Study of microscale urban air dispersion by ADMS - Urban

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1 Study of microscale urban air dispersion by ADMS - Urban Jason P.L. Huang Atmospheric, Marine and Coastal Environment Program (AMCE), The Hong Kong University of Science & Technology Jimmy C.H. Fung Department of Mathematics, The Hong Kong University of Science & Technology Alexis K.H. Lau Center for Coastal & Atmospheric Research, The Hong Kong University of Science & Technology Abstract In the urban areas of Hong Kong, air pollution is mainly caused by motor vehicles, particularly diesel vehicles such as trucks, buses and light buses which emit a large amount of respirable suspended particulates and nitrogen oxides. These pollutants are then often trapped in between very tall buildings along the streets. The objective of this study is to understand how these pollutants being dispersed under different atmospheric conditions. This study provides some examples and preliminary results of model simulations due to road traffic with its street canyon features in air quality studies in the urban areas of Hong Kong - Tsim Sha Tsui. Keywords: Dispersion modelling, ADMS-Urban, Boundary layer 1 Introduction Hong Kong has two main air pollution issues. One is street-level pollutant and the other is smog. Diesel vehicles are the main source of street-level pollution. They are also one of the contributors to smog, which is caused by a combination of pollutants from within Hong Kong and the surrounding region. The motor vehicles in Hong Kong are the main causes of high concentrations of respirable suspended particulates (RSPs) and nitrogen oxides (NOx) at street level. In order to understand the problem, we have used an urban scale dispersion model (ADMS-Urban) to calculate concentrations of NOx, NO2, RSP and O3 in the urban areas of Hong Kong. ADMS was developed by the Cambridge Environmental Research Consultants (CERC) and the UK Meteorological Office. This model is based on up-to-date physics and has been the subject of extensive validation studies. 1,2 The dispersion model used in this study is a second generation Gaussian dispersion model which is characterised by the use of boundary layer similarity profiles to parameterise the variation of turbulence with height within the boundary layer. The model ADMS-Urban run on a PC under Windows and are suitable for modelling of industrial, domestic and road traffic sources in urban dispersion problems for comparison with limits, planning, and what if ƒscenarios. Each model can be linked to a GIS (geographical information system) for simple entry of sources. ADMS-Urban can also access emissions inventory databases directly. The model has an integrated chemistry model which is used to predict concentrations of various pollutants. This model is capable of calculating concentrations of pollutants at a very high resolution and allowing explicit treatment of roads, including street canyons, point and area sources. In ADMS-Urban, chemical reactions occurring in the boundary layer is modelled by the Generic Reaction Set (GRS) chemistry scheme. It is a semi-empirical photochemical model which reduces the complicated series of chemical reaction involving nitrogen oxides (NO, NO2), ozone (O3) and volatile organic compounds (VOCs) to just seven reactions. It was developed originally by CSIRO in Australia. 3 In addition, the system has a number of distinct features such as advanced dispersion model in which the boundary layer structure is characterised by the height of the boundary layer and the Monin-Obukhov length. This length provides a measure of relative important of buoyancy generated by heating of the ground and mechanical mixing generated by the frictional effect of the earth s surface. Physically, it can be thought of as representing the depth of the boundary layer within which mechanical mixing is the dominant form of turbulence generation. This model also have a full range of explicit source types which allow up to 3000 grid sources, 1000 road sources and 100 industrial sources. The model capable of calculating the boundary layer parameters from a variety of input data such as wind speed, day, time, cloud cover, wind speed, surface heat flux and boundary layer height. Meteorological data may be raw, hourly values or statistically analysed. PS-44-1

2 The paper examines the performance of the integrated modelling system to predict concentrations from vehicles emission sources in the urban areas of Hong Kong. 2 Model input 2.1 Traffic data The model requires the traffic flow data in order to calculate the emission rates of various pollutants. The approach taken was to model roads as discrete line sources where the annual average daily traffic flow exceeded vehicles per day. The required input data includes the locations of roads (vertices), characteristic road widths, characteristic building heights along each road (see Figure 1), vehicle types, vehicle count (the number of vehicles passing along a stretch of road per unit time), vehicle average speed. In total, 36 road links were modelled as discrete sources in this study. Disaggregation of vehicle composition was applied dividing the flow into heavy duty and light duty vehicles. Speed-dependent emission factors were used for the road traffic sources. The daily and diurnal variation of the traffic flows and therefore emissions from road traffic were described by diurnal curves provided as part of the emissions inventory. The ADMS-Urban model allowed only one input to describe the diurnal variation for each source type. The emission rates were then calculated using a default database of emission factors 4. These input data provide emission rates in kg/m/s for NO x, NO 2, O 3 and RSP (PM10), and the model also includes predictions of emission factors into the next century. The data used in this current study was based on the Annual Traffic Census 2000 report 5 from the Transport Department, HKSAR. No account has been taken of other emissions within the computational domain. 2.2 Meteorology data In ADMS-Urban, the boundary structure is characterised by the boundary layer height h and the Monin- Obukhov length L MO, not by a Pasquill-Gifford stability category. In unstable conditions, the Monin- Obukhov length is negative. Under such conditions, the magnitude of the length is a measure of the height above the ground above which convective turbulence, that is turbulent motions caused by convective motions, are more important than mechanical turbulence generated by friction at the earth s surface. Then it is a measure to the height above the ground above which vertical turbulent motion is greatly inhibited by the stable stratification. In stable conditions, the Monin-Obukhov length is positive. All the turbulence in the stable boundary layer is mechanically generated, it means there is no generation of turbulence due to convective motions. Usually the level of turbulence decreases with height, as the relative effects of stratification increase although it can be enhanced by wave motions at the top of the boundary layer. However, the effect of the wave motions is not considered by ADMS-Urban. In order to calculate the boundary layer height and the Monin-Obukhov length for the model run, it requires the user to enter a number of meteorological parameters, the minimum requirement of input parameters for running the model are: date, time, cloud cover amount, wind speed and wind direction. Other input parameters are such as surface temperature, precipitation, sea surface temperature and surface albedo, etc. These extra input parameters will generally improve the estimation of the boundary layer height in the atmosphere. For this study, hourly meteorological data were input into the model and these data were obtained by the weather station in Tsim Sha Tsui which is maintained from the Hong Kong Observatory. This site was chosen because it is flat, with relatively uncomplex terrain, may be considered generally representative of weather conditions throughout the conurbation. 2.3 Background pollutant data In order to model chemical reactions correctly, other than the emitted pollutants due to traffic, the model required appropriate background concentrations for a range of pollutants such as NO x, NO 2, O 3 and nonmethane volatile organic compounds (VOCs). Constant background concentrations may be used, in this study we used the hourly data of NO x, NO 2 and O 3. The background data is estimated based on raw data taken directly from the HKEPD s Air-Quality Monitoring Network just outside our computational domain as shown in Figure 1. PS-44-2

3 2.4 Topography data Before running the model, the area of interest should be selected first. In this study, we selected one of the most crowded districts of Hong Kong, such as Yau Ma Tei, Mongkok and Tsim Sha Tsui as our area of interest. The major road sources based on the Annual Traffic Census 2000 were added into this area as shown in Figure 1. After inputting all the required parameters such as the background pollutant data, traffic emission data and meteorology data, the model can then be run. Figure 1. Computational domain, topographical information (Road sources indicated by thick black line) 3 Results The ADMS model was run for the whole year of 2000 and the monthly average of various pollutants was then computed and the results were compared with two monitoring stations from HKEPD s Air Quality Monitoring Network. One station is so-called the road-side station, mainly measure the pollutants (RSP, NOx and NO 2 ) at the street level, which is located at 5.5 meters above ground and is nearby the road-side on Mongkok. The other is so-called the general station, mainly measure the background pollutants (RSP, NOx, NO 2 and O 3 ), which is located at 17 meters above ground and the monitoring equipments are situated at the top of the building in Sham Shui Po. Based on our model ADMS runs, the monthly average values of the four pollutants, namely Respirable Suspended Particulates (RSP), Nitrogen Dioxide (NO 2 ), Ozone (O 3 ) and Nitrogen Oxides (NO x ) are shown in Figure 2 to Figure 5 respectively. The figures also included the monitored monthly average values obtained from the Annual Air Quality Report 6 by the HKEPD in Some of the monitored data from HKEPD are missing because the record of that month is below the respective minimum data requirements within that period. For the Respirable Suspended Particulates (RSP) in Figure 2, the simulated monthly average concentration is compared well with that of the monitored monthly average concentration values. The modelled results were slightly underestimated for the general station when compared with the monitored data for the whole year. The winter concentration of RSP is about 80% higher than that of the summer. Figure 3 shows the results of the concentration of nitrogen dioxide (NO 2 ), the modelled results are reasonable when compared with that of monitored data, and the trend is also the same concentration in summer is lower than winter for both modelled and monitored data. Figure 4 shows the values of ozone, PS-44-3

4 the modelled trend is correct when compared with that of monitored data, but the concentration is about 50% higher than the reported values, this may be due to the fact that we have not included any volatile organic compounds (VOCs) in our current simulation, which will probably decrease the concentration of ozone by chemical reactions. Figure 5 shows the values of nitrogen oxides, the modelled road side results is about 20% overestimated when compared with the monitored data, however the comparison between modelled and monitored data for the general station. In addition to the station comparisons, in Figure 6 we have also plotted the spatial concentration of the monthly average of RSP in Jan (Winter) and in June (summer) at 5.5m above ground level, we can see that the concentration is higher in the winter than in the summer throughout the whole area of Tsim Sha Tsui. This result is consistent with the results showed in Figure 2. Figure 2. Simulation result of PM by ADMS-Urban Figure 3. Simulation result of NO2 by ADMS-Urban PS-44-4

5 Figure 4. Simulation result of O3 by ADMS-Urban Figure 5. Simulation result of NOx by ADMS-Urban PS-44-5

6 Figure 6. Contour plot of PM in January and June Conclusions The aim of this study was to access the suitability of the ADMS-Urban dispersion model for predicting the concentration of various pollutants mainly due to traffic flows. This paper has shown that this model can predict a reasonable results when modelled values were compared with measured data. However, certain areas were identified for further investigation including the model s tendency to overestimate concentrations of NOx, especially during the summer. 5 References 1.Carruthers DJ, Mckeown AM, Hall DJ & Porter S 1999 Validation of ADMS against wind tunnel data of dispersion from chemical warehouse fires. Atmospheric Environment 33(12): Owen B, Edmunds HA, Carruthers DJ, Singles RJ, 2000 Prediction of total oxides of nitrogen and nitrogen dioxide concentrations in a large urban area using a new generation urban scale dispersion model with integral chemistry model. Atmospheric Environment 34(3): Azzi M & Johnson G An introduction to the Generic Reaction Set Photochemical Smog Mechanism, Proc. 11 th Clean Air Conf. 4 th Regional IUAPPA Conf., Brisbane, Australia, July Design Manual for Roads and Bridges, Vol 11, Environment Assessment, Dept. of Transport, UK, June The Annual Traffic Census 2000, Transport Department, HKSAR, Air Quality in Hong Kong 2000, Environmental Protection Department, HKSAR, 2000 PS-44-6