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Source apportionment of atmospheric deposition species in an agricultural Brazilian region using Positive Matrix Factorization
* 1 , 2 , * 1
1  Universidade Federal de Lavras
2  Universidade de São Paulo
Academic Editor: Anthony Lupo

https://doi.org/10.3390/ecas2021-10698 (registering DOI)
Abstract:

A key mechanism for cycling chemical compounds between natural reservoirs is the atmospheric deposition, and it could provide valuable information on air quality and apportion of pollution sources. In this context, we investigated the influence of natural and anthropogenic sources on bulk atmospheric deposition chemistry in the Lavras city, South of Minas Gerais, a Brazilian area with rural background. A dataset of 66 bulk samples were collected from November 2017 until October 2019 and major ionic species were quantified by ionic chromatography. To attend the quality criteria suggested by the World Meteorological Organization, the ion balance was calculated and 58 samples were validated. The pH values ranged from 5.52 to 8.46, with an average of 5.99 and most deposition samples (~98%) were alkaline (pH > 5.60). For the whole sampling campaign, the ions profile in volume weighted mean (VWM) was described as follows: Ca2+ (45.7) > Cl- (19.1) > Na+ (16.6) > NH4+ (14.4) > Mg2+ (12.8) > NO3- (9.46) > K+ (5.48) > F- (4.00) > SO42- (3.88) > HCO2- (1.92) > C2H3O2- (1.41) > C2O42- (1.26) > H+ (0.77) µmolL-1. We identified Ca2+ as the most predominant specie accounting for 33% of the total ionic species distribution. In order to identify the sources and atmospheric processes for the ionic compounds, we applied Principal Component Analysis (PCA). PCA produced 4 significant principal components, which explained 80% of the data variation. In general, the analysis suggested different scenarios for the sources, such as soil, agricultural activity, cement manufacturing and atmospheric processes neutralization. As perspective, we intend to perform Kruskal–Wallis tests to assess the temporal and seasonal variability of the major ionic species. In addition, to apply Positive Matrix Factorization (PMF) model to identify pollutants sources and their relative contributions.

Keywords: Atmospheric Deposition; Source apportionment; PMF; PCA; Brazil
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