Water-Soluble Anions in PM10 Samples Collected in the Metropolitan Area of Costa Rica: Temporal and Spatial Variations

• Published in: Atmosphere Vol. 12; no. 10; p. 1264
• Main Authors: Herrera-Murillo, Jorge, Soto-Murillo, Tomas, Rojas-Marín, José Félix, Beita-Guerrero, Victor Hugo, Hidalgo-Gutiérrez, María
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2021
• Subjects: Additives; Air; Air quality; Air sampling; Airborne particulates; Anions; Biogenic emissions; Biomass burning; Burning; Chromatography; Cost analysis; Costa Rica; Emissions; Flow control; Ions; Leaves; Mass flow; Metropolitan areas; Particle formation; Particulate matter; Pollutants; Principal components analysis; Samplers; Sampling; Seasonal variations; source apportionment; Spatial variations; Statistical analysis; time series; Urban areas; Variance analysis; Water chemistry; United States > US; Massachusetts; Costa Rica
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos12101264

The concentrations of water-soluble anions present in PM10 collected from ambient air in four sites of Costa Rica’s greater metropolitan area (GAM) were analyzed. Samples were collected over a 24-h period for three days during the 2011–2018 period, using high-volume air samplers with mass flow controller. The PM10 samples were collected using quartz fiber filters, which were analyzed using ion chromatography to identify organic and inorganic ions. The annual averages for PM10, F−, Cl−, and SO42− showed significant differences between the sampling sites, with Uruca (UR) consistently registering annual averages above the maximum concentration established by Costa Rica’s air quality regulations (30 µgm−3). The ions analyzed contributed 33%, 34%, 35%, and 37% of the PM10 mass for sampling sites UR, HA, AL, and CA, respectively. Using Spearman correlations and principal component analysis (PCA), the following contributions were identified: biomass burning, secondary particle formation processes from mobile and stationary emissions, and biogenic emissions. For each variable, seasonal patterns and trends were analyzed using time series with additive decomposition.