A 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 Mirafores (SJM) (PM10-SJM: 78.7 µg/m3) and the lowest in Santiago de Surco (SS) (PM10 -SS: 40.2 µg/m3). The PCA showed the infuence 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 m...