Aerosol Optical Depth as a Proxy for Air Pollution in Bangladesh: Temporal Trends, Emission Drivers, and Implications for Public Health and Sustainable Development

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This study examines 25 years (2000–2024) of MODIS Terra Aerosol Optical Depth (AOD) data to evaluate long-term trends in aerosol concentration in Bangladesh. Annual mean AOD values were obtained using Google Earth Engine and examined by linear regression, Mann–Kendall trend analysis, and Sen’s slope estimation in R. The results indicate a statistically significant upward trend in AOD, with mean values escalating from 0.49 in 2001 to 0.87 in 2024 (R² = 0.83; p < 0.001), implying a continual increase in atmospheric particle concentrations. Z-score analysis indicated that recent years, specifically 2023 and 2024, exhibited statistically significant increases in aerosol pollution. The rise in AOD is associated with both local and transboundary sources, such as automotive emissions, brick kilns, consumption of fossil fuels, and regional dust transport. These findings underscore the escalating public health risks associated with PM₂.₅ exposure and affirm the utility of AOD as a reliable proxy. The study emphasizes the imperative for targeted emission reduction measures, comprehensive planning, and improved monitoring systems to achieve Sustainable Development Goals (SDGs) related to health and urban resilience. Despite its limitations, especially the use of coarse-resolution AOD, the study provides essential evidence for informed decision-making and future research on aerosol pollution in Bangladesh. Atmospheric Sciences Aerosol Optical Depth Air Pollution Emission Drivers Public Health Risks Sustainable Development Figures Figure 1 Figure 2 Highlights • A significant increasing trend of aerosol pollution in Bangladesh is found from a long term analysis of MODIS AOD data. • AOD can be a reliable proxy for surface-level PM₂.₅ concentrations in a data-scarce environment • Evaluation of the escalating public health risks related to increasing aerosol concentrations, highlighting effects on susceptible populations and correlating results with Sustainable Development Goals. • Recommendations for integrated policy approaches focusing on emission reductions and intersectoral collaboration to reduce air pollution and advance sustainable development in Bangladesh. Introduction Air pollution stands as a pressing environmental and public health issue in the 21st century, particularly affecting countries with low or median incomes (LMICs) that are undergoing fast urbanization and industrial expansion (Stanaway et al., 2018 ). Aerosol pollution in South Asia constitutes a significant component of ambient air pollution, caused by a variety of complex factors such as biomass burning, natural dust, fossil fuel combustion, and atmospheric influences (Pan et al., 2015 ). Aerosol Optical Depth (AOD) determined by satellite sensors has been validated as a valuable indicator of aerosol presence in the atmosphere and acts as a dependable proxy for predicting surface-level PM2.5 concentrations, especially in areas with limited ground-based air quality monitoring (Van Donkelaar et al., 2010 ). In recent decades, Bangladesh has experienced a significant increase in aerosol pollution, attributed to its high population density and substantial human activities (Khandker et al., 2023 ). The geographical setting of the country, characterized as a low-lying deltaic basin next to the Indo-Gangetic Plain, intensifies the effects of local emissions as well as transboundary pollution from neighboring areas like India and others (Rana et al., 2016 ; Mongo et al., 2021). Although air pollution presents significant health and environmental challenges, Bangladesh's air quality monitoring infrastructure remains underdeveloped, characterized by limited continuous ground-based data coverage (Mahmood et al., 2019 ; Begum & Hopke, 2018 ). In this context, datasets derived from satellites that measure aerosol optical depth are crucial for monitoring trends in aerosol loading across Bangladesh, utilizing their broad spatial and temporal coverage (Islam et al., 2019 ; Sayed et al., 2022 ). The incorporation of satellite AOD data into air quality management provides a significant tool for evaluating pollution dispersion and supporting policy-making initiatives focused on reducing aerosol pollution (Islam et al., 2022 ; Holloway et al., 2025 ). Over the last two decades, a variety of studies have investigated aerosol pollution in Bangladesh through the use of satellite remote sensing and ground-based data. Haque et al. (2010) identified urban hotspots of particulate pollution associated with traffic and industrial emissions in Dhaka. Ali et al., 2025 examined the seasonal variability of PM₂.₅ and AOD, emphasizing that biomass burning and brick kilns are significant contributors. Studies utilizing remote sensing have mapped aerosol distributions throughout Bangladesh, investigating connections between urban-industrial development, seasonal weather patterns, and changes in land use (Mao et al., 2014 ; Singh et al., 2020 ). For example, MODIS AOD data have been utilized to identify regional haze events and investigate aerosol climate interactions (Chang et al., 2015 ; Raza, 2025 ). Chatterjee et al. ( 2023 ) conducted a regional investigation into aerosol mobility from the Indo-Gangetic Plain to Bangladesh, establishing a connection between transboundary pollution and seasonal peaks. Ahmed ( 2024 ) evaluated the health effects of pollution from PM2.5 in Bangladesh, noting a rise in respiratory illnesses while highlighting the shortcomings in the integration of satellite data with health metrics. Mohammad et al. (2024) established statistically significant increasing trends in aerosol concentrations throughout Bangladesh by employing Sen’s slope and Mann-Kendall tests, yet they recognized an absence of studies directly connecting these trends to health or policy outcomes. Despite growing aerosol pollution, limited studies in Bangladesh have examined long-term AOD trends in conjunction with emission sources and public health risks, and none have contextualized these trends within sustainable development priorities. Therefore, this study addresses this gap by analyzing 25 years (2000–2024) of satellite-derived AOD data to assess temporal dynamics, identify key pollution drivers, and interpret the findings in relation to health impacts and relevant SDG targets. Methodology Study area The research encompasses all areas of Bangladesh (Fig. 1 ), with locations spanning 20°34′N to 26°38′N latitude and 88°01′E to 92°41′E longitude (Hossain et al., 2008 ). The country is located in northeastern South Asia, sharing borders with India to the west, north, and northeast, Myanmar to the southeast, and the Bay of Bengal to the south. Bangladesh covers an area of around 147,570 square kilometers (Haque et al., 2017). Bangladesh has a high population density, with approximately 171.5 million residents (World Bank, 2023 ). The geographic and climatic conditions of Bangladesh's low-lying deltaic terrain are especially crucial in the trapping and accumulation of pollutants (Shamsudduha et al. 2009 ). Data Used This study utilized the MODIS Terra Aerosol Optical Depth (AOD) product to assess long-term variations in air pollution in Bangladesh. The data were sourced from the Moderate Resolution Imaging Spectroradiometer (MODIS) within the Terra satellite, a sun-synchronous, near-polar orbiting platform that NASA launched on December 18, 1999. MODIS offers global data at various spatial resolutions of 1 km, 500 m, and 250 m, that includes a temporal revisit frequency of 1 to 2 days (Islam et al., 2019 ). The MODIS Collection 6.1 Level 3 monthly gridded product (MOD08_M3) was utilized, providing the average Aerosol Optical Depth (AOD) across terrestrial and marine environments at a wavelength of 550 nm, with a spatial resolution of 1° × 1° (Hsu et al., 2013 ). The AOD values had been normalized by dividing by 1000, following standard preprocessing procedures for MODIS aerosol datasets (Sayer et al., 2019 ; Ali et al., 2022 ). The annual mean Aerosol Optical Depth (AOD) for each year from January 2000 through December 2024 was calculated using Google Earth Engine (GEE). The analysis involved calculating the average monthly MODIS AOD values within the national boundaries of Bangladesh. Only normalized AOD values were used to evaluate interannual variations and evaluate long-term trends (Streets et al., 2009 ). Assessment methods The spatiotemporal assessment of AOD over Bangladesh was conducted in two stages. First, Google Earth Engine (GEE) was used to preprocess and extract normalized AOD data; second, statistical trend analysis was performed