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Effect of PM(2.5) pollution on perinatal mortality in China

Using ArcGIS to analyze satellite derived PM(2.5) estimates, this paper obtains the average concentration and maximum concentration of fine particulate matter (PM(2.5)) in China's 31 provinces from 2002 to 2015. We adopt fixed effects model and spatial Durbin model to investigate the associatio...

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Detalles Bibliográficos
Autores principales: Li, Guangqin, Li, Lingyu, Liu, Dan, Qin, Jiahong, Zhu, Hongjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026972/
https://www.ncbi.nlm.nih.gov/pubmed/33828199
http://dx.doi.org/10.1038/s41598-021-87218-7
Descripción
Sumario:Using ArcGIS to analyze satellite derived PM(2.5) estimates, this paper obtains the average concentration and maximum concentration of fine particulate matter (PM(2.5)) in China's 31 provinces from 2002 to 2015. We adopt fixed effects model and spatial Durbin model to investigate the association between PM(2.5) and perinatal mortality rates. The results indicate that PM(2.5) has a significantly positive association with perinatal mortality rates. A 1% increase of log-transformed average concentration and maximum concentrations of PM(2.5) is associated with 1.76‰ and 2.31‰ increase of perinatal mortality rates, respectively. In spatial econometrics analysis, we find PM(2.5) has significant spatial autocorrelation characteristics. The concentrations of log-transformed average and maximum PM(2.5) increase 1% is associated with a 2.49% increase in a 2.49‰ and 2.19‰ increase of perinatal mortality rates, respectively. The potential mechanism is that air pollution has an impact on infant weight to impact perinatal mortality rates.