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, B.L. Madhavan, Chaithanya D. Jain, 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.