Description I need a poster followed by the template and rubric from my term paper please make it attractive and color full no ai please and all the informations from my term paper UNFORMATTED ATTACHMENT PREVIEW Department of Biological and Environmental Sciences Student name: Supervisor name: XXXXXXXXXX Abstract Results and Discussion 24-28 Background Objectives Methodology Conclusion Acknowledgments References 3-7 Qatar University College of Arts and Sciences Department of Biological and Environmental Sciences Senior Project Course 495 Thesis Title Modeling Carbon Monoxide Dispersion Along Roadways: Integration Of GIS With CALINE 4 Approach By AlMaha AlMohannadi Supervisor/s name/s Dr. Perumal Balakrishnan May 2025 1 ARABIC ABSTRACT ? ?????? ?????????CLINE-4 ?( ?? ????? ??? ???????? ????? ??????CO) ????? ??? ??????? ?? ??????? ??? ????? ???????? ? ?????? ???? ??????? ??? ?? ???? ??? ?? ???????? ??? ??? ????? ??????? ?? ??????? ??? ????.???? ????? ????? ????? ???????? ? ???? ??????? ??????? ??? ?????? ????? ??????? ??????????????? ???? ????? ???????? ??? ????? ??????? ?? ???? ???????? ? ??? ????? ?? ?? ??? ???? ?????? ?? ????? ????? ??? ??????? ????.? ??? ???? ?? ?????? ???? ??????????????? ??????? ? ??? ?????????? ??? ???? ????????.? ??? ?? ???? ?????? ?????? ???? ???? ? ????? ? ?? ????? ???? ?????? ?????????????? ???????? ? ????? ???????8 ?( ???????WHO) ???? ????? ??????? ?????? ????? ? ????? ?????? ???????? ??????? ????? ????? ????????? ? ???? ??? ??????? ??? ??? ?????? ???????.? ??? ?????? ??? ???? ?????? ?????? ???? ???? ???????? ??? ?? ????????3.5 ? ?????? ???????? ??? ?? ??? ????? ????? ????????? ???? ??????? ?????? ?????? ?????? ?????? ????????.??????? ???? ????? ???????? ? ???? ??????? ????? ??? ??????? ?????? ??? ??????? ????????.? ?????? ????? ?????? ???? ?????? ?? ????? ?????????????? ?????????? .? ????? ??????? ???? ?????? ???? ?????? ?? ????? ????????????????? ??????? ?? ???? ?????? ??????? ENGLISH ABSTRACT This study investigates carbon monoxide (CO) concentrations at Qatar University using outputs from the CLINE-4 model to analyze variations by time of day and weekday. Results indicate that the highest number of vehicles pass through the campus in the morning; however, peak mean CO concentrations occur at noon, driven by environmental factors such as higher temperatures and reduced wind speeds, which limit pollutant dispersion. Although traffic volume significantly influences CO levels, dispersion conditions are critical in determining ambient air quality. Across weekdays, CO concentrations remained relatively stable but consistently exceeded the World Health Organization’s (WHO) 8-hour mean guideline of 3.5 ppm despite staying within the 1-hour exposure limit. These findings point to a chronic exposure risk for the university population. The study highlights the urgent need for mitigation actions, including optimizing traffic patterns, promoting sustainable transportation, and enhancing real-time air quality monitoring. Implementing such measures is essential for reducing long-term health risks associated with CO exposure. The results underscore the complex interaction between human activities and environmental conditions in urban 2 air pollution and provide important guidance for targeted air quality management interventions on university campuses. 3 DEDICATION A simple, optional note dedicating the work to a single person or small group of persons. The dedication is centered, typically in italic and rarely more than 3-4 lines. 4 DECLARATION Plagiarism Using of ChatGPT tools "In accordance with Article 6 of the Student Code of Conduct at Qatar University, academic violations include a range of actions, one of which pertains to submitting work that is not the individual's own production. This includes using creative artificial intelligence tools such as ChatGPT to produce content, images, videos, or programming code and presenting it as original work. Therefore, students are cautioned that using artificial intelligence tools such as ChatGPT or any similar tools to produce academic content and present it as their own work is considered plagiarism, exposing the student to disciplinary penalties as stipulated in Qatar University's Student Code of Conduct.” I concur with the aforementioned statements and hereby assert that the subsequent portion of this document has been generated by ChatGPT or any other artificial intelligence. YES, Pages or Sections ............................................... No 5 TABLE OF CONTENT CHAPTER 1 - Introduction ................................................................................................................ 