Formulation and Performance of NBR/CR-Based High-Damping Rubber Composites for Soundproof Using Orthogonal Test.

Multiple functional-material-filled nitrile butadiene rubber/chloroprene rubber (NBR/CR) acoustic composites were extensively studied and prepared. According to the orthogonal test table L25 (56), 25 groups of samples were prepared by using a low-temperature one-time rubber mixing process. With tensile strength, average transmission loss, and damping peak as indexes, the influence degree of different factors and levels on the properties of acoustic composites was quantitatively discussed and analyzed. The matrix weight analysis was employed to optimize the material formula of rubber composites, and the corresponding influence weight was given. Results showed that the acoustic composite with blending ratio of 70/30 for NBR/CR matrix had preferable mechanical and acoustic properties; adding mica powder (MP) and montmorillonite (MMT) in matrix contributed to improve all above three indexes owing to their specific lamellar structures; hollow glass beads (HGB) had a positive influence on improving acoustic property due to its hollow microcavities, however, it had a negative impact on damping property because of the smooth spherical surfaces. Accordingly, the optimal formulation was found to be NBR/CR blending ratio of 70/30, MP of 10 phr (per hundred rubber), HGB of 4 phr, and MMT of 10 phr.

Seawater intrusion alters nitrogen cycling patterns through hydrodynamic behavior and biochemical reactions: Based on Bayesian isotope mixing model and microbial functional network.

Seawater intrusion is a global coastal environmental issue of great concern and significantly impacts the regional biogeochemical environment and material cycles, including nitrogen cycling. To reveal the mechanism of seawater intrusion altering nitrogen cycling patterns through hydrodynamic behavior and biochemical reactions, the Bayesian mixing model (δ15N-NO3- and δ18O-NO3-) and 16S rDNA gene amplicon sequencing are used to establish nitrogen cycling pathways and microbial functional network. The results show that the nitrate in the coastal groundwater is from manure and septic waste (M&S, over 44 %), soil organic nitrogen (SON, over 20 %), and nitrogen fertilizer (FN, over 16 %). The hydrological interaction has promoted the coupling between material cycling and microbial community in the coastal groundwater systems. Among them, precipitation infiltration has caused the gradual decrease of specific microbes along the flow direction, such as Lactobacillus, Acinetobacter, Bifidobacterium, etc. And seawater intrusion has caused the mutations of specific microbes (Planktomarina, Clade_Ia, Wenyingzhuangia, Glaciecola, etc.) and convergence of microbial community at the salt-freshwater interface in the aquifer. In the coastal intruded aquifer systems, the nitrogen cycling pattern can be divided into oxidation and reduction processes. The oxidation process involves the enhancement of nitrification while the weakening of denitrification and anammox with the increase of aquifer depth. The reduction process consists of the enhancement of denitrification and anammox while the erosion of nitrification and ammonification with increased seawater intrusion. In addition, seawater intrusion can mitigate nitrate contamination by promoting denitrification and anammox in coastal areas.

Eugenol-derived Organic Liquids as an In-Situ CO2 Capturing and Conversion System for Eugenol-based Polycarbonate Synthesis.

The significant development of catalytic biomass conversion has provided a large library of chemicals ready for subsequent upgrading to polymerisable monomers for the design and preparation of sustainable polymers. In this study, hydroxyethylation of eugenol by using green ethylene carbonate as alkylation reagent and cheap tetrabutylammonium iodide ionic liquids as green solvents and catalysts produced 2-(4-allyl-2-methoxyphenoxy)ethan-1-ol with a 85% yield, which could be used to construct an in situ CO2 capture and conversion system by taking the reversible chemistry of alcoholic compounds with CO2 in the presence of superbases, on which α,ω-diene functionalized carbonate monomers were successfully prepared and were applied in thiol-ene click and acyclic diene metathesis polymerisation (ADMET), producing a series of poly(thioether carbonate)s and unsaturated aromatic aliphatic polycarbonates with moderate molecular weights and satisfactory thermal properties. The structures of the formed CO2 reversible ILs, the polymerisable monomers and the corresponding polymers were fully characterized by various technologies.

Insight into the mechanism of phosphate and cadmium co-transport in natural soils.

