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"George, Justin"
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Assessment of soil erosion rates, carbon stocks, and erosion-induced carbon loss in dominant forest types of the Himalayan region using fallout-137Cs
Forest plays a crucial role in mitigating soil erosion and preserving organic carbon, especially in mountainous regions of Himalayas. However, limited information exists on soil erosion rate, soil organic carbon stock (SOCS), and associated carbon loss in these areas because of the rugged terrain, which poses challenges for reliable estimation using both traditional and modelling approaches. This study used Fallout Radionuclide-
137
Cs to assess soil erosion and carbon loss across various forest types. Results showed that mixed forests had the lowest erosion rates, while degraded forests had the highest, following the order of mixed forest < oak (
Quercus
) <
Rhododendron
< deodar (
Cedrus
) < pine (
Pinus
) < apple (
Malus
) < degraded forests. Forests with dense canopy and understory cover experiences reduced erosion (5.9 ± 3.6 t ha
−1
year
−1
) while degraded forests showed high soil erosion rates (15.5 ± 6.4 t ha
−1
year
−1
) with corresponding carbon displacement of 0.75 ± 0.48 and 1.42 ± 0.71 t ha
−1
year
−1
and carbon emission of 0.23 ± 0.14 and 0.43 ± 0.21 t ha
−1
year
−1
respectively. SOCS (0–15 cm) was inversely correlated with erosion rates, being highest in mixed forests (73.7 ± 32.2 t ha
−1
) and lowest in apple orchard (23.41 ± 4.3 t ha
-1
) and degraded forests (46.3 ± 19.9 t ha
−1
). These findings underscore the need to maintain forest diversity and canopy cover to arrest soil erosion, enhance carbon sequestration, and to improve ecosystem resilience. Conservation and restoration in degraded areas are essential for climate change mitigation and environmental stability in the mountainous landscapes of Himalayas.
Journal Article
Soil erodibility mapping using remote sensing and in situ soil data with random forest model in a mountainous catchment of Indian Himalayas
Land degradation is accelerating in the Himalayan ecosystem, resulting in the loss of soil nutrients due to severe erosion. Soil erosion presents a significant environmental challenge, resulting in both on-site and off-site consequences, such as reduced soil productivity and siltation in reservoirs. Soil erodibility (K factor), an inherent soil property, determines the susceptibility of soils to erosion. Sampling across hilly and mountainous terrain pose challenges due to its complex landscape. Despite these challenges, it is essential to study K factor variations in different land use/land cover types to comprehend the threat of erosion. Digital soil mapping offers an opportunity to overcome this limitation by providing spatial predictions of soil properties. The objective of our study is to map the spatial distribution of soil erodibility using the Random Forest (RF) model, a machine learning method based on sampled in situ soil data and environmental covariates. We collected 556 surface soil samples from the mountainous catchment (Tehri dam catchment) using the stratified random sampling approach. The model performed satisfactorily in both training (
r
2
= 0.91; RMSE = 0.00185) and testing (
r
2
= 0.45; RMSE = 0.00318) phases. Subsequently, we generated a digital map with a resolution of 12.5 m to depict the distribution of the K factor. Our analysis revealed that key environmental variables influencing the prediction of the K factor included geology, mean NDVI, and climatic factors. The average K factor value was estimated at 0.0304 and ranging from 0.0251 to 0.0400 t ha h ha
−1
MJ
−1
mm
−1
. A higher K factor was observed in the barren land (0.0344) primarily located in the higher and trans-Himalayan region of seasonally snow-covered areas. These areas typically feature young soils with weak soil formation and unstable soil aggregates. Subsequently cropland/cultivated soils (0.0307) exhibited higher K factor values due to the breakdown of soil aggregates by ploughing activities and exposing carbon to decomposition. The average K factor value of evergreen (0.0294) and deciduous (0.0295) forests were the lowest compared to other land use/land cover types indicating the role of forests in resisting soil erosion. By assessing and predicting soil erodibility, land planners and farmers can implement erosion control measures to protect soil health, prevent sedimentation in water bodies, and sustain agricultural productivity in the Himalayas.
Journal Article
They called us enemy
\"A stunning graphic memoir recounting actor/author/activist Takei's childhood imprisoned within American concentration camps during World War II. Experience the forces that shaped an icon in this gripping tale of courage, country, loyalty, and love.\"-- Provided by publisher.
