Department of Civil Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India, 600036.
Environ Monit Assess. 2024 Oct 25;196(11):1102. doi: 10.1007/s10661-024-13272-z.
This research study investigates hourly data on concentrations of five major air pollutants such as particulate matter (PM, PM) and gaseous pollutants (SO, NO, CO) measured during 2022 at four hotspot sites (industrial site, traffic site, commercial site, harbour, and one residential site) in Chennai, India. The analysis encompasses temporal variations spanning annual, seasonal, and diurnal variations in the pollutants. Notably, PM and CO emerge as the predominant pollutants, with the highest concentrations at industrial and traffic sites (PM: 67.64 ± 40.77 µg/m, CO: 1.41 ± 0.84 mg/m; traffic site: PM: 58.67 ± 20.05 µg/m, CO: 0.99 ± 0.57 mg/m). Seasonal dynamics reveal prominent winter spikes in particulate matter (PM, PM) and carbon monoxide (CO) concentrations, while nitrogen dioxide (NO) and sulphur dioxide (SO) levels peak during the summer season, particularly in the harbour area. The proximity to roadways exerts a discernible influence on diurnal patterns, with traffic sites showcasing broader rush hour peaks compared to sharper spikes observed at other sites. Furthermore, distinct bimodal patterns are evident for PM and PM concentrations in residential and harbour areas. A common lognormal distribution pattern is identified across the studied sites, suggesting consistent air quality trends despite contrasting locations. The conditional probability function (CPF) is used in conjunction with local meteorological conditions for identifying key pollution sources in each location. The implementation of polar plots emphasizes industries as principal local sources of pollution, at industrial sites significantly contributing to PM, SO, and NO concentrations under specific wind conditions. The main objective of the present study is to facilitate a good understanding of pollutant dynamics, pollution sources, and their intricate interplay with meteorological factors, thereby contributing to the formulation and implementation of effective air pollution control and mitigation strategies.
本研究调查了 2022 年在印度钦奈的四个热点地点(工业地点、交通地点、商业地点、港口和一个居民区)测量的五个主要空气污染物(如颗粒物(PM,PM)和气态污染物(SO,NO,CO)的每小时浓度。该分析包括污染物的年度、季节性和日变化的时间变化。值得注意的是,PM 和 CO 是主要污染物,在工业和交通地点浓度最高(PM:67.64±40.77μg/m,CO:1.41±0.84mg/m;交通地点:PM:58.67±20.05μg/m,CO:0.99±0.57mg/m)。季节性动态显示颗粒物(PM,PM)和一氧化碳(CO)浓度在冬季明显升高,而二氧化氮(NO)和二氧化硫(SO)浓度在夏季达到峰值,特别是在港口地区。与道路的接近程度对日变化模式有明显影响,交通地点的高峰时段较宽,而其他地点的高峰时段较尖。此外,在居民区和港口地区,PM 和 PM 浓度呈现明显的双峰模式。在研究的所有地点都发现了一个共同的对数正态分布模式,表明尽管地理位置不同,但空气质量趋势一致。条件概率函数(CPF)与当地气象条件结合使用,用于识别每个地点的关键污染源。极坐标图的实现强调了工业是污染的主要本地来源,在特定的风条件下,工业地点显著导致 PM、SO 和 NO 浓度增加。本研究的主要目的是促进对污染物动态、污染源及其与气象因素的复杂相互作用的深入了解,从而为制定和实施有效的空气污染控制和缓解策略做出贡献。