Achieving environmental standards with stochastic discharges

The inherent stochastic nature of the levels and composition of environmental waste discharges are well known to environmental control agencies, who recognize that realistically environmental quality constraints must be formulated in probabilistic terms. Given the specification of environmental constraints in such terms, it is shown that the set of activity vectors which satisfy the constraint will, in general, be nonconvex. This militates against the determination of an efficient solution. A means of avoiding the potentially high computational and informational costs associated with the determination of an efficient solution in such cases is proposed.     

Environmental policy choice under uncertainty

The Markovian model provides an insightful structure for analyzing environmental policy decisions. This framework is applied to a variety of conceptual issues. The analysis of reswitching phenomena shows that when multiple risks are involved, there is unlikely to be an unambiguous ranking of policies according to their future-mindedness. The discussion of irreversibilities demonstrates that irreversibilities differ only in degree from other probabilistic structures; they create no special analytic complications. In the final section, the Markovian decision framework is extended to incorporate the impact of budgetary constraints on optimal decisions.     

An interval joint-probabilistic programming method for solid waste management: a case study for the city of Tianjin, China

Currently, environmental protection and resources conservation continue to be challenges faced by solid-waste managers in China. These challenges are being further compounded by rapid socioeconomic devel- opment and population growth associated with increased waste generation rates and decreased waste disposal capacities. In response to these challenges, an interval joint-probabilistic mixed-integer programming （IJMP） method is developed for supporting long-term planning of waste management activities in the city of Tianjin, which is one of the largest municipalities in the northern part of China. In the IJMP, joint probabilistic constraints are introduced into an interval-parameter mixed-integer programming framework, such that uncertainties presented in terms of interval values and random variables can be reflected. Moreover, a number of violation levels for the waste-management-capacity constraints are examined, which can facilitate in-depth analyses of tradeoffs among economic objective and system-failure risk. The results indicate that reasonable solutions have been generated. They are valuable for supporting the adjustment of the city＇s existing waste-management practices and the long- term planning of the city＇s waste-management facilities.     

Interaction structures analysed from water-quality data

Fortnightly observations of water quality parameters, discharge and water temperature along the River Elbe have been subjected to a multivariate data analysis. In a previous study [Petersen, W., Bertino, L., Callies, U., Zorita, E., 2001. Process identification by principal component analysis of river-quality data. Ecol. Model. 138, 193–213] applied principal component analysis (PCA) to show that 60% of variability in the data set can be explained through just two linear combinations of eight original variables. In the present paper more advanced multivariate methods are applied to the same data set, which are supposed to suit better interpretations in terms of the underlying system dynamics.The first method, graphical modelling, represents interaction structures in terms of a set of conditional independence constraints between pairs of variables given the values of all other variables. Assuming data from a multinormal distribution conditional independence constraints are expressed by zero partial correlations. Different graphical structures with nodes for each variable and connecting edges between them can be assessed with regard to their likelihood. The second method, canonical correlation analysis (CCA), is applied for studying the correlation structures of external forcing and water quality parameters.Results of CCA turn out to be consistent with the dominant patterns of variability obtained from PCA. The percentages of variability explained by external forcing, however, are estimated to be smaller. Fitting graphical models allows a more detailed representation of interaction structures. For instance, for given discharge and temperature correlated variations of the concentrations of oxygen and nitrate, respectively, can be modelled as being mediated by variations of pH, which is a representer for algal activity. Considerably simplified graphical models do not much affect the outcomes of both PCA and CCA, and hence it is concluded that these graphical models successfully represent the main interaction structures represented by the covariance matrix of the data. The analysed conditional independence patterns provide constraints to be satisfied by directed probabilistic networks, for instance.     

Probabilistische Modelle der Expositionsabschätzung

Probabilistic exposure and risk assessment can show and help to explicate the uncertainties of deterministic single-point estimates. Heterogeneity in population and exposure parameters (variability) is a part of probabilistic models which makes use of distributions for the main influential factors. The distributions of the target variables are calculated as a model-based combination of all influence factors by Monte Carlo-simulation methods. The differences between classical and probabilistic exposure assessment are demonstrated using a residential area with soil contamination as an example. The estimated arsenic exposure due to soil and dust is calculated by deterministic single-point estimates and probabilistic exposure assessment. The results are compared to the biomonitoring results of an epidemiological study. A criteria catalogue for exposure model validation is given and discussed for this example. In this scenario, the probabilistic exposure prognosis fits the empirical data better than that of a deterministic, single-point assessment. Both approaches seem to overestimate exposure with respect to empirical data.     

