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.

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.

Urban areas have very complex spatial structures. These spatial structures are primarily composed of a complex network of built environments, which evolve rapidly as the cities expand to meet the growing population’s demand and economic development. Therefore, studying the impact of spatial structures on urban heat patterns is extremely important for sustainable urban planning and growth. We investigated the relationship between surface temperature obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, at 90 m spatial resolution) and different urban components based on high-resolution QuickBird satellite imagery classification. We further investigated the relationships between ASTER-derived surface temperature and building footprint and land use information acquired by the New York City (NYC) Department of City Planning. The ASTER image reveals fine-scale urban heat patterns in the NYC metropolitan region. The impervious-medium and dark surfaces, along with bright covers, generate higher surface temperatures. Even with highly reflective urban surfaces, the presence of impervious materials leads to an increased surface temperature. At the same time, trees and shadows cast by buildings effectively reduce urban heat; on the contrary, grassland does not reduce or amplify urban heat. The data aggregated to the census tract reveals high-temperature hotspots in Queens, Brooklyn, and the Bronx region of NYC. These clusters are associated with industrial and manufacturing areas and multi-family walk-up buildings as dominant land use. The census tracts with more trees and higher building height variability showed cooling effects, consistent with shadows cast by high-rise buildings and trees. The results of this study can be valuable for urban heat island modeling on the impact of shadow generated by building heights variability and trees on small-scale surface temperature patterns since recent image reveals similar hotspot locations. This study further helps identify the risk areas to protect public health.