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