Characterization of background particulate matter concentrations using the combination of two clustering techniques in zones with heterogeneous emission sources

Abstract

The estimation of the background atmospheric concentration allows to assess local contributions and helping to the design of air quality improvement policies. Using clustering techniques and bivariate analysis, this study aims to characterize the background concentration of PM10 (particulate matter with an aerodynamic diameter less than or equal to 10 μm) and PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) in environments with heterogeneous emission sources. Background PM10 and PM2.5 pollution was characterized using Hidden Markov and Finite Mixture Models in four air quality monitoring stations, from 2011 to 2017. Average background concentrations in all stations were of 12.7 ± 2.2 μg m-3 for PM10 and 4.6 ± 0.4 μg m-3 for PM2.5. The contribution of background concentration to ambient pollution (both PM10 and PM2.5) was high (more than 40%) in all studied stations, being a 10% higher in background stations (Camping Temisas and Parque de San Juan) compared with stations influenced by an anthropogenic source (Castillo Romeral and San Agustín). Estimated background concentration showed significant differences among studied areas according to Kruskal-Wallis test (p < 0.001) and coefficients of divergence, which were greater than 0.2. PM10 and PM2.5 monthly profiles (concentration level) showed that the traffic urban station presented seasonality, probably due to the summer tourism, and daily profiles exhibited a differentiated bimodal distribution. The estimation of background concentrations in this study will allow to quantify local contributions from Saharan outbreaks and to study its possible effects on human health and marine biota.