Delineating dengue risk zones in Jaipur: An interdisciplinary approach to inform public health strategies.

Dengue fever (DF) is a pervasive public health concern in tropical climates, with densely populated regions, such as India, disproportionately affected. Addressing this issue requires a multifaceted understanding of the environmental and sociocultural factors that contribute to the risk of dengue infection. This study aimed to identify high-risk zones for DF in Jaipur, Rajasthan, India, by integrating physical, demographic, and epidemiological data in a comprehensive risk analysis framework. We investigated environmental variables, such as soil type and plant cover, to characterize the potential habitats of Aedes aegypti, the primary dengue vector. Concurrently, demographic metrics were evaluated to assess the population's susceptibility to dengue outbreaks. High-risk areas were systematically identified through a comparative analysis that integrated population density and incidence rates per ward. The results revealed a significant correlation between high population density and an increased risk of dengue, predominantly facilitated by vertical transmission. Spatially, these high-risk zones are concentrated in the northern and southern sectors of Jaipur, with the northern and southwestern wards exhibiting the most acute risk profiles. This study underscores the importance of targeted public health interventions and vaccination campaigns in vulnerable areas. It further lays the groundwork for future research to evaluate the effectiveness of such interventions, thereby contributing to the development of robust evidence-based strategies for dengue risk mitigation.

The effects of COVID-19 on agriculture supply chain, food security, and environment: a review.

COVID-19 has a deep impact on the economic, environmental, and social life of the global population. Particularly, it disturbed the entire agriculture supply chain due to a shortage of labor, travel restrictions, and changes in demand during lockdowns. Consequently, the world population faced food insecurity due to a reduction in food production and booming food prices. Low-income households face food security challenges because of limited income generation during the pandemic. Thus, there is a need to understand comprehensive strategies to meet the complex challenges faced by the food industry and marginalized people in developing countries. This research is intended to review the agricultural supply chain, global food security, and environmental dynamics of COVID-19 by exploring the most significant literature in this domain. Due to lockdowns and reduced industrial production, positive environmental effects are achieved through improved air and water quality and reduced noise pollution globally. However, negative environmental effects emerged due to increasing medical waste, packaging waste, and plastic pollution due to disruptions in recycling operations. There is extensive literature on the effects of COVID-19 on the environment and food security. This study is an effort to review the existing literature to understand the net effects of the pandemic on the environment and food security. The literature suggested adopting innovative policies and strategies to protect the global food supply chain and achieve economic recovery with environmental sustainability. For instance, food productivity should be increased by using modern agriculture technologies to ensure food security. The government should provide food to vulnerable populations during the pandemic. Trade restrictions should be removed for food trade to improve international collaboration for food security. On the environmental side, the government should increase recycling plants during the pandemic to control waste and plastic pollution.

Assessing Hydropower Potential in Nepal's Sunkoshi River Basin: An Integrated GIS and SWAT Hydrological Modeling Approach.

This study assessed the hydropower potential of a mountain watershed within the Sunkoshi River basin in Sindhupalchok, Nepal, utilizing geographic information systems (GIS) and the soil and water assessment tool (SWAT) hydrological model. Topographical, soil, land use, meteorological, and discharge data were employed to assess the study area for the appropriateness of hydropower generation. SWAT was utilized to delineate the Sunkoshi basin into 23 distinct subbasins and involved the creation of a detailed river network, incorporating various hydrological attributes including stream links, stream order, stream length, and slope gradient. After that, it was employed to simulate river discharges within these subbasins. The Sequential Uncertainty Fitting Version 2 (SUFI-2) algorithm, integrated within the SWAT Calibration and Uncertainty Program (SWAT-CUP), was employed to calibrate and validate the model. This step involved the adjustment of 25 selected parameters to enhance the model's accuracy and reliability in representing the hydrological processes of the Sunkoshi basin. Model performance was assessed utilizing three well-established efficiency criteria: coefficient of determination (R2 = 0.79), Nash-Sutcliffe efficiency (NSE = 0.73), and percent bias (PBIAS = 17.59). The study identified 36 sites across streams of order 3, 4, and 5 as having potential for hydropower generation. The hydropower potential at each identified site was evaluated using estimated stream flow and topographical head at various probability of exceedance (PoE) levels (40%, 45%, 50%, and 60%). The aggregate hydropower potential of the basin was quantified, yielding a potential of 371.30 MW at a 40% PoE. The findings suggest that an integrated approach combining SWAT-based hydrological modeling within a GIS can accurately assess a river basin's hydropower potential and provide insights into further evaluation of the comprehensive environmental assessment of the fragile Himalayan watersheds.

A review on sea cucumber (Bengali: Somuddro Sosha) as a bioindicator of heavy metal contamination and toxicity.

This review paper exhibits the underexplored realm of heavy metal contamination and associated risks in sea cucumbers (SCs), which hold significant importance in traditional Asian marine diets and are globally harvested for the Asian market. The assessment focuses on heavy metals (HMs) presence in various SC species, revealing a global trend in HMs concentrations across anatomical parts: Fe > Zn > As > Cu > Hg > Pb > Mn > Cr > Ni > Cd. Specific species, such as Eupentacta fraudatrix, Holothuria mammata, Holothuria polii, Holothuria tubulosa, and Holothuria atra, exhibit heightened arsenic levels, while Stichopus herrmanni raises concerns with mercury levels, notably reaching 3.75 mg/kg in some instances, posing potential risks, particularly for children. The study sheds light on anthropogenic activities such as cultivation, fishing, and shipping, releasing HMs into marine ecosystems and thereby threatening ocean and coastal environments due to the accumulation and toxicity of these elements. In response to these findings, the paper suggests SCs as promising bioindicator species for assessing metal pollution in marine environments. It underscores the adverse effects of human actions on sediment composition and advocates for ongoing monitoring efforts both at sea and along coastlines.

Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm.

Frequent floods are a severe threat to the well-being of people the world over. This is particularly severe in developing countries like India where tropical monsoon climate prevails. Recently, flood hazard susceptibility mapping has become a popular tool to mitigate the effects of this threat. Therefore, the present study utilized four distinctive Machine Learning algorithms i.e., K-Nearest Neighbor, Decision Tree, Naive Bayes, and Random Forest to estimate flood susceptibility zones in the Agartala Urban Watershed of Tripura, India. The latter experiences debilitating floods during the monsoon season. A multicollinearity test was conducted to examine the collinearity of the chosen flood conditioning factors, and it was seen that none of the factors were compromised by multicollinearity. Results showed that around three-fourths of the AUW area was classified as moderate to very high flood-prone zones, while over 20 percent was between low and very low flood-prone zones. The models applied performed well with ROC-AUC scores greater than 70 percent and MAE, MSE, and RMSE scores less than 30 percent. DT and RF algorithms were suggested for places with similar physical characteristics based on their outstanding performance and the training datasets. The study provides valuable insights to policymakers, administrative authorities, and local stakeholders to cope with floods and enhance flood prevention measures as a climate change adaptation strategy in the AUW.

Interactions of urbanisation, climate variability, and infectious disease dynamics: insights from the Coimbatore district of Tamil Nadu.

Climate change and shifts in land use/land cover (LULC) are critical factors affecting the environmental, societal, and health landscapes, notably influencing the spread of infectious diseases. This study delves into the intricate relationships between climate change, LULC alterations, and the prevalence of vector-borne and waterborne diseases in Coimbatore district, Tamil Nadu, India, between 1985 and 2015. The research utilised Landsat-4, Landsat-5, and Landsat-8 data to generate LULC maps, applying the maximum likelihood algorithm to highlight significant transitions over the years. This study revealed that built-up areas have increased by 67%, primarily at the expense of agricultural land, which was reduced by 51%. Temperature and rainfall data were obtained from APHRODITE Water Resources, and with a statistical analysis of the time series data revealed an annual average temperature increase of 1.8 °C and a minor but statistically significant rainfall increase during the study period. Disease data was obtained from multiple national health programmes, revealing an increasing trend in dengue and diarrhoeal diseases over the study period. In particular, dengue cases surged, correlating strongly with the increase in built-up areas and temperature. This research is instrumental for policy decisions in public health, urban planning, and climate change mitigation. Amidst limited research on the interconnections among infectious diseases, climate change, and LULC changes in India, our study serves as a significant precursor for future management strategies in Coimbatore and analogous regions.

