Kolkata Megacity Research Articles

Groundwater sustainability in the face of urban expansion: A case study on Kolkata's ongoing challenge

This study investigates the impact of urbanization on groundwater sustainability in the megacity of Kolkata, India, amidst the global trend of increasing urban populations. Utilizing a site-specific approach and multidisciplinary framework, the research analyses three decades (1990–2020) of urban development, correlating the expansion of built-up areas with changes in groundwater levels. Remote sensing and Geographic Information System (GIS) techniques, specifically the Normalized Difference Built-up Index (NDBI), are employed to assess built-up area dynamics. Groundwater data from governmental sources, combined with spatial analysis using Geographically Weighted Regression (GWR), reveal a complex relationship. Descriptive analysis indicates a significant rise in average built-up areas over the decades, with a notable increase in 2020. Correlation coefficients shift from negative in 1990 to strongly positive in 2020, signifying a growing dependence of groundwater levels on built-up areas. Spatial autocorrelation showing a strong clustered pattern in case of groundwater scenario over the years. Geographical analysis highlights varying impacts across different wards, especially in the southern part of Kolkata. Time series analysis forecasts a continued increase in built-up areas, potentially leading to a decline in groundwater levels. The study emphasizes the urgent need for site-specific interventions to ensure the sustainability of groundwater resources in Kolkata, as urbanization continues unabated. The methodology and findings offer valuable insights for policymakers and city planners to address the challenges posed by urban development and safeguard groundwater resources in the pursuit of a sustainable future.

Exploring the relationship between quality of living and green spaces in cities: Evidence from an Indian megacity region of global south

Green space (GS) provides a number of environmental and social benefits to the urban residents and these GS are very crucial for a sustainable urban environment and human well-being. However, spatial distributions of GS are rarely uniform over the space, thus creating green injustice. In this, an attempt has been made to assess the relationship between quality of living (QLI) and GS developing greenness Index (GI) across the urban centers in the Kolkata megacity region (KMR), Eastern India. Eighteen variables under three domains were selected to develop the QLI, and three remote sensing indices were used to develop the quality of GS Correlation anslysis was carried out to assess the relationship between QL and GS. Explanatory factor analysis was used to understand the association between factors affecting QL and GS. The Gini coefficient (GC) was used to measure the spatial equity of GS across urban centers. The findings showed that there was a negative correlation between QLI and GI (−0.278). The urban centers with 'very high' and 'high' QLI were characterized by 'very low' and 'low' GI. Most of the urban centers around Kolkata were characterized by 'very high' and 'high' QLI and 'very low and 'low' GI. GS results showed that there was an inequality of GS distribution across the urban centers in KMR. The findings suggest that GS in the cities should be increased and improved for GS justice by enhancing per capita availability and accessibility.

Developing an integrated urban ecological efficiency framework for spatial ecological planning: A case on a tropical mega metropolitan area of the global south

Rapid urbanization has emerged as one of the most critical challenges to ecological sustainability in urban areas. In developing countries, the degradation of the ecosystem is more prominent due to the lack of urban planning. Thus, it has become urgent for researchers to identify the ecological efficiency (EE) changes imposed by urban expansion and promote sustainable land use planning. This study aims to develop a comprehensive urban ecological efficiency (UEE) framework in the Kolkata Metropolitan Area (KMA), India, from 2000 to 2020. Principal component analysis (PCA) was used to develop a remote sensing-based UEE index (UEEI) based on five effective ecological parameters (Greenness, Dryness, Heat, Wetness and vegetation health. A single sensitivity parameter was also calculated to determine the role of a single parameter based on which management strategies can be carried out. The findings showed that (i) there were substantial deteriorations of UEE in the last 20 years. In 2000 the areas with good EE were about 65.5% which declined to 53.72% in 2010 and 20.87% in 2020. The areas with good UEE decreased 68% and 61% from 2000 to 2020 and 2010 to 2020, respectively; (ii) the areas with good UEE were 52% in 2000, while 38% in 2010. Most urban centres (Bhadreshwar, Champdani, Srirampur, Bally, Howrah, Kamarhati, Baranagar, Dum Dum, South Dum Dum, Rajarhat, Bidhannagar) located around the Kolkata megacity are characterized by poor and very poor EE (ranges of 0.60–1.00). Thus, spatiotemporal pattern of UEE could assist to clarify the administrative responsibilities as well as obligations. In addition to this, the UEE framework can help for scientific guidance of urban ecosystem protection and restoration through comprehensive spatial landscape planning.