by using R to quantify and validate the temporal analysis. AOD Extraction and Preprocessing MODIS Terra Level 3 monthly AOD data were filtered and processed within the GEE environment. The dataset’s monthly mean AOD was averaged over both land and ocean surfaces and normalized by dividing 1000 to convert into standard AOD units. Annual mean AOD images were generated by averaging monthly values for each year from 2020 to 2024. The data were spatially clipped to Bangladesh's administrative boundary, and mean annual AOD values were extracted as a country-level time series. Trend Analysis and Statistical Evaluation The exported time series was analyzed in R using both parametric and non-parametric techniques. A simple linear regression model was fitted: $$\:{AOD}_{t}=\:{\beta\:}_{0}+\:{\beta\:}_{1}t+\:\epsilon\:$$ Where, \(\:{AOD}_{t}\) is the normalized AOD in year t, \(\:{\beta\:}_{0}\) is the intercept (baseline AOD when t = 0), \(\:{\beta\:}_{1}\) is the slope (rate of annual change), and \(\:\epsilon\:\) is the random error. The model provided projections for the intercept, p-values, slope and coefficients of determination (R 2 ). To ensure robustness and address non-linearity, the Mann-Kendall trend test was employed to identify the presence of a statistically significant linear trend. Furthermore, the Sen’s slope estimator was utilized to assess the magnitude of the annual trend. All analyses and visualizations were performed utilizing R packages, including trend, ggplot2, and readr. Results Temporal variation of AOD over Bangladesh Normalized annual AOD over Bangladesh from 2000 to 2024 shows a clear increasing trend (Table 1 ), with significant interannual variation. In the early 2000s, values consistently remained below the long-term mean of 0.6555, with the lowest AOD recorded in 2001 (0.4918; ~25% below the mean). AOD first exceeded the 0.6 threshold in 2009 (0.6683), after which values mainly remained above average. Table 1 Temporal variation of AOD over Bangladesh from 2000 to 2024 Year AOD Deviation from Mean % Deviation Z-Score Trend Category 3-Year Moving Average 2000 0.523138 -0.13238 -20.19% -1.35 Below Mean - 2001 0.491761 -0.16376 -24.98% -1.67 Below Mean - 2002 0.559885 -0.09563 -14.59% -0.98 Below Mean 0.524928 2003 0.559312 -0.0962 -14.67% -0.98 Below Mean 0.556319 2004 0.558357 -0.09716 -14.82% -0.99 Below Mean 0.558327 2005 0.546635 -0.10888 -16.61% -1.11 Below Mean 0.554101 2006 0.554007 -0.10151 -15.49% -1.04 Below Mean 0.553666 2007 0.557351 -0.09817 -14.97% -1.01 Below Mean 0.552664 2008 0.593314 -0.0622 -9.49% -0.64 Below Mean 0.568224 2009 0.668251 0.012735 1.94% 0.13 Above Mean 0.606972 2010 0.632361 -0.02316 -3.53% -0.24 Below Mean 0.631309 2011 0.698611 0.043095 6.57% 0.44 Above Mean 0.664987 2012 0.69401 0.038494 5.87% 0.39 Above Mean 0.675964 2013 0.623228 -0.03229 -4.93% -0.33 Below Mean 0.67195 2014 0.665695 0.010179 1.55% 0.12 Above Mean 0.661644 2015 0.750984 0.095468 14.56% 0.97 Above Mean 0.696969 2016 0.733195 0.077679 11.85% 0.79 Above Mean 0.713799 2017 0.69142 0.035904 5.48% 0.37 Above Mean 0.733462 2018 0.775772 0.120256 18.34% 1.22 Significantly Above 0.733462 2019 0.685178 0.029662 4.53% 0.31 Above Mean 0.717457 2020 0.6862 0.030684 4.68% 0.32 Above Mean 0.71505 2021 0.75356 0.098044 14.95% 1 Above Mean 0.745646 2022 0.706974 0.051458 7.85% 0.52 Above Mean 0.750479 2023 0.810964 0.155448 23.71% 1.59 Significantly Above 0.757166 2024 0.867727 0.212211 32.37% 2.17 Significantly Above 0.795222 Mean 0.655516 0 0 0 - - Between 2009 and 2024, over 85% of the years recorded AOD above the mean, with recent years such as 2023 (0.8110) and 2024 (0.8677) showing deviations of over 23% and 32%, respectively. These were also classified as "significantly above" based on z-scores (> 1.5), indicating statistically elevated aerosol loading. A three-year moving average confirms the rise in AOD, with values increasing from ~ 0.55 in the early 2000s to ~ 0.80 by 2024. The consistent increase in aerosol pollution over Bangladesh is further confirmed by the moving average, which smooths out short-term fluctuations and emphasizes the underlying long-term trend (Pope & Dockery, 2006 ). These results confirm a persistent upward shift in aerosol concentrations over Bangladesh, which is in line with the findings of MODIS-based analyses (Tariq et al., 2022 ; Sayed et al., 2022 ) emphasizing the importance of urgent policy attention to the expanding air quality concerns. Temporal trend of AOD over Bangladesh The normalized Aerosol Optical Depth (AOD) trend over Bangladesh from 2000 to 2024 shows a gradual and statistically significant increase in the concentrations of aerosols in the atmosphere (Fig. 2 ). A consistent increasing trend in aerosol loading over the 25 years is confirmed by the linear regression analysis of the yearly mean AOD values, which shows a positive slope of 0.0120 AOD units per year with a p-value < 0.001. This pattern shows deteriorating air quality over time, with an average annual rise of about 1.8%. According to Islam et al. ( 2019 ), AOD increased between 2002 and 2016, with values primarily exceeding 0.5 across a broad area of Bangladesh. Reflecting a notable long-term rise in atmospheric aerosol loading, the AOD value ranged from a minimum of 0.492 in 2001 to a maximum of 0.868 in 2024. A coefficient of determination (R 2 ) of 0.83 from the linear regression analysis demonstrated a good model fit and supported the stability of the observed increasing trend. These results are in line with earlier findings about the rise in aerosol burden linked to the burning of biomass, emissions from cities and industries, and transboundary air pollution that affects Bangladesh (J. Lelieveld et al., 2001 ). Statistical Validation of AOD Trend The reliability of the observed trend was validated through the application of non-parametric methods. The Mann-Kendall test revealed a significant positive monotonic trend, with a Z-value of 4.928 (p < 0.001). This indicates that the rise in AOD is statistically strong and unaffected by outliers or non-normal data distribution. In addition, Sen’s slope estimator indicated a median annual increase of 0.0120 (95% confidence interval: 0.0093–0.0146), which closely aligns with the slope derived from linear regression. The findings confirm the trend's consistency and support previous satellite-based studies that indicated increasing aerosol loadings in Bangladesh and neighboring areas, reinforcing the evidence of a continuous rise in aerosol pollution (Mohammad et al., 2022 ). Discussion AOD as a Proxy for PM2.5 It is commonly acknowledged that satellite-derived Aerosol Optical Depth (AOD) is a reliable proxy for surface-level PM 2.5 , allowing for spatiotemporal air quality monitoring, particularly in areas with limited data (Van Donkelaar et al., 2010 ). Numerous investigations conducted in various regions have revealed statistically significant correlations between AOD and PM 2.5 . For example, Krishna et al. ( 2019 ) used MODIS AOD and WRF-Chem simulations to establish a significant monthly association (R 2 = 0.77), in India. Regional humidity and aerosol features altered the regression slopes between AOD and PM₂.₅ in China, which ranged from 13 to 90 with R² values up to 0.75 (Zheng et al., 2016 ). Similarly, urban places like Nanjing and Beijing showed moderate to high associations (R² = 0.53–0.64) (Kong et al., 2016 ; Zheng et al., 2017 ), whilst in Delhi, a 1% increase in AOD was linked to a 0.4–0.5% increase in PM 2.5 concentrations (Kumar et al., 2007 ). The long-term increase in AOD over Bangladesh in the current study, with normalized values regularly exceeding 0.6, indicates persistently high levels of surface particulate matter, confirming the usefulness of AOD as a stand-in for PM 2.5 . Drivers of AOD accumulation in Bangladesh Domestic emissions, transboundary pollution, and meteorological conditions that retain pollutants contributed to increased aerosol loading in Bangladesh. Domestic particle emissions result from rapid urbanization, unregulated industrial expansion, vehicular traffic, biomass combustion, and coal-fired brick kiln activities (Begum et al., 2011 ). The aged fleet of the transportation industry and the lack of emission restrictions constantly elevate urban PM2.5 levels, while seasonal agricultural residue burning increases aerosol concentrations from November to January​ (Pavel et al., 2021 ). Transboundary pollution significantly impacts Bangladesh's air quality because the country is situated downwind of the heavily polluted Indo-Gangetic Plain (IGP) region.