Next-generation sequencing in the biodiversity conservation of endangered medicinal plants

Environmental Science and Pollution Research - Medicinal plants have been used as traditional herbal medicines in the treatment of various types of diseases. However, the increased demand for these...     

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...     

Effects of arsenic on reactive oxygen species and antioxidant defense system in tomato plants

In this study, the generation of reactive oxygen species, the induction of oxidative stress, and the response of the antioxidative system in hydroponically grown tomato plants as the cause of arsenic-induced phytotoxicity are investigated. Reduction in plant growth was measured in terms of dry weight and length of roots and shoots, the latter accumulating more arsenic than the roots. The treatment resulted in increased formation of superoxide anion (O₂^.?), H₂O₂, and thiobarbituric acid reactive substances, which indicate augmented lipid peroxidation. Superoxide dismutase, catalase (CAT), and ascorbate peroxidase activities were increased in arsenic-treated tomato plants while CAT activity was insignificantly increased.     

Catchment-Wide Wetland Assessment and Prioritization Using the Multi-Criteria Decision-Making Method TOPSIS

It is widely accepted that wetland ecosystems are under threat worldwide. Many communities are now trying to establish wetland rehabilitation programs, but are confounded by a lack of objective information on wetland condition or significance. In this study, a multi-criteria decision-making method, TOPSIS (the Technique for Order Preference by Similarity to Ideal Solution), was adapted to assist in the role of assessing wetland condition and rehabilitation priority in the Clarence River Catchment (New South Wales, Australia). Using 13 GIS data layers that described wetland character, wetland protection, and wetland threats, the wetlands were ranked in terms of condition. Through manipulation of the original model, the wetlands were prioritized for rehabilitation. The method offered a screening tool for the managers in choosing potential candidate wetlands for rehabilitation in a region.     

Heat storage material: a hope in solar thermal

Environmental Science and Pollution Research - Solar energy is a vast renewable energy source, but uncertainty in the demand and supply of energy due to various geographical regions raises a...     

The impact of sequestration on the bioaccessibility of arsenic in long-term contaminated soils

Arsenic (As) contamination of soil poses a potential threat to human health, particularly for small children, through the incidental ingestion of soil from hand-to-mouth activity. In this study, we examined the relationship between As bioaccessibility using the simplified bioaccessibility extraction test (SBET) and the soil fractions that contribute to bioaccessible As in 12 long-term contaminated soils. Sequential fractionation of soils prior to As bioaccessibility assessment found that As was primarily associated with the specifically sorbed (3-26%), amorphous and poorly crystalline (12-82%), and the well crystalline (3-25%) oxyhydroxide Fe/Al phases with proportions varying depending on the mode of As input. Arsenic bioaccessibility in these soils ranged from less than 1% in the gossan soil to 48% in railway corridor soils. Soil fractions contributing to As bioaccessibility were found to be from the non-specifically (<1-11%), the specifically (<1-29%) sorbed and the amorphous and poorly-crystalline (30-93%) oxyhydroxide Fe/Al fractions. Significant correlations (p<0.05) were found between the As bioaccessible fraction and the amorphous and poorly-crystalline oxyhydroxide Fe/Al fractions indicating that this fraction is a key factor influencing As bioaccessibility in many anthropogenically contaminated soils.     

Simulating impacts,potential adaptation and vulnerability of maize to climate change in India

Climate change associated global warming, rise in carbon dioxide concentration and uncertainties in precipitation has profound implications on Indian agriculture. Maize (Zea mays L.), the third most important cereal crop in India, has a major role to play in country’s food security. Thus, it is important to analyze the consequence of climate change on maize productivity in major maize producing regions in India and elucidate potential adaptive strategy to minimize the adverse effects. Calibrated and validated InfoCrop-MAIZE model was used for analyzing the impacts of increase in temperature, carbon dioxide (CO₂) and change in rainfall apart from HadCM3 A2a scenario for 2020, 2050 and 2080. The main insights from the analysis are threefold. First, maize yields in monsoon are projected to be adversely affected due to rise in atmospheric temperature; but increased rainfall can partly offset those loses. During winter, maize grain yield is projected to reduced with increase in temperature in two of the regions (Mid Indo-Gangetic Plains or MIGP, and Southern Plateau or SP), but in the Upper Indo-Gangetic Plain (UIGP), where relatively low temperatures prevail during winter, yield increased up to a 2.7°C rise in temperature. Variation in rainfall may not have a major impact on winter yields, as the crop is already well irrigated. Secondly, the spatio-temporal variations in projected changes in temperature and rainfall are likely to lead to differential impacts in the different regions. In particular, monsoon yield is reduced most in SP (up to 35%), winter yield is reduced most in MIGP (up to 55%), while UIGP yields are relatively unaffected. Third, developing new cultivars with growth pattern in changed climate scenarios similar to that of current varieties in present conditions could be an advantageous adaptation strategy for minimizing the vulnerability of maize production in India.     

Performance of a diesel engine with water emulsified diesel prepared with optimized process parameters

An attempt has been made to produce stable water–diesel emulsion with optimal formulation and process parameters and to evaluate the performance and emission characteristics of diesel engine using this stable water–diesel emulsion. A total of 54 samples were prepared with varying water/diesel ratio, surfactant amount and stirring speed and water separation was recorded after 24 and 48 hr of emulsification. The recorded data were used in artificial neural network (ANN)-particle swarm optimization (PSO) technique to find the optimal parameters to produce water–diesel emulsion for engine testing. The predicted optimal parameters were found as 20% water to diesel ratio, 0.9% surfactant and 2200 rpm of stirrer for a water separation of 14.33% in one day with a variation of 6.54% against the actual value of water separation. Water–diesel emulsion fuel exhibited similar fuel properties as base fuel. The peak cylinder gas pressure, peak pressure rise rate and peak heat release rate for water–diesel were found higher as compared to diesel at medium to full engine loads. The improved air-fuel mixing in water–diesel emulsion enhanced brake thermal efficiency (BTE) of engine. The absorption of heat by water droplets present in water–diesel emulsion led to reduced exhaust gas temperature (EGT). With water–diesel emulsion fuel, the mean carbon monoxide (CO), unburned hydrocarbon and oxides of nitrogen (NO_x) emissions reduced by 8.80, 39.60, and 26.11%, respectively as compared to diesel.     

Evaluation of ecological restoration success in mining‐degraded lands

This review article analyzes the importance of assessing the success of ecological restoration by using four indicators: assemblage of the plant and animal communities; enzyme activity; litter accumulation and decomposition; and the improvement of soil quality. These indicators can be used alone or in combinations. Even though the Society for Ecological Restoration International provided a primer containing nine attributes to use as standards for measuring ecological restoration, only three of these attributes could be easily applied due to their low costs and low time requirements. These three attributes include: diversity, vegetation structure, and ecological processes. This review article emphasizes that the criteria for the selection of the indicator species should be based upon: habitat types, abundance of species, ease of measuring, quantifying and interpreting the results, gradual enhancement with time and cost‐effectiveness, sensitivity, variability of response, size, residential status, and requirements of the area. Principal component analysis was applied to calculate the reclaimed mine soil quality index (RMSQI) and the forest soil quality index (FSQI) and the RMSQI value was compared with FSQI (optimum index value of reference ecosystem) to evaluate the restoration success. Available phosphorus, exchangeable magnesium, organic carbon, clay content, field moisture, available nitrogen, electrical conductivity, and pH are identified as the most influential parameters that regulate the health of reclaimed mine soil. Exchangeable calcium, magnesium, cation exchange capacity, sand, silt, clay content, field moisture, available phosphorus, and pH are the controlling properties for forest soil. The observed values of the above‐stated soil indicator properties were converted into a unitless score (0–1.00) and integrated into index calculations (RMSQI and FSQI). The contribution of each soil indicator properties on the calculated index was analyzed, which provides insight into the reason for the measured index. A higher RMSQI indicates better ecological restoration success. The calculated RMSQI was found to be 0.473 in the reclaimed dump, which is 6% lower than the reference ecosystem.     

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.