Embedded Generative Air Pollution Model with Variational Autoencoder and Environmental Factor Effect in Ulaanbaatar City
Air pollution is one of the most pressing modern-day issues in cities around the world. However, most cities have adopted air quality measurement devices that only measure the past pollution levels without paying attention to the influencing factors. To obtain preliminary pollution information with...
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| Format: | Article |
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MDPI AG
2021-12-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/13/1/71 |
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| author | Bulgansaikhan Baldorj Munkherdene Tsagaan Lodoysamba Sereeter Amanjol Bulkhbai |
| author_facet | Bulgansaikhan Baldorj Munkherdene Tsagaan Lodoysamba Sereeter Amanjol Bulkhbai |
| author_sort | Bulgansaikhan Baldorj |
| collection | DOAJ |
| description | Air pollution is one of the most pressing modern-day issues in cities around the world. However, most cities have adopted air quality measurement devices that only measure the past pollution levels without paying attention to the influencing factors. To obtain preliminary pollution information with regard to environmental factors, we developed a variational autoencoder and feedforward neural network-based embedded generative model to examine the relationship between air quality and the effects of environmental factors. In the model, actual <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>O</mi></mrow></semantics></math></inline-formula> measurements from 2016 to 2020 were used, which were assembled from 15 differently located ground monitoring stations in Ulaanbaatar city. A wide range of weather and fuel measurements were used as the data for the influencing factors, and were collected over the same period as the air pollution data were recorded. The prediction results concerned all measurement stations, and the results were visualized as a spatial–temporal distribution of pollution and the performance of individual stations. A cross-validated <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> was used to estimate the entire pollution distribution through the regions as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></semantics></math></inline-formula>: 0.81, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula>: 0.76, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula>: 0.89, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>O</mi></mrow></semantics></math></inline-formula>: 0.83. Pearson’s chi-squared tests were used for assessing each measurement station, and the contingency tables represent a high correlation between the actual and model results. The model can be applied to perform specific analysis of the interdependencies between pollution and environmental factors, and the performance of the model improves with long-range data. |
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| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T01:56:01Z |
| publishDate | 2021-12-01 |
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| spelling | doaj.art-707a980475804f4bb87347e41510f54c2023-11-23T12:56:40ZengMDPI AGAtmosphere2073-44332021-12-011317110.3390/atmos13010071Embedded Generative Air Pollution Model with Variational Autoencoder and Environmental Factor Effect in Ulaanbaatar CityBulgansaikhan Baldorj0Munkherdene Tsagaan1Lodoysamba Sereeter2Amanjol Bulkhbai3Department of Physics, Mongolian University of Science and Technology, Ulaanbaatar 14191, MongoliaDepartment of Mathematics, Mongolian University of Science and Technology, Ulaanbaatar 14191, MongoliaDepartment of Engineering, German-Mongolian Institute for Resource and Technology, Ulaanbaatar 14191, MongoliaDepartment of Remote Sensing, Information and Research Institute of Meteorology, Hydrology and Environment, Ulaanbaatar 15160, MongoliaAir pollution is one of the most pressing modern-day issues in cities around the world. However, most cities have adopted air quality measurement devices that only measure the past pollution levels without paying attention to the influencing factors. To obtain preliminary pollution information with regard to environmental factors, we developed a variational autoencoder and feedforward neural network-based embedded generative model to examine the relationship between air quality and the effects of environmental factors. In the model, actual <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>O</mi></mrow></semantics></math></inline-formula> measurements from 2016 to 2020 were used, which were assembled from 15 differently located ground monitoring stations in Ulaanbaatar city. A wide range of weather and fuel measurements were used as the data for the influencing factors, and were collected over the same period as the air pollution data were recorded. The prediction results concerned all measurement stations, and the results were visualized as a spatial–temporal distribution of pollution and the performance of individual stations. A cross-validated <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> was used to estimate the entire pollution distribution through the regions as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></semantics></math></inline-formula>: 0.81, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula>: 0.76, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula>: 0.89, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>O</mi></mrow></semantics></math></inline-formula>: 0.83. Pearson’s chi-squared tests were used for assessing each measurement station, and the contingency tables represent a high correlation between the actual and model results. The model can be applied to perform specific analysis of the interdependencies between pollution and environmental factors, and the performance of the model improves with long-range data.https://www.mdpi.com/2073-4433/13/1/71air pollutionembedded generative modelenvironmental factor effectlatent variable |
| spellingShingle | Bulgansaikhan Baldorj Munkherdene Tsagaan Lodoysamba Sereeter Amanjol Bulkhbai Embedded Generative Air Pollution Model with Variational Autoencoder and Environmental Factor Effect in Ulaanbaatar City Atmosphere air pollution embedded generative model environmental factor effect latent variable |
| title | Embedded Generative Air Pollution Model with Variational Autoencoder and Environmental Factor Effect in Ulaanbaatar City |
| title_full | Embedded Generative Air Pollution Model with Variational Autoencoder and Environmental Factor Effect in Ulaanbaatar City |
| title_fullStr | Embedded Generative Air Pollution Model with Variational Autoencoder and Environmental Factor Effect in Ulaanbaatar City |
| title_full_unstemmed | Embedded Generative Air Pollution Model with Variational Autoencoder and Environmental Factor Effect in Ulaanbaatar City |
| title_short | Embedded Generative Air Pollution Model with Variational Autoencoder and Environmental Factor Effect in Ulaanbaatar City |
| title_sort | embedded generative air pollution model with variational autoencoder and environmental factor effect in ulaanbaatar city |
| topic | air pollution embedded generative model environmental factor effect latent variable |
| url | https://www.mdpi.com/2073-4433/13/1/71 |
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