Analyzing Land Cover Change and Urban Growth Trajectories of the Mega-Urban Region of Dhaka Using Remotely Sensed Data and an Ensemble Classifier

Heat Index: An Alternative Indicator for Measuring the Impacts of Meteorological Factors on Diarrhoea in the Climate Change Era: A Time Series Study in Dhaka, Bangladesh

Heat index (HI) is a biometeorological indicator that combines temperature and relative humidity. This study aimed to investigate the relationship between the Heat Index and daily counts of diarrhoea hospitalisation in Dhaka, Bangladesh. Data on daily diarrhoea hospitalisations and meteorological variables from 1981 to 2010 were collected. We categorised the Heat Index of >94.3 °F (>34.6 °C), >100.7 °F (>38.2 °C) and >105 °F (>40.6 °C) as high, very high and extremely high Heat Index, respectively. We applied a time series adjusted generalised linear model (GLM) with negative binomial distribution to investigate the effects of the Heat Index and extreme Heat Index on hospitalisations for diarrhoea. Effects were assessed for all ages, children under 5 years old and by gender. A unit higher HI and high, very high and extremely high HI were associated with 0.8%, 8%, 7% and 9% increase in diarrhoea hospitalisations in all ages, respectively. The effects varied slightly by gender and were most pronounced in children under 5 years old with a rise of 1°F in high, very high and extremely high HI associated with a 14.1% (95% CI: 11.3-17.0%), 18.3% (95% CI: 13.4-23.5%) and 18.1% (95% CI: 8.4-28.6%) increase of diarrhoea, respectively. This suggests that the Heat Index may serve as an alternative indicator for measuring the combined effects of temperature and humidity on diarrhoea.

Spatio-temporal assessment and prediction of wetlands: Examining the changes in ecosystem service value of RAJUK DAP area using Artificial Neural Network and Geospatial Techniques

Wetlands are a crucial component of the earth's socio-ecological structure, providing significant ecosystem services to people. Changes in wetlands, driven by both natural and manmade causes, are altering these ecosystem services. Although Bangladesh is developing, natural resources like wetlands are changing in the country at different scales, with urban areas experiencing significant impacts. This study intends to evaluate the past, present, and future scenarios of wetlands and examine the changes in ecosystem service value (ESV) of the RAJUK (Rajdhani Unnayan Kartripakkha or Capital Development Authority) Detailed Area Plan (DAP) region. This research examined the effects of five different Land Use and Land Cover (LULC) classes on ESVs for 27 years from 1995 to 2022. Findings reveal that the current wetland area is 80.16 km2 in the post-monsoon season and 306.67 km2 in the pre-monsoon season. Composite post-monsoon wetland map from 1995 to 2015 that 19.48 km2 of wetlands are classified as hydro-ecologically consistent wetlands. Wetland area has decreased by 140.926 km2 between 1995 and 2022, according to simulations, and is predicted to do so by another 27.11 km2 during the following eight years. The total ESV of wetlands dropped by about 26.72 percent between 1995 and 2022, primarily due to conversion to habitation and agricultural use. Also, the projection of LULC and associated ESV for the year 2030 demonstrates how ESV evolves throughout this period and which LULC classes are more susceptible to change, while the kappa coefficient was used to compare the simulated models to the actual wetland area. The current study will undoubtedly be helpful to decision-makers who make a substantial contribution to preserving ecosystem services and the wetland landscape.