Phosphate is ubiquitous in the environment and can affect the transport of heavy metals in the subsurface systems. In this study, column experiments were conducted to systematically evaluate the effects of phosphate on the transport of Cd in natural soils (RS, BS) under different ionic strength (IS) conditions. The presence of phosphate significantly retarded the transport of Cd in the soils. The extent of retardation was closely associated with phosphate concentrations, IS and soil properties. Increasing phosphate adsorption induced more negative surface charges on soils, thereby contributing to greater retention of Cd through electrostatic attraction. In contrast, higher IS not only promoted mobility of Cd, but also reduced the retardation effect of phosphate on Cd transport in soils. Moreover, higher Fe/Al oxides contents in RS exhibited a more pronounced effect of phosphate on Cd retardation. Our results indicated that electrostatic interaction was the predominant mechanism controlling co-transport of Cd with phosphate, but no ternary surface complexes was observed in the Cd LIII-edge XANES spectra. Our findings highlight the critical role of phosphate in retarding Cd transport in natural soils, which should be considered in assessing environmental risks of heavy metals in the subsurface.

Improved comprehensive ecological risk assessment method and sensitivity analysis of polycyclic aromatic hydrocarbons (PAHs).

Based on the existing comprehensive ecological risk assessment methods of PAHs, this paper proposed an improved hierarchical Archimedean copula integral assessment (HACIA) model with the optimization in the model selection mechanism and accelerating the calculation speed, and according to which performed the sensitivity analysis of the integrated risk relative to the underlying grouped risk probability. Taihu Lake in China and the Bay of Santander in Spain were taken as study areas, whose samples were obtained and extracted concentrations of 16 priority polycyclic aromatic hydrocarbons (PAHs). After briefly analyzing their concentration characteristics and source, their comprehensive ecological risks were evaluated by the improve HACIA model and their sensitivity was also analyzed. The results proved that, for Taihu Lake, pyrogenic sources occupied the dominance, especially grass, coal and wood combustion, while the risk proportion of 5-rings PAHs was the lowest indeed based on the improved HAICA model. For the Bay of Santander, source apportionment indicated both petrogenic and pyrogenic sources, mainly from vehicle emissions including gasoline and diesel engines, and 4-ring PAHs were urgently needed to be managed. However, the sensitivity analysis results of two study areas showed that the most effective control target for reducing integral risk has no obvious relationship with the maximum grouped risk. And a clear linear relationship between the maximum sensitivity range and the logarithm of the initial overall risk only presented in one of study areas, which required further research to clarify. In brief, the improved HACIA model is helpful to evaluate the comprehensive ecological risk of 16 PAHs, and formulate risk management strategies based on grouped risk assessment and sensitivity analysis, with the former points out the admonitory risk and the latter helps to find the most effective mitigation measures.

Sustainable Aromatic Aliphatic Polyesters and Polyurethanes Prepared from Vanillin-Derived Diols via Green Catalysis.

The design and preparation of polymers by using biobased chemicals is regarded as an important strategy towards a sustainable polymer chemistry. Herein, two aromatic diols, 4-(hydroxymethyl)-2-methoxyphenol and 2-(4-(hydroxymethyl)-2-methoxyphenoxy)ethanol, have been prepared in good yields through the direct reduction of vanillin and hydroxyethylated vanillin (4-(2-hydroxyethoxy)-3-methoxybenzaldehyde) using NaBH4, respectively. The diols were submitted to traditional polycondensation and polyaddition with acyl chlorides and diisocyanatos, and serials of new polyesters and polyurethanes were prepared in high yields with moderate molecular weight ranging from 17,000 to 40,000 g mol-1. Their structures were characterized by 1H NMR, 13C NMR and FTIR, and their thermal properties were studied by TGA and differential scanning calorimetry (DSC), indicating that the as-prepared polyesters and polyurethanes have Tg in the range of 16.2 to 81.2 °C and 11.6 to 80.4 °C, respectively.

Transport of polystyrene nanoplastics in natural soils: Effect of soil properties, ionic strength and cation type.

Nanoplastics as emerging pollutants have caused growing concerns and posed potential threats to the environment. Nonetheless, only few studies investigated transport behaviors of nanoplastics in natural soils. In this study, column experiments were conducted to investigate the effect of soil properties, ionic strength and cation type on the transport of polystyrene nanoplastics (PSNPs) in a desert soil (DS), a black soil (BS) and a red soil (RS). The effluent recovery of PSNPs in three soils followed the order of DS (0%-96.8%) > BS (0%-87.5%) > RS (0%). The retention of PSNPs was positively correlated with Fe/Al oxides contents (DS: Fe-2.69%, Al-12.6%; BS: Fe-4.04%, Al-15.9%; RS: Fe-6.57%, Al-26.9%), whereas negatively correlated with soil pH (DS: 9.75; BS: 6.57; RS: 4.97). Soil minerals and pH were thus identified as the crucial soil properties determining transport of PSNPs, due to their coupled effects on surface charges to affect electrostatic interactions between soils and PSNPs. In addition, increasing solution ionic strength strongly inhibited the transport of PSNPs in the DS (0%-96.8%) and BS (0%-87.5%). Ca2+ (IS: 1-5 mM) was more pronounced in enhancing PSNP retention than Na+ (IS: 1-20 mM). Our findings highlight that the transport and fate of PSNPs in natural soils are highly sensitive to soil physicochemical properties, ionic strength and cation type, and reveal that nanoplastics have strong mobility ability in soils with high pH and low Fe/Al oxides contents, which may pose potential risks to the soil and groundwater environment.

