Source sectors underlying PM2.5-related deaths among children under 5 years of age in 17 low- and middle-income countries. (February 2023)

• Record Type: Journal Article
• Authors: Li, Pengfei; Wu, Jingyi; Wang, Ruohan; Liu, Hengyi; Zhu, Tong; Xue, Tao
• Is Part Of: Environment international. Volume 172(2023)
• Publication Date: 2023-02
• Subjects: Fine particulate matter -- Child mortality -- Source sector -- Exposure-response function -- Low- and middle-income countries; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2023.107756
• Languages: English
• ISSNs: 0160-4120

Abstract

Abstract: Background: Fine particulate matter (PM2.5 ) from different source sectors might differ in toxicity. However, data from large-scale studies on vulnerable children in low- and middle-income countries (LMICs) are insufficient. Objective: To analyze the association of under-five death (U5D) with long-term exposure to PM2.5 from different sources. Method: We evaluated demographic and health survey data for 79, 995 babies born in 2017 in 16 Asian and African LMICs (AA-LMICs) and a Latin America low-income country (i.e., Haiti). Long-term exposure to PM2.5 was assessed by a well-established product that attributed the annual concentration to 20 source sectors in 2017. The associations of survival during < 5-year periods with each source-specific concentration of PM2.5 were analyzed by Cox regression with multiple adjustments. We derived a multiple-pollutant ridge regression model to estimate the joint exposure–response function (JERF) between U5D and PM2.5 mixtures. To evaluate how sources affected PM2.5 toxicity, we evaluated the number of U5Ds attributable to PM2.5 based on the source profiles for 88 AA-LMICs. Results: According to the single-pollutant model, the risk of U5D increased by 7% (95% confidence interval [CI]: 5%, 9%) for each 10 μg/m 3 increment in total PM2.5 concentration. The model performance was lower than that of the multiple-pollutant ridge regression model. For each 10 μg/m 3 increment in PM2.5, the excess risk of U5D ranged from 6% (95% CI: 4%, 9%) in Nepal to 10% (95% CI: 6%, 14%) in Mauritania. Based on the JERF, PM2.5 contributed to 817, 647 (95% CI: 585, 729, 1, 050, 439), i.e., 28.0% (95% CI: 20.1%, 35.8%), of all U5Ds across the 88 AA-LMICs. The PM2.5 -related U5Ds were mostly attributable to PM2.5 produced by desert dust, followed by solid biofuel combustion and open fires. Conclusion: The average toxicity of PM2.5 varied by source profile, which should be taken into consideration when planning public health interventions. For some AA LMICs, natural sources of PM2.5 had the most significant health effects, and should not be ignored to ensure the protection of child health.