​ During winter, prevailing northwesterly winds carry fine particulate matter, including sulfate, organic carbon, and dust, from northern India, Nepal, and Pakistan into Bangladesh (Dihan et al., 2023 ). The rapid rise in energy demand has increased dependence on fossil fuels such as coal and natural gas for power and industrial use, intensifying emissions of aerosol precursors and primary particles. Power plants near urban and industrial areas notably contribute to regional haze and aerosol loading (Zaman et al., 2022 ). Construction activities and road dust resuspension, driven by urban expansion in cities like Dhaka and Chattogram, are essential contributors to aerosol load. These generate coarse particulate matter that raises AOD and serves as nuclei for further aerosol growth (Kormoker et al., 2022 ). Climate change affects regional aerosol dynamics by modifying wind patterns, precipitation, and atmospheric stability, impacting pollutant transport and deposition. Increased temperature variability and drought frequency prolong pollutant residence and elevate aerosol levels across South Asia, including Bangladesh (Banik, 2017 ; Nair et al., 2012 ). Public Health Risks and Sustainable Development Needs These data reveal the escalating trend of aerosol optical depth (AOD) in Bangladesh from 2000 to 2024, during which normalized AOD values increased from 0.49 to 0.86, with an average of roughly 0.66. The consistent increase in AOD corresponds with rapid urbanization, industrial expansion, and higher energy consumption in Bangladesh during the past twenty years. This degree of aerosol concentration indicates a significant exceeding of PM₂.₅ levels beyond the WHO's yearly recommended limit of 5 µg/m³. The WHO estimates that more than 94% of the global population is subjected to hazardous PM₂.₅ levels, with concentrations over 35 µg/m³ associated with a 24% rise in mortality risk (Rentschler & Leonova, 2023 ). The WHO does not specify an exact AOD limit; nonetheless, the proven association between AOD and PM₂.₅ indicates that consistently elevated AOD levels, such those observed in Bangladesh, pose considerable environmental and public health risks (Khan, 2021 ). In 2023, Bangladesh was designated the most unhealthy county globally, demonstrating an annual average fine particulate matter (PM2.5) concentration of 79.9 µg/m³—surpassing the national limit of 35 µg/m³ by over twice and exceeding the World Health Organization’s (WHO) standard of 5 µg/m³ by fifteen times. Air pollution is responsible for 102,456 deaths each year in Bangladesh (Nesan et al., 2025 ). This is especially crucial in Bangladesh, where terrestrial air quality monitoring is limited, rendering satellite-derived AOD data essential for evidence-based decision-making (Gupta et al., 2006 ). Prolonged exposure to elevated PM₂.₅ correlates with cardiovascular diseases, respiratory disorders, hindered lung development in children, and premature mortality, disproportionately affecting vulnerable populations, including pregnant women, elderly, and those with existing health issues, thereby exacerbating health disparities across the country (Di et al., 2017 ; Hoek et al., 2013 ; Kioumourtzoglou et al., 2016 ). Effective emission reduction policies that focus on critical sources like transportation, industry, brick kilns, and agricultural burning are vital for reducing pollutant concentrations in Bangladesh (Begum et al., 2011 ; Khandker et al., 2023 ). These policies and initiatives play a crucial role in advancing the Sustainable Development Goals (SDGs), especially SDG 3.9, which aims to significantly decrease deaths and illnesses caused by hazardous air pollution, and SDG 11.6, which emphasizes enhanced monitoring and mitigation of negative environmental effects in urban areas, including air quality management (WHO, 2018; Kanlayanatam et al., 2022 ; Zusman et al., 2023 ). Moreover, enhancing intersectoral coordination connecting health ministries, urban planners, and environmental regulators can improve the effectiveness of air quality management strategies. The ongoing increase in AOD across Bangladesh highlights an escalating air quality crisis that requires thorough and collaborative actions from health, environmental, and policy sectors (Islam et al., 2019 ). In the absence of prompt and integrated actions, the rising aerosol pollution will persist in undermining public health and hindering the nation’s advancement toward sustainable development goals (Mahmood et al., 2024 ). Limitations and Future Directions This study provides a significant long-term evaluation of aerosol pollution in Bangladesh utilizing MODIS AOD data from 2000 to 2024; nevertheless, it has some limitations. AOD indicates columnar aerosol load instead of surface-level concentrations, and its accuracy in assessing human exposure may fluctuate due to atmospheric mixing and boundary layer dynamics. The spatial resolution (1° × 1°) of the MODIS Level 3 data restricts the identification of localized pollution hotspots, especially in urban-industrial areas. Seasonal and meteorological influences on aerosol dynamics were also out of the scope of this study. Future research should integrate higher-resolution products like MODIS MAIAC or Sentinel-5P and expanded ground-monitoring networks to better capture surface-level pollution. Coupling AOD with chemical transport models or machine learning techniques could enhance prediction accuracy. Investigating health impacts through empirical linkage between AOD and morbidity or mortality data would strengthen the public health relevance. Additionally, source apportionment and chemical characterization of aerosols and analysis of climate-aerosol interactions would provide deeper insights into rising air pollution's environmental and developmental implications. Conclusion This research presents a thorough, 25-year spatiotemporal evaluation of aerosol pollution in Bangladesh, utilizing MODIS-derived Aerosol Optical Depth (AOD). The notable and steady increase in AOD throughout the country underscores an escalating environmental and public health issue, with normalized values surpassing long-term averages in over 85% of the study years. The rise can be linked to local emissions, industrial expansion, energy use, and cross-border pollution. The significant upward trend, confirmed through both linear and non-parametric analyses, highlights the critical need to incorporate satellite-based aerosol monitoring into national air quality management. Considering the constraints of Bangladesh’s ground monitoring network, AOD acts as a significant proxy for PM₂.₅, highlighting the crucial role of satellite data in informed policymaking. The health implications are significant, particularly for vulnerable populations, as elevated aerosol concentrations correlate with heightened risks of respiratory and cardiovascular diseases. Bangladesh needs immediate and notable air quality improvement initiatives to achieve the Sustainable Development Goals, especially SDG 3.9 and SDG 11.6. Enhancing ground-based monitoring, utilizing high-resolution satellite datasets, and incorporating health impact models are crucial for formulating thorough air quality strategies. Without timely, coordinated, and multisectoral interventions, the increasing aerosol burden will persist in undermining public health and environmental sustainability in Bangladesh. Declarations Acknowledgement The author would like to thank the open-access platforms and data providers whose freely available resources enabled this research, particularly NASA’s MODIS team for the AOD data. Ethics Approval and Consent to Participate Not applicable Consent for Publication Not applicable Availability of Data and Material MODIS Terra AOD data (MOD08_M3) used in this study are publicly available from NASA’s Earthdata portal (https://earthdata.nasa.gov). 