, Dec. 15, 2009. Both the Federal Register Notice and the Technical Support Document (TSD) for Endangerment and Cause or Contribute Findings are found in the public docket, Docket No. EPA-OAR-2009-0171.

8 EPA, Endangerment Finding , 74 FR 66535.

9 . U.S. Global Change Research Program, Climate Change Impacts in the United States: The Third National Climate Assessment , May 2014.

10 EPA, Endangerment Finding , 74 FR 66498.

11 National Research Council (2011) America’s Climate Choices: Report in Brief , Committee on America’s Climate Choices, Board on Atmospheric Sciences and Climate, Division on Earth and Life Studies, The National Academies Press, Washington, D.C., p. 2.

12 EPA, 2005 National-Scale Air Toxics Assessment (2011).

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Adversity-hope hypothesis: Air pollution raises lottery demand in China

• Open access
• Published: 13 August 2021
• Volume 62 , pages 247–280, ( 2021 )

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• Soo Hong Chew 1 , 2 ,
• Haoming Liu 2 , 3 &
• Alberto Salvo 2  

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“The heart that is well prepared for any fate hopes in adversity and fears in prosperity. ” – Horace, 65-8 BC “ It is principally at games of chance that a multitude of illusions support hope and sustain it against unfavorable chances. ” – Laplace, A Philosophical Essay on Probabilities, 1796/1902

The empirical literature points to a stylized phenomenon of increased demand for hope following adversity. Clotfelter and Cook ( 1989 ) suggest that hope is a key sentiment underpinning recreational gambling. Chew and Ho (1994, this journal) offer the view of hope being experienced in lottery products when people enjoy delaying the resolution of uncertainty. Taking air quality as an indicator of subjective well-being, we hypothesize a positive causal relationship between air pollution and lottery sales. We test this hypothesis using data from China and find that air pollution measured by particle concentration increases demand for a popular lottery for which province-level daily sales records exist. The relationship can readily be seen on combining high-frequency, spatially resolved lottery sales and particle pollution data. Our findings support the adversity-hope hypothesis in the context of air quality and lottery sales and point to further tests using other measures of adversity and proxies of demand for hope.

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1 Introduction

Risk taking underpins much of the economic activity in any modern society. At one extreme of the risk-taking spectrum lies financial investment, which is deliberate, consequential, and focused especially in the capital markets. At the other extreme, we have recreational risk taking and gambling which tend to be impulsive, low-stake, augmented by non-conscious cues and insensitivity to the inherently unfavorable odds. The perception of risk ranges from being a necessary evil to be tamed and managed in financial risk taking to an object of fun and entertainment to be enjoyed in moderation in recreational risk taking. Apart from pathological gamblers, recreational risk takers tend to be conscious of the prospect of losing while they enjoy gaming activities in the hope of winning, as prefaced by the quotation from Laplace in the epigraph.

Recreational gambling such as participating in lotteries has been part and parcel of human societies since time immemorial. In Selling Hope , Clotfelter and Cook ( 1989 ) argue that lottery products, characterized by small chances of sizable winnings at low prices, fulfill a demand for hope often by those of lower socioeconomic status. On the supply side, the state lottery has been viewed as a form of regressive taxation (Price & Novak, 1999 ). Earlier attempts to modeling a demand for lottery products in various non-expected utility models center around a sense of optimism arising from the overweighting of small winning probabilities. From this perspective, Chark et al. ( 2020 ) investigate the popular approach to modeling optimism using a probability weighting function directly as in Kahneman and Tversky ( 1979 ), and alternatively by applying an outcome-based salience function based on Chew ( 1983 ). Intuitively, optimism may underpin the consumer’s hope to win despite unfavorable odds (Lopes, 1987 ).

Chew and Ho (1994, this journal, henceforth CH94) and Lovallo and Kahneman ( 2000 ) offer a novel strand of thinking of hope as a sentiment underpinning lottery demand, in which individuals enjoy delaying the resolution of uncertainty. CH94 provide preference specifications that capture a hope-induced utility for preferring later resolution of uncertainty. For some lottery products, such as New York State’s Instant Scratch-off, the lottery outcome can be resolved immediately after purchase. Most other lottery products involve some delay between the time of purchase and the drawing of winning outcomes. A popular form of gambling in mainland China today is the daily three-digit lottery (3D). A 3D ticket costs CNY 2 and pays CNY 1,000 if the three digits picked by a bettor are drawn during the live broadcast on national TV the evening of purchase. Footnote 1 On average 21 million 3D lottery tickets are sold daily by China’s Welfare Lottery authority, equivalent to about 2 daily bets for every 100 persons aged 16 years and above.

The empirical literature points to a stylized phenomenon of an increase in demand for hope following adversity, for instance, during economic downturns or after natural disasters. This is corroborated by studies that find a positive relation between economic hardship and gambling or lottery purchases. Mikesell ( 1994 ) discusses the positive relationship between state unemployment and lottery sales in the US. Olason et al. ( 2017 ) find an increase in gambling participation but not problem gambling after the 2008 economic collapse in Iceland. Writing in the aftermath of the Great Recession, Zezima ( 2008 ) notes that “sweet dreams in hard times add to lottery sales.” In the wake of heavy snow and an earthquake in China in 2008, Li et al. ( 2011 ) report increased lottery demand (a 0.1% chance to win CNY 10,000 at a price of CNY 10) in affected vs. non-affected areas. Footnote 2 Isidore ( 2017 ) reports on the “billions of dollars [that] go unclaimed in lottery prizes” pointing out that some people may even not bother to check if they won. For such consumers, the hope component of a lottery itself may be worth the ticket price.

We are led to posit an “adversity-hope hypothesis,” as prefaced by Horace in the epigraph, with lottery sales serving as a proxy for hope. To test this hypothesis, we examine a routine source of moderate-level adversity in the form of environmental pollution. Particles in ambient air—that contribute to haze or smog—are arguably the most visible time-varying manifestation of environmental pollution in developing countries, including China (Lelieveld et al., 2015 ; Marlier et al., 2016 ). When it is visible, this form of environmental degradation can affect a healthy human not only through inhalation but also through perception (Watson, 2002 ; Zhang et al., 2015 ). It is hard to miss a hazy vs. blue-sky day in Beijing, Delhi, or Jakarta even if one is able to stay inside, behind windows that properly shut. Recent papers have also documented the acute effect of air pollution on subjective well-being (Levinson, 2012 ; Zhang et al., 2017 ; Zheng et al., 2019 ). In this context of air pollution, our adversity-hope hypothesis would entail an increase in lottery ticket sales when air quality declines—with the mechanism being a preference for later resolution of uncertainty but not optimism.

We examine a possible relation between air quality proxied by PM2.5 (particulate matter up to 2.5 μ m in diameter) and daily sales of the 3D lottery which is generally purchased during the day and is drawn at 9 pm. The fixed-prize-fixed-odds nature of the 3D lottery, paying CNY 1,000 at 1/1000 winning odds and for which we have daily provincial-level sales data over multiple years is ideal for our investigation. We find evidence that is compatible with the adversity-hope hypothesis. Controlling for month-of-sample, day-of-week, and weather, flexibly by province, 3D betting rises and falls in step with PM2.5 (haze) pollution. The positive causal relationship that we document can readily be seen in Fig.  1 on combining the high-frequency, spatially resolved lottery sales and PM2.5 records.

Demeaned 3D lottery sales against demeaned daytime PM2.5, after subtracting province-level means. An observation is a province by day in the five years from 2013 to 2017. There are 32 provinces in the data. The top panel shows sales in CNY million, the bottom panel reports the natural logarithm of sales. For better visualization, we include locally weighted polynomials of best fit and omit a few outlying realizations (e.g., percentiles 1 and 99 of the distribution of demeaned log 3D sales are -0.6 and 0.7)

We provide regression evidence based on alternative identifying assumptions. Studies of the socioeconomic impact of pollution need to contend with the possibility that pollution is associated with unobserved day-to-day variation in economic activity. For instance, high-frequency shifts in labor demand and changes in daily wages could directly impact lottery sales. For this reason, one estimator we implement focuses on air quality variation attributable to naturally and exogenously occurring shifts in atmospheric regime (“thermal inversions”). We are then able to rule out economic activity as a candidate explanation for the positive relationship between lottery sales and pollution, which is co-produced with economic activity.

We further find that, fixing a day’s average pollution dose, daily 3D sales are higher when the pollution dose is allocated toward the morning and away from the afternoon. This is consistent with the expectation sense of hope since morning purchases deliver more time for the consumption of hope in a lottery ticket purchase prior to its resolution in the evening.

The empirical analysis in this paper supports the adversity-hope hypothesis with air quality shifting hope attitudes. Our paper contributes to a recent literature documenting the acute impacts of air pollution on socioeconomic outcomes. These include cognitive performance (Zhang et al., 2018 ; Archsmith et al., 2018 ), self-reported happiness and subjective well-being (Levinson, 2012 ; Zhang et al., 2017 ; Zheng et al., 2019 ), individual investor activity (Meyer and Pagel, 2017 ; Li et al., 2017 ), decision-making (Chew et al., 2021 ), test scores (Ham et al., 2014 ; Ebenstein et al., 2016 ; Graff Zivin et al., 2020 ), and crime and unethical behavior (Lu et al., 2018 ; Herrnstadt et al., 2019 , 2021 ). Footnote 3

In the next section, we posit the adversity-hope hypothesis. Section  3 tests the adversity-hope hypothesis in the setting of daily lottery sales and an environment with routinely high and exogenously varying air pollution in China. We present the institutional setting, show evidence of a relationship in the raw data, and implement empirical models of lottery demand that seek to establish both internal and external validity. Section  4 summarizes and provides a discussion.

2 Adversity-hope hypothesis

To model the adversity-hope hypothesis exposited in the Introduction, we apply the CH94 model about hope being experienced “when there is enjoyment in delaying the resolution of uncertainty.” A lottery ticket typically involves a future period at which the final uncertainty is resolved. For such lotteries, attitude toward timing of uncertainty resolution has some bearing on the perceived attitude toward risk. Even if one is averse toward risks that resolve in the present period, one may still buy “hope” by purchasing a lottery ticket to enjoy the anticipation of winning.

We adopt the notation from CH94 in a two-period setting. Period-1 consumption is certain and is denoted by y whereas the uncertain consumption in period 2 is denoted by \(m = \sum q_{i}[z_{i}]\) , a lottery which pays z i with probability q i (subscript i denotes a realization). Together, these flows constitute a consumption plan [ y , m ] in which y is consumed in period 1 and lottery m is consumed as period 2 begins but the consumer does not yet know which z i payoff she will receive until uncertainty is resolved.

