Articles | Volume 24, issue 2
https://doi.org/10.5194/acp-24-807-2024
© Author(s) 2024. 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-24-807-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jan 2024
Research article |
| 19 Jan 2024
| 19 Jan 2024
Improving 3-day deterministic air pollution forecasts using machine learning algorithms
Zhiguo Zhang
Dept. of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
Christer Johansson
CORRESPONDING AUTHOR
Department of Environmental Science, Stockholm University, Stockholm, Sweden
Environment and Health Administration, SLB-analys, Stockholm, Sweden
Magnuz Engardt
Environment and Health Administration, SLB-analys, Stockholm, Sweden
Massimo Stafoggia
Department of Epidemiology, Lazio Region Health Service, Rome, Italy
Dept. of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
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Cited
23 citations as recorded by crossref.
- Hybrid Mixed-Effect Diffusion model (H-MED) for longitudinal air quality analysis I. Tanriverdi & C. Yozgatligil
- Passive satellite hourly precipitation estimation over mainland China by combining cloud and meteorological parameters S. Xu et al.
- Forecasting the Exceedances of PM2.5 in an Urban Area S. Logothetis et al.
- Assessing the Impact of a Low-Emission Zone on Air Quality Using Machine Learning Algorithms in a Business-As-Usual Scenario M. Doval-Miñarro et al.
- A neural operator for forecasting carbon monoxide evolution in cities S. Bedi et al.
- Spatial–temporal distribution and variation of atmospheric NO2 dry deposition in the Yellow River Basin from 2015 to 2023 Z. Rao et al.
- Advances in amelioration of air pollution using plants and associated microbes: An outlook on phytoremediation and other plant-based technologies A. James et al.
- Comparative Evaluation of Machine Learning Models for Residential PM1 Prediction in Zagreb (Croatia): Identifying Key Predictors and Indoor/Outdoor Dynamics M. Lovrić Štefiček et al.
- Investigating the causes and reduction approaches of nocturnal ozone increase events over Tai'an in the North China Plain J. Li et al.
- Advancing Spatiotemporal Pollutant Dispersion Forecasting with an Integrated Deep Learning Framework for Crucial Information Capture Y. Wang et al.
- Interactions between nocturnal ozone enhancement and daytime ozone pollution in Guangxi, South China Y. Mu et al.
- Improving PM2.5 simulations using LSTM: a study on spatiotemporal generalization X. Chen et al.
- A deep learning model incorporating frequency domain information for ultra multi-step air pollutant forecasting: A case study of Shanghai H. Huang et al.
- Integration of Chemical Transport Model and Artificial Neural Network for PM10 Concentration Forecasting D. Borisov & I. Kuznetsova
- Interpretable long-horizon air pollution forecasting using a transformer-based framework Z. Zhang et al.
- An Assessment of the Multi-Input Spatiotemporal RF–XGBoost Hybrid Framework for PM10 Estimation in Lithuania M. Fahim & J. Sužiedelytė Visockienė
- Forecasting particulate matter concentration in Shanghai using a small-scale long-term dataset A. Salcedo-Bosch et al.
- Meteorology-normalized ozone enhancement during the 2022 late-spring COVID-19 lockdown in Beijing Z. Liao et al.
- Estimating marine atmospheric PM2.5 over the Bohai Sea: Spatiotemporal dynamics under policy transition in China's emission reduction R. Wang et al.
- Integrating land-use change into probabilistic forecasting and sensitivity analysis of urban greenhouse gas emissions W. Liu et al.
- Harnessing Machine Learning for Accurate Smog Level Prediction: A Study of Air Quality in India . Sahil Jatoi et al.
- X2-AQFormer: unveiling dynamic drivers in multi-day hourly air pollution forecasting Z. Zhang et al.
- A hybrid approach leveraging meta-heuristic and ensemble learning for time-sensitive prediction of pollutant concentrations P. Kansal et al.
23 citations as recorded by crossref.
- Hybrid Mixed-Effect Diffusion model (H-MED) for longitudinal air quality analysis I. Tanriverdi & C. Yozgatligil
- Passive satellite hourly precipitation estimation over mainland China by combining cloud and meteorological parameters S. Xu et al.
- Forecasting the Exceedances of PM2.5 in an Urban Area S. Logothetis et al.
- Assessing the Impact of a Low-Emission Zone on Air Quality Using Machine Learning Algorithms in a Business-As-Usual Scenario M. Doval-Miñarro et al.
- A neural operator for forecasting carbon monoxide evolution in cities S. Bedi et al.
- Spatial–temporal distribution and variation of atmospheric NO2 dry deposition in the Yellow River Basin from 2015 to 2023 Z. Rao et al.
- Advances in amelioration of air pollution using plants and associated microbes: An outlook on phytoremediation and other plant-based technologies A. James et al.
- Comparative Evaluation of Machine Learning Models for Residential PM1 Prediction in Zagreb (Croatia): Identifying Key Predictors and Indoor/Outdoor Dynamics M. Lovrić Štefiček et al.
- Investigating the causes and reduction approaches of nocturnal ozone increase events over Tai'an in the North China Plain J. Li et al.
- Advancing Spatiotemporal Pollutant Dispersion Forecasting with an Integrated Deep Learning Framework for Crucial Information Capture Y. Wang et al.
- Interactions between nocturnal ozone enhancement and daytime ozone pollution in Guangxi, South China Y. Mu et al.
- Improving PM2.5 simulations using LSTM: a study on spatiotemporal generalization X. Chen et al.
- A deep learning model incorporating frequency domain information for ultra multi-step air pollutant forecasting: A case study of Shanghai H. Huang et al.
- Integration of Chemical Transport Model and Artificial Neural Network for PM10 Concentration Forecasting D. Borisov & I. Kuznetsova
- Interpretable long-horizon air pollution forecasting using a transformer-based framework Z. Zhang et al.
- An Assessment of the Multi-Input Spatiotemporal RF–XGBoost Hybrid Framework for PM10 Estimation in Lithuania M. Fahim & J. Sužiedelytė Visockienė
- Forecasting particulate matter concentration in Shanghai using a small-scale long-term dataset A. Salcedo-Bosch et al.
- Meteorology-normalized ozone enhancement during the 2022 late-spring COVID-19 lockdown in Beijing Z. Liao et al.
- Estimating marine atmospheric PM2.5 over the Bohai Sea: Spatiotemporal dynamics under policy transition in China's emission reduction R. Wang et al.
- Integrating land-use change into probabilistic forecasting and sensitivity analysis of urban greenhouse gas emissions W. Liu et al.
- Harnessing Machine Learning for Accurate Smog Level Prediction: A Study of Air Quality in India . Sahil Jatoi et al.
- X2-AQFormer: unveiling dynamic drivers in multi-day hourly air pollution forecasting Z. Zhang et al.
- A hybrid approach leveraging meta-heuristic and ensemble learning for time-sensitive prediction of pollutant concentrations P. Kansal et al.
Saved (final revised paper)
Latest update: 30 Apr 2026
Short summary
Up-to-date information on present and near-future air quality help people avoid exposure to high levels of air pollution. We apply different machine learning models to significantly improve traditional forecasts of PM10, NOx, and O3 in Stockholm, Sweden. It is shown that forecasts of all air pollutants are improved by the input of lagged measurements and taking calendar information into account. The final modelled errors are substantially smaller than uncertainties in the measurements.
Up-to-date information on present and near-future air quality help people avoid exposure to high...
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