Examining the informal urban growth trends in a Port city

Rapid urban developmental growth is a heated debate worldwide due to environmental challenges. This research has examined the spatiotemporal trend of informal built-up growth in Karachi city. Using a geo-information system, the past twenty years (2000-2020) trends of informal built-up growth are examined. For attaining the research objectives, geo-referenced high-resolution maps and satellite images are used for accuracy based spatial data. Karachi is divided into five different land use and land cover (LULC): formal built-up, informal built-up, vacant, water bodies, and green spaces. Spatial data of informal built-up growth change of five different years, 2000, 2005, 2010, 2015, and 2020 are generated through acquired maps digitization using ArcMap. Subsequently, the gains and transfers of Karachi's informal built-up growth based on five years 2000-2005, 2005-2010, 2010-2015, and 2015-2020 are analyzed using the Land Change Modeler (LCM) in IDRISI software. Also, land use land cover changes (LULCC) are predicted for the next 40 years (2020-2060) using the integrated Cellular Automata Markov (CA-Markov) simulation model in IDRISI. The results revealed that Karachi's built-up is expanding rapidly. Land conversion into the informal built-up area is alarming, as it has changed from 144.31 km2 to 217.19 km2 with 72.88 km2 in the past twenty years (2000-2020) and has occupied green and agricultural land. Most informal built-up areas have transitioned from vacant (71.01 km2) land use land cover (LULC). The informal built-up area could expand from 217.19 km2 to 317.63 km2, with about 100.44 km2 up to 2060. The planned and unplanned development will be towards the city's East (E) direction and will convert and ruin agriculture and vacant land. The present study provides suggestions to urban planners, administrative authorities, and policymakers to control informal growth and achieve sustainable development goals in developing countries.

Sustainable futures in agricultural heritage: Geospatial exploration and predicting groundwater-level variations in Barind tract of Bangladesh

Groundwater resources are one of the essential aspects of achieving self-sufficiency in a country's agricultural production, poverty alleviation, and socioeconomic development, particularly in agricultural heritage management and practices. In the Barind Tract in Bangladesh, groundwater levels have steadily declined due to growing irrigation demand. Surface water sources become scarce during the dry season, and groundwater levels fall to levels that make minimum cultivation challenging. In these circumstances, determining the current status of groundwater is key to any action in the future. This study investigated the existing geospatial pattern and critical zone of groundwater level in Chapainawabganj District, a significant area of the Barind Tract of Bangladesh, and predicted future groundwater levels considering multiple factors. Kriging, a sophisticated geostatistical method, was performed to examine the geographical pattern and groundwater variations, and time series analysis was employed to determine data trends and make future projections. The current study used groundwater level data from 23 monitoring stations over 10 years (2009-2018). Exponential, Gaussian, and Spherical models were cross-matched here for the best predictor model in four fitness measures to determine groundwater concentrations (RMSE, ME, RMSS, ASE), and Box-Jenkins ARIMA (3,1,0) was found best-fit for predictions, and variance estimation. Likewise, cross-validation has been assessed for the accuracy of anticipated results across spatial scales. Although more research is needed to identify the underlying mechanisms, critical zones, and their pattern of modification, possible recharge zones and their locations have been identified. Future groundwater levels, critical zones, and recharge locations have been indicated for the research area and potential recommendations.

A GIS and remote sensing approach for measuring summer-winter variation of land use and land cover indices and surface temperature in Dhaka district, Bangladesh

Rapid urbanization has induced land use and land cover change (LULC) that increases land surface temperature (LST). Analyzing seasonal variations of LULC and LST is a precondition for mitigating heat island effects and promoting a sustainable living environment. The objective of this study is to explore the association between the seasonal LST dynamics and LULC indices for the Dhaka district of Bangladesh. The LULC indices are comprised of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBAI), and Modified Normalized Difference Water Index (MNDWI). The results show that the LULC effect on LST in Dhaka is significant, with an increase in summer season LST from 34.58 °C to 37.66 °C and in winter season LST from 24.710C to 26.24 °C. Predictably, the highest and lowest LST values were observed in the built-up and vegetation-covered areas, respectively. Secondly, the correlation values indicate a significant inverse correlation (R² > 0.50) between NDVI and LST, as well as MNDWI and LST. On the contrary, positive correlations were observed between NDBI and LST, and between NDBAI and LST for both the summer and winter seasons. Finally, subsequent vegetation decline (-69.34%) and increasing built-up area (+11.30%) between 2000 and 2020 in Dhaka district were found to be the most significant factors for the increasing trend and spatial heterogeneity of LST in Dhaka. The methodological approach of this study offers a low-cost efficient technique for monitoring LST hotspots, which can guide land use planners and urban managers for spatial intervention to ensure a livable environment.

