高温状态下受热时间对膨润土物性指标的影响∗

高温状态下膨润土的物性指标变化是高放核废物深地质处置库设计的重要参数之一。通过高温状态下不同受热时间MX80钠基膨润土的比重、液塑限、自由膨胀率试验,研究了受热时间对膨润土基本物性指标的影响规律;并通过热重分析(TGA)、X衍射(XDR)、电镜扫描(SEM)试验,从微观角度对上述变化规律进行了合理解释。研究结果表明:随着受热时间的增加,膨润土的比重、液塑限、自由膨胀率在受热15～30天内急剧下降,降幅分别为2.5%、3.1%和28.3%,之后各值变化量很小;高温状态下,膨润土的矿物成分转化、结合水脱附、微观形貌改变是膨润土物性指标发生变化的根本原因,三者之间相互作用和彼此影响。     

生态社会主义及其双重价值研究

生态社会主义是当代西方一种具有重要影响的左翼社会思潮,它致力于生态原则与社会主义的结合,要求超越资本主义与现存的社会主义模式,主张在对资本主义进行批判和建议的基础上,建立新的绿色社会、生态现代化的社会和全面发展的社会。生态社会主义具有历史的进步性与理论和实践上的局限性双重社会价值,我们应当汲取生态社会主义的积极思想,为中国生态文明建设提供有益借鉴。     

表观遗传学机制与空气污染的健康效应

空气污染是一个全球性的问题,并且具有深远的环境影响。暴露于空气污染会对人体健康产生许多不同的影响,理解空气污染的健康效应又是一个复杂命题,既要考虑不同类型的污染物同时也要考虑相关疾病的复杂性。然而越来越多的研究表明,表观遗传学在空气污染相关疾病的发生、发展中发挥着重要的作用。空气污染物可引起DNA甲基化、组蛋白修饰和miRNA表达等表观遗传学改变,这种改变往往发生在疾病产生的早期,因此相关研究不仅可以了解疾病的发病机制,而且还为疾病早期诊断和预防筛选可能的标志物。本文综述了表观遗传学的几种修饰方式和空气污染物造成不良健康损伤机制的一些研究进展。     

预测沉积物-水微宇宙系统中化学品浓度变化的多介质模型

沉积物-水微宇宙系统是经济合作发展组织(Organisation for Economic Co-Operation and Development,OECD)颁布的化学品测试准则中推荐的试验系统之一,可用来测试化学品对底栖生物的慢性毒性。为了在试验前对化学品的浓度变化进行预测,进而确定试验方法,以摇蚊慢性毒性试验系统为例,采用环境多介质模型的建模方法,构建了一种可通过化学品理化性质和试验系统参数,对化学品在沉积物-水试验系统中浓度变化进行预测的模型。结合试验数据和文献资料,给出了模型中试验系统参数的推荐取值,并使用Matlab软件中的Simulink工具对模型进行编程和求解。以此模型为基础,给出了模型在3个方面的应用,即预测蓄积时间、预测平衡时间以及拟合试验数据。对80种已有或假想化学品的蓄积时间和平衡时间进行了计算,得出的范围分别为1~204 d和1~73 d。此外,适当修改模型结构和模型参数,也可将其应用于其他暴露场景中。但使用模型对化学品浓度进行预测时发现,模型仅对沉积物中化学品浓度的预测结果较为准确,而对水中化学品浓度的预测结果与实测值相差1~2个数量级。模型对浓度的预测精度未来仍需进一步提高。上述研究结果完善了沉积物-水微宇宙系统试验方法。     

Pollution levels and characteristics of phthalate esters in indoor air in hospitals

