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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.)

Vector control strategies recommended by the World Health Organization are threatened by resistance of Anopheles mosquitoes to insecticides. Information on the distribution of resistant genotypes of malaria vectors is increasingly needed to address the problem. Ten years of published and unpublished data on malaria vector susceptibility/resistance and resistance genes have been collected across Togo. Relationships between the spatial distribution of resistance status and environmental, socio-economic, and landscape features were tested using randomization tests, and calculating Spearman rank and Pearson correlation coefficients between mosquito mortality and different gridded values. Anopheles gambiae sensu lato was resistant to DDT, pyrethroids, and the majority of carbamates and organophosphates. Three sibling species were found (i.e., An. gambiae, Anopheles coluzzii, and Anopheles arabiensis) with four resistance genes, including kdr (L1014F, L1014S, and N1575Y) and ace1 (G119S). The most frequent resistance gene was L1014F. Overall, no association was found between the susceptibility/resistance status and environmental features, suggesting that evolution of resistance may be most closely related to extreme selection from local insecticide use. Nevertheless, further research is necessary for firm conclusions about this lack of association, and the potential role of landscape characteristics such as presence of crops and percentage of tree cover., (© The Author(s) 2020. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)