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

Viaene, Peter; Belis, Claudio A.; Blond, Nadège; Bouland, Catherine; Juda-Rezler, Katarzyna; Karvosenoja, Niko; Martilli, Alberto; Miranda, Ana Isabel; Pisoni, Enrico; Volta, Marialuisa

doi:10.1016/j.envsci.2016.05.024

Cited by 24 publications

(15 citation statements)

References 38 publications

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“…There are many methods for air quality assessment, mainly including dynamic models [ 15 – 17 ], fuzzy assessments [ 18 – 20 ], integrated assessment methodologies [ 21 , 22 ], principal component analysis [ 23 – 25 ] and grey system theory [ 26 , 27 ]. Thunis & Clappier [ 16 ] proposed common indicators and diagrams which have since proven useful for determining the magnitude of locally produced emissions effects.…”

Section: Introductionmentioning

confidence: 99%

Assessment of air quality using a cloud model method

Xu¹,

Xu²

2018

R. Soc. open sci.

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To effectively control air pollution, it is necessary to obtain a preliminary assessment of air quality. The purpose of this study was to introduce a cloud model method in air pollution assessment. First, the standard cloud models of air pollution indicators were obtained, and the calculating process of numerical characteristics employed by the standard cloud model was explained. Second, the levels of air pollution indicators were presented based on the qualitative and quantitative analysis of cloud models, which realized the uncertainty conversion between qualitative concepts and their corresponding quantitative values, as well as taking the fuzziness and randomness into account. Air quality assessment results including SO2, NO2, CO, O3, PM10 and PM2.5 were analysed. Third, the cloud model adopted in the assessment process of air quality was validated by grey relational analysis, and the results confirmed the validity of cloud model assessment. Fourth, the air pollution level of the air quality index (AQI) was determined, and the fuzziness and randomness of the assessment results were thoroughly analysed by taking entropy and hyper entropy into consideration. Fifth, seasonal variations in different air pollution indicators were analysed to proffer a series of recommendations for government policy decision-makers and travellers. The cloud model provided a new method for air quality assessment.

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Section: Introductionmentioning

confidence: 99%

Assessment of air quality using a cloud model method

Xu¹,

Xu²

2018

R. Soc. open sci.

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“…The methodologies used to identify effective plans can be classified in two main categories [4]; [8]: (1) Scenario analysis [9][10][11] includes the source-apportionment and the multi-criteria approaches (e.g., ELECTRE [12]; MCDM [13]). Using this approach, a set of abatement measures is first defined by a panel of experts and stakeholders, and its impact is then assessed through the application of a chemical and transport model, with the possibility of modifying the proposed set of measures if they are deemed unsatisfactory under certain viewpoints.…”

Section: Air Quality Plans: the European State Of The Artmentioning

confidence: 99%

“…The following examples, also collected in the APPRAISAL project database [8], represent applications of the mentioned approaches at different levels of complexity.…”

Section: Air Quality Plans: the European State Of The Artmentioning

confidence: 99%

Assessing the Economic and Environmental Sustainability of a Regional Air Quality Plan

Carnevale

Ferrari²,

Guariso

et al. 2018

Sustainability

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Air quality plans must be demonstrated to be economically sustainable and environmentally effective. This paper presents a full cost–benefit and environmental analysis of a large regional air quality plan involving several different actions covering a large spectrum of fields, from domestic heating to passenger and freight transport, from electricity generation to agriculture. The impact of each action is analyzed looking at the possible energy savings, greenhouse gases (GHG) emission reductions, the improvement in air quality, and the consequent decrease in external costs, namely the reduced impact on population health. The analysis is performed by applying a flexible and fast computer tool (RIAT+) that allows for a rapid simulation of different pollutant emission scenario, to assess different air quality indices (AQIs) over a regional scale domain. The results show that, in most cases, the economic savings exceed the implementation costs and thus that these actions can be introduced in air quality plans for the domain under study. The reduced health and climate costs, though relevant in absolute terms, are, in general, only a fraction of the economic benefits of energy savings. This is not true for the measures acting on improvements in electricity generation, since a reduction in power plant emissions (generally with high stacks, far from populated areas) does not significantly impact the air quality inside the region. A shift in energy production to renewable sources can instead provide noticeable effects on GHG emissions. This research raises some interesting and general questions about the adequacy of the methodologies applied to attribute costs (and benefits) to actions, improving a variety of sectors that are different from the one in which the measures are applied here.

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“…They can therefore be used to evaluate, for example, policy scenarios and their consequent emission reductions in terms of pollutant concentrations changes and environmental, health and economic impacts. IAMs applied to AQ can provide (i) an analysis of exceedances (reasons for air quality non-compliance), (ii) details of possible measures and the time required to attain the reduction objectives, (iii) cost-benefit/cost-effectiveness analysis, (iv) information on the impact on human health and the environment and (v) information on related uncertainties and robustness (Viaene et al 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-level policies for air quality: implications of national and sub-national emission reductions on population exposure

Peduzzi

Pisoni

Clappier

et al. 2018

Air Qual Atmos Health

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Poor air quality and related health impacts are still an issue in many cities and regions worldwide. Integrated assessment models (IAMs) can support the design of measures to reduce the emissions of precursors affecting air pollution. In this study, we apply the SHERPA (screening for high emission reduction potentials for air quality) model to compare spatial and sectoral emission reductions, given country-scale emission targets. Different approaches are tested: (a) country ”uniform” emission reductions, (b) emission reductions targeting urban areas, (c) emission reductions targeting preferential sectors. As a case study, we apply the approaches to the implementation of the National Emission Ceiling Directive. Results are evaluated in terms of the reduction in average population exposure to PM2.5 overall in a country and in its main cities. Results indicate that the reduction of population exposure to PM2.5 highly depends on the way emission reductions are implemented. This work also shows the usefulness of the SHERPA model to support national authorities implementing national emission reduction targets while, at the same time, addressing their local air quality issues.Electronic supplementary materialThe online version of this article (10.1007/s11869-018-0613-1) contains supplementary material, which is available to authorized users.

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

Cited by 24 publications

References 38 publications

Assessment of air quality using a cloud model method

Assessment of air quality using a cloud model method

Assessing the Economic and Environmental Sustainability of a Regional Air Quality Plan

Multi-level policies for air quality: implications of national and sub-national emission reductions on population exposure

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