Exploring the factors affecting urban ecological risk: A case from an Indian mega metropolitan region

Assessment of ecological risk (ER) is a key approach to adapting and mitigating ecological deterioration in cities of developing countries. In developing countries, the ecological landscapes such as vegetation cover, water bodies, and wetlands are highly vulnerable due to rapid urban expansion. Therefore, urban ER (UER) assessment and its drivers are crucial to guide ecological protection as well as restoration. This study aims to explore the spatiotemporal pattern of UER and the impact of urban spatial form on UER in the Kolkata Megacity Region (KMR), India. This study developed a UER index and used spatial regression models across the urban centres. The ER has been assessed at city scale as well as grid-scale (2 km × 2 km and 5 km × 5 km) from 2000 to 2020. The results showed that ER has substantially increased over the last 20 years. The urban centres with very high and high ER substantially increased, i.e. from 21.95% in 2000 to 31.70% in 2020. Kolkata and its surrounding urban centres were mostly characterized by very high and high ER. ER was influenced by spatial variables (such as land use and landscapes pattern). However, remote sensing parameters were weakly related to ER. The spatial lag model (SLM) (R2 = 0.8686) was found to be better fit model than spatial error model (SEM) (R2 = 0.8661) and ordinary linear regression model (OLS) (R2 = 0.8641). Thus, the findings of the study can improve research and a comprehensive framework for urban ecological resources and provide a scientific basis for urban ecosystem planning and restoration. In addition to this, it will guarantee the sustainable utilization of urban ecosystems.

Land Suitability Investigation for Solar Power Plant Using GIS, AHP and Multi-Criteria Decision Approach: A Case of Megacity Kolkata, West Bengal, India

Renewable energy sources are the most necessitated natural energy to reduce fossil fuels globally. Fossil fuel is the most valuable and limited resource on the planet, but on the other hand, renewable energy creates less pollution. Solar energy is the most effective renewable resource for daily use. Solar power plants are necessary for domestic and daily use. Remote sensing and geographic information technology (GIS) were used for this study to delineate the possible site selection of solar power plants in Kolkata and the surrounding area in West Bengal, India. The analytical hierarchy process (AHP) and the multi-criteria decision-making process (MCDA) were used for each weight calculation and ArcGIS v10.8 was applied for weighted overlay analysis (WOA) for delineation of the result. The site suitability map was developed using a pairwise comparison matrix and the weights were calculated for each criterion. The suitability map was divided into five categories, from not suitable to very highly suitable. A total of 474.21 km2 (10.69%) of the area was classified as very highly suitable whereas 249.54 km2 (5.62%) area was classified as not suitable because of the water area and east Kolkata wetland. A total of 1438.15 km2 (32.43%) of the area was classified as highly suitable for a solar power plant. The Kolkata megacity and water body locations were identified as moderate to not suitable sites. Very high and high-potential sites were identified 2 to 5 km from the central business district (CBD) location, which is Dharmotala. Renewable energy source is needed in the megacity of Kolkata. If solar power plants are contracted then the demand for fossil fuel will be reduced one day, and that will help the environment as well as the society in terms of sustainable development. This study result is helpful for administrators, urban planners, developers, and other stakeholders for the implementation and development of a new solar power plant in the study area.

COVID-19 Lockdown: Lessons learnt using multiple air quality monitoring station data from Kolkata city in India

On March 16, 2020, Kolkata megacity in India announced partial lockdown due to COVID-19 crisis. This study presents an analysis for multiple pollutants with special focus on NO2 and O3 based on data from different monitoring stations located to across Kolkata city for the period from 16 March- 17 May 2020 compared to the pre-lockdown period. Most significant reduction was observed in the concentration of nitrogen dioxide (NO2) (- 76.8%), volatile organic compounds (VOCs) (- 69.5%), PM10 (- 64.6%) and PM2.5 (- 60.9%). A lower reduction percentage was found for CO, sulfur dioxide (SO2) and ammonia (-48.6%, - 41.7% and – 41.1% respectively). However, during partial lockdown, lockdown phase-1, phase-2 and phase-3, surface-level ozone (O3) has changed respectively by 31.72%, 31.13%, -14.28% and -14.05% which resulted in an overall increase of 8.17% in the entire study period. The air quality index (AQI) in Kolkata which usually remains poor or very poor, even during lockdown period, failed to attain the ‘good’ standard. This needs special attention in human health impact assessment and public health management. We recommend additional attention to be drawn towards stickiness in O3 which had adverse human health and which went up during lockdown period compared to pre-lockdown period. We highlight some major policy implications of the observed trends to combat city air pollution along with climate co-benefits by shifting transport fuel and related infrastructure.

Wintertime direct radiative effects due to black carbon (BC) over the Indo-Gangetic Plain as modelled with new BC emission inventories in CHIMERE

Abstract. To reduce the uncertainty in climatic impacts induced by black carbon (BC) from global and regional aerosol–climate model simulations, it is a foremost requirement to improve the prediction of modelled BC distribution, specifically over the regions where the atmosphere is loaded with a large amount of BC, e.g. the Indo-Gangetic Plain (IGP) in the Indian subcontinent. Here we examine the wintertime direct radiative perturbation due to BC with an efficiently modelled BC distribution over the IGP in a high-resolution (0.1∘ × 0.1∘) chemical transport model, CHIMERE, implementing new BC emission inventories. The model efficiency in simulating the observed BC distribution was assessed by executing five simulations: Constrained and bottomup (bottomup includes Smog, Cmip, Edgar, and Pku). These simulations respectively implement the recently estimated India-based observationally constrained BC emissions (Constrainedemiss) and the latest bottom-up BC emissions (India-based: Smog-India; global: Coupled Model Intercomparison Project phase 6 – CMIP6, Emission Database for Global Atmospheric Research-V4 – EDGAR-V4, and Peking University BC Inventory – PKU). The mean BC emission flux from the five BC emission inventory databases was found to be considerably high (450–1000 kg km−2 yr−1) over most of the IGP, with this being the highest (> 2500 kg km−2 yr−1) over megacities (Kolkata and Delhi). A low estimated value of the normalised mean bias (NMB) and root mean square error (RMSE) from the Constrained estimated BC concentration (NMB: < 17 %) and aerosol optical depth due to BC (BC-AOD) (NMB: 11 %) indicated that simulations with Constrainedemiss BC emissions in CHIMERE could simulate the distribution of BC pollution over the IGP more efficiently than with bottom-up emissions. The high BC pollution covering the IGP region comprised a wintertime all-day (daytime) mean BC concentration and BC-AOD respectively in the range 14–25 µg m−3 (6–8 µg m−3) and 0.04–0.08 from the Constrained simulation. The simulated BC concentration and BC-AOD were inferred to be primarily sensitive to the change in BC emission strength over most of the IGP (including the megacity of Kolkata), but also to the transport of BC aerosols over megacity Delhi. Five main hotspot locations were identified in and around Delhi (northern IGP), Prayagraj–Allahabad–Varanasi (central IGP), Patna–Palamu (mideastern IGP), and Kolkata (eastern IGP). The wintertime direct radiative perturbation due to BC aerosols from the Constrained simulation estimated the atmospheric radiative warming (+30 to +50 W m−2) to be about 50 %–70 % larger than the surface cooling. A widespread enhancement in atmospheric radiative warming due to BC by 2–3 times and a reduction in surface cooling by 10 %–20 %, with net warming at the top of the atmosphere (TOA) of 10–15 W m−2, were noticed compared to the atmosphere without BC, for which a net cooling at the TOA was exhibited. These perturbations were the strongest around megacities (Kolkata and Delhi), extended to the eastern coast, and were inferred to be 30 %–50% lower from the bottomup than the Constrained simulation.

Roadside Vegetation in and Around the Megacity of Kolkata Along an Urbanisation Gradient

Combustion of fossil fuels by the on-road vehicles is major contributor of air pollution which affects the surrounding vegetation and their habitat in addition to human health hazards. Study on the concurrence between vehicular greenhouse gas emissions and associated plant community is important to assess the present day problem scenario on environmental equilibrium. An ecological analysis has been carried out from five locations along roadside of the suburban interiors to the highly vehicle congested urban areas of Kolkata megacity. Quantitative study on naturally grown road side vegetation covering seedlings of tree and shrubby species, herbaceous annuals and perennials along the suitable length of each study area was conducted following standard methodology. Increasing vehicular pollution shows reciprocal correlation with species richness and species diversity. From community structure analyses across the emission gradient it was revealed that a few species exhibited tolerance to withstand increasing air contamination by successful population growth. Nevertheless, the present study might be worthwhile in assessing ecological status of the local plant communities subjected to varying level of vehicle traffic.

Size-segregated aerosols over a high altitude Himalayan and a tropical urban metropolis in Eastern India: Chemical characterization, light absorption, role of meteorology and long range transport

A study has been conducted focusing on the chemical and optical characterization of water soluble inorganic and organic components of aerosols at different sizes, over a high altitude Himalayan station, Darjeeling (27.1° N and 88.15° E, 2200 m amsl) and a tropical urban metropolis, Kolkata (22.5° N, 88.3° E, ~6 m amsl) for two-year long period (March 2016–February 2018). It was observed that local meteorology and long range transport of pollution plumes have played the pivotal role in governing the temporal variation of the mass distribution and concentration of aerosols and its various components over both the sites. The aerosol mass-size distributions were found to be bimodal in nature with the relative dominance of accumulation mode (0.1–1.0 μm) over coarse mode (1.8–10 μm) over both the stations, indicating dominance of the anthropogenic emissions. Among the size classes, accumulation mode aerosols alone contributed 55–75% over Darjeeling and 40–60% over Kolkata to PM10. The coarse mode aerosols were mainly consisted of primary inorganic species over Kolkata and water soluble organic carbons (WSOC) over Darjeeling whereas the fine mode aerosols (accumulation: 0.1–1.0 μm) were primarily composed of the secondary inorganic aerosols for both the stations. Irrespective of the seasons and stations, SO42− and NH4+ exhibited peaks in the mass-size distribution at 1.0–0.1 μm whereas that of NO3− varied with the seasons. The photochemical oxidation and the aqueous phase oxidation of SO2 (g) were the pathways for SO42− formation over Darjeeling whereas the later dominated over Kolkata. Concentration weighted trajectory (CWT) model has revealed that the secondary inorganic aerosols and WSOC were local/regional in origin over Kolkata megacity whereas Darjeeling was influenced by mixed sources. The accumulation mode aerosols were found to be the highest light absorbing over both the stations irrespective of the seasons. Over Darjeeling, the absorption coefficient (babs_365) and the mass absorption efficiency (MAE) of WSOC were maximum for local biomass burning aerosols than the transported plumes whereas those over Kolkata were maximum for transported biomass burning plumes from Eastern Ghats.

The Development of a Framework for the Integrated Assessment of SDG Trade-Offs in the Sundarban Biosphere Reserve

The United Nations Sustainable Development Goals (SDGs) and their corresponding targets are significantly interconnected, with many interactions, synergies, and trade-offs between individual goals across multiple temporal and spatial scales. This paper proposes a framework for the Integrated Assessment Modelling (IAM) of a complex deltaic socio-ecological system in order to analyze such SDG interactions. We focused on the Sundarban Biosphere Reserve (SBR), India, within the Ganges-Brahmaputra-Meghna Delta. It is densely populated with 4.4 million people (2011), high levels of poverty, and a strong dependence on rural livelihoods. It is adjacent to the growing megacity of Kolkata. The area also includes the Indian portion of the world’s largest mangrove forest––the Sundarbans––hosting the iconic Bengal Tiger. Like all deltaic systems, this area is subject to multiple drivers of environmental change operating across scales. The IAM framework is designed to investigate socio-environmental change under a range of explorative and/or normative scenarios and explore associated policy impacts, considering a broad range of subthematic SDG indicators. The following elements were explicitly considered: (1) agriculture; (2) aquaculture; (3) mangroves; (4) fisheries; and (5) multidimensional poverty. Key questions that can be addressed include the implications of changing monsoon patterns, trade-offs between agriculture and aquaculture, or the future of the Sundarbans’ mangroves under sea-level rise and different management strategies. The novel, high-resolution analysis of SDG interactions allowed by the IAM will provide stakeholders and policy makers the opportunity to prioritize and explore the SDG targets that are most relevant to the SBR and provide a foundation for further integrated analysis.

COVID-19 induced lockdown and decreasing particulate matter (PM10): An empirical investigation of an Asian megacity

The air quality in the cities of developing countries is deteriorating with the proliferation of anthropogenic activities that add pollutant matters in the lower part of the troposphere. Particulate matter with an aerodynamic diameter lower than 10 μm (PM10) is considered one of the direct indicators of air quality in an urban area as it brings health morbidities. The article empirically investigates the role COVID-19 related lockdown has played in bringing down pollution level (PM10) in the megacity of Kolkata. It does so by taking account of PM10 level in three stages – pre, presage and complete-lockdown timelines. The extracted results show a significant declining trend (about 77% vis-a-vis the pre-lockdown period) with 95% of the geographical area under 100 μm/m3 and a strong fit with the station-based records. The feasibility and robustness showed by the remotely sensed data along with other earth observatory information for larger-scale pollution prevalence make its adoption imperative. Simultaneously, it becomes urgent in times of lockdown when the physical mobility of maintenance and research staff to stations is significantly curtailed. The work contributes to study on PM10 by its ability to replicate in examining cities of both the global north and global south.

Living environment matters: Unravelling the spatial clustering of COVID-19 hotspots in Kolkata megacity, India

The emergence of COVID-19 has brought a serious global public health threats especially for most of the cities across the world even in India more than 50 % of the total cases were reported from large ten cities. Kolkata Megacity became one of the major COVID-19 hotspot cities in India. Living environment deprivation is one of the significant risk factor of infectious diseases transmissions like COVID-19. The paper aims to examine the impact of living environment deprivation on COVID-19 hotspot in Kolkata megacity. COVID-19 hotspot maps were prepared using Getis-Ord-Gi* statistic and index of multiple deprivations (IMD) across the wards were assessed using Geographically Weighted Principal Component Analysis (GWPCA).Five count data regression models such as Poisson regression (PR), negative binomial regression (NBR), hurdle regression (HR), zero-inflated Poisson regression (ZIPR), and zero-inflated negative binomial regression (ZINBR) were used to understand the impact of living environment deprivation on COVID-19 hotspot in Kolkata megacity. The findings of the study revealed that living environment deprivation was an important determinant of spatial clustering of COVID-19 hotspots in Kolkata megacity and zero-inflated negative binomial regression (ZINBR) better explains this relationship with highest variations (adj. R2: 71.3 %) and lowest BIC and AIC as compared to the others.

Research note: Ecosystem Health (EH) assessment of a rapidly urbanizing metropolitan city region of eastern India – A study on Kolkata Metropolitan Area

Assessment of ecosystem health (EH) in Indian megacities is urgently required where ineffective environmental protection policies turned them into hotspots of a bundle of eco-environmental problems. In this study, the classic Vigor-Organization-Resilience (VOR) framework was employed to devise the ecosystem health index (EHI) of Kolkata megacity which is similar to the work of Peng et. al. (2015). Results show that EH of this megacity city region was rapidly deteriorating trend, from 0.627 to 0.377 with strong spatial heterogeneity. In 2000, areal coverage of EH status of relatively well was 73.10% reduced to 52.00% and areal coverage of weak EH status increased to 21.80% in 2019. The relatively weak and weak status of EH are concentrated in Kolkata, Howrah and its adjacent areas. The study proposed a new improvement and deterioration index to track the changes of EH at the city scale to overcome the limitation of the percentage change method. Ultimately the outcome of the study provides a better understanding of EH in Kolkata megacity region that is helpful for policymakers in developing measures for eco-environmental management.

