Temporal and Spatial Analysis of PM2.5 Concentrations in Accra Using Integrated Ground-Based and Satellite Sensor Data

Temporal and Spatial Analysis of PM2.5 Concentrations in Accra Using Integrated Ground-Based and Satellite Sensor Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Temporal and Spatial Analysis of PM2.5 Concentrations in Accra Using Integrated Ground-Based and Satellite Sensor Data RICHARD ADDO SOWAH, CONFIDENCE MAWUFEMOR AFEMEKU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4841971/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 Air pollution, particularly fine particulate matter, poses a significant public health challenge in urban areas worldwide. This study presents a comprehensive temporal and spatial analysis of PM 2.5 concentrations in Accra, Ghana, utilizing an integrated approach combining data from low-cost ground-based sensors, reference-grade monitors, and satellite-derived sources. Over a 21-month period from April 2019 to December 2020, PM 2.5 levels were monitored across five strategic locations within the Greater Accra Metropolitan Area (GAMA). The findings reveal distinct seasonal trends, with higher PM 2.5 concentrations during the dry season (November to March) and lower levels during the wet season (April to October). This seasonal variation underscores the influence of meteorological conditions on particulate matter levels. Spatial analysis indicates significant variations in PM 2.5 concentrations, with higher levels recorded in densely populated and industrial zones such as Amasaman and Akweteyman. These areas are particularly affected by emissions from vehicular traffic, industrial activities, and biomass burning. A key outcome of this study is the strong positive correlation between ground-based sensor data and MERRA-2 satellite-derived PM 2.5 concentrations, validating the use of satellite data to complement traditional ground-based monitoring. Despite some discrepancies, where satellite data tended to overestimate PM 2.5 levels in 2020 and underestimate in 2019, the integration of these data sources provides a more robust and comprehensive assessment of air quality. The elevated PM 2.5 levels observed in industrial and densely populated areas have significant public health implications. Prolonged exposure to high PM 2.5 concentrations is linked to increased risks of respiratory and cardiovascular diseases. This study highlights the urgent need for targeted air quality management strategies and public health interventions to mitigate these risks. By demonstrating the feasibility and effectiveness of using low-cost sensors in combination with satellite-derived data, this research offers a scalable and cost-effective solution for air quality monitoring in resource-constrained settings. The insights gained from this study can inform policy-making and contribute to improved public health outcomes in Accra and similar urban environments. PM2.5 air quality monitoring low-cost sensors satellite data spatial analysis temporal trends Accra MERRA-2. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Air pollution is a significant environmental and public health issue, particularly in urban areas of developing countries. Fine particulate matter (PM 2.5 ) is a major pollutant of concern due to its ability to penetrate deep into the lungs and enter the bloodstream, causing various health problems, including respiratory and cardiovascular diseases (Du, Xu, Chu, Guo, & Wang, 2016). The World Health Organization (2016) estimates that millions of deaths annually can be attributed to exposure to PM 2.5 , highlighting the need for effective air quality monitoring and management. In Accra, the capital city of Ghana, rapid urbanization and industrial activities contribute to elevated PM 2.5 levels. Common sources of PM 2.5 include vehicular emissions, industrial processes, and biomass burning (Acquah, Tschakert, & Sagoe-Addy, 2019). The Environmental Protection Agency of Ghana monitors air quality, but the limited number of high-grade reference monitors provides incomplete data, necessitating alternative monitoring methods (EPA Ghana, 2018). Satellite-derived data offer extensive spatial coverage and can complement ground-based measurements. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), provides high-resolution data for estimating PM 2.5 levels (Randles, Sliva, Buchard, & Flynn, 2017). However, satellite data accuracy can be influenced by atmospheric conditions and cloud cover, which necessitates ground-based validation (Belle et al., 2017). Low-cost sensors (LCS) like Clarity nodes have emerged as valuable tools for air quality monitoring due to their affordability and ease of deployment. These sensors can provide real-time data and have been utilized in various urban air quality studies and community-driven projects (Eilenberg et al., 2020). By integrating data from low-cost sensors with satellite-derived measurements, it is possible to achieve a comprehensive understanding of PM 2.5 distribution and trends in urban environments (Ford et al., 2019). This research aims to provide a detailed temporal and spatial analysis of PM 2.5 concentrations in Accra by integrating data from ground-based low-cost sensors, reference-grade monitors, and satellite-derived sources. This study seeks to address the gaps in air quality data collection and support the development of effective air quality management strategies and public health policies. Overall Objective: The overall objective of this study is to conduct a comprehensive temporal and spatial analysis of PM 2.5 concentrations in Accra using integrated ground-based and satellite sensor data. Significance of the Study: By integrating multi-sensor data, this study provides a detailed understanding of PM 2.5 distributions and its health impacts in Accra. The findings will inform effective air quality management and policy-making, ultimately contributing to improved public health outcomes. Results The study integrates data from Clarity node sensors, BAM 1020 reference-grade monitor, and the MERRA-2 satellite model over a period of 21 months from April 2019 to December 2020. The key findings are as follows: 1. Temporal Patterns of PM 2.5 Concentrations: The temporal analysis of PM 2.5 concentrations reveals distinct seasonal trends, with higher levels during the dry season and lower levels during the wet season. Figures 1 & 2 illustrate these patterns for both ground-based sensors and MERRA-2 data. 2. Spatial Distribution of PM2.5: The spatial distribution analysis shows significant variations in PM2.5 levels across different areas of Accra. Higher concentrations are recorded in densely populated and industrial zones such as Amasaman and Akweteyman (Figures 3 and 4). 3. Comparison of Ground-Based and Satellite Data: The comparison between ground-based sensor data and MERRA-2 derived data shows a significant positive correlation, with seasonal trends aligning closely between the two datasets. However, the satellite data tends to overestimate PM 2.5 levels during certain months in 2020 and underestimate in 2019 (Figures 5 and 6). 4. Health Implications: The high PM 2.5 levels observed during the raining season of June 2020, in densely populated and industrial areas have significant public health implications. The study highlights the need for targeted interventions in these areas to mitigate the health risks associated with prolonged exposure to high PM 2.5 concentrations (Figure 7). Discussion The integration of ground-based sensors with satellite-derived data provides a comprehensive approach to monitoring PM 2.5 concentrations, addressing the limitations of each method when used in isolation. The significant positive correlation between ground-based and satellite-derived data confirms the reliability of MERRA-2 in estimating PM 2.5 levels, as supported by previous studies (Randles et al., 2017). However, the observed tendency of satellite data to overestimate PM 2.5 levels during certain months in 2020 and underestimate in 2019 highlights the importance of continuous calibration and validation (Belle et al., 2017). Temporal analysis reveals distinct seasonal trends, with higher PM 2.5 concentrations during the dry season and lower levels during the wet season. These findings are consistent with known patterns where dry conditions lead to increased emissions and dust, exacerbating particulate matter levels (Du et al., 2016). Understanding these seasonal variations is crucial for developing effective air quality management strategies and mitigating the health impacts of pollution. Dansoman, as a matured settlement with many paved roads, generally experiences lower levels of PM2.5 compared to Amasaman, which has a high number of unpaved roads. Paved roads significantly reduce the amount of dust and particulate matter generated by vehicular movement. The smoother surfaces of paved roads minimize the abrasion and dispersion of particles into the air. Additionally, the established nature of Dansoman likely includes more regulated traffic patterns, better-maintained infrastructure, and possibly more stringent environmental regulations that collectively contribute to lower PM 2.5 levels. In contrast, Amasaman, being a newer settlement with numerous unpaved roads, is more prone to higher PM2.5 levels. Unpaved roads are a significant source of dust and particulate matter, especially