Spatiotemporal Heterogeneity and the Key Influencing Factors of PM2.5 and PM10 in Heilongjiang, China from 2014 to 2018

Longhui Fu; Qibang Wang; Jianhui Li; Huiran Jin; Zhen Zhen; Qingbin Wei

doi:10.3390/ijerph191811627

Spatiotemporal Heterogeneity and the Key Influencing Factors of PM_2.5 and PM₁₀ in Heilongjiang, China from 2014 to 2018

Int J Environ Res Public Health. 2022 Sep 15;19(18):11627. doi: 10.3390/ijerph191811627.

Authors

Longhui Fu^{1

2}, Qibang Wang¹, Jianhui Li¹, Huiran Jin³, Zhen Zhen^{1

2

4}, Qingbin Wei^{2

5}

Affiliations

¹ School of Forestry, Northeast Forestry University, Harbin 150040, China.
² Key Laboratory of Forest Plant Ecology, Ministry of Education, Northeast Forestry University, Harbin 150040, China.
³ School of Applied Engineering and Technology, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.
⁴ Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea.
⁵ School of Geographical Sciences, Harbin Normal University, Harbin 150025, China.

Abstract

Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial-temporal heterogeneity of PM (PM_2.5 and PM₁₀) 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., SO₂, NO₂, 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: GTWR; GWR; NDVI; PCA; TWR; meteorological factors; particulate matter.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollutants* / analysis
Air Pollution*
China
Cities
Environmental Monitoring
Humans
Nitrogen Dioxide
Particulate Matter / analysis
Seasons

Substances

Air Pollutants
Particulate Matter
Nitrogen Dioxide

Grants and funding

This work was supported by the Key Laboratory of Forest Plant Ecology, Ministry of Education Northeast Forestry University (K2020C02), National Undergraduate Training Program for Innovation and Entrepreneurship (202110225060). This study was also supported by China Scholarship Council (CSC-No. 202006605008).