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Soil is one of the most complex systems on Earth, functioning at the interface between the lithosphere, biosphere, hydrosphere, and atmosphere and generating a multitude of functions. Moreover, soil constitutes the belowground environment from which plants capture water and nutrients. Despite their great importance, soil properties are often not sufficiently considered in other disciplines, especially in spatial studies of plant distributions. Most soil properties are available as point data and, to be used in spatial analyses, need to be generalised over entire regions (i.e. digital soil mapping). Three categories of statistical approaches can be used for such purpose: geostatistical approaches (GSA), predictive-statistical approaches (PSA), and hybrid approaches (HA) that combine the two previous ones. How then to choose the best approach in a given soil study context? Does it depend on the soil properties to be spatialized, the study area’s characteristics, and/or the availability of soil data? The main aims of this study was to review the use of these three approaches to derive maps of soil properties in relation to the soil parameters, the study area characteristics, and the number of soil samples. We evidenced that the approaches that tend to show the best performance for spatializing soil properties were not necessarily the ones most used in practice. Although PSA was the most widely used, it tended to be outperformed by HA in many cases, but the latter was far less used. However, as the study settings were not always properly described and not all situations were represented in the set of papers analysed, more comparative studies would be needed across a wider range of regions, soil properties, and spatial scales to provide robust conclusions on the best spatialization methods in a specific context. [ABSTRACT FROM AUTHOR]

The body of science evaluating the potential impacts of unconventional natural gas development (UNGD) has grown significantly in recent years, although many data gaps remain. Still, a broad empirical understanding of the impacts is beginning to emerge amidst a swell of research. The present categorical assessment provides an overview of the peer-reviewed scientific literature from 2009–2015 as it relates to the potential impacts of UNGD on public health, water quality, and air quality. We have categorized all available original research during this time period in an attempt to understand the weight and direction of the scientific literature. Our results indicate that at least 685 papers have been published in peer-reviewed scientific journals that are relevant to assessing the impacts of UNGD. 84% of public health studies contain findings that indicate public health hazards, elevated risks, or adverse health outcomes; 69% of water quality studies contain findings that indicate potential, positive association, or actual incidence of water contamination; and 87% of air quality studies contain findings that indicate elevated air pollutant emissions and/or atmospheric concentrations. This paper demonstrates that the weight of the findings in the scientific literature indicates hazards and elevated risks to human health as well as possible adverse health outcomes associated with UNGD. There are limitations to this type of assessment and it is only intended to provide a snapshot of the scientific knowledge based on the available literature. However, this work can be used to identify themes that lie in or across studies, to prioritize future research, and to provide an empirical foundation for policy decisions. [ABSTRACT FROM AUTHOR]

Accurate reporting and forecasting of PM2.5 concentration are important for improving public health. In this paper, we propose a partial differential equation (PDE) model, specially, a linear diffusive equation, to describe the spatial-temporal characteristics of PM2.5 in order to make short-term prediction. We analyze the temporal and spatial patterns of a real dataset from China’s National Environmental Monitoring and validate the PDE-based model in terms of predicting the PM2.5 concentration of the next day by the former days’ history data. Our experiment results show that the PDE model is able to characterize and predict the process of PM2.5 transport. For example, for 300 continuous days of 2016, the average prediction accuracy of the PDE model over all city-regions is 93% or 83% based on different accuracy definitions. To our knowledge, this is the first attempt to use PDE-based model to study PM2.5 prediction in both temporal and spatial dimensions. [ABSTRACT FROM AUTHOR]

Although studies have increasingly linked air pollution to specific health outcomes, less well understood is how public perceptions of air quality respond to changing pollutant levels. The growing availability of air pollution measurements and the proliferation of social media provide an opportunity to gauge public discussion of air quality conditions. In this paper, we consider particulate matter (PM) measurements from four Chinese megacities (Beijing, Shanghai, Guangzhou, and Chengdu) together with 112 million posts on Weibo (a popular Chinese microblogging system) from corresponding days in 2011–2013 to identify terms whose frequency was most correlated with PM levels. These correlations are used to construct an Air Discussion Index (ADI) for estimating daily PM based on the content of Weibo posts. In Beijing, the Chinese city with the most PM as measured by U.S. Embassy monitor stations, we found a strong correlation (R = 0.88) between the ADI and measured PM. In other Chinese cities with lower pollution levels, the correlation was weaker. Nonetheless, our results show that social media may be a useful proxy measurement for pollution, particularly when traditional measurement stations are unavailable, censored or misreported. [ABSTRACT FROM AUTHOR]

Objective: Excess emissions are pollutant releases that occur during periods of startups, shutdowns or malfunctions and are considered violations of the U.S. Clean Air Act. They are an important, but understudied and under-regulated, category of pollution releases given their frequency and magnitude. In this paper, we examine the demographic correlates of excess emissions, using data from industrial sources in Texas. Methods: We conduct two complementary sets of analyses: one at the census tract level and one at the facility level. At the census tract level, we use a multinomial logit model to examine the relationships between racial, ethnic, and income characteristics and the incidence of excess emissions. At the facility level, we first estimate a logit model to examine whether these characteristics are associated with facilities that emit excess emissions, and then, conditional on the presence of excess emissions, we use ordinary least square regression to estimate their correlation with the magnitude of releases. Results: Across our analyses, we find that the percentage of Black population and median household income are positively associated with excess emissions; percentage of college graduate, population density, median housing value, and percentage of owner-occupied housing unit are negatively associated with excess emissions. We, however, have not found a clear and significant relationship between the percentage of Hispanic population and excess emissions. [ABSTRACT FROM AUTHOR]

Plant cell walls development is an integrated process during which several components are deposited successively. In the cell walls in grass, the accessibility of structural polysaccharides is limited by the cell walls structure and composition mainly as a result of phenolic compounds. Here, we studied the patterns of cell walls establishment in the internode supporting the ear in three distinct maize genotypes. The developmental patterns observed in the internode cell walls in terms of its composition are reported with an emphasis on lignification, p-coumaroylation and feruloylation. We combined biochemical and histological approaches and revealed that internode cell walls development in maize before flowering is characterized by the rapid deposition of secondary cell walls components and robust lignification in both the pith and the rind. After flowering and until silage maturity, the slow deposition of secondary walls components occurs in the cortical region, and the deposited lignins are rich in β-O-4 bonds and are highly p-coumaroylated. We conclude the paper by proposing a revised spatiotemporal model based on that proposed by Terashima et al. (1993) for cell walls development in grass. [ABSTRACT FROM AUTHOR]

Compared with the idealized isotropic coal seam, the effectively cracking distances of deep-hole pre-splitting blasting in different directions have significant difference in the structural anisotropy of coal seam. In this paper, based on the test results of coal mechanical parameters in different coring directions, the blasting crack distances in these coring directions of coal seam are theoretically calculated and analyzed, and then a new method of blasthole layout in coal seam is designed and the blasting crack effect is investigated according to the test data of gas drainage. The research results show that both compressive strength and tensile strength of the direction of perpendicular to bedding are larger than that in other directions, especially parallel to bedding. The blasting crack distances of calculation results are in great agreement with that of underground in-situ measurement, so the blasting crack zone caused by the differences of mechanical properties in different directions can be approximately treated as an ellipse. Comparing the conventional method of blasthole layout, the blasting crack effect with new method has greatly enhanced the methane concentration and gas drainage quantity. Pure methane quantities with the new method and conventional method are about 2.8 times and 1.67 times as large as that before blasting, and pure methane quantity with the new method is about 1.46 times that with the conventional method. Therefore, considering the anisotropic characteristics of coal seams, the reasonable blasthole layout plays an important role for enhancing gas drainage in low permeability and outburst working face. [ABSTRACT FROM AUTHOR]

This paper aims to develop a novel computational technique for the detection of the transit through the anaerobic threshold. This technique uses only cardiac intervals derived from the electrocardiogram and is based on algebraic relationships between RR and QRS intervals. Electrocardiograms are measured during the load and the recovery processes. Algebraic relationships between cardiac intervals are used not only to identify the anaerobic threshold but also to characterise individual features of the person during the transit through the threshold. The ratio between carbon dioxide and oxygen in the exhaled air is used to validate the results. The algebraic relationship between cardiac intervals serves as a stand-alone indicator for both the determination of the anaerobic threshold and the characterization of the performance of the person during the load and the recovery processes. [ABSTRACT FROM AUTHOR]

When assessing the short-term effect of air pollution on health outcomes, it is common practice to consider one pollutant at a time, due to their high correlation. Multi pollutant methods have been recently proposed, mainly consisting of collapsing the different pollutants into air quality indexes or clustering the pollutants and then evaluating the effect of each cluster on the health outcome. A major drawback of such approaches is that it is not possible to evaluate the health impact of each pollutant. In this paper we propose the use of the Bayesian hierarchical framework to deal with multi pollutant concentrations in a two-component model: a pollutant model is specified to estimate the ‘true’ concentration values for each pollutant and then such concentration is linked to the health outcomes in a time-series perspective. Through a simulation study we evaluate the model performance and we apply the modelling framework to investigate the effect of six pollutants on cardiovascular mortality in Greater London in 2011-2012. [ABSTRACT FROM AUTHOR]

The data collected by mobile methane (CH4) sensors can be used to find natural gas (NG) leaks in urban distribution systems. Extracting actionable insights from the large volumes of data collected by these sensors requires several data processing steps. While these survey platforms are commercially available, the associated data processing software largely constitute a black box due to their proprietary nature. In this paper we describe a step-by-step algorithm for developing leak indications using data from mobile CH4 surveys, providing an under-the-hood look at the choices and challenges associated with data analysis. We also describe how our algorithm has evolved over time, and the data-driven insights that have prompted these changes. Applying our algorithm to data collected in 15 cities produced more than 6100 leak indications and estimates of the leaks’ size. We use these results to characterize the distribution of leak sizes in local NG distribution systems. Mobile surveys are already an effective and necessary tool for managing NG distribution systems, but improvements in the technology and software will continue to increase its value. [ABSTRACT FROM AUTHOR]

In environmental epidemiology, it is often encountered that multiple time series data with a long-term trend, including seasonality, cannot be fully adjusted by the observed covariates. The long-term trend is difficult to separate from abnormal short-term signals of interest. This paper addresses how to estimate the long-term trend in order to recover short-term signals. Our case study demonstrates that the current spline smoothing methods can result in significant positive and negative cross-correlations from the same dataset, depending on how the smoothing parameters are chosen. To circumvent this dilemma, three classes of time series smoothers are proposed to detrend time series data. These smoothers do not require fine tuning of parameters and can be applied to recover short-term signals. The properties of these smoothers are shown with both a case study using a factorial design and a simulation study using datasets generated from the original dataset. General guidelines are provided on how to discover short-term signals from time series with a long-term trend. The benefit of this research is that a problem is identified and characteristics of possible solutions are determined. [ABSTRACT FROM AUTHOR]

Super-critical carbon dioxide energy-storage (SC-CCES) technology is a new type of gas energy-storage technology. This paper used orthogonal method and variance analysis to make significant analysis on the factors which would affect the thermodynamics characteristics of the SC-CCES system and obtained the significant factors and interactions in the energy-storage process, the energy-release process and the whole energy-storage system. Results have shown that the interactions in the components have little influence on the energy-storage process, the energy-release process and the whole energy-storage process of the SC-CCES system, the significant factors are mainly on the characteristics of the system component itself, which will provide reference for the optimization of the thermal properties of the energy-storage system. [ABSTRACT FROM AUTHOR]

Biomass gasification stations are facing many hazard factors, therefore, it is necessary to make hazard assessment for them. In this study, a novel hazard assessment method called extended set pair analysis (ESPA) is proposed based on set pair analysis (SPA). However, the calculation of the connection degree (CD) requires the classification of hazard grades and their corresponding thresholds using SPA for the hazard assessment. In regard to the hazard assessment using ESPA, a novel calculation algorithm of the CD is worked out when hazard grades and their corresponding thresholds are unknown. Then the CD can be converted into Euclidean distance (ED) by a simple and concise calculation, and the hazard of each sample will be ranked based on the value of ED. In this paper, six biomass gasification stations are introduced to make hazard assessment using ESPA and general set pair analysis (GSPA), respectively. By the comparison of hazard assessment results obtained from ESPA and GSPA, the availability and validity of ESPA can be proved in the hazard assessment for biomass gasification stations. Meanwhile, the reasonability of ESPA is also justified by the sensitivity analysis of hazard assessment results obtained by ESPA and GSPA. [ABSTRACT FROM AUTHOR]

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction. [ABSTRACT FROM AUTHOR]

In this paper, a statistically-based experimental design with response surface methodology (RSM) was employed to examine the effects of functional conditions on the photoelectrocatalytic oxidation of landfill leachate using a Cu/N co-doped TiO2 (Ti) electrode. The experimental design method was applied to response surface modeling and the optimization of the operational parameters of the photoelectro-catalytic degradation of landfill leachate using TiO2 as a photo-anode. The variables considered were the initial chemical oxygen demand (COD) concentration, pH and the potential bias. Two dependent parameters were either directly measured or calculated as responses: chemical oxygen demand (COD) removal and total organic carbon (TOC) removal. The results of this investigation reveal that the optimum conditions are an initial pH of 10.0, 4377.98mgL-1 initial COD concentration and 25.0 V of potential bias. The model predictions and the test data were in satisfactory agreement. COD and TOC removals of 67% and 82.5%, respectively, were demonstrated. Under the optimal conditions, GC/MS showed 73 organic micro-pollutants in the raw landfill leachate which included hydrocarbons, aromatic compounds and esters. After the landfill leachate treatment processes, 38 organic micro-pollutants disappeared completely in the photoelectrocatalytic process. [ABSTRACT FROM AUTHOR]

Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results. [ABSTRACT FROM AUTHOR]

To attain cleaner air, it is important that authorities make informed decisions when selecting a strategy. Concentrations of particulate matter with an aerodynamic diameter of less than or equal to 2.5 μm (PM 2.5) are high in the Tokyo metropolitan area, even though concentrations of particulate matter with an aerodynamic diameter of less than or equal to 10 μm (PM10) have dropped dramatically since the implementation of the NOx-PM Act. Currently, monitored concentration levels continue to exceed the designated ambient air quality standard set by the Japanese Ministry of the Environment. To our knowledge, no study has investigated a cost-efficient strategy for reducing PM 2.5 concentration levels in the Tokyo metropolitan area. This is the first study to examine a proper control strategy for Japan by developing an integrated model that includes both aerosol and economic models. The simulation results show that prefectures in the Tokyo metropolitan area cannot achieve the standards by relying on their own efforts to reduce PM 2.5. That is, prefectural governments in the Tokyo metropolitan areas need to cooperate with prefectures outside of the area to improve their PM 2.5 concentration levels. Thus, we simulated policies under the assumption that emissions from other sources are reduced to levels such that the PM 2.5 concentration declines by approximately 18 μg/m3. We first simulated an efficient policy, i.e., the implementation of a pollution tax. We found that the total abatement cost to meet the air quality standard using the cost-efficient strategy is approximately 142.7 billion yen. [ABSTRACT FROM AUTHOR]

Based on carbon spot prices selected from seven carbon pilots, we assess the financial performances related to carbon volatility in China on the overall perspective. According to the results, the Chinese carbon market fluctuated severely at the beginning of carbon trading, but has stabilised in general, despite several dramatic changes related to ‘yearly compliance events’. Long-term memory exists in the volatility series. Moreover, asymmetry exists in the Chinese carbon market, and volatility reacts more severely to good news than to bad news. Finally, we discuss our empirical results, and make certain suggestions regarding firms’ awareness, international cooperation and individual investors not only for policy makers in China but also for other developing countries who are contemplating either commencing carbon trading or improving the current market. [ABSTRACT FROM AUTHOR]

This article builds on the premise that human consumption of goods, food and transport are the ultimate drivers of climate change. However, the nature of the climate change problem (well described as a tragedy of the commons) makes it difficult for individuals to recognise their personal duty to implement behavioural changes to reduce greenhouse gas emissions. Consequently, this article aims to analyse the climate change issue from a human-scale perspective, in which each of us has a clearly defined personal quota of CO2 emissions that limits our activity and there is a finite time during which CO2 emissions must be eliminated to achieve the “well below 2°C” warming limit set by the Paris Agreement of 2015 (COP21). Thus, this work’s primary contribution is to connect an equal per capita fairness approach to a global carbon budget, linking personal levels with planetary levels. Here, we show that a personal quota of 5.0 tons of CO2 yr-1 p-1 is a representative value for both past and future emissions; for this level of a constant per-capita emissions and without considering any mitigation, the global accumulated emissions compatible with the “well below 2°C” and 2°C targets will be exhausted by 2030 and 2050, respectively. These are references years that provide an order of magnitude of the time that is left to reverse the global warming trend. More realistic scenarios that consider a smooth transition toward a zero-emission world show that the global accumulated emissions compatible with the “well below 2°C” and 2°C targets will be exhausted by 2040 and 2080, respectively. Implications of this paper include a return to personal responsibility following equity principles among individuals, and a definition of boundaries to the personal emissions of CO2. [ABSTRACT FROM AUTHOR]

Climate warming and organic matter decomposition are connected in a recursive manner; this recursion can be described by temperature sensitivity. We conducted a multifactorial laboratory experiment to quantify the temperature sensitivity of organic carbon (C) and nitrogen (N) decomposition processes of common boreal organic soils. We incubated 36 mor and 36 slightly decomposed Carex-Sphagnum peat samples in a constant moisture and ambient temperature for 6 months. The experiment included three temperature and two moisture levels and two food web manipulations (samples with and without fungivore enchytraeid worms). We determined the release of carbon dioxide (CO2) and dissolved organic carbon (DOC) in seven molecular size classes together with ammonium N and dissolved organic N in low molecular weight and high molecular weight fractions. The temperature sensitivity function Q10 was fit to the data. The C and N release rate was almost an order of magnitude higher in mor than in peat. Soil fauna increased the temperature sensitivity of C release. Soil fauna played a key role in N release; when fauna was absent in peat, the N release was ceased. The wide range of the studied C and N compounds and treatments (68 Q10 datasets) allowed us to recognize five different temperature sensitivity patterns. The most common pattern (37 out of 68) was a positive upwards temperature response, which was observed for CO2 and DOC release. A negative downward pattern was observed for extractable organic nitrogen and microbial C. Sixteen temperature sensitivity patterns represented a mixed type, where the Q10function was not applicable, as this does not allow changing the sign storage change rate with increasing or decreasing temperature. The mixed pattern was typically connected to intermediate decomposition products, where input and output fluxes with different temperature sensitivities may simultaneously change the storage. Mixed type was typical for N processes. Our results provide useful parameterization for ecosystem models that describe the feedback loop between climate warming, organic matter decomposition, and productivity of N-limited vegetation. [ABSTRACT FROM AUTHOR]

In order to support decision making on how to most effectively improve broiler welfare an innovative expert survey was conducted based on principles derived from semantic modelling. Twenty-seven experts, mainly broiler welfare scientists (n = 20; and 7 veterinarians), responded (response rate 38%) by giving welfare scores (GWS, scale 0–10) to 14 benchmarking housing systems (HSs), and explaining these overall scores by selecting, weighing and scoring main welfare parameters, including both input and output measures. Data exploration followed by REML (Linear Mixed Model) and ALM (Automatic Linear Modelling) analyses revealed 6 clusters of HSs, sorted from high to low welfare, i.e. mean GWS (with superscripts indicating significant differences): 1. (semi-natural backyard) Flock (8.8a); 2. Nature (7.7ab), Label Rouge II (7.4ab), Free range EU (7.2ab), Better Life (7.2ab); 3. Organic EU (7.0bc), Freedom Food (6.2bc); 4. Organic US (5.8bcd), Concepts NL (5.6abcdef), GAP 2 (4.9bcd); 5. Conventional EU (3.7de), Conventional US (2.9ef), Modern cage (2.9abcdef); 6. Battery cage (1.3f). Mean weighting factors (WF, scale 0–10) of frequently (n> = 15) scored parameters were: Lameness (8.8), Health status (8.6), Litter (8.3), Density (8.2), Air quality (8.1), Breed (8.0), Enrichment (7.0) and Outdoor (6.6). These did not differ significantly, and did not have much added value in explaining GWS. Effects of Role (Scientist/Vet), Gender (M/F) and Region (EU/non-EU) did not significantly affect GWS or WF, except that women provided higher WF than men (7.2 vs 6.4, p<0.001). The contribution of welfare components to overall welfare has been quantified in two ways: a) using the beta-coefficients of statistical regression (ALM) analyses, and b) using a semantic-modelling type (weighted average) calculation of overall scores (CalcWS) from parameter level scores (PLS) and WF. GWS and CalcWS were highly correlated (R = ~0.85). CalcWS identified Lameness, Health status, Density, Breed, Air quality and Litter as main parameters contributing to welfare. ALM showed that the main parameters which significantly explained the variance in GWS based on all PLS, were the output parameter Health status (with a beta-coefficient of 0.38), and the input parameters (stocking) Density (0.42), Litter (0.14) and Enrichment (0.27). The beta-coefficients indicated how much GWS would improve from 1 unit improvement in PLS for each parameter, thus the potential impact on GWS ranged from 1.4 welfare points for Litter to 4.2 points for Density. When all parameters were included, 81% of the variance in GWS was explained (77% for inputs alone; 39% for outputs alone). From this, it appears that experts use both input and output parameters to explain overall welfare, and that both are important. The major conventional systems and modern cages for broilers received low welfare scores (2.9–3.7), well below scores that may be considered acceptable (5.5). Also, several alternatives like GAP 2 (4.9), Concepts NL (5.6), Organic US (5.8) and Freedom Food (6.2) are unacceptable, or at risk of being unacceptable due to individual variation between experts and farms. Thus, this expert survey provides a preliminary semi-quantified decision-support tool to help determine how to most effectively improve broiler welfare in a wide range of HSs. [ABSTRACT FROM AUTHOR]

