4.1 Indicators analysis related to wetland health assessment
The DPSI framework is a tool to measure the different environmental problems and their management techniques. It begins with (D) driving forces which involves human induced activities. Drivers may affect wetland ecosystem which could lead to (P) pressure and changes that to (S) state and (I) impact on wetlands which affects the quantity and quality of fishes, and foods from the encroached the wetland area.
4.1.1 Drivers
(A1) Demography
Demography incorporates urban and rural population, density and sex ratio. According to the 2011 Census, West Bengal's total urban population was around 29 million while KMA consisted of 15.87 million people. In 2025, the population of KMA is expected to reach 21.1 million with approximate density of 8500 people per square kilometer (KMDA). It is visible that how commercial and industrial activities influence urban growth as well as unplanned and uncontrolled expansion results in high-specialized region that directly puts a threat on natural ecosystem of the region (Labianca et al., 2020).
(A2) Agriculture
In West Bengal as well as surrounding rural areas in Kolkata urban agglomeration, agriculture is one of the most important sources of income. Agriculture intensity has changed the land-use pattern significantly and uses of chemicals and pesticides runoff from the agricultural field into the wetlands causes the deterioration in terms of soil quality, water contamination and aquatic life. Furthermore, the doom is that the most of decision-makers are not aware of the services provided by wetlands(Shewit et al., 2017;Gebremedhin et al., 2018).
(A3)Industry
West Bengal has been a resource-rich state with great industrial estates in both pre and post-independence period. Initially, in middle of the nineteenth century, Bengal's industrial landscape was dominated by cottage enterprises. Modern industrialization began with the establishment of jute industry and later Britishers developed the industrial center in Kolkata and its adjacent areas. The prominent industrial centers in KMA are with leather complexes in South 24 Parganas, where 22 to 24% of India’s tanning exports, garment park near East Kolkata Wetland, food park, foundry park, chemical park, industrial park, apparel park in Pujali and Uluberia municipality. This upward trend gives a great stress to wetland ecosystem.
(A4)Transport
In Bengal, landscape has been consistently changing. Earlier, the landscape was majorly covered with towns which with time and development transformed into cities, cities into metropolis and megacities. As Kolkata city grew with population and developmental activities, people required efficient transport system for the spread of roads and to carry the loads of increasing number of vehicles (Ghose et al., 2004). According to Kolkata metropolitan development authority (KMDA), the vehicles that deliver goods would increase from 41000 to 71000 (73%), motorized vehicles 10 to 30 lakhs (300%), and river traffic range from 1.43 to 3.12 lakhs/day (118%) from 2001 to 2025. Though, the dense transport system reduces the time from one place to another but the unintended growth and expansion of connectivity also have negative impact on environment as automobiles and roads are maintained poorly and causing land and air pollution (Haq et al, 2002; Joseph et al, 2009).
(A5)Water
Wetlands play a major role in balancing environmental equilibrium with economic activities such as agriculture, industrial, recreational but on the other hand the agricultural waste, and industrial effluent degrading the habitat of wetlands. This problem is more severe in developing countries than in developed countries. The water Indicators to measure the wetland health are pH, dissolved oxygen, Biological oxygen demand and total Coliform. The water in the KMA was found contaminated with dissolved particles, concentration of algae and bacteria, eutrophication concentration, iron, chromium and arsenic concentration that deteriorated the wetland health( Rahman et al., 1997, Sekovski et al., 2012).
4.1.2 Pressure (P)
(B1)Population density and (B2) No of Households
The population density and households can be regarded as social anthropogenic activities. According to United Nations, in 1950 around 30% of the world's population lived in cities; however, it was expected to be 60% of the world's population resides by 2030. The urban population have been increased continuously as rural peoples migrated to urban areas(Faulkner, 2004). Land use change has been prominent in mainly high density population, built up region which stress the ecosystem of that areas (Eppink et al., 2004). The maximum densely populated region, in Titagarh (35530 km2), Baranagar (32180 km2), Kamahati (30128 km2), and Rishra (27807 km2) have the highest density, whereas Gayeshpur (1966 km2), Kalyani (3443 km2), and Pujali (4452 km2) have the lowest(Census of India, 2011)density. The maximum number of households can be observed in Kolkata MC (1024928) followed by Howrah MC (296008) and the least found in Pujali (10500) and Budge Budge (14738) MC and municipalities in 2011(Census of India, 2011). Further these pressure factors that determine the wetlands are become unable to offer ecosystem services, recharging, and maintenance.