The above \(\sum q_{i}[z_{i}]\) notation has the advantage of enabling us to differentiate it from another case, denoted by \(\sum q_{i}[y, z_{i}]\) , in which uncertainty about period-2 consumption z i is resolved early along with the certain period-1 consumption y , i.e., the consumer already knows the specific z i payoff to be consumed in period 2 as period 1 begins. More generally, we refer to a temporal lottery , denoted by \(\sum q_{i}[y_{i}, m_{i}]\) , as a stochastic consumption plan [ y i , m i ] which delivers the specific plan [ y i , m i ] with probability q i . Footnote 4

Let M denote the set of temporal lotteries. Consider two alternative temporal lotteries, each involving consumption of y in period 1:

The LHS of ( 1 ) represents early resolution in period 1 of the period-2 consumption that obtains—i.e., m versus \(m^{\prime }\) , with probabilities α and 1 − α . By contrast, in the RHS the uncertainty of period-2 consumption resolves later. The enjoyment of hope corresponds to a preference for the temporal lottery described in the RHS of ( 1 ) rather than in the LHS.

We refer to a situation in which a consumer is indifferent between either temporal lottery, LHS vs. RHS irrespective of α , y , m , and \(m^{\prime }\) , as one of timing indifference , following Kreps and Porteus ( 1978 ), which offers the following preference specification over temporal lotteries. Let W , called an aggregator (in the sense that it aggregates utility across periods), be a continuous and increasing function on Y × Y . For instance, consider a simple form of the intertemporal aggregator function of consumption y and z in periods 1 and 2: Footnote 5

The recursive expected utility (REU) of a temporal lottery d which yields [ y i , m i ] with probability q i is given by:

where μ e ( m ) = u − 1 ( E [ u , m ]) is the certainty equivalent of lottery m under expected utility, i.e., accounting for the within-period aversion to risk under concave utility function u . As shown in CH94, an REU consumer is hopeful if \([y, \alpha m + (1 - \alpha )m^{\prime }]\) is preferred to early resolution \(\alpha [y, m] + (1 - \alpha )[y, m^{\prime }]\) when the concavity of the v function dominates and the composite h = v ∘ u − 1 is concave, i.e.,

Note that standard discounted additive expected utility theory corresponds to the case of a linear h function. This case necessarily satisfies timing indifference and cannot exhibit hopefulness. To capture our adversity-hope hypothesis, we posit that the hope function h would become more concave at times of adversity. Footnote 6 To induce a baseline demand for a state lottery, we can augment the above analysis by applying some level of probability overweighting through a non-expected utility (NEU) model, such as weighted utility (WU) or reference-dependent utility (RDU), of the certainty equivalent of the period-2 uncertainty (Chark et al., 2020 ). The hope function then plays a modulating role of the baseline demand through changes in its curvature.

In the next section, we test the implication of the CH94 hope model in the setting of air quality and daily sales of a fixed-prize-fixed-odds lottery in China. An adverse environment would tend to increase the value of a lottery ticket purchased in the morning whose outcome will be revealed in the evening, giving rise to greater demand for the lottery. Absent a hope channel, deteriorating air quality associated with increased risk aversion or reduced optimism might lower, rather than increase, lottery demand.

3 Evidence from daily lottery sales

3.1 institutional setting and data.

Gambling was banned in China from 1949, the year the People’s Republic of China was founded, until 1984. That year, the Chinese Sports Association was allowed to offer betting on sporting events with the aim of funding the Beijing International Marathon. The following year, the State Council authorized the National Sports Commission to offer betting on a regular basis. In 1987, the Ministry of Civil Affairs was allowed to sell lottery products to fund social welfare and provide for public goods. Three decades later, the state-run Welfare Lottery and Sports Lottery authorities have similar revenue shares of China’s regulated lottery duopoly (Betting Guide, 2018 ; AGTech, 2018 ). Footnote 7

“Number picking” (hereafter, number) lotteries, supplied by the two national-level agencies, are the most popular accounting for 62% of lottery sales (Ministry of Finance, 2017 ). These consist of 3D, Double Color Balls, and Lotto 7 supplied by the Welfare Lottery, and PL3, PL5, Big Lotto, and Seven Stars from the Sports Lottery. The Double Color Balls, Lotto 7, Big Lotto, and Seven Stars run thrice weekly and have variable prizes that depend on whether there are rollovers from the preceding rounds. By contrast, the 3D, PL3, and PL5 have fixed winning prizes and are run daily.

Table  1 describes the sales data available for 32 areas, including 22 provinces, five Autonomous Regions, and five major cities, hereafter 32 provinces. Footnote 8 Detailed daily sales data are publicly available for 3D for all 32 provinces. Sales for the other number lotteries are available only as aggregated data for the entire country without geographic resolution. With about 21 million bets placed daily, 3D sales in 2017 of CNY 16 billion were double that of the other two daily fixed-prize number lotteries, PL3 and PL5, combined. With the availability of daily ambient particle levels by province starting in 2013, we examine daily 3D sales data over the five years 2013 to 2017.

In the 3D lottery, a player pays CNY 2 and picks three integers (0-9) and one of three possible formats: (i) exact order, (ii) two numbers repeated, and (iii) no repeated numbers. The winning odds is 1/1000 in format (i). For format (ii), the winning odds is 3/1000 since there are three equivalent combinations of any three winning numbers, e.g., 122, 212 or 221. Similarly, the winning odds for format (iii) is 6/1000 since there are six equivalent combinations of three winning numbers, e.g., 149, 194, 419, 491, 914 or 941. The corresponding winning amounts are CNY 1,040, CNY 346, and CNY 173 with expected payoffs of CNY 1.04 for format (i) and CNY 1.038 for formats (ii) and (iii). Online distribution remains restricted, and there were no officially sanctioned online platforms by 2018 (Betting Guide, 2018 ; xinhuanet.com, 2018b ). The day’s sales, winning numbers, and number of winners by bet format are broadcast live, at 9 pm, on channel one of the Chinese Education TV Network (CETV-1) (cwl.gov.cn, 2017 ; AGTech, 2018 ).

Starting on January 18, 2013, PM2.5 concentrations at all surface-level air monitoring sites—about 1,500 across the country—became available, by hour, through the Chinese Ministry of Ecology and Environment (MEE). For each day and hour, we average across sites within a province to obtain a province-level mean. Footnote 9 For each day, we then average these province-level means over daytime/daylight hours, from 6 am to 8 pm inclusive.

Figure  2 shows wide PM2.5 variability within province over the 1,760 days in the study period, as well as across the 32 provinces. The 10th percentile in the combined lottery-environment sample over days and provinces is 17 μ g/m 3 , already in the range of human visibility (Watson, 2002 ). Most province-day observations amply exceed the US National Ambient Air Quality Standards, marked by the vertical lines (NAAQS annual at 12 μ g/m 3 , 24-hour at 35 μ g/m 3 ). The 90th percentile at 99 μ g/m 3 is severe. At such a level it is conceivable that individuals reduce their time outdoors including, where not too costly, staying more at home (He et al., 2017 ). Such potential avoidance behavior, as we subsequently discuss, would render our estimates conservative.

Distribution of daily mean PM2.5 mass concentrations (× 10 2 μ g/m 3 ) over days (within panel) and across provinces (across panels). We compute daily mean PM2.5 during daytime hours from 6 am to 8 pm, based on hourly PM2.5 averaged across monitoring sites within each province. The sample period is January 2013 to December 2017. For better visualization, we show density up to 200 μ g/m 3 and rescale the density function such that the maximum is 1 (otherwise plots for provinces with high PM2.5 such as Hebei are too flat). The annual and 24-hour US National Ambient Air Quality Standards of 12 and 35 μ g/m 3 are marked by the vertical lines. Source: MEE

Besides PM2.5 and seasonal factors, our empirical models allow ambient temperature and humidity to directly affect lottery demand. For example, people may behave differently on a hot and humid summer day. From the US National Oceanic and Atmospheric Administration (NOAA), we obtain surface temperature and dew point depression, recorded every 12 hours, for 93 observatories across China. Footnote 10

In addition, NOAA data for these locations and times include wind speed and direction at surface as well as atmospheric thermal gradients (Table  1 ). These gradients, which fluctuate exogenously, indicate how temperature varies with altitude and whether there is a layer of warmer air overhead that traps pollutants close to the surface where they are emitted. We use atmospheric thermal gradients to instrument for PM2.5, to the extent that high-frequency unobservable shocks to economic activity are present and significantly influence PM2.5 (Seibert et al., 2000 ; Tang et al., 2016 ).

3.2 Adversity-hope hypothesis in the raw data

With lottery proxying for the demand for hope, the adversity-hope hypothesis is corroborated by Fig.  1 showing a positive relationship between 3D lottery sales against PM2.5 by province-day observation in the absence of any regression controls. To help visualize the data, we subtract province-level means from each variable and show locally weighted polynomial fits. Lottery sales are high in province-days with high and visible environmental degradation. The relationship seen in the raw data is further confirmed by the rigorous regression evidence provided next. The hypothesis is that, via a hope channel, smog and haze shift households’ preferences over the timing of uncertainty resolution, thus raising lottery sales.

3.3 Empirical models of lottery demand

We estimate variations of the empirical model of lottery demand:

where the natural logarithm of 3D lottery sales in province i and day t , q i t , is regressed on a parametric (e.g., quadratic) function of the key variable of interest P i t , province-day specific PM2.5 exposure, and controls. As in other developing countries, PM2.5 dominates China’s local air quality indices, with particle levels in outdoor air well in the visible range. The interpretation is that of PM2.5 as a general indicator (Dominici et al., 2010 ) of the severity of haze, including even smaller particles (ultrafine PM 0.01 to 0.1) that are not routinely monitored in China or elsewhere (He et al., 2019 ). Footnote 11

Province-day specific weather controls W i t include two complete sets of bins, each bin 5 ∘ C wide, for ambient temperature and dew point depression. Footnote 12 We average variables across a day’s two readings, at 8 am and 8 pm local time, prior to taking 5 ∘ C bins. We interact each set of bins with a complete set of province fixed effects (FE), flexibly allowing for the impact of weather on lottery sales to vary by province.

Time controls γ i t include complete sets of month(-of-year) FE, year FE, day-of-week FE, and a dummy variable for public holidays, to allow for systematic demand shifts over the years in the sample, within year, within week, and with the holiday calendar. We interact each set of time controls (month, year, day-of-week, public holiday) with province FE. This flexibly allows the effects of season on lottery sales to vary by province. We note that 3D sales and PM2.5 co-vary over the seasons, both variables increasing in the more adverse winter months of December-January relative to June-August. Since many factors can explain seasonal correlation, it is absorbed by the γ i t . A model variant replaces month-by-province FE and year-by-province FE with even tighter month-by-year-by-province FE, i.e., month-of-sample interacted with province.

Winning prizes for the 3D lottery were raised in August 2014, namely from CNY 1,000 to CNY 1,040 (for the exact-order format), from CNY 320 to CNY 346 (two numbers repeated format), and from CNY 160 to CNY 173 (no repeated numbers format). To allow for a change in winning prizes to shift demand, akin to a price change, we include a post August 2014 dummy interacted with province FE in the vector of time controls; this allows for cross-sectional variation in prize sensitivity. Beyond these increases in winning prizes, we note that the price per 3D bet has not changed from CNY 2 and that the supply of 3D bets is elastic. We thus interpret 𝜖 i t as an idiosyncratic demand shock.