8 Background ...................................................................................................................................... 8 Literature Review ............................................................................................................................. 9 Overview of Vehicular Pollution Dispersion.................................................................................... 9 Dispersion Models for Vehicular Pollution .................................................................................. 9 Application of CALINE4 in Dispersion Modelling ................................................................... 10 Integration of GIS in Air Quality Modeling ............................................................................... 10 Limitations of the CALINE4 Model........................................................................................... 11 Future Directions in Vehicular Pollution Modelling................................................................... 11 Research Questions/Hypothesis ..................................................................................................... 12 Research Hypotheses .................................................................................................................. 12 Research Questions ..................................................................................................................... 12 Objectives ....................................................................................................................................... 12 Significance .................................................................................................................................... 13 CHAPTER 2 - Materials and Methods .............................................................................................. 14 Materials ......................................................................................................................................... 14 Study Area ...................................................................................................................................... 14 Data Collection .............................................................................................................................. 15 CLINE 4 Dispersion Model ........................................................................................................... 19 The Gaussian Dispersion Formula.............................................................................................. 19 CHAPTER 3 - Results and Discussion .............................................................................................. 23 CLINE 4 Dispersion Model Outputs .............................................................................................. 23 Wind Rose................................................................................................................................... 23 Sunday ........................................................................................................................................ 24 Monday ....................................................................................................................................... 26 Tuesday ....................................................................................................................................... 29 Wednesday .................................................................................................................................. 31 Thursday ..................................................................................................................................... 34 Discussion ...................................................................................................................................... 37 CHAPTER 4 - Conclusion ................................................................................................................. 41 Recommendations .......................................................................................................................... 41 References .......................................................................................................................................... 