Foraminifera are expected to be particularly susceptible to future changes in ocean carbonate chemistry as a function of increased atmospheric CO2. Studies in an experimental recirculating seawater system were performed with a dominant benthic foraminiferal species collected from intertidal mudflats. We investigated the experimental impacts of ocean acidification on survival, growth/calcification, morphology and the biometric features of a calcareous species Elphidium williamsoni. Foraminifera were exposed for 6 weeks to four different pH treatments that replicated future scenarios of a high CO2 atmosphere resulting in lower seawater pH. Results revealed that declining seawater pH caused a decline in foraminiferal survival rate and growth/calcification (mainly through test weight reduction). Scanning electron microscopy image analysis of live specimens at the end of the experimental period show changes in foraminiferal morphology with clear signs of corrosion and cracking on the test surface, septal bridges, sutures and feeding structures of specimens exposed to the lowest pH conditions. These findings suggest that the morphological changes observed in shell feeding structures may serve to alter: (1) foraminiferal feeding efficiency and their long-term ecological competitiveness, (2) the energy transferred within the benthic food web with a subsequent shift in benthic community structures and (3) carbon cycling and total CaCO3 production, both highly significant processes in coastal waters. These experimental results open-up the possibility of modelling future impacts of ocean acidification on both calcification and dissolution in benthic foraminifera within mid-latitude intertidal environments, with potential implications for understanding the changing marine carbon cycle. [ABSTRACT FROM AUTHOR]

A simultaneous treatment of lignocellulosic biomass (LCB) and low density oxodegradable polyethylene (LDPEoxo) was carried-out using Pleurotus ostreatus at microcosm scale to obtain biotransformed plastic and oxidized lignocellulosic biomass. This product was used as raw matter (RM) to produce biochar enriched with phosphate solubilizing bacteria (PSB). Biochar potential as biofertilizer was evaluated in Allium cepa culture at greenhouse scale. Experiments including lignocellulosic mix and LDPEoxo were performed for 75 days in microcosm. Biotransformation progress was performed by monitoring total organic carbon (TOC), CO2 production, laccase (Lac), manganese peroxidase (MnP), and lignin peroxidase (LiP) enzymatic activities. Physical LDPEoxo changes were assessed by atomic force microscopy (AFM), scanning electron microscopy (SEM) and static contact angle (SCA) and chemical changes by Fourier transform infrared spectroscopy (FTIR). Results revealed P. ostreatus was capable of LCB and LDPEoxo biotransformation, obtaining 41% total organic carbon (TOC) removal with CO2 production of 2,323 mg Kg-1 and enzyme activities of 169,438 UKg-1, 5,535 UKg-1 and 5,267 UKg-1 for LiP, MnP and Lac, respectively. Regarding LDPEoxo, SCA was decreased by 84%, with an increase in signals at 1,076 cm-1 and 3,271 cm-1, corresponding to C-O and CO-H bonds. A decrease in signals was observed related to material degradation at 2,928 cm-1, 2,848 cm-1, agreeing with CH2 asymmetrical and symmetrical stretching, respectively. PSB enriched biochar favored A. cepa plant growth during the five-week evaluation period. To the best of our knowledge, this is the first report of an in vitro circular production model, where P. ostreatus was employed at a microcosmos level to bioconvert LCB and LDPEoxo residues from the agroindustrial sector, followed by thermoconversion to produce an enriched biochar with PSB to be used as a biofertilizer to grow A. cepa at greenhouse scale. [ABSTRACT FROM AUTHOR]

Task-evoked Blood-oxygenation-level-dependent (BOLD-fMRI) signal activation is widely used to interrogate eloquence of brain areas. However, data interpretation can be improved, especially in regions with absent BOLD-fMRI signal activation. Absent BOLD-fMRI signal activation may actually represent false-negative activation due to impaired cerebrovascular reactivity (BOLD-CVR) of the vascular bed. The relationship between impaired BOLD-CVR and BOLD-fMRI signal activation may be better studied in healthy subjects where neurovascular coupling is known to be intact. Using a model-based prospective end-tidal carbon dioxide (CO2) targeting algorithm, we performed two controlled 3 tesla BOLD-CVR studies on 17 healthy subjects: 1: at the subjects’ individual resting end-tidal CO2 baseline. 2: Around +6.0 mmHg CO2 above the subjects’ individual resting baseline. Two BOLD-fMRI finger-tapping experiments were performed at similar normo- and hypercapnic levels. Relative BOLD fMRI signal activation and t-values were calculated for BOLD-CVR and BOLD-fMRI data. For each component of the cerebral motor-network (precentral gyrus, postcentral gyrus, supplementary motor area, cerebellum und fronto-operculum), the correlation between BOLD-CVR and BOLD-fMRI signal changes and t-values was investigated. Finally, a voxel-wise quantitative analysis of the impact of BOLD-CVR on BOLD-fMRI was performed. For the motor-network, the linear correlation coefficient between BOLD-CVR and BOLD-fMRI t-values were significant (p<0.01) and in the range 0.33–0.55, similar to the correlations between the CVR and fMRI Δ%signal (p<0.05; range 0.34–0.60). The linear relationship between CVR and fMRI is challenged by our voxel-wise analysis of Δ%signal and t-value change between normo- and hypercapnia. Our main finding is that BOLD fMRI signal activation maps are markedly dampened in the presence of impaired BOLD-CVR and highlights the importance of a complementary BOLD-CVR assessment in addition to a task-evoked BOLD fMRI to identify brain areas at risk for false-negative BOLD-fMRI signal activation. [ABSTRACT FROM AUTHOR]

