Impact of precipitation disparity on groundwater fluctuation in a semi-arid region (Vellore district) of southern India using geospatial techniques

Environmental Science and Pollution Research - In the present study, impact of precipitation disparity on groundwater level fluctuation was carried out in Vellore district, Tamil Nadu, India, using...     

Subcritical water oxidation of propham by H2O2 using response surface methodology (RSM)

This study was conducted to investigate the degradation of propham, which is a compound that pollutes water and seriously threatens human health, by subcritical water oxidation and using H₂O₂ as an oxidising agent. The maximum total organic carbon removal rate of propham was obtained as 73.65% at 40 min of treatment time and 60 mM of H₂O₂ concentration and 373 K of temperature. In addition, response surface method based on the Box-Behnken design was applied to design the degradation experiments of propham for determination of the combined effects of process variables, namely temperature, concentration of oxidising agent and treatment time. The proposed quadratic model of propham degradation, which was examined with the analysis of variance, was used for navigating the design space. The R² and adjusted R² values of the model were determined as 0.9921 and 0.9819 respectively. It was shown that propham was effectively degraded, thus could be removed from the water by using an environmentally friendly method.     

Patterns and biases of climate change threats in the IUCN Red List

International Union for Conservation of Nature (IUCN) Red List assessments rely on published data and expert inputs, and biases can be introduced where underlying definitions and concepts are ambiguous. Consideration of climate change threat is no exception, and recently numerous approaches to assessing the threat of climate change to species have been developed. We explored IUCN Red List assessments of amphibians and birds to determine whether species listed as threatened by climate change display distinct patterns in terms of habitat occupied and additional nonclimatic threats faced. We compared IUCN Red List data with a published data set of species’ biological and ecological traits believed to infer high vulnerability to climate change and determined whether distributions of climate change‐threatened species on the IUCN Red List concur with those of climate change‐threatened species identified with the trait‐based approach and whether species possessing these traits are more likely to have climate change listed as a threat on the IUCN Red List. Species in some ecosystems (e.g., grassland, shrubland) and subject to particular threats (e.g., invasive species) were more likely to have climate change as a listed threat. Geographical patterns of climate change‐threatened amphibians and birds on the IUCN Red List were incongruent with patterns of global species richness and patterns identified using trait‐based approaches. Certain traits were linked to increases or decreases in the likelihood of a species being threatened by climate change. Broad temperature tolerance of a species was consistently related to an increased likelihood of climate change threat, indicating counterintuitive relationships in IUCN assessments. To improve the robustness of species assessments of the vulnerability or extinction risk associated with climate change, we suggest IUCN adopt a more cohesive approach whereby specific traits highlighted by our results are considered in Red List assessments. To achieve this and to strengthen the climate change‐vulnerability assessments approach, it is necessary to identify and implement logical avenues for further research into traits that make species vulnerable to climate change (including population‐level threats).     

Association of extinction risk of saproxylic beetles with ecological degradation of forests in Europe

To reduce future loss of biodiversity and to allocate conservation funds effectively, the major drivers behind large‐scale extinction processes must be identified. A promising approach is to link the red‐list status of species and specific traits that connect species of functionally important taxa or guilds to resources they rely on. Such traits can be used to detect the influence of anthropogenic ecosystem changes and conservation efforts on species, which allows for practical recommendations for conservation. We modeled the German Red List categories as an ordinal index of extinction risk of 1025 saproxylic beetles with a proportional‐odds linear mixed‐effects model for ordered categorical responses. In this model, we estimated fixed effects for intrinsic traits characterizing species biology, required resources, and distribution with phylogenetically correlated random intercepts. The model also allowed predictions of extinction risk for species with no red‐list category. Our model revealed a higher extinction risk for lowland and large species as well as for species that rely on wood of large diameter, broad‐leaved trees, or open canopy. These results mirror well the ecological degradation of European forests over the last centuries caused by modern forestry, that is the conversion of natural broad‐leaved forests to dense conifer‐dominated forests and the loss of old growth and dead wood. Therefore, conservation activities aimed at saproxylic beetles in all types of forests in Central and Western Europe should focus on lowlands, and habitat management of forest stands should aim at increasing the amount of dead wood of large diameter, dead wood of broad‐leaved trees, and dead wood in sunny areas.     

Voluntary participation for sustainability transition: experiences from the ‘Commitment to Sustainable Development 2050’

Voluntary and active participation by a wide variety of actors is a prerequisite for successful societal transitions towards sustainability. The ‘Commitment to Sustainable Development 2050’ is a national-level initiative in Finland, aiming to mobilise a large-scale transition involving various societal actors through openly communicated commitments to concrete actions. Each commitment should focus on at least one of the eight nationally defined sustainability objectives connected to the global Sustainable Development Goals. This article assesses the implementation and the development needs of the commitment process based on a range of materials. The results highlight the importance of securing adequate resources for long-term coordination and continuous development of the commitment process, trust creation through long-term and open communication, and flexibility allowing for experimentation aimed at finding new modes of interaction between the public and private sectors.     

Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness

Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time.

Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5?min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student’s t test.

Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations.

Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.