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Description i need to combine all my work in the template as ( formal research) . it should be Introduction Literature Review. Methodology Results Discussion Conclusion Recommendations References Do for me a 1 page Conclusion + recommendations UNFORMATTED ATTACHMENT PREVIEW CLINE 4 Dispersion Model Outputs Wind Rose Figure 1. wind rose histogram 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 but less intense airflow. Lighter winds, around 2-4 m/s, are recorded in the South-East (SE) and North-Northwest (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) concentrations across Qatar University during three key time intervals on Sunday, 2 February 2025: morning (7:30-8:30 AM), afternoon (1:30-2:30 PM), and evening (5:00-6:00 PM) (Figures2,3, and 4). These results were evaluated alongside corresponding meteorological parameters and estimated vehicular counts to better understand the contributing factors influencing air pollution. Figure 2. Sunday 2 February 2025: Morning Carbon Monoxide(CO) Concentration (7:30-8:30 AM) Figure 3. Sunday 2 February 2025: Afternoon Carbon Monoxide(CO) Concentration (1:30-2:30 PM) Figure 4. Sunday 2 February 2025: Evening Carbon Monoxide (CO) Concentration (5:00-6:00 PM) Monday The CLINE-4 dispersion model outputs were analyzed to assess carbon monoxide (CO) concentrations across Qatar University during three key time intervals on Monday, 3 February 2025: morning (7:30-8:30 AM), afternoon (1:30-2:30 PM), and evening (5:00-6:00 PM) (Figures 5,6, and 7). These results were evaluated alongside corresponding meteorological parameters and estimated vehicular counts to better understand the contributing factors influencing air pollution. Figure 5. Monday 3 February 2025: Morning Carbon Monoxide(CO) Concentration (7:30-8:30 AM) Figure 6. Monday 3 February 2025: Afternoon Carbon Monoxide(CO) Concentration (1:30-2:30 PM) Figure 7. Monday 3 February 2025: Evening Carbon Monoxide(CO) Concentration (5:00-6:00 PM) Tuesday The CLINE-4 dispersion model outputs were analyzed to assess carbon monoxide (CO) concentrations across Qatar University during three key time intervals on Tuesday, 4 February 2025: morning (7:30-8:30 AM), afternoon (1:30-2:30 PM), and evening (5:00-6:00 PM) (Figures 8,9, and 10). These results were evaluated alongside corresponding meteorological parameters and estimated vehicular counts to better understand the contributing factors influencing air pollution. Figure 8.Tuesday 4 February 2025: Morning Carbon Monoxide(CO) Concentration (7:30-8:30 AM) Figure 9. Tuesday 4 February 2025: Afternoon Carbon Monoxide(CO) Concentration (1:30-2:30 PM) Figure 10. Tuesday 4 February 2025: Evening Carbon Monoxide (CO) Concentration (5:00-6:00 PM) Wednesday The CLINE-4 dispersion model outputs were analyzed to assess carbon monoxide (CO) concentrations across Qatar University during three key time intervals on Wednesday, 5 February 2025: morning (7:30-8:30 AM), afternoon (1:30-2:30 PM), and evening (5:00-6:00 PM) (Figures 11,12, and 13). These results were evaluated alongside corresponding meteorological parameters and estimated vehicular counts to better understand the contributing factors influencing air pollution. Figure 11. Wednesday 5 February 2025: Morning Carbon Monoxide(CO) Concentration (7:308:30 AM) Figure 12. Wednesday 5 February 2025: Afternoon Carbon Monoxide (CO) Concentration (1:302:30 PM) Figure 13. Wednesday 5 February 2025: Evening Carbon Monoxide(CO) Concentration (5:00-6:00 PM) Thrusday The CLINE-4 dispersion model outputs were analyzed to assess carbon monoxide (CO) concentrations across Qatar University during three key time intervals on Thrusday, 6 February 2025: morning (7:30-8:30 AM), afternoon (1:30-2:30 PM), and evening (5:00-6:00 PM) (Figures 11,12, and 13). These results were evaluated alongside corresponding meteorological parameters and estimated vehicular counts to better understand the contributing factors influencing air pollution. Figure 14. Thursday 6 February 2025: Morning Carbon Monoxide (CO) Concentration (7:308:30 AM) Figure 15. Thursday 6 February 2025: Afternoon Carbon Monoxide (CO) Concentration (1:302:30 PM) Figure 16. Thursday 6 February 2025: Evening Carbon Monoxide (CO) Concentration (5:006:00 PM) Discussion From the analysis of the CLINE-4 model outputs, one can see that there are clear variations in mean carbon monoxide (CO) concentrations at the Qatar University campus based on time of day as well as day of the week. Studying the diurnal variations, the records reflect that though the largest number of motor vehicles pass through the campus during the morning hours (1,332 vehicles), the highest mean CO concentration is registered at noon at 5.99 ppm. This indicates that traffic volume alone is not responsible for CO concentrations, but factors like higher temperatures at noon (22°C versus 20°C at morning) and lower wind speed (5.66 m/s at noon versus 7.20 m/s at morning) increase the effectiveness of pollution dispersion, thus raising the mean CO concentration. Conversely, the morning hours, though suffering