Next Article in Journal
Improved Evaluation of Wind Turbine Lightning Exposure: Modeling Upward Leader Effects on Equivalent Collection Area
Previous Article in Journal
An Artificial Intelligence-Driven Precipitation Downscaling Method Using Spatiotemporally Coupled Multi-Source Data
Previous Article in Special Issue
High-Resolution Spatial Forecasting of Hourly Air Quality: A Fast Method for a Better Representation of Industrial Plumes and Traffic Emissions Contributions
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Atmosphere
Volume 16
Issue 11
10.3390/atmos16111227
atmosphere-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Characteristics and Driving Factors of PM2.5 Concentration Changes in Central China

by
Yue Zhao
1,2,
Ke Wang
3,4,
Xiaoyong Liu
5,
Qixiang Xu
3,4,
Le Luo
3,4,
Panpan Liu
3,4,
Yanhua He
3,4,
Yan Yu
3,4,
Fangcheng Su
3,4,* and
Ruiqin Zhang
3,4
1
College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
2
Department of Science and Technology Innovation, Zhengzhou Non-ferrous Metals Research Institute Co., Ltd. of CHINALCO, Zhengzhou 450041, China
3
School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
4
Research Institute of Environmental Science, Zhengzhou University, Zhengzhou 450001, China
5
School of Geographic Sciences, Xinyang Normal University, Xinyang 464000, China
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(11), 1227; https://doi.org/10.3390/atmos16111227 (registering DOI)
Submission received: 2 September 2025 / Revised: 19 October 2025 / Accepted: 20 October 2025 / Published: 23 October 2025
(This article belongs to the Special Issue Secondary Atmospheric Pollution Formations and Its Precursors)
Browse Figure
Versions Notes

Abstract

Despite nationwide control efforts, central China experiences persistently high annual PM2.5 concentrations (~50 μg/m3), which are particularly severe in January (exceeding 110 μg/m3). This study employs an integrated approach combining a Multiple Linear Regression (MLR) model derived from random forest analysis with the WRF-CMAQ chemical transport modeling system to quantitatively disentangle the driving factors of PM2.5 concentrations in central China. Key findings reveal significant spatiotemporal heterogeneity in anthropogenic contributions, evidenced by consistently higher north–south gradients in regression residuals (reflecting emission impacts), linked to spatially varying industrial and transportation influences. Critically, the reduction in anthropogenic impacts over six years was substantially smaller in winter (January: 27 to 23 μg/m3) compared to summer (15 to −18 μg/m3, July), highlighting the profound role of emissions in driving severe January pollution events. Furthermore, WRF-CMAQ simulations demonstrated that adverse meteorological conditions in January 2020 counteracted emission controls, causing a net increase in PM2.5 of +13 μg/m3 relative to 2016, thereby offsetting ~68% of the reductions achieved through emission abatement (−19 μg/m3). Significant regional transport, especially affecting northern and central Henan, further weakened local control efficacy. These quantitative insights into the mechanisms of PM2.5 pollution, particularly the counteracting effects of meteorology on emission reductions in critical winter periods, provide a vital scientific foundation for designing more effective and targeted air quality management strategies in central China.
Keywords: PM2.5; driving factors; MLR; WRF-CMAQ PM2.5; driving factors; MLR; WRF-CMAQ
Graphical Abstract

Share and Cite

MDPI and ACS Style

Zhao, Y.; Wang, K.; Liu, X.; Xu, Q.; Luo, L.; Liu, P.; He, Y.; Yu, Y.; Su, F.; Zhang, R. Characteristics and Driving Factors of PM2.5 Concentration Changes in Central China. Atmosphere 2025, 16, 1227. https://doi.org/10.3390/atmos16111227

AMA Style

Zhao Y, Wang K, Liu X, Xu Q, Luo L, Liu P, He Y, Yu Y, Su F, Zhang R. Characteristics and Driving Factors of PM2.5 Concentration Changes in Central China. Atmosphere. 2025; 16(11):1227. https://doi.org/10.3390/atmos16111227

Chicago/Turabian Style

Zhao, Yue, Ke Wang, Xiaoyong Liu, Qixiang Xu, Le Luo, Panpan Liu, Yanhua He, Yan Yu, Fangcheng Su, and Ruiqin Zhang. 2025. "Characteristics and Driving Factors of PM2.5 Concentration Changes in Central China" Atmosphere 16, no. 11: 1227. https://doi.org/10.3390/atmos16111227

APA Style

Zhao, Y., Wang, K., Liu, X., Xu, Q., Luo, L., Liu, P., He, Y., Yu, Y., Su, F., & Zhang, R. (2025). Characteristics and Driving Factors of PM2.5 Concentration Changes in Central China. Atmosphere, 16(11), 1227. https://doi.org/10.3390/atmos16111227

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop