Arsenic exposure from food exceeds that from drinking water in endemic area of Bihar, India

Extensive evidence of elevated arsenic (As) in the food-chain, mainly rice, wheat and vegetables exists. Nevertheless, the importance of exposure from food towards total As exposure and associated health risks in areas with natural occurring As in drinking water is still often neglected, and accordingly mitigations are largely focused on drinking water only. In this study, the contribution of food over drinking water to overall As exposure was estimated for As exposed populations in Bihar, India. Increased lifetime cancer risk was predicted using probabilistic methods with input parameters based on detailed dietary assessment and estimation of As in drinking water, cooked rice, wheat flour and potato collected from 91 households covering 19 villages. Median total exposure was 0.83 μg/kgBW/day (5th and 95th percentiles were 0.21 and 11.1 μg/kgBW/day) and contribution of food (median = 49%) to overall exposure was almost equal to that from drinking water (median = 51%). More importantly and contrary to previous studies, food was found to contribute more than drinking water to As exposure, even when drinking water As was above the WHO provisional guide value of 10 μg/L. Median and 95th percentile excess lifetime cancer risks from food intake were 1.89 x 10-4 and 7.32 x 10-4 respectively when drinking water As was below 10 µg/L and 4.00 x 10-4 and 1.83 x 10-3 respectively when drinking water As was above 10 µg/L. Our results emphasise the importance of food related exposure in As-endemic areas, and, perhaps surprisingly, particularly in areas with high As concentrations in drinking water – this being partly ascribed to increases in food As due to cooking in high As water. These findings are timely to stress the importance of removing As from the food chain and not just drinking water in endemic areas

Predicting climate change impacts on the distribution of the threatened Garcinia indica in the Western Ghats, India

In recent years, climate change has become a major threat and has been widely documented in the geographic distribution of many plant species. However, the impacts of climate change on the distribution of ecologically vulnerable medicinal species remain largely unknown. The identification of a suitable habitat for a species under climate change scenario is a significant step towards the mitigation of biodiversity decline. The study, therefore, aims to predict the impact of current, and future climatic scenarios on the distribution of the threatened Garcinia indica across the northern Western Ghats using Maximum Entropy (MaxEnt) modelling. The future projections were made for the year 2050 and 2070 with all Representative Concentration Pathways (RCPs) scenario (2.6, 4.5, 6.0, and 8.5) using 56 species occurrence data, and 19 bioclimatic predictors from the BCC-CSM1.1 model of the Intergovernmental Panel for Climate Change’s (IPCC) 5th assessment. The bioclimatic variables were minimised to a smaller number of variables after a multicollinearity test, and their contributions were assessed using jackknife test. The AUC value of 0.956 ± 0.023 indicates that the model performs with excellent accuracy. The study identified that temperature seasonality (39.5 ± 3.1%), isothermality (19.2 ± 1.6%), and annual precipitation (12.7 ± 1.7%) would be the major influencing variables in the current and future distribution. The model predicted 10.50% (19318.7 sq. km) of the study area as moderately to very highly suitable, while 82.60% (151904 sq. km) of the study area was identified as ‘unsuitable’ or ‘very low suitable’. Our predictions of Climate change impact on habitat suitability suggest that there will be a drastic reduction in the suitability by 5.29% and 5.69% under RCP 8.5 for 2050 and 2070, respectively. Objective and Significance: Primary objective of this study is to identify the potential distribution of medicinally and ecologically important but threatened Garcinia indica species in the northern Western Ghats on the basis of species occurrence data and nineteen bioclimatic predictors. Using MaxEnt modelling, current and future species distribution and suitability has been predicted using the BCC-CSM1.1 and four RCP scenarios of 2.6, 4.5, 6.0, and 8.5. The results also signify the bioclimatic variables contribution to the species distribution in northern Western Ghats.Finally, the results signify that the model might be an efficient tool for biodiversity protection, ecosystem management, and species re-habitation planning under future climate change scenarios. Keywords: Garcinia indica, Maximum entropy modelling, Western Ghats, Medicinal plants, Climate chang

