Application of the CALPUFF model in emergency response assessment for radiological events in metropolitan area.

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Jian-Pu Chen, Jen-Hsin Teng, Teng-Ping Zeng, Yu-Xia Hsu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4518587/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Due to the possibility of radioactive events or dirty bomb events occurring in urban areas near buildings with a relatively small impact radius (within 1-2 kilometers), an effective and rapid assessment of their extent and impact is essential for emergency response strategies. In this research, the CALPUFF model is introduced as the basis for conducting a series of tests and improvements to create a localized radioactive events impact assessment system. The CALPUFF model is recommended by the US EPA and will be combined with meteorological data from the Central Weather Bureau's mesoscale numerical weather forecast model and terrain data with a 30-meter horizontal resolution from Taiwan's Ministry of the Interior. This localized system aims to simulate and predict the outdoor radiation intensity or pollution range for future scenarios, reducing the health risks and pressure on emergency personnel exposed to radiation, and facilitating necessary protective action decisions. Environmental Policy radioactive events dirty bomb events CALPUFF model emergency response Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction International terrorist organizations employ a diverse range of terrorist tactics, primarily including kidnapping for ransom and hostage-taking, bombings using various methods such as parcel bombs, gasoline bombs, car bombs, human bombs, incendiary devices, and acts of arson. Additionally, they engage in activities aimed at disrupting communication, networks, and computer information systems, all with the intention of causing societal chaos. In the future, there is a growing concern that these organizations may escalate their tactics to include nuclear terrorism using radiological weapons. Therefore, the threat of "nuclear, biological, and chemical terrorism" is expected to become increasingly severe. Preventing the misuse of nuclear weapons by terrorist groups, addressing illegal transportation, and implementing measures for nuclear disaster prevention and protection, as well as post-disaster response and sampling, are all of paramount importance.。 A radiological dispersal device (RDD), also known as a "dirty bomb," is a device that combines conventional explosives with radioactive materials. When a dirty bomb is detonated, radioactive material is dispersed along with the explosive energy and carried by the wind. Most of the radioactive particles are likely to fall to the ground within a few city blocks or a few miles from the explosion site, leading to contamination of the environment and potential exposure to the public. The radioactive materials dispersed by a dirty bomb may not necessarily cause immediate radiation injuries, but the psychological concerns and anxiety among the public can often outweigh the actual physical harm. Incidents involving dirty bombs are referred to as "radioactive events". In the 2002 Police White Paper Report of Taiwan's Ministry of the Interior, there is a section titled "Research on the Anti-Terror Crisis Response Mechanism," which discusses potential forms of terrorist attacks that could occur within the country. While bomb attacks are the most common, what concerns the Taiwanese people the most is the possibility of a RDD nuclear terrorist attack. To safeguard against international terrorist organizations smuggling RDD into Taiwan for the purpose of nuclear terrorist attacks, Taiwan's Atomic Energy Council (AEC) initiated the "Radiological Dispersal Device Protection Mechanism" for the first time in March 2003. The initial hours following a radiological events are critical for response and can significantly impact the overall effectiveness of disaster relief execution at all stages. Therefore, frontline responders are tasked with a multitude of responsibilities, such as assessing radiological release conditions, contamination distribution, implementing life-saving actions, advising on public protection measures, reporting on the on-site situation, and more.These tasks are initiated within minutes of frontline responders arriving at the scene. The efficiency of initial response activities and coordination will determine when other supporting organizational teams and resources arrive at the scene and the subsequent effectiveness of disaster relief operations. Therefore, conducting research and development in radiation event response technology is of paramount importance for enhancing our country's disaster prevention and rescue capabilities. To meet the requirements for radiation protection and emergency response, the selected model needs to have the following capabilities: fast computational speed, the ability to use regional real-time numerical weather forecast data, compatibility with atmospheric dispersion models that can utilize such meteorological data, suitability for employing point source atmospheric dispersion models, model simulation resolution capable of resolving down to the urban scale, and adaptability to the complex terrain of Taiwan. These criteria were comprehensively used to select a suitable model. 2. Literature Review Numerical simulation is a powerful tool to study the underlying physics of atmospheric transportation and reasonably quantify and analyze the impact of radionuclides dispersion on the environment. There are a large number of numerical studies on varied spatial scales of atmospheric dispersion models (Holmes and Morawska, 2006 , Leelőssy et al., 2018 ). Several systems based on local, regional and global models have been recognized, including AERMOD with Gaussian plume model (Cimorelli et al., 2005 ), CALPUFF with Gaussian puff model (Scire et al., 2000 ), HYSPLIT with Lagrangian model (Draxler and Hess, 1998 ), CMAQ with Eulerian model (Byun et al., 1997 ), Fluent with Computational Fluid Dynamic (CFD) models (Riddle et al., 2004 ), etc. For decision support of nuclear emergency, several software have integrated atmospheric models, dose assessment modules and preprocessing funtions, such as European RODOS/JRODOS (Ehrhardt and Weis, 2000 ), Japanese SPEEDI/WSPEEDI (Chino et al., 1993 ), American NARAC (Nasstrom et al., 2005 ), etc. Among these studies and applications, the Lagrangian model is usually applied to long distance (thousands of kilometers) simulation (Draxler et al., 2015 , Jones et al., 2007 , Katata et al., 2012 , Mohammed Saeed et al., 2019 , Stohl et al., 2005 ). Due to its capability of tracing the stochastic physics of particles, the trajectory of atmospheric transportation can be better revealed. However, the simulation can be computationally intensive since countless sources and particles should be considered in most cases. Practically, sufficient accuracy and resolution can be obtained when a model is chosen to best matches the need and computational resources of the modelers. Therefore, some researchers turned to Gaussian plume model to obtain short-distance but computationally effective results of radionuclides dispersion (Connan et al., 2013 , Jeong et al., 2013 , Leelőssy et al., 2011 , Subramanian et al., 2019 ). The Gaussian plume simulation cost little run time and small requirement of data input, which is very suitable for fast response to nuclear emergency. But when the topography and meteorology conditions are not steady, the pitfalls of Gaussian plume model may limit its performance. Taking both calculated accuracy and computational cost into consideration, the Gaussian puff model is a compromising but reliable approach especially for fast response to emergency. The puff model was designed to resolve the most fatal weaknesses of plume model by dividing the main plume into a series of puffs. Each puff will follow the local changes of the wind both in space and time, which is the key advantage of a Lagrangian system. Inside the puff, the concentration profile is still gaussian (Ludwig et al., 1977 ). Based on this concept, 3 most popular atmospheric simulation system ATSTEP (Päsler-Sauer, 2000 ), RIMPUFF (Thykier-Nielsen et al., 1999 ) and CALPUFF (Scire et al., 2000 ) have been built. This article simulates the dispersion of radioactive isotopes from a radiological event using the Gaussian puff model. The United States Environmental Protection Agency (EPA) recommended system CALPUFF/CALMET is applied to solving the meteorology field and radionuclides transportation in sequence. 3. Research Methodology 3.1 System architecture The dose impact assessment for radiological disaster events primarily requires three computational modules: meteorological, dispersion, and dose calculation. Since radiological disaster events are likely to occur in places with crowds, the dispersion assessment involves a street-level evaluation of the dispersion of radioactive materials post-explosion. Therefore, the meteorological module needs to rapidly calculate turbulent wind fields at the street level, considering temporal and spatial variations in weather conditions, to facilitate the subsequent use of small-scale dispersion modules. In addition to rapid assessment of accident-type pollutant dispersion, small-scale dispersion modules need to interface with the pollutant release module for radiological events. The dispersion assessment results are then integrated into the dose assessment module, along with the relevant information required for the application of the three main modules in radiological disaster event response and emergency decision-making. The architectural diagram for the design of our country's radioactive events dispersion assessment system (as shown in Fig. 1 ) is as follows: Components: Real-time Meteorological Data Downscaling System: This component handles the processing of real-time meteorological data and downscales it to a finer spatial resolution suitable for dispersion modeling. Radiological Dispersion Assessment System for Radiological Source Release: This system is responsible for modeling the dispersion of radioactive materials released from radiological sources. Urban Three-Dimensional Building Database: This database contains detailed information about urban buildings, which is crucial for modeling how radiation spreads in urban environments. Explosion Parameter Database: This database stores essential parameters related to explosive events, which are used in dispersion modeling calculations. Meteorological Data Validation System: Ensures the accuracy and reliability of meteorological data used in the dispersion models. Dose Assessment System: This component calculates and assesses the radiation doses received by different locations and populations based on dispersion modeling results. Post-Processing and Visualization System: After the dispersion assessment, this system processes the results and generates visual representations and reports for decision-makers. Expert Decision Support System: Provides decision-makers with expert guidance and recommendations based on the assessment results. Data Flow: Real-time meteorological data is obtained from Weather Research and Forecasting model of Central Weather Bureau (CWB/WRF) and processed to provide high-resolution weather information. The explosion parameter database and radiological source release information are fed into the radiological dispersion assessment system. The dispersion modeling results, along with building data from the urban three-dimensional building database, are used in the dose assessment system. The results are then post-processed and visualized for decision-makers. The expert decision support system utilizes the assessment results to provide informed guidance. This system architecture ensures that our country is well-prepared to assess and respond to radioactive events, with a focus on accurate dispersion modeling, dose assessment, and expert decision support. 