Identifying key factors of the seawater intrusion model of Dagu river basin, Jiaozhou Bay.

Seawater intrusion is a complex groundwater - seawater interaction process, and it is influenced by many factors from ground surface to underground, from groundwater to seawater. Generally, for seawater intrusion model, some model parameters and boundary conditions are always specified by model users' personal experiences or literature's reference value. The defective model would damage the groundwater management for controlling and preventing seawater intrusion when making decisions are based on this model. In order to improve the reliability of seawater intrusion model, the influences of model inputs on output should be identified prior at optimizing model inputs. Dagu river basin, Jiaozhou Bay is one of the most serious areas of seawater intrusion in China, and it is chosen as the study area in this study. The seawater intrusion model of Dagu river basin is built based on a general program SEAWAT4. The key influence factors of model output are analyzed by two sensitivity analysis methods, i.e., stepwise regression and mutual entropy. The results demonstrated that the most important influence factors which have largest sensitivities to groundwater Cl- concentration are the precipitation rate and groundwater pumping in agriculture area. In addition, the hydraulic conductivity of zone 1 has a non-negligible influence on seawater intrusion process. Stepwise regression analysis is capable of identifying most important influence factor, and it can't handle complicated nonlinear input-output relationship. Mutual entropy analysis is reliable for identifying the influence factors for complex seawater intrusion model.

A hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series.

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

A multidimension cloud model-based approach for water quality assessment.

Lakes are vitally important, because they perform a multitude of functions, such as water supply, recreation, fishing, and habitat. However, eutrophication limits the ability of lakes to perform these functions. In order to reduce eutrophication, the first step is its evaluation. The process of evaluation entails randomness and fuzziness which must therefore be incorporated. This study proposes an eutrophication evaluation method, named Multidimension Normal Cloud Model (MNCM). The model regards each evaluation factor as a one-dimension attribute of MNCM, chooses reasonable parameters and determines the weights of evaluation factors by entropy. Thus, all factors of MNCM belonging to each eutrophication level are generated and the final eutrophication level is determined by the certainty degree. MNCM is then used to evaluate eutrophication of 12 typical lakes and reservoirs in China and its results are compared with those of the reference method, one-dimension normal cloud model, related weighted nutrition state index method, scoring method, and fuzzy comprehensive evaluation method. Results of MNCM are found to be consistent with the actual water status; hence, MNCM can be an effective evaluation tool. With respect to the former one-dimension normal cloud model, parameters of MNCM are improved without increasing its complexity. MNCM can directly determine the eutrophication level according to the degree of certainty and can determine the final degree of eutrophication; thus, it is more consistent with the complexity of water eutrophication evaluation.

A cloud model-based approach for water quality assessment.

Water quality assessment entails essentially a multi-criteria decision-making process accounting for qualitative and quantitative uncertainties and their transformation. Considering uncertainties of randomness and fuzziness in water quality evaluation, a cloud model-based assessment approach is proposed. The cognitive cloud model, derived from information science, can realize the transformation between qualitative concept and quantitative data, based on probability and statistics and fuzzy set theory. When applying the cloud model to practical assessment, three technical issues are considered before the development of a complete cloud model-based approach: (1) bilateral boundary formula with nonlinear boundary regression for parameter estimation, (2) hybrid entropy-analytic hierarchy process technique for calculation of weights, and (3) mean of repeated simulations for determining the degree of final certainty. The cloud model-based approach is tested by evaluating the eutrophication status of 12 typical lakes and reservoirs in China and comparing with other four methods, which are Scoring Index method, Variable Fuzzy Sets method, Hybrid Fuzzy and Optimal model, and Neural Networks method. The proposed approach yields information concerning membership for each water quality status which leads to the final status. The approach is found to be representative of other alternative methods and accurate.

Mid- and long-term runoff predictions by an improved phase-space reconstruction model.

