Spatio-temporal variation and daily prediction of PM 2.5 concentration in world-class urban agglomerations of China
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ORIGINAL PAPER
Spatio-temporal variation and daily prediction of PM2.5 concentration in world-class urban agglomerations of China Dan Yan . Ying Kong . Bin Ye . Haitao Xiang
Received: 26 December 2019 / Accepted: 26 August 2020 Ó Springer Nature B.V. 2020
Abstract 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 PM2.5 concentration in 40 cities from January 2014 to December 2016, this research explored the spatial–temporal characteristics of PM2.5 concentrations in these three urban agglomerations. The annual mean PM2.5 concentrations in Beijing–Tianjin–Hebei, the Yangtze River Delta and the Pearl River Delta are 35.39 lg/m3, 53.72 lg/m3 and 78.54 lg/m3, respectively. Compared with the
other two urban agglomerations, abundant rainfall causes the Pearl River Delta to have the lowest PM2.5 level. Furthermore, a general regression neural network (GRNN) method is developed to predict the PM2.5 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 PM2.5 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-dayahead forecasting of PM2.5 concentrations can raise awareness among the public to improve their precautionary behaviours and help urban planners to provide corresponding support.
D. Yan Y. Kong H. Xiang Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China D. Yan B. Ye (&) School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China e-mail: [email protected] Y. Kong Department of Economics, York University, Toronto M3J1P3, Canada
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Environ Geochem Health
Graphic abstract Average temperature Maximum temperature Minimum temperature Average atmosphere Maximum atmosphere
Influencing factors
Minimum atmosphere
Forecast PM2.5 concentration
Total rainfall Average humidity Average wind speed Maximum wind speed PM2.5 one day ahead
Keywords Spatio-temporal variation PM2.5 concentration prediction GRNN Urban agglomerations
Introduction The prominent environmental problems related to air climate change, water and soil, extinct
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