
==== Front
iScience
iScience
iScience
2589-0042
Elsevier

S2589-0042(24)01922-9
10.1016/j.isci.2024.110697
110697
Article
Uncovering the impact and mechanisms of air pollution on eye and ear health in China
Fang Jingwei 17
Yu Yanni yanniyu@sdu.edu.cn
127∗
Zhang Guanglai 3
Zhu Penghu 1
Shi Xin 20221010@cmu.edu.cn
4∗∗
Zhang Ning nz293@cam.ac.uk
128∗∗∗
Zhang Peng 56
1 Institute of Blue and Green Development, Shandong University, Weihai 264209, China
2 Department of Land Economy, University of Cambridge, Cambridge CB2 1TN, UK
3 School of Economics, Jiangxi University of Finance and Economics, Nanchang 330013, China
4 School of Health Management, China Medical University, Shenyang 110122, China
5 School of Management and Economics, The Chinese University of Hong Kong, Shenzhen 518172, China
6 Shenzhen Finance Institute, Shenzhen 518038, China
∗ Corresponding author yanniyu@sdu.edu.cn
∗∗ Corresponding author 20221010@cmu.edu.cn
∗∗∗ Corresponding author nz293@cam.ac.uk
7 These authors contributed equally

8 Lead contact

10 8 2024
20 9 2024
10 8 2024
27 9 1106973 5 2024
8 7 2024
6 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Summary

Increasing air pollution could undermine human health, but the causal link between air pollution and eye and ear health has not been well-studied. Based on four-week-level records of eye and ear health over 1991–2015 provided by the China Health and Nutrition Survey, we estimate the causal effect of air pollution on eye and ear health. Using two-stage least squares estimation, we find that eye or ear disease possibility rises 1.48% for a 10 μg/m3 increase in four-week average PM2.5 concentration. The impacts can last about 28 weeks and will be insignificant afterward. Females, individuals aged 60 years and over, with high exposure environments, relatively poor economic foundations, and low knowledge levels are more vulnerable to such negative influences. Behavioral channels like more smoking activities and less sleeping activities could partly explain this detrimental effect. Our findings enlighten how to minimize the impact of air pollution and protect public health.

Graphical abstract

Highlights

• Air pollution is robustly associated with eye and ear health

• Air pollution’s detrimental effect is cumulative

• Women, the elderly, and those with limited resources are more vulnerable

• Smoking and sleeping are the behavioral channels

Pollution; Neuroscience

Subject areas

Pollution
Neuroscience
Published: August 10, 2024
==== Body
pmcIntroduction

A large body of literature has increasingly documented the harmful effect of air pollution on socioeconomic outcomes. It lowers labor productivity and educational outcomes through biological mechanisms and behavioral channels,1,2,3,4,5 reflecting on diverse mortality and morbidity.6,7,8,9,10,11,12 Existing clinical and economic literature mainly focuses on the significant diseases induced by air pollution (respiratory disease, diabetes, and hypertension13,14,15). However, the continuous growth of air pollution raises public attention on some subtle diseases.16,17,18,19,20,21,22,23,24,25 For instance, several studies have investigated the relationship between air pollution and outpatient visits of eye and ear health.26,27,28,29 These problems were easily neglected in the past but resulted in considerable social burdens. The World Health Organization reports that the annual global costs of productivity losses associated with vision impairment are about USD 411 billion and the international number of years lived with disability (YLDs) attributable to hearing loss in 2019 was about 43.5 million.30,31

While eye and ear health is affected by dimensional factors, ranging from age-related sensorineural degeneration to chronic disease,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46 their causal linkages to air pollution remain scarcely vindicated, at least because of data limitation and endogeneity. Some medical literature investigates the negative effect of air pollution on eye and ear health but is only confined to the correlation and based on laboratory or workplace.47,48,49,50,51,52,53,54,55,56 For instance, Yang et al.57 demonstrate that fine particulate matter (PM) is cytotoxic to cultured human corneal epithelial cells and the ocular surface. Song et al.58 suggest the immortalized human middle ear epithelial cell lines have decreased cell viability after exposure to diesel exhaust particles. The controlled experiments in the laboratory or workplace may provide a helpful benchmark, but do not fully capture individual responses and behaviors in natural environments, according to Somanathan et al.59 Besides, the correlation ignores the disturbance of the potential endogeneity problem, which biases the authentic influence of air pollution.

