A Comprehensive Assessment of PM2.5 and PM10 Pollution in Cusco, Peru: Spatiotemporal Analysis and Development of the First Predictive Model (2017–2020)

This study presents the first comprehensive assessment of air pollution by PM2.5 and PM10 in the city of Cusco, aiming to determine atmospheric pollution levels, characterize air quality, and develop predictive models. The research, conducted during 2017–2020, systematically evaluated particulate matter (PM) contamination using a high-volume sampler (HiVol ECOTEC 3000) installed at 18 monitoring sites distributed across five urban districts. Multiple linear regression (MLR) models were developed and evaluated, incorporating meteorological, seasonal, and temporal variables under two approaches: direct linear (Model 1) and logarithmic transformation (Model 2). The model evaluation employed R², RMSE, MAE, MAPE, IOA, and CV statistical indicators. The results revealed concentrations significantly exceeding WHO guideline values, with PM2.5 ranging between 41.10 ± 3.2 μg/m3 (2020) and 82.01 ± 5.1 μg/m3 (2018), while PM10 values ranged from 45.07 ± 2.8 μg/m3 (2020) to 72.35 ± 4.3 μg/m3 (2017). A notable reduction was observed during 2020, attributable to COVID-19 pandemic restrictions. The Air Quality Index (AQI) indicated predominantly “Unhealthy” and “Very Unhealthy” levels during 2017–2018, improving to “Unhealthy for Sensitive Groups” in 2020. MLR models achieved maximum efficiency using logarithmic transformation, obtaining R² = 0.98 (p < 0.001) for PM2.5 in the 2020 rainy season and R² = 0.44 (p < 0.001) for PM10 in the 2018 annual model. These findings demonstrate the existence of nonlinear relationships between pollutants and predictor variables in Cusco’s atmospheric basin.