By way of sensitivity analysis, we specify a cubic function of PM2.5 instead of a quadratic. Another variant of demand model ( 5 ) includes PM2.5 on each of the three preceding calendar days in addition to PM2.5 on the concurrent day of 3D sales. We also interact PM2.5 with dummies for China’s six different regions, allowing for geographic variation in the estimated impact of pollution on lottery demand.

Allowing for endogenous PM2.5. We estimate models both by ordinary least squares (OLS) and by two-stage least squares (2SLS). The OLS estimator assumes that PM2.5 exposure is measured without error and that omitted variables that correlate with lottery demand and PM2.5 are absent. This assumption may fail in the presence of components of economic activity that co-vary with emissions and that are not already accounted for by controls γ i t that capture systematic province-specific seasonal and day-of-week influences and time trends.

An alternative 2SLS estimator assumes that whether the atmosphere is stagnant or well ventilated has no bearing on other possible drivers of lottery demand, such as economic activity. These atmospheric regime shifts, which we observe through recorded thermal gradients, are excluded from equation ( 5 ), only affecting lottery demand through PM2.5 (Zhang et al., 2015 ; Tang et al., 2016 ; Liu & Salvo, 2018 ; He et al., 2019 ). Province-day specific atmospheric regime conditions, denoted by A i t , are a key determinant of local PM2.5 (Figs. A.4 and A.5 ) and are unlikely to correlate with unobserved lottery demand shocks, 𝜖 i t (exogeneity). Footnote 13

The 2SLS identifying assumption would be violated were a special kind of pollution control policy in place. Consider a hypothetical scenario in which emissions permits issued by a strong and well-informed regulator responded to day-to-day atmospheric regime shifts. In this hypothetical scenario, 2SLS estimates may be downward biased (conservative). For instance, atmospheric stagnation would lower industrial activity as regulators attempted to mitigate the high PM2.5 levels; canceled work shifts would reduce the purchase of lottery tickets through an income channel or a decline in commuter traffic (less workers passing by street-based lottery outlets on their way to work). We do not believe that such a scenario befits China’s environmental regulations and, in any case, such a scenario would render our 2SLS estimates conservative.

To use a household analogy for air pollutant dispersion and removal, shifting from a stagnant to a well-ventilated atmosphere is akin to flushing the toilet. Figs. A.4 and 5 illustrate the strong correlation between PM2.5 and key elements of A i t : (i) thermal gradients and (ii) wind. PM2.5 tends to be high: (i) when there is a positive (less negative) atmospheric temperature gradient in altitude, as the thermal inversion traps surface emissions; Footnote 14 and (ii) when horizontal dissipation is poor, characterized by low wind speeds.

We thus use atmospheric removal conditions A i t to form an instrument for measured pollution P i t , estimating the following auxiliary equation by OLS flexibly by province i :

Included in A i t are linear and quadratic terms for thermal gradients in the atmosphere’s five layers closest to the surface and wind speed, as well as wind direction. Footnote 15 To account for the build-up of particles during multi-day inversion episodes, we include same-day and prior-day conditions, and interact a dummy for light wind (up to 1 m/s) with each thermal gradient. δ i t are time fixed effects (month-by-year, day-of-week, public holiday), and ν i t is a disturbance.

We fit \(\hat {P}_{it}\) using the auxiliary atmospheric regime-pollution model ( 6 ) and employ these fitted values to instrument for measured P i t in the lottery demand equation ( 5 ). To obtain 2SLS estimates, we still generate first-stage predictions of P from the excluded instrument \(\hat {P}_{it}\) and the non-pollution covariates in the second-stage lottery demand equation. Footnote 16

To be clear, A i t does not include surface temperature or humidity, which we allow to directly affect lottery demand. Our results are robust to dropping wind speed from A i t and instead modeling this variable as a lottery demand shifter W i t in ( 5 ). In our setting, winds are mild and unlikely to affect lottery demand. Wind speeds average 2.2 m/s compared to 4.6 m/s and 3-4 m/s in Chicago and Los Angeles (Herrnstadt et al., 2019 , 2021 ; Anderson 2020 ).

3.4 Adversity in environment increases lottery demand

Table  2 reports estimates for alternative models of the causal effect of PM2.5 pollution on daily 3D lottery demand. Impacts estimated with 2SLS, in specifications 5 to 8, are somewhat larger than with OLS, in specifications 1 to 4. This is consistent with either some measurement error in pollution exposure or some unobserved day-to-day variation in economic activity and emissions, which the 2SLS models account for. For instance, on unobservable busy days workers/individuals have less time to purchase lotteries and emissions are higher, suggesting downward biased OLS estimates. In specifications 1 (OLS) and 5 (2SLS), with a quadratic in daytime PM2.5, a shift from 10 to 50 μ g/m 3 increases a day’s 3D sales by 1.1-1.3 log points, with day-clustered standard errors of 0.2-0.3 log points (the dependent variable is the log of sales). 2SLS estimates are generally less precise. The coefficient on the quadratic term is negative such that pollution’s marginal effect falls as PM2.5 rises from 10 μ g/m 3 (the first percentile of PM2.5 over days and provinces). Figure  3 visualizes the impact of a large—but still in sample—PM2.5 shift from 10 to 200 μ g/m 3 , converted to a 95% confidence interval (CI) of percentage increase: Lottery demand increases by about 3.0 percent in specifications 1 and 5.

Percent increase in daily 3D lottery demand due to a 10 to 200 μ g/m 3 in-sample shift in daytime PM2.5 levels (6 am to 8 pm mean). The figure plots the 95% confidence interval for the PM2.5 impact based on each of the 8 OLS and 2SLS regression models reported in Table  2 , implemented on the 3D province-day sample

In specifications 2 and 6, estimates grow on replacing month-by-province and year-by-province intercepts with more flexible month-by-year-by-province intercepts. The effect of PM2.5 on lottery demand is now estimated from co-variation within month-of-sample and province. These detailed controls absorb a few isolated spikes (e.g., Beijing in October 2014) and breaks (e.g., Jianxi from March 2015 on) that we observe in the sales data. In specifications 3 and 7, estimated impacts are similar when we add a cubic term in PM2.5, suggesting that the quadratic form is not restrictive. Estimates on same-day PM2.5 are similar, in specifications 4 and 8, when we include linear and quadratic terms for PM2.5 in each of the three days preceding the specific daily draw. Across all models in Fig.  3 (Table  2 ), the impact of a 10 to 200 μ g/m 3 PM2.5 shift on daily 3D lottery demand ranges from + 2.9 to + 5.1 percent, and is significant at the 1% level throughout.

In Table A.1 , we repeat the quadratic PM2.5 specification on separate sub-samples by region of China (north, northeast, northwest, east, southwest, and southcentral). OLS estimates are positive for all regional sub-samples, and statistically significant for six of the seven regions. 3D sales impacts range from + 1.6 to + 7.4 percent for a 10 to 200 μ g/m 3 PM2.5 shift. 2SLS estimates are more dispersed, ranging from 0.0 to + 13.3 percent, and less precise. (We discuss the case of independently managed Taiwan below.)

Table  3 restricts the sample to the two municipalities with province status in the 3D lottery data, Beijing and Shanghai, where the US State Department monitors PM2.5 since 2008 and 2011, respectively. We use US State Department rather than MEE PM2.5 records at these granular geographic areas (cities, not provinces), add controls for daytime precipitation (obtained from NASA), and temporally extend the sample over one decade. Similar estimates based on alternative US State Department pollution records for two major cities reassure us of our findings. Footnote 17

Table  4 shows evidence that morning PM2.5 impacts lottery sales more than afternoon PM2.5. Fixing a change in the daytime mean PM2.5 (6 am to 8 pm), say from 10 to 200 μ g/m 3 , the impact on lottery sales is more pronounced when the morning is polluted (and the afternoon has cleared) than when the onset of haze happens in the afternoon (and the morning was clear). We interpret this pattern as being consistent with an individual spending the day savoring the prospect of winning, ahead of the resolution of uncertainty.

3.4.1 Robustness

The Appendix provides further sub-sample analysis and robustness tests. Table A.2 shows OLS and 2SLS estimates for the quadratic PM2.5 specification implemented separately on (i) the 16 provinces with higher PM2.5 in the sample; (ii) the 16 less polluted provinces; (iii) the colder and often more polluted season from October to March; and (iv) the warmer April to September season. PM2.5’s estimated impact on daily 3D demand is similar and significant whether we stratify the sample by provinces’ typical air quality or by season.

Table A.3 reports on several robustness tests, with similar positive and significant effects estimated throughout. Estimates hardly change when we replace the 5 ∘ C-wide ambient temperature bins by a quadratic in temperature, continuing to interact temperature covariates with province intercepts for flexibility. Effects shrink slightly when we progressively drop dew point depression and temperature controls, or when we correct for seasonality using week-of-year rather than month-of-year intercepts (always interacted with province). Very granular week controls likely absorb some variation of interest—and Table A.4 follows Isen et al. ( 2017b ) and goes further still, specifying day-of-year rather than month-of-year or week-of-year.

Continuing with Table A.3 , similar results obtain when replacing year intercepts with a trend (again province-specific), or when the estimation sample is trimmed at the 1st and 99th percentiles of the province-specific distribution of daily 3D sales or restricted to weekdays outside of public holidays. We also specify daily PM2.5 as the 24-hour mean rather than the 15-hour daytime mean. Table A.7 shows sensitivity analysis to the parametric form of PM2.5, specifying PM2.5 (i) in bins, (ii) as a linear spline function with knots set at 50 and 100 μ g/m 3 , and (iii) in logarithmic form, as alternatives to the quadratic and cubic functional forms of Table  2 . We find a robust concave lottery demand response to air pollution. Finally, weighting the regression by a province’s 2010 Census population yields similar estimates (not shown).

3.4.2 Demand for other number lotteries

We find that demand for other number lotteries, with available data aggregated for the entire country, is positively associated with a weighted average for PM2.5 across China. Besides the 3D examined above, we consider the two other fixed-prize lotteries and the four variable-prize lotteries offering highly skewed prospects of winning millions of CNY (in contrast to CNY 1,000 in the 3D). The PL3 lottery has the same rules as the 3D. The PL5 lottery pays CNY 100,000 if a player picks five winning integers (0-9) in the exact order. Like the 3D, each PL3 and PL5 bet costs CNY 2. The rules for variable-prize lotteries are detailed in the Appendix .

Table  5 reports OLS estimates for the log of each product’s nationwide sales—varying daily in the case of fixed-prize lotteries, and thrice weekly otherwise—regressed on a quadratic function of average PM2.5 with controls for weather and time. Nationally aggregated environmental variables are weighted averages of their provincial-level counterparts, where the weights are the provincial shares of China’s 2013-2017 3D lottery sales (results are similar if we use population shares as weights).