This study analyzes the core energy consumption among countries’ specific variables by Environmental Kuznets Curve hypothesis (EKC), for a panel data of 29 (14 developed and 15 developing) countries during the period of 1977–2014. By assessing Generalized Method of Moments (GMM) regressions with first generation tests such as common root, individual Augmented Dickey-Fuller (ADF), and individual root-Fisher-PP which have been computed individually, the results confirm the EKC hypothesis in the case of emissions of solid, liquid, gases, manufacturing industries and also construction. Hence, we computed the cointegration test by Pedroni Kao from Engle-Granger based and Fisher. Since the variables are co-integrated, a panel vector error correction model is estimated in GDP per capita, emission from manufacturing industries, arms import, commercial service export, and coal rent, in order to perform Pairwise Granger Causality test and indicate Vector Error Correction (VEC), with co-integration restrictions. Moreover, the statistical finding from VEC short-run unidirectional causality from GDP per capita growth to manufacturing industries and coal rent, as well as the causal link with manufacturing industries and commercial service export. Additionally, there occurred no causal link among economic growth, arm import and coal rent. [ABSTRACT FROM AUTHOR]

As in many nations, air pollution linked to rapid industrialization is a public health and environmental concern in Malaysia, especially in cities. Understanding awareness of air pollution and support for environmental protection from the general public is essential for informing governmental approaches to dealing with this problem. This study presents a cross-sectional survey conducted in the Klang Valley and Iskandar conurbations to examine urban Malaysians’ perception, awareness and opinions of air pollution. The survey was conducted in two languages, English and Malay, and administered through the online survey research software, Qualtrics. The survey consisted of three sections, where we collected sociodemographic information, information on the public perception of air quality and the causes of air pollution, information on public awareness of air pollution and its related impacts, and information on attitudes towards environmental protection. Of 214 respondents, over 60% were positive towards the air quality at both study sites despite the presence of harmful levels of air pollution. The air in the Klang Valley was perceived to be slightly more polluted and causing greater health issues. Overall, the majority of respondents were aware that motor vehicles represent the primary pollution source, yet private transport was still the preferred choice of transportation mode. A generally positive approach towards environmental protection emerged from the data. However, participants showed stronger agreement with protection actions that do not involve individual effort. Nonetheless, we found that certain segments of the sample (people owning more than three vehicles per household and those with relatives who suffered from respiratory diseases) were significantly more willing to personally pay for environmental protection compared to others. Implications point to the need for actions for spreading awareness of air pollution to the overall population, especially with regards to its health risks, as well as strategies for increasing the perception of behavioural control, especially with regards to motor vehicles’ usage. [ABSTRACT FROM AUTHOR]

Soil heterogeneity is a major contributor to the uncertainty in near-surface biogeochemical modeling. We sought to overcome this limitation by exploring the development of a new classification analogy concept for transcribing the largely qualitative criteria in the pedomorphologically based, soil taxonomic classification systems to quantitative physicochemical descriptions. We collected soil horizons classified under the Alfisols taxonomic Order in the U.S. National Resource Conservation Service (NRCS) soil classification system and quantified their properties via physical and chemical characterizations. Using multivariate statistical modeling modified for compositional data analysis (CoDA), we developed quantitative analogies by partitioning the characterization data up into three different compositions: Water-extracted (WE), Mehlich-III extracted (ME), and particle-size distribution (PSD) compositions. Afterwards, statistical tests were performed to determine the level of discrimination at different taxonomic and location-specific designations. The analogies showed different abilities to discriminate among the samples. Overall, analogies made up from the WE composition more accurately classified the samples than the other compositions, particularly at the Great Group and thermal regime designations. This work points to the potential to quantitatively discriminate taxonomically different soil types characterized by varying compositional datasets. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) is one of the most commonly used gas euthanasia agents in mice, despite reports of aversion and nociception. Inert gases such as nitrogen (N2) may be a viable alternative to carbon dioxide. Here we compared behavioural and electrophysiological reactions to CO2 or N2 at either slow fill or rapid fill in C57Bl/6 mice undergoing gas euthanasia. We found that mice euthanised with CO2 increased locomotor activity compared to baseline, whereas mice exposed to N2 decreased locomotion. Furthermore, mice exposed to CO2 showed significantly more vertical jumps and freezing episodes than mice exposed to N2. We further found that CO2 exposure resulted in increased theta:delta of the EEG, a measure of excitation, whereas the N2 decreased theta:delta. Differences in responses were not oxygen-concentration dependent. Taken together, these results demonstrate that CO2 increases both behavioural and electrophysiological excitation as well as producing a fear response, whereas N2 reduces behavioural activity and central neurological depression and may be less aversive although still produces a fear response. Further studies are required to evaluate N2 as a suitable euthanasia agent for mice. [ABSTRACT FROM AUTHOR]

Ischemia-induced left ventricular (LV) wall thickening compromises the hemodynamic effectiveness of cardiopulmonary resuscitation (CPR). However, accurate assessment of the severity of ischemia-induced LV wall thickening during CPR is challenging. We investigated, in a swine model, whether hemodynamic parameters, including end-tidal carbon dioxide (ETCO2) level, are linearly associated with the severity of ischemia-induced LV wall thickening during CPR of consistent quality. We retrospectively analyzed 96 datasets for ETCO2 level, arterial pressure, LV wall thickness, and the percent of measured end-diastolic volume (%EDV) relative to EDV at the onset of ventricular fibrillation from eight pigs. Animals underwent advanced cardiovascular life support based on resuscitation guidelines. During CPR, LV wall thickness progressively increased while %EDV progressively decreased. Systolic and diastolic arterial pressure and ETCO2 level were significantly correlated with LV wall thickness and %EDV. Linear mixed effect models revealed that, after adjustment for significant covariates, systolic and diastolic arterial pressure were not associated with LV wall thickness or %EDV. ETCO2 level had a significant linear relationship with %EDV (P = 0.004). However, it could explain only 28.2% of the total variance of %EDV in our model. In conclusion, none of the hemodynamic parameters examined in this study appeared to provide sufficient information on the severity of ischemia-induced LV wall thickening. [ABSTRACT FROM AUTHOR]

Knowledge about the relative importance of influencing-factors on rice yield gap is crucial to rice production, especially in southwestern China where topography is extremely complicated. In the current study, the data of rice yield from a total of 76 experiments were collected in 2008 and 2009 in Chongqing, southwest China. For each location, two treatments with fertilizer and without fertilizer were carried out, each treatment was performed with three replications, and yield gap was calculated using fertilized yield minus unfertilized yield. Seventeen influencing-factors including variety, fertilization, climate, terrain, and soil properties were obtained at each location. Regression tree (RT) model were employed to investigate relative important of influencing-factors to rice yield gap variability. The result of Pearson correlation analysis suggested yield gap of rice was positively correlated with sunshine hours, phosphorous and potassium fertilizers, while negatively correlated with soil available nitrogen content. The results of RT showed that the selected influencing-factors explained about 74.1% of rice yield gap variation. Meanwhile, the result also indicated variety followed by others had more influence on rice yield gap variation. Our findings analyzed by regression model at a regional scale suggested that more precise fertilization recommendation should be formulated based on comprehensive factors (e.g., soil, climate, terrain, variety), which reasonably guided farmer and government for rice production. [ABSTRACT FROM AUTHOR]