Determine the Land-Use Land-Cover Changes, Urban Expansion and Their Driving Factors for Sustainable Development in Gazipur Bangladesh

At present, urbanization is a very common phenomenon around the world, especially in developing countries, and has a significant impact on the land-use/land-cover of specific areas, producing some unwanted effects. Bangladesh is a tightly inhabited country whose urban population is increasing every day due to the expansion of infrastructure and industry. This study explores the land-use/land-cover change detection and urban dynamics of Gazipur district, Bangladesh, a newly developed industrial hub and city corporation, by using satellite imagery covering every 10-year interval over the period from 1990 to 2020. Supervised classification with a maximum likelihood classifier was used to gather spatial and temporal information from Landsat 5 (TM), 7 (ETM+) and 8 (OLI/TIRS) images. The Geographical Information System (GIS) methodology was also employed to detect changes over time. The kappa coefficient ranged between 0.75 and 0.90. The agricultural land was observed to be shrinking very rapidly, with an area of 716 km2 in 2020. Urbanization increased rapidly in this area, and the urban area grew by more than 500% during the study period. The urbanized area expanded along major roads such as the Dhaka–Mymensingh Highway and Dhaka bypass road. The urbanized area was, moreover, concentrated near the boundary line of Dhaka, the capital city of Bangladesh. Urban expansion was found to be influenced by demographic-, economic-, location- and accessibility-related factors. Therefore, similarly to many countries, concrete urban and development policies should be formulated to preserve the environment and, thereby, achieve sustainable development goal (SDG) 11 (sustainable cities and communities).

Impact of Land Use and Land Cover Changes on Urban Ecosystem Service Value in Dhaka, Bangladesh

Urban ecosystem services provide a wide range of services to sustain life, social relation, health, etc., and address most of the challenges, including climate change and environmental pollution. While it is recognized that the urban ecosystem substantially contributes to human well-being in cities, there is less attention to consider the value of urban ecosystem service in urban planning and policymaking. This study analyzed the land use and land cover (LULC) dynamics of city of Dhaka over the past three decades (1990–2020) to evaluate the impact of LULC on ecosystem services value (ESV). The estimation of ESV in relation to LULC has been done using the globally used benefits transfer method (BTM). Findings of the study show that built-up area has increased by 188.35% from 1990 to 2020, with an average annual growth rate is about 6.28%The analysis of ESV shows that it has decreased by 59.55% (85 million USD) from 142.72 million USD in 1990 to 57.72 million USD in 2020 due to the development of the built-up area through conversion of agricultural land, waterbodies, and forest and vegetation land. This study also identified that waterbodies are the greatest contributor to ESV. The result on the elasticity of ESV in relation to LULC implies that about 1% transition in LULC would result in about 0.33% change in total ESV during the study period. We believe that the findings of this study would serve as a reference for the policy maker and urban planner to devise appropriate land use decision to ensure sustainable urban development of Dhaka.

Impacts of Large-Scale Groundwater Exploitation Based on Long-Term Evolution of Hydraulic Heads in Dhaka City, Bangladesh

Dhaka city has emerged as the fastest-growing megacity, having more than 20 million inhabitants, with a growth rate of 3.62%. Unplanned and rapid urbanization, coupled with exponential population growth, has significantly altered the groundwater dynamics in Dhaka city. This study concentrates on the evolution of long-term piezometric heads of the Upper Dupi Tila aquifer (UDA) and the Middle Dupi Tila aquifer (MDA) based on long-term hydrographs, piezometric maps and synthetic graphical overviews of piezometric trends. Due to over-exploitation, the piezometric level (PL) has declined deeper than −85 and −65 m PWD (Public Works Department reference datum) in UDA and MDA, respectively. The highest rate of decline was observed in the south-central to southeastern parts of the city both in UDA (4.0 m/year) and MDA (5.74 m/year). The results clearly show that the rates of decline in PL vary from 2.25 to 5.74 m/year in both aquifers of the city, and urban expansion has greatly affected the shape and extent of the depression cone over the past four decades. The magnitudes of the depression cones in both aquifers seem to pose a considerable threat to groundwater resources, indicating that the current exploitation is not sustainable at all.

How Does Peri-Urbanization Trigger Climate Change Vulnerabilities? An Investigation of the Dhaka Megacity in Bangladesh

This paper aims to scrutinize in what way peri-urbanization triggers climate change vulnerabilities. By using spatial analysis techniques, the study undertakes the following tasks. First, the study demarcates Dhaka’s—the capital of Bangladesh—peri-urban growth pattern that took place over the last 24-year period (1992–2016). Afterwards, it determines the conformity of ongoing peri-urban practices with Dhaka’s stipulated planning documents. Then, it identifies Dhaka’s specific vulnerabilities to climate change impacts—i.e., flood, and groundwater table depletion. Lastly, it maps out the socioeconomic profile of the climate change victim groups from Dhaka. The findings of the study reveal that: (a) Dhaka lacks adequate development planning, monitoring, and control mechanisms that lead to an increased and uncontrolled peri-urbanization; (b) Dhaka’s explicitly undefined peri-urban growth boundary is the primary factor in misguiding the growth pockets—that are the most vulnerable locations to climate change impacts, and; (c) Dhaka’s most vulnerable group to the increasing climate change impacts are the climate migrants, who have been repeatedly exposed to the climate change-triggered natural hazards. These study findings generate insights into peri-urbanization-triggered climate change vulnerabilities that aid urban policymakers, managers, and planners in their development policy, planning, monitoring and control practices.

Rank-size Distribution of Cities and Municipalities in Bangladesh

This paper examines and updates the rank-size distribution of cities and municipalities in Bangladesh between 1990 and 2019 based on two criteria: (1) built-up urban areas; and (2) population. The distribution of built-up urban areas and population are compared to provide a robust theoretical underpinning of Zipf’s law for future urban developmental planning framework. The data on built-up urban areas is extracted from land cover classification using Google Earth Engine and the population data is obtained from the decennial censuses. The comparison of the conformity to Zipf’s law indicated contradictory results. While a greater proportion of the population has been increasingly concentrated in the smaller and midsized cities over the last three decades, built-up urban areas, on the other hand, have been mostly clustered in two largest cities— Dhaka and Chittagong—accounting for 50 to nearly 60 percent of the total built-up urban areas. These results shed light on the magnitude of continued spatial inequalities in urban development amongst cities and municipalities in Bangladesh despite there being an overall increase of evenness in the distribution of population over time. These results imply an unsustainable rate of urban expansion in Bangladesh and reinforce the need for the exploration of policies and regulations targeted at guiding the rate and direction of evenness in urban expansion.

Mapping Urbanization and Evaluating Its Possible Impacts on Stream Water Quality in Chattanooga, Tennessee, Using GIS and Remote Sensing

Impervious surfaces (IS) produced by urbanization can facilitate pollutants’ movement to nearby water bodies through stormwater. This study mapped and estimated the IS changes in Chattanooga, Tennessee, using satellite imagery acquired in 1986 and 2016. A model was developed utilizing the Normalized Difference Vegetation Index coupled with density slicing to detect and map urbanization through IS growth. Urban growth was quantified at USGS HUC12 watershed level including stream riparian areas. The obtained results show a net growth of 45.12 km2 of IS with a heterogeneous distribution. About 9.96 km2 of this growth is within 90 m of streams, about 6% of the study site’s land cover. The Lower South Chickamauga Creek watershed experienced the largest urban growth with a change from 24.2 to 48.5 km2. Using the riparian zone percent imperviousness, a stream risk assessment model was developed to evaluate potential stream impairment due to this growth. Approximately 87, 131, and 203 km lengths of streams identified as potentially at high, very high, and extreme risks, respectively, to be impaired due to urban growth from the last 30 years. These findings would benefit to proactively implement sustainable management plans for the streams near rapidly urbanizing areas in the study site.

Remote Sensing-Based Research for Monitoring Progress towards SDG 15 in Bangladesh: A Review

The Sustainable Development Goals (SDGs) have been in effect since 2015 to continue the progress of the Millennium Development Goals. Some of the SDGs are expected to be achieved by 2020, while others by 2030. Among the 17 SDGs, SDG 15 is particularly dedicated to environmental resources (e.g., forest, wetland, land). These resources are gravely threatened by human-induced climate change and intense anthropogenic activities. In Bangladesh, one of the most climate-vulnerable countries, climate change and human interventions are taking a heavy toll on environmental resources. Ensuring the sustainability of these resources requires regular monitoring and evaluation to identify challenges, concerns, and progress of environmental management. Remote sensing has been used as an effective tool to monitor and evaluate these resources. As such, many studies on Bangladesh used various remote-sensing approaches to conduct research on the issues related to SDG 15, particularly on forest, wetland, erosion, and landslides. However, we lack a comprehensive view of the progress, challenges, concerns, and future outlook of the goal and its targets. In this study, we sought to systematically review the remote-sensing studies related to SDG 15 (targets 15.1–15.3) to present developments, analyze trends and limitations, and provide future directions to ensure sustainability. We developed several search keywords and finally selected 53 articles for review. We discussed the topical and methodological trends of current remote-sensing works. In addition, limitations were identified and future research directions were provided.

Mapping Time-Space Brickfield Development Dynamics in Peri-Urban Area of Dhaka, Bangladesh

Due to the high demand for cheap construction materials, clay-made brick manufacturing has become a thriving industry in Bangladesh, with manufacturing kilns heavily concentrated in the peripheries of larger cities and towns. These manufacturing sites, known as brickfields, operate using centuries-old technologies which expel dust, ash, black smoke and other pollutants into the atmosphere. This in turn impacts the air quality of cities and their surroundings and may also have broader impacts on health, the environment, and potentially contribute to global climate change. Using remotely sensed Landsat imagery, this study identifies brickfield locations and areal expansion between 1990 and 2015 in Dhaka, and employs spatial statistics methods including quadrat analysis and Ripley’s K-function to analyze the spatial variation of brickfield locations. Finally, using nearest neighbor distance as density functions, the distance between brickfield locations and six major geographical features (i.e., urban, rural settlement, wetland, river, highway, and local road) were estimated to investigate the threat posed by the presence of such polluting brickfields nearby urban, infrastructures and other natural areas. Results show significant expansion of brickfields both in number and clusters between 1990 and 2015 with brickfields increasing in number from 247 to 917 (total growth rate 271%) across the Dhaka urban center. The results also reveal that brickfield locations are spatially clustered: 78% of brickfields are located on major riverbanks and 40% of the total are located in ecologically sensitive wetlands surrounding Dhaka. Additionally, the average distance from the brick manufacturing plant to the nearest urban area decreased from 1500 m to 500 m over the study period. This research highlights the increasing threats to the environment, human health, and the sustainability of the megacity Dhaka from brickfield expansion in the immediate peripheral areas of its urban center. Findings and methods presented in this study can facilitate data-driven decision making by government officials and city planners to formulate strategies for improved brick production technologies and decreased environmental impacts for this urban region in Bangladesh.