In recent years, the phase-space reconstruction method has usually been used for mid- and long-term runoff predictions. However, the traditional phase-space reconstruction method is still needs to be improved. Using the genetic algorithm to improve the phase-space reconstruction method, a new nonlinear model of monthly runoff is constructed. The new model does not rely heavily on embedding dimensions. Recognizing that the rainfall-runoff process is complex, affected by a number of factors, more variables (e.g. temperature and rainfall) are incorporated in the model. In order to detect the possible presence of chaos in the runoff dynamics, chaotic characteristics of the model are also analyzed, which shows the model can represent the nonlinear and chaotic characteristics of the runoff. The model is tested for its forecasting performance in four types of experiments using data from six hydrological stations on the Yellow River and the Yangtze River. Results show that the medium-and long-term runoff is satisfactorily forecasted at the hydrological stations. Not only is the forecasting trend accurate, but also the mean absolute percentage error is no more than 15%. Moreover, the forecast results of wet years and dry years are both good, which means that the improved model can overcome the traditional ''wet years and dry years predictability barrier,'' to some extent. The model forecasts for different regions are all good, showing the universality of the approach. Compared with selected conceptual and empirical methods, the model exhibits greater reliability and stability in the long-term runoff prediction. Our study provides a new thinking for research on the association between the monthly runoff and other hydrological factors, and also provides a new method for the prediction of the monthly runoff.

Assessing the pollution risk of a groundwater source field at western Laizhou Bay under seawater intrusion.

Coastal areas have great significance for human living, economy and society development in the world. With the rapid increase of pressures from human activities and climate change, the safety of groundwater resource is under the threat of seawater intrusion in coastal areas. The area of Laizhou Bay is one of the most serious seawater intruded areas in China, since seawater intrusion phenomenon was firstly recognized in the middle of 1970s. This study assessed the pollution risk of a groundwater source filed of western Laizhou Bay area by inferring the probability distribution of groundwater Cl(-) concentration. The numerical model of seawater intrusion process is built by using SEAWAT4. The parameter uncertainty of this model is evaluated by Markov Chain Monte Carlo (MCMC) simulation, and DREAM(ZS) is used as sampling algorithm. Then, the predictive distribution of Cl(-) concentration at groundwater source field is inferred by using the samples of model parameters obtained from MCMC. After that, the pollution risk of groundwater source filed is assessed by the predictive quantiles of Cl(-) concentration. The results of model calibration and verification demonstrate that the DREAM(ZS) based MCMC is efficient and reliable to estimate model parameters under current observation. Under the condition of 95% confidence level, the groundwater source point will not be polluted by seawater intrusion in future five years (2015-2019). In addition, the 2.5% and 97.5% predictive quantiles show that the Cl(-) concentration of groundwater source field always vary between 175mg/l and 200mg/l.

Analysis of spatial-temporal distributions of nitrate-N concentration in Shitoukoumen catchment in northeast China.

This article discusses the generation and migration process of nitrate-N pollution in shallow groundwater caused by agricultural nonpoint source pollution in the catchment area of Shitoukoumen Reservoir in northeast China. By monitoring the shallow groundwater nitrate-N in the low-water period, the normal season, and high-flow period in the study area for a year, it was found that the nitrate-N concentration in the shallow groundwater of this area had a seasonal variation in both spatial and time distribution. In the time distribution, the peak value appeared in July, the high-flow period, and the valley value appeared in April, the low-water period, and showed a significant correlation with the time distribution of fertilization rate and rainfall. In the spatial distribution of nitrate-N pollution, when the distribution in shallow groundwater was analyzed separately in the three different periods (low-water period, the normal season, and high-flow period) and the discipline transference and enrichment of nitrate-N pollution in shallow groundwater was determined, this indicated that the region in the southeast study area where runoff conditions were better was less contaminated, and the region where runoff conditions were poor, as well as the region along the river were seriously polluted. The nitrate-N concentration in shallow groundwater was distributed mainly along the path of groundwater flow and was excreted in the drainage region. This showed that the spatial distribution of nitrate-N concentration in the shallow groundwater of the entire region was mainly controlled by the groundwater flow system. At the same time, in the middle and lower reaches of the study area, the seasonal changes in the recharged-excreted relationship between groundwater and river caused seasonal differences in the spatial distribution of nitrate-N pollution in groundwater. The combined effects of the groundwater mobility and the surface river resulted in a poor correlation between the groundwater nitrate-N concentration and land-use types. Only in the plain area where there was little influence from groundwater runoff and the surface river did the groundwater nitrate-N concentration correlate with land-use types. The spatial and time distribution of nitrate-N concentration in the shallow groundwater of the study area was impacted by agricultural nonpoint source pollution, the groundwater flow system, and the surface river and formed a concentration response system which uses basins as a unit.