Spatiotemporal Analysis of Urban Growth and PM2.5 Concentrations in Sylhet, Bangladesh

Abstract

Environmental impacts of urbanization, such as increased air pollution, pose significant challenges for developing countries. This study examines land cover changes and their relationship with particulate matter _2.5 (PM_2.5) concentrations in two upazilas of Bangladesh from 2001 to 2019 using GIS and remote sensing techniques. Results show significant urban expansion (i.e., increase in built-up area) in both upazilas, corresponding with increasing PM_2.5 levels. Linear regression reveals correlations between land cover types and PM_2.5 levels. Mixed forests and waterbodies tend to be negatively associated with PM_2.5 concentrations; on the other hand, built-up and barren land show a positive correlation with PM_2.5. The most significant increase in built-up land and PM_2.5 was in Madhabpur, indicating urgent environmental and health issues. This study emphasizes the critical role of sustainable urban planning and environmental conservation in mitigating urbanization’s adverse effects on air quality, advocating for preserving natural landscapes to maintain ecological balance, protecting urban health, and providing policymakers with insights to develop strategies addressing urban expansion and air pollution.

1. Introduction

Bangladesh is one of the fastest-growing developing economies [1]. Its rapid economic growth and increased energy use have raised concerns about air, water, and noise pollution. It has been demonstrated that Bangladesh’s district and divisional cities are plagued by air pollution due to inadequate urban planning and persistent social inequalities [2]. World Health Organization (WHO) data suggest that 99% of the world’s population breathes significantly polluted air, and amongst the world’s major cities, Dhaka in Bangladesh is one of the worst polluted [3,4]. The World Bank estimated that approximately 80,000 people died in 2019 in Bangladesh due to various health conditions caused by air pollution [5].

Particulate matter (PM), fine particles with diameters of 2.5 μm or less (PM_2.5), is increasingly recognized as a critical component of air pollution [6,7]. Yu et al. conducted a national study on PM_2.5 exposure across Brazil from 2010 to 2018 and found that long-term exposure significantly reduced life expectancy, with potential improvements of 0.78 years if PM_2.5 levels met WHO guidelines [8]. De La Cruz et al. analyzed PM_2.5 in the Metropolitan area of Huancayo, Peru, observing that concentrations exceeded WHO and national standards, largely driven by vehicular emissions and natural sources [9]. Zareba et al. used machine learning to predict smog events in Krakow, Poland, and found that transitioning from coal to sustainable energy was key to improving air quality [10]. Mataveli et al. studied fire-related PM_2.5 emissions in the Brazilian Cerrado, identifying a decrease in emissions over time but with significant spatial variability across the region [11]. According to the Emission Database for Global Atmospheric Research (EDGAR), PM_2.5 emissions worldwide were predominantly driven by the residential sector, particularly through the use of biofuels and biomass burning. In 2019, Bangladesh’s PM_2.5 emissions from biomass burning reached 36.652 Gg, while emissions from the residential and other sectors were substantially higher at 177.036 Gg, according to EDGAR’s report. In addition, unplanned rapid urbanization, motorization, and industrialization in Bangladesh’s major cities have also significantly contributed to rising air pollution levels [12]. Moniruzzaman et al. have identified key sources of PM_2.5 in Dhaka, such as automobile emissions, industrial activities, reduction of urban spaces, and solid waste combustion [13]. Jawaa et al. investigated the dominant sources of trace elements in PM_2.5 in Dhaka city and found four primary sources to be soil dust (65%), heavy engine oil combustion (25%), industrial emissions (5%), and non-exhaust emission (5%). Kawashima et al. identified sources of ammonium in PM_2.5 in Dhaka and found that non-agricultural sources (NH3 slip and fossil fuel combustion) contribute more than 50% of the ammonium in PM_2.5 in Dhaka [14]. Mahmood et al. found that brick kilns were the dominant PM source in Bangladesh from 2014 to 2017 [15]. We suspect the primary sources of PM_2.5 in the study should be very similar to those in Dhaka city [16].

The land use and land cover (LULC) change has been identified to be one of the important factors influencing PM_2.5 levels. For example, Hassan et al. focused on five major industrial districts in central Bangladesh from 2002 to 2021, integrating PM_2.5 data with various LULC categories such as barren lands, forests, croplands, and urban areas [17]. Their study found a notable rise in PM_2.5 concentrations, particularly in urban areas, with the most significant increases observed between 2012 and 2021. Similarly, Mazumder et al. employed a GIS-based spatial modeling approach to assess PM_2.5 concentrations across six zones within the Khulna Metropolitan area, utilizing geospatial variables like the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Digital Elevation Model (DEM) data [18]. The study revealed that PM_2.5 concentrations were significantly higher in urban areas with lower NDVI values and higher LST, particularly near major roads and industrial regions. Zarin and Esraz-Ul-zannat assessed the impacts of LULC changes on air quality in Dhaka and found that PM_2.5 concentration is negatively correlated with vegetation coverage and waterbodies [19]. Lu et al. investigated the response of PM_2.5 to land use change in China and found that low PM_2.5 concentration is highly associated with forests and grasslands [20].