Climate change is affecting the growth, phenology, and distribution of species across northeastern United States. In response to these changes, some species have been adversely impacted while others have benefited. One species that has benefited from climate change, historically and in response to experimental treatments, is common ragweed (Ambrosia artemisiifolia), a widely distributed annual weed and a leading cause of hay fever in North America. To better understand how climate change may affect the distribution of common ragweed, we built a maximum entropy (Maxent) predictive model using climate and bioclimatic data and over 700 observations across the eastern U.S. Our model performed well with an AUC score of 0.765 using four uncorrelated variables, including precipitation seasonality, mean diurnal temperature range, August precipitation, and January maximum temperature. After building and testing our model, we then projected potential future common ragweed distribution using a suite of 13 global climate models (GCMs) under two future greenhouse gas scenarios for mid and late-century. In addition to providing georeferenced hot spots of potential future expansion, we also provide a metric of confidence by evaluating the number of GCMs that agree. We show a substantial contraction of common ragweed in central Florida, southern Appalachian Mountains, and northeastern Virginia and areas of potential expansion at the northern margins of its current distribution, notably in northeastern U.S. However, the vast majority of this increase is projected to occur by mid-century and may be moderated somewhat by the 2070s, implying that common ragweed may be sensitive to climatic variability. Although other factors and modeling approaches should be explored, we offer preliminary insight into where common ragweed might be a new concern in the future. Due to the health impacts of ragweed, local weed control boards may be well advised to monitor areas of expansion and potentially increase eradication efforts. [ABSTRACT FROM AUTHOR]

While stress is a widely utilized concept, no direct methods facilitating its measurement are currently available. In our previous work we proposed stress entropic load (SEL) as a potential new marker of stress response in the human body. However, at that time no method for SEL measurement existed. In this pilot study we devised and then tested methodology for SEL measurement. Healthy male participants were monitored by indirect calorimetry and thermography while resting and subsequently while under prolonged mental effort. The acquired data was then used to calculate the temporal development of SEL for each participant. Our results show that SEL production increased significantly in participants subjected to prolonged mental effort. Furthermore, we observed that the calculated development of SEL over time may be used to accurately determine the time point at which participants started performing stressful tasks. [ABSTRACT FROM AUTHOR]

Neurological dysfunction has been reported in Gulf War Illness (GWI), including abnormal cerebral blood flow (CBF) responses to physostigmine challenge. However, it is unclear whether the CBF response to normal physiological challenges and regulation is similarly dysfunctional. The goal of the present study was to evaluate the CBF velocity response to orthostatic stress (i.e., sit-to-stand maneuver) and increased fractional concentration of carbon dioxide. 23 cases of GWI (GWI+) and 9 controls (GWI) volunteered for this study. Primary variables of interest included an index of dynamic autoregulation and cerebrovascular reactivity. Dynamic autoregulation was significantly lower in GWI+ than GWI- both for autoregulatory index (2.99±1.5 vs 4.50±1.5, p = 0.017). In addition, we observed greater decreases in CBF velocity both at the nadir after standing (-18.5±6.0 vs -9.8±4.9%, p = 0.001) and during steady state standing (-5.7±7.1 vs -1.8±3.2%, p = 0.042). In contrast, cerebrovascular reactivity was not different between groups. In our sample of Veterans with GWI, dynamic autoregulation was impaired and consistent with greater cerebral hypoperfusion when standing. This reduced CBF may contribute to cognitive difficulties in these Veterans when upright. [ABSTRACT FROM AUTHOR]

Despite substantial research and policy interest in pixel level cropland allocation data, few sources are available that span a large geographic area. The data used for much of this research are derived from complex modeling techniques that may include model simulation and other data processing. We develop a transparent econometric framework that uses pixel level biophysical measurements and aggregate cropland statistics to develop pixel level cropland allocation predictions. Such pixel level land use data can be used to investigate the impact of human activities on the environment. Validation exercises show that our approach is effective at downscaling cropland allocation to multiple levels of resolution. [ABSTRACT FROM AUTHOR]

Carbon isotope discrimination (Δ13C) in plant leaves generally decreases with increasing altitude in mountains. Lower foliar Δ13C at high elevation usually is associated with higher leaf mass per area (LMA) in thicker leaves. However, it is unclear if lower foliar Δ13C in high-altitude plants is caused by improved photosynthetic capacity as an effect of higher nutrient, especially nitrogen, content in thicker leaves. We investigated trends of foliar Δ13C in four species, each belonging to a different plant functional type (PFT), across two altitudinal gradients, each on a different bedrock type (carbonate and silicate bedrock, respectively) in a region of the southern Alps (Italy) where the foliar Δ13C was not affected by water limitation. Our objective was to assess whether the altitudinal patterns of foliar Δ13C in relation to leaf morphology and foliar nutrients were conditioned by indirect control of bedrock geology on soil nutrient availability. The foliar Δ13C of the four species was mainly affected by LMA and, secondarily, by stomatal density (SD) but the relative importance of these foliar traits varied among species. Area-based nutrient contents had overall minor importance in controlling C discrimination. Relationships among foliar Δ13C, foliar nutrient content and leaf growth rate strongly depended on soil nutrient availability varying differently across the two gradients. In the absence of water limitation, the foliar Δ13C was primarily controlled by irradiance which can shape anatomical leaf traits, especially LMA and/or SD, whose relative importance in determining C isotope discrimination differed among species and/or PFT. Decreasing foliar Δ13C across altitudinal gradients need not be determined by improved photosynthetic capacity deriving from higher nutrient content in thicker leaves. [ABSTRACT FROM AUTHOR]

In the European Union (EU) millions of laboratory mice are used and killed for experimental and other scientific purposes each year. Although controversially discussed, the use of carbon dioxide (CO2) is still permitted for killing rodents according to the Directive 2010/63/EU. Within the scope of refinement, our aim was to investigate if isoflurane and sevoflurane are an appropriate alternative killing method to CO2 in mice. Different concentrations of CO2 (filling rates of 20%, 60%, 100%; CO2 20, 60, 100), isoflurane (Iso 2%, 5%) and sevoflurane (Sevo 4.8%, 8%) were compared in two mouse strains (NMRI, C57Bl/6J) using a broad spectrum of behavioral parameters, including the approach-avoidance test, and analyzing blood for stress parameters (glucose, adrenaline, noradrenaline). We focused in our study on the period from the beginning of the gas inlet to loss of consciousness, as during this period animals are able to perceive pain and distress. Our results show that only higher concentrations of CO2 (CO2 60, 100) and isoflurane (5%) induced surgical tolerance within 300 s in both strains, with CO2 100 being the fastest acting inhalant anesthetic. The potency of halogenated ethers depended on the mouse strain, with C57Bl/6J being more susceptible than NMRI mice. Behavioral analysis revealed no specific signs of distress, e. g. stress-induced grooming, and pain, i. e. audible vocalizations, for all inhalant gases. However, adrenaline and noradrenaline plasma concentrations were increased, especially in NMRI mice exposed to CO2 in high concentrations, whereas we did not observe such increase in animals exposed to isoflurane or sevoflurane. Escape latencies in the approach-avoidance test using C57Bl/6J mice did not differ between the three inhalant gases, however, some animals became recumbent during isoflurane and sevoflurane but not during CO2 exposure. The rise in catecholamine concentrations suggests that CO2 exposure might be linked to a higher stress response compared to isoflurane and sevoflurane exposure, although we did not observe a behavioral correlate for that. Follow-up studies investigating other fast-acting stress hormones and central anxiety circuits are needed to confirm our findings. [ABSTRACT FROM AUTHOR]

