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Air Quality Integrated Assessment A European Perspective

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Nguyễn Gia Hào

Academic year: 2023

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Finally, the contribution from the energy sector is somehow mixed: most pollutants have decreased (eg NOx by more than 70%). is shown in Fig.1.6.

Fig. 1.1 Distributions of the 36-th highest PM10 daily value in EU MS (source EEA 2015)
Fig. 1.1 Distributions of the 36-th highest PM10 daily value in EU MS (source EEA 2015)

Introduction

The DPSIR Framework Concept

This should translate into the ability to evaluate the pros and cons of increasing the level of detail of the description of each block in a given IAM implementation, and thus compare the potential improvement with the corresponding effort. The ultimate idea is to be able to classify existing European plans and projects, in order not to provide an evaluation value of the plans themselves, but to indicate possible "directions" for improvement, for each building block of each plan.

A General Overview of the IAM Framework

PRESS (emissions): this block describes "discharge of pollutants into the atmosphere from stationary sources such as chimneys, and from surface areas of commercial or industrial facilities and mobile sources, for example motor vehicles, locomotives and aircraft." LEADS, and is calculated as a function of the activity levels and the amount of emitted pollution per activity unit (emission factor). ANSWER: this block describes "attempts to prevent, compensate, improve or adapt to changes in the state of the environment".

Fig. 2.1 The general DPSIR scheme (source http://www.eea.europa.eu/)
Fig. 2.1 The general DPSIR scheme (source http://www.eea.europa.eu/)

A Detailed Analysis of the IAM Framework Modules

Drivers

Activities based on city-level traffic counts or other estimates (bottom-up), and assigning vehicle and technology categories based on national averages (top-down). Projections may be based on city-level residential combustion for each type of fuel/heating (bottom-up).

Pressures

Emission factors and activity data must correspond to the specific activities in the investigated area. To obtain this SEI, the spatial information about the distribution of the sources (point, line and area) must be projected onto the grid (usually a matrix of square cells).

State

There is also a semi-quantitative method that can be used to estimate uncertainties, which consists of assessing the quality of the data. Concentrations used as model boundary conditions can be extrapolated from measurements or derived from a larger-scale model.

Impact

For example: the number of hospital emergency visits related to increased ozone levels for a city or region. As for IMPACT and especially those on human health, the scale is closely related to the level of uncertainties.

Responses

They can be related to a macro sector or a reduction of pollution levels (aggregate approach), or to a single technology that acts on one or more pollutants (detailed approach). In this context, the costs of the emission reduction measures can be assessed as a result of the procedure (although in many cases they are not taken into account). Such measures can be related to people's behavioral changes (e.g. using bicycles instead of cars for personal mobility, lowering the temperature in buildings) or to technologies that reduce fuel consumption (using high-efficiency boilers or building heat-insulating coats, which reduce the overall energy demand).

Introduction

While AQP, which represents 58% of the database information coming from 10 MS, is representative of current practices in the decision-making process, RP (31% of the Systems Research Institute of the Polish Academy of Sciences (IBS- PAN), Warsaw University of Technology, Warsaw, Poland The current status (September 2015) of the databases is presented in Fig. 3.1 where the contributions per country are presented.

Fig. 3.1 Screenshot of the query to the online APPRAISAL database relative to the contributions in terms of countries
Fig. 3.1 Screenshot of the query to the online APPRAISAL database relative to the contributions in terms of countries

Actual Use of IA Components

Drivers and Pressures

According to the database, there is no relationship between the category disaggregation level and the spatial scale of the study. A top-down approach alone is used in few cases (8 %), while bottom-up approaches alone represent about 22 % of the cases. Urban, regional and street-level studies represent more than 80 % of the studies using a bottom-up approach.

State

However, with increasing computational power, their importance could increase in the future, as they could gradually take over the role of the current generation of empirical or Gaussian models for local and street level modelling. Regarding the Design of Experiment needed to identify this S/R, most approaches adopt the OaT (Once at a Time) approach (11 studies), where one emission is varied at a time and the variation in concentration or securities in one place. Only in the case of more complex research projects does the number of scenarios considered in the Design of Experiment exceed 50 (Fig. 3.5).

