TY - JOUR
T1 - Understanding the Patterns and Drivers of Air Pollution on Multiple Time Scales
T2 - The Case of Northern China
AU - Liu, Yupeng
AU - Wu, Jianguo
AU - Yu, Deyong
AU - Hao, Ruifang
N1 - Funding Information:
Acknowledgements This research was funded by the National Basic Research Program of China (973 Program) (2014CB954303, 2014CB954301) and the Fund for Creative Research Groups of National Natural Science Foundation of China (No. 41621061). We thank the members of the Center for Human-Environment System Sustainability (CHESS) at Beijing Normal University for their suggestions on this study.
Funding Information:
This research was funded by the National Basic Research Program of China (973 Program) (2014CB954303, 2014CB954301) and the Fund for Creative Research Groups of National Natural Science Foundation of China (No. 41621061). We thank the members of the Center for Human-Environment System Sustainability (CHESS) at Beijing Normal University for their suggestions on this study.
Publisher Copyright:
© 2018, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2018/6/1
Y1 - 2018/6/1
N2 - China’s rapid economic growth during the past three decades has resulted in a number of environmental problems, including the deterioration of air quality. It is necessary to better understand how the spatial pattern of air pollutants varies with time scales and what drive these changes. To address these questions, this study focused on one of the most heavily air-polluted areas in North China. We first quantified the spatial pattern of air pollution, and then systematically examined the relationships of air pollution to several socioeconomic and climatic factors using the constraint line method, correlation analysis, and stepwise regression on decadal, annual, and seasonal scales. Our results indicate that PM2.5 was the dominant air pollutant in the Beijing–Tianjin–Hebei region, while PM2.5 and PM10 were both important pollutants in the Agro-pastoral Transitional Zone (APTZ) region. Our statistical analyses suggest that energy consumption and gross domestic product (GDP) in the industry were the most important factors for air pollution on the decadal scale, but the impacts of climatic factors could also be significant. On the annual and seasonal scales, high wind speed, low relative humidity, and long sunshine duration constrained PM2.5 accumulation; low wind speed and high relative humidity constrained PM10 accumulation; and short sunshine duration and high wind speed constrained O3 accumulation. Our study showed that analyses on multiple temporal scales are not only necessary to determine key drivers of air pollution, but also insightful for understanding the spatial patterns of air pollution, which was important for urban planning and air pollution control.
AB - China’s rapid economic growth during the past three decades has resulted in a number of environmental problems, including the deterioration of air quality. It is necessary to better understand how the spatial pattern of air pollutants varies with time scales and what drive these changes. To address these questions, this study focused on one of the most heavily air-polluted areas in North China. We first quantified the spatial pattern of air pollution, and then systematically examined the relationships of air pollution to several socioeconomic and climatic factors using the constraint line method, correlation analysis, and stepwise regression on decadal, annual, and seasonal scales. Our results indicate that PM2.5 was the dominant air pollutant in the Beijing–Tianjin–Hebei region, while PM2.5 and PM10 were both important pollutants in the Agro-pastoral Transitional Zone (APTZ) region. Our statistical analyses suggest that energy consumption and gross domestic product (GDP) in the industry were the most important factors for air pollution on the decadal scale, but the impacts of climatic factors could also be significant. On the annual and seasonal scales, high wind speed, low relative humidity, and long sunshine duration constrained PM2.5 accumulation; low wind speed and high relative humidity constrained PM10 accumulation; and short sunshine duration and high wind speed constrained O3 accumulation. Our study showed that analyses on multiple temporal scales are not only necessary to determine key drivers of air pollution, but also insightful for understanding the spatial patterns of air pollution, which was important for urban planning and air pollution control.
KW - Air quality index
KW - Constraint line analysis
KW - Correlation analysis
KW - PM
KW - Stepwise regression
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U2 - 10.1007/s00267-018-1026-5
DO - 10.1007/s00267-018-1026-5
M3 - Article
C2 - 29564496
AN - SCOPUS:85044225436
SN - 0364-152X
VL - 61
SP - 1048
EP - 1061
JO - Environmental Management
JF - Environmental Management
IS - 6
ER -