component b3.2. analysis of the current practice in the

Contract № ENPI/2014/350-889 B3.2 - Report on Software in the air management sector

Europe Aid/135512/DH/SER/BY

"Technical Assistance to Support the Development of Green Economy in Belarus"

Contract № ENPI/2014/350-889

COMPONENT B3.2. Analysis of the current practice

in the European Union related to the software

programs used for air emission assessment

Report on the analysis of different software

programs used in the EU

May 2016


HUMAN DYNAMICS-IDOM CONSORTIUM

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Component B.3.2 - Report on software in the air management sector

Проект: " Report on software in the air management sector "

Номер проекта: ЕNPI/2014/350-889

Страна: Беларусь

Адрес:

Hulla & Co Human Dynamics KG 13

Lothringer Strasse 16

A-1030 Wien

Austria

тел.: +359 2 935 99 80

факс: +359 2 935 99 70

Контактное лицо: Надя Бонева

Подписи:

Дата доклада: 09.06.2016

Авторы доклада: Raquel Navarrete

Проект выполняется консорциумом под

руководством Hulla and Co. Human Dynamics KG

Disclaimer. The content of this report does not reflect the official opinion of the European Union. Responsibility for the

information and views expressed lies entirely with the author(s).



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CONTENTS

LIST OF ABBREVIATIONS ............................................................................................................... 5

1 INTRODUCTION. ........................................................................................................................ 6

2 INVENTORY PREPARATION PROCESS. SITUATION IN EUROPE ..................................... 7

2.1 SPAIN .................................................................................................................................................... 7

2.1.1 Introduction ................................................................................................................................ 7

2.1.2 Methods and Data Sources ....................................................................................................... 7

2.1.3 Software ...................................................................................................................................... 8

2.2 BULGARIA.............................................................................................................................................. 8

2.2.1 Introduction ................................................................................................................................ 8

2.2.2 Methods and Data Sources ....................................................................................................... 9

2.2.3 Software ...................................................................................................................................... 9

2.3 PORTUGAL ........................................................................................................................................... 10

2.3.1 Introduction .............................................................................................................................. 10

2.3.2 Methods and Data Sources ..................................................................................................... 10

2.3.3 Software .................................................................................................................................... 13

2.4 DENMARK ............................................................................................................................................ 13

2.4.1 Introduction .............................................................................................................................. 13


2.4.3 Software .................................................................................................................................... 15

2.5 GERMANY ............................................................................................................................................ 15

2.5.1 Introduction .............................................................................................................................. 15


2.5.3 Software .................................................................................................................................... 17

2.6 GEORGIA ............................................................................................................................................. 17

2.6.1 Introduction .............................................................................................................................. 17


2.6.3 Software .................................................................................................................................... 19

2.7 ITALY .................................................................................................................................................. 19

2.7.1 Introduction .............................................................................................................................. 19


2.7.3 Software .................................................................................................................................... 19

3 AIR EMISSION INVENTORY SOFTWARE TOOLS ............................................................... 21

4 AIR DISPERSION SOFTWARE ............................................................................................... 23

4.1 AERMOD .............................................................................................................................................. 24

4.2 THOR................................................................................................................................................... 27

4.3 CHIMERE ............................................................................................................................................. 28



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5 AIR MANAGEMENT IN EUROPE ............................................................................................ 29

5.1 AIR MANAGEMENT SOFTWARE .............................................................................................................. 29

5.1.1 UKIAM model — United Kingdom .......................................................................................... 29

5.1.2 Regional integrated assessment tool plus (RIAT+) ............................................................ 30

5.1.3 GAINS ........................................................................................................................................ 34

5.1.4 GEM-E3 macro-economic model ............................................................................................ 34

6 CONCLUSION AND PROPOSAL OF FURTHER ACTIONS ................................................... 36

6.1 CONCLUSIONS ..................................................................................................................................... 36

6.2 PROPOSAL FOR BELARUS ...................................................................................................................... 37



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LIST OF ABBREVIATIONS

AELs Associated Emission Levels

BAT Best Available Technique

BDR Baseline Data Report

CA Contracting Authority

CBA Cost–benefit analysis

CLRTAP Convention on Long-range Transboundary Air Pollution

EMEP guidebook EMEP/EEA air pollutant emission inventory guidebook - 2013

ENPI European Neighbourhood and Partnership Instrument

EU European Union

GAINS) Greenhouse gas and Air pollution Interactions and Sinergies

Protocol Gothenburg Protocol

CEIP Centre on Emission Inventories and Projections

ToR Terms of Reference

IIR Informative Inventory Report

CLRTAP Convention on Long-Range Transboundary Air Pollution

EMEP European Monitoring and Evaluation Programme



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1 INTRODUCTION.

The aim of the component B is the elaboration of the draft programme of actions to reduce pollutant

emissions until 2030, covered by the Gothenburg Protocol, with the application of an integral emissions

evaluation. In order to facilitate the elaboration of this programme, as well as to support the Beneficiary in

future updates, the component B.3.2 aims to select/compile a software product, adapted for the Republic

of Belarus, to calculate the reduction of main pollutants emissions with cost-benefit analysis of each

pollutant individually and all the main pollutants together, which allows, inter alia, carry out reporting on the

pollutants emissions in the format, provided for by the program EMEP.

As previous stage for the determination of this software, this report pretends showing the current practice

in the European Union related to the software programs used to:

• calculate present and forecast the future emissions to the air

• prepare cost-benefit analysis for emission reduction at different scenarios

• prepare reports on the pollutants emission in the format, provided for by the program EMEP for key

emissions sector.

For the fulfilment of the task, the following information has been taking into account:

• the submissions of the IIR (Informative inventory report) of the parties to the Secretariat of the

Geneva Convention and EMEP Programme INFORMATIVE INVENTORY REPORT 1990-2013.

Considering the section related to the inventory preparation process and methods and data sources.

• Outcomes of the present project

• Publicly available sources of information

• Information obtained through contacts with different stakeholders and third parties.

The report is structured in 5 main sections:

- The first one is related to emission inventory. In this section the way the emission inventory is

developed in 6 countries is explained.

- The second one evaluates three representative air emissions dispersion software.

- The third one is related to Cost-benefit analysis for emission reduction software.

- The fourth section describes how the air quality is managed in some European countries and

software used for this management

- The last section is about findings and proposal for further continuation and successful

implementation on the Activity B.3.2.



