Selective separation of cobalt versus nickel by split-phosphinate complexation using a phosphonium-based ionic liquid

Environmental Chemistry Letters - The rising demand of metals for batteries is calling for advanced methods to separate metallic mixtures in mineral ores and electronic waste. In particular, the...     

含多种分布式电源的微电网分层控制策略

随着分布式电源接入微电网数量的增加,微电网系统的稳定性受到了很大的影响;微电源的运行特性及控制方法、微电源的接人点和容量、微电网运行方式和控制方法、电力电子装置、储能设备和负荷特性等都会影响到电能质量。为了改善微电网的电能质量,提出了包括上层中心控制器及下层分布式电源信息交互的微电网分层控制策略。微电网中的分布式电源控制由下垂控制、功率控制、电压和电流控制组成。通过Matlab/simulink仿真,对微电网运行中各分布式电源的功率、电压和频率的变化规律进行了分析。仿真结果表明:分层控制策略能使各分布式电源之间较好地协调,满足了改善电能质量的要求。     

Improving construction site safety through leader-based verbal safety communication

Background

The construction industry is one of the most injury-prone industries, in which production is usually prioritized over safety in daily on-site communication. Workers have an informal and oral culture of risk, in which safety is rarely openly expressed. This paper tests the effect of increasing leader-based on-site verbal safety communication on the level of safety and safety climate at construction sites. Method: A pre-post intervention-control design with five construction work gangs is carried out. Foremen in two intervention groups are coached and given bi-weekly feedback about their daily verbal safety communications with their workers. Foremen-worker verbal safety exchanges (experience sampling method, n = 1,693 interviews), construction site safety level (correct vs. incorrect, n = 22,077 single observations), and safety climate (seven dimensions, n = 105 questionnaires) are measured over a period of up to 42 weeks. Results: Baseline measurements in the two intervention and three control groups reveal that foremen speak with their workers several times a day. Workers perceive safety as part of their verbal communication with their foremen in only 6-16% of exchanges, and the levels of safety at the sites range from 70-87% (correct observations). Measurements from baseline to follow-up in the two intervention groups reveal that safety communication between foremen and workers increases significantly in one of the groups (factor 7.1 increase), and a significant yet smaller increase is found when the two intervention groups are combined (factor 4.6). Significant increases in the level of safety are seen in both intervention groups (7% and 12% increases, respectively), particularly in regards to 'access ways' and 'railings and coverings' (39% and 84% increases, respectively). Increases in safety climate are seen in only one of the intervention groups with respect to their 'attention to safety.' No significant trend changes are seen in the three control groups on any of the three measures. Conclusions: Coaching construction site foremen to include safety in their daily verbal exchanges with workers has a significantly positive and lasting effect on the level of safety, which is a proximal estimate for work-related accidents. It is recommended that future studies include coaching and feedback at all organizational levels and for all involved parties in the construction process. Building client regulations could assign the task of coaching to the client appointed safety coordinators or a manager/supervisor, and studies should measure longitudinal effects of coaching by following foremen and their work gangs from site to site.     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Road-deposited sediments mediating the transfer of anthropogenic organic matter to stormwater runoff

Environmental Geochemistry and Health - It has been regarded that road-deposited sediment (RDS) is one of the important sinks of anthropogenic pollutants as well as the major source of pollutants...     

Inhibition of pyrite oxidation by surface coating: a long-term field study

Pyrite and other iron sulfides are readily oxidized by dissolved oxygen in aqueous phase, producing acidity and Fe²⁺, which causes significant environmental problems. Applications of surface coating agents (Na₂SiO₃ and KH₂PO₄) were conducted at Boeun (Chungbuk, South Korea) outcrop site, and their efficiencies to inhibit the oxidation of sulfide minerals were monitored for a long-term period (449 days). The rock sample showed positive Net Acid Production Potential (NAPP = 20.23) and low Net Acid Generation pH (NAGpH = 2.42) values, suggesting that the rock sample was categorized in the potential acid-forming group. For the monitored time period (449 days), field study results showed that the application of Na₂SiO₃ effectively inhibited the pyrite oxidation as compared to KH₂PO₄. Na₂SiO₃ as a surface coating agent maintained pH 5–6 and reduced oxidation of pyrite surface up to 99.95 and 97.70 % indicated by Fe²⁺ and SO₄ ^2? release, respectively. The scanning electron microscope and energy-dispersive X-ray spectrometer analysis indicated that the morphology of rock surface was completely changed attributable to formation of iron silicate coating. The experimental results suggested that the treatment with Na₂SiO₃ was highly effective and it might be applicable on field for inhibition of iron sulfide oxidation.     

