Department of Urban and Regional Planning, University of Colorado Denver, Denver, CO, USA.
Centre for Atmospheric Sciences, Indian Institute of Technology (IIT) Delhi, New Delhi, India.
Sci Rep. 2023 Oct 4;13(1):16690. doi: 10.1038/s41598-023-43628-3.
Due to the lack of timely data on socioeconomic factors (SES), little research has evaluated if socially disadvantaged populations are disproportionately exposed to higher PM 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 PM 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 PM 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-PM levels corresponding to 0.127 μg/m (95% CI 0.062 μg/m, 0.192 μg/m) and 0.199 μg/m (95% CI 0.116 μg/m, 0.283 μg/m, respectively. We noted substantial differences when evaluating such associations in urban/rural locations, and when considering source-specific PM 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 PM levels and different SES parameters.
由于缺乏关于社会经济因素 (SES) 的及时数据,很少有研究评估社会弱势群体是否不成比例地暴露于印度更高的 PM 浓度中。我们通过使用一种精确加权的方法,从国家家庭健康调查 (NFHS-4) 中为 28081 个簇(印度农村的村庄和印度城市的普查块)创建了 SES 参数的丰富数据集,该方法考虑了调查设计。然后,我们使用完全调整的多层次模型评估了总 PM、人为 PM 和来源特定 PM 暴露与 SES 变量之间的关联。我们观察到 SES 因素,如种姓、宗教、贫困、教育和获得各种家庭设施,是 PM 暴露的重要风险因素。例如,我们注意到,在簇级中,预定种姓和其他落后阶层家庭的比例每增加一个标准差,与总 PM 水平的增加显著相关,相应的总 PM 水平增加 0.127μg/m(95%CI:0.062μg/m,0.192μg/m)和 0.199μg/m(95%CI:0.116μg/m,0.283μg/m)。我们注意到,在评估城乡地区的这种关联以及考虑特定来源的 PM 暴露时,存在很大差异,这表明需要为印度制定一个概念化的、细致入微的环境正义框架,以考虑到这些经验差异。我们还通过报告 PM 水平的最近变化与不同 SES 参数之间的关联,评估了印度新出现的不平等轴。