43 6 7 CHAPTER 1 - Introduction Background Carbon monoxide (CO) is a significant air pollutant resulting from vehicular emissions, posing health and environmental concerns, especially in urban areas with heavy traffic (EPA, 2020). As motor vehicles are one of the primary sources of CO, understanding its dispersion along roadways is crucial for effective air quality management and public health. Vehicular emissions contribute substantially to the urban air pollution problem, particularly in developing countries where traffic congestion and aging vehicle fleets exacerbate the issue (Karner, Eisinger, & Niemeier, 2010). The CALINE4 model, developed by the California Department of Transportation, is a widely used air quality dispersion model specifically designed to predict CO concentrations near roadways (Benson, 1992). This model considers factors such as wind speed, traffic volume, road geometry, and emission rates, making it a valuable tool for evaluating CO dispersion patterns. When combined with Geographic Information Systems (GIS), CALINE4 can provide a spatially detailed analysis of CO pollution, helping researchers, policymakers, and urban planners to identify high-risk areas and implement effective mitigation strategies (McNally et al., 2015). The integration of GIS with the CALINE4 model enhances the model’s ability to simulate complex environments by providing geospatial data on road networks, land use, topography, and meteorological conditions. This approach allows for a more comprehensive analysis of CO dispersion, making it possible to assess the impact of different scenarios, such as changes in traffic flow or road configurations, on air quality (Abdul-Wahab, 2004). As urban populations continue to grow, understanding and modeling vehicular CO pollution dispersion is essential for developing sustainable transportation systems and ensuring public health and safety. 8 Literature Review The relationship between vehicular emissions and air pollution has been a subject of extensive research over the years. Motor vehicles emit various pollutants, including CO, nitrogen oxides (NOx), particulate matter (PM), and hydrocarbons, which contribute to poor air quality, particularly in urban environments (Zhu, Hinds, Kim, & Sioutas, 2002). CO, a colorless and odorless gas, is primarily produced by the incomplete combustion of fossil fuels, and prolonged exposure can lead to severe health effects, such as headaches, dizziness, and even death at high concentrations (World Health Organization, 2018). Overview of Vehicular Pollution Dispersion Vehicular pollution is a critical concern in urban areas, primarily due to its impact on human health and the environment (Chen et al., 2020). Various studies have demonstrated that pollutants emitted from vehicles, such as carbon monoxide (CO), nitrogen oxides (NOx), particulate matter (PM), and volatile organic compounds (VOCs), contribute significantly to the deterioration of air quality (Seinfeld & Pandis, 2016). The dispersion of these pollutants depends on numerous factors, including meteorological conditions, road configurations, traffic volume, and vehicle types (Kumar et al., 2015). Understanding these factors is crucial for predicting pollutant concentrations in different urban environments and for formulating effective mitigation strategies. Dispersion Models for Vehicular Pollution The modeling of vehicular pollution dispersion is an important tool for understanding the way in which roadways spread their pollutants into the surrounding environment. Such models can simulate the transport of a pollutant-their CO, NOx, and PM-with variables related to wind speed, temperature, and traffic density (Vardoulakis et al., 2003). These are quite enlightening with regard to air quality and important in regulatory assessments and urban planning. Gaussian models, such as CALINE 4, are among the most common dispersion models used for pollutant dispersion prediction in urban areas. In fact, the development of such models was based on 9 the Gaussian plume theory, where pollutant dispersion takes a bell curve due to atmospheric conditions (Zannetti, 1990). Other powerful models dealing with complex environmental factors include topography and surface roughness, AERMOD, which quite often is applied in combination or for comparison with CALINE 4 (U.S. EPA, 2004). Such models do field simulations of actual conditions for the understanding of dispersion and accumulation of pollutants; hence, data useful in traffic management and control of pollution. Application of CALINE4 in Dispersion Modelling CALINE 4 is a roadway Gaussian dispersion model specifically designed to predict the concentration of vehicular emissions near roadways. Developed by the California Department of Transportation, it simulates the dispersion of pollutants such as CO, NOx, and PM, considering factors like vehicle type, speed, traffic volume, and meteorological conditions (Benson, 1989). CALINE 4 finds a wide range of applications in environmental impact assessments and urban planning due to its abilities in simulating various pollution