The top panel controls for month and year intercepts whereas the bottom panel specifies month-by-year intercepts to better account for fluctuating sales over the period. For comparability, columns 1 and 8 report on aggregated 3D lottery sales. Figure  4 shows the corresponding 95% CI. In columns 2 and 3, an in-sample aggregate PM2.5 shift from 10 to 140 μ g/m 3 is associated with statistically significant increases in aggregate PL3 and PL5 lottery sales, of 4.2 and 5.7 percent respectively. With more flexible month-by-year intercepts, columns 9 and 10 show increases of 3.3 and 6.0 percent. Despite the use of a different data source, namely the Sports Lottery, the observed pattern is consistent with that of the 3D from the Welfare Lottery.

Percent increase in the nationwide sales of seven number lotteries associated with a 10 to 140 μ g/m 3 in-sample shift in PM2.5, averaged across China. The figure plots the 95% confidence interval associated with a PM2.5 shift based on the OLS regression models reported in Table  5 , implemented on daily time series (3D, PL3, and PL5 fixed-prize lotteries drawn every day) or thrice weekly time series (Double Color Balls, Lotto 7, Big Lotto, and 7 Stars variable-prize lotteries drawn three days every week). Models 1-7 specify month and year intercepts (top panel). Models 8-14 specify month-by-year intercepts (bottom panel)

Estimated associations for PM2.5 and variable-prize lottery sales are mostly significantly positive. In columns 11 to 14, a 10 to 140 μ g/m 3 PM2.5 shift is associated with Double Color Balls, Lotto 7, Big Lotto, and 7 Stars sales increases of 6.8, 6.5, 6.9, and 4.4 percent, all positive and the first three separately statistically significant (7 Stars is the smaller of the four by sales). Since the variable-prize lotteries are drawn only three times per week, on spaced-out days, here we add lagged PM2.5 exposure (and report the cumulative impact) to account for sales on days preceding a draw. Despite the loss of geographic resolution, we are reassured by the results from the nationwide time series. These are illustrated in the scatterplots of Fig.  5 , showing aggregated sales for the different lotteries against aggregated PM2.5.

Residuals of log nationwide sales, for the six largest number lotteries, against residuals of nationally aggregated PM2.5 on the day of the draw, after partialing out time fixed effects (month-by-year, day-of-week, and public holiday) and weather (quadratics in temperature and dew point depression) from each time series. An observation is a day (draw) in the period January 2013 to December 2017. The fixed-prize lotteries in panels a to c have draws every day, so the time series are daily. The variable-prize lotteries in panels d to f have three draws per week, so the frequency of the time series are thrice weekly. Environmental variables are aggregated across provinces as described in Table  1 . For better visualization, the panels omit a few outlying residuals and show locally weighted polynomial fits

3.4.3 External validity: Taiwan

Authorities in Taiwan sell daily fixed-prize number lotteries that are very similar to (mainland) China’s 3D, PL3, and PL5 (Taiwanlottery.com.tw, 2019 ). Taiwan’s three- and four-digit 3D and 4D are similarly popular as their counterparts in China. A time series of daily sales across Taiwan is publicly available for the period 2014 to 2018. Exposure to particle pollution in Taiwan tends to be lower than in mainland China, yet PM2.5 routinely rises above the US NAAQS and varies in the range of human visibility. Here we look to this independently provided dataset for validity of our main analysis. We find that in Taiwan, too, PM2.5 significantly raises daily lottery demand.

The provider, Taiwan’s state-run Welfare Lottery, is not related to China’s lottery authorities. Similar to China’s 3D, Taiwan’s 3D and 4D run daily (except on Sunday), with winning numbers drawn between 8 and 9 pm. To place a bet in either the 3D or the 4D, a player pays 25 New Taiwan dollars (NT$), or about US$ 0.80, and picks three integers in the 3D, or four integers in the 4D. Top prizes are paid out if a bet matches the winning number in exact order, with winning odds and winning amounts of, respectively, 1/1000 and NT$12,500 in the 3D, and 1/10,000 and NT$125,000 in the 4D (and expected payoffs of NT$12.5 alike). As in China’s 3D, betting formats other than the exact-order format can be purchased. We obtained publicly available daily sales spatially aggregated for Taiwan over the five-year period between January 2014 and December 2018. Footnote 18

Hourly PM2.5 concentrations at all surface-level air monitoring sites—76 sites across 22 Taiwanese cities—are available from Taiwan’s Environmental Protection Administration. Following our main analysis of lottery demand in China, for each day and hour, we average PM2.5 across sites within a city; for each day, we then average these city-level means over daytime hours, from 6 am to 8 pm. Finally, Taiwan-aggregated PM2.5 are population-weighted averages of city-level daytime PM2.5. We obtain Taipei’s surface and atmospheric meteorological conditions, recorded every 12 hours, from NOAA. Again following our main analysis, NOAA data provides times series of weather controls W t , namely ambient temperature and dew point depression, and of atmospheric pollutant removal variables A t .

Table  6 reports estimates for OLS and 2SLS models of the causal effect of PM2.5 pollution on daily 3D and 4D lottery demand in Taiwan. Since the 90th and 99th percentiles of daytime PM2.5 in the combined lottery-environment sample for Taiwan are 35 μ g/m 3 and 50 μ g/m 3 (compared with 99 μ g/m 3 and 214 μ g/m 3 in the mainland China sample), we report the impact of a PM2.5 shift from 10 to 50 μ g/m 3 . We find that PM2.5 significantly increases 3D and 4D lottery sales. While point estimates tend to be larger, particularly under the 2SLS identifying assumption, evidence based on Taiwanese daily time series is consistent with our results using the province-day sample for mainland China. We note that some presence of online sales in Taiwan (Taiwanlottery.com.tw, 2019 ) can, by diminishing the potential role of outdoor avoidance in reducing lottery sales, explain the larger PM2.5 impact compared to what we find in mainland China.

3.4.4 Heterogeneity

We return to the province-day sample for 3D lottery sales in China. We cautiously explore whether the relationship between haze-induced adversity and lottery demand exhibits heterogeneity along some margins, for example, over the weekly cycle, with colder weather, and with short-run variation in the increasingly popular Shanghai Stock Exchange (SSE) composite index. To our baseline specifications in Table  2 (OLS and 2SLS), we include as regressors interaction terms between PM2.5 (and its square) and variables that might be related to the demand for hope. We control for the levels of these variables as well, allowing them to correlate with lottery demand directly. Footnote 19 Table  7 reports on a subset of the specifications we have implemented.

We first allow the economic and ambient environments to interact. As motivation, recall that Mikesell ( 1994 ) finds that US state lottery sales increase with state-level unemployment, suggesting that lottery demand rises when the economic environment deteriorates. Kumar ( 2009 ) documents that lottery expenditure is positively correlated with investment in lottery-type stocks, and that the demand for lottery-type stocks increases during economic downturns. It is plausible that stock market performance (a variable that moves daily) may correlate with lottery demand, and the participation rate in China’s stock market is already quite high, e.g., 138 million Chinese owned stocks in 2018 (xinhuanet.com, 2018a ).

Columns 1 and 5 consider short-run stock market performance. A sharp fall in the stock market can be adversely perceived by individuals and potentially affect their hope attitudes and demand for lotteries. We generate a binary indicator that equals one if the one-day return in the Shanghai Stock Exchange on the previous day is in the bottom 10% of the distribution of one-day returns over the study period. The estimation sample thus excludes Sunday and Monday as there is no trading on Saturday and Sunday. As explained, we interact PM2.5 covariates with the Poor Stock Market indicator.

We find that the positive relationship between a day’s lottery sales and PM2.5 grows stronger when the stock market performed badly the day before, that is, when high-frequency economic adversity and environmental adversity interact. We also explored interactions between PM2.5 and five-day or seven-day returns, obtaining similar associations. One possible interpretation is that when “bad news” interact, such as haze and a poor stock market, the desire for “good news” to look forward to may grow stronger. While finding this association interesting, we caution that both short-run stock market performance and lottery demand may be responding to wider economic variables.

Research suggests that daily returns in the US stock market may vary systematically with day of the week (Birru, 2018 ). We consider whether the effect of PM2.5 on lottery demand differs by type of day. To our baseline specifications we include interactions between PM2.5 and separate indicators for Monday, Saturdays and Sundays/public holidays (we have explored other specifications). Columns 2 and 6 show that the PM2.5 effect is not significantly heterogeneous over the different weekdays and is perhaps stronger on Saturday.

We consider the interaction between PM2.5 and cold weather. We generate an indicator for cold days, defined as temperature in the bottom 10% of the province-day sample. Columns 3 and 7 report that the effect of PM2.5 on lottery demand is not significantly different on cold vs. normal days. If anything, the effect of PM2.5 on lottery demand is somewhat lower on cold days, which is consistent with avoidance of the cold outdoors (less gamblers visiting street-based lottery outlets irrespective of haze).

It is possible that individuals stay inside with space-heating on cold days. Studies have documented that, unlike their northern counterparts, cities south of the Huai river and Qin mountains do not enjoy subsidized heating, despite winter temperatures occasionally falling below 0 ∘ C (e.g., Chen et al., 2013 ; Chu et al., 2018 ). We generate an indicator for the absence of subsidized heating, equal to one if the province’s capital city lies south of the Huai-Qin boundary. To check whether the impact of haze on lottery demand depends on temperature when it is difficult for households to seek comfort indoors, to our baseline specification we add a triple interaction between the cold-day indicator, the no-subsidized-heating indicator, and PM2.5. Columns 4 and 8 suggest that the impact of PM2.5 on lottery demand may be stronger in cold vs. non-cold days in cities where heating is not subsidized, but this difference is not statistically significant.

4 Discussion

Drawing on both laboratory and field observations, the economics and psychology literatures reveal a tendency for individuals to display longshot preference, i.e., risk affinity towards small chances of sizable gains, while simultaneously exhibiting aversion to risk involving moderate chances of winning (Kahneman and Tversky, 1979 ; Chark et al., 2020 ). Theoretical models offer preferential foundations for the observed concurrence of longshot preference behavior revealed in lottery purchases and pervasive risk aversion evident in capital markets (see, e.g., Quiggin 1991 ; Chew & Tan 2005 ). The literature has also developed models of preference over the timing of uncertainty resolution—or hope attitude—being distinct from attitude towards risk (Kreps & Porteus, 1978 ; Chew & Epstein, 1989 ).

Empirical evidence supports the notion that individuals tend to exhibit hopefulness when facing longshots, even choosing to delay their resolution (Chew & Ho, 1994 ). Lotteries have been described as “selling hope” (Clotfelter & Cook, 1989 ), and “are perhaps best understood in terms of the anticipatory fantasies and hopeful excitement that they permit” (Lovallo & Kahneman, 2000 ). In this regard, one may draw a further distinction between hope underpinned by optimism as in probability overweighting versus hope in terms of savoring the prospect of a positive resolution of uncertainty in the future.