We can attempt to bridge the linkages by relying on the medical evidence from previous studies. Irritation caused by air pollutants and the subsequent inflammation have been proven to be harmful to the ocular surface.35,60 In air pollution, ototoxic chemicals (CO, lead, and mercury) could cause cochleotoxic and neurotoxic effects, inducing hearing loss.61 Persistent exposure to air pollution may also lead to oxidative stress at the site of PM deposition due to the overloading of the antioxidative defense system and finally result in eye and ear diseases.62,63,64,65,66 By disrupting the circadian rhythm through sleep disorder, which declines the expression of the core clock proteins,67,68 air pollution might contribute to neurodegeneration and metabolic diseases with inflammatory features, hurting the eye and ear.69,70,71 Air pollution–related emotional problems like anxiety9,21,72,73 might promote personal smoking behavior through which people can alleviate and escape the negative feelings,74 but would incur oxidative stress and inflammatory processes that damage the eye and ear.75,76,77,78,79 The increase of indoor time to avoid pollutants would stimulate the use of electronic devices,80,81,82,83,84 thus creating a detrimental optical and acoustic environment for the eye and ear.30,31,43,85

In this study, we develop rational and reliable estimations by establishing causal links between air pollution and eye and ear health in natural environments with data from the China Health and Nutrition Survey (CHNS: https://www.cpc.unc.edu/projects/china)86 and instrumental variable method. The average volume of PM2.5 in China had soared from 40.7 μg/m3 in 1991 to 66.9 μg/m3 in 2015 (Figure 1). Meanwhile, China’s YLDs attributable to vision and hearing loss maintains the first place among the WHO member states. The severe situation and the substantial population make China representative and effective in identifying the influence of air pollution.Figure 1 Changes in regional PM2.5 concentrations in China during 1991-2015

(A–C) portray the annual average concentrations of PM2.5 at county level in China in 1991, 2001, and 2015, respectively. PM2.5 data are provided by the National Aeronautics and Space Administration (NASA).

The main novelties and contributions are: (1) Analyzing the causal links between air pollution and eye and ear health from the perspective of economics, which extends the research on the influence of air pollution on the field of common sensory diseases. (2) Exploring individual behavior when exposed to a natural environment. It captures a relatively more actual reaction compared with laboratory evidence. (3) Accurate exposure window matching based on the calculated daily air pollution data enables us to examine the cumulative effect.

Results

Descriptive statistics

During 1991–2015, 16,307 individuals from 62 counties across 8 provinces made 42,774 observations. Around 3315 and 2420 respondents were interviewed twice and three times, respectively. And our sample is an unbalanced panel. Males account for 48.44% (n = 20,718), and females account for 51.56% (n = 22,056). 39.13% (n = 16,737) observations fall within 0–39 years old, 37.49% (n = 16,035) are between 40 and 59, and 23.38% (n = 10,002) are more than 59 years old. Rural and urban residents constitute 72.49% (n = 31,008) and 27.51% (n = 11,766) observations. The annual household income of 66.43% (n = 28,033) observations is below CNY40000. 54.69% (n = 21,020) observations have completed the nine-year compulsory education. Detailed descriptive statistics and distribution of demographic characteristics can be found in Tables S1 and S2.

Figure 2 portrays the distribution of every year’s disease rate across genders. In our sample, around 2.33% of interviewees have had eye or ear disease at least once in the last four weeks before the interview. Females take up around 52% of the observations and have remarkably higher eye or ear disease rates than males.Figure 2 Distribution of every year’s disease rate across genders

We merge the air pollution, thermal inversion, and weather data at the county’s 4-week level with eye or ear disease data. Table 1 shows the descriptive statistics used in our main regression. Based on the 4-week exposure window, PM2.5 concentration averages 38.97 μg/m3, nearly 4 times higher than the standard of 10 μg/m3 of annual mean recommended by WHO.87 The data presented in this article indicate that the total number of days with inversions, sunshine duration, and precipitation average 12.76 days, 155 h, and 66.13 mm, respectively. Mean relative humidity, temperature, and wind speed are 73.62%, 23.1°C and 1.83 m/s.Table 1 Descriptive statistics of key variables in baseline regression

Variable	Definition	Obs	Mean	Std.Dev.	Min	Max	
Eye or ear disease (1 = yes)	Answer to “Did you have eye or ear disease during the past 4 weeks?” (1 = yes,0 = no)	42,774	0.010	0.098	0	1	
PM2.5 (μg/m3)	Average of PM2.5 concentration during the past 4 weeks	42,774	38.97	14.52	7.061	77.14	
Thermal inversion day	Total number of days with inversions during the past 4 weeks	42,774	12.76	7.900	0	28	
Wind speed (m/s)	Average of wind speed during the past 4 weeks	42,774	1.834	0.759	0.153	7.933	
Relative humidity (%)	Average of relative humidity during the past 4 weeks	42,774	73.62	7.573	40.95	90.06	
Temperature (。c)	Average of temperature during the past 4 weeks	42,774	23.10	6.282	−4.388	35.07	
Sunshine duration (hour)	Total of sunshine duration during the past 4 weeks	42,774	155.0	44.13	7.329	270.4	
Precipitation (millimeter)	Total of precipitation during the past 4 weeks	42,774	66.13	62.36	0.025	426.5	