3.2 CALPUFF/CALMET model The modeling system consists of many modules, among which CALMET and CALPUFF are the key components. With single-point meteorological data input, CALMET is capable of estimating the local deviation and interpolating to the whole area. Therefore, the more single-point data are available, the higher resolution of calculation will be obtained. The wind field calculation is primarily significant as affecting the airborne transportation of particles and gases. following coordinate is applied, as Eqs. ( 1 ) and ( 2 ): $$Z=z+{h}_{t}$$ 1 $$W=w-u\frac{{\partial h}_{t}}{\partial x}-v\frac{{\partial h}_{t}}{\partial y}$$ 2 where Z is the following vertical coordinate, z is the original vertical coordinate at Cartesian system and \({h}_{t}\) is the altitude of the terrain. W and w are vertical wind speeds at following and Cartesian coordinates respectively. u and v are horizontal wind speed components, corresponding to x and y directions. The CALPUFF model has several options of taking a diversified relief into account, including an option to perform simulations over flat surfaces. The most important options include: the ISC obstacle resolving method (to remove the area obstacles like hills and mountains, thus facilitating unobstructed movement of pollution puffs), and the “partial plume” method (to account for variable altitude of the area in the path of travelling puffs). The altitude Hp of the puff center point over a hill is given by, as Eq. ( 3 ): $${H}_{p}={H}_{p0}\bullet C$$ 3 where \({H}_{p}\) is the altitude of the puff center point over the receptor, \({H}_{p0}\) is the altitude of the puff center point after an “initial” uplift, C is the correction factor (0.35 for a stable atmosphere, 0.5 for all other atmosphere states). CALPUFF is the most advanced option of obstacle resolving. Parameters taken into account include the size of travelling puffs, the altitude of travelling puffs, and interactions of travelling puffs with obstacles that depend on the given obstacle slope angle (Environmental Protection Agency, 1995 , Environmental Protection Agency, 2000). 4. Preliminary Simulation Results 4.1 Simulation configuration Since a radiological dirty bomb is likely to be detonated in open urban spaces with a high population density, such as city streets, and the resulting atmospheric dispersion of pollutants will occur around buildings, building models were constructed based on major landmarks in Taiwan. First, the topographical settings for the CALMET model were processed, with 30-meter horizontal resolution topographical data obtained from the open data provided by the Ministry of the Interior, Republic of China. The data was imported into the CALMET model, focusing on an area with a radius of five kilometers from Taipei 101 as the central point (as shown in Figure 2). The extracted topography is shown in Figure 3, which illustrates that the terrain near Taipei 101 is predominantly flat, with elevations around 20 meters. To the east of Taipei 101 lies the Four Beast Mountains, with the highest peak reaching an elevation of 344.7 meters. Meteorological data from the CWB/WRF model was used, providing hourly three-dimensional meteorological data with a horizontal resolution of 1 kilometer (as shown in Figure 4). This data was introduced into the CALMET model using the CALWRF program and downscaled to a 250-meter horizontal resolution, as depicted in Figure 5. The figure shows that the predominant wind direction is from the northeast. The model for a single building was designed based on the appearance of Taipei 101, a landmark skyscraper located in Xinyi District, Taipei, Taiwan. Taipei 101 has a total height of approximately 509 meters, including its spire and antenna, and a height of approximately 449 meters without the spire and antenna. It consists of 101 above-ground floors and 5 basement levels, with a total floor area of 371,000 square meters. Additionally, there is a six-story shopping mall, approximately 60 meters high, known as the "podium," located adjacent to the main building. The widest point of the main building is approximately 55 meters. Therefore, for this study, a rectangular prism model was constructed with a height of 449 meters (excluding the spire and antenna) and a width of 55 meters. The CALPUFF model setup parameters corresponding to the radiological dirty bomb are provided in Table 1. The initial bomb size was set with a diameter of 0.3 meters, and the pollutant emission rate was 1.0E01 Bq/s. The bomb's location was set 10 meters northeast of Taipei 101. Table 1 Radiological Dirty Bomb Configuration Parameters. 4.2 Simulation Results The simulation results are illustrated in Figure 8 to Figure 10. Figure 8 represents the results without any building influence, showing pollutant dispersion following a Gaussian distribution. In contrast, Figure 9 shows higher cumulative pollutant concentrations on the southwest side of the building. For multiple simplified building simulations, the same modeling approach as for a single building was used, involving the CALPUFF model setup parameters under Subgroup (13c) in the model setup document. For this test, a building model with dimensions 70 meters long, 30 meters wide, and 45 meters high was constructed 15 meters west (left side) of Taipei 101, as shown in Table 2. The simulation results are presented in Figure 10, which indicates higher cumulative pollutant concentrations on the northeast side of the two buildings, with the second-highest cumulative concentration on the southwest side of the building. To assess the impact of increasing horizontal resolution by a factor of 10, a simulation was performed with a horizontal resolution of 3 meters, which is ten times higher than the original 30-meter resolution. The model setup parameters are provided in Table 3, and the data output in terms of the number of calculations increased from 2 seconds to 3600 seconds. The simulation results with enhanced horizontal resolution are displayed in Figure 11 to Figure 13. In the absence of buildings, as seen in Figure 11, pollutant dispersion follows a Gaussian distribution, downstream in the wind direction. Under the influence of a single building, the results for a 3-meter resolution (Figure 12) are identical to those with a 30-meter resolution. There is a noticeable higher concentration of pollutants on the southwest side of the building. With the configuration of multiple buildings, the 3-meter resolution results also show higher cumulative concentrations on the northeast side of the buildings, similar to the 30-meter resolution results. Comparing the results of 30-meter and 3-meter resolutions, the increase in resolution provides more detailed insights into pollutant distribution patterns. However, it comes at the cost of computational speed. Considering the need for emergency response and the required information on pollutant concentration distribution when personnel are deployed, a smaller simulation area should be considered. Table 2 Building Configuration. Table 3 Model Horizontal Resolution Configuration. 5. Conclusions The initial hours following a radiation disaster event or a dirty bomb incident are crucial, as they significantly impact the overall disaster response strategy and its effectiveness across various stages. Consequently, first responders are tasked with multitasking responsibilities, such as assessing the radiation release status, understanding the pollution distribution, initiating life-saving actions, recommending protective measures for the public, and reporting the on-site conditions, among other tasks. These duties begin within minutes of first responders arriving at the scene. The efficiency and coordination of these initial response activities determine the arrival of other supporting organizations and resources at the scene, thereby influencing the effectiveness of subsequent disaster relief efforts. In the early stages of the research, during the model selection process, it was imperative to choose a model that aligns with the requirements of emergency response. This model needed to be capable of rapid computation and utilize regional real-time numerical weather forecast data to simulate the affected areas. This information would serve as a reference for emergency response. Based on these criteria, the CALPUFF model was selected for development and implementation. This research aims to understand how a dirty bomb, when detonated in open urban spaces such as city streets, disperses atmospheric pollutants around nearby buildings. To achieve this, building models were created based on Taiwan's major landmarks and their surroundings. Initially, the focus was on the main building of Taipei 101, and both single-building and two-building models were designed. Additionally, the study evaluated the results of the CALPUFF model by increasing its horizontal resolution. It's important to note that increasing the model resolution reduces computation speed but provides more detailed information about pollutant concentration distribution. To meet the requirements of emergency response and provide necessary information for personnel on-site, it's essential to assess the impact of different horizontal resolutions on computation speed. In the future, there is a need to establish a three-dimensional approximate building model database for urban areas and integrate it into the CALPUFF model for simulating multiple-building scenarios. Furthermore, the CALPUFF model lacks parameters for pollutant horizontal initial velocity in its pollutant setup. Therefore, it's necessary to refer to international literature to establish parameters for radiation bomb explosion. This information will be used to create a radiation bomb explosion parameter database, allowing CALPUFF to systematically simulate the impact range of explosions under different conditions. This will enable a more accurate assessment of the effects of radiation bombs in urban environments. Declarations Funding statement No funds, grants, or other support was received. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jian-Pu Chen, Teng-Ping Zeng and Yu-Xia Hsu. The first draft of the manuscript was written by Jian-Pu Chen and Jen-Hsin Teng reviewed and editied on previous versions of the manuscript. All authors read and approved the final manuscript. Availability of Data and Materials The data sources are properly cited in the manuscript. References Byun, D., Young, J., Gipson, G., Godowitch, J., Binkowski, F., 1997. Description of the Models-3 Community Multiscale Air Quality(CMAQ) Modeling System. Chino, M., Ishikawa, H., Yamazawa, H., 1993. SPEEDI and WSPEEDI: Japanese Emergency Response Systems to Predict Radiological Impacts in Local and Workplace Areas due to a Nuclear Accident. Cimorelli, A.J., Perry, S.G., Venkatram, A., Weil, J.C., Paine, R.J., Wilson, R.B., Lee, R.F., Peters, W.D., Brode, R.W., 2005. AERMOD: A dispersion model for industrial source applications. Part I: General model formulation and boundary layer characterization. Journal of applied meteorology 44, 682-693. Connan, O., Smith, K., Organo, C., Solier, L., Maro, D., Hébert, D., 2013. Comparison of RIMPUFF, HYSPLIT, ADMS atmospheric dispersion model outputs, using emergency response procedures, with 85Kr measurements made in the vicinity of nuclear reprocessing plant. Journal of Environmental Radioactivity 124, 266-277. Draxler, R., Arnold, D., Chino, M., Galmarini, S., Hort, M., Jones, A., Leadbetter, S., Malo, A., Maurer, C., Rolph, G., Saito, K., Servranckx, R., Shimbori, T., Solazzo, E., Wotawa, G., 2015. World Meteorological Organization's model simulations of the radionuclide dispersion and deposition from the Fukushima Daiichi nuclear power plant accident. Journal of Environmental Radioactivity 139, 172-184. Draxler, R.R., Hess, G., 1998. An overview of the HYSPLIT_4 modelling system for trajectories. Australian meteorological magazine 47, 295-308. Ehrhardt, J., Weis, A., 2000. RODOS: Decision support system for off-site nuclear emergency management in Europe. European Commission, Brussels, Report EUR 19144. Environmental Protection Agency, 2000, A User’s Guide for the CALPUFF Dispersion Model. Environmental Protection Agency, 1995. User’s Guide for the Industrial Source Complex (ISC3) Dispersion Models, Volumes I–III. Holmes, N.S., Morawska, L., 2006. A review of dispersion modelling and its application to the dispersion of particles: an overview of different dispersion models available. Atmospheric environment 40, 5902-5928. Jeong, H., Park, M., Hwang, W., Kim, E., Han, M., 2013. The effect of calm conditions and wind intervals in low wind speed on atmospheric dispersion factors. Annals of Nuclear Energy 55, 230-237. Jones, A., Thomson, D., Hort, M., Devenish, B., 2007. 