Fig. 3.3 Model types as used in AQP (blue) and RP (red)
Fig. 3.3 Model types as used in AQP (blue) and RP (red)

Impact

Other pollutants such as heavy metals (arsenic, nickel, cadmium and lead) are mainly considered in RPs (Fig.3.6and3.7). The exposure indicators, for both AQPs and RPs, were estimated based on intake fraction (emissions), air quality monitored data and air quality modeled data (Fig.3.8and3.9). In addition, exposure indicators based on individual exposure data were also used in the scope of one research project.

Fig. 3.6 Air pollutants assessed in HIA for AQPs
Fig. 3.6 Air pollutants assessed in HIA for AQPs

AQP (10 answers)

The temporal resolution used for the concentration data differs between the two types of activities: 5 of the assessed CPAs use daily temporal resolution, 2 hourly and 2 annual. In the case where monitored concentration levels were used for exposure assessment, 2 studies processed data recorded at traffic station sites, 5 studies used data from urban background stations, 4 from suburban background areas and only one from rural background stations.

RP (12 answers)

Responses

All articles stored in the database implemented modeling systems to determine mitigation measures and planning (Fig. 3.12). In the scenario analysis approach, source apportionment can be used to identify the main sources of emissions that contribute to air pollution concentrations. In the optimization approach, emission reduction measures are selected by an optimization algorithm that evaluates their impact on air quality, health exposure and implementation costs.

Fig. 3.11 IAM approaches within the DPSIR scheme: scenario analysis (left) and optimization approach (right)
Fig. 3.11 IAM approaches within the DPSIR scheme: scenario analysis (left) and optimization approach (right)

Scale and Resolution Issues

The reported priorities are focused on compliance performance and population exposure followed by emission reduction costs (internal costs) and costs mainly related to the negative impact of air pollution on human health (external costs) (Fig.3.16). Type of emission reduction measures: the number of non-technical and technical measures considered is very similar (41 and 39 % respectively).

Sensitivity and Uncertainty

Some questionnaires did not provide an answer on the methodology used (local or global), especially in the case of AQPs. However, local uncertainty analysis methods (sensitivity methods, OaT) are also significantly represented in the answers, especially in the case of RPs (Fig. 3.21). As expected, modeling was not one of the priority aspects examined in the case of AQPs; however, it was considered in a significant number of RPs.

Fig. 3.18 Overview of evaluation methodologies used for the assessment of AQPs and RPs
Fig. 3.18 Overview of evaluation methodologies used for the assessment of AQPs and RPs

Classifying the AQ Plans in Europe

  • AQP for Athens
  • AQP for Emilia Romagna
  • AQP for the Warsaw Agglomeration

For the complexity of the Impact block, details in spatial and temporal resolution for exposure and population data are important. The impact of meteorological patterns on PM10 concentrations was analysed, particularly in relation to the long-range transport of PM10 from other areas (e.g. the Sahara Desert). Ammonia (mainly released by agriculture) is an important particulate precursor in the Po Valley.

Table 3.1 Levels of complexity distinguished for the different DPSIR blocks DPSIR block Level Description
Table 3.1 Levels of complexity distinguished for the different DPSIR blocks DPSIR block Level Description

Introduction

Obligations arising from other EU directives (e.g. the National Emission Ceilings Directive) and targeting more specific sectors (e.g. transport, industry, energy, agriculture) should also be considered when designing and assessing local and regional air quality plans (Syri et al To deal with these different elements, over the last decade Member States have developed and applied a wide variety of modeling approaches to assess the impacts of local and regional emission reduction policy options on air quality and human health (e.g.

Available Tools

In RIAT (Carnevale et al.2012), the main objective is to calculate the most effective mix of local policies required to reduce secondary pollution exposure, in accordance with air quality regulations, while taking into account characteristics of the area under consideration. The Luxembourg Energy Air Quality Model (LEAQ) (Zachary et al. 2011) integrated assessment tool focuses on projected energy policy and related air quality at the urban and small nation scale. The UKIAM model (Oxley et al. 2003) was developed to examine the achievement of UK emission ceilings, while meeting other environmental objectives, including urban air quality and human health, as well as natural ecosystems.

