SPATIAL ANALYSIS AND MODELLING OF AIR POLLUTION AND DEATH RATES IN HEBEI PROVINCE, CHINA

MA Jing; ZHOU Chuang-wen; PRYCE Gwilym

doi:10.13959/j.issn.1003-2398.2019.06.006

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HUMAN GEOGRAPHY ›› 2019, Vol. 34 ›› Issue (6) : 45-52,98. DOI: 10.13959/j.issn.1003-2398.2019.06.006

SPATIAL ANALYSIS AND MODELLING OF AIR POLLUTION AND DEATH RATES IN HEBEI PROVINCE, CHINA

MA Jing¹, ZHOU Chuang-wen¹, PRYCE Gwilym²

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Abstract

Air pollution is one of the major environmental problems associated with rapid urbanization in many countries, like China. Based on the data of population census and air quality at the sub-district level in Hebei province in 2010, distributional analysis and spatial regression models were used to examine the spatial distribution, social distribution and determinants of air pollution and death rates at the level of townships in Hebei province. Here, the principle of spatial adjacency was adopted to define the spatial weight matrix, and spatial auto-correlation in the residuals of the OLS model was identified by Moran's I. We discussed environmental injustice and health inequality in Hebei province in 2010, based on estimates from spatial regression models. The following conclusions can be drawn: 1) Environmental injustices exists in Hebei province, as the air pollution exposure for the elderly (aged ≥60 years old), and the unemployed are significantly higher than that of other groups. 2) There is health inequality in Hebei province. At the level of townships, there are significant differences in the death rates. 3) Air pollution has a significant effect on death rates. Areas with higher air pollution tend to have higher death rates in Hebei province. 4) The air pollution and death rates in urban areas and rural areas are quite different.

Key words

air pollution / environmental justice / the number of deaths / spatial regression model / Hebei province

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MA Jing, ZHOU Chuang-wen, PRYCE Gwilym. SPATIAL ANALYSIS AND MODELLING OF AIR POLLUTION AND DEATH RATES IN HEBEI PROVINCE, CHINA[J]. HUMAN GEOGRAPHY. 2019, 34(6): 45-52,98 https://doi.org/10.13959/j.issn.1003-2398.2019.06.006

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