An empirical literature points to increased demand for hope products, including lotteries and lottery-type stocks, at times of economic and environmental adversity (e.g., Mikesell 1994 ; Kumar 2009 ; Li et al., 2011 ; Olason et al. 2017 ). Footnote 20 To some, candles are symbols of hope, and the press commonly shows footage of crowds holding lit candles in the aftermath of adverse events—whether natural or manmade, for example, an earthquake, a mass shooting, or the death of a celebrity (such as the Princess of Wales killed in a car accident in 1997). Besides an increase in hope, the literature finds that adversity may also induce less optimism and thus more pessimism in people (Kivimäki et al., 2005 ).

The empirical relationship we present adds to these findings across disciplines. Our design and robustness across data sources heightens our confidence that the result is not a statistical artifact or due to confounding factors. We study adversity in the form of pollution shocks that vary widely and visibly over time and space in a country of land area comparable to the US. The purchase of lottery tickets is widespread across Chinese households, not confined to problem or pathological gamblers. Footnote 21 Morning pollution has a larger impact than pollution in the afternoon, when the time to uncertainty resolution is shorter. We estimate a similarly large and statistically significant relationship using data from different state lottery providers—two separate authorities for mainland China, yet another for Taiwan.

We obtain a positive relationship despite possible avoidance behavior (Chu et al., 2020 ). Were routine haze to induce some individuals to stay at home, and given the limited opportunities for online lottery purchase directly from home, we conjecture that pollution’s impact on lottery demand would be higher still in the absence of avoidance or the presence of online sales. Indeed, this is consistent with our estimates for Taiwan.

Recent papers point to a positive association between air pollution and risk aversion based on overall stock market data (Levy & Yagil, 2011 ; Li & Peng, 2016 ; Li et al., 2017 ). This is in line with the direct finding of increased aversion to even-chance risk in (Chew et al., 2021 ), because financial investments generally involve deliberation over more-or-less evenly distributed risks with positive returns in which risk aversion plays a key role. In contrast, lotteries concern skewed prospects with negative actuarial values, typically involving delayed resolutions, in which a more impulsive motive such as hope may be at play. In this regard, there also are lottery-type stocks (Kumar, 2009 ) which exhibit positive skewness and excess kurtosis with poorer returns relative to the overall market.

Future work using observational or experimental evidence can verify whether adversity shocks—including pollution, snow, stock markets, conflict, terrorism, pandemics—shift the demand for lotteries in terms of the timing of uncertainty resolution, or induce substitution across lottery and investment products. Lottery sales data at a granular spatial level such as neighborhood or city may be available and combined with demographic data such as income. Spain’s two-century-old multi-billion Christmas lottery, with advance sales taking place as early as July, points to the prevalence of consumption of hope. Our adversity-hope hypothesis predicts that, all else equal, such lottery sales would grow with, for example, terrorist-group ETA bombings in time and space. Footnote 22 Individual-level data can further allow one to assess whether adversity induces a compositional change in the demand for lottery and investment products.

In the context of a theoretical literature that models hope and optimism as drivers of lottery demand, we interpret our results for pollution-induced daily lottery sales in China through the lens of the adversity-hope hypothesis. In the face of adversity, an increase in lottery demand is plausibly due to hope rather than to optimism (feeling lucky or confident). An increase in optimism when confronted with air pollution would be hard to square with studies that find that haze reduces subjective well-being (Zhang et al., 2017 ; Zheng et al., 2019 ). By the adversity-hope hypothesis, hazy and smoggy days shift preferences over the timing of uncertainty resolution. As the environment around them grows more severe, individuals may seek “good news” with greater fervor. Says an Arab proverb (noted in CH94), “there is fear in every hope, and hope in every fear.”

The winning odds are 1/1000. One US dollar is equivalent to about 6.5 Chinese yuan (CNY).

In the context of a major earthquake in New Zealand, Sibley and Bulbulia ( 2012 ) provide evidence that secular people turn to religion at times of crisis. North et al. ( 2005 ) document that residents of Nairobi, Kenya relied on religious support after terrorist bombings.

For a review of this cross-disciplinary literature, see Lu ( 2020 ).

In this general form the period-2 realization is itself a lottery m i rather than a specific outcome z i .

We omit the discount factor between period 1 and period 2 given the short duration of the uncertainty. In our empirical setting, a consumer can purchase a 3D lottery ticket during the day and resolve the uncertainty by the evening, when the winning numbers are announced. Some bettors may choose to learn of the winning numbers later, in subsequent days, as they have up to 60 days to collect any prize.

Suppose v = x ρ and u = x λ so that h = x ρ / λ , then the hope parameter given by ρ / λ would decrease with adversity.

2017 official lottery sales were CNY 427 billion.

The cities are the four officially designated Municipalities with provincial status plus Shenzhen. The data exclude the Special Administrative Zones of Hong Kong and Macau. Mainland China further excludes Taiwan, but we subsequently examine Taiwanese lottery sales for external validity.

This simple averaging procedure places more weight in more populated locations, such as cities, which tend to host more air monitoring sites, besides more potential gamblers.

Each province is matched to the observatory that is nearest to a province’s capital city, with distance ranging from 0 km (Heilongjiang) to 136 km (Anhui). The median and 75th percentile observatory-to-capital distance across provinces are 12 km and 37 km.

That the US State Department monitors PM2.5 in several Chinese cities (and elsewhere in the developing world) underscores the relative threat particles pose. To our knowledge, the US State Department does not monitor air pollutants other than PM2.5.

At a given temperature, relative humidity decreases in the dew point depression. Figs. A.1 to A.3 describe how de-seasoned daily ambient temperature, dew point depression, and PM2.5 co-vary, by province.

Using A i t -induced PM2.5 circumvents the unobservable presence of lottery demand influences that may co-vary with PM2.5 and confound our estimate of the causal effect of haze on lottery demand. Intuitively, while OLS relies on all PM2.5 variation after accounting for weather, season, and trends at the provincial level, 2SLS relies only on regime-induced PM2.5 shifts that plausibly do not co-vary with potentially confounding economic activity.

The label inversion reflects the fact that gradients are usually negative, i.e., temperatures drop at higher altitudes. The reported patterns, and the recent PM2.5 records (Ghanem & Zhang, 2014 ), bolster our confidence both in the quality of the environmental data and in the identifying strategy.

Table  1 describes these variables. We compute temperature differences between the following pressure points in the lower atmosphere: (1) from surface to 1000 mb, (2) 1000 to 925 mb, (3) 925 to 850 mb, (4) 850 to 700 mb, and (5) 700 to 500 mb. Table A.5 further lists elements of A i t .

Isen et al. ( 2017a ), Liu and Salvo ( 2018 ), and He et al. ( 2019 ) similarly instrument for pollution using fitted pollution imputed, respectively, from policy and atmospheric interventions. As an alternative to using fitted values from ( 6 ), we can use A i t to instrument for pollution (Angrist and Krueger, 2001 )—see Table A.5 for sensitivity analysis. Fig. A.6 reports on instrument strength.

These regressions further indicate that 3D lottery sales fall by about 4 percent in both Beijing and Shanghai when daytime rainfall exceeds 4 mm. Controlling for rain slightly reduces the impact of PM2.5 on 3D sales. This is consistent with the notion that heavy rain, while uncommon, may wash out particles and, at the same time, lead to less gamblers visiting lottery outlets (due to avoidance of rain).

For brevity, Table  1 does not describe this additional lottery-environment sample. Sources include http://www.taiwanlottery.com.tw/Lotto/4D/history.aspx , Taiwan’s Ministry of the Interior, and https://taqm.epa.gov.tw/taqm/en/default.aspx .

To implement 2SLS we interact these variables with instruments for PM2.5 and its square.

In this context, 2020 has been marked by significant growth in non-institutional (retail or “day”) traders in lottery-type stocks and rising equity prices in conjunction with a once-in-a-century pandemic.

By way of identification, we specify up to month-by-year-by-province intercepts to control for time-varying omitted determinants of lottery demand—particularly local economic activity—thus relying on co-variation in lottery demand and pollution within month-of-sample and province. To allow for even higher frequency shocks to economic activity, from one day to the next, we condition on pollution variation induced by local atmospheric regime shifts, to which economic activity is unlikely to respond.

(Bagues & Esteve-Volart, 2016 ) and (Kent & Martinez-Marquina, 2020 ) examine income shocks from Spain’s Christmas lottery. As another example of spatial data, (Guryan & Kearney, 2010 ) use lottery sales at the store level available for Texas (across 1,386 cities and 3,660 nine-digit zip codes). Much of the economic literature studying lottery data uses lottery wins as exogenous income shocks (e.g., Imbens et al., 2001 ; Lindahl 2005 ; Hankins et al., 2011 ; Kuhn et al., 2011 ), rather than a variable to be explained as is our case (lottery demand). In principle, these empirical settings provide further opportunities to test the adversity-hope hypothesis.

AGTech. (2018). The People’s Republic of China (PRC): A large but still under-penetrated lottery market. www.agtech.com/html/lottery-reg/index-en.html , Accessed on June 17, 2018.

Anderson, M.L. (2020). As the wind blows: the effects of long-term exposure to air pollution on mortality. Journal of the European Economic Association , 18 (4), 1886–1927.

Article   Google Scholar  

Angrist, J.D., & Krueger, A.B. (2001). Instrumental variables and the search for identification: from supply and demand to natural experiments. Journal of Economic Perspectives , 15 (4), 69–85.

Archsmith, J., Heyes, A., & Saberian, S. (2018). Air quality and error quantity: Pollution and performance in a high-skilled, quality-focused occupation. Journal of the Association of Environmental and Resource Economists , 5 (4), 827–863.

Bagues, M., & Esteve-Volart, B. (2016). Politicians’ luck of the draw: Evidence from the Spanish Christmas lottery. Journal of Political Economy , 124 (5), 1269–1294.

Betting Guide. (2018). Gambling law of Chinese Mainland. https://www.onlinebetting.com/legal/china/ , Accessed on June 17, 2018.

Birru, J. (2018). Day of the week and the cross-section of returns. Journal of Financial Economics , 130 (1), 182–214.

Chark, R., Chew, S.H., & Zhong, S. (2020). Individual preference for longshots. Journal of the European Economic Association , 18 (2), 1009–1039.

Chen, Y., Ebenstein, A., Greenstone, M., & Li, H. (2013). Evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River policy. Proceedings of the National Academy of Sciences , 110 (32), 12936–12941.

Chew, S.H. (1983). A generalization of the quasilinear mean with applications to the measurement of income inequality and decision theory resolving the Allais paradox. Econometrica , 51 (4), 1065–1092.

Chew, S.H., & Epstein, L.G. (1989). The structure of preferences and attitudes towards the timing of the resolution of uncertainty. International Economic Review , 30 (1), 103–117.

Chew, S.H., & Ho, J.L. (1994). Hope: an empirical study of attitude toward the timing of uncertainty resolution. Journal of Risk and Uncertainty , 8 (3), 267–288.

