Overview of current regional and local scale air quality modelling practices: Assessment and planning tools in the EU

Thunis, Philippe; Miranda, Ana Isabel; Baldasano, Jose J.M.; Blond, Nadège; Douros, John; Graff, A.; Janssen, Stijn; Juda-Rezler, Katarzyna; Karvosenoja, Niko; Maffeis, Giuseppe; Martilli, Alberto; Rasoloharimahefa, Michèle; Real, Elsa; Viaene, Peter; Volta, Marialuisa; White, L.

doi:10.1016/j.envsci.2016.03.013

Cited by 90 publications

(51 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…More details on the state of the art and current practice to assess the STATE can be found in the companion paper by Thunis et al (2016). Here the discussion will focus on source-receptor relations and SA.…”

Section: State: Concentration and Depositionmentioning

confidence: 98%

Air quality integrated assessment modelling in the context of EU policy: A way forward

Viaene

Belis

Blond

et al. 2016

Environmental Science & Policy

Self Cite

View full text Add to dashboard Cite

Section: State: Concentration and Depositionmentioning

confidence: 98%

Air quality integrated assessment modelling in the context of EU policy: A way forward

Viaene

Belis

Blond

et al. 2016

Environmental Science & Policy

Self Cite

View full text Add to dashboard Cite

“…The specific objectives for addressing data complexity are Air quality modelling is an area of strong research interest for many reasons. Within the European Union (EU), for instance, member states are required to design appropriate air quality plans for zones where the air quality does not comply with specified limit values which has led to a wide range of air quality modelling tools and techniques [9]. A thorough assessment of the effects of local and regional emission on air quality and human health and to identify methodologies and their limitations is proposed in [9].…”

Section: General: To Develop a General Framework For Training And Tesmentioning

confidence: 99%

A statistical downscaling framework for environmental mapping

et al. 2018

View full text Add to dashboard Cite

In recent years, knowledge extraction from data has become increasingly popular, with many numerical forecasting models, mainly falling into two major categorieschemical transport models (CTMs) and conventional statistical methods. However, due to data and model variability, data-driven knowledge extraction from highdimensional, multifaceted data in such applications require generalisations of global to regional or local conditions. Typically, generalisation is achieved via mapping global conditions to local ecosystems and human habitats which amounts to tracking and monitoring environmental dynamics in various geographical areas and their regional and global implications on human livelihood. Statistical downscaling techniques have been widely used to extract high-resolution information from regional-scale variables produced by CTMs in climate model. Conventional applications of these methods are predominantly dimensional reduction in nature, designed to reduce spatial dimension of gridded model outputs without loss of essential spatial information. Their downside is twofold-complete dependence on unlabelled design matrix and reliance on underlying distributional assumptions. We propose a novel statistical downscaling framework for dealing with data and model variability. Its power derives from training and testing multiple models on multiple samples, narrowing down global environmental phenomena to regional discordance through dimensional reduction and visualisation. Hourly ground-level ozone observations were obtained from various environmental stations maintained by the US Environmental Protection Agency, covering the summer period (June-August 2005). Regional patterns of ozone are related to local observations via repeated runs and performance assessment of multiple versions of empirical orthogonal functions or principal components and principal fitted com

show abstract

“…In particular, the 2008 European Air Quality Directive (AQD) 2008/50/EC [1] requires EU Member States to design appropriate air quality plans for zones where the air quality does not comply with the AQD limit values. In the last few decades, European countries implemented various modeling methods to assess the effects of local and regional emission abatement policy options on air quality and human health [2]. On the one side, they include scenario approaches, in which running a chemical-physical simulation model with and without a specific emission source allows for quantifying the impact on air quality levels [3,4].…”

Section: Introductionmentioning

confidence: 99%

Statistical Modeling of the Early-Stage Impact of a New Traffic Policy in Milan, Italy

Maranzano

Fassò

Pelagatti

et al. 2020

IJERPH

View full text Add to dashboard Cite

Most urban areas of the Po basin in the North of Italy are persistently affected by poor air quality and difficulty in disposing of airborne pollutants. In this context, the municipality of Milan started a multi-year progressive policy based on an extended limited traffic zone (Area B). Starting on 25 February 2019, the first phase partially restricted the circulation of some classes of highly polluting vehicles on the territory, in particular, Euro 0 petrol vehicles and Euro 0 to 3 diesel vehicles, excluding public transport. This is the early-stage of a long term policy that will restrict access to an increasing number of vehicles. The goal of this paper is to evaluate the early-stage impact of this policy on two specific vehicle-generated pollutants: total nitrogen oxides (NO x ) and nitrogen dioxide (NO 2 ), which are gathered by Lombardy Regional Agency for Environmental Protection (ARPA Lombardia). We use a statistical model for time series intervention analysis based on unobservable components. We use data from 2014 to 2018 for pre-policy model selection and the relatively short period up to September 2019 for early-stage policy assessment. We include weather conditions, socio-economic factors, and a counter-factual, given by the concentration of the same pollutant in other important neighbouring cities. Although the average concentrations reduced after the policy introduction, this paper argues that this could be due to other factors. Considering that the short time window may be not long enough for social adaptation to the new rules, our model does not provide statistical evidence of a positive policy effect for NO x and NO 2 . Instead, in one of the most central monitoring stations, a significant negative impact is found.

show abstract

Overview of current regional and local scale air quality modelling practices: Assessment and planning tools in the EU

Cited by 90 publications

References 35 publications

Air quality integrated assessment modelling in the context of EU policy: A way forward

Air quality integrated assessment modelling in the context of EU policy: A way forward

A statistical downscaling framework for environmental mapping

Statistical Modeling of the Early-Stage Impact of a New Traffic Policy in Milan, Italy

Contact Info

Product

Resources

About