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Aerosol and Air Quality Research , 19 (2), 272-281. Mahmood, D., Khan, F., Ahmed, D., Rahman, S., Motalib, A., Moniruzzaman, M., ... & Xiang, J. (2024). Transboundary Air Quality Challenges in South Asia: A Comprehensive Analysis of Climatological Circulation Patterns and Implications for Bangladesh and Beyond. In EGU General Assembly Conference Abstracts (p. 561). Mohammad, L., Mondal, I., Bandyopadhyay, J., Pham, Q. B., Nguyen, X. C., Dinh, C. D., & Al-Quraishi, A. M. F. (2022). Assessment of spatio-temporal trends of satellite-based aerosol optical depth using Mann–Kendall test and Sen’s slope estimator model. Geomatics, Natural Hazards and Risk , 13 (1), 1270-1298. Mogno, C., Palmer, P. I., Knote, C., Yao, F., & Wallington, T. J. (2021). Seasonal distribution and drivers of surface fine particulate matter and organic aerosol over the Indo-Gangetic Plain. Atmospheric Chemistry and Physics , 21 (14), 10881-10909. Nair, V. S., Solmon, F., Giorgi, F., Mariotti, L., Babu, S. S., & Moorthy, K. K. (2012). Simulation of South Asian aerosols for regional climate studies. Journal of Geophysical Research: Atmospheres , 117 (D4). Nesan, D., Thieriot, H., Kelly, J. (2025). Public health impacts of fine particle air pollution in Bangladesh.https://energyandcleanair.org/wp/wp-content/uploads/2025/01/ CREA_HIA_Ambient_Pm2.5_Bangladesh.pdf Pan, X., Chin, M., Gautam, R., Bian, H., Kim, D., Colarco, P. R., ... & Bellouin, N. (2015). A multi-model evaluation of aerosols over South Asia: common problems and possible causes. Atmospheric Chemistry and Physics , 15 (10), 5903-5928. Pavel, M. R. S., Zaman, S. U., Jeba, F., Islam, M. S., & Salam, A. (2021). Long-term (2003–2019) air quality, climate variables, and human health consequences in Dhaka, Bangladesh. Frontiers in Sustainable Cities , 3 , 681759. Pope III, C. A., & Dockery, D. W. (2006). Health effects of fine particulate air pollution: lines that connect. Journal of the air & waste management association , 56 (6), 709-742. Rana, M. M., Mahmud, M., Khan, M. H., Sivertsen, B., & Sulaiman, N. (2016). Investigating incursion of transboundary pollution into the atmosphere of Dhaka, Bangladesh. Advances in Meteorology , 2016 (1), 8318453. Raza, A. (2025). Air Quality Under a Changing Climate: Trends and Implications for Respiratory Diseases. Journal of Environmental Science and Health , 1 (1). Rentschler, J., & Leonova, N. (2023). Global air pollution exposure and poverty. Nature communications , 14 (1), 4432. Sayer, A. M., Hsu, N. C., Lee, J., Kim, W. V., & Dutcher, S. T. (2019). Validation, stability, and consistency of MODIS Collection 6.1 and VIIRS Version 1 Deep Blue aerosol data over land. Journal of Geophysical Research: Atmospheres , 124 (8), 4658-4688. Sayed, M. A., Islam, M. M., Ali, M. A., & Tusher, M. N. J. (2022). Spatiotemporal Changes in aerosol loadings across Bangladesh from 2010-2021. In 2022 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET) (pp. 1-6). IEEE. Shamsudduha, M., Marzen, L. J., Uddin, A., Lee, M. K., & Saunders, J. A. (2009). Spatial relationship of groundwater arsenic distribution with regional topography and water-table fluctuations in the shallow aquifers in Bangladesh. Environmental Geology , 57 (7), 1521-1535. Singh, T., Ravindra, K., Sreekanth, V., Gupta, P., Sembhi, H., Tripathi, S. N., & Mor, S. (2020). Climatological trends in satellite-derived aerosol optical depth over North India and its relationship with crop residue burning: rural-urban contrast. Science of the Total Environment , 748 , 140963. Stanaway, J. D., Afshin, A., Gakidou, E., Lim, S. S., Abate, D., Abate, K. H., ... & Bleyer, A. (2018). Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The lancet , 392 (10159), 1923-1994. Streets, D. G., Yan, F., Chin, M., Diehl, T., Mahowald, N., Schultz, M., ... & Yu, C. (2009). Anthropogenic and natural contributions to regional trends in aerosol optical depth, 1980–2006. Journal of Geophysical Research: Atmospheres , 114 (D10). Tariq, S., Qayyum, F., Ul-Haq, Z., & Mehmood, U. (2022). Long-term spatiotemporal trends in aerosol optical depth and its relationship with enhanced vegetation index and meteorological parameters over South Asia. Environmental Science and Pollution Research , 29 (20), 30638-30655. Van Donkelaar, A., Martin, R. V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., & Villeneuve, P. J. (2010). Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environmental health perspectives , 118 (6), 847-855. World Bank, 2023. World Development Indicators. https://datacatalog.worldbank.org/search/dataset/0037712 World Health Organization (WHO). (2018). Air pollution and child health: prescribing clean air: summary. Zaman, S. U., Pavel, M. R. S., Rani, R. I., Jeba, F., Islam, M. S., Khan, M. F., ... & Salam, A. (2022). Aerosol climatology characterization over Bangladesh using ground-based and remotely sensed satellite measurements. Elem Sci Anth , 10 (1), 000063. Zheng, Y., Zhang, Q., Liu, Y., Geng, G., & He, K. (2016). Estimating ground-level PM2. 5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements. Atmospheric Environment , 124 , 232-242. Zheng, C., Zhao, C., Zhu, Y., Wang, Y., Shi, X., Wu, X., ... & Qiu, Y. (2017). Analysis of influential factors for the relationship between PM 2.5 and AOD in Beijing. Atmospheric Chemistry and Physics , 17 (21), 13473-13489. Zusman, E., Elder, M., & Sussman, D. D. (2023). A clean air sustainable development goal (SDG). In Handbook of air quality and climate change (pp. 1-12). Singapore: Springer Nature Singapore. Additional Declarations The authors declare no competing interests. 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-7342293","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498494120,"identity":"03824eaf-bb89-407f-9292-2f9688924c8a","order_by":0,"name":"Md Shahinur Rahman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDACCQY2hgQY5wMQs7GTooVxBkgLMzFaYICZB0wS0CE/u/nZg4c7DuczSKQ/3Wzza5s8HzMD44ePObi1GNw5Zm6QeOawZYNEjtnt3L7bhm3MDMySM7fh0SKRYCaR2HbYgEEih+12bs9tRqAWNmZePFrkZ6R/g2pJf3bbsue2PUEtDDdyYLYkmN1m+HE7kaAWgxs5ZUAt6QZsPG/MbvY23E5uY2ZsxusXoMO2Sf5sszbgZ09/duPHn9u289ubD374iM9hMACOHcY2MNlAhHo4+EOK4lEwCkbBKBgpAACa601aXJX/BAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-7962-3766","institution":"Environment and Sustainability Research Initiative","correspondingAuthor":true,"prefix":"","firstName":"Md","middleName":"Shahinur","lastName":"Rahman","suffix":""}],"badges":[],"createdAt":"2025-08-11 04:46:26","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7342293/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7342293/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88853226,"identity":"eece213f-facc-4446-aef3-136f2e7023c5","added_by":"auto","created_at":"2025-08-12 06:05:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":153903,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area map showing the administrative boundary and the major divisions of Bangladesh\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7342293/v1/5796c9854ed27dc98c0687e1.png"},{"id":88852029,"identity":"abbb2356-081b-4583-9e4c-0f09b2049267","added_by":"auto","created_at":"2025-08-12 05:49:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14647,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trend of normalized AOD from 2000 to 2024\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7342293/v1/bebb41939f556401d932329c.png"},{"id":88854231,"identity":"29ff805f-780a-46e3-8e5c-e396692bee54","added_by":"auto","created_at":"2025-08-12 06:13:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":976285,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7342293/v1/7bf854f8-7dba-4822-8d80-665219302488.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAerosol Optical Depth as a Proxy for Air Pollution in Bangladesh: Temporal Trends, Emission Drivers, and Implications for Public Health and Sustainable Development\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; A significant increasing trend of aerosol pollution in Bangladesh is found from a long term analysis of MODIS AOD data.\u003c/p\u003e\u003cp\u003e\u0026bull; AOD can be a reliable proxy for surface-level PM₂.₅ concentrations in a data-scarce environment\u003c/p\u003e\u003cp\u003e\u0026bull; Evaluation of the escalating public health risks related to increasing aerosol concentrations, highlighting effects on susceptible populations and correlating results with Sustainable Development Goals.\u003c/p\u003e\u003cp\u003e\u0026bull; Recommendations for integrated policy approaches focusing on emission reductions and intersectoral collaboration to reduce air pollution and advance sustainable development in Bangladesh.