A dynamic air pollution prediction system for Cape Town, South Africa M. Zunckel 1, E. C. Cairncross 2, E. Marx 3, V. Singh 4 & V. Reddy 5 1 CSIR Environmentek, Durban, South Africa 2 Peninsula Technikon, Cape Town, South Africa 3 South African Weather Service, Pretoria, South Africa 4 CSIR Material & Manufacturing, Pretoria, South Africa 5 Steffen Robertson and Kirsten (Pty) Ltd, Durban, South Africa Abstract The objective of the Dynamic Air Pollution Prediction System (DAPPS) is to combine a meteorological forecast model and a photochemical dispersion model to predict the ambient concentration of selected pollutants. The Penn State/UCAR MM5 mesoscale model, nested to 1.7 km, is used to predict gridded 12 hour, 24 hour and 36 hour meteorological fields. A comprehensive emission data base comprises emissions from industry, petrol and diesel powered motor vehicles and domestic wood and paraffin burning. It includes information on their physical and chemical characteristics, together with their spatial attributes and temporal variations. Emissions data and forecast meteorological data are input to the Comprehensive Air Quality Model with Extensions (CAMx) to estimate air pollutant concentrations over the metropolitan area for the following day. The forecast information is conveyed to the users by means of an Air Pollution Index (API), based on health risk of exposure to individual pollutants and combinations of pollutants via a web site and in other forms of media. A Haze Index (HI) is used to convey forecast visibility degradation or atmospheric haze, based on the concentrations of haze precursors at various levels in the near surface layer. DAPPS is being piloted in the City of Cape Town. Keywords: DAPPS, CAMx, mobile emissions, point sources, Air Pollution Index, Haze Index, IPIECA Toolkit.
276 Air Pollution XII 1 Introduction The air pollution cocktail that exist in major urban centres is the result of chemical transformations between compounds emitted from a range of source types. In many cities these emissions typically include those from diesel and gasoline driven motor vehicles and other transport modes, and from large and small industry. In developing countries emissions from domestic biomass burning in non-electrified residential areas are also added into the urban air shed. The multitude of sources, each with different physical and chemical characteristics, together with the high density populations, and meteorological variation all pose substantial challenges to air quality management in urban environments. Factors that pose additional challenges to air quality management in cities in developing countries are the reliance on relatively older vehicle fleets, often without emission control devices, and a lack of transport alternatives as a result of unreliable or non-existent public transport systems. The Dynamic Air Pollution Prediction System (DAPPS) is an air quality information and management system that is being developed for cities in the developing countries of southern Africa and elsewhere. The DAPPS technical team is a consortium of South African institutes who are funded by the National Research Foundation, through the Innovation Fund. This paper discusses DAPPS as a pilot system that is being developed in the City of Cape Town (Fig. 1). Figure 1: Map of Cape Town and modelling domain with a 1.7 km grid.
Air Pollution XII 277 Cape Town is a world renowned tourist destination, located on the southwestern tip of southern Africa (Fig. 1) at the foot of Table Mountain. It is a harbour city with a population of approximately 3.15 million people [1]. Complex topography, generally poor air pollution ventilation, and emissions from a range of source types are some of the air quality management challenges in the City of Cape Town. South Africa has recently implemented a new Air Quality Management Act. The Act moves away from source-based controls to adopting a holistic air quality management philosophy the focuses on obtaining and maintaining specified ambient air quality standards. The use of air quality management tools such as DAPPS is fundamental in realising the objectives of the Act at various decision making levels in urban environments. 2 The DAPPS modelling system The Dynamic Air Pollution Prediction System (DAPPS) is a modular system that consists of three input modules, a photochemical dispersion module, and a webbased output module for communication of results. The modules are interfaced into the air pollution prediction system (Fig. 2). DAPPS Emissions Inventory Module Prognostic Meteorological Module (MM5) Photochemical dispersion module (CAMx) Web-based communication module Initial and boundary condition module Figure 2: Schematic of the modules of DAPPS and the inter-module linkages. 2.1 Emissions inventory The DAPPS emission inventory module for the City of Cape Town includes a range of sources. These are emissions from a growing motor vehicle fleet, emissions from large, medium and small scale industry and emissions from wood and coal burning for cooking and space heating in homes in unelectrified
278 Air Pollution XII informal residential areas and other diffuse sources e.g. waste landfills, waste water treatment plants etc. 2.1.1 Point sources Point source emissions from large and medium industry have mostly been calculated from fuel and raw material usage, or emission factors from the US EPA AP-42 [2] are applied to estimate emission rates. The temporal characteristics of each source are considered in deriving hourly emission rates for CO, SO 2, NOx, particulate matter and total organic carbons. Scheduled emission data from the Department of Environmental Affairs and Tourism and from the local council in Bellville South were used to initiate the data collection process for the larger industries. Questionnaires were used to complete the data gaps. Smaller point emission sources located in a 1 km 2 grid block are to be summed and grid block are to be represented as area sources. Annual emissions CO, SO 2, NOx, particulate matter and total organic carbons from the largest point sources are compared in Figure 3. 