{"title":"利用对印度德里 PM2.5 的多模式分析评估控制策略","authors":"Ummed Singh Saharan, Tuhin Kumar Mandal*, Sudhir Kumar Sharma, Siddhartha Singh, Sakshi Ahlawat, Naveeta Kumari Jangir, Jitender Kumar, Rajesh Kumar and Ibrahim Hoteit, ","doi":"10.1021/acsestair.4c0008810.1021/acsestair.4c00088","DOIUrl":null,"url":null,"abstract":"<p >Over the past decade, Delhi has implemented various air quality control measures, but their effectiveness remains unclear. The present study addresses this gap by employing multimodal analysis to quantify the contribution of various sources to ambient PM<sub>2.5</sub> and evaluate their spatiotemporal distribution. Isotopic analysis (δ<sup>13</sup>C and δ<sup>15</sup>N) reveals that PM<sub>2.5</sub> in Delhi comprises a mix of sources, including coal combustion, crop residue burning, residential solid biofuel, vehicle emissions, and unidentified contributors. Moreover, positive matrix factorization (PMF) quantified the mixed combustion, and secondary aerosols (MCSAs) contributed the highest loading (34%), followed by vehicular emissions (26.7%), soil dust (28.9%), industries (6.6%), and solid waste burning (2.9%) from 2017 to 2019. The contribution of different sources varies throughout the year. Dust dominated during warm seasons, while MCSAs and vehicles, during cold seasons. The major sources are spread relatively uniform across Delhi and neighboring cities. Compared to 2013–2016, a decline in the contribution of MCSA_SWB [MCSA with solid waste burning] (∼15%) and industries (∼4%) were observed during 2017–2019. However, this is counterbalanced by a rise in vehicle emissions (∼10%) and construction dust (∼8%), highlighting the need for multifaceted strategies. The present study provides valuable insights for developing future air quality management strategies in Delhi to achieve the National Clean Air Programme target and contribute to sustainable development goals. Furthermore, the analysis paves the way for assessing the impact of control measures in other megacities worldwide.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 11","pages":"1362–1372 1362–1372"},"PeriodicalIF":0.0000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Evaluation of Control Strategies Using Multimodal Analysis of PM2.5 in Delhi, India\",\"authors\":\"Ummed Singh Saharan, Tuhin Kumar Mandal*, Sudhir Kumar Sharma, Siddhartha Singh, Sakshi Ahlawat, Naveeta Kumari Jangir, Jitender Kumar, Rajesh Kumar and Ibrahim Hoteit, \",\"doi\":\"10.1021/acsestair.4c0008810.1021/acsestair.4c00088\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >Over the past decade, Delhi has implemented various air quality control measures, but their effectiveness remains unclear. The present study addresses this gap by employing multimodal analysis to quantify the contribution of various sources to ambient PM<sub>2.5</sub> and evaluate their spatiotemporal distribution. Isotopic analysis (δ<sup>13</sup>C and δ<sup>15</sup>N) reveals that PM<sub>2.5</sub> in Delhi comprises a mix of sources, including coal combustion, crop residue burning, residential solid biofuel, vehicle emissions, and unidentified contributors. Moreover, positive matrix factorization (PMF) quantified the mixed combustion, and secondary aerosols (MCSAs) contributed the highest loading (34%), followed by vehicular emissions (26.7%), soil dust (28.9%), industries (6.6%), and solid waste burning (2.9%) from 2017 to 2019. The contribution of different sources varies throughout the year. Dust dominated during warm seasons, while MCSAs and vehicles, during cold seasons. The major sources are spread relatively uniform across Delhi and neighboring cities. Compared to 2013–2016, a decline in the contribution of MCSA_SWB [MCSA with solid waste burning] (∼15%) and industries (∼4%) were observed during 2017–2019. However, this is counterbalanced by a rise in vehicle emissions (∼10%) and construction dust (∼8%), highlighting the need for multifaceted strategies. The present study provides valuable insights for developing future air quality management strategies in Delhi to achieve the National Clean Air Programme target and contribute to sustainable development goals. Furthermore, the analysis paves the way for assessing the impact of control measures in other megacities worldwide.</p>\",\"PeriodicalId\":100014,\"journal\":{\"name\":\"ACS ES&T Air\",\"volume\":\"1 11\",\"pages\":\"1362–1372 1362–1372\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS ES&T Air\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acsestair.4c00088\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS ES&T Air","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsestair.4c00088","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Evaluation of Control Strategies Using Multimodal Analysis of PM2.5 in Delhi, India
Over the past decade, Delhi has implemented various air quality control measures, but their effectiveness remains unclear. The present study addresses this gap by employing multimodal analysis to quantify the contribution of various sources to ambient PM2.5 and evaluate their spatiotemporal distribution. Isotopic analysis (δ13C and δ15N) reveals that PM2.5 in Delhi comprises a mix of sources, including coal combustion, crop residue burning, residential solid biofuel, vehicle emissions, and unidentified contributors. Moreover, positive matrix factorization (PMF) quantified the mixed combustion, and secondary aerosols (MCSAs) contributed the highest loading (34%), followed by vehicular emissions (26.7%), soil dust (28.9%), industries (6.6%), and solid waste burning (2.9%) from 2017 to 2019. The contribution of different sources varies throughout the year. Dust dominated during warm seasons, while MCSAs and vehicles, during cold seasons. The major sources are spread relatively uniform across Delhi and neighboring cities. Compared to 2013–2016, a decline in the contribution of MCSA_SWB [MCSA with solid waste burning] (∼15%) and industries (∼4%) were observed during 2017–2019. However, this is counterbalanced by a rise in vehicle emissions (∼10%) and construction dust (∼8%), highlighting the need for multifaceted strategies. The present study provides valuable insights for developing future air quality management strategies in Delhi to achieve the National Clean Air Programme target and contribute to sustainable development goals. Furthermore, the analysis paves the way for assessing the impact of control measures in other megacities worldwide.