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Research Article Design and Implementation of a Dynamic Simulation System for Air Pollutant Diffusion - A Case Study of the Fangshan District, Beijing, China Zhenxv Lan1, Fengyuan Zhang1, Jia Wang1, Min Chen4* 1School of Geography, Nanjing Normal University, China 2College of Forestry, Beijing Forestry University, China
Abstract With the rapid development of China’s industrialization and urbanization, urban air pollution has become an urgent problem to be solved. Industrial air pollutants in local areas of cities and towns directly harm the health of residents. Under the UNEP initiative, research on urban air pollution has become a main aspect of air pollution research. This paper focuses on the study of atmospheric pollution in small urban areas using GIS spatial analysis and simulation as the methods and the improved Gaussian plume diffusion model as the mathematical principle, based on ArcGIS Engine components and C#.Net. A simulation system platform for the diffusion process of atmospheric pollutants is designed and implemented, which has various functions including dynamic simulation display expression, GIS spatial analysis, spatial data processing, attribute information extraction and simulation result thematic mapping and export. Based on the Fangshan District in Beijing as an example, the dynamic simulation and spatial analysis of pollutant diffusion were conducted using the system platform based on industrial air pollution resource data in the region. The results show that the system provides significant decision-making guidance for effective urban air pollution warnings and the improvement of urban air quality. Keywords: Air Pollution, Simulation, Gaussian Plume Models, Arc Engine
Introduction Fuzhou to carry out a GIS simulation analysis of urban air pollution diffusion and obtained relevant conclusions [11]. Tang Since the industrial revolution, the problem of air pollution et al. [12] and others used components of GIS development has been inherent in the development of the industrial technology to support an R&D system and simulated the economy, from the London smog a hundred years ago to pollutant diffusion in the Liuzhi Steel Plant in Chongqing. the haze and fog of the Beijing, Tianjin and Hebei Regions Zhang [13] used Net as a development tool and Arc Engine today [1-4]. According to the first survey of pollution sources, to implement concentration prediction and analysis software China’s secondary industry has emitted most of the sulfur functions. Arystanbekova, N. K predicted air pollution based dioxide while promoting per capita GDP growth, which is an on a Gaussian model [14]. D Briggs noted that GIS technology important cause of air pollution [5]. Air pollution has become could make a great contribution to air pollution control and one of the most serious environmental pollution problems in conducted significant research on methods of interpolation and China [6].Therefore, the search for a more efficient alternative dynamic modeling techniques [15]. to traditional air pollution prevention measures is no longer just about the system or the public [7, 8]. GIS, which allows Tolga Elbir designed a system for large Turkish cities to for powerful spatial analysis and has intuitive and accurate perform basic spatial analysis [16]. Gulliver and Briggs [17] visual display characteristics, complements the advantages discussed a GIS system that can be used to simulate traffic- of a Gaussian diffusion model to scientifically simulate related air pollution. Kumar et al. [18] used a GIS-designed the diffusion, spread and dilution of polluted gases in the simulation system to evaluate and predict the spread of urban space-time range, which will be beneficial to atmospheric traffic air pollution. Kwak et al. [19] performed asimple environmental protection and pollution control [9]. The simulation of air pollution diffusion has been the focus of many scholars. Zhao Wei and others developed the City Air 1.0 Correspondence to: Min Chen, School of Geography, Nanjing Normal urban air quality management system to simulate and visualize University, China, Email: chenmin0902[AT]163[DOT]com the spread of pollution [10]. Chen Hongmei and others used Received: Nov 12, 2018; Accepted: Nov 15, 2018; Published: Nov 19, 2018
J Geol Geosci 1 Volume 3(1): 2018 Chen M (2018) Design and Implementation of a Dynamic Simulation System for Air Pollutant Diffusion - A Case Study of the Fangshan District, Beijing, China