(B1)Population density and (B2) Number of Households
The population density, total population, per capita income and households can be regarded as social anthropogenic activities(Pinto et al., 2011). According to United Nations, around 30% of the world's population lived in cities by 1950; however, it was expected to be 60% by 2030. The urban populations have been increasing as rural people migrate to urban areas(Faulkner, 2004). Land use change was observed hugely in high density population areas where built up region encroaching the natural ecosystem of those areas(Eppink et al., 2004). In KMA maximum densely populated region were in Titagarh (35530 km2), Baranagar (32180 km2), Kamahati (30128 km2), and Rishra (27807 km2) have the highest density, whereas Gayeshpur (1966 km2), Kalyani (3443 km2), and Pujali (4452 km2) have the lowest density (Census of India, 2011).The maximum number of households could be observed in Kolkata MC (1024928) followed by Howrah MC (296008) and the least found in Pujali (10500) and Budge Budge (14738) MC and municipalities in 2011(Census of India, 2011). Furthermore, pressure factors that determine the wetlands have become unable to offer ground water recharge facility and ecosystem services.
(B3) Road Density
Mega cities of the world usually have high density of road network and accompanying construction operation(Chenery et al., 2020). Road density is one of the vital components to analyze the wetland health status in urban landscape. In order to assess pressure KMA’s wetland, the efforts were made to establish an explicit connection between the driving forces and pressures. Each indicator of pressure has favorable and unfavorable condition on the state of wetland health. The expansion of expressways (Belghoria, Dum Dum, Srerampur-Barrackpur-Barasat expressway), major arterial roads (Maheshtala connector, Kamalgazi-Baruipur road, Thakurpukur-Budge Budge road), bridge construction (Iswar Gupta setu, Bhatpara-Chandannagar), bus terminals (Bansberia, Kalyani, Chinsura) and truck terminals (Amtala, Dankuni, Barasat) lead to high density of road network in KMA reference. The maximum density of roads found in Khardah municipality followed by Baidyabati, Kamarhati and Rishra municipalties(BAES, 2011).
(B4) Total sewerage generation per day and (B5) Per capita Sewerage generation
The inappropriate management of huge amount of domestic, agriculture and industrial waste and untreated poses serious challenges to environment and human health (Nadella & Sen, 2021). In west Bengal total sewage generation in 2008 was about 2345.21 million liters/day and 180.42 million liters/day in class I cities and II towns(CPCB, 2011).The majority of total sewage is generated from Kolkata MC (172 MLD) and Howrah MC (63.9 MLD). The maximum and lowest per capita sewage is generated from Kalyani (198 MLD/person) and Maheshtala (15.49 MLD/person) municipalities under Kolkata Metropolitan Area(CPCB, 2011). Due to untreated waste disposal, poor sewerage and drainage supply, KMA wetland ecosystem presently dotted with many big waste-fed areas (Bunting et al., 2010a; Makoni et al., 2016). Therefore, the current research work indicates the direct linkages.
(B6) Generation of solid waste per day and (B7) Per capita solid waste generation
Wetland ecosystems are crucial for resource production because they provide food and shelter to terrestrial and aquatic biota (Singh, & Sinha, 2020; Gujre et al., 2021). One of the most serious environmental issue in this area is collection and dumping of municipal solid waste (MSW) (Gohain & Bordoloi, 2021). The generated MSW from industry and urban areas create pressure on wetlands. Simultaneously, the growing population has also direct impact on amount of municipal solid waste generation. In 2001, Kolkata MC and Howrah MC produced 3000 and 600 metric tons (MT) MSW per day respectively, which increased to 4590 and 910 MT per day in 2020. The per capita MSW in 2001was maximum found in Kolkata MC (873gm/day) and Champadany (728gm/day), and in 2020, it increased to (1023gm/day) Kolkata MC and (1016gm/day) Gayeshpur (Das, et al., 2011, ; WBPCB, 2021). So, the generation of solid waste per day and per capita solid waste generation have been growing with time (Chattopadhyay et al., 2009; Hazra & Goel, 2009)
(B8) Total income
Primary, secondary, tertiary economy of any country may govern positive and negative impact on wetlands (Cole, 2007; Castiglione et al., 2015). The most predominant economic activities in KMA are agriculture, fishing, manufacturing and services. According to West Bengal Bureau of Applied Economics and Statistics, Kolkata MC has the highest total income (Rs 266684 lakhs), followed by Bhatpara (Rs 30289 lakhs), and Howrah MC (Rs 18034 lakhs). In Naihati (533 lakhs Rs), Kanchrapara (Rs 634 lakhs), and Bally (Rs 724 lakhs) municipalities have the lowest total income. However, the investment on environmental sector, policy implication to protect the ecology is highly profitable.
Others: Urbanization growth
According to United Nations 2011, the Kolkata urban agglomeration is on tenth place in terms of population in the world that constituted around 14.72 million people(Census of India, 2011). This growth occurred owing to birth rate, migration, employment opportunities, education, medical facilities and economic investment(Brockerhoff, 1999; Sekovski et al., 2012). But in KMA, the major concerning issue is environmental degradation due to the high pace of unplanned urbanization(Bardhan et al., 2015; Peng et al., 2017; Ghosh et al., 2021).The urban population growth rate is highly correlated with loss of cultivable areas, loss of biodiversity, and degradation of water bodies.
Use of fertilizer
Mostly fertilizer and pesticides used to produce vegetables, grains, and fish in short time period. But excessive usage of these chemicals, as well as poor management, have a negative influence on the wetland environment (Coulter et al., 2004, Carey et al., 2012).
Number of Registered Industry
Number of registered and unregistered industries in developed and developing countries have contributed to economic success (Majumdar et al., 2020, Parveen et al., 2021) but, it is a matter of concern that rapid industrial expansion has put a severe strain on environment. In West Bengal, the major pollution contribution to environment is from industrial sector.The red category industries such as dyeing and bleaching industries were restricted within kolkata metropolitan area because these were producing excess toxic material. Six districts (Kolkata, Howrah, Hooghly, South 24 Parganas, North 24 Parganas, and Nadia) are included in Kolkata Metropolitan Area, and the maximum registered industries are found in North 24 Parganas followed by Kolkata and Howrah. The discharge of trash and pollutants produced by industrial activities caused pollution in water bodies and change the land use pattern in the study area.
4.1.3 State (S)
(C1) Change of Water Area (NDWI, MNDWI, NDPI, NDTI), (C2) Change of Vegetation Area (NDVI) and (C3) Change of Built-Up Area (NDBI)
The land use land cover in KUA is changing rapidly owing to the urban and industrial development (Song & Deng, 2017, Makwinja et al., 2021). It has significant impact on vegetation cover and water bodies. In this study the major components to assess health of water bodies and vegetation cover are NDWI, MNDWI, NDPI, NDTI, NDVI and NDBI indices. The values of all indices range vary from + 1 to -1.
The KMA wetland ecosystem changed enormously from 2011 to 2020 due to intensive agriculture and encroachment by built-up cover. The NDWI was used to calculate the area of water and non-water bodies and differentiate water from terrestrial vegetation and soil cover(McFeeters, 1996). In 2011 and 2020, the result showed NDWI range from 0.714 to -0.704 and 0.122 to -0.386, respectively (Fig. 3 & Table 6). MNDWI determines water and non-water areas particularly where water bodies and built-up areas intermixes(Xu, 2006; Sagar et al., 2017). The MNDWI value in KUA ranged from 0.695 to -0.678 and 0.288 to -0.617 for the years 2011 and 2020 respectively (Fig. 3 & Table 6). In 2011 and 2020, the NDWI and MNDWI positive values were noticed majorly in Hooghly River, east Kolkata wetland, and minor values were observed in small extent in entire KMA. While, NDWI and MNDWI was immensely found in negative values in Gayeshpur, Kalyani, Maheshtala, Uluberia, Barasat municipality and central part of north-west, and extreme south direction of KMA. Therefore, the result implies that the natural wetland has been encroached by built up, agriculture and others land uses.
Sometimes, NDVI could not make clear difference of vegetation in water bodies and on ground between the natural vegetation cover and agriculture (Lacaux et al., 2007). It might give the distorted result of same spectral reflectance. Hence, NDPI fills in to detect the vegetation even in very small pond without any spectral reflectance distortion and presence of water turbidity helps in assessing the pond cover (Mondal & Bandyopadhyay, 2014). In 2011 and 2020, the NDPI values were ranged from 0.678 to 0.695 and from 0.617 to 0.288 respectively (Fig. 5 & Table 6). In 2011 the highest concentration of NDPI noticed along Hooghly River, North of Panihati, North Dum Dum, south of East Kolkata Wetland (EKW), Kalyani, Gayeshpur, Barasat and Uluberia municipalities. In 2020, it got decreased and majorly found in Uluberia, south of EKW and sparsely seen in KMA areas.