Chew, S.H., Huang, W., & Li, X. (2021). Does haze cloud decision making? a natural laboratory experiment. Journal of Economic Behavior & Organization , 182 , 132–161.

Chew, S.H., & Tan, G. (2005). The market for sweepstakes. Review of Economic Studies , 72 (4), 1009–1029.

Chu, J., Liu, H., & Png, I.P.L. (2018). Nonlabor income and age at marriage: Evidence from China’s Heating Policy. Demography , 55 (6), 2345–2370.

Chu, J., Liu, H., & Salvo, A. (2020). Air pollution as a determinant of food delivery and related plastic waste. Nature Human Behaviour October , 19 , 1–14.

Google Scholar  

Clotfelter, C.T., & Cook, P.J. (1989). Selling Hope: State Lotteries in America . Cambridge: Harvard University Press.

cwl.gov.cn. (2017). China’s Welfare Lottery. www.cwl.gov.cn/c/2017-11-15/418907.shtml , Accessed on June 17, 2018.

Dominici, F., Peng, R.D., Barr, C.D., & Bell, M.L. (2010). Protecting human health from air pollution: Shifting from a single-pollutant to a multipollutant approach. Epidemiology , 21 (2), 187–194.

Ebenstein, A., Lavy, V., & Roth, S. (2016). The long-run economic consequences of high-stakes examinations: Evidence from transitory variation in pollution. American Economic Journal: Applied Economics , 8 (4), 36–65.

Ghanem, D., & Zhang, J. (2014). Effortless perfection: Do Chinese cities manipulate air pollution data? Journal of Environmental Economics and Management , 68 (2), 203–225.

Graff Zivin, J., Liu, T., Song, Y., Tang, Q., & Zhang, P. (2020). The unintended impacts of agricultural fires: Human capital in China. Journal of Development Economics , 147 , 102560.

Guryan, J., & Kearney, M.S. (2010). Is lottery gambling addictive?. American Economic Journal: Economic Policy , 2 (3), 90–110.

Ham, J.C., Zweig, J.S., & Avol, E. (2014). Pollution, test scores and the distribution of academic achievement: Evidence from California schools, 2002-2008. Unpublished manuscript.

Hankins, S., Hoekstra, M., & Skiba, P.M. (2011). The ticket to easy street? the financial consequences of winning the lottery. Review of Economics and Statistics , 93 (3), 961–969.

He, J., Gouveia, N., & Salvo, A. (2019). External effects of diesel trucks circulating inside the Sao Paulo megacity. Journal of the European Economic Association , 17 (3), 1–43.

He, J., Liu, H., & Salvo, A. (2019). Severe air pollution and labor productivity:, Evidence from industrial towns in China. American Economic Journal: Applied Economics , 11 (1), 1–31.

He, X., Luo, J., & Zhang, J. (2017). When airpocalypse met blockbusters: The impact of air pollution on the box office. Unpublished manuscript.

Herrnstadt, E., Heyes, A., Muehlegger, E., & Saberian, S. (2019). Air pollution as a cause of violent crime. Unpublished manuscript.

Herrnstadt, E., Heyes, A., Muehlegger, E., & Saberian, S. (2021). Air pollution and criminal activity: Microgeographic evidence from Chicago. American Economic Journal: Applied Economics Forthcoming.

Imbens, G.W., Rubin, D.B., & Sacerdote, B.I. (2001). Estimating the effect of unearned income on labor supply, earnings, savings, and consumption: Evidence from a sample of lottery players. American Economic Review , 91 (4), 778–794.

Isen, A., Rossin-Slater, M., & Walker, W.R. (2017a). Every breath you take, every dollar you’ll make: The long-term consequences of the Clean Air Act of 1970. Journal of Political Economy , 125 (3), 848–902.

Isen, A., Rossin-Slater, M., & Walker, W.R. (2017b). Relationship between season of birth, temperature exposure, and later life wellbeing. Proceedings of the National Academy of Sciences , 114 (51), 13447–13452.

Isidore, C. (2017). Why billions of dollars go unclaimed in lottery prizes. New York Times, August 24.

Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica , 47 (2), 263–91.

Kent, C., & Martinez-Marquina, A. (2020). When a town wins the lottery: Evidence from Spain. Unpublished manuscript.

Kivimäki, M., Vahtera, J., Elovainio, M., Helenius, H., Singh-Manoux, A., & Pentti, J. (2005). Optimism and pessimism as predictors of change in health after death or onset of severe illness in family. Health Psychology , 24 (4), 413–421.

Kreps, D.M., & Porteus, E.L. (1978). Temporal resolution of uncertainty and dynamic choice theory. Econometrica , 46 (1), 185–200.

Kuhn, P., Kooreman, P., Soetevent, A.R., & Kapteyn, A. (2011). The effects of lottery prizes on winners and their neighbors: Evidence from the Dutch Postcode Lottery. American Economic Review , 101 (5), 2226–2247.

Kumar, A. (2009). Who gambles in the stock market? Journal of Finance , 64 (4), 1889–1933.

Lelieveld, J., Evans, J.S., Fnais, M., Giannadaki, D., & Pozzer, A. (2015). The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature , 525 (7569), 367–371.

Levinson, A. (2012). Valuing public goods using happiness data: the case of air quality. Journal of Public Economics , 96 (9), 869–880.

Levy, T., & Yagil, J. (2011). Air pollution and stock returns in the US. Journal of Economic Psychology , 32 (3), 374–383.

Li, J., Massa, M., Zhang, H., & Zhang, J. (2017). Behavioral bias in haze: Evidence from air pollution and the disposition effect in China. Unpublished manuscript.

Li, J.-Z., Li, S., Wang, W.-Z., Rao, L.-L., & Liu, H. (2011). Are people always more risk averse after disasters? Surveys after a heavy snow-hit and a major earthquake in China in 2008. Applied Cognitive Psychology , 25 (1), 104–111.

Li, Q., & Peng, C.H. (2016). The stock market effect of air pollution: Evidence from China. Applied Economics , 48 (36), 3442–3461.

Lindahl, M. (2005). Estimating the effect of income on health and mortality using lottery prizes as an exogenous source of variation in income. Journal of Human Resources , 40 (1), 144–168.

Liu, H., & Salvo, A. (2018). Severe air pollution and child absences when schools and parents respond. Journal of Environmental Economics and Management , 92 , 300–330.

Lopes, L.L. (1987). Between hope and fear: the psychology of risk. Advances in Experimental Social Psychology , 20 , 255–295.

Lovallo, D., & Kahneman, D. (2000). Living with uncertainty: Attractiveness and resolution timing. Journal of Behavioral Decision Making , 13 (2), 179–190.

Lu, J.G. (2020). Air pollution: a systematic review of its psychological, economic, and social effects. Current Opinion in Psychology , 32 , 52–65.

Lu, J.G., Lee, J.J., Gino, F., & Galinsky, A.D. (2018). Polluted morality: Air pollution predicts criminal activity and unethical behavior. Psychological Science , 29 (3), 340–355.

Marlier, M.E., Jina, A.S., Kinney, P.L., & DeFries, R.S. (2016). Extreme air pollution in global megacities. Current Climate Change Reports , 2 (1), 15–27.

Meyer, S., & Pagel, M. (2017). Fresh air eases work - the effect of air quality on individual investor activity. NBER Working Paper No. 24048.

Mikesell, J.L. (1994). State lottery sales and economic activity. National Tax Journal , 47 (1), 165–171.

Ministry of Finance. (2017). December 2017 lottery sales in People’s Republic of China. http://zhs.mof.gov.cn/zhengwuxinxi/zonghexinxi/201801/t20180123_2799468.html , Accessed on October 9, 2018.

North, C.S., Pfefferbaum, B., Narayanan, P., Thielman, S., McCoy, G., Dumont, C., Kawasaki, A., Ryosho, N., Kim, Y.S., & Spitznagel, E.L. (2005). Comparison of post-disaster psychiatric disorders after terrorist bombings in Nairobi and Oklahoma City. British Journal of Psychiatry , 186 (6), 487–493.

Olason, D.T., Hayer, T., Meyer, G., & Brosowski, T. (2017). Economic recession affects gambling participation but not problematic gambling: Results from a population-based follow-up study. Frontiers in Psychology , 8 , 1247.

Price, D.I., & Novak, E.S. (1999). The tax incidence of three Texas lottery games: Regressivity, race, and education. National Tax Journal , 52 (4), 741–751.

Quiggin, J. (1991). On the optimal design of lotteries. Economica , 58 (229), 1–16.

Seibert, P., Beyrich, F., Gryning, S.-E., Joffre, S., Rasmussen, A., & Tercier, P. (2000). Review and intercomparison of operational methods for the determination of the mixing height. Atmospheric Environment , 34 (7), 1001–1027.

Sibley, C.G., & Bulbulia, J. (2012). Faith after an earthquake: A longitudinal study of religion and perceived health before and after the 2011 Christchurch New Zealand earthquake. PLOS ONE , 7 (12), e49648.

Taiwanlottery.com.tw. (2019). Taiwan’s Welfare Lottery. www.Taiwanlottery.com.tw/about/lottery.asp , Accessed on March 29, 2019.

Tang, G., Zhang, J., Zhu, X., Song, T., Münkel, C., Hu, B., Schäfer, K.J., Liu, Z., Zhang, J., Wang, L., Xin, J., Suppan, P., & Wang, Y. (2016). Mixing layer height and its implications for air pollution over Beijing, China. Atmospheric Chemistry and Physics , 16 , 2459–2475.

Watson, J.G. (2002). Visibility: Science and regulation. Journal of the Air & Waste Management Association , 52 (6), 628–713.

xinhuanet.com. (2018a). 138 million investors on average hold stocks of 1.3 companies. http://www.xinhuanet.com/money/2018-05/08/c_129866753.htm . Accessed on March 4, 2019.

xinhuanet.com. (2018b). Twelve departments’ joint announcement on prohibiting online lottery sales. http://www.xinhuanet.com/tech/2018-08/22/c_1123306129.htm . Accessed on August 16, 2018.

Zezima, K. (2008). Sweet dreams in hard times add to lottery sales. New York Times, September 13.

Zhang, Q., Quan, J., Tie, X., Li, X., Liu, Q., Gao, Y., & Zhao, D. (2015). Effects of meteorology and secondary particle formation on visibility during heavy haze events in Beijing, China. Science of the Total Environment , 502 , 578–584.

Zhang, X., Chen, X., & Zhang, X. (2018). The impact of exposure to air pollution on cognitive performance. Proceedings of the National Academy of Sciences , 115 (37), 9193–9197.

Zhang, X., Zhang, X., & Chen, X. (2017). Happiness in the air: How does a dirty sky affect mental health and subjective well-being? Journal of Environmental Economics and Management , 85 , 81–94.

Zheng, S., Wang, J., Sun, C., Zhang, X., & Kahn, M.E. (2019). Air pollution lowers Chinese urbanites’ expressed happiness on social media. Nature Human Behaviour , 3 , 237–243.