chemical simulation of NO, NO2 and O3 using a Gaussian the ground, the simplest study is idealized and only the plume smoke model and established a comprehensive urban air quality is considered. It is completely reflected on the ground and model.The research of these scholars is very representative, is not absorbed by the ground. The reference source method but additional research on air pollution at small urban scales is used to treat the gas concentration at any position as the is still needed. In this paper, software for the simulation of concentration of the position when there is no ground and the atmospheric pollutant diffusion is designed and implemented concentration of the pollutant when the ground is present [20]. based on close integration of the Gaussian diffusion model, Then, the model of the downdraft to any point of polluted gas which is widely applied, and GIS. The system first establishes concentration C(x, y, z) is calculated as follows (Yuan J, 2016): the model relationship between the atmospheric pollutant Qy2 = − (1) diffusion phenomenon and influencing factors such as wind Cxyz( , , ) exp(2 )* F 22πσyz σu σ y speed and direction, then the spatial distribution of the polluting gas centered at the pollution source and the diffusion ()z−+ He22 () z He F = exp[ ]+ exp[ ] (2) process over time can be dynamically displayed. The software 22 22σσzz is a complete system, with functions such as a GIS attribute query, spatial analysis and thematic production output. where x is the distance between the calculated wind direction and the downwind direction (m); y is the distance between the Gaussian plume model calculated position and the location of the source of the wind Assumptions (m); z is the height of the calculated position from the ground (m); Q is the pollution source Or release rate (mg/s); is the The Gaussian diffusion model, which ignores the effect of average wind speed at the source of the pollution (m/s); He is gravity or buoyancy on a gas with an average density close the geometric height at which the source leaks from the ground to that of air, is based on the hypothesis that the diffused gas (m); σy σz are horizontal and vertical diffusion parameters, concentration obeys a Gaussian distribution in an approximately respectively (m); σy σz is a function of the ground roughness, stable turbulent flow field, and mainly considers the turbulence the calculated position distance from the downwind position leading to gas diffusion [20-22]. x between the pollution source, and the atmospheric stability. The hypotheses of Gaussian diffusion model are as follows: Model parameter calculation 1. The quasi-regional flow field is stable, the cutting edge Based on the two kinds of nighttime cloud volume design, of the wind field is not considered, and the underlying three different daytime insolation and five different ground surface is open, uniform and flat. wind speeds designed by Pasquill-Gifford, the description of 2. During the diffusion period, the polluting gas should be atmospheric stability is divided into six types. Stabilization passive and conservative. The polluting gas has no loss includes stability and stability, and instability includes or conversion during the diffusion process, and the air strong instability, instability, slight instability, and neutrality. does not move relative to each other and is reflected on The types are represented by the letters A to F, from strong the ground. instability to neutral to stable [25]. The Chinese national standards, “Technical Principles and Methods for Establishing 3. The diffusion process occurs in the same temperature Local Air Pollution Emission Standards” (GB3840-83) and layer, the average wind speed is greater than 1 m / s, “Technical Principles for Environmental Impact Assessment” and the diffusion description is generally less than 10 (HJ/T2.1-93) are mainly based on the improved Pasqual to 20 km. stability classification method, in which the first simulation The Gaussian diffusion model is derived from a large number step is based on the cloud amount and solar elevation angle; of actual measurement studies and analyses, mainly for the the corresponding solar radiation levels are shown in Table 1. spread of pollutants from elevated point sources. The model In the second step, the number of solar radiation levels assumes that the pollutant concentration in the uniform obtained in the first step and the ground wind speed value in the turbulent atmosphere obeys a positive distribution, but the simulation area at the corresponding simulation time is queried actual atmospheric conditions are very complicated and in (Table 2) and the number of stability levels is determined. varied. Therefore, in practical applications, the Gaussian The Chinese national standards, “Technical Principles and model holds in a small area where the underlying surface is Methods