Another important factor influencing PM_2.5 levels is meteorological conditions. Studies have suggested that relative humidity (RH), temperatures, wind speeds, and surface pressures can significantly influence PM_2.5 levels. However, the impact of meteorological conditions on PM_2.5 varies from region to region and from season to season. For example, Yang et al. found that RH is positively correlated with PM_2.5 in north China and Urumqi, and a totally opposite relationship is found in other areas of China. It was also found that the positive correlation between RH and PM_2.5 is stronger in the winter and spring seasons [21]. In Krakow, Poland, Danek et al. investigated the impact of meteorological factors and terrain on air pollution concentrations using geostatistical methods [22]. The study highlighted how the city’s topography, situated in a valley with limited natural ventilation, traps pollutants, which are further influenced by wind speed, temperature, and pressure. The researchers found that even low wind speeds exacerbated pollutant accumulation, while temperature drops correlated with increased PM₁₀ concentrations. Similarly, Oleniacz and Gorzelnik explored air pollutant concentrations in Krakow and emphasized that topographic factors, combined with meteorological variables like wind and humidity, significantly influenced pollution dispersion from industrial and traffic sources [23]. Faisal et al. found that seasonal PM_2.5 is inversely related to precipitation, temperature, and humidity in Dhaka [24]. Previous studies have demonstrated that the impact of meteorological conditions on air pollution generally occurs on a sub-annual time scale, as the varying seasonal impacts on air pollution could potentially offset each other over the course of a year.

Previous studies have suggested a linkage between land cover and PM_2.5 concentration. However, very few studies have focused on Sylhet, another metropolitan area in Bangladesh. This study will analyze trends in land cover changes and PM_2.5 concentrations in these cities and investigate their relationships on an annual scale. This study period ends before the COVID-19 lockdown, and because of that, we do not consider the impact of suspended economic activities on PM_2.5. In this study, we focus on an annual average of PM_2.5 to minimize the impact of daily, monthly, or seasonal meteorological factors. The two main objectives of this study are (1) investigate spatiotemporal change in land cover and the annual mean concentration of PM_2.5 in ambient air in two upazilas in Sylhet Division, Bangladesh, from 2001 to 2019, and (2) to determine associations between land use and air pollution in each study area.

2. Study Area, Data and Methods

2.1. Study Area

Sylhet Division, one of eight divisions in Bangladesh, is essential due to its well-developed road network that connects major cities. Remittances from this region’s substantial tea plants are a significant export. Tourists flock to Sylhet’s natural beauty, boosting its economic and cultural importance. Bangladesh’s lowest air pollution is in Sylhet Division [25]. Sylhet Division has four districts and 35 upazilas. Two upazilas, including Sylhet and Madhabpur were selected for investigation based on geography, population density, urbanization, industrialization, etc. (see Table 1 and Figure 1). Sylhet Upazila is a densely populated and rapidly urbanizing divisional city, while Madhabpur is a growing industrial hub. Sylhet’s economic potential has spurred urbanization and industrialization in recent years, and the air pollution level in the study area has increased.

2.2. Data and Methods

The study period spans from 2001 to 2019 for the two selected upazilas of Bangladesh. Annual PM_2.5 data were obtained for 2001, 2005, 2010, 2015, and 2019 from the NASA Socioeconomic Data and Applications Center (SEDAC) [26]. The SEDAC data set was created through the combination of Aerosol Optical Depth (AOD) retrievals from multiple satellite algorithms including the NASA MODerate resolution Imaging Spectroradiometer Collection 6.1 (MODIS C6.1), Multi-angle Imaging SpectroRadiometer Version 23 (MISRv23), MODIS Multi-Angle Implementation of Atmospheric Correction Collection 6 (MAIAC C6), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Deep Blue Version 4. The near-surface PM_2.5 concentration is calculated in the GEOS-Chem chemical transport model through its relationship with the total column measurement of aerosol with adjustments. These data have been used in previous studies of PM_2.5 concentration in Dhaka [24,27]. Further, Faisal et al. compared the SEDAC PM_2.5 concentration data against Landsat-derived PM_2.5 in Dhaka and found that Landsat PM_2.5 and the SEDAC data are highly correlated with the Pearson correlation coefficient ranging from 0.85 to 0.97 [24]. Although both are not direct measurements of PM_2.5, the high correlation between PM_2.5 derived from the two different sources suggests that the SEDAC data provides a reliable measure of PM_2.5 concentration in Bangladesh.

Landsat 5 and 8 images, with cloud cover less than 2%, for land cover analysis data were collected from the United States Geological Survey (USGS). City boundary data were collected from the Local Government Engineering Department (LGED) in shapefile format (http://gis.lged.gov.bd/, accessed on 3 July 2024), and human settlements data were obtained from the Bangladesh Bureau of Statistics (BBS) in tabular format. Landsat imagery at 30-m spatial resolution was used to examine urban land cover changes over time; specifically, Landsat 5 data for 2001, 2005, and 2010, and Landsat 8 for 2015 and 2019.

Landsat images were classified as built-up, barren land, mixed forest, and waterbodies land cover types. The “Built-up” class encompasses industrial and residential areas, infrastructure, and impervious surfaces. “Barren land” includes non-vegetated areas such as exposed soil, sand, and vacant land. The “Mixed Forest” class refers to green lands with dense plantations, trees, and gardens. “Waterbodies” covers rivers, lakes, ponds, streams, and channels. These categories represent different types of land cover, each with unique characteristics and implications for environmental and land use studies.

The study used the Support Vector Machine (SVM) supervised image classification technique (Figure 2) within ArcGIS Pro to generate categorical land cover maps. Classification results were used to create land cover change maps from 2001 to 2019.

