Conceptual Models as Hypotheses in Monitoring Urban Landscapes

Many problems and challenges of ecosystem management currently are driven by the rapid pace and spatial extent of landscape change. Parks and reserves within areas of high human population density are especially challenged to meet the recreational needs of local populations and to preserve valued environmental resources. The complex problem of managing multiple objectives and multiple resources requires an enormous quantity of information, and conceptual models have been proposed as tools for organizing and interpreting this information. Academics generally prefer a bottom-up approach to model construction that emphasizes ecologic theory and process, whereas managers often use a top-down approach that takes advantage of existing information to address more pragmatic objectives. The authors propose a formal process for developing, applying, and testing conceptual models to be used in landscape monitoring that reconciles these seemingly opposing perspectives. The four-step process embraces the role of hypothesis testing in the development of models and evaluation of their utility. An example application of the process to a network of national parks in and around Washington, DC illustrates the ability of the approach to systematically identify monitoring data that would both advance ecologic theory and inform management decisions.     

Predicting geogenic groundwater fluoride contamination throughout China

Endemic fluorosis exists in almost all provinces of China. The long-term ingestion of groundwater containing high concentrations of fluoride is one of the main causes of fluorosis. We used artificial neural network to model the relationship between groundwater fluoride concentrations from throughout China and environmental variables such as climatic, geological. and soil parameters as proxy predictors. The results show that the accuracy and area under the receiver operating characteristic curve of the model in the test dataset are 80.5% and 0.86%, respectively, and climatic variables are the most effective predictors. Based on the artificial neural network model, a nationwide prediction risk map of fluoride concentrations exceeding 1.5 mg/L with a 0.5 × 0.5 arc minutes resolution was generated. The high risk areas are mainly located in western provinces of Xinjiang, Tibet, Qinghai, and Sichuan, and the northern provinces of Inner Mongolia, Hebei and Shandong. The total number of people estimated to be potentially at risk of fluorosis due to the use of untreated high fluoride groundwater as drinking water is about 89 million, or 6% of the population. The high fluoride groundwater risk map helps the authorities to prioritize areas requiring mitigation measures and thus facilitates the implementation of water improvement and defluoridation projects.     

贸易、外商直接投资、经济增长与环境污染

选取1990—2002年中国30个省市贸易、外商直接投资(FDI)、经济和环境相关数据，从定性和定量描述的角度探讨贸易、FDI对我国环境库兹涅茨曲线(ECK)的影响。研究表明：贸易对中国的ECK没有直接影响。但考虑到贸易对经济增长的贡献，以及它在引进先进污染防治技术和环境管理思想方法方面的积极作用，积极发展对外贸易将有助于改善我国经济增长带来的环境污染问题。另一方面。FDI与污染物排放之间呈现出显著的正相关关系。在中国接受经济全球化影响的过程中，由于部分地区急于吸引外资，加之环境管理体系的不完善，外商直接投资在某些方面对我国环境造成了一定的负面影响。     

浅谈我国冰雹的分布规律及其保险对策

本文从冰雹灾害的分布规律着手,研究我国雹灾对策形式之一——雹灾保险,在对世界各国雹灾保险借鉴的基础上,选择我国雹灾保险的模式,建立适合我国国情的雹灾保险。     

Parameterizing,evaluating and comparing metapopulation models with data from individual-based simulations

Due to the lack of sufficient data and appropriate ecological information parameterizing predictive population dynamical models usually is a difficult task. The approach proposed in this study is meant to overcome this problem by using detailed individual-based simulations to generate artificial data. With short-term data samples, the models to be investigated can be parameterized and their predictions be compared. The flexibility of individual-based simulations as experimental tools also facilitates the evaluation and comparison of different (aggregated) model types. The presented approach is a step towards unifying models of different complexity. As an example we applied it to two metapopulation models of insect species in a highly fragmented landscape: the well-known incidence function model with a patch-based representation of space and a grid-based analogue. The models are tested with respect to their data requirement and recommendations for a better data sampling are derived.     

The models of niche and their application

Having studied the definitions of niche proposed by different ecologists, I have proposed a quantitative method of niche which can be applied to plants. Accordingly, the niche of an operational taxonomic unit (OTU) has been described by a mapping from its environmental set to the unit interval [0, 1], which enables a model of niche to be mathematically operational. The concepts of fundamental niche, realized niche, time niche, etc., may be described by using mathematical models related to each other, and the geometrical relationships between them can be revealed by a multi-dimensional surface. The uni-factor models are built upon the condition that the other factors are optimal for OTU, which are particular cases of the multi-factor models. The establishment of the quantitative relationship between these two kinds of models makes it possible to find out the plants' fundamental niche by doing uni-factor experiments. This may simplify the experiments in which the parameters in a practically applied model are to be estimated. The niche index introduced in this paper is related to average level and aftereffect of plant responses to the effects of its environment (i.e. “inertia”), thus it should be the basis of the simulation of plant seed yield and of its environmental evaluation. Accordingly, models of niche index, of plant seed yield, of plant growth and of environmental evaluation have been built which can be applied to the environmental evaluation or the prediction and management of plant (crop) production, etc.As an example of application, the models of wheat yield and its environmental assessment have been established and practically tested. The results of testing the model of wheat yield showed that the relative errors are 8% and 7.2%, respectively, in 1984 and 1986. The results of the environmental assessment of wheat reveal the fact that the insufficiency of the soil moisture at the 2th and 3th stages is the main restriction of the production of the wheat in Dinxi, Gansu Province, China.     

