Real-Time GIS Model for Air Pollution

Transcription

1 International Conference on Chemical, Civil and Environment engineering (ICCEE'01 March 4-5, 01 Dubai Real-Time GIS Model for Air Pollution Abdulla Galadari Abstract This paper proposes an analytical model to identify causes and locations of pollutants. The model is developed based on the types of pollutants, features found in the surrounding areas, while factoring wind speed and direction. The model uses a modified Gaussian air dispersion model to fit the needs of rendering the data of limited number of monitoring stations. It models real-time data captured through air quality monitoring stations. Real-time traffic data is integrated with data from air quality monitoring stations with GIS to model to identify possible causes of pollutants from surrounding roads. The data is analysed for air pollutants and its dispersion to the surrounding areas. The modified Gaussian model implemented quickly analyses and identifies the potential sources of pollution and predicts the locale of the affected areas. The model allows for crisis response of haardous pollutants in real-time. Keywords air quality model, dispersion, geographic information systems, pollution, real-time model. A I. INTRODUCTION IR quality is one of the important issues addressed for managing urban pollution. The two basic paradigms that build the dimensions of urban environmental management are GIS (Geographic Information Systems and dynamic simulations. The integration with monitoring data is essential for accurate decision-based models. Adding another dimension, real-time data is an ample tool to integrate with GIS from monitoring data. Since monitoring data are normally tabular, it does not provide the complete solution of visualiing the causes and effects of the elements being monitored. Most of the data being monitored are spatially referenced and dynamic. Hence visualising and analysing the data is a best candidate for the usage of GIS as a tool. GIS can capture, manipulate, process, and display spatial data. Obviously air quality fits the description well, since it is a problem of spatial dimension. The model proposed in this paper describes an application used for real-time data collection and analysis [1]. The proposed model also considers a regression-based approach developed by Briggs et al. [] This paper defines a logical model to be used to analyse air quality from monitoring stations using GIS. The method applied uses monitoring results that are tabulated in databases, which are analysed using a spatial simulation, and then visualised by the use of GIS. II. INTEGRATING WITHIN GIS Integrating Air Quality Analysis within a GIS environment is very important to visualise the possible areas for the source of the pollutants. GIS technology has capabilities in analysing geospatial data, which are essential to the analysis of air quality. The model used for analysis is a modified Gaussian model that can readily utilise GIS techniques for spatial calculations, such as evaluating the distance from the source to the monitoring station, as well as extracting height values from DEM (Digital Elevation Model data to use those values in the model discussed in this paper. The model illustrated in Fig. 1 determines the analytical methodology employed by the GIS to determine the potential sources of the pollutants and the affected areas. This model paves the way for future developments of realtime emergency responses to haardous air pollution from chemicals or toxic gases. It has been shown that such emergency responses are necessary as part of crisis management established by governments to respond to emergencies such as wild fires, oil fires (as that experienced in Kuwait during the Gulf War [3], and nuclear reactor leaks and failures (as that experienced by the Fukushima nuclear reactors [4]. In addition to crisis management, it may also help assess the real-time dispersion of eruptions of volcano ash clouds that could cause risks to flights or that could cause health haards. There are many applications that are possible for real-time dispersion of air pollutants within society, as it ensures the health and safety of the community, as well as allowing crises to be managed more efficiently within a GIS framework. GIS is a structure for a decision support system and it is for that main reason that the integration between real-time air monitoring and real-time traffic data are integrated within GIS to identify the sources of air pollution in real-time and predicting its dispersion. Land use data is also necessary to be integrated within the GIS framework to identify locations of industrial communities that could be culprit in the source of pollution. It also helps identify residential areas along with the demographics to predict the health haards of the residents in the affected areas. Abdulla Galadari is an adjunct faculty with the Higher Colleges of Technology, Dubai, United Arab Emirates and the American University in Dubai (aigaladari@gmail.com. 41

