Examining Spatial Association of Air Pollution and Suicide Rate Using Spatial Regression Models

Yeran Sun, Ting On Chan, Jing Xie, Xuan Sun and Ying Huang
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Yeran Sun: School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Ting On Chan: School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Jing Xie: Faculty of Architecture, The University of Hong Kong, Hong Kong, China
Xuan Sun: Zhou Enlai School of Government, Nankai University, Tianjin 300350, China
Ying Huang: School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China

Sustainability, 2020, vol. 12, issue 18, 1-10

Abstract: Air pollution can have adverse impacts on both the physical health and mental health of people. Increasing air pollution levels are likely to increase suicide rates, although the causal mechanisms underlying the relationship between pollution exposure and suicidal behaviour are not well understood. In this study, we aimed to further examine the spatial association of air pollution and suicidal behaviour. Specifically, we investigated whether or how PM 2.5 levels are spatially associated with the adult suicide rates at the district level across London. As the data used are geospatial data, we used two newly developed specifications of spatial regression models to investigate the spatial association of PM 2.5 levels and suicide. The empirical results show that PM 2.5 levels are spatially associated with the suicide rates across London. The two models show that PM 2.5 levels have a positive association with adult suicide rates over space. An area with a high percentage of White people or a low median household income is likely to suffer from a high suicide rate.

Keywords: air pollution; suicide rate; spatial regression models; matrix exponential spatial specification models; eigenvector spatial filtering model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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