Leaching of atrazine, metolachlor and diuron in the field in relation to their injection depth into a silt loam soil

A field experiment was conducted on a Calcaric Cambisol soil to study the consequences of the penetration depth and properties of pesticides on the risk of subsequent leaching. Three pesticides with different mobility characteristics and bromide were injected at 30 cm (where soil organic matter (OM) was 2%) and 80 cm (soil OM 0.5%) on irrigated plots without a crop. The migration of injected solutes was assessed for two years by sampling the soil solution using six porous cups installed at 50 and 150 cm depth and by relating solute contents to drainage water flux estimated by the STICS model (Simulateur mulTIdisciplinaire pour les Cultures Standard). Pesticides injected at 30 cm were strongly retained so that no metolachlor or diuron was detected at 50 and 150 cm. The ratio of atrazine peak concentration in the soil solution to concentration in the injected solution (C/C₀) was 1 × 10⁻³ and 0.2 × 10⁻³, respectively, at 50 and 150 cm. When injected at 80 cm, (C/C₀) of atrazine, metolachlor and diuron were 10 × 10⁻³, 1 × 10⁻³ and 0.3 × 10⁻³ at 150 cm, respectively; 1/(C/C₀) was correlated with K_oc values reported from databases. The ratio of drainage volume to the amount of water at field capacity in the soil layer between the injection point at 30 cm and the water sampling level (V/V₀) at 50 and 150 cm was 0.6 and 0.9, respectively, for bromide and 1.6 and 1.0 for atrazine. V/V₀ of the injected solutes at 80 cm was for bromide, atrazine, metolachlor and diuron 0.6, 0.9, 1.2 and 1.7, respectively; pesticide V/V₀ was correlated with K_oc. The retardation factor was a good indicator of migration risk, but tended to overestimate retardation of molecules with high K_oc. Atrazine desorption represented an additional leaching risk as a source of prolonged low contamination. The large variability in soil solution of bromide and pesticide concentrations in the horizontal plane was attributed to flow paths and clods in the tilled soil layer. This heterogeneity was assumed to channel water fluxes into restricted areas and thereby increase the risk of groundwater contamination. The methodology used in the field proves to provide consistent results.     

A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

中国北方沙漠化地区生态重建的环境政策研究——基于Bioe-conomic Model

论文以内蒙古沙漠化地区为例,运用Bioe-conom ic M odel,比较自由放牧与耕垦、禁牧与禁垦、控制放牧规模与禁垦的土地利用政策情景下,引进舍饲养牛技术与信贷服务和增加非农就业机会对农户土地利用决策及其家庭收入的影响,据此探讨中国北方地区沙漠化发展的人文驱动机制以及生态重建的环境政策。结果表明:实施控制放牧规模与禁垦的环境政策,同时引进舍饲养牛技术能够有效地促进生态重建并提高农户收入;引进舍饲养牛技术需要启动基金,提供信贷服务和增加非农就业机会是贫困农户引进该技术的前提。因此,迫切需要调整宏观环境政策,提高农业集约化经营水平和城乡联系力度,从而提高农户家庭收入、减轻农民对环境的依赖性和促进农村的可持续发展。     

Scenario derivation and consequence evaluation of dust explosion accident based on dynamic Bayesian network

In order to clarify the correlation between the evolution path of dust explosion accidents and emergency decision-making, and to accurately predict the disaster damage levels of various disaster bearing bodies. This paper extracts 56 key scenario elements from four aspects, namely state, answer, goal and environment, based on the analysis of typical dust explosion accident cases. At the same time, a general scenario evolution path of dust explosion accident is constructed. Using fuzzy number set theory and dynamic Bayes joint probability model, the accurate solution of scenario state probability was realized. With the help of accident cases and dynamic Bayes approach, the dust explosion consequence prediction index system and evaluation criteria were constructed, covering factors such as dust explosion intensity, casualties, direct economic losses, equipment damage, building damage, environmental damage and other factors. A polyethylene wax dust explosion accident in a city of China was used to verify the dust explosion accident scenario evolution model and consequences prediction model. The predicted results were in good agreement with the actual damage of various carriers of the accident, which indicated that the model could be used for dust explosion accident prediction and disaster loss prediction. The research results provided reference and technical support for the prediction of dust explosion accident evolution direction, emergency aid measures decision and deployment, disaster damage prediction and evaluation.