Identifying source apportionment of atmospheric particulate matter and gaseous pollutants using receptor models : A case study of Bengaluru, India

IF 0.7 4区 地球科学 Q4 METEOROLOGY & ATMOSPHERIC SCIENCES MAUSAM Pub Date : 2023-12-31 DOI:10.54302/mausam.v75i1.6080
H. N. Sowmya, Channabasavaraj Wollur, G. P. Shivashankara, H. K. Ramaraju
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Abstract

The data of Particulate matter PMs (PM2.5, PM10) and Gaseous Pollutants such as carbon monoxide (CO), methane (CH4), oxides of nitrogen (NOx: NO and NO2), non-methane hydrocarbons (NMHCs), sulfur dioxide (SO2), along with ammonia (NH3) at five different locations across Bengaluru from 1st January, 2017 to 20th March, 2018 were collected. The primary objective of this research work is to identify the sources of atmospheric particulate matter and gaseous pollutants using receptor models in Bengaluru, India. To execute this, receptor models, namely Conditional Bivariate Probability Function (CBPF) and Concentrated Weighted Trajectory (CWT) Analysis, are applied. Conditional Bivariate Probability Function (CBPF) shows that, annually, the maximum concentrations of PMs over receptor sites were detected during low wind speed (< 2 knots) along the north-east direction specifying that the long-range transport does not play an essential role in the transportation of higher concentrations of PM and their primary source region may be localized. Concentrated Weighted Trajectory (CWT) analysis shows that, seasonally, the highest air mass contribution of about 37% was noticed in summer, whereas the lowest was in the post-monsoon season (13%). The significant contribution of PM2.5 transported from long distances was during monsoon, and in the case of PM10, it was in summer. The study suggests that the long-range transport of PMs and gaseous Pollutants was not vital and was observed to be localized.
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利用受体模型确定大气颗粒物和气体污染物的来源分配:印度班加罗尔案例研究
本研究收集了 2017 年 1 月 1 日至 2018 年 3 月 20 日期间班加罗尔五个不同地点的颗粒物 PMs(PM2.5、PM10)和气体污染物数据,如一氧化碳 (CO)、甲烷 (CH4)、氮氧化物 (NOx:NO 和 NO2)、非甲烷碳氢化合物 (NMHC)、二氧化硫 (SO2) 以及氨 (NH3)。这项研究工作的主要目的是利用受体模型确定印度班加罗尔大气颗粒物和气态污染物的来源。为此,应用了受体模型,即条件双变量概率函数(CBPF)和集中加权轨迹分析(CWT)。条件双变量概率函数(CBPF)显示,每年在沿东北方向风速较低(< 2 海里)时,受体点上检测到的可吸入颗粒物浓度最高,这说明长程飘移在高浓度可吸入颗粒物的飘移中并没有发挥重要作用,其主要来源区域可能是局部的。集中加权轨迹(CWT)分析表明,从季节上看,夏季的气团贡献率最高,约为 37%,而季风后季节的贡献率最低(13%)。PM2.5 的长程飘移主要发生在季风季节,而 PM10 的长程飘移主要发生在夏季。研究表明,可吸入颗粒物和气态污染物的长程飘移并不重要,观察到的是局部飘移。
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来源期刊
MAUSAM
MAUSAM 地学-气象与大气科学
CiteScore
1.20
自引率
0.00%
发文量
1298
审稿时长
6-12 weeks
期刊介绍: MAUSAM (Formerly Indian Journal of Meteorology, Hydrology & Geophysics), established in January 1950, is the quarterly research journal brought out by the India Meteorological Department (IMD). MAUSAM is a medium for publication of original scientific research work. MAUSAM is a premier scientific research journal published in this part of the world in the fields of Meteorology, Hydrology & Geophysics. The four issues appear in January, April, July & October.
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