Additionally, hourly particulate matter (PM) data were obtained from the Sylhet Continuous Air Monitoring Station (CAMS), which is the only CAMS managed by the Department of Environment (DoE) under the CASE project. These data were obtained using a beta gauge instrument specifically designed to measure the mass concentration of particulate matter. The instrument measures the volume of gas extracted from the stack or duct during each sampling interval and then calculates the mass concentration, typically expressed in units such as micrograms per cubic meter (µg/m³). The device has a precision of about ±2 to ±5 micrograms per cubic meter. The data were then processed to get the monthly average minimum, maximum, mean, and annual mean PM_2.5 in the study area.

A statistical model examines the relationship between land cover changes and PM_2.5 concentrations. The study quantifies the relationship between land use changes and air pollution by fitting a linear model, illustrating the association between land cover changes and PM_2.5 levels. The Scheffe test is used to determine the significance of differences in PM_2.5 among the four land cover types. To analyze the association between PM_2.5 concentrations and land cover types, each research region was divided into grids of 1 km. PM_2.5 and land cover data are joined together using the spatial join tool in ArcGIS Pro. The dissolve tool in ArcGIS Pro combines homogenous land cover classes to determine surface areas, identifying the primary land cover class in each grid by area percentage. For example, if 51% of a grid’s area is classified as built-up, it will be classified as built-up land. Then the correlation between PM_2.5 concentration and percentage of land cover over the corresponding grid cell is calculated for the whole study area.

Approximately 100 ground-based training samples were generated for each land cover class to assess the accuracy of the produced land cover maps. The accuracy of the classified maps is assessed using 150 random samples of ground truth data from Google Earth Pro. Overall accuracy, user accuracy, producer accuracy, and kappa statistics are calculated based on Equations (1)–(4). This will help quantitatively assess image classification accuracy.

(1)$\mathrm{Overall} \mathrm{accuracy}={\displaystyle \frac{(\mathrm{Total} \mathrm{number} \mathrm{of} \mathrm{correctly} \mathrm{classified} \mathrm{pixels} \left(\mathrm{diagonal}\right)}{ \mathrm{total} \mathrm{number} \mathrm{of} \mathrm{reference} \mathrm{pixels}} \times 100}$

(2)$\mathrm{User} \mathrm{Accuracy}={\displaystyle \frac{\mathrm{number} \mathrm{of} \mathrm{correctly} \mathrm{classified} \mathrm{pixels} \mathrm{in} \mathrm{each} \mathrm{category} \left(\mathrm{diagonal}\right)}{\mathrm{total} \mathrm{number} \mathrm{of} \mathrm{reference} \mathrm{pixels} \mathrm{in} \mathrm{each} \mathrm{category} \left(\mathrm{row} \mathrm{total}\right)}} \times 100$

(3)$\mathrm{Producer} \mathrm{Accuracy}={\displaystyle \frac{\mathrm{number} \mathrm{of} \mathrm{correctly} \mathrm{classified} \mathrm{pixels} \mathrm{in} \mathrm{each} \mathrm{category} \left(\mathrm{diagonal}\right)}{\mathrm{total} \mathrm{number} \mathrm{of} \mathrm{reference} \mathrm{pixels} \mathrm{in} \mathrm{each} \mathrm{category} \left(\mathrm{column} \mathrm{total}\right)}} \times 100$

(4)$\mathrm{Kappa} \mathrm{Coefficient}={\displaystyle \frac{\mathrm{Total} \mathrm{number} \mathrm{of} \mathrm{Samples} \ast \mathrm{Total} \mathrm{Number} \mathrm{of} \mathrm{Corrected} \mathrm{Samples} - \mathsf{\Sigma }\left(\mathrm{col}\_\mathrm{tot} \ast \mathrm{row}\_\mathrm{tot}\right)}{\left(\left(\mathrm{Total} \mathrm{number} \mathrm{of} \mathrm{Samples}\right)^2- \mathsf{\Sigma }\left(\mathrm{col}\_\mathrm{tot} \ast \mathrm{row}\_\mathrm{tot}\right)\right)}} \times 100$

User Accuracy measures commission errors and indicates the likelihood that a pixel is correctly identified. Higher values show fewer false positives and more accurate classification. Producer Accuracy assesses how well a ground-truth class is represented on the map, with higher values indicating fewer exclusions and better accuracy. Overall Accuracy calculates the ratio of correctly classified pixels to the total number, providing a straightforward percentage of classification accuracy. The Kappa Statistic accounts for chance agreement, offering a detailed assessment of classification reliability. Values above 0.8 typically indicate a high level of agreement beyond chance.

This study involves two minor uncertainties: errors in land cover classification due to potential misclassification and variations in image quality over time, further affected by the 30-m resolution, which may simplify complex land use patterns. Additionally, relying on data from a single air quality monitoring station introduces spatial uncertainty.

3. Results

3.1. Classification Accuracy

Our assessment shows very high user accuracy for both Sylhet and Madhabpur for all four classification types, ranging from 81% to 93% (Table 2). The producer accuracy ranges from 79% to 96%, suggesting consistent satisfactory performance. Overall accuracy varies from ~86% to 91%, indicating substantial agreement between classified and reference data. Kappa statistics show a modest to significant level of reliability in classification algorithms over time (Table 2). Overall, our accuracy assessment suggests that the supervised classification technique provides a reliable and accurate classification of the four land cover types.