We describe and correlate environmental, floristic and structural vegetation traits of a large portion of Australian rangelands. We analysed 351 one hectare vegetation plots surveyed by Australia’s Terrestrial Ecosystem Research Network (TERN) using the AusPlots Rangelands standardized method. The AusPlots Rangelands method involves surveying 1010 one meter-spaced point-intercepts (IPs) per plot. At each IP, species were scored, categorised by growth-form, converted to percentage cover as the input for the plot x species matrix. Vegetation structure is depicted by growth-form configuration and relative importance. The floristic and structural distance matrices were correlated with the Mantel test. Canonical correspondence analysis (CCA) related floristic composition to environmental variables sourced from WorldClim, the Atlas of Living Australia and TERN’s Soil and Landscape Grid. Differences between clusters were tested with ANOVA while principal component analysis (PCA) ordered the plots within the environmental space. Our plot x species matrix required segmentation due to sparsity and high β-diversity. Based on the ordination of plots latitude within environmental space, the matrix was segmented into three “superclusters”: the winter rain and temperate Mediterranean, the monsoonal rain savannas and the arid deserts. Further classification, with the UPGMA linkage method, generated two, four and five clusters, respectively. All groupings are described by species richness, diversity indices and growth form conformation. Several floristic disjunctions were apparent and their possible causes are discussed. For all superclusters, the correspondence between the floristic and the structural or growth form matrices was statistically significant. CCA ordination clearly demarcated all groupings. Aridity, rainfall, temperature, seasonality, soil nitrogen and pH are significant correlates to the ordination of superclusters and clusters. At present, our results are influenced by incomplete sampling. As more sites are surveyed, this pioneer analysis will be updated and refined providing tools for the effective management of Australian rangelands. [ABSTRACT FROM AUTHOR]

Climate change communication efforts grounded in the information deficit model have largely failed to close the gap between scientific and public understanding of the risks posed by climate change. In response, simulations have been proposed to enable people to learn for themselves about this complex and politically charged topic. Here we assess the impact of a widely-used simulation, World Climate, which combines a socially and emotionally engaging role-play with interactive exploration of climate change science through the C-ROADS climate simulation model. Participants take on the roles of delegates to the UN climate negotiations and are challenged to create an agreement that meets international climate goals. Their decisions are entered into C-ROADS, which provides immediate feedback about expected global climate impacts, enabling them to learn about climate change while experiencing the social dynamics of negotiations. We assess the impact of World Climate by analyzing pre- and post-survey results from >2,000 participants in 39 sessions in eight nations. We find statistically significant gains in three areas: (i) knowledge of climate change causes, dynamics and impacts; (ii) affective engagement including greater feelings of urgency and hope; and (iii) a desire to learn and do more about climate change. Contrary to the deficit model, gains in urgency were associated with gains in participants’ desire to learn more and intent to act, while gains in climate knowledge were not. Gains were just as strong among American participants who oppose government regulation of free markets–a political ideology that has been linked to climate change denial in the US–suggesting the simulation’s potential to reach across political divides. The results indicate that World Climate offers a climate change communication tool that enables people to learn and feel for themselves, which together have the potential to motivate action informed by science. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the energy metabolism and partition of lactating Gyr and F1 Holstein x Gyr (F1 HxG) cows in different planes of nutrition. Six F1 HxG and six Gyr cows with 130 days in milking (DIM) fed corn silage and concentrate were evaluated. The experiment consisted of four periods with different levels of feeding: 1st ad libitum dry matter intake (DMI) and the others with 5, 10 and 20% restricted DMI, related to the first one. An apparent digestibility assay was performed before measurements in the respiration chamber. Total feces were collected for three days. The cows were confined for 24h in the chamber in each period to determine methane and heat production (HP). F1 HxG had higher gross energy intake (GEI), metabolisable energy intake (MEI) and digestible energy intake (DEI). GE lost in feces was higher in F1 HxG (23.7% GEI) than in Gyr (20.5%) cows. Energy lost as methane and urine was similar between the groups. The metabolisability (q) was 0.67, and the efficiency of converting ME to NE (k) was 0.56. There was no difference in the energy requirements for maintenance between breeds (426.6 MJ/kg BW0,75 average value). The energy requirements for lactation were higher in F1 HxG animals due to the higher volume of milk produced, since there was no difference in energy requirements for production of one kg of milk. [ABSTRACT FROM AUTHOR]

A multivariate linear regression model was proposed to achieve short period prediction of PM2.5 (fine particles with an aerodynamic diameter of 2.5 μm or less). The main parameters for the proposed model included data on aerosol optical depth (AOD) obtained through remote sensing, meteorological factors from ground monitoring (wind velocity, temperature, and relative humidity), and other gaseous pollutants (SO2, NO2, CO, and O3). Beijing City was selected as a typical region for the case study. Data on the aforementioned variables for the city throughout 2015 were used to construct two regression models, which were discriminated by annual and seasonal data, respectively. The results indicated that the regression model based on annual data had (R2 = 0.766) goodness-of-fit and (R2 = 0.875) cross-validity. However, the regression models based on seasonal data for spring and winter were more effective, achieving 0.852 and 0.874 goodness-of-fit, respectively. Model uncertainties were also given, with the view of laying the foundation for further study. [ABSTRACT FROM AUTHOR]