Main results

PM2.5 exposure imposes a statistically significant negative effect on eye and ear health. The 2SLS estimates-adjusting for permanent unobserved year-by-month, individual determinants of the eye and ear health and under a series of weather control shows that the possibility of having eye or ear disease rises by 1.48% (95% CI 0.47%–2.49%) for a 10 μg/m3 (25.66% of the mean) increase in average PM2.5 concentrations in the past 4 weeks (Table 2). The ordinary least squares (OLS) results are close to zero in magnitude and not significant under all the control sets, mainly due to the endogeneity caused by the potential for omitted bias, reverse causality, and measurement error (Detailed results are provided in Tables S3–S5).Table 2 Estimates of the causal effects of air pollution

	OLS	2SLS	
β	S.E.	p value	95% CI	β	S.E.	p value	95% CI	
PM2.5 (μg/m3)	−0.0004	0.0011	0.7355	[-0.0025,0.0017]	0.0148	0.0051	0.0041	[0.0047,0.0249]	
Relative humidity (%)	0.0002	0.0002	0.2822	[-0.0002,0.0007]	0.0007	0.0003	0.0107	[0.0002,0.0013]	
Temperature (。c)	0.0003	0.0003	0.3265	[-0.0003,0.0010]	−0.0005	0.0005	0.2840	[-0.0014,0.0004]	
Sunshine duration (hour)	0.0000	0.0000	0.1863	[0.0000,0.0001]	0.0001	0.0000	0.0028	[0.0000,0.0002]	
Wind speed (m/s)	−0.0008	0.0022	0.7252	[-0.0050,0.0035]	0.0057	0.0030	0.0576	[-0.0002,0.0117]	
Precipitation (millimeter)	0.0000	0.0000	0.4732	[-0.0001,0.0000]	0.0001	0.0000	0.0827	[0.0000,0.0002]	
Precipitation2	0.0000	0.0000	0.7436	[0.0000,0.0000]	0.0000	0.0000	0.0451	[0.0000,0.0000]	
N = 42,774. The dependent variable is eye and ear health status over the 4 weeks before the interview day. The fixed effects are controlled at individual level and year-by-month level. Standard errors listed in parentheses are clustered at individual level.

The results are robust to a vast range of alternative models, including the exclusion of weather controls, the use of more restrictive fixed effects, cluster level and the IV-Logit functional form, alternation layer of IV, exclusion of extreme values, inclusion of population mobility, health condition, exclusion of interview day. Our model also passes the falsification test that replaces the exposure window with the 4 weeks after the interview, mitigating the concern of model misspecification (Table S6).

We test for cumulative effects of air pollution by altering the exposure window (STAR Methods). Figure 3 illustrates that the cumulative effects of air pollution on eye and ear health persist for 28 weeks or so and then cease to be meaningful in magnitude, which means the impact of air pollution 28 weeks before on present eye and ear health is not significant (see details in the Table S7).Figure 3 The cumulative effects of PM2.5 on eye or ear disease

This figure depicts the cumulative effect of air pollution on eye or ear disease using a 2SLS model with distributed lag structures increasing from 0 to 7 lag terms before the interview day. Each term manifests four weeks. The instrument for each lag term of PM2.5 is the corresponding lag term of thermal inversion days. The dependent variable is eye and ear health status over the 4 weeks before the interview day. The line denotes the point estimate (sum of the current and lag period), and the shadow denotes the 95% confidence intervals. ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1.

Heterogeneous check

Heterogeneity across gender and age groups

The probabilities significantly increase by 1.94% (95%CI 0.49%–3.39%) and 3.90% (95%CI 0.98%–6.83%) among females and older people (individuals aged 60 years and over), respectively, for an additional 10 μg/m3 p.m.2.5. By contrast, the effects are insignificant among males (0.90% 95%CI -0.49%–2.30%), young (age below 40) (0.39%, 95%CI -0.44%–1.23%), and middle (age between 40 and 60) people (0.20%, 95%CI -1.28%–1.67%).

Heterogeneity across exposure environment

First, the exposure level of samples is classified according to work characteristics. People who usually work outside, exposed more to air pollution, are more liable to gain eye or ear health problems. The probabilities significantly increase by 1.43% (95%CI -0.05%–2.92%) for an additional 10 μg/m3 p.m.2.5.

