Modeling NO 2 air pollution variation during and after COVID-19-regulation using principal component analysis of satellite imagery.

Autor: Kovács KD; Université de Lorraine, Laboratoire LOTERR-EA7304, Île Du Saulcy, 57045, Metz, France. Electronic address: kamill-daniel.kovacs@univ-lorraine.fr., Haidu I; Université de Lorraine, Laboratoire LOTERR-EA7304, Île Du Saulcy, 57045, Metz, France.
Jazyk: angličtina
Zdroj: Environmental pollution (Barking, Essex : 1987) [Environ Pollut] 2024 Feb 01; Vol. 342, pp. 122973. Date of Electronic Publication: 2023 Nov 19.
DOI: 10.1016/j.envpol.2023.122973
Abstrakt: By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO 2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO 2 data. The models were used to predict actual near-surface NO 2 concentrations. Model-predicted NO 2 concentrations were validated with NO 2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO 2 was assessed. It was found that the weather component had substantial impact on NO 2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO 2 . Both models accurately estimated near-surface NO 2 concentrations. Modeled NO 2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO 2 concentrations were found to highly correlate with monitoring station NO 2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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