Valdiviezo Gonzáles, LorgioRomero Cabello, Edison AlessandroGarcía Ávila, Fausto FernandoCastañeda Olivera, Carlos AlbertoQuispe Eulogio, Carlos EnriqueLópez Gonzáles, Javier LinkolkHuamán De la Cruz, Alex RubénSánchez Ccoyllo, Odón RománCabello Torres, Rita JaquelinePonce Estela, Manuel Angel2023-01-172023-01-1720222045-2322http://dspace.ucuenca.edu.ec/handle/123456789/40757https://www.scopus.com/record/display.uri?eid=2-s2.0-85139286126&doi=10.1038%2fs41598-022-20904-2&origin=inward&txGid=56c31c9a62e9ac571f8666d52fe69680A total of 188,859 meteorological-PM10 data validated before (2019) and during the COVID-19 pandemic (2020) were used. In order to predict PM10 in two districts of South Lima in Peru, hourly, daily, monthly and seasonal variations of the data were analyzed. Principal Component Analysis (PCA) and linear/nonlinear modeling were applied. The results showed the highest annual average PM10 for San Juan de Miraflores (SJM) (PM10-SJM: 78.7 μ g/m3) and the lowest in Santiago de Surco (SS) (PM10-SS: 40.2 μ g/m3). The PCA showed the influence of relative humidity (RH)-atmospheric pressure (AP)-temperature (T)/dew point (DP)-wind speed (WS)-wind direction (WD) combinations. Cool months with higher humidity and atmospheric instability decreased PM10 values in SJM and warm months increased it, favored by thermal inversion (TI). Dust resuspension, vehicular transport and stationary sources contributed more PM10 at peak times in the morning and evening. The Multiple linear regression (MLR) showed the best correlation (r = 0.6166), followed by the three-dimensional model LogAP-LogWD-LogPM10 (r = 0.5753); the RMSE-MLR (12.92) exceeded that found in the 3D models (RMSE < 0.3) and the NSE-MLR criterion (0.3804) was acceptable. PM10 prediction was modeled using the algorithmic approach in any scenario to optimize urban management decisions in times of pandemic.es-ESStatistical modelingPM10COVID-19ARTÍCULO10.1038/s41598-022-20904-2