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Spatial and temporal analyses of air pollutants and meteorological driving forces in Beijing–Tianjin–Hebei region, China

Author:
Hu, Zizhan, Tang, Xuguang, Zheng, Chen, Guan, Menglin, Shen, Jingwei
Source:
Environmental earth sciences 2018 v.77 no.14 pp. 540
ISSN:
1866-6280
Subject:
air pollutants, air pollution, air quality, autumn, carbon monoxide, cities, climate change, human health, meteorological parameters, nitrogen dioxide, ozone, particulates, prediction, regression analysis, spring, sulfur dioxide, summer, temporal variation, winter, China
Abstract:
Due to its negative impact on the living environment of human beings, ambient air pollution has become a global challenge to human health. In this study, surface observations of six criteria air pollutants, including PM₂.₅, PM₁₀, SO₂, NO₂, CO and O₃, were collected to investigate the spatial and temporal variation in the Beijing–Tianjin–Hebei (BTH) region during 2013–2016 and to explore the relationships between atmospheric pollutants and meteorological variables using quantile regression model (QRM) and multiple linear regression model (MLRM). The results show that BTH region has experienced significant air pollution, and the southern part generally has more severe conditions. The annual average indicates clear decreasing trends of the particulate matters, SO₂ and CO concentrations over the last 4 years and slight increasing trends of NO₂ and O₃ in several cities. The seasonal and monthly characteristics indicate that the concentrations of five species reach their maxima in the winter and their minima in the summer, whereas O₃ has the opposite behaviour. Finally, the pseudo R² values show that the QRMs have the best performance in the winter, followed by spring, fall, and summer. Specifically, all the meteorological factors have significant impacts on air pollution but change with pollutants and seasons. The MLRM results are generally consistent with the QRM results in all seasons, and the inconsistencies are more common in the fall and winter. The results of this research provide foundational knowledge for predicting the response of air quality to climate change in the BTH region.
Agid:
6049961