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2 INVENTORY PREPARATION PROCESS. SITUATION IN EUROPE

2.1 SPAIN

2.1.1 Introduction

The Inventory preparation process is managed by the Inventories Unit of the Spanish National Inventory

System (SEI), together with a technical assistance. The main stages in the elaboration process are:

1) Analysis of the key categories identified in the previous edition of the inventory. This constitutes the

starting point for assigning the priorities for improving the inventory and for maintaining the

remaining activities.

2) Choice of methods for estimating the emissions. This stage includes the initial selection of a category

not previously considered, as well as the revision of the selected methods for those categories where

a methodological change is proposed.

3) Collection of the necessary information for applying the selected methods according to the activity

(activity parameters and variables, algorithms and emission factors, measured or estimated

emissions).

4) Processing of data. This phase involves integrating the base data with the methods for estimating

emissions in order to apply the procedures for calculating these emissions.

5) Preparation of air pollutant emissions report and result tables required.

6) Submission of the results for approval. Once the inventory has been approved, the reports and the

related information –in the format required for each case– are published and sent to the

international bodies through the National Focal Points.

2.1.2 Methods and Data Sources

The emission estimation methods applied in the Inventory depend on the nature of the activity being

considered and the availability of basic data. Based on the availability of information on the emissions

themselves, the estimation methods applied in the preparation of this Inventory can be classified into two

major categories:

I) Methods based on observed emission data. These methods are based on direct observation of the key

variable, i.e. the emission itself. Two types of methods can be distinguished:

a) Continuous measurement.

b) Measurement at regular intervals.

These Methods are mainly used in the Large Point Sources, excluding airports. The required data are

frequently available due to their environmental importance and the size of the activity involved. This

information is collected from the plants themselves through individualized questionnaires previously

elaborated by SEA. (The questionnaires are available if required and could be included as annex).

Specifically, direct measurements have been used for the determination of:

a. SOx: in thermal power plants, oil refineries, sulphuric acid plants, paper pulp plants, municipal and

industrial waste incineration plants.

b. NOx: in thermal power plants, oil refineries, nitric acid plants, paper pulp plants and in urban waste

incineration plants and in industrial waste incineration plants.

c. NMVOC: in thermal power plants (in part: in some plants and in certain years), in integrated iron and

steelworks, municipal waste incineration plants and industrial waste incineration plants.

d. CO: in thermal power plants; oil refineries, integrated iron and steelworks and paper pulp plants;

municipal waste incineration plants and industrial waste incineration plants.

e. NH3 nitric acid manufacturing plants and municipal waste incineration plants.



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f. TSP: in thermal power plants, oil refineries, integral iron and steelworks, municipal waste

incinerators, paper pulp and aluminium production.

g. Other pollutants in all those point sources for which it has been possible to collect direct data. (E.g

Coal-fired thermal power plants (1995-1998) for cadmium, mercury and lead, or urban waste

incinerators, mainly with respect to heavy metals and dioxins).

h.

II) Methods based on calculation procedures: this category can be split depending on the procedures:

a. Simple balance of materials. This method has been applied for the estimation of Sulphur dioxide in

combustion facilities where information regarding the amount of sulphur present in the fuel used,

and the retention coefficients for ash and specific parts of the combustion facilities is available. In

installations with de-sulphurisation units where there were available information on emission

abatement techniques, the estimation of potential emissions has been accordingly corrected. This

procedure was also used to estimate lead emissions and other heavy metals in internal combustion

engines in vehicles for road transport and mobile machinery. This has been also the adopted

approach for estimating NMVOC emissions from painting lines at automobile manufacturing plants.

b. Complete balance. This method comprises the determination of all inputs and outputs of different

chemical elements (using data on the types of process and facilities as well as the amounts of

materials and the elements in their composition), although in fact it was not possible to apply it

effectively in the estimation due to its complexity. In any case it has been retained as a reference

method for validating atypical estimates.

c. Methods based on functional statistical models: Modelling-correlation. This method is based on the

results of earlier works into the estimation of functional relationships or correlations between certain

physical and chemical variables and emissions from certain activities. It has the advantage of

providing specific relationships and making it possible to estimate the emissions as if there were

continuous monitoring of the activity. Specifically, it has been applied to estimate emissions from

categories 1A3a Air transport and 1A3b Road transport when functional ratios adjusted by

regression analysis were available.

d. Methods based on emission factors: activity factors and variables. This method has been the most

generally used in preparing the Inventory and has always been considered as the backup option, only

applied when no other more precise option was available to estimate the emissions for an activity.

These default emission factors, given by unit of socio-economic variable, constitute subrogated

information on plants or activities that can be assimilated to those for which estimations are required

in this inventory.

2.1.3 Software

There is not a commercial software for the development of the Spanish National Inventory. The inventory is

developed in an own database based on Oracle. This database is updated every year by means of the

particularities found in the development of each annual inventory.

2.2 BULGARIA

2.2.1 Introduction

Bulgaria’s reporting obligations are being administered by the Ministry of Environment and Water (MOEW).

All activities on preparation of GHGs and air pollutant inventory in Bulgaria are coordinated and managed on



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the state level by the MOEW. The Executive Environment Agency (ExEA) has been identified as the

responsible organization for preparation of Bulgaria’s National air pollutant inventory under UNECE/CLRTAP

and it is designated as single national entity.

Organizational Chart of the Bulgarian National Inventory System


There is a national methodology (approved with Order RD 77/03.02.2006 of MoEW) for the calculation of

the emissions according to the UNECE/CLRTAP. The information is collected on the annual basis.

The emission inventory is prepared based on:

A) Emission factors taken from:

a. National common methodology for emissions inventory under UNECE/CLRTAR b. The EMEP/EEA Guidebook 2009/2013 (where EF are not available in the national

methodology) c. International emission factor data base d. Country specific EF.

B) Activity data from the NSI, MI, MTITC, MEE, MAF, EAF, ExEA, MOEW

a. Sources of activity data for preparation of national air pollutant emission inventory are

presented in Table 1.

2.2.3 Software

All calculation and reporting rely in a set of different Excel spreadsheet workbooks which had been

developed in order that all information and calculations occur automatically.