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The authors acknowledge support from China’s National Science Foundation key project no. 72033006 (S.H.C.) and Singapore’s Ministry of Education Academic Research Fund Tier 1 grant R-122-000-250-115 (A.S. and H.L.).

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Chew, S.H., Liu, H. & Salvo, A. Adversity-hope hypothesis: Air pollution raises lottery demand in China. J Risk Uncertain 62 , 247–280 (2021). https://doi.org/10.1007/s11166-021-09353-w

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DOI : https://doi.org/10.1007/s11166-021-09353-w

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Smog over Shanghai in 2018. Over the last decade, China has sharply cut air pollution. Johannes Eisele / AFP via Getty Images

Pollution Paradox: How Cleaning Up Smog Drives Ocean Warming

New research indicates that the decline in smog particles from China’s air cleanups caused the recent extreme heat waves in the Pacific. Scientists are grappling with the fact that reducing such pollution, while essential for public health, is also heating the atmosphere.

By Fred Pearce • May 28, 2024

They call it “The Blob.” A vast expanse of ocean stretching from Alaska to California periodically warms by up to 4 degrees Celsius (7 degrees F), decimating fish stocks, starving seabirds, creating blooms of toxic algae, preventing salmon returns to rivers, displacing sea lions, and forcing whales into shipping lanes to find food.

The Blob first formed in 2013 and spread across an area of the northeast Pacific the size of Canada. It lasted for three years and keeps coming back — most recently last summer . Until now, scientists have been unable to explain this abrupt ocean heating. Climate change, even combined with natural cycles such as El Niño, is not enough.

But new analysis suggests an unexpected cause. Xiaotong Zheng, a meteorologist at the Ocean University of China, and international colleagues argue that this extraordinary heating is the result of a dramatic cleanup of Chinese air pollution. The decline in smog particles, which shield the planet from the sun’s rays, has accelerated warming and set off a chain of atmospheric events across the Pacific that have, in effect, cooked the ocean.

Other researchers spoken to by Yale Environment 360 see the finding, made with the help of in-depth climate modeling, as having potentially critical implications for future climate in the Pacific and elsewhere. Emissions of the tiny particles that cause smogs, collectively known as aerosols, are in decline across most of the world — apart from South Asia and Africa. Scientists are concerned that the cleanups will both heat the global atmosphere and lead to more intense regional ocean heat waves.

The idea that cleaning up air pollution can worsen atmospheric warming sounds counterintuitive.

Yangyang Xu, an atmospheric scientist at Texas A&M University not involved in the study, said it shows that “aerosol reductions will perturb the climate system in ways we have not experienced before. It will give us surprises.”

Indeed, that may already be happening in the Atlantic. Some researchers we spoke to argue that the exceptional heat wave that spread across the North Atlantic from spring last year until April this year, sending fish fleeing for cooler Arctic waters, may have owed its intensity to international efforts to reduce aerosol emissions from ships crossing the ocean.

The idea that cleaning up air pollution can worsen atmospheric warming sounds counterintuitive. But small particles suspended in the atmosphere, collectively known as aerosols, are very different from greenhouse gases. Instead of warming the planet by trapping solar radiation, they shade it by scattering incoming sunlight and sometimes creating clouds.

They don’t stick around in the air for more than a few days. But climate modelers calculate that while they are there, they fend off as much as a third of greenhouse warming.

The Blob, a long-lasting marine heat wave, off the Pacific coast of North America, shown here in August 2019. NASA

In recent years, however, this cooling influence has begun to decline in much of the world. Thanks to clean-air legislation intended to protect public health, aerosol emissions have been reduced in Europe and North America since the 1980s. And over the past decade, the same has happened in China, where tough government controls on dirty industries, introduced by President Xi Jinping in 2013, have cut overall aerosol emissions by 70 percent, according to Zheng.

Globally, there are now fewer anthropogenic aerosols in the air at any one time than for decades. Susanne Bauer, a climate modeler at the NASA Goddard Institute for Space Studies, says this “turning point of the aerosol era” occurred in the first decade of this century, and seems set to continue, as more countries seek to banish smogs.

As a result, scientists say, the aerosol mask is slipping, causing a boost to global warming in many regions. “We are currently experiencing greenhouse-gas driven global warming enhanced by aerosol removal,” says Ben Booth, a climate modeler at the U.K. Met Office.

The climatic repercussions of this are not unexpected. Predicted declines in aerosol cooling are already factored into projections of future global warming by the Intergovernmental Panel on Climate Change (IPCC). But Zheng’s new findings on the cause of the warm Pacific blob suggest that we can also expect more and bigger regional climatic surprises.

Without aerosols’ cooling effect, the world would already have reached the temperature threshold of dangerous climate change.

Why so? The answer lies in the fact that aerosols do not remain aloft for long enough in the air to mix thoroughly in the atmosphere. So national pollution cleanups will create radically new maps of aerosol distribution.

Some areas will heat much more than others, and this differential warming has the potential to destabilise atmospheric circulation patterns, which are largely heat-driven. This is what appears to have been happening in the northeast Pacific, says Zheng.

When he and Hai Wang, also of the Ocean University of China, along with colleagues in the United States and Germany, modeled the likely impacts on circulation systems of the recent cleaning of the air over eastern China, they found that clearing the country’s smogs caused exceptional atmospheric heating downwind over the Pacific.

This altered air pressures and intensified the Aleutian Low, a semi-permanent area of low pressure in the Bering Sea. This in turn reduced wind speeds further east, limiting the ability of the winds to cool the ocean below, providing “a favorable condition for extreme ocean warming.”

Zheng and colleagues warn that the findings are a harbinger of future “disproportionately large” warm-blob events.

Smog shrouds the Taj Mahal in Agra, India, last November. Pawan Sharma / AFP via Getty Images

Aerosols come in many shapes and sizes, from dust and soot to tiny particles invisible to the eye. They have many natural sources, such as forest fires and dust storms. But since the Industrial Revolution the aerosol load in the atmosphere has been dramatically increased by anthropogenic sources, primarily the burning of fossil fuels such as coal and oil.

These emissions include large volumes of sulfur dioxide (SO2), a gas that reacts readily with other compounds in the air to create tiny particles that both shade the planet and can act as condensation nuclei that cause atmospheric moisture to coalesce into water droplets that form clouds.

Burning fossil fuels produces both planet-warming carbon dioxide and aerosols that mask much of the warming. Atmospheric temperatures depend on the balance between the two. The last IPCC assessment of climate science, published in 2021, calculated that greenhouse gases were producing a warming effect of around 1.5 degrees C, with 0.4 degrees of this masked by aerosols.

“Without the cooling effect of the aerosols, the world would already have reached the 1.5- degree temperature threshold of ‘dangerous’ climate change as set out by the Paris agreement,” says Johannes Quaas, a meteorologist at the University of Leipzig and former IPCC lead author.

But the balance is shifting as ever more countries act to reduce aerosol emissions.

Until recently, ships’ aerosol emissions probably cooled the planet more than their greenhouse-gas emissions warmed it.

They do so because of a growing awareness of the public health impacts of aerosols, which the World Health Organization calculates cause more than 4 million premature deaths from cancers and respiratory and cardiovascular diseases each year. Air pollution reduced life expectancy in parts of China by up to five years, according to a 2013 study .

Countries are requiring power companies, industries, and vehicle manufacturers to filter particulates and either burn low-sulfur fuel or fit equipment to strip SO2 from stack emissions — thus cleaning up aerosol and SO2 emissions without reducing the energy produced by burning the fuel.

Europe and North America have had clean air laws in place for almost half a century. Since 2013 — following a run of debilitating smogs in many cities — China has followed, at break-neck speed. Its anthropogenic aerosol emissions have fallen by 70 percent in a decade, and SO2 emissions have been reduced even more, from 20.4 million tons in 2013 to 2.4 million tons in 2022.

Chinese researchers have tracked the impact of this on local climate in some detail. Yang Yang, an atmospheric physicist at Nanjing University of Information Science and Technology, calculates that by 2017, it had boosted the existing greenhouse warming trend in eastern China by 0.1 degrees C. As the cleanup extends, including to transportation, he expects this extra heating to increase to between 0.2 and 0.5 degrees C by 2030, and to more than 0.5 degrees C by 2060.

Yang predicts it will also trigger changes in local atmospheric circulation that will result in more rainfall over southern China and beyond, in nearby countries such as the Philippines. Zheng’s new research suggests that the effects are already far more long-ranging, stretching across the Pacific to create The Blob on the shores of the U.S.

Where else can we expect disrupting local climate change? Outside of China, researchers are exploring the potential for oceanic climate surprises arising from recent efforts to cut SO2 emissions from shipping.

Dirty, sulfurous diesel has long been the fuel of choice in ships’ boilers. As a result, the world’s shipping fleets until recently emitted more than 10 million tons of SO2 annually, contributing between 10 and 20 percent of the total anthropogenic climate “forcing” from aerosols, says Michael Diamond, who studies aerosols and climate at Florida State University.

Ships are a major cause of aerosol buildup over oceans, where there are usually few other anthropogenic sources. Satellite images show clear tracks of clouds stretching along major shipping routes.

Burning ships’ fuel also emits carbon dioxide, of course. But until recently, ships’ aerosol emissions have probably cooled the planet more than their greenhouse-gas emissions have warmed it. That is changing, however. Ships seem set to turn from planetary coolers to planetary warmers.

Eliminating methane, a short-lived greenhouse gas, can provide a quick fix for some of the impacts of lost aerosols.

In 2020, the U.N.’s International Maritime Organization (IMO) responded to rising pressure to clear the air around ports by reducing the sulfur content allowed in shipping fuel from 3.5 percent to 0.5 percent. Reduced ships’ SO2 emissions have already resulted in fewer clouds over shipping lanes and and higher ocean temperatures.

Diamond says he has a paper currently under peer review whose “takeaway is that something like a third of the North Atlantic marine heat wave [of the past year] might be attributable to the IMO regulations.” Booth, meanwhile, is coauthor of a paper preprinted online this month which argues that shipping emissions reductions “may help explain part of the rapid jump in global temperatures over the last 12 months.”

Where are we headed?

If the world works successfully toward lowering greenhouse gas emissions in the coming decades, while also continuing to curb aerosols, then we can still expect continued warming for which aerosol reductions are a growing cause.

A satellite view of aerosol trails left by ships crossing the North Pacific. NASA

Yang recently coauthored a paper that forecasts a mid-century world in which the warming impact of the clearer air will “far outweigh those of greenhouse gases.” There will be “increased humid heat waves with longer duration and stronger amplitudes,” he says.

So what can be done? Can the world have clean air while also keeping warming to bearable levels and avoiding worsening ocean heat waves?

Most scientists spoken to for this article agreed that the best route remains doubling down on reducing greenhouse gas emissions. But Diamond suggests the aerosol dilemma shines a spotlight on the need to give priority to cutting methane emissions .

