Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú

Rita Cabello Torres; Manuel Angel Ponce Estela; Odón R. Sánchez-Ccoyllo; Edison Alessandro Romero-Cabello; Fausto Fernando García Ávila; Carlos Alberto Castañeda Olivera; Lorgio Valdiviezo-Gonzáles; Carlos Quispe Eulogio; Alex Rubén Huamán De La Cruz; Javier Linkolk López‐Gonzales

doi:10.1038/s41598-022-20904-2

Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú

Author(s)

Rita Cabello Torres

Manuel Angel Ponce Estela

Odón R. Sánchez-Ccoyllo

Edison Alessandro Romero-Cabello

Fausto Fernando García Ávila

Carlos Alberto Castañeda Olivera

Lorgio Valdiviezo-Gonzáles

Carlos Quispe Eulogio

Alex Rubén Huamán De La Cruz

Javier Linkolk López‐Gonzales

Date Issued

6 de octubre de 2022

Type

Article

Volume

12

Issue

1

Start Page

16737

End Page

16737

DOI

10.1038/s41598-022-20904-2

Abstract

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.