An environmental justice analysis of air pollution in India

被引:4
作者
deSouza, Priyanka N. [1 ,2 ]
Chaudhary, Ekta [2 ]
Dey, Sagnk [2 ,3 ,4 ]
Ko, Soohyeon [5 ,6 ]
Nemeth, Jeremy [1 ]
Guttikunda, Sarath [7 ,8 ]
Chowdhury, Sourangsu [9 ]
Kinney, Patrick [10 ]
Subramanian, S. V. [11 ,12 ]
Bell, Michelle L. [13 ]
Kim, Rockli [6 ,14 ]
机构
[1] Univ Colorado Denver, Dept Urban & Reg Planning, Denver, CO 80204 USA
[2] Indian Inst Technol IIT Delhi, Ctr Atmospher Sci, New Delhi, India
[3] IIT Delhi, Ctr Excellence Res Clean Air, New Delhi, India
[4] IIT Delhi, Sch Publ Policy, New Delhi, India
[5] Korea Univ, Grad Sch, Dept Publ Hlth Sci, Seoul, South Korea
[6] Korea Univ, Dept Publ Hlth Sci, Interdisciplinary Program Precis Publ Hlth, Grad Sch, Seoul, South Korea
[7] Indian Inst Technol, Transportat Res & Injury Prevent TRIP Ctr, New Delhi 110016, India
[8] Urban Emiss, New Delhi 110019, India
[9] CICERO Ctr Int Climate Res, Oslo, Norway
[10] Boston Univ, Sch Publ Hlth, Boston, MA USA
[11] Harvard Ctr Populat & Dev Studies, Bow St, Cambridge, MA 02138 USA
[12] Harvard TH Chan Sch Publ Hlth, Dept Social & Behav Sci, 677 Huntington Ave, Boston, MA 02115 USA
[13] Yale Univ, Sch Environm, New Haven, CT USA
[14] Korea Univ, Coll Hlth Sci, Div Hlth Policy & Management, Seoul, South Korea
来源
SCIENTIFIC REPORTS | 2023年 / 13卷 / 01期
基金
比尔及梅琳达.盖茨基金会; 新加坡国家研究基金会;
关键词
DISCRIMINATION; DISPARITIES; EXPOSURE;
D O I
10.1038/s41598-023-43628-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Due to the lack of timely data on socioeconomic factors (SES), little research has evaluated if socially disadvantaged populations are disproportionately exposed to higher PM2.5 concentrations in India. We fill this gap by creating a rich dataset of SES parameters for 28,081 clusters (villages in rural India and census-blocks in urban India) from the National Family and Health Survey (NFHS-4) using a precision-weighted methodology that accounts for survey-design. We then evaluated associations between total, anthropogenic and source-specific PM2.5 exposures and SES variables using fully-adjusted multilevel models. We observed that SES factors such as caste, religion, poverty, education, and access to various household amenities are important risk factors for PM2.5 exposures. For example, we noted that a unit standard deviation increase in the cluster-prevalence of Scheduled Caste and Other Backward Class households was significantly associated with an increase in total-PM2.5 levels corresponding to 0.127 mu g/m3 (95% CI 0.062 mu g/m3, 0.192 mu g/m3) and 0.199 mu g/m3 (95% CI 0.116 mu g/m3, 0.283 mu g/m3, respectively. We noted substantial differences when evaluating such associations in urban/rural locations, and when considering source-specific PM2.5 exposures, pointing to the need for the conceptualization of a nuanced EJ framework for India that can account for these empirical differences. We also evaluated emerging axes of inequality in India, by reporting associations between recent changes in PM2.5 levels and different SES parameters.
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页数:13
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