Spatial-Temporal Evolution of Health Impact and Economic Loss upon Exposure to PM2.5 in China

Sun, Xialing; Zhang, Rui; Wang, Geyi

doi:10.3390/ijerph19041922

Cited by 20 publications

(4 citation statements)

References 50 publications

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“…However, in 2016, these figures decreased to 2,372.745 deaths and 2,938.809 million CNY in economic losses, respectively, accounting for 0.20% of GDP. At the national level, Sun et al reported that in China, PM 2.5 affected an average of 15.03 million people per year and caused economic losses of 1.71% of China's GDP, about US$86,886.94 million 54 . Diao et al found that in China’s 338 cities, the overall economic losses caused by exposure to PM 2.5 in 2015 was 1.846 trillion CNY, which was 2.73% of their total annual GDP 51 .…”

Section: Discussionmentioning

confidence: 99%

Estimation of health risk and economic loss attributable to PM2.5 and O3 pollution in Jilin Province, China

Ma,

Zhang,

Wang

et al. 2023

Sci Rep

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Ambient pollutants, particularly fine particulate matter (PM2.5) and ozone (O3), pose significant risks to both public health and economic development. In recent years, PM2.5 concentration in China has decreased significantly, whereas that of O3 has increased rapidly, leading to considerable health risks. In this study, a generalized additive model was employed to establish the relationship of PM2.5 and O3 exposure with non-accidental mortality across 17 districts and counties in Jilin Province, China, over 2015–2016. The health burden and economic losses attributable to PM2.5 and O3 were assessed using high-resolution satellite and population data. According to the results, per 10 µg/m3 increase in PM2.5 and O3 concentrations related to an overall relative risk (95% confidence interval) of 1.004 (1.001–1.007) and 1.009 (1.005–1.012), respectively. In general, the spatial distribution of mortality and economic losses was uneven. Throughout the study period, a total of 23,051.274 mortalities and 27,825.015 million Chinese Yuan (CNY) in economic losses were attributed to O3 exposure, which considerably surpassing the 5,450.716 mortalities and 6,553,780 million CNY in economic losses attributed to PM2.5 exposure. The O3-related health risks and economic losses increased by 3.75% and 9.3% from 2015 to 2016, while those linked to PM2.5 decreased by 23.33% and 18.7%. Sensitivity analysis results indicated that changes in pollutant concentrations were the major factors affecting mortality rather than baseline mortality and population.

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Section: Discussionmentioning

confidence: 99%

Estimation of health risk and economic loss attributable to PM2.5 and O3 pollution in Jilin Province, China

Ma,

Zhang,

Wang

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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“…This type of pollution has the potential to worsen symptoms and result in serious adverse health effects. Previous studies have underscored the significant impact of PM 2.5 pollution on the health and welfare of individuals residing in mainland Southeast Asia, as noted by Sun et al [ 16 ]. Several research studies have been carried out to investigate the impact of fine particulate matter on human health in mainland Southeast Asia.…”

Section: Introductionmentioning

confidence: 99%

Perspective on Particulate Matter: From Biomass Burning to the Health Crisis in Mainland Southeast Asia

Amnuaylojaroen

Parasin

2023

Toxics

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Air pollution, notably particulate matter pollution, has become a serious concern in Southeast Asia in recent decades. The combustion of biomass has been recognized to considerably increase air pollution problems from particulate matter in this region. Consequently, its effect on people in this area is significant. This article presents a synthesis of several datasets obtained from satellites, global emissions, global reanalysis, and the global burden of disease (GBD) to highlight the air quality issue and emphasize the health crisis in mainland Southeast Asia. We found that the death rates of people have increased significantly along with the rise of hotspots in mainland Southeast Asia over the last two decades (2000–2019). In comparison, most countries saw a considerable increase in the predicted fatality rates associated with chronic respiratory illnesses during those two decades. Several reports highlight the continued prevalence of chronic respiratory diseases likely related to poor air quality in Southeast Asia.

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“…In recent years, with accelerated urbanization, industrialization, and modernization, air pollution problems have become increasingly serious, and PM 2.5 , as one of the main pollutants in air pollution in China, has garnered significant widespread concern in scientific fields, including the atmospheric environmental protection field. Furthermore, concern has grown among the general public [1][2][3]. PM 2.5 is highly active, small in size but large in surface area, suspended in the air for a long time, and easily adsorbs heavy metals, microorganisms, and other toxic and harmful substances, which can not only directly reduce atmospheric visibility by scattering and absorbing sunlight, causing disturbance to people's daily lives, but can also enter the end of the human respiratory tract through airflow, directly endangering human health [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Spatial Estimation of Regional PM2.5 Concentrations with GWR Models Using PCA and RBF Interpolation Optimization

et al. 2022

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In recent years, geographically weighted regression (GWR) models have been widely used to address the spatial heterogeneity and spatial autocorrelation of PM2.5, but these studies have not fully considered the effects of all potential variables on PM2.5 variation and have rarely optimized the models for residuals. Therefore, we first propose a modified GWR model based on principal component analysis (PCA-GWR), then introduce five different spatial interpolation methods of radial basis functions to correct the residuals of the PCA-GWR model, and finally construct five combinations of residual correction models to estimate regional PM2.5 concentrations. The results show that (1) the PCA-GWR model can fully consider the contributions of all potential explanatory variables to estimate PM2.5 concentrations and minimize the multicollinearity among explanatory variables, and the PM2.5 estimation accuracy and the fitting effect of the PCA-GWR model are better than the original GWR model. (2) All five residual correction combination models can better achieve the residual correction optimization of the PCA-GWR model, among which the PCA-GWR model corrected by Multiquadric Spline (MS) residual interpolation (PCA-GWRMS) has the most obvious accuracy improvement and more stable generalizability at different time scales. Therefore, the residual correction of PCA-GWR models using spatial interpolation methods is effective and feasible, and the results can provide references for regional PM2.5 spatial estimation and spatiotemporal mapping. (3) The PM2.5 concentrations in the study area are high in winter months (January, February, December) and low in summer months (June, July, August), and spatially, PM2.5 concentrations show a distribution of high north and low south.

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Spatial-Temporal Evolution of Health Impact and Economic Loss upon Exposure to PM2.5 in China

Cited by 20 publications

References 50 publications

Estimation of health risk and economic loss attributable to PM2.5 and O3 pollution in Jilin Province, China

Estimation of health risk and economic loss attributable to PM2.5 and O3 pollution in Jilin Province, China

Perspective on Particulate Matter: From Biomass Burning to the Health Crisis in Mainland Southeast Asia

Spatial Estimation of Regional PM2.5 Concentrations with GWR Models Using PCA and RBF Interpolation Optimization

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