Driving factors behind the continuous increase of long-term PM2.5-attributable health burden in India using the high-resolution global datasets from 2001 to 2020.
Driving factors behind the continuous increase of long-term PM2.5-attributable health burden in India using the high-resolution global datasets from 2001 to 2020.
复制标题
DOI：
10.1016/j.scitotenv.2023.161435
复制
发表时间：
2023-01
期刊：
影响因子：
--
通讯作者：
K. Maji;A. Namdeo;Lindsay Bramwell
中科院分区：
文献类型：
--
作者：
K. Maji;A. Namdeo;Lindsay Bramwell
空气污染是全球第四大风险因素，而在印度，空气污染是最高风险因素，每年有数百万人过早死亡。尽管实施了几项空气污染控制计划，但印度的PM2.5水平并没有明显下降。在过去的几十年里，印度与PM2.5相关的健康负担显著增加。对PM2.5可归因过早死亡的精细分辨率（0·01° × 0·01°）分析（而不是粗略水平分析）可能会阐明这种增加的原因，并为最先进的国家和国家排放控制策略提供信息。本研究量化了2001 - 2020年PM2.5导致的过早死亡的时空动态，并应用分解分析来剖析各种相关参数的贡献，如PM2.5浓度，人口分布和疾病特异性基线死亡率。结果显示，在印度的PM2.5和相关的健康负担显着的时空变化。在研究期间，人口加权PM2.5值从46.0增加到59.5 μg/m3，相关非传染性死亡增加约87.6%，从1050 [95%（CI）：880-1210]千增加到1970（95% CI：1658-2259）千。北方邦、比哈尔邦、西孟加拉、马哈拉施特拉邦、拉贾斯坦邦和中央邦的可归因于PM2.5的死亡人数最多。在这些州，非意外死亡人数从2001年的232.1、112.7、81.4、79.1、66.3和58.5万人增加到2020年的424.1、226.7、156.2、154.5、123.3和119.7万人。在人均人口（/105人口）中，德里、北方邦、比哈尔邦、哈里亚纳邦和旁遮普的PM2.5可归因死亡人数最高。在整个研究期间，人口统计学变化超过了健康负担，导致印度PM2.5相关非意外死亡增加约62.8%，而PM2.5浓度变化仅影响18.7%。基线死亡率的变化对估计特定疾病死亡率变化的影响不同。我们的研究结果表明，在具体的邦一级，特别是在印度北部，制定更动态和全面的政策对于印度PM2.5相关死亡的总体减少是不可或缺的。
Air pollution is the fourth leading global risk factor, whereas in India air pollution is reported as the highest risk factor with millions of premature deaths every year. Despite implementation of several air pollution control plans, PM2.5levels over India have not noticeably reduced. PM2.5-associated health burdens in India have increased significantly in past decades. A fine resolution (0·01° × 0·01°) analysis of PM2.5-attribulable premature deaths (rather than the coarse-level analysis) may elucidate the reason for this increase and inform and effective start-of-the-art state-level and national emission control strategies. This study quantified the spatiotemporal dynamics of PM2.5-attributable premature deaths from 2001 to 2020 and applied a decomposition analysis to dissect the contribution of various associated parameters, such as PM2.5concentration, population distribution and disease-specific baseline death rate. Results show significant spatiotemporal variations of PM2.5and associated health burden in India. During the study period, population weighted PM2.5value increased from 46.0 to 59.5 μg/m3and associated non-communicable death increased around 87.6 %, from 1050 [95 % (CI): 880–1210] thousand to 1970 (95 % CI: 1658-2259) thousand. The states of Uttar Pradesh, Bihar, West Bengal, Maharashtra, Rajasthan, and Madhya Pradesh had the highest PM2.5-attributable deaths. In these states, non-accidental deaths increased from 232.1, 112.7, 81.4, 79.1, 66.3 and 58.5 thousand in 2001 to 424.1, 226.7, 156.2, 154.5, 123.3 and 119.7 thousand in 2020. In per capita population (/105population), the highest PM2.5-attributable deaths were observed in Delhi, Uttar Pradesh, Bihar, Haryana and Punjab. Throughout the study period, demographic changes outweighed the health burden and were responsible for ~62.8 % increase of PM2.5-related non-accidental deaths across India, whereas the change in PM2.5concentration influenced only 18.7 %. The change in baseline mortality rate impacts differently for the estimation of disease-specific mortality changes. Our findings suggest more dynamic and comprehensive policies at state-specific level, especially for North India is very indispensable for the overall decrease of PM2.5-related deaths in India.