Predicting modes of spatial change from state-and-transition models

State-and-transition models (STMs) can represent many different types of landscape change, from simple gradient-driven transitions to complex, (pseudo-) random patterns. While previous applications of STMs have focused on individual states and transitions, this study addresses broader-scale modes of spatial change based on the entire network of states and transitions. STMs are treated as mathematical graphs, and several metrics from algebraic graph theory are applied—spectral radius, algebraic connectivity, and the S-metric. These indicate, respectively, the amplification of environmental change by state transitions, the relative rate of propagation of state changes through the landscape, and the degree of system structural constraints on the spatial propagation of state transitions. The analysis is illustrated by application to the Gualalupe/San Antonio River delta, Texas, with soil types as representations of system states. Concepts of change in deltaic environments are typically based on successional patterns in response to forcings such as sea level change or river inflows. However, results indicate more complex modes of change associated with amplification of changes in system states, relatively rapid spatial propagation of state transitions, and some structural constraints within the system. The implications are that complex, spatially variable state transitions are likely, constrained by local (within-delta) environmental gradients and initial conditions. As in most applications, the STM used in this study is a representation of observed state transitions. While the usual predictive application of STMs is identification of local state changes associated with, e.g., management strategies, the methods presented here show how STMs can be used at a broader scale to identify landscape scale modes of spatial change.     

Modelling the fate of marine turtle hatchlings

The SLIM oceanographic model was used to examine the fate of hatchling flatback turtles (Natator depressus) in the first two weeks of their dispersal starting at Wild Duck Island, a major turtle-nesting site in the central Great Barrier Reef region of Australia. We ran simulations to investigate the effects of spring versus neap tides, hatchling's swimming behaviour during their first three days at sea, and the location of nesting beaches. The model predicted that up to 50% of the turtle hatchlings entering the sea from the windward, southern beach remained after 14 days in shallow, nearshore waters, irrespective of tides and swimming. These waters are turbid and may be inhospitable to hatchlings. In contrast >80% of hatchlings dispersing from the leeward, northern beach were dispersed to deeper water (10-40 m) in a quasi-stationary dispersion core centred around 10 km north of the nesting beach after 14 days and the offshore spread of the turtle plume was enhanced by the hatchling's seaward swimming during the first three days. This was due to the presence of a coastal boundary layer and a stagnation zone around the northern side of island, but not the southern side. The model confirmed that dispersal from eastern Queensland flatback turtle rookeries is restricted to the lagoons and coastal waters, and that water circulation and hatchling's swimming control dispersion. The model explains why more turtles nest on the northern than the southern side. This study highlights the usefulness of oceanographic models to increase knowledge about a cryptic life stage of marine turtles.     

What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods

Species distribution model is the term most frequently used in ecological modelling, but other authors used instead predictive habitat distribution model or species-habitat models. A consensual ecological modelling terminology that avoids misunderstandings and takes into account the ecological niche theory does not exist at present. Moreover, different studies differ in the type of niche that is represented by similar distribution models. I propose to use as standard ecological modelling terminology the terms “ecological niche”, “potential niche”, “realized niche” models (for modelling their respective niches), and “habitat suitability map” (for the output of the niche models). Therefore, the user can understand more easily that models always forecast species’ niche and relate more closely the different types of niche models.     

重点源大气砷铅污染排放模型及特征

在我国的非常规性污染物质中,大气重金属砷、铅已越来越多地被关注和重视。文章综述了国内外人为源对大气中重金属排放的贡献,结果显示中国是全球人为活动向大气排放重金属最多的国家之一,燃煤和有色金属冶炼行业在相当长的时间内都将是最主要且最为重要的人为排放源。通过系统调研燃煤及有色金属冶炼业资源及产业布局状况,构建目前我国大气重金属相关清单模型,进行了我国重点源大气砷、铅排放清单分析,结果表明：（1）2000-2008年我国燃煤大气砷、铅排放量共为93733t,年均增长率为7．93％,2004-2008年有色金属冶炼业大气砷、铅排放量共为18836t,年均增长率为15．2％;（2）2000-2008年各经济部门中电力部门燃煤大气砷、铅放量始终最高,占燃煤大气排放总量的44．6％-57．1％,且呈逐年升高的趋势;（3）2000-2008年各省区中山西、河北、河南和湖南省是大气砷、铅的排放大户。其中,燃煤大气砷、铅排放量主要集中在人口密集、工业集中、经济发展速度较快的北部和中东部省区,包括山西、山东、河北、河南和江苏五省,占全国燃煤排放总量的39．1％,有色金属冶炼大气砷、铅排放量主要集中在我国有色金属工业较为发达的河南及湖南省,占全国有色金属冶炼业排放总量的47．3％。可以看出,我国需要高度重视大气重金属砷、铅的污染防治,加强排放控制基础能力建设,加快建立适合中国的大气砷、铅污染防治技术政策体系。     

