Literature DB >> 26452312

Predicting exposure-response associations of ambient particulate matter with mortality in 73 Chinese cities.

Lina Madaniyazi1, Yuming Guo2, Renjie Chen3, Haidong Kan4, Shilu Tong5.   

Abstract

Estimating the burden of mortality associated with particulates requires knowledge of exposure-response associations. However, the evidence on exposure-response associations is limited in many cities, especially in developing countries. In this study, we predicted associations of particulates smaller than 10 μm in aerodynamic diameter (PM10) with mortality in 73 Chinese cities. The meta-regression model was used to test and quantify which city-specific characteristics contributed significantly to the heterogeneity of PM10-mortality associations for 16 Chinese cities. Then, those city-specific characteristics with statistically significant regression coefficients were treated as independent variables to build multivariate meta-regression models. The model with the best fitness was used to predict PM10-mortality associations in 73 Chinese cities in 2010. Mean temperature, PM10 concentration and green space per capita could best explain the heterogeneity in PM10-mortality associations. Based on city-specific characteristics, we were able to develop multivariate meta-regression models to predict associations between air pollutants and health outcomes reasonably well.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Keywords:  China; Mortality; Multivariate meta-regression model; Particulate matter

Mesh:

Substances:

Year:  2016        PMID: 26452312     DOI: 10.1016/j.envpol.2015.09.011

Source DB:  PubMed          Journal:  Environ Pollut        ISSN: 0269-7491            Impact factor:   8.071


  1 in total

1.  Estimating Ground-Level Concentrations of Multiple Air Pollutants and Their Health Impacts in the Huaihe River Basin in China.

Authors:  Deying Zhang; Kaixu Bai; Yunyun Zhou; Runhe Shi; Hongyan Ren
Journal:  Int J Environ Res Public Health       Date:  2019-02-16       Impact factor: 3.390

  1 in total

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