under dry conditions. When vehicles travel on these unpaved surfaces, they disturb the soil and generate large amounts of dust, which contributes directly to elevated PM2.5 concentrations. The less developed infrastructure in Amasaman also suggests that there may be less control over construction activities and other dust-generating operations, further exacerbating the problem. Temporal analysis reveals seasonal variations in PM2.5 levels influenced by weather conditions. During the dry season, Amasaman likely experiences a pronounced increase in PM2.5 due to the greater generation of dust from unpaved roads and construction activities. The lack of precipitation during this period means that dust is not effectively settled, leading to higher concentrations of airborne particles. In Dansoman, while there may also be some increase in PM2.5 during the dry season, the effect is less pronounced due to the paved infrastructure that mitigates dust generation. Conversely, during the rainy season, PM2.5 levels may decrease in both areas as rainfall helps to settle airborne particles. However, Amasaman might still exhibit relatively higher PM2.5 levels due to the potential for muddy conditions and ongoing construction activities that do not cease with rain, continuing to disturb the soil. The spatial analysis shows significant variations in PM 2.5 levels across different areas of Accra. Higher concentrations are observed in densely populated and industrial zones, such as Tetteh Quarshie and Dansoman. This spatial distribution is critical for identifying pollution hotspots and prioritizing interventions in areas with the highest health risks (Acquah et al., 2019). The findings underscore the need for targeted air quality management strategies to address pollution sources in these areas. The health implications of high PM 2.5 levels, particularly in urban and industrial areas, are significant. Prolonged exposure to elevated PM 2.5 concentrations can lead to serious health issues, including respiratory and cardiovascular diseases (Du et al., 2016). This study highlights the urgent need for effective air quality management and public health policies to mitigate the adverse health impacts of air pollution in Accra. The study demonstrates the feasibility of using low-cost sensors for continuous air quality monitoring. These sensors provide valuable real-time data, which is essential for timely interventions and public health protection. The integration of low-cost sensors with satellite data offers a cost-effective and scalable solution for air quality monitoring, particularly in resource-constrained settings (Ford et al., 2019). However, the study acknowledges several limitations. The accuracy of low-cost sensors can be influenced by environmental factors such as humidity and temperature, which may affect sensor performance. Additionally, satellite data can be affected by cloud cover and atmospheric conditions, leading to potential discrepancies in PM 2.5 estimates (Belle et al., 2017). Future research should focus on improving the calibration and validation processes for both low-cost sensors and satellite-derived data to enhance the accuracy and reliability of air quality assessments. Conclusion This study demonstrates that integrating ground-based sensors with satellite-derived data provides a reliable and comprehensive approach to monitoring PM 2.5 concentrations. The findings highlight the potential for using low-cost sensors in combination with satellite data for continuous air quality monitoring, particularly in developing countries. The spatial and temporal insights gained from this research can inform effective air quality management and policy-making, ultimately contributing to improved public health outcomes. Declarations Author Contribution R.A.S. prepared and edited the whole manuscriptC.M.A. contributed to data analysis and edited the manuscript References Acquah, H., Tschakert, P., & Sagoe-Addy, K. (2019). Urban air pollution and its health impacts in Accra. Journal of Environmental Studies, 45 (3), 210-222. Belle, J. H., Burkart, K., Huang, W., & Zhang, W. (2017). Air pollution impacts on avian species via impacts on terrestrial ecosystems. Journal of Environmental Management, 206 , 170-181. https://doi.org/10.1016/j.jenvman.2017.10.005 Du, Y., Xu, X., Chu, M., Guo, Y., & Wang, J. (2016). Air particulate matter and cardiovascular disease: The epidemiological, biomedical and clinical evidence. Journal of Thoracic Disease, 8 (1), 8-19. https://doi.org/10.3978/j.issn.2072-1439.2016.01.77 Eilenberg, S. R., Pacheco, M. J., van Donkelaar, A., Martin, R. V., & Cohen, A. J. (2020). Assessing PM2.5 air pollution exposure using