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A platform for research: civil engineering, architecture and urbanism
Air pollution prediction using machine learning techniques – An approach to replace existing monitoring stations with virtual monitoring stations
https://orcid.org/0000-0002-9862-9354
Abstract Air pollution in the modern world is a matter of grave concern. Due to rapid expansion in commercial social, and economic aspects, the pollutant concentrations in different parts of the world continue to increase and disrupt human life. Thus, monitoring the pollutant levels is of primary importance to keep the pollutant concentrations under control. Regular monitoring enables the authorities to take appropriate measures in case of high pollution. However, monitoring the pollutant concentrations is not straightforward as it requires installing monitoring stations to collect the relevant pollutant data, which comes with high installation and maintenance costs. In this research, an attempt has been made to simulate the concentrations of PM2.5, PM10, and NO2 at two sites in Stuttgart (Marienplatz and Am Neckartor) using Machine Learning methods. These pollutants are measured with the help of monitoring stations at these locations. Five Machine Learning methods, namely ridge regressor, support vector regressor, random forest, extra trees regressor, and xtreme gradient boosting, were adopted for this study. Meteorological parameters, traffic data, and pollutant information from nearby monitoring stations for the period from January 01, 2018 to 31.03.2022 were considered as inputs to model the pollutants. From the results, it was concluded that the pollutant information from the nearby stations has a significant effect in predicting the pollutant concentrations. Further, it was investigated if a similar methodology can be applied at other locations to estimate pollutant concentrations. This procedure was tested on the data of the monitoring station Karlsruhe-Nordwest which is located in another German city named Karlsruhe. The results demonstrated that this method is applicable in other areas as well.
Highlights Machine Learning models are suitable for pollutant concentration prediction. Pollutant concentrations from nearby monitoring stations proved the most effective input parameter. The developed methodology is applicable to estimate pollutant concentrations at other locations. Virtual monitoring stations can substitute existing monitoring stations.
Air pollution prediction using machine learning techniques – An approach to replace existing monitoring stations with virtual monitoring stations
https://orcid.org/0000-0002-9862-9354
Abstract Air pollution in the modern world is a matter of grave concern. Due to rapid expansion in commercial social, and economic aspects, the pollutant concentrations in different parts of the world continue to increase and disrupt human life. Thus, monitoring the pollutant levels is of primary importance to keep the pollutant concentrations under control. Regular monitoring enables the authorities to take appropriate measures in case of high pollution. However, monitoring the pollutant concentrations is not straightforward as it requires installing monitoring stations to collect the relevant pollutant data, which comes with high installation and maintenance costs. In this research, an attempt has been made to simulate the concentrations of PM2.5, PM10, and NO2 at two sites in Stuttgart (Marienplatz and Am Neckartor) using Machine Learning methods. These pollutants are measured with the help of monitoring stations at these locations. Five Machine Learning methods, namely ridge regressor, support vector regressor, random forest, extra trees regressor, and xtreme gradient boosting, were adopted for this study. Meteorological parameters, traffic data, and pollutant information from nearby monitoring stations for the period from January 01, 2018 to 31.03.2022 were considered as inputs to model the pollutants. From the results, it was concluded that the pollutant information from the nearby stations has a significant effect in predicting the pollutant concentrations. Further, it was investigated if a similar methodology can be applied at other locations to estimate pollutant concentrations. This procedure was tested on the data of the monitoring station Karlsruhe-Nordwest which is located in another German city named Karlsruhe. The results demonstrated that this method is applicable in other areas as well.
Highlights Machine Learning models are suitable for pollutant concentration prediction. Pollutant concentrations from nearby monitoring stations proved the most effective input parameter. The developed methodology is applicable to estimate pollutant concentrations at other locations. Virtual monitoring stations can substitute existing monitoring stations.
Air pollution prediction using machine learning techniques – An approach to replace existing monitoring stations with virtual monitoring stations
https://orcid.org/0000-0002-9862-9354
Abstract Air pollution in the modern world is a matter of grave concern. Due to rapid expansion in commercial social, and economic aspects, the pollutant concentrations in different parts of the world continue to increase and disrupt human life. Thus, monitoring the pollutant levels is of primary importance to keep the pollutant concentrations under control. Regular monitoring enables the authorities to take appropriate measures in case of high pollution. However, monitoring the pollutant concentrations is not straightforward as it requires installing monitoring stations to collect the relevant pollutant data, which comes with high installation and maintenance costs. In this research, an attempt has been made to simulate the concentrations of PM2.5, PM10, and NO2 at two sites in Stuttgart (Marienplatz and Am Neckartor) using Machine Learning methods. These pollutants are measured with the help of monitoring stations at these locations. Five Machine Learning methods, namely ridge regressor, support vector regressor, random forest, extra trees regressor, and xtreme gradient boosting, were adopted for this study. Meteorological parameters, traffic data, and pollutant information from nearby monitoring stations for the period from January 01, 2018 to 31.03.2022 were considered as inputs to model the pollutants. From the results, it was concluded that the pollutant information from the nearby stations has a significant effect in predicting the pollutant concentrations. Further, it was investigated if a similar methodology can be applied at other locations to estimate pollutant concentrations. This procedure was tested on the data of the monitoring station Karlsruhe-Nordwest which is located in another German city named Karlsruhe. The results demonstrated that this method is applicable in other areas as well.
Highlights Machine Learning models are suitable for pollutant concentration prediction. Pollutant concentrations from nearby monitoring stations proved the most effective input parameter. The developed methodology is applicable to estimate pollutant concentrations at other locations. Virtual monitoring stations can substitute existing monitoring stations.
Air pollution prediction using machine learning techniques – An approach to replace existing monitoring stations with virtual monitoring stations
Samad, A. (author) / Garuda, S. (author) / Vogt, U. (author) / Yang, B. (author)
Atmospheric Environment ; 310
2023-07-27
Article (Journal)
Electronic Resource
English
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