Assessment of the capability of SWAT model to predict surface runoff in open cast coal mining areas.

The hydrological response of watersheds affected by large-scale coal mining activities is complex and difficult to simulate. The present study aims to bridge this gap by simulating the effects of land-use and topographical changes due to coal mining on surface runoff in the Jamunia basin of Jharkhand, India. The derivatives of digital elevation model (DEM) have been used to simulate the changes in topography of the study area and the runoff has been calculated using Soil and Water Assessment Tool (SWAT) hydrological model. The study results revealed significant increase in surface runoff (mm) during the simulation period. The findings of this study established that unplanned mining activities can reduce the water holding capacity of downstream reservoirs and increase the runoff. The outcome of the study will be helpful for mine planners to design sustainable mining operations which will have less adverse impact on the hydrological regime of the watershed.

Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis.

Forests play a vital role in maintaining the global carbon balance. However, globally, forest ecosystems are increasingly threatened by climate change and deforestation in recent years. Monitoring forests, specifically forest biomass is essential for tracking changes in carbon stocks and the global carbon cycle. However, developing countries lack the capacity to actively monitor forest carbon stocks, which ultimately adds uncertainties in estimating country specific contribution to the global carbon emissions. In India, authorities use field-based measurements to estimate biomass, which becomes unfeasible to implement at finer scales due to higher costs. To address this, the present study proposed a framework to monitor above-ground biomass (AGB) at finer scales using open-source satellite data. The framework integrated four machine learning (ML) techniques with field surveys and satellite data to provide continuous spatial estimates of AGB at finer resolution. The application of this framework is exemplified as a case study for a dry deciduous tropical forest in India. The results revealed that for wet season Sentinel-2 satellite data, the Random Forest (adjusted R2 = 0.91) and Artificial Neural Network (adjusted R2 = 0.77) ML models were better-suited for estimating AGB in the study area. For dry season satellite data, all the ML models failed to estimate AGB adequately (adjusted R2 between -0.05 - 0.43). Ensemble analysis of ML predictions not only made the results more reliable, but also quantified spatial uncertainty in the predictions as a metric to identify its robustness.

Hydroclimatic adaptation critical to the resilience of tropical forests.

Forest and savanna ecosystems naturally exist as alternative stable states. The maximum capacity of these ecosystems to absorb perturbations without transitioning to the other alternative stable state is referred to as 'resilience'. Previous studies have determined the resilience of terrestrial ecosystems to hydroclimatic changes predominantly based on space-for-time substitution. This substitution assumes that the contemporary spatial frequency distribution of ecosystems' tree cover structure holds across time. However, this assumption is problematic since ecosystem adaptation over time is ignored. Here we empirically study tropical forests' stability and hydroclimatic adaptation dynamics by examining remotely sensed tree cover change (ΔTC; aboveground ecosystem structural change) and root zone storage capacity (Sr ; buffer capacity towards water-stress) over the last two decades. We find that ecosystems at high (>75%) and low (<10%) tree cover adapt by instigating considerable subsoil investment, and therefore experience limited ΔTC-signifying stability. In contrast, unstable ecosystems at intermediate (30%-60%) tree cover are unable to exploit the same level of adaptation as stable ecosystems, thus showing considerable ΔTC. Ignoring this adaptive mechanism can underestimate the resilience of the forest ecosystems, which we find is largely underestimated in the case of the Congo rainforests. The results from this study emphasise the importance of the ecosystem's temporal dynamics and adaptation in inferring and assessing the risk of forest-savannah transitions under rapid hydroclimatic change.

COVID-19 pandemic in Uttarakhand, India: Environmental recovery or degradation?

The human coronavirus disease-2019 (COVID-19) caused by SARS-CoV-2 is now a global pandemic. Personal hygiene such as hand-washing, the use of personal protective equipment, and social distancing via local and national lockdowns are used to reduce the risk of transmission of SARS-CoV-2. COVID-19 and the associated lockdowns may have significant impacts on environmental quality and ergonomics. However, limited studies exists on the impacts of COVID-19 and the associated lockdowns on environmental quality and ergonomics in low-income settings. Therefore, the present study investigated the impacts of the COVID-19 outbreak on socioeconomics, ergonomics and environment (water quality, air quality and noise) in Uttarakhand, India. Approximately 55% of respondents experienced headaches, and the other common health-related issue was back pain, with 45% of respondents having problems with their backs. Water and air quality significantly improved during the lockdown relative to the pre-lockdown period, but was observed to return to their previous characteristics afterwards. Lockdowns significant increased the concentration of indoor air pollutants while noise pollution levels significantly declined. In summary, lockdowns have adverse impacts on ergonomics, resulting in work-related human health risks. The impacts of lockdowns on environmental quality are mixed: temporary improvements on water and air quality, and noise reduction were observed, but indoor air quality deteriorated. Therefore, during lockdowns there is a need to minimize the adverse environmental and ergonomic impacts of lockdowns while simultaneously enhancing the beneficial impacts.