the highest vehicular flow, has the least mean CO concentration (5.21 ppm), due to the stronger ventilation resulting from higher wind velocities. The evening hours depict the declining mean CO concentration (5.63 ppm), corresponding to the huge decrease in the number of vehicles (to 300), suggesting that the drop in emission outweighs the lower wind velocity (4.12 m/s) effect on pollutant dispersion to result in lower mean CO concentrations. Examining the fluctuations between the workdays (Sunday to Thursday), the CLINE-4 model results indicate fairly consistent mean CO levels between the days, with the range spanning only between 5.48 ppm on Sunday to 5.76 ppm on Tuesday. This indicates the fairly constant amount of activity and resultant emissions throughout the average workweek. These slightly lower mean values measured on Sunday and Thursday likely reflect lower overall activity on these days than the more active mid-week period. The implications of such findings are substantial and call for the enforcement of targeted mitigation measures to ensure the well-being and health of the community at Qatar University. Although the modeled highest 1-hour CO level at Sunday (6.30 ppm) was less than the WHO 1hour target of 8.7 ppm (WHO, 2021), the mean CO levels throughout the week consistently exceed the WHO 8-hour mean target value of 3.5 ppm (WHO, 2021). This constant exceedance is likely to cause potential adverse effects to the human health due to long-term CO exposures, as indicated by earlier studies (Brook et al., 2010; Gurjar et al., 2010). Hence, preventive measures are urgently needed, such as optimizing traffic flow, with careful focus around peak hours of the day, specifically high-activity areas, encouraging, inducing, and promoting the use of alternative modes of transport to minimize vehicular exhaust, and integrating real-time atmospheric information into the overall air quality monitoring framework to facilitate adaptive management decisions based on informed inputs. Barcharts Figure 1: Comparison of CO Levels: Morning, Noon, and Evening Figure 2 Comparison of Average CO Levels Across Weekdays (Sunday to Thursday) Discussion The analysis of the outputs of the CLINE-4 indicates different patterns of average concentrations of carbon monoxide (CO) at varying times of the day and weekdays at Qatar University. Diurnal pattern (Figure 1) shows that even with maximum motor vehicle flow during the day (1,332 vehicles, according to earlier text), maximum average CO is observed at noon (5.99 ppm). That this is related to reasons other than mere vehicle flow, like higher temperature at noon (22C vs. 20C) and lower wind speed (5.66 m/s vs. 7.20 m/s), is evidenced by the fact that there is less effective pollutant dispersing at this time. The minimum average of the morning (5.21 ppm) in spite of maximum vehicles is an indication of better ventilation due to higher winds. Decrease in the evening average CO (5.63 ppm) goes along with an appreciable fall in vehicles (300) as emission reduction is greater than poorer dispersion (4.12 m/s wind speed). The daily variation (Figure 2) indicates fairly constant average CO levels, varying from 5.48 ppm (Sunday) to 5.76 ppm (Tuesday). This is indicative of uniform levels of activity and emissions across the workweek. The slightly weaker averages on Sunday and Thursday may reflect less overall activity than on mid-week days. A comparison with World Health Organization (WHO) guidelines indicates that although the modeled maximum 1-hour concentration on Sunday (6.30 ppm) was less than the 1-hour guideline value of 8.7 ppm (WHO, 2021), average levels at various times throughout the weekdays consistently exceed the 8-hour mean guideline value of 3.5 ppm (WHO, 2021). This correlates with the previous results and indicates a possible risk for chronic health effects of long-term CO exposure (Brook et al., 2010; Gurjar et al., 2010). These results have important implications for the implementation of focused mitigation actions. The diurnal and weekday patterns of CO concentrations unveiled by the modeling require proactive action. The high average levels consistently over WHO's 8-hour guideline (WHO, 2021) indicate an emergent chronic health risk to the campus community (Brook et al., 2010; Gurjar et al., 2010). Peak exposure mitigation and reductions of sustained levels across weekdays are key priorities for target-oriented strategies. Optimizing flow of vehicles, especially around high-activity areas, and offering incentives to adopt sustainable travel to minimize vehicle emissions are essential. References Brook, R. D., Rajagopalan, S., Pope, C. A., Brook, J. R., Bhatnagar, A., Diez-Roux, A. V., & Kaufman, J. D. (2010). Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation, 121(21), 2331-2378. https://doi.org/10.1161/CIR.0b013e3181dbece1 Gurjar, B. R., Jain, A., Sharma, A., Agarwal, A., Gupta, P., Nagpure, A. S., & Lelieveld, J. (2010). Human health risks in megacities due to air pollution. Atmospheric Environment, 44(36), 4606-4613. https://doi.org/10.1016/j.atmosenv.2010.08.011 World Health Organization (WHO). (2021). WHO global air quality guidelines: Particulate matter (PM?.? and PM??), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. Geneva: WHO. https://www.who.int/publications/i/item/9789240034228 Department of Biological and Environmental Sciences Course Number BIOL 496 Project Title Modeling Carbon Monoxide Dispersion Along Roadways: Integration Of GIS With CALINE 4 Approach Main supervisor Name Position Title Department, Research Center, laboratory or equivalent E-mail Address Phone/mobile Dr.Perumal Balakrishnan Lecturer in Environmental Sciences Department of Biological and Environmental Sciences bala@qu.edu.qa 44034736 Students Information Name AlMaha AlMohannadi Current Standing Undergraduate (Graduate/ Undergraduate) College, Dept. Art and science – environmental science Expected Graduate Spring 2025 Semester Email address Aa1901830@qu.edu.qa Phone/mobile 50889998 Co- supervisor Information (if applicable) Name Position Title Department, Research Center, laboratory or equivalent E-mail Address Phone/mobile First reader Information Name Position Title Department, Research Center, laboratory or equivalent E-mail Address Phone/mobile Compliance and Ethical Considerations Senior Project Proposal Template Department of Biological and Environmental Sciences Will the project involve using any of the following: Human subjects? Animal subjects? Hazardous materials? Laboratory Name Lab Location and number Lab Director Responsible technician Lab Phone number No No No No Practical work Yes, approval pending - Yes, approval granted - GIS Lab C01 – A220 - Research Project Proposal Plan and expected outcomes (6-10 pages), please describe the project background/literature survey, and then list project objectives/significance, methods and time lines, and expected outcomes: 1. Background and Literature Review (1-2 pages) 1.1.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 Senior Project Proposal Template Department of Biological and Environmental Sciences 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. 1.2.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). 1.2.1. 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. 1.2.2. 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 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. 1.2.3. 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 Senior Project Proposal Template Department of Biological and Environmental Sciences 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. 1.2.4. 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 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. 1.2.5. 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 1.2.6. 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. 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). Senior Project Proposal Template Department of Biological and Environmental Sciences 2. Objectives/significance (1 page) 2.1.Objectives 1. To model and analyze the dispersion of vehicular CO pollution along selected roadways using the CALINE4 model. 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. 4. 2.2.Research hypothesis 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. 2.3.Research Question 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? 2.4.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. 3. Research Materials and Methods (3-5 pages) 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. Senior Project Proposal Template Department of Biological and Environmental Sciences 3.1. Materials 1. 2. 3. 4. 5. 6. 7. 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 3.2.Study area The research will concentrate on Qatar University Road, a popular urban artery that sees severe traffic congestion, particularly during peak hours. Figure 1. shows Qatar map precisely Qatar University Senior Project Proposal Template Department of Biological and Environmental Sciences 3.3.Data collection The monitoring of air quality will be performed using a land-use/land cover map where sampling points are to be selected strategically. These sampling points are to be selected such that air quality across regions with the majority of the vulnerable population can be represented. Data will be collected across several locations along Qatar University Road for comprehensive study of vehicular activity in these critical areas and of carbon monoxide emissions and this will be measured over five continuous days to get real and representative data; this also means that it is a continuous and consistent data set. Monitoring will be carried out during peak hours of traffic congestion-from 7:30 AM to 9:00 AM in the morning, between 1:30 PM and 3:00 PM in the afternoon, and from 5:00 PM to 7:00 PM evening. These time slots correspond to the morning and afternoon rush hours traffic when traffic volume is at its peak, and thus carbon monoxide emissions from vehicles are also likely to be high. The study will focus on these periods so that the maximum impact of traffic on air quality can be captured. Data to be collected would include vehicular carbon monoxide levels, the volume of traffic passing each monitoring point. The