Spatiotemporal Heterogeneity and the Key Influencing Factors of PM 2.5 and PM 10 in Heilongjiang, China from 2014 to 2018

Longhui Fu, Qibang Wang, Jianhui Li, Huiran Jin, Zhen Zhen () and Qingbin Wei ()
Additional contact information
Longhui Fu: School of Forestry, Northeast Forestry University, Harbin 150040, China
Qibang Wang: School of Forestry, Northeast Forestry University, Harbin 150040, China
Jianhui Li: School of Forestry, Northeast Forestry University, Harbin 150040, China
Huiran Jin: School of Applied Engineering and Technology, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Zhen Zhen: School of Forestry, Northeast Forestry University, Harbin 150040, China
Qingbin Wei: Key Laboratory of Forest Plant Ecology, Ministry of Education, Northeast Forestry University, Harbin 150040, China

IJERPH, 2022, vol. 19, issue 18, 1-20

Abstract: Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial–temporal heterogeneity of PM (PM 2.5 and PM 10 ) concentration in Heilongjiang Province during 2014–2018 and the key impacting factors were investigated based on principal component analysis-based ordinary least square regression (PCA-OLS), PCA-based geographically weighted regression (PCA-GWR), PCA-based temporally weighted regression (PCA-TWR), and PCA-based geographically and temporally weighted regression (PCA-GTWR). Results showed that six principal components represented the temperature, wind speed, air pressure, atmospheric pollution, humidity, and vegetation cover factor, respectively, contributing 87% of original variables. All the local models (PCA-GWR, PCA-TWR, and PCA-GTWR) were superior to the global model (PCA-OLS), and PCA-GTWR has the best performance. PM had greater temporal than spatial heterogeneity due to seasonal periodicity. Air pollutants (i.e., SO 2 , NO 2 , and CO) and pressure were promoted whereas temperature, wind speed, and vegetation cover inhibited the PM concentration. The downward trend of annual PM concentration is obvious, especially after 2017, and the hot spot gradually changed from southwestern to southeastern cities. This study laid the foundation for precise local government prevention and control by addressing both excessive effect factors (i.e., meteorological factors, air pollutants, vegetation cover) and spatial-temporal heterogeneity of PM.

Keywords: PCA; GTWR; GWR; TWR; particulate matter; meteorological factors; NDVI (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/18/11627/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/18/11627/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:18:p:11627-:d:915805

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().