low-cost sensors and satellite data. Environmental Research Letters, 15 (3), 034029. https://doi.org/10.1088/1748-9326/ab70b2 EPA Ghana. (2018). Annual report on air quality in Accra. Environmental Protection Agency Ghana. Ford, B., Val Martin, M., Zelasky, S. E., Fischer, E. V., Anenberg, S. C., Heald, C. L., & Pierce, J. R. (2019). Future fire impacts on smoke concentrations, visibility, and health in the contiguous United States. GeoHealth, 3 (5), 122-136. https://doi.org/10.1029/2019GH000203 Randles, C. A., Silva, A. M. d., Buchard, V., & Flynn, C. J. (2017). The MERRA-2 aerosol reanalysis, 1980 onward. Earth System Science Data, 9 (2), 515-543. https://doi.org/10.5194/essd-9-515-2017 World Health Organization. (2016). Ambient air pollution: A global assessment of exposure and burden of disease. World Health Organization. https://apps.who.int/iris/handle/10665/250141 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4841971","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338293613,"identity":"04e1b551-297a-43aa-8a2f-6cb40fecc01e","order_by":0,"name":"RICHARD ADDO SOWAH","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYPCCA3IQmg2IJYjUYgxWTZKWxAaitZi3H3/4mKfmTnr//B4Dhg9lhxnkoxvwa5E5k2NszHPsWe6MYzwGjDPOHWYwvHMAvxYJhhw26Ry2w7kNQC3MvG1ALTMSCGjhf/5MOuff4XR5kJa/RGmRSDCTzm07nGAA0sII1CIvQVDLG2Pjv32HDTceSys42HMunceAoBb+9IcPZ3w7LC93+PDGBz/KrOXkCTkMBRwAYh6DAyTogAD5BpK1jIJRMApGwTAHABJRQ6rHBPqtAAAAAElFTkSuQmCC","orcid":"","institution":"ACCRA TECHNICAL UNIVERSITY, ACCRA","correspondingAuthor":true,"prefix":"","firstName":"RICHARD","middleName":"ADDO","lastName":"SOWAH","suffix":""},{"id":338293614,"identity":"0b72939d-bc33-4f57-a66e-8d36158d4cff","order_by":1,"name":"CONFIDENCE MAWUFEMOR AFEMEKU","email":"","orcid":"","institution":"ACCRA TECHNICAL UNIVERSITY, ACCRA","correspondingAuthor":false,"prefix":"","firstName":"CONFIDENCE","middleName":"MAWUFEMOR","lastName":"AFEMEKU","suffix":""}],"badges":[],"createdAt":"2024-08-01 11:54:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4841971/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4841971/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62299282,"identity":"370edac7-1f28-48ba-83b2-83fb6ac68116","added_by":"auto","created_at":"2024-08-12 16:19:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":12700,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variations of PM\u003csub\u003e2.5\u003c/sub\u003e concentrations derived from MERRA-2 reanalysis, and the ground based sensors across all sites\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4841971/v1/f6c63697b9721bf9b5ef4948.png"},{"id":62299275,"identity":"48afdff6-dd47-4b27-bc0b-cc3d89630445","added_by":"auto","created_at":"2024-08-12 16:18:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12100,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variations of PM\u003csub\u003e2.5\u003c/sub\u003e concentrations derived from MERRA-2 reanalysis, and the ground based sensors across all sites\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4841971/v1/24e96a233cd186f1cffc5091.png"},{"id":62299362,"identity":"0f15a276-5b7b-40a7-8762-6105f2bc4fe0","added_by":"auto","created_at":"2024-08-12 16:19:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":112751,"visible":true,"origin":"","legend":"\u003cp\u003eTime-series, Area-Average of PM2.5, time average monthly for the ground-based measurement sites (Site B, Site C, \u0026amp; Site D,)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4841971/v1/725bf62a69461c4a289b12b4.png"},{"id":62299372,"identity":"5e1e13e0-f7c5-4618-82c7-d743086415ad","added_by":"auto","created_at":"2024-08-12 16:19:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":25236,"visible":true,"origin":"","legend":"\u003cp\u003eTime-series, Area-Average of PM2.5, time average monthly for the ground-based measurement at Cantonments.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4841971/v1/c33d2f9c7c09b40d797dab39.png"},{"id":62299269,"identity":"5b416b42-5605-44f9-8339-33060a1cae82","added_by":"auto","created_at":"2024-08-12 16:18:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":84918,"visible":true,"origin":"","legend":"\u003cp\u003eTime-series, Area-Average of PM2.5, time average daily for LCS, Reference grade monitor and MERRA-2 derived data (April – December 2019).