Showing papers by "Bibhash Nath published in 2022"

Growth, Nutrient Accumulation, and Drought Tolerance in Crop Plants with Silicon Application: A Review

Abstract: Plants take up silicon (Si) from the soil which impacts their growth and nutrient accumulation. It increases plant resistance to abiotic and biotic stresses such as drought, salinity, and heavy metal, diseases, and pest infestation. However, until recently, research of Si application on the crop is limited. This article reviews the recent progress of research on Si application on crop growth and yield, nutrient availability in soil and accumulation, and drought tolerance of crop plants. The review’s findings show that Si improves crop development and output under stressful environmental conditions. Silicon increases the availability and accumulation of both macronutrients (nitrogen, potassium, calcium, and sulphur) and micronutrients (iron and manganese). It improves drought resistance by increasing plant water usage efficiency and reducing water loss during transportation. Silicon application is a crucial aspect of crop productivity because of all of these favorable attributes. The gaps in current understandings are identified. Based on the outcome of the present research, future scopes of research on this field are proposed.

Spatial variation of organic carbon sequestration in large lakes and implications for carbon stock quantification

Abstract: Lakes are recognized as critical zones for carbon transformation and storage, and lacustrine sediments sequestrate considerable amounts of organic carbon (OC). Understanding sedimentation processes and OC burial patterns is crucial to clarifying lakes’ role in global carbon cycling. However, OC sedimentation may be quite spatially heterogeneous within an aquatic system, owing to the differences in OC production and sources, hydrodynamic conditions and underwater topography. The uncertainties in estimating OC sequestration in the world’s large lakes remain poorly constrained. This study takes the test case of two large lakes (50 and 249 km2) with different water depth and trophic status, using a multi-core paleolimnological technique, to identify the spatial variation in OC accumulation and its main influencing factors over the past century. Results of multi-core comparisons revealed similar temporal trends in major organic and nutrient parameters, suggesting coherent processes of whole-lake sedimentary environment changes for each lake. The OC preserved in sediments was primarily of autochthonous origin. However, OC standing stocks varied ∼3-fold spatially, and average OC accumulation rates ranged between 9.5–27.4 g m−2 yr−1 (post–1963 in oligo-mesotrophic deep-lake Lugu) and between 17.4–43.5 g m−2 yr−1 (post–1980 in eutrophic shallow-lake Erhai), respectively. These variations were primarily attributable to the spatial differences in aquatic primary production and terrestrial detritus supply relating to anthropogenic land-use change and phosphorus loading, rather than intra-lake sediment focusing-related transport and redistribution. The single central-core approach from Lugu Lake would overestimate whole-lake OC stock by 32% or underestimate the value by 48%, indicating spatial variability is an important source of uncertainty for OC stock quantification in similar large and/or morphometrically complex waterbodies. Therefore, spatial heterogeneity of OC accumulation in inland waters requires considerable research with well-placed multi-cores to provide a deeper understanding of carbon sequestration patterns and mechanisms.

Predicting the Distribution of Arsenic in Groundwater by a Geospatial Machine Learning Technique in the Two Most Affected Districts of Assam, India: The Public Health Implications

Abstract: Arsenic (As) is a well-known carcinogen and chemical contaminant in groundwater. The spatial heterogeneity in As distribution in groundwater makes it difficult to predict the location of safe areas for tube well installations, consumption, and agriculture. Geospatial machine learning techniques have been used to predict the location of safe and unsafe areas of groundwater As. We used a similar machine learning technique and developed a habitation-level (spatial resolution 250 m) predictive model to determine the risk and extent of As >10 μg/L in groundwater in the two most affected districts of Assam, India, with an aim to advise policymakers on targeted interventions. A random forest model was employed in Python environments to predict the probabilities of As at concentrations >10 μg/L using intrinsic and extrinsic predictor variables, which were selected for their inherent relationship with As occurrence in groundwater. The relationships between predictor variables and proportions of As occurrences >10 μg/L follow the well-documented processes leading to As release in groundwater. We identified potential As hotspots based on a probability of ≥0.7 for As >10 μg/L, including regions not previously surveyed and extending beyond previously known As hotspots. Of the total land area (6,500 km2), 25% was identified as a high-risk zone, with an estimated 155,000 people potentially consuming As through drinking water or cooking food. The ternary hazard probability map (showing high, moderate, and low risk for As >10 μg/L) could inform policymakers on establishing newer drinking water treatment plants and providing safe drinking water connections to rural households.

Predicting the Distribution of Arsenic in Groundwater by a Geospatial Machine Learning Technique in the Two Most Affected Districts of Assam, India: The Public Health Implications

Abstract: Arsenic (As) is a well‐known carcinogen and chemical contaminant in groundwater. The spatial heterogeneity in As distribution in groundwater makes it difficult to predict the location of safe areas for tube well installations, consumption, and agriculture. Geospatial machine learning techniques have been used to predict the location of safe and unsafe areas of groundwater As. We used a similar machine learning technique and developed a habitation‐level (spatial resolution 250 m) predictive model to determine the risk and extent of As >10 μg/L in groundwater in the two most affected districts of Assam, India, with an aim to advise policymakers on targeted interventions. A random forest model was employed in Python environments to predict the probabilities of As at concentrations >10 μg/L using intrinsic and extrinsic predictor variables, which were selected for their inherent relationship with As occurrence in groundwater. The relationships between predictor variables and proportions of As occurrences >10 μg/L follow the well‐documented processes leading to As release in groundwater. We identified potential As hotspots based on a probability of ≥0.7 for As >10 μg/L, including regions not previously surveyed and extending beyond previously known As hotspots. Of the total land area (6,500 km2), 25% was identified as a high‐risk zone, with an estimated 155,000 people potentially consuming As through drinking water or cooking food. The ternary hazard probability map (showing high, moderate, and low risk for As >10 μg/L) could inform policymakers on establishing newer drinking water treatment plants and providing safe drinking water connections to rural households.