Wetland conversion risk assessment of East Kolkata Wetland: A Ramsar site using random forest and support vector machine model

East Kolkata Wetland (EKW) is a Ramsar site located adjacent to the Kolkata megacity. EKW is one of the resourceful wetland ecosystems of the world which offers a bundle of direct and indirect ecosystem services to the Kolkata megacity region. The rapid expansion of built-up in the surrounding urban agglomeration of the EKW is putting immense pressure on the EKW and the rate of wetland loss has been highest in recent decades. To ensure that this distinct ecosystem is conserved, an efficient means of identifying wetland conversion risk is needed. This study aims to assess the risk of EKW conversion using two advanced data-driven Machine Learning (ML) models, viz, Random Forest (RF), and Support Vector Machine (SVM). The novelty of the paper is in the fact that ML models have been widely applied to groundwater potential, flood susceptibility, and landslide susceptibility, their applicability to wetland conversion risk assessment has not yet been explored. The advantage of RF and SVM is that both of the ML models can overcome the limitations of pre-assumption based conventional methods of wetland risk assessment. A total of eight factors are selected which can be categorized into ecological, bio-physical, demographic, and physical infrastructure groups. Both results indicate that around 60% area under medium to very high-risk zones. A comparison is also made between these two methods to identify the most precise prediction method for this study area. The results of the models are quantitatively validated applying the Receiver Operating Characteristics (ROC) method, where both of this method identifies SVM as a more precise predictive model for this study with 91.12% accuracy. The spatial pattern of encroachment and shrinkage of EKW triggered by urban expansion is successfully captured by RF and ME. Policy analysts and land-use planners can use the outcome derived from RF and SVM models and associated maps to identify the risk zones, assess the effectiveness of wetland conservation programs, design effective policies to stop further degradation of the wetlands, and adopt long-term sustainable planning for this precious ecosystem.

Significant decrease of lightning activities during COVID-19 lockdown period over Kolkata megacity in India

The outbreak of COVID-19 has now created the largest pandemic and the World health organization (WHO) has declared social distancing as the key precaution to confront such type of infections. Most of the countries have taken protective measures by the nationwide lockdown. The purpose of this study is to understand the effect of lockdown on air pollutants and to analyze pre-monsoon (April and May) cloud-to-ground and inter-cloud lightning activity in relation to air pollutants i.e. suspended Particulate matter (PM10), Nitrogen dioxides (NO2) Sulfur dioxide (SO2), Ozone (O3) and Aerosol concentration (AC) in a polluted tropical urban megacities like Kolkata. After the strict lockdown the pollutants rate has reduced by more than 40% from the pre-lockdown period in the Kolkata megacity. So, decreases of PM10, NO2, SO2, O3 and AC have a greater effect on cloud lightning flashes in the pre-monsoon period. In the previous year (2019), the pre-monsoon average result shows a strong positive relation between the lightning and air pollutants; PM10 (R2 = 0.63), NO2 (R2 = 0.63), SO2 (R2 = 0.76), O3 (R2 = 0.68) and AC (R2 = 0.83). The association was relatively low during the lock-down period (pre-monsoon 2020) and the R2 values were 0.62, 0.60, 0.71, 0.64 and 0.80 respectively. Another thing is that the pre-monsoon (2020) lightning strikes decreased by 49.16% compared to the average of previous years (2010 to 2019). The overall study shows that the reduction of surface pollution in the thunderstorm environment is strongly related to the reduction of lightning activity where PM10 and AC are the key pollutants in the Kolkata megacity.

Access to basic amenities and services to urban households in West Bengal: Does its location and size of settlements matter?