3.2. Land Cover Maps

3.2.1. The Sylhet Upazila

The Sylhet Upazila map (Figure 3) suggests that there are significant land cover changes in built-up areas, waterbodies, barren land, and mixed forests from 2001 to 2019. The developed area (built-up area) has increased by 27.28% from 2001 to 2019 (Table 3), indicating that rapid urban growth with a focus on infrastructure and population growth, economic growth, and urbanization policies has made Sylhet a growing metropolitan hub. The water body areas increased by 86.70% (Table 3) from 2001 to 2010, followed by a decrease, possibly due to different water management adjustments.

The area of barren land has experienced less change (−0.04%) from 2001–2019, reflecting dynamic equilibrium, where land development and abandonment are balanced. Despite urban growth and waterbody changes, the barren land area’s stability suggests efforts to maintain and sustain the upazila’s natural environments. However, mixed forests have declined by 39.48% (Table 3) from 2001–2019, possibly due to widespread destruction of vegetation. Mixed forest coverage is decreasing due to the growing need for urban and agricultural spaces, which requires tree clearance for infrastructure construction, farmland growth, and other human operations. Urbanization, water management, land cover dynamics, and environmental changes in Sylhet are interconnected, as shifts in land cover dynamics directly impact water management practices and contribute to environmental changes. Sustainable land cover planning and management are crucial to balancing development and environmental conservation.

3.2.2. The Madhabpur Upazila

Madhabpur has experienced a spectacular 65.66% increase in built-up areas (Table 3 and Figure 4) from 2001 to 2019, showing rapid urban expansion and infrastructure investment. Growing urbanized regions show the upazila’s transformation into an advanced urban hub with more demand for residential, commercial, and industrial properties.

Figure 4 and Table 3 also show that the area of the waterbodies in Madhabpur increased by 286.61% from 2001–2010, followed by a decrease of 52.40% from 2010–2019. This variance suggests a dynamic connection between environmental management and water flow mechanisms. These variations could potentially be attributed to land use changes like deforestation or agricultural development, as well as natural variables like precipitation patterns or hydrological cycles. However, the significant loss in waterbodies from 2010 to 2019 underscores the importance of sustainable water resource management measures to mitigate land cover changes and ensure water ecosystems’ ecological resilience. Table 3 shows a 17.71% reduction in barren land from 2010 to 2019, suggesting continuous land development. In comparison, the area of mixed forest has declined by 47.49% (Table 3) during the period of 2001 to 2019, posing a threat to ecological integrity. It underscores the need for targeted conservation efforts in Madhabpur to safeguard biodiversity and ecosystems. These land cover changes in this research region indicate urbanization, expanded agricultural activities, and enhanced natural resource management in Bangladesh. These changes underscore the intricate dynamics of regional development and environmental conservation.

3.3. Annual PM_2.5 Concentration in Sylhet and Madhabpur Upazilas

To validate the satellite-based data, this study acquired ground-based measurements. Table 4 shows that the average annual PM_2.5 concentrations were 59.47 µg/m³ in 2015 and 51.17 µg/m³ in 2019, which closely matches the satellite-based annual average PM_2.5 data presented in Table 5 for the same years. This suggests that the satellite-based annual average data provide a reasonable estimate of PM_2.5 in our study area.

Table 5 shows changes in PM_2.5 values in Sylhet and Madhabpur from 2001–2019. The average PM_2.5 concentration in Sylhet increased significantly from 43.15 µg/m³ in 2001 to 49.80 µg/m³ in 2019. The increase was particularly noticeable in 2015, reaching an average of 53.00 µg/m³. PM_2.5 concentrations have a larger increase in Madhabpur than in the Sylhet Upazila. The average PM_2.5 concentrations in Madhabpur increased from 50.50 µg/m³ in 2001 to 64.30 µg/m³ in 2015, then decreased to 58.20 µg/m³ in 2019. These patterns highlight the growing problem of air pollution in the study area and the urgent need for PM_2.5 emission reduction and public health interventions.

From 2001 to 2010, PM_2.5 concentrations in the air ranged from 39.1 µg/m³ to 50 µg/m³ across the study area, indicating that the air quality was unsatisfactory based on the World Health Organization’s (WHO) guidelines (Figure 5). According to WHO, the recommended mean annual PM_2.5 concentration is 5 µg/m³. This suggests that the PM_2.5 concentration in Sylhet Sadar Upazila is approximately 8 to 10 times higher than the WHO recommendations. It also shows a significant rise in PM_2.5 levels from 2015 to 2019 and a further decline in air quality. This deterioration is likely due to rapid urbanization, increased traffic, and growing industrial pollution. Reducing tree cover for construction or agricultural purposes may have further exacerbated the situation, as fewer trees reduce the region’s capacity to absorb pollutants, leading to higher PM_2.5 concentrations in the air.

PM_2.5 concentration maps (Figure 6) of Madhabpur from 2001 to 2019 demonstrate a steady rise in PM_2.5 concentration. As shown in the 2015 and 2019 maps (Figure 6), PM_2.5 levels have increased in Madhabpur. This change indicates a decline in air quality, possibly caused by urbanization, increased car usage, and industrial growth, which elevates the concentration of typical airborne particulate matter. Clearing trees for urban growth or cultivation reduces the land’s ability to filter air, worsening the problem. Maps show that higher annual PM_2.5 concentration locations are usually located in densely inhabited cities, industrial districts, or transit hubs. Such a trend in PM_2.5 suggests a need for effective air pollution management and land cover planning.

