Spatial, temporal features and influence of meteorology on PM2.5 and O3 association across urban and rural environments of India

IF 3.8 Q2 ENVIRONMENTAL SCIENCES
A. Sai Krishnaveni, B.L. Madhavan, Chaithanya D. Jain, M. Venkat Ratnam
{"title":"Spatial, temporal features and influence of meteorology on PM2.5 and O3 association across urban and rural environments of India","authors":"A. Sai Krishnaveni,&nbsp;B.L. Madhavan,&nbsp;Chaithanya D. Jain,&nbsp;M. Venkat Ratnam","doi":"10.1016/j.aeaoa.2024.100265","DOIUrl":null,"url":null,"abstract":"<div><p>This study provides an extensive analysis of the spatio-temporal association between particulate matter of 2.5 μm or less (PM<sub>2.5</sub>) and ground-level Ozone (O<sub>3</sub>) across four selected urban settlements (Delhi, Bengaluru, Ahmedabad, and Kolkata), and a rural (Gadanki) area in India. Utilizing 4 years (2019–2022) data from multiple sites in India, the study employed the robust linear regression, and deweathering techniques to elucidate the dynamics of PM<sub>2.5</sub> and O<sub>3</sub> under varying environmental conditions. Key findings include, in urban areas like Kolkata and Bengaluru, PM<sub>2.5</sub> and O<sub>3</sub> exhibited a consistent year-round positive relationship pre- and post-deweathering. This implies that within these cities, emission sources, and atmospheric chemistry are crucial in shaping the association between PM<sub>2.5</sub>, and O<sub>3</sub> than meteorological conditions. In contrast, negative correlations were more dominant over Delhi and Ahmedabad, which were unaffected by meteorology except in a few seasons. Typically, in Ahmedabad, this relationship differed from the general trend, displaying a positive correlation in winter and a negative in the pre-monsoon season. The rural area of Gadanki presents a unique case where deweathering alters the observed correlations significantly (shifted from positive to negative association), highlighting the dominant role of meteorological factors in driving PM<sub>2.5</sub> and O<sub>3</sub> relationship in rural settings. Relative humidity (RH), temperature (T), and wind direction (WD) were the key factors influencing PM<sub>2.5</sub> and O<sub>3</sub> relationship, although their impact varied seasonally and by location. However, the analysis during COVID-19 lockdown highlights the combined impact of meteorology and anthropogenic emissions on PM<sub>2.5</sub> and O<sub>3</sub> association, rather than the effect of each factor individually. These outcomes emphasize the need to account for both meteorological and non-meteorological factors in the air quality analysis. The findings offer valuable insights into coordinating the control of these pollutants, suggesting that effective air quality control strategies should be tailored to the specific needs and conditions of each region. This approach is crucial for developing more effective and targeted air quality management policies, especially in a diverse and rapidly developing country like India.</p></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590162124000327/pdfft?md5=23356889a1508935544e72426bd2555d&pid=1-s2.0-S2590162124000327-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric Environment: X","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590162124000327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

This study provides an extensive analysis of the spatio-temporal association between particulate matter of 2.5 μm or less (PM2.5) and ground-level Ozone (O3) across four selected urban settlements (Delhi, Bengaluru, Ahmedabad, and Kolkata), and a rural (Gadanki) area in India. Utilizing 4 years (2019–2022) data from multiple sites in India, the study employed the robust linear regression, and deweathering techniques to elucidate the dynamics of PM2.5 and O3 under varying environmental conditions. Key findings include, in urban areas like Kolkata and Bengaluru, PM2.5 and O3 exhibited a consistent year-round positive relationship pre- and post-deweathering. This implies that within these cities, emission sources, and atmospheric chemistry are crucial in shaping the association between PM2.5, and O3 than meteorological conditions. In contrast, negative correlations were more dominant over Delhi and Ahmedabad, which were unaffected by meteorology except in a few seasons. Typically, in Ahmedabad, this relationship differed from the general trend, displaying a positive correlation in winter and a negative in the pre-monsoon season. The rural area of Gadanki presents a unique case where deweathering alters the observed correlations significantly (shifted from positive to negative association), highlighting the dominant role of meteorological factors in driving PM2.5 and O3 relationship in rural settings. Relative humidity (RH), temperature (T), and wind direction (WD) were the key factors influencing PM2.5 and O3 relationship, although their impact varied seasonally and by location. However, the analysis during COVID-19 lockdown highlights the combined impact of meteorology and anthropogenic emissions on PM2.5 and O3 association, rather than the effect of each factor individually. These outcomes emphasize the need to account for both meteorological and non-meteorological factors in the air quality analysis. The findings offer valuable insights into coordinating the control of these pollutants, suggesting that effective air quality control strategies should be tailored to the specific needs and conditions of each region. This approach is crucial for developing more effective and targeted air quality management policies, especially in a diverse and rapidly developing country like India.

Abstract Image

印度城市和农村环境中 PM2.5 和 O3 关联的时空特征和气象影响
本研究广泛分析了印度四个选定城市住区(德里、班加罗尔、艾哈迈达巴德和加尔各答)和一个农村地区(Gadanki)的 2.5 μm 或以下颗粒物(PM2.5)与地面臭氧(O3)之间的时空关联。利用来自印度多个地点的 4 年(2019-2022 年)数据,该研究采用了稳健线性回归和去重技术来阐明不同环境条件下 PM2.5 和 O3 的动态变化。主要发现包括:在加尔各答和班加罗尔等城市地区,PM2.5 和 O3 在风化前后呈现出一致的全年正相关关系。这意味着,在这些城市中,与气象条件相比,排放源和大气化学对形成 PM2.5 和 O3 之间的关联至关重要。相比之下,负相关在德里和艾哈迈达巴德更为突出,这两个城市除少数季节外不受气象条件的影响。在艾哈迈达巴德,这种关系通常与总体趋势不同,在冬季呈正相关,而在季风前季节呈负相关。Gadanki 的农村地区是一个独特的案例,在这里,风化显著改变了观察到的相关性(从正相关转为负相关),突出了气象因素在推动农村地区 PM2.5 和 O3 关系中的主导作用。相对湿度(RH)、温度(T)和风向(WD)是影响 PM2.5 和 O3 关系的关键因素,尽管它们的影响因季节和地点而异。然而,COVID-19锁定期间的分析突出了气象和人为排放对PM2.5和O3关系的综合影响,而不是每个因素的单独影响。这些结果强调了在空气质量分析中考虑气象和非气象因素的必要性。研究结果为协调控制这些污染物提供了宝贵的见解,表明有效的空气质量控制策略应适合每个地区的具体需求和条件。这种方法对于制定更有效、更有针对性的空气质量管理政策至关重要,尤其是在印度这样一个多样化和快速发展的国家。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Atmospheric Environment: X
Atmospheric Environment: X Environmental Science-Environmental Science (all)
CiteScore
8.00
自引率
0.00%
发文量
47
审稿时长
12 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信