Immission levels and identification of sulfur dioxide sources in La Oroya city, Peruvian Andes

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

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.

Temporal variation of the PM2.5/PM10 ratio and its association with meteorological factors in a South American megacity: Metropolitan Area of Lima-Callao, Peru

The Metropolitan Area of Lima-Callao (MALC) is a South American megacity that has suffered a serious deterioration in air quality due to high levels of particulate matter (PM2.5 and PM10). Studies on the behavior of the PM2.5/PM10 ratio and its temporal variability in relation to meteorological parameters are still very limited. The objective of this study was to analyze the temporal trends of the PM2.5/PM10 ratio, its temporal variability, and its association with meteorological variables over a period of 5 years (2015-2019). For this, the Theil-Sen estimator, bivariate polar plots, and correlation analysis were used. The regions of highest mean concentrations of PM2.5 and PM10 were identified at eastern Lima (ATE station-41.2 µg/m3) and southern Lima (VMT station-126.7 µg/m3), respectively. The lowest concentrations were recorded in downtown Lima (CDM station-16.8 µg/m3 and 34.0 µg/m3, respectively). The highest average PM2.5/PM10 ratio was found at the CDM station (0.55) and the lowest at the VMT station (0.27), indicating a predominance of emissions from the vehicular fleet within central Lima and a greater emission of coarse particles by resuspension in southern Lima. The temporal progression of the ratio of PM2.5/PM10 showed positive and highly significant trends in northern and central Lima with values of 0.03 and 0.1 units of PM2.5/PM10 per year, respectively. In the southern region of Lima, the trend was also significant, showcasing a value of 0.02 units of PM2.5/PM10 per year. At the hourly and monthly level, the PM2.5/PM10 ratio presented a negative and significant correlation with wind speed and air temperature, and a positive and significant correlation with relative humidity. These findings offer insights into identifying the sources of PM pollution and are useful for implementing regulations to reduce air emissions considering both anthropogenic sources and meteorological dispersion patterns.

Human Health Risk Assessment for Exposure to Heavy Metals via Dietary Intake of Rainbow Trout in the Influence Area of a Smelting Facility Located in Peru

Abandoned mining-metallurgical sites can significantly impact the environment and human health by accumulating heavy metals in aquatic ecosystems. The water in the sub-basin near an abandoned smelting facility in the city of La Oroya, Peru, is primarily used for pisciculture. The objective of this study was to assess the risk to human health from exposure to heavy metals via dietary intake of rainbow trout (Oncorhynchus mykiss) in the influence area of a smelting facility located in the central Andean region of Peru. The bioconcentration factor, biosediment accumulation factor, and consumption risks were evaluated using the Monte Carlo method. The results showed that the concentrations of elements (Zn > Pb > Cu > As) in rainbow trout muscle did not exceed the maximum limit (ML). However, the water significantly exceeded the ML for Pb in all sectors and As in the lower and middle parts of the river. The concentration of Pb in sediments also significantly exceeded the ML in the upper and lower parts of the river. Consequently, rainbow trout consumption in the study area presents risks to human health due to the bioaccumulation of heavy metals, with a 1.27% carcinogenic risk in samples from the lower part of the river.