The challenge of population aging for mitigating deaths from PM2.5 air pollution in China

Abstract

Estimating the health burden of air pollution against the background of population aging is of great significance for achieving the Sustainable Development Goal 3.9 which aims to substantially reduce the deaths and illnesses from air pollution. Here, we estimated spatiotemporal changes in deaths attributable to PM2.5 air pollution in China from 2000 to 2035 and examined the drivers. The results show that from 2019 to 2035, deaths were projected to decease 15.4% (6.6%–20.7%, 95% CI) and 8.4% (0.6%–13.5%) under the SSP1-2.6 and SSP5-8.5 scenario, respectively, but increase 10.4% (5.1%–20.5%) and 18.1% (13.0%–28.3%) under SSP2-4.5 and SSP3-7.0 scenarios. Population aging will be the leading contributor to increased deaths attributable to PM2.5 air pollution, which will counter the positive gains achieved by improvements in air pollution and healthcare. Region-specific measures are required to mitigate the health burden of air pollution and this requires long-term efforts and mutual cooperation among regions in China.

Continued Rise in Health Burden from Ambient PM2.5 in India under SSP Scenarios Until 2100 despite Decreasing Concentrations

“…We applied the relative change to the baseline period satellite-derived data from the baseline period to estimate future PM 2.5 exposure at a 1°× 1°grid. This method has been applied to Xu et al; 33 this process can be represented by eq 5 Population Data. In our study, we focused solely on assessing the burden of disease in individuals aged 25 and above.…”

“…We applied the relative change to the baseline period satellite-derived data from the baseline period to estimate future PM 2.5 exposure at a 1° × 1° grid. This method has been applied to Xu et al; this process can be represented by eq P normalM 2.5 normalF normalu normalt normalu normalr normale normalC normala normall normali normalb normalr normala normalt normale normald = P normalM 2.5 normalB normala normals normale normall normali normaln normale normalS normala normalt + P normalM 2.5 normalB normala normals normale normall normali normaln normale normalS normala normalt × true[ normalP M 2.5 F u t u r e mod e l − normalP M 2.5 B a s e l i n e mod e l normalP M 2.5 B a s e l i n e mod e l ] where PM 2.5 Future Calibrated rep...…”

“…We applied the relative change to the baseline period satellite-derived data from the baseline period to estimate future PM 2.5 exposure at a 1° × 1° grid. This method has been applied to Xu et al; this process can be represented by eq where PM 2.5 Future Calibrated represents the future (2016–2100) PM 2.5 concentration after calibration; PM 2.5 Baseline s at represents the satellite-derived PM 2.5 concentration of the baseline period (2016–2100); PM 2.5 Baseline model represents the CMIP6 model-derived PM 2.5 concentration of the baseline period (2016–2020); and PM 2.5 Future model represents the CMIP6 model-derived PM 2.5 concentration in the future (2016–2100).…”

Development of a Method to Determine the Environmental Burden of Diseases and an Application to Identify Factors Driving Changes in the Number of PM2.5-Related Deaths in China between 2000 and 2010

“…7−10 However, the baseline mortality rate is ambiguous. Previous studies typically described changes in the baseline mortality rate in terms of improvements or worsening of healthcare, 7 medical status, 10 or socioeconomic status without any estimation, 11,12 which might mislead readers. According to the classical method used to calculate premature deaths attributable to PM 2.5 (see eq 1), the baseline mortality rate reflects total mortality, which is affected by not only nontarget factors (e.g., unhealthy behaviors) but also target factors (PM 2.5 in the present study).…”

Data Insights for Sustainable Cities: Associations between Google Street View-Derived Urban Greenspace and Google Air View-Derived Pollution Levels

“…These include meteorology, physicochemical transformations, and urban morphology including the size, structure, and growth of cities . The WHO estimates that 4.2 million premature deaths globally were attributed to ambient air pollution in 2019. , Indeed, a plethora of evidence has shown associations between air pollution and adverse health issues including cognitive decline, chronic respiratory diseases, , daily cardiorespiratory hospitalizations, change in thyroid hormones, and premature deaths …”