2.1.2 Mobile sources The International Petroleum Industries Environmental Conservation Association (IPIECA) Toolkit [3] has been used to generate a base case mobile source emissions inventory for Cape Town for 2001. The IPIECA Toolkit uses data on the characteristics of the vehicle fleet by classes, average vehicle speeds in each class, the annual average distance traveled in each class, and the driving mode, i.e. urban, rural or highway. Reasonable assumptions based on an understanding of Cape Town vehicular traffic were made where actual data are not available. The vehicle fleet is divided by size class into small cars (< 1400 cc), medium cars (1400 2000 cc) and large cars (> 2000 cc). Historical car populations and the vehicle survival rates are modeled using the two-parameter Weibull distribution function in order to predict future car populations. The Weibull distribution is commonly used to model the failure of mechanical components as a function of time. The Toolkit makes provision for five gasoline and diesel scenarios, which were manipulated to simulate the different phases of gasoline and diesel composition before and after the 2001 base year. Default values for annual kilometers traveled were used and urban, rural and highway speeds of 25, 80 and 90 km/h were assumed. Fuel consumption data for these speeds, for each of the three vehicle classes, were applied. Hourly emission rates are calculated for defined road strips for SO 2, NOx, CO, Pb and hydrocarbons. Annual emission rates from mobile sources for the base year are compared with industrial sources in Figure 3. 2.1.3 Domestic sources Burning of wood, paraffin and other fuels in unelectrified informal residential areas for space heating and cooking are treated as area sources in the inventory. Emission rates are determined by applying emission factors to data on fuel type and quantity burnt in defined areas. Burning of used tyres to recover the metal is a common practice well demarcated areas. These emissions are also considered as area sources. Similarly, emissions from landfills are treated as area sources.
Air Pollution XII 279 Where possible, the inventory included seasonal and diurnal characteristics of activities. 2.1.4 Other source types Emissions from shipping and aircraft movement in the modelling domain have not yet been included in the emissions inventory. Similarly, biogenic emissions from vegetation and soils are currently omitted from the inventory. The degree of agreement between the initial model results with the ambient monitoring network will determine whether it is necessary to include these emissions. 1000000 100000 10000 Emission t/yr 1000 100 10 1 Medical Waste Abattoir Textile Brick & Clay Oil Refining Paper Clothing Oil Refinery Glass Motor Vehicles Sources Sulphur dioxide Carbon dioxide Carbon Monoxide Nitrogen Oxides Total particulates Volatile organic compounds Figure 3: Annual emission rates from major point sources and mobile sources in Cape Town in tons per annum. 2.2 Prognostic meteorology The defined DAPPS modelling domain for the City of Cape Town is approximately 27 000 km 2, from 33 S to 34 30 S and 18º E to 19º30 E (Fig. 1). This domain is gridded at a resolution of 1.7 km. It is planned to reduce the resolution to 1 km which will provide a grid of approximately 150 by 180 blocks that will cover the entire municipal area. The Pennsylvania State University / National Center for Atmospheric Research Penn mesoscale meteorological model, known as MM5, is used to generate gridded forecast meteorological fields of pressure/height, temperature, wind, cloud/rain and vertical diffusivity for this domain. MM5 is initially run for a larger mother domain with a grid resolution of 15 km and nested down to the finer grid. When converting MM5 output fields to CAMx-ready file formats the vertical dimension of the domain is defined at 4 km which is divided into 10 equally spaced layers. MM5 is to be run daily to generate prognostic meteorological input fields for DAPPS at time t = 0 hours, t = 12 hours, t = 24 hours and t = 36 hours. The gridded meteorological
280 Air Pollution XII fields include the following parameters in each of the 10 layers: Horizontal wind speed and direction; temperature; pressure and height; clouds and rain; water vapour and vertical diffusivity. 2.3 Photochemical dispersion model The Comprehensive Air Quality Model with extensions (CAMx) [4] is the core component of DAPPS. 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 chemical species on a system of nested three-dimensional grids. The Carbon Bond 4 chemistry mechanism [5] is used in the DAPPS project which allows for 91 reactions and 36 chemical species, providing an integrated assessment of gaseous and particulate air pollutants. Emissions of CO, SO 2, NOx, particulate matter and total organic carbons are speciated into 16 chemical species based on the definition of the industrial process, raw materials, the fuel used and the air pollution abatement equipment. The variation in land cover type across the modelling domain is input to CAMx through the distribution of one of 11 landuse types to each grid cell. In this way, surface roughness and UV albedo are defined for each grid cell. Topography is not an explicit input CAMx, but is used in MM5 in the calculation of the meteorological fields, which are in turn applied in CAMx. Mean monthly concentrations of O 3, CO, and CH 4 measured at the Global Atmosphere Watch station at Cape Point [6] are used to set boundary conditions in CAMx. Initial conditions for each model run will be set using data from the monitoring network in the City of Cape Town. After initialising CAMx, model runs are conducted for the four meteorological forecast data sets. From these CAMx generates ambient concentrations of pollutants of interest in each of the grid blocks for each hour of the 36 hour forecast period. 