PM2.5作为空气污染物,对人体健康构成了潜在威胁。中国和印度是全球人口最多的两个发展中国家,PM2.5污染造成的疾病负担问题尤为严重。因此,本文基于长时间序列高分辨率(0.01°×0.01°)卫星反演的PM2.5浓度数据,分析了中国和印度19年(2000-2018年)的PM2.5时空格局变化和人口暴露情况；基于综合暴露响应模型全面评估了两个国家因PM2.5长期暴露导致的六种疾病(急性下呼吸道感染、慢性阻塞性肺病、二型糖尿病、缺血性心脏病、肺癌和中风)的过早死亡人数。结果表明,中国PM2.5浓度的高值区集中在新疆、四川盆地、华北平原以及长江经济带等地区,年人口加权浓度总体呈减少趋势(2000年为50 ug×m-3,2018年为40.8ug×m-3)；印度PM2.5浓度的高值区集中在北部,年人口加权浓度一直呈上升趋势(2000年为51.5 ug×m-3,2018年为76.4 ug×m-3)。对于中国而言,PM_2.5暴露造成的过早死亡人数从2000年的90.8万人增长至2018年的137.8万人,增长了47万人(34.1%)；中风是过早死亡主要原因,占总死亡人数的45.9%(56.3万人)。印度PM2.5造成的过早死亡人数从2000年的34.3万人增长至2018年的75万人,增长了40.7万人(54.2%)；缺血性心脏病和中风是过早死亡两大主要原因,分别占39.9%(20.2万人)和25.5%(12.9万人)。研究结果有望为决策者和污染控制机构提供参考,有助于制定空气污染治理政策。

With frequent air pollution incidents happening worldwide, many studies have focused on the disease burden from long-term exposure to PM2.5 pollution. In China and India, the world"s two most populous developing countries, the burden of disease attributable to PM2.5 exposure are particularly serious. There is a critical need to develop a multi-year and comprehensive dataset of PM2.5-related premature deaths in both countries, so as to support future air pollution prevention policy. However, not enough relevant studies were conducted over the past years. Therefore, this study analyzed the spatial and temporal patterns of PM2.5 concentrations and changes of population exposure to PM2.5 in China and India over the past 19 years (2000?2018) by using high-resolution (0.01°×0.01°) satellite data. Combined with the integrated exposure response (IER) model, this study comprehensively assessed the premature deaths from six diseases due to long-term PM2.5 exposure: acute lower respiratory infection (ALRI), chronic obstructive pulmonary disease (COPD), type 2 diabetes (DIA), ischemic heart disease (IHD), lung cancer (LNC) and stroke (STR). The results showed that the high value areas of PM2.5 concentrations in China were concentrated in Xinjiang, Sichuan Basin, North China Plain and Yangtze River Economic Belt. The annual population-weighted PM2.5 concentrations showed a decreasing trend (50 ug×m-3, in 2000 and 40.8 ug×m-3, in 2018). In India, high levels of PM2.5 concentrations were concentrated in the north, such as Punjab, Haryana, and Uttar Pradesh. The annual population-weighted PM2.5 concentrations in India increased from 51.5 ug×m-3, in 2000 to 76.4ug×m-3, in 2018. For China, the number of premature deaths caused by PM2.5 exposure increased by 34.1% from 908,000 in 2000 to 1,378,000 in 2018. The annual average premature deaths were 1,228,000 in China. STR was the major contributor to total premature deaths, accounting for 45.9% (563,000) of total. In India, the number of premature deaths attributable to PM2.5 increased rapidly from 343,000 in 2000 to 750,000 in 2018, with a net increase of 407,000. The annual average premature deaths were 506,000 in India. IHD and STR were the two major contributors, accounting for 39.9% (202,000) and 25.5% (129,000), respectively. Moreover, DIA was responsible for 29,000 (2.3%) and 30,000 (6%) premature deaths in China and India, respectively, whose contributions to the total could not be ignored. Overall, this study established a long-term series of high-resolution datasets on premature deaths due to PM2.5 exposure in the two developing countries (China and India). The premature deaths caused by air pollution remain high in China and India, where PM2.5 concentrations and population density are high and stricter air pollution control policies were needed. These results are expected to provide a reference for the formulation of air pollution policies in the two countries. However, in the estimation of premature deaths of PM2.5, the baseline mortality rate did not consider the differences caused by the level of development and medical treatment within a country in this study. The incorporation of sub-national baseline mortality rate for assessment of premature death will be the focus in the future.