Supporting conservationists’ mental health through better working conditions

Biodiversity conservation work can be challenging but rewarding, and both aspects have potential consequences for conservationists’ mental health. Yet, little is known about patterns of mental health among conservationists and its associated workplace protective and risk factors. A better understanding might help improve working conditions, supporting conservationists’ job satisfaction, productivity, and engagement, while reducing costs from staff turnover, absenteeism, and presenteeism. We surveyed 2311 conservation professionals working in 122 countries through an internet survey shared via mailing lists, social media, and other channels. We asked them about experiences of psychological distress, working conditions, and personal characteristics. Over half were from and worked in Europe and North America, and most had a university-level education, were in desk-based academic and practitioner roles, and responded in English. Heavy workload, job demands, and organizational instability were linked to higher distress, but job stability and satisfaction with one's contributions to conservation were associated with lower distress. Respondents with low dispositional and conservation-specific optimism, poor physical health, and limited social support, women, and early-career professionals were most at risk of distress in our sample. Our results flag important risk factors that employers could consider, although further research is needed among groups underrepresented in our sample. Drawing on evidence-based occupational health interventions, we suggest measures that could promote better working conditions and thus may improve conservationists’ mental health and abilities to protect nature.     

A quantitative investigation of methane gas and solid waste management in mountainous Srinagar city-A case study

Journal of Material Cycles and Waste Management - Methane has tremendous potential for energy production, and enormous amounts of energy can be produced using suitable technology. To address this...     

Exploring potential of C band synthetic aperture radar imagery to investigate rice crop growth mechanism and productivity

Environmental Science and Pollution Research - Development of satellite technology over decades established unique approach to analyze rice crop phenological parameters and supervise growth and...     

Current trends in diagnosis and treatment strategies of COVID-19 infection

Environmental Science and Pollution Research - Coronaviruses are terrifically precise and adapted towards specialized respiratory epithelial cells, observed in organ culture and human volunteers...     

Dynamics of oxytetracycline and resistance genes in soil under long-term intensive compost fertilization in Northern China

In the present study, we explored the dynamics of antibiotics (ciprofloxacin, norfloxacin, enrofloxacin, and oxytetracycline), tetracycline resistance genes (TRGs), and bacterial communities over 2013–2015 in soils fertilized conventionally or with two levels (82.5 and 165 t/ha) of compost for 12 years. In the soil receiving 165 t/ha of compost, only oxytetracycline was 46% higher than that in the conventionally fertilized soil. Transient enrichment of both tetM (20% to 9-fold) and tetK (25% to 67-fold) was observed in multiple instances immediately after the application of compost. The majority of genera which positively correlated with tetM or tetK were affiliated to Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes. The structural equation model analysis indicated that fertilization regimes directly affected the bacterial composition and antibiotics and had an indirect effect on the abundance of tetK and tetM via these antibiotics. In summary, this study shed light into the complex interactions between fertilization, antibiotics, and antibiotic resistance pollution in greenhouse soil.

     

A novel bagging ensemble approach for predicting summertime ground-level ozone concentration

Ozone pollution appears as a major air quality issue, e.g. for the protection of human health and vegetation. Formation of ground level ozone is a complex photochemical phenomenon and involves numerous intricate factors most of which are interrelated with each other. Machine learning techniques can be adopted to predict the ground level ozone. The main objective of the present study is to develop the state-of-the-art ensemble bagging approach to model the summer time ground level ozone in an industrial area comprising a hazardous waste management facility. In this study, the feasibility of using ensemble model with seven meteorological parameters as input variables to predict the surface level O3 concentration. Multilayer perceptron, RTree, REPTree, and Random forest were employed as the base learners. The error measures used for checking the performance of each model includes IoAd, R2, and PEP. The model results were validated against an independent test data set. Bagged random forest predicted the ground level ozone better with higher Nash-Sutcliffe coefficient 0.93. This study scaffolded the current research gap in big data analysis identified with air pollutant prediction.

Implications: The main focus of this paper is to model the summer time ground level O₃ concentration in an Industrial area comprising of hazardous waste management facility. Comparison study was made between the base classifiers and the ensemble classifiers. Most of the conventional models can well predict the average concentrations. In this case the peak concentrations are of importance as it has serious effect on human health and environment. The models developed should also be homoscedastic.     

Common property resources in drylands of India

Common property resources are areas of land or water being used by a community or a group of communities. These have special significance to peoples and communities who depend on them for their livelihood. The commons in all arid districts of India include village pastures, community forests, wasteland, common threshing grounds, waste dumps, watershed drainage, village baoris¹, talabs², nadis³ and ponds, and tanks, rivers, rivulets, wetlands, riverbeds, community conserved areas, protected areas, Dhaam⁴ or Dhooni⁵, culturable wastelands, barren & un-culturable land, etc. The area under commons often ranged from 9 to 28% of total village area. Appropriation of the commons by the state for building essential infrastructure such as schools, clinics, veterinary hospitals, housing for government functionaries, SEZ and industrial corridors, etc. is a cause of serious concern. Presently the ownership rights over CPRs are not clear and there are many who claim ownership, some at State level but also like local bodies. The 12^th plan of the Planning Commission of India recognized and highlighted the need for favourable land tenure arrangements, institutional design and programme architecture in order to achieve effective governance and management of the commons. The revitalization of CPRs is crucial for protecting livelihoods, as well as for biodiversity conservation and for the improvement in arid microclimatic conditions. Dialogue continues on the status of common property resources, the available legal framework and some policy related issues for its conservation through strengthening of local institutions and capacity building for stakeholders.     

Distribution and ecotoxicological concerns of persistent organic pollutants in sediment from creek ecosystem

In order to study the distribution and ecotoxicological concerns of persistent organic pollutants, grab sediment samples were collected from different locations across Thane creek, India. Analyses of samples were carried out using gas chromatography (GC)–electron capture detector and GC–mass spectrometry techniques. In organochlorine pesticides (OCPs), DDT (1,1,1,-trichloro-2,2-bis(p-chlorophenyl) ethane), DDE (1,1-dichloro-2,2-bis(p-chlorophenyl) ethylene), DDD (1-chloro-4-(2,2-dichloro-1-(4-chlorophenyl)ethyl) benzene) and α, β, and γ conformer of hexachlorocyclohexane (HCH), and 9 polychlorinated biphenyls (PCBs) congeners were analyzed in surface sediment samples. Concentrations of these pollutants in grab sediment samples may indicate their current use and impact on marine ecosystem. Average concentrations of total DDT (including DDD and DDE), HCH, and Σ₉PCBs were found to be 4.9, 12.5, and 2.9 µg kg^?1(dry weight) respectively. High concentrations of OCPs and PCBs were found at discharge locations in creek compared to other locations. Location-wise distribution of OCPs and PCBs indicates their high concentrations at the waste water receiving point. Data were compared for ecotoxicological impacts based on the levels specified in the sediment quality standards of the US Environmental Protection Agency and the Canadian Council of Ministers of the Environment. γ-HCH was found to have maximum potential to induce ecotoxicological impacts.     

Mercury in the sediments of Vembanad Lake,western coast of India

Mercury, a global pollutant, is popping up in places where it was never expected before and it burdens in sediments and other non-biological materials. It is estimated to have increased up to five times the pre-human level due to anthropogenic activities. Vembanad backwaters, one of the largest Ramsar site in India, which have extraordinary importance for its hydrological function, are now considered as one of the mercury hot spots in India. In this study, surface sediment samples of Vembanad Lake and nearshore areas have been seasonally analysed for total mercury and methyl mercury concentrations while the core sediment samples were analysed for total mercury. The results showed that the northern part of the lake was more contaminated with mercury than the southern part. The mercury concentration was relatively high in the subsurface sediment samples, indicating the possibility of historic industrial mercury deposition. A decreasing trend in the mercury level towards the surface in the core sediment was also observed. The geochemical parameters were also analysed to understand the sediment mercury chemistry. Anoxic conditions, pH and organic carbon, sulphur and Fe determined the presence of various species of mercury in the sediments of Vembanad Lake. The prevailing physical and geochemical conditions in Vembanad Lake have indicated the chances of chemical transformation of mercury and the potential hazard if the deposited mercury fractions are remobilised.     

Breakthrough Curves Characterization and Identification of an Unknown Pollution Source in Groundwater System Using an Artificial Neural Network (ANN)

Contamination of groundwater constrains its uses and poses a serious threat to the environment. Once groundwater is contaminated, the cleanup may be difficult and expensive. Identification of unknown pollution sources is the first step toward adopting any remediation strategy. The proposed methodology exploits the capability of a universal function approximation by a feed-forward multilayer artificial neural network (ANN) to identify the sources in terms of its location, magnitudes, and duration of activity. The back-propagation algorithm is utilized for training the ANN to identify the source characteristics based on simulated concentration data at specified observation locations in the aquifer. Uniform random generation and the Latin hypercube sampling method of random generation are used to generate temporal varying source fluxes. These source fluxes are used in groundwater flow and the transport simulation model to generate necessary data for the ANN model-building processes. Breakthrough curves obtained for the specified pollution scenario are characterized by different methods. The characterized breakthrough curves parameters serve as inputs to ANN model. Unknown pollution source characteristics are outputs for ANN model. Experimentation is also performed with different number of training and testing patterns. In addition, the effects of measurement errors in concentration measurements values are used to show the robustness of ANN based methodology for source identification in case of erroneous data.