3.4. Land Cover Type and PM_2.5 Concentration in Sylhet Sadar Upazila and Madhabpur

The analysis of PM_2.5 concentrations in different land cover types between 2001 and 2019 reveals significant variations in air quality due to land use changes in both Sylhet (Table 6) and Madhabpur (Table 7). In Sylhet, built-up areas had the highest mean PM_2.5 concentration (44.2 µg/m³), followed by barren land (41.9 µg/m³), mixed forest (41.22 µg/m³), and waterbodies (40.56 µg/m³) in 2001 (Table 6). By 2019, the mean PM_2.5 concentrations in Sylhet had increased across all land cover types (Table 6). Built-up areas again showed the highest mean concentration at 50.92 µg/m³, followed by barren land (48.90 µg/m³), waterbodies (47.31 µg/m³), and mixed forest (45.99 µg/m³).

For Madhabpur, the mean PM_2.5 concentration in built-up areas was 52.6 µg/m³, followed by barren land (51.3 µg/m³), waterbodies (50.3 µg/m³), and mixed forest (48.7 µg/m³) in 2001 (Table 7). The relatively higher PM_2.5 levels in Madhabpur compared to Sylhet in 2001 could be attributed to the more extensive industrial activities and lesser forest cover in Madhabpur. By 2019, the PM_2.5 concentrations in Madhabpur had further escalated, with built-up areas recording the highest mean value of 61.57 µg/m³, followed by barren land (59.31 µg/m³), waterbodies (57.67 µg/m³), and mixed forest (55.64 µg/m³), as detailed in Table 7. The substantial increase in PM_2.5 levels in built-up areas highlights the intensifying impact of urbanization and industrialization on air quality. Additionally, the broader range of PM_2.5 concentrations in 2019, as indicated by the higher standard deviations, points to an increasingly complex and deteriorating air quality scenario in Madhabpur.

Overall, both Sylhet and Madhabpur experienced significant increases in PM_2.5 concentrations across all land cover types from 2001 to 2019. However, the rate of increase and the absolute concentrations were higher in Madhabpur, particularly in built-up areas, which may reflect the more aggressive urban and industrial expansion in that region. This result underscores the need for more effective land management and pollution control strategies to mitigate the adverse effects of urbanization and industrialization on air quality.

Comparison of PM_2.5 Among Different Land Use Types

We further conducted the Scheffe test to determine where there are significant differences in PM_2.5 among the four land use groups (Table 8 and Table 9). It is shown that significant differences exist between the four land use groups, except between mixed forest and waterbody and between barren land and built-up in Sylhet at the 95% confidence level. In Madhabpur, only mixed forest is not significantly different from waterbody in terms of PM_2.5 concentration at the same significance level. This clearly indicates that forested areas and waterbody tend to have lower PM_2.5 concentration than the barren land and built-up areas. The growing urbanization and reduction in forest cover are likely contributing to the worsening air quality in the study area.

3.5. Relationship Between Land Cover Types and PM_2.5 Concentration

To investigate how land cover change influences PM_2.5 concentrations, the relationship between the percentage of land cover type and corresponding PM_2.5 concentration in each grid was calculated and shown in scatter plots (Figure 7). It can be seen from Figure 7 that land cover types are correlated with PM_2.5 concentration in our study area. PM_2.5 positively correlates with the percentage of barren land when barren land occupies less than 75% of the corresponding grid cell (Figure 7A). The lack of vegetation barriers that inhibit wind erosion and soil particle suspension may explain this. Further increases in barren land lead to no changes in PM_2.5 level when the barren land percentage is 75% or higher.

Figure 7B shows that built-up land cover—urbanized or industrialized areas—positively affects PM_2.5 concentration. The positive correlation coefficient (r = 0.79) suggests that when built-up regions increase, the PM_2.5 concentration also increases. This shows that urban growth and industrial operations often produce more particulate matter due to increased traffic, construction, and industrial emissions.

A negative correlation (r = −0.87) between mixed forests and PM_2.5 was shown in Figure 7C, suggesting that an increase in mixed forests may help decrease PM_2.5 concentration. This is possible because vegetative cover filters air pollution by trapping particulate matter in leaves and branches, improving air quality. However, it is also noted that the PM_2.5 level does not respond to a further increase in the mixed forest when the mixed forest is 80% or more (Figure 7C).

Water bodies are negatively correlated with PM_2.5 levels (r = −0.87) (Figure 7D). These findings underscore the importance of land cover planning and environmental conservation in lowering air pollution. Urban development needs green spaces to filter air pollutants and act as “lungs”. Avoiding deforestation and encouraging reforestation could significantly reduce area PM_2.5 concentrations. This research may lay the groundwork for future studies on land cover, land cover changes, and air quality. It can also inform policymakers about the benefits of sustainable land management.

4. Discussion

The intricate relationship between human activities, environmental management, and air quality is investigated in this study. Our study shows that land cover types affect the air quality of the study region. There have been significant changes in land cover in Sylhet Sadar and Madhabpur Upazilas from 2001 to 2019. From 2001 to 2019, built-up areas increased by 27.28% in Sylhet Sadar Upazila, whereas mixed forest decreased by 39.48%. Seyam et al. discovered that built-up areas expanded from 3.09 sq km to 5.97 sq km while studying Bhaluka Upazila from 2001 to 2019 [12]. Another study from 2002 to 2008 found that Rawalpindi’s forest acreage declined by 9% [28]. These studies are consistent with the results of this study. Our study indicates that the PM_2.5 concentration in Sylhet Sadar Upazila increased significantly from an average of 43.15 µg/m³ in 2001 to 49.80 µg/m³ in 2019, a substantial rise that far exceeds the WHO guidelines, which recommend that the annual average PM_2.5 concentration should be 5 µg/m³ or less. The progressive rise in PM_2.5 concentration suggests that governments should take action to reduce it in order to minimize its negative impacts on human health and the environment. It was also found that locations with more water and forest cover have lower PM_2.5 concentrations in both study areas. Zhang et al. critically reviewed various methods used to estimate PM_2.5 concentrations within specified research regions, highlighting that spatial interpolation and remote sensing are the most commonly utilized techniques. The results of their study showed that industrialization is one of the main sources of PM_2.5 [29]. However, built-up areas tend to have higher PM_2.5 levels than other land cover types [30,31,32,33]. This study found a statistically significant positive association (r = 0.78) between built-up areas and PM_2.5 concentration and a negative correlation (r = −0.87) between PM_2.5 and mixed forest, suggesting that increased urbanization has raised PM_2.5 levels and that mixed forests improve air quality.

