The Impact of COVID-19 Lockdowns on Particulate Matter Emissions in Lombardy and Italian Citizens' Consumption Habits
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Abstract
Italy has been one of the first nations in the world to be heavily affected by COVID-19. A wide range of containment measures has been adopted from February to December 2020 to mitigate the pandemic. In this regard, the present research sets out to evaluate two aspects: (i) the impact of lockdowns on the concentrations of particulate matter (PM) 10 and 2.5 in the Lombardy region, and (ii) how anti-COVID-19 restrictions influenced Italian citizens' consumption habits. To do this, the average daily concentrations of PM10 and PM2.5 during 2020 in all the provinces of Lombardy were compared with those of the previous years through Welch's t -test. The same procedure was adopted to estimate the change in Google relative search volumes of home delivery services and smart working on a national scale. Two mean values were considered statistically confident when t < 1.5, suspiciously non-confident when 1.5 ≤ t < 1.9, and non-confident when t ≥ 1.9. Seasonalities and trends were assessed both graphically and with Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski-Phillips-Schmidt-Shin tests. Finally, Pearson and Spearman correlations between changes in citizens' behavior and specific key events related to COVID-19 have been dealt with. The P -value threshold was indicatively set at 0.05. Microsoft Excel 2020 and Google Sheets were used as data analysis software. This paper showed: (i) the limited or insufficient effectiveness of lockdowns in reducing PM10 and PM2.5 concentrations in Lombardy, and (ii) a significant change in the consumption habits of Italian citizens, thus leading to both positive and negative results in terms of sustainability. Therefore, it is high time that both Italian and international environmental protection authorities thoroughly investigated the role of non-mobility-related sources of particulate emissions to impose effective rules on home delivery services. Moreover, further research is required for the understanding of anthropogenic, environmental, and atmospheric phenomena that influence the concentrations of PM10 and PM2.5.
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SciScore for 10.1101/2020.12.15.20248285: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Meteorological data was collected from the website “Il Meteo.it”7. 2.2 Statistical Analysis: This paper quantifies the impact of COVID-19 restrictions and containment measures on the web queries RSVs and average concentrations of PM10 and 2.5 through the percentage increases Δ% = (x1 − x0)/x0. 100 and their related t-test t = (x1 − x0)/σ. x1 − x0)/σsuggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the …SciScore for 10.1101/2020.12.15.20248285: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Meteorological data was collected from the website “Il Meteo.it”7. 2.2 Statistical Analysis: This paper quantifies the impact of COVID-19 restrictions and containment measures on the web queries RSVs and average concentrations of PM10 and 2.5 through the percentage increases Δ% = (x1 − x0)/x0. 100 and their related t-test t = (x1 − x0)/σ. x1 − x0)/σsuggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:4.1 Strengths and limitations: As far as the author knows, this is the first study that compares PM10 and PM2.5 concentrations during 2019 and 2020 in Lombardy including data from all the measuring stations of all its provinces also quantifying their meteorological differences. Furthermore, this study demonstrates a substantial reliability of Google Trends in estimating the demand for e-commerce and home delivery services of Italian web users as well as the ineffectiveness of lockdowns in the reduction of PM10 and PM2.5 concentrations in the most populated Italian region. However, some limitations must be considered: the search for statistical correlations cannot provide details on any possible causal nature of the phenomena investigated. The web interest of Google users is not certain to reflect the general interest of all Italian citizens. In some provinces, the number of measuring stations could be insufficient to represent in detail the overall trend of PM10 and PM2.5 concentrations. Finally, some weather differences may have biased the comparison between the particulate emissions during 2019 and 2020.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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