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4841971/v1/c8eb1deb1516253b41cb6489.png"},{"id":62299378,"identity":"2611681a-a99c-44ca-addb-5b9580a25dbe","added_by":"auto","created_at":"2024-08-12 16:19:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":75391,"visible":true,"origin":"","legend":"\u003cp\u003eTime Series, Area-Average of PM2.5, time average daily for LCS, Reference grade monitor and MERRA-2 derived data (January – December 2020)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4841971/v1/037dfd1494f2aa8fdccf04aa.png"},{"id":62299357,"identity":"46db6333-f07e-4a0f-8afb-e0906029e1e0","added_by":"auto","created_at":"2024-08-12 16:19:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":34397,"visible":true,"origin":"","legend":"\u003cp\u003eThe distributions of daily PM2.5 concentrations across the measurement sites, MERRA-2 derived PM2.5 for the month of June 2020\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4841971/v1/a7087c232c96a8e5e7a0f30b.png"},{"id":62300081,"identity":"37593b6a-8de3-42e1-82f9-2f6d68a467d8","added_by":"auto","created_at":"2024-08-12 16:24:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":594753,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4841971/v1/51bde9b7-73d0-47c0-865c-c98048aa6f5e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Temporal and Spatial Analysis of PM2.5 Concentrations in Accra Using Integrated Ground-Based and Satellite Sensor Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAir pollution is a significant environmental and public health issue, particularly in urban areas of developing countries. Fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) is a major pollutant of concern due to its ability to penetrate deep into the lungs and enter the bloodstream, causing various health problems, including respiratory and cardiovascular diseases (Du, Xu, Chu, Guo, \u0026amp; Wang, 2016). The World Health Organization (2016) estimates that millions of deaths annually can be attributed to exposure to PM\u003csub\u003e2.5\u003c/sub\u003e, highlighting the need for effective air quality monitoring and management.\u003c/p\u003e\n\u003cp\u003eIn Accra, the capital city of Ghana, rapid urbanization and industrial activities contribute to elevated PM\u003csub\u003e2.5\u003c/sub\u003e levels. Common sources of \u0026nbsp;PM\u003csub\u003e2.5\u003c/sub\u003e include vehicular emissions, industrial processes, and biomass burning (Acquah, Tschakert, \u0026amp; Sagoe-Addy, 2019). The Environmental Protection Agency of Ghana monitors air quality, but the limited number of high-grade reference monitors provides incomplete data, necessitating alternative monitoring methods (EPA Ghana, 2018).\u003c/p\u003e\n\u003cp\u003eSatellite-derived data offer extensive spatial coverage and can complement ground-based measurements. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), provides high-resolution data for estimating PM\u003csub\u003e2.5\u003c/sub\u003e levels (Randles, Sliva, Buchard, \u0026amp; Flynn, 2017). However, satellite data accuracy can be influenced by atmospheric conditions and cloud cover, which necessitates ground-based validation (Belle et al., 2017).\u003c/p\u003e\n\u003cp\u003eLow-cost sensors (LCS) like Clarity nodes have emerged as valuable tools for air quality monitoring due to their affordability and ease of deployment. These sensors can provide real-time data and have been utilized in various urban air quality studies and community-driven projects (Eilenberg et al., 2020). By integrating data from low-cost sensors with satellite-derived measurements, it is possible to achieve a comprehensive understanding of PM\u003csub\u003e2.5\u003c/sub\u003e distribution and trends in urban environments (Ford et al., 2019).\u003c/p\u003e\n\u003cp\u003eThis research aims to provide a detailed temporal and spatial analysis of PM\u003csub\u003e2.5\u003c/sub\u003e concentrations in Accra by integrating data from ground-based low-cost sensors, reference-grade monitors, and satellite-derived sources. This study seeks to address the gaps in air quality data collection and support the development of effective air quality management strategies and public health policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverall Objective:\u003c/strong\u003e The overall objective of this study is to conduct a comprehensive temporal and spatial analysis of PM\u003csub\u003e2.5\u003c/sub\u003e concentrations in Accra using integrated ground-based and satellite sensor data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificance of the Study:\u003c/strong\u003e By integrating multi-sensor data, this study provides a detailed understanding of PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003edistributions and its health impacts in Accra. The findings will inform effective air quality management and policy-making, ultimately contributing to improved public health outcomes.