Between June and September 2018, particulate matter (PM) samples were taken in the Sartenejas Valley, southeast of Greater Caracas, Venezuela. The aim was to evaluate the morphology and the elemental chemical composition of particulate matter and establish possible emission sources during the rainy season. Functional groups were identified by FTIR spectroscopic analysis, and morphology and elemental composition were obtained by SEM–EDX analysis. The sampling period coincided with a Sahara dust storm. The SEM–EDX and FTIR analyses found evidence of mineral elements related to soil and crustal origins. The presence of C-rich or C-containing aerosols is related to biological sources or mineral carbon. SEM–EDX analysis of PM revealed the following particle groups: geogenic, metallic, C-rich, and secondary aerosols. Quantitative source appointments through principal component analysis (PCA) corroborated PM sources, including soil dust, sea salts, and reacted aerosols. According to the authors’ knowledge, this study represents the first report to indicate that an episode of African dust could influence the particles collected in an intertropical continental sector in Venezuela, South America.

muestras de material particulado (PM, por sus siglas en inglés) usando un impactador de cascada que segrega el PM en seis rangos de tamaños de partículas: > 7.2 μm, 3.0 a 7.2 μm, 1.5 a 3.0 μm, 0.95 a 1.5 μm, 0.49 a 0.95 μm y < 0.49 μm, junto con datos de la meteorología del lugar. Como complemento se recolectaron las fechas de ocurrencia de incendios forestales y lluvias para el periodo de muestreo, así como la precipitación acumulada histórica mensual de la Gran Caracas. El objetivo de esta investigación fue obtener un modelo lineal multivariante para la predicción de PM1 a partir de variables ambientales, meteorológicas y eventualidades físicas en una región intertropical del centro-norte de Venezuela. Utilizando la información del muestreo y de fuentes secundarias se construyó una matriz de datos con variables ambientales, meteorológicas y eventualidades capaces de predecir el comportamiento de las partículas finas (PM1) a partir de otros tamaños de partículas, temperatura, histórico de precipitación, ocurrencia de incendios y de lluvias. Finalmente, se construyó un modelo de regresión lineal múltiple para estimar concentraciones promedio de PM1 a partir de la ocurrencia de incendios forestales, la concentración de PM en el rango de 3.0 a 0.95 µm y el histórico del promedio mensual de precipitación acumulada. La varianza de PM1 se explica en más del 75 % de los casos a partir de esas variables (R 2 = 0.759; p < 0.000). Se validó el modelo usando el indicador de error de sesgo promedio, el cual subestima los valores pronosticados.

Between June 2018 and April 2019, a sampling campaign was carried out to collect PM2.5, monitoring meteorological parameters and anthropogenic events in the Sartenejas Valley, Venezuela. We develop a logistic model for PM2.5 exceedances (≥ 12.5 µg m−3). Source appointment was done using elemental composition and morphology of PM by scanning electron microscopy coupled with energy dispersive spectroscopy (SEM–EDS). A proposal of an early warning system (EWS) for PM pollution episodes is presented. The logistic model has a holistic success rate of 94%, with forest fires and motor vehicle flows as significant variables. Source appointment analysis by occurrence of events showed that samples with higher concentrations of PM had carbon-rich particles and traces of K associated with biomass burning, as well as aluminosilicates and metallic elements associated with resuspension of soil dust by motor-vehicles. Quantitative source appointment analysis showed that soil dust, garbage burning/marine aerosols and wildfires are three majority sources of PM. An EWS for PM pollution episodes around the Sartenejas Valley is proposed considering the variables and elements mentioned.

No abstract