空气污染对心理健康的影响：基于微观追踪调查的分析

自从改革开放以来，中国着重推动工业化进程。为了实现经济的快速发展，我们在很长一段探索时期里不断地牺牲环境，最终造成了一定的空气污染。实际上，空气污染对居民的身心健康产生危害的问题已演变成一个全国性问题。过往的研究并未过多关注空气质量与心理健康的因果关系。本论文的目的是验证如下假说：空气污染降低了心理健康水平，进而使得心理疾病发病率上升。

基于四种污染物（一氧化碳（CO）、二氧化氮（NO₂）、可吸入颗粒物（PM₁₀、PM_2.5））浓度，本文将相邻两期中国家庭追踪调查（China Family Panel Studies, CFPS）中的流行病学研究中心抑郁量表（Centre for Epidemiological Studies Depression, CES-D）评分的变化与受访者居住地点和受访者回答记录日期的空气污染物浓度变化作精准匹配。对两时期面板数据进行一阶差分处理，我们利用双向固定效应模型和线性概率模型探究空气污染对心理健康的影响。除此之外，本文还将个人CES-D量表得分及记录时间与前段时间的空气污染的数据进行一一匹配，以较为准确地捕捉空气污染物在不同时期对心理健康的持续性影响和累积效应。总的来说，本文基于考虑分布滞后结构的双向固定效应模型估计和分析了不同空气污染物对心理健康结果的短期和长期影响。

本文发现CO、NO₂、PM₁₀和PM_2.5浓度的上升可能导致居民们产生心理健康问题，严重者则有可能出现非常棘手的心理疾病。我们观察到当期的CO和NO₂浓度每上升10 mg/m³、100 μg/m³分别会显著导致反映居民心理健康的CES-D量表得分提升2.65和1.72。如果考虑一个月的累积效应，可吸入颗粒物PM₁₀和PM_2.5浓度每增加100 μg/m³分别会显著导致严重心理疾病（Severe Mental Illness, SMI）的概率提高5个百分点和7个百分点。空气污染导致的这些后果甚大且不可忽视。针对非线性效应、SMI截断标准、多分类逻辑回归以及双污染物效应模型的一系列稳健性检验也支持我们的主要发现。其次，在异质性分析中，女性、非老年人和受教育程度低的人群的心理健康受到空气污染负面影响的边际效应更大。此外，机制分析表明更差的空气质量会导致居民身体健康水平下降，进一步间接诱发相关的心理疾病。

因此，一系列的发现提醒我们，在经济社会发展过程中，我们必须通过建立保护环境的相关规章制度并不断加以提升完善，进而提高空气质量，最终减少空气污染对居民心理健康造成的负面影响。

China has sacrificed high environmental cost for rapid social and economic progress since the formulation of opening-up policy and the development in industry. The health consequence of air pollution has become a nationwide issue. But previous studies rarely focused on the causal impact of air quality on mental health during initial economic and social development. An objective of this paper is to test the hypothesis: Exposure to air contamination is correlated with increasing incidence of psychological illness.

Using concentration dataset of four air pollutants (carbon monoxide (CO), nitrogen dioxide (NO₂), and particulate matters (PM₁₀ and PM_2.5)), this paper precisely matched the Centre for Epidemiological Studies Depression Scale (CES-D) variation from two adjacent periods from the China Family Panel Studies (CFPS) to air pollutant concentration changes at the respondent’s residence area and on the date the questionnaire’s responses were recorded. First-order differencing was performed on the two-period panel data, and we then explored the effects of air pollution on mental health using a two-way fixed effects model and a linear probability model. In this paper, individual CES-D scores and recording dates were also matched one-by-one to the prior air pollution concentrations to explore the lagged effects of air pollutants on mental health. In brief, this paper estimated and analyzed both the short- and long-term effects of four air pollutants on mental health outcomes based on a two-way fixed effects model with distributed lag model structure.

This paper found that elevated concentrations of CO, NO₂, PM₁₀ and PM_2.5 may lead to mental health problems among residents and, in some cases, to the development of severe mental illnesses. We observed that each 10 mg/m³ and 100 μg/m³ increase in current CO and NO₂ concentrations significantly increased residents’ CES-D scores by 2.65 and 1.72 respectively. When the one-month lag effect was taken into account, each 100 μg/m³ increase in PM₁₀ and PM_2.5 concentrations significantly increased the probability of severe mental illness (SMI) by 0.05 and 0.07 respectively. These consequences of air pollution were significant and cannot be ignored. Secondly, a series of robustness tests for nonlinear effects, SMI cut-off criteria, multinomial logistic regression, and two-pollutant effect models also support our major findings. Thirdly, in the heterogeneity analysis, the negative marginal effects of air pollution on mental health were greater for women, non-elderly people and those with poorer education backgrounds. In addition, mechanistic analyses suggested that poorer air quality would lead to an exacerbation in people’s physical health, which further indirectly contributed to relevant mental disorders.

This paper suggests that in the process of economic and social development, environmental quality must be taken into consideration through appropriate improvement of laws and regulations on the protection of the environment, so that the negative effects of air contamination on residents' psychological health can be effectively reduced and the optimization driven by a trade-off between environment cost and people’s well-being can be reached.

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