Annual estimates of CO2 and dissolved carbon concentrations in the soil profile provide valuable insight into the dynamics of organic matter in soil and the effect of changes to vegetation cover. The aim of this study was to observe the spatial influence of litter decomposition in the first few centimeters of the soil for CO2 fluxes and to describe the processing of soil organic matter throughout the soil profile by comparing three small tropical watersheds. Data were collected biweekly for six months, from December 2015 to May 2016. CO2 was measured using an infrared gas analyzer in fixed chambers and the dissolved carbon of soil solution was analyzed in a TOC analyzer. No differences were found in the total soil CO2 fluxes (control flux treatments) between the three study areas. In both cacao agroforestry systems (managed and unmanaged), total CO2 fluxes were influenced by the decomposition of litter. CO2 emissions in the soil profile of the cacao agroforestry systems were highly variable, compared to the preserved forest, and highly dependent on the soil characteristics attributed to the type of vegetation cover. Although a definite pattern between the temperature and soil moisture was not identified, these parameters showed a strong relationship in controlling the release of CO2 between treatments. The organic and inorganic dissolved carbon patterns in the soil solution of the three areas revealed different responses of soil organic matter processing related to soil characteristics and vegetation. The results confirm the hypothesis that the top of soils (total CO2 fluxes) of both cacao agroforestry systems (managed and unmanaged) emits fluxes of CO2, which do not differ statistically from the preserved forest. However, depending on the soil characteristics, the cacao agroforestry system can result in an accumulation of CO2 and dissolved inorganic carbon in the soil profile that is prone to being transported by hydrological routes to groundwater and stream water. [ABSTRACT FROM AUTHOR]

To solve the hidden danger of high methane and low permeability gas in the coal mining process, potentially affecting the safety production in an orderly way, we propose the use of deep hole blasting technology to improve the permeability of the coal seam gas drainage, increase the quantity and rate of extraction, and reduce methane output. Taking the geological conditions of the 201 working surface of Tingnan Coal Mine as an example, it is calculated that the single drilled fracture crack extension range is 3.11~5.24 m according to the coal seam deep-hole pre-splitting blasting joint mechanism and fracture propagation mechanics model, providing a theoretical basis for choosing the appropriate hole spacing. Using COMSOL simulation software to simulate the effective gas drainage radius of a coal seam from a two-dimensional perspective on a single borehole radial, the least squares fitting method was used to analyze the simulated data, and obtained the effective drilling extraction radius after pre-split blasting in a deep hole that is 3.6 m, which is in accordance with the theoretical calculations. In order to obtain accurate and scientific calculations, Fast lagrangian analysis of continua (FLAC3D) numerical simulation software was used. After simulating the distribution of plastic zone between two blast holes at different intervals from a three-dimensional angle, and evaluating the development of cracks in the blasting hole, the white zone of the blasting space was completely eliminated when the interval between blasting holes was 7 m, and the cracks could be propagated throughout the surroundings. Therefore, a blasting hole spacing of 7 m is optimal. On-site monitoring in the Nanting coal mine showed that the quantity and rate of extraction of the single hole after pre-splitting blasting were 2.36 times and 1.62 times as much as before. By integrating the borehole drainage amount and the optimized calculation equation, it could be concluded that the permeability coefficient of the coal seam after blasting was 7.78 times as much as before. The function of time-variated drilling methane emission was obtained using multivariate statistical regressions based on the on-site monitored borehole methane emission (qt), and the drilling limit after pre-splitting blasting revealed that the limitation of methane extraction volume was 5.27 times as much as before. [ABSTRACT FROM AUTHOR]

In a two-echelon new energy vehicle (NEV) supply chain consisting of a risk-neutral manufacturer and a risk-averse retailer, the coordination and sustainability problem is investigated. The risk-averse retailer, who makes sales effort and undertakes the incurred effort cost, decides the order quantity and sales effort level under the Conditional Value-at-Risk (CVaR) criterion. We derive the optimal centralized decisions of a vertically integrated supply chain where the retailer is owned by the manufacturer. Taking such a centralized case as the benchmark, we prove that the subsidy-sharing-based wholesale price (SS-WP) contract fails to coordinate the NEV supply chain under the decentralized case where the retailer makes decisions independently. Then we design a subsidy-sharing-based sales rebate/penalty (SS-SRP) contract and derive the contract parameters to achieve coordination. We evaluate the coordination efficiency of this contract and find that a well-designed SS-SRP contract can promote the NEV sales and lead to a Pareto-improving win-win situation for both the NEV manufacturer and retailer compared to the non-coordination case. A series of numerical experiments are carried out to compare the effects of significant parameters under the SS-WP and SS-SRP contract and provide additional observations and implications, including an indication of the necessary conditions to sustainably maintain the NEV supply chain. [ABSTRACT FROM AUTHOR]

The use of rice husks (RH) to reinforce polymers in biocomposites are increasing tremendously. However, the incompatibility between the hydrophilic RH fibers and the hydrophobic thermoplastic matrices leads to unsatisfactory biocomposites. Surface modification of the fiber surface was carried out to improve the adhesion between fiber and matrix. In this study, the effect of surface modification of RH via alkali, acid and ultraviolet-ozonolysis (UV/O3) treatments on the properties of composites recycled high density polyethylene (rHDPE) composites was investigated. The untreated and treated RH were characterized by Fourier Transform Infrared (FTIR) and Scanning Electron Microscope (SEM). The composites containing 30 wt% of RH (treated and untreated) were then prepared via extrusion and followed by compression molding. As compared to untreated RH, all surface treated RH exhibited rougher surface and showed improved adhesion with rHDPE matrix. Tensile strength of UV/O3-treated RH composites showed an optimum result at 18.37 MPa which improved about 5% in comparison to the composites filled with untreated RH. UV/O3 treatment promotes shorter processing time and lesser raw material waste during treatment process where this is beneficial for commercialization in the future developments of wood plastic composites (WPCs). Therefore, UV/O3 treatment can be served as an alternative new method to modify RH surface in order to improve the adhesion between hydrophilic RH fibre and hydrophobic rHDPE polymer matrix. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to verify whether transient inflammatory reactions induced by intra-articular medicinal ozone administration affect joint components, by in vivo evaluation of inflammatory (prostaglandin E2, Substance P, Interleukin-6, Interleukine-1, Tumor Necrosis Factor), anti-inflammatory (Interleukin-10) and oxidative (superoxide dismutase activity and oxidative burst) biomarkers and extracellular matrix degradation products (chondroitin sulphate and hyaluronic acid) in synovial fluid. Methods: The effects of medicinal ozone were analyzed at two ozone concentrations (groups A and B, 20 and 40 μg/ml, respectively), using oxygen-injected joints as controls (group C); each group received ten treatments (15 ml gas per treatment). Physical evaluation, evaluation of lameness, ultrasonography, and synovial fluid analysis were performed. Results: All joints presented mild and transient effusion throughout the study. Group B exhibited the highest lameness score on day 14 (P<0.05), detected by the lameness measurement system, probably because of the higher ozone concentration. All groups exhibited increased ultrasonography scores on day 14 (P < 0.05). Groups A and B exhibited increased proteins concentrations on day 21 (P<0.05). There was no change in hyaluronic acid concentration or the percentage of high-molecular weight hyaluronic acid throughout the experiment. Chondroitin sulfate concentrations decreased in group B, and did not change in group A and C, indicating that neither treatment provoked extracellular matrix catabolism. Cytokine and eicosanoid concentrations were not significantly changed. Conclusions: The ozonetherapy did not cause significant inflammation process or cartilage degradation, therefore, ozonetherapy is safe at both evaluated doses. [ABSTRACT FROM AUTHOR]