利用对印度德里 PM2.5 的多模式分析评估控制策略

Ummed Singh Saharan, Tuhin Kumar Mandal*, Sudhir Kumar Sharma, Siddhartha Singh, Sakshi Ahlawat, Naveeta Kumari Jangir, Jitender Kumar, Rajesh Kumar and Ibrahim Hoteit, 
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摘要

过去十年来,德里实施了各种空气质量控制措施,但其效果仍不明确。本研究采用多模式分析来量化各种来源对环境 PM2.5 的贡献并评估其时空分布,从而弥补了这一不足。同位素分析(δ13C 和 δ15N)显示,德里的 PM2.5 由多种来源组成,包括燃煤、农作物秸秆燃烧、住宅固体生物燃料、汽车尾气排放和不明来源。此外,正矩阵因式分解(PMF)量化了混合燃烧,从 2017 年到 2019 年,二次气溶胶(MCSAs)的贡献负荷最高(34%),其次是车辆排放(26.7%)、土壤尘埃(28.9%)、工业(6.6%)和固体废物燃烧(2.9%)。不同来源的贡献在一年四季中各不相同。粉尘在温暖季节占主导地位,而 MCSA 和车辆则在寒冷季节占主导地位。主要污染源在德里及周边城市的分布相对均匀。与 2013-2016 年相比,2017-2019 年期间,MCSA_SWB[燃烧固体废物的 MCSA](∼15%)和工业(∼4%)的贡献率有所下降。然而,汽车尾气排放(∼10%)和建筑扬尘(∼8%)的增加抵消了这一趋势,突出了多方面战略的必要性。本研究为德里未来空气质量管理战略的制定提供了宝贵的见解,以实现国家清洁空气计划的目标,并为可持续发展目标做出贡献。此外,本分析还为评估控制措施对全球其他特大城市的影响铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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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.

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