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Simulation of Concentration Distributions of Primary Gaseous Pollutants Using Air Quality Modeling System in Bang Pakong Area, Thailand CHATCHAWAN VONGMAHADLEK* 1 , MEIGEN ZHANG 2 , BOONSONG SATAYOPAS 3 , PHAM THI BICH THAO 1 1 The Joint Graduate School of Energy and Environment (JGSEE) King Mongkut’s University of Technology Thonburi (KMUTT) Bangkok 10140, THAILAND 2 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS) Beijing 100029, CHINA 3 Department of Civil Engineering Chiang Mai University (CMU) Chiang Mai 50002, THAILAND Abstract: - For this study, the coupling of air quality modeling system of the Models-3 Community Multi-scale Air Quality (CMAQ) and the Regional Atmospheric Modeling System (RAMS) was applied to investigate the concentration distributions of primary gaseous pollutants (i.e., NO x , SO 2 , and CO) over Bang Pakong and its surrounding area, Thailand. The up-to-date emissions inventory was prepared as input into CMAQ. To perform modeling evaluation, air quality monitoring data from the Pollution Control Department (PCD), Thailand was used. The evaluating results between modeling simulation and monitoring observation show a good agreement within a factor of two, representing the acceptable level of emissions inventory and simulation of concentration distributions. This model could be employed to support decision making for emission planning and control strategies. Key-Words: - Photochemical model; CMAQ; RAMS; Emissions Inventory; Model evaluation . 1 Introduction Bang Pakong , and its surrounding area , is located in the Central and Eastern regions of Thailand (Fig.1), where exists a number of power plants and large industrial facilities [1-3]. This area has been considered as an area of pollution threat caused by both emissions and influences of local wind and heat circulation (i.e., complex terrain and land/sea breeze over the Gulf of Thailand) [4]. Air quality index is found to be high during the summer and wintertime (around April and December, respectively) [5]. There was an investigation showing that the concentration level in the area could have impacts on environment and human health. A study on Environmental Impact Assessment (EIA) of Bang Pakong power plant [2] was undertaken. Industrial Source Complex for Short-range Transport (ISCST) model was applied to investigate the impacts of various power plant operation modes on air quality. ISCST is an air quality model, being conceived to limit and treat problems in the steady state with uniform wind field and not capable to account for the effects of local meteorology on pollutant dispersion. It is a transport-dispersion model, thus ignoring some key mechanisms for atmospheric photochemistry reactions and removal processes (i.e., acid deposition). In this study, the concentration distribution of primary criteria gaseous pollutants (i.e, Oxides of Nitrogen (NO x ), Sulfur Dioxide (SO 2 ), and Carbon Monoxide (CO)) are of interests. These pollutants are primary gaseous phase resulting from anthropogenic activities, and are indicators of air quality for a particular area [5]. In addition, they are primary precursors of acid deposition (i.e., Nitrate and Sulfate) and atmospheric photochemistry (i.e., 3rd IASME/WSEAS Int. Conf. on Energy & Environment, University of Cambridge, UK, February 23-25, 2008 ISSN: 1790-5095 Page 254 ISBN: 978-960-6766-43-5

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Page 1: Simulation of Concentration Distributions of Primary ... · concentration distributions of primary gaseous pollutants (i.e., NOx, SO2, and CO) over Bang Pakong and its surrounding

Simulation of Concentration Distributions of Primary Gaseous Pollutants Using Air Quality Modeling System in

