Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic Acal González, Christian José Aguilera Del Pino, Ana María COVID-19 This research was funded by project PID2020-113961GB-I00 of the Spanish Ministry of Science and Innovation (also supported by the FEDER program), project FQM-307 of the Government of Andalusia (Spain) and the PhD grant (FPU18/01779) awarded to Christian Acal. The authors also thank the support of the University of Granada, Spain, under project for young researchers PPJIB2020-01. Faced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of NO2, PM10, PM2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the PM10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest. 2021-10-11T12:00:31Z 2021-10-11T12:00:31Z 2021-08-24 info:eu-repo/semantics/article Acal, C., Aguilera, A. M., Sarra, A., Evangelista, A., Di Battista, T., & Palermi, S. (2021). Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic. Stochastic Environmental Research and Risk Assessment, 1-19. [ https://doi.org/10.1007/s00477-021-02071-4] http://hdl.handle.net/10481/70784 10.1007/s00477-021-02071-4 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España Springer