Areas for Future Research of DPSIR Blocks

  • Drivers (Activities)
  • Pressures (Emissions)
  • State (Concentration Levels)
  • Impact (Human Health)
  • Responses (Methodologies to Design Measures)

This means that new land use policies (location of activities) must be considered as part of the IAM problem. In the IAM database collected by APPRAISAL, 70% of respondents identified emission values ​​as the main weakness of their modeling approach. Relevant information on preferred practice for the preparation of such local emissions records can be found in the FAIRMODE Working Group Guidelines.

Areas for Future Research of IAM Systems

In terms of problem formulation, one major area of ​​investigation for the future is the consideration of dynamic evolution of the physical, economic and social environment. This is probably due to the increasing computational capacity and the relative novelty of the problem treatment in the context, which causes scientists to start the study directly from the numerical approaches for both uncertainty and sensitivity analysis. Non-technical measures for the reduction of air pollution emissions: the integrated assessment of the local air quality management plans through the Italian national model.

Introduction

The RIAT+ System

This is achieved by considering a quadrant-shaped input configuration as shown in Fig.5.1 where the emissions Sj(x, y) are summed according to these quadrants, the dimension of which depends on the specific area and pollutant under study, and then used to calculate the AQI value at position (x, y). The development of surrogate models thus means: first, determining the input variables and the shape of the so-called "activation function", generally a strongly nonlinear function of its argument, which in turn is a weighted sum of the input values; secondly, the definition of all model parameters (respectively, weights and threshold). This second step (training) is performed by forcing the surrogate model to represent, as much as possible, a set of CTM calculations that are representative of the range of emissions/AQI that can be covered by the plan to be developed.

Fig. 5.2 A sketch of an elementary neuron
Fig. 5.2 A sketch of an elementary neuron

Brussels Capital Region

  • Proposed Abatement Measures
  • The 2010 Scenario
  • Design of Experiments and Source/Receptor Models
  • Results

The starting situation for the simulations was a reconstruction of the 2010 situation based on the emissions inventory from the previous year. For the meteorological inputs, ECMWF ERA INTERIM data with a resolution of 0.25° were used and interpolated to the model grid. For the results to be acceptable, the method requires that they lie within the unit circle.

Table 5.2 reports all the reductions per each pollutant and each measure that can be obtained by their full adoption.
Table 5.2 reports all the reductions per each pollutant and each measure that can be obtained by their full adoption.

Great Porto Area

  • Proposed Abatement Measures
  • The Chemical Transport Model
  • Design of Experiments and Source/Receptor Models
  • Results

The results of the meteorological module are one of the inputs to the air pollution component. The target diagram for NO2 shows that 69 % of the values ​​meet the unit circle criterion, while values ​​are similar to PM10 for the other performance indicators. Figure 5.13 presents the spatial distribution of the expected reductions in PM10 concentration levels for the Point C of the Pareto curve.

Fig. 5.10 Target diagram for the observation stations for PM10 inside the model domain for 2012
Fig. 5.10 Target diagram for the observation stations for PM10 inside the model domain for 2012

Conclusions

In fact, TM includes and extends the concept of Integrated Assessment Modeling, a method that, as seen in the previous chapters, has been developed over the last two decades to select "optimized" policies aimed at reducing the negative effects of air pollution and climate change. A central issue in the development of a transition process is the assessment of the social acceptance of political decisions. The tools could be identified and structured based on the DPSIR method, which, as shown in the previous chapters, is suitable for describing the interaction between society and the environment.

Hình ảnh

Fig. 1.1 Distributions of the 36-th highest PM10 daily value in EU MS (source EEA 2015)
Fig. 1.2 Geographical distribution of the 36-th highest PM10 daily value (source EEA 2015)
Fig. 1.6 Evolution of PM10 compliance according to GAINS results (source Amann 2013)
Fig. 2.1 The general DPSIR scheme (source http://www.eea.europa.eu/)
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