There has been rapid urbanization in the study area due to population increase and economic development. The built-up area in Madhabpur has increased by 65.66% from 2001 to 2019. This substantial increase suggests fast urbanization that influences air quality. Divergent trends in a waterbody, defined by initial increase and eventual reduction, suggest agricultural or climatic changes may alter water management methods. The fishing industry’s growth has made aquaculture a prominent justification for developing water bodies, in addition to aesthetic features like ponds and lakes. Aquaculture ponds may cause water body expansion. Flash flooding and water remaining in the lowlands have increased water bodies in those research locations [34]. This study indicated that water body areas rose 72.92% and 75.44% in Sylhet and Madhabpur, respectively, during the period 2001 to 2019. A similar situation happened in Jiangsu province in China. Because of aquaculture, from 1975 to 2023, Jiangsu province in China experienced water body expansion from 459.3 sq km to 2373.1 sq km [35].

Areas with various vegetation types have also declined, suggesting that deforestation and urban and agricultural activities have reduced vegetated land. This study finds approximately 39% and 48% loss in mixed forests in Sylhet and Madhabpur Upazilas, respectively, from 2001 to 2019. These changes emphasize the need for sustainable land cover planning to balance environmental protection and development. However, the conversion of developed areas into barren land may indicate economic shifts or land cover changes. Urban pressure is evident in Sylhet and Madhabpur, with high conversion rates from water bodies and barren land to developed areas.

Madhabpur data reveal a substantial transition from barren land to built-up land, suggesting urbanization. However, the significant shift of previously developed areas into unproductive (barren) land by 2019 suggests a dramatic reversal in city expansion, potentially due to land management objectives or economic trends. Wang et al. and Zhang et al. found that, in China, barren land is the type most converted into built-up areas [36,37]. This study found that PM_2.5 concentrations increased in both study areas from 2001 to 2019. In Madhabpur, the average PM_2.5 concentration peaked at 64.30 µg/m³ in 2015, with an overall rise from 50.50 µg/m³ in 2001 to 58.20 µg/m³ in 2019, exceeding WHO ambient air quality guidelines. This increase may be attributed to industrial activity, disorganized urbanization, population growth, and burning fossil fuels. Similarly, Li et al. reported that PM_2.5 concentrations were 7–19 points higher in urban regions than rural areas across 324 Chinese cities, attributed to differences in physical characteristics, development levels, population density, and vehicle usage [38]. If the current rising PM_2.5 concentration trend continues in Madhapur, WHO predicts that its conditions may soon resemble Dhaka, Bangladesh’s capital city, with an annual average of 77.1 μg/m³, seven times higher than the global average PM_2.5 concentrations.

This study found statistically significant positive correlations between built-up land and barren land, and PM_2.5 (r = 0.78 and r = 0.74, respectively). In contrast, mixed forest and waterbodies have substantial negative associations (r = −0.87 and r = −0.87, respectively). This suggests that forests, waterbodies, and planned urbanization are needed to maintain air quality. Global studies support our findings on the significant impact of land cover changes on PM_2.5 concentrations. Research by Wong et al., Lu et al., Zhou et al., and Song et al. shows that transitions such as deforestation and urbanization can either mitigate or exacerbate PM_2.5 levels, depending on the land cover type [33,39,40,41]. These results highlight the importance of sustainable land management for improving air quality. Our study aligns with these findings, emphasizing that preserving natural landscapes and implementing strategic land cover planning can significantly enhance air quality. Transportation and biological land coverings play crucial roles in influencing PM_2.5 concentrations, with forested and industrial areas having the most substantial impact and rural regions being more effective at removing PM_2.5 than urban ones [42,43]. This confirms that land cover types can dominate air quality, particularly PM_2.5 levels. The transition to cleaner energy sources, modern heating systems, and sustainable transportation plays a critical role in addressing the air quality challenges. Several cities worldwide have implemented changes to reduce their reliance on fossil fuels, improve heating systems, and transform transportation, resulting in reduced emissions of PM_2.5 and other pollutants. In Krakow, Poland, Oleniacz and Gorzelnik found that the shift from coal-based heating to cleaner alternatives significantly lowered PM₁₀ and NO₂ concentrations, demonstrating the effectiveness of transitioning to sustainable energy sources and modernized heating systems [23]. Similarly, de la Cruz et al. showed that urban areas with robust public transportation systems and a reduced dependency on personal vehicles experienced better air quality, underscoring the link between transportation policies and pollution reduction [9]. These findings align with the results of our study, where increased urbanization and industrialization in Sylhet Sadar and Madhabpur Upazilas were accompanied by rising PM_2.5 concentrations. Addressing these issues through the promotion of cleaner energy, better heating systems, and sustainable transportation infrastructure is essential for improving air quality and mitigating the negative impacts of air pollution on public health. Our study provides valuable insights for policymakers and environmental managers to prioritize sustainable land management, reforestation, and pollution control, addressing urbanization concerns and enhancing the adaptability of ecosystems and communities.

