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A total of 188,859 meteorological-PM[Formula: see text] data validated before (2019) and during the COVID-19 pandemic (2020) were used. In order to predict PM[Formula: see text] 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 PM[Formula: see text] for San Juan de Miraflores (SJM) (PM[Formula: see text]-SJM: 78.7 [Formula: see text]g/m[Formula: see text]) and the lowest in Santiago de Surco (SS) (PM[Formula: see text]-SS: 40.2 [Formula: see text]g/m[Formula: see text]). 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 PM[Formula: see text] values in SJM and warm months increased it, favored by thermal inversion (TI). Dust resuspension, vehicular transport and stationary sources contributed more PM[Formula: see text] 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-LogPM[Formula: see text] (r = 0.5753); the RMSE-MLR (12.92) exceeded that found in the 3D models (RMSE [Formula: see text]) and the NSE-MLR criterion (0.3804) was acceptable. PM[Formula: see text] prediction was modeled using the algorithmic approach in any scenario to optimize urban management decisions in times of pandemic., (© 2022. The Author(s).)

Introduction: There have been no time-series studies of air pollution in Peru. Here we evaluate the effect of ambient PM 2.5 on emergency room (ER) visits in Lima., Methods: We estimated daily PM 2.5 levels at a 1 km 2 resolution during 2010-2016 using ground measurements, satellite data, and chemical transport model simulations. Population-weighted average daily PM 2.5 levels were calculated for each district in Lima (n = 40), and assigned to patients based on residence. ER visits for respiratory and circulatory diseases were gathered from nine large public hospitals. Poisson regression was used to estimate the rate ratio for daily ER visits with change in daily PM 2.5 , controlling for meteorology, time trends, and district., Results: For each interquartile range (IQR) increase in PM 2.5 , respiratory disease ER visits increased 4% (95% CI: 0-5%), stroke visits 10% (3-18%), and ischemic heart disease visits (adults, 18-64 years) 11% (-1, 24%). Districts with higher poverty showed significantly stronger associations of PM 2.5 and respiratory disease ER visits than districts with lower poverty. Effects were diminished 24-42% using Lima-wide instead of district-specific PM 2.5 levels., Conclusions: Short-term exposure to ambient PM 2.5 is associated with increases in ER visits in Lima for respiratory diseases and stroke, and among middle-aged adults, ischemic heart disease.