Growth of water hyacinth biomass and its impact on the floristic composition of aquatic plants in a wetland ecosystem of the Brahmaputra floodplain of Assam, India.

Inland water plants, particularly those that thrive in shallow environments, are vital to the health of aquatic ecosystems. Water hyacinth is a typical example of inland species, an invasive aquatic plant that can drastically alter the natural plant community's floral diversity. The present study aims to assess the impact of water hyacinth biomass on the floristic characteristics of aquatic plants in the Merbil wetland of the Brahmaputra floodplain, NE, India. Using a systematic sampling technique, data were collected from the field at regular intervals for one year (2021) to estimate monthly water hyacinth biomass. The total estimate of the wetland's biomass was made using the Kriging interpolation technique. The Shannon-Wiener diversity index (H'), Simpson's diversity index (D), dominance and evenness or equitability index (E), density, and frequency were used to estimate the floristic characteristics of aquatic plants in the wetland. The result shows that the highest biomass was recorded in September (408.1 tons/ha), while the lowest was recorded in March (38 tons/ha). The floristic composition of aquatic plants was significantly influenced by water hyacinth biomass. A total of forty-one plant species from 23 different families were found in this tiny freshwater marsh during the floristic survey. Out of the total, 25 species were emergent, 11 were floating leaves, and the remaining five were free-floating habitats. Eichhornia crassipes was the wetland's most dominant plant. A negative correlation was observed between water hyacinth biomass and the Shannon (H) index, Simpson diversity index, and evenness. We observed that water hyacinths had changed the plant community structure of freshwater habitats in the study area. Water hyacinth's rapid expansion blocked out sunlight, reducing the ecosystem's productivity and ultimately leading to species loss. The study will help devise plans for the sustainable management of natural resources and provide helpful guidance for maintaining the short- to the medium-term ecological balance in similar wetlands.

Geospatial modeling to assess the past and future land use-land cover changes in the Brahmaputra Valley, NE India, for sustainable land resource management.

Satellite remote sensing and geographic information system (GIS) have revolutionalized the mapping, quantifying, and assessing the land surface processes, particularly analyzing the past and future land use-land cover (LULC) change patterns. Worldwide river basins have observed enormous changes in the land system dynamics as a result of anthropogenic factors such as population, urbanization, development, and agriculture. As is the scenario of various other river basins, the Brahmaputra basin, which falls in China, Bhutan, India, and Bangladesh, is also witnessing the same environmental issues. The present study has been conducted on the Brahmaputra Valley in Assam, India (a sub-basin of the larger Brahmaputra basin) and assessed its LULC changes using a maximum likelihood classification algorithm. The study also simulated the changing LULC pattern for the years 2030, 2040, and 2050 using the GIS-based cellular automata Markov model (CA-Markov) to understand the implications of the ongoing trends in the LULC change for future land system dynamics. The current rate of change of the LULC in the region was assessed using the 48 years of earth observation satellite data from 1973 to 2021. It was observed that from 1973 to 2021, the area under vegetation cover and water body decreased by 19.48 and 47.13%, respectively. In contrast, cultivated land, barren land, and built-up area increased by 7.60, 20.28, and 384.99%, respectively. It was found that the area covered by vegetation and water body has largely been transitioned to cultivated land and built-up classes. The research predicted that, by the end of 2050, the area covered by vegetation, cultivated land, and water would remain at 39.75, 32.31, and 4.91%, respectively, while the area covered by built-up areas will increase by up to 18.09%. Using the kappa index (ki) as an accuracy indicator of the simulated future LULCs, the predicted LULC of 2021 was validated against the observed LULC of 2021, and the very high ki observed validated the generated simulation LULC products. The research concludes that significant LULC changes are taking place in the study area with a decrease in vegetation cover and water body and an increase of area under built-up. Such trends will continue in the future and shall have disastrous environmental consequences unless necessary land resource management strategies are not implemented. The main factors responsible for the changing dynamics of LULC in the study area are urbanization, population growth, climate change, river bank erosion and sedimentation, and intensive agriculture. This study is aimed at providing the policy and decision-makers of the region with the necessary what-if scenarios for better decision-making. It shall also be useful in other countries of the Brahmaputra basin for transboundary integrated river basin management of the whole region.

Integrated remote sensing and field-based approach to assess the temporal evolution and future projection of meanders: A case study on River Manu in North-Eastern India.

A common phenomenon associated with alluvial rivers is their meander evolution, eventually forming cutoffs. Point bar deposits and ox-bow lakes are the products of lateral bend migration and meander cutoff. The present study focuses on identifying the meanders of River Manu and their cutoffs. Moreover, this study compares the temporal evolution and predicts the progress of selected meanders of River Manu. In the present research, the Survey of India topographical map, satellite imagery, and geographic information system (GIS) technique were used to examine the evolution of the Manu River meander. Subsequently, a field visit was done to the selected cutoffs and meanders of River Manu to ascertain the present status and collect data. It has been observed that many cutoffs have undergone temporal changes, and their sizes have decreased. Some have become dried or converted to agricultural fields. The width of River Manu has decreased in all the selected bends from 1932 to 2017. The sinuosity index has changed from 2.04 (1932) to 1.90 (2017), and the length of the river has decreased by 7 km in 85 years (1932-2017). The decrease in length is evident from lowering the number of meanders. Uniformity coefficient and coefficient of curvature of the bank soil samples were calculated, indicating that the soil is poorly graded and falls under the cohesionless category. Based on cross-section analysis, sediment discharge, grain-size analysis of the bank material, channel planform change, and radius of curvature, it can be stated that almost all the selected bends have the probability of future cutoff. The highest probabilities were observed in bend 3 (Jalai) and bend 4 (Chhontail). This work is aimed to provide planners with decisions regarding the construction of roads and bridges in areas that show the huge dynamicity of river meandering.

Analyzing the Risk to COVID-19 Infection using Remote Sensing and GIS.

Globally, the COVID-19 pandemic has become a threat to humans and to the socioeconomic systems they have developed since the industrial revolution. Hence, governments and stakeholders call for strategies to help restore normalcy while dealing with this pandemic effectively. Since till now, the disease is yet to have a cure; therefore, only risk-based decision making can help governments achieve a sustainable solution in the long term. To help the decisionmakers explore viable actions, we propose a risk-based assessment framework for analyzing COVID-19 risk to areas, using integrated hazard and vulnerability components associated with this pandemic for effective risk mitigation. The study is carried on a region administrated by Jaipur municipal corporation (JMC), India. Based on the current understanding of this disease, we hypothesized different COVID-19 risk indices (C19Ri) of the wards of JMC such as proximity to hotspots, total population, population density, availability of clean water, and associated land use/land cover, are related with COVID-19 contagion and calculated them in a GIS-based multicriteria risk reduction method. The results showed disparateness in COVID-19 risk areas with a higher risk in north-eastern and south-eastern zone wards within the boundary of JMC. We proposed prioritizing wards under higher risk zones for intelligent decision making regarding COVID-19 risk reduction through appropriate management of resources-related policy consequences. This study aims to serve as a baseline study to be replicated in other parts of the country or world to eradicate the threat of COVID-19 effectively.

Coronavirus pandemic versus temperature in the context of Indian subcontinent: a preliminary statistical analysis.