We also assess exposure environment based on household cooking fuel. The estimation shows that air pollution has an apparent influence on people using cooking fuel with high pollution, thus suffering dual air pollution, although both groups’ health is significantly affected by air pollution (2.17%, 95%CI 0.21%–4.12% compared with 1.07%, 95%CI -0.14%–2.29%).

Heterogeneity across economic foundation

We initially compare urban residents with rural residents. The results show that the positive effect of air pollution on inducing eye or ear disease is only statistically significant for the rural group (1.30%, 95%CI 0.16%–2.45%).

We also examine heterogeneity concerning household income. We define “low income” if the interviewee’s annual household income is below the median. Otherwise, they will be recognized as “high-income”. The estimation suggests that low-income people are more susceptible to air pollution (3.29%, 95%CI 0.46%–6.12%).

Heterogeneity across knowledge level

As is expected, air pollution significantly stimulates eye or ear disease for people who do not finish the nine-year compulsory education. An additional 10 μg/m3 p.m.2.5 increases the possibility of eye or ear disease by 2% (95%CI 0.12%–3.88%).

We also find that people without recognition of dietary knowledge are more likely to be affected by air pollution (1.48%, 95%CI 0.34%–2.61%). (Figure 4) (see details in the Tables S8–S11).Figure 4 Heterogeneous effect across gender, age groups, exposure environment, economic foundation and knowledge level

The bars in the four charts identify the corresponding coefficients of PM2.5’s effects on eye and ear health within different groups, and the whisker denotes the 95% confidence intervals. (A) heterogeneous effect across gender, age groups. (B) heterogeneous effect across exposure environment. (C) heterogeneous effect across economic foundation. (D) heterogeneous effect across knowledge level. ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1.

Mechanism tests

The adequate personal information contained by the CHNS database allows us to explore the potential channels through which air pollution imposes its negative influence. The differences like personal recognition ability and economic development between urban and rural areas can be reflected in individual behavior when exposed to air pollution,88 thus, it is advisable to distinguish the heterogeneous effect even during the mechanism analysis to observe the intra-group trends. Figure 5 portrays the results.Figure 5 Mechanism tests

The three bars shown in each chart identify the corresponding coefficients of PM2.5’s effects on different behaviors within full, rural, and urban samples, and the whisker denotes the 95% confidence intervals.

(A) the effect of air pollution on smoking behavior.

(B) the effect of air pollution on sleep.

(C) the effect of air pollution on electronic equipment usage. ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1.

The estimation suggests that air pollution significantly shifts up smoking behavior. 10 μg/m3 increase in county’s 4-week average PM2.5 concentration will significantly raise the number of cigarettes the interviewers take by 164.86% (95%CI -4.61%–334.34%). It’s noteworthy that the positive impact of air pollution does not work for the urban residents (−5.70%, 95%CI -468.76%–457.37%) but for the rural residents (194.09%, 95%CI -19.03%–407.21%).

Air pollution displays a statistically significant negative relationship with sleep. Compared to the whole sample (−11.28%, 95%CI -23.15%–0.58%), the coefficient of the rural place sample behaves larger in magnitude while becoming more precise in significance at the 1% level (−28.37%, 95%CI -42.42%–14.33%).

No significant effect on electronic equipment usage is found for urban (0.38%, 95%CI -9.57%–10.32%) or rural residents (−0.62%, 95%CI -4.83%–3.60%).

Economic costs estimation of air pollution’s negative effect

On average, a 1 μg/m3 increase in average PM2.5 concentration will result in a per-capita eye or ear health costs of USD 0.17 and a total eye or ear health costs of USD 0.23 billion (0.17∗1.38 billion population) (For the record, we use 1 μg/m3 increase in average PM2.5 concentrations as the air pollution variation for better calculation and comparison.). We also calculate the future total eye or ear health costs change when the PM2.5 concentration undulates under different scenarios. Figure 6 depicts an increasing trend of total eye or ear health costs in the SSP3-7.0 scenario due to higher population growth and emissions than in the other three scenarios but a decreasing trend in the other three scenarios (STAR Methods).Figure 6 Future total eye or ear health cost change under different scenarios

Discussion

This paper proves that air pollution is robustly associated with eye and ear health. Our two-stage least squares (2SLS) estimator indicates that a 10 μg/m3 increase in county’s 4-week average PM2.5 concentration will raise the possibility of having eye and ear disease by 0.0148. That negative impact is cumulative and can last 28 weeks or so. Heterogeneity analysis is conducted across gender, age, exposure environment, economic foundation, and knowledge level. Increasing smoking activities and lessening sleeping activities are proven to be able to explain this detrimental effect partly.

Our main findings are supported by epidemiological studies that have demonstrated the adverse effect of air pollution on eye and ear health from various perspectives based on laboratory evidence.49,50,51 Transforming the coefficient into Odd Ratio (OR 1.03, 95% CI 1.01 to 1.05, 1 μg/m3 p.m.2.5 increase) with the IV-Logit estimate, we find the results are similar to the associations between PM2.5 exposure and depression, autism spectrum disorders (OR 1.01, 95% CI 0.99–1.04) (OR 1.06, 95% CI 1.01–1.11),89,90 but smaller than that between PM2.5 exposure and dementia (OR 1.16 95% CI 1.07–1.26) and Parkinson’s disease (OR 1.34, 95% CI 1.04–1.73).91 The cumulative effects of air pollution observed in our study are also mentioned by previous literature, whereas the lag terms vary according to different topics.92,93

The differences in physiological structure and cognitive level between sexes and all ages might cause heterogeneous inner-group trends. Thus, estimating the differential impact of air pollution is indispensable.94,95 The elevation in the possibility of suffering eye or ear health problems is primarily driven by females and older people (individuals aged 60 years and over), due to the vulnerability.7,96 It is in line with literature focusing on diabetes and pulmonary health.13,97 We compare the impact of air pollution across exposure environments, which would directly lead to different exposure levels.98,99 Observations frequently exposed to air pollution behave more vulnerable. It accords with other studies. For instance, Fang et al.100 indicates a possible association between occupational pollution exposures and heart disease. The poor economic foundation would potentially undermine the eye and ear.30,31 Lack of access to nutrition and health care services would lead to disparities in risk of disease.101 Indeed, it has been shown that the effect of air pollution is only statistically significant for rural residents and low-income groups, consistent with the widely acknowledged concept.102,103 According to the health capital model proposed by Grossman104 and Cropper,105 health investment and knowledge level are closely related. Sufficient knowledge could create better self-awareness on personal health, help people gain decent work, and make healthcare more accessible.106 The detrimental effect of air pollution on people with low knowledge is noteworthy. Figure S1 illustrates people not completing the nine-year compulsory education are more likely to take a high exposure work. Lack of dietary knowledge might make people eat unhealthy and short of the necessary nutrition for eye and ear health. The results align with the main studies.107,108

Our evaluation indicates that air pollution significantly promotes personal smoking behavior, which is recognized as an essential risk factor for eye and ear health.77,79 The insignificant influence on urban people might be explained by the strict norm of city workers, which directly restrains their smoking behavior. Besides, a clearer recognition of the harmful effects of cigarettes on oneself and families could also inhibit urban residents’ smoking behavior.109 The proven negative relationship displayed between air pollution and sleep would induce the eye and ear health problems through the disturbance of circadian rhythm.69,70,71 Urban residents’ sleep quality is closely related to other important factors, such as the nature of jobs and lifestyle, which would cause more obvious disturbance than air pollution and improve personal adaptive capability.110 By contrast, rural residents might be more sensitive to air quality degeneration. The insignificant estimate of electronic equipment mechanisms may be explained by the deficient popularization of smartphones and computers in the early years. Thus, the changing lifestyle caused by air pollution can hardly be captured.

We can compare our health cost calculation with the main literature estimating the economic costs of air pollution. Regarding avoided mortality, Deryugina et al.10 find that a 1 μg/m3 decrease in air pollution in U.S. will bring an annual benefit of USD 4.11 billion, 17 times the magnitude of our estimation. Chang et al.111 document that 1 μg/m3 decrease in air pollution will shift up labor productivity by USD 6.99 billion annually in China, which is 30 times as large as our estimates. Since the costs related to mortality and productivity are considerable, we also compare our results with medical costs caused by air pollution. Deschenes et al.11 find that a 1 μg/m3 increase in PM2.5 will induce USD 0.27 billion in overweight and obesity-related medical costs, which is similar to our estimation in magnitude. Chen et al.9 calculate that a 1 μg/m3 increase in air pollution will induce USD 1.26 billion in additional medical expenditure on mental illness, which is 5 times larger than our estimation. All the estimates suggest that the effect of air pollution on eye and ear health is sizable by comparison.

The empirical evidences offer insights into improving the public’s eye and ear health. (1) Lowering the level of air pollution would be a practical approach to decreasing eye and ear disease. (2) Health target should pay more attention to female, the old generations, rural residents and low-income workers, who are easier to suffer hearing and visual problems caused by air pollution. For example, completing the medical security system would reduce the medical burden of disadvantaged people. (3) Regional sector should accelerate the alteration of traditional dirty cooking fuel by improving infrastructure, which can also alleviate the detrimental combustion phenomena in rural place. (4) The government should enhance authentic propaganda regarding the necessity of a healthy diet and less smoking to protect personal eye and ear health. Thus, the residents can benefit from good daily habits when air pollution happens.

Under the background of substantial global number of YLDs attributable to vision and hearing loss nowadays, even a modest elevation in eye and ear health problems due to air pollution could portend a considerable global health burden, particularly in developing countries where current air pollution is relatively high and/or on the rise.30,31 Our investigations strongly complement dominant themes in the existing air pollution-health literature and other recent studies needing empirical evidence on the effects of changes in air pollution on eye and ear health. The heterogeneous check helps identify the weak group and adopt corresponding measures. The mechanism accounting for this relationship could be widespread and enhance reliability in generalizing these findings to other contexts and into the future.

Limitations of the study

It is noteworthy that some limitations of our study can be further discussed. First, we cannot clearly distinguish the effects of air pollution on eye and ear health because of the indicators set in the database. Although the mechanisms of air pollution inducing eye and ear diseases are similar, it would be more credible and meaningful to analyze the causal effects separately. Moreover, we cannot measure the precise exposure level of an individual and identify the influence of indoor air pollution due to the technique limitation; neither can we capture the entire mobility of the individuals, like travel. Additionally, examining the differential effects of air pollution between weekdays and weekends would be exciting and valuable despite the existing data’s limitations in discerning such distinctions. We leave those questions for future research and await new data collection on eye and ear health.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER	
Deposited data	
	
China Health and Nutrition Survey database	The Carolina Population Center at the University of North Carolina at Chapel Hill and the National Institute for Nutrition and Health (NINH, former National Institute of Nutrition and Food Safety) at the Chinese Center for Disease Control and Prevention (CCDC)	https://www.cpc.unc.edu/projects/china	
Air pollution and thermal inversion data	National Aeronautics and Space Administration (NASA)	http://disc.sci.gsfc.nasa.gov/mdisc	
Meteorological data	Meteorological data are available at National Meteorological Information Center (NMIC)	http://www.nmic.cn	
	
Software and algorithms	
	
Stata software	StataCorp	https://www.stata.com/	

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Ning Zhang (nz293@cam.ac.uk).

Materials availability

This study did not generate new unique reagents.

Data and code availability

• This paper analyzes existing, publicly available data. These accession numbers for the datasets are listed in the key resources table.

• This paper does not report original code.

• Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Experimental model and study participant details

Eye and ear health data are drawn from the China Health and Nutrition Survey (CHNS: https://www.cpc.unc.edu/projects/china), an international collaborative survey conducted by the Carolina Population Center and the National Institute for Nutrition and Health (NINH) from 1989-2015, of which the survey contents cover nutrition and physical examination, individual activities and other research topics. The survey was conducted 9 times in 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011 and 2015 respectively. The CHNS uses a multistage, random cluster process to draw samples. A weighted sampling scheme selects two cities and four counties in each province. Also, urban districts in cities, villages, and towns in counties are selected similarly. More specifically, the sample of CHNS is from 12 provinces and 3 municipalities, covering about 9700 households and some 37000 individuals aged from 0 to 101 years old. The CHNS index “last 4 weeks: eye/ear disease” is the proxy of personal eye and ear health. It is a binary variable that takes the value 1 if the respondent had eye or ear disease in the past 4 weeks (28 days) before the interview. Otherwise, it takes a value of 0.

The original data contains 127761 observations and 71943 observations that report usable eye and ear health data. Only eight provinces’ county identifiers are made public and can be matched with corresponding air pollution and meteorological data at county-4-week level, which are Liaoning, Jiangsu, Shandong, Henan, Hubei, Hunan, Guangxi, and Guizhou respectively. Besides, the initial year’s data (Year 1989) are dropped due to the considerable missing value. Finally, 42774 observations are kept to conduct the examination. The specific information on the observation changes during estimation is provided in Table S13.

Method details

Air pollution exposure assessment

We use PM2.5 (particulate matter with an aerodynamic equivalent diameter of less than 2.5 μm) to measure air pollution due to its proven detrimental effect on human health and high concentration in China.87,112,113,114 The data are obtained from the satellite-based Aerosol Optical Depth (AOD) retrievals drawn from the public website of National Aeronautics and Space Administration of the U.S (NASA: http://disc.sci.gsfc.nasa.gov/mdisc).115 The longitude and latitude grid data of AOD (0.5°×0.625°, around 50km×60km) is available since 1980. According to the formulation proposed by Buchard et al.,116 we calculate the spatial grid data of PM2.5 concentration. We aggregate that to the county level each week and average it to a 4-week exposure window. Specifically, if the respondent is interviewed on November 21, 2004, the exposure window will be constructed from October 25, 2004.