Estimation of the generation rate of different types of plastic wastes and possible revenue recovery from informal recycling.

Plastic waste generation is an inevitable product of human activities, however its management faces challenges in many cities. Understanding the existing patterns of plastic waste generation and recycling is essential for effective management planning. The present study established a relationship between plastic waste generation rate and the identified socioeconomic groups, higher socioeconomic group (HSEG), middle socioeconomic group (MSEG), and lower socioeconomic group (LSEG) of the study area (Dhanbad, India). For identification of the socioeconomic groups, four different socioeconomic parameters were considered (total family income, education, occupation and type of houses). The information related to the identified parameters were obtained using questionnaire survey conducted in the selected households. One week plastic waste sampling was carried out in the households of all the socioeconomic groups. The plastic waste generated in the study area was 5.7% of the total municipal solid waste. In terms of total plastic waste generation rate, it was found that HSEG had maximum (51 g/c/d) and LSEG had minimum (8 g/c/d) generation rate. The present study area does not have any formal waste recycling system. Thus, the amount of plastic waste recovered and the revenue generated from recycling of plastic waste by the active informal recyclers (waste pickers, itinerant waste buyers and scrap dealers) in the study area have been evaluated. Additionally, three non-linear machine learning models i.e., artificial neural network (ANN), support vector machine (SVM) and random forest (RF) have been developed and compared for the prediction of plastic waste generation rate.

Amyand's Hernia: Rare Presentation of a Common Ailment.

Inguinal hernia with vermiform appendix as content is known as Amyand's hernia. It is a rare entity but we encountered four cases within six months. A 52-year-old female had high grade fever and evidence of inflammatory pathology involving the ileocaecal region. She was initially managed conservatively and subsequently underwent exploratory laparatomy. The appendix was perforated and herniating in the inguinal canal. Appendectomy was done with herniorrhaphy without mesh placement. A 74-year-old male with bilateral inguinal hernia, of which, the right side was more symptomatic, underwent open exploration. Operative findings revealed a lipoma of the sac and a normal appearing appendix as content. Contents were reduced without appendectomy and mesh hernioplasty was performed. A 63-year-old male with an obstructed right sided hernia underwent emergency inguinal exploration which revealed edematous caecum and appendix as content without any inflammation. Contents were reduced without any resection. Herniorrhaphy was performed without mesh placement. A 66-year-old male with an uncomplicated right inguinal hernia underwent elective surgery. The sac revealed an appendix with adhesions at the neck. Contents were reduced after adhesiolysis and hernioplasty was performed with mesh placement. Emphasis is made to the rarity of disease, variation in presentation, and difference in treatment modalities depending upon the state of appendix.

Anorectal gastrointestinal stromal tumor: a case report and literature review.

Gastrointestinal stromal tumors or "GIST" are mesenchymal neoplasms expressing KIT(CD117) tyrosine kinase and showing the presence of activating mutations in KIT or PDGFR α (platelet-derived growth factor alpha). GIST of anal canal is an extremely rare tumor, accounting for only 3% of all anorectal mesenchymal tumors and 0.1-0.4% of all GIST. GIST with large tumor size and high mitotic activity are highly malignant, but the biological behavior of anorectal GIST is less clear. Abdominoperineal resection (APR) or conservative surgery is the best treatment option. Imatinib mesylate, a tyrosine kinase inhibitor, has shown promising results in its management. We present a case of anorectal GIST diagnosed by computed tomography (CT) scan, magnetic resonance imaging (MRI), and colonoscopy with biopsy. The patient underwent abdominoperineal resection (APR) and was confirmed on histopathology to have anal canal GIST with tumor size more than 5 cm in maximum dimension and mitotic figures more than 5/50 high power field (HPF). The CD117-immunoreactive score-was 3+ in spindled cells. Therefore the patient was put on adjuvant imatinib mesylate 400 mg daily.