Factors Influencing Spatiotemporal Variability of NO2 Concentration in Urban Area: A GIS and Remote Sensing-Based Approach

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Shakhaoat Hossain This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4672218/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2025 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 11 You are reading this latest preprint version Abstract The growing global attention on urban air quality underscores the need to understand the spatial and temporal dynamics of nitrogen dioxide (NO2), especially in cities like Dhaka (Gazipur), Bangladesh, known for having some of the world's poorest air quality. The present study utilizes the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5P (S5P) satellite and Google Earth Engine (GEE) to analyse NO2 concentrations in Gazipur, Bangladesh, from 2019 to 2022. Utilizing S5P TROPOMI data, we investigate the correlations between NO2 levels and various environmental factors, including land surface temperature (LST), normalized difference vegetation index (NDVI), land use and land cover (LULC), population density, road density, settlement density, and industry density. Our results reveal significant seasonal variations, with peak NO2 levels during pre-monsoon and post-monsoon periods and the lowest levels during monsoon seasons. The study demonstrates a positive correlation between NO2 concentrations and LST, road density, settlement density, and industrial density, and a negative correlation with NDVI. These findings underscore the detrimental impact of rapid urbanization and deforestation on air quality. Through linear regression analysis, we highlight the influence of these environmental factors on NO2 levels, providing a comprehensive understanding of the urban pollution dynamics in a rapidly growing city. This research offers critical insights for policymakers and urban planners, advocating for enhanced green infrastructure, stringent emission controls, and sustainable urban development strategies to mitigate air pollution in Gazipur. Our methodological approach and findings contribute to the broader discourse on urban air quality management in developing countries. NO2 Concentration Urban Air Quality Spatiotemporal Patterns GIS Gazipur-Dhaka Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction In tackling the global air pollution situation, diverse programs and policies at the international, regional, and local scales have been put in place aimed at fostering environmental sustainability and enhancing overall well-being (Melamed et al., 2016 ). Despite these efforts, air pollution persists as the fourth leading cause of premature death globally (Health Effects Institute, 2020 ) and remains the most substantial external threat to human life expectancy (Greenstone & Hasenkopf, 2023 ). Shockingly, it contributes to an annual toll of 6.67 million premature deaths (Health Effects Institute, 2020 ), with nitrogen dioxide (NO 2 ), an ambient air pollutant, playing a pivotal role among others (Song et al., 2023 ). NO 2 exerts adverse effects on both human health (Song et al., 2023 ) and the ecological environment (L. Li & Wu, 2021 ). It serves as a crucial precursor for anthropogenic ozone (O3) and urban smog. This gaseous pollutant is a key player in the formation of other pollutants, such as nitric acid (HNO3), fine particulate matter (PM2.5), and nitro-polycyclic aromatic hydrocarbons (nitro-PAHs) (L. Li & Wu, 2021 ). A global analysis in 2019 found that 81% of cities exceeded WHO standards for NO 2 , contributing to 2.7% of all mortalities that year (Song et al., 2023 ). Existing epidemiological evidence firmly establishes the link between ambient NO 2 exposure and an increased risk of adverse health outcomes (Stieb et al., 2021 ). Inhaling of increased NO 2 can lead to throat and upper respiratory tract inflammation, resulting in breathing difficulties, throat spasms, and lung fluid accumulation (DoE, 2018 ). Additionally, it diminishes blood oxygen-carrying capacity, causing symptoms like headaches, fatigue, and dizziness (DoE, 2018 ). Recent research also indicates a positive correlation between atmospheric NO 2 levels and susceptibility to COVID-19 infection (Amoroso et al., 2022 ; Zhu et al., 2020 ). Long-term exposure to NO 2 poses significant health risks, including cardiovascular disease, lung cancer, and other potentially fatal respiratory conditions (Atkinson et al., 2018 ). Recognizing the severity, the World Health Organization (WHO) and the United States Environmental Protection Agency (US EPA) designate NO 2 as a key indicator of outdoor air pollution (Islam et al., 2022 ; Melamed et al., 2016 ). Also, the WHO has reduced the ambient NO 2 annual mean limit from 40 µg/m3 to 10 µg/m3 in the latest air quality guideline (WHO, 2021 ). This yellowish-orange to reddish-brown gaseous pollutant with a pungent, irritating odour (DoE, 2018 ), exhibits a brief atmospheric photochemical longevity, ranging from 2–5 hours during the daytime in summer to 12–24 hours during winter, which indicates spatiotemporal variability based on emission sources (Goldberg et al., 2021 ; Islam et al., 2022 ). Worldwide population-weighted nitrogen dioxide (NO 2 ) estimates, spanning from 1996 to 2012, reveal a yearly decline of 4.7% in the United States and Canada, 2.5% in Western Europe, and a notable increase of around 6.7% in East Asia and major Indian cities (Geddes et al., 2016 ; Singh et al., 2023 ). The observed trends in NO 2 concentration are highly influenced by various spatiotemporal factors (Singh et al., 2023 ). However, obtaining an accurate estimate of NO 2 remains a challenge in many developing nations like Bangladesh (Bechle et al., 2013 ; Islam et al., 2022 ) due to the high costs associated with establishing and maintaining monitoring stations (Rabiei-Dastjerdi et al., 2022 ). Advanced remote sensing (RS) (Bechle et al., 2013 ; Islam et al., 2022 ) and geographic information system (GIS) (Ashwini & Sil, 2022 ; Chawala et al., 2023 ) technologies play a crucial role in addressing this gap, enabling scientists to monitor atmospheric NO 2 and study its determinants effectively (Bechle et al., 2013 ; Islam et al., 2022 ). The most recent tropospheric vertical column of NO 2 data from the European Space Agency's Sentinel 5P, also known as the TROPOMI (ESA, 2018 ), offers a reliable estimate of NO 2 emission values in comparison with on-site data recordings (Goldberg et al., 2021 ; Islam et al., 2022 ). In recent literature, diverse factors including the NDVI, LULC patterns, and LST have been explored, using GEE-based RS (Ashwini & Sil, 2022 ; Rahaman et al., 2023 ); on the other hand, road density, industry density, and settlement density have been investigated, using GIS platforms to quantify and understand the variations in NO 2 levels (Ashwini & Sil, 2022 ; Grzybowski et al., 2023 ). Despite limited studies on NO 2 pollution in Bangladesh (Islam et al., 2022 ; Mukta et al., 2020 ; Rabbi, 2018 ; R. R. Rahman & Kabir, 2023 ; Rana et al., 2021 ), several researchers have employed GIS and RS techniques to measure and analyze NO 2 concentration (R. R. Rahman & Kabir, 2023 ; Rana et al., 2021 ), yet there remains a need to address crucial factors (NDVI, LULC, LST, road density, industry density, and settlement density) to comprehensively grasp the variability of NO 2 concentration levels, using RS and GIS techniques. Given the context, Gazipur City, a part of the Greater Dhaka Division, suffers from some of the worst air quality in Bangladesh, as noted in recent studies [3]. However, there has been limited research on how nitrogen dioxide (NO2) levels vary on spatiotemporal levels within the city, and what drives these variations. To address this research gap, the present study aims to: (1) Investigate the spatiotemporal distribution of NO 2 concentration in urban areas like Gazipur city, using RS and GIS techniques. (2) Identify the factors that influence the variation in NO 2 concentration. 2. Materials and methods A flowchart-based depiction of the complete methodological process of the study is available in Fig S1 2.1. Study Area The present study focuses on Gazipur, a rapidly urbanizing area located in the north-eastern region of Dhaka, Bangladesh (Arifeen et al., 2021 ). Gazipur district lies centrally in Bangladesh, between latitudes 23°53′ and 24°20′N and longitudes 90°9′ and 90°42′E, covering an area of 321 square kilometres (Fig. 01 ). The Gazipur city has a population of approximately six million and a total road length of around 1552 kilometres (Abdullah et al., 2019 ). Gazipur's proximity to Dhaka has transformed it into a major hub for employment and commerce, contributing significantly to the country's Gross Domestic Product (GDP). Notably, 65% of Bangladesh's garment factories are situated in this region (Oeurng et al., 2016 ). Consequently, people from surrounding districts, particularly rural areas, are migrating to Gazipur. This rapid urbanization, along with an increase in vehicles and industrial activities, has led to a substantial rise in NO2 levels in the area (Uddin et al., 2023 ). 2.2. Data description In this study, we utilized Sentinel 5P TROPOMI satellite NO 2 concentration data from 2019 to 2022, along with various environmental variables including population density, LST intensity, NDVI, LULC, road density, settlement density, and industry density for the year 2022. Detailed information about these datasets is provided in Table 1 . Furthermore, thematic maps illustrating the 2022 data for Gazipur city, including population density, LST intensity, NDVI, LULC, road density, settlement density, and industry density, are presented in Fig. 06 . Table 1 Geospatial dataset with their different attributes used in the study domain. Data used Data type Special Resolution Temporal Resolution Dataset Provider Dataset Availability Range/Units Sentinel 5P TROPOMI NO 2 Raster Data 1000 Meters Daily European Union/ESA/Copernicus 2018 to Present mol/m 2 Sentinel 2A Dynamics (LULC) [32] Raster Data 10 Meters 6 Days European Space Agency 2017 to Present Square Meter Grid LST Raster Data 1000 Meters Daily MODIS 2000 to Present K NDVI Raster Data 30 Meters 16 Days Landsat 8 2013 to Present Square Meter Grid Human Modification Raster Data 1000 Meters 5 Year Interval Conservation Science Partners February 2016 km^ 2 Road Network Vector Data 100 Meters Random European Union/ESA/Copernicus 2018 to Present km Settlement Vector Data Point Values Yearly OpenStreetMap 2004 to Present Point Industry Vector Data Point Daily OpenStreetMap 2004 to Present Point 2.3. Methods Numerous studies have effectively utilized the GEE platform to analyze factors such as LULC, LST, NDVI, population density, road density, settlement density, and industry density. This study focuses on the following analyses: (1) the spatiotemporal distribution of NO2 concentration using satellite images, with seasonal and monthly statistics extracted for the years 2019 and 2022, and (2) the comparison of Sentinel 5P TROPOMI NO2 values with seven environmental variables (LST, NDVI, LULC, population density, road density, settlement density, and industry density) in Gazipur city. The study leverages free image collections from TROPOMI, MODIS, Dynamic World, and Global World instruments available in the GEE datasets, along with road, settlement, and industry data from the OpenStreetMap (OSM) platform. GEE and OSM are open-source geospatial analysis platforms that allow users to visualize and analyze changes, map trends, and quantify differences in Earth's environment. Gazipur city was divided into 356 grids, each with a size of 1x1 square kilometer, based on NO2 concentration. Environmental variable’s values for each grid were extracted based on NO2 concentration points. The study computed and processed the NO2 concentration, along with all the seven environmental parameters for each grid. 2.3.1 NO 2 Concentration data The impacts of air pollution can be assessed using different methods, including ground-based, ship-based, and satellite-based monitoring. Satellite-based monitoring stands out for its extensive spatial and temporal coverage, offering comprehensive global and regional insights into air pollution (Gao et al., 2023 ). This study uses satellite-based observation for the spatiotemporal distribution of NO 2 concentration. The Sentinel 5P TROPOMI is an on-board satellite system used to measure NO 2 concentrations (Prunet et al., 2020 ). NO2 concentration data from TROPOMI, spanning January 2019 to December 2022, were acquired daily through the GEE platform (Harper et al., 2021 ). Daily tropospheric NO2 vertical column concentration data from TROPOMI version-1 level-3, featuring a spatial resolution of 1×1 square kilometre, were sourced from GEE platform (X. Li et al., 2023 ). For the analysis, a JavaScript program was created in GEE to access, process, and display the data from the image collections. The scripts utilized in this study are available in supplementary material Algorithm S1. During this phase, the chosen images undergo the following processing steps: (a) filtering the imaging time for the clipped region of interest (Gazipur City), (b) eliminating cloudy pixels using a condition on TROPOMI, MODIS, and Landsat-based products and selecting the highest quality satellite data, (c) extracting daily images by mosaicking overlapping scenes across the entire study area, (d) extracting hourly pixel values for AOD (Aerosol Optical Depth) and NO2 column density from the relevant images, (e) computing monthly and seasonal images by averaging the daily images to create spatiotemporal maps and to analyse the relationship between environmental variables from January 2019 to December 2022. 2.3.2 Normalized Difference Vegetation Index (NDVI) To calculate NDVI, the raw scenes from USGS Landsat 8 Collection 1 Tier 1 and real-time data were utilized. A custom script was employed on the GEE platform to derive NDVI values from the Landsat 8 dataset composite, with a 30 meters spatial resolution and a temporal resolution of 16 days. NDVI, which ranges from − 1 to + 1, quantifies the earth's vegetation cover. Negative values indicate non-vegetated surfaces, a value of 0 denotes minimal vegetation, and a value of 1 signifies dense vegetation (Ashwini & Sil, 2022 ). Eq. 1, presented below, illustrates the calculation of NDVI, while Algorithm S2 provides the NDVI extraction code. Equation 1 NDVI = (NIR - RED) / (NIR + RED) In this formula, 'RED' refers to the Red Reflectance, and 'NIR' refers to the Near Infrared Reflectance. 2.3.3 Land Surface Temperature (LST) LST images from the MOD11A1 dataset, obtained by the MODIS sensor on the Terra satellite, were utilized. This dataset offers daily LST data with a 1x1 km grid resolution, available in both day and night temperature bands (Suthar et al., 2024 ). For this study, only daytime satellite imagery was considered. The LST, derived from the difference between outgoing thermal radiation and surface temperature, was calculated using the GEE platform. The calculation involves several steps, starting with Equation 2 LST = (K2 / ln (K1 / radiance) + 1) − 273.15 ( Algorithm S3 ), where K1 and K2 are calibration constants specific to the MODIS sensor and thermal infrared bands. ArcGIS Pro software was employed to convert the pixel/grid data of Gazipur city into a point vector grid. The final output was then used to create and visualize the LST map, providing insights into the LST patterns in the Gazipur city area. 2.3.4 Land Use Land Cover (LULC) Classification This research utilizes the 2022 Sentinel-2 Dynamic World LULC satellite dataset to illustrate these land use changes. The land is classified into six categories: built-up areas, water bodies, trees, grasslands, flooded vegetation, cropland, and shrubland or bare land (Agarwal & Kumar, 2022 ). The images in the Dynamic World collection correspond to specific Sentinel-2 L1C asset ( Algorithm S4 ) For instance, the image 'COPERNICUS/S2/20160711T084022_20160711T084751_T35PKT' matches the Dynamic World image 'GOOGLE/DYNAMICWORLD/V1/20160711T084022_20160711T084751_ T35PKT'. All probability bands in these images, except the 'label' band, collectively sum to one. 2.3.5 Grided Population Density To explore the interactions with LST, NDVI, LULC, settlement density, road density, and NO2 concentration, the Global Human Modification (GHM) data or population density information was utilized. The Gridded Population of the World, version 4 (GPWv4), offers a comparative analysis of human population density (Suthar et al., 2023 ). This dataset employs a proportional allocation gridding algorithm to distribute the population into 30 arc-second grid cells, covering approximately 13.5 million administrative units worldwide. The GHM data for the years 2005, 2010, 2015, and 2020 are available for download. For this study, the 2020 data was obtained through the GEE platform ( Algorithm S5) from the following website: https://csp-inc.org/ (CSP, 2024 ). 