2 International Conference on Chemical, Civil and Environment engineering (ICCEE'01 March 4-5, 01 Dubai stochastic or Gaussian dispersion model would not significantly differ [5], and therefore, the simpler approach is considered in this model, since the objective is not precision, but real-time analysis. The Gaussian Plume Equation is as follows: Q C x, y, = e πσ xσ yu y H + H σ y σ σ ( + e + e (1 The mathematical equation expresses concentration of pollutant at position (x resulting from emissions from the stack with distance across the plume (y and with height (. (Q is the emission rate, while (u is the wind speed component from the source to the monitoring station. The model used to identify the potential cause of the pollution uses a weighting system for each gas. The weight is a percentage of the gas caused by different types of land use and roads as shown in Table 1. The model is mainly concerned in evaluating the potential (Q downwind. Therefore, referring to ( is the expression for evaluating the downwind emission rate. Q d w(πcuσ yσ = ( e y σ y + e H σ + e + H σ Potential upwind emission rate is also necessary in the analysis of air quality. Although most air quality analysis considers downwind effects of pollution, the dispersion of pollution during turbulent atmospheric impermanence at low wind speeds can cause the spread of pollution upwind according to the concentration flux of pollutants evaluated in (3. Fig. 1: GIS Model to determine areas of potential cause and likely affected areas. III. MODIFIED GAUSSIAN PLUME DISPERSION MODEL Forecasting the impact of air emissions from various sources are very challenging, especially when a spatial analysis is required to identify the diverse sources. GIS is a very good tool to analyse air dispersion to render the analysis on a map. The Gaussian dispersion model is used and modified to suit the needs of rendering the data and the source of pollutants in real-time within a GIS environment. Processing of the data is done in real-time as soon as the information is available from the monitoring station. Once the processing has been complete, it is published on a real-time online map. Although there is a stochastic approach in predicting measurements of pollutants, it is requires more processing time, and therefore further load on the GIS server for evaluations. It has been found that for traffic pollutants, a wcu Q u = (3 x Every pollutant is more likely to be derived from different sources. Some pollutants are primarily based on emissions, while others are primarily based on industrial waste. Thence, these parameters are considered in the model to determine the source of the pollutants as weighting factors, as shown in Table I. TABLE I: WEIGHT VALUES FOR DIFFERENT POLLUTANTS (SOURCE: EUROPEAN ENVIRONMENT AGENCY 4

3 International Conference on Chemical, Civil and Environment engineering (ICCEE'01 March 4-5, 01 Dubai The coefficients σ y and σ are defined by, TABLE III: MODIFIED PASQUILL STABILITY CLASS σ.893 y = ax (4 σ = cx d f (5 (a, (c, (d, and (f are coefficients based on the best fit line equation formulating the atmospheric stability represented by the Pasquill stability class [6], [7]. This scheme classifies the boundary layer into six stability classes that can be determined by routine surface observations. The stability classes are determined by the surface wind speed and by day by the amount of incident solar radiation and by night by the amount of cloud cover, using the values in Table II [8]. TABLE II: PASQUILL STABILITY CLASS The classes A-F determine the stability as the following: According to Pasquill [8], solar radiation is considered strong if the solar insolation exceeds 700 Wm -, moderate if the solar insolation is between 350 and 700 Wm -, and slight if less than 350 Wm -. Under unstable conditions the Gaussian plume model predicts the mean concentration, averaged over the tortuous plume. This causes the actual concentrations to be underestimated, and therefore one of the limitations of accuracy of the Gaussian model. Since the analysis from the air quality monitoring stations is live, and streaming data for the cloud cover by night as required by Pasquill classification is not available, it is assumed that the category falls within a cloud cover of 4/8, to be more conservative in the analysis in identifying the source of the pollutants due to a wider dispersion model. Being conservative in this parameter is important to try to reduce the propagation of error by balancing errors in the parameters that cause the values to sway above or below the actual concentration levels to try to achieve the mean value. Furthermore, to maintain the conservative analysis, wherever the Pasquill class is a range between two different classes (i.e. A B, the class that is more stable is used. Hence, the Pasquill class table is slightly modified to reflect conservative values, as shown in Table III. Generally, pollution travels a greater distance downwind when the atmosphere is more stable. However, the pollution covers a greater area when the atmosphere is less stable. Therefore, identifying the source of the pollution becomes less accurate the longer the distance to the monitoring station. Hence, a limit has been imposed on the modified model to only select potential causes of pollution within a region of up to 0 km from the monitoring station. Although it is possible that causes of the pollution can be travelling from a greater distance than 0 km, the model will be less reliable. This is especially important if the monitoring station is placed close to a source of high pollution. Hence using GIS data in air quality analysis can significantly help regulators to identify the source of the problem. Consider the scenario of Industrial Area A emitting certain gaseous pollutants situated within 1 km upwind from a monitoring station and another Industrial Area B emitting the same gaseous pollutants situated within 30 km upwind