The NDTI mainly use to detect varying degree of turbidity and muddy concentration in water (Lacaux et al., 2007). A turbidity index is applied for wetland water quality assessment(Singh et al., 2020). It is used for inland wetlands including ponds, reservoirs, ox-bow-lakes and rivers (Panigrahy, 2017). The NDTI results showed the turbidity value of 0.287 and − 0.666 in 2011 and 0.097 to -0.081 in 2020. The NDTI values of KMA’s wetland manifest that the very high turbidity found in Bidhannagar, Madhyamgram, North and South Dum Dum region for the years 2011 and 2020 (Fig. 4 & Table 6). In 2011, maximum small water bodies or higher NDTI values were observed in north-west, middle-west and south-east of KMA but in 2020, it encroached by built up area (Fig. 4).
In this study NDVI employed to estimate the green surface of the region. Greater NDVI value represents, green vegetation presence in the region. The range of NDVI was from 0.809 to 565 and from 0.446 to 0.115 in the year 2011 and 2020 respectively. The maximum vegetation was found along the margin of KMA in 2011 and west and north-east of KMA in 2020 (Fig. 5 & Table 6). Figure 5 shows that disturbed vegetation areas were observed in Rajpur-Sonarpur to Baruipur, along the NH 12, south of EKW which indicate vegetation area transformed into settlement or the agriculture area.
Wetland habitats in urban areas are under threat from rapidly growing urban populations specially in developing nations (Ehrenfeld, 2000; Zhang et al., 2016). Uncontrolled built-up growth has significant problem to the natural ecosystem(Zhang et al., 2017; Ghosh et al., 2019).Therefore, built-up area change is shown using the NDBI indices of study area (Fig. 5 & Table 6). The values of NDBI range from − 1 to + 1, with vegetation cover surface being 0, water cover and built-up indicate negative and positive values. The result of NDBI varies from 0.771 to -0.5 in 2011 and from 0.447 to -0.388 in 2020. The maximum extension of the built-up area was noticed along the highways, outskirt of cities, most of municipalities have higher concentration of urban areas in KMA both in 2011 and 2020. The built-up area encroached the water bodies, green space, and agricultural areas resulting in municipal wastes, and degrading ecological health while posing a danger to ecological security (Ghosh et al, 2018; Das et al., 2020; Dutta et al, 2020).
Table 6
Range of different indices values and formulas for wetland
Sensor | NDVI | NDBI | NDWI | MNDWI | NDPI | NDTI |
(NIR -Red)/(NIR + Red) | (SWIR-NIR)/(SWIR + NIR) | (Green-NIR)/(Green + NIR) | (Green-MIR)/(Green + MIR) | (MIR-Green)/(MIR + Gree) | (Red-Green)/(Red + Green) |
Min | Max | Min | Max | Min | Max | Min | Max | Min | Max | Min | Max |
LISS-III(2011) | -0.5652 | 0.8095 | -0.5 | 0.7714 | -0.7049 | 0.7142 | - | - | - | - | -0.6666 | 0.2870 |
Landsat 7(2011) | - | - | - | - | - | - | -0.6785 | 0.6956 | -0.6956 | 0.6785 | - | - |
Landsat 8 (2020) | -0.1157 | 0.4467 | -0.3883 | 0.4477 | -0.3864 | 0.1229 | -0.6173 | 0.2880 | -0.2881 | 0.6173 | -0.0815 | 0.0971 |
4.1.4 Impact (I)
(D1)Water quality change: (D1a) BOD, (D1b) DO, (D1c) pH, (D1d) Total Coliform (TC)
The water bodies offer ground to fishes and vegetables those become the source of local livelihood(Bunting et al., 2010; Chen & Wong, 2016). It replenishes groundwater, manages floods, balances the local weather phenomena, filters pollution and is a carbon sink(Mitsch et al., 2013; Cao et al., 2017). The quality of surface water in urban areas continuously is challenging, particularly with the rise of population and climate change (Green et al., 2015; Miller & Hutchins, 2017). Therefore partially treated sewage, dumping on wetlands, insufficient solid waste collection, and chemical usage in agricultural and manufacturing factories are the predominant concerns in KMA(Ellis, 1991). The standard limit for biological oxygen demand (BOD) is 3.0 mg/liter. In comparison to upstream, the downstream in the Hooghly River had crossed the limit. In 2011, the Kharda canal had the highest BOD (79 mg/L), followed by Noai canal and East Kolkata Wetland (EKW), while in 2020 the EKW, mainly in the Nalban bheries, had the highest (80.32 mg/L) during monsoon since garbage in cities drain into the fisheries or bheries (IMPEKW, 2021;WBPCB., 2021).
The dissolved oxygen (DO), pH, and Total Coliform acceptable limits are 4.0 mg/L, 6.5–8.5, and 5000 MPN/100mL, respectively. For the year 2011 and 2020, the greatest DO levels were observed in Serampore locations and the Rabindra Sarovar Lake. In 2011, EKW (8.4) had the highest pH, followed by Palta (8.15) in 2020 (IMPEKW. 2021, WBPCB,. 2021). In both periods, the TC was greatest in the Khardah canal.
The quality of wetland health changes seasonally, according to the aforementioned analysis. The DO was found to be lowest in the post-monsoon season and greatest in the summer (Saha, 2021). Throughout the year, the Coliform concentration is greater in lake habitats. Pollution of static water bodies such as lakes was caused mainly by untreated sewage water, agricultural runoff, and waste from birds. The health of the wetland in KMA has deteriorated, as wetland region was unsuitable for recreation and direct drinking.
Others: Total fish production in wetlands and Area under cereals production
The demand for fish and food in megacities of Asia is rapidly increasing and it is putting pressure on fish stocks (Yasmeen & D, 2001). The developing nations have a problem of under nutrition caused by inaccessibility of food due to unclean water for production of fish, agricultural products, low income and lack of technology use (Satterthwaite et al., 2010). In comparison to other parts of India, the demand for fish and grains in KMA is higher. According to recent studies, East Kolkata Wetland Management Authority (EKWMA) and Wetland International South Asia (WISA) 2021, 20,000 metric tonnes (MT) of fish and 50,000 MT of vegetables along with huge rice farming produced in KMA wetland farming areas (EKWMA & WISA 2021). The surrounding districts of KMA, the highest production of fish found in North 24 Parganas i.e 1502176.5 quintals. But fishes and foods in KMA are often found contaminated with heavy metals, chemicals, and pesticides and the area of cereals become shrinking due to the grave impact of industrialization and urbanization (Nadella & Sen, 2021).
The very poor health of wetland found in Howrah MC, Garulia, and Hooghly-Chinsura was analyzed using the status of health in each municipality under pressure (P) indicator. Bally, Bansberia, Basirhat, Bhatpara, Dum Dum, Kamarhati, and Khardaha are in the bad health category, whereas all municipalities are in the sub-healthy category. Kolkata and Kalyani are part of the poor health categories, Bally, Bansberia, Basirhat, Bhatpara, Dum Dum, Kamarhati, and Khardaha are sub healthy groups and whereas all municipalities are included in healthy wetland health types(Table 7)
Wetland Health Analysis
In Kolkata metropolitan region, the wetland health index (WHI) results were based on the municipality and station level data assessment (Table 7, 8 & Fig. 6). Five categories were used to examine the status of the driver forces, pressure, state, and impact subsystems. The wetland health conditions in different municipalities were explored using the DPSI conceptual model. In WHI, the results of each municipality was ranked and then divided into five categories i.e. healthy, sub-healthy, unhealthy, poor and very (Table 5). Wetlands with very poor health were observed in Howrah MC, Garulia, and Hooghly-Chinsura from B1 to B8 under pressure (P) indicator. Kolkata and Kalyani are part of the poor category. Bally, Bansberia, Basirhat, Bhatpara, Dum Dum, Kamarhati, and Khardaha were in the unhealthy category, Chandannagar MC, Baidyabati and Baranagar are sub healthy groups and whereas all municipalities are included in healthy wetland health types (Table 7)