levels from roads, intersections, and parking lots with respect to distances. The model has been used in various studies related to the prediction of air quality impacts in urban settings. For instance, it is often applied in the assessment of pollution concentrations around schools, residential areas, and other sensitive receptors along major highways. -Benson, 1992. It is especially useful for analyses of the impact of traffic congestion on air quality, because it accounts for both moving and idling vehicles. It also works with current software, including CALRoads View, to bring convenience in handling big data volumes and complicated situations in the simulation of the flow of vehicles. Integration of GIS in Air Quality Modeling The use of GIS technology in air quality modeling has gained popularity due to its ability to manage, analyze, and visualize spatial data. When combined with models like CALINE4, GIS can provide detailed insights into pollutant dispersion patterns across different geographic regions (Abdul-Wahab, 2004). For instance, a study by McNally et al. (2015) demonstrated the effectiveness of using GIS 10 with CALINE4 in evaluating CO pollution dispersion in an urban setting, enabling researchers to identify pollution hotspots and assess the impact of traffic management strategies. Additionally, it can enable real-time integration of information with traffic and meteorological data for dynamic updating of models in pollution aspects, thereby increasing prediction accuracy manifold to make informed decisions on urban planning and policy matters. Limitations of the CALINE4 Model Aside from its popularity and efficiency, CALINE 4 possesses a number of limitations. In the model, it is presumed that it works on a steady-state condition, which means it cannot account for the variation in emission with time since the amount of traffic flow changes at different hours of the day. According to Benson (1992), this is considered to be one limitation of the model regarding the accurate modeling of dynamic traffic flow, especially at the peak time of traffic. Though the model is designed to predict pollutant dispersion in open countryside, it may not simulate air quality in complex environments such as areas with high-rise buildings or with varying topography accurately. The simplistic model treatment of urban canyons or areas with sharp changes in terrain may lead to localized under or over-estimation of pollutant concentrations. Another limitation is its inability to handle reactive pollutants like ozone, O3, because it cannot handle the chemical reaction within the air. It becomes less applicable, therefore, for studying secondary pollutants that are generated through chemical transformation in the air. Future Directions in Vehicular Pollution Modelling The future development of vehicular pollution modeling works toward greater accuracy and adaptability to diverse urban scenarios. Hybrid models, which couple Gaussian dispersion methods with either computational fluid dynamics or machine learning methodologies, could become potential options for handling such complex environments and dynamic traffic conditions (Wang et al., 2020). In this way, such methods would be better able to incorporate real-time variation in traffic, changes in meteorological conditions, and urban obstructions like buildings and tunnels. 11 Recently, there has been a growing interest in integrating real-time traffic and meteorological data with emission models for highly accurate predictions. These tools are able to use real-time traffic data from sensors or cameras with cutting-edge dispersion algorithms that provide more accurate and local predictions of air quality. (Sun et al., 2020). Finally, vehicle electrification and the increasing use of zero-emission transportation will redefine the emphasis of future modeling efforts. As vehicular emissions composition shifts, models will have to adjust to new pollutants, such as those associated with tire wear or production of used electricity for electric vehicles (Hooftman et al., 2018). Research Questions/Hypothesis Research Hypotheses 1. There is a significant correlation between traffic volume and CO concentration levels along roadways. 2. The integration of GIS with CALINE4 improves the accuracy of predicting CO dispersion patterns. 3. Meteorological conditions, such as wind speed and direction, have a significant effect on the dispersion of CO pollution along roadways. Research Questions 1. How does traffic volume impact CO concentration levels along roadways? 2. How effective is the integration of GIS with the CALINE4 model in predicting CO dispersion patterns? 