气候变化领域集成服务门户(CCPortal): A hybrid model approach for estimating health burden from NO2 in megacities in China: A case study in Guangzhou

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DOI	10.1088/1748-9326/ab4f96
	A hybrid model approach for estimating health burden from NO2 in megacities in China: A case study in Guangzhou
	He B.; Heal M.R.; Humstad K.H.; Yan L.; Zhang Q.; Reis S.
发表日期	2019
ISSN	17489318
卷号	14 期号:12
英文摘要	Background: Nitrogen dioxide (NO2) poses substantial public health risks in large cities globally. Concentrations of NO2 shows high spatial variation, yet intra-urban measurements of NO2 in Chinese cities are sparse. The size of Chinese cities and shortage of some datasets is challenging for high spatial resolution modelling. The aim here was to combine advantages of dispersion and land-use regression (LUR) modelling to simulate population exposure to NO2 at high spatial resolution for health burden calculations, in the example megacity of Guangzhou. Methods: Ambient concentrations of NO2 simulated by the ADMS-Urban dispersion model at 83 'virtual' monitoring sites, selected to span both the range of NO2 concentration and weighting by population density, were used to develop a LUR model of 2017 annual-mean NO2 across Guangzhou at 25 m × 25 m spatial resolution. Results: The LUR model was validated against both the 83 virtual sites (adj R 2: 0.96, RMSE: 5.48 μg m-3; LOOCV R 2: 0.96, RMSE: 5.64 μg m-3) and, independently, against available observations (n = 11, R 2:: 0.63, RMSE: 18.0 μg m-3). The modelled population-weighted long-term average concentration of NO2 across Guangzhou was 52.5 μg m-3, which contributes an estimated 7270 (6960-7620) attributable deaths. Reducing concentrations in exceedance of the China air quality standard/WHO air quality guideline of 40 μg m-3 would reduce NO2-attributable deaths by 1900 (1820-1980). Conclusions: We demonstrate a general hybrid modelling method that can be employed in other cities in China to model ambient NO2 concentration at high spatial resolution for health burden estimation and epidemiological study. By running the dispersion model with alternative mitigation policies, new LUR models can be constructed to quantify policy effectiveness on NO2 population health burden. © 2019 The Author(s). Published by IOP Publishing Ltd.
英文关键词	air quality; dispersion model,NO2; health burden; LUR
语种	英语
scopus关键词	Air quality; Dispersions; Health; Health risks; Image resolution; Land use; Population statistics; Public risks; Urban growth; Ambient concentrations; Average concentration; Dispersion modeling; Epidemiological studies; High spatial resolution; Mitigation policies; Population densities; Urban dispersion modeling; Nitrogen oxides; air quality; estimation method; health impact; megacity; nitrous oxide; numerical model; China; Guangdong; Guangzhou
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154308
作者单位	School of Chemistry, University of Edinburgh, Joseph Black Building, David Brewster Road, Edinburgh, EH9 3FJ, United Kingdom; NERC Centre for Ecology and Hydrology, Bush Estate, Penicuik, Midlothian, EH26 0QB, United Kingdom; School of GeoSciences, University of Edinburgh, Institute of Geography, Drummond Street, Edinburgh, EH8 9XP, United Kingdom; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China; University of Exeter Medical School, European Centre for Environment and Health, Knowledge Spa, Truro, TR1 3HD, United Kingdom
推荐引用方式 GB/T 7714	He B.,Heal M.R.,Humstad K.H.,et al. A hybrid model approach for estimating health burden from NO2 in megacities in China: A case study in Guangzhou[J],2019,14(12).
APA	He B.,Heal M.R.,Humstad K.H.,Yan L.,Zhang Q.,&Reis S..(2019).A hybrid model approach for estimating health burden from NO2 in megacities in China: A case study in Guangzhou.Environmental Research Letters,14(12).
MLA	He B.,et al."A hybrid model approach for estimating health burden from NO2 in megacities in China: A case study in Guangzhou".Environmental Research Letters 14.12(2019).