RESUMEN
The Sundarban mangrove or tidal influenced natural ecosystem is extremely productive and providing multiple goods and services to millions of people. In the last few decades, the quality and quantity of mangrove ecosystem are being deteriorated. The main objectives of this current research are (i) to investigate the ecosystem service values (ESVs) using a time series satellite data (1975, 2000 and 2020) and different unit values (ii) to analyze the dynamic pattern of carbon sequestration through InVEST model and (iii) determination of ESVs change hotspots by Getis-Ord Gi* method. Here, mangrove forest has the highest ecosystem service value and highest carbon sinker. The total loss of ESVs was estimated 3310.79 million USD during last 45 years in Sundarban Biosphere Reserve (SBR) due to high natural and anthropogenic adversities. InVEST model also revealed that the total static carbon storage over the study area was 48.87, 46.65 and 43.33 Tg for the year 1975, 2000 and 2020 respectively. Total 6313944 mg/6.31Tg loss of carbon has been observed in the case of mangrove forest during the overall study period (1975-2020). So, illegal human encroachment should be strictly (through law and regulations) restricted within Sundarban mangrove ecosystem for the benefits of people.
Asunto(s)
Carbono , Ecosistema , Secuestro de Carbono , Conservación de los Recursos Naturales , Humanos , HumedalesRESUMEN
Worldwide spread out of COVID-19 in a short-time has brought a significant decline of road traffic, tourist flow and industrial ventures. During this emergency period, the restricted human dealings with nature have appeared as blessing for health of the total environment. The variation of atmospheric O3 may modulate the range of UV index (UVI) at any region of the earth. The objective of the study is to examine the variation of UV index over the megacities of India with respect to tropospheric O3 level modification during COVID-19 lockdown. The meteorological or environmental data (temperature in °C, gust in km/h, wind speed km/h, relative humidity in %, air pressure in mb and cloud cover in okta) of four selective megacities of India (Kolkata, Chennai, Delhi, Mumbai) during and pre lockdown period have been obtained to comprehend about the variation of UV index and tropospheric O3. The descriptive statistical applications i.e. standard deviation, standard errors and K-means clustering have been done through standard statistical software. In the present study, t-test has been used to understand level of significance of surface O3 and UVI during pre-lockdown (2019) and lockdown (2020) phase. The result shows that the four major megacities in India namely New Delhi, Mumbai, Kolkata and Chennai have experienced the vibrant diminution in terms of the concentration of UV index with slightly increasing the tropospheric O3 level during the lockdown phase. The higher accumulation of O3 during the lockdown in the lower atmosphere of four megacities does not exceed the permissible limit. The excess amount of O3 has remarkably contributed to trap the harmful UV radiation which has lowered the UVI in these worst polluted megacities of India. In the meantime, the prominent reduction of NOx during the lockdown period decreases the titration impact to O3 and this mechanism helps to revitalize the ozone concentration level. The uniqueness of the current study is highlighted the ground reality regarding reduction of UV index and amplification of tropospheric O3 concentration during lockdown phase. This study definitely assists to make new environmental policy, act and law for recover the health of the total environment.
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The higher concentration of PM10 and PM2.5 in the lower atmosphere is severely harmful for human health and it also makes visibility diminution along with weather and climate modifications. The main objective is to find out the spatiotemporal variation and dispersal of PM10 and PM2.5 along with COVID-19 infection in the dusty city Kolkata. The consecutive two years PM10 and PM2.5 data of different stations have been obtained from State Pollution Control Board, Govt. of West Bengal. Forward trajectory analysis has been done through HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model to find the path and direction of air particles. The result showed that the various meteorological or environmental factors (such as temperature, relative humidity, wind, wind speed, pressure and gusty wind) and geographical location regulate the spatiotemporal variation of PM10 and PM2.5. These factors like high temperature with relative humidity and strong wind influence to disperse the particulate matters from north to south direction from city to outside during summer in Kolkata metropolitan city. During summer (both pre and lockdown years), the height of particles is extended up to 1000 m owing to active atmospheric ventilation whereas in winter it is confined within 100 m. The HYSPLIT model clearly specified that the particles dispersed from south, south-west to north and north east direction due to strong wind. The constant magnification of PM10 and PM2.5 in the lower atmosphere leads to greater frequency of COVID-19 infections and deaths. In Kolkata, the one of the crucial reasons of high infection and deaths (COVID-19) is co-morbidity of people.
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Kolkata is the third densely populated city of India and Kolkata stands in the World's 25 most polluted cities along with 10 worse polluted cities in India. The relevant study claims that due to the imposition of lockdown during COVID-19 pandemic, the atmospheric pollution level has been significantly reduced over the metropolitan city Kolkata like other cities of the world. The main objective of this study is to predict the concentration of PM2.5 using multiple linear regression (MLR) and artificial neural network (ANN) models and similarly, to compare the accuracy level of two models. The concentration of PM2.5 data has been obtained from state pollution control board, Govt. of West Bengal and daily meteorological data have been collected from the world weather website. The results show that non-linear artificial neural network model is more rational compared with multiple linear regression model due to its high precision and accuracy level (in respect to RMSE, MAE and R2). In this research artificial neural network (ANN) model exhibited higher accuracy during the training and testing phases (root mean square error (RMSE), mean absolute error (MAE) and R2 indicate 3.74, 1.14 and 0.91 respectively in training phase and 2.55, 4.32 and 0.69 in testing phase respectively). This model (ANN)) can be applied to predict the concentration of PM2.5 during the execution of urban air quality management plan.
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The fatal novel coronavirus (COVID-19) pandemic disease smashes the normal tempo of global socio-economic and cultural livelihood. Most of the countries impose a lockdown system with social distancing measures to arrest the rapid transmission of this virus into the human body. The objective of this study is to examine the status of air quality during and pre-COVID-19 lockdown and to recommend some long-term sustainable environmental management plan. The pollution data like PM10, PM2.5, O3, SO2, NO2 and CO have been obtained from State Pollution Control Board under Govt. of West Bengal. Similarly, various land surface temperature (LST) maps have been prepared using LANDSAT-8 OLI and LANDSAT-7 ETM + images of USGS. The maps of NO2 and aerosol concentration over Indian subcontinent have been taken from ESA and NASA. The digital thematic maps and diagrams have been depicted by Grapher 13 and Arc GIS 10.3 platforms. The result shows that the pollutants like CO, NO2 and SO2 are significantly decreased, while the average level of O3 has been slightly increased in 2020 during the lockdown due to close-down of all industrial and transport activities. Meanwhile, around 17.5% was the mean reduction of PM10 and PM2.5 during lockdown compared with previous years owing to complete stop of vehicles movement, burning of biomass and dust particles from the construction works. This study recommends some air pollution-tolerant plant species (in urban vacant spaces and roof tops) for long-term cohabitation among environment, society and development.
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Since the first case of COVID-19 traced in India on 30th January, 2020, the total no. of confirmed cases is amplified. To assess the inter-state diversity in spreading potentiality of COVID-19, the exposure, readiness and resilience capability have been studied. On the basis of the extracted data, the outbreak scenario, growth rate, testing amenities have been analysed. The study reflects that there is an enormous disparity in growth rate and total COVID-19 cases. The major outbreak clusters associated with major cities of India. COVID-19 cases are very swiftly amplifying with exponential growth in every four to seven days in main affected states during first phase of lockdown. The result shows the vibrant disproportion in the aspects of, hospital bed ratio, coronavirus case-hospital bed ratio, provision of isolation and ventilators, test ratio, distribution of testing laboratories and accessibility of test centres all over India. The study indicates the sharp inequality in transmission potentiality and resilience capacity of different states. Every state and union territory are not well-prepared to contain the spreading of COVID-19. The strict protective measures and uniform resilience system must be implemented in every corner of India to battle against the menace of COVID-19.