Development of a probabilistic blood lead prediction model

A partially probabilistic blood lead prediction model has been developed, based on the US Environmental Protection Agency integrated exposure-uptake-biokinetic blood lead model (IEUBK model). This study translated the IEUBK model into a spreadsheet format. The uptake submodel incorporates uncertainty distributions for exposure and bioavailability parameters. The biokinetic submodel is duplicated with a table incorporating partitioning and decay of lead levels in the body. As a case study, the probabilistic model is applied to a lead exposure scenario involving a former smelter site in Sandy, Utah. The probabilistic model produces less biased estimates of means and standard deviations than the deterministic model. Parameter uncertainty is propagated in the model by the use of Monte Carlo simulation. Thus, sensitivity analysis is possible, and driving variables can be determined.     

Berücksichtigung von Variabilität und Unsicherheit in quantitativen Risikoabschätzungen (QRA)

Using the example of residential living on a contaminated site, a probabilistic exposure assessment is performed with variability and uncertainty being modelled separately. Probability distributions are used in the exposure model in order to characterize person-related variables (e.g. body weight) only; chemical-specific parameters are being held constant. In addition, uncertainty concerning one selected variable (soil ingestion rate) was modelled. Comparing these results to conventional “worst case” estimates, we find those estimates located in the uppermost range of the probabilistic estimates. The worst case estimates tend to be highly conservative and possibly unrealistic.     

Probabilistic prediction of exposures to arsenic contaminated residential soil

Probabilistic modelling using Monte Carlo simulation has been proposed as a more scientifically valid method of estimating soil contaminant exposures than conservative deterministic methods currently used by regulatory agencies. A retrospective application of probabilistic modelling to an exposure scenario involving arsenic-contaminated residential soil near the former ASARCO smelter near Tacoma, Washington is presented. The population of interest is children, aged 2–6 years, living within one-half mile (0.3 km) of the smelter site. Models that predict urinary arsenic levels based on unintentional soil ingestion and inhalation exposure pathways are used. Distributions of exposure variables are based on site-specific data and previous exposure studies. Simulated urinary arsenic levels are compared with data from two biomonitoring studies performed during the late 1980s. Arsenic distributions produced by simulation and biomonitoring are significantly different, and likely contributors to this difference are discussed. However the probabilistic model provides closer estimations of urinary arsenic levels than conservative deterministic models similar to those used by regulatory agencies, and provides useful information regarding parameter uncertainty. Soil ingestion rate was a driving variable in the probabilistic models. Further quantification of soil ingestion rates is warranted.     

Complex national sampling design for long-term monitoring of protected dry grasslands in Switzerland

We describe a probabilistic sampling design of circular permanent plots for the long-term monitoring of protected dry grasslands in Switzerland. The population under study is defined by the perimeter of a national inventory. The monitoring focus is on the species composition of the protected grassland vegetation and derived conservation values. Efficient trend estimations are required for the whole country and for some predefined target groups (six biogeographical regions and eleven vegetation types). The target groups are equally important regardless of their size. Consequently, intensified sampling of the less frequent groups is essential for sample efficiency. The prior information needed to draw a targeted sample is obtained from the sampling frame and external databases. The logistics and generalized delineation of the target population may pose further problems. Thus, investments in fieldwork and travel time should be well balanced by selecting a cluster sample. Second, any access problems in the field and non-target units in the sample should be compensated for by selecting reserve plots as they otherwise may considerably reduce the effective sample size. Finally, the design has to be flexible as the sampling frame may change over time and sampling intensity might have to be adjusted to redefined budgets or requirements. Likewise, the variables and biological items of interest may change. To fulfil all these constraints and to optimally use the available prior information, we propose a multi-stage self-weighted unequal probability sampling design. The design uses modern techniques such as: balanced sampling, spreading, stratified balancing, calibration, unequal probability sampling and power allocation. This sampling design meets the numerous requirements of this study and provides a very efficient estimator.     

Mathematical equivalence of geometric mean fitness with probabilistic optimization under environmental uncertainty

Natural selection can be considered as optimising fitness. Because ‘mean’ fitness is maximized with respect to the genotypes of carriers, traditional theory can be viewed as a statistical theory of natural selection. Probabilistic optimisation is a way to incorporate such uncertainty into optimality analyses of natural selection, where environmental uncertainty is expressed as a probability distribution. Its canonical form is a weighted average of fitness with respect to a given probabilistic distribution. This concept should be applicable to three different levels of uncertainty: (1) behavioural variations of an individual, (2) individual variations within a generation, and (3) temporal change over generations (geometric mean fitness). The former two levels are straightforward with many empirical evