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. 2024 Aug 10;27(9):110697.
doi: 10.1016/j.isci.2024.110697. eCollection 2024 Sep 20.

Uncovering the impact and mechanisms of air pollution on eye and ear health in China

Jingwei Fang  1 , Yanni Yu  1   2 , Guanglai Zhang  3 , Penghu Zhu  1 , Xin Shi  4 , Ning Zhang  1   2 , Peng Zhang  5   6
Affiliations

Uncovering the impact and mechanisms of air pollution on eye and ear health in China

Jingwei Fang et al. iScience. .

Abstract

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.

Keywords: Neuroscience; Pollution.

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Conflict of interest statement

The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
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).
Figure 2
Figure 2
Distribution of every year’s disease rate across genders
Figure 3
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.
Figure 4
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.
Figure 5
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.
Figure 6
Figure 6
Future total eye or ear health cost change under different scenarios
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