Table 7
Municipality-Wise Weights of Different Assessment Indicators in KMA
Indicators | B1 | B2 | B3 | B4 | B5 | B6 | B7 | B8 | Wetland Health Index (Rank) |
Municipalities | Final Weight 2011 | Final Weight 2011 | Final Weight 2011 | Final Weight 2011 | Final Weight 2011 | Final Weight 2011 | Final Weight 2011 | Final Weight 2011 |
Kolkata MC | 0.0497 | 0.0860 | 0.0060 | 0.0430 | 0.0279 | 0.0440 | 0.0314 | 0.2220 | 0.0387 (4) |
Howrah MC | 0.0472 | 0.0199 | 0.0024 | 0.0081 | 0.0282 | 0.0087 | 0.0203 | 0.0219 | 0.0604 (5) |
Chandannagar MC | 0.0141 | 0.0028 | 0.0234 | 0.0007 | 0.0173 | 0.0004 | 0.0083 | 0.0021 | 0.0194 (2) |
Bidhannagar MC | 0.0209 | 0.0034 | 0.0000 | 0.0011 | 0.0282 | 0.0038 | 0.0193 | 0.0054 | 0.0030 (1) |
Baidyabati | 0.0203 | 0.0018 | 0.0494 | 0.0006 | 0.0282 | 0.0005 | 0.0138 | 0.0013 | 0.0194 (2) |
Bally | 0.0572 | 0.0040 | 0.0253 | 0.0019 | 0.0166 | 0.0001 | 0.0014 | 0.0000 | 0.0216 (3) |
Bansberia | 0.0238 | 0.0013 | 0.0159 | 0.0006 | 0.0218 | 0.0003 | 0.0114 | 0.0001 | 0.0222 (3) |
Baranagar | 0.0756 | 0.0046 | 0.0237 | 0.0018 | 0.0104 | 0.0014 | 0.0151 | 0.0047 | 0.0184 (2) |
Barasat | 0.0153 | 0.0052 | 0.0278 | 0.0016 | 0.0282 | 0.0012 | 0.0000 | 0.0047 | 0.0114 (1) |
Barrackpur | 0.0311 | 0.0024 | 0.0049 | 0.0009 | 0.0237 | 0.0004 | 0.0099 | 0.0016 | 0.0103 (1) |
Baruipur | 0.0091 | 0.0011 | 0.0156 | - | - | 0.0001 | 0.0097 | 0.0028 | 0.0087 (1) |
Basirhat | 0.0093 | 0.0021 | 0.0043 | 0.0007 | 0.0282 | 0.0002 | 0.0053 | 0.0022 | 0.0297 (3) |
Bhadreswar | 0.0258 | 0.0012 | 0.0158 | 0.0006 | 0.0330 | 0.0002 | 0.0068 | 0.0030 | 0.0127 (1) |
Bhatpara | 0.0246 | 0.0067 | 0.0318 | 0.0034 | 0.0282 | 0.0019 | 0.0162 | 0.0089 | 0.0250 (3) |
Budge Budge | 0.0163 | 0.0008 | 0.0068 | 0.0001 | 0.0166 | 0.0004 | 0.0159 | 0.0017 | 0.0155 (1) |
Champdany | 0.0373 | 0.0013 | 0.0146 | 0.0006 | - | 0.0001 | 0.0048 | 0.0006 | 0.0067 (1) |
Dum Dum | 0.0246 | 0.0016 | 0.0182 | 0.0006 | 0.0160 | 0.0006 | 0.0165 | 0.0026 | 0.0299 (3) |
Garulia | 0.0280 | 0.0008 | 0.0126 | 0.0001 | 0.0220 | 0.0003 | 0.0125 | 0.0022 | 0.0908 (5) |
Gayeshpur | 0.0000 | 0.0005 | 0.0009 | 0.0000 | 0.0175 | 0.0005 | 0.0312 | 0.0019 | 0.0152 (1) |
Halisahar | 0.0328 | 0.0018 | 0.0194 | 0.0007 | 0.0282 | 0.0005 | 0.0127 | 0.0018 | 0.0068 (1) |
Hooghly-Chinsura | 0.0207 | 0.0031 | 0.0220 | 0.0011 | 0.0345 | 0.0007 | 0.0138 | 0.0012 | 0.0594 (5) |
Kalyani | 0.0037 | 0.0013 | 0.0066 | 0.0001 | 0.0430 | 0.0005 | 0.0170 | 0.0035 | 0.0551 (4) |
Kamarhati | 0.0705 | 0.0052 | 0.0392 | 0.0027 | 0.0282 | 0.0015 | 0.0147 | 0.0076 | 0.0322 (3) |
Kanchrapara | 0.0283 | 0.0016 | 0.0045 | 0.0008 | 0.0282 | 0.0004 | 0.0098 | 0.0005 | 0.0043 (1) |
Khardaha | 0.0346 | 0.0014 | 0.1370 | 0.0007 | 0.0196 | 0.0006 | 0.0180 | 0.0026 | 0.0216 (3) |