of the monitoring station. If it is assumed that the Industrial Area B is the real cause of the pollutants concentration detected by the monitoring station, the analytical process by using the Gaussian Plume Dispersion Model will give a much greater weight to Industrial Area A as the potential cause of the pollutant due to its spatial distance upwind from the monitoring station. Thus, the procedure deems the whole analysis as unreliable. Nevertheless, the corrective measure to this misanalysis is not to include Industrial Area B when analysing the source of the pollutants as detected by the monitoring station, but to actually place a new monitoring station within a short proximity of Industrial Area B. The Air Dispersion Model used for analysis in determining the cause of pollutants is an abstraction of dispersion due to common meteorological phenomena. To get more accurate analysis of the source of pollutants and its dispersion in relation to the wind speed, a component of the wind speed from the potential source to the monitoring station is used (Fig.. u = W s cos(θ (6 The (u evaluated in (6 is used in the equation for the Gaussian Plume Dispersion (1 to identify the source of the pollutant. Since the Air Monitoring Stations detect the concentration of the pollutants, the theoretical emission rate (Q is calculated. 43

4 International Conference on Chemical, Civil and Environment engineering (ICCEE'01 March 4-5, 01 Dubai Fig. : Wind speed component from potential source to Monitoring Station The distance across the plume (y is a variable that is dependent on the distance (x and the angle (θ as defined in (7. y = x sin(θ (7 The model evaluates the potential causes of pollution from different types of land use and roads. However, a system is being initiated to actually include every single factory and the pollutants emitted by them to have a more accurate and precise location of the source of the pollutant. Nevertheless, when evaluating for the land use, and since the facilities within the study area are mainly homogeneous in their general elevation profile that the difference would not significantly change the concentration of pollutants, the elevation ( used is the average computed from the DEM data available for the land use. Similarly, for road networks, the ( value used is the average computed on the DEM overlaid by the area of each road segment within GIS. The model evaluates the potential emission rate (Q for every source, which includes land use and road segments for both downwind and upwind. Then, a percentage is computed for each source identifying the most probable source for the pollution, as shown in (8 and (9. n n ( Qd i + ( Q u i Q = (8 i=0 i=0 Qi Pi = (9 Q Since a percentage is given to every potential source within a radius of 0 km from the monitoring station, there are areas which may overlap for more than one monitoring station. In such a case, the percentages are calculated mutually exclusive from each other. For analytical and rendering purposes, the monitoring station that gives the same source a higher percentage is used for analysis purposes for that area. IV. PROPAGATION OF ERROR Even when using the most accurate data and the most complex models, uncertainty is inherent in the process. Necessarily, caution in the accuracy due to uncertainty of the data model must be emphasied. When rendering a map in real-time within a GIS environment, the processing time required to analyse the data must remain at minimal required. More precise data can be evaluated, but that would require greater processing time for a precision unwarranted for to suit the needs of rendering a map for air quality analysis. Hence, simplification of the process is necessary, as long as they do not radically influence the values evaluated by the model. The propagation of error in the model proposed is mainly due to three parameters, wind speed, standard deviation of lateral and vertical wind direction fluctuations. The model proposed assumes that conditions are horiontally homogeneous and steady-state. In other words, the wind speed is assumed not to change in the direction of the source identified and the monitoring station. Although this can be argued as statistically insignificant when the source is an industrial area, this is not completely true in concentrations measured close to highways. The turbulence of the wind due to the drag of vehicles may cause fluctuation in the wind speed measured. In principle, uncertainties cannot be reduced by further advancement in knowledge, but they can be characterised and identified [9]. Few assumptions are implemented in the model since not all monitoring stations in the study area detect wind speed, direction, and solar radiation. Therefore, the values used by each monitoring station are by identifying the closest monitoring station that includes these data and using them for the analysis of the measured concentrations. Another fail-safe method that needs to be applied is by determining if all the data are not statistically significantly different from each other. However, this has not been employed in our study area, since we have limited monitoring stations where only a very few actually detects wind speed, direction, and solar radiation. Therefore, statistical analysis to check of the reliability of the data is not possible. Nevertheless, whenever possible, such a method must be included within the model. Nonetheless, few fool-proof checks of the quality of the data has been