Rainwater quality is influenced by air pollutants and can affect sensitive ecosystems. This study was conducted to identify the sources of rainwater contamination in a receptor investigated in the southern part of Brazil. A total of 22 rainwater samples were collected at Florianópolis, Brazil. The sampling station is influenced by continental emissions (soil resuspension, traffic emissions and combustion) and marine aerosols. Over the sampling period, the average pH and electrical conductivity (EC) of the precipitation was found to be 4.97 +/- 0.41 and 14.68 microS cm(-1) +/- 13.47, respectively. In addition topH and EC, ions and trace metals in the collected rainwater were quantified. The results were investigated by a combination of techniques including principal component analysis (PCA), a back trajectory model and other statistical and graphical interpretation methodologies. A PCA showed that Cl(-), Na+, Mg2+ and part of the K+ and SO4(2-) content were mainly contributed by marine aerosols, whereas the contribution from continental sources (combustion, traffic emissions and other urban activities) was dominant in the content of NO3(-) and part of the SO4(2-) and Mn content. Soil resuspension was responsible for the concentrations of most of the trace metals (apart from Mn) and Ca2+ in the rainwater. An inverse correlation among the elemental concentrations, amount of rainfall and wind speed was observed. The northern transport pathway was identified as being associated with high concentrations of NO3(-) and slightly decreased pH values. However, the low standard deviation observed for the pH values during the sampling campaign also showed a small variation in the data, suggesting that the acidity is most probably being constantly sourced from a natural origin, such as organic acids.
Epidemiological studies have documented that elevated airborne particulate matter (PM) concentrations, especially those with an aerodynamic diameter less than 10 microm (PM10), are associated with adverse health effects. Two receptor models, UNMIX and positive matrix factorization (PMF), were used to identify and quantify the sources of PM10 concentrations in Tubarão and Capivari de Baixo, Santa Catarina, Brazil. This region is known for its high pollution levels due to intense industrial activity and exploitation of natural resources. PM10 samples were collected using high volume samplers at two sites in the region and statistical exploratory analysis techniques were applied to identify and assess PM10 sources. The two primary PM10 sources were identified as soil re-suspension/road dust emissions and coal burning emissions, contributing 65-75% and 15-25% of the PM10, respectively. The study confirmed the significance of the influence of local PM10 emissions (power plants, soil re-suspension and road dust emissions) on regional air quality, although no violations of the Brazilian PM10 standards (limit of 150 microg/m3) were observed, with a mean concentration of 27.6 microg/m3 measured in this study. This study demonstrated the usefulness of statistical exploratory analysis techniques in assessing the validity of modelling results and contributing to the interpretation of ambient air quality data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.