Konnagar | 0.0359 | 0.0009 | 0.0319 | 0.0001 | 0.0235 | 0.0003 | 0.0120 | 0.0014 | 0.0086 (1) |
Madhyamgram | 0.0178 | 0.0034 | 0.0331 | 0.0010 | 0.0111 | 0.0005 | 0.0076 | 0.0041 | 0.0068 (1) |
Maheshtala | 0.0188 | 0.0079 | 0.0041 | 0.0029 | 0.0001 | 0.0017 | 0.0125 | 0.0135 | 0.0038 (1) |
Naihati | 0.0413 | 0.0024 | 0.0204 | 0.0010 | 0.0158 | 0.0016 | 0.0197 | 0.0026 | 0.0080 (1) |
New Barrackpur | 0.0230 | 0.0009 | 0.0233 | 0.0002 | 0.0166 | 0.0002 | 0.0115 | 0.0004 | 0.0059 (1) |
North Barrackpur | 0.0223 | 0.0020 | 0.0298 | 0.0009 | 0.0282 | 0.0004 | 0.0108 | 0.0011 | 0.0068 (1) |
North Dum Dum | 0.0187 | 0.0046 | 0.0149 | 0.0015 | 0.0072 | 0.0014 | 0.0184 | 0.0130 | 0.0048 (1) |
Panihati | 0.0438 | 0.0065 | 0.0125 | 0.0026 | 0.0144 | 0.0016 | 0.0122 | 0.0051 | 0.0079 (1) |
Pujali | 0.0062 | 0.0000 | 0.0197 | - | - | 0.0000 | 0.0025 | 0.0003 | 0.0032 (1) |
Rajarhat Gopalpur | 0.0239 | 0.0078 | 0.0053 | 0.0020 | 0.0282 | 0.0029 | 0.0399 | 0.0056 | 0.0046 (1) |
Rajpur-Sonarpur | 0.0166 | 0.0083 | 0.0184 | 0.0018 | 0.0168 | 0.0016 | 0.0122 | 0.0020 | 0.0054 (1) |
Rishra | 0.0647 | 0.0016 | 0.0388 | 0.0005 | 0.0208 | 0.0004 | 0.0122 | 0.0029 | 0.0131 (1) |
South dum dum | 0.0513 | 0.0081 | 0.0110 | 0.0030 | 0.0282 | 0.0055 | 0.0440 | 0.0116 | 0.0088 (1) |
Srerampore | 0.0209 | 0.0028 | 0.0070 | 0.0013 | 0.0140 | 0.0008 | 0.0150 | 0.0049 | 0.0038 (1) |
Titagarh | 0.0840 | 0.0013 | 0.0074 | 0.0007 | 0.0135 | 0.0006 | 0.0170 | 0.0021 | 0.0116 (1) |
Uluberia | 0.0116 | 0.0036 | 0.0053 | 0.0014 | 0.0000 | 0.0002 | 0.0025 | 0.0043 | 0.0026 (1) |
Uttarpara Kotrung | 0.0268 | 0.0027 | 0.0095 | 0.0010 | 0.0282 | 0.0008 | 0.0171 | 0.0002 | 0.0049 (1) |
Table 8
Station-Wise Weights of Different Assessment Indicators in KMA
Name of Stations | D3a | D3b | D3c | D3d | Wetland Health Index(Rank) 2011 | Wetland Health Index(Rank) 2020 |
Final Weight 2011 | Final Weight 2020 | Final Weight 2011 | Final Weight 2020 | Final Weight 2011 | Final Weight 2020 | Final Weight 2011 | Final Weight 2020 |
Tribeni (Bansberia) | 0.0000 | 0.0042 | 0.0000 | 0.1297 | 0.1073 | 0.1222 | 0.0002 | 0.0000 | 0.0269 (2) | 0.0640 (4) |
Palta(North Barrackpur) | 0.0000 | 0.0033 | 0.1188 | 0.1297 | 0.0655 | 0.1355 | 0.0025 | 0.0000 | 0.0467 (4) | 0.0671 (3) |
Serampore(Serampore) | 0.0020 | 0.0029 | 0.1430 | 0.1430 | 0.0775 | 0.1430 | 0.0014 | 0.0000 | 0.0560 (4) | 0.0722 (4) |
Garden Reach(Kolkata MC) | 0.0037 | 0.0036 | 0.1074 | 0.1020 | 0.0715 | 0.0690 | 0.0124 | 0.0002 | 0.0488 (4) | 0.0437 (2) |
Dakshineswar(Kamarhati) | 0.0038 | 0.0033 | 0.1188 | 0.1001 | 0.0775 | 0.0865 | 0.0052 | 0.0000 | 0.0513 (4) | 0.0475 (4) |
Howrah-Shivpur(Howrah MC) | 0.0030 | 0.0027 | 0.1264 | 0.1011 | 0.0417 | 0.0000 | 0.0035 | 0.0000 | 0.0436 (3) | 0.0259 (4) |
Uluberia(Uluberia) | 0.0044 | 0.0018 | 0.1090 | 0.1039 | 0.0775 | 0.0308 | 0.0012 | 0.0000 | 0.0480 (4) | 0.0341(4) |
Rabindra Sarovar(Kolkata MC) | 0.0106 | 0.0022 | 0.1271 | 0.1430 | 0.1013 | 0.1014 | 0.0007 | 0.0000 | 0.0599 (4) | 0.0617 (3) |
Kharda Canal(Khardah) | 0.1430 | 0.0948 | 0.0000 | 0.0000 | 0.0119 | 0.0158 | 0.1430 | 0.1430 | 0.0745 (4) | 0.0634 (5) |
Noai Canal(New Barrackpur) | 0.0443 | 0.0283 | 0.0000 | 0.0048 | 0.0417 | 0.0382 | 0.1171 | 0.0025 | 0.0508 (4) | 0.0185 (3) |