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2.3 PORTUGAL

2.3.1 Introduction

The Portuguese Environmental Agency (APA)/ Ministry for the Environment, Spatial Planning and Energy, is

the Responsible Body for: the overall coordination and updating of the National Inventory of Emissions by

Sources and Removals by Sinks of Air Pollutants (INERPA); the inventory’s approval, after consulting the Focal

Points and the involved entities; and its submission to EC and international bodies to which Portugal is

associated, in the several communication and information formats, thus ensuring compliance with the

adopted requirements and directives.

All the participating organizations are represented in the INERPA’s working group set up to support the

annual production of the national inventories and the fulfilment of the reporting requirements. Each year

the coordinator, organizes a kick off meeting to plan and launch, in coordination with the sectoral focal points

and the involved entities, the work for the following inventory submission(s). Bilateral meetings occur as

necessary as consequence of this meeting aiming at discussing the specific issues related to each sector and

to agree on the actions to be implemented in the framework of INERPA during the inventory compilation

regarding the next submission.


The emissions calculations is performed by APA. However many other institutions and agencies contribute

to the inventory process, providing activity data, sectoral expert judgement, technical support and

comments. All calculation and reporting rely in a set of different Excel spreadsheet workbooks which had

been developed in order that all information and calculations occur automatically. The structure of the

information system is outlined below.

Electronic System Structure of the estimation and reporting system



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The information received from the several data suppliers is stored in its original format (paper or magnetic).

A copy of this information is converted into the working workbooks, where data is further processed, linkage

made and calculations performed, maintaining hence the integrity of the original data sources. The following

table gives an overview of the institutions and data sources providing data for the compilation of the

Portuguese emission inventories.



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Inventory data sources

Default methods and emission factors used and the choice between Tier 1 and Tier 2 approaches, are

dictated, case by case, by the availability of proper background information, from national circumstances

and the availability of resources.



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2.3.3 Software

All calculation and reporting rely in a set of different Excel spreadsheet workbooks which had been

developed in order that all information and calculations occur automatically.

2.4 DENMARK

2.4.1 Introduction

DCE (Danish Centre for Environment and Energy, Aarhus University), is responsible for the annual

preparation and submission to the UNECE-LRTAP Convention of the Annual Danish Emissions Report, and

the inventories in the NFR Format in accordance with the guidelines. DCE is contracted by the Ministry of the

Environment and the Ministry of Climate, Energy and Building to complete emission inventories for Denmark.


The Danish emission inventory for stationary combustion plants is based on the former CORINAIR system.

The CORINAIR calculation principle is to calculate the emissions as activities multiplied by emission factors.

The most consistent emission factors are used, either as national values or default factors proposed by

international guidelines. Information on activities to carry out the inventory is largely based on official

statistics.

The process of inventory preparation is showed in the following diagram:

Schematic diagram of the process of inventory preparation

The emission inventory for stationary combustion is based on activity rates from the Danish energy statistics.

General emission factors for various fuels, plants and sectors are determined.

A number of large plants, e.g. power plants, municipal waste incineration plants and large industrial plants

are registered individually as large point sources. This enables use of plant-specific emission factors that refer

to emission measurements stated in annual environmental reports. Emission factors of SO2, NOX, HM and

PM are often plant specific.



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An internal DCE model with a structure similar to the European COPERT IV emission model (EEA, 2013) is

used to calculate the Danish annual emissions for road traffic.

The Danish agricultural emissions are calculated and managed in a comprehensive model complex called IDA

(Integrated Database model for Agricultural emissions), which is used to calculate both air pollutants

compounds and greenhouse gas related emissions. The livestock production has a great influence on the

Danish agricultural emissions. IDA works with approximately 40 different livestock categories, dependent on

livestock category, weight class and age.

The distribution of emissions in the Danish emission inventory is carried out in databases and in a

geographical information system, GIS. The methodology applied in the part of the distribution carried out in

GIS is described for the Industrial Processes sector as a case, as this distribution is rather simple: the emission

inventory for Industrial Processes covers both point sources and area sources. Emissions from point sources

are allocated to the coordinates for the individual plants included in the Danish inventory system and are not

relevant in relation to the GIS procedure. Emissions from area sources are calculated from production

statistics and the resulting emissions are national totals as allocation of the sources (industrial plants) is not

possible with the available data. Instead a proxy for the distribution is applied, in this case the location of

industrial areas. The map of industrial areas is not reflecting differences in the location for different

industries, but only holds industrial buildings (referred to as the industrial area as the buildings are treated

as areas rather than units). The map is a shape file and the industrial areas are polygons.

The remaining part of the emission distribution for industrial processes is carried out in a database. The share

of the national emissions that should be allocated to each grid cell is calculated as the industrial area of the

cell divided by the total industrial area. The same distribution key is applied for all pollutants.

The Danish high resolution gridded emissions are aggregated on the 50 km x 50 km EMEP grid for reporting

to CLRTAP. The share of each 1 km x 1 km grid cell located in the relevant EMEP grid cells are calculated and

the aggregated emissions are calculated as the weighted sum of emissions in the 1 km grid cells intersecting

each EMEP grid cell being partial or fully part of the Danish Exclusive Economic Zone, EEZ.

National total gridded emissions



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2.4.3 Software

The background data (activity data and emission factors) for estimation of the Danish emission inventories

is collected and stored in central databases located at DCE. The databases are in Access format and handled

with software developed by the European Environmental Agency (EEA) and DCE.

As input to the databases, various sub-models are used to estimate and aggregate the background data in

order to fit the format and level in the central databases.

For each submission, databases and additional tools and submodels are frozen together with the resulting

NFR-reporting format. This material is placed on central institutional servers, which are subject to routine

back-up services.

For data handling, the software tool is CollectER (Pulles et al., 1999) and for reporting the software tool is

developed by DCE. Data files and programme files used in the inventory preparation process are listed in the

following table.

List of current data structure;, data files and programme files in use

2.5 GERMANY

2.5.1 Introduction

In Germany, emissions reporting is coordinated by a Single National Entity in the Federal Environment Agency

(UBA). Since the mid-1990s, when reporting obligations for preparation of emissions inventories of air

pollutants and green-house gases increased sharply, efforts to harmonise emissions calculation and



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reporting have been intensified. At the same time, requirements from reporting obligations relative to the

UNECE Geneva Convention on Long-range Transboundary Air Pollution and its protocols, to the EU NEC

Directive and to EU plant specific reporting obligations, must be taken into account.