2.3.6 Industry, Road, and Settlement Density Data for industry, settlements, and roads were sourced from the OSM portal ( https://www.openstreetmap.org)(OpenStreetMap contributors, 2024 ). The dataset includes point features for industry and settlements, and polyline features for roads. To analyse density, we used ArcMap commands specifically designed for point density (for industry and settlements) and line density (for roads). The spatial resolution of the dataset is 100 meters. The density calculation process involves measuring the quantity of features per unit area within a specified radius around each cell. This method enables the assessment of the density of linear features across different regions. ArcGIS 10.8 software facilitates the conversion of polygons to points and subsequently calculates point densities, which are then categorized by height, settlement type, and settlement classification. 2.3.1. Kernel Density Analysis The Kernel Density tool is utilized to determine the concentration of features within a specific area. This analysis can be applied to both point and line features, providing an index that represents the intensity of influence that certain factors have on their surroundings (Mitchell, 1999 ). In this study, three environmental variables are examined using kernel density analysis with ArcGIS 10.8 software: Road Network (line feature), Settlements (point feature), and Industry (point feature). Firstly, the road network data, initially a line feature, is converted into a kernel density distribution. This conversion produces a weighted value based on the density of the road network, highlighting areas with higher road concentrations. Secondly, the settlement and industry data, originally point features, are converted into kernel point densities. This process focuses on the distribution patterns of settlements and industries within the Gazipur city area. The results of this analysis provide detailed density maps for both settlements and industries, fulfilling the study's requirements. 2.3.2. Regression Analysis To examine the relationship between NO2 levels and various environmental factors such as NDVI, LST, LULC, population density, industry density, road density, and settlement density in Gazipur City, linear regression analysis was employed. The NO2 concentrations across 356 grid cells were visualized using scatter plots. Microsoft Excel was utilized for correlation analysis, while Python was used to perform multiple linear regression analysis to explore the connections between NO2 concentrations and the other variables. 2.3.3. Final Visualization The spatiotemporal distribution maps of NO2 concentrations, along with other environmental variable maps, were created using ArcGIS 10.8 software. The methodology to summarize the study results for the entire Gazipur City area included: i) Mapping the spatiotemporal distribution of NO2 concentrations across the city ii) Developing a JavaScript program in GEE to calculate and visualize the average NO2 concentrations on a monthly and seasonal basis iii) Comparing and analysing satellite-based NO2 data with various environmental variables. 3. Result and Discussion The study analyses NO2 levels from 2019 to 2022, highlighting trends over different seasons and months. It examines how NO2 concentrations relate to environmental factors such as LST, LULC, NDVI, and the density of populations, settlements, roads, and industries. The findings demonstrate the impact of these factors on NO2 levels through correlation and regression analysis. 3.1. Spatial and temporal variation of NO 2 concentration The NO 2 seasonal concentrations exhibited fluctuations over four consecutive years (2019–2022), as illustrated in Fig. 02 (Table S2) . The seasonal shifts in NO 2 concentration are further demonstrated with the use of geospatial mapping for each investigated years in Fig. 03 . Additionally, the monthly variations in NO2 concentrations are provided in Table S3 . The findings revealed a diverse pattern of NO 2 concentration across the studied years. In 2019, mean NO 2 concentration showed almost similar levels during post-monsoon (2.5 × 10 − 4 mol/m 2 ), pre-monsoon (2.3 × 10 − 4 mol/m 2 ), and winter (2.2 × 10 − 4 mol/m 2 ) seasons; on the other hand, monsoon recorded the minimum concentration (1.4 × 10 − 4 mol/m 2 ). In 2020, the pre-monsoon season exhibited the highest average NO 2 concentration (2.9 × 10 − 4 mol/m 2 ), with winter and post-monsoon showing comparable levels (2.0 × 10 − 4 mol/m 2 and 2.3 × 10 − 4 mol/m 2 , respectively), and monsoon recording the lowest average (1.5 × 10 − 4 mol/m 2 ). The winter of 2021 marked the peak mean NO 2 concentration (3.1 × 10 − 4 mol/m 2 ) of all the studied years, with post-monsoon, pre-monsoon, and monsoon seasons following with concentrations of 2.1 × 10 − 4 mol/m 2 , 1.2 × 10 − 4 mol/m 2 , and 1.7 × 10 − 4 mol/m 2 , respectively. Winter 2022 nearly matched the highest average NO 2 concentration that was observed across all study years (3.0 × 10 − 4 mol/m2), with other three seasons following a descending trend: post-monsoon (2.0 × 10 − 4 mol/m 2 ) > pre-monsoon (1.8 × 10 − 4 mol/m 2 ) > monsoon (1.1 × 10 − 4 mol/m 2 ). During 2019, average NO 2 levels were substantially higher in the pre-monsoon and post-monsoon seasons by 4.3% and 12% respectively, compared to winter. In 2020, this trend continued with NO 2 concentrations as 31% higher in the pre-monsoon season and 4.8% higher in the post-monsoon season compared to winter. Increased levels of NO 2 during the dry pre-monsoon and post-monsoon periods are a common occurrence across Bangladesh, including Dhaka (Azad & Kitada, 1998 ; Islam et al., 2022 ; Mukta et al., 2020 ). A comprehensive analysis spanning from 2005 to 2019 in the Indo-Gangetic Plain similarly noted increased NO 2 concentrations during the dry post-monsoon season(Chawala et al., 2023 ). Previous studies in Dhaka have consistently identified winter as the season with the highest NO 2 pollution levels (Azad & Kitada, 1998 ; Rahaman et al., 2023 ; R. R. Rahman & Kabir, 2023 ), and the results of (Azad & Kitada, 1998 ; Rahaman et al., 2023 ; R. R. Rahman & Kabir, 2023 ) align with the results of the present study’s findings for 2021 and 2022; winter exhibited the highest average NO 2 concentration. Various sources contribute to excessive NO 2 pollution, that take place because of vehicular emissions, burning of biomass fuels, industrial processes, such as electroplating, and operations of electric power plants. Out of the years examined, the monsoon season consistently recorded the lowest average NO 2 levels, reaching a minimum of 0.00011 mol/m 2 in 2022. This observation is consistent with previous research findings and is likely attributed to heavy precipitation during the monsoon period(Hoque, 2020 ; M. M. Rahman et al., 2019 ; Shobnom et al., 2023 ). The distribution of mean NO 2 concentration varied throughout the studied years, and this shows fluctuations between months with higher and lower pollution levels (Fig. 04 ), consistent with seasonal changes. The spatial distribution patterns of NO 2 concentration also support the similar monthly variations as shown in Fig. 05 . In 2019 (Fig. 04 a), the highest average NO 2 concentration occurred in November (3.7 × 10 − 4 mol/m 2 ), the last month of the post-monsoon season, followed by January and February of the winter season with the second highest mean concentration (2.6 × 10 − 4 mol/m 2 ). During the pre-monsoon months of March, April, and May, as well as extension to the moth of June (1.5 × 10 − 4 mol/m 2 ), concentrations fluctuated between 1.1 × 10 − 4 mol/m 2 and 1.6 × 10 − 4 mol/m 2 . The latter part of the monsoon season, July, and August displayed the lowest mean NO 2 concentration levels at 0.9 × 10 − 4 mol/m 2 . In 2020 (Fig. 04 b), December, the onset of winter, saw the highest mean NO 2 concentration at (3.9 × 10 − 4 mol/m 2 ), whereas August, marking the end of the monsoon, had the lowest at (0.9 × 10 − 4 mol/m 2 ). As it shown in 2021 (Fig. 04 c) and 2022 (Fig. 04 d), the lowest concentrations were in August (1.4 × 10 − 4 mol/m 2 ) and July (1.3 × 10 − 4 mol/m 2 ) respectively, with the peak levels recorded in January 2021 (4.3 × 10 − 4 mol/m 2 ) and November 2022 (3.8 × 10 − 4 mol/m 2 ). In general, the analysis of the current study reveals a consistent rise in NO2 levels from November to March across all years that were under investigation. Starting in August 2019, the researchers observed a notable rise, with NO2 levels peaking at 35.7% by 2021. Over the period from July 2019 to July 2022, there was a 30.7% surge in NO2 concentrations. Among the nine cities the researchers studied, Narayanganj and Dhaka recorded the highest levels, with Gazipur City ranking third. This rise in pollution is primarily due to rapid unchecked urbanization and heavy loss of vegetation (Rahaman et al., 2023 ). The average NO2 concentrations across four seasons are detailed in Table S2 and Fig. 02 . 3.2. Vegetation scenario (NDVI) and NO2 concentration This study explores the relationship between vegetation density and nitrogen dioxide (NO2) levels in the atmosphere. An inverse correlation, indicated by a correlation coefficient of -0.4, between the NDVI and NO2 concentrations was found. This suggests that as vegetation density increases, NO2 levels decrease. This relationship is visually supported in our data ( Table S1 ). Further analysis reveals that 16% of the increase in NO2 levels can be directly attributed to the reduction of green spaces, as shown in Fig. 07 a. This finding highlights the protective role of vegetation in urban and industrial settings where NO2 levels are often increased due to vehicle and industrial emissions. In areas with dense vegetation, NO2 levels are typically lower, and this demonstrates how plants absorb and mitigate these pollutants. To summarize, NDVI, effectively illustrates how vegetation can influence air quality by reducing harmful NO2 concentrations in the atmosphere. This underscores the importance of maintaining and expanding green spaces to mitigate air pollution, particularly in densely populated urban areas. 3.3. LST condition and NO2 concentration This study found a correlation between LST and nitrogen dioxide (NO2) levels in urban areas, with an observed correlation coefficient of 0.47 ( Table S1 ) . This finding indicates that as the temperature of the land surface increases, the concentration of NO2 also tends to rise. The analysis shows that about 22.5% of the variation in NO2 pollution can be explained by fluctuations in LST, as reflected by the calculated R 2 value of 0.2251. Additionally, our results highlight the impact of diminishing green spaces in urban settings. Specifically, the reduction of green areas contributes to 16% of the increase in NO2 concentrations ( Fig. 07 b ) . This underscores the environmental challenges faced in densely built metropolitan areas where concrete structures dominate and green spaces are limited. The urban heat island effect, which is prevalent in such areas, not only leads to higher temperatures compared to surrounding rural zones but also appears to exacerbate air pollution levels due to increased vehicular and industrial activities. 3.4. Population density and NO2 concentration This study investigates how population migration impacts nitrogen dioxide (NO 2 ) levels in the Gazipur City region amid significant demographic shifts as illustrated in Fig. 06 e and Fig. 07 c. The detailed analysis reveals a correlation coefficient of 0.4, which indicates a positive association between NO 2 concentrations and population density. Areas experiencing substantial population mobility show higher NO 2 levels, likely due to increased motor vehicle usage and subsequent NO 2 emissions. Conversely, regions with lower population mobility tend to have lower NO 2 concentrations. Furthermore, our calculated R 2 value of 0.17 suggests that population density explains up to 17% of the variation in NO 2 concentration. 3.5. Industry density and NO2 concentration This study assessed the relationship between industrial density and NO2 levels in Gazipur city. The findings reveal that areas with a greater concentration of industrial facilities consistently show higher NO2 levels. Specifically, the correlation coefficient of 0.35, as shown in Table S1 , indicates a positive relationship between the number of industries and the increase in NO2 levels. The found result aligns with prior research, which suggests a significant impact of industrial activities on air quality (Jiang et al., 2021 ; Leffel et al., 2022 ). Additionally, although the influence appears modest, the coefficient of determination (0.0124) depicted in Fig. 07 d, confirms that industrial density does affect NO2 levels during the study period. The data points to industrial emissions as a likely contributor to increased NO2 levels in the surrounding regions. This underscores the urgent need for effective pollution control measures and regulatory actions in areas that are heavily populated by industrial activities, to mitigate their impact on air quality. 3.6. Road density and NO2 concentration This study reveals a moderately strong correlation (0.55) between road density and NO2 concentrations, as detailed in Table S1 . This suggests that areas with more roads tend to have higher levels of NO2, likely due to vehicle emissions. This pattern is supported by similar findings in other research (CARSLAW, 2005 ; Zheng et al., 2009 ), which confirm the link between road networks and air pollution. Additionally, a spatial map presented in Fig. 06 f visually illustrates this relationship across the study area. Further analysis, shown in Fig. 07 e, reveals that road density accounts for approximately 30.32% (R squared = 0.3032) of the variation in NO2 levels, which indicate a moderate influence of roads on NO2 concentration. These findings are crucial for urban planners, environmental managers, and policymakers. The findings highlight the need for improved transportation and fuel standards, as well as thoughtful urban planning. By addressing these factors, we can mitigate the adverse effects of road density on air quality and public health. This evidence strongly supports the importance of strategic settlement planning to reduce the negative impact of road networks on NO2 pollution. 3.7. Settlement Density and NO 2 concentration In this investigation, the researchers explore the relationship between building density and nitrogen dioxide (NO 2 ) levels in Gazipur City, considering the studied years. The analysis reveals a moderate correlation coefficient of 0.44 between settlement density and NO 2 concentrations (refer to Table S1 ). The results indicate that settlement density has a moderate influence (R squared 0.1243) on NO 2 concentration levels in the current study (Fig. 07 f). Overall, the findings of this study suggest that higher settlement density corresponds to increased population movement, reduced vegetation, more commuting roads, and consequently, higher settlement density leads higher NO 2 concentrations (Erbertseder et al., 2023 ; Reinmann et al., 2016 ). 