nature of the analysis may be done in such a way that the correlation between traffic density and air quality on Qatar University Road is elaborated. It analyzes the data of successive days peak traffic, attempting to find regularity and correlation within the aspect of vehicle emissions with respect to road traffic flow. Such information is crucial in understanding the environmental impact of vehicular traffic; thus, it will enable academics and policymakers to develop urban planning and traffic management policies. However, apart from the field data, secondary data will be sought from the Ministry of Environment that has two air quality monitoring stations currently operating in Doha. The latter information, while useful for validation purposes, is insufficient to result in comprehensive spatial interpolation, so additional data collection shall be undertaken by the research team with the help of air pollution meters. It will also include meteorological data, which will provide information on wind direction, speed of wind, temperature, and altitude-all these being factors that affect the dispersion of pollution. Complementing this, relevant spatial and non-spatial data, including administrative boundaries, population statistics, land use/land cover maps, and road networks, will be obtained from Qatar GISNet to account for holistic air quality over this area. 3.4.Data Entry and Geocoding Sampling locations will be determined randomly, taking into consideration the land use types mentioned in section 3.3. Students will receive training from faculty members on the methods for geographically sampling locations. These locations will then be pinpointed using a handheld GPS device. Students will visit these sampling locations to collect air quality data using ArcGIS Survey123, which will enable direct data logging into the ArcGIS format. Data on the pollutants mentioned in section 3.3 will be gathered using an air pollution meter. Addioinallly, the data will be aggregated and transmitted in real time to a central database, where it shall be analyzed and visualized immediately to present the level of pollution in the study area, hence enabling the identification of temporal patterns and emerging trends of pollution. Senior Project Proposal Template Department of Biological and Environmental Sciences 3.5.Analysis A Land Use/Land Cover (LULC) cluster map for the study area will be generated using data from the Qatar GISNet, and students will classify this map following the criteria outlined in section 7.2. Air pollution levels across the study area will be modeled using the CALRoads View software, developed by Lakes Environmental Software and approved by the US Environmental Protection Agency (EPA). This software integrates meteorological parameters with pollutant levels to create a comprehensive air pollution map for each pollutant (Diem & Komrie, 2002; Beelen et al., 2009). High pollution areas for each parameter will then be identified based on national and World Health Organization (WHO) standards, with potential pollution sources also being determined. Additionally, a population density map for the study area will be created and correlated with the air pollution maps. An overlay analysis combining the population density data with the composite air quality map will be performed to identify pollution hotspots in residential and sensitive areas. 4. Research Timeline The timeline chart below displays the schedule of the major project tasks and highlights how these tasks are distributed. Timeline Chart of Activities = Activity Months X = Complete 1 2 Step 1: Review of the Literature X Step 2: Base map creation (begins with student training) X Step 3: Identify sampling locations 3 4 5 6 7 8 9 X Step 4: Field Data collection X Step 5: Submission of Progress Report X Step 6: Creating Thematic Maps and Spatial Modeling (begins with student training) Step 7: Highlighting Hot-spot Areas X X X Step8: Final Report X 5. Expected Outcomes (Training Skills, Research output) (1 paragraph) 5.1. Training skills I took an internship at MECC, I also got practical experience in current analysis and monitoring in the Air Quality Department. The section responsible for Ambient Air Quality maintains the three fixed stations within Qatar: Al Corniche, Aspire Zone, and Qatar University, apart from mobile stations, which can be mobilized at any moment against emergencies. These monitor the critical pollutants of CO2, SO2, NO2, CO, O3, particulate matter (PM2.5, PM10), among others. Each station has a series of analyzers linked to computers that provide current air quality data, which in turn are networked with the Ministry for automatic recording and evaluation. The Air Quality Department takes this data to evaluate the Air Quality Index, which determines the grade of pollution in a given area at any moment. However, Through the process, I gained an intimate understanding of how air quality is Senior Project Proposal Template Department of Biological and Environmental Sciences managed and assessed. Other than that, I attained technical skills in Geographic Informa
