Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand,India

Landslide poses severe threats to the natural landscape of the Lesser Himalayas and the lives and economy of the communities residing in that mountainous topography. This study aims to investigate whether the landscape change has any impact on landslide occurrences in the Kalsi-Chakrata road corridor by detailed investigation through correlation of the landslide susceptibility zones and the landscape change, and finally to demarcate the hotspot villages where influence of landscape on landslide occurrence may be more in future. The rational of this work is to delineate the areas with higher landslide susceptibility using the ensemble model of GIS-based multi-criteria decision making through fuzzy landslide numerical risk factor model along the Kalsi-Chakrata road corridor of Uttarakhand where no previous detailed investigation was carried out applying any contemporary statistical techniques. The approach includes the correlation of the landslide conditioning factors in the study area with the changes in land use and land cover (LULC) over the past decade to understand whether frequent landslides have any link with the physical and hydro-meteorological or, infrastructure, and socioeconomic activities. It was performed through LULC change detection and landslide susceptibility mapping (LSM), and spatial overlay analysis to establish statistical correlation between the said parameters. The LULC change detection was performed using the object-oriented classification of satellite images acquired in 2010 and 2019. The inventory of the past landslides was formed by visual interpretation of high-resolution satellite images supported by an intensive field survey of each landslide area. To assess the landslide susceptibility zones for 2010 and 2019 scenarios, the geo-environmental or conditioning factors such as slope, rainfall, lithology, normalized differential vegetation index (NDVI), proximity to road and land use and land cover (LULC) were considered, and the fuzzy LNRF technique was applied. The results indicated that the LULC in the study area was primarily transformed from forest cover and sparse vegetation to open areas and arable land, which is increased by 6.7% in a decade. The increase in built-up areas and agricultural land by 2.3% indicates increasing human interference that is continuously transforming the natural landscape. The landslide susceptibility map of 2019 shows that about 25% of the total area falls under high and very high susceptibility classes. The result shows that 80% of the high landslide susceptible class is contained by LULC classes of open areas, scrubland, and sparse vegetation, which point out the profound impact of landscape change that aggravate landslide occurrence in that area. The result acclaims that specific LULC classes, such as open areas, barren-rocky lands, are more prone to landslides in this Lesser Himalayan road corridor, and the LULC-LSM correlation can be instrumental for landslide probability assessment concerning the changing landscape. The fuzzy LNRF model applied has 89.6% prediction accuracy at 95% confidence level which is highly satisfactory. The present study of the connection of LULC change with the landslide probability and identification of the most fragile landscape at the village level has been instrumental in delineation of landslide susceptible areas, and such studies may help the decision-makers adopt appropriate mitigation measures in those villages where the landscape changes have mainly resulted in increased landslide occurrences and formulate strategic plans to promote ecologically sustainable development of the mountainous communities in India's Lesser Himalayas.

     

Prospects and challenges of conservation agriculture in Bangladesh for sustainable sugarcane cultivation

Environment, Development and Sustainability - Sugarcane is an industrial crop globally used for producing biofuels and bioproducts and is the only source of white sugar in Bangladesh. However,...     

Dynamics of Tree and Shrub Vegetation in the Eastern Sayan Mountain Tundra

Russian Journal of Ecology - Climate change entails shifts in the ranges of woody plants along both latitudinal and altitudinal gradients in the boreal forest biome. In this study,...     

Organizational resilience and social-economic sustainability: COVID-19 perspective

Environment, Development and Sustainability - COVID-19 has affected the global economy like no other crisis in the history of mankind. It forced worldwide lockdown and economic shutdown to the...     