3.8. Land Use Land Cover Classification and NO2 concentration In this study, the researchers explored how different types of land uses influence the levels of nitrogen dioxide (NO2. The researchers categorized the land into six groups: settlements, vegetation, water bodies, bare land, farm fields, and others, as part of the LULC classification. The analysis of this study demonstrated a moderate relationship between the types of land use and NO2 concentrations, with a correlation coefficient of 0.37 and an R² value of 0.112. This suggests that as the land use changes, the levels of NO2 also vary moderately. Interestingly, this research found that NO2 levels were consistently low in areas of reduced settlement, vegetation, agricultural fields, and near water bodies (Fig. 07 g). In contrast, higher concentrations of NO2 are usually observed in industrial and urban areas due to denser human activities (Erbertseder et al., 2023 ). This pattern underscores the impact of human development and land use on environmental quality, particularly air pollution levels. The findings can help guide urban planning and public health initiatives to better manage and mitigate NO2 pollution in various environments. 4. Conclusion This study explored the changing levels of tropospheric vertical column NO 2 concentration in Gazipur city from January 2019 to December 2022, utilizing TROPOMI satellite data that were analysed through the GEE platform. The researchers investigated the influences of environmental factors on NO 2 concentrations, using regression analysis, and map visualizations. Gazipur is experiencing significant environmental challenges due to high levels of NO 2 , unstable LST, and transformations in land use and cover. These issues, combined with increasing population density and expanding road networks, are leading to heavy vegetation loss and eventually reshaping the urban landscape. The findings of the present study underscored the link between monthly and seasonal fluctuations in NO 2 concentrations and environmental variables like LST and LULC. The analysis provided insights into the spatial and temporal patterns of NO 2 concentration, with a focus on its geographic relationships with LST, NDVI, road density, settlement density, LULC, industrial activity, and population density. To mitigate the challenges posed by high NO 2 concentrations and related environmental factors in Gazipur city, policymakers could consider implementing stricter emission controls, promoting green infrastructure development, enhancing public transportation systems to reduce reliance on individual vehicles, and implementing urban planning strategies that prioritize environmental sustainability. Additionally, community engagement and awareness campaigns can play a crucial role in fostering environmental stewardship and encouraging behavioural changes towards reducing air pollution levels. 5. Strength and limitations The present study, one of the first in the Gazipur Dhaka region, explores the changing patterns of NO2 levels and their causes, using advanced geographic and remote sensing techniques. The methods used in this study are scientific, and the finding are pioneering and trustworthy. However, to fully appreciate the implications of these findings, especially for policymaking, it’s important to acknowledge some limitations of the study. Firstly, Bangladesh currently has only 11 air monitoring stations, which are too few for detailed satellite-based studies. Since 2019, the S5P satellite has been providing data with a resolution of 1x1 square kilometres. This resolution is not fine enough for analysing small areas effectively. Secondly, in Gazipur City, where there is only one air monitoring station, the researchers relied on satellite data to examine the link between road traffic and NO2 levels. However, this data does not allow the researchers to identify specific types of vehicles that contribute to NO2 pollution. Although daily NO2 data is available from Sentinel-5P, other important variables (could you please insert the names of some of the variables which S-5P doesn’t provide in detailed resolution) do not have similarly detailed daily data. Thirdly, considering meteorological factors, such as wind speed, humidity, and precipitation could have given us a clearer picture of how NO2 levels vary with time and location. Abbreviations NO 2 : Nitrogen Dioxide; NDVI: Normalized Difference Vegetation Index; LULC: Land Use Land Cover; LST: Land Surface Temperature; GEE: Google Earth Engine; OSM: OpenStreetMap; MODIS: Moderate Resolution Imaging Spectroradiometer; CSP: Conservation Science Partners. Declarations Funding: This research received funding from the Institute for Advanced Research (IAR), United International University (UIU). Author Contribution Al Jubaer: Formal analysis, Methodology, Software, Visualization, Writing - Original Draft; Rakib Hossain: Formal analysis, Visualization, Writing - Original Draft, Writing – reviewing & editing; Md. Shakhaoat Hossain: Supervision, Validation, Conceptualization;Afzal Ahmed: Super Vision, Funding acquisition; Acknowledgement The authors would like to express their appreciation to the United International University (UIU), We are thankful to European Space Agency (ESA) for NO2, LULC and NDVI for data, Conservation Science Partners (CSP) for Population density data, United States Geological Survey (USGS) and National Aeronautics and Space Administration (NASA) and also thankful to OpenStreetMap (OSM) for providing Road, Settlement and Industry free data and other support for the completion of this work. Data Availability Statement: The datasets used in the findings of this study are mentioned within this article. References Abdullah, H. M., Islam, I., Miah, M. G., & Ahmed, Z. (2019). Quantifying the spatiotemporal patterns of forest degradation in a fragmented, rapidly urbanizing landscape: A case study of Gazipur, Bangladesh. Remote Sensing Applications: Society and Environment , 13 (August 2018), 457–465. https://doi.org/10.1016/j.rsase.2019.01.002 Agarwal, V., & Kumar, A. (2022). The changes in the air quality of Wazirpur, Delhi due to the COVID-19 shutdown. Cleaner Chemical Engineering , 1 (2), 100001. https://doi.org/10.1016/j.clce.2022.100001 Amoroso, N., Cilli, R., Maggipinto, T., Monaco, A., Tangaro, S., & Bellotti, R. (2022). Satellite data and machine learning reveal a significant correlation between NO2 and COVID-19 mortality. Environmental Research , 204 (PA), 111970. https://doi.org/10.1016/j.envres.2021.111970 Arifeen, H. M., Phoungthong, K., Mostafaeipour, A., Yuangyai, N., Yuangyai, C., Techato, K., & Jutidamrongphan, W. (2021). Determine the land-use land-cover changes, urban expansion and their driving factors for sustainable development in gazipur Bangladesh. Atmosphere , 12 (10). https://doi.org/10.3390/atmos12101353 Ashwini, K., & Sil, B. S. (2022). Impacts of Land Use and Land Cover Changes on Land Surface Temperature over Cachar Region, Northeast India—A Case Study. Sustainability (Switzerland) , 14 (21), 100862. https://doi.org/10.3390/su142114087 Atkinson, R. W., Butland, B. K., Anderson, H. R., & Maynard, R. L. (2018). Long-term Concentrations of Nitrogen Dioxide and Mortality. Epidemiology , 29 (4), 460–472. https://doi.org/10.1097/EDE.0000000000000847 Azad, A. K., & Kitada, T. (1998). Characteristics of the air pollution in the city of Dhaka, Bangladesh in winter. Atmospheric Environment , 32 (11), 1991–2005. https://doi.org/10.1016/S1352-2310(97)00508-6 Bechle, M. J., Millet, D. B., & Marshall, J. D. (2013). Remote sensing of exposure to NO2: Satellite versus ground-based measurement in a large urban area. Atmospheric Environment , 69 , 345–353. https://doi.org/10.1016/j.atmosenv.2012.11.046 CARSLAW, D. (2005). Evidence of an increasing NO2/NOX emissions ratio from road traffic emissions. Atmospheric Environment , 39 (26), 4793–4802. https://doi.org/10.1016/j.atmosenv.2005.06.023 Chawala, P., Priyan R, S., & SM, S. N. (2023). Climatology and landscape determinants of AOD, SO2 and NO2 over Indo-Gangetic Plain. Environmental Research , 220 . https://doi.org/10.1016/j.envres.2022.115125 CSP. (2024). Conservation Science Partners . https://csp-inc.org/ DoE. (2018). AMBIENTAIR QUALITY IN BANGLADESH (M. Shahjahan (ed.)). Clean Air and Sustainable Environment Project, Department of Environment, Ministry of Environment, Forest and Climate Change of the Government of Bangladesh. http://doe.portal.gov.bd/sites/default/files/files/doe.portal.gov.bd/page/cdbe516f_1756_426f_af6b_3ae9f35a78a4/2020-06-10-11-02-5a7ea9f58497800ec9f0cea00ce7387f.pdf Erbertseder, T., Taubenböck, H., Esch, T., Gilardi, L., Paeth, H., Marconcini, M., & Dech, S. (2023). Earth Observation-based analysis of NO2 pollution and settlement growth in megacities. 2023 Joint Urban Remote Sensing Event (JURSE) , 1–4. https://doi.org/10.1109/JURSE57346.2023.10144190 ESA. (2018). 5P: TROPOMI Level 2 Nitrogen Dioxide total column products, Version 01, processed by: ESA . Gao, Y., Wang, S., Zhang, C., Xing, C., Tan, W., Wu, H., Niu, X., & Liu, C. (2023). Assessing the impact of urban form and urbanization process on tropospheric nitrogen dioxide pollution in the Yangtze River Delta, China. Environmental Pollution , 336 (July), 122436. https://doi.org/10.1016/j.envpol.2023.122436 Geddes, J. A., Martin, R. V., Boys, B. L., & van Donkelaar, A. (2016). Long-Term Trends Worldwide in Ambient NO 2 Concentrations Inferred from Satellite Observations. Environmental Health Perspectives , 124 (3), 281–289. https://doi.org/10.1289/ehp.1409567 Goldberg, D. L., Anenberg, S. C., Kerr, G. H., Mohegh, A., Lu, Z., & Streets, D. G. (2021). TROPOMI NO 2 in the United States: A Detailed Look at the Annual Averages, Weekly Cycles, Effects of Temperature, and Correlation With Surface NO 2 Concentrations. Earth’s Future , 9 (4). https://doi.org/10.1029/2020EF001665 Greenstone, M., & Hasenkopf, C. (2023). Air Quality Life Index | 2023 Annual Update . Energy Policy Institute at the University of Chicago (EPIC). https://aqli.epic.uchicago.edu/reports/?utm_medium=partner&utm_source=twitter&utm_campaign=aqlinews Grzybowski, P. T., Markowicz, K. M., & Musiał, J. P. (2023). Estimations of the Ground-Level NO2 Concentrations Based on the Sentinel-5P NO2 Tropospheric Column Number Density Product. Remote Sensing , 15 (2), 378. https://doi.org/10.3390/rs15020378 Harper, A., Baker, P. N., Xia, Y., Kuang, T., Zhang, H., Chen, Y., Han, T. L., & Gulliver, J. (2021). Development of spatiotemporal land use regression models for PM2.5 and NO2 in Chongqing, China, and exposure assessment for the CLIMB study. Atmospheric Pollution Research , 12 (7). https://doi.org/10.1016/j.apr.2021.101096 Health Effects Institute. (2020). State of Global Air 2020. Special Report. https://www.stateofglobalair.org/sites/default/files/documents/2020-10/soga-2020-report-10-26_0.pdf Hoque, M. M. M. (2020). Meteorological Influences on Seasonal Variations of Air Pollutants (SO2, NO2, O3, CO, PM2.5, and PM10) in the Dhaka Megacity. American Journal of Pure and Applied Biosciences , 15–23. https://doi.org/10.34104/ajpab.020.15023 Islam, K. M. A. A., Adnan, M. S. G., Zannat, K. E., & Dewan, A. (2022). Spatiotemporal dynamics of NO2 concentration with linear mixed models: A Bangladesh case study. Physics and Chemistry of the Earth , 126 (2), 103119. https://doi.org/10.1016/j.pce.2022.103119 Jiang, L., Zhou, H., He, S., Cui, Y., & Wang, J. (2021). Identifying the driving factors of NO2 pollution of One Belt One Road countries: satellite observation technique and dynamic spatial panel data analysis. Environmental Science and Pollution Research , 28 (16), 20393–20407. https://doi.org/10.1007/s11356-020-12113-z Leffel, B., Tavasoli, N., Liddle, B., Henderson, K., & Kiernan, S. (2022). Metropolitan air pollution abatement and industrial growth: Global urban panel analysis of PM 10 , PM 2.5 , NO 2 and SO 2. Environmental Sociology , 8 (1), 94–107. https://doi.org/10.1080/23251042.2021.1975349 Li, L., & Wu, J. (2021). Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks. Remote Sensing of Environment , 254 , 112257. https://doi.org/10.1016/j.rse.2020.112257 Li, X., Wang, P., Wang, W., Zhang, H., Shi, S., Xue, T., Lin, J., Zhang, Y., Liu, M., Chen, R., Kan, H., & Meng, X. (2023). Mortality burden due to ambient nitrogen dioxide pollution in China: Application of high-resolution models. Environment International , 176 (December 2022), 107967. https://doi.org/10.1016/j.envint.2023.107967 Melamed, M. L., Schmale, J., & von Schneidemesser, E. (2016). Sustainable policy—key considerations for air quality and climate change. Current Opinion in Environmental Sustainability , 23 , 85–91. https://doi.org/10.1016/j.cosust.2016.12.003 Mitchell, A. (1999). The Esri Guide to GIS Analysis, Volume 1: Geographic Patterns and Relationships (2nd ed.). Esri. https://www.esri.com/en-us/esri-press/browse/the-esri-guide-to-gis-analysis-volume-1-geographic-patterns-and-relationships-second-edition Mukta, T. A., Hoque, M. M. M., Sarker, M. E., Hossain, M. N., & Biswas, G. K. (2020). Seasonal variations of gaseous air pollutants (SO2, NO2, O3, CO) and particulates (PM2.5, PM10) in Gazipur: An industrial city in Bangladesh. Advances in Environmental Technology , 6 (4), 195–209. https://doi.org/10.22104/AET.2021.4890.1320 Oeurng, C., Cochrane, T. A., Arias, M. E., Shrestha, B., & Piman, T. (2016). Assessment of changes in riverine nitrate in the Sesan, Srepok and Sekong tributaries of the Lower Mekong River Basin. Journal of Hydrology: Regional Studies , 8 , 95–111. https://doi.org/10.1016/j.ejrh.2016.07.004 OpenStreetMap contributors. (2024). OpenStreetMap . https://www.openstreetmap.org Prunet, P., Lezeaux, O., Camy-Peyret, C., & Thevenon, H. (2020). Analysis of the NO2 tropospheric product from S5P TROPOMI for monitoring pollution at city scale. City and Environment Interactions , 8 (2). https://doi.org/10.1016/j.cacint.2020.100051 Rabbi, M. A. (2018). Assessment of Nitrogen Oxides and Sulphur Dioxide Content in the Ambient Air near the Garments Industries of Bangladesh Environmental and Social Sciences Open Science Publications . January . Rabiei-Dastjerdi, H., Mohammadi, S., Saber, M., Amini, S., & McArdle, G. (2022). Spatiotemporal Analysis of NO2 Production Using TROPOMI Time-Series Images and Google Earth Engine in a Middle Eastern Country. Remote Sensing , 14 (7), 1725. https://doi.org/10.3390/rs14071725 Rahaman, S. N., Ahmed, S. M. M., Zeyad, M., & Zim, A. H. (2023). Effect of vegetation and land surface temperature on NO2 concentration: A Google Earth Engine-based remote sensing approach. Urban Climate , 47 . https://doi.org/10.1016/j.uclim.2022.101336 Rahman, M. M., Mahamud, S., & Thurston, G. D. (2019). Recent spatial gradients and time trends in Dhaka, Bangladesh, air pollution and their human health implications. Journal of the Air & Waste Management Association , 69 (4), 478–501. https://doi.org/10.1080/10962247.2018.1548388 Rahman, R. R., & Kabir, A. (2023). Spatiotemporal analysis and forecasting of air quality in the greater Dhaka region and assessment of a novel particulate matter filtration unit. Environmental Monitoring and Assessment , 195 (7). https://doi.org/10.1007/s10661-023-11370-y Rana, S. M. S., Ahmed, S. M. F., & Akter, H. (2021). Analysis of NO2 Pollution over Bangladesh between the Two COVID-19 Caused Lockdowns in 2020 and 2021 Using Sentinel-5P Products †. Engineering Proceedings , 11 (1), 0–5. https://doi.org/10.3390/ASEC2021-11139 Reinmann, A. B., Hutyra, L. R., Trlica, A., & Olofsson, P. (2016). Assessing the global warming potential of human settlement expansion in a mesic temperate landscape from 2005 to 2050. Science of The Total Environment , 545 – 546 , 512–524. https://doi.org/10.1016/j.scitotenv.2015.12.033 Shobnom, N., Hossain, M. S., & Roni, R. (2023). Monitoring spatiotemporal changes of NO2 using TROPOMI and sentinel-5 images for Dhaka city and its surrounding areas of Bangladesh. Journal of Air Pollution and Health , 8 (3), 269–284. https://doi.org/10.18502/japh.v8i3.13785 Singh, N., Dey, S., & Knibbs, L. D. (2023). Spatio-temporal patterns of tropospheric NO2 over India during 2005–2019. Atmospheric Pollution Research , 14 (3). https://doi.org/10.1016/j.apr.2023.101692 Song, J., Wang, Y., Zhang, Q., Qin, W., Pan, R., Yi, W., Xu, Z., Cheng, J., & Su, H. (2023). Premature mortality attributable to NO2 exposure in cities and the role of built environment: A global analysis. Science of The Total Environment , 866 , 161395. https://doi.org/10.1016/j.scitotenv.2023.161395 Stieb, D. M., Berjawi, R., Emode, M., Zheng, C., Salama, D., Hocking, R., Lyrette, N., Matz, C., Lavigne, E., & Shin, H. H. (2021). Systematic review and meta-analysis of cohort studies of long term outdoor nitrogen dioxide exposure and mortality. PLOS ONE , 16 (2), e0246451. https://doi.org/10.1371/journal.pone.0246451 Suthar, G., Kaul, N., Khandelwal, S., & Singh, S. (2024). Dynamics of land surface temperature: Insights into vegetation, elevation, and air pollution in Bengaluru. Remote Sensing Applications: Society and Environment , 33 (December 2023), 101145. https://doi.org/10.1016/j.rsase.2024.101145 Suthar, G., Singhal, R. P., Khandelwal, S., & Kaul, N. (2023). Spatiotemporal variation of air pollutants and their relationship with land surface temperature in Bengaluru, India. Remote Sensing Applications: Society and Environment , 32 (June), 101011. https://doi.org/10.1016/j.rsase.2023.101011 Uddin, M. S., Mahalder, B., & Mahalder, D. (2023). Assessment of Land Use Land Cover Changes and Future Predictions Using CA-ANN Simulation for Gazipur City Corporation, Bangladesh. Sustainability (Switzerland) , 15 (16). https://doi.org/10.3390/su151612329 WHO. (2021). WHO global air quality guidelines. Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide , 1–360. https://creativecommons.org/licenses/by-nc-sa/3.0/igo Zheng, J., Che, W., Wang, X., Louie, P., & Zhong, L. (2009). Road-Network-Based Spatial Allocation of On-Road Mobile Source Emissions in the Pearl River Delta Region, China, and Comparisons with Population-Based Approach. Journal of the Air & Waste Management Association , 59 (12), 1405–1416. https://doi.org/10.3155/1047-3289.59.12.1405 Zhu, Y., Xie, J., Huang, F., & Cao, L. (2020). Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China. Science of The Total Environment , 727 , 138704. https://doi.org/10.1016/j.scitotenv.2020.138704 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2025 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 18 Oct, 2024 Reviews received at journal 18 Oct, 2024 Reviewers agreed at journal 26 Sep, 2024 Reviewers agreed at journal 23 Sep, 2024 Reviews received at journal 30 Aug, 2024 Reviewers agreed at journal 25 Aug, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviewers invited by journal 08 Aug, 2024 Editor assigned by journal 27 Jul, 2024 Submission checks completed at journal 27 Jul, 2024 First submitted to journal 02 Jul, 2024 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. 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Shakhaoat Hossain","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDACCQZmZjCDvSGBgcEAxEogUgsPzwGStUjAVRLQIj+7+bFxQY2NvL3kg2fSBQWHGfjZcwzwajG4c8w4ecaxNMMe6YQ06RkGhxkke94Q0CKRYHyYh+0wI1gLj0Eag8ENArbIz0j/fJjn32H7HskDEC32hLQw3MgxTuZtO5zYI8EA0mIDtJeQX27kFBvz9qUl95xJSLYGauGROPOsgJDDNkvzfLOxbW8/k3ib54+EHH978gb8DkMAngQwSaxyEGA/QIrqUTAKRsEoGEEAAPx0PnvGiUTyAAAAAElFTkSuQmCC","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":true,"prefix":"","firstName":"Md.","middleName":"Shakhaoat","lastName":"Hossain","suffix":""}],"badges":[],"createdAt":"2024-07-02 07:26:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4672218/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4672218/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-024-13531-z","type":"published","date":"2025-01-13T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63055717,"identity":"9d2f6078-2a3a-4c0c-a4a5-9b4cb0f8f0aa","added_by":"auto","created_at":"2024-08-22 15:04:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":809800,"visible":true,"origin":"","legend":"\u003cp\u003eThe study area in Gazipur City.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4672218/v1/70cb5b720a83681d897dc1f7.png"},{"id":63055714,"identity":"5896cb69-f584-4c29-b587-c30efb009bd2","added_by":"auto","created_at":"2024-08-22 15:04:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77541,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal Variation of NO\u003csub\u003e2\u003c/sub\u003e concentration at Gazipur City over the study period.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4672218/v1/198d3874a54672b32abbe8ee.png"},{"id":63056398,"identity":"dd524db8-6408-4ff6-84a1-45b348de0387","added_by":"auto","created_at":"2024-08-22 15:12:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":695817,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal distribution NO2 concentration in Gazipur City from 2019 to 2022- (a) Winter, (b) Pre-monsoon, (c) Monsoon, (d) post-monsoon.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4672218/v1/85052fbdf1de3a979e889efd.png"},{"id":63055712,"identity":"b8816573-6710-4153-9132-a58b46dc92ef","added_by":"auto","created_at":"2024-08-22 15:04:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":359651,"visible":true,"origin":"","legend":"\u003cp\u003eMonth-wise temporal distribution NO2 concentration in Gazipur City- (a) year 2019, (b) year 2020, (c) year 2021, (d) year 2022.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4672218/v1/0e807685d81fde076b816060.png"},{"id":63055719,"identity":"cd986564-9d62-4ae0-a09e-8e1d7e3c0e74","added_by":"auto","created_at":"2024-08-22 15:04:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":885392,"visible":true,"origin":"","legend":"\u003cp\u003eMonth-wise spatial distribution NO2 concentration in Gazipur City- (a) year 2019, (b) year 2020, (c) year 2021, (d) year 2022.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4672218/v1/6026881ad2d5b6ce8fc3744a.png"},{"id":63055715,"identity":"0a01d9b7-2046-4427-bf06-38dfb99a737e","added_by":"auto","created_at":"2024-08-22 15:04:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":815199,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Nitrogen Dioxide (NO\u003csub\u003e2\u003c/sub\u003e) map 2022 (b) Normalized Difference Vegetation Index (NDVI) map 2022 (c) Land surface temperature (LST) map 2022 (d) Land Use Land Cover map 2022 (e) Population density map 2022 (f) Road Density map 2022 (g) Settlement Density map 2022 (h) Industry Density map 2022 and (i) Study domain overview.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4672218/v1/687b1de6f29280212115324f.png"},{"id":63055713,"identity":"1789d4e0-fb3f-40f6-9c35-79f723db44fc","added_by":"auto","created_at":"2024-08-22 15:04:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":467988,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Grid based analysis for extracted NO\u003csub\u003e2\u003c/sub\u003e concentration with Normalized Difference Vegetation Index (NDVI) through a scatter plot producing an R\u003csup\u003e2\u003c/sup\u003e value. (b) Grid based analysis for extracted NO\u003csub\u003e2\u003c/sub\u003e concentration with Land Surface Temperature (LST) through a scatter plot producing an R\u003csup\u003e2\u003c/sup\u003e value. (c) Grid based analysis for extracted NO\u003csub\u003e2\u003c/sub\u003e concentration with Population Density through a scatter plot producing an R\u003csup\u003e2\u003c/sup\u003e value. (d) Grid-based analysis for extracted NO\u003csub\u003e2\u003c/sub\u003e concentration with Industry Density through a scatter plot producing an R\u003csup\u003e2\u003c/sup\u003e value. (e) Grid-based analysis for extracted NO\u003csub\u003e2\u003c/sub\u003e concentration with Road Density through a scatter plot producing an R\u003csup\u003e2 \u003c/sup\u003evalue. (f) Grid-based analysis for extracted NO\u003csub\u003e2\u003c/sub\u003e concentration with Settlement density through a scatter plot producing an R\u003csup\u003e2\u003c/sup\u003e value. (g) Grid-based analysis for extracted NO\u003csub\u003e2\u003c/sub\u003e concentration with Land Use Land Cover Classification through a scatter plot producing an R\u003csup\u003e2\u003c/sup\u003e value.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4672218/v1/a23a0db4f9b0a8f9179c7df4.png"},{"id":74284836,"identity":"483b2f44-9ba7-4994-b00b-c3cf1c277110","added_by":"auto","created_at":"2025-01-20 16:12:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5361468,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4672218/v1/9d844fa8-5fb0-4308-b0f1-0c307030ba47.pdf"},{"id":63055718,"identity":"191751db-7798-4360-b7f7-f9c200dd1811","added_by":"auto","created_at":"2024-08-22 15:04:57","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":673823,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4672218/v1/857b7649996f127242717ee1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors Influencing Spatiotemporal Variability of NO2 Concentration in Urban Area: A GIS and Remote Sensing-Based Approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn tackling the global air pollution situation, diverse programs and policies at the international, regional, and local scales have been put in place aimed at fostering environmental sustainability and enhancing overall well-being (Melamed et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Despite these efforts, air pollution persists as the fourth leading cause of premature death globally (Health Effects Institute, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and remains the most substantial external threat to human life expectancy (Greenstone \u0026amp; Hasenkopf, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Shockingly, it contributes to an annual toll of 6.67\u0026nbsp;million premature deaths (Health Effects Institute, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e), an ambient air pollutant, playing a pivotal role among others (Song et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e exerts adverse effects on both human health (Song et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the ecological environment (L. Li \u0026amp; Wu, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It serves as a crucial precursor for anthropogenic ozone (O3) and urban smog. This gaseous pollutant is a key player in the formation of other pollutants, such as nitric acid (HNO3), fine particulate matter (PM2.5), and nitro-polycyclic aromatic hydrocarbons (nitro-PAHs) (L. Li \u0026amp; Wu, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA global analysis in 2019 found that 81% of cities exceeded WHO standards for NO\u003csub\u003e2\u003c/sub\u003e, contributing to 2.7% of all mortalities that year (Song et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Existing epidemiological evidence firmly establishes the link between ambient NO\u003csub\u003e2\u003c/sub\u003e exposure and an increased risk of adverse health outcomes (Stieb et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Inhaling of increased NO\u003csub\u003e2\u003c/sub\u003e can lead to throat and upper respiratory tract inflammation, resulting in breathing difficulties, throat spasms, and lung fluid accumulation (DoE, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, it diminishes blood oxygen-carrying capacity, causing symptoms like headaches, fatigue, and dizziness (DoE, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recent research also indicates a positive correlation between atmospheric NO\u003csub\u003e2\u003c/sub\u003e levels and susceptibility to COVID-19 infection (Amoroso et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Long-term exposure to NO\u003csub\u003e2\u003c/sub\u003e poses significant health risks, including cardiovascular disease, lung cancer, and other potentially fatal respiratory conditions (Atkinson et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recognizing the severity, the World Health Organization (WHO) and the United States Environmental Protection Agency (US EPA) designate NO\u003csub\u003e2\u003c/sub\u003e as a key indicator of outdoor air pollution (Islam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Melamed et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Also, the WHO has reduced the ambient NO\u003csub\u003e2\u003c/sub\u003e annual mean limit from 40 \u0026micro;g/m3 to 10 \u0026micro;g/m3 in the latest air quality guideline (WHO, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis yellowish-orange to reddish-brown gaseous pollutant with a pungent, irritating odour (DoE, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), exhibits a brief atmospheric photochemical longevity, ranging from 2\u0026ndash;5 hours during the daytime in summer to 12\u0026ndash;24 hours during winter, which indicates spatiotemporal variability based on emission sources (Goldberg et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Worldwide population-weighted nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) estimates, spanning from 1996 to 2012, reveal a yearly decline of 4.7% in the United States and Canada, 2.5% in Western Europe, and a notable increase of around 6.7% in East Asia and major Indian cities (Geddes et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The observed trends in NO\u003csub\u003e2\u003c/sub\u003e concentration are highly influenced by various spatiotemporal factors (Singh et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, obtaining an accurate estimate of NO\u003csub\u003e2\u003c/sub\u003e remains a challenge in many developing nations like Bangladesh (Bechle et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) due to the high costs associated with establishing and maintaining monitoring stations (Rabiei-Dastjerdi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Advanced remote sensing (RS) (Bechle et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and geographic information system (GIS) (Ashwini \u0026amp; Sil, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chawala et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) technologies play a crucial role in addressing this gap, enabling scientists to monitor atmospheric NO\u003csub\u003e2\u003c/sub\u003e and study its determinants effectively (Bechle et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The most recent tropospheric vertical column of NO\u003csub\u003e2\u003c/sub\u003e data from the European Space Agency's Sentinel 5P, also known as the TROPOMI (ESA, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), offers a reliable estimate of NO\u003csub\u003e2\u003c/sub\u003e emission values in comparison with on-site data recordings (Goldberg et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In recent literature, diverse factors including the NDVI, LULC patterns, and LST have been explored, using GEE-based RS (Ashwini \u0026amp; Sil, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rahaman et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); on the other hand, road density, industry density, and settlement density have been investigated, using GIS platforms to quantify and understand the variations in NO\u003csub\u003e2\u003c/sub\u003e levels (Ashwini \u0026amp; Sil, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Grzybowski et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite limited studies on NO\u003csub\u003e2\u003c/sub\u003e pollution in Bangladesh (Islam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mukta et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rabbi, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; R. R. Rahman \u0026amp; Kabir, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rana et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), several researchers have employed GIS and RS techniques to measure and analyze NO\u003csub\u003e2\u003c/sub\u003e concentration (R. R. Rahman \u0026amp; Kabir, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rana et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), yet there remains a need to address crucial factors (NDVI, LULC, LST, road density, industry density, and settlement density) to comprehensively grasp the variability of NO\u003csub\u003e2\u003c/sub\u003e concentration levels, using RS and GIS techniques.