Author:
Cabello-Torres Rita Jaqueline,Estela Manuel Angel Ponce,Sánchez-Ccoyllo Odón,Romero-Cabello Edison Alessandro,Ávila Fausto Fernando García,Castañeda-Olivera Carlos Alberto,Valdiviezo-Gonzales Lorgio,Eulogio Carlos Enrique Quispe,De La Cruz Alex Rubén Huamán,López-Gonzales Javier Linkolk
Abstract
AbstractA total of 188,859 meteorological-PM$$_{10}$$
10
data validated before (2019) and during the COVID-19 pandemic (2020) were used. In order to predict PM$$_{10}$$
10
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$$_{10}$$
10
for San Juan de Miraflores (SJM) (PM$$_{10}$$
10
-SJM: 78.7 $$\upmu$$
μ
g/m$$^{3}$$
3
) and the lowest in Santiago de Surco (SS) (PM$$_{10}$$
10
-SS: 40.2 $$\upmu$$
μ
g/m$$^{3}$$
3
). 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$$_{10}$$
10
values in SJM and warm months increased it, favored by thermal inversion (TI). Dust resuspension, vehicular transport and stationary sources contributed more PM$$_{10}$$
10
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$$_{10}$$
10
(r = 0.5753); the RMSE-MLR (12.92) exceeded that found in the 3D models (RMSE $$<0.3$$
<
0.3
) and the NSE-MLR criterion (0.3804) was acceptable. PM$$_{10}$$
10
prediction was modeled using the algorithmic approach in any scenario to optimize urban management decisions in times of pandemic.
Publisher
Springer Science and Business Media LLC
Cited by
13 articles.
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