黄河中上游能源化工区大气污染跨界环境影响分析

对黄河中上游能源化工区近地面和边界层顶气象流场及该区域大气污染长距离输送产生的跨界环境影响趋势特征进行了分析。结果表明,该区域近地面和边界层顶的流场总体上为西北向东南流动,大气污染物长距离输送可能影响的区域主要为华中地区;区域冷空气的活动对其大气污染物的清除起重要作用;通过CALPUFF模型的模拟结果表明,该区域SO2的影响程度和范围明显大于NO2,在区域内部存在污染物浓度较高的区域,但未对区外造成显著跨界环境影响。     

通榆河南段控制单元最大日负荷估算及分配研究

以通榆河南段控制单元为研究区，利用现有的平原感潮河网区水量模型、面源污染负荷统计模型和水环境容量模型，估算研究区2010年污染物最大日负荷总量（TMDL），并进行负荷削减和分配研究。结果表明：研究区COD、氨氮（NH3-N）90％保证率（2004年）下的水环境容量分别为7．76万t,0．37万t；xCN2010年污染物入河量，COD、NH3-N的最大年负荷分别为1．99万t,0．28万t。在研究区涉及的各县市中，海安、姜堰、东台是负荷削减的重点区域；对于不同的污染源，城镇生活点源和农业面源是研究区污染物总量控制的关键。     

多介质模型在海湾突发性有机污染中的应用及案例分析

为弥补海洋环境监测能力不足,利用受潮汐动力控制海湾中的多介质模型,预测突发性污染发生后海洋环境中污染物的浓度变化信息,并对突发性污染物的迁移转化进行了计算.模型中使用逸度方法和质量平衡算法,预测非挥发性有机物在水、沉积物和鱼中的浓度.在象山港的应用结果表明,该模型可较快速地给出污染物在不同介质中的浓度变化信息,为环境监...     

同位素技术在环境科学研究中的应用进展

综述了近年来同位素技术在国内外环境科学研究中的应用状况,简要介绍了同位素技术的应用原理与分析方法。总结了放射性同位素技术和稳定同位素技术在环境史重建、污染物示踪及源解析等方面的应用,重点介绍了稳定同位素技术在水、大气和土壤污染物源解析中的应用进展。对未来同位素技术在环境科学研究中的应用进行了展望,提出了多种同位素联合解析、建立和完善定量源解析模型等建议。     

钢铁厂及其周围环境中二噁英和多氯联苯土-气交换研究

利用逸度模型及其相关算法,研究了东北某钢铁厂及其周围居住区、背景对照区二噁英和多氯联苯污染物土－气迁移方向和交换通量。结果表明,二噁英主要由大气向土壤迁移,其土－气交换通量为每月618~5373mg/km²,多氯联苯污染物主要由土壤向大气迁移,交换通量为每月47~230mg/km²。基于实测污染物的浓度和逸度模型能较好地估算该地区二噁英 、多氯联苯等持久性有机污染物的土－气界面迁移规律。     

典型行业废水特征有机污染物排放控制标准探讨

通过对典型行业废水有机污染物排放现状的调查研究，探讨我国现有污水排放标准存在的问题，即现有的标准不能有效控制废水特征有机污染物排放。依据美国环保局多介质环境模型，用不同方法计算水介质排放环境目标值，用该值作为标准限值，评价典型行业废水有机污染物监测结果。针对典型行业废水有机污染物排放特征，提出制订排放控制标准的建议。     

机动车排放尾气中挥发性有机污染物对环境空气质量影响初探

采用气相色谱/质谱检测技术,对汽油和柴油、汽油车和柴油车排放气体以及天津市环境空气中主要挥发性有机污染物(VOCs)的组成种类及比例进行探析.结果表明,机动车排放的尾气中主要有机污染物为芳香烃类、烷烃类和烯烃类化合物,其中芳香烃类含量最高;汽油车排放的芳香烃类化合物浓度高于柴油车排放的浓度;机动车尾气对交通区域空气质量的影响高于工厂区(非化工区)、生活小区和公园.     

