Environmental inequality in eastern China: socio-economic status and air pollution

被引:0
|
作者
Tan, Jialong [1 ]
Yan, Peizhe [1 ]
Wang, Jian [1 ,3 ]
Chen, Shuaizhen [1 ]
Bai, Jing [1 ]
Zhang, Zemin [1 ]
Nicholas, Stephen [4 ,5 ]
Maitland, Elizabeth [6 ]
Li, Peilong [7 ]
Hu, Yukang [1 ]
Sun, Jingjie [7 ]
Chen, Chen [2 ]
机构
[1] Wuhan Univ, Dong Fureng Inst Econ & Social Dev, Wuhan, Hubei, Peoples R China
[2] Wuhan Univ, Sch Publ Hlth, Dept Global Hlth, Wuhan, Hubei, Peoples R China
[3] Wuhan Univ, Ctr Hlth Econ & Management, Sch Econ & Management, Wuhan, Hubei, Peoples R China
[4] Australian Natl Inst Management & Commerce, Australian Technol Pk, Sydney, NSW, Australia
[5] Univ Newcastle, Newcastle Business Sch, Newcastle, NSW, Australia
[6] Univ Liverpool, Management Sch, Liverpool, England
[7] Shandong Hlth Commiss Med Management Serv Ctr, 6 Yandongxin Rd, Jinan 250012, Shandong, Peoples R China
关键词
Socioeconomic status; Air pollutant; Environmental inequality; Spatial regression; HEALTH; EQUITY; DISPARITIES; EXPOSURE; QUALITY; NO2; RESIDENTS; EMISSIONS; IMPACTS; INDEXES;
D O I
10.1007/s11111-024-00454-7
中图分类号
C921 [人口统计学];
学科分类号
摘要
Exposure to ambient air pollution has serious adverse impacts on human health. Yet air pollution does not affect all individuals in the same way. Existing evidence of environmental inequality in low- and middle-income countries (LMIC) is limited and contradictory, despite 91% of premature death due to air pollution in LMIC. This study aims to estimate the association between community socioeconomic status (CSES) and ambient air pollution in eastern China. The study comprised 19,622 individuals. CSES was measured by income, occupation and education. Air pollution was measured by 4-year-average ambient levels of PM2.5, PM10, NO2, and CO. Spatial autoregressive models and U-test was applied. Subsample analyses were conducted based on participants' rural/urban location and hukou status. Air pollutant exposure had an inverted U-shaped correlation with CSES. Before (after) the inflection point, increasing CSES by 1% increased(decreased) community exposure to PM2.5 by 0.527% (0.379%), PM10 by 0.460%(0.215%), NO2 by 0.584%(0.288%), and CO by 0.582% (0.382%). All results remained robust in sensitivity analysis. Subsample analysis showed that compared to rural (urban) residents, the increment of air pollution exposure concentration for migrants was 4.042 (4.556) mu g/m3 for PM2.5, 5.839 (10.624) mu g/m3 for PM10, 3.212 (5.719) mu g/m3 for NO2 and 0.205(0.208) mg/m3 for CO. Our study finds moderate SES communities facing the highest level of exposure. Our results aid policymakers to understand the locality-specific patterns of environmental pollution and to design intervention strategies to improve the environment, especially for economically vulnerable groups, such as migrants.
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页数:35
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