Scoping Report
Air Quality Modelling Activities in
Southern Africa and the Feasibility of a
Regional Modeling Centre
Prepared by:
Dr Mark Zunckel, Task Team Leader, Modelling Task Team
CSIR
Natural Resources and the Environment
P O Box 17001, Congella, 4013
South Africa
Contributions from:
Tiroyaone Tshukudu (Botswana)
James Chimphamba (Malawi)
Genito Maure (Mozambique)
Msafiri Jackson (Tanzania)
Richard Mugara (Zambia)
Barnabas Chipindu (Zimbabwe)
TABLE OF CONTENTS
Executive Summary...............................................................................................................................iii
Acknowledgements……………………………………………………………………………………………..iv
Glossary of Acronyms and Abbreviations…………………………………………………………………..v
1
INTRODUCTION ........................................................................................................................................ 1
2
TERMS OF REFERENCE .......................................................................................................................... 2
3
METHODOLOGY ....................................................................................................................................... 2
3.1
Establishment of the Atmospheric Transport and Deposition Task Team ........................... 2
3.2
Scoping study .............................................................................................................................. 2
3.3
Feasibility study........................................................................................................................... 3
4
URBAN AND REGION-SCALE MODELLING ........................................................................................... 3
4.1
Air quality modelling overview ................................................................................................... 3
4.1.1
4.1.2
4.1.3
4.1.4
4.1.5
4.1.6
4.2
5
Plume-rise Models.......................................................................................................... 4
Gaussian Models ............................................................................................................ 5
Eulerian Models .............................................................................................................. 5
Lagrangian Models......................................................................................................... 6
Chemical Models ............................................................................................................ 6
Receptor Models ............................................................................................................ 7
The quality modelling process ................................................................................................... 7
EXISTING MODELLING ACTIVITIES IN THE REGION ........................................................................... 7
5.1
Botswana ...................................................................................................................................... 7
5.1.1
5.1.2
5.2
Malawi ........................................................................................................................................... 8
5.2.1
5.2.2
5.3
Modelling and modelling capabilities ........................................................................ 12
Data, models and processing ..................................................................................... 12
Zimbabwe ................................................................................................................................... 13
5.7.1
5.7.2
5.8
Modelling and modelling capabilities ........................................................................ 11
Data, models and processing ..................................................................................... 11
Zambia ........................................................................................................................................ 12
5.6.1
5.6.2
5.7
Modelling and modelling capabilities .......................................................................... 9
Data, models and processors ..................................................................................... 10
Tanzania ..................................................................................................................................... 11
5.5.1
5.5.2
5.6
Modelling capabilities and capacity ............................................................................. 8
Data, models and processors ....................................................................................... 9
South Africa ................................................................................................................................. 9
5.4.1
5.4.2
5.5
Modelling capabilities and capacity ............................................................................. 8
Data, models and processors ....................................................................................... 8
Mozambique ................................................................................................................................. 8
5.3.1
5.3.2
5.4
Modelling capabilities and capacity ............................................................................. 7
Data, models and processors ....................................................................................... 8
Modelling and modelling capabilities ........................................................................ 13
Data, models and processing ..................................................................................... 13
Summary for southern Africa ................................................................................................... 13
6
FEASIBILITY OF ESTABLISHING A CENTRE FOR REGIONAL SCALE MODELLING ...................... 14
6.1
THE CAMx MODEL .................................................................................................................... 14
6.2
Establishment of a regional centre for modelling .................................................................. 16
7
CONCLUSION AND RECOMMENDATIONS FOR FUTURE APINA ACTIVITIES ................................. 17
8
REFERENCES ......................................................................................................................................... 18
ii
EXECUTIVE SUMMARY
As a component of the Air Pollution Information Network for Africa (APINA) Phase III, this scoping
report provides an overview of the current modelling activities in southern Africa as well as an
assessment of the current resource base and the need for such expertise and capabilities in
Botswana, Malawi, Mozambique, South Africa, Tanzania, Zambia and Zimbabwe. The feasibility of
establishing a regional-scale modelling centre is also examined considering the importance
associated with a regionally shared atmosphere and trans-boundary transport of air pollution in the
Southern African Development Community (SADC) region.
APINA Country representatives (ACR) and APINA National Focal Points (NFP) assisted the
Atmospheric Transport and Deposition task team leader in identifying appropriate individuals in the
respective countries to participate in the scoping exercise. The task team comprises the following
members:







Tiroyaone Tshukudu
James Chimphamba
Genito Maure
Mark Zunckel
Msafiri Jackson
Richard Mugara
Barnabas Chipindu
Botswana
Malawi
Mozambique
South Africa (task team leader)
Tanzania
Zambia
Zimbabwe
Air quality management is gaining prominence at the local and urban scale and there is a growing
need for skilled professionals. The findings of this scoping study reveal that there is a dire shortage of
qualified and experienced modellers in most southern African countries with the exception of Tanzania
and South Africa. Two options are recommended to addressing the capacity gap. These are:


for modellers to attend courses run specifically for the models that are deployed in their
area of interest, or
for an appropriate institution in Tanzania or South Africa to develop a short course for a
selected local scale model. Such a course could be run and repeated for groups in the
region.
The nature of the air pollution issues at the regional scale emphasises the need to build this capability.
The lack of capacity in southern Africa to conduct regional scale transport and deposition modelling is
evident. Only one group at the Council for Scientific and Industrial Research (CSIR) in South Africa
has developed capacity and competence in this aspect of modelling. In order to bridge this capacity
gap in a cost effective and efficient manner it is recommended to use this foundation as a point of
entry.
Given the technical complexity associated with regional scale photochemical modelling a two phase
approach is recommended. The initial phase should be a workshop to create awareness, to scope
regional scale modelling issues and to identify suitable candidates for the second phase. Phase two
should take the form of secondments of identified individuals to the proposed modelling centre.
Secondements provide the benefits of working on identified projects in a mentorship mode with an
established research team and provide the opportunity of sustained interaction over a period of time.
It is believed that in this manner a core of individuals will develop and constitute a regionally strong
team. The science will become rigorous and credible and the potential impacts on air quality
management decision making on a regional scale will be enhanced.
iii
ACKNOWLEDGEMENTS
The authors acknowledge the funding from the Swedish International Development Cooperation
Agency (Sida) and the support and encouragement for the APINA leadership team. The contributions
by the APINA Modelling task team members are acknowledged. Prof Hannes Rautenbach at the
University of Pretoria and Greg Scott from CSIR are acknowledged for their inputs.
iv
GLOSSARY OF ACRONYMS AND ABBREVIATIONS
ACR
APINA
CAMx
CAPIA
CB4
CSIR
DAPPS
DEAT
EMU
INAM
NFP
SADC
SAFARI
SAWS
Sida
TMA
UCLAS
UNFCCC
USEPA
APINA Country Representative
Air Pollution Information Network for Africa
Comphrehensive Air Quality Model with extensions
Crossborder Air Pollution Impact Assessment
Carbon Bond 4
Council for Scientific and Industrial Research
Dynamic Air Pollution Prediction Project
Department of Environmental Affairs and Tourism
Eduardo Mondlane University
Mozambican Weather Service
National Focal Point
Southern African Development Community
Southern African Regional Science Initiative
South African Weather Service
Swedish International Development Cooperation Agency
Tanzania Meteorological Agency
University College of Lands and Architectural Studies
United Nations Framework Convention on Climate Change
United States Environment Protection Agency
Models:
ADMS - Urban
A comprehensive tool for tackling air pollution problems in cities and towns. It can be used to examine
emissions from 6,000 sources simultaneously, including road traffic, industrial sources and grid
sources.
AERMOD
The AERMOD air pollution dispersion modelling system is an integrated system that includes three
modules:
 A steady-state dispersion model designed for short-range (up to 50 kilometers) dispersion of
air pollutant emissions from stationary industrial sources.
 A meteorological data pre-processor (AERMET) that accepts surface meteorological data,
upper air soundings, and data from on-site instrument towers. It then calculates atmospheric
parameters needed by the dispersion model, such as atmospheric turbulence characteristics,
mixing heights, friction velocity, Monin-Obukov length and surface heat flux.
 A terrain preprocessor (AIRMAP) whose main purpose is to provide a physical relationship
between terrain features and the behavior of air pollution plumes. It generates location and
height data for each receptor location. It also provides information that allows the dispersion
model to simulate the effects of air flowing over hills or splitting to flow around hills.
CALINE3
A steady-state Gaussian dispersion model designed to determine air pollution concentrations at
receptor locations downwind of highways located in relatively uncomplicated terrain.
CALPUFF
A multi-layer, multi-species non-steady-state puff dispersion model that simulates the effects of timeand space-varying meteorological conditions on pollution transport, transformation and removal.
CALPUFF can be applied on scales of tens to hundreds of kilometers. It includes algorithms for
v
subgrid scale effects (such as terrain impingement), as well as, longer range effects (such as pollutant
removal due to wet scavanging and dry deposition, chemical transformation, and visibility effects of
particulate matter concentrations).
CAMx
The Comprehensive Air Quality Model with extensions is a publicly available open-source computer
modelling system for the integrated assessment of gaseous and particulate air pollution. Built on
today’s understanding that air quality issues are complex, interrelated, and reach beyond the urban
scale, CAMx is designed to:




Simulate air quality over many geographic scales.
Treat a wide variety of inert and chemically active pollutants:
- Ozone
- Inorganic and organic PM2.5/PM10
- Mercury and toxics.
Provide source-receptor, sensitivity, and process analyses.
Be computationally efficient and easy to use.
CONCX
Used to calculate short term downwind concentration at ground level or in specified receptor point for
various distances and also for a selected meteorological condition. Inputs to the model consist of
information of the source and the meteorological condition to be considered.
CONDEP
Used to calculate long term sector averaged concentrations for twelve 30 0 sector in specified receptor
points or in a given grid. The input to the model consists of a source data for up to 50 point sources
and a meteorological joint frequency matrix of four wind speed classes, four stability classes and
twelve wind sectors.
COPERT 3
An emissions model for calculating emissions from motor vehicle traffic.
CG-MATHEW (Conjugated-Gradient Mass Adjusted THrEe dimensional Wind field)
A diagnostic model designed to produce a gridded three dimensional mass conservative mean wind
fields from time average measured meteorological data. The model has the following main
characteristics:
 It incorporates terrain explicitly, in order to be site independent.
 It uses available meteorological measurements (wind direction, wind speed, temperature, and
temperature gradient).
 It is computationally stable.
 It calculates three dimensional velocity field with a relatively large number of grid points in
relatively short computer time.
EPISODE
This is a comprehensive air pollution dispersion model for urban and local-to-regional scale
applications. EPISODE provides the numerical solution of the atmospheric (mass) continuity equation
on a 3D Eulerian grid. The numerical solution of the continuity equation involves description of the
transport (advection) and dispersion (diffusion) of air pollutants using a 3D coordinate system
(Eulerian grid). The model is typically used to calculate air pollution in urban areas from emission
sources such as road traffic, industry stacks and domestic burning. It may be applied on any scale
vi
ranging from local to regional scale. EPISODE calculates ground level hourly average concentrations
and dry and wet depositions as gridded data and/or at individually placed receptor points.
EPS 3
A modular emissions processing system used to generate temporally and spatially resolved grid
emissions for photochemical modelling from basic emissions data.
ETA
New generation prognostic meteorological model.
IPIECA
A spreadsheet type model for the calculation of emissions from motor vehicle traffic.
ISC3
A steady-state Gaussian plume model which can be used to assess pollutant concentrations from a
wide variety of sources associated with an industrial complex. This model can account for the
following: settling and dry deposition of particles; downwash; point, area, line, and volume sources;
plume rise as a function of downwind distance; separation of point sources; and limited terrain
adjustment. ISC3 operates in both long-term and short-term modes.
HAWK
A Gaussian puff model based on CALPUFF for single and multiple sources and source types. An
advantage is the capability of modelling very short averaging periods (5 sec- 5min typically), unlike
normally 1 hour minimum averaging times. Includes the comprehensive treatment of fires.
HRM
A high resolution regional weather prediction model able to simulate mesoscale features such a
tropical cyclone movement.
LED
Is a Eluerian-Lagrangian Diffusion model that has been developed in South Africa.
MEPDIM (MEteorological Processor for DIspersion Modelling)
A new generation dispersion model based on the parameterization of the structure of the atmospheric
boundary layer to provide improved meteorological modelling. This meteorological processor is based
on two optimal methods; the profile method and the energy budget method. The profile method needs
both vertical profiles of wind speed and temperature as input while the energy budget needs either
cloud cover or direct measurement of net radiation.
MATCH
Is an Elurian multi-layer three-dimensional model that includes horizontal and vertical transport,
vertical diffusion, dry deposition, wet scavenging and chemical transformations.
MM5
The PSU/NCAR mesoscale model (known as MM5) is a limited-area, nonhydrostatic, terrain-following
sigma-coordinate model designed to simulate or predict mesoscale atmospheric circulation.
NSM
The Non-hydrostatic Sygma-Coordinate-Model which can simulate cloud processes and wind over
obstacles on a very fine resolution. The model uses the full set of atmospheric equations.
vii
PUM
The ported Unified Model is a weather prediction model.
SCREEN 3
A single source Gaussian plume model which provides maximum ground-level concentrations for
point, area, flare, and volume sources, as well as concentrations in the cavity zone, and
concentrations due to inversion break-up and shoreline fumigation.
TANKS
Is used to predict the annual average emissions of volatile organic compounds from organic liquids
storage tanks.
TAPM
The Air Pollution Model used to model surface and upper air meteorology at selected sites based on
gridded reanalysis data and observed data.
Water 9
Is a Windows based computer program. It consists of analytical expressions for estimating air
emissions of individual waste constituents in wastewater collection, storage, treatment, and disposal
facilities; a data base listing many of the organic compounds; and procedures for obtaining reports of
constituent fates, including air emissions and treatment effectiveness.
WRF
The Weather Research and Forecasting model.
viii
1
INTRODUCTION
Air quality management is a systematic and holistic approach of assessing the status of air quality in a
given area, setting air quality objectives, designing and implementing air pollution control strategies,
and reassessing and measuring progress in meeting the objectives (Fig. 1). Fundamental to the air
quality management cycle is a sound scientific and technical core. Skills, capabilities and
technologies that are included among the scientific and technical requirements are the compilation
and maintenance of emission inventories, air quality dispersion and deposition modelling, emissions
monitoring and ambient air quality monitoring.
Figure 1: The air quality management cycle (National Research Council, 2004).
This systematic approach to air quality management is accepted and practiced in the United States,
Europe, Australia and other developed countries where it is backed by solid scientific and technical
resources. In southern Africa the approach to air quality management is generally not systematic and
in many cases the technological resources are poor and it is not backed by appropriately skilled
1
scientific and technical personnel. This short coming is recognised by the Air Pollution Information
Network for Africa (APINA) who has embarked on a number of focused technical tasks in Phase III of
the Sida funded APINA project, to improve the skills in the scientific and technical personnel that
support air quality management in southern African countries.
As a component of APINA Phase III, this report focuses on air quality transport and deposition
modelling. An overview of the current modelling activities in southern Africa is provided as well as an
assessment of the current resource base and the need for such expertise and capabilities in each of
the countries. Regional-scale modelling and deposition is important for air quality management at this
scale, considering issues such as the regionally shared atmosphere and trans-boundary air pollution.
The feasibility of establishing a regional-scale modelling centre is also examined.
2
TERMS OF REFERENCE
The terms of reference for this component of the Atmospheric Transport and Deposition Task Team’s
study are to:





compile a scoping study on the existing modelling activities in the region;
discuss the best approaches to urban and regional scale modelling;
provide and overview of the Comprehensive Air Quality Model with extensions (CAMx) for
application at different scales;
conduct a feasibility study for the establishment of a regional modelling centre; and
make recommendations for future APINA transport and deposition modelling activities.
3
METHODOLOGY
3.1
Establishment of the Atmospheric Transport and Deposition Task Team
APINA Country representatives (ACRs) and APINA National Focal Points (NFPs) assisted in
identifying appropriate individuals in the respective APINA countries to participate in the Atmospheric
Transport and Deposition Task Team. The team is coordinated by Dr Mark Zunckel of South Africa
and comprises the following members from seven southern African countries. The task team is:







3.2
Tiroyaone Tshukudu
James Chimphamba
Genito Maure
Mark Zunckel
Msafiri Jackson
Richard Mugara
Barnabas Chipindu
Botswana
Malawi
Mozambique
South Africa
Tanzania
Zambia
Zimbabwe
Scoping study
Task team members have compiled country specific scoping reports with emphasis on a few key
issues, including models, the current modelling expertise, model input data, projects involving
modelling and the requirement in the respective countries for modelling. The individual country
reports have been integrated into Section 5 of this report to provide an overview of the existing
modelling transport and deposition activities in southern Africa.
2
3.3
Feasibility study
Expert opinion on the requirements of establishing a centre for regional scale modelling has been
used to evaluate the feasibility of developing such a centre.
4
URBAN AND REGION-SCALE MODELLING
4.1
Air quality modelling overview
Atmospheric dispersion models are mathematical simulations of the physics and chemistry that control
the transport and transformation of pollutants in the atmosphere. They provide a means of estimating
air pollutant concentrations in the ambient environment based on information on emissions and the
prevailing meteorology. Atmospheric transport and deposition modelling at urban and regional scale
is usually conducted with air quality management objectives in mind. Examples of these objectives
may be:










to assess compliance of emissions with air quality guidelines or standards;
in planning new facilities;
to determine appropriate stack heights;
to manage existing emissions;
to design ambient air monitoring networks;
to identify the contributors to existing air pollution problems;
to evaluate policy and mitigation strategies and interventions;
for air pollution forecasting;
to estimate the influence of geophysical features on dispersion; or
as a surrogate for monitoring that would otherwise be too costly.
The information required for atmospheric dispersion modelling usually includes:





pollutant characteristics including emissions rates;
characteristics of the emission source;
topography of the area of interest, i.e. the modelling domain;
surface and lower tropospheric meteorology; and
ambient or background concentrations of the pollutants of interest.
After the selection of the most appropriate model, the air quality modelling process typically comprises
four stages (Fig. 2). It is important to understand the accuracy and uncertainty of each stage in order
to evaluate the modelling results and to provide an assessment of the confidence in the modelling.
A great number of dispersion models have been developed by a number of groups, each for a specific
purpose. With this array of models available to the modeller, one of the fundamental elements of
atmospheric dispersion modelling is the choice of the correct model for the modelling job at hand. A
number of key questions need to be asked when selecting an atmospheric model. These include:






Is the study for screening purposes or is a more complex modelling exercise required?
What is the spatial scale of interest?
Is chemistry important?
Does the modelling domain comprise complex terrain or a coastal environment?
Is the atmosphere very convective?
Are surface and upper air meteorological data available?
3



What are the physical characteristics of the sources?
What are the physical and chemical properties of the pollutants to be modelled?
Are representative emissions data available for the sources and pollutants?
Figure 2: Four stages of air quality modelling (NZ MFE, 2004).
Atmospheric transport and deposition models that are recommended by the United States
Environment Protection Agency (US EPA) have undergone rigorous validation. These models are
available and may be downloaded from the EPA’s Support Centre for Regulatory Atmospheric
Modelling at http://www.epa.gov/scram001/dispersionindex.htm.
These models may be classified into the following categories (Zannetti, 1993):
4.1.1
Plume-rise Models
A pollutant plume that is released into the atmosphere normally has a higher temperature than the air
around it. Pollutants emitted from industries (normally through their stacks) have an initial vertical
momentum. These factors are referred to as thermal buoyancy and vertical momentum and have a
role in determining the effective height of the plume above the point of its release. Plume-rise models
are therefore used to determine the vertical displacement and to describe the general behaviour of the
plume dispersion in its initial stages.
4
4.1.2
Gaussian Models
Gaussian models are based on the assumption that plume concentration, at each downwind distance,
has independent Gaussian (normal) distributions both in the horizontal and in the vertical dimensions
(Fig. 3). These models are regarded as the most common type of air pollution models and can be
used to calculate long-term averages from single or multiple sources.
Figure 3: Gaussian plume dispersion (Schulze and Turner, 1996).
4.1.3
Eulerian Models
In an Eulerian model, chemical species are transported in a fixed grid (Fig. 4). Eulerian models use
numerical terms to solve the atmospheric diffusion equation (i.e. the equation for conservation of mass
of the pollutant), and are therefore able to model the transport of inert air pollutants. The numerical
solution of the transport term in the Eulerian framework becomes more difficult and often requires
substantial computational resources to be accurate enough compared to the Lagrangian approach.
The main advantage of the Eulerian models is the well defined three dimensional formulations which
are needed for the more complex regional scale air pollution problems. Eulerian models can simulate
turbulence and are usually embedded in prognostic meteorological models.
5
Figure 4: Structure of a basic Eulerian model (Environ, 2005a).
4.1.4
Lagrangian Models
In Lagrangian models a specific parcel of air is followed and the concentrations of a pollutant are
assumed to be homogeneously mixed in the parcel. The Lagrangian model is based on fluid elements
that follow instantaneous flow - transport is determined by trajectories of the air flow. They include all
models in which plumes are broken up into constituents such as segments, puffs, or particles.
Lagrangian models use fictitious particles to simulate the dynamics of a selected physical parameter.
Transport is a result of the average wind and the turbulent term is taken into account. The main
advantage of the Lagrangian approach is the simple numerical treatment of the transport term in the
mass balance equation. The main disadvantage is that it is difficult to account for exchange processes
between air parcels and windshear, making three dimensional Lagrangian models not very reliable.
4.1.5
Chemical Models
Air pollution models which include a chemical component to simulate chemical transformation in the
atmosphere are referred to as chemical models and range from simple chemistry to complex
photochemical reactions. Reaction schemes for simulating the dynamics of interacting chemical
species have been incorporated into both Lagrangian- and Eulerian-based photochemical models. In
Eulerian photochemical models, a three-dimensional grid is superimposed across the entire modelling
domain, and all chemical reactions are simulated in each grid cell at successive time steps. In the
6
Langrangian photochemical models, a single cell is advected according to the predominant wind
direction, such that any emission encountered along the cell trajectory can be injected into the cell.
4.1.6
Receptor Models
Receptor models work differently to dispersion models in that they start with observed concentration of
a pollutant at a receptor point rather than from the source. Receptor models are based on massbalance equations which are used to determine the concentration of a pollutant at its source or at
other points within the modelling domain. Receptor models do not provide a deterministic relationship
between emissions and concentration.
4.2
The air quality modelling process
After the selection of the most appropriate model, the air quality modelling process typically comprises
four stages (Fig. 2). Air quality modelling is typically data intensive (Stage 1) and it is important that
the best available and most representative data sources are used. It is important to understand the
accuracy and uncertainty of each of the input data classes (emissions, topography, meteorology, etc).
Models typically have a variety of set up and run-mode options. It is critically important in Stage 2 that
the modeller has a thorough understanding of the model and of theory and implications of each option.
Selection of an inappropriate modelling option can result in unrealistic model simulations.
It is important to emphasise that a good understanding of all the input data and its strengths and
weaknesses, as well as an understanding of the model set up, will put the modeller in an
advantageous position when interpreting the model output.
5
EXISTING MODELLING ACTIVITIES IN THE REGION
In this section the modelling activities in each of the seven APINA countries are presented.
Information is provided on the modelling capabilities, the available capacity and on the models that are
commonly used. The information is drawn together at the end of the section when conclusions and
recommendations on the future needs are made.
5.1
Botswana
5.1.1
Modelling capabilities and capacity
Atmospheric transport and deposition modelling is conducted by three groups. These are the:



Department of Waste and Pollution Control.
Meteorological Services.
University of Botswana.
Officers in the Department of Waste Management and Pollution Control have had exposure to air
quality modelling and meteorological processing either through their attachment to the Norwegian
Institute for Air Research or through on the job training.
7
5.1.2
Data, models and processors
The Department of Waste Management and Pollution Control measures wind speed and direction at
10 m in all its 21 air pollution monitoring stations throughout Botswana. The Department also has a
25 m meteorological tower in Selebi-Phikwe measuring the same parameters including vertical
temperature difference.
An emission processing model is available as a component of the Air Quality Information System. This
system has the capability to calculate hourly emissions for area, line and point sources, i.e.:



Area sources: Using annual consumption of fossil fuels for area sources, emission factors,
time variations and temperature variation to calculate hourly emissions.
Line sources: Using road and traffic data, road and traffic classification, emission factors, traffic
dependencies and time variations to calculate hourly emissions.
Point sources: Using process consumption or emission data, emission factors and time
variations to calculate emissions from point sources.
Air quality models that are available are:





CONCX for the calculation of short term downwind concentration at ground level or in specified
receptor point for various distances from source.
CONDEP for the calculation of long term sector averaged concentrations for twelve 30 0 sectors
in specified receptor points or in a given grid.
MEPDIM is a meteorological processor.
CG-MATHEW is a diagnostic gridded three dimensional mass conservative mean model.
EPISODE is a comprehensive air pollution dispersion model for urban and local-to-regional
scale applications.
5.2
Malawi
5.2.1
Modelling capabilities and capacity
No air quality models or modelling capacity exists in Malawi.
Related skills reside in the
Meteorological Department where meteorological forecasting is conducted.
5.2.2
Data, models and processors
Emission data for Malawi were compiled for the country’s response to the initial national
communication under the United Nations Framework Convention on Climate Change in 1994. The
emissions are categorized by sector and not by type of sources.
5.3
Mozambique
5.3.1
Modelling capabilities and capacity
No dispersion model is currently being used in Mozambique, but one individual has exposure to some
air quality modelling.
Mozambique has expertise in the field of meteorological modelling. Meteorological models are run by
two institutions, namely:
8


Eduardo Mondlane University (EMU), and
Mozambican Weather Service (INAM).
The expertise base consists of the following:



5.3.2
One expert at EMU trained at MSc level in modelling (in atmospheric sciences), with additional
training in HRM (Germany), MM5 and WRF (self taught); also exposure to air quality models
MATCH, Airviro (Sweden), and CAMx.
Four experts at INAM trained by EMU at BSc. Honors level in modelling (with MM5) and by the
UK Meteorological Office (with PUM).
One expert in trans-boundary air pollution currently pursuing his PhD in Durban.
Data, models and processors
Emission data for Mozambique were produced in 2000 during the emission inventory activity carried
out for the First National Communication to the United Nations Framework Convention on Climate
Change (UNFCCC). These and other data may be found at the following places:
 The Ministry of Energy.
 The Mozambican Statistical Institute.
 The Ministry of Environment.
 The Ministry of Agriculture.
 The Ministry of Transport.
Basic observational meteorological data exists since 1961 in digitized format for most of the stations at
INAM. Also, data for case studies can be generated by MM5 or HRM.
Three meteorological models are available in Mozambique:
 MM5 for mesoscale meteorological modelling.
 Deutscher WetterDienst HRM, is run operationally for national weather prediction at the
Eduardo Mondlane University.
 The UK Meteorological Office PUM is run operationally for national weather prediction at the
Mozambican Weather Service.
5.4
South Africa
5.4.1
Modelling and modelling capabilities
Air transport and deposition modelling is not taught as a subject at any South African tertiary education
institute, but the capability has been well established in a number of research institutions and
consulting companies through courses and hands-on training. The focus of application is historically
local scale modelling and contributing to impact assessment, but the recently promulgated National
Environmental Management Air Quality Act (Act 39 of 2004) has necessitated a shift to using
modelling for planning. Only recently has modelling work been done on a regional scale. A well
established capability in operational and medium term forecast meteorological modelling exists in the
South African Weather Service and at Universities.
9
The following institutions have skilled capacity in atmospheric transport and deposition modelling:










5.4.2
Airshed Planning Professionals.
City of Johannesburg.
CSIR.
Ethekweni Municipality.
Gondwana Environmental Solutions.
Richards Bay Clean Air Association.
SRK Consulting.
Stuart Scott Consulting Engineers.
The South African Weather Service.
The University of Pretoria.
Data, models and processors
Some of the models that are commonly used in South Africa include the following:
Screening models:
Dispersion models:
Photochemical models:
Emissions models:
Meteorological models:
SCREEN 3
ISC3
CALPUFF
AERMOD
ADMS - Urban
HAWK
CALINE
CAMx
LED
EPS 3
COPERT 3
IPIECA
MM5
TAPM
ETA
Unified Model
WRF
NSM
Air quality and meteorological monitoring is conducted by 35 agencies in South Africa, who collectively
operate 266 monitoring sites (DEAT, 2006). Most of the monitoring is conducted in metropolitan and
industrial areas. Air quality monitoring focuses mostly on criteria pollutants. The South African
Weather Service (SAWS) operates a national meteorological monitoring network, with stations located
in most of the larger towns and cities. Upper air meteorological soundings are conducted at the larger
airports and at Irene near Pretoria.
South Africa does not have a coordinated national emissions inventory. A number of emissions
inventories have been compiled by metropolitan councils and by consultants and researchers for
specific projects. In some cases local councils have emission inventories of industrial boilers.
Industry specific and sector emissions are well understood in many instances, e.g. power generation
and refining industry. Examples of existing inventories are:


Gridded emissions for southern Africa for SAFARI 2000.
Richards Bay Clean Air Association.
10





The City of Cape Town.
Bellville South emissions inventory.
Ethekweni Municipality.
The Fund for Research into Industrial Development Growth and Equity.
The Department of Environmental Affairs and Tourism’s (DEAT) database of certificates for
scheduled processes.
More recently capabilities in photochemical modelling have been developed and applied at regional
and urban scales. CAMx and LED have been used for regional air quality assessment, while CAMx
has also been applied at an urban scale. Applicable to the intent of APINA, a solid capability base
comprising five modellers has been built in CAMx in conjunction with the developers, Environ Inc. The
development process has involved two southern African workshops, the first during the Crossborder
Air Pollution Impact Assessment (CAPIA) project and the second as a component of the Dynamic Air
Pollution Prediction (DAPPS) project. Capacity development in this group is ongoing with two PhD
studies and two MSc studies and study visits to the USA. Computing infrastructure to support this
capability has also been established.
5.5
Tanzania
5.5.1
Modelling and modelling capabilities
Seven academic staff with experience ranging from 10 to 15 years and students (about 20) at the
University College of Lands and Architectural Studies (UCLAS) and College of Engineering and
Technology, conduct research using atmospheric dispersion modelling to evaluate impacts and
mitigation options. In addition, University of Dar es Salaam has 10 years experience in emissions
modelling using the Computer Programme to calculate Emissions from Road Transport (COPERT).
The Department of Physics at the University of Dar-es-Salaam has more than 25 years experience in
meteorological modelling. Three Physics Department staff members are involved in research on
meteorological modelling and have published internationally. Five Tanzania Meteorological Agency
(TMA) staff members are trained and operate the Weather Research and Forecasting (WRF) Model
for meteorological prediction.
Over the last 10 years more than 300 students have graduated from local universities in Tanzania with
a sound knowledge of atmospheric modelling. Air Pollution and Control courses which include
atmospheric pollutants dispersion modelling are taught at three University Colleges in Tanzania.
5.5.2
Data, models and processing
A number of air quality models are used in Tanzania (Chikira, 2000; Munishi, 2002; Furgence, 2003;
Temu, 2004; Jackson, 2005; Jefta, 2005; Stefano, 2005; Sanzage, 2005; Jackson, 2006.). These are:







Gaussian air dispersion model.
Modified Gaussian (for line sources).
Modified Gaussian (for area sources).
CALPUFF.
Hybrid model- receptor dispersion model (Chemical and Mass balance Model).
ISC-AERMOD view air dispersion modelling system (ISCST3).
Weather Research and Forecasting (WRF) Model is operated by TMA.
11
A number of models are used to estimate emissions (Temu, 2004; Sanzage, 2005; Jackson, 2006):
 COPERT (Computer Programme to calculate Emissions from Road Transport) to estimate
emissions from traffic.
 WATER9 Ver 2: Air emissions estimate model.
 TANKS 4.9b: The US EPA standard regulatory storage tanks emission model.
A meteorological station is located in each district. TMA also operate meteorological stations at every
airport and seaport measuring ambient temperature, wind speed and direction, atmospheric pressure,
solar radiation, rainfall and relative humidity.
Tanzania does not have a formal emissions inventory. There is however a relatively good
understanding of emissions developed through a number of projects over a 10 year period.
Atmospheric transport and deposition modelling at a local scale is well established in Tanzania
(Jackson, 2005). Local scale modelling has been conducted successfully in impact assessments and
planning projects by university groups for a number of years. Projects include the assessment of motor
vehicle emissions in Dar es Salaam (Munishi, 2002; Furgence, 2003; Jackson, 2005), the impact of
emissions from power and steam generation plants (Jefta, 2005) and waste incinerators (Stefano,
2005) and cement factories (Chikira, 2000). There is limited air quality monitoring in Tanzania so air
quality modelling is an important scientific assessment tool for air quality management.
5.6
Zambia
5.6.1
Modelling and modelling capabilities
Meteorological forecast models are run by the Zambian Meteorological Department, but limited air
quality modelling has been conducted in the country. The mines in northern Zambia (Copperbelt
region) have conducted internal modelling projects, but these have unfortunately remained on the
shelves of the mining companies. Zambian delegates have been involved in introductory modelling
workshops through the APINA associated CAPIA project’s workshops on CAMx, held in Durban.
These activities have yet to spread to operational levels. Current efforts in the country are directed at
building capacity (human and computing) in air quality and transport modelling.
5.6.2
Data, models and processing
Meteorological data is available for much of Zambia through the Zambian Meteorological Department.
These include wind speed and direction, temperature, relative humidity and stability index. Some
emissions data was collected by the mining companies for the Copperbelt, but these monitoring
stations are no longer operational. No air quality models are currently used in Zambia. Some air
quality data are available from monitoring programmes conducted by the Environmental Council of
Zambia. These include industrial activity data, emission factors, emission data [nitrogen oxides (NOx),
sulphur oxides (SOx), ozone (O3) and carbon dioxide (CO2)].
The Zambian Copperbelt and the city of Lusaka may be regarded as air pollution hot spots in the
country, the Copperbelt from the perspective of smelting and mining emissions and the city from the
perspective of a growing motor vehicle fleet and urbanisation. There is a clear need to build air quality
modelling capacity and capability in the country to support air quality management initiatives and
informed decision making.
12
5.7
Zimbabwe
5.7.1
Modelling and modelling capabilities
The Zimbabwe Meteorological Department does not run any predictive models, but uses several
products from general circulation models for operational weather forecasting. No dispersion models
are operated in Zimbabwe, but some expertise exists in dispersion modelling in personnel based at
the Meteorological Department and the local university.
5.7.2
Data, models and processing
Emissions data for Zimbabwe were compiled for the country’s response to the initial national
communication under the United Nation Framework Convention on Climate Change in 1994. The
emissions are categorized by sector and not by type of sources. The Zimbabwe Meteorological
Department operates 15 synoptic stations and some automatic stations around the country. Similar to
many other countries, the temporal resolution of the synoptic scale data collection is not adequate for
the optimum running of air pollution models. The data is not freely available.
A member of the University of Zimbabwe’s Physics Department has been carrying out a project on
computing to calculate backward trajectories for trace gases at a single site in northern Zimbabwe.
The model can be adapted and applied to dispersion modelling. The findings of the research will be
published once the studies are completed. In addition, a copy of the model will be made available. In
a second study, the mesoscale meteorological numerical weather prediction model (MM5) has been
adapted to canopy areas near Harare. The skills with the Community Multi-Scale Air Quality numerical
modelling system for simulating ozone concentration, dispersion and deposition are being developed
in the Physics Department.
With air pollution a growing issue in Zimbabwe, the Air Pollution Control Unit has been set up within
the Environmental Protection Agency. Unfortunately the shortage of skilled manpower inhibits their
effectiveness. Within this group there is need for air quality modellers and modelling to support air
quality management and related activities.
5.8
Summary for southern Africa
Capabilities in the field of atmospheric transport and deposition modelling exist in all of the
participating southern African countries to a greater or lesser extent. In Botswana a relatively high
degree of staff turnover in institutions where atmospheric modelling is conducted impacts negatively
on continuity and the establishment of a strong modelling foundation. In Mozambique, Zambia and
Zimbabwe the skills base is limited with the focus primarily on meteorological modelling. A strong
modelling capability exists in South Africa with a wide experience base in a number of local scale
models, and a growing base in the field of regional scale and photochemical modelling. Local and
regional scale modelling is conducted by 10 or more research, consulting and government institutions.
The Tanzania Government recognises the importance of clean air and the country is fortunate to have
a relatively strong body of researchers and practitioners who have been trained in atmospheric
pollutants dispersion and meteorological modelling.
13
Thus, there are some strong aspects with regard to modelling in the southern African region, notably
in Tanzania and in South Africa. Despite this, there are some common weaknesses with respect to
modelling in the region which include:





Lack of customized models from within the region.
Lack of reliable ambient monitored data for model calibration and verification.
Lack or scarcity of model input data, including:
Representative time resolved surface and upper air meteorological data.
High resolution geophysical and topographical data.
Comprehensive and representative emissions data.
Chemical transformation data.
Lack of experienced local and regional scale modellers in some countries.
No centre in the region that specializes in atmospheric pollution modelling.
These shortcomings in air quality management in southern Africa have been recognised by APINA,
hence the establishment of task teams to address deficiencies in emission inventories, ambient
monitoring and air quality transport and deposition modelling. With respect to the latter task, APINA
has initiated this scoping study to identify the gaps, conduct a training workshop on regional scale
modelling, and to explore the feasibility of establishing a centre for regional scale modelling.
6
FEASIBILITY OF ESTABLISHING A CENTRE FOR REGIONAL SCALE MODELLING
Capability for regional scale photochemical modelling is important in southern Africa to evaluate and
address the topical and important issue of trans-boundary air pollution in a scientifically defendable
manner. It is therefore important to build capacity for regional scale modelling. Initial regional scale
modelling using the Swedish MATCH model was conducted by Zunckel et al (2000) which
emphasised the regional scale significance of wet and dry sulphur deposition resulting from emissions
on the South African Highveld and the Zambian Copperbelt. Later the CAPIA project assessed the
risk posed to maize by ozone in southern Africa on a regional scale using the CAMx model (van
Tienhoven et al, 2006; Zunckel et al, 2006, http://dbn.csir.co.za/capia/). CAPIA again stressed the
importance of understanding regional scale air quality, supporting the key drivers in APINA of
providing scientific guidance to policy making on a regional scale.
Through the execution of the CAPIA project, and later the DAPPS project (Zunckel et al, 2004;
http://dapps.csir.co.za/docs/index.htm) a sound modelling capability of five researchers has been
developed in South Africa at CSIR. The skills include meteorological modelling with MM5, emissions
processing using EPS3, skills using the necessary LINUX operating system, and CAMx modelling. On
the strength of having this regional scale modelling capability with CAMx available in southern Africa, it
is considered appropriate to build a regional scale capability from this foundation. An overview of
CAMx is therefore pertinent.
6.1
THE CAMx MODEL
CAMx is an Eulerian photochemical model that simulates the emission, dispersion, chemical reaction
and removal processes in the lower troposphere by solving the chemical continuity equation for each
species on a system of nested three-dimensional grids (Environs, 2003). The integrated capability
allows for simultaneous assessment of gaseous and particulate air pollutants [including ozone,
particulate matter with an aerodynamic diameter of less than or equal to a nominal 2.5 microns and 10
microns (PM2.5 and PM10) respectively] over scales ranging from sub-urban to regional and continental.
14
CAMx is a 3-dimensional grid model that runs on a LINUX operating system. The modelling domain is
divided into a 3-dimensional array of grid cells. Horizontal and vertical transport and chemical
transformation is resolved in each grid cell and at the interfaces between cells. The selection of the
grid resolution and size of the modelling domain therefore have immediate implications on the
computing resources that are available. The inter-cell processes are illustrated in Fig. 5 and the intergrid relationships are in the discussion on Eulerian models in Fig. 4.
Two chemistry mechanisms are available in CAMx that provide for a comprehensive simulation of the
chemistry in the modelling domain. Each caters for a different number of gases, radicles and
chemical reactions (Table 1). The Carbon Bond 4 (CB4) chemistry mechanism (Gery et al., 1989) was
developed in the late 1990s. It has a wide support base and is founded on the principle of dealing
with groups of chemical species with similar carbon bonds, rather than with individual species. CB4
with radicals is considered the most appropriate chemical mechanism for regional scale modelling and
allows for 91 reactions and 36 species, providing an integrated assessment of gaseous and
particulate air pollutants. SAPRC-99 has more detail than CB4, its (volatile organic compound) VOC
chemistry is more reactive, but its model has run times that can be 60% longer than CB4.
Figure 5: Inter-cell processes in the CAMx model (Environ, 2005a)
15
Table 1: Comparison between the four CB4 chemical mechanisms and SAPRC-99.
Mechanism
Gases
Radicals Reactions
SAPRC-99
56
18
211
CB4 & chlorine
56
18
211
CB4 & radicals
24
12
91
CB4 & isoprene
25
12
96
CB4 & aerosols
34
12
100
The input requirements for CAMx are:

Meteorology
CAMx requires 3-dimensional meteorological data, i.e. information on wind speed and direction,
temperature, pressure/height relationship, vertical diffusivity, water vapour and precipitation for
each grid cell. Such data is generated by MM5, a gridded mesoscale meteorological model.

Emissions
CAMx requires emissions to be resolved to the defined modelling grid. Emissions may include
point sources, area sources and line sources such as those from traffic. Spatial allocation of total
emissions to each grid cell, with the definition of temporal profiles and profiles for chemical
speciation is done using processing emissions data with systems such as the Emissions
Processing System (EPS V3) (Environ, 2005b).
CAMx is a US EPA approved photochemical model and is therefore available to users at their SCRAM
web site or at http://www.camx.com/.
6.2
Establishment of a regional centre for modelling
It is important to distinguish here between the need for a centre for regional scale or a centre for local
scale modelling. It is fair to say that local scale modelling is easily taught through short courses,
typically by the distributors of the various models, and local scale modelling is relatively easily applied.
To the contrary, regional scale modelling is complex, the data requirements are significant, instruction
is ongoing and best provided through a series of focussed interactions. The establishment of a centre
for regional scale atmospheric modelling has a number of benefits. These include:





Applying a consistent approach to teaching.
Growing a regional resource of modellers and instructors to address regional issues.
The ability to pool resources and build regional project teams.
The opportunity of centralising hardware for regional benefit.
Being able to attract international experts participation in projects.
There are a number of prerequisites that need to be in place when considering where such a centre
could be established. In considering these the country/institution should have:

experience and credibility in the field of modelling;
16


appropriate infrastructure e.g. computing hardware, office space, network, etc;
selected models operationally installed.
Table 2: Comparison of availability of prerequisites for the establishment of a regional centre for
modelling.
Modelling experience
Botswana
Malawi
Mozambique
South Africa
Tanzania
Zambia
Zimbabwe
Limited
Very Limited
Very limited
Extensive (local scale)
Well established (photochemistry)
Well established (regional scale)
Extensive (local scale)
Very Limited
Very Limited
Modelling
infrastructure
Limited
Very Limited
Very limited
Established
Established
Established
Established
Very Limited
Very Limited
Models
Limited
Very Limited
Very limited
Extensive
Appropriate model
Appropriate model
Moderate
Very Limited
Very Limited
Analysis of the information in Table 2 indicates that South Africa is the only suitable candidate country
for the establishment of a centre for regional scale air pollution modelling. Tanzania and South Africa
on the other hand would be suitable candidates for centres for local scale modelling. The focus
should fall on regional scale modelling considering the points raised above and the regional scale
nature of the APINA agenda.
7
CONCLUSION AND RECOMMENDATIONS FOR FUTURE APINA ACTIVITIES
Two of APINA’s primary objectives are to assist air pollution decision making in the region through
scientific input, and to assist in building appropriate capacity in southern Africa in disciples of air
pollution technologies and management. With respect to air pollution transport and deposition
modelling there is generally a poor resource base in all countries except Tanzania and South Africa.
Both of these countries have well established institutional capacity on local scale modelling and have
both established project and research track records. South Africa has also developed a strong
research team in the field of regional scale photochemical modelling.
The findings of this scoping study reveal a need for well qualified air pollution modellers in all
represented countries to conduct local scale modelling. Air quality management is gaining
prominence on a local or urban scale and there is a growing need for skilled professionals. Two
options are recommended in addressing the capacity gaps that exist for this scale of modelling:


Model distributors run short courses specific to their products. Such courses must be attended
in order to fully optimise models that are implemented.
Institutions and consulting groups in Tanzania and South Africa are well experienced in the
application of a number of local scale models. These groups could be contracted to conduct
individual or group training.
17
This scoping report emphasises the lack of capacity in southern Africa for regional scale transport and
deposition modelling. Only one group in the region has developed capacity and competence in this
aspect of modelling. The nature of the air pollution issues at the regional scale emphasises the need
to build this capability to be able to effectively address and communicate the science. In order to
bridge this capacity gap in a cost effective and efficient manner it is recommended to use the
foundation that has been established at CSIR in South Africa with CAMx.
Given the complexity associated with regional scale photochemical modelling it is evident that
although awareness training through workshops can be done, this will not be sufficient to produce
capable regional scale modellers. It is therefore recommended that such workshops be used to
create awareness, scope regional scale modelling issues and to identify suitable candidates for follow
up training. The in depth follow up training should be in the form of secondments of appropriate
skilled individuals to the proposed modelling centre. Secondements provide the benefits of working
on identified projects in a mentorship mode with an established research team and providing the
opportunity of sustained interaction over a longer period. In this manner individuals and teams will
develop and the science will become rigorous and credible. This will enhance the potential impacts on
air quality management decision making on a regional scale.
8
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19