\u003c/p\u003e \u003cp\u003eGiven the context, Gazipur City, a part of the Greater Dhaka Division, suffers from some of the worst air quality in Bangladesh, as noted in recent studies [3]. However, there has been limited research on how nitrogen dioxide (NO2) levels vary on spatiotemporal levels within the city, and what drives these variations. To address this research gap, the present study aims to: (1) Investigate the spatiotemporal distribution of NO\u003csub\u003e2\u003c/sub\u003e concentration in urban areas like Gazipur city, using RS and GIS techniques. (2) Identify the factors that influence the variation in NO\u003csub\u003e2\u003c/sub\u003e concentration.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003eA flowchart-based depiction of the complete methodological process of the study is available in \u003cb\u003eFig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area\u003c/h2\u003e \u003cp\u003eThe present study focuses on Gazipur, a rapidly urbanizing area located in the north-eastern region of Dhaka, Bangladesh (Arifeen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Gazipur district lies centrally in Bangladesh, between latitudes 23\u0026deg;53\u0026prime; and 24\u0026deg;20\u0026prime;N and longitudes 90\u0026deg;9\u0026prime; and 90\u0026deg;42\u0026prime;E, covering an area of 321 square kilometres (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e01\u003c/span\u003e). The Gazipur city has a population of approximately six million and a total road length of around 1552 kilometres (Abdullah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Gazipur's proximity to Dhaka has transformed it into a major hub for employment and commerce, contributing significantly to the country's Gross Domestic Product (GDP). Notably, 65% of Bangladesh's garment factories are situated in this region (Oeurng et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Consequently, people from surrounding districts, particularly rural areas, are migrating to Gazipur. This rapid urbanization, along with an increase in vehicles and industrial activities, has led to a substantial rise in NO2 levels in the area (Uddin et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data description\u003c/h2\u003e \u003cp\u003eIn this study, we utilized Sentinel 5P TROPOMI satellite NO\u003csub\u003e2\u003c/sub\u003e concentration data from 2019 to 2022, along with various environmental variables including population density, LST intensity, NDVI, LULC, road density, settlement density, and industry density for the year 2022. Detailed information about these datasets is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Furthermore, thematic maps illustrating the 2022 data for Gazipur city, including population density, LST intensity, NDVI, LULC, road density, settlement density, and industry density, are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e06\u003c/span\u003e.\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\u003eGeospatial dataset with their different attributes used in the study domain.\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=\"left\" 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\u003eData used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecial Resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTemporal Resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDataset Provider\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDataset Availability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRange/Units\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSentinel 5P\u003c/p\u003e \u003cp\u003eTROPOMI NO\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRaster Data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1000 Meters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eDaily\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEuropean Union/ESA/Copernicus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2018 to Present\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003emol/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSentinel 2A Dynamics (LULC) [32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRaster Data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e10 Meters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6 Days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEuropean Space Agency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2017 to Present\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eSquare Meter Grid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRaster Data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1000 Meters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eDaily\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMODIS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2000 to Present\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eK\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRaster Data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e30 Meters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e16 Days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLandsat 8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2013 to Present\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eSquare Meter Grid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman Modification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRaster Data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1000 Meters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5 Year Interval\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eConservation Science Partners\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eFebruary 2016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ekm^\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoad Network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eVector Data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100 Meters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRandom\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEuropean Union/ESA/Copernicus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2018 to Present\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ekm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eVector Data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePoint Values\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eYearly\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eOpenStreetMap\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2004 to Present\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ePoint\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eVector Data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePoint\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eDaily\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eOpenStreetMap\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2004 to Present\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ePoint\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\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Methods\u003c/h2\u003e \u003cp\u003eNumerous studies have effectively utilized the GEE platform to analyze factors such as LULC, LST, NDVI, population density, road density, settlement density, and industry density. This study focuses on the following analyses: (1) the spatiotemporal distribution of NO2 concentration using satellite images, with seasonal and monthly statistics extracted for the years 2019 and 2022, and (2) the comparison of Sentinel 5P TROPOMI NO2 values with seven environmental variables (LST, NDVI, LULC, population density, road density, settlement density, and industry density) in Gazipur city.\u003c/p\u003e \u003cp\u003eThe study leverages free image collections from TROPOMI, MODIS, Dynamic World, and Global World instruments available in the GEE datasets, along with road, settlement, and industry data from the OpenStreetMap (OSM) platform. GEE and OSM are open-source geospatial analysis platforms that allow users to visualize and analyze changes, map trends, and quantify differences in Earth's environment.\u003c/p\u003e \u003cp\u003eGazipur city was divided into 356 grids, each with a size of 1x1 square kilometer, based on NO2 concentration. Environmental variable\u0026rsquo;s values for each grid were extracted based on NO2 concentration points. The study computed and processed the NO2 concentration, along with all the seven environmental parameters for each grid.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 NO\u003csub\u003e2\u003c/sub\u003e Concentration data\u003c/h2\u003e \u003cp\u003eThe impacts of air pollution can be assessed using different methods, including ground-based, ship-based, and satellite-based monitoring. Satellite-based monitoring stands out for its extensive spatial and temporal coverage, offering comprehensive global and regional insights into air pollution (Gao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This study uses satellite-based observation for the spatiotemporal distribution of NO\u003csub\u003e2\u003c/sub\u003e concentration. The Sentinel 5P TROPOMI is an on-board satellite system used to measure NO\u003csub\u003e2\u003c/sub\u003e concentrations (Prunet et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). NO2 concentration data from TROPOMI, spanning January 2019 to December 2022, were acquired daily through the GEE platform (Harper et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Daily tropospheric NO2 vertical column concentration data from TROPOMI version-1 level-3, featuring a spatial resolution of 1\u0026times;1 square kilometre, were sourced from GEE platform (X. Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For the analysis, a JavaScript program was created in GEE to access, process, and display the data from the image collections. The scripts utilized in this study are available in supplementary material \u003cb\u003eAlgorithm S1.\u003c/b\u003e During this phase, the chosen images undergo the following processing steps:\u003c/p\u003e \u003cp\u003e(a) filtering the imaging time for the clipped region of interest (Gazipur City), (b) eliminating cloudy pixels using a condition on TROPOMI, MODIS, and Landsat-based products and selecting the highest quality satellite data, (c) extracting daily images by mosaicking overlapping scenes across the entire study area, (d) extracting hourly pixel values for AOD (Aerosol Optical Depth) and NO2 column density from the relevant images, (e) computing monthly and seasonal images by averaging the daily images to create spatiotemporal maps and to analyse the relationship between environmental variables from January 2019 to December 2022.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Normalized Difference Vegetation Index (NDVI)\u003c/h2\u003e \u003cp\u003eTo calculate NDVI, the raw scenes from USGS Landsat 8 Collection 1 Tier 1 and real-time data were utilized. A custom script was employed on the GEE platform to derive NDVI values from the Landsat 8 dataset composite, with a 30 meters spatial resolution and a temporal resolution of 16 days. NDVI, which ranges from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1, quantifies the earth's vegetation cover. Negative values indicate non-vegetated surfaces, a value of 0 denotes minimal vegetation, and a value of 1 signifies dense vegetation (Ashwini \u0026amp; Sil, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Eq.\u0026nbsp;1, presented below, illustrates the calculation of NDVI, while \u003cb\u003eAlgorithm S2\u003c/b\u003e provides the NDVI extraction code.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEquation 1\u003c/strong\u003e \u003cp\u003eNDVI = (NIR - RED) / (NIR\u0026thinsp;+\u0026thinsp;RED)\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn this formula, 'RED' refers to the Red Reflectance, and 'NIR' refers to the Near Infrared Reflectance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Land Surface Temperature (LST)\u003c/h2\u003e \u003cp\u003eLST images from the MOD11A1 dataset, obtained by the MODIS sensor on the Terra satellite, were utilized. This dataset offers daily LST data with a 1x1 km grid resolution, available in both day and night temperature bands (Suthar et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For this study, only daytime satellite imagery was considered. The LST, derived from the difference between outgoing thermal radiation and surface temperature, was calculated using the GEE platform.\u003c/p\u003e \u003cp\u003eThe calculation involves several steps, starting with\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEquation 2\u003c/strong\u003e \u003cp\u003eLST = (K2 / ln (K1 / radiance)\u0026thinsp;+\u0026thinsp;1) \u0026minus;\u0026thinsp;273.15 (\u003cb\u003eAlgorithm S3\u003c/b\u003e),\u003c/p\u003e \u003c/p\u003e \u003cp\u003ewhere K1 and K2 are calibration constants specific to the MODIS sensor and thermal infrared bands.\u003c/p\u003e \u003cp\u003eArcGIS Pro software was employed to convert the pixel/grid data of Gazipur city into a point vector grid. The final output was then used to create and visualize the LST map, providing insights into the LST patterns in the Gazipur city area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Land Use Land Cover (LULC) Classification\u003c/h2\u003e \u003cp\u003eThis research utilizes the 2022 Sentinel-2 Dynamic World LULC satellite dataset to illustrate these land use changes. The land is classified into six categories: built-up areas, water bodies, trees, grasslands, flooded vegetation, cropland, and shrubland or bare land (Agarwal \u0026amp; Kumar, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The images in the Dynamic World collection correspond to specific Sentinel-2 L1C asset (\u003cb\u003eAlgorithm S4\u003c/b\u003e) For instance, the image 'COPERNICUS/S2/20160711T084022_20160711T084751_T35PKT' matches the Dynamic World image 'GOOGLE/DYNAMICWORLD/V1/20160711T084022_20160711T084751_\u003c/p\u003e \u003cp\u003eT35PKT'. All probability bands in these images, except the 'label' band, collectively sum to one.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Grided Population Density\u003c/h2\u003e \u003cp\u003eTo explore the interactions with LST, NDVI, LULC, settlement density, road density, and NO2 concentration, the Global Human Modification (GHM) data or population density information was utilized. The Gridded Population of the World, version 4 (GPWv4), offers a comparative analysis of human population density (Suthar et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This dataset employs a proportional allocation gridding algorithm to distribute the population into 30 arc-second grid cells, covering approximately 13.5\u0026nbsp;million administrative units worldwide. The GHM data for the years 2005, 2010, 2015, and 2020 are available for download. For this study, the 2020 data was obtained through the GEE platform (\u003cb\u003eAlgorithm S5)\u003c/b\u003e from the following website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://csp-inc.org/\u003c/span\u003e\u003cspan address=\"https://csp-inc.