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A user’s guide for the CALPUFF dispersion model. Earth Tech, Inc. Concord, MA 10. Stohl, A., Forster, C., Frank, A., Seibert, P., Wotawa, G., 2005. The Lagrangian particle dispersion model FLEXPART version 6.2. Atmospheric Chemistry and Physics 5, 2461-2474. Subramanian, V., Kumar, A., Pujala, U., Sujatha, P.N., Srinivas, C.V., Bagavathsingh, A., Goplakrishnan, V., Ananthanarayanan, R., Kumar, A.A., Kumar, S.K., Chandramouli, S., Baskaran, R., Nashine, B.K., 18 Venkatraman, B., 2019. Studies on sodium aerosols dispersion in open environment for fast reactor safety. Annals of Nuclear Energy 125, 63-73. Thykier-Nielsen, S., Deme, S., Mikkelsen, T., 1999. Description of the atmospheric dispersion module RIMPUFF. Riso National Laboratory, PO Box 49. Additional Declarations The authors declare no competing interests. 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Diagram.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4518587/v1/28b30e4c788ec8c84ec9f733.png"},{"id":57785824,"identity":"bf97c2aa-ceba-47d0-9cb6-a798885fde06","added_by":"auto","created_at":"2024-06-05 16:07:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1588050,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of a Five-Kilometer Radius Around Taipei 101 on Google Maps (Highlighted in Black Border).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4518587/v1/e37198509f0aae406a5cf85b.png"},{"id":57785825,"identity":"11f6d423-c393-4555-a94e-fb88641ab5ce","added_by":"auto","created_at":"2024-06-05 16:07:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":891009,"visible":true,"origin":"","legend":"\u003cp\u003eTopographic Map of the Five-Kilometer Radius Area Around Taipei 101.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4518587/v1/43ca2f5e23eb6e5bdfc2cb1c.png"},{"id":57785827,"identity":"98f2daa8-499a-470f-a35b-8a4fec4ab631","added_by":"auto","created_at":"2024-06-05 16:07:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1179016,"visible":true,"origin":"","legend":"\u003cp\u003eWind Field Distribution Around Taiwan with 1-Kilometer Horizontal Resolution, Hourly, from CWB/WRF.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4518587/v1/64c7ee6d7cbc7396ef1b34ca.png"},{"id":57785830,"identity":"0d4d876c-e365-47cd-a589-ee3e9d9d1fac","added_by":"auto","created_at":"2024-06-05 16:07:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":399092,"visible":true,"origin":"","legend":"\u003cp\u003eCALMET Downscaled 250-Meter Horizontal Resolution Wind Field Distribution Map (Contour Lines Representing Topography, Red Line Indicates Taipei City Boundary).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4518587/v1/6109d4dae99461e8f578cc16.png"},{"id":57786188,"identity":"792f86d4-4781-4b6b-ad4e-215e7b312d13","added_by":"auto","created_at":"2024-06-05 16:15:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":352381,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic Diagram of Taipei 101 Building.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4518587/v1/0e592163f97556fe62ff3b59.png"},{"id":57785831,"identity":"070ef135-afa4-4558-ab9d-fe93e5367f63","added_by":"auto","created_at":"2024-06-05 16:07:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":327048,"visible":true,"origin":"","legend":"\u003cp\u003eCALPUFF Model Configuration File Sample.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4518587/v1/db839a738536e8dc55a05c5a.png"},{"id":57786189,"identity":"55989e9b-ddc6-445a-b779-4656f243749e","added_by":"auto","created_at":"2024-06-05 16:15:39","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":429076,"visible":true,"origin":"","legend":"\u003cp\u003eThe simulation results without building influence with a CALPUFF horizontal resolution of 30 meters are shown in the shaded area, with concentration expressed in units of Bq/m2.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4518587/v1/841747b085169ac4fd1a72c1.png"},{"id":57785832,"identity":"0b4a550c-7332-4a93-a298-a3c5443d7dc1","added_by":"auto","created_at":"2024-06-05 16:07:39","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":534401,"visible":true,"origin":"","legend":"\u003cp\u003eThe simulation results with the influence of a single building and a CALPUFF horizontal resolution of 30 meters are shown in the shaded area, with concentration expressed in units of Bq/m2.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4518587/v1/c125116be85e918ec8d1e5ea.png"},{"id":57785833,"identity":"f5634b32-f9a0-4020-a306-920ca5260c8d","added_by":"auto","created_at":"2024-06-05 16:07:39","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":538629,"visible":true,"origin":"","legend":"\u003cp\u003eThe simulation results with the influence of multiple buildings and a CALPUFF horizontal resolution of 30 meters are shown in the shaded area, with concentration expressed in units of Bq/m2.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4518587/v1/03981bf40b54996cdec772d4.png"},{"id":57785835,"identity":"a7109a9c-dc27-445d-9331-98bc638028ca","added_by":"auto","created_at":"2024-06-05 16:07:39","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":575051,"visible":true,"origin":"","legend":"\u003cp\u003eThe simulation results with a CALPUFF horizontal resolution of 3 meters and no influence of buildings are shown in the shaded area, with concentration expressed in units of Bq/m2.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4518587/v1/c09603b723d5e523c07b8d32.png"},{"id":57785828,"identity":"2180a748-284e-4660-9ef4-e6c9d0eff354","added_by":"auto","created_at":"2024-06-05 16:07:39","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":711278,"visible":true,"origin":"","legend":"\u003cp\u003eThe simulation results with a CALPUFF horizontal resolution of 3 meters and the influence of a single building are shown in the shaded area, with concentration expressed in units of Bq/m2.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-4518587/v1/bdaa66f5104d969300b5ae20.png"},{"id":57785837,"identity":"53351355-4b48-45be-80f6-118b00bb8d55","added_by":"auto","created_at":"2024-06-05 16:07:39","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":718388,"visible":true,"origin":"","legend":"\u003cp\u003eThe simulation results with a CALPUFF horizontal resolution of 3 meters and the influence of a single building are shown in the shaded area, with concentration expressed in units of Bq/m2.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-4518587/v1/fcc580fc73ff815f00f660b2.png"},{"id":57786676,"identity":"0ee7872e-d22f-4d16-992b-5e1a2c4bdddf","added_by":"auto","created_at":"2024-06-05 16:23:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11613180,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4518587/v1/e0f86a1b-7b9b-466c-a621-96901ddf9248.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eApplication of the CALPUFF model in emergency response assessment for radiological events in metropolitan area.\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eInternational terrorist organizations employ a diverse range of terrorist tactics, primarily including kidnapping for ransom and hostage-taking, bombings using various methods such as parcel bombs, gasoline bombs, car bombs, human bombs, incendiary devices, and acts of arson. Additionally, they engage in activities aimed at disrupting communication, networks, and computer information systems, all with the intention of causing societal chaos. In the future, there is a growing concern that these organizations may escalate their tactics to include nuclear terrorism using radiological weapons. Therefore, the threat of \"nuclear, biological, and chemical terrorism\" is expected to become increasingly severe. Preventing the misuse of nuclear weapons by terrorist groups, addressing illegal transportation, and implementing measures for nuclear disaster prevention and protection, as well as post-disaster response and sampling, are all of paramount importance.。\u003c/p\u003e \u003cp\u003eA radiological dispersal device (RDD), also known as a \"dirty bomb,\" is a device that combines conventional explosives with radioactive materials. When a dirty bomb is detonated, radioactive material is dispersed along with the explosive energy and carried by the wind. Most of the radioactive particles are likely to fall to the ground within a few city blocks or a few miles from the explosion site, leading to contamination of the environment and potential exposure to the public. The radioactive materials dispersed by a dirty bomb may not necessarily cause immediate radiation injuries, but the psychological concerns and anxiety among the public can often outweigh the actual physical harm. Incidents involving dirty bombs are referred to as \"radioactive events\".\u003c/p\u003e \u003cp\u003eIn the 2002 Police White Paper Report of Taiwan's Ministry of the Interior, there is a section titled \"Research on the Anti-Terror Crisis Response Mechanism,\" which discusses potential forms of terrorist attacks that could occur within the country. While bomb attacks are the most common, what concerns the Taiwanese people the most is the possibility of a RDD nuclear terrorist attack. To safeguard against international terrorist organizations smuggling RDD into Taiwan for the purpose of nuclear terrorist attacks, Taiwan's Atomic Energy Council (AEC) initiated the \"Radiological Dispersal Device Protection Mechanism\" for the first time in March 2003.\u003c/p\u003e \u003cp\u003eThe initial hours following a radiological events are critical for response and can significantly impact the overall effectiveness of disaster relief execution at all stages. Therefore, frontline responders are tasked with a multitude of responsibilities, such as assessing radiological release conditions, contamination distribution, implementing life-saving actions, advising on public protection measures, reporting on the on-site situation, and more.These tasks are initiated within minutes of frontline responders arriving at the scene. The efficiency of initial response activities and coordination will determine when other supporting organizational teams and resources arrive at the scene and the subsequent effectiveness of disaster relief operations.\u003c/p\u003e \u003cp\u003eTherefore, conducting research and development in radiation event response technology is of paramount importance for enhancing our country's disaster prevention and rescue capabilities. To meet the requirements for radiation protection and emergency response, the selected model needs to have the following capabilities: fast computational speed, the ability to use regional real-time numerical weather forecast data, compatibility with atmospheric dispersion models that can utilize such meteorological data, suitability for employing point source atmospheric dispersion models, model simulation resolution capable of resolving down to the urban scale, and adaptability to the complex terrain of Taiwan. These criteria were comprehensively used to select a suitable model.