The methodology followed for the inventory preparation for both, National Inventory Report as submitted

under the UNFCCC an the one used for air pollution emissions is exactly the same. Mainly differences are

related to the use of emission factors. Following figures shows the process for the elaboration of the emission

inventory:

Overview of the emissions-reporting process

In practice many experts are involved in the inventory compilation process, which requires an efficient

organisation. The major advantage of this concept is the provision of additional expertise for quality control

and verifications. The cooperation with the experts, who are responsible for legislation and Best Available

Technology (BAT) ensures a detailed technical knowledge for the inventory compilation process. The

knowledge of abatement technologies and limit values is essential for the evaluation of emission factors.

Since the Federal Environment Agency (UBA) operates several analytical laboratories and monitoring

stations, it's possible to draw on the specialist expertise in order to get a better understanding of

measurements and uncertainties.

As a general rule, Germany uses many country-specific process information and emission factors where

available.

Another factor that is critical to the success of the overall process is selection and review of, and (where

necessary) changes in, data sources, since the quality of results of all downstream processes (data



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preparation, calculation, reporting) cannot be better than that of the primary data used. Data sources may

be oriented to the activity data, emission factors or emissions for/of a specific category. In many cases, the

data sources used have been relied on for a number of years. It can become necessary to select new data

sources – for example, as a result of required changes in methods, of the elimination of an existing data

source, of a need for additional data or of findings from quality checks of previously used data sources.

The suitability of a given data source depends on various criteria. These include:

• Long-term availability,

• Institutionalisation of data provision,

• Good documentation,

• Execution of quality assurance and control measures, by the persons/organisations providing

data,

• Identification of uncertainties,

• Representative nature of the data in question, and

• Completeness of the expected data.

In each case, it is vital that the reasons for choosing a particular data source be documented and, where the

data source has significant deficits, that suitable measures for improving the data be planned.

2.5.3 Software

The Federal Environment Agency's Central System on Emissions (CSE) database is the national, central

database for emissions calculation and reporting. It is used for central storage of all information required for

emissions calculation (methods, activity rates, emission factors). The CSE is the main instrument for

documentation and quality assurance at the data level.

2.6 GEORGIA

2.6.1 Introduction

Georgia joined the Convention on Long-Range Transboundary Pollution in 1999. In Georgia, the Ministry of

Environment and Natural Resource Protection (MoENRP) is responsible for preparation of the inventory. This

task is located within the Ambient Air Protection Service, which collects data from GEOSTAT (the Statistical

Office) and from various companies. The transport sector is covered by the Ministry of Internal Affairs of

Georgia.


MoENRP carries out the emission calculation based on the collected data. Quality checking/control is also

carried out by MoENRP.

The responsibilities for preparing the inventory are shown in the following figure.



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In the first step of inventory preparation, MoENRP receives data from the Statistical office and other data

suppliers. Information on county’s car fleet are received from the Ministry of Internal Affairs of Georgia.

Experts at MoENRP use emission factors from the EMEP/EEA Guidebook to calculate air pollutant emissions

in the agriculture sector (Tier 1 method). Emissions from road transport are calculated based on the

EMEP/EEA Guidebook as well. For other sectors, a national methodology is applied.

Emissions from the Agriculture sector are calculated based on Tier 1 EMEP/EEA methodology, along with the

recommended Tier 1 emission factors from GB2013. Road transport emissions are calculated by software

tool COPERT 4 (Tier 2/3 method). For other sectors, a national methodology is applied.

Data sources for the inventory comprise the National Statistical Office and the Ministry of Internal Affairs. In

addition, information for large point sources is provided in reports by companies, verified by regional offices

of Environment Inspectorate and by the MoENRP.

Emission inventory structure



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2.6.3 Software

Activity data and emission factors are stored in Excel files. Data is backed-up and archived at MoENRP

(Ambient Air Protection Service) in different computers and virtual server.

2.7 ITALY

2.7.1 Introduction

The Institute for Environmental Protection and Research (ISPRA) has the overall responsibility for the

emission inventory submission to CLRTAP, as well as to the United Nations Framework Convention on

Climate Change (UNFCCC), and is in charge of all the work related to inventory compilation.

ISPRA has established fruitful cooperation with a number of governmental and research institutions as well

as industrial associations, which helps improving information about some leading categories of the inventory.

Specifically, these activities aim at the improvement of provision and collection of basic data and emission

factors, through plant-specific data, and exchange of information on scientific researches and new sources.

Moreover, when in depth investigation is needed and estimates are affected by a high uncertainty, sectoral

studies are committed to ad hoc research teams or consultants.


The main basic data needed for the preparation of the national emission inventory are energy statistics,

published by the Ministry of Economic Development (MSE) in the National Energy Balance (BEN), statistics

on industrial and agricultural production, published by the National Institute of Statistics (ISTAT), statistics

on transportation, provided by the Ministry of Transportation (MIT), and data supplied directly by the

relevant professional associations.

Emission factors and methodologies used in the estimation process are consistent with the EMEP/EEA

Guidebook, the IPCC Guidelines and Good Practice Guidance as well as supported by national experiences

and circumstances.

For the industrial sector, emission data collected through the National Pollutant Release and Transfer

Register (PRTR), the Large Combustion Plant (LCP) Directive and in the framework of the European Emissions

Trading Scheme have yielded considerable developments in the inventory of the relevant sectors.

In fact, these data, even if not always directly used, are taken into account as a verification of emission

estimates and improve national emissions factors as well as activity data figures.

For large industrial point sources, emissions are registered individually, when communicated, based upon

detailed information such as fuel consumption.

Other small plants communicate their emissions which are also considered individually.

2.7.3 Software

The inventory is composed by spreadsheets to calculate emission estimates; activity data and emission

factors as well as methodologies are referenced to their data sources.

All the reference material, estimates and calculation sheets, as well as the documentation on scientific

papers and the basic data needed for the inventory compilation, are stored and archived at the Institute for



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Environmental Protection and Research. After each reporting cycle, all database files, spreadsheets and

electronic documents are archived as ‘read-only-files’ so that the documentation and estimates could be

traced back during the new year inventory compilation or a review process.



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3 AIR EMISSION INVENTORY SOFTWARE TOOLS

As commented in the previous section, only Denmark uses a commercial software for the development of

the inventory. This software is named “CollectER”.

