The present manuscript attempts to contribute to the Citizen science philosophy, based on the collaboration between public institutions, research and active citizenship, by offering advanced statistical tools for air quality monitoring and for the analysis of environmental protection actions. The main focus will be on the temporal modelling, hence involving the use of statistical methodologies for time series analysis, of air quality in Lombardy, the economic-financial heart of northern Italy, which has suffered from high pollution for decades and enjoys poor air quality despite the efforts made by the institutions. In this context we will introduce and discuss new tools having the aim of estimate the impact on the air quality we breath of extraordinary events and unexpected shocks. In particular, we will consider both artificial events, such as the introduction of new restricted traffic zones in urban areas, and natural, such as the forced closure of all productive activities and restrictions on individual mobility following the events of the COVID-19 pandemic in 2020. Overall, the work has various objectives: on the one hand, it intends to offer a technicalscientific evaluation of the above events in order to validate environmental policy choices in the region and to open a discussion on public policies based on scientific evidence and datadriven approaches; on the other hand, it attempts to import from other realities, such as the financial world, statistical techniques useful for the analysis of data on air quality, opening up new research fronts; eventually, it wants to make data on environmental conditions in Lombardy region available to international users on a large scale through the development of a specific software for downloading, visualising and managing this information. Some of the new methodological proposals discussed in the paper are: the development of an algorithm, presented as a pipeline chain of distinct statistical methodologies, covering all the steps of the impact analysis: from the selection of key environmental factors to explain pollutant concentrations (model selection) to the estimation of the effects; the application of methodologies born in the world of financial statistics to air quality data and able to identify level shift in multiple time series due to unexpected events; the application of filtering techniques to multiple time series in order to check their properties of stationarity and cointegration in the context of highly-noisy and non-Gaussiantime series, such as pollution concentrations data; the introduction of a new statitical software written in R language having the scope of download, visualization and management of large databases provided by the Lombardy regional agency for environmental protection (ARPA Lombardia).
Il presente manoscritto cerca di contribuire alla filosofia della Citizen science, basata sulla collaborazione tra istituzioni pubbliche, ricerca e cittadinanza attiva, offrendo strumenti statistici avanzati per il monitoraggio della qualità dell'aria e per l'analisi delle azioni di protezione ambientale. Il focus principale sarà sulla modellazione temporale, che prevede quindi l'utilizzo di metodologie statistiche per l'analisi delle serie temporali, della qualità dell'aria in Lombardia, cuore economico-finanziario del Nord Italia, che da decenni soffre di un elevato inquinamento e gode di una scarsa qualità dell'aria nonostante gli sforzi compiuti dalle istituzioni. In questo contesto introdurremo e discuteremo nuovi strumenti con l'obiettivo di stimare l'impatto sulla qualità dell'aria che respiriamo di eventi straordinari e shock inaspettati. In particolare, prenderemo in considerazione sia eventi artificiali, come l'introduzione di nuove zone a traffico limitato nelle aree urbane, sia naturali, come la chiusura forzata di tutte le attività produttive e le restrizioni alla mobilità individuale a seguito degli eventi della pandemia COVID-19 nel 2020. Nel complesso, il lavoro ha diversi obiettivi: da un lato, intende offrire una valutazione tecnico-scientifica dei suddetti eventi al fine di convalidare le scelte di politica ambientale della regione e di aprire un dibattito sulle politiche pubbliche basate su evidenze scientifiche e su approcci datadriven; dall'altro, cerca di importare da altre realtà, come il mondo finanziario, tecniche statistiche utili all'analisi dei dati sulla qualità dell'aria, aprendo nuovi fronti di ricerca; infine, vuole mettere a disposizione degli utenti internazionali su larga scala i dati sulle condizioni ambientali della regione Lombardia attraverso lo sviluppo di un software specifico per il download, la visualizzazione e la gestione di queste informazioni. Alcune delle nuove proposte metodologiche discusse nel documento sono: lo sviluppo di un algoritmo, presentato come una pipeline chain di metodologie statistiche distinte, che copre tutte le fasi dell'analisi d'impatto: dalla selezione dei fattori ambientali chiave per spiegare le concentrazioni di inquinanti (selezione del modello) alla stima degli effetti; l'applicazione di metodologie nate nel mondo della statistica finanziaria ai dati sulla qualità dell'aria e in grado di identificare lo spostamento di livello in più serie temporali dovuto ad eventi imprevisti; l'applicazione di tecniche di filtraggio a più serie temporali per verificarne le proprietà di stazionarietà e cointegrazione nel contesto di serie temporali altamente rumorose e non gaussiane, come i dati sulle concentrazioni di inquinanti; l'introduzione di un nuovo software statico scritto in linguaggio R che ha lo scopo di scaricare, visualizzare e gestire grandi banche dati fornite dall'Agenzia regionale lombarda per la protezione dell'ambiente (ARPA Lombardia).
(2021). Essays on air quality statistics: time series methods for evaluating environmental protection policies in Lombardy, Italy. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2021).
Essays on air quality statistics: time series methods for evaluating environmental protection policies in Lombardy, Italy
MARANZANO, PAOLO
2021
Abstract
The present manuscript attempts to contribute to the Citizen science philosophy, based on the collaboration between public institutions, research and active citizenship, by offering advanced statistical tools for air quality monitoring and for the analysis of environmental protection actions. The main focus will be on the temporal modelling, hence involving the use of statistical methodologies for time series analysis, of air quality in Lombardy, the economic-financial heart of northern Italy, which has suffered from high pollution for decades and enjoys poor air quality despite the efforts made by the institutions. In this context we will introduce and discuss new tools having the aim of estimate the impact on the air quality we breath of extraordinary events and unexpected shocks. In particular, we will consider both artificial events, such as the introduction of new restricted traffic zones in urban areas, and natural, such as the forced closure of all productive activities and restrictions on individual mobility following the events of the COVID-19 pandemic in 2020. Overall, the work has various objectives: on the one hand, it intends to offer a technicalscientific evaluation of the above events in order to validate environmental policy choices in the region and to open a discussion on public policies based on scientific evidence and datadriven approaches; on the other hand, it attempts to import from other realities, such as the financial world, statistical techniques useful for the analysis of data on air quality, opening up new research fronts; eventually, it wants to make data on environmental conditions in Lombardy region available to international users on a large scale through the development of a specific software for downloading, visualising and managing this information. Some of the new methodological proposals discussed in the paper are: the development of an algorithm, presented as a pipeline chain of distinct statistical methodologies, covering all the steps of the impact analysis: from the selection of key environmental factors to explain pollutant concentrations (model selection) to the estimation of the effects; the application of methodologies born in the world of financial statistics to air quality data and able to identify level shift in multiple time series due to unexpected events; the application of filtering techniques to multiple time series in order to check their properties of stationarity and cointegration in the context of highly-noisy and non-Gaussiantime series, such as pollution concentrations data; the introduction of a new statitical software written in R language having the scope of download, visualization and management of large databases provided by the Lombardy regional agency for environmental protection (ARPA Lombardia).File | Dimensione | Formato | |
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phd_unimib_800603.pdf
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Descrizione: PhD_Thesis_PaoloMaranzano_revised
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Doctoral thesis
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