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eNumerical simulation is a powerful tool to study the underlying physics of atmospheric transportation and reasonably quantify and analyze the impact of radionuclides dispersion on the environment. There are a large number of numerical studies on varied spatial scales of atmospheric dispersion models (Holmes and Morawska, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Leelőssy et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Several systems based on local, regional and global models have been recognized, including AERMOD with Gaussian plume model (Cimorelli et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), CALPUFF with Gaussian puff model (Scire et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), HYSPLIT with Lagrangian model (Draxler and Hess, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), CMAQ with Eulerian model (Byun et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), Fluent with Computational Fluid Dynamic (CFD) models (Riddle et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), etc. For decision support of nuclear emergency, several software have integrated atmospheric models, dose assessment modules and preprocessing funtions, such as European RODOS/JRODOS (Ehrhardt and Weis, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), Japanese SPEEDI/WSPEEDI (Chino et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), American NARAC (Nasstrom et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), etc. Among these studies and applications, the Lagrangian model is usually applied to long distance (thousands of kilometers) simulation (Draxler et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Jones et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Katata et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Mohammed Saeed et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Stohl et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Due to its capability of tracing the stochastic physics of particles, the trajectory of atmospheric transportation can be better revealed. However, the simulation can be computationally intensive since countless sources and particles should be considered in most cases. Practically, sufficient accuracy and resolution can be obtained when a model is chosen to best matches the need and computational resources of the modelers. Therefore, some researchers turned to Gaussian plume model to obtain short-distance but computationally effective results of radionuclides dispersion (Connan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Jeong et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Leelőssy et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Subramanian et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Gaussian plume simulation cost little run time and small requirement of data input, which is very suitable for fast response to nuclear emergency. But when the topography and meteorology conditions are not steady, the pitfalls of Gaussian plume model may limit its performance. Taking both calculated accuracy and computational cost into consideration, the Gaussian puff model is a compromising but reliable approach especially for fast response to emergency. The puff model was designed to resolve the most fatal weaknesses of plume model by dividing the main plume into a series of puffs. Each puff will follow the local changes of the wind both in space and time, which is the key advantage of a Lagrangian system. Inside the puff, the concentration profile is still gaussian (Ludwig et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Based on this concept, 3 most popular atmospheric simulation system ATSTEP (P\u0026auml;sler-Sauer, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), RIMPUFF (Thykier-Nielsen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and CALPUFF (Scire et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) have been built.\u003c/p\u003e \u003cp\u003eThis article simulates the dispersion of radioactive isotopes from a radiological event using the Gaussian puff model. The United States Environmental Protection Agency (EPA) recommended system CALPUFF/CALMET is applied to solving the meteorology field and radionuclides transportation in sequence.\u003c/p\u003e"},{"header":"3. Research Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 System architecture\u003c/h2\u003e \u003cp\u003eThe dose impact assessment for radiological disaster events primarily requires three computational modules: meteorological, dispersion, and dose calculation. Since radiological disaster events are likely to occur in places with crowds, the dispersion assessment involves a street-level evaluation of the dispersion of radioactive materials post-explosion. Therefore, the meteorological module needs to rapidly calculate turbulent wind fields at the street level, considering temporal and spatial variations in weather conditions, to facilitate the subsequent use of small-scale dispersion modules.\u003c/p\u003e \u003cp\u003eIn addition to rapid assessment of accident-type pollutant dispersion, small-scale dispersion modules need to interface with the pollutant release module for radiological events. The dispersion assessment results are then integrated into the dose assessment module, along with the relevant information required for the application of the three main modules in radiological disaster event response and emergency decision-making. The architectural diagram for the design of our country's radioactive events dispersion assessment system (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is as follows:\u003c/p\u003e \u003cp\u003eComponents:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eReal-time Meteorological Data Downscaling System: This component handles the processing of real-time meteorological data and downscales it to a finer spatial resolution suitable for dispersion modeling.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRadiological Dispersion Assessment System for Radiological Source Release: This system is responsible for modeling the dispersion of radioactive materials released from radiological sources.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUrban Three-Dimensional Building Database: This database contains detailed information about urban buildings, which is crucial for modeling how radiation spreads in urban environments.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExplosion Parameter Database: This database stores essential parameters related to explosive events, which are used in dispersion modeling calculations.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMeteorological Data Validation System: Ensures the accuracy and reliability of meteorological data used in the dispersion models.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDose Assessment System: This component calculates and assesses the radiation doses received by different locations and populations based on dispersion modeling results.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePost-Processing and Visualization System: After the dispersion assessment, this system processes the results and generates visual representations and reports for decision-makers.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExpert Decision Support System: Provides decision-makers with expert guidance and recommendations based on the assessment results.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eData Flow:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eReal-time meteorological data is obtained from Weather Research and Forecasting model of Central Weather Bureau (CWB/WRF) and processed to provide high-resolution weather information.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe explosion parameter database and radiological source release information are fed into the radiological dispersion assessment system.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe dispersion modeling results, along with building data from the urban three-dimensional building database, are used in the dose assessment system.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe results are then post-processed and visualized for decision-makers.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe expert decision support system utilizes the assessment results to provide informed guidance.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis system architecture ensures that our country is well-prepared to assess and respond to radioactive events, with a focus on accurate dispersion modeling, dose assessment, and expert decision support.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 CALPUFF/CALMET model\u003c/h2\u003e \u003cp\u003eThe modeling system consists of many modules, among which CALMET and CALPUFF are the key components. With single-point meteorological data input, CALMET is capable of estimating the local deviation and interpolating to the whole area. Therefore, the more single-point data are available, the higher resolution of calculation will be obtained. The wind field calculation is primarily significant as affecting the airborne transportation of particles and gases. following coordinate is applied, as Eqs.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Z=z+{h}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$W=w-u\\frac{{\\partial h}_{t}}{\\partial x}-v\\frac{{\\partial h}_{t}}{\\partial y}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere Z is the following vertical coordinate, z is the original vertical coordinate at Cartesian system and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({h}_{t}\\)\u003c/span\u003e\u003c/span\u003e is the altitude of the terrain. W and w are vertical wind speeds at following and Cartesian coordinates respectively. u and v are horizontal wind speed components, corresponding to x and y directions.\u003c/p\u003e \u003cp\u003eThe CALPUFF model has several options of taking a diversified relief into account, including an option to perform simulations over flat surfaces. The most important options include: the ISC obstacle resolving method (to remove the area obstacles like hills and mountains, thus facilitating unobstructed movement of pollution puffs), and the \u0026ldquo;partial plume\u0026rdquo; method (to account for variable altitude of the area in the path of travelling puffs). The altitude Hp of the puff center point over a hill is given by, as Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${H}_{p}={H}_{p0}\\bullet C$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({H}_{p}\\)\u003c/span\u003e\u003c/span\u003e is the altitude of the puff center point over the receptor, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({H}_{p0}\\)\u003c/span\u003e\u003c/span\u003e is the altitude of the puff center point after an \u0026ldquo;initial\u0026rdquo; uplift, C is the correction factor (0.35 for a stable atmosphere, 0.5 for all other atmosphere states). CALPUFF is the most advanced option of obstacle resolving. Parameters taken into account include the size of travelling puffs, the altitude of travelling puffs, and interactions of travelling puffs with obstacles that depend on the given obstacle slope angle (Environmental Protection Agency, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e, Environmental Protection Agency, 2000).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Preliminary Simulation Results","content":"\u003cp\u003e\u003cstrong\u003e4.1\u0026nbsp;Simulation configuration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince a radiological dirty bomb is likely to be detonated in open urban spaces with a high population density, such as city streets, and the resulting atmospheric dispersion of pollutants will occur around buildings, building models were constructed based on major landmarks in Taiwan. First, the topographical settings for the CALMET model were processed, with 30-meter horizontal resolution topographical data obtained from the open data provided by the Ministry of the Interior, Republic of China. The data was imported into the CALMET model, focusing on an area with a radius of five kilometers from Taipei 101 as the central point (as shown in Figure 2). The extracted topography is shown in Figure 3, which illustrates that the terrain near Taipei 101 is predominantly flat, with elevations around 20 meters. To the east of Taipei 101 lies the Four Beast Mountains, with the highest peak reaching an elevation of 344.7 meters. Meteorological data from the CWB/WRF model was used, providing hourly three-dimensional meteorological data with a horizontal resolution of 1 kilometer (as shown in Figure 4). This data was introduced into the CALMET model using the CALWRF program and downscaled to a 250-meter horizontal resolution, as depicted in Figure 5. The figure shows that the predominant wind direction is from the northeast.