Thermal inversion calculation

The thermal inversion data, our instrumental variable, are also acquired from NASA. We use the same approach as air pollution calculation to aggregate all data from grid to county. When the temperature in the second layer (320 m) goes beyond that of the first layer (110 m) every 6-h period, we identify the existence of a thermal inversion and then aggregate the number of days with inversions to the exposure window.

Meteorological data calculation

The weather data employed as control variables in our paper are secured from the National Meteorological Information Center (NMIC: http://www.nmic.cn), reporting meteorological data for over 800 weather stations in China.117 We use the inverse-distance weighting (IDW) method to convert weather data from station to county level and choose a radius of 200 km. Finally, we select average relative humidity, wind speed, surface temperature and cumulative precipitation, sunshine duration, and the quadratic of cumulative precipitation as the control variables.

Baseline regression model

Typical endogenous problems might bias the causal effect of air pollution on eye and ear health. Thus, we exploit thermal inversion, a common meteorological phenomenon only occurring when air temperature rarely and abnormally increases with height, as our instrumental variable.118,119,120,121 Reduced vertical circulation will trap pollution near the ground, augmenting the air pollution concentration. We also include year-by-month and individual fixed effects, controlling for all nationwide seasonality in air pollution, individual health, and economic factors, netting out all time-invariant differences across individuals. The corresponding two-stage-least-square model is listed as follows:(Equation 1) Yict=β0+β1Pct+f(Wct)+γi+σym+εict

(Equation 2) Pct=α0+α1Ict+f(Wct)+γi+σym+μict

Where Yict denotes the eye and ear health status of each respondent i residing in county c at date t. To measure Yict, we use CHNS index “last 4 weeks: eye/ear disease” as the proxy of personal eye and ear health. This index is a binary variable, which equals to 1, if the individual i had eye or ear disease during the 4 weeks (28 days) before date t, otherwise equaling to 0. Pct represents the average concentration of PM2.5. We choose an exposure window of 4 weeks(28days) as a starting point according to our dependent variable.

We instrument PM2.5 using the total number of days that thermal inversions occur, denoted by Ict. f(Wct) represents weather variables in flexible specifications in the same exposure window. γi is the individual fixed effect, and σym is the year-by-month fixed effect. To resolve potential heteroscedasticity and autocorrelation, we cluster the standard errors at the individual level.

Quantification and statistical analysis

Robustness checks

We alternate the fixed effect to the date level, further restricting unobserved nationwide temporal time shocks. Clustering the standard errors at family level allows for the potential heteroscedasticity and autocorrelation within a family. To examine the robustness of our instrumental variable, we redefine the thermal inversion day when the temperature of the third layer (540 m) goes beyond that of the first layer (110 m). The top and bottom 0.5% observations are winsorized according to age distribution to eliminate the extreme values of the eye or ear disease data.

Given that the respondent may reside in one county and work in another, we use two methods, as proposed by Deschenes et al.,11 to avoid the interference caused by population mobility. The prime method aggregates the county statistics to the prefecture level, which generally consists of 5-15 counties. It can, therefore, capture the population mobility between counties. The second approach focuses only on rural counties, where the residents are more liable to work within their counties. We also eliminate student samples with relatively strong mobility, especially on holidays. To exclude the interference of chronic diseases and personal physical condition, we introduce disease control and personal health control and drop the observations suffering from other diseases besides eye or ear health problems. The detailed interview date data in the exposure window are dropped to test the robustness. The IV-Logit model is also exploited in the robustness test. For falsification, we replaced the exposure window with the 4 weeks after the interview to test whether the unobserved secular trends confound our results.

Cumulative effects

We start with the baseline model and gradually add one additional lag term of pollution (4 weeks) into the model. In particular, each lag term of air pollutants is instrumented with the corresponding lag term of thermal inversion days. The coefficient is the sum of the current period (βt) and lag terms (βt-n), manifesting the cumulative effects of the current and previous periods.

Heterogeneity analysis

We compare the air pollution effect across gender and age groups (young people aged less than 40, middle people aged 40-60, and older people aged 60 years old or more). For exposure environment, we define high exposure if the interviewee works outside more according to job characteristics. People are reckoned as facing low pollution if they use clear fuel for cooking in contrast with traditional high pollution fuel. For economic foundation, people residing in rural places are compared to those in urban places. We also divide the full sample into “high income” and “low income” by the median number of interviewees’ annual household incomes. We test the distinguishable effect for knowledge level between people who complete nine-year compulsory education and those who do not. The effects among respondents grasping the dietary knowledge are compared to respondents not grasping it.