for Establishing Local Air Pollution Emission approximately uniform, and the airflow is relatively stable. In Standards” (GB3840-83) and “Technical Principles for diffusion studies, the calculated value is more relevant than the Environmental Impact Assessment” (HJ/T2.1-93) are mainly real value, and the simulation effect is good [23, 24]. based on the improved Pasqual stability classification method, Model calculation in which the first simulation step is based on the cloud amount and solar elevation angle; the corresponding solar radiation Continuous leakage of elevated point source pollutants occurs levels are shown in Table 1. In the second step, based on the when aspects such as the wind direction, wind speed, and number of solar radiation levels and the ground wind speed atmospheric stability do not change over a certain period of in the simulation area at the corresponding simulation time time. Due to the complexity and diversity of the objects on that were obtained in the first step are queried to determine the
J Geol Geosci 2 Volume 3(1): 2018 Chen M (2018) Design and Implementation of a Dynamic Simulation System for Air Pollutant Diffusion - A Case Study of the Fangshan District, Beijing, China
Cloud amount Solar radiation level Total cloud amount/ low cloud cover at night h0<=15° 15
Ground wind Solar radiation level height and atmospheric stability, and the model diffusion Speed (m/s) +3 +2 +1 0 -1 -2 parameter can be obtained by calculation. <=1.9 A A~B B D E F System design and implementation 2~2.9 A~B B C D E F 3~4.9 B B~C C D D E Main data processed by the system 5~5.9 C C~D D D D D The data processed by the system designed in this paper >=6 D D D D D D has three main parts: the pollution source data of industrial Table 1: Cloud volume and solar radiation levels (Yuan J, 2016). enterprises in the Fangshan District of Beijing, the vector data of the administrative area map of the Fangshan District and stability grade. The diffusion parameter σ 、σ is a function y z the model parameter data input by the user. The industrial of atmospheric stability and the distance x, which is a variable enterprise pollution source data include tabular data related to and a function of wind speed and time, between the calculated the name of the company, the person in charge, the industry position and the leaking source. As follows (Yuan J, 2016): and address of the company, and point-like vector layer qy qz σ yy= PX , σ zz= PX (3) data describing the location of each pollution source. The administrative area vector layer data describes the basic area Based on the calculation of the diffusion coefficient of the of the Fangshan District and the county boundaries of the elevated point source diffusion in the large roughness of the district to define the approximate spatial extent and structure simulated area recommended by the International Atomic of the Fangshan District. The main data framework is shown Energy Agency (IAEA), the corresponding coefficient is in (Figure 1). obtained from (Table 3) according to the pollutant release Demand analysis Freed Return Diffusion range height coefficient A B C D E F In response to the demand for air pollution control and Py 1.503 0.876 0.659 0.640 0.801 1.294 the needs of environmental management personnel in the qy 0.833 0.823 0.807 0.784 0.754 0.718 Fangshan District, managers and decision makers can input 50 Pz 0.151 0.127 0.165 0.215 0.264 0.241 actual conditions of the environment including meteorological qz 1.219 1.108 0.996 0.885 0.774 0.662 data and other parameters into the system designed in this Py 0.170 0.324 0.466 0.504 0.411 0.253 paper. The system diffusion model uses a GIS spatial analysis qy 1.296 1.025 0.866 0.818 0.882 1.057 function to simulate the diffusion and dilution of pollutants 100 Pz 0.051 0.070 0.137 0.265 0.487 0.717 discharged by factory enterprises over a certain period of qz 1.317 1.151 0.985 0.818 0.652 0.486 time, which is useful for managers to conduct pollution Py 0.671 0.415 0.232 0.208 0.345 0.671 prediction, monitoring, pollution simulation analyses, early qy 0.903 0.903 0.903 0.903 0.903 0.903 warning and forecasting, pollution emergency plans and 180 Pz 0.0245 0.0330 0.104 0.307 0.546 0.484 environmental management decisions. User requirements are qz 0.500 1.320 0.997 0.734 0.557 0.500 shown in Figure 2. Table 3: Diffusion parameter coefficients (Yuan J, 2016).
Industrial Fangshan Model enterprise District parameter pollution Map Data source data
wind Wind Release ... Fangshan direction speed height Industrial Enterprise District enterprise pollution administrati ... detailed source ve division map data data sheet location map Figure 1: The main data framework.