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe model for a single building was designed based on the appearance of Taipei 101, a landmark skyscraper located in Xinyi District, Taipei, Taiwan. Taipei 101 has a total height of approximately 509 meters, including its spire and antenna, and a height of approximately 449 meters without the spire and antenna. It consists of 101 above-ground floors and 5 basement levels, with a total floor area of 371,000 square meters. Additionally, there is a six-story shopping mall, approximately 60 meters high, known as the \"podium,\" located adjacent to the main building. The widest point of the main building is approximately 55 meters. Therefore, for this study, a rectangular prism model was constructed with a height of 449 meters (excluding the spire and antenna) and a width of 55 meters. The CALPUFF model setup parameters corresponding to the radiological dirty bomb are provided in Table 1. The initial bomb size was set with a diameter of 0.3 meters, and the pollutant emission rate was 1.0E01 Bq/s. The bomb's location was set 10 meters northeast of Taipei 101.\u003c/p\u003e\n\u003cp\u003eTable 1 Radiological Dirty Bomb Configuration Parameters.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2\u0026nbsp;Simulation Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe simulation results are illustrated in Figure 8 to Figure 10. Figure 8 represents the results without any building influence, showing pollutant dispersion following a Gaussian distribution. In contrast, Figure 9 shows higher cumulative pollutant concentrations on the southwest side of the building. For multiple simplified building simulations, the same modeling approach as for a single building was used, involving the CALPUFF model setup parameters under Subgroup (13c) in the model setup document. For this test, a building model with dimensions 70 meters long, 30 meters wide, and 45 meters high was constructed 15 meters west (left side) of Taipei 101, as shown in Table 2. The simulation results are presented in Figure 10, which indicates higher cumulative pollutant concentrations on the northeast side of the two buildings, with the second-highest cumulative concentration on the southwest side of the building.\u003c/p\u003e\n\u003cp\u003eTo assess the impact of increasing horizontal resolution by a factor of 10, a simulation was performed with a horizontal resolution of 3 meters, which is ten times higher than the original 30-meter resolution. The model setup parameters are provided in Table 3, and the data output in terms of the number of calculations increased from 2 seconds to 3600 seconds. The simulation results with enhanced horizontal resolution are displayed in Figure 11 to Figure 13. In the absence of buildings, as seen in Figure 11, pollutant dispersion follows a Gaussian distribution, downstream in the wind direction. Under the influence of a single building, the results for a 3-meter resolution (Figure 12) are identical to those with a 30-meter resolution. There is a noticeable higher concentration of pollutants on the southwest side of the building. With the configuration of multiple buildings, the 3-meter resolution results also show higher cumulative concentrations on the northeast side of the buildings, similar to the 30-meter resolution results. Comparing the results of 30-meter and 3-meter resolutions, the increase in resolution provides more detailed insights into pollutant distribution patterns. However, it comes at the cost of computational speed. Considering the need for emergency response and the required information on pollutant concentration distribution when personnel are deployed, a smaller simulation area should be considered.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 2 Building Configuration.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 Model Horizontal Resolution Configuration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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neV+++2Xnuemm25aVopNY8vRpzIOU5vpKey7J/ff04yp819wwQXLxx57rPFtYKDfv+6668rVV1+9nGeeeco999xzpPx5//33ywMOOCDJn8zVV19d/uQnP0myXlv3/5VXXtlYW5ZHHnlkOcUUUyS5stdee5Xrr79+OXz48MbaETgGufq1r30tyRbbOPacc85Z/uIXvyhfe+21dJ8r4yPph/Qa+uRFF13U2EOZ9BLnQFc4/fTTky430CBfmvuk7qDHDQGceuqpSYny0Sjae2EXWWSRtJ3OsTv45z//WS677LLl1ltv3VjSMTToiSeeuPGtbd59991yww03TOdM6GcIfQ3aPrxUY4JjjjkmKSyUldHlX//6V3pWH3zwQWPJmGNMGwJ47733ktJGWN1yyy2NpWVS/CiBjIE333yzsTRAXzQERgfPMyvXdbwTjIS///3v6fttt91WPvPMM+n/7uA///lPefLJJ6eOrdXxMzpH7/h9993XWNJ1XMcZZ5xRnn322Y0l/YsxZQhccMEFSdZSaCjtdTxDCg2lsTPKiT7m/PPPLx999NHGku6FrHf+999/f2NJ/4Jxz/HlPextBqIh0BM8++yzHZIrZB7diTFCrpFxdTjlOFeGDRuWnLCDjZ4yBHolR8AwtlqzJrYxbCoerBUnnHBCWicGtzq3xtK+T6tzFV5g2N71GPIaE3TlHh588MEpNEA1kaBIFQ6EijWHQ6irLLSi6kSLSjg1lgYDAcPVJg1rRmnQHNuKXPO6uxALq3qR+QdaHT8jXMdsvx0pEdpRXIeJxfKwfTBqhG55VuZpMClds8z0DD0nMuKss85qLO04KrQIOTWnQ08gpEzYo9jv/ohcFvXqldMN+ibymzoiV7xHdCflRsm1HJKYET4pPFmJ52984xuNpUFX6RVDQCULs8+pxkEYKjvVjFhYk0qYfEK8F4WrGTH5jz/+eIrRplybWKIV4lopZbYTiyZWtjmpRfKKGrf2Y/KeT2txu91FPdGmFab8N0kGA6iepAdxcRdddFEScLLuxak3x/u6F+L/Ke1qfbd1Dc6D8mp7915Sjv3DPu1fyUSxvzoF+Rknn3xySsKRFyDGDwSuGL3mpB7HtZ3rELdnopG6sixeUOyfygCQ6Cj/4/bbbx8ZX9jXcT2jOtdmw4sB7F7oZMVzu//ucR33/9RTT03viDZAUTB5lDr2dexbrLFZZK1X193+JLTm4/pLWTDbK8VBPHfzOQVB0H3I51BPXX+l72pWXDKUF3NCeNfJYFVUGBDe4VxekXyVn0bWU8rt89Zbb005IYwB23qnxUSTLWSB44unvuCCC4rzzz9/pNywjJyV7P3GG2+kZWSPPlOfKPbb/sltzgxyyL4dw3mMCn3DxRdfnPoex2j+jXNUc15uoHwJ22fUhHdc/YjrgO31w5bpgzJ+d+211yY5psyk+6ffzpCJKtgwAnx8b6v6TBAErekVQwAUHVnm6lwPHTo0fa9DaLAafSj8zRAElGKJeQQYBWvPPfdMSlZdCBESBCdF13aEIcHKo5shKAgn++T1Vo/2gAMOSMKzuyDcTYWtZq7rboahYiIg5yiRRm1chhCMIrhHOgvnLWlHMqDkmczll19e7LPPPun3rp/RYwSirYRsxgBhe9JJJ6UEnrpgppCbxVQn4Hwpl+5fNmQcgwG3yy67FBtuuGG6pxnrGAD2q9Si8l4E8957753uM+HtubHgGRc6TUZAXqbj60+4J/WqMSqteF68w8suu2xj6QhOPPHEdG+0dR22xCbXDb9hVPGSmDDKfWAsSEL1XaK3e5thIDCylHT0fLTX9dZbL80Eq5wbtBPHkzBK2eC13m+//b5QsSEIgu6BjOOMYQBIoGwLslHfR6ZTdI0acL6YEIkTB2Q9ZXbHHXdMCi8lmQygtGdHltEicpqsV8HKPiR6k6VGkXbeeee0PTlBHtiOwZB/Swk3cR4ZnJ0Slo3KaVWHEq/Pykr8BhtskI6bFXCy7owzzkiy3zEp5+a7cE3Qx5Cb+hL9L2zn2jkM9W32fc4556QZuHmR9fMcVPbhWjk74L44/3B4BEEXqF6gTjE6OQLiqHfbbbf0v6Q2h60ni4iptN4yCXTyAyTAZcTMSpwTHyUBC2LI9t1335RcIl4XlXJbzj777CknoRKgadm9996bjpfjqsSbS4KT4ALbiYW3jRi2zOjkCFSCMP1ekmBl0KREGbGqkuqOPfbYL+REiPeUUFYZHum7uNFvfvObKZdBYmFlzJRzzz13WQnJtL4SkCkR8YYbbkjfJepIWr300kvTd4jxdA4Sa/K1H3300V/IEagEdtpOwmRGbKjEQ+cOiW5Vx1ZWnUi69/b38ssvl5Uim37r/mYkMk455ZQjk8A9y+222y5t5z6Is3aevounrDqRsjLiyosvvjglJlbGTIrxbI++kCMgSXTzzTdPbVMcsHslWVJSprZy9913N7YcgWemvUq4kl/gvmgfM888c1l15KlNVEZb2sa98UzzvVl33XXL+eefv6yMqrQvSZrucb19eo5VJ5iSOcWqVx1nucIKK6R9Q9zvj3/847Tvyy67LC3rbfp7jkDQ/+jtHAFxyt4x8in3Ta2QbJn7CXkdeRl5UpfP9uGd8e5+8MEHSW5su+226f3XL0C/oD+rlOdyvfXWS32mD1lj/+Q2mUNmTTrppKn/zXgnJblXCvfInKZdd9019Sf1BM+2qJT6cskllxyZLKpvIHf8Pvcf5KPk6Nx/6SMV0JAwmvPlJHlK+Kwnm7t250+3gGO5F5WRku7ZAw88kIo2yNNaccUVy1deeSVtp/+mL4yJvJbIEQh6k36dI4DqWOlvngWOdznDI8I7sdRSS6Xt8rYZoSmVMpM8Dz/4wQ/SMjFkvKm8n4ZFYZY5YUWVQBzp4TABht/wUkA4Di8EbwmvK29ODn3hiehKHKZh2d/+9rfJM2OolSdGzGhz3XGhHZbZlofDcV2PcA/lVo2IGM698MIL0/ZGSXiD87AzrzHvk1CrDM/w+uuvn+4FD0xbVIZB47/PcA/rnm6hPpWATx/Pwjq1xk0N3ox7yLNjinDwdHnGSh0KT+GZEkMNMdeGz9VB580WO+ueNT/vvor7oB0J4eFR47nnrdJ+1Uuu45qMEPC+VZ19WmZyJqUntQ332j0yWROEDuR7Y5IWQ/g8XeApM6rAE5bhXfR8HNdohLAAowBGv4zOeF+yh84ojNGgIAh6Bu+797Et9Hnf//730/9G6Mh4E9jV5W6d+jLb1vcvJ0nOHc+60dfKaEifvfbaK/UVvO1GBaznMW/OWbAv+899pP2jvfPPGD02IklOwT70u0asl1tuuTQiLATU+TlP6F/09/q0yoBIy/QFzq8VuT+Yb7750kiKc9Wnzj///KmfMxJi9D6fd/7bX8JMg6Cv0WuGQEZC1ZJLLpmGRynElE6hJuInKUVZYa8jLEXs4BRTTNFYMgLCdfHFF0/Cx1Alhck+KJ8ZgpDAycKFskRgCMUhUNTn3nfffZMBQMHOwqszUOQMZwq9oezZZx7SzbhugpRSTRlm3DBoKGsMEvVyJewZJiY81eyl3FMa/YYhIyaT8lcX8GbwY/DYd1Ygexr3i2LbnLQj2WeWWWZJQ8gMvNzhNHdIlrs/eX1fR7vRqTG45Hbo2A1ZSx5tRodsKFuHaFttjZKuU2u+D6jvg1FoaDw/R23Be8JozTR34mKKtXttyrEYwwxJbVAYXFudbhAEPQ/nAUcPFM3wTndE8W4L8kCfNvvsszeWFMUCCyyQZJNj5byA7sR+Oa04oaabbrrG0iL1RUIVyRhx/sKVKPB1yE2OH3kRQlyFAuU+uSPU+/S87/7SbwRBX6fXDQHwZBOCEqHEyVMY26t8kS395rwCgoBA8ZfwoTjZb7OAqS+zXc4Z4KnlleGd4K1lWDQr7qODcwEvDUG58sorj5wgI+P4PEKUeYKTgKTEM3L8hsAj3AlcRopcA3GkYiUp+a7D9Uiubu5I8rmPjoDtCjmGlTHQzEAV0u6x5zzTTDMlhVs7Ev+a42UztjPqpNPUcYnxpwA0P7P2yNtm75qcE7kFlADefkZwHqWRc2J7Iz7atHbFKNaumzvlIAi6B33Hpptumvqe9ia/9M7m/qtulJOTXelzmsl9UJ3u2j+nAzlntKFezMA15GvKjrxmOUcGMVT8vpWzLwiCMccYMQQk51JshMHwxgvfMezXFjwJlHTKMa9EHUKO8sNzTqE2upBDIkBIUa6NFkiAZXD4jeTkOoQcoyQnL3UFHn2eYp5fyaF1oZm9KRI8c7JWxqiHJGhKvqFXyaSMpRVXXDGFfvguhIS33RArpbBONgDq3pOO0pkOycgOD7eZoJvJ5zKqfbbquPoi7k8ers/XJqlPxQ3PzeiIzj4jUc5wvWQ9XnrPpLPXyoAwQ6kRIwnFRhokiEvIy6Mxm2++eepkc+JwxrkaUWs2ooMg6DpG73JIa3uFD8h6fYtkWApxhpOrXqSCvMyy2N/RhZLNOMmj5xTyehWevO/O7N+1ci7oz5qdP0IP9a95ZFNfWpc5juXDgWJ0m6z0vSdGLoIgGD16zRBo9piKeVdBQVk0NZYz9djFjLAhYTe8LnVF3T6FBfGM8p4L1eBtr1e1oeATWoSt8AkxhuLdKerCbHg3hE8Io/A7Bsfo0krZFeIjPlzIj/jGjP1T8h3XPRBC5P9nn302hTcRjDy/Ri3AcDHtv3AblX7cG4IUhG0d+zENt/uBrLi2Or/681A+VIfEAIHORAfC2w3PqRVGLhgl9sXQyhDyRjIMF+uUmr1DGdfimtpa35dwf9wH51y/n54tRV0uCEM1I//D6E8eutcGtTPGoevNxkRH0O4p/6oQqcakWhTDQJvIOA7jQBiQoXfvlfauXXse+VkG3QtjnKKVRy27C+1De+toJTPKGdk4Jt4lx3TsLD8GGxw1Kvdwzih12Yy2MXz48PSMdtttt5TfA7KE8pwr4MB9pECTF7lN+UuWe4e9y3XZ4R2voy0us8wyI+e4IMuVoc5oT47pWWXHhb9kmv17ls0OqowcNWGv+iqyPRs+ZDgHhT5L/2rEn6Ot7rSzT3Js7rnnTtfiGh3TueXz4DxBs67QjOvnVMly2P1xLzlbrBus7XAgoN9qFWEwpvA+eKfqlSkHIr1iCHjpeSqFSmR4Rigv4top+hnCg/Lu5c6ChneDx5MQUyZTHKJayHIMKJyEK4RqCJUQc69+sZAjCasalw9BPcccc6S4/GwU8Nby3IpdlNRqiNNDd3yNoC0lOONcKXigiNe9IMqYLr300qmkKcOD0ULoUeYo80YLxHQyGlwf7y7vEmVReFGOKbVfy/KoCQPDda677roj6zGLQ5ekJYyI8g3X7Hj1zoLSapjW+VAuebIprISnJFPC2L6N2Nx5551puzxfQ74X9REXSbMEt+uwnY9Scjogk4MQ0FmZqSesOi/3mPEj5Kmv415K/va83aOscOnUlezkCVPOVggQY9QIFQ8axVwnecwxx6T7bp3620Z+kDu9usLXfJ+1zVwuV9s9+uij03PRxvM7okQthUSbkWysXQ8ZMqS44YYb0ohbNgqD7oPSQQaRN1mJ6Q68G0bZhAMa9WkP7dEzlqzJYUDJ6004LrRF7z+HwmCErFT0gXwmt02aSaZRIOSkcfiQzd7bhRZaqPGrEYaASQrdQ4a995njiKxXSjrXy+e4IhO0hZyXld9n7ziZQt46jv6Ac0JoIBRxsL1jk0NG4e2fnHAMcswINfmvLTtGe32eAgjkC0eHZ57LGLtW/adReaFS+hznajTSh0NCIQ9lscEZpl/njNN+yMns/FJ8QVKy/sK74D3L8pG8NDIqDEu/BffH9TO2yGLnEvQ/tAVtp5UxPaagazJsB7psG3v/isb/o4WXlbBpL6THS+zhUmSyRUUwsdwJI0YARTkrruojE1DWU4p1aryejsNocCwvPw+EvxRJgpdyCwLNsCsh5LgEiX0TOrbxl4KmugHhQWBQ3IUeqfGe90NoEzbiqyl4vBitcHzb8pAQaoSzc+Ul979rJBwpCer78wg5LsOHIWDfDB/bUeQo8ToHHYOP37kHlD35Bgwd6/Oogt/x/so3YBAJueKZso396kgYNmLGjZZQRP1Ox0U4G1Ew0kKg2w9vD8XWvXZP7Nc9pND7qxINoUzBFeLkWt1PoVDWu0bC3HoVLFyn7YcNG5auQ0fAU+Wv+8bA4WXiiaoPlzfDm8TQ4XUfEzBgna8O0n2hfGmf2bNn2ayzzprupzas/cobcK3aoU5WO9dhagPul86UMeseG27XXvz1zL1bOmghbypnqAykEpQO3b3IIWTuN+OEB1A7167de+1ahymMjNFoOF6b6G0oQ7yd2m7d2O+veKe8s/URIYoeA19noV13B94ZHRClhrxYc801G2ta411mOGh33v9WlcF6Cu2Mg8VHm25LVvYWCivoVxi/vYl8M7k8ZDNniDZBplLMvdtGvb0HWYGHd5JcJhPkkpGlimnok9xLfaW+xLVoE5wRFHHGAzlDqTdXiFFlx9IvClckDzL2b9RKv6otCefUbzkOh499kw887GSP9ZbXz7MOOWOU2l99FPmlmpkQ3zzqqG8jt1yTcyKn7J8DzLHg92Sovo3cZCCRka5FG/YukZ1GSPTDuc/Rp9EnvIOUfzKOPNUX0QnISp/eCjnVL+k3c0W/wQYjVNsc3ftNX9Cfea4Zeps2o5/UVscE9D7vcu4v9f36Wu8u/XJMo0qjd0jkR3cyVvUQOx6jUEN4ghsjBrotCBydJ+VFJ0UhojDlOMJmCDoKo47MaXnRKdj1cB3edw2IINBYWiVC2kYHRZki6OyTQlxvrFmw2kZjrL/Ide+e9SrAtML1EXKuh1JOQSQMnVf9WM6XR4OAd8/y9XgZKG22dfz8G0aT63Md/tcw3bdm7M81OA/b5HhxeEEprvbjGghW3how0AhY56tR+a4zInDreHZ+4/o8Q0I6/+9YjpnR2Thf9zUnXsN3nZR77JkQ2AwTL73OxsfyephLM86NwK+PKPUm2TBzDa7Hc2NMUdwz7nF+FtAGtAXKsOt1r/JvtRXr8rvhuVvuGXkmnrl3wDqKgEpUjqdzrWMbI2Q6dZ0R4eX3hBmcn/s9pmC8qM7FU62KUX+HUUNJqcsv7d6zy46A7sJoEOeA/dZDR9pCCV/vO89rXV72BpRB98XkdUZHxiQMMk4dI6tjCu+1Z+F99J6T+XWFpxn9g76RY4Es1ld47vWkYrKAXCAHwODh+SdzyF2/04dk50Qdv9OerCOnyH6ypn5O+pK8TUfQN9uP4zJgWjkayKHcl7kHrfp925CZ9uE9ct+cg/Oxzjvl945DFrq3+gv3Vb9umftkvfPpbWWNAWMUJTsRBxtGkURZ1Ee6OgJnFqehkcw69AKOtTGBvlpYNhmmj4Z2pU+lv3anfO8s2hljnZOoW6mUk04xOhOKBUFXqJTwMT6h2JjCJEKVUjByIp5mhg0b9rlJefoSlaAfMBOKVZ1EOe+886a/vUGlHJZrrLFGmoCxI2y88cZpkqW33nqrsaT3qAz9sjLky9NPP72xZMyxfC9PKDamOP/888sJJpigrJT3xpJgTDCYJxT7y1/+Us4zzzyfm9i0I1TGYTlkyJA06VxfYpVVVil32mmnUU5wOibpqQnFejQ0KAi6A96wMRkaNCbhJeMRllfAmyfUwIe3Xc4A7y9vbF/wVjTTG6FBPISOIWRAsj/PZPYoGdKVYC1sjseRF5F3h+zyG6EEvKhGbCpZmDyT9mH0h2xTpctolFExw9XW8aIa4cqhbDyyQrxsUw/J4QkV6ypkhpfLCFHdA6tNi9EWa22kzQhbfT2vp3U8U3KIRoXQNR5Sowj183D+1vnk+2AESgy2eyAPyEhbfXT15JNPTveIBzd7iIXxiX2Xj+DcjCTy1ML90j6FGQpFyTie2HcjrEZOeG5bjeB2J2MqNKi30E61cd5U+VWVUpCedyuPfNDzdDY0SNiT3xq5J6/IpjpkZ04+N2KU5w3y/H2MlpA75BS5YdRVHLuRc23B6Ij3VQELoybkE/lGLtlOX2LURTVCMsz7bP/ee+8y2Wj0iWwU3mWkUbgi2WHUhfw65JBDUlK443vHc9g3OUS2tZJ9juu8cigZueDcjICRzVn++k0d4XbkVQ61y9EP7gWvvWVGZoSlGV0zUk5O52sYFXJJ5ffByJh3KkdIuBb9sGfsHrpv7qHrtH+VHT1LkSOuxWiZXB+yyLnUox2cr32Ri0ZFnFs9smBU9FRoUK8kCwdB0DkIV8OVYoMJe4qtxD8dhARkIXrNnchgQuelI5NsqGiATiGjQ3HPxCDnDmrIkCEpLpmwpvAyosRb67AMU4vNdr/luNhWQr8OSKfgPuvIdFgMMRVPVOiSMKlDyzgfxRB0IGKhCW8dVE66Fw7oeepMxFtLuBfO4vy6E8oi45lCziAQSiKPSOfkOuSXMCbNRFs3QnS22lZW9IcPH14cfPDBqfMX+84w0e7soy3EpLufOnTPSGEDxwm6hraj3Wm7DCvPVHhG0D+g1EuO9swovxK4hdXkcE4w8OR6UEgZzvJLhIMIm/LuCsfOk7h5x+WG+V9xEvkaFHdJ2UpbSyBXRILDRN8hJEwRAoooBw2F3PtMjpERFH3n4hjkFsgBIdC25TggO/0lC+qQqwwQOSoMCrKNcs45IHwQ8pny/6DAqyY1bNiwlOtBLjKS6pA1JtMk2ynRrle7B0PK/aSQy7FkkAjt8X5ss8026d65j+1BDusjXCcjyvW5rxwxQnEUlXFP4TwYIRwu5KT+wf3L91NfQnYq/uJ+y+usv5/HHXdcuv+uOzuUFBoY41QWSqeI0KCgtxjMoUH9md4MDao6SrlOZdVZNpaUZdWhlFVnWFYdVvpeCeq0LFN1AOXXv/71NERdKeFpqHuiiSZK21VKe3nTTTeVlRKc1l1++eXlhBNOWL7yyiuNX5dl1WGUVWdTzjfffGXVAaVlVUddVh1fufPOO6fvqDqqdG5nnnlm+l518uUcc8yR/kfVWaf1t99+e2NJWVaGRbn66quXP/7xjxtL2qc5NMi1rLTSSuXrr7+evqPqtMqqAyrPOeec9F1Ig+NWnWr6nqmUipHbVAZnWXVc6R5kfK8Mh/KWW25J35966qmy6sQ/FxpUKRJlZZw1vo0IZakMgca3nmOwhAYFfYPRCQ366KOP0ru//vrrN5aU5TXXXFPONNNM5V577ZW+VwppCrOrlMf0Hd4bYWCLLbZY+v7iiy+WSy21VFkpv2m7jz/+OC0nL7zP1ukzccIJJ6R3nq5WGfLl0ksvnbb54Q9/mN5b8qpS3NOySnlNv7nnnnvSd7KzjuPV3+lK4U3bXXnllY0lZfnrX/+6nGSSSUa+65WyXE4//fTleuutl0KCUBkV6Xdbbrll+p45/vjjy/HHHz+dc6ZSsFNIpnOFsJ1DDjkk/X633XZLy1AZB2nZJptskr7/73//S3KsMpjKhx56KC1rD/JdX/Dzn//8c6FBlUGR9qsfyNB9K4MphejkUNF87p7T3XffnZYJ2dX/rbrqquk+C/OtDIgU9onKaEoyetppp019UUfoqdCgGBEIgqDfY3ieh9SwdoZ3iJfJ0LhKI7xRhnZ55g19865JwOaJ5zWSkM8jxottJIZ3ireHt7w+0pDh6eaZs8+MECVhNDx6Gd4rIxO5motwyu222y79b9TC/g1F1/fTVYwYuX7XxkvF+8QbKETL6Ad40YQSuC85Ed+wt98pU+t/++Hlc572wzPoPglncJ1tYUSAR00IknvLq5bnNwmCwYjRHIUfJPZnVGiS+Cn00PvvfRNeYgQtY3TRdkJJeNp5k41KCvMTBpdHhFWTEipE3uRwE/LPqGT2whuZNBJhJFCSrxEHZcSFvQhl6Sq85eZ08q7z3vOkkyNk5ahoHmE0kul+kB3OFbz2KmW5TiXpyao6vPHg2SfrjM5m2dYelS488jMqPCfy2r3PoZPus1BKIwE5bNRzdA5GPIwYC1dyjapMkqNksvtvVKN5dKW3CUMgCIJ+DwFM6Bp6BeWVgM2VlnQGOiTx+IRu7qAMnytrSEmGjkZn2VnUN9dR5xhWqBi17bbbpthOUAQo2uJKdWY6Bp1mDsXpCjlW3PVRFuzb9TJ8GDiUjqyI6MTEmAtzomDAX7H+lAvnpBNjTLh/9mPiQErJaaedlsKo2sLQvFABw+UUE51eR2J1g2CgIqYfQk8y4u2FJ3I8cEwIkSGL6hWjwCFBSfX+5RAWSmVdcc3f606LvCzLllbbUFwprdZ1FSV0OT0YHvIPyEOKc5ZLo4N9kEfN+T7OVTU6hpWQp7bI1yzkqbtxD+v3K9/X+rP13bUzgpwDecopkuWo/kn45NVXX/25kr9jgjAEgiDo9/AQqUOulKIORLKdMoyUVlDOIR5XJ2KeByVNKbPiQOUQZLrimTeiIJk2d/qt0BGYm0PMr3lGxPVS0luNOnQWnSAPlVEN12ouAp5F98gISUbdfwaKOFnoeNVhr8ODqKOyHx/KvfNur1Sj41BqlPOUY0GRMUIQBIMRSiHjnBxqK6eLEsmDTcHNo3YZMmVMQ5lvNlCacZ4SbzkKyF6ygte7M0YGRZksJs8p0Rn76gv3Y3RxLQy+3P/4K9+BjCb/xyRhCARBMCCQGEvYSnQz0ZcE3+wV0snqjA3PNqMahg64O+CFV5lDQmczPD+GiBkBtjHZm3PUqXWHN66O/fHmC0VohkKejR0TDBqdMAusEAHLc7UM98v9kxiYRwwyzllSdVvw1JmxXSUOE18ZITHPQEeG6YNgoEGJpvhRboUPNqP6DmcExwRDwEheHe8hz7LJPrtbVsC7XncQtIKjQsWf9iBHfCRBSxZm+GRZM7oIQXROqr9xmmTy6IKQp96eN6IzeF7CMDmrVKIzEl2HoWPkZEwShkAQBAMCMbOqNDACKLsq5GTE3PJsU4xVBMpQZs1mWZ/sKBsPrdAJ6RDFeLZCRQgYFs9VNyBkyTF0BkJmcpgQxNoLZcqjFp3FuenQoXKGkB7VoSkZGd9V/6lfI6+dDlfcsHjcHCalozXrOMXEDO7OG47BiBEnC8fNnXNGzDOUt7VPIwOGw1VNCYLBCGWQQSyPqf5OqvijAg3ZoNqPUc1mw1t+jjBHE+V158ghGO1kgne6LYxmMP6NZraFHAajgOQCJR5ZNrSSERlGj+M3r3e9Rm3di3o+km2FeDKaRmW8dJRsXJHBnEbdrZgzhpQRNUotfyzvn0NIjpZ8iDFJGAJBEAwYdKqSbyWs1TsWnZNSdrxqys1JjvMhlHU4lF+eGZ2MYe1m7MtvN9100xQDq7Pg/ed110lRlmGIV4k5gl3pOnH4lGEdgLCkfE5qbFPWTzrppDQiYficIq4Mqk5JByGu3+9G1UnwljEkdC65jKnEQfsSYkCZdx48/ZLvlBOs3xs5AYanJQ4350eIX5Z/4d6Ywdp+eOF49g1rwzGUHNSh5yF7525kJhsmlgsPqtfUDoLBBMN65513Tu+0dzLLICNmDAB4F8kNjgPLwSOuVr18JtRHBLK33Xvm472ur2/+nwOAvKg7Msgg77SSmOBQYXBwZpBJnBveZ/JPHgOvNjgzyFref++2cEz5BowFThjlTCXv8oDnOV0gZ8pvlUkVIqOwAAU8n3+WGXAvyE37I2MYQfKqxNorF5rJv6kbSfna6/egLchLCdZku5KgcpyQfzuq+1w/93wO9fUcIPKz5LHpH4RfckwJzRROKvF4TBITigV9HsqZxMrBOKFYf0bHID6cx5kA7C10tGpeN8N7RODygFGuxear4azz4vHK8fFyBnQ6OocMBZlHTwiRkBexnurwMwZUyGAQUHJ5/a0TH6tDZDDozHT0OkDo2NwbnTKvltAc3nudMUVBDLH2bh8S5ZyrddkDX0eH6DzIYkPxjAGGjTADstk1S9TVYRsJUVdcsl0zjBmjJq69jv24ZxJ9KQM6cAaG2t46b0aADp3iIH7Yu2ob16pDlJgsVICRISnbefYkA31CsaBv4T3t6IRi3nfvgVFLo2PeJ+FC3o9cfYayLeGW80CsvXA6yuXw4cOTXGFQm3TL+0g+2YdwIbKGM4IhQSaQS95zhQC805Rcx7c9pdy7qvY9pZTizxhgqMC7S5aZB4XnndFC0TeSscsuuyTHAFTyIS8dmxHhPpjvQGKsc3Rttid7yLsDDzwwOTycu3WW2QeFnhxRAcj+yJw80Za/nBRkNsOCMk1+Ou88cmnyLso0WZo97f76yG2SX6DKmXvaFvYl74msNTGYc3Xtcr04PIR0tbrP5LtzzPfZftzfeeedN1Wq0x8Iv/QMVZjTF7gfcrK0GYZSvULUqDDKTL5294RiY1Un+Jn5NRroCHROvD5B0JMQLARoxBf3LwhHAp+XxRBvEPQ0QpBMzsYwCYKehlJqpLHZgO6LMMw508T5Mz44aIL+hXbG2GIQdScRGhQEQRAEQTDAyXlI9RyhIIjWEARBEARBMIAR5y4ER8x+zmkKAoQhEARBEARBMICRy2NEQO6QOHbx7kGAMASCIAiCIAgGMJJgVUxTQnnIkCEpaTUIEIZAEARBEATBAEbVIcaAqjMq2eSJA4OgS1WDlD/abrvtUqmqIOgJ1OZ94403UolGQ5nR1voHSmDee++9qZ7/8ccfn8rLxbMLehJtTqk/EyMpJRjtLehJtDcle5V0VLJXKckg6Cm0N8bc0ksv3e1VgzptCBC2TkY92iDoSTRRddbVN+5kcw3GAJLT1HAmI9R5jmcX9CScBuSEDlM99iDoSbQ3E/+Rb7kaTxD0FNqbCSPNNWM+mO6k04aAGedMQmOyh/psbkHQnWj8ZjM87LDD0gQr4eXrH1DEjOCYodGENDxn8eyCnkTMs1lBTbYj9CH6paAnMcGfiQtNTGhSrjz7bBD0BAxOc6SY0NJkcN1Jl0KDzNZWn+Y5CHqCmFCsf/Lwww+nCcVMLR8TigW9QUwoFvQm/WlCsaD/0ycnFPvf//7X+C8IguDz8DF00s8QBEEQBEEvEFWDgiAIgiAIgmAQEoZAEARBEARBEAxCwhAIgiAIgiAIgkFIGAJBEARBEARBMAgJQ6CJjz/+uMNJ0BIhP/roo8a3nqMjSZdKl0V5xiAIgiAIgqCjhCHQgEL/zDPPpLrAV111VfH888+PUvl+6aWXigMPPLDxrfuh2D/22GPFMcccU1x00UXFK6+80ljzGc7x5ZdfLs4///zi8MMPTyU2//WvfzXWBkEQBEEQBEFrwhCoMApwzTXXFHvssUdx2223pQmQ1Go1IVJbULa32WabYvjw4Y0l3c/pp59e/PKXvyyuuOKKYrPNNksT5TAM6jz66KPFQQcdVJx11llpqvNvf/vbxaWXXtpYGwRBX4Yh/9xzz6U5F9oaXXzhhReKm266qbj44ouLxx9/fJQOiiAIgiDoKGEIVPDsv/baa8Vxxx1X3HHHHcUFF1xQvP3228W2227b2OKLnHrqqcXtt9+eZr7tCZyPDv/oo48ubrnlljQqwNtvptasMAhh+sMf/pBmNrzzzjuLa6+9tlh00UWLXXbZpfjzn/+ctgmCoO9h1ttnn322OOecc4o11lij2HvvvdPEec28/vrrafIYzoC77767OOCAA9L/QRAEQdAdhCFQMd100xUbbbRRmil53HHHLdZaa61iwgknTB11K3jvPvzwwzRrak8h5v9nP/tZMe200xZf+cpXitVWW62Yb775ir/97W/FO++8k7b50pe+VKy77rrFYostlr7PO++8aQZeCoXzC4Kg7+L9fffdd9OIAKN+nHHGaawZgZHKQw45JDkFOATM0GzG3KFDh7YMEwyCIAiC0SUMgYrxxhuvmGSSSRrfRqCT3nXXXRvfPuP9998vjj/++GKdddYpZplllnaH6Xn9hg0bVlx22WXpY3ifgm7UwTD/hRde2ObvGSd1xcC+nOeKK65YTD311I2lRTHFFFM0/hvBv//972SgzDrrrI0lQRD0Nbzb3tGVV165+OY3v1l88sknjTWfIezvtNNOKxZccMH07mPzzTcvPvjgg2QUBEEQBEFXCUOgBYceemix++67F0OGDGks+Ywdd9yxWGaZZVIsfqvOu47Ofq655kr747lnAHzta18rXnzxxeLss89OHvyOhBYxAuQu/PCHPyx22GGHxtIvcuuttxbjjz9+CiNqNmyCIOh7GPnzfjfLAQb9eeedV0w22WTFPPPM01hapPfb9hwLQRAEQdBVwhCoIQZ3q622Ko444ohin332Ke6///7GmhHomHnqV1999caSUUPZ572baKKJiqeffjotk4Ow5557FnPOOWf63hZGC5zTXnvtVWyxxRYpL4Gy34x8BtWOGClHHnlkcckll4zSSAmCoO/i/f3HP/6RwgKFK9YZe+yxU0hRlidBEARB0FnCEKiRE23XW2+9FAIkiU+cLiQU65hXXXXVYoIJJkgJuzrrHNrTXg1/MfxnnHFGquYz22yzpf3nuP724CV0TjvvvHOxwQYbpDKh++233xdyF77+9a+nbRgxM8wwQzIy7rrrrsbaIAj6IxR+8qV5XhPVylQt4yQIgiAIgq4QhkATs88+e/K851jcM888My0XykOZ9926n/70p8WNN95YvPHGGynR2GhBezAglAAVFiSsiKevo0w55ZSpushuu+1W3HvvvcWTTz7ZWPMZE088cbHddtulpEKJzs7V+QdBMLBQrYyRQC4EQRAEQVcIQ6ANJApT1nnjjA68+uqrqVrP1VdfXfzud79LH9U+eObMQdBKOa/Dm09B96Gs58o/HUW+wQorrJB+317Yj0Th+eefP51Xe4nMQRD0beQPtMJcInKAVllllcaSIAiCIOgcYQi0gXAbhoCSnZRvOQNm91Xpx8f/cgWmmmqqZAgYIWgLMfzi9lUIOfbYY4vLL788jSA0D/mPCsaA0CLn1ha2+fKXv5zO+6tf/WpjaRAE/QnvsYpgqpd5n4OgryJ5PeatCYL+SxgCFW+++WZxww03pLCdzFFHHZUm6sqx/Or5L7fccqmOt4+Zh5UPVcXjBz/4QTHHHHOk7Zrhvb/yyitTvP/yyy+f9mnmYlWJ2pqnACYSMkmYvISMycPMLqx6EHj8VRN64IEH0nc4FkNBuJLwgaB3YfQxDJtnpX7mmWeSASjx+09/+lNjaRC0hhH/ox/9KMmm5tnEGQkLLbRQ41swJlBIQohWe3D0XH/99R3K5dBPKCKhUt1OO+2U8kA23XTTNJeMstPNbaAv8Zvf/Cad94MPPthYEvRVtElzEXlmHJr0jIGKa+OAFd0xqoiNwc6gNwQo05J4xfAbalcadOutt07K9EEHHdTYqjXCb9rDS2dWUAnIuQ44RVEVEIJfGI9jt8Isx2uvvXY6H4aHeQu+8Y1vpMmEcn7BH//4x5R4LKnZdrYnjI1eMFKC3sPzVB2KsZgngarDYGRcnn766e0agMHggsefk6BVGWGFAhj9yg7nMD8OC6OC2lLQ+7z33nspF4uRRiZvvPHGn3PWZISSks1CSOWX/fznP2+3v9AOVJjzO7L/oYceSk4o5aZVhHMcRkVfROisuTDq89sEfQ99lKIiyy67bHJIKi6iHWtXAzGMmBOF/qWS4t///vfG0qAlVQPoFHvvvXd5yimnNL71XyplvXzppZfKe+65p7z77rvLqtMtH3744fKDDz5obNE2L7/8clkp441vX8S+//rXv5b3339/+corr6Rl//73v8vnn3++fOSRR9IxHbsVfnffffeNPKdKwS+rTqixdgTvvvtu2ve9996btrG/qiNprB04vPHGG+Wss87a+NZ3+fOf/1xWHXY588wzp+dS5+OPPy433HDDcooppiivu+66xtKBjTZbGa/lFVdc0VgS1PGu/uIXvyinmWaaslKkygMPPLCslKrG2hE8/fTT5bbbblvuscceZaVQlquuump5wQUXNNYGzSy//PLlrbfe2vjWvbz11lvlbrvtVp511lllpaiXlZJBeyrXWGONxhafsdJKK5Vrrrlm+dprr6VtF1hggXKfffZprG2bXXfdNe1z8803T32QPuT3v/99OeWUU5YzzDBDedNNNzW27Ds4z7fffrvxrfv58MMPy5VXXrnxrW+x4IILlo899ljjW9/mD3/4Q7nCCiskufzkk0+W++23XznWWGOVCy20UGqnAxF6qvfp2muvbSzp38w111zl9ttv3/jWfQx6QyDo+/QXQwBXX311Oe20037BEPjPf/5TbrPNNuXkk08ehkCQ+OSTT8o333wzdcL/+Mc/kqL53//+t7H2M2w3bNiw8rzzzkvOh08//bSxJmimJw2B5557LilQngf83WKLLZJxX1fQyYCJJ544bZ+56KKLyvHGG6+8/vrrG0tG8NFHHyVDIZMNgU022WSkM4qSvc4666TlBx10UFpWh7HQE7S13546Xlscfvjh5fTTT9/49kWa3xkOMUpvb9CfDIHjjz/+CwbbRhttlNoVJ9ao6I3n3l3HyPs58sgj2zQEersddwc9ZQhEjkAQdCOtQjwyleBp/DcCoQKvvPJKCi0QclC9j6malPCADz/8MH3MX2GbXGXKjLOGOS0fVWha0LcR6z/ppJOmggNKgQpHFCLSjO023HDDlPcjVylyf8YMM800U/Gd73wnPQ9414Vpyg8TbpE55ZRT0oRv5pvJCOfy3Oqx/uSBPIP2ZAbycZrxeyGI1113XXHLLbeksFOVpnzIDqWthX0ICREjLcSsWQbBXDnCex5//PF0fn7/+9//Ps1HI9fJbyQEm8Dul7/8ZVEZ9iNDSfyO/BIC5Ti2JZeci2XOw3qx2ibDzPPtkGPO3Sfvy3GFc5B/1lsuL27//fdP68zdU5d57rGPmfRfeOGFdC5+s/DCC6f9VAZBY8sA2267bao2VmfddddNk516du3x1ltvpefnr/4oz68E99z9tk5787cZy7QJ623bjDaSj+H553biuec2pi0piS6XU/+nT8xtJ+O7HAjvhDbvt804D7+XuyOPwH4HO2EIBMEYQgUpccFijnWUOjn5JJQ9c0YMHz68WHPNNZMCIgeE8DrttNNSHoJZqSmHAznZK/gMBkIrIyHoPZoV9qzMqiCXoYQ8//zzKXlWtbkMQ48R8fDDDyejH37nnSYDmqk/a0oaRQi5EhyFSMKnCSaVsaagkwnmtvnnP/9ZXHzxxWm+GnloJ510UlKOzU4vybkOxemRRx5J8mbuuecuzjrrrLRfxSzMRWMfZJPcJtswcsikPL/Ob3/72xRn/uMf/zgVQ6AgUsIU0FDumiJvxvu99947FcuQvwbHtH6aaaYZeT8o9dtvv32Sf7/+9a+TbFNkIRsXzk0BDVjn2n/1q1+le+J4zisXzpBfY13ePhgxQWEz2qPlctjagkHn+T311FOp/5GzqChJhjGrCIZKiD4MY0VMMtqk9mQupPPPP7/41re+lRLgM7Zda621Utt64oknUm7MEUcckZ65dq7NTDfddMkRcsABB6R24xhyOhkNGcaqdqo9MoK10cMPP7yxdgSMGBUer7322mQoaifej0FPJQg6RYQGBb1FfwoNuuaaa9oMDaoEUAojuPnmm1NIyJ577plCBaqOrrFVmeK/vZbf/e53R4YMVMIvhRVUwnRkaMG5556btqsEcvreF4nQoKC36cnQoMw777yTcrKE8Sy++OLls88+21hTphChqaaaqtx0001TPlimMhDKShlPuQJCht56662UG/Lhhx82thhBDg1abbXVUu6XHJFKyU/LKmUp5ZehUrxSzlE+RqUwJxkpBpy8tP8ZZ5wx/U44kVyzE088sc1Y/krxTtsuueSSI49RKWjlRBNNVFaKW3nsscemZfJabLPccsuVlTKellXGRgqFqgyG9N05rbjiiml/m222Wbo/5J3fOae77rorbeeYlbGUzjVz4YUXpt9VRkhjSZnOoVIEG99GhAKtvvrqZaXoN5aMkDV+t9VWWzWWlOWhhx6a2kN9/91NfwoNaoWQs5NPPrnxrTXaeO539FVCryoDMH2vjISUF5fbjOc82WSTlZWCnUIYPevKQC0rAy2tl+c4yyyzlMsss0z6Xin+KT9KHkxG/+hZVgZk+l4ZH+XXvva19F7l/Ch/xx133PKyyy5L33HppZem/lV+ZUYOln3l0KB999035VxlhPFdddVVjW99nwgNCoJ+Do8ir4Xhd56RRRZZJHnJ6p7GmWeeOf1VxcpcEFBKUPUQYSRVB5uWWWfbUQ3pBkHQvfBUeodV9uFxNmN8HpnjyW8v1Mc6FeTMXm+kL4cZNSOkgaffdqrDCY/h3awU6eTF59E0AsDD7xyE7ZhIkjwQOiHMTHU6c1Dw1pI1Rh6bQ0Myucqc0QnHgIpV9iGUg1cfKgN973vfS57b7Ml3Hs4p4/rycXhzhUWRXe6T88vX7H+/a+9+tYI32aiH4/Dq5jLblSGUQkpyWIlRAh5vozTBFzHyw9Ou6mB7CF1UbUiFQ2E02oj2BCM32ojQLKFnRnpUy7LM3BJGjFQ7zP2aUTLltXfYYYf0Xclcy2aYYYb0HUam7N9IgbY8++yzp1EfYUw+8O4YffJ8M0aA9Kf5WGiefd256HvPPffcNBqXqzIOdsIQCIJewhCs+EQds2F0Jfc6Eu4x+eSTf2E7BoVPEAS9i3AWSrXQPeF5FFGzxUNMc10pzlB2vf+UX//7zRJLLNGmIUCZWW211VLZ0GHDhqUwGIouKL9CkihI5IJjivMWVnjooYcm5a4nYVw4vphuOH5HoJQJ8+kqFEzK/sQTT5xkoOPLx7jssstSmdYc802ZJDvFpbd6JoMZhiNjksNpVGVftXHGr/mPzG3hfvqNZ6kNuLf+91d7VpqUAUt5ZzBaV3/uwnq0bSF0woomm2yyz+XTyIdhGNg3g89+fTzztp6jfABhZIzY9gxLYUPf/e53iy233DJdz1VXXZWcbIOdMASCoJfQYfGM8bTdddddSQgFQdA/8S5TvCmkeW6Q6aefPn2nENUVEvHvPPzefUoURZpy1RZZuZ5nnnnS3zrinyXOGoUQT21yS0aFEQETzTV7QQcacjPkAjDIFl988ZHX717NNddcI2fe9wx4psWFt6VADka0w5NPPjkZmUsvvXRjadtQniWIc2AZRTBCffXVV6fnwLFFQf/+97+f2raPNshIdf8ZZRT7Vsq5d4Ky38oY1v7bU+ibMTJmhKrVvupoM0Yh5Oc9+OCDKW/GveioMTtQCUMgCHoJnRFvmmF2yVCHHXZY8vZFJxUE/RNKJ0Uph9NIrhW2IGmyXrFEGILExhwC0ZV33jF4uoU41Ku3wGiB6j8DGcYW6jPqg2J67733pjCVOqNSDgcTQqj0PZJzGVAdQZU6o9dGpVSTggkN9WXCwvRhvPt1/vKXv6Q2LxlX8vjLL7/cWDOiShUl3EiAUFcjDM3PjGIuJMn70hElfYMNNkgGtgTh9t4tIxRG2yQMe1eEBemHLR/MhCHQQcSq1WenMyQpJu6+++5rt+GxiMW6ybjvLuwrD8sGfQ/toVWbMDxKqAkREC8r1naTTTb53EzD7bWl+vBqPkZ72wedoznkyn23rH7/O4Pfj+p5aR/tdXzWjepccrhEV3GMrl7zQMKzc1/rz5BXk0eUZzEjNIKSRDHNMAp4I8Wtg7ezPY9ne+tUXVFZh9LLq5vPyQiBmOlm46CnyefaUQ9uvn95e2EkmfzutfeeMIQooDvttFMyhnJOgHAX1Wzq8eb205Hwy8GANrj11lun6lVGUHJ+mTh/s2Sr0NMK1Xwo+u6jERhlSLV7v9eHMbQo1qoLafOqYTGEhWblkQHH1T49X4aD2bLF68tPMIIlRCg/e3+FCq200krJWMjPtxW5ncgXcTyGDg9/JsvBvO+bb745VbJyTkaQGBBGjdq69sFCvCGjgCIvsUTCVvb6sCQJe42IYPdiiVtsxvCv+DrxawcffHAqb5aTyroCIciK9cJFR913YJzpiAnJP/3pT42lI7Du0UcfTQJLSIBkulwqlDHAa0KIMiyRBRcINp275MR6p0ewKxNYL6EWdA33Upm7jI7GMLhp6pXQE2Ywunh2albzxEm6s79W2ObEE09M291www2NpZ8hHET5PvHoykX63gyng33w2KnJ3Vl0oMr+6dCzwjDYEc6ndK9wIOEVuRQhD6M+IKPM4VZbbZWUdGE8PNfHHXdcceCBBxYLLLBA2kZ4BWUky++s0OT4djIkzx3SCu2Al1YCp2RLITFin3lRHUOYRE7GdZ6jeobZsZSP7/nbRzYG8+iG/50b/IYsEx5ie38zuW3mmvEULQqYPlFCKZyn3Aa5Ft4L8lDCJ+RQeQfsVwiWc6GAavfCUOQCWO4eK31JqWOMif3O8Eq7h0OHDg1joIIu4p4K8RG+ZlTJX/eQ8UQhbgXdxm/JRl5+ckFCsNh6Cj7dho5DqbdPslKfBs/DM7MPCelG0JT4FJoDoxLOxz48fzkBztG+OctA98phdNnIzd+9h7ndaUOuw/wXzs9f/SPHm3Nw/t4H64QSORbjxRwg2tRgZuz9lSPoBIaIvMTiEgcyvC4ajQYPDcnwk0ZPuBNCBJmhr5zRDsLS8JuXjICSHEPZ06jrk890FtnxJ5xwQupocnWZgYqX3j3PlQb6IgQUpUmnvPLKK6eOS0fFowFGm2oLPCoEE2+INsK75/npaCn/Ok5tK1fecO2GTbUZQk5HSkhSLtRRZpzq5Oyvr3V2BK8a5O4HL15fR6fhGfJW8VZ611VqoUgxAvLkRhQ9Ht+Oov65/VLcJDRS4njK6sojBZ7BpzPUOZMZOqfs3dQOKJfqwXNC8NLJMVl77bXTeuhAHcv5Mxi9L7xkjM3RRVvScTM+HacjscR9AfG/nDP1yiHdBWXGxF3CGrzPvrvXlKRmhDwI0RFbTUmixPK6ZjxjHm3P1LtOcb7wwguTYqJfJde1Qc+v1XtN4SITyAzKu31oo5xTvKIUMPKFYcDwpHCRR62