Mechanism test

We replace the original eye and ear health variable with the variable of interest provided by the CHNS and re-run the 2SLS model to confirm that the effect is partly working through behavioral channels. Specifically, we utilize the average number of cigarettes the respondents take daily as the proxy of smoking behaviour. CHNS reports the interviewees’ average bedtime per day, which we use to index sleep behaviour. Here, we construct a new indicator to manifest electronic equipment usage behaviour, ranging from 0 to 3, containing whether the respondents have three electronic equipment usage like watching TV, Videotapes, VCDs, DVDs, or video usage.

Back-of-the-envelope calculation

According to Ye and He122 and Ding et al.,123 the per capita direct cost attributable to hearing and visual impairment was USD109.94 and USD122.32 in 2011 respectively. However, both estimations only investigate individuals over 45 years old instead of considering people of all ages. What’s more, not all eye and ear health problems will lead to impairment. The costs of eye or ear health problems won’t stop at medical costs, either. Related productivity loss and mental health loss are not expected to be negligible. Therefore, the economic costs statistic may be underestimated, and our empirical strategy is likely to provide a lower bound to the total cost of air pollution on eye and ear health. Since the effect of air pollution is estimated for eye or ear disease, we use the average value of per capita direct cost of visual impairment and hearing impairment, which is equal to USD 116.13.

Xu et al. 124 provide the projection of annual average PM2.5 concentrations and population in China under 4 scenarios: the sustainability (SSP1-2.6) scenario, featuring lower birth, death and migration rate; the population is more educated and older in this scenario; the middle of the road scenario (SSP2-4.5), characterizing by similar social, economic and technological trends to historical ones; the regional rivalry scenario (SSP3-7.0), a combined scenario of regional competing paths, representing higher social vulnerability and higher mitigation challenges with a moderate to severe climatic change. Population in this scenario grows fast; the regional development always loses its balance, and the per capita economic and technological development level is low; the fossil-fueled development (SSP5-8.5) scenario, in which traditional fossil fuel combustion is dominant, typically emphasizing economic growth, while the fertility rate, mortality rate, and migration rate are relatively low.125,126,127 We multiply the variation of PM2.5 volume with the per-capita eye or ear health costs led by a 1 μg/m3 increase in average PM2.5 concentration and population to calculate the change in the total eye or ear health costs (We use 1 μg/m3 rather than 10 μg/m3 as the variation value because it is convenient to predict the health cost change and make comparisons with other studies.) The calculation formula is as follows:(Equation 3) Costtotal=Coefficient∗Costindiv∗Population

(Equation 4) ΔCostforecast=ΔPM2.5forecast∗Coefficient∗Costindiv∗Populationforecast

Costtotal denotes the total eye or ear health cost caused by a 1 μg/m3 increase in average PM2.5 concentration. Coefficient represents the probability of having eye or ear disease with a 1 μg/m3 increase in average PM2.5 concentration, corresponding to the coefficient β1 in our baseline regression. Costindiv is the per-capita eye or ear health cost. Population denotes the total population of China.

ΔCostforecast means the future total eye or ear health costs change. ΔPM2.5forecast and Populationforecast represent the variation of average PM2.5 and the predicted population of China under different scenarios, respectively.

Statistical analyses were performed using Stata. Statistical significance of results are manifested by p value and 95% confidence intervals. (∗∗∗p< 0.01, ∗∗p < 0.05, ∗p < 0.1).

Supplemental information

Document S1. Figure S1 and Tables S1–S13

Acknowledgments

Y.Y. acknowledges the research support from the 10.13039/501100001809 National Natural Science Foundation of China [grant number 72022009 ]; N.Z. acknowledges the research support from the 10.13039/501100012456 National Social Science Foundation of China [grant number 21ZDA065 ]; N.Z. acknowledges the research support from the 10.13039/501100001809 National Natural Science Foundation of China [grant number 72033005 ]; G.Z. acknowledges the research support from the 10.13039/501100001809 National Natural Science Foundation of China [grant number 72303084 ]. This research uses data from China Health and Nutrition Survey (CHNS). We thank the National Institute of Nutrition and Food Safety, China Center for Disease Control and Prevention, Carolina Population Center, the University of North Carolina at Chapel Hill, the 10.13039/100000002 NIH (R01-HD30880 , DK056350 , and R01-HD38700 ) and the Fogarty International Center, 10.13039/100000002 NIH for financial support for the CHNS data collection and analysis files from 1989 to 2006 and both parties plus the China-Japan Friendship Hospital, Ministry of Health for support for CHNS 2009 and future surveys.

Author contributions

J.F. and Y.Y. conceptualized the study. J.F., G.Z., and N.Z. obtained and processed the data. J.F. and P.Z. conducted data analysis. J.F. and P.Z. designed and made display items. J.F. wrote the paper. X.S. and P.Z edited the paper.

Declaration of interests

The authors declare no competing interests.

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2024.110697.
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