Identification of Pollution Sources in Urban Wind Environments Using the Regularized Residual Method
The scale of cities is increasing with continuous urban development. Effective methods, such as the source term estimation (STE) method, must be established for identifying the sources of air pollution in cities to prevent economic losses and casualties caused by pollutant leakage. Herein, methods f...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/14/12/1786 |
| _version_ | 1827575677805658112 |
|---|---|
| author | Shibo Tang Xiaotong Xue Fei Li Zhonglin Gu Hongyuan Jia Xiaodong Cao |
| author_facet | Shibo Tang Xiaotong Xue Fei Li Zhonglin Gu Hongyuan Jia Xiaodong Cao |
| author_sort | Shibo Tang |
| collection | DOAJ |
| description | The scale of cities is increasing with continuous urban development. Effective methods, such as the source term estimation (STE) method, must be established for identifying the sources of air pollution in cities to prevent economic losses and casualties caused by pollutant leakage. Herein, methods for optimizing sensor configuration and identifying pollution sources are discussed, and an STE method based on the regularized minimum residual method is proposed. Urban wind environments were simulated using a computational fluid dynamics (CFD) model, and the results were compared with experimental data pertaining to the wind tunnel of an architectural ensemble to verify the model’s accuracy. The sensor layout was optimized using the simulated annealing (SA) algorithm and adjoint entropy, and the relationship between sensor responses and potential pollution sources was established using the CFD model. Pollutant concentrations measured using sensors were combined with the regularization method to extrapolate the pollution source strength, and the regularized minimum residual method was used to obtain the locations of the real pollution sources. The results show that compared with the Bayesian methods, the proposed method can more accurately identify pollution sources (100%), with a smaller source strength error of 2.01% for constant sources and one of 2.62% for attenuation sources. |
| first_indexed | 2024-03-08T21:00:26Z |
| format | Article |
| id | doaj.art-3dc4af7bd1b34e7eb8364758142dc1c4 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-08T21:00:26Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-3dc4af7bd1b34e7eb8364758142dc1c42023-12-22T13:52:52ZengMDPI AGAtmosphere2073-44332023-12-011412178610.3390/atmos14121786Identification of Pollution Sources in Urban Wind Environments Using the Regularized Residual MethodShibo Tang0Xiaotong Xue1Fei Li2Zhonglin Gu3Hongyuan Jia4Xiaodong Cao5Tianmushan Laboratory, Yuhang District, Hangzhou 311115, ChinaCollege of Urban Construction, Nanjing Tech University, Nanjing 211816, ChinaTianmushan Laboratory, Yuhang District, Hangzhou 311115, ChinaCollege of Urban Construction, Nanjing Tech University, Nanjing 211816, ChinaInstitute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, JapanTianmushan Laboratory, Yuhang District, Hangzhou 311115, ChinaThe scale of cities is increasing with continuous urban development. Effective methods, such as the source term estimation (STE) method, must be established for identifying the sources of air pollution in cities to prevent economic losses and casualties caused by pollutant leakage. Herein, methods for optimizing sensor configuration and identifying pollution sources are discussed, and an STE method based on the regularized minimum residual method is proposed. Urban wind environments were simulated using a computational fluid dynamics (CFD) model, and the results were compared with experimental data pertaining to the wind tunnel of an architectural ensemble to verify the model’s accuracy. The sensor layout was optimized using the simulated annealing (SA) algorithm and adjoint entropy, and the relationship between sensor responses and potential pollution sources was established using the CFD model. Pollutant concentrations measured using sensors were combined with the regularization method to extrapolate the pollution source strength, and the regularized minimum residual method was used to obtain the locations of the real pollution sources. The results show that compared with the Bayesian methods, the proposed method can more accurately identify pollution sources (100%), with a smaller source strength error of 2.01% for constant sources and one of 2.62% for attenuation sources.https://www.mdpi.com/2073-4433/14/12/1786urban wind environmentsource identificationCFDregularized method |
| spellingShingle | Shibo Tang Xiaotong Xue Fei Li Zhonglin Gu Hongyuan Jia Xiaodong Cao Identification of Pollution Sources in Urban Wind Environments Using the Regularized Residual Method Atmosphere urban wind environment source identification CFD regularized method |
| title | Identification of Pollution Sources in Urban Wind Environments Using the Regularized Residual Method |
| title_full | Identification of Pollution Sources in Urban Wind Environments Using the Regularized Residual Method |
| title_fullStr | Identification of Pollution Sources in Urban Wind Environments Using the Regularized Residual Method |
| title_full_unstemmed | Identification of Pollution Sources in Urban Wind Environments Using the Regularized Residual Method |
| title_short | Identification of Pollution Sources in Urban Wind Environments Using the Regularized Residual Method |
| title_sort | identification of pollution sources in urban wind environments using the regularized residual method |
| topic | urban wind environment source identification CFD regularized method |
| url | https://www.mdpi.com/2073-4433/14/12/1786 |
| work_keys_str_mv | AT shibotang identificationofpollutionsourcesinurbanwindenvironmentsusingtheregularizedresidualmethod AT xiaotongxue identificationofpollutionsourcesinurbanwindenvironmentsusingtheregularizedresidualmethod AT feili identificationofpollutionsourcesinurbanwindenvironmentsusingtheregularizedresidualmethod AT zhonglingu identificationofpollutionsourcesinurbanwindenvironmentsusingtheregularizedresidualmethod AT hongyuanjia identificationofpollutionsourcesinurbanwindenvironmentsusingtheregularizedresidualmethod AT xiaodongcao identificationofpollutionsourcesinurbanwindenvironmentsusingtheregularizedresidualmethod |