危险化学品道路运输安全管理现状及发展趋势研究

为降低危化品运输风险,从危化品运输风险评价、道路选线模型、技术应用、应急联动机制及平台建设、统一联控监管平台等方面进行研究分析。通过对微观视角与宏观视角、单一应用与综合应用的归类和对比,进行分析并指出存在的问题。分析表明,获取精确连续数据,考虑多种外在变量来完善评估模型,注重理论、技术和监管资源的综合应用,发展大区域联控平台将是今后研究的发展趋势。     

Random parameter models for accident prediction on two-lane undivided highways in India

Introduction

Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation.

Method

The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models.

Results

The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations.

Conclusions

The paper is concluded with a discussion on modeling results and the limitations of the present study.     

Highlights of LNG risk technology

The authors have recently undertaken a major review of LNG consequence modeling, compiling a wide range of historical information with more recent experiments and modeling approaches in a book entitled “LNG Risk-Based Safety: Modeling and Consequence Analysis”. All the main consequence routes were reviewed – discharge, evaporation, pool and jet fire, vapor cloud explosions, rollover, and Rapid Phase Transitions (RPT’s). In the book, experimental data bases are assembled for tests on pool spread and evaporation, burn rates, dispersion, fire and radiation and effects on personnel and structures. The current paper presents selected highlights of interest: lessons learned from historical development and experience, comparison of predictions by various models, varying mechanisms for LNG spread of water, a modeling protocol to enable acceptance of newer models, and unresolved technical issues such as cascading failures, fire engulfment of a carrier, the circumstances for a possible LNG BLEVE, and accelerated evaporation by LNG penetration into water.     

我国林地土壤有效态镉的影响因素及拟合模型

选取全国13个具有代表性的不同气候带和植被类型的森林土壤,通过外源添加重金属镉（Cd）,比较分析土壤Cd的固-液分配系数（K_d）和有效态Cd的分布特征,探讨了土壤/土壤溶液性质对K_d及土壤有效态Cd的影响,并建立了土壤K_d及有效态Cd的拟合模型.结果表明,在不同浓度Cd处理的土壤中,其K_d值的变化范围为0.91~623.66L/kg,平均值为53.11L/kg,最大值和最小值的差异达到684.37倍;土壤孔隙水中Cd浓度（PW-Cd）的变化范围为0.309~104.450mg/L,其最大值与最小值的差异为338;DTPA提取态Cd含量（DTPA-Cd）从未添加Cd处理的本底土壤C0至最大添加量128mg/kg,其最大值与最小值的差异分别为9和1.4倍.土壤溶液pH值与lgK_d呈显著正相关（R²=0.49,P<0.001）,其与PW-Cd含量呈显著负相关（R²=0.41,P<0.05）;单一的土壤溶液Mg²⁺可解释46%的DTPA-Cd的变异.在对土壤性质的回归分析中,并未发现有单一的主控土壤性质影响Cd的固液分配,当回归方程中加入其它土壤或溶液性质时,可在一定程度上提高拟合模型的预测能力.总之,土壤溶液pH值和Mg²⁺对K_d和有效态Cd含量的影响比较显著.     

Review of Urban Stormwater Quality Models: Deterministic,Stochastic, and Hybrid Approaches1

Abstract: The growing impact of urban stormwater on surface‐water quality has illuminated the need for more accurate modeling of stormwater pollution. Water quality based regulation and the movement towards integrated urban water management place a similar demand for improved stormwater quality model predictions. The physical, chemical, and biological processes that affect stormwater quality need to be better understood and simulated, while acknowledging the costs and benefits that such complex modeling entails. This paper reviews three approaches to stormwater quality modeling: deterministic, stochastic, and hybrid. Six deterministic, three stochastic, and three hybrid models are reviewed in detail. Hybrid approaches show strong potential for reducing stormwater quality model prediction error and uncertainty. Improved stormwater quality models will have wide ranging benefits for combined sewer overflow management, total maximum daily load development, best management practice design, land use change impact assessment, water quality trading, and integrated modeling.     

Translating habitat class to land cover to map area of habitat of terrestrial vertebrates

Area of habitat (AOH) is defined as the “habitat available to a species, that is, habitat within its range” and is calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land-cover classes, which generates a source of uncertainty in AOH maps. We developed a data-driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 species of terrestrial mammals, birds, amphibians, and reptiles. We extracted the land-cover class at each point locality and matched it to the IUCN habitat class or classes assigned to each species occurring there. Then, we modeled each land-cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land-cover class. We then compared the performance of our data-driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land-cover classes with more confidence than others (e.g., wetlands and artificial). The data-driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups.