Spatial Distribution of PM_2.5‐Related Premature Mortality in China

Abstract

PM2.5 is a major component of air pollution in China and has a serious threat to public health. It is very important to quantify spatial characteristics of the health effects caused by outdoor PM2.5 exposure. This study analyzed the spatial distribution of PM2.5 concentration (45.9 μg/m3 national average in 2016) and premature mortality attributed to PM2.5 in cities at the prefectural level and above in China in 2016. Using the Global Exposure Mortality Model (GEMM), the total premature mortality in China was estimated to be 1.55 million persons, and the per capita mortality was 11.2 per 10,000 persons in the year 2016, resulting in higher estimates compared to the integrated exposure‐response model. We assessed the premature mortality attributed to PM2.5 through common diseases, including ischemic heart disease (IHD), cerebrovascular disease (CEV), chronic obstructive pulmonary disease (COPD), lung cancer (LC), and lower respiratory infections (LRI). The premature mortality due to IHD and CEV accounted for 68.5% of the total mortality, and the per capita mortality (per 10,000 persons) for all ages due to IHD was 3.86, the highest among diseases. For the spatial distribution of disease‐specific premature mortality, the top two highest absolute numbers of premature mortality associated with IHD, CEV, LC, and LRI, respectively, were found in Chongqing and Beijing. In 338 cities of China, we have found a significant positive spatial autocorrelation of per capita premature mortality, indicating the necessity of coordinated regional governance for an efficient control of PM2.5.

Specific analysis of PM2.5-attributed disease burden in typical areas of Northwest China

“…The study results showed the number of cardiovascular diseases (IHD and stroke: 7,200 individuals) deaths attributable to PM 2.5 pollution in Gansu Province was higher than respiratory diseases (COPD, LC, and LRI: 4,579 individuals) deaths. This finding is consistent with previous research conducted in other regions of China ( 16 , 26 , 44 ). The higher number of premature cardiovascular deaths can be attributed to the generally high baseline mortality rate from cardiovascular diseases ( 45 ).…”

“…Meanwhile, the reliability of the WRF-Chem model simulation was evaluated using common evaluation metrics. Second, the exposure-response relationship between PM 2.5 and health outcomes was also a major source of uncertainty ( 44 ). Previous studies mainly utilized the IER model, which only incorporated cohort study data from European and American regions, potentially underestimating the health burden in areas with higher PM 2.5 concentrations.…”

Sources of PM2.5‐Associated Health Risks in Europe and Corresponding Emission‐Induced Changes During 2005–2015

“…( 2019 ) since the latter study calculated the PM 2.5 ‐associated premature deaths over a larger European region using the Global Exposure Mortality Model, which accounts for a larger range of PM 2.5 exposure by including new cohort data from China, and providing larger hazard ratio predictions for nearly all concentrations than the GBD estimates (Burnett et al., 2018 ). IHD and stroke were estimated to be the top two causes of premature death attributable to PM 2.5 exposure, which is similar to the calculation results reported in recent studies over Europe (Tarín‐Carrasco et al., 2022 ), China (Zheng et al., 2021 ), and the US (Kazemiparkouhi et al., 2022 ).…”

High-resolution mapping of premature mortality induced by atmospheric particulate matter in China

“…A previous study in China reported PM 2.5 with a rather coarse spatial resolution of 10 km, and population data lacked the use of raster data and were not gender-specified (Li et al 2019). Previous investigations of health effects in China are mainly based on statistical studies of whole regions, and lack high spatial resolution of the data (Wang et al 2018;Chowdhury et al 2020;Zheng et al 2021). It has been shown that the coarse-level mortality statistics would bias the results while the finer resolution of the data opens up the possibility to improve the estimation of PM 2.5 -induced health impacts (Goto et al 2016;Liu et al 2019).…”