J Geol Geosci 3 Volume 3(1): 2018 Chen M (2018) Design and Implementation of a Dynamic Simulation System for Air Pollutant Diffusion - A Case Study of the Fangshan District, Beijing, China
Air pollutant diffusion simulation system
Industrial enterprise pollution source information view
Simulated scene meteorological parameter input
Contaminant diffusion simulation display
Static diffusion Dynamic diffusion simulation display simulation display
Diffusion simulation result output
GIS spatial query analysis processing
Pollution management decision
User Interface
Figure 2: Demand frame diagram.
User Interface layer
Business Logic Layer Auxiliary File data editing classes and Air pollutant diffusion simulation methods GIS basic space analysis, query, processing, output
Data access layer
Fangshan District Industrial Enterprise Fangshan District Information space map data (Access database） Figure 3: System architecture diagram.
Functional framework of the system local Fangshan space map data into the system for processing. The overall architecture design is shown in Figure 3. System architecture design Functional framework of the system The system was built with .Net technology and adopts a three-layer architecture design, namely, a presentation layer, According to the previous demand analysis and overall a business logic layer and a data access layer [26].The architecture design, the system’s functions can be divided into presentation layer is the system interface layer: the system is an atmospheric pollutant diffusion simulation module, a GIS based on a user-friendly design of relevant pollution source basic function module and a data addition and deletion module. characteristics and environmental meteorological parameter The core module of the system is the air pollutant diffusion input and a function button interface. The design of the business simulation module, including the main industrial enterprise logic layer comprises the atmospheric pollutant diffusion pollution source information view, model parameter input, simulation. The data access layer main design includes the dynamic and static simulation display, dynamic simulation detailed industrial enterprise data in Microsoft Office Access, time input and simulation result output [27, 28]. The functional displayed in the front-end interface, and accesses and inputs the framework is shown in Figure 4.
J Geol Geosci 4 Volume 3(1): 2018 Chen M (2018) Design and Implementation of a Dynamic Simulation System for Air Pollutant Diffusion - A Case Study of the Fangshan District, Beijing, China
Air pollutant diffusion simulation system
Data file editing Air pollutant diffusion GIS basic function module simulation module module
Industrial enterprise basic information view Attribute query Position coordinate display Data file addition Dynamic analog duration input Spatial query Map browsing
Model parameter input Map decoration Hawkeye Data file cleanup Dynamic analog display Map symbolization Layer operation
storage Static analog display Map measurement Thematic mapping Simulation result export
Data
Industrial Spatial layer data enterprise information
Figure 4: System functional framework design.
Model simulation process region is dominated by bituminous coal anthracite mining, lime and gypsum manufacturing and bottled/canned water The functions of the system include basic data processing, manufacturing, and the southeast region is dominated by core atmospheric pollutant diffusion simulation and integrated cement manufacturing and metal structure manufacturing. In GIS basic spatial analysis, query, processing and output. The general, most industrial pollution sources in the region are simulation process is shown in Figure 5. densely distributed in the southeast, as shown in Figure 7. The Model parameter input interface polluting gases emitted by industrial enterprises are mainly The system diffusion simulation interface consists of three sulfides, nitrogen oxides and particulate matter. This paper main parts. The first is to select the pollution source of different focuses on the pollution diffusion simulation of SO2. pollution source layers and display the basic information Simulation process and results of the pollution source through the interface. The second part is the input of several analog parameters, including the The pollution source layer of the Fangshan Industrial solar radiation level of the simulation area, intensity of the Enterprise was input into the system for