applied in the model, such as checking whether the values detected for wind speed are not negative values, nor values that exceed 100 m/s, which is a value that greatly exceeds a severe hurricane, which are usually no more than 67 m/s. For even if such wind speed severity is achieved, there is no available research that defines the reliability of the Gaussian model when applying such values. Evaluating uncertainty propagation may include analytic techniques, Monte Carlo simulation, response surface approaches, differential sensitivity techniques, Bayesian forecasting, and classical statistical confidence bounds [10]. No one technique seems to be able to completely characterise all aspects of the uncertainty [11]. V. CONCLUSION Modelling air quality is not an exact science. However, with the use of mathematical models, an estimate can be assessed. The model applied is conservative in its evaluation to reduce the risk of ignoring what can otherwise be a significant effect of air quality. The model simplifies the data to help in rendering the analysis on a map using GIS, because 44

5 International Conference on Chemical, Civil and Environment engineering (ICCEE'01 March 4-5, 01 Dubai what can be considered as statistically insignificant changes in the values would not delay the processing time on the GIS servers of what needs to be in real-time. Otherwise, we can be more precise if we do not attempt to simplify the data, but risk being inaccurate in the analysis. Nevertheless, the parameters used in this model can be modified to the needs of analysis and calibrated according to statistical field information for more precise analysis depending on regional meteorological factors. The air pollution sources can be controlled through this model in real-time. Policies can also be enforced in real-time. For example, a quota can be established for each area on how much pollution emission can be tolerated. If any more exist, then certain policies can be enforced, such as not allowing the use of charcoal in an area for a certain period. Other policies can be established such as encouraging carpooling or tighter vehicle emission tests. The geospatial analysis can also help determine the area for health risk assessment and to establish air quality standards. The next step of this research is to determine the areas affected by the air pollution dispersion, now that the model can identify the source of the pollution. from the University of Colorado. He is also currently pursuing a Ph.D. in Arabic and Islamic studies from the University of Aberdeen. He is an Adjunct Faculty at the Higher Colleges of Technology and the American University in Dubai. He has various publications in the fields of engineering and science, but also in the fields of business and management, as well as fields in social sciences and humanities, including economics, law, comparative religion, and spirituality. Dr. Galadari looks into the world from a macroscopic point of view. Consequently, most of his research considers multivariate approaches, whether in engineering, science, social sciences, or the humanities. Some physicians are specialied in treating a specific disease, while others treat the human being. Similarly, Dr. Galadari attempts to treat and develop humanity, as a whole in its pursuit for identity and the Truth. REFERENCES [1] A. Galadari, Mapping Urban Air Quality Analysis in GIS to Identify Sources of Ambient Pollution in Real-Time, Map Middle East Conference, 3 5 April 005. [] D. J. Briggs, S. Collins, P. Elliott, P. Fischer, S. Kingham, E. Lebret, K. Pryl, H. van Reeuwijk, K. Smallbone, and A. van der Veen, Mapping Urban Air Pollution Using GIS: A Regression-based Approach, International Journal of Geographical Information Science, vol. 11, no. 7, pp , [3] S. Alhajraf, L. Al-Awadhi, S. Al-Fadala, A Al-Khubaii, A. R. Khan, and S. Baby, Real-time Response System for the Prediction of the Atmospheric Transport of Haardous Materials, Journal of Loss Prevention in the Process Industries, vol. 18, no. 4 6, pp , July November 005. [4] A. Leelossy, R. Mesaros, and I. Lagi, Short and Long Term Dispersion Patterns of Radionuclides in the Atmosphere around the Fukushima Nuclear Power Plant, Journal of Environmental Radioactivity, vol. 10, no. 1, pp , December 011. [5] J. Cyrys, M. Hochadel, U. Gehring, G. Hoek, V. Diegmann, B. Brunekreef, and J. Heinrich, GIS-Based Estimation of Exposure to Particulate Matter and NO in an Urban Area: Stochastic versus Dispersion Modeling, Environmental Health Perspectives, vol. 113, no. 8, pp , August 005. [6] F. A. Gifford Jr., Use of Routine Meteorological Observations for Estimating Atmospheric Dispersion, Nuclear Safety, vol., no. 4, pp , June [7] J. H. Seinfeld, Atmospheric Chemistry and Physics of Air Pollution, New York: Wiley, [8] F. Pasquill, The Estimation of the Dispersion of Windborne Material Meteorological Magaine, vol. 90, pp , [9] Environmental Protection Agency, Guiding Principles for Monte Carlo Analysis, EPA/630/R-97/00., Washington, D.C.: Office of Research and Development, [10] D. C. Cox and P. Baybutt, Methods of Uncertainty Analysis: A Comparative Survey, Risk Analysis, vol. 1, no. 4, pp , [11] R. Krystofowic, Bayesian Forecasting via Deterministic Model, Risk Analysis, vol. 19, no. 4, pp , Abdulla Galadari holds a B.Sc. in civil engineering (00, a B.Sc. in applied mathematics (00, M.Sc. in civil engineering (003, M.Eng. in GIS and engineering management (003, and Ph.D. in civil engineering (008 all 45