The CollectER emission inventory software tool is developed by European Environment Agency (EEA) and its

European Topic Centre on Air and Climate Change (ETC-ACC) since the year 1998. The objectives of the

software are to facilitate preparation of transparent, consistent, complete, comparable and accurate data

for emissions reporting procedures in accordance with the requirements of international conventions,

protocols and EU legislation. The CollectER tool is designed to help national experts on air emissions to collect

the relevant air emission data for delivery to the European Commission and to international conventions.

CollectER III is an executable programme, running under the Windows operating system, which manages and

compiles an MS Access type of relational database. CollectER III integrates inventory compilation and

inventory reporting into one software tool. This tool uses a tailored database platform to help with emission

inventory compilation. It allows the input of activity data, selection of different emission factors to give

emissions. It also includes point source functionality.

The software can be downloaded free of charge from ETC-ACC’s web site at http://air-

climate.eionet.europa.eu/country_tools/ae/CollectER_III.html

Start data collection. - Activity rates and emission factors have to be collected for the actual inventory year.

Two reporting functions are available in the tool:



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o UNECE_LRTAP – allows user generate flat file containing emission data from the Inventory. Flat file

has format as required by UNECE LRTAP (and NECD) reporting. This format is fully compatible with

the latest reporting requirements of UNFCCC in NFR09. Data exported into flat files may be filtered.

o UNFCCC – allows user generate XML files containing emission data and fuels background data that

can be imported into UNFCCC Reporter database file. It is possible export one or all years emission

data or fuel background data into single files.



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4 AIR DISPERSION SOFTWARE

Air quality models are complex tools which include detailed representations of the transport, diffusion and

chemical processes taking place in the atmosphere.

Models have become a primary tool for analysis in most air quality assessments mainly for the following

reasons:

• A picture of the air quality in a zone may be obtained - in contrast to the limitations in the spatial

coverage of air quality measurements.

• The relation between air concentrations and the emissions causing these can be made explicitly

and quantitatively by modelling, which is most important for supporting air quality management.

• Models are the only available tool if the impact on air quality of possible future sources or of

alternative future emission scenarios is to be investigated.

Air pollution models can be used in a complementary manner to air quality measurements, with due regard

for the strengths and weaknesses of both analysis techniques. Modelled information is necessarily uncertain

due to deficiencies in our knowledge of emissions and atmospheric processes; this disadvantage may be

largely offset by validation of models with the help of measurements, or by assessing air quality by

combination of information from modelling and measurements. In fact, if a concentration map is to be made

on the basis of measurements, model results provide essential information for interpolation. The use of

interpolation in assessments of air quality measurements alone is to be recommended only if emission

information cannot be made available or if acceptable models cannot be found, and if monitoring data with

sufficient spatial and temporal coverage are available.

The European Topic Centre on Air Pollution and Climate Change Mitigation CM (ETC/ACM) is a consortium

of 14 European organisations with the Netherlands Institute for Public Health and the Environment (RIVM)

as its lead organisation. The centres are part of EIONET (European Environment Information and Observation

Network). This partnership network of the European Environment Agency (EEA) and its member- and

cooperating countries involves approximately 1000 experts and more than 350 national institutions. The

network supports the collection and organisation of data and the development and dissemination of

information concerning Europe’s environment.

The European Topic Centre on Air Quality has prepared a pilot model documentation centre accessible via

the Internet (ETC-AQ home page: http://www.etcaq.rivm.nl; model documentation centre:

http://aix.meng.auth.gr/lhtee/database.html). Here, descriptions of the models, their application areas and

their status with respect to evaluation and validation are to be provided. These air quality models catalogue

and meta information database are set up to provide guidance to model users in the selection of the most

appropriate model for his or her application. The Model Documentation System aims to provide guidance to

any model user in the selection of the most appropriate air quality model for his application. Inclusion of an

air quality model in the system is by no means associated with any form of endorsement for using the

particular model: it helps select the most appropriate by using the specifications submitted by the modellers.

In short, the procedure for modelling involves the following steps:

1) Define the pollutant, and the output quantity to be modelled (concentration fields, or (spatial

maximum) concentrations in streets or near point sources, usually for concentration statistics,

for instance annual average, 98 percentile of hourly values ...)

2) Define the time resolution needed (the averaging time for the concentration)



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3) Define the "model output area" for which the model calculations should be made (usually a zone

or agglomeration) and the spatial resolution needed.

4) Define the accuracy in the output quantity that is required

5) Determine the model area (this may extend considerably beyond the output area, particularly in

case of pollutants with long range transport!)

6) Investigate the availability of emission data (in the model area)

7) Investigate the availability meteorological and topographical data (in the model area)

8) Investigate available air quality data (in the model output area)

9) Check available computer resources

10) Select models that are suitable for the pollutant (taking into account its chemistry and

deposition), for the relevant output quantity, with the appropriate resolution in space and time,

within the required accuracy, and for the area under consideration (taking into account its

topography and meteorological characteristics)

11) Consider the computer requirements of the model(s); if these surpass available computer

resources, reconsider model choice.

12) Reconsider the requirements on emission and meteorological data of the model(s) selected and,

if necessary, collect more detailed input data (or reconsider the model choice)

13) Prepare input data

14) Run the model

15) Compare results to available air quality data and critically evaluate. If necessary, re-run model.

16) Map output

Dispersion modelling is a complex process and, as with all models, the results are only as useful as the model

itself and how it is used. Correct interpretation of modelling results against the national environmental

standards and determination of the potential effects of a discharge are as important as accurate modelling

results. The process of air pollution modelling contains four stages (data input, dispersion calculations,

deriving concentrations, and analysis). The accuracy and uncertainty of each stage must be known and

evaluated to ensure a reliable assessment of the significance of any potential adverse effects.

For air quality assessment by modelling, a wide variety of models have been developed, some of which have

been made readily accessible and easy to use by combination with user-friendly software. Others can only

be operated by specialists, or even exclusively by the developers.Following sections include a short

description of three different models: AERMOD, THOR and CHIMERE.

4.1 AERMOD

AERMOD is a ‘near-field, steady-state’ guideline model. It is designed to predict pollutant concentrations

from continuous point, flare, area, line, and volume sources. It uses boundary-layer similarity theory to

define turbulence and dispersion coefficients as a continuum, rather than as a discrete set of stability classes.

Variation of turbulence with height allows a better treatment of dispersion from different release heights.