Basic urban services refer to the important civic services required for the citizens of urban areas and for the functioning of cities viz., water supply, sanitation, waste management, drainage, roads, power and shelter etc. This study focuses on the availability and access to basic services and amenities in class I (having population > 1,00,000) and VI (having population < 5000) towns across West Bengal based on census data. To measure the access to basic urban services and amenities, a composite index is devised which is consisting of 3 (three) domains, namely, assets index, basic amenities index and housing quality index and 14 representative indicators. The principal component analysis is used to reduce the dimensions and assign weight to the component domains. The results show that the access to and availability of basic urban services and amenities is relatively better in those towns located in and around Kolkata megacity. There is a huge disparity in terms of availability of basic services and amenities between the towns of North and South Bengal. Kolkata centric development leads to high levels of disparity between KMA (towns included under Kolkata Metropolitan Area) and non-KMA (towns not included under KMA) towns in West Bengal. The research study may assist the urban planners and policy makers to identify the intra-urban as well as well inter-urban spatial disparity in terms of basic urban services and amenities.

Urban expansion induced vulnerability assessment of East Kolkata Wetland using Fuzzy MCDM method

Wetlands provide multi-dimensional services to the society and environment but recent anthropogenic interference becomes a threat for the conservation of existing wetlands. Wetlands adjacent to megacities of South Asian countries are mostly affected by numerous adverse environmental impact of rapid urban development. The East Kolkata Wetland located adjacent to the Kolkata megacity is also struggling for its existence as the Kolkata megacity is growing rapidly by converting its surrounding areas to urban landscapes. Encroachment of settlements identified through land use change analysis that makes EKW more vulnerable. Vulnerability assessment is an essential part of formulating any conservation strategies. This study tries to identify the wetland conversion vulnerable areas within the East Kolkata Wetland. Well established Fuzzy multi-criteria decision-making approach is applied by incorporating different wetland conversion influencing factors and knowledge-based approach and the result shows near about 60% areas are lying in high to very high-vulnerable zones. This high vulnerability condition demands immediate plan formulation for protecting this ecologically rich wetland. The wetland areas which are adjacent to already develop urban built-up belongs to high and very high vulnerable zones. Validation of the result is required for understanding the applicability of Fuzzy MCDM method and for formulating an appropriate plan. In this study Receiver Operating Characteristics (ROC) is used to evaluate the result, where, Area Under Curve (AUC) accounts for 93.7% which indicates a high reliability and accuracy of the Fuzzy MCDM. The outcome of the study demands immediate attention of the local authority for conserving this wetland.

Step-wise Land-class Elimination Approach for extracting mixed-type built-up areas of Kolkata megacity

The extraction of urban built-up areas is an important aspect of urban planning and understanding the complex drivers and biophysical mechanism of urban climate processes. However, built-up area extraction using Landsat data is a challenging task due to spatio-temporal dynamics and spatially intermixed nature of Land Use and Land Cover (LULC) in the cities of the developing countries, particularly in tropics. In the light of advantages and drawbacks of the Normalized Difference Built-up Index (NDBI) and Built-up Area Extraction Method (BAEM), a new and simple method i.e. Step-wise Land-class Elimination Approach (SLEA) is proposed for rapid and accurate mapping of urban built-up areas without depending exclusively on the band specific normalized indices, in order to pursue a more generalized approach. It combines the use of a single band layer, Normalized Difference Vegetation Index (NDVI) image and another binary image obtained through Logit model. Based on the spectral designation of the satellite image in use, a particular band is chosen for identification of water pixels. The Double-window Flexible Pace Search (DFPS) approach is employed for finding the optimum threshold value that segments the selected band image into water and non-water categories. The water pixels are then eliminated from the original image. The vegetation pixels are similarly identified using the NDVI image and eliminated. The residual pixels left after elimination of water and vegetation categories belong either to the built-up areas or to bare land categories. Logit model is used for separation of the built-up areas from bare lands. The effectiveness of this method was tested through the mapping of built-up areas of the Kolkata Metropolitan Area (KMA), India from Thematic Mapper (TM) images of 2000, 2005 and 2010, and Operational Land Imager (OLI) image of 2015. Results of the proposed SLEA were 95.33% accurate on the whole, while those derived by the NDBI and BAEM approaches returned an overall accuracy of 83.67% and 89.33%, respectively. Comparisons of the results obtained using this method with those obtained from NDBI and BAEM approaches demonstrate that the proposed approach is quite reliable. The SLEA generates new patterns of evidence and hypotheses for built-up areas extraction research, providing an integral link with statistical science and encouraging trans-disciplinary collaborations to build robust knowledge and problem solving capacity in urban areas. It also brings landscape architecture, urban and regional planning, landscape and ecological engineering, and other practice-oriented fields to bear together in processes for identifying problems and analyzing, synthesizng, and evaluating desirable alternatives for urban change. This method produced very accurate results in a more efficient manner compared to the earlier built-up area extraction approaches for the landscape and urban planning.

