Articles | Volume 22, issue 16
https://doi.org/10.5194/acp-22-10551-2022
© 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-22-10551-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Aug 2022
Research article |
| 19 Aug 2022
| 19 Aug 2022
Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions
Minghao Qiu
CORRESPONDING AUTHOR
Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
current address: Earth System Science Department, Stanford University, Stanford, California, USA
Corwin Zigler
Department of Statistics and Data Science, University of Texas at Austin, Texas, USA
Noelle E. Selin
Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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Cited
16 citations as recorded by crossref.
- A comparison of meteorological normalization of PM2.5 by multiple linear regression, general additive model, and random forest methods L. Qi et al. 10.1016/j.atmosenv.2024.120854
- Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022 H. Zheng et al. 10.1016/j.envint.2023.108361
- Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China S. Guha et al. 10.1016/j.atmosenv.2024.120793
- Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US M. Childs et al. 10.1021/acs.est.2c02934
- An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models H. Zheng et al. 10.1038/s41612-023-00536-7
- Constructing transferable and interpretable machine learning models for black carbon concentrations P. Fung et al. 10.1016/j.envint.2024.108449
- Quantifying the contributions of meteorology, emissions, and transport to ground-level ozone in the Pearl River Delta, China J. Li et al. 10.1016/j.scitotenv.2024.173011
- Assessment of meteorological and air quality drivers of elevated ambient ozone in Beijing via machine learning approach M. Hassan et al. 10.1007/s11356-023-29665-5
- Improving the Air Pollution Control Measures More Efficiently and Cost-Effectively: View from the Practice in the 7th Military World Games in Wuhan S. Kong et al. 10.1007/s41810-024-00245-5
- Estimating particulate matter concentrations and meteorological contributions in China during 2000–2020 S. Wang et al. 10.1016/j.chemosphere.2023.138742
- Extracting regional and temporal features to improve machine learning for hourly air pollutants in urban India S. Wang et al. 10.1016/j.atmosenv.2024.120834
- Characterization of Synanthropic Habitats on Shallow Seabeds Using Map Clustering Techniques: A Case Study in Taranto, Apulia, Italy C. Massarelli et al. 10.3390/ecologies5040037
- Impact of meteorological conditions and reductions in anthropogenic emissions on PM2.5 concentrations in China from 2016 to 2020 Z. Xu et al. 10.1016/j.atmosenv.2023.120265
- Predicting Atmospheric Water-Soluble Organic Mass Reversibly Partitioned to Aerosol Liquid Water in the Eastern United States M. El-Sayed et al. 10.1021/acs.est.3c01259
- 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 Y. Lin et al. 10.5194/acp-22-16073-2022
- Unraveling complex causal processes that affect sustainability requires more integration between empirical and modeling approaches M. Schlüter et al. 10.1073/pnas.2215676120
16 citations as recorded by crossref.
- A comparison of meteorological normalization of PM2.5 by multiple linear regression, general additive model, and random forest methods L. Qi et al. 10.1016/j.atmosenv.2024.120854
- Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022 H. Zheng et al. 10.1016/j.envint.2023.108361
- Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China S. Guha et al. 10.1016/j.atmosenv.2024.120793
- Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US M. Childs et al. 10.1021/acs.est.2c02934
- An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models H. Zheng et al. 10.1038/s41612-023-00536-7
- Constructing transferable and interpretable machine learning models for black carbon concentrations P. Fung et al. 10.1016/j.envint.2024.108449
- Quantifying the contributions of meteorology, emissions, and transport to ground-level ozone in the Pearl River Delta, China J. Li et al. 10.1016/j.scitotenv.2024.173011
- Assessment of meteorological and air quality drivers of elevated ambient ozone in Beijing via machine learning approach M. Hassan et al. 10.1007/s11356-023-29665-5
- Improving the Air Pollution Control Measures More Efficiently and Cost-Effectively: View from the Practice in the 7th Military World Games in Wuhan S. Kong et al. 10.1007/s41810-024-00245-5
- Estimating particulate matter concentrations and meteorological contributions in China during 2000–2020 S. Wang et al. 10.1016/j.chemosphere.2023.138742
- Extracting regional and temporal features to improve machine learning for hourly air pollutants in urban India S. Wang et al. 10.1016/j.atmosenv.2024.120834
- Characterization of Synanthropic Habitats on Shallow Seabeds Using Map Clustering Techniques: A Case Study in Taranto, Apulia, Italy C. Massarelli et al. 10.3390/ecologies5040037
- Impact of meteorological conditions and reductions in anthropogenic emissions on PM2.5 concentrations in China from 2016 to 2020 Z. Xu et al. 10.1016/j.atmosenv.2023.120265
- Predicting Atmospheric Water-Soluble Organic Mass Reversibly Partitioned to Aerosol Liquid Water in the Eastern United States M. El-Sayed et al. 10.1021/acs.est.3c01259
- 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 Y. Lin et al. 10.5194/acp-22-16073-2022
- Unraveling complex causal processes that affect sustainability requires more integration between empirical and modeling approaches M. Schlüter et al. 10.1073/pnas.2215676120
Latest update: 13 Dec 2024
Short summary
Evaluating impacts of emission changes on air quality requires accounting for meteorological variability. Many studies use simple regression methods to correct for meteorology, but little is known about their performance. Using cases in the US and China, we show that widely used regression models do not perform well and can lead to biased estimates of emission-driven trends. We propose a novel machine learning method with lower bias and provide recommendations to policymakers and researchers.
Evaluating impacts of emission changes on air quality requires accounting for meteorological...
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