Water Reservoir St. Helens School(Howrah MC) | 0.0084 | 0.0000 | 0.1052 | 0.1297 | 0.0000 | 0.0100 | 0.0000 | 0.0000 | 0.0284 (2) | 0.0349 (4) |
Nalban Bheries, East Kolkata Wetland(Kolkata MC) | 0.0378 | 0.1430 | 0.0507 | 0.0986 | 0.1430 | 0.1147 | 0.0002 | 0.0000 | 0.0579 (4) | 0.0891 (1) |
The impact (I) indicator of DPSI model revealed a clear health condition of water. The weight of 2011 and 2020 year data was used for a better understanding of wetland health in impact (I) section. In 2011, the KMA wetland health status index found no stations in the healthy and very poor categories. Tribeni in Bansberia and the water reservoir St. Helens School in Howrah MC were in the sub-healthy category and remaining stations were in the poor category. In 2020, the D1a to D1d indicator found the dynamicity of wetland health that is improved, declined, and showed a moderate trend in the value of WHI under the impact (I) Indicator of the DPSI model. The health of the Nalban bheries in east Kolkata wetland has improved from poor to healthy, and the Garden Reach of Kolkata MC has also improved from poor to sub-healthy. Palta (North Barrackpur municipality), Rabindra Sarovar Lake, and Noai Canal (New Barrackpur municipality) were all changed from poor to sub-healthy. The sole Kharda Canal in Khardah municipality was in very poor health, while the other stations are part of the wetland's poor health categories (Table 8 and Fig. 6).
The fundamental reason for the improvement in wetland health is a municipal investment in drainage; water supply, and environmental management. Local people's involvement and government responsibilities for urban wetland protection were the major factors that contribute in the improvement of health. The majority of categories fall into healthy groups when compared from one municipality, station to another, but wetlands of these areas are suffering from solid waste, population pressure, landfill, road construction, industrial development, economic growth, urban area expansion, sewage water mixed up, and others.
Limitations of the Study
This study investigated the WHI in KMA using the technique DPSI and AHP. In DPSI model, factors such as vegetables, fruits, paddy production, soil quality, tourism and the impact of wetland water on human health, water management strategy, heavy metals in wetland, wetland biodiversity, and others were not utilized to detect the wetland assessment. The other method and techniques can be used for weight analysis and future wetland health estimation; these techniques are Principal Component Analysis (PCA), Delphi method, sophisticated machine learning methods like Artificial Neural Network (ANN), Fuzzy AHP, ecosystem services, ecological integrity, logistic regression model, Pearson correlation, Cellular Automata. In order to get accurate health result, this study concentrated on wetland health at the municipality and station level from 2011 to 2020. As it was very difficult to cover each and every municipality to collect information regarding water conditions of KMA’s wetland due to lack of resources. Therefore data was gathered from sites along rivers, lakes, and canals of selected wetlands.