Effect of pumping on hydrologic system of a young satellite city in south Bengal Basin through numerical modelling: past, present and future

Hydrogeologic analysis based on groundwater modelling was used to evaluate the past, present and possible future states of the groundwater system that will supply water to a future population of more than 1.5 million of the newly developed urban centre of Rajarhat New Town (RNT). This city is located in the north eastern fringe of Kolkata megacity. Pumping of groundwater beneath this satellite city will modify the subsurface hydrologic system. Drastic changes in head distribution patterns, recharge areas of the RNT aquifer and outflow areas of arsenic-affected wells in adjacent contiguous aquifer under current and future development conditions indicate that human impacts far outweigh topographic and geologic forces on the groundwater flow system. Interference effects were also observed in wells of the nearby urban centres. Future groundwater abstraction in RNT may lead to further reduction in groundwater levels in the neighbouring Kolkata megacity and may increase the rate of transport of contaminants to the RNT aquifer.

Passive air sampling of persistent organic pollutants (POPs) and emerging compounds in Kolkata megacity and rural mangrove wetland Sundarban in India: An approach to regional monitoring

Polyurethane foam (PUF) disk passive air samplers were deployed concurrently at five sites across Kolkata megacity and the rural mangrove wetland of Sundarban (UNESCO World Heritage Site) between January–March in 2014. Samples were analyzed for hexachlorocyclohexanes (HCHs), dichlorodiphenyltricholoroethanes (DDTs), polychlorinated biphenyls (PCBs) and, polybrominated diphenyl ethers (PBDEs) using gas chromatography and mass spectrometry (GC-MS). Derived air concentrations (pg/m3) for Kolkata ranged: for ∑α- and γ-HCH between 70 and 207 (114 ± 62), ∑6DDTs: 127–216 (161 ± 36), ∑7PCBs: 53–213 (141 ± 64), and ∑10PBDEs: 0.30–23 (11 ± 9). Low values for all the studied POPs were recorded in the remote area of the Sundarban site (with the exception of DDTs: o,p’-DDT and p,p’-DDT), where ∑4DDTs was 161 ± 36. In particular, the site of Ballygunge, located in the southern part of Kolkata, showed the highest level of all the metabolites/congeners of POPs, suggesting a potential hot spot of usage and emissions. From HCHs, α-/γ-HCH isomers ratio was low (0.67–1.96) indicating a possible sporadic source of lindane. γ-HCH dominated the HCH signal (at 3 sites) reflecting wide spread use of lindane both in Kolkata and the Sundarban region; however, isomeric composition in Kolkata also suggests potential technical HCHs use. Among DDT metabolites, both o,p’-DDT and p,p’-DDT shared the dominant percentages accounting for ∼26–46% of total DDTs followed by p,p’-DDE (∼12–19%). The PCB congener profile was dominated by tri- and tetra-Cl at the southern and eastern part of Kolkata. These results are one of the few contributions that reports air concentrations of POPs, concurrently, at urban and remote villages in India. These data are useful to assess atmospheric pollution levels and to motivate local and regional authorities to better understand the potential human exposure risk associated to urban areas in India.