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe study integrates data from Clarity node sensors, BAM 1020 reference-grade monitor, and the MERRA-2 satellite model over a period of 21 months from April 2019 to December 2020. The key findings are as follows:\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; \u003cstrong\u003eTemporal Patterns of PM\u003csub\u003e2.5\u003c/sub\u003e Concentrations:\u003c/strong\u003e The temporal analysis of PM\u003csub\u003e2.5\u003c/sub\u003e concentrations reveals distinct seasonal trends, with higher levels during the dry season and lower levels during the wet season. Figures 1 \u0026amp; 2 illustrate these patterns for both ground-based sensors and MERRA-2 data.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; \u003cstrong\u003eSpatial Distribution of PM2.5:\u003c/strong\u003e The spatial distribution analysis shows significant variations in PM2.5 levels across different areas of Accra. Higher concentrations are recorded in densely populated and industrial zones such as Amasaman and Akweteyman (Figures 3 and 4).\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; \u003cstrong\u003eComparison of Ground-Based and Satellite Data:\u003c/strong\u003e The comparison between ground-based sensor data and MERRA-2 derived data shows a significant positive correlation, with seasonal trends aligning closely between the two datasets. However, the satellite data tends to overestimate PM\u003csub\u003e2.5\u003c/sub\u003e levels during certain months in 2020 and underestimate in 2019 (Figures 5 and 6).\u003c/p\u003e\n\u003cp\u003e4. \u0026nbsp; \u003cstrong\u003eHealth Implications:\u003c/strong\u003e The high PM\u003csub\u003e2.5\u003c/sub\u003e levels observed during the raining season of June 2020, in densely populated and industrial areas have significant public health implications. The study highlights the need for targeted interventions in these areas to mitigate the health risks associated with prolonged exposure to high PM\u003csub\u003e2.5\u003c/sub\u003e concentrations (Figure 7).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe integration of ground-based sensors with satellite-derived data provides a comprehensive approach to monitoring PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, addressing the limitations of each method when used in isolation. The significant positive correlation between ground-based and satellite-derived data confirms the reliability of MERRA-2 in estimating PM\u003csub\u003e2.5\u003c/sub\u003e levels, as supported by previous studies (Randles et al., 2017). However, the observed tendency of satellite data to overestimate PM\u003csub\u003e2.5\u003c/sub\u003e levels during certain months in 2020 and underestimate in 2019 highlights the importance of continuous calibration and validation (Belle et al., 2017).\u003c/p\u003e\n\u003cp\u003eTemporal analysis reveals distinct seasonal trends, with higher PM\u003csub\u003e2.5\u003c/sub\u003e concentrations during the dry season and lower levels during the wet season. These findings are consistent with known patterns where dry conditions lead to increased emissions and dust, exacerbating particulate matter levels (Du et al., 2016). Understanding these seasonal variations is crucial for developing effective air quality management strategies and mitigating the health impacts of pollution.\u003c/p\u003e\n\u003cp\u003eDansoman, as a matured settlement with many paved roads, generally experiences lower levels of PM2.5 compared to Amasaman, which has a high number of unpaved roads. Paved roads significantly reduce the amount of dust and particulate matter generated by vehicular movement. The smoother surfaces of paved roads minimize the abrasion and dispersion of particles into the air. Additionally, the established nature of Dansoman likely includes more regulated traffic patterns, better-maintained infrastructure, and possibly more stringent environmental regulations that collectively contribute to lower PM\u003csub\u003e2.5\u003c/sub\u003e levels.\u003c/p\u003e\n\u003cp\u003eIn contrast, Amasaman, being a newer settlement with numerous unpaved roads, is more prone to higher PM2.5 levels. Unpaved roads are a significant source of dust and particulate matter, especially under dry conditions. When vehicles travel on these unpaved surfaces, they disturb the soil and generate large amounts of dust, which contributes directly to elevated PM2.5 concentrations. The less developed infrastructure in Amasaman also suggests that there may be less control over construction activities and other dust-generating operations, further exacerbating the problem.