Detecting the causality influence of individual meteorological factors on local PM2.5 concentration in the Jing-Jin-Ji region

被引:127
作者
Chen, Ziyue [1 ,2 ]
Cai, Jun [3 ]
Gao, Bingbo [4 ]
Xu, Bing [1 ,3 ]
Dai, Shuang [3 ]
He, Bin [1 ]
Xie, Xiaoming [4 ]
机构
[1] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, 19 Xinjiekouwai St, Beijing 100875, Peoples R China
[2] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China
[3] Tsinghua Univ, Dept Earth Syst Sci, Beijing 100084, Peoples R China
[4] Natl Engn Res Ctr Informat Technol Agr, 11 Shuguang Huayuan Middle Rd, Beijing 100097, Peoples R China
来源
SCIENTIFIC REPORTS | 2017年 / 7卷
基金
中国国家自然科学基金;
关键词
PARTICULATE MATTER PM2.5; AIR-POLLUTION; CHEMICAL-COMPOSITIONS; TEMPORAL PATTERNS; PM10; POLLUTANTS; MORTALITY; QUALITY; AEROSOL; CHINA;
D O I
10.1038/srep40735
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Due to complicated interactions in the atmospheric environment, quantifying the influence of individual meteorological factors on local PM2.5 concentration remains challenging. The Beijing-Tianjin-Hebei (short for Jing-Jin-Ji) region is infamous for its serious air pollution. To improve regional air quality, characteristics and meteorological driving forces for PM2.5 concentration should be better understood. This research examined seasonal variations of PM2.5 concentration within the Jing-Jin-Ji region and extracted meteorological factors strongly correlated with local PM2.5 concentration. Following this, a convergent cross mapping (CCM) method was employed to quantify the causality influence of individual meteorological factors on PM2.5 concentration. The results proved that the CCM method was more likely to detect mirage correlations and reveal quantitative influences of individual meteorological factors on PM2.5 concentration. For the Jing-Jin-Ji region, the higher PM2.5 concentration, the stronger influences meteorological factors exert on PM2.5 concentration. Furthermore, this research suggests that individual meteorological factors can influence local PM2.5 concentration indirectly by interacting with other meteorological factors. Due to the significant influence of local meteorology on PM2.5 concentration, more emphasis should be given on employing meteorological means for improving local air quality.
引用
收藏
页数:11
相关论文
共 39 条
[21]  
Lanzinger S, 2015, ENVIRON INT, V88, P44
[22]   Ambient temperature enhanced acute cardiovascular-respiratory mortality effects of PM2.5 in Beijing, China [J].
Li, Yi ;
Ma, Zhiqiang ;
Zheng, Canjun ;
Shang, Yu .
INTERNATIONAL JOURNAL OF BIOMETEOROLOGY, 2015, 59 (12) :1761-1770
[23]   Temporal Patterns in Fine Particulate Matter Time Series in Beijing: A Calendar View [J].
Liu, Jianzheng ;
Li, Jie ;
Li, Weifeng .
SCIENTIFIC REPORTS, 2016, 6
[24]   Oxidative Potential and Inflammatory Impacts of Source Apportioned Ambient Air Pollution in Beijing [J].
Liu, Qingyang ;
Baumgartner, Jill ;
Zhang, Yuanxun ;
Liu, Yanju ;
Sun, Yongjun ;
Zhang, Meigen .
ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2014, 48 (21) :12920-12929
[25]   Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing [J].
Ma, Zongwei ;
Hu, Xuefei ;
Huang, Lei ;
Bi, Jun ;
Liu, Yang .
ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2014, 48 (13) :7436-7444
[26]   Short-term impacts of particulate matter (PM10, PM10-2.5, PM2.5) on mortality in nine French cities [J].
Pascal, Mathilde ;
Falq, Gregoire ;
Wagner, Verene ;
Chatignoux, Edouard ;
Corso, Magali ;
Blanchard, Myriam ;
Host, Sabine ;
Pascal, Laurence ;
Larrieu, Sophie .
ATMOSPHERIC ENVIRONMENT, 2014, 95 :175-184
[27]   Quantifying the influence of local meteorology on air quality using generalized additive models [J].
Pearce, John L. ;
Beringer, Jason ;
Nicholls, Neville ;
Hyndman, Rob J. ;
Tapper, Nigel J. .
ATMOSPHERIC ENVIRONMENT, 2011, 45 (06) :1328-1336
[28]   PM2.5 Constituents and Hospital Emergency-Room Visits in Shanghai, China [J].
Qiao, Liping ;
Cai, Jing ;
Wang, Hongli ;
Wang, Weibing ;
Zhou, Min ;
Lou, Shengrong ;
Chen, Renjie ;
Dai, Haixia ;
Chen, Changhong ;
Kan, Haidong .
ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2014, 48 (17) :10406-10414
[29]   Tools for validating causal and predictive claims in social science models [J].
Sliva, Amy ;
Reilly, Scott Neal ;
Casstevens, Randy ;
Chamberlain, John .
6TH INTERNATIONAL CONFERENCE ON APPLIED HUMAN FACTORS AND ERGONOMICS (AHFE 2015) AND THE AFFILIATED CONFERENCES, AHFE 2015, 2015, 3 :3925-3932
[30]   Detecting Causality in Complex Ecosystems [J].
Sugihara, George ;
May, Robert ;
Ye, Hao ;
Hsieh, Chih-hao ;
Deyle, Ethan ;
Fogarty, Michael ;
Munch, Stephan .
SCIENCE, 2012, 338 (6106) :496-500