The novel coronavirus (COVID-19) has unleashed havoc across different countries and was declared a pandemic by the World Health Organization. Since certain evidences indicate a direct relationship of various viruses with the weather (temperature in particular), the same is being speculated about COVID-19; however, it is still under investigation as the pandemic is advancing the world over. In this study, we tried to analyze the spread of COVID-19 in the Indian subcontinent with respect to the local temperature regimes from March 9, 2020, to May 27, 2020. To establish the relation between COVID-19 and temperature in India, three different ecogeographical regions having significant temperature differences were taken into consideration for the analysis. We observed that except Maharashtra, Rajasthan and Kashmir showed a significantly positive correlation between the number of COVID-19 cases and the temperature during the period of study. The evidences based on the results presented in this research lead us to believe that the increasing temperature is beneficial to the COVID-19 spread, and the cases are going to rise further with the increasing temperature over India. We, therefore, conclude that the existing data, though limited, suggest that the spread of COVID-19 in India is not explained by the variation of temperature alone and is most likely driven by a host of other factors related to epidemiology, socioeconomics and other climatic factors. Based on the results, it is suggested that temperature should not be considered as a yardstick for planning intervention strategies for controlling the COVID-19 pandemic.

Satellite-observed glacier recession in the Kashmir Himalaya, India, from 1980 to 2018.

The study, first of its kind in the Kashmir Valley, uses a time series of satellite data (1980-2018) to determine the glacier health, which is critical for sustaining the perenniality of the rivers originating from the area. The role of topography, morphology and climate on the observed glacier recession was investigated. In total, 147 glaciers were mapped from 1980 image; ~ 72% of the glaciers have area ≤ 3 km2 and a majority of them (123) are having size < 1 km2. The glaciers have reduced from 101.73 ± 16.79 km2 in 1980 to 72.41 ± 4.7 km2 in 2018 showing a recession of 29.32 ± 12.09 km2 during the period (28.82%). The observed glacier loss is higher (0.77 ± 0.31 km2 a-1) compared with the other Himalayan regions. The results indicated that there is strong influence of altitude, aspect, slope and climate on glacier recession. The glaciers with area ≤ 1 km2 have receded significantly more (41.20 ± 6.20%) than the larger glaciers > 3 km2 in area (15.97 ± 5.13%). The glaciers situated between 4200 and 4400 m altitudes have receded more (~ 55 ± 5.01%) than those situated at altitudes > 4800 m (~ 19 ± 6.9%). Furthermore, the glaciers with steep slope (> 25) have witnessed lower recession (0.25 ± 0.15 km2 a-1) compared to the glaciers with gentle slope (0.51 ± 0.22 km2 a-1). The south-facing glaciers showed higher recession (~ 38%) compared with the north-facing glaciers (~ 27%). The findings suggest that the increase in temperature and decline in winter solid precipitation have resulted in the glacier recession with the consequent depletion of the streamflows, which, if continued in the future, would adversely affect the economy in the region.

Morphometry and land cover based multi-criteria analysis for assessing the soil erosion susceptibility of the western Himalayan watershed.

Complex mountainous environments such as Himalayas are highly susceptibility to natural hazards particular those that are triggered by the action of water such as floods, soil erosion, mass movements and siltation of the hydro-electric power dams. Among all the natural hazards, soil erosion is the most implicit and the devastating hazard affecting the life and property of the millions of people living in these regions. Hence to review and devise strategies to reduce the adverse impacts of soil erosion is of utmost importance to the planners of watershed management programs in these regions. This paper demonstrates the use of satellite based remote sensing data coupled with the observational field data in a multi-criteria analytical (MCA) framework to estimate the soil erosion susceptibility of the sub-watersheds of the Rembiara basin falling in the western Himalaya, using geographical information system (GIS). In this paper, watershed morphometry and land cover are used as an inputs to the MCA framework to prioritize the sub-watersheds of this basin on the basis of their different susceptibilities to soil erosion. Methodology included the derivation of a set of drainage and land cover parameters that act as the indicators of erosion susceptibility. Further the output from the MCA resulted in the categorization of the sub-watersheds into low, medium, high and very high erosion susceptibility classes. A detailed prioritization map for the susceptible sub-watersheds based on the combined role of land cover and morphometry is finally presented. Besides, maps identifying the susceptible sub-watersheds based on morphometry and land cover only are also presented. The results of this study are part of the watershed management program in the study area and are directed to instigate appropriate measures to alleviate the soil erosion in the study area.