Subsurface nutrient modelling using finite element model under Boro rice cropping system

Boro rice, an emerging low-risk crop variety of rice, cultivated using residual or stored water after Kharif season. To enhance the quality and production of rice, potassium (K) and phosphorus (P) are the common constituents of agricultural fertilizers. However, excess application of fertilizers causes leaching of nutrients and contaminates the groundwater system. Therefore, assessment and optimization of fertilizer dose are needed for better management of fertilizers. Towards this, the present study determines the path, persistence, and mobility of K and P under the Boro rice cropping system. The experimental site consisted of four plots having Boro rice with four different fertilizer doses of nitrogen (N), P, K viz. 100%, 75%, 50%, and 25% of the recommended dose. Disturbed soil samples were analysed for K and P from pre-sown land to tillering stage at 0–5, 5–10, 10–15, 15–30, 30–45, and 45–60 cm depths. Simultaneously, K and available P were also simulated in the subsurface soil layers through the HYDRUS-1D model. The statistical comparisons were made with RMSER, E, and PBIAS between the modelled values and laboratory-measured values. Although, the results showed that all the treatments considered had agreeable simulations for both K and P, the K simulations were found to be better as compared to P simulations except for 25% where P simulations outperformed K. The simulated concentration at all doses was found most appropriate when measured for the subsurface layers (up to 45 cm), while showed an underestimation in the bottom layers (45–60 cm) of soil.

     

Immunohistochemical,histopathological, and biochemical studies of the NF-ҡB P65 marker in rat ovaries experimentally intoxicated by cadmium and the protective effect of the purslane plant extract

Environmental Science and Pollution Research - The aim of this study is to describe the existence of the inflammatory marker nuclear factor kappa light chain B lymphocyte protein (NF-?B P65)...     

How does eliminating mercury from artisanal and small-scale gold mining lead to achieving sustainable development goals?

Artisanal and small-scale gold mining (ASGM) is the principal anthropogenic activity that globally contributes to overloading our environment with mercury. Although the Minamata Convention, led by the United Nations, is a crucial instrument to eliminate its use progressively, novel approaches to accelerate this difficult transition are welcome. This article proposes a framework for policy-making or improvement, fostering the enforcement of mercury elimination through the lens of the 17 Sustainable Development Goals (SDGs), focusing on the excluded artisanal and small-scale gold miners and their dependents. We move forward with a literature review of the Artisanal and Small Mining topic, taking each SDG as a unit of analysis. Understanding the problem as a puzzle of four sets of pieces, namely: (1) social, (2) environmental, (3) economic, and (4) institutional, the paper offers potential opportunities for the decision-makers and practitioners to accelerate the substitution of this heavy metal and develop sustainable futures for the ASGM communities. We conclude by proposing a pragmatic framework that synthesizes the means, actions, and ends to accelerate a sustainable transition.     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Ingestion and egestion of microplastic by aquatic insects in Egypt wastewater

Microplastics (MPs) are frequently regarded as environmental and biota contaminants. Yet, research on the accumulation of MPs in living entities, particularly aquatic insects that serve as food resources in the aquatic food chain, is limited. This study to investigate the accumulation of MPs in aquatic insects from water and sediment in an Egyptian wastewater basin. Four typical freshwater insect groups were used. The highest MP load per gram wet weight was reported by collector-gatherers (Chironomus sp. and Hydrophilus sp.), followed by collector-filterers (Culex sp.) with the second highest MP load. However, Predators (Aeshna sp.) had the lowest values. Also, the present results showed a reduction in the number of MPs in all insect taxa tested after a 24 h depuration time, with differences in the observed egestion ability. The mean number of MPs per individual significantly reduced after 24 h in both Chironomus sp. and Culex sp. larvae, where 53% and 40% of MPs particles were ejected from them, respectively. However, the ability of MP egestion decreases in Aeshna sp. nymph (25%), and the lowest proportion of ejection was observed in Hydrophilus sp. adults (9%). Polyethylene terephthalate fibers were the most abundant type of MP in both sediment and water, followed by fragments (polyethylene and polypropylene). Yet, only polyester fibers were detected in the various insect species. The average length of fibers in the various insects was somewhat shorter than in the surrounding environment. The current study reveals that MP ingestion by aquatic insects is not always related to levels of pollution in the environment, since other factors such as feeding strategies may play a role in MP ingestion. Based on these observations, further studies should be carried out on studies on toxicological impacts of MPs on freshwater/aquatic biota.     

Impact of green trust and green perceived quality on green purchase intentions: a moderation study

Environment, Development and Sustainability - The current study explores the role of green trust, green perceived risk and green perceived quality in changing green purchase intention....