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eAir pollution stands as a pressing environmental and public health issue in the 21st century, particularly affecting countries with low or median incomes (LMICs) that are undergoing fast urbanization and industrial expansion (Stanaway et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Aerosol pollution in South Asia constitutes a significant component of ambient air pollution, caused by a variety of complex factors such as biomass burning, natural dust, fossil fuel combustion, and atmospheric influences (Pan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Aerosol Optical Depth (AOD) determined by satellite sensors has been validated as a valuable indicator of aerosol presence in the atmosphere and acts as a dependable proxy for predicting surface-level PM2.5 concentrations, especially in areas with limited ground-based air quality monitoring (Van Donkelaar et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In recent decades, Bangladesh has experienced a significant increase in aerosol pollution, attributed to its high population density and substantial human activities (Khandker et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The geographical setting of the country, characterized as a low-lying deltaic basin next to the Indo-Gangetic Plain, intensifies the effects of local emissions as well as transboundary pollution from neighboring areas like India and others (Rana et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mongo et al., 2021). Although air pollution presents significant health and environmental challenges, Bangladesh's air quality monitoring infrastructure remains underdeveloped, characterized by limited continuous ground-based data coverage (Mahmood et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Begum \u0026amp; Hopke, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this context, datasets derived from satellites that measure aerosol optical depth are crucial for monitoring trends in aerosol loading across Bangladesh, utilizing their broad spatial and temporal coverage (Islam et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sayed et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The incorporation of satellite AOD data into air quality management provides a significant tool for evaluating pollution dispersion and supporting policy-making initiatives focused on reducing aerosol pollution (Islam et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Holloway et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOver the last two decades, a variety of studies have investigated aerosol pollution in Bangladesh through the use of satellite remote sensing and ground-based data. Haque et al. (2010) identified urban hotspots of particulate pollution associated with traffic and industrial emissions in Dhaka. Ali et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e examined the seasonal variability of PM₂.₅ and AOD, emphasizing that biomass burning and brick kilns are significant contributors. Studies utilizing remote sensing have mapped aerosol distributions throughout Bangladesh, investigating connections between urban-industrial development, seasonal weather patterns, and changes in land use (Mao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For example, MODIS AOD data have been utilized to identify regional haze events and investigate aerosol climate interactions (Chang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Raza, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Chatterjee et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) conducted a regional investigation into aerosol mobility from the Indo-Gangetic Plain to Bangladesh, establishing a connection between transboundary pollution and seasonal peaks. Ahmed (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) evaluated the health effects of pollution from PM2.5 in Bangladesh, noting a rise in respiratory illnesses while highlighting the shortcomings in the integration of satellite data with health metrics. Mohammad et al. (2024) established statistically significant increasing trends in aerosol concentrations throughout Bangladesh by employing Sen\u0026rsquo;s slope and Mann-Kendall tests, yet they recognized an absence of studies directly connecting these trends to health or policy outcomes.\u003c/p\u003e\u003cp\u003eDespite growing aerosol pollution, limited studies in Bangladesh have examined long-term AOD trends in conjunction with emission sources and public health risks, and none have contextualized these trends within sustainable development priorities. Therefore, this study addresses this gap by analyzing 25 years (2000\u0026ndash;2024) of satellite-derived AOD data to assess temporal dynamics, identify key pollution drivers, and interpret the findings in relation to health impacts and relevant SDG targets.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area\u003c/h2\u003e\u003cp\u003eThe research encompasses all areas of Bangladesh (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with locations spanning 20\u0026deg;34\u0026prime;N to 26\u0026deg;38\u0026prime;N latitude and 88\u0026deg;01\u0026prime;E to 92\u0026deg;41\u0026prime;E longitude (Hossain et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The country is located in northeastern South Asia, sharing borders with India to the west, north, and northeast, Myanmar to the southeast, and the Bay of Bengal to the south. Bangladesh covers an area of around 147,570 square kilometers (Haque et al., 2017). Bangladesh has a high population density, with approximately 171.5\u0026nbsp;million residents (World Bank, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The geographic and climatic conditions of Bangladesh's low-lying deltaic terrain are especially crucial in the trapping and accumulation of pollutants (Shamsudduha et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Used\u003c/h3\u003e\n\u003cp\u003eThis study utilized the MODIS Terra Aerosol Optical Depth (AOD) product to assess long-term variations in air pollution in Bangladesh. The data were sourced from the Moderate Resolution Imaging Spectroradiometer (MODIS) within the Terra satellite, a sun-synchronous, near-polar orbiting platform that NASA launched on December 18, 1999. MODIS offers global data at various spatial resolutions of 1 km, 500 m, and 250 m, that includes a temporal revisit frequency of 1 to 2 days (Islam et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe MODIS Collection 6.1 Level 3 monthly gridded product (MOD08_M3) was utilized, providing the average Aerosol Optical Depth (AOD) across terrestrial and marine environments at a wavelength of 550 nm, with a spatial resolution of 1\u0026deg; \u0026times; 1\u0026deg; (Hsu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The AOD values had been normalized by dividing by 1000, following standard preprocessing procedures for MODIS aerosol datasets (Sayer et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ali et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe annual mean Aerosol Optical Depth (AOD) for each year from January 2000 through December 2024 was calculated using Google Earth Engine (GEE). The analysis involved calculating the average monthly MODIS AOD values within the national boundaries of Bangladesh. Only normalized AOD values were used to evaluate interannual variations and evaluate long-term trends (Streets et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAssessment methods\u003c/h3\u003e\n\u003cp\u003eThe spatiotemporal assessment of AOD over Bangladesh was conducted in two stages. First, Google Earth Engine (GEE) was used to preprocess and extract normalized AOD data; second, statistical trend analysis was performed by using R to quantify and validate the temporal analysis.\u003c/p\u003e\n\u003ch3\u003eAOD Extraction and Preprocessing\u003c/h3\u003e\n\u003cp\u003eMODIS Terra Level 3 monthly AOD data were filtered and processed within the GEE environment. The dataset\u0026rsquo;s monthly mean AOD was averaged over both land and ocean surfaces and normalized by dividing 1000 to convert into standard AOD units. Annual mean AOD images were generated by averaging monthly values for each year from 2020 to 2024. The data were spatially clipped to Bangladesh's administrative boundary, and mean annual AOD values were extracted as a country-level time series.\u003c/p\u003e\n\u003ch3\u003eTrend Analysis and Statistical Evaluation\u003c/h3\u003e\n\u003cp\u003eThe exported time series was analyzed in R using both parametric and non-parametric techniques. A simple linear regression model was fitted:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{AOD}_{t}=\\:{\\beta\\:}_{0}+\\:{\\beta\\:}_{1}t+\\:\\epsilon\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{AOD}_{t}\\)\u003c/span\u003e\u003c/span\u003e is the normalized AOD in year t, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the intercept (baseline AOD when t\u0026thinsp;=\u0026thinsp;0), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e is the slope (rate of annual change), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003e is the random error. The model provided projections for the intercept, p-values, slope and coefficients of determination (R\u003csup\u003e2\u003c/sup\u003e). To ensure robustness and address non-linearity, the Mann-Kendall trend test was employed to identify the presence of a statistically significant linear trend. Furthermore, the Sen\u0026rsquo;s slope estimator was utilized to assess the magnitude of the annual trend. All analyses and visualizations were performed utilizing R packages, including trend, ggplot2, and readr.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eTemporal variation of AOD over Bangladesh\u003c/h2\u003e\u003cp\u003eNormalized annual AOD over Bangladesh from 2000 to 2024 shows a clear increasing trend (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with significant interannual variation. In the early 2000s, values consistently remained below the long-term mean of 0.6555, with the lowest AOD recorded in 2001 (0.4918; ~25% below the mean). AOD first exceeded the 0.6 threshold in 2009 (0.6683), after which values mainly remained above average.