Formation Mechanisms and Multi-Scale Control Strategies for Atmospheric Composite Pollution

Topic Information

Dear Colleagues,

Atmospheric composite pollution, characterized by the synergistic effects of pollutants such as fine particulate matter, ozone, and volatile organic compounds, has become a major global challenge, threatening ecological security and public health. Rapid urbanization and industrialization have increased the complexity of its formation, with cross-regional transmission and multi-source superposition making pollution control increasingly arduous. Traditional single-pollutant and single-region control measures have shown obvious limitations, highlighting the urgency of systematic research on pollution’s formation mechanisms and multi-scale collaborative governance.

This Topic aims to explore the complex formation pathways, key driving factors, and multi-scale transmission rules of atmospheric composite pollution. It also focuses on developing targeted control strategies covering local, regional, and inter-regional scales. We invite interdisciplinary researchers from environmental science, atmospheric chemistry, ecology, and environmental management to contribute original research, case studies, and reviews that provide novel insights and actionable solutions for mitigating atmospheric composite pollution and advancing sustainable environmental development. By bridging atmospheric science, environmental engineering, and sustainability studies, this Topic aims to advance systematic solutions for achieving cleaner air and a sustainable future. 

Prof. Dr. Guoliang Shi
Dr. Jie Gao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Atmosphere atmosphere	2.6	5.4	2010	20.4 Days	CHF 2400	Submit
Processes processes	3.4	5.7	2013	14.7 Days	CHF 2400	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit
Standards standards	-	-	2021	28.5 Days	CHF 1000	Submit
Toxics toxics	4.9	7.8	2013	17 Days	CHF 2600	Submit

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Open AccessArticle

Predicting Short-Term Air Quality Index in the Beijing–Tianjin–Hebei Urban Agglomeration: A Comparative Assessment of Linear, Ensemble, and Recurrent Forecasting Models

by Xiaofeng Ling, Mujun Han, Zhen Xu, Baohua Li, Xin Chen, Fude Liu and Hailong Wu

Atmosphere 2026, 17(7), 651; https://doi.org/10.3390/atmos17070651 - 30 Jun 2026

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Abstract

The Beijing–Tianjin–Hebei (BTH) region faces complex air pollution driven by alternating particulate matter (PM) and ozone (O₃) dominance, regional transport, topography, and meteorology. This study develops a hybrid framework integrating air quality index (AQI) records, pollutants, meteorological variables, and MEIC emissions [...] Read more.

The Beijing–Tianjin–Hebei (BTH) region faces complex air pollution driven by alternating particulate matter (PM) and ozone (O₃) dominance, regional transport, topography, and meteorology. This study develops a hybrid framework integrating air quality index (AQI) records, pollutants, meteorological variables, and MEIC emissions from the BTH region (2018–2025) to capture spatiotemporal evolution and short-term predictability. Results show a seasonal AQI cycle (winter/spring highs, summer/autumn lows) with a summer PM–O₃ seesaw. Spatially, three zones were identified: the northern and coastal ecological barrier zone, the central compound-pollution plain zone, and the southern heavy-industrial zone. Random Forest identifies PM as the dominant AQI compositional contributor, with visibility, dew point, humidity, and MEIC emissions (particulates, NH₃, organics) as key correlates. Forecast evaluation reveals progressive improvement: ARMA captures linear baselines (R² = 0.318, MAPE = 33.26%), XGBoost improves statistical prediction by incorporating nonlinear feature interactions and lagged meteorology (R² = 0.567, MAPE = 24.81%), and LSTM shows the strongest statistical predictive performance (R² = 0.613, MAPE = 22.32%). The improvement of LSTM over XGBoost is incremental and reflects enhanced data-driven representation of short-term AQI–meteorology temporal dependence, rather than identification of physical pollution mechanisms. Regional disparities persist, with higher predictability in the southern heavy-industrial zone and lower accuracy in the northern and coastal ecological barrier zone affected by intermittent dust intrusions and frontal passages. Overall, the results suggest that LSTM may support data-driven short-term AQI warning, but source-oriented mitigation still requires process-based tools, such as chemical-transport or source-apportionment models. Full article

(This article belongs to the Topic Formation Mechanisms and Multi-Scale Control Strategies for Atmospheric Composite Pollution)

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