The Spatiotemporal Variation Trends of Major Air Pollutants in Beijing from 2014 to 2023

Based on the hourly concentration data of PM2.5, PM10, SO2, NO2, CO, and O3 from 35 environmental monitoring sites in Beijing between 1 January 2014 and 31 December 2023, this paper investigated the annual average concentration variation of these pollutants, the differences between regions, and the factors influencing these changes and differences. Seasonal variations in the pollutants are examined through monthly average concentrations, and Pearson correlation coefficients are used to study their relationships. The results are as follows: (1) Over the past decade, the concentrations of PM2.5, PM10, SO2, NO2, and CO have decreased by −67.5%, −58.6%, −81.4%, −51.9%, and −59.3%, respectively, indicating significant progress in controlling these pollutants. However, O3 fluctuates significantly between 57 μg/m3 and 66 μg/m3, suggesting the need to improve O3 management. (2) Air pollution levels exhibit distinct spatial variations, with better air quality in mountainous and suburban areas compared to more heavily trafficked urban zones, emphasizing the need for localized control strategies. (3) The correlation coefficients between PM2.5, PM10, SO2, NO2, and CO all exceeded 0.90, indicating strong positive correlations. In contrast, O3 showed negative correlations with these five pollutants, with its most pronounced negative correlation being NO2.

Authors

Institutions

Publication Details

Journal
Atmosphere
Published
2025-04-24
DOI
https://doi.org/10.3390/atmos16050494
Citations
3
Primary Topic
Environmental Policies and Emissions
Type
article
Field-Weighted Citation Impact
1.51
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Spatiotemporal Variation Trends of Major Air Pollutants in Beijing from 2014 to 2023

Yangyang Xie, Jiaqing Zhao
3 citations
Atmosphere
Environmental Policies and Emissions
1.51
article

The Spatiotemporal Variation Trends of Major Air Pollutants in Beijing from 2014 to 2023

Yangyang Xie, Jiaqing Zhao
article en
3 citations

Abstract

Based on the hourly concentration data of PM2.5, PM10, SO2, NO2, CO, and O3 from 35 environmental monitoring sites in Beijing between 1 January 2014 and 31 December 2023, this paper investigated the annual average concentration variation of these pollutants, the differences between regions, and the factors influencing these changes and differences. Seasonal variations in the pollutants are examined through monthly average concentrations, and Pearson correlation coefficients are used to study their relationships. The results are as follows: (1) Over the past decade, the concentrations of PM2.5, PM10, SO2, NO2, and CO have decreased by −67.5%, −58.6%, −81.4%, −51.9%, and −59.3%, respectively, indicating significant progress in controlling these pollutants. However, O3 fluctuates significantly between 57 μg/m3 and 66 μg/m3, suggesting the need to improve O3 management. (2) Air pollution levels exhibit distinct spatial variations, with better air quality in mountainous and suburban areas compared to more heavily trafficked urban zones, emphasizing the need for localized control strategies. (3) The correlation coefficients between PM2.5, PM10, SO2, NO2, and CO all exceeded 0.90, indicating strong positive correlations. In contrast, O3 showed negative correlations with these five pollutants, with its most pronounced negative correlation being NO2.

AtmosphereVol. 16(5)
University of Science and Technology Beijing (CN)
Openalex Percentile: Top 20%
Environmental Policies and Emissions
1.51
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.