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.

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.

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.