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Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú.
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.
  • Cabello-Torres RJ; Universidad César Vallejo, Escuela de Ingeniería Ambiental, Lima, Peru. rcabello@ucv.edu.pe.
  • Estela MAP; Dirección General de Salud Ambiental, Lima, Peru.
  • Sánchez-Ccoyllo O; Universidad Nacional Tecnológica de Lima Sur, Lima, Peru.
  • Romero-Cabello EA; Universidad Nacional Agraria La Molina, Escuela de Ingeniería Ambiental, Lima, Peru.
  • Ávila FFG; Universidad de Cuenca, Facultad de Ciencias Químicas, Grupo RISKEN, Cuenca, Ecuador.
  • Castañeda-Olivera CA; Universidad César Vallejo, Escuela de Ingeniería Ambiental, Lima, Peru.
  • Valdiviezo-Gonzales L; Universidad Tecnológica del Perú, Facultad de Ingeniería Mecánica e Industrial, Lima, Peru.
  • Eulogio CEQ; Facultad de Ciencias de la Salud, Universidad Peruana Los Andes, Huancayo, Peru.
  • De La Cruz ARH; E.P. de Ingenieria Ambiental, Universidad Nacional Intercultural de la Selva Central Juan Santos Atahualpa, La Merced, Peru.
  • López-Gonzales JL; Facultad de Ingeniería y Arquitectura, Universidad Peruana Unión, Lima, Peru.
Sci Rep ; 12(1): 16737, 2022 10 06.
Article in English | MEDLINE | ID: covidwho-2151072
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Air Pollutants / COVID-19 Type of study: Observational study / Prognostic study Topics: Variants Limits: Humans Country/Region as subject: South America / Peru Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-20904-2

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Air Pollutants / COVID-19 Type of study: Observational study / Prognostic study Topics: Variants Limits: Humans Country/Region as subject: South America / Peru Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-20904-2