\u003c/p\u003e\n\u003cp\u003eTemporal analysis reveals seasonal variations in PM2.5 levels influenced by weather conditions. During the dry season, Amasaman likely experiences a pronounced increase in PM2.5 due to the greater generation of dust from unpaved roads and construction activities. The lack of precipitation during this period means that dust is not effectively settled, leading to higher concentrations of airborne particles. In Dansoman, while there may also be some increase in PM2.5 during the dry season, the effect is less pronounced due to the paved infrastructure that mitigates dust generation.\u003c/p\u003e\n\u003cp\u003eConversely, during the rainy season, PM2.5 levels may decrease in both areas as rainfall helps to settle airborne particles. However, Amasaman might still exhibit relatively higher PM2.5 levels due to the potential for muddy conditions and ongoing construction activities that do not cease with rain, continuing to disturb the soil.\u003c/p\u003e\n\u003cp\u003eThe spatial analysis shows significant variations in PM\u003csub\u003e2.5\u003c/sub\u003e levels across different areas of Accra. Higher concentrations are observed in densely populated and industrial zones, such as Tetteh Quarshie and Dansoman. This spatial distribution is critical for identifying pollution hotspots and prioritizing interventions in areas with the highest health risks (Acquah et al., 2019). The findings underscore the need for targeted air quality management strategies to address pollution sources in these areas.\u003c/p\u003e\n\u003cp\u003eThe health implications of high PM\u003csub\u003e2.5\u003c/sub\u003e levels, particularly in urban and industrial areas, are significant. Prolonged exposure to elevated PM\u003csub\u003e2.5\u003c/sub\u003e concentrations can lead to serious health issues, including respiratory and cardiovascular diseases (Du et al., 2016). This study highlights the urgent need for effective air quality management and public health policies to mitigate the adverse health impacts of air pollution in Accra.\u003c/p\u003e\n\u003cp\u003eThe study demonstrates the feasibility of using low-cost sensors for continuous air quality monitoring. These sensors provide valuable real-time data, which is essential for timely interventions and public health protection. The integration of low-cost sensors with satellite data offers a cost-effective and scalable solution for air quality monitoring, particularly in resource-constrained settings (Ford et al., 2019).\u003c/p\u003e\n\u003cp\u003eHowever, the study acknowledges several limitations. The accuracy of low-cost sensors can be influenced by environmental factors such as humidity and temperature, which may affect sensor performance. Additionally, satellite data can be affected by cloud cover and atmospheric conditions, leading to potential discrepancies in PM\u003csub\u003e2.5\u003c/sub\u003e estimates (Belle et al., 2017). Future research should focus on improving the calibration and validation processes for both low-cost sensors and satellite-derived data to enhance the accuracy and reliability of air quality assessments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that integrating ground-based sensors with satellite-derived data provides a reliable and comprehensive approach to monitoring PM\u003csub\u003e2.5\u003c/sub\u003e concentrations. The findings highlight the potential for using low-cost sensors in combination with satellite data for continuous air quality monitoring, particularly in developing countries. The spatial and temporal insights gained from this research can inform effective air quality management and policy-making, ultimately contributing to improved public health outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.A.S. prepared and edited the whole manuscriptC.M.A. contributed to data analysis and edited the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAcquah, H., Tschakert, P., \u0026amp; Sagoe-Addy, K. (2019). Urban air pollution and its health impacts in Accra. \u003cem\u003eJournal of Environmental Studies, 45\u003c/em\u003e(3), 210-222.\u003c/li\u003e\n \u003cli\u003eBelle, J. H., Burkart, K., Huang, W., \u0026amp; Zhang, W. (2017). Air pollution impacts on avian species via impacts on terrestrial ecosystems. \u003cem\u003eJournal of Environmental Management, 206\u003c/em\u003e, 170-181. https://doi.org/10.1016/j.jenvman.2017.10.005\u003c/li\u003e\n \u003cli\u003eDu, Y., Xu, X., Chu, M., Guo, Y., \u0026amp; Wang, J. (2016). Air particulate matter and cardiovascular disease: The epidemiological, biomedical and clinical evidence. \u003cem\u003eJournal of Thoracic Disease, 8\u003c/em\u003e(1), 8-19. https://doi.org/10.3978/j.issn.2072-1439.2016.01.77\u003c/li\u003e\n \u003cli\u003eEilenberg, S. R., Pacheco, M. J., van Donkelaar, A., Martin, R. V., \u0026amp; Cohen, A. J. (2020). Assessing PM2.5 air pollution exposure using low-cost sensors and satellite data. \u003cem\u003eEnvironmental Research Letters, 15\u003c/em\u003e(3), 034029. https://doi.org/10.1088/1748-9326/ab70b2\u003c/li\u003e\n \u003cli\u003eEPA Ghana. (2018). Annual report on air quality in Accra. Environmental Protection Agency Ghana.