2.4 Air pollution index A number of countries use some form of air pollution index to communicate the quality of the air in their urban environments [7, 8]. These air quality index systems are simplified methods to communicate potential health impacts of the prevailing air pollution levels. In an overwhelming majority of examples, the air quality index is based on the ambient concentrations of the classical or criteria pollutants. In most cases these systems rely on relating ambient concentrations of air pollutants to a numerical scale, which may be enhanced by verbal descriptors, such as low, moderate and high. The use of a single figure index creates several difficulties. For example, different pollutants may have different health end points and information may be lost in the use of a single index. It may also prove difficult to translate a unified pollution index back to the disaggregated actual pollutant levels. In addition, comparison of the index with national or international guidelines and standards may be problematic. In DAPPS the predicted ambient concentrations in each of the domain s grid blocks from CAMx are translated into a human health risk-based Air Pollution
Air Pollution XII 281 Index (API), based on the methodology of the World Health Organisation (WHO) and the European Centre for Environment and Health (ECEH) [7] for estimating the health effect of ambient air pollution. Specific criteria are defined for including specific pollutants in the DAPPS API. The criteria are: Health hazard (either as a primary pollutant or as a precursor to the formation of a secondary pollutant that is a recognised health hazard); Released in significant quantities in the study area; DAPPS modelling capabilities; Availability of exposure response relationships; Requested by public; International practice; Response to acute (<=1 hour) to short-term (<=24 hours) time-averaged exposures, with response occurring within 3 days of exposure; Based on these criteria, an inclusive approach is adopted for the selection of pollutants and averaging periods (Table 1). All combinations of pollutants and time-averaging periods are to be found in one or other jurisdiction internationally. Table 1: Pollutants and averaging periods to be included in the calculation of the API in DAPPS. Pollutant SO 2 NO 2 O 3 PM 10 PM 2.5 CO Averaging 1h 1h 1h 24h 24h 1h periods 24h 24h 8h 8h The relative risks of respiratory and other diseases and health endpoints associated with increasing air pollution have been documented by the WHO [9]. The health endpoint Total mortality is the only one that is common to all the pollutants and time-averaged values under consideration in developing the API for DAPPS. One approach under evaluation is to use a common increment of risk attributable to each pollutant/ time averaged value combination under a given exposure scenario to establish breakpoints for the APIs. 2.5 Haze index The relationship between atmospheric haze and air pollution has been the subject of a number of studies, particularly in large cities with high vehicle population densities, and meteorological conditions that favour the formation of the haze. More recently, the impact of atmospheric haze and deteriorating visibility on tourist areas remote from densely populated cities is receiving increasing attention. Concern regarding the so-called Cape Town Brown Haze that is usually apparent during the April to September months led to the Brown Haze Study [10] in 1990. The Cape Town Brown Haze Study revealed that the PM 2.5 fraction of particulate matter to be the most important component of the Brown
282 Air Pollution XII Haze, with diesel and petrol vehicle emissions the largest contributing sources at 42% and 25% respectively. It is intended to incorporate the prediction of haze via a Haze Index in DAPPS. This could be achieved by predicting the concentrations and examining the relationships between PM 2.5, PM 10, NO 2, secondary sulphates and nitrates, ozone and meteorology at the levels where the Brown Haze preferentially occurs. 3 DAPPS validation The modular nature of DAPPS makes it important to validate each component of the system as well as the system as a whole. It this respect, the nested prognostic meteorological module is validated at time t = 0 against measured data, and forecasts are validated retrospectively to determine the model performance. Validation of the emission inventory is an on going exercise at improving the existing data and augmenting the inventory with new data or data not initially included. The outputs of the photochemical and dispersion module will be validated against measured data from the Cape Town ambient monitoring network. 4 Benefits of DAPPS DAPPS forecasts in the form of the API and the Haze Index will be made available to the public via a web site and possibly in the newspapers. The intention is that the DAPPS information is used by a wide range of stakeholders to proactively manage the activities that may impact on air quality in an urban environment, or in turn, to manage daily activities so that potential exposure to undesirable levels of air pollution are reduced. The anticipated benefits of DAPPS depend on the level of key role player buy-in, and these may include: A common platform for an understanding air quality in the urban environment, and the root of Air Quality Management problems. A decision support tool for integrated planning and sustainable growth and development. A tool to assist decision makers at different levels of Government to make informed decisions on development options, with due consideration of human health and environmental impact implications. An aid to industries and other source contributors to evaluate source reduction mitigatory options with the biggest reduction effect in the ambient environment. An educational tool to enhance the understanding and awareness of air quality issues. An information system for environmental non-governmental organisations and community groups concerned about air quality.