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTemporal variation of AOD over Bangladesh from 2000 to 2024\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAOD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeviation from Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ-Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTrend Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3-Year Moving Average\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.523138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.13238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-20.19%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBelow Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.491761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.16376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-24.98%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBelow Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.559885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.09563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-14.59%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBelow Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.524928\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.559312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-14.67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBelow Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.556319\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.558357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.09716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-14.82%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBelow Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.558327\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.546635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.10888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-16.61%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBelow Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.554101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.554007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.10151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-15.49%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBelow Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.553666\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.557351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.09817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-14.97%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBelow Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.552664\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.593314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-9.49%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBelow Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.568224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.668251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.012735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.94%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAbove Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.606972\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.632361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.02316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.53%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBelow Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.631309\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.698611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.043095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.57%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAbove Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.664987\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.69401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.038494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.87%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAbove Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.675964\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.623228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.03229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.93%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBelow Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.67195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.665695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.010179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.55%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAbove Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.661644\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.750984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.095468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.56%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAbove Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.696969\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.733195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.077679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.85%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAbove Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.713799\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.69142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.035904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.48%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAbove Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.733462\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.775772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.120256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.34%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignificantly Above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.733462\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.685178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.029662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.53%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAbove Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.717457\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.6862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.030684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.68%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAbove Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.71505\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.75356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.098044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.95%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAbove Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.745646\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.706974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.051458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.85%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAbove Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.750479\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.810964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.155448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.71%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignificantly Above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.757166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.867727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.212211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.37%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignificantly Above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.795222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.655516\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBetween 2009 and 2024, over 85% of the years recorded AOD above the mean, with recent years such as 2023 (0.8110) and 2024 (0.8677) showing deviations of over 23% and 32%, respectively. These were also classified as \"significantly above\" based on z-scores (\u0026gt;\u0026thinsp;1.5), indicating statistically elevated aerosol loading.\u003c/p\u003e\u003cp\u003eA three-year moving average confirms the rise in AOD, with values increasing from ~\u0026thinsp;0.55 in the early 2000s to ~\u0026thinsp;0.80 by 2024. The consistent increase in aerosol pollution over Bangladesh is further confirmed by the moving average, which smooths out short-term fluctuations and emphasizes the underlying long-term trend (Pope \u0026amp; Dockery, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). These results confirm a persistent upward shift in aerosol concentrations over Bangladesh, which is in line with the findings of MODIS-based analyses (Tariq et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sayed et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) emphasizing the importance of urgent policy attention to the expanding air quality concerns.