3. What role do meteorological conditions play in the dispersion of vehicular CO pollution? Objectives 1. To model and analyze the dispersion of vehicular CO pollution along selected roadways using the CALINE4 model. 12 2. To integrate GIS technology with the CALINE4 model for spatial analysis and visualization of CO dispersion patterns. 3. To evaluate the impact of traffic volume, meteorological conditions, and road configurations on CO pollution levels. Significance Understanding the dispersion of vehicular CO pollution is crucial for developing effective traffic management and air quality control measures. This research will provide valuable insights into how traffic volume, meteorological conditions, and road configurations impact CO levels along roadways. The integration of GIS with the CALINE4 model offers a powerful tool for spatial analysis, enabling policymakers and urban planners to identify pollution hotspots and implement targeted interventions to improve air quality and protect public health. 13 CHAPTER 2 - Materials and Methods The materials used in this study include the CALINE4 air dispersion model software, GIS software (such as ArcGIS), traffic volume data, emission factors, and meteorological data. The CALINE4 model, originally developed by the California Department of Transportation, is essential for simulating CO dispersion patterns along roadways (Benson, 1992). The GIS software will be used to manage and analyze spatial data, allowing for the visualization of CO concentration levels across the study area (Abdul-Wahab, 2004). Traffic data, including vehicle counts, speeds, and fleet composition, will be obtained from traffic monitoring agencies, while emission factors will be sourced from emission inventory databases, such as the EPA's MOVES (Motor Vehicle Emission Simulator) model (EPA, 2020). Meteorological data, such as wind speed, wind direction, temperature, and humidity, will be collected from nearby weather stations to account for their influence on CO dispersion (Hagler et al., 2009). This combination of data sources and modeling tools will provide a comprehensive analysis of CO pollution along roadways, contributing to improved air quality management strategies. Materials • CARBON MONOXIDE Detector CO • CALRoads View – Air Dispersion Modeling software, • ArcGIS, ArcGIS Survey123 and excel • Meteorological Data • Land use/Land cover data • Field survey data • Administrative boundary data Study Area The research will concentrate on Qatar University Road, a popular urban artery that sees severe traffic congestion, particularly during peak hours. 14 Figure 1: Qatar map, precisely Qatar University Data Collection Air quality monitoring was conducted using a land-use/land cover (LULC) map, with sampling points strategically selected to ensure representative coverage across regions inhabited predominantly by vulnerable populations. Sampling locations were distributed along Qatar University Road to enable a comprehensive study of vehicular activity and associated carbon monoxide (CO) emissions in these critical areas. Measurements were carried out continuously over five consecutive days to generate real, representative, and consistent datasets. In addition to air quality measurements, meteorological parameters—including wind direction, wind speed, temperature, and altitude—were collected, as these factors significantly influence pollutant dispersion. Monitoring activities were scheduled during peak traffic congestion periods: between 7:30 AM and 8:30 AM, between 1:30 PM and 2:30 PM, and between 5:00 PM and 6:00 PM. These timeframes corresponded to the morning and afternoon rush hours, when traffic volumes—and consequently 15 vehicular CO emissions—were at their highest. The study specifically targeted these periods to capture the maximum impact of traffic on local air quality. The data collected included vehicular carbon monoxide concentrations and traffic volume at each monitoring point. Subsequent analysis focused on investigating the correlation between traffic density and air quality along Qatar University Road. Peak traffic data from successive days were analyzed to identify patterns and correlations between vehicular emissions and traffic flow. This information proved essential for understanding the environmental impact of vehicular activity and supported the development of informed urban planning and traffic management strategies by academics and policymakers. Data will be collected at the Qatar University Road by observing two roads inside the university campus. Each road will be observed for 15 minutes. This will up to a total of 30 minutes per session. Observation will be done thrice and repeated on six days, excluding Fridays as shown in tables 1 to 5 below. The levels of CO will be measured in the atmosphere with the use of Device A, which is a special device that detects and records the amount of CO. At the end of each session, the average levels of CO to represent the air quality conditions will be accurately calculated. The analysis of these findings will enable a better understanding of how traffic density and environmental factors contribute to air pollution on campus. The data collected will enable the study of patterns and trends and will be useful in further research studies on air quality and measures of sustainability taken within the university. 