线性修正趋势分析技术在环境质量预警预测中的应用

线性修正趋势分析法根据历年数据建立线性模型并进行修正,预测年度、季环境要素污染物变化情况,可应用于空气质量、水环境质量、噪声环境质量、污染源污染物排放等预警预测数据统计分析评价,为环境决策提供技术支持。本文通过实例论述了该方法在实际工作中的应用。     

典型产业集聚区地下水优先控制污染物筛选

为明确地下水污染高风险区环境管控目标，快速识别优先控制污染物，于2022年以典型产业集聚区为研究对象，利用污染源产排污状况和地下水基础环境状况调查结果，将污染评价和环境风险相结合，优选污染源超标率、地下水环境污染等级、生物毒性参数和环境迁移转化参数作为筛选因子，利用层次分析法和综合分析法，计算各污染指标综合得分，建立筛选地下水优先控制污染物的方法体系。筛选结果显示，该产业集聚区的优先控制污染物相关指标包括：硝酸盐、耗氧量、石油类、总硬度、亚硝酸盐、镍、锌、硫酸盐、甲苯、氨氮等。筛选结果与园区主导行业产排污特征和地下水质量现状相符，具有一定的可靠性，可为科学、精准管控高风险区地下水质量提供依据。     

江苏响水爆炸事件环境应急监测工作几点思考

2019年3月21日,盐城市响水县陈家港化工园区的江苏天嘉宜化工有限公司发生爆炸事故,江苏省环境监测力量于事故发生后第一时间奔赴现场开展应急监测。通过收集和整理事故发生后环境应急监测工作的详细开展过程、事故现场污染物浓度变化数据,评估了事故应急处置期间污染物对周边环境的影响情况;通过对应急监测的工作措施进行简要分析,总结了应急监测工作在突发性环境污染事件中的作用与影响;指出该事故中监测机构应急监测技术能力的瓶颈和不足,并从根本上为提高应急监测能力提出建议。     

Water Conservation Versus Soil Salinity Control

This paper tackles the increasingly significant problem of irrigation-induced soil salinity within a groundwater management model. Irrigation can result not only in heavier salt concentrations but also in the removal of salt from the soil through return flows. Given these contradictory observations, we are interested in the effects on soil salt concentration if irrigation efficiency is improved. We develop a model of salt concentration patterns in both soil and groundwater. We introduce a negative externality to the production process by assuming that soil degradation due to higher soil salinity affects total factor productivity. Within this framework, we show that in the presence of this externality, increasing irrigation efficiency can lead to higher or lower soil salt concentration, depending on the social cost of transferring salt from one reservoir to another.     

一种基于可靠性理论的区域环境质量风险评价模型探讨

首先从理论上研究了用可靠性理论进行区域环境质量风险评价需要解决的几个基本问题,在此基础上,构建了环境污染故障树和区域环境质量风险评价的数学模型.这是一种以概率为基础的、新的区域环境质量风险评价模型,对于研究污染型工业系统的环境质量风险具有重要意义.     

A two-stage DEA approach for environmental efficiency measurement

The slacks-based measure (SBM) model based on the constant returns to scale has achieved some good results in addressing the undesirable outputs, such as waste water and water gas, in measuring environmental efficiency. However, the traditional SBM model cannot deal with the scenario in which desirable outputs are constant. Based on the axiomatic theory of productivity, this paper carries out a systematic research on the SBM model considering undesirable outputs, and further expands the SBM model from the perspective of network analysis. The new model can not only perform efficiency evaluation considering undesirable outputs, but also calculate desirable and undesirable outputs separately. The latter advantage successfully solves the "dependence" problem of outputs, that is, we can not increase the desirable outputs without producing any undesirable outputs. The following illustration shows that the efficiency values obtained by two-stage approach are smaller than those obtained by the traditional SBM model. Our approach provides a more profound analysis on how to improve environmental efficiency of the decision making units.     

Soil Organic Carbon (SOC) Equilibrium and Model Initialisation Methods: an Application to the Rothamsted Carbon (RothC) Model

Carbon (C) emissions from anthropogenic land use have accelerated climate change. To reduce C emissions, dynamic models can be used to assess the impact of human drivers on terrestrial C sequestration. Model accuracy requires correct initialisation, since incorrect initialisation can influence the results obtained. Therefore, we sought to improve the initialisation of a process-based SOC model, RothC, which can estimate the effect of climate and land-use change on SOC. The most common initialisation involves running the model until equilibrium (‘spin-up run’), when the SOC pools stabilise (method 1). However, this method does not always produce realistic results. At our experimental sites, the observed SOC was not at equilibrium after 10 years, suggesting that the commonly used spin-up initialisation method assuming equilibrium might be improved. In addition to method 1, we tested two alternative initialisations for RothC that involved adjusting the total or individual SOC pool equilibrium values by regulating the C input during the entire spin-up initialisation period (method 2) and initialising each SOC pool with recently measured SOC values obtained by SOC fractionation (method 3). Analysis of the simulation accuracy for each model initialisation, quantified using the root mean square error (RMSE), indicated that a variant of method 2 that involved adjusting the equilibrium total SOC to observed values (method 2-T) generally showed less variation in the individual SOC pools and total SOC. Furthermore, as total SOC is the sum of all SOC pools, and because total SOC data are more readily available than the individual SOC pool data, we conclude that method 2-T is best for initialising RothC.     