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (CSP, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.6 Industry, Road, and Settlement Density\u003c/h2\u003e \u003cp\u003eData for industry, settlements, and roads were sourced from the OSM portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.openstreetmap.org)(OpenStreetMap contributors, 2024\u003c/span\u003e\u003cspan address=\"https://www.openstreetmap.org)(OpenStreetMap contributors, 2024\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The dataset includes point features for industry and settlements, and polyline features for roads. To analyse density, we used ArcMap commands specifically designed for point density (for industry and settlements) and line density (for roads). The spatial resolution of the dataset is 100 meters. The density calculation process involves measuring the quantity of features per unit area within a specified radius around each cell. This method enables the assessment of the density of linear features across different regions. ArcGIS 10.8 software facilitates the conversion of polygons to points and subsequently calculates point densities, which are then categorized by height, settlement type, and settlement classification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Kernel Density Analysis\u003c/h2\u003e \u003cp\u003eThe Kernel Density tool is utilized to determine the concentration of features within a specific area. This analysis can be applied to both point and line features, providing an index that represents the intensity of influence that certain factors have on their surroundings (Mitchell, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In this study, three environmental variables are examined using kernel density analysis with ArcGIS 10.8 software: Road Network (line feature), Settlements (point feature), and Industry (point feature). Firstly, the road network data, initially a line feature, is converted into a kernel density distribution. This conversion produces a weighted value based on the density of the road network, highlighting areas with higher road concentrations. Secondly, the settlement and industry data, originally point features, are converted into kernel point densities. This process focuses on the distribution patterns of settlements and industries within the Gazipur city area. The results of this analysis provide detailed density maps for both settlements and industries, fulfilling the study's requirements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Regression Analysis\u003c/h2\u003e \u003cp\u003eTo examine the relationship between NO2 levels and various environmental factors such as NDVI, LST, LULC, population density, industry density, road density, and settlement density in Gazipur City, linear regression analysis was employed. The NO2 concentrations across 356 grid cells were visualized using scatter plots. Microsoft Excel was utilized for correlation analysis, while Python was used to perform multiple linear regression analysis to explore the connections between NO2 concentrations and the other variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Final Visualization\u003c/h2\u003e \u003cp\u003eThe spatiotemporal distribution maps of NO2 concentrations, along with other environmental variable maps, were created using ArcGIS 10.8 software. The methodology to summarize the study results for the entire Gazipur City area included: i) Mapping the spatiotemporal distribution of NO2 concentrations across the city ii) Developing a JavaScript program in GEE to calculate and visualize the average NO2 concentrations on a monthly and seasonal basis iii) Comparing and analysing satellite-based NO2 data with various environmental variables.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Result and Discussion","content":"\u003cp\u003eThe study analyses NO2 levels from 2019 to 2022, highlighting trends over different seasons and months. It examines how NO2 concentrations relate to environmental factors such as LST, LULC, NDVI, and the density of populations, settlements, roads, and industries. The findings demonstrate the impact of these factors on NO2 levels through correlation and regression analysis.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Spatial and temporal variation of NO\u003csub\u003e2\u003c/sub\u003e concentration\u003c/h2\u003e \u003cp\u003eThe NO\u003csub\u003e2\u003c/sub\u003e seasonal concentrations exhibited fluctuations over four consecutive years (2019\u0026ndash;2022), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e02\u003c/span\u003e \u003cb\u003e(Table S2)\u003c/b\u003e. The seasonal shifts in NO\u003csub\u003e2\u003c/sub\u003e concentration are further demonstrated with the use of geospatial mapping for each investigated years in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e03\u003c/span\u003e. Additionally, the monthly variations in NO2 concentrations are provided in \u003cb\u003eTable S3\u003c/b\u003e. The findings revealed a diverse pattern of NO\u003csub\u003e2\u003c/sub\u003e concentration across the studied years. In 2019, mean NO\u003csub\u003e2\u003c/sub\u003e concentration showed almost similar levels during post-monsoon (2.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e), pre-monsoon (2.3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e), and winter (2.2 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e) seasons; on the other hand, monsoon recorded the minimum concentration (1.4 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e). In 2020, the pre-monsoon season exhibited the highest average NO\u003csub\u003e2\u003c/sub\u003e concentration (2.9 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e), with winter and post-monsoon showing comparable levels (2.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e and 2.3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e, respectively), and monsoon recording the lowest average (1.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e). The winter of 2021 marked the peak mean NO\u003csub\u003e2\u003c/sub\u003e concentration (3.1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e) of all the studied years, with post-monsoon, pre-monsoon, and monsoon seasons following with concentrations of 2.1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e, 1.2 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e, and 1.7 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e, respectively. Winter 2022 nearly matched the highest average NO\u003csub\u003e2\u003c/sub\u003e concentration that was observed across all study years (3.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m2), with other three seasons following a descending trend: post-monsoon (2.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;pre-monsoon (1.8 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;monsoon (1.1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring 2019, average NO\u003csub\u003e2\u003c/sub\u003e levels were substantially higher in the pre-monsoon and post-monsoon seasons by 4.3% and 12% respectively, compared to winter. In 2020, this trend continued with NO\u003csub\u003e2\u003c/sub\u003e concentrations as 31% higher in the pre-monsoon season and 4.8% higher in the post-monsoon season compared to winter. Increased levels of NO\u003csub\u003e2\u003c/sub\u003e during the dry pre-monsoon and post-monsoon periods are a common occurrence across Bangladesh, including Dhaka (Azad \u0026amp; Kitada, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mukta et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A comprehensive analysis spanning from 2005 to 2019 in the Indo-Gangetic Plain similarly noted increased NO\u003csub\u003e2\u003c/sub\u003e concentrations during the dry post-monsoon season(Chawala et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Previous studies in Dhaka have consistently identified winter as the season with the highest NO\u003csub\u003e2\u003c/sub\u003e pollution levels (Azad \u0026amp; Kitada, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Rahaman et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; R. R. Rahman \u0026amp; Kabir, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the results of (Azad \u0026amp; Kitada, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Rahaman et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; R. R. Rahman \u0026amp; Kabir, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) align with the results of the present study\u0026rsquo;s findings for 2021 and 2022; winter exhibited the highest average NO\u003csub\u003e2\u003c/sub\u003e concentration. Various sources contribute to excessive NO\u003csub\u003e2\u003c/sub\u003e pollution, that take place because of vehicular emissions, burning of biomass fuels, industrial processes, such as electroplating, and operations of electric power plants.\u003c/p\u003e \u003cp\u003eOut of the years examined, the monsoon season consistently recorded the lowest average NO\u003csub\u003e2\u003c/sub\u003e levels, reaching a minimum of 0.00011 mol/m\u003csup\u003e2\u003c/sup\u003e in 2022. This observation is consistent with previous research findings and is likely attributed to heavy precipitation during the monsoon period(Hoque, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; M. M. Rahman et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shobnom et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe distribution of mean NO\u003csub\u003e2\u003c/sub\u003e concentration varied throughout the studied years, and this shows fluctuations between months with higher and lower pollution levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e04\u003c/span\u003e), consistent with seasonal changes. The spatial distribution patterns of NO\u003csub\u003e2\u003c/sub\u003e concentration also support the similar monthly variations as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e05\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn 2019 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e04\u003c/span\u003ea), the highest average NO\u003csub\u003e2\u003c/sub\u003e concentration occurred in November (3.7 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e), the last month of the post-monsoon season, followed by January and February of the winter season with the second highest mean concentration (2.6 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e). During the pre-monsoon months of March, April, and May, as well as extension to the moth of June (1.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e), concentrations fluctuated between 1.1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e and 1.6 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e. The latter part of the monsoon season, July, and August displayed the lowest mean NO\u003csub\u003e2\u003c/sub\u003e concentration levels at 0.9 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e04\u003c/span\u003eb), December, the onset of winter, saw the highest mean NO\u003csub\u003e2\u003c/sub\u003e concentration at (3.9 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e), whereas August, marking the end of the monsoon, had the lowest at (0.9 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e). As it shown in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e04\u003c/span\u003ec) and 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e04\u003c/span\u003ed), the lowest concentrations were in August (1.4 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e) and July (1.3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e) respectively, with the peak levels recorded in January 2021 (4.3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e) and November 2022 (3.8 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e mol/m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eIn general, the analysis of the current study reveals a consistent rise in NO2 levels from November to March across all years that were under investigation. Starting in August 2019, the researchers observed a notable rise, with NO2 levels peaking at 35.7% by 2021. Over the period from July 2019 to July 2022, there was a 30.7% surge in NO2 concentrations. Among the nine cities the researchers studied, Narayanganj and Dhaka recorded the highest levels, with Gazipur City ranking third. This rise in pollution is primarily due to rapid unchecked urbanization and heavy loss of vegetation (Rahaman et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe average NO2 concentrations across four seasons are detailed in \u003cb\u003eTable S2\u003c/b\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e02\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Vegetation scenario (NDVI) and NO2 concentration\u003c/h2\u003e \u003cp\u003eThis study explores the relationship between vegetation density and nitrogen dioxide (NO2) levels in the atmosphere. An inverse correlation, indicated by a correlation coefficient of -0.4, between the NDVI and NO2 concentrations was found. This suggests that as vegetation density increases, NO2 levels decrease. This relationship is visually supported in our data (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFurther analysis reveals that 16% of the increase in NO2 levels can be directly attributed to the reduction of green spaces, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e07\u003c/span\u003ea. This finding highlights the protective role of vegetation in urban and industrial settings where NO2 levels are often increased due to vehicle and industrial emissions. In areas with dense vegetation, NO2 levels are typically lower, and this demonstrates how plants absorb and mitigate these pollutants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo summarize, NDVI, effectively illustrates how vegetation can influence air quality by reducing harmful NO2 concentrations in the atmosphere. This underscores the importance of maintaining and expanding green spaces to mitigate air pollution, particularly in densely populated urban areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3. LST condition and NO2 concentration\u003c/h2\u003e \u003cp\u003eThis study found a correlation between LST and nitrogen dioxide (NO2) levels in urban areas, with an observed correlation coefficient of 0.47 (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e. This finding indicates that as the temperature of the land surface increases, the concentration of NO2 also tends to rise. The analysis shows that about 22.5% of the variation in NO2 pollution can be explained by fluctuations in LST, as reflected by the calculated \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e value of 0.2251.\u003c/p\u003e \u003cp\u003eAdditionally, our results highlight the impact of diminishing green spaces in urban settings. Specifically, the reduction of green areas contributes to 16% of the increase in NO2 concentrations \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e07\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. This underscores the environmental challenges faced in densely built metropolitan areas where concrete structures dominate and green spaces are limited. The urban heat island effect, which is prevalent in such areas, not only leads to higher temperatures compared to surrounding rural zones but also appears to exacerbate air pollution levels due to increased vehicular and industrial activities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Population density and NO2 concentration\u003c/h2\u003e \u003cp\u003eThis study investigates how population migration impacts nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) levels in the Gazipur City region amid significant demographic shifts as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e06\u003c/span\u003ee \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e07\u003c/span\u003ec.