16 Figure 2: CARBON MONOXIDE Detector CO 17 Date Time Tem Humi Wind perat dity % speed ure (kt) (c) Wind direction Sunday 2 Februar y 2025 Sunday 2 Februar y 2025 Sunday 2 Februar y 2025 Monda y3 Februar y 2025 Monda y3 Februar y 2025 Monda y3 Februar y 2025 Tuesda y4 Februar y 2025 Tuesda y4 Februar y 2025 Tuesda y4 Februar y 2025 Wednes day 5 Februar y 2025 Wednes day 5 Februar y 2025 7:30AM – 8:30PM 20 40 14 NNW Co Cars Bus Truc Tota levels (Hourly (Hourly ks l Estimate) Estimate) (Hour ly Estim ate) 5 1212 84 36 1332 1:30PM – 2:30PM 22 33 11 NNE 5 1108 68 4 1180 5:00PM – 7:00PM 20 17 8 NW 5 236 56 8 300 7:30AM – 8:30PM 16 59 7 NW 5 1100 128 12 1240 1:30PM – 2:30PM 22 31 10 NNE 5 788 116 16 920 5:00PM – 7:00PM 22 43 9 NW 5 264 72 4 340 7:30AM – 8:30PM 15 82 4 SE 5 1148 144 16 138 1:30PM – 2:30PM 18.1 68 9 WNW 5 1136 104 20 1260 5:00PM – 7:00PM 20 37 9 NE 5 284 80 4 368 7:30AM – 8:30PM 17 48 5 SE 5 1172 148 24 1344 1:30PM – 2:30PM 22 51 14 ESE 5 844 64 20 928 18 Wednes day 5 Februar y 2025 Thursd ay 6 Februar y 2025 Thursd ay 6 Februar y 2025 Thursd ay 6 Februar y 2025 5:00PM – 7:00PM 20 49 11 SE 5 312 72 16 400 7:30AM – 8:30PM 20 75 15 SE 5 1340 68 32 1440 1:30PM – 2:30PM 22 53 12 SE 5 804 64 16 884 5:00PM – 7:00PM 21 64 13 E 5 324 84 12 420 The aim is to monitor and analyze environmental and traffic-related factors that may affect air quality and overall conditions in the area. The Q Weather application will be used to obtain weather-related data on real-time measurement of temperature, wind speed, wind direction, and humidity. These variables are important in understanding how environmental conditions may influence air pollution levels and vehicle emissions, especially CO. Therefore, in this respect, the counting of buses and cars that pass through the roads will be carried out personally in the area under study. Truck traffic is disregarded because those types of vehicles do not cross within the vicinity of the campus. The obtained data will assist in estimating road traffic volume, which contributes to deterioration or modification in air quality. CLINE 4 Dispersion Model The Gaussian Dispersion Formula The Gaussian dispersion equation serves as the basis for determining the concentration of CO at different distances from the roadway. The formula: ???? ????(????, ????, ????, ????) = (2????????????????????????) × [???????????? (? (????+2????)2 ????2 ????2 )] × [???????????? (? 2 ) + ???????????? (? )] 2 2???????? 2???????? 2???????? 2 This formula will be useful in predicting the concentration of CO at various points of the roadways. All the components of the equation are important in the model as described below: 19 ????(????, ????, ????, ????) - Pollutant concentration at a given point. GIS will assist in presenting the CO concentrations on a spatial grid to show the dispersion of pollution along the roads. ???? - Emission rate of CO. This depends on the type of vehicle, type of fuel used and traffic density which can be obtained from traffic sensors or traffic surveys and fed into GIS. ???? ? Wind speed in the x-direction. Information from meteorological stations or GIS-based climate models will be used to understand how CO disperses in various wind conditions. ???????? ???????????? ???????? ? Horizontal and vertical dispersion coefficients. These parameters are affected by atmospheric stability and this will be done using GIS layers of meteorological data to enhance the model. ???? ? Effective stack height. This addresses the issue of exhaust heights and the structures of buildings around the roads which GIS can capture as 3D objects of the road network in urban areas. Therefore, although the Gaussian dispersion formula forms the basis of mathematical modeling of air pollution, GIS enriches its practical application with spatial analysis tools. The use of GIS will first, allow input and process of spatial data by importing road network shape files, elevation models, and land use data. This will enhance the overlay of traffic and meteorological