The concentrations of phthalate esters(PAEs) in Chinese hospitals were investigated by simultaneously determining concentrations of gas- and particle-phase PAEs. PAEs were detected in two third-class first-grade hospitals, two second-class first-grade hospitals, and a community health service center. Hospital drugstores had the highest concentration(24.19 μg/m3), which was 1.54 times that of newly decorated houses. The second highest concentration was found in the transfusion rooms, averaging 21.89 μg/m3; this was followed by the concentrations of PAEs in the nurse's workstations, the wards, and the doctor's offices, with mean concentrations of 20.66, 20.0, and 16.92 μg/m3, respectively. The lowest concentrations were found in the hallways(16.30 μg/m3). Of the six different kinds of PAEs found, major pollutants included diethyl phthalates, dibutyl phthalates, butylbenzyl phthalates and di(2-ethylhexyl) phthalates, comprising more than 80% of all PAEs present.Meanwhile, a comparison between different wards showed that PAE concentrations in the maternity wards were 1.63 times higher than in the main wards. Based on known health hazards, our results suggest that the PAEs seriously influence the health of the pregnant women and babies; therefore, it is of great importance to take the phthalate concentrations in hospitals into consideration. In addition, hospital indoor air was more seriously contaminated than the air of newly decorated houses.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

川中遂宁地区红层地下水水化学特征及主要离子来源分析

四川红层区居民用水主要为就近开采的地下水,水质参差不齐,研究红层地下水水文地球化学特征,对于宜井区选择,红层水源地用水安全具有重要意义。利用在遂宁船山区采集的70组浅层地下水水样,采用数理统计分析、离子比例关系等方法,分析了红层浅层地下水水化学特征及主要离子来源,研究得出:区内地下水中阴离子以HCO_3~-(164.00～571.91 mg·L~(-1))为主,其次是SO_4~(2-)(13.96～1 000.00 mg·L~(-1)),阳离子以Ca~(2+)(45.70～313.00 mg·L~(-1))为主,其中孔隙水的水化学类型主要为HCO_3,SO_4-Ca,SO_4-Mg型,裂隙水以HCO_3-Ca,Mg型与HCO_3-Ca型为主;地下水中离子成分主要由岩石风化产生,来源于地层中的方解石、白云石、石膏、芒硝等矿物的风化溶解,蒸发浓缩、地下水酸碱环境及人类活动对地下水成分造成了一定的影响;浅层地下水质量以Ⅲ～Ⅳ类为主,污染物主要为TDS、Fe~(3+)等原生污染,也存在NO_3~-、NO_2~-等人类活动污染;红层宜井区主要为平坝、丘坡坡脚及沟谷一带,为避免污染指标超标,井深不宜超过25m,远离养殖场、农厕等地下水易受人类活动污染的环境,防止鱼塘、农田等地表水进入井中。     

Polynomial Surrogates for Open-Channel Flows in Random Steady State

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on the more realistic case of the Garonne River (France) for which a global sensitivity analysis using sparse least-angle regression was performed to reduce the size of the stochastic problem. For both cases, Galerkin projection approximation coupled to Gaussian quadrature that involves a limited number of forward model evaluations is compared with least-square regression for computing the coefficients when the surrogate is parameterized with respect to the local friction coefficient and the upstream discharge. The results showed that a gPC-surrogate with total polynomial degree equal to 6 requiring 49 forward model evaluations is sufficient to represent the water level distribution (in the sense of the \(\ell _2\) norm), the probability density function and the water level covariance matrix for further use in the framework of data assimilation. In locations where the flow dynamics is more complex due to bathymetry, a higher polynomial degree is needed to retrieve the water level distribution. The use of a surrogate is thus a promising strategy for uncertainty quantification studies in open-channel flows and should be extended to unsteady flows. It also paves the way toward cost-effective ensemble-based data assimilation for flood forecasting and water resource management.     

Irrigation Water Allocation Using an Inexact Two‐Stage Quadratic Programming with Fuzzy Input under Climate Change

Agricultural irrigation accounts for nearly 70% of the total water use around the world. Uncertainties and climate change together exacerbate the complexity of optimal allocation of water resources for irrigation. An interval‐fuzzy two‐stage stochastic quadratic programming model is developed for determining the plans for water allocation for irrigation with maximum benefits. The model is shown to be applicable when inputs are expressed as discrete, fuzzy or random. In order to reflect the effect of marginal utility on benefit and cost, the model can also deal with nonlinearities in the objective function. Results from applying the model to a case study in the middle reaches of the Heihe River basin, China, show schemes for water allocation for irrigation of different crops in every month of the crop growth period under various flow levels are effective for achieving high economic benefits. Different climate change scenarios are used to analyze the impact of changing water requirement and water availability on irrigation water allocation. The proposed model can aid the decision maker in formulating desired irrigation water management policies in the wake of uncertainties and changing environment.