This virulent greenhouse gas is second to carbon dioxide in importance as a planetary warmer. Right now, notes Diamond, its warming effect is almost identical to the average cooling effect of continued aerosol emissions. And because methane is a relatively short-lived greenhouse gas, persisting in the atmosphere for only around a decade, its elimination can provide a quick fix for some of the impacts of the lost aerosols. Luckily, there is low-hanging fruit to achieve this: The easiest and cheapest actions include preventing the venting of methane from gas and oil wells and pipelines.

To be clear, nobody — but nobody — suggests that we should stop the cleanup of aerosols. The death toll would just be too great.

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Focus on cities will boost benefits of air pollution action for most vulnerable

by Hayley Dunning, Imperial College London

Meeting UK air pollution targets by focusing on urban areas will maximize health benefits for the most deprived communities.

A study led by Imperial College London researchers shows that reducing typically urban sources of fine-particle air pollution like roads, wood burners, and machinery would also reduce inequalities in how different communities suffer the health impacts .

Air pollution can reach the lungs, causing short-term irritation and more harmful long-term impacts on heart and lung function. For people with existing conditions like heart failure and asthma, this can worsen already serious health problems. Residents or workers in more deprived areas are more likely to suffer these conditions, and as such are disproportionately impacted by air pollution.

The new study shows that while there are many ways to reduce the UK population's exposure to air pollution overall, focusing on these typically urban sources benefits deprived areas more, reducing the health inequalities across the country. The research is published in Environmental Advances .

Lead researcher Dr. Huw Woodward, from the Centre for Environmental Policy at Imperial College London, said, "People facing higher air pollution in deprived areas suffer health inequalities, which have a profound impact on their quality of life. Reducing air pollution will benefit everyone, but thinking more deeply about how we get there can also help us alleviate the impact on the most vulnerable in society."

Reducing bias

There are several types of air pollution, including nitrous dioxide and fine particles. This study focused on a type of fine-particle pollution called PM2.5 (pollution particles that are less than 2.5 microns across).

The UK Environment Act of 2021 set a target for cutting the population's exposure to PM2.5 by 35% by 2040, compared to 2018 levels. In practice, this means reducing the sources of the pollution, which include industry, road transport, energy production and agriculture.

Experts and policymakers use models to explore different ways of reaching the target by reducing pollution from these sources by different proportions. While all reductions in pollutant emissions will reduce the population's exposure, previous studies have not considered how different ways of reaching the target would influence the health inequality.

To track how different scenarios impact the inequality, the team created a new metric, called the Indicator of Exposure Bias (IoEB). They paired this with the UK Integrated Assessment Model, used to investigate the impact of future emissions scenarios on air quality in England.

The team modeled several of these scenarios, including two that meet the 2040 target, and used the IoEB to assess their impact on the exposure bias. The successful scenarios both achieved the target by reducing PM2.5 sources from all sectors, but one focused more on urban sources, including road transport and wood burners.

While both these scenarios reduced the exposure bias, the one focusing more on urban sources had a larger impact, reducing the bias by 59% (compared to 43% for the other scenario).

North-south divide

There is also a bias between Southern and Northern areas of England, with the former experiencing higher levels of PM2.5 air pollution. This bias is due to the south receiving a greater proportion of pollution from shipping channels and continental neighbors. The south of England has fewer deprived areas than the north, and as such this north-south divide in PM2.5 from non-UK sources reduces the overall bias towards deprived areas.

Despite this, deprived areas still experience higher levels of PM2.5 pollution. Of the sources under English control, the bias towards deprived areas is greater than that assessed by considering all sources including those from outside of the UK.

The study looked at pollution at the level of populations, as individual exposure is very difficult to estimate accurately.

The team believes their new measure can be applied to different countries or regions using models that estimate population exposure and socioeconomic status. This could allow policymakers to identify the sectors which contribute disproportionately to the bias in exposure and to identify effective strategies for reducing this bias .

Provided by Imperial College London

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• Public Health

How Air Quality Affects Asthma—and What to Do About It

A lana Yañez’s severe asthma had been completely under control for years. But when the 2020 wildfires started pumping thick plumes of ugly black smoke into the southern California sky, the 41-year-old Los Angeles resident began to wheeze. 

She felt her chest tighten and then become painful. Yañez shut all the windows in her house, cranked the air conditioner, and turned on an air filter. But those measures barely made a dent in her symptoms.   

“I was sucking on my inhaler every couple of hours,” Yañez says, adding that no matter what medications she was given, the pain in her chest persisted.  

After several miserable days, Yañez remembered that she’d always breathed easier on the coast. When she checked local air quality maps, she saw that the air was far cleaner by the ocean. So she packed up her work and her little boy and headed for Redondo Beach.

“With every mile, my lungs felt better,” she says. “By the time I took the exit for the beach, I was able to breathe without pain.”

While dirty air —whether it’s due to diesel exhaust, traffic fumes, industrial pollution, or wildfires—can make breathing difficult for anyone, it hits people with severe asthma much harder, with some ending up in the emergency room or even hospitalized.

During the spring and summer of 2023, when Canadian wildfires were shooting thick clouds of smoke into the air, asthma-associated emergency room visits in the U.S. spiked 17% higher than what would normally be expected. 

The research linking air pollution exposure to asthma attacks “is very consistent,” says Dr. Akhgar Ghassabian, an associate professor of pediatrics and population health at the NYU Grossman School of Medicine. Even low levels of exposure can trigger an exacerbation, she says, and the most at-risk groups are children and seniors.

Read More: What to Know About the Latest Advances in Managing Severe Asthma

How does dirty air harm the respiratory system and exacerbate asthma?

Over the past few decades, volunteers, one at a time, have entered a small chamber in a lab at the University of North Carolina and either pedaled on a stationary bike or sat quietly while components of diesel exhaust or smoke from burning wood were pumped into the room.  

The volunteers had been carefully selected to avoid any severe reactions. They were all relatively young, under 45, and healthy overall, although some had mild asthma. After a few hours in the chamber, the study participants gave sputum samples, which helped researchers identify those who were sensitive to the fumes and exactly how their airways and lungs were being affected.

Early experiments by the researchers from UNC and the U.S. Environmental Protection Agency (EPA) looked at the impact of exposure to diesel exhaust components, which included fine particles (PM2.5), ozone, and other gases. In some volunteers, the fumes sparked increases in airway inflammation, says Dr. David Peden, senior associate dean of translational research and medical director of the Center for Environmental Medicine, Asthma and Lung Biology at the University of North Carolina School of Medicine.  

“Our studies are designed to get an idea of the underlying biology, and to use this information and these methods to identify particular interventions,” Peden says. Inhaling exhaust constituents sparked neutrophilic and eosinophilic inflammation. “The most important thing we find with most air pollution is that it irritates the airway epithelium.”

Most people will acutely experience some degree of airway inflammation when they encounter air pollution or wildfire smoke, Peden says. “For many, it’s simply an annoyance, and they may not worry about it,” he adds.

But for those with severe asthma, the impact can be much greater, Peden says. That’s especially true for children: Their respiratory rates tend to be higher than those of adults, so even a small amount of polluted air can make a big impact.

The center’s most recent research has focused on potential treatments for exposure to pollution and wildfire smoke in the volunteers who were found to be sensitive. It’s yielded promising results. For example, people who overproduce mucus in response to dirty air may be helped by inhaling hypertonic saline solution. “When they inhale the solution, it loosens up the mucus,” Peden explains. 

The research has also suggested a role for a certain type of vitamin D (gamma-tocopherol) that appears to calm the eosinophil response to pollution. But, Peden cautions, “this is a very early phase study. It’s not definitive.”

Ongoing research is examining the genetics that impact sensitivity to wildfire smoke and air pollution, as well as ways to protect people with respiratory diseases, such as studies to determine the efficacy of N95 masks.

Read More: An N95 Mask Is Your Best Outdoor Defense Against Wildfire Smoke

Dirty air’s impact on people with severe asthma

Exposure to any kind of dirty air can make asthma a lot worse, says Dr. Stokes Peebles, section chief for allergy and immunology at Vanderbilt University Medical Center. “It can lead to a feeling of tightness in the chest, coughing and shortness of breath,” he says. “The fine particulate matter, PM2.5, can get down into the very lowest parts of the airways.”

Those ultrafine particles can also get deep inside the lungs, says Dr. Barbara Mann, an associate professor of medicine in the division of pulmonary, critical care and sleep medicine at the Icahn School of Medicine and at Mount Sinai in New York City. “They can evade most of the body’s defenses and wreak havoc.”

Air pollution can cause two airway issues: constriction and inflammation. And it doesn’t stop there, Mann says. The tiniest particles can leach into the bloodstream and cause systemic inflammation. The more severe a person’s asthma is at baseline, the smaller the dose of polluted air it takes to kick off an exacerbation, and the worse those flare-ups might be.

Wildfire smoke: an urgent danger 

Wildfire smoke is an especially troublesome type of air pollution. It “dwarfs other kinds of air pollution," Mann says. “It’s a toxic mix of both organic and inorganic materials that have been burned.”

As Peden points out, wildfires can significantly raise the amount of fine particles in the atmosphere. “In 2018, when the Camp Fire was burning, the amount of fine particles in San Francisco was up three- to five-fold,” he says. 

Unlike industrial and traffic related air pollution, wildfire smoke is likely to also contain fumes from the burning of manmade items, such as houses and vehicles. That can be a nefarious combination.

Read More: What Wildfire Smoke Does to the Human Body

New asthma kicked off by air pollution

Along with exacerbating asthma, air pollution can spark new onset airway disease in those who are exposed, says Matt Perzanowski, an associate professor of environmental health sciences at the Columbia University Mailman School of Public Health. 

Moreover, studies done at Columbia have shown that when people are exposed to diesel smoke, they can develop allergies to proteins they weren’t previously allergic to. “We study children in the South Bronx,” Perzanowski says. “When they’re exposed to cockroaches and diesel exhaust, they are more likely to develop an allergy to cockroaches.”

Perzanowski recommends that parents limit their children’s exposure to pollution, especially wildfire smoke. 

How to protect yourself

If you have asthma, the most important step you can take to avoid an exacerbation due to wildfire smoke and pollution is to check local air quality reports daily. “There’s good data available in real time,” Ghassabian says. AirNow.gov , for example, is a terrific resource.

On bad air quality days, take precautions to protect yourself from exposure to the dirty air. These are doctors' favorite strategies:

• Check ozone levels online and stay inside if they’re high. Close all the windows and block other spots where outside air could seep in.
• Invest in a HEPA filter. According to the EPA, these can remove at least 99.97% of dust, pollen, mold, bacteria, and other airborne particles.
• If the air quality index goes over 100, don’t exercise outside. If it’s over 150, don’t exercise at all. 
• When the index is lower than 100 but still relatively high, you can exercise outside, but only in the early morning or evening.
• Switch the setting on your home and car air conditioners to recycle, so you’re not bringing in outside air.
• Use an N95 mask when you go outside.
• When wildfire smoke is at high levels, consider temporarily relocating to a spot where air quality is better.

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