Assessment of urban environmental change using multi-source remote sensing time series (2000-2016): A comparative analysis in selected megacities in Eurasia

Excessive urban growth has led to an urban environmental degradation in megacities in less developed countries. Using fine particulate matter (PM_2.5) concentration, land surface temperature (LST), and normalized difference vegetation index (NDVI) data obtained by satellite remote sensing, we analysed the inter-annual variations and trends in the urban environment of 17 megacities in Eurasia from 2000 to 2016. Taking the average environmental condition for all the megacities in 2000 as the baseline, the urban environmental conditions were evaluated by a Comprehensive Environmental Index (CEI) from 2001 to 2016. The variation and trends analysis of CEI revealed that the overall environmental conditions in Chennai, Dhaka, Kolkata and Tianjin showed significant deterioration trends. Environmental qualities in newly developed urban areas experienced degradation in Bangalore, Beijing, and Mumbai. The area of environmentally deteriorated urban land has been expanding in Bangalore, Chennai, Delhi, Kolkata, and Mumbai in India and Dhaka in Bangladesh since 2001. By contrast, the area of environmentally degraded urban land in Chinese megacities expanded to the largest extent in the period of 2007-2009 and decreased afterwards. The result suggests that greening and strong emission control strategies significantly contributed to urban environmental quality enhancement in rapidly developing megacities.

Assessing long-term urban surface water changes using multi-year satellite images: A tale of two cities, Dhaka and Hong Kong

Water is a fundamental component of an urban environment. Management of water resources is important to facilitate a liveable environment and urban sustainability. Several factors affect water resources, including urbanization, climate change and seasons. Moreover, the nature of urban expansion and unsustainable water management practices have been associated with water scarcity, loss of biodiversity and increase of flood risk. Knowledge of the changes in urban surface water in relation to changes in seasons, land covers, anthropogenic activities, and topographical characteristics are important for managing watersheds and urban planning, and developing adaptation strategies to address environmental challenges posed by urbanization. However, existing studies rarely consider all the above factors when monitoring surface water changes in the urban environment. To address this problem, this study uses satellite images from multiple seasons and years, and assess the changes in surface water in relation to changes of several important factors, e.g., seasons, urbanization, land cover and topography. Moreover, this study applies advanced geostatistical tools to assess the local relationship between changes in surface water and the driving factors, and compares the findings in two cities Dhaka (Bangladesh) and Hong Kong with a large contrast in many aspects. First, seasonal influence in the distribution of water area is evaluated. Second, land cover classifications are assigned, and then the contingency matrix and decadal maps are formulated to investigate and evaluate the influence of urbanization on the occurrence and transitions in surface water. Third, an advanced geostatistical regression model is used to investigate the spatially varying relationships for change in surface water in relation to change in other land covers and topography. The investigations confirm that the temporal and seasonal variation, and urbanization induced land cover changes largely affect surface water. In addition, topography influences the nature of the city's expansion, which in turn entails the changes in surface water. The flat terrain of Dhaka facilitates the easy invasion of water bodies and horizontal expansion, in contrast, the steep terrain of Hong Kong prohibits horizontal expansion thus it's surface water is more stable than Dhaka.

Mapping Methodology of Public Urban Green Spaces Using GIS: An Example of Nagpur City, India

Faced with a lack of fine grain data availability, in rapidly emerging urban centers of developing nations, the study explored a mapping methodology to create thematic map of public urban green space (UGS). Using GIS, a thematic map of Nagpur city, India was prepared. The objective was to prepare spatial data that are relevant for planners and policy makers, with detailed UGS typologies and to update the status of overall availability and distribution of hierarchical recreational green spaces in the city. The spatial and non-spatial data with added attributes gathered through fieldwork resulted in a holistic dataset, with high accuracy of thematic map (0.93 kappa coefficient). The recorded status of different typologies as well as the distribution of recreational UGS shows disparity in the distribution of UGS. The eastern part of the city grossly lacks UGS provisions, which is compensated by the western part with larger availability of natural green spaces. The mapping methodology is novel and effective for recording qualitative status, analyzing their spatial distribution and prioritizing the provisions of UGS. Future research integrating these spatial data with more qualitative research can provide a holistic view on benefits of UGS provisions and thus facilitate effective UGS governance aiming towards better green infrastructure and hence broader urban sustainability.

