TY - JOUR
T1 - A study on PM2.5 distribution over China using principal component analyses
AU - Chen, Di
AU - Zhang, Yong Wen
AU - Tie, Xue Xi
N1 - Publisher Copyright:
© 2016, Chinese Academy of Sciences. All Rights Reserved.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Recently China has been suffering from air pollution. The ministry of environmental protection has been publishing air quality index data since 2013, which has enabled us to better understand the situation of air pollution. We used the PM2.5 data in 2015, to study whether the spatial variation of PM2.5 is local or regional. We combined Correlation analysis with the Principal Component Analysis method, and discovered that the PM2.5 concentration in China can be divided into different modes. For the first mode, most of the country experiences cyclonic growth and falls, which means that there is a nationwide regional trend. The only accident appears in the third season, probably a result of the east Asia summer monsoon. For the second mode, all seasons contributions are close, and there’s no evident cluster formed, attributing to local emission and meteorological conditions. We also compared the mean value of the correlation efficient with the geographical distance for all stations. There’s a double-log relationship as m(x)–β in the 1st, 2nd and 4th season, where β equals 0.73 and 1. The equation doesn’t exist on the 3rd season, which means there is some other mechanism. In whole This indicates that the PM2.5 concentration around the country is not local but regional related.
AB - Recently China has been suffering from air pollution. The ministry of environmental protection has been publishing air quality index data since 2013, which has enabled us to better understand the situation of air pollution. We used the PM2.5 data in 2015, to study whether the spatial variation of PM2.5 is local or regional. We combined Correlation analysis with the Principal Component Analysis method, and discovered that the PM2.5 concentration in China can be divided into different modes. For the first mode, most of the country experiences cyclonic growth and falls, which means that there is a nationwide regional trend. The only accident appears in the third season, probably a result of the east Asia summer monsoon. For the second mode, all seasons contributions are close, and there’s no evident cluster formed, attributing to local emission and meteorological conditions. We also compared the mean value of the correlation efficient with the geographical distance for all stations. There’s a double-log relationship as m(x)–β in the 1st, 2nd and 4th season, where β equals 0.73 and 1. The equation doesn’t exist on the 3rd season, which means there is some other mechanism. In whole This indicates that the PM2.5 concentration around the country is not local but regional related.
KW - Correlation function
KW - PM2.5
KW - Principle component analysis
UR - http://www.scopus.com/inward/record.url?scp=85018332373&partnerID=8YFLogxK
U2 - 10.1360/SSPMA2016-00367
DO - 10.1360/SSPMA2016-00367
M3 - Article
AN - SCOPUS:85018332373
SN - 1674-7275
VL - 47
JO - Scientia Sinica: Physica, Mechanica et Astronomica
JF - Scientia Sinica: Physica, Mechanica et Astronomica
IS - 2
M1 - 020501
ER -