Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic

Abstract

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 $$\hbox {NO}_{{2}}$$ NO 2 , $$\hbox {PM}_{{10}}$$ PM 10 , $$\hbox {PM}_{2.5}$$ PM 2.5 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 $$\hbox {PM}_{{10}}$$ PM 10 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.

The lock-down effects of COVID-19 on the air pollution indices in Iran and its neighbors

“…The recent studies indicate that NO 2 , PM 10 , PM 2.5 , and benzene in the urban territory of Chieti-Pescara (Central Italy) is changed due to the lock-down with an analysis of variance for functional data (FANOVA) and it is based on the multivariate functional principal component analysis. (Acal 2021 ).…”

“…data (FANOVA) and it is based on the multivariate functional principal component analysis. (Acal 2021).…”

The Lock-Down Effects of COVID-19 on the Air Pollution Indices in Iran and Its Neighbors

“…The recent studies indicate that NO2, PM10, PM2.5, and benzene in the urban territory of Chieti-Pescara (Central Italy) is changed due to the lock-down with an analysis of variance for functional data (FANOVA) and it is based on the multivariate functional principal component analysis. [59] The limitation of this research is that the air pollution indices are not adjusted due to the metrological conditions such as temperature, wind, rain, etc. We also show that Precipitation as an important weather condition is not the same among countries and time [60].…”

Basis expansion approaches for functional analysis of variance with repeated measures

“…Additionally, several authors have also focused their efforts on providing some tools to deal with the multivariate ANOVA problem for functional data (Górecki and Smaga 2017 ; Acal et al. 2021 ).…”

“…These statistics were extended by considering the basis expansion of the sample curves with the aim of detecting changes in air pollution during the COVID-19 pandemic in Acal et al. ( 2021 ). In the more general context of testing homogeneity of paired functional data, a Cramér-von-Mises type test statistic is introduced in Ditzhaus and Gaigall ( 2021 ) with application to stock market returns.…”