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is critical. To overcome the challenges in wind speed forecasting, this paper proposes a new convolutional neural network algorithm for short-term forecasting. In this paper, the forecasting performance of the proposed algorithm was compared to that of four other artificial intelligence algorithms commonly used in wind speed forecasting. Numerical testing results based on data from a designated wind site in Taiwan were used to demonstrate the efficiency of above-mentioned proposed learning method. Mean absolute error (MAE) and root-mean-square error (RMSE) were adopted as accuracy evaluation indexes in this paper. Experimental results indicate that the MAE and RMSE values of the proposed algorithm are 0.800227 and 0.999978, respectively, demonstrating very high forecasting accuracy.

Carbon dioxide emission and bio-capacity indexing for transportation activities: A methodological development in determining the sustainability of vehicular transportation systems

CO₂ emissions from urban traffic are a major concern in an era of increasing ecological disequilibrium. Adding to the problem net CO₂ emissions in urban settings are worsened due to the decline of bio-productive areas in many cities. This decline exacerbates the lack of capacity to sequestrate CO₂ at the micro and meso-scales resulting in increased temperatures and decreased air quality within city boundaries. Various transportation and environmental strategies have been implemented to address traffic related CO₂ emissions, however current literature identifies difficulties in pinpointing these critical areas of maximal net emissions in urban transport networks. This study attempts to close this gap in the literature by creating a new lay-person friendly index that combines CO₂ emissions from vehicles and the bio-capacity of specific traffic zones to identify these areas at the meso-scale within four ranges of values with the lowest index values representing the highest net CO₂ levels. The study used traffic volume, fuel types, and vehicular travel distance to estimate CO₂ emissions at major links in Dhaka, Bangladesh's capital city's transportation network. Additionally, using remote-sensing tools, adjacent bio-productive areas were identified and their bio-capacity for CO₂ sequestration estimated. The bio-productive areas were correlated with each traffic zone under study resulting in an Emission Bio-Capacity index (EBI) value estimate for each traffic node. Among the ten studied nodes in Dhaka City, nine had very low EBI values, correlating to very high CO₂ emissions and low bio-capacity. As a result, the study considered these areas unsustainable as traffic nodes going forward. Key reasons for unsustainability included increasing use of motorized traffic, absence of optimized signal systems, inadequate public transit options, disincentives for fuel free transport (FFT), and a decline in bio-productive areas.

A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities

In modern society, air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment. Among air pollutants, Particulate Matter (PM_2.5) consists of suspended particles with a diameter equal to or less than 2.5 μm. Sources of PM_2.5 can be coal-fired power generation, smoke, or dusts. These suspended particles in the air can damage the respiratory and cardiovascular systems of the human body, which may further lead to other diseases such as asthma, lung cancer, or cardiovascular diseases. To monitor and estimate the PM_2.5 concentration, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined and applied to the PM_2.5 forecasting system. To compare the overall performance of each algorithm, four measurement indexes, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) Pearson correlation coefficient and Index of Agreement (IA) are applied to the experiments in this paper. Compared with other machine learning methods, the experimental results showed that the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper. For the CNN-LSTM model, its feasibility and practicability to forecast the PM_2.5 concentration are also verified in this paper. The main contribution of this paper is to develop a deep neural network model that integrates the CNN and LSTM architectures, and through historical data such as cumulated hours of rain, cumulated wind speed and PM_2.5 concentration. In the future, this study can also be applied to the prevention and control of PM_2.5.