Optimal Sampling and Problematic Likelihood Functions in a Simple Population Model

Markov chains provide excellent statistical models for studying many natural phenomena that evolve with time. One particular class of continuous-time Markov chain, called birth–death processes, can be used for modelling population dynamics in fields such as ecology and microbiology. The challenge for the practitioner when fitting these models is to take measurements of a population size over time in order to estimate the model parameters, such as per capita birth and death rates. In many biological contexts, it is impractical to follow the fate of each individual in a population continuously in time, so the researcher is often limited to a fixed number of measurements of population size over the duration of the study. We show that, for a simple birth–death process, with positive Malthusian growth rate, subject to common practical constraints, there is an optimal schedule for measuring the population size that minimises the expected confidence region of the parameter estimates. Throughout our exposition of the optimal experimental design, we compare it to a simpler equidistant design, where the population is sampled at regular intervals. This is an experimental design worthy of comparison since it can represent a much simpler design to implement in practice. In order to find optimal experimental designs for our population model, we make use of a combination of useful statistical machinery. Firstly, we use a Gaussian diffusion approximation of the underlying discrete-state Markov process, which allows us to obtain analytical expressions for Fisher’s information matrix (FIM), which is crucial to optimising the experimental design. We also make use of the cross-entropy method of stochastic optimisation for the purpose of maximising the determinant of FIM to obtain the optimal experimental designs. Our results show that the optimal schedule devised by others for a simple model of population growth without death can be extended, for large populations, to the two-parameter model that incorporates both birth and death. For the simple birth–death process, we find that the likelihood surface is also problematic and poses serious problems for point estimation and easily defining confidence regions. A Bayesian approach to inference is proposed as a way in which these problems could be circumvented.     

Oracle-based optimization applied to climate model calibration

In this paper, we show how oracle-based optimization can be effectively used for the calibration of an intermediate complexity climate model. In a fully developed example, we estimate the 12 principal parameters of the C-GOLDSTEIN climate model by using an oracle-based optimization tool, Proximal-ACCPM. The oracle is a procedure that finds, for each query point, a value for the goodness-of-fit function and an evaluation of its gradient. The difficulty in the model calibration problem stems from the need to undertake costly calculations for each simulation and also from the fact that the error function used to assess the goodness-of-fit is not convex. The method converges to a ‘best fit’ estimate over 10 times faster than a comparable test using the ensemble Kalman filter. The approach is simple to implement and potentially useful in calibrating computationally demanding models based on temporal integration (simulation), for which functional derivative information is not readily available.     

工业居民混合区降尘含量的灰色GM(1,1)预测

本文采有灰色理论中的预测方法，以９０—９４年度的降尘监测数据为基础，建立了邯郸市工业居民混合区降尘含量的ＧＭ（１，１）预测模型，其数学表达式为：＾Ｘ（１）（ｋ＋１）＝５６０２９ｅ００５６４１２ｋ－５３０８３，经三种方法对模型精度检验，由模式计算的预测值与历年的实测值的平均相对误差仅为－０００８％，关联度系数γ＝０９０５，后验差比值Ｃ＝００３，模型精度为一级，经与９５年度预测值与实测值对比验证，误差２７４％，说明本模型能真实地反映该区降尘的发展变化规律，预测数据可作为制定该区降尘防治规划的依据。     

Slow-fast dynamics in Wonderland

Taking Wonderland—a simple model of demographic, economic and environmental interactions—as our artificial world, we illustrate the use of geometric singular perturbation theory in environmental demoeconomics. The theory of slow-fast dynamics helps us to gain new insights into the system's behaviour and enables one to reduce the inherent unpredictability of a “natural catastrophe” in Wonderland. Though we cannot predict the exact date of such an “environmental crash”, we can state the specific demographic, economic and environmental constellations of our artificial world at which the sustainability of nature becomes endangered.     

Anticipating Climate Threshold Damages

Several integrated assessment studies have concluded that future learning about the uncertainties involved in climate change has a considerable effect on welfare but only a small effect on optimal short-term emissions. In other words, learning is important but anticipation of learning is not. We confirm this result in the integrated assessment model “model of investment and technological development” for learning about climate sensitivity and climate damages. If learning about an irreversible threshold is included, though, we show that anticipation can become crucial both in terms of necessary adjustments of pre-learning emissions and resulting welfare gains. We specify conditions on the time of learning and the threshold characteristic, for which this is the case. They can be summarized as a narrow “anticipation window.”