Air Pollution XII 283 A tool to provide supporting information for air-quality related health risk assessments and epidemiological studies. A tool that will enable decision makers to plan, implement and assess air quality improvement plans in a holistic manner. 5 Summary The Dynamic Air Pollution Prediction System (DAPPS) is being developed by a consortium of South African institutions as a system to aid air quality management in urban environments. DAPPS is being developed as a pilot system in the City of Cape Town, an international renowned tourist destination. The objective is to provide a wide range of stakeholders with air quality information to make informed air quality management decisions. The corner stone of DAPPS is the CAMx photochemical dispersion model, which utilises spatially and temporally resolved point and mobile source emission data and forecast meteorology fields to model air pollutant concentrations in a forecast manner. DAPPS forecast information is via a health risk-based air pollution index by means of a web page, and possibly in the newspapers. The system will also include a haze index. DAPPS will also have the capability of scenario-mode operation to assist in the evaluation of development scenarios and episodic emission events. Acknowledgements The South African National Research Foundation is acknowledged for making funding available for the development of DAPPS through the Innovation Fund. Personnel at the Department of Environmental Affairs and Tourism and at the Bellville South Local Council are acknowledged for providing emission data. The South African Weather Service are thanked for the Cape Point Global Atmosphere Watch data. References [1] City of Cape Town, State of the Environment Report for the City of Cape Town, ISBN 0-620-29402-7, 2001. [2] US EPA, Compilation of Air Pollution Emission Factors (AP-42), 8 th Edition, Volume 1, as contained in the AirCHIEF (AIR Clearinghouse for Inventories and Emission Factors), CD-ROM, US Environmental Protection Agency, Research Triangle Park, North Carolina, 2000. [3] IPIECA, Urban Air Quality Management Volume 2 The preparation and application of pollutant emission inventories: exploring the capacity of the IPIECA Toolkit, IPIECA, London, 2001.
284 Air Pollution XII [4] ENVIRON, User s guide to the Comprehensive Air Quality Model with Extensions (CAMx), ENVIRON International Corporation, 101 Rowland Way, Novato, CA 94945, 2003. [5] Gery M. W., Whitten G. Z., Killus J. P. and Dodge M. C., A photochemical kinetics mechanism for urban and regional scale computer modelling, Journal of Geophysical Research, 94, 925-956, 1989. [6] Brunke, E-G and Sheel, H.E., Surface ozone measurements at Cape Point (34 S, 18 E): 'Atmospheric Ozone, Proceedings of the XVIII Quadrennial Ozone Symposium, L'Aquila, Italy, 12-21 September 1996', R.D. Bojkov, G. Visconti (eds.), 331-334, 1998. [7] Maynard R.L. and Coster S.M. Informing the public about air pollution. In Air pollution and health. Holgate, Samet, Karen & Maynard (eds), 1999. [8] EPA, Federal Register, Vol. 63, No. 236, Air Quality Index Reporting: Proposed Rules, December 1998. [9] WHO/ECEH, Health Impact Assessment of Air Pollution in the WHO European Region, WHO/Euro product no: 876.03.01 (50263446), November 2001. [10] Wicking-Baird, M. C., de Villiers, M. G. and Dutkiewicz, R. K., Cape Town Brown Haze Study, Energy Research Institute, University of Cape Town, September 1997.