Articles | Volume 22, issue 24
https://doi.org/10.5194/acp-22-16073-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-16073-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Dec 2022
Research article |
| 22 Dec 2022
| 22 Dec 2022
Decoupling impacts of weather conditions on interannual variations in concentrations of criteria air pollutants in South China – constraining analysis uncertainties by using multiple analysis tools
Yu Lin
Sanya Oceanographic Institution (Ocean University of China), Yazhou Bay Science & Technology City, Sanya, China
Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, Toronto, ON, M3H 5T4, Canada
Qinchu Fan
Key Laboratory of Marine Environment and Ecology (MoE) and Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Ocean University of China, Qingdao, China
He Meng
Qingdao Eco-Environment Monitoring Center of Shandong Province, Qingdao, China
Sanya Oceanographic Institution (Ocean University of China), Yazhou Bay Science & Technology City, Sanya, China
Key Laboratory of Marine Environment and Ecology (MoE) and Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Ocean University of China, Qingdao, China
Laboratory for Marine Ecology and Environmental Sciences, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
Huiwang Gao
Key Laboratory of Marine Environment and Ecology (MoE) and Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Ocean University of China, Qingdao, China
Laboratory for Marine Ecology and Environmental Sciences, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
Xiaohong Yao
CORRESPONDING AUTHOR
Sanya Oceanographic Institution (Ocean University of China), Yazhou Bay Science & Technology City, Sanya, China
Key Laboratory of Marine Environment and Ecology (MoE) and Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Ocean University of China, Qingdao, China
Laboratory for Marine Ecology and Environmental Sciences, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
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Cited
13 citations as recorded by crossref.
- Statistical analysis and environmental impact of pre-existing particle growth events in a Northern Chinese coastal megacity: A 725-day study in 2010–2018 X. Wei et al.
- Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China B. Wu et al.
- Identifying decadal trends in deweathered concentrations of criteria air pollutants in Canadian urban atmospheres with machine learning approaches X. Yao & L. Zhang
- Spatial Distribution Simulation of PM10 Concentrations Based on Dynamic Constraint Interpolation 佳. 李
- Separation of meteorological and anthropogenic contributions to major ambient pollutants using an integrated explainable machine learning framework: RMT-PAS H. Ke et al.
- An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models H. Zheng et al.
- Seasonally Dependent Daytime and Nighttime Formation of Oxalic Acid Vapor and Particulate Oxalate in Tropical Coastal and Marine Atmospheres L. Yan et al.
- Advancing Aerosol Chemistry with Machine Learning: A Short Review Y. Wang et al.
- Associations of interannual variation in summer tropospheric ozone with the Western Pacific Subtropical High in China from 1999 to 2017 X. Zhang et al.
- Using machine learning to quantify drivers of aerosol pollution trend in China from 2015 to 2022 Y. Ji et al.
- Co-evolving emission controls and climate impacts: A multi-decadal machine learning decomposition of urban O3 and NO2 air quality measurements M. Brancher
- Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022 H. Zheng et al.
- Novel insights on causes of disproportionate trends between particulate NO3− and NOx emissions in Canadian urban atmospheres Q. Fan et al.
13 citations as recorded by crossref.
- Statistical analysis and environmental impact of pre-existing particle growth events in a Northern Chinese coastal megacity: A 725-day study in 2010–2018 X. Wei et al.
- Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China B. Wu et al.
- Identifying decadal trends in deweathered concentrations of criteria air pollutants in Canadian urban atmospheres with machine learning approaches X. Yao & L. Zhang
- Spatial Distribution Simulation of PM10 Concentrations Based on Dynamic Constraint Interpolation 佳. 李
- Separation of meteorological and anthropogenic contributions to major ambient pollutants using an integrated explainable machine learning framework: RMT-PAS H. Ke et al.
- An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models H. Zheng et al.
- Seasonally Dependent Daytime and Nighttime Formation of Oxalic Acid Vapor and Particulate Oxalate in Tropical Coastal and Marine Atmospheres L. Yan et al.
- Advancing Aerosol Chemistry with Machine Learning: A Short Review Y. Wang et al.
- Associations of interannual variation in summer tropospheric ozone with the Western Pacific Subtropical High in China from 1999 to 2017 X. Zhang et al.
- Using machine learning to quantify drivers of aerosol pollution trend in China from 2015 to 2022 Y. Ji et al.
- Co-evolving emission controls and climate impacts: A multi-decadal machine learning decomposition of urban O3 and NO2 air quality measurements M. Brancher
- Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022 H. Zheng et al.
- Novel insights on causes of disproportionate trends between particulate NO3− and NOx emissions in Canadian urban atmospheres Q. Fan et al.
Saved (final revised paper)
Latest update: 16 May 2026
Short summary
In this study, we analyzed 7-year (from May 2014 to April 2021) concentration data of six criteria air pollutants (PM2.5, PM10, O3, NO2, CO and SO2) as well as the sum of NO2 and O3 in six cities in South China. Three different analysis methods were used to identify emission-driven interannual variations and perturbations from varying weather conditions. In addition, a self-developed method was further introduced to constrain analysis uncertainties.
In this study, we analyzed 7-year (from May 2014 to April 2021) concentration data of six...
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