5. Conclusions

This study investigated spatiotemporal changes in land cover and PM_2.5 concentration in Sylhet, Bangladesh. Two upazilas, Sylhet and Madhabpur, were selected for investigation based on geography, population density, urbanization, industrialization, etc. Landsat images were used to derive land cover types for the period of 2001 to 2019. Annual PM_2.5 concentration data were obtained from the NASA Socioeconomic Data and Applications Center (SEDAC).

Our analysis suggested that there was significant urban expansion and a substantial increase in PM_2.5 from 2001 to 2019. There is substantial land conversion from forested to built-up areas. Results indicate that land cover types are highly correlated with PM_2.5 concentrations. PM_2.5 concentration has a positive correlation with the percentage of barren and built-up land, and a negative correlation with mixed forest and waterbody. Our analysis also suggests that different land cover types are associated with significantly different levels of PM_2.5 concentration. Overall, areas with mixed forest and water body coverages tend to have lower PM_2.5 concentrations than barren land and built-up areas. It is also demonstrated that mixed forest and waterbody areas have similar PM_2.5 concentrations, as the difference in PM_2.5 concentrations is not statistically significant at the 95% confidence level.

This study improves understanding of the relationship between land cover changes and air quality, particularly PM_2.5 concentration. Strong evidence of rising PM_2.5 levels in the analyzed locations emphasizes the need to address regional pollution. Incorporating comprehensive urban planning techniques that prioritize socio-economic development, environmental sustainability, and public health is crucial. This is essential for health and socioeconomic improvement. Controlling PM_2.5 pollution and protecting natural landscapes is crucial to local climates, water cycles, and community well-being in Bangladesh and beyond. The main findings of the study include the following:

• Both Sylhet and Madhabpur experienced significant urban growth between 2001 and 2019, with Sylhet’s built-up areas increasing by 27.28% and Madhabpur’s by 65.66%.

• There was a notable increase in PM_2.5 levels in both study areas. In Sylhet, PM_.2.5 levels rose from 43.15 µg/m³ in 2001 to 49.80 µg/m³ in 2019, while in Madhabpur, PM_2.5 peaked at 64.30 µg/m³ in 2015, then slightly decreased to 58.20 µg/m³ by 2019.

• PM_2.5 concentrations were positively correlated with built-up areas and barren land, indicating that urbanization and industrialization contribute to higher PM_2.5 levels. In contrast, PM_2.5 levels were negatively correlated with mixed forests and waterbodies, suggesting that green spaces and water help mitigate air pollution.

• Sylhet saw a 39.48% decline in mixed forest coverage, while Madhabpur’s mixed forest area decreased by 47.49%, contributing to the overall rise in PM_2.5 concentrations in both areas.

• The findings underscore the importance of sustainable urban planning, reforestation, and water management to reduce air pollution and improve public health in rapidly urbanizing regions.

Author Contributions

Conceptualization and methods, M.R. and L.M.; formal analysis, M.R.; data curation, M.R.; writing—original draft preparation, M.R.; writing—review and editing, L.M., A.J.M. and S.B.; visualization, M.R.; supervision, L.M.; project administration, L.M.; funding acquisition, M.R., L.M. and A.J.M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Acknowledgments

We thank the three anonymous reviewers for their constructive comments which greatly improved the quality of this paper.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Study area map.

Figure 2. Land cover classification workflows.

Figure 3. Land cover maps of Sylhet Sadar Upazila.

Figure 4. Land cover maps of Madhabpur Upazila.

Figure 5. Annual PM2.5 concentration map of Sylhet Sadar Upazila.

Figure 6. Annual PM2.5 concentration map of Madhabpur.

Figure 7. Correlation between land cover types (Barrenland (A), Built-in (B), Mixed Forest (C), and Waterbody (D)) and PM2.5 concentration.

Table 1

Study area profile.

Study Area Name	Sylhet	Madhabpur
Area (sq km)	327	295
Population	544,412	250,069
Pop Density (Per Km)	1071	850
Household	86,074	44,053
Literacy	87.60	44.82
Rivers	Surma	Matian, Sansti

Table 2

Accuracy assessment results.

User Accuracy
Study Area	Built-up	Waterbody	Barren Land	Mixed Forest
Year	2001–2019	2001–2019	2001–2019	2001–2019
Sylhet	84.85–89.29	88.57–93.75	81.08–83.33	84.21–91.43
Madhabpur	84.38–92.31	88.89–93.94	85.71–92.11	83.78–89.19
Producer Accuracy
Sylhet	79.31–89.29	83.78–90.91	85.71–90.32	88.24–89.47
Madhabpur	87.50–96.88	84.21–90.91	82.02–89.74	88.57–94.12
Overall Accuracy
Year	2001	2005	2010	2015	2019
Sylhet	87.40	85.71	85.93	90.23	88.36
Madhabpur	85.92	87.76	87.58	88.89	91.16
Kappa Statistics
Sylhet	0.83	0.80	0.80	0.86	0.84
Madhabpur	0.81	0.83	0.83	0.84	0.88

Table 3

Areal distribution (in hectares) of different land cover classes in the study area.

