Identification of particulate matter (PM10 and PM2.5) sources using bivariate polar plots and k-means clustering in a South American megacity: Metropolitan Area of Lima-Callao, Peru
José Abel Espinoza-Guillen, Marleni Beatriz Alderete-Malpartida, Franchesco David Roncal-Romero, Joycy Claudia Vilcanqui-Sarmiento
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引用次数: 0
Abstract
The identification of different air pollution sources is essential to effectively control atmospheric pollution, particularly in megacities of emerging countries with rapid economic development, such as the Metropolitan Area of Lima-Callao (MALC). The objective of this research was to identify the main sources of particulate matter pollution by applying bivariate polar plots and the k-means clustering algorithm. These statistical techniques were applied to hourly in situ data of four variables collected over a 5-year period (2015–2019) by the Automatic Air Quality Monitoring Network of the MALC: wind direction, wind speed, PM10, and PM2.5 concentrations. Average PM10 concentrations ranged from 34 μg m−3 (CDM station) to 126.7 μg m−3 (VMT station), while average PM2.5 concentrations ranged from 16.8 μg m−3 (CDM station) to 41.2 μg m−3 (ATE station). The diurnal variation of PM presented two peaks, one in the morning (from 0800 to 1000 h) and the other at night (from 1900 to 2300 h), with the highest concentrations of PM10 recorded at the ATE (0800 h: 155.8 μg m−3) and VMT (2100 h: 154.6 μg m−3) stations, and PM2.5 at the ATE station (0800 h: 60.3 μg m−3 and 2300 h: 37.5 μg m−3). The results showed that the contributions of PM10 are directly related to emissions from industrial activities, automotive fleet, construction, demolition, wind erosion, and the suspension and resuspension of particulates from unpaved roads. Meanwhile, high concentrations of PM2.5 are mainly attributed to vehicle exhaust emissions, industrial emissions, secondary particulate formation, and drag by the action of the winds. The major source of particulate matter contamination is the vehicle fleet, and within this, automobiles, station wagons, combi vans, and 2 and 3-wheel motorcycles are those that have the greatest contribution. These results were supported by non-parametric statistical tests such as Kruskal–Wallis and Mann–Whitney U and validated by the conditional bivariate probability function. The findings of this work may help to implement pollution prevention and control strategies in the future within this South American megacity.
期刊介绍:
Environmental Monitoring and Assessment emphasizes technical developments and data arising from environmental monitoring and assessment, the use of scientific principles in the design of monitoring systems at the local, regional and global scales, and the use of monitoring data in assessing the consequences of natural resource management actions and pollution risks to man and the environment.