simulation effect pollution source, wind direction, wind speed, release height display. After the layer data was input into the system, the of the polluted gas, and the calculated height. The third part atmospheric pollutant diffusion simulation function menu is based on the simulated time and time interval of the inputs is selected and the model parameter input window is called for dynamic simulation. In addition, the Track Bar, which is to define the parameters. For example, a source of pollution one of the Windows Forms controls, can be used to adjust the called Beijing Saint-Gobaintex Glass Fiber Co., Ltd. in display frame-by-frame. Parameter input interface is shown in Fangshan District was selected. The system displays basic Figure 6. information such as the name, industry, person in charge and address of the company. Then, based on the locations of the Simulating the spread of atmospheric pollution in the pollution sources of enterprises in the Fangshan District, the Fangshan District cloud amount and solar elevation angle at the simulation time The simulation data was obtained from the Fangshan District and the corresponding solar radiation level are determined. In Environmental Protection Bureau of Beijing and includes the this simulation, the total traffic volume and low cloud volume basic attributes and spatial information of industrial factories are less than or equal to 4 and sunny, respectively. The solar and enterprises in the Fangshan District from 1969 to 2015, radiation level of the altitude angle between 35 degrees and as well as administrative maps of the streets and towns in the 65 degrees is +2. It is assumed that the release rate of the district. polluting gas from the pollution source is 500 mg/s. The wind direction at the pollution source is at an angle of 45 degrees Distribution of atmospheric pollution sources in the clockwise with an east wind direction, the wind speed is 5 Fangshan District m/s, and the pollutants are released at a height of 50 m from The simulation data is characterized by the distribution of the ground. The system simulates the diffusion of pollutants industrial sources in Fangshan District. The northwestern from the ground at a height of 200 m. The parameter input is
J Geol Geosci 5 Volume 3(1): 2018 Chen M (2018) Design and Implementation of a Dynamic Simulation System for Air Pollutant Diffusion - A Case Study of the Fangshan District, Beijing, China
start
Pollution source layer selection Pollution source name selection Solar radiation Solar elevation angle Cloud level amount Pollution source information view
Release Regression Atmospheric Ground height coefficients stability wind speed
Diffusion parameter
Source Gaussian intensity Application model Pollution source parameter calculation point coordinates
Analog duration, unit time input Prediction point concentration Analog time IDW input interpolation Y Layered Dynamic tinting simulation
Static Dynamic simulation simulation Layered color Dynamic display display
Result
Figure 5: Model simulation process.
Figure 6: Model parameter input interface.
J Geol Geosci 6 Volume 3(1): 2018 Chen M (2018) Design and Implementation of a Dynamic Simulation System for Air Pollutant Diffusion - A Case Study of the Fangshan District, Beijing, China
Figure 7: Industrial plant distribution map of Fangshan district.
Figure 8: Simulation effect display parameter input interface.
shown in Figure 8. Finally, “Execute Calculation” is selected “Start System” is selected to stream the simulation animations to make the system calculate and display the simulation. The frame-by-frame. simulation effect is shown in Figure 9. Discussion In the dynamic simulation, within 60s of the release of the First, the effect of the model simulation is discussed. In this simulated pollutants, the diffusion result is taken as a frame paper, the Gaussian diffusion model is used to simulate the every 15s. The “Apply Parameters” button is selected to point source pollution diffusion of industrial enterprises in prompt the system to simulate the model of different frame the Fangshan District. The simulation effect depends on the positions. The parameter definition is shown in Figure 10. model assumptions, the relevance of the input meteorological
J Geol Geosci 7 Volume 3(1): 2018 Chen M (2018) Design and Implementation of a Dynamic Simulation System for Air Pollutant Diffusion - A Case Study of the Fangshan District, Beijing, China
Figure 9: System simulation effect display.
