Landfill site selection in a mining dominated area using a fuzzy AHP and DRASTIC vulnerability based multicriteria decision analysis: a case study of Dhanbad, India.

Landfilling is the least preferred method in the hierarchy of solid waste management options, but it is the most widely practiced option. Thus, identification of environmentally and economically suitable landfill sites should be of prime importance. The main objective of this study is to identify environmentally and economically suitable landfill sites using fuzzy analytical hierarchy process-based weighted linear combination model within a GIS environment. This study also used the DRASTIC-based groundwater vulnerability index and distance of landfills from densely populated areas to protect groundwater and reduce cost of transportation of solid waste which were not considered by the previous studies. Using the previously reported methods, a total of 132 landfill sites were found environmentally suitable in the study area. But, after applying DRASTIC-based groundwater vulnerability index, the number of environmentally suitable sites reduced to 95. When the proximity of the 95 sites to densely populated areas was considered to reduce waste transportation cost, the number of selected sites further reduced to 21 site and they can be considered the most environmentally and economically suitable landfill sites. This study will help the policy makers and the concerned SWM authorities to construct the engineered landfills at environmentally and economically suitable landfill sites in the study area and in other similar areas.

Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis.

Forests play a vital role in maintaining the global carbon balance. However, globally, forest ecosystems are increasingly threatened by climate change and deforestation in recent years. Monitoring forests, specifically forest biomass is essential for tracking changes in carbon stocks and the global carbon cycle. However, developing countries lack the capacity to actively monitor forest carbon stocks, which ultimately adds uncertainties in estimating country specific contribution to the global carbon emissions. In India, authorities use field-based measurements to estimate biomass, which becomes unfeasible to implement at finer scales due to higher costs. To address this, the present study proposed a framework to monitor above-ground biomass (AGB) at finer scales using open-source satellite data. The framework integrated four machine learning (ML) techniques with field surveys and satellite data to provide continuous spatial estimates of AGB at finer resolution. The application of this framework is exemplified as a case study for a dry deciduous tropical forest in India. The results revealed that for wet season Sentinel-2 satellite data, the Random Forest (adjusted R2 = 0.91) and Artificial Neural Network (adjusted R2 = 0.77) ML models were better-suited for estimating AGB in the study area. For dry season satellite data, all the ML models failed to estimate AGB adequately (adjusted R2 between -0.05 - 0.43). Ensemble analysis of ML predictions not only made the results more reliable, but also quantified spatial uncertainty in the predictions as a metric to identify its robustness.

Altered Food Habits? Understanding the Feeding Preference of Free-Ranging Gray Langurs Within an Urban Settlement.

Urbanization affects concurrent human-animal interactions as a result of altered resource availability and land use pattern, which leads to considerable ecological consequences. While some animals have lost their habitat due to urban encroachment, few of them managed to survive within the urban ecosystem by altering their natural behavioral patterns. The feeding repertoire of folivorous colobines, such as gray langur, largely consists of plant parts. However, these free-ranging langurs tend to be attuned to the processed high-calorie food sources to attain maximum benefits within the concrete jungle having insignificant greenery. Therefore, besides understanding their population dynamics, the effective management of these urbanized, free-ranging, non-human primate populations also depends on their altered feeding habits. Here, we have used a field-based experimental setup that allows gray langurs to choose between processed and unprocessed food options, being independent of any inter-specific conflicts over resources due to food scarcity. The multinomial logit model reveals the choice-based decision-making of these free-ranging gray langurs in an urban settlement of West Bengal, India, where they have not only learned to recognize the human-provisioned processed food items as an alternative food source but also shown a keen interest in it. However, such a mismatch between the generalized feeding behavior of folivorous colobines and their specialized gut physiology reminds us of Liem's paradox and demands considerable scientific attention. While urbanization imposes tremendous survival challenges to these animals, it also opens up for various alternative options for surviving in close proximity to humans which is reflected in this study, and could guide us for the establishment of a sustainable urban ecosystem in the future.