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTemporal trend of AOD over Bangladesh\u003c/h3\u003e\n\u003cp\u003eThe normalized Aerosol Optical Depth (AOD) trend over Bangladesh from 2000 to 2024 shows a gradual and statistically significant increase in the concentrations of aerosols in the atmosphere (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A consistent increasing trend in aerosol loading over the 25 years is confirmed by the linear regression analysis of the yearly mean AOD values, which shows a positive slope of 0.0120 AOD units per year with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001. This pattern shows deteriorating air quality over time, with an average annual rise of about 1.8%. According to Islam et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), AOD increased between 2002 and 2016, with values primarily exceeding 0.5 across a broad area of Bangladesh.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eReflecting a notable long-term rise in atmospheric aerosol loading, the AOD value ranged from a minimum of 0.492 in 2001 to a maximum of 0.868 in 2024. A coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) of 0.83 from the linear regression analysis demonstrated a good model fit and supported the stability of the observed increasing trend. These results are in line with earlier findings about the rise in aerosol burden linked to the burning of biomass, emissions from cities and industries, and transboundary air pollution that affects Bangladesh (J. Lelieveld et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Validation of AOD Trend\u003c/h2\u003e\u003cp\u003eThe reliability of the observed trend was validated through the application of non-parametric methods. The Mann-Kendall test revealed a significant positive monotonic trend, with a Z-value of 4.928 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates that the rise in AOD is statistically strong and unaffected by outliers or non-normal data distribution. In addition, Sen\u0026rsquo;s slope estimator indicated a median annual increase of 0.0120 (95% confidence interval: 0.0093\u0026ndash;0.0146), which closely aligns with the slope derived from linear regression. The findings confirm the trend's consistency and support previous satellite-based studies that indicated increasing aerosol loadings in Bangladesh and neighboring areas, reinforcing the evidence of a continuous rise in aerosol pollution (Mohammad et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAOD as a Proxy for PM2.5\u003c/h2\u003e\u003cp\u003eIt is commonly acknowledged that satellite-derived Aerosol Optical Depth (AOD) is a reliable proxy for surface-level PM\u003csub\u003e2.5\u003c/sub\u003e, allowing for spatiotemporal air quality monitoring, particularly in areas with limited data (Van Donkelaar et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Numerous investigations conducted in various regions have revealed statistically significant correlations between AOD and PM\u003csub\u003e2.5\u003c/sub\u003e. For example, Krishna et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) used MODIS AOD and WRF-Chem simulations to establish a significant monthly association (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.77), in India. Regional humidity and aerosol features altered the regression slopes between AOD and PM₂.₅ in China, which ranged from 13 to 90 with R\u0026sup2; values up to 0.75 (Zheng et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, urban places like Nanjing and Beijing showed moderate to high associations (R\u0026sup2; = 0.53\u0026ndash;0.64) (Kong et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), whilst in Delhi, a 1% increase in AOD was linked to a 0.4\u0026ndash;0.5% increase in PM\u003csub\u003e2.5\u003c/sub\u003e concentrations (Kumar et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The long-term increase in AOD over Bangladesh in the current study, with normalized values regularly exceeding 0.6, indicates persistently high levels of surface particulate matter, confirming the usefulness of AOD as a stand-in for PM\u003csub\u003e2.5\u003c/sub\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDrivers of AOD accumulation in Bangladesh\u003c/h2\u003e\u003cp\u003eDomestic emissions, transboundary pollution, and meteorological conditions that retain pollutants contributed to increased aerosol loading in Bangladesh. Domestic particle emissions result from rapid urbanization, unregulated industrial expansion, vehicular traffic, biomass combustion, and coal-fired brick kiln activities (Begum et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The aged fleet of the transportation industry and the lack of emission restrictions constantly elevate urban PM2.5 levels, while seasonal agricultural residue burning increases aerosol concentrations from November to January​ (Pavel et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Transboundary pollution significantly impacts Bangladesh's air quality because the country is situated downwind of the heavily polluted Indo-Gangetic Plain (IGP) region.​ During winter, prevailing northwesterly winds carry fine particulate matter, including sulfate, organic carbon, and dust, from northern India, Nepal, and Pakistan into Bangladesh (Dihan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The rapid rise in energy demand has increased dependence on fossil fuels such as coal and natural gas for power and industrial use, intensifying emissions of aerosol precursors and primary particles. Power plants near urban and industrial areas notably contribute to regional haze and aerosol loading (Zaman et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Construction activities and road dust resuspension, driven by urban expansion in cities like Dhaka and Chattogram, are essential contributors to aerosol load. These generate coarse particulate matter that raises AOD and serves as nuclei for further aerosol growth (Kormoker et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Climate change affects regional aerosol dynamics by modifying wind patterns, precipitation, and atmospheric stability, impacting pollutant transport and deposition. Increased temperature variability and drought frequency prolong pollutant residence and elevate aerosol levels across South Asia, including Bangladesh (Banik, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nair et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003ePublic Health Risks and Sustainable Development Needs\u003c/h2\u003e\u003cp\u003eThese data reveal the escalating trend of aerosol optical depth (AOD) in Bangladesh from 2000 to 2024, during which normalized AOD values increased from 0.49 to 0.86, with an average of roughly 0.66. The consistent increase in AOD corresponds with rapid urbanization, industrial expansion, and higher energy consumption in Bangladesh during the past twenty years. This degree of aerosol concentration indicates a significant exceeding of PM₂.₅ levels beyond the WHO's yearly recommended limit of 5 \u0026micro;g/m\u0026sup3;. The WHO estimates that more than 94% of the global population is subjected to hazardous PM₂.₅ levels, with concentrations over 35 \u0026micro;g/m\u0026sup3; associated with a 24% rise in mortality risk (Rentschler \u0026amp; Leonova, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The WHO does not specify an exact AOD limit; nonetheless, the proven association between AOD and PM₂.₅ indicates that consistently elevated AOD levels, such those observed in Bangladesh, pose considerable environmental and public health risks (Khan, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In 2023, Bangladesh was designated the most unhealthy county globally, demonstrating an annual average fine particulate matter (PM2.5) concentration of 79.9 \u0026micro;g/m\u0026sup3;\u0026mdash;surpassing the national limit of 35 \u0026micro;g/m\u0026sup3; by over twice and exceeding the World Health Organization\u0026rsquo;s (WHO) standard of 5 \u0026micro;g/m\u0026sup3; by fifteen times. Air pollution is responsible for 102,456 deaths each year in Bangladesh (Nesan et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This is especially crucial in Bangladesh, where terrestrial air quality monitoring is limited, rendering satellite-derived AOD data essential for evidence-based decision-making (Gupta et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Prolonged exposure to elevated PM₂.