This study presents the first comprehensive assessment of air pollution by ${\mathrm{PM}}_{2.5}$ and ${\mathrm{PM}}_{10}$ 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 ${\mathrm{PM}}_{2.5}$ ranging between 41.10 ± 3.2 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$ (2020) and 82.01 ± 5.1 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$ (2018), while ${\mathrm{PM}}_{10}$ values ranged from 45.07 ± 2.8 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$ (2020) to 72.35 ± 4.3 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$ (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 ${\mathrm{PM}}_{2.5}$ in the 2020 rainy season and R² = 0.44 (p < 0.001) for ${\mathrm{PM}}_{10}$ in the 2018 annual model. These findings demonstrate the existence of nonlinear relationships between pollutants and predictor variables in Cusco’s atmospheric basin.

En este artículo científico, se llevó a cabo una evaluación exhaustiva de la contaminación del aire por material particulado PM2.5 en diferentes áreas de monitoreo de la ciudad del Cusco durante los años 2017 y 2018. El objetivo central de la investigación fue analizar concentración de PM2.5 en varios puntos de la ciudad, utilizando un enfoque de muestreo predeterminado. Se recolectaron múltiples mediciones en cada punto, realizadas en intervalos de 24 horas, y obteniendo los valores de concentración mediante gravimetría y posteriormente se calculó el promedio de estas mediciones. Este proceso de recolección y análisis de datos siguió las pautas normativas establecidas por la Agencia de Protección Ambiental de Estados Unidos (EPA). Los resultados obtenidos revelaron un panorama preocupante de la contaminación del aire en la ciudad del Cusco. Más del 84% de los sitios monitoreados presentaron una calificación mala en los Índices de Calidad Ambiental (INCA) establecidos por el Ministerio del Ambiente del Gobierno Peruano. Estos hallazgos indican que la población de Cusco está expuesta a niveles perjudiciales de PM2.5. Un punto destacado de la investigación es el distrito de San Jerónimo en Cusco, donde se registró una concentración promedio de 125 ug/m3 de PM2.5. Esto coloca esta área en el umbral de cuidado, representado por el color rojo en los INCA. Estos niveles elevados de PM2.5 en San Jerónimo se correlacionan con el riesgo de desarrollar enfermedades pulmonares crónicas y cardiovasculares, concluyendo que según los INCA el aire de la ciudad del Cusco es mala.

The study utilizes data corresponding to the average monthly concentration of particulate matter 2.5 (P.M. 2.5), relative humidity, and precipitation during the period 2016-2018 collected by the High Vol station of the Energy and Atmosphere Center, and the meteorological center SENAHMI (Kayra-UNSAAC). In order to estimate the statistical relationship between two quantitative variables, the Pearson and Spearman correlation coefficients are used, which were obtained using the Python programming language. The obtained t-value is compared with the Student's t-distribution table for a certain level of significance $(α= 0.05)$. Positive and negative correlation values are established based on the obtained coefficients, and the interpretation of the Pearson and Spearman correlation values is used to determine if the data is related or not.

Wildfires represent a growing threat to air quality in fire-prone regions; however, the spatiotemporal dynamics of pollutant dispersion to distant localities remain inadequately characterized. This study quantifies particulate matter concentrations (PM1.0, PM2.5, PM10) in localities distant from the February 2024 Valparaíso wildfire, evaluating their spatiotemporal variation and association with fire intensity and meteorological parameters using a network of low-cost sensors. Four PurpleAir sensors distributed along the Valparaíso-Santiago corridor (70-120 km) were integrated with meteorological stations and satellite-derived Fire Radiative Power (FRP) data. Analysis of variance revealed significant differences in PM concentrations between pre-fire, during-fire, and post-fire phases, with the proximal station (S1) showing the strongest response. Pearson correlations with False Discovery Rate adjustment demonstrated immediate associations between FRP and PM at S1 (r = 0.71-0.72, p < 0.05), while distal stations (S2-S4) exhibited significant correlations only after applying 1-2 day temporal lags (r = 0.44-0.49, p < 0.05), providing quantitative evidence of regional-scale pollutant transport. These findings establish an empirical foundation for developing early warning systems in fire-prone regions and underscore the necessity of considering temporal lags in air quality management strategies during wildfire events.