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<p>Air pollution poses a serious threat to public health with the continuous process of urbanization and industrialization. The relationship between particulate matter smaller than 2.5μm (PM<sub>2.5</sub>) and cardiovascular and cerebrovascular diseases (CCVD) has attracted considerable global attention. However, the spatial clustering characteristics and environmental influencing factors for cardiovascular and cerebrovascular disease mortality (CCVDM) were seldom explored at the urban scale. In particular, the modification effects of PM<sub>2.5</sub> on CCVDM under different socio-economic levels and microclimatic conditions have not yet received adequate attention. This study focused on Xi'an City, China, and utilized exploratory spatial data analysis methods to reveal the spatial clustering characteristics of CCVDM. The random forest (RF) model was developed to assess the relative importance of various influencing factors on CCVDM. Furthermore, the geographically and temporally weighted regression (GTWR) model was employed to determine the spatial heterogeneity of influencing factors and assess the modification effects of PM<sub>2.5</sub> on CCVDM. The results showed that from 2014 to 2016, a total of 38,150 male and 32,717 female CCVD deaths were recorded. Spatially, significant clustering was observed, with hotspots mainly concentrated in the central and western areas. Secondly, the RF model effectively quantified the relationship between CCVDM and its influencing factors (<i>R</i><sup>2</sup> = 0.90). The importance ranking showed that socio-economic factors had a greater impact on CCVDM than natural factors. Additionally, the GTWR model achieved higher accuracy for females (Adjusted <i>R</i><sup>2</sup> = 0.41) than for males (Adjusted <i>R</i><sup>2</sup> = 0.35) in revealing the effects of influencing factors on CCVDM. The model results confirmed significant spatiotemporal heterogeneity in the effects of these factors on CCVDM. Finally, PM<sub>2.5</sub> exhibited significant modification effects on CCVDM. The adverse impact of PM<sub>2.5</sub> on CCVDM was stronger in areas with lower economic levels or when the temperature was below 26&#xa0;°C, and the effect was greater in males than in females. This study provides valuable insights and evidence for the prevention and control of CCVD and related epidemics.</p>

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Revealing the influence of socio-economic and meteorological factors on PM2.5 related cardiovascular and cerebrovascular disease mortality: insights from Xi'an, China

  • 期刊论文

  • 发表日期：

    2025年7月2日

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  • Wencai Zhang,
  • Bin Guo,
  • Lin Pei,
  • Yan Wang,
  • Tengyue Guo

摘要

Air pollution poses a serious threat to public health with the continuous process of urbanization and industrialization. The relationship between particulate matter smaller than 2.5μm (PM2.5) and cardiovascular and cerebrovascular diseases (CCVD) has attracted considerable global attention. However, the spatial clustering characteristics and environmental influencing factors for cardiovascular and cerebrovascular disease mortality (CCVDM) were seldom explored at the urban scale. In particular, the modification effects of PM2.5 on CCVDM under different socio-economic levels and microclimatic conditions have not yet received adequate attention. This study focused on Xi'an City, China, and utilized exploratory spatial data analysis methods to reveal the spatial clustering characteristics of CCVDM. The random forest (RF) model was developed to assess the relative importance of various influencing factors on CCVDM. Furthermore, the geographically and temporally weighted regression (GTWR) model was employed to determine the spatial heterogeneity of influencing factors and assess the modification effects of PM2.5 on CCVDM. The results showed that from 2014 to 2016, a total of 38,150 male and 32,717 female CCVD deaths were recorded. Spatially, significant clustering was observed, with hotspots mainly concentrated in the central and western areas. Secondly, the RF model effectively quantified the relationship between CCVDM and its influencing factors (R2 = 0.90). The importance ranking showed that socio-economic factors had a greater impact on CCVDM than natural factors. Additionally, the GTWR model achieved higher accuracy for females (Adjusted R2 = 0.41) than for males (Adjusted R2 = 0.35) in revealing the effects of influencing factors on CCVDM. The model results confirmed significant spatiotemporal heterogeneity in the effects of these factors on CCVDM. Finally, PM2.5 exhibited significant modification effects on CCVDM. The adverse impact of PM2.5 on CCVDM was stronger in areas with lower economic levels or when the temperature was below 26 °C, and the effect was greater in males than in females. This study provides valuable insights and evidence for the prevention and control of CCVD and related epidemics.