Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6)
Abstract Euro 6 is the latest vehicle emission standards for pollutants such as CO, NO2 and PM, that all new vehicles must comply, and it was introduced in September 2015 in South Korea. This study examined the effect of Euro 6 by comparing the measured pollutant concentrations after 2016 (Euro 6–er...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-24886-z |
| _version_ | 1811207918878457856 |
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| author | Hyemin Hwang Sung Rak Choi Jae Young Lee |
| author_facet | Hyemin Hwang Sung Rak Choi Jae Young Lee |
| author_sort | Hyemin Hwang |
| collection | DOAJ |
| description | Abstract Euro 6 is the latest vehicle emission standards for pollutants such as CO, NO2 and PM, that all new vehicles must comply, and it was introduced in September 2015 in South Korea. This study examined the effect of Euro 6 by comparing the measured pollutant concentrations after 2016 (Euro 6–era) to the estimated concentrations without Euro 6. The concentration without Euro 6 was estimated by first modeling the air quality using various environmental factors related to diesel vehicles, meteorological conditions, temporal information such as date and precursors in 2002–2015 (pre–Euro 6–era), and then applying the model to predict the concentration after 2016. In this study, we used both recurrent neural network (RNN) and random forest (RF) algorithms to model the air quality and showed that RNN can achieve higher R2 (0.634 ~ 0.759 depending on pollutants) than RF, making it more suitable for air quality modeling. According to our results, the measured concentrations during 2016–2019 were lower than the concentrations predicted using RNN by − 1.2%, − 3.4%, and − 4.8% for CO, NO2 and PM10. Such reduction can be attributed to the result of Euro 6. |
| first_indexed | 2024-04-12T04:13:18Z |
| format | Article |
| id | doaj.art-8205096ce8904ced8eac861f4b478df6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T04:13:18Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-8205096ce8904ced8eac861f4b478df62022-12-22T03:48:27ZengNature PortfolioScientific Reports2045-23222022-12-011211910.1038/s41598-022-24886-zEvaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6)Hyemin Hwang0Sung Rak Choi1Jae Young Lee2Environmental Engineering Department, Ajou UniversityEnvironmental and Safety Engineering Department, Ajou UniversityEnvironmental and Safety Engineering Department, Ajou UniversityAbstract Euro 6 is the latest vehicle emission standards for pollutants such as CO, NO2 and PM, that all new vehicles must comply, and it was introduced in September 2015 in South Korea. This study examined the effect of Euro 6 by comparing the measured pollutant concentrations after 2016 (Euro 6–era) to the estimated concentrations without Euro 6. The concentration without Euro 6 was estimated by first modeling the air quality using various environmental factors related to diesel vehicles, meteorological conditions, temporal information such as date and precursors in 2002–2015 (pre–Euro 6–era), and then applying the model to predict the concentration after 2016. In this study, we used both recurrent neural network (RNN) and random forest (RF) algorithms to model the air quality and showed that RNN can achieve higher R2 (0.634 ~ 0.759 depending on pollutants) than RF, making it more suitable for air quality modeling. According to our results, the measured concentrations during 2016–2019 were lower than the concentrations predicted using RNN by − 1.2%, − 3.4%, and − 4.8% for CO, NO2 and PM10. Such reduction can be attributed to the result of Euro 6.https://doi.org/10.1038/s41598-022-24886-z |
| spellingShingle | Hyemin Hwang Sung Rak Choi Jae Young Lee Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6) Scientific Reports |
| title | Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6) |
| title_full | Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6) |
| title_fullStr | Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6) |
| title_full_unstemmed | Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6) |
| title_short | Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6) |
| title_sort | evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy euro 6 |
| url | https://doi.org/10.1038/s41598-022-24886-z |
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