Bang Pakong Area, Thailand

CHATCHAWAN VONGMAHADLEK*1, MEIGEN ZHANG2, BOONSONG SATAYOPAS3, PHAM THI BICH THAO1

1 The Joint Graduate School of Energy and Environment (JGSEE) King Mongkut’s University of Technology Thonburi (KMUTT)

Bangkok 10140, THAILAND

2 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry

Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS) Beijing 100029, CHINA

3 Department of Civil Engineering

Chiang Mai University (CMU) Chiang Mai 50002, THAILAND

Abstract: - For this study, the coupling of air quality modeling system of the Models-3 Community Multi-scale Air Quality (CMAQ) and the Regional Atmospheric Modeling System (RAMS) was applied to investigate the concentration distributions of primary gaseous pollutants (i.e., NOx, SO2, and CO) over Bang Pakong and its surrounding area, Thailand. The up-to-date emissions inventory was prepared as input into CMAQ. To perform modeling evaluation, air quality monitoring data from the Pollution Control Department (PCD), Thailand was used. The evaluating results between modeling simulation and monitoring observation show a good agreement within a factor of two, representing the acceptable level of emissions inventory and simulation of concentration distributions. This model could be employed to support decision making for emission planning and control strategies. Key-Words: - Photochemical model; CMAQ; RAMS; Emissions Inventory; Model evaluation .1 Introduction Bang Pakong , and its surrounding area , is located in the Central and Eastern regions of Thailand (Fig.1), where exists a number of power plants and large industrial facilities [1-3]. This area has been considered as an area of pollution threat caused by both emissions and influences of local wind and heat circulation (i.e., complex terrain and land/sea breeze over the Gulf of Thailand) [4]. Air quality index is found to be high during the summer and wintertime (around April and December, respectively) [5]. There was an investigation showing that the concentration level in the area could have impacts on environment and human health. A study on Environmental Impact Assessment (EIA) of Bang Pakong power plant [2] was undertaken. Industrial Source Complex for Short-range Transport (ISCST) model was applied to investigate the

impacts of various power plant operation modes on air quality. ISCST is an air quality model, being conceived to limit and treat problems in the steady state with uniform wind field and not capable to account for the effects of local meteorology on pollutant dispersion. It is a transport-dispersion model, thus ignoring some key mechanisms for atmospheric photochemistry reactions and removal processes (i.e., acid deposition). In this study, the concentration distribution of primary criteria gaseous pollutants (i.e, Oxides of Nitrogen (NOx), Sulfur Dioxide (SO2), and Carbon Monoxide (CO)) are of interests. These pollutants are primary gaseous phase resulting from anthropogenic activities, and are indicators of air quality for a particular area [5]. In addition, they are primary precursors of acid deposition (i.e., Nitrate and Sulfate) and atmospheric photochemistry (i.e.,

3rd IASME/WSEAS Int. Conf. on Energy & Environment, University of Cambridge, UK, February 23-25, 2008

ISSN: 1790-5095 Page 254 ISBN: 978-960-6766-43-5

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Ozone). To better understand the relationship between pollutants, emissions/meteorology and area wide concentration distributions, an air quality modeling system was applied for simulation. Emissions inventory is of concern due to the sporadic and out-to-date information related to emissions. Most of the previous studies focused on different scale, location, purpose [6-8] that have limitations. Examples are of limited area of study and chemical species. In this work, inventory was estimated by considering a high resolution grid-based 1 km x 1 km with temporal profiles including key anthropogenic and natural sources (i.e., large point source, mobile, nonroad, biogenic, and biomass burning) of multiple species (i.e., NOx, SO2, CO, VOC, NH3, and PM10), specially developed to support the air quality study. The Regional Atmospheric Modeling System (RAMS) [9, 10], developed by the Colorado State University (CSU) and the ASTER division of Mission Research Corporation, was used to provide geo-terrestrial information (i.e., topography, vegetation, and soil) and meteorological fields (i.e., wind and temperature). The results were then used as input in air quality modeling [[4, 11].The reliability of RAMS to predict the historical wind and temperature fields for this studied domain has been discussed in the previous study [4, 11]. The Models-3 Community Multi-scale Air Quality modeling system (CMAQ) [12, 13], developed by the U.S. Environmental Protection Agency (US EPA), was used to simulate photochemical transport and transformation in the atmosphere. CMAQ integrates the concept of “one atmosphere” that can treat both gaseous and aerosol phase while simulating of wind-driven, turbulence, chemistry and removal of the pollutants at the same time. In addition, CMAQ is considered as a good technical tool for understanding of the interactions of atmospheric pollutants resulting from emission contributions and meteorological influences [14, 15]. CMAQ applies Eulerian-based governing equation and uses computational algorithm to solve the basic governing mass continuity equation [12, 13, 16]. This study focuses on modeling simulation of Emissions-RAMS-CMAQ to investigate the dispersion characteristics related to concentration distribution. Modeling evaluation of CMAQ has been compared using available data of air quality monitoring networks from the Pollution Control Department, Thailand (PCD). The purpose of this study is to evaluate CMAQ modeling performance.