wnetwHHJIO9WvcTZR9p0rZcs+DjnkkHRNwnPIJY4t/RoPLkeGii88zPpNv6OoCZ3iPFMthiHhnMk8yc/2y2nC46yNUdKMkIo1J9/cCzLT770v3gvL/J7uYZTesclK9y9XP4KKNe6NOQ7aq5PfFfRL+nb3ua+jjWmX6vDTH+of7bOtpFntxn31nLR529cNLu8beeav5GMKuTaUER5E/mvLeV4L7Tcjp4Bzk2EsREeS8R577JHaj7asDebcGn2ntkKP4li1zIjCFFNMkf6XxO9/CfOOR1Z6T8ht16EdODcGjcpfZKxr0W/3B8h9oYdGS7qVqrPrFINhHoGHH3441QmuLMfGkjLVqK2UvMa3EXVxK+GVajirZZxRq7ZqXI1vIzjqqKO4fUbWUe4q1UuZ6rRXymFjycBEXWz3N+hfeB+0z/4wj4A672o0a2sZddEroZtqw2dmm2228uyzz258GzX298gjj5SVgpW+O07VYaXa2plKUUz7rZT49L1SlFK966qjS99RdXCpBn1lXKXv5JBzy7LEvVZrPdf3rpSysuqQUz37rkD22U8+t75OpaT0+DwC3cURRxxRnnDCCY1vQXfy3//+tzz44IPTu1sZH42l3U9/n0cg6F/EPAK9THVvkmXL85G9umAZ161H2/kYmqzPEGn4rDm2zfAtK1cIAM8IhIsYwjQEyto2+pCHsHx854FqTsYBy1ayi1n7giDoPEI1eNDzu84zytPJ88/DlBEaksMXOgLPF9mQy+Px8DvG5ptvnr6DJ58MUG8dvF68a8LEeL54PZWbFGPO8wseUJ4h8bZQzcOoZPbE2a5SUtLvyBUYVeBhI3vIGKNKucY6zykPthGAehiJc+U1M5LhHIPuQ//iWbjnQfehLRtB4Kk3itHR3IUgGKyEIdAGhj0J6FENwehIDQsaRs1DuDpUikQe1s9QCgxjCasSOnL88cenoSthR4Z8DGVS6oWG6NQlUeno8xAyg6AOAWefhpMpDEEQjD4UfmE/hqiz0iDMwTtoyDor6BCKZVhZfGlnEPLBMPA+ZyzLsiFDdgi7MHxNlggZEyIhLALiecmdv/71r+m7fXBa5ImcYB+KHFDgGRVqgEuuFJ/rL7kiJEToCFlD2SeLhHDUcb7OJRTW7kV/IceAEyjoPtxPRjOjWLhJEATtE4ZAG4gF5RnTCbeCl008rrwA3v96+SsJo5Kj8iywGQLKRydO4RDzKH7UvnTq8gnE2FkmgUacKy8lpUEMsbjUOvYhD8E5hiEQBJ2DR55hzhOf8f6aoIbXvR476/3lWScfRgd5BpwF3m9x07lKBRlDdoitrsdwOxfryBJKvtwQFTqys8Foofefom+E0TY89/XRSvswKkmBZ9CIvZbPJJ6Xt5RhIKaeEWDfjAAGifK2OSkPvNa8rOTcYK+u0d3oC4zcBN0HY9nIfR49C4KgfcIQaAOKNyFNOW+F5EwJLDpVFVxkqOcheBNo6LDzxCat0JEr4QXJfBLJ7FMSFK+eBC7r7V9yD2+lzrgZ6xkCMTlVEHQOyi3loa7wSyqTfJZD+DKU72wMeO+FHlA6JPlKkMzVnnjQ6+8kJV/SovfbdpJ8jSwIPbKO4t18LHi3hec4PzKjGedhH2RN8z7y6AYjh4HA2cBTalp9yXuSOJ2P6xTyJMGOgWD0ob4fxyXPhCeGIRAEQTCwCEOgDRgCOsC6l66OmF8hAzxqJtoQyy9OF7kDzkpBxvL6J1P/P48a6Pyz949nj2LSShHIv81l2oIgGD28pypM5LAbeOcYBq2Uc8t5zuX3KN8orEjpRiU7VW1RPu+YY45JccoZ3klVUoQDqiCkOkZeb53j1N/v/L+qUsIbOCWaz8V3SrzcI+fbLB/IEeTQJutdY5YrHBbyH/J3kDP17+CgyFV46rIqCIIg6P+EIdAGOmcdZ32IvBU6Vt58HjlxxVh//fWTl1+iXh0ds/0pe8ZD19xx12nu9NvbNgiC7oVizdgXg1+v5y5ch2wQ4sOrT5HmZTdyqPyo2HvvqvhkoUDNUOjlHdXfZ8q30L66MS+kx8ggjz3F3DZyhLJzwe9tIw/A+RgVUJ+d9z9jvfNjKGTqBoe/HZEzzsNxXHur9UEQBEH/JQyBNhAvq+M744wzGkvahlI/xxxzpE4+s9hii42cOCUjdEisr1rgOvfu6FTzPnK1kyAIRg91q1XQqSvR3icx9OL56zM/33TTTSMLCDAWMt5nyjgZsOuuu36uKhBlu65wcwZwHJAD4DhwPPH+8E4bkeSJN9IgjEfojlyAfI7+mmFc1TCor+58yBdwOnBEGIWoGwJGF7JX39/6NaB5NABGL5yPimfN2wdBEAT9mzAE2oCnX0epc6+jE9dh15OIKfg8b/WJZXgLeRTrlTZ05CoCtTcJm47YcQ3b1zvs5vPIUFIYImJ7gyAYfXjdTWzUXBhADpA8HVW+JPRSrL2H8gE6in2adMhkR+QEpVrSfy5NDIm8ig6o1qPimEpBqvyYPVUIEkyUw5hgsBilMCU/I0HVMUgUNlmPyj8MBKMH/kr8pcCDY8O+VUOCmH+TBOVZYMk2VdBsV58ZO4cmKWlaL6UaBEEQ9H/CEGgDCbq8dOpu19FZ67RNoW22OjMt6uDNzFef1ZDXcJ999il+9rOfpaRByoDh/zwzYp51FRKDKRo6etsIMdLRU0AsUx5U9RDH0cHXw5VMxc4QoEwEQTD6eNcp0qp11aGEm1+AcW7WX5WCzjnnnJSg31EYAt5pI4vyBvxvBKJ5lmyzwqoeY/9GHXj6hwwZ0lhbJKVfqUmGhHkMlC89++yzRyr5MI2+kCTHuuOOO9KMznm2c4aO74we8saog3Nj6FDwyRzLhQAZDc3GAjgbhDpKLg6CIAgGFmNVHUKn4lMouapM6JwGKpKAKeH1pD9e/bvuuivF3/Kcid3ljW+liIvnlUDsFlPWGRY6e4qFcoIUC2X/7MP06koV2j9lQSdtWx218oaUFCMManpbzkNn+N/03Y5NgRioUHqEUcSERv0Lo2G81BTX1VdfvbG0byLpV+K/GP960jBUylEhSIhOW9PwtwdZwYtvqnyjgfUQwmaEE0oAlnfQCu8CT357o4ruu9GG+rwEXYGs53w44ogjGkv6Low6c7HkkKkg6ElU82N4K78bBD2NdqZS3QknnNBY0j2EIdAOvPSUfHW2czxvX0KdcyUJGQqUh4FKGAL9k/5kCDDqheyIgd9oo40aSwPv3M0331ysueaa3WZY9CRhCAS9SRgCQW/SU4ZAhAa1g2Fys3Ca9Zfnvq+hTCFP5kA2AoKgNxD7r/SnsB3x+4Mdo5nyFMxdIpm5PxgBQRAEwegThsAo4FmSBHzooYf2mVr9vHQnnXRSmn1YTHAQBF2HMaAs6B577NFYMngxMmK2Y9XQ6nkIQRAEwcAiDIEOsOyyyybve1uTi/U2SgJK6JNTEARB98EYyJV6BjMqlZmfQPWyIAiCYOAShkAHkNxbr789pnEezicIgiAIgiAIOkuXDIFWk88EQRCAwdpXjOcgCIIgCL5Ip6sGiU+XUKZOtYobQdATUCTVQDf/gjkc8uRHQd9GSInJscyyawKu5ZdfPp5d0KMo0bzZZpsVG2+8cbHIIouksq1B0FMo503/kVOktDB9KAh6CoUsVOGTx3bcccc1lnYPnTYElGhTVWLeeedN9eyDoCdgCPzrX/8qhg8fnoyBMDr7B5JNX3vtteLqq69O5c5MwhUdZdCTmJ/h17/+dZpjYcYZZ4x+KehRtLdhw4YVyyyzTKqqZfLPIOgpONdMImkyW5NTdiedNgTMhClO3RT3oZwFPQVDwERM6623Xpot1QyoQd+H0DI5lhm3zTmyyiqrxIhA0KOYCM68KltuuWWx+OKLx4hA0KMYgTJb94EHHlh85zvfCUdH0KOYzJLRudJKKxXHH398Y2n30KUJxcxeudVWWzWWBEHPEBOK9U8efvjhNJR54okn9vkJxYKBQUwoFvQmMaFY0Jv0yQnFYigsCIK24GPopJ8hCIIgCIJeIMr+BEEQBEEQBMEgJAyBIAiCIAiCIBiEhCEQBEEQBEEQBIOQMASCIAiCIAiCYBAShkAQBEEQBEEQDEIGhSHw+uuvp9n/ll566WK77bYrHnrooVFWMzFhw6WXXtr49hnvvPNOceSRRxannnpqKo340ksvNdZ0P87xvPPOS7OyqqNvUq3mOvq2eeCBB4qtt966WHbZZVNZV9fbjBrHV155ZTp3M2+a6CkIgiAIgiAYvAx4Q+D5559PRgAF2gQzZmYzIYPa9G1xzTXXFIceemjxxBNPNJaM4L333kuT1fgt5XybbbYp1lxzzR4zBn7+858XTz75ZJpI4tZbb03TmZs5s84999xTHH744WlyN0bKwQcfXOy///7p/wxjwXWrPasG7X777VccddRR6TqDIBizmKGdYW4G7VY89thjxfbbb18cdNBBadso2xwEQRB0FwPaEPjggw+KK664othiiy2KI444Is1MS3l/4403ipNOOqnlqMBzzz2XRgLefffdNDtqHft6/PHHk0I+yyyzFD/84Q+LWWedNRkH3c35559fLLbYYklpv+yyy4oLL7ywmGCCCYoLLrhgpOFhCv3TTz89fY499tjk8TeD6ymnnFI888wzaRu8/PLLxWmnnZbOd4EFFkjnnGfgdJ1BEPQ+r732WpqZfdFFF02G+vvvv99Y8xm33XZbmrTRTLlG+372s58VN9xwQ2NtEARBEHSNAW0I8KTzpC2yyCJJqf/Sl76U/p944onTDG1jjTVWY8vPOPfcc9OIwRRTTPE5z5vRgN/85jfF1FNPXUw77bRp2bjjjlv86Ec/Sl666667Li1r5uOPP06evo8++ih97NMyIxQffvhhY6svssEGGxRrr712Uv7HHnvs4pvf/Gbx7W9/u5hhhhmKySefPG1juVkNJ5100nR90003XfGtb32rmHLKKYuvf/3raRvGDsXByMiCCy6YlsGIBoQKBUHQ+5Alu+yyS3qvyZdmTz+Df9ddd00GPMMdP/3pT4sNN9ywuPvuu9P3IAiCIOgKA9oQoOhTlutQjHn0l1lmmcaSz7jooouSwi0e/8tf/vLnRgz+/Oc/F48++mgx4YQTfm6fPOxvv/128eCDDzaWfIaO/cYbbyx23HHHFMNPaefF5+03SnHIIYe0HJVA83mL8f/+97+flIDxxhuvsbRI51nH+e28885pxAKMjmuvvTYZLT4Z1zn++OMXN910U2NJEAS9jffXu05WNTsmhCY+/PDDScZkjA5waFx11VWNJUEQBEHQeQZFsjAo0sJ+/vCHPxSbbbZZGhWoI9beMPw888yTvjcr6EKCXnjhhTREb7Qgkztv+27GummmmSYN+Q8bNiz9XsefO3je++bOvxVyEn7/+98nz2ErAwYUfqFBRkF4D7MhYeRBSNP000//OYUi05wHEQTBmMf77J03ymcUsI5cp4svvrjxLQiCIAg6z6AwBHSc999/f0oaPvHEE5NXjUGQoWjrWCn5c801V2NpayjUHYWST/k+8MADi5lnnrm48847k7HxjW98o7jkkkuKTTbZpLFl2/zlL39JMf/O/YADDkjnT0moI+RI4q8wH6EEwgdefPHFxtoRGCkQZhQEQd/HO/7Xv/41yZDmkKH55psvvfNkQxAEQRB0hUFhCOhIKcJGAiTc8cwL1TFKAOU3v/vd76bEvTr1EJyuIAxHxR4xvyoBifmde+65G2vbRxjAEkssUfzyl79MSb5iiu+9997G2hEINzJK4dpWXXXVFI6kupDr7siIQxAEfY/m8MCMssVGGSOsLwiCIOgqg8IQoNDz9K+wwgqpCs9SSy2VYv4p2bxq2267bYrfX2211YqVV145JQCrLGSZMBujCX4/0UQTFZ988kljr6OHfW600UbFI488Ujz77LONpaMmVyeSUyDx2ehGc+lT4UCMBdcn70DIkVAnYUHyAPw+Gz1BEPRv5BiRad/5zncaS4IgCIKgcwyaHIE6JhajIMsDkEArCVf8vdCZSSaZJFXcYSTYRi6BGv2TTTZZCguSeCs5OCN3wHbLLbdcY0lrjDowLoQIKfWpdODoYtRCzHB7Sj2DxQhEhleRYSCHgUGTMSoivIBxEgRB36M5JCjDkZCN/yAIgiDoCoPSEKDYL7zwwqmjFa8vkVct/rPOOit9fFeS0xC8+QaE8cw222xpJEF4T57d1+jAzTffnJL55Be0hXh9cxjsueeexa9+9av0neeed390YLjMMcccqTxopq2qQ+KIeQ1d6+abb57KEzJEMnfddVcKL3CNQffT6rkI4WLEtfXMgiDz1a9+tZh33nlTm2lFW2FDQf+ETCAbPG/9Uv1vX5cXzltfGHKt93HP23IYtCK3s1bPKre3UdEf2mR34Drbc7oOJAa0IcDjbXKwW265JXnxKfAPPfRQcfzxxyeFXwUfMfQ8+vWPZT46W/X5c6e78cYbp8m5eNYp8a+88koqKSp8qLmyR4YCft555xVvvvlmivEXeqQEKIPA5GCtJhGCiiEmEnM88xA41jHHHJPCfxgkcD1mETa64PokGNpmqqmmSsp/Pm+jHeYNMEeCF9h2kpZ/8IMfjNxX0HW0CRO5eW5///vfG0tHIJxrp512Ss++VYWpYHCSZU1zx8qAN6Egx4Mwxow2ZhRzt912aywJehuTMJLLr776arsTMr711lvFP/7xj/TRD7QHeWGGeAUhPFv5XopMkO+cRo7TV5US52ykO5LXew9tgR6ifQwdOjT1O+0ZBOQLB6Syw0KezStU5/XXX095iEcddVRyEraCjvHb3/42zXAuPLEjRkN/xf2hM5mAdjAwoA0BnSbvPuVZjP6ZZ56ZlHKhOUYC2kP8rdKfdVQAkmNgFtDdd989Jf4q50lgt4VKQRJ3jShkZpppphSOdNxxx6VJylpx++23p5l/efa9nF5e/6t4lPnTn/6UXkghQ9ttt106N52TTsRkRRm5DV5egkJFIR+zLvstwyfoOu6tMq3amZEfgrUOg5QRygCrK3bB4IZRzqBnDDRjJHKNNdZIo4nZUCB3yCXvcND7eBbytTwbE0sKCb366qsbaz+DHFacQhU3OWhkModOW+gTzP/CSUDe33rrrWlUWhEIjiu5Yvquvog+cM455/zcPDXdCdkqlDX4DAo5JVWfc84556TwX3pGKxiidB8h0RyPZidnuGUUHZh//vnTKKS/J598cop0qON3+jbt0oSrQp332muvAWkMGN067bTT0rVyyAwKqg6mU+y9997lKaec0vjWd6ms2LJ6QcoLL7ywrBSwxtKu8dJLL5WVYtdt+2uLa665Jp379ddf31jyRZ555ply+PDhZdUZlZXy2Vjamkr5L2+88cayMjLKjz76qLG07/PGG2+Us846a+Nb36QSiOVjjz1WVp15OeOMM5b3339/Y80IKqO0rBSCcvLJJy+vu+66xtKBzYMPPlhWBndZdVaNJUEdbaLqoMvZZ5+9/L//+7+y6njKTz/9tLF2BE888US5+uqrl6effnp5ww03lGuuuWZaFrRm+eWXT/exJ3jvvffS+33YYYeVV155ZbnvvvuWX/nKV8rKKPjcc7PdkksuWZ5xxhnpO7m70047lZXxluREe1SGA4uvHDJkSPodKuOjnGSSScrJJptsUL5L5P/iiy/e+DZqPvzww/TpDRZccMEk93uTp59+uqyU/7JSztN3/1dKfPm9730vfa/zyiuvlJUBUM4222zlU0891Vj6GR9//HE533zzlUccccTItnnBBReU008/fXnXXXel7xg6dGg5xxxzjGznrnneeectDznkkPR9oPH444+n97AymhpL+gZzzTVXuf322ze+dR8DPkdA8q/QnQ022CBZzd2BhN2111672/bXFqussko69xVXXLGx5ItIDF5nnXWStV6f6KwVEgwNd0ky7CnvzWBFcnkliFOoVVuVpVp5T6p38AthIfXvba1vXhb0Pwy1K0xw6qmnJu+eUBLhjHWM9hnBrAzIolJu0ginZUHvI3xH2WmjMirM8fIL9TSqUw/xFOJj5I9MBrkrh8xzbp4RmreWd7WZepiH0QfHE17aagLI9uRBZ+VEW/tsb3kz9WWtfteRZdq8kW6hGs3bIm9fX2c01oi692kgQv+gG+R5gTbddNNUzKR5FBp77713KmhiRFp+YTNClE1uan/6MAgZ1maFEkNI0eWXX16sueaaI8ON9XV0CZ7zP/7xj2lZZ2l+fs10dX2djm7XFl39fV9lUCYLB0FfgLIgvEwIgXAAnf9jjz2WOr7rrrsuCWCCXBiI4VtCiCKw7777pjhik+L11bjhYNRI+te5Mh6FmKy11lqpA27GML4QIdu2Wh/0DgywekU24Vy+yw/jcIL3UTioeWvkoGVUnBOiqaxzhmPAe98qLKyO976VE0H4kUkkKYJCQ6+//vpklEBhCEajMEVGilhyc9m0Ck+yf8oiucNQkf9AxpA7wkRAIZdXJ/csK+YZssqEl0pjQ9ipWe7JNrlRN9xwQ9qX0CcKOpyrc95hhx1GGr+OK9xE+CvjSF6E4zkHSr0cvXw+oMD6vYk55V/5DqFz7heZ6VwGGmRAs9OPEu/51tEOVTkU+8+Y9JybFdmsxNeLDwgRmnHGGVNfI/9QLsI777zzBQcX+SVkS/5iW3jeBx98cHHYYYeltiXcJmN/zo8TZMiQISkErrk/M2eS0DrPnkNEG6mjHWqrcu+OOOKI1GdmtBm/UfDlb3/7W3oftJe2QuzkRnifvJMXXnhhY+ln2J92ZjJY99QkrgOFMASCYAxB8OhcKRgqxOjIVK+Sz6Hj5m15+umni9/97ndJWBJkBKrykeJBCchI0AuCMYOZn43q1JXTBx98MI0cMOzq+VeUJqWdKVVZmVHYQW6AuOxmsncWckjye87zCyMJhx56aBqVNmLMyDCZpLwzx6doU84dgzdY8QmK4pNPPpl+X4cCL4ZcrpvEXyMX8pgo+BwOilpQ/p27PCeKHQXLeYlVdwyz38tZk5enOId9OReKPfllf+6TmfE5PGafffa0LRmXjRfXZplzoYQypozkq36naIdcvzx3BkNDvsZCCy2UchQU7WAQKNRgtNtojGdhm4EMxV4fsuSSS6ZRowxDyL3QTijq6667btqG0ZirB7rXrUZNssHAEGMA+j1jzfZ1QyIbsNpBK+67777UFuSPeE6M4JyITOG/+OKLkyEi50AOjDam74M2IhdTu5JP6XkeffTR6Tryego9pd3+XTujwnrGiXNnAGinDAztkGEoJ1PeTjZIM/IlOeGMdBg58X8d98zxFV5xPKMxjIYBQ/VgO0V/yREI+j/9IUcgI69j2mmnbZkjIEZYjkDVKZc333xzii8Va5wR13r55ZeTtOVmm22Wrvudd94pDz/88BSLvNZaa5WV8pFikB2nUi76dMxw5AgEvU1P5gjUkSNGJk099dRlpeg2lpZlpWyUU001Vbn55puXlYLUWFqWf/vb38qFF164XGCBBdL/8szICTlsdXKOQKU4pVwucsR7P84446T8BDHd2GeffcpK0Sk/+eSTslKKyueee66cZ5550u/kkJAllXJVVgZFkhWOJ+6Z7GjGtpUSlY670UYbJbnjUxksKS9h5plnTrlolVKZ4tIr5bxcaqmlUk7aa6+9luRaZYikfDa47koxS/sju1599dX0cf7O6eGHH07bXXLJJWWl7JeVMpq+Qy6f3+X8Ctj3dNNN1/g2Igdj6623TvfdtfuIm/c7OTQZ+Te+1/ff3YyJHIHMm2++WVaKfXrG2mE9dtzz0t7cX/epMuBSnyN/rVK8y0pRTu2hMiTLJZZYIj3vjOfsPaoU3rJSqstKuU73tjIiG1uMoDIA0/J6H1anUpzLxRZbLLU9aAPaAyrDLbUR55SfYWW4pvOtjMv0PJ2D/MfMKqusUlYKePrf9clRqIzh9B0vvvhi+v2iiy6avnt/5E1UhnZZGSSpveZ2ov1m3JvKyCwfeOCBxpKyvO2229J2OUdAfpb+1nuGytgeI/kRkSMQBAMA3i1eNJ4Sw485jhiGZHkOoQSsuHAT2u24447J41d19MlzwlMmTpTHMUKDgqD34WnlyfRXFSEeePCS+rQK5YF1ZABPpZAi73wreMl58XkxeSBVcTECwDMOHk1eTvJAGIe8rzxpJG+m/ap4IuRDLDd5YUSC7GimLneEnJA7PqoiGZkQWiQEyjY8oSrSOTdhGOSUPBdU+kT66/rydcmls72PqjVCk/IIANnlN6MKjWrGiKjrXG+99dKIiutfdtll036MSvBEw4z6RgUqwyF9H2joG4zG8FS7Rh50IyMQdsObr9KgURR/3X9ed21VOI2RE9t51vUwtmby8/KsRwf7F74qJEyIkd9XSmxa570RKvSTn/wkPUPhj0Y2vDfCyYyaPfXUU+nZZvSXeWRM2zNSIZ8hoxKkkXNhbvpY74a26DxUbdS2HQtCnTJGFox21EvAq9hVx/rKWEmhakKuvBu77LJLY23/JwyBIOgldMzqwhseF/JDkNdDANpC558ViIwOlGAKgqD3UYJRqI1QA6EIlDBQvL2r9ZhrWOZdt9z7LGRFSFBb5QmFKHAASEwWJkipzUaAkB3Kvhwjyr8a8pTjN954Iylt66+/ftou090lELPino2d9uRQXb7le5J/31lcv2MLNcnX7+Pa3ZcFF1wwbUf5k7shRGkgykr3kxGmnYi9d68p3sj3OLdH+KsdQeK58CzfKc0U9Tq2tT+Ggvh7BiSDoxVt9WEbbbRRyn8Txipvg5JOofbs7r777jSpK4NOiJ3QMe8Rg0b4kvejuZ3oLxkTDFMhtPrDZgOGoepZ51wY/WT+tEJoFIODsdDWdUDYnXLu2pd7ZiLWbHQNBMIQCIJeggdM0iCvDE9VrhTTUdoSZkEQ9C6UBoqKuGuJmBQm+J/3nbeyPipAMVHVxXreRuuaFZ061lHgHadZQWEQ+BglsI5C5JMNhYHuIHCtPP+8yhTdfP1ZKaxX3nIfB4PDhPHjXuR26Jq1sdxO24JyjXrf4neSrMXD89TL3dC2mvejTTMQjPa0wjpefjkfRrX8NQLuHK2Ty0L51849P8q4a+Ct9/64hjwaUYfBy7vPIKh79pGvo/7utYeEe0ZQ9vi3hfUm9jMaIGfBiL4E5lY5N/2RMASCoBch2AiSX/ziFymZS5JSKPhB0D+huFCWcmgNDy1lX/hFvWKNEUBhDdlb3RXl1D4YGzy0kpXrcDAYHehPCGEZHSiolEYhHfV77H+e5uZ7MhjCJ91DbZGXH4xNoTGWU2Izua/JVYck11Lk60UnGKwSyPVTlH9VzbRxbTj/XjKu4hZCsmaddda0rBnec9WfhNNQnFWRErbD+y9UiRIuwbdeycrzUwlIyJDwIOvr7cMomARjieGcaIzhOtlINhrUkXdMsr2QOQp+c0WiOqoXOS8GjdAzScrOX9L6QCAMgU6iEWq0GRaoRm8YUja5YcuuCPuMfRiCqpfdCvou2kV7HhjPk+dK6TyxxcqRCTFor63werRaz9vT3OkFXcN7nGOMMzoqHePoKizNCF+wn/a8VdY3D9PX8bxVbmkLHbUOmtesKzhHHjtet2AEjHieaEpQhkdTu1CJJCOch6JOEYJnQgGiUCkDC4paW/kBoNS1hXwhShpFRGUgoTLOi2KspGGO2e8tKOVkXnvnDPeB9zeHKrmflpkZ119tjaKH+j2G/edtFl100RQbLhxLFTXtXR/J+yynQvw2GACOISylPZnc3xAORc/gjc73RGUcVZXMawHXLIadUp/bIcgOuWZDhw5N31Wro9zan+o/jAZVpijqQ4YMSduAYu7eit93TDkqDAN9WFvtjXKd581gbBg9E9rj3CjqQmzkvegDVQcy/4b/vTsMDzkNlqmOR9fST5rPQJiOZywvhiGg7TsnYUX0JGWWF1tssZGjI+2hLcr10SebF4Tsd88YIDAy4T0TvqSalr5Yv6sqmGtx7wYCY+8vALETSBYikFqVPhvoEFLqyEpkYXVrhGolK48m5k1Si/vDc9GWtdxRCDCdjanECdyc7DKYoIgZVlQDuK+iDeiMCEseCc9K8lFOziNYKFaWS2riNSRUCFZ5A2pDays6coKIsCSMXTvhRwGkPEgQti2Po3U6O8leBG1f6+wIUDWXdU7ehf6A++oZCeGCTlQnySOk4/XO55rxHYUyIi9Ex3zRRRclz5POuD4ngHAGSg1FyHpeXdvU4X0644wz0jlRBnj/DKlneESVf3SuZIZPZycf09npYJ0vD3R7yYR9CaUuKYkUie7Gs1EPn7JBgdBWeDot865mvKeeJyWHvBa37b2WV5C3IwvsR3gBBZrh5j33jCVCes+FbUj0bPVekx+er75GngKFiyNKfLXyh56bPoNyp72SOfrrZsgtBuqNN96YlCrnrs0wboxYXn311Wkb/bzzIONMNKWNan8cHxKfxZx7Nxzb9RrpJNt8JwPJOIo65dD1LrLIIknhIy9ds/1p094xbd9v3DsKn/1pi/bBeOLBdX8YzX7rGTgeGam0ab5Oyqp+mbHUVvhKV9EvKV05uom0XcGoj/h7zybfN8eXK1AvWet5aKfusTh495FCSw5luWC9Z2S5NqTNePbKv9Z1F9swOLR599xzUtrVhKdt4floh9q2tul35koxiqBtk7Geq77R++H5UsaVGvUMnb/1jknmOb7yotZpH3Qvss7z9276a78SqLUxRo/5A7R/8kB70s4dR9uTAK+f9S4Jf1LOlBGk3+KMYeg4F+1cW3YN3hHGEnnuHkqE7s1+l8HkOhgv3clY1UveqbgED8zNJAQHE6xOlrIGLSlLI+DtGTZsWBquIkjVTfZSGn7rrjrvkmc0AvvVOAcTXkKJRd11L3sCrxGhTFgxBChlDIHsLSE4KVMEr229O7bRnnynOBA4hA9BZx0hZXv7pZzaxl9hCIS/xChCiVDMFT76EgSxpCqzV66++uqNpX0XoRZmgM3hG9qdCd28095tBhpFm8Gfh+A7AtngeVFgyAoT36jPXvfBOI72LWRMJ6Yz4xGj2EH7sU7invPTKVEcKVc5zldFDu1P56Qzd98pKe111u2hvVH6KFiG9bW9vg6vKI8873t34z2kKPHA8whSjryDvI/NeC9tx2nkvffsKfX5HRXfzhtL6WUQet8pO+41T6Znqt1JGm7rvabQUAp9PBvVgfQNnj2FXfsFecKobMtzS/FhfPjYhoLluihhFHztkVJI7jhvbZkDggxzXIaEv75TUuyHkWJUiQy03P3Qhp2ba3Oe/rpm99R6FYbsV615hgLjhQy0P15/+3OvyTojpGSlczESoj/mbKDUZSihPLpysdobfekKroNx7jn1FvoMnmvP13VRqB1fX9GM+0QO15+j59uM55WNNJ72PKpSh+PJsyEHKcHudXtKsLaiDXm20Ea0+Wyo6feE2wgVYrDaJ+W8vk/rGTGujdxrdsJY7/rIPcYeJ4C2qx15Xx3bcThMtENthrHkHfNeZoeB7Z2r9dq58zCXhfvK0eaatV3t0G8Y8kY0ckhgb+F8jKw0Tx7XZaqb1CkG6zwCxx9/fKp/WzWMxpIRdY2rhtH4NoLqhSurh9b41j3sueeeZaUcpNrNg4k3+tE8AsFnVApKv5lH4Nhjjy0rA7+sFLLGkrK87LLLykrh/1x9aXWxt9hii8a3UVN1hmXVaae63ag6oFTXes4550zfUXV0qX1ff/316XvVeaca8ZXyOLK+t7rp6n+rHY5KCSyrzqqslP30veoMPzd/RdWxpTrXlUKV5qLoLPaz1157lUceeWRjSd+mp+cRqJTRVJe+UjzScxoVlfLwufkE6lQGYjl06NDGt85TKTupXfVX6ufv/mpzo0N+J+p4b7bccstUr74nqZTTMTaPQDD40CfEPAJjGMOgho94p3lrMjyy9Rq04BU2alKHZ8Nsd4bhqnufvH68dv7P63n9hw8fnqzPZnj3eJAG+myJQdDbKKEn7jp7DnnQhBvwGtWH23ldhRt0FHJCOEj2gvHues+NKmYMR5MJPFngEeOV4/GVGMdjJtTQ+uz1sz8eZsshzpr3M58/zx7vmP3yxIJ3kEeLl4w3UQm+LHvyOsP4deyHF4rM6csjcr2F58dr7ll0JCTA86iHb9UxumMEyohRV8gjjf2V+vm7v9rc6FDvizNCRozi1UcIgiBoTRgCo4EhTAJGslJbwsrwpXg1paZyOIQhdsOXhpMliYqV09HLMzApjKFLv1lllVVSbJ64UfGkhsvqGEJmdFBQKCpBEHQd8alCKShlWbnzHgvrMPxL4c5QWAwjW98RbJ+VHAq4cBGOhPpkNIb6hUIYts9QjmzPIeCvoW9hQtmgcJ72K/8A1jNS6kP69mG983V9wpnIHLHpCyywQDqHHGZE3vg9h0auiZ+REGd4XuhA0H1QeoW0MAiC7oMBLRSrVchWEARfJAyB0SB7xHj7W6HTlRxpxkPxvLlD5RHinfCBmEoJMOLOttpqq5SNbnRARyte06x/ksCaKydQFHTmPH9ZAQiCoGswzCnS9Rh4ij4lvNngp4BT3MXmQxwpj7sY7+xdBw978zvKay9eXzyuUYAMR4Hj1D3Mvju+cwNjoPlcGAIUfIq83zavZwiQIZIAxYiLoTbyYbkkOnHG4sslKUv4NErAUdHKCeFYjtO8POg6zc8t6BpyCySaBkHQMcIQ6CA6dgq4RJG2EkQkm1AGlIjTqUrslczUjPJWhpYpGzp4Getbb731yKQ/HTbvf12xgBAFx5ccw2AIgqDrSBzj9c7vHyi8Kke0lahpvRLBEoyV7hNmI7E2J0RKJjPKV4chL0lYEp1ykxJDyQAhh20dx3LrKfR1QwG++5A11re1j4zrVG0ke0ol16k+Qf7kkCKjH3WDCBwZRiIl0jU7J4IgCIL+TRgCHYTnTcZ8LofWCl43Ha04/5wfUPf8tQUjw/B9xvcgCHoHyriRtqwMgyGuAkpzCBDvre14HMXfy/lhCFCqyYfTTjstlY1Usq+5SouyeWaVNgJoNFDVH/sTzuNvXZHPTgDnoPoYZb0uF7JRwCkhfEmcdPM+8vZ17yhjoJ4XUJc7aCV71PwWa+1eNBsjQRAEQf8mDIEOwksv6c+QujJao8Jsejz4o/LSBUEwZvGOCtcRopNRMo7nXKJufRp7nn5x9Mqimh+BwcAwUFNbuJA4fl51Mfi5DGkz8gF42XNOgLJ54pqNOGaUxRPiYEp+5f4YKkYgsuJuJNGIRU5wtg8hh/XcIftQBm/FFVdsLOkcrt+xGBDNo5RBEARB/ya01A6is+UZE/5jaH9UqHcrrra7J1KiADiPPEV4EARdQ7idyW+E6mR41yUPq9JlksCMOULU60eraiUUc0aBetmZZgWaM8F6VX1gvgCGgao94Hk3+Q9DglHBoSDB2MQ7uaqPcCFhh2b8hEIDZIORSzBqnDdjhhGRcV3Zq+9vs6OileOC8cPIIHdyZaMgCIJgYBCGwGiQS4Q2JwHyJlIQcofvoxpQnkWvPeqdcib/X1ceIBRBgh8vXz2MIQiCzmOkj2efIl2HIs7TbgZPqOglj2B0JuhiSJADZsX27sojkFegYg+lHOSKibAUF6Do205lMNPtZyj8DBUFBYT37Lnnnql6WTYmyIRlllkmTXYmcVl+gmpIpufPOJ4JzbL8kiCtGlA+D0h+ZkTkkqPg0CCLTJ4UhkAQBMHAIgyB0YBSwBOvI86IqdXhChMQDiAGWOKvhEHVOHIN8pw8SMk3T4DOWtKvjpkH0HTuPIK8eGKHoayoSh0Z1UooChQRoQtBEHQd83PI71EauI537De/+U2ayVHc/7e+9a00h8DoGOEUZwo7OWAKfHJAhR6lOuscfPDB6RhmQzUacPfdd3+u/KFyiOSD3AKzSpqHwIymdeQq7LHHHmm5kCb7MUKB22+/Pc0HIDmYMUL+qHBm9MLIBoNAXoORCUUO/DbDAcHQENIUBEEQDCzGKpvdzh1EMqwpm3/60582lgwOlADViSoFmGt6q6ShHKjOV/UR8wUIDap7+d3memJe9rLVkxGzZy4vs952ebiekqD835FHHjlyauzBgKooQiNiQqP+BUNXLL1J8/KcGn0VFXFU8uGVl5hbh7HvUw+rGR28xz724fd1D3wz3v383rdiVOuR5Ux9m3oScL4W+/LX/pD/kjfW+b38gMMPPzyFKCmE0NcxsrL77rsXSy21VGNJEPQcRskY3ozsIOhptDOOKc6g7iRGBEaTI444IoUR8JJlhAuYO2D//fdP3nrVQpoVBt/V784f33W49WW+15eZryAbAZL1jBbojAeTERAEvQFvPyFrBMBoXh3vIKW4M0YA8rtuH+0ZAbC+PSV/VOthffM2WbbUryX/zcvt2yevQ55EbNVVV01/gyAIgoFFGAKjyVRTTVVsttlmaVhdPG1vwMMnrGCaaaZJHq8gCLqfgw46qHj55Zc/F443mJEgLF9APkN9joUgCIJg4BCGQCcwNCPWVtwtxaEnMYwv5Ei9cDkDOecgCILuR45PzEo6wvlgdGCdddb5QqhUEARBMHAIQ6CTMAbWXnvtkXG1PQVDQKiRvIO2JjILgqB7ECIToXcjwovyPAdBEATBwCUMgS5gNlGT/vQkOmRJ2UEQBEEQBEHQnXTJEOA9C4IgaEVnk2uDIAiCIOgdOl0+VIk2s+yqdZ3LXQZBd0OZVK711FNPTTXSmyd9CvomRrLUzDcxltKhKvLU598Igu5G/pQJ1ZZYYolUBaperjkIuhtzbpgTRIlko/b1Er1B0N2MO+64qZTz+uuvXxx33HGNpd1Dpw0Bs2yedNJJKaEsCHoazTQ8zP2PeG5BbxLtLehNor0FvQmnu9ni991338aS7qHThkAQBEEQBEEQBP2XCPIPgiAIgiAIgkFIGAJBEARBEARBMAgJQyAIgiAIgiAIBiFhCARBEARBEATBoKMo/h+YCGZk6CMuSgAAAABJRU5ErkJggg==\"\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe initial hours following a radiation disaster event or a dirty bomb incident are crucial, as they significantly impact the overall disaster response strategy and its effectiveness across various stages. Consequently, first responders are tasked with multitasking responsibilities, such as assessing the radiation release status, understanding the pollution distribution, initiating life-saving actions, recommending protective measures for the public, and reporting the on-site conditions, among other tasks. These duties begin within minutes of first responders arriving at the scene.\u003c/p\u003e \u003cp\u003eThe efficiency and coordination of these initial response activities determine the arrival of other supporting organizations and resources at the scene, thereby influencing the effectiveness of subsequent disaster relief efforts. In the early stages of the research, during the model selection process, it was imperative to choose a model that aligns with the requirements of emergency response. This model needed to be capable of rapid computation and utilize regional real-time numerical weather forecast data to simulate the affected areas. This information would serve as a reference for emergency response. Based on these criteria, the CALPUFF model was selected for development and implementation.\u003c/p\u003e \u003cp\u003eThis research aims to understand how a dirty bomb, when detonated in open urban spaces such as city streets, disperses atmospheric pollutants around nearby buildings. To achieve this, building models were created based on Taiwan's major landmarks and their surroundings. Initially, the focus was on the main building of Taipei 101, and both single-building and two-building models were designed. Additionally, the study evaluated the results of the CALPUFF model by increasing its horizontal resolution.\u003c/p\u003e \u003cp\u003eIt's important to note that increasing the model resolution reduces computation speed but provides more detailed information about pollutant concentration distribution. To meet the requirements of emergency response and provide necessary information for personnel on-site, it's essential to assess the impact of different horizontal resolutions on computation speed. In the future, there is a need to establish a three-dimensional approximate building model database for urban areas and integrate it into the CALPUFF model for simulating multiple-building scenarios.\u003c/p\u003e \u003cp\u003eFurthermore, the CALPUFF model lacks parameters for pollutant horizontal initial velocity in its pollutant setup. Therefore, it's necessary to refer to international literature to establish parameters for radiation bomb explosion. This information will be used to create a radiation bomb explosion parameter database, allowing CALPUFF to systematically simulate the impact range of explosions under different conditions. This will enable a more accurate assessment of the effects of radiation bombs in urban environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eNo funds, grants, or other support was received.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jian-Pu Chen, Teng-Ping Zeng and Yu-Xia Hsu. The first draft of the manuscript was written by Jian-Pu Chen and Jen-Hsin Teng reviewed and editied on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e \u003cp\u003eThe data sources are properly cited in the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eByun, D., Young, J., Gipson, G., Godowitch, J., Binkowski, F., 1997. Description of the Models-3 Community Multiscale Air Quality(CMAQ) Modeling System.\u003c/li\u003e\n \u003cli\u003eChino, M., Ishikawa, H., Yamazawa, H., 1993. SPEEDI and WSPEEDI: Japanese Emergency Response Systems to Predict Radiological Impacts in Local and Workplace Areas due to a Nuclear Accident.\u003c/li\u003e\n \u003cli\u003eCimorelli, A.J., Perry, S.G., Venkatram, A., Weil, J.C., Paine, R.J., Wilson, R.B., Lee, R.F., Peters, W.D., Brode, R.W., 2005. AERMOD: A dispersion model for industrial source applications. Part I: General model formulation and boundary layer characterization. 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International Journal of Emergency Management (IJEM) Special Issue, vol. 4, no. 3, April 27, 2007, pp. 004.\u003c/li\u003e\n \u003cli\u003eP\u0026auml;sler-Sauer, J., 2000. Description of the atmospheric dispersion model ATSTEP. RODOS (WG2)-TN (99)-11.\u003c/li\u003e\n \u003cli\u003eRiddle, A., Carruthers, D., Sharpe, A., McHugh, C., Stocker, J., 2004. Comparisons between FLUENT and ADMS for atmospheric dispersion modelling. Atmospheric environment 38, 1029-1038.\u003c/li\u003e\n \u003cli\u003eScire, J.S., Strimaitis, D.G., Yamartino, R.J., 2000. A user\u0026rsquo;s guide for the CALPUFF dispersion model. Earth Tech, Inc. Concord, MA 10.\u003c/li\u003e\n \u003cli\u003eStohl, A., Forster, C., Frank, A., Seibert, P., Wotawa, G., 2005. The Lagrangian particle dispersion model FLEXPART version 6.2. Atmospheric Chemistry and Physics 5, 2461-2474.\u003c/li\u003e\n \u003cli\u003eSubramanian, V., Kumar, A., Pujala, U., Sujatha, P.N., Srinivas, C.V., Bagavathsingh, A., Goplakrishnan, V., Ananthanarayanan, R., Kumar, A.A., Kumar, S.K., Chandramouli, S., Baskaran, R., Nashine, B.K., 18 Venkatraman, B., 2019. Studies on sodium aerosols dispersion in open environment for fast reactor safety. Annals of Nuclear Energy 125, 63-73.\u003c/li\u003e\n \u003cli\u003eThykier-Nielsen, S., Deme, S., Mikkelsen, T., 1999. Description of the atmospheric dispersion module RIMPUFF. Riso National Laboratory, PO Box 49.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"radioactive events, dirty bomb events, CALPUFF model, emergency response","lastPublishedDoi":"10.21203/rs.3.rs-4518587/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4518587/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to the possibility of radioactive events or dirty bomb events occurring in urban areas near buildings with a relatively small impact radius (within 1-2 kilometers), an effective and rapid assessment of their extent and impact is essential for emergency response strategies. In this research, the CALPUFF model is introduced as the basis for conducting a series of tests and improvements to create a localized radioactive events impact assessment system. The CALPUFF model is recommended by the US EPA and will be combined with meteorological data from the Central Weather Bureau's mesoscale numerical weather forecast model and terrain data with a 30-meter horizontal resolution from Taiwan's Ministry of the Interior. This localized system aims to simulate and predict the outdoor radiation intensity or pollution range for future scenarios, reducing the health risks and pressure on emergency personnel exposed to radiation, and facilitating necessary protective action decisions.\u003c/p\u003e","manuscriptTitle":"Application of the CALPUFF model in emergency response assessment for radiological events in metropolitan area.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-05 16:07:34","doi":"10.21203/rs.3.rs-4518587/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70c54159-d276-418e-bd63-2186b55465e1","owner":[],"postedDate":"June 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32722704,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2024-06-05T16:07:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-05 16:07:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4518587","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4518587","identity":"rs-4518587","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}