Model selection criteria for overdispersed data and their application to the characterization of a host-parasite relationship

In the statistical modeling of a biological or ecological phenomenon, selecting an optimal model among a collection of candidates is a critical issue. To identify an optimal candidate model, a number of model selection criteria have been developed and investigated based on estimating Kullback’s (Information theory and statistics. Dover, Mineola, 1968) directed or symmetric divergence. Criteria that target the directed divergence include the Akaike (2nd international symposium on information theory. Akadémia Kiadó, Budapest, Hungary, pp 267–281, 1973, IEEE Trans Autom Control AC 19:716–723, 1974) information criterion, AIC, and the “corrected” Akaike information criterion (Hurvich and Tsai in Biometrika 76:297–307, 1989), AICc; criteria that target the symmetric divergence include the Kullback information criterion, KIC, and the “corrected” Kullback information criterion, KICc (Cavanaugh in Stat Probab Lett 42:333–343, 1999; Aust N Z J Stat 46:257–274, 2004). For overdispersed count data, simple modifications of AIC and AICc have been increasingly utilized: specifically, the quasi Akaike information criterion, QAIC, and its corrected version, QAICc (Lebreton et al. in Ecol Monogr 62(1):67–118 1992). In this paper, we propose analogues of QAIC and QAICc based on estimating the symmetric as opposed to the directed divergence: QKIC and QKICc. We evaluate the selection performance of AIC, AICc, QAIC, QAICc, KIC, KICc, QKIC, and QKICc in a simulation study, and illustrate their practical utility in an ecological application. In our application, we use the criteria to formulate statistical models of the tick (Dermacentor variabilis) load on a white-footed mouse (Peromyscus leucopus) in northern Missouri.     

The Effect of Aqueous Urea on the Processing, Structure and Properties of CGM

Corn gluten meal (CGM) is potentially a cost-effective raw material for producing a bioderivable thermoplastic. However, CGM disintegrates to a powder subsequent to processing with polar plasticisers, such as water. The hypothesis of this study was that aqueous urea could be used to denature protein within CGM and therefore encourage protein?Cprotein interactions leading to consolidated bioplastics when using water as a plasticiser. To assess this, the effects of aqueous urea on structure and properties of CGM with particular focus on storage were assessed. Processing of CGM with aqueous urea produced consolidated materials. FTIR analysis showed secondary structure was modified during processing, leading to increased amounts of ??-helices and random coils and reduction of the amount of intermolecular ??-sheets and turns. Above 6?wt% free water, the plasticising efficiency of water in processed CGM increased as the amount of denatured proteins increased. Below 6?wt% free water, protein secondary structure did not have a significant influence on thermal and flexural properties. It was found that storage environment and urea concentration influenced the rate of drying, however, the final water content was constant relative to CGM, and not urea. The materials were resistant to cracking at urea concentrations above 8?M, provided the mass loss during storage did not exceed 15?wt%.     

Evaluation of the potential methane yield of industrial wastewaters used in bioreactor landfills

To increase the moisture content of waste disposed in a bioreactor landfill, it is critical to secure the moisture source. In this study, industrial wastewater was evaluated as a potential moisture source for a bioreactor landfill with respect to the impact on methane generation. Industrial wastewater samples were collected from fishery, brewery and dairy industries. These samples were tested for basic water chemistry parameters, heavy metals, and methane generation using the regular and modified biochemical methane potential (BMP) assays. Despite high sodium and total phosphate concentrations in the fishery wastewater, a significant methane yield was observed (0.313?m³ CH₄/kg COD). However, a relatively large amount of fishery wastewater added at the phase of methane generation acceleration adversely affected microbial activity. Samples from the brewery and dairy wastewater did not indicate significant inhibitory effects on methane generation in either the regular or the modified BMP assay. This study demonstrates the utility of wastewater as an alternative to traditional moisture sources when used to enhance methane production in a bioreactor landfill.     

Arsenic species in the well water and sediments of the blackfoot disease area in Taiwan

The blackfoot disease (BFD) observed in southwestern Taiwan is due to drinking high arsenic concentrations in well water. This paper presents some results concerning the distributions of arsenic species in water and sediments collected in the BFD (well, river and coastal) area and the background (lake and ocean) area for comparison. The results show that the concentrations of arsenate (870 ± 26 ug/L) and arsenite (70.2 ± 2.6 ug/ L) in well waters, and the contents of arsenic (1640 ug/g) with high percentage of easily reducible (Fe and Mn oxides, 91.7%) and exchangeable and carbonate (4.6%) phases in well sediments were much higher than those in river, lake and coastal samples. Low arsenic (3.46–31.8 ug/g) with high percentages (73.8–97.3%) of detritus and minerals phase with low percentages (0.4–9.8%) of total carbonate and exchangeable phases were found in the river and coastal samples in the BFD area as well as the lake and ocean samples in background area. It might suggest that the higher concentrations of toxic As(III) in well water and arsenic with higher values of easily reducible, carbonate and exchangeable phases in the well sediments, combined with the higher values of dissolved organic carbon, humic and fulvic acids and aromatic carbon as well as the higher fluorescence indensity in the well water are the key factors to cause the BFD in Taiwan.