Name	Class Name	Area 2001	Area 2005	Area 2010	Area 2015	Area 2019	Change (2001–2010) (%)	Change (2010–2019) (%)	Change (2001–2019) (%)
Sylhet	Built-up	9894.69	9976.07	9918.63	11,324.16	12,593.88	0.24	26.97	27.28
	Waterbody	1142.37	987.59	2132.82	1782.39	1975.41	86.70	−7.38	72.92
	Barren land	11,451.15	14,251.47	14,081.82	13,443.16	11,455.83	22.97	−18.64	−0.04
	Mixed Forest	8959.59	6256.47	5315.31	4921.89	5422.68	−40.67	2.02	−39.48
Madhabpur	Built-up	7524.99	7926.92	8774.01	11,875.37	12,466.26	16.59	42.08	65.66
	Waterbody	486.27	791.35	1792.44	1337.23	853.11	268.61	−52.40	75.44
	Barren land	15,022.53	14,933.64	15,016.32	11,958.71	12,356.19	0.04	−17.71	−17.74
	Mixed Forest	5562.63	4942.73	3013.65	3423.33	2920.86	−45.82	−3.08	−47.49

Table 4

Monthly average minimum, maximum, and average PM_2.5 (ug/m³) in Sylhet Upazila (ground-based data).

Year		Jan	Feb	Mar	April	May	June	July	Aug	Sept	Oct	Nov	Dec
2015	Min	55.3	71.5	55.7	30.3	21.9	6.74	11.4	15.1	18.5	13.1	24.7	42.1
	Max	172	161	142	107	83.1	47.8	41.9	43.4	45.9	66.7	67.4	187
	Avg	108	113	95.9	75.7	42.3	18.5	21.3	26.3	27.5	33.9	49.3	102
Annual Average					59.47
2019	Min	49.8	26.6	44.4	12.9	10.7	9.31	9.18	7.56	6.99	6.45	19.6	33.7
	Max	147	135	114	85.2	44.2	39.1	38.7	46.1	55.3	72.1	178	155
	Avg	90.9	91.0	79.2	43.4	28.4	20.1	16.1	18.8	27.9	36.9	90.6	70.7
Annual Average					51.17

Table 5

Minimum, maximum, and average PM_2.5 (ug/m³) concentration in the study area (satellite-based data).

Study Area	Year	Minimum	Maximum	Average
Sylhet	2001	39.70	46.60	43.15
	2005	40.80	49.50	45.15
	2010	40.90	48.80	44.85
	2015	48.90	57.10	53.00
	2019	45.50	54.10	49.80
Study Area	Year	Minimum	Maximum	Average
Madhabpur	2001	47	54	50.50
	2005	44.60	51.70	48.15
	2010	45.30	53.50	49.40
	2015	58.20	70.40	64.30
	2019	52.90	63.50	58.20

Table 6

Mean and standard deviations of PM_2.5 concentration in the four land cover types in Sylhet for 2001 and 2019.

Land Cover Types	Mean (2001)	Std. Deviation (2001)	Mean (2019)	Std. Deviation (2019)
Barren Land	41.9	0.72	48.90	0.61
Built-up	44.2	1.74	50.92	0.86
Mixed Forest	41.22	0.48	45.99	0.29
Waterbody	40.56	0.29	47.31	0.36

Table 7

Mean and standard deviations of PM_2.5 concentration in the four land cover types in Madhabpur for 2001 and 2019.

Land Cover Types	Mean (2001)	Std. Deviation (2001)	Mean (2019)	Std. Deviation (2019)
Barren Land	51.30	0.40	59.31	0.51
Built-up	52.60	0.49	61.57	0.80
Mixed Forest	48.70	0.71	55.64	1.61
Waterbody	50.30	0.23	57.67	0.49

Table 8

Comparison of PM_2.5 in different land cover categories using the post-hoc analysis—Sylhet Upazila.

Multiple Comparisons
Dependent Variable: PM2.5
Scheffe
(I) Landcover	(J) Landcover	Mean Difference (I-J)	Std. Error	Sig.	95% Confidence Interval
					Lower Bound	Upper Bound
Barren Land	Built-up	4.62	2.041	0.166	−1.12	10.37
	Mixed Forest	7.40 *	2.400	0.025	0.64	14.15
	Waterbody	15.54 *	4.053	0.003	4.13	26.95
Built-up	Waterbody	10.91 *	3.923	0.047	1.00	20.82
	Mixed Forest	8.00 *	2.172	0.031	−14.89	−1.11
Mixed Forest	Waterbody	8.14	4.121	0.275	−3.46	19.74

* The mean difference is significant at the 0.05 level.

Table 9

Comparison of PM_2.5 in different land cover categories using the post-hoc analysis—Madhabpur Upazila.

Multiple Comparisons
Dependent Variable: PM2.5
Scheffe
(I) Landcover	(J) Landcover	Mean Difference (I-J)	Std. Error	Sig.	95% Confidence Interval
					Lower Bound	Upper Bound
Barren land	Built-up	−15.84 *	2.815	<0.001	−23.76	−7.92
	Mixed Forest	−10.56 *	2.877	0.004	−18.66	−2.46
	Waterbody	−6.43	2.690	0.012	−10.86	2.00
Built-up	Waterbody	9.41 *	2.399	0.002	2.66	16.16
	Mixed Forest	9.00 *	2.608	0.014	2.00	16.00
Mixed Forest	Waterbody	4.13	2.473	0.427	−2.83	11.09

* The mean difference is significant at the 0.05 level.

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