Structure and indentation behavior of nanocomposite Ti–B–N films

Titanium–boron–nitrogen (Ti–B–N) films were deposited by DC reactive magnetron sputtering using single titanium diboride (TiB2) target in argon, argon–nitrogen mixtures and pure nitrogen gas. The effects of nitrogen incorporation on the microstructure and mechanical properties of the deposited films were investigated. The films were characterized by X-ray diffraction, atomic force microscope, scanning electron microscope, and X-ray photoelectron spectroscopy. Under pure argon the films showed the formation of TiB2. The films deposited in Ar–N2 mixture showed a transition from a hexagonal P6/mmm structure of TiB2 to the cubic (Fm3m) one of TiN and then again to hexagonal P6/mmm structure of TiB2 with decreasing nitrogen partial pressure. TiN was the major crystalline phase for the films deposited in pure nitrogen gas. Mechanical properties of the films were evaluated by nano indentation. The hardness varied from 45 GPa for pure TiB2 films to 11.15 GPa for Ti–B–N films. The energy of indentation analysis from the load–depth curves, % elastic recovery (%Er) and plots of H3/E2, where H is hardness and E is elastic modulus, at different indentation depths has been used to study the Ti–B–N film deformation process

Mechanical and deformation behaviour of titanium diboride thin films deposited by magnetron sputtering

Nanoindentation studies have been carried out for TiB2 films deposited on Si, glass and steel by sputtering for studying the influence of the substrates. It was observed that the modulus of the film was influenced by the substrates from 30 nm onwards. Plastic energy analysis has shown that as load increases more energy is absorbed by the substrate. Quantitative indentation depth limits for obtaining film only hardness, using a combination of log–log plot of load vs displacement and load vs (displacement)2 functions, have shown the dependence on the threshold load for crack formation. Comparison of the hardness data with composite hardness models has been performed. Fracture toughness of the coatings was also evaluated using two methods which resulted in comparable result

Urban heat island intensity and its mitigation strategies in the fast-growing urban area

Climate change especially rising temperature in the urban areas has become a major focus of attention worldwide because of the impacts having on human beings, biodiversity, and urban ecosystem. Time series Landsat (TM and ETM+) satellite data products have been employed in this study to quantify the spatiotemporal Land Surface Temperature (LST) and Urban Heat Island (UHI) intensity for the year of 2000, 2005, 2010 and 2015, respectively. Biophysical characteristics of the city have been assessed through Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Bareness Index (NDBaI). The thermal behavior of the city varied distinctly. Seasonal LST and biophysical composition of the city has been analyzed to explore the temperature and greenness sensitivity across the city region. The per capita electricity consumption of the city was positively correlated with the surface LST for both summer and autumn/spring season. A relative brightness temperature approach was employed to examine the nature of UHI across the city. It is evident from the observation that the temperature is very high within the city core as well as certain surrounding areas of the city, especially on the southern side. The temperature is comparatively lower on the western side of the city than the eastern region. Certain peripheral regions, however, show a higher temperature. This can be due to the development taking place in the outer areas of the city and destruction of vegetation in the outlying parts of the city. Studied NDVI indicates that vegetation in the city is not balanced. It is high in the western part which maybe because of the locations of different academic institutions, botanical gardens, seminary hills, agricultural land, etc. Whereas, the eastern part is devoid of vegetation. Also, the areas in the periphery, especially near the airport and Ambajhari Lake, has very low vegetation. The bareness is also high in the peripheral regions. Result also shows that street-based heat intensity mitigation helps for urban planning

Comparison of spatial modelling approaches to simulate urban growth: a case study on Udaipur city, India

