Spatio-temporal variation and daily prediction of PM2.5 concentration in world-class urban agglomerations of China

Yan, Dan1,2; Kong, Ying1,3; Ye, Bin2; Xiang, Haitao1

2020-09-08

10.1007/s10653-020-00708-x

ENVIRONMENTAL GEOCHEMISTRY AND HEALTH   影响因子和分区

0269-4042

1573-2983

The contradiction between the development of urban agglomerations and ecological protection has long been a challenging issue. China has experienced an astonishing expansion of its urban scale in the past 40 years, and nearly 783 million of the nation's people now live in cities. Beijing-Tianjin-Hebei, the Yangtze River Delta and the Pearl River Delta have been prioritized to become world-class clusters by 2020. The health effects of air pollution in these three urban agglomerations are becoming increasingly formidable. Given these conditions, using the daily mean PM(2.5)concentration in 40 cities from January 2014 to December 2016, this research explored the spatial-temporal characteristics of PM(2.5)concentrations in these three urban agglomerations. The annual mean PM(2.5)concentrations in Beijing-Tianjin-Hebei, the Yangtze River Delta and the Pearl River Delta are 35.39 mu g/m(3), 53.72 mu g/m(3)and 78.54 mu g/m(3), respectively. Compared with the other two urban agglomerations, abundant rainfall causes the Pearl River Delta to have the lowest PM(2.5)level. Furthermore, a general regression neural network (GRNN) method is developed to predict the PM(2.5)concentration in these clusters on the second day, with inputs including the average, maximum and minimum temperature; average, maximum and minimum atmosphere; total rainfall; average humidity; average and maximum wind speed; and the PM(2.5)concentration measured 1 day ahead. The results indicate that the GRNN method can precisely predict the concentration level in these clusters, and it is especially useful for the Pearl River Delta, as the underlying influence mechanism is more specified in this cluster than in the others. Importantly, this 1-day-ahead forecasting of PM(2.5)concentrations can raise awareness among the public to improve their precautionary behaviours and help urban planners to provide corresponding support.

Spatio-temporal variation PM2.5 concentration prediction GRNN Urban agglomerations

SCI

英语

通讯

China Postdoctoral Science Foundation[2019M650733] ; National Natural Science Foundation of China[71803074] ; High-level Special Funding of the Southern University of Science and Technology[G02296302][G02296402]

Engineering ; Environmental Sciences & Ecology ; Public, Environmental & Occupational Health ; Water Resources

Engineering, Environmental ; Environmental Sciences ; Public, Environmental & Occupational Health ; Water Resources

WOS:000567373600002

SPRINGER

ENVIRONMENT/ECOLOGY

Web of Science

1.Tsinghua Univ, Tsinghua Berkeley Shenzhen Inst, Shenzhen 518055, Peoples R China
2.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518055, Peoples R China
3.York Univ, Dept Econ, N York, ON M3J 1P3, Canada

Yan, Dan,Kong, Ying,Ye, Bin,et al. Spatio-temporal variation and daily prediction of PM2.5 concentration in world-class urban agglomerations of China[J]. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH,2020,43(1).

Yan, Dan,Kong, Ying,Ye, Bin,&Xiang, Haitao.(2020).Spatio-temporal variation and daily prediction of PM2.5 concentration in world-class urban agglomerations of China.ENVIRONMENTAL GEOCHEMISTRY AND HEALTH,43(1).

Yan, Dan,et al."Spatio-temporal variation and daily prediction of PM2.5 concentration in world-class urban agglomerations of China".ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 43.1(2020).