data hence improving the dispersion model. GIS will also do spatial analysis by applying geostatistical methods like interpolation to produce CO concentration maps and also to determine areas of high pollution and those that exceed air quality standards (Khayyal et al., 2022). Ultimately, GIS will improve visualization and decision-making since it can provide pollution dispersion maps in real time to urban planners and policymakers. This can also provide vital solutions to the ‘what if’ analysis of traffic flow and emission control measures. Input variables 1- Traffic and Emissions Data • Traffic volume (vehicles per hour): Number of vehicles on each road link. • Vehicle emission factors (g/mile or g/km): Amount of pollutant emitted per distance per vehicle (depends on vehicle type, speed, fuel type) • Fleet composition: percentage of different vehicle types (e.g., light – duty cars, heavy trucks) 20 2- Roadway Geometry • Link location: the geographical coordinates or layout of the layout of the roadway (straight, curved, multiple segments) • Link length: Length of the roadway segment (meters or kilometers) • Roadway width: Total width (meters) • Mixing Zone width: Area over which initial mixing of pollutants occurs (important for near – road dispersion) 3- Meteorological conditions • Wind speed (m/s): speed of wind near ground level. • Wind direction (degrees): Direction from which the wind is blowing (relative to the roadway) • Atmospheric stability class: (A to F): stability of the atmosphere (e.g., A= very unstable, F= very stable), affecting dispersion coefficients. • Ambient temperature (C°): used to adjust dispersion parameters and emission rates. • Mixing height (m): The vertical extent of the atmospheric layer where pollutants mix. 4- Dispersion Parameters • ????????: Horizontal dispersion coefficient (m) determined by stability class and distance downwind. • ????????: vertical dispersion coefficient (m), similarly stability-dependent 5- Receptor Information • Receptor locations: coordinates where pollutant concentrations will be estimated (e.g., sidewalks, building) 21 • Receptor height (m): Height above the ground (important for pedestrian exposure modelling) 6- Background Concentration • Ambient pollutant concentration (ppm or µg/m3): Existing background level of pollutants not caused by the modelled roadway. Example Calculation: Suppose a roadway segment has a traffic volume generating an emission factor E= 0.0015 g/m/s. The wind speed u= 2 m/s, and the dispersion parameters are y= 5m, z=3 m. for a receptor located directly downwind (i.e., y=0) at a height z= 1.5 m above the ground, and assuming H= 0 (ground-level emissions): 0.0015 1 1.5 2 1 1.5 2 ????= × exp(0) × [exp (? ( ) ) + exp (? ( ) )] 5×3×2 2 3 2 3 ????= 0.0015 1 × 1 × [2 × exp (? (0.25))] 30 2 ???? = 5 × 10?5 × 2 × exp(?0.125) exp (?0.125) ? 0.8825 ???? ???? = 5 × 10?5 × 2 × 0.8825 = 8.825 × 10?5 ?????3 This result indicates the predicted CO concentration at the receptor point is approximately ???????? 0.088 ?????3 . 22 CHAPTER 3 - Results and Discussion CLINE 4 Dispersion Model Outputs Wind Rose Figure 3: Wind rose diagram showing wind speed and wind direction The Wind Rose Histogram (Sunday - Thursday) visually captures how wind speed and direction are distributed throughout the week. It breaks wind directions into 16 categories, with wind speeds grouped into different ranges, shown in varying shades of blue. The labels (N, NE, E, SE, S, SW, W, NW) provide a clear directional reference, making it easy to interpret wind patterns. This visualization highlights dominant wind directions and their intensity, offering valuable insights for applications like air quality studies, aviation safety, and urban planning. The Wind Rose Diagram shows that the wind primarily blows from the Southeast (SE) and Northwest (NW), making these the dominant wind directions in the area. The strongest winds, reaching around 7.7 m/s, come from the Southeast (SE), indicating a significant prevailing wind from this direction. Moderate wind speeds, around 5-6 m/s, are common in the Northeast (NNE) and East-Southeast (ESE) directions, reflecting steady 23 but less intense airflow. Lighter winds, around 2-4 m/s, are recorded in the South-East (SE) and NorthNorthwest (NNW) regions, suggesting weaker wind activity. Interestingly, there are no calm wind conditions, meaning air movement is constant in some direction. This has important implications for air quality management, urban development, and wind energy potential. Sunday The CLINE-4 dispersion model outputs were analyzed to assess carbon monoxide (CO) concentration
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