₅ correlates with cardiovascular diseases, respiratory disorders, hindered lung development in children, and premature mortality, disproportionately affecting vulnerable populations, including pregnant women, elderly, and those with existing health issues, thereby exacerbating health disparities across the country (Di et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hoek et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kioumourtzoglou et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEffective emission reduction policies that focus on critical sources like transportation, industry, brick kilns, and agricultural burning are vital for reducing pollutant concentrations in Bangladesh (Begum et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Khandker et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These policies and initiatives play a crucial role in advancing the Sustainable Development Goals (SDGs), especially SDG 3.9, which aims to significantly decrease deaths and illnesses caused by hazardous air pollution, and SDG 11.6, which emphasizes enhanced monitoring and mitigation of negative environmental effects in urban areas, including air quality management (WHO, 2018; Kanlayanatam et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zusman et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, enhancing intersectoral coordination connecting health ministries, urban planners, and environmental regulators can improve the effectiveness of air quality management strategies. The ongoing increase in AOD across Bangladesh highlights an escalating air quality crisis that requires thorough and collaborative actions from health, environmental, and policy sectors (Islam et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the absence of prompt and integrated actions, the rising aerosol pollution will persist in undermining public health and hindering the nation\u0026rsquo;s advancement toward sustainable development goals (Mahmood et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e\u003cp\u003eThis study provides a significant long-term evaluation of aerosol pollution in Bangladesh utilizing MODIS AOD data from 2000 to 2024; nevertheless, it has some limitations. AOD indicates columnar aerosol load instead of surface-level concentrations, and its accuracy in assessing human exposure may fluctuate due to atmospheric mixing and boundary layer dynamics. The spatial resolution (1\u0026deg; \u0026times; 1\u0026deg;) of the MODIS Level 3 data restricts the identification of localized pollution hotspots, especially in urban-industrial areas. Seasonal and meteorological influences on aerosol dynamics were also out of the scope of this study.\u003c/p\u003e\u003cp\u003eFuture research should integrate higher-resolution products like MODIS MAIAC or Sentinel-5P and expanded ground-monitoring networks to better capture surface-level pollution. Coupling AOD with chemical transport models or machine learning techniques could enhance prediction accuracy. Investigating health impacts through empirical linkage between AOD and morbidity or mortality data would strengthen the public health relevance. Additionally, source apportionment and chemical characterization of aerosols and analysis of climate-aerosol interactions would provide deeper insights into rising air pollution's environmental and developmental implications.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research presents a thorough, 25-year spatiotemporal evaluation of aerosol pollution in Bangladesh, utilizing MODIS-derived Aerosol Optical Depth (AOD). The notable and steady increase in AOD throughout the country underscores an escalating environmental and public health issue, with normalized values surpassing long-term averages in over 85% of the study years. The rise can be linked to local emissions, industrial expansion, energy use, and cross-border pollution. The significant upward trend, confirmed through both linear and non-parametric analyses, highlights the critical need to incorporate satellite-based aerosol monitoring into national air quality management. Considering the constraints of Bangladesh\u0026rsquo;s ground monitoring network, AOD acts as a significant proxy for PM₂.₅, highlighting the crucial role of satellite data in informed policymaking. The health implications are significant, particularly for vulnerable populations, as elevated aerosol concentrations correlate with heightened risks of respiratory and cardiovascular diseases. Bangladesh needs immediate and notable air quality improvement initiatives to achieve the Sustainable Development Goals, especially SDG 3.9 and SDG 11.6. Enhancing ground-based monitoring, utilizing high-resolution satellite datasets, and incorporating health impact models are crucial for formulating thorough air quality strategies. Without timely, coordinated, and multisectoral interventions, the increasing aerosol burden will persist in undermining public health and environmental sustainability in Bangladesh.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author would like to thank the open-access platforms and data providers whose freely available resources enabled this research, particularly NASA\u0026rsquo;s MODIS team for the AOD data.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMODIS Terra AOD data (MOD08_M3) used in this study are publicly available from NASA\u0026rsquo;s Earthdata portal (https://earthdata.nasa.gov). The scripts and processed datasets are available from the author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author independently designed the study, conducted the data analysis, analyzed the findings, and prepared the report.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed, A. (2024). \u003cem\u003eExamining the Impact of Environmental Factors on the Risk of Cerebral Palsy\u003c/em\u003e (Doctoral dissertation, Universit\u0026eacute; d\u0026apos;Ottawa/University of Ottawa).\u003c/li\u003e\n\u003cli\u003eAli, M. 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Singapore: Springer Nature Singapore.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Not Applicable","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Aerosol Optical Depth, Air Pollution, Emission Drivers, Public Health Risks, Sustainable Development","lastPublishedDoi":"10.21203/rs.3.rs-7342293/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7342293/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAerosol pollution constitutes a substantial risk to environmental health in South Asia, especially in Bangladesh, where rapid urbanization and industrial growth have exacerbated particulate emissions. This study examines 25 years (2000\u0026ndash;2024) of MODIS Terra Aerosol Optical Depth (AOD) data to evaluate long-term trends in aerosol concentration in Bangladesh. Annual mean AOD values were obtained using Google Earth Engine and examined by linear regression, Mann\u0026ndash;Kendall trend analysis, and Sen\u0026rsquo;s slope estimation in R. The results indicate a statistically significant upward trend in AOD, with mean values escalating from 0.49 in 2001 to 0.87 in 2024 (R\u0026sup2; = 0.83; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), implying a continual increase in atmospheric particle concentrations. Z-score analysis indicated that recent years, specifically 2023 and 2024, exhibited statistically significant increases in aerosol pollution. The rise in AOD is associated with both local and transboundary sources, such as automotive emissions, brick kilns, consumption of fossil fuels, and regional dust transport. These findings underscore the escalating public health risks associated with PM₂.₅ exposure and affirm the utility of AOD as a reliable proxy. The study emphasizes the imperative for targeted emission reduction measures, comprehensive planning, and improved monitoring systems to achieve Sustainable Development Goals (SDGs) related to health and urban resilience. Despite its limitations, especially the use of coarse-resolution AOD, the study provides essential evidence for informed decision-making and future research on aerosol pollution in Bangladesh.\u003c/p\u003e","manuscriptTitle":"Aerosol Optical Depth as a Proxy for Air Pollution in Bangladesh: Temporal Trends, Emission Drivers, and Implications for Public Health and Sustainable Development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 05:49:46","doi":"10.21203/rs.3.rs-7342293/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":"2f4a6f3b-ebd7-4077-b6c0-47025a5885f2","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52941545,"name":"Atmospheric Sciences"}],"tags":[],"updatedAt":"2025-08-13T14:59:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 05:49:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7342293","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7342293","identity":"rs-7342293","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}