Book
Carbon Nanofiber-Bridged Carbon Nitride-Fe2O3 Photocatalyst: Hydrogen Generation and Degradation of Aqueous Organics
Graphitic carbon nitride (g-C3N4)-supported ferric oxide (Fe2O3) nanocomposite modified with carbon nanofibers (CNFs) has been synthesized for the first time for photocatalytic H2-generation and the degradation of aqueous organics. In its first dual role, Fe2O3 served as the photocatalyst and the chemical vapor deposition (CVD) catalyst to grow CNFs over the g-C3N4 substrate. Time of CVD was found to be critical for synthesizing Fe2O3-CNF/g-C3N4 with a good photocatalytic efficiency. The synthesized materials were characterized for various physicochemical and photocatalytic properties. The creation of a Z-scheme heterostructure between g-C3N4 and Fe2O3 resulted in the greater H2-generation rate (2095 µmol/g h) than that (1119 µmol/g h) over the substrate. The photocatalytic degradation data showed ~ 95 and 91% removal of methylene blue and 4-nitrophenol in 180 and 300 min, respectively at 22 °C using the Fe2O3-CNF/g-C3N4 dose of 0.5 mg/mL. The photocatalytic reactions followed the indirect Z-scheme charge transfer path, with the graphitic CNFs acting as the electron-transfer medium between Fe2O3 and g-C3N4. The simple fabrication route and high photocatalytic efficiency of the ternary CNF-bridged Fe2O3-g-C3N4 composite system indicate the utility of the material in integrated environmental and energy applications including microbial fuel cells and microelectrolysis cells.
Journal Article
Improvement of Electrical and Mechanical Properties of PLA/PBAT Composites Using Coconut Shell Biochar for Antistatic Applications
Biochar-based environment-friendly polymer composites are suitable substitutes for conventional non-biodegradable polymer composites. In this work, we developed polylactic acid (PLA)/polybutylene adipate-co-terephthalate (PBAT)/biochar (BC) composites with improved mechanical and electrical properties for antistatic applications. Coconut shell biochar was obtained through the pyrolysis of coconut shell in an inert atmosphere, and characterised using scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD), to investigate the morphology and structural properties. The biochar was converted to powder form, sieved to reduce the particle size (≤30 μm diameters), and melt-mixed with PLA and PBAT to form composites. The composites were extruded to produce 3D printing filaments and, eventually, 3D-printed tensile specimens. The tensile strength and tensile modulus of the 3D-printed PLA/PBAT/BC (79/20/1) composite with 1 wt% of biochar improved by 45% and 18%, respectively, compared to those of PLA/PBAT (80/20). The interfacial interaction between the biochar and polymer matrix was strong, and the biochar particles improved the compatibility of the PLA and PBAT in the composites, improving the tensile strength. Additionally, the electrical resistivity of the composite did reduce with the addition of biochar, and PLA/PBAT/BC (70/20/10) showed the surface resistivity of ~1011 Ω/sq, making it a suitable material for antistatic applications.
Journal Article
Latest Developments in Insect Sex Pheromone Research and Its Application in Agricultural Pest Management
Since the first identification of the silkworm moth sex pheromone in 1959, significant research has been reported on identifying and unravelling the sex pheromone mechanisms of hundreds of insect species. In the past two decades, the number of research studies on new insect pheromones, pheromone biosynthesis, mode of action, peripheral olfactory and neural mechanisms, and their practical applications in Integrated Pest Management has increased dramatically. An interdisciplinary approach that uses the advances and new techniques in analytical chemistry, chemical ecology, neurophysiology, genetics, and evolutionary and molecular biology has helped us to better understand the pheromone perception mechanisms and its practical application in agricultural pest management. In this review, we present the most recent developments in pheromone research and its application in the past two decades.
Journal Article
Soil organic carbon prediction using visible–near infrared reflectance spectroscopy employing artificial neural network modelling
Visible–near infrared (VNIR) spectroscopy is a relatively fast and cost-effective analytical technique for estimating soil organic carbon (SOC). The present study was undertaken for predicting SOC using VNIR reflectance spectroscopy employing artificial neural network (ANN). Surface soil samples (0–15 cm) were collected from 75 georeferenced locations through grid sampling approach in a hilly watershed of Himachal Pradesh, India, and analysed for SOC. The reflectance spectra of soil samples was measured using a spectroradiometer in the wavelength range of 350–2500 nm. Various spectral indices were generated using the sensitive bands in the visible region. The SOC-sensitive spectral indices and reflectance transformations were utilized for predictive modelling of SOC using the ANN model. This model could predict SOC values with R² of 0.92 and MSE value of 0.24, indicating that this technique can be used to predict SOC in a spatial domain when coupled with highresolution hyperspectral satellite/airborne data.
Journal Article