Assessment of past and future urban growth processes helps the decision makers to evaluate and formulate the policy documents. In an attempt to make such assessments, this study compares three commonly used urban growth models: Multicriteria Cellular Automata-Markov Chain (MCCA-MC), Multi-Layer Perception Markov Chain (MLP-MC), and the Slope, Land use, Exclusion, Urban Extent, Transportation and Hillshade (SLEUTH). This study has taken into account the land use and land cover data for the years, 1977, 1992, 2000, 2008, 2016 and prepared driving variables for urban growth. The KAPPA index of agreement indicates that the MCCA-MC, MLP-MC and SLEUTH models avoid errors by 94%, 93%, and 92% respectively. Models forecast that about 156.96 km2, 157.43 km2 and 142.43 km2 built-up areas will emerge through the process of urbanization by 2031 in the city of Udaipur. However, this assessment identified that all the models are embodied with their own advantages and disadvantages while serving specific purposes. While the MCCA-MC and MLP-MC provides a good account of the urban spread, the SLEUTH identifies the new isolated growth centres more accurately

Evaluating landscape capacity to provide spatially explicit valued ecosystem services for sustainable coastal resource management

Ecosystem Services (ESs) are the direct and indirect benefits and opportunities that human obtained from the ecosystem. This study evaluated landscape capacity of providing multiple key ESs in a tropical coastal ecosystem (Sundarbans Biodiversity Region (SBR)India). Multiple supervised machine learning algorithms were utilized to classify the regions into several landscape zones. The provisioning capacities of ESs for each landscape type were derived separately from an expert opinion survey and the remote sensing based methods, and the association of the outcomes between these two approaches was evaluated using the Pearson correlation coefficient test. A total of nine ESs were selected to quantify their economic values for several reference years. The benefit transfer and equivalent value coefficient approaches were used to aggregate the economic values for each ES. Research results indicated that the water bodies are the most important landscape units in the SBR region. This ecosystem has the highest relevant capacity to provide the necessary regulatory, supporting, provisioning, and cultural ESs. Water regulation (WR), waste treatment (WT), aesthetic, recreation, and cultural (ARC), and climate regulation (CR) are the main ESs of the SBR. These services are immensely important not only for upgrading the livelihood status of coastal communities but also for the climatic and environmental suitability of the Kolkata urban region. The correlation results between the remote sensing and expert-based capacity estimates have suggested that the proposed remote sensing approach could be an alternative to evaluate the landscape capacity of providing multiple ESs in any given ecosystem. Except for the mangrove region, a very high (>0.7) correlation was observed between the model and expert-derived capacity values. The outcome of this study could be an important reference to the land administrators, planners, decision makers for adopting suitable land resource management plans for sustainable uses of natural resources in coastal region.</p

Ecosystem service value assessment of a natural reserve region for strengthening protection and conservation

Ecosystem Services (ESs) refer to the direct and indirect contributions of ecosystems to human well-being and subsistence. Ecosystem valuation is an approach to assign monetary values to an ecosystem and its key ecosystem goods and services, generally referred to as Ecosystem Service Value (ESV). We have measured spatiotemporal ESV of 17 key ESs of Sundarbans Biosphere Reserve (SBR) in India using temporal remote sensing (RS) data (for years 1973, 1988, 2003, 2013, and 2018). These mangrove ecosystems are crucial for providing valuable supporting, regulatory, provisioning, and cultural ecosystem services. We have adopted supervised machine learning algorithms for classifying the region into different ecosystem units. Among the used machine learning models, Support Vector Machine (SVM) and Random Forest (RF) algorithms performed the most accurate and produced the best classification estimates with maximum kappa and an overall accuracy value. The maximum ESV (derived from both adjusted and non-adjusted units, million US$ year −1 ) is produced by mangrove forest, followed by the coastal estuary, cropland, inland wetland, mixed vegetation, and finally urban land. Out of all the ESs, the waste treatment (WT) service is the dominant ecosystem service of SBR. Additionally, the mangrove ecosystem was found to be the most sensitive to land use and land cover changes. The synergy and trade-offs between the ESs are closely associated with the spatial extent. Therefore, accurate estimates of ES valuation and mapping can be a robust tool for assessing the effects of poor decision making and overexploitation of natural resources on ESs. </p