Figure 10: Dynamic simulation parameter setting. parameters and the actual situation. However, the situation in technology, GIS software requires an Internet-based approach the simulated area is complex and fluctuating. For example, to make GIS functions more flexible, convenient, efficient, the model assumes that the pollutants are diffused under and low-threshold. This is how WebGIS came into being. constant wind speed and atmospheric stability. However, the WebGIS has improved the deficiencies of component GIS in wind speed and atmospheric stability are not in a completely many aspects. Component GIS is not as powerful as a cross- constant state for any period of time, and are continually platform capability or as useful as data sharing via the Internet. changing. Therefore, the system model simulates the diffusion It is better for users to employ GIS without other complicated under ideal conditions. We can continuously correct the input installations. The functional convenience is not as good, as its parameters or models according to the situation to be as close huge mobile application is based on a mobile network. With the as possible to the actual diffusion so that the simulation is as future development of the Internet and related technologies, accurate as possible. Therefore, the model simulation effect the development of component GIS is full of unknowns. and actuality cannot be ignored. There are some deviations and Finally, the pollutant diffusion simulation system designed and it is necessary to consider the actual situation of the simulation implemented in this paper is based on a two-dimensional angle. area and the system simulation. Simulation of the same effect However, in practice, pollution diffusion is three-dimensional. as the actual diffusion situation requires additional study. The diffusion of pollutant gas is a two-dimensional motion process that includes fluid dynamics at different heights and Second, the development of the GIS components is discussed. complex processes such as gravity. Simulating the diffusion In this paper, the components of the GIS method are used to of atmospheric pollution in three-dimensions will more design and implement the simulation system, which allows for accurately simulate the diffusion of pollutants, rationally and the advantages of component GIS, such as easy development, scientifically. This presents a prospect for further improvement strong embedding and powerful functions. However, with the of the simulation model of atmospheric pollutant dispersion. continuous rapid development of Internet technology, technical Many of the present models for simulating diffusion are too methods of cloud computing, cloud platforms, the Internet of idealistic or demanding, and the model was improved so that Things, distributed-grid computing, big data, and mobile and the application conditions are reasonable and true and the wireless networks, data sharing and interoperability between simulation results are real and reliable. the concepts of platform and collaborative computing are becoming increasingly popular and GIS software is bound Summary to be further upgraded. Although component GIS has great Studying urban air pollution is related to urban health and advantages, in the context of the growing maturity of Internet the development and progress of human civilization. The
J Geol Geosci 8 Volume 3(1): 2018 Chen M (2018) Design and Implementation of a Dynamic Simulation System for Air Pollutant Diffusion - A Case Study of the Fangshan District, Beijing, China simulation software for atmospheric pollutant diffusion in integration of GIS and atmospheric environment model. urban small areas that is designed and implemented in this Environ Sci Technol 26: 27-29. [View Article] paper is based on previous scholars’ research, and more fully 11. Chen H, Wang X, Chen C, et al (2005) GIS simulation combines GIS spatial analysis and simulation technology analysis of urban air pollution diffusion-a case study of to produce vivid and convenient spatial simulation display. Fuzhou city. J Earth Information Science 7: 101-106. Studying urban air pollution is related to urban health and [View Article] the development and progress of human civilization. The simulation software for atmospheric pollutant diffusion in 12. Tang S, Zhang X, Huang Q, et al (2011) Design urban small areas that is designed and implemented in this and implementation of simulation analysis system paper is based on previous scholars’ research, and more fully for atmospheric pollutant dispersion based on GIS. combines GIS spatial analysis and simulation technology Hydroelectric Energy Science 46-48. [View Article] to produce vivid and convenient spatial simulation display. 