Also, dispersion coefficients for unstable conditions are non-Gaussian, to represent the high concentrations

that can be observed close to a stack under convective conditions.

AERMOD allows emission sources considered puntual, surface or volumetric type, and is applicable to areas

of both simple and complex topography. AERMOD considers the complex terrain through the concept of



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dividing flow path. With this assumption, the plume behavior resulting from topographic effect is an average

offset of two streams: one over the obstacle and another around it.

The dispersion modeling system AERMOD is structured in three different modules, self AERMOD module,

and two modules called AERMAP and AERMET.

AERMET utiliza la información meteorológica y las características del terreno suministradas para calcular los

parámetros necesarios para representar la capa límite planetaria (altura de la capa de mezcla, velocidad de

fricción, etc.) de acuerdo a los requerimientos de AERMOD. Como datos de entrada AERMET acepta datos

de estaciones meteorológicas o datos procedentes del acoplamiento de algún modelo meteorológico.

AERMET crea dos ficheros requeridos por AERMOD, un archivo de información meteorológica en superficie

y un archivo del perfil en altura.

AERMET uses meteorological information and the land cover to calculate the necessary parameters to

represent the planetary boundary layer (height of the mixing layer, friction velocity, etc.) according to the

requirements of AERMOD. As AERMET accepts input data from meteorological stations or link data from a

meteorological model. AERMET creates two files required by AERMOD, meteorological surface file and a

meteorological height profile file.

On the other hand, AERMAP is responsible for processing topography from field data in order to calculate

the coordinates of the receptors and terrain characteristics that may influence local issues in relation to the

dispersion of pollutants. Besides, this module defines the number of discrete receptors where the AERMOD

model will calculate the concentration of pollutants and / or define the coordinates of a Cartesian domain

where AERMOD predict the air pollutant concentration.

AERMOD needs, as input data, the following:

• Meteorological data in the point where the emission sources are located. Specifically it requires

hourly values of wind speed, wind direction, temperature, relative humidity, cloud cover and cloud height.

• Digital terrain model with a specific resolution.



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• Receptors / or definition of a Cartesian domain with a specific horizontal resolution.

• Definition of albedo, Bowen ratio and roughness according to land use of the area where the source

is located.

• Location, physical parameters and flow rate of the source. For punctual sources, the data are: stack

diameter and height; temperature and flow rate.

Several National Authorities (p. e Hungarian Ministry on Environment and Water Management and

Hungarian Meteorological Service) decided to promulgate AERMOD as national standard. In Spain and in

Bulgaria, AERMOD has been the model used for modelling the air dispersion emissions included in the

Environmental Impact Assessment.

AERMOD supports regulatory modeling programs. Currently it calculates concentration values, dry, wet and

total deposition rates. It generates gridded vertical profile of potential temperature for use in plume rise

calculations. Effective release height for flare sources is available. Exponential decay of pollutant is taken

into account. Point, volume and area sources are treated. Line sources are handled as volume source.



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4.2 THOR

THOR is a comprehensive and unique integrated air pollution model system. The model system includes

several meteorological and air pollution models capable of operating for different applications and different

scales. The system is capable of accurate and high resolution three-days forecasting of weather and air

pollution from regional scale over urban background scale and down to individual street canyons in cities -

on both sides of the streets. Coupling models over different scales makes it possible to account for

contributions from local, near-local as well as remote emission sources in order to describe the air quality at

a specific location - e.g. in a street canyon or in a park. The system is used in connection with the urban and

background monitoring programs in Denmark. Furthermore, the system can be used to forecast air pollution

from accidental releases as e.g. power plants, industrial sites and natural or human made fires.

The main purposes of the THOR system are:

• forecasting,

• now casting,

• emission reduction scenarios,

• retrospective analyses and

• air pollution assessments and management.



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The system can be used for information and warning of the public in cases of high air pollution levels and for

policy management (e.g. by emission reduction or traffic scenarios) of many different chemical compounds.

The system can be applied operationally for any location all over the world. The system consists of several

different air pollution models - all developed at NERI during the last decades.

Present capabilities of the THOR system include all aspects within forecasting, nowcasting, supplement to

monitoring programs, scenarios, retrospective analyses, assessment and management of air pollution.

4.3 CHIMERE

The CHIMERE atmospheric chemistry-transport model. CHIMERE simulates and predicts the impacts of

different emission control scenarios on air quality (ozone, nitrogen dioxide and particulate matter

concentrations) in Europe at different scales.

The CHIMERE multi-scale model is primarily designed to produce daily forecasts of ozone, aerosols and other

pollutants and make long-term simulations for emission control scenarios. CHIMERE runs over a range of

spatial scales from the regional scale (several thousand kilometers) to the urban scale (100-200 Km) with

resolutions from 1-2 Km to 100 Km. CHIMERE proposes many different options for simulations which make

it also a powerful research tool for testing parameterizations, hypotheses. Its use is relatively simple so long

as input data is correctly provided. It can run with several vertical resolutions, and with a wide range of

complexity. It can run with several chemical mechanisms, simplified or more complete, with or without

aerosols.

CHIMERE is one of the models used in the frame of the French national platform for air quality forecasting

(PREV'AIR). The model is also used in the frame of EMEP through the EURODELTA and CityDelta projects. The

model is also used in Spain along with MM5, to evaluate the air quality in Spain with models. For both ozone

and PM, the model has undergone extensive inter-comparisons on aerosol concentrations at European and

city scales.

CHIMERE runs over a range of spatial scale from the regional scale (several thousand kilometers) to the urban

scale (100-200 Km) with resolutions from 1-2 Km to 100 Km. On this server, documentation and source codes

are proposed for the complete multi-scale model. However most data are valid only for Europe and should

be revisited for applications on other continents.

CHIMERE proposes many different options for simulations which make it also a powerful research tool for

testing parameterizations

The data sources used in CHIMERE are: Meteorological data from WRF, MM5 models, or ECMWF; Emission

data (usually from EMEP); Boundar

The model is freely available with a full documentation at : http://www.lmd.polytechnique.fr/chimere/



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5 AIR MANAGEMENT IN EUROPE

5.1 AIR MANAGEMENT SOFTWARE

5.1.1 UKIAM model — United Kingdom

The UKIAM model has been developed to investigate emissions control strategies in the UK that are cost-

effective in maximizing improvement in protection of the environment while helping to comply with the UK's

international commitments on national emission ceilings and air quality legislation.