\u003c/p\u003e \u003cp\u003eThe detailed analysis reveals a correlation coefficient of 0.4, which indicates a positive association between NO\u003csub\u003e2\u003c/sub\u003e concentrations and population density. Areas experiencing substantial population mobility show higher NO\u003csub\u003e2\u003c/sub\u003e levels, likely due to increased motor vehicle usage and subsequent NO\u003csub\u003e2\u003c/sub\u003e emissions. Conversely, regions with lower population mobility tend to have lower NO\u003csub\u003e2\u003c/sub\u003e concentrations. Furthermore, our calculated \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e value of 0.17 suggests that population density explains up to 17% of the variation in NO\u003csub\u003e2\u003c/sub\u003e concentration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Industry density and NO2 concentration\u003c/h2\u003e \u003cp\u003eThis study assessed the relationship between industrial density and NO2 levels in Gazipur city. The findings reveal that areas with a greater concentration of industrial facilities consistently show higher NO2 levels. Specifically, the correlation coefficient of 0.35, as shown in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e, indicates a positive relationship between the number of industries and the increase in NO2 levels. The found result aligns with prior research, which suggests a significant impact of industrial activities on air quality (Jiang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Leffel et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, although the influence appears modest, the coefficient of determination (0.0124) depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e07\u003c/span\u003ed, confirms that industrial density does affect NO2 levels during the study period. The data points to industrial emissions as a likely contributor to increased NO2 levels in the surrounding regions. This underscores the urgent need for effective pollution control measures and regulatory actions in areas that are heavily populated by industrial activities, to mitigate their impact on air quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Road density and NO2 concentration\u003c/h2\u003e \u003cp\u003eThis study reveals a moderately strong correlation (0.55) between road density and NO2 concentrations, as detailed in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. This suggests that areas with more roads tend to have higher levels of NO2, likely due to vehicle emissions. This pattern is supported by similar findings in other research (CARSLAW, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), which confirm the link between road networks and air pollution. Additionally, a spatial map presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e06\u003c/span\u003ef visually illustrates this relationship across the study area. Further analysis, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e07\u003c/span\u003ee, reveals that road density accounts for approximately 30.32% (R squared\u0026thinsp;=\u0026thinsp;0.3032) of the variation in NO2 levels, which indicate a moderate influence of roads on NO2 concentration.\u003c/p\u003e \u003cp\u003eThese findings are crucial for urban planners, environmental managers, and policymakers. The findings highlight the need for improved transportation and fuel standards, as well as thoughtful urban planning. By addressing these factors, we can mitigate the adverse effects of road density on air quality and public health. This evidence strongly supports the importance of strategic settlement planning to reduce the negative impact of road networks on NO2 pollution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Settlement Density and NO\u003csub\u003e2\u003c/sub\u003e concentration\u003c/h2\u003e \u003cp\u003eIn this investigation, the researchers explore the relationship between building density and nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) levels in Gazipur City, considering the studied years. The analysis reveals a moderate correlation coefficient of 0.44 between settlement density and NO\u003csub\u003e2\u003c/sub\u003e concentrations (refer to \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The results indicate that settlement density has a moderate influence (R squared 0.1243) on NO\u003csub\u003e2\u003c/sub\u003e concentration levels in the current study (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e07\u003c/span\u003ef). Overall, the findings of this study suggest that higher settlement density corresponds to increased population movement, reduced vegetation, more commuting roads, and consequently, higher settlement density leads higher NO\u003csub\u003e2\u003c/sub\u003e concentrations (Erbertseder et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Reinmann et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Land Use Land Cover Classification and NO2 concentration\u003c/h2\u003e \u003cp\u003eIn this study, the researchers explored how different types of land uses influence the levels of nitrogen dioxide (NO2. The researchers categorized the land into six groups: settlements, vegetation, water bodies, bare land, farm fields, and others, as part of the LULC classification. The analysis of this study demonstrated a moderate relationship between the types of land use and NO2 concentrations, with a correlation coefficient of 0.37 and an R\u0026sup2; value of 0.112. This suggests that as the land use changes, the levels of NO2 also vary moderately. Interestingly, this research found that NO2 levels were consistently low in areas of reduced settlement, vegetation, agricultural fields, and near water bodies (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e07\u003c/span\u003eg). In contrast, higher concentrations of NO2 are usually observed in industrial and urban areas due to denser human activities (Erbertseder et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis pattern underscores the impact of human development and land use on environmental quality, particularly air pollution levels. The findings can help guide urban planning and public health initiatives to better manage and mitigate NO2 pollution in various environments.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study explored the changing levels of tropospheric vertical column NO\u003csub\u003e2\u003c/sub\u003e concentration in Gazipur city from January 2019 to December 2022, utilizing TROPOMI satellite data that were analysed through the GEE platform. The researchers investigated the influences of environmental factors on NO\u003csub\u003e2\u003c/sub\u003e concentrations, using regression analysis, and map visualizations. Gazipur is experiencing significant environmental challenges due to high levels of NO\u003csub\u003e2\u003c/sub\u003e, unstable LST, and transformations in land use and cover. These issues, combined with increasing population density and expanding road networks, are leading to heavy vegetation loss and eventually reshaping the urban landscape.\u003c/p\u003e \u003cp\u003eThe findings of the present study underscored the link between monthly and seasonal fluctuations in NO\u003csub\u003e2\u003c/sub\u003e concentrations and environmental variables like LST and LULC. The analysis provided insights into the spatial and temporal patterns of NO\u003csub\u003e2\u003c/sub\u003e concentration, with a focus on its geographic relationships with LST, NDVI, road density, settlement density, LULC, industrial activity, and population density. To mitigate the challenges posed by high NO\u003csub\u003e2\u003c/sub\u003e concentrations and related environmental factors in Gazipur city, policymakers could consider implementing stricter emission controls, promoting green infrastructure development, enhancing public transportation systems to reduce reliance on individual vehicles, and implementing urban planning strategies that prioritize environmental sustainability. Additionally, community engagement and awareness campaigns can play a crucial role in fostering environmental stewardship and encouraging behavioural changes towards reducing air pollution levels.\u003c/p\u003e"},{"header":"5. Strength and limitations","content":"\u003cp\u003eThe present study, one of the first in the Gazipur Dhaka region, explores the changing patterns of NO2 levels and their causes, using advanced geographic and remote sensing techniques. The methods used in this study are scientific, and the finding are pioneering and trustworthy. However, to fully appreciate the implications of these findings, especially for policymaking, it\u0026rsquo;s important to acknowledge some limitations of the study.\u003c/p\u003e \u003cp\u003eFirstly, Bangladesh currently has only 11 air monitoring stations, which are too few for detailed satellite-based studies. Since 2019, the S5P satellite has been providing data with a resolution of 1x1 square kilometres. This resolution is not fine enough for analysing small areas effectively.\u003c/p\u003e \u003cp\u003eSecondly, in Gazipur City, where there is only one air monitoring station, the researchers relied on satellite data to examine the link between road traffic and NO2 levels. However, this data does not allow the researchers to identify specific types of vehicles that contribute to NO2 pollution. Although daily NO2 data is available from Sentinel-5P, other important variables (could you please insert the names of some of the variables which S-5P doesn\u0026rsquo;t provide in detailed resolution) do not have similarly detailed daily data.\u003c/p\u003e \u003cp\u003eThirdly, considering meteorological factors, such as wind speed, humidity, and precipitation could have given us a clearer picture of how NO2 levels vary with time and location.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e: Nitrogen Dioxide; NDVI: Normalized Difference Vegetation Index; LULC: Land Use Land Cover; LST: Land Surface Temperature; GEE: Google Earth Engine; OSM: OpenStreetMap; MODIS: Moderate Resolution Imaging Spectroradiometer; CSP: Conservation Science Partners.\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research received funding from the Institute for Advanced Research (IAR), United International University (UIU).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAl Jubaer: Formal analysis, Methodology, Software, Visualization, Writing - Original Draft; Rakib Hossain: Formal analysis, Visualization, Writing - Original Draft, Writing \u0026ndash; reviewing \u0026amp; editing; Md. Shakhaoat Hossain: Supervision, Validation, Conceptualization;Afzal Ahmed: Super Vision, Funding acquisition;\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to express their appreciation to the United International University (UIU), We are thankful to European Space Agency (ESA) for NO2, LULC and NDVI for data, Conservation Science Partners (CSP) for Population density data, United States Geological Survey (USGS) and National Aeronautics and Space Administration (NASA) and also thankful to OpenStreetMap (OSM) for providing Road, Settlement and Industry free data and other support for the completion of this work.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eThe datasets used in the findings of this study are mentioned within this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdullah, H. M., Islam, I., Miah, M. G., \u0026amp; Ahmed, Z. (2019). Quantifying the spatiotemporal patterns of forest degradation in a fragmented, rapidly urbanizing landscape: A case study of Gazipur, Bangladesh. \u003cem\u003eRemote Sensing Applications: Society and Environment\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(August 2018), 457\u0026ndash;465. https://doi.org/10.1016/j.rsase.2019.01.002\u003c/li\u003e\n\u003cli\u003eAgarwal, V., \u0026amp; Kumar, A. (2022). The changes in the air quality of Wazirpur, Delhi due to the COVID-19 shutdown. \u003cem\u003eCleaner Chemical Engineering\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(2), 100001. https://doi.org/10.1016/j.clce.2022.100001\u003c/li\u003e\n\u003cli\u003eAmoroso, N., Cilli, R., Maggipinto, T., Monaco, A., Tangaro, S., \u0026amp; Bellotti, R. (2022). Satellite data and machine learning reveal a significant correlation between NO2 and COVID-19 mortality. \u003cem\u003eEnvironmental Research\u003c/em\u003e, \u003cem\u003e204\u003c/em\u003e(PA), 111970. https://doi.org/10.1016/j.envres.2021.111970\u003c/li\u003e\n\u003cli\u003eArifeen, H. M., Phoungthong, K., Mostafaeipour, A., Yuangyai, N., Yuangyai, C., Techato, K., \u0026amp; Jutidamrongphan, W. (2021). Determine the land-use land-cover changes, urban expansion and their driving factors for sustainable development in gazipur Bangladesh. \u003cem\u003eAtmosphere\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(10). https://doi.org/10.3390/atmos12101353\u003c/li\u003e\n\u003cli\u003eAshwini, K., \u0026amp; Sil, B. S. (2022). Impacts of Land Use and Land Cover Changes on Land Surface Temperature over Cachar Region, Northeast India\u0026mdash;A Case Study. \u003cem\u003eSustainability (Switzerland)\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(21), 100862. https://doi.org/10.3390/su142114087\u003c/li\u003e\n\u003cli\u003eAtkinson, R. W., Butland, B. K., Anderson, H. R., \u0026amp; Maynard, R. L. (2018). Long-term Concentrations of Nitrogen Dioxide and Mortality. \u003cem\u003eEpidemiology\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(4), 460\u0026ndash;472. https://doi.org/10.1097/EDE.0000000000000847\u003c/li\u003e\n\u003cli\u003eAzad, A. K., \u0026amp; Kitada, T. (1998). Characteristics of the air pollution in the city of Dhaka, Bangladesh in winter. \u003cem\u003eAtmospheric Environment\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(11), 1991\u0026ndash;2005. https://doi.org/10.1016/S1352-2310(97)00508-6\u003c/li\u003e\n\u003cli\u003eBechle, M. J., Millet, D. B., \u0026amp; Marshall, J. D. (2013). Remote sensing of exposure to NO2: Satellite versus ground-based measurement in a large urban area. \u003cem\u003eAtmospheric Environment\u003c/em\u003e, \u003cem\u003e69\u003c/em\u003e, 345\u0026ndash;353. https://doi.org/10.1016/j.atmosenv.2012.11.046\u003c/li\u003e\n\u003cli\u003eCARSLAW, D. (2005). Evidence of an increasing NO2/NOX emissions ratio from road traffic emissions. \u003cem\u003eAtmospheric Environment\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(26), 4793\u0026ndash;4802. https://doi.org/10.1016/j.atmosenv.2005.06.023\u003c/li\u003e\n\u003cli\u003eChawala, P., Priyan R, S., \u0026amp; SM, S. N. (2023). Climatology and landscape determinants of AOD, SO2 and NO2 over Indo-Gangetic Plain. \u003cem\u003eEnvironmental Research\u003c/em\u003e, \u003cem\u003e220\u003c/em\u003e. https://doi.org/10.1016/j.envres.2022.115125\u003c/li\u003e\n\u003cli\u003eCSP. (2024). \u003cem\u003eConservation Science Partners\u003c/em\u003e. https://csp-inc.org/\u003c/li\u003e\n\u003cli\u003eDoE. (2018). \u003cem\u003eAMBIENTAIR QUALITY IN BANGLADESH\u003c/em\u003e (M. Shahjahan (ed.)). Clean Air and Sustainable Environment Project, Department of Environment, Ministry of Environment, Forest and Climate Change of the Government of Bangladesh. http://doe.portal.gov.bd/sites/default/files/files/doe.portal.gov.bd/page/cdbe516f_1756_426f_af6b_3ae9f35a78a4/2020-06-10-11-02-5a7ea9f58497800ec9f0cea00ce7387f.pdf\u003c/li\u003e\n\u003cli\u003eErbertseder, T., Taubenb\u0026ouml;ck, H., Esch, T., Gilardi, L., Paeth, H., Marconcini, M., \u0026amp; Dech, S. (2023). Earth Observation-based analysis of NO2 pollution and settlement growth in megacities. \u003cem\u003e2023 Joint Urban Remote Sensing Event (JURSE)\u003c/em\u003e, 1\u0026ndash;4. https://doi.org/10.1109/JURSE57346.2023.10144190\u003c/li\u003e\n\u003cli\u003eESA. (2018). \u003cem\u003e5P: TROPOMI Level 2 Nitrogen Dioxide total column products, Version 01, processed by: ESA\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eGao, Y., Wang, S., Zhang, C., Xing, C., Tan, W., Wu, H., Niu, X., \u0026amp; Liu, C. (2023). Assessing the impact of urban form and urbanization process on tropospheric nitrogen dioxide pollution in the Yangtze River Delta, China. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, \u003cem\u003e336\u003c/em\u003e(July), 122436. https://doi.org/10.1016/j.envpol.2023.122436\u003c/li\u003e\n\u003cli\u003eGeddes, J. A., Martin, R. V., Boys, B. L., \u0026amp; van Donkelaar, A. (2016). Long-Term Trends Worldwide in Ambient NO 2 Concentrations Inferred from Satellite Observations. \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e, \u003cem\u003e124\u003c/em\u003e(3), 281\u0026ndash;289. https://doi.org/10.1289/ehp.1409567\u003c/li\u003e\n\u003cli\u003eGoldberg, D. L., Anenberg, S. C., Kerr, G. H., Mohegh, A., Lu, Z., \u0026amp; Streets, D. G. (2021). TROPOMI NO 2 in the United States: A Detailed Look at the Annual Averages, Weekly Cycles, Effects of Temperature, and Correlation With Surface NO 2 Concentrations. \u003cem\u003eEarth\u0026rsquo;s Future\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(4). https://doi.org/10.1029/2020EF001665\u003c/li\u003e\n\u003cli\u003eGreenstone, M., \u0026amp; Hasenkopf, C. (2023). \u003cem\u003eAir Quality Life Index | 2023 Annual Update\u003c/em\u003e. Energy Policy Institute at the University of Chicago (EPIC). https://aqli.epic.uchicago.edu/reports/?utm_medium=partner\u0026amp;utm_source=twitter\u0026amp;utm_campaign=aqlinews\u003c/li\u003e\n\u003cli\u003eGrzybowski, P. T., Markowicz, K. M., \u0026amp; Musiał, J. P. (2023). Estimations of the Ground-Level NO2 Concentrations Based on the Sentinel-5P NO2 Tropospheric Column Number Density Product. \u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 378. https://doi.org/10.3390/rs15020378\u003c/li\u003e\n\u003cli\u003eHarper, A., Baker, P. N., Xia, Y., Kuang, T., Zhang, H., Chen, Y., Han, T. L., \u0026amp; Gulliver, J. (2021). Development of spatiotemporal land use regression models for PM2.5 and NO2 in Chongqing, China, and exposure assessment for the CLIMB study. \u003cem\u003eAtmospheric Pollution Research\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(7). https://doi.org/10.1016/j.apr.2021.101096\u003c/li\u003e\n\u003cli\u003eHealth Effects Institute. (2020). \u003cem\u003eState of Global Air 2020. Special Report.\u003c/em\u003e https://www.stateofglobalair.org/sites/default/files/documents/2020-10/soga-2020-report-10-26_0.pdf\u003c/li\u003e\n\u003cli\u003eHoque, M. M. M. (2020). Meteorological Influences on Seasonal Variations of Air Pollutants (SO2, NO2, O3, CO, PM2.5, and PM10) in the Dhaka Megacity. \u003cem\u003eAmerican Journal of Pure and Applied Biosciences\u003c/em\u003e, 15\u0026ndash;23. https://doi.org/10.34104/ajpab.020.15023\u003c/li\u003e\n\u003cli\u003eIslam, K. M. A. A., Adnan, M. S. G., Zannat, K. E., \u0026amp; Dewan, A. (2022). Spatiotemporal dynamics of NO2 concentration with linear mixed models: A Bangladesh case study. \u003cem\u003ePhysics and Chemistry of the Earth\u003c/em\u003e, \u003cem\u003e126\u003c/em\u003e(2), 103119. https://doi.org/10.1016/j.pce.2022.103119\u003c/li\u003e\n\u003cli\u003eJiang, L., Zhou, H., He, S., Cui, Y., \u0026amp; Wang, J. (2021). Identifying the driving factors of NO2 pollution of One Belt One Road countries: satellite observation technique and dynamic spatial panel data analysis. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(16), 20393\u0026ndash;20407. https://doi.org/10.1007/s11356-020-12113-z\u003c/li\u003e\n\u003cli\u003eLeffel, B., Tavasoli, N., Liddle, B., Henderson, K., \u0026amp; Kiernan, S. (2022). Metropolitan air pollution abatement and industrial growth: Global urban panel analysis of PM 10 , PM 2.5 , NO 2 and SO 2. \u003cem\u003eEnvironmental Sociology\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 94\u0026ndash;107. https://doi.org/10.1080/23251042.2021.1975349\u003c/li\u003e\n\u003cli\u003eLi, L., \u0026amp; Wu, J. (2021). Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, \u003cem\u003e254\u003c/em\u003e, 112257. https://doi.org/10.1016/j.rse.2020.112257\u003c/li\u003e\n\u003cli\u003eLi, X., Wang, P., Wang, W., Zhang, H., Shi, S., Xue, T., Lin, J., Zhang, Y., Liu, M., Chen, R., Kan, H., \u0026amp; Meng, X. (2023). Mortality burden due to ambient nitrogen dioxide pollution in China: Application of high-resolution models. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e176\u003c/em\u003e(December 2022), 107967. https://doi.org/10.1016/j.envint.2023.107967\u003c/li\u003e\n\u003cli\u003eMelamed, M. L., Schmale, J., \u0026amp; von Schneidemesser, E. (2016). Sustainable policy\u0026mdash;key considerations for air quality and climate change. \u003cem\u003eCurrent Opinion in Environmental Sustainability\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e, 85\u0026ndash;91. https://doi.org/10.1016/j.cosust.2016.12.003\u003c/li\u003e\n\u003cli\u003eMitchell, A. (1999). \u003cem\u003eThe Esri Guide to GIS Analysis, Volume 1: Geographic Patterns and Relationships\u003c/em\u003e (2nd ed.). Esri. https://www.esri.com/en-us/esri-press/browse/the-esri-guide-to-gis-analysis-volume-1-geographic-patterns-and-relationships-second-edition\u003c/li\u003e\n\u003cli\u003eMukta, T. A., Hoque, M. M. M., Sarker, M. E., Hossain, M. N., \u0026amp; Biswas, G. K. (2020). Seasonal variations of gaseous air pollutants (SO2, NO2, O3, CO) and particulates (PM2.5, PM10) in Gazipur: An industrial city in Bangladesh. \u003cem\u003eAdvances in Environmental Technology\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(4), 195\u0026ndash;209. https://doi.org/10.22104/AET.2021.4890.1320\u003c/li\u003e\n\u003cli\u003eOeurng, C., Cochrane, T. A., Arias, M. E., Shrestha, B., \u0026amp; Piman, T. (2016). Assessment of changes in riverine nitrate in the Sesan, Srepok and Sekong tributaries of the Lower Mekong River Basin. \u003cem\u003eJournal of Hydrology: Regional Studies\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 95\u0026ndash;111. https://doi.org/10.1016/j.ejrh.2016.07.004\u003c/li\u003e\n\u003cli\u003eOpenStreetMap contributors. (2024). \u003cem\u003eOpenStreetMap\u003c/em\u003e. https://www.openstreetmap.org\u003c/li\u003e\n\u003cli\u003ePrunet, P., Lezeaux, O., Camy-Peyret, C., \u0026amp; Thevenon, H. (2020). Analysis of the NO2 tropospheric product from S5P TROPOMI for monitoring pollution at city scale. \u003cem\u003eCity and Environment Interactions\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(2). https://doi.org/10.1016/j.cacint.2020.100051\u003c/li\u003e\n\u003cli\u003eRabbi, M. A. (2018). \u003cem\u003eAssessment of Nitrogen Oxides and Sulphur Dioxide Content in the Ambient Air near the Garments Industries of Bangladesh Environmental and Social Sciences Open Science Publications\u003c/em\u003e. \u003cem\u003eJanuary\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eRabiei-Dastjerdi, H., Mohammadi, S., Saber, M., Amini, S., \u0026amp; McArdle, G. (2022). Spatiotemporal Analysis of NO2 Production Using TROPOMI Time-Series Images and Google Earth Engine in a Middle Eastern Country. \u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(7), 1725. https://doi.org/10.3390/rs14071725\u003c/li\u003e\n\u003cli\u003eRahaman, S. N., Ahmed, S. M. M., Zeyad, M., \u0026amp; Zim, A. H. (2023). Effect of vegetation and land surface temperature on NO2 concentration: A Google Earth Engine-based remote sensing approach. \u003cem\u003eUrban Climate\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e. https://doi.org/10.1016/j.uclim.2022.101336\u003c/li\u003e\n\u003cli\u003eRahman, M. M., Mahamud, S., \u0026amp; Thurston, G. D. (2019). Recent spatial gradients and time trends in Dhaka, Bangladesh, air pollution and their human health implications. \u003cem\u003eJournal of the Air \u0026amp; Waste Management Association\u003c/em\u003e, \u003cem\u003e69\u003c/em\u003e(4), 478\u0026ndash;501. https://doi.org/10.1080/10962247.2018.1548388\u003c/li\u003e\n\u003cli\u003eRahman, R. R., \u0026amp; Kabir, A. (2023). Spatiotemporal analysis and forecasting of air quality in the greater Dhaka region and assessment of a novel particulate matter filtration unit. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e, \u003cem\u003e195\u003c/em\u003e(7). https://doi.org/10.1007/s10661-023-11370-y\u003c/li\u003e\n\u003cli\u003eRana, S. M. S., Ahmed, S. M. F., \u0026amp; Akter, H. (2021). Analysis of NO2 Pollution over Bangladesh between the Two COVID-19 Caused Lockdowns in 2020 and 2021 Using Sentinel-5P Products \u0026dagger;. \u003cem\u003eEngineering Proceedings\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 0\u0026ndash;5. https://doi.org/10.3390/ASEC2021-11139\u003c/li\u003e\n\u003cli\u003eReinmann, A. B., Hutyra, L. R., Trlica, A., \u0026amp; Olofsson, P. (2016). Assessing the global warming potential of human settlement expansion in a mesic temperate landscape from 2005 to 2050. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e545\u003c/em\u003e\u0026ndash;\u003cem\u003e546\u003c/em\u003e, 512\u0026ndash;524. https://doi.org/10.1016/j.scitotenv.2015.12.033\u003c/li\u003e\n\u003cli\u003eShobnom, N., Hossain, M. S., \u0026amp; Roni, R. (2023). Monitoring spatiotemporal changes of NO2 using TROPOMI and sentinel-5 images for Dhaka city and its surrounding areas of Bangladesh. \u003cem\u003eJournal of Air Pollution and Health\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(3), 269\u0026ndash;284. https://doi.org/10.18502/japh.v8i3.13785\u003c/li\u003e\n\u003cli\u003eSingh, N., Dey, S., \u0026amp; Knibbs, L. D. (2023). Spatio-temporal patterns of tropospheric NO2 over India during 2005\u0026ndash;2019. \u003cem\u003eAtmospheric Pollution Research\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(3). https://doi.org/10.1016/j.apr.2023.101692\u003c/li\u003e\n\u003cli\u003eSong, J., Wang, Y., Zhang, Q., Qin, W., Pan, R., Yi, W., Xu, Z., Cheng, J., \u0026amp; Su, H. (2023). Premature mortality attributable to NO2 exposure in cities and the role of built environment: A global analysis. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e866\u003c/em\u003e, 161395. https://doi.org/10.1016/j.scitotenv.2023.161395\u003c/li\u003e\n\u003cli\u003eStieb, D. M., Berjawi, R., Emode, M., Zheng, C., Salama, D., Hocking, R., Lyrette, N., Matz, C., Lavigne, E., \u0026amp; Shin, H. H. (2021). Systematic review and meta-analysis of cohort studies of long term outdoor nitrogen dioxide exposure and mortality. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(2), e0246451. https://doi.org/10.1371/journal.pone.0246451\u003c/li\u003e\n\u003cli\u003eSuthar, G., Kaul, N., Khandelwal, S., \u0026amp; Singh, S. (2024). Dynamics of land surface temperature: Insights into vegetation, elevation, and air pollution in Bengaluru. \u003cem\u003eRemote Sensing Applications: Society and Environment\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(December 2023), 101145. https://doi.org/10.1016/j.rsase.2024.101145\u003c/li\u003e\n\u003cli\u003eSuthar, G., Singhal, R. P., Khandelwal, S., \u0026amp; Kaul, N. (2023). Spatiotemporal variation of air pollutants and their relationship with land surface temperature in Bengaluru, India. \u003cem\u003eRemote Sensing Applications: Society and Environment\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(June), 101011. https://doi.org/10.1016/j.rsase.2023.101011\u003c/li\u003e\n\u003cli\u003eUddin, M. S., Mahalder, B., \u0026amp; Mahalder, D. (2023). Assessment of Land Use Land Cover Changes and Future Predictions Using CA-ANN Simulation for Gazipur City Corporation, Bangladesh. \u003cem\u003eSustainability (Switzerland)\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(16). https://doi.org/10.3390/su151612329\u003c/li\u003e\n\u003cli\u003eWHO. (2021). WHO global air quality guidelines. \u003cem\u003eParticulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide\u003c/em\u003e, 1\u0026ndash;360. https://creativecommons.org/licenses/by-nc-sa/3.0/igo\u003c/li\u003e\n\u003cli\u003eZheng, J., Che, W., Wang, X., Louie, P., \u0026amp; Zhong, L. (2009). Road-Network-Based Spatial Allocation of On-Road Mobile Source Emissions in the Pearl River Delta Region, China, and Comparisons with Population-Based Approach. \u003cem\u003eJournal of the Air \u0026amp; Waste Management Association\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(12), 1405\u0026ndash;1416. https://doi.org/10.3155/1047-3289.59.12.1405\u003c/li\u003e\n\u003cli\u003eZhu, Y., Xie, J., Huang, F., \u0026amp; Cao, L. (2020). Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e727\u003c/em\u003e, 138704. https://doi.org/10.1016/j.scitotenv.2020.138704\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"NO2 Concentration, Urban Air Quality, Spatiotemporal Patterns, GIS, Gazipur-Dhaka","lastPublishedDoi":"10.21203/rs.3.rs-4672218/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4672218/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe growing global attention on urban air quality underscores the need to understand the spatial and temporal dynamics of nitrogen dioxide (NO2), especially in cities like Dhaka (Gazipur), Bangladesh, known for having some of the world's poorest air quality. The present study utilizes the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5P (S5P) satellite and Google Earth Engine (GEE) to analyse NO2 concentrations in Gazipur, Bangladesh, from 2019 to 2022. Utilizing S5P TROPOMI data, we investigate the correlations between NO2 levels and various environmental factors, including land surface temperature (LST), normalized difference vegetation index (NDVI), land use and land cover (LULC), population density, road density, settlement density, and industry density.\u003c/p\u003e \u003cp\u003eOur results reveal significant seasonal variations, with peak NO2 levels during pre-monsoon and post-monsoon periods and the lowest levels during monsoon seasons. The study demonstrates a positive correlation between NO2 concentrations and LST, road density, settlement density, and industrial density, and a negative correlation with NDVI. These findings underscore the detrimental impact of rapid urbanization and deforestation on air quality. Through linear regression analysis, we highlight the influence of these environmental factors on NO2 levels, providing a comprehensive understanding of the urban pollution dynamics in a rapidly growing city.\u003c/p\u003e \u003cp\u003eThis research offers critical insights for policymakers and urban planners, advocating for enhanced green infrastructure, stringent emission controls, and sustainable urban development strategies to mitigate air pollution in Gazipur. Our methodological approach and findings contribute to the broader discourse on urban air quality management in developing countries.\u003c/p\u003e","manuscriptTitle":"Factors Influencing Spatiotemporal Variability of NO2 Concentration in Urban Area: A GIS and Remote Sensing-Based Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-22 15:04:52","doi":"10.21203/rs.3.rs-4672218/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-18T22:33:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-18T16:34:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190299650655073903661158253393581747408","date":"2024-09-26T14:14:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319217786675583641745844334404513526832","date":"2024-09-23T14:07:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-30T17:00:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249713665979103723677608299279156525405","date":"2024-08-25T11:18:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229623479935551950711664110328459653624","date":"2024-08-23T02:29:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-08T13:30:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-27T10:38:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-27T10:36:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2024-07-02T07:24:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"530cc336-a4d2-4ef9-8dfd-7d9a79ec876d","owner":[],"postedDate":"August 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T16:06:11+00:00","versionOfRecord":{"articleIdentity":"rs-4672218","link":"https://doi.org/10.1007/s10661-024-13531-z","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2025-01-13 15:57:27","publishedOnDateReadable":"January 13th, 2025"},"versionCreatedAt":"2024-08-22 15:04:52","video":"","vorDoi":"10.1007/s10661-024-13531-z","vorDoiUrl":"https://doi.org/10.1007/s10661-024-13531-z","workflowStages":[]},"version":"v1","identity":"rs-4672218","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4672218","identity":"rs-4672218","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}