13. Zhang F (2009) Research and application of GIS-based In the system, the simulation results of different heights are air pollution diffusion simulation and evaluation system. realized dynamically on a frame-by-frame basis. The system Chengdu University of Technology, China. [View Article] is more flexible than those of previous scholars through the use of user-defined parameters such as source intensity of 14. Arystanbekova NK (2004) Application of Gaussian plume pollution. In summary, the research in this paper is based on models for air pollution simulation at instantaneous GIS technology using the improved Gaussian model under emissions. Math Comput Simul 67: 451-458. [View Article] ideal conditions and, considering the public atmosphere 15. Briggs D (2005) The role of GIS: coping with space (and health of urban residents, it focuses on simulation and time) in air pollution exposure assessment. J Toxicol analysis of the diffusion of atmospheric pollution in small Environ Health A 68: 1243-1261. [View Article] urban areas. 16. Elbir T (2004) A GIS based decision support system for References estimation, visualization and analysis of air pollution for 1. Ashby E (1987) The big smoke. Methuen. [View Article] large Turkish cities. Atmos Environ 38: 4509-4517. [View Article] 2. Sun Y, Zhuang G, Tang AA, Wang Y, An Z (2006) Chemical characteristics of PM2.5 and PM10 in Haze-Fog episodes 17. Gulliver J, Briggs DJ (2005) Time-space modeling of in Beijing. Environ Sci Technol 40: 3148-3155. [View journey-time exposure to traffic-related air pollution using Article] GIS. Environ Res 97: 10-25. [View Article] 3. Che H, Xia X, Zhu J, Li Z, Dubovik O, et al. (2014) Column 18. Kumar A, Mishra RK, Singh SK (2015) GIS application aerosol optical properties and aerosol radiative forcing during in urban traffic air pollution exposure study: a research a serious Haze-Fog month over North China Plain in 2013 review. Suan Sunandha Science and Technology Journal based on ground-based sunphotometer measurements. Atmos 2: 25-37. [View Article] Chem Phys 14: 2125-2138. [View Article] 19. Kwak KH, Baik JJ, Ryu YH, Lee SH (2015) Urban air 4. Dai H (2014) Talking about controlling urban air pollution quality simulation in a high-rise building area using a Yangtze River delta energy forum. [View Article] CFD model coupled with mesoscale meteorological and chemistry-transport models. Atmos Environ 100: 167-177. 5. Wang X, Wang W, Shan Y, et al. (2016) Research on [View Article] the relationship between economic development and atmospheric pollutant emissions in China Based on the 20. Sykes RI, Lewellen WS, Parker SF (1986) A Gaussian empirical analysis of the first national pollution source plume model of atmospheric dispersion based on second- census data. Ecol Econ 32: 165-169. [View Article] order closure. Journal of Climate and Applied Meteorology 25: 322-331. [View Article] 6. Chan CK, Yao X (2008) Air pollution in mega cities in China. Atmos Environ 42: 1-42. [View Article] 21. Spijkerboer HP, Beniers JE, Jaspers D, Schouten HJ, Goudriaan J, et al. (2002) Ability of the Gaussian plume 7. Zhang H, Wang S, Hao J, Wang X, Wang S, et al. (2016) model to predict and describe spore dispersal over a potato Air pollution and control action in Beijing. J Clean Prod crop. Ecol Modell 155: 1-18. [View Article] 112: 1519-1527. [View Article] 22. Islam MA (1999) Application of a Gaussian plume model 8. Lan Q, Chen C (2015) The impact of system softening and to determine the location of an unknown emission source. public identity on air pollution control efficiency. China Water Air Soil Pollut 112: 241-245. [View Article] Population Resources and Environment 25: 145-152. [View Article] 23. Wei Z, Guo Z, Zhang L, et al (2008) Application of air pollution model based on Gaussian model. Journal of 9. Fotheringham S, Rogerson P (2014) Spatial analysis and Ocean University of China (Natural Science) 38: 327-330. GIS. CRC Press. [View Article] [View Article] 10. Zhao W, Lin B, Yan L (2003) Research and practice of
J Geol Geosci 9 Volume 3(1): 2018 Chen M (2018) Design and Implementation of a Dynamic Simulation System for Air Pollutant Diffusion - A Case Study of the Fangshan District, Beijing, China
24. Yuan J (2016) Design and implementation of GIS-based 26. Gat E, Bonnasso RP, Murphy R (1998) On three-layer atmospheric pollutant diffusion simulation system. Tianjin architectures. Mobile Robots 195: 210. [View Article] University, China. [View Article] 27. Li H (2010) Simulation of air pollution diffusion in Huizhou 25. Paine RJ, Strimatis OG, Dennis MG (1987) User’s guide to City. Central South University, China. [View Article] the complex terrain dispersion model. EPA/600/8–87/058a. 28. Wang Z (2013) Design and implementation of Fangshan Department of Commerce. 1: 1-173, 2: 1-266. [View Article] atmospheric quality management system. Beijing Forestry University, China. [View Article]
Citation: Lan Z, Zhang F, Wang J, Chen M (2018) Design and Implementation of a Dynamic Simulation System for Air Pollutant Diffusion - A Case Study of the Fangshan District, Beijing, China. J Geol Geosci 2: 001-010. Copyright: © 2018 Lan Z, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
J Geol Geosci 10 Volume 3(1): 2018