\u003c/li\u003e\n \u003cli\u003eFord, B., Val Martin, M., Zelasky, S. E., Fischer, E. V., Anenberg, S. C., Heald, C. L., \u0026amp; Pierce, J. R. (2019). Future fire impacts on smoke concentrations, visibility, and health in the contiguous United States. \u003cem\u003eGeoHealth, 3\u003c/em\u003e(5), 122-136. https://doi.org/10.1029/2019GH000203\u003c/li\u003e\n \u003cli\u003eRandles, C. A., Silva, A. M. d., Buchard, V., \u0026amp; Flynn, C. J. (2017). The MERRA-2 aerosol reanalysis, 1980 onward. \u003cem\u003eEarth System Science Data, 9\u003c/em\u003e(2), 515-543. https://doi.org/10.5194/essd-9-515-2017\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. (2016). Ambient air pollution: A global assessment of exposure and burden of disease. World Health Organization. https://apps.who.int/iris/handle/10665/250141\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":"PM2.5, air quality monitoring, low-cost sensors, satellite data, spatial analysis, temporal trends, Accra, MERRA-2.","lastPublishedDoi":"10.21203/rs.3.rs-4841971/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4841971/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAir pollution, particularly fine particulate matter, poses a significant public health challenge in urban areas worldwide. This study presents a comprehensive temporal and spatial analysis of PM\u003csub\u003e2.5\u003c/sub\u003e concentrations in Accra, Ghana, utilizing an integrated approach combining data from low-cost ground-based sensors, reference-grade monitors, and satellite-derived sources. Over a 21-month period from April 2019 to December 2020, PM\u003csub\u003e2.5\u003c/sub\u003e levels were monitored across five strategic locations within the Greater Accra Metropolitan Area (GAMA). The findings reveal distinct seasonal trends, with higher PM\u003csub\u003e2.5\u003c/sub\u003e concentrations during the dry season (November to March) and lower levels during the wet season (April to October). This seasonal variation underscores the influence of meteorological conditions on particulate matter levels. Spatial analysis indicates significant variations in PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, with higher levels recorded in densely populated and industrial zones such as Amasaman and Akweteyman. These areas are particularly affected by emissions from vehicular traffic, industrial activities, and biomass burning. A key outcome of this study is the strong positive correlation between ground-based sensor data and MERRA-2 satellite-derived PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, validating the use of satellite data to complement traditional ground-based monitoring. Despite some discrepancies, where satellite data tended to overestimate PM\u003csub\u003e2.5\u003c/sub\u003e levels in 2020 and underestimate in 2019, the integration of these data sources provides a more robust and comprehensive assessment of air quality. The elevated PM\u003csub\u003e2.5\u003c/sub\u003e levels observed in industrial and densely populated areas have significant public health implications. Prolonged exposure to high PM\u003csub\u003e2.5\u003c/sub\u003e concentrations is linked to increased risks of respiratory and cardiovascular diseases. This study highlights the urgent need for targeted air quality management strategies and public health interventions to mitigate these risks. By demonstrating the feasibility and effectiveness of using low-cost sensors in combination with satellite-derived data, this research offers a scalable and cost-effective solution for air quality monitoring in resource-constrained settings. The insights gained from this study can inform policy-making and contribute to improved public health outcomes in Accra and similar urban environments.\u003c/p\u003e","manuscriptTitle":"Temporal and Spatial Analysis of PM2.5 Concentrations in Accra Using Integrated Ground-Based and Satellite Sensor Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-12 16:08:47","doi":"10.21203/rs.3.rs-4841971/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":"33a48eee-d77f-4af8-a958-32c89e4855da","owner":[],"postedDate":"August 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-27T04:23:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-12 16:08:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4841971","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4841971","identity":"rs-4841971","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}