Protection of the environment includes human health with respect to human exposure and air quality

standards, and protection of natural ecosystems through reducing exceedance of critical loads as the

maximum annual deposition sustainable to avoid adverse effects. It brings together UK data based on the

work of other DEFRA contractors, with emissions data based on the National Atmospheric Emissions

Inventory, source apportionment reflecting the response of concentrations and deposition to changes in UK

emissions based on the FRAME model of CEH, and information on abatement measures on SO2, NOx and

primary PM10 from AMEC Environment & Infrastructure UK, and from the North Wyke Research for NH3

from agriculture as the main NH3 source. The latter are embodied in cost curves, ranking the potential

abatement measures as a series of emission reduction steps for each pollutant and source represented in

UKIAM in order of increasing cost per unit emission reduction. The model can then be used in scenario

analysis mode to examine the effect of selected measures, or in optimization mode selecting measures in

order of cost effectiveness in moving towards targets for environmental protection.

At the urban scale UKIAM has been modified to model NO2 and PM10 in London and other major cities,

using a finer grid resolution and with the road network superimposed. So far this work has been concentrated

mainly on scenarios for transport up to 2020, and the effectiveness of both technical measures aimed at

reducing exhaust emissions, and non-technical measures such as congestion charging that reduce traffic

volumes (and emissions from brakes and tyres as well as exhausts). More work is now needed on non-traffic

emissions, including those due to space heating and energy use.



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UKIAM has being extended to consider greenhouse gases as well as air quality pollutants, and investigate

synergies in combined strategies for control. This development parallels corresponding developments in

European scale modelling by IIASA for the European Commission with their new GAINS model, and involves

extending the range of measures considered to include, for example, changes in energy generation as well

as add-on or "end-of-pipe" measures to control specific pollutants. Instead of cost-curves for individual

pollutants, each measure is associated with corresponding effects on the whole range of pollutants and

greenhouse gas emissions from the sources affected, with only a single associated cost. This will enable us

to study the environmental and economic advantages of an integrated approach to air quality issues and

climate change.

5.1.2 Regional integrated assessment tool plus (RIAT+)

RIAT+ is a regional Integrated Assessment Modelling tool developed during the OPERA project (LIFE09

ENV/IT/000092). It has been designed to help regional decision makers to select optimal air pollution

reduction policies that will improve the air quality at minimum costs. To achieve this, the system incorporates

explicitly the specific features of the area of interest with regional input data-set for the:



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• precursor emissions of local and surrounding sources

• abatement measures (technical and non-technical/energy) described per activity sector and

technology with information on application rates, emission removal efficiency factor and cost

• the effect of meteorology and prevailing chemical regimes through the use of site specific source-

receptor (S/R) functions

The tool allows two possible decision pathways: scenario analysis and optimization. The main outputs from

RIAT+ are a summary of emission reductions on the domain, a table of the application rates for the different

measures, maps of a set of relevant air quality indexes (AQIs) and, for the optimization pathway, the Pareto

Curve providing the efficient solutions of a specific AQI ranked by costs.

The S/R model is used, internally, to link emissions to an AQI. The S/R model can be as simple as a linear

relationship, or as complex as a chemical transport model. To limit the computational time, RIAT+ currently

uses a nonlinear relations identified by means of Artificial Neural Networks (ANNs), tuned to replicate the

results of a limited set of simulations performed by the users with deterministic air quality model calibrated

of the specific site.

RIAT+ has been already tested and applied in different EU Regions with various aims:

• in Emilia-Romagna Region (IT) in the optimization way to estimate the effectiveness of measures

(both technical and efficiency measures) contained in the AQP

• in Alsace Region (FR) in the optimization way to support the implementation of an action plan like

SRCAE (Regional Scheme on Climate, Air and Energy) identifying the most effective technical and

energy measures

• in Lombardy Region (IT) in scenario mode to estimate the costs and the benefits of both technical

and efficiency measures contained in the AQP



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• in Brussels Capital Region (BCR) in scenario mode to evaluate a reduced set of traffic and domestic

heating measures provided by BIM (http://www.ibgebim.be), responsible for the study, monitoring

and management of air, water, soil, waste, noise and nature

• in Porto Region (PT) in the optimization way applied in Great Porto Area to focus the AQP on RIAT+

selected measures

Below the Brussels case study (2015) is explained in detail in order to show how the model works.

The Brussels Capital Region (BCR) has an area of 161 km² and is home to more than 1.1 million people. The

region consists of 19 municipalities, one of which is the Brussels Municipality, the capital of Belgium.

Brussels Capital Region.

The proposed abatement measures were provided by BIM (Brussels Environment): they were a list of 13

measures consisting of 9 traffic measures and 4 domestic heating measures approved by Brussels authorities.

For these abatement measures, BIM provided order-of-magnitude estimations of the costs and emission

reductions.

The RIAT+ database with abatement technologies that are available for the macro-sectors of interest - non-

industrial combustion (2) and transport (7) - was derived from GAINS Europe in the frame of the OPERA LIFE+

project.

For air quality modelling of the BCR, the AURORA chemical transport model was used with a domain of 49 x

49 grid cells at 1 km resolution with base emissions for the year 2009. For the vertical discretization, 20 layers

were used for a domain extending up to 5 km.



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Brussels domain simulation.

The results of the 1 km resolution model setup were validated by comparison to the observed values at the

measurement stations inside the model domain. For the model validation, the FAIRMODE methodology

(http://fairmode.jrc.ec.europa.eu/) was adopted.

For the Design of Experiment phase, three levels of emission reduction were distinguished: base case (B),

high emission reductions (H) and low emission reductions (L). In order to determine the emission reduction

scenarios, the three levels B, H, L were combined to produce 14 emission scenarios..

RIAT+ could be also run to look for optimal policies beyond the 2020 Current legislation. As the emission

changes, that can be obtained with the selected set of measures, are limited, unsurprisingly, the

concentration changes are also limited. Following figure 16 shows an example of NO2 concentration changes

due to the emission abatement measures.



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NO2 concentration changes traffic&non industrial heating measures.

5.1.3 GAINS

GAINS explores cost-effective multi-pollutant emission control strategies that meet environmental

objectives on air quality impacts (on human health and ecosystems) and greenhouse gases.

It is well known and used broadly by MNREP and their external experts.

5.1.4 GEM-E3 macro-economic model

The GEM-E3 model is a model connected with gains used to model the macro-economic impacts of emission

control strategies for all Member States of the European Union.

GEM-E3 is a model that covers the interactions between the economy, the energy system and the

environment, especially designed to evaluate energy, climate and environmental policies. GEM-E3 can

evaluate consistently the distributional and macro-economic effects of policies for the various economic

sectors and agents across the countries. GEM-E3 is used regularly to provide analytical support to European

Commission services, particularly with regards to the economics of climate change. The model is extensively

used as a tool of policy analysis and impact assessment.

GEM-E3 allows for a consistent comparative analysis of policy scenarios since it ensures that in all scenarios,

the economic system remains in general equilibrium. In addition it incorporates micro-economic

mechanisms and institutional features within a consistent macro-economic framework and avoids the

representation of behaviour in reduced form. Particularly valuable are the insights the model provides

regarding the distributional aspects of long-term structural adjustments.

The model intends, in particular, to analyse the global climate change issue a theme that embraces several

aspects and interactions within the economy, energy and environment systems. To reduce greenhouse gas

emissions it is necessary to achieve substantial gains in energy conservation and in efficiency in electricity



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generation, as well as to perform important fuel substitutions throughout the energy system, in favour of

less carbon intensive energy forms.

Moreover, within the context of increasingly competitive markets, new policy issues arise. For example, it is

necessary to give priority to market-oriented policy instruments, such as carbon taxes and pollution permits,

and to consider market-driven structural changes, in order to maximise effectiveness and alleviate

macroeconomic consequences. Re-structuring of economic sectors and re-location of industrial activities

may be also induced by climate change policies. This may have further implications on income distribution,

employment, public finance and the current account.

The input from other EC4MACS models are:

− Energy prices , obtained from the PRIMES model

− Emission control costs, obtained from the GAINS model

The data source used by the model is the economic statistics from EUROSTAT.



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6 CONCLUSION AND PROPOSAL OF FURTHER ACTIONS

6.1 CONCLUSIONS

• The main stages in the emission inventory preparation has been described for 6 different

countries in Europe. In all cases except one, the inventories are developed in their own database

(based in Oracle, excel, access, …etc.), without the use of a commercial software.

• The NKE has found in Europe a specific software able to carry out reporting on the pollutants

emissions in the EMEP format “Collecter”. It could be a very useful tool for national emission

inventory compilers- particularly for countries which are starting out with their inventory

compilation, and therefore don't already have their own database/data collection system.

However only Denmark uses this tool for supporting the emission inventory. The rest of the

European countries develops their own inventory using their own databases and the results are

adapted depending on the required report.

• The main data sources are national statistics, energy balances, agricultural statistics, etc. or any

other reporting in line with other national/international reporting requirements.

• In most of the countries large combustion plants, e.g. power plants, municipal waste incineration

plants and large industrial plants are registered individually as large point sources. This enables

use of plant-specific emission factors that refer to emission measurements stated in annual

environmental reports.

• Another factor that is critical to the success of the overall process in the emission inventory is

the selection and review of, and (where necessary) changes in, data sources, since the quality of

results of all downstream processes (data preparation, calculation, reporting) cannot be better

than that of the primary data used.

• Further, Member States are encouraged to report their emission inventories on the basis of fuel

sold for the ‘Road transport’ sector, in line with the reporting guidelines (UNECE, 2014a).

Reporting of “fuel sold” is a minimum requirement although a number of countries may choose

to additionally report road transport emissions on the basis of “fuel used” for compliance

checking purposes.

• The best international practice is to have the same team of experts working both on air quality

and greenhouse gases emission inventories.

• The quality of the inventory should be improved by the organization and participation in sector

specific workshops.

• A specific procedure undertaken for improving the inventory regards the establishment of

national expert panels (in particular, in road transport, land use change and forestry and energy

sectors) which involve, on a voluntary basis, different institutions, local agencies and industrial

associations cooperating for improving activity data and emission factors accuracy.

• Focusing on the energy sector and industrial processes, it is important the maintaining of an air

quality emission inventory, not only for the purpose of reporting under the Convention, but also

for:

• formulating national policies and measures,

• for various projection and modelling studies,

• for the assessment of impacts on health, economy and environment, and



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• for the evaluation of energy efficiency measures.

• Atmospheric dispersion modelling is an essential tool in air quality management by providing

the link between environmental effects and discharges to air.

• Air dispersion

6.2 PROPOSAL FOR BELARUS

• The Belarus inventory will be continuously amended due to the use of more detailed

methodologies, better access to underlying data and better knowledge, and development of

complementary studies. This is the normal process in Europe. It is not necessary to develop an

specific software for the inventory and reporting.

• In order to improve the emission inventory in Belarus, emission inventory questionnaires should

be used by specific sector in order to report their emissions. Some questionnaires are annexed

to this report to serve as an example.

• Air dispersion models are needed to be used by large companies or specific municipalities in

order to know the current impact of their air emissions.

• Air dispersion models can be used as tool for cost-benefit analysis in order to evaluate the

environmental benefit of a specific measure projected for reducing the emissions to the air.

• GAINS model provides a consistent framework for the analysis of cost-benefits reduction

strategies from air pollution. It is recommended to use GAINS as a tool for assess in a global

perspective the effect of emissions reduction after the implementation of one of the 2000

specific emission control measures and their costs.

• With regards to limited number of sectors simple models (e.g. Excel sheets) could be developed

to easy calculate the expected costs and benefits adherent to certain actions for pollution

reduction. These are fast working tools but their uncertainty is high.

• NKE also recommends following complementary actions.

• Implementation of automated measuring systems in the stationary source emissions, at least

in the large combustion plants.

• Improvement of the air quality network.

• Elaboration of air quality planning. Plans and Programs should be developed for regions

where limit values are exceeded. These plans should include different measures to reduce

emissions from different sectors, e.g. reduce industrial or residential combustion emissions,

introduction of Low Emission Zones ...etc. The plan also should include a cost analysis for

each proposed measure. For the development of these plans, following aspects should be

taken into account:

• Selection of activities and categories that will be considered.

• Identifying the main industries in Belarus

• To determine the air quality areas and to identify the critical emissions areas

component b3.2. analysis of the current practice in the

Documents