The study results will give the information on potential of the model to support decision making in air quality planning and control strategies [17].

BPK

meter

The Gulf of Thailand

Figure 1. Studied domain for RAMS (left) and CMAQ (right) where Bang Pakong (BPK) area is located in the middle of the outer domain. 2 Methodology Fig.2 shows the components of coupling RAMS-CMAQ air quality modeling system used in this study. The system requires emissions input from emissions inventory, meteorology input from RAMS, and gives the output on air quality.

Figure 2. Components of the RAMS-CMAQ

Air Quality Modeling System The year 2005 emissions inventory for Thailand was developed with spatial and temporal profiles, using Geographic Information System (GIS) and emission modeling technique. For emission inputs to CMAQ, some species (i.e., VOC) is needed to allocate for use in chemical transportation. To do so, VOC speciation was obtained from the PCD and allocated chemical speciation in a lumped structure chemical reaction mechanism scheme, Carbon Bond IV (CB-IV), in CMAQ [18].

3rd IASME/WSEAS Int. Conf. on Energy & Environment, University of Cambridge, UK, February 23-25, 2008

ISSN: 1790-5095 Page 255 ISBN: 978-960-6766-43-5

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The simulation of selected episode of RAMS was run for 4 days with 1-day spinning up during the summer and wintertime (April and December 2005, respectively). A 30-second elevation datasets of topography from the global United State Global Survey (USGS) and vegetation data from Global Land Cover Center (GLCC) were used in geo-processing step of RAMS. Initial and lateral boundary conditions were obtained from daily report (which recorded 4 times per day) of the National Center for Environmental Prediction (NCEP) of global FiNaL (FNL) analyses on 1 x 1 degree of resolution. Weekly updated Sea Surface Temperature (SST) from NCEP is at 1 km x 1 km resolutions. RAMS configuration used one-way nesting grid with a nesting ratio of 4:1 and 25 vertical layers with vertical grid stretch ratio of 1.20. The domains have grid dimension of 90×120 and 98×98, respectively. The grid cells have 16 km × 16 km grid resolutions. It is noted that the domain covers all of the Central and Eastern regions of Thailand including some part of the Gulf of Thailand (Fig.1). The meteorological outputs of RAMS were then converted into CMAQ meteorological inputs through the modified interface of RAMS Evaluation and Visulation Utilities (REVU) [19] and Meteorological-Chemical Interface Process (MCIP) [12]. CMAQ spatial domain was configured with 94 x 94 grid cells, which is smaller than meteorological one, to avoid the undesirable effects of boundary condition. Emissions domain was aggregated conforming to CMAQ domain. Map projection center was at (13.5°N, 101.0°E) with 4 km x 4 km resolutions (see Fig.1). CMAQ vertical domain extended to 15 levels to cover the top boundary. It was considered that the first through ninth layer was lower than 1 km within the mixing height while the last layer ~12 km was included to calculate impacts over troposphere. Photo-dissociation is an important process to address solar radiation in the form of chemical energy to initiate photochemical reaction [12]. To do so, photolysis rates of sky and cloud factor were prepared for numerous trace gases, which depended on altitude, latitude, and hour angle. Initial and boundary conditions were prepared as representatives of background concentration considering the contribution of anthropogenic activities on the concentration distribution. The selection of those conditions was chosen from monitoring observations at the minimum value during the starting time [15].

The simulation of CMAQ Chemical Transportation model (CCTM) was conducted using the same episode in RAMS. One day spinning up was applied to delay of photochemical transport and transformation process. Chemical mechanisms used here is CB-IV [18]. The major components consist of the production rate and loss rate of concentrations using the concept of lumped structure where organic compounds are classified by chemical bonds (i.e., single, double, aromatic, carbonyl, etc) and detailed reactions are specified for the bonding structure The pair comparison of RAMS-CMAQ modeling simulation and available monitoring observation provides an evaluation for the performance of model in simulating the spatial distribution of air pollutants. Observation data was obtained from hourly report of the PCD air quality networks. Several statistical methods (i.e., standard deviation, correlation coefficient, and factor of two) were used for model evaluation [15, 20]. Therefore, not only CMAQ but also emission inventory was evaluated. In this paper, the modeling evaluation will be applied for pollutant concentration only. For RAMS, the evaluation was discussed by the previous study [4]. 3 Results and discussions Figs.3a-f shows spatial distributions on emissions level of NOx, SO2, and CO. Emissions of NOx are high in industrialized, on-road, and urbanized area. This is due to a high temperature in combustion chamber and a high consumption of fuel. SO2 emissions are high in the industrial areas, particularly industrial estates by the reason of high sulfur contents in fuel and the unused of SO2 control technology. CO emissions are mainly from vehicular sources over road networks. A poor internal combustion engine, resulting to incomplete combustion, is responsible for a large contribution of CO. Meteorological parameters are important inputs to drive the chemical transport. Wind vectors from RAMS simulations during the selected episodes indicated that the diurnal wind patterns are influenced by land/sea breeze at the coasts [4]. The wind from the Gulf of Thailand developing over in the land near coastlines within 4 km causes an increase temperature difference between the land and sea surface. This creates a pressure over the land to drive wind fields. In addition, due to its relative warmer and higher pressure, cooler air from the sea moves in land. For complex terrain (i.e., mountainous area, where some parts of inland area are of highly

3rd IASME/WSEAS Int. Conf. on Energy & Environment, University of Cambridge, UK, February 23-25, 2008

ISSN: 1790-5095 Page 256 ISBN: 978-960-6766-43-5

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complex topography), RAMS was able to well simulate the pattern of wind fields over the complex terrain at some boundary regions. A variation in wind vector around these areas can fairly be distinguished from other urbanized or flatted area, indicating the effects on topography to the pattern of wind circulation [4]. CMAQ simulation of Bang Pakong area was successfully run for selected episodes during the summer and wintertime. A post-processing tool was used to convert CMAQ’s NetCDF-based I/O API to the Grid Analysis and Display System (GrADS) for visualized aids interface. The results of tempo-spatial concentration distribution of NOx, SO2, and CO are shown in Fig.3a-f in hourly basis. Concentrations here are displayed in units of ppmV or ppbV based on the national standard [5] (i.e., 170 ppb of 1-hourly NO2, 300 ppb of 1-hourly SO2, and 30 ppmV of 8-hourly CO). It should be noted that there is no standard level of NOx so that NO2 would be used to evaluate. Simulation of NOx, SO2, and CO distributions are displayed during 13th-15th April and 27th-29th December 2005 in hourly and daily average basis. The concentration distributions and time series plot (Figs.3-4) clearly show that these gaseous distributions were strongly influenced by the wind field. In general, the simulated results showed that maximum of gaseous pollutants were near the emissions sources. The affected distance can be as far as 100 km for NOx and SO2 because they were released from tall stacks. In reality, the plume can go at a longer distance than the simulated ones since this study simplified all emissions source as grid-based area source. This is one of the limitations of this study due to the lack of data on elevated point sources. Photochemical reactions are important factors to enhance or reduce chemical transformation, particularly NOx. In this study, gaseous phase species were treated by CB-IV chemical mechanisms. The level of NOx concentration in the summer time was found to be less than in wintertime. For the summer time, the atmosphere induced a high photolysis rate so that photochemical reaction can be formed faster, thus more NOx converted into secondary pollutants (i.e., O3). However, the delay of photochemistry process can result in NOx depletion to a minimum around 12:00-16:00. In wintertime, high level of NOx concentration increased due to a cooler ambient air temperature, which causes a decrease of mixing

height. This produces less vertical advection and diffusion. Table 1 shows the statistical tests results on modeling evaluation of NO2, SO2, and CO, comparing between simulated and observed values. It indicated a good agreement within the factor of two. The correlation coefficients between simulated and observed NO2, SO2, and CO are 0.44, 0.46, and 0.50, respectively. The trend line and standard deviation of observed and simulated pairs are satisfactory. 4 Conclusion and recommendation An updated and improved EI as well as its temporal profile of each particular emission source was developed to represent the current emission situation. The emissions contribution from various sources was found to have more influence on regional air quality. In urbanized and industrialized area, the major contributions of NOx and CO are from mobile sources while SO2 is from large point sources. The coupling of RAMS-CMAQ air quality modeling system can be used to simulate a historical concentration distribution of NOx, SO2, and CO. The results showed reasonable agreement for two selected episodes during a summer time and wintertime. Concentration distributions of gaseous pollutants were influenced by the wind fields. The impacts from local wind and heat circulation along the coastline of the Gulf of Thailand influenced spatial distribution. The performance of modeling simulation is satisfactory in term of statistical evaluation within a factor of two. 5 Acknowledgements The authors would like to thank the PCD officers for his technical assistant and providing observation data. Computer resources for the simulation were provided by the High Performance Cluster (HPC), Department of Mathematics, KMUTT , Bangkok, Thailand (http://hpcmath.kmutt.ac.th), and the Institute of Atmospheric Physics (IAP) Laboratory. This research has been supported and funded under the Joint Graduate of School of Energy and Environment (JGSEE). References: [1] DEDE, Annual report 2004. Thailand Energy

Situation. Ministry of Energy, Thailand. 2005. [2] SECOT, Final Report of Environmental Impact

Assessment of Bang Pakong Combined Cycle Power Plant Unit 5. Chachengsao, Thailand. , , 2006.

3rd IASME/WSEAS Int. Conf. on Energy & Environment, University of Cambridge, UK, February 23-25, 2008

ISSN: 1790-5095 Page 257 ISBN: 978-960-6766-43-5

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[3] DIW, Elaboration of Economic Instruments for Industrial Air Pollution, Prepared by Thailand Environment Institute, Nonthaburi, Thailand., 2003.

[4] Vongmahadlek, C., Satayopas, B., Applicability of RAMS for A Simulation of Wind and Temperature Fields in Bang Pakong Area, Thailand In Proceedins of the 7th International Conference on Simulation, Modelling, and Optimization, Beijing, China, September 15-17, 2007.

[5] PCD, Daily Report of the Regional Air Quality in Thailand, [http://www.pcd.go.th/AirQuality/Regional/defaultThai.cfm, accessed: Aug., 2007]. 2005.

[6] PCD, Air Emission Database of Vehicles and Industry in Bangkok Metropolitan Region 1992, Prepared by Faculty of Engineering, Chulalongkorn

University, Bangkok, Thailand. 1994. [7] PCD, J. a., Acid Deposition Control Strategy in the

Kingdom of Thailand. Prepared by Suuri-Keikaku Co. Ltd and Pacific Consultants International. 2003

[8] Pongprueksa, P., Hydrocarbons and Nitrogen Oxides Emissions Database for the Bangkok Metropolitan Region. Master Thesis, Chulalongkorn University, Bangkok, Thailand. 2003.

[9] Pielke, R. A., Cotton, W. R., Walko, R. L., Tremback, C. J., Lyons, W. A., Grasso, L. D., Nicholls, M. E., Moran, M. D., Wesley, D. A., Lee, T. J., Copeland, J. H., A Comprehensive Meteorological Modeling System – RAMS. Meteorology and Atmospheric Physics 49, 1992, 69-91.

[10] Cotton, W. R., Pielke, R. A., Walko, R. L., Liston, G. E., Tremback, C. J., Jiang, H., Mcanelly, R. L., Harrington, J. Y., Nicholls, M. E., Carrio, G. G., Mcfadden, J. P., RAMS 2001 - Current Status and Future Directions. Meteorology Atmospheric Physics 82, 2003, 5-29.

[11] Lynos, W. A., Treamback, C. J., Pielke, R. A., Applications of the Regional Atmospheric Modeling System (RAMS) to Provide Input to Photochemical Grid Models for Lake Michigan Ozone Study (LMOS). Journal of Applied Meteorology 34, 1994, 1762-1786.

[12] Byun, D. W., Ching, J. K. S., Science Algorithms of the EPA Models-3 Community Multi-scale Air Quality (CMAQ) Modeling System. NERL,ResearchvTriangle Park,NC., 1999.

[13] Byun, D. W., Schere, K. L., Review of the Governing Equations,Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. Applied Mechanics Reviews 59, 2001, 51-77.

[14] Zhang, M. G., Gao, L. J., Ge, C., Xu, Y. P., Simulation of nitrate aerosol concentrations over East Asia with the model system RAMS-CMAQ. Tellus Series B-Chemical and Physical Meteorology 59, (3)2007, 372-380.

[15] Zhang, M. G., Uno, I., Zhang, R. J., Han, Z. W., Wang, Z. F., Pu, Y. F., Evaluation of the Models-3 Community Multi-scale Air Quality (CMAQ)

modeling system with observations obtained during the TRACE-P experiment: Comparison of ozone and its related species. Atmospheric Environment 40, (26)2006, 4874-4882.

[16] Seinfeld, J. H., Atmospheric Chemistry and Physics of Air Pollution, John Wiley & Sons. Inc. 1986.

[17] Hogrefe, C., Rao, S. T., Kasibhatla, P., Kallos, G., Tremback, C. J., Hao, W., Olerud, D., Xui, A., JcHenry, J., Alapaty, K., Evaluating the performance of regional-scale photochemical modeling system: Part I - meteorological predictions. Atmospheric Environment 35, 2001, 4159-4174.

[18] Gery, M. W., Whitten, G. Z., Killus, J. P., Dodge, M. C., A Photochemical Kinetics Mechanism for Urban and Regional Scale Computer Modeling. Journal of Geophysical Research 94, 1989, 12925-12956.

[19] Tremback, C. J., Walko, R. L., Bell, M. J., User’s Guide: REVU RAMS/HYPACT Evaluation and Visualization Utilities Version 2.3.1, ASTER Division Mission Research Corporation. Fort Collins, CO, USA., 2001.

[20] Yu, S., Eder, B., Dennis, R., Chu, S.-H., Schwartz, S., On the Development of New Metrics for the Evaluation of Air Quality Models. Atmospheric Science Letters, (7)2005, 26-34.

Table 1. Modeling evaluation using statistical tests between hourly pair of simulation and observation in the period of

a) 13-15, Apr 2005 (summer), and b) 27-29, Dec 2005 (winter)

UMMER TYPE MEAN STDa CORRELb FAC1.5c FAC2d S

NO2 Observed 22.00 15.51 NO2 Simulated 20.06 13.20

0.47 69.44% 83.33%

SO2 Observed 11.77 4.97 SO2 Simulated 7.98 2.43

0.40 97.22% 98.61%

CO Observed 2.68 1.31 CO Simulated 1.21 0.45

0.67 20.83% 34.72%

WINTER TYPE MEAN STDsa CORRELb FAC1.5c FAC2d

NO

2 Observed 48.37 20.93 NO2 Simulated 56.08 12.45

0.41 77.78% 87.50%

2 Observed 8.96 3.51 SO

2 Simulated 8.54 2.34 0.53 83.33% 91.67%

SO Observed 1.86 0.44 CO Simulated 1.97 0.18

0.32 46.11% 64.44% CO

a) Standard deviation of a temporal distribution b) Correlation coefficient of a pair between observed and

simulated temporal distribution c) Factor of 1.5 of observed over simulated temporal distribution d) Factor of two of observed over simulated temporal distribution

3rd IASME/WSEAS Int. Conf. on Energy & Environment, University of Cambridge, UK, February 23-25, 2008

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(a) NOx emissions (b) 1-hourly average of NOx concentration

(c) SO2 emissions (d) 1-hourly average of SO2 concentration

(e) CO emissions (f) 8-hourly average of CO concentration

g/grid/s ppmV

ppbV g/grid/s

g/grid/s ppbV

Figure 3. Spatial distribution of NOx, SO2 and CO during the episode

Figure 4. Time series plot of simulation and observation in the period of 13-15 Apr 2005

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3rd IASME/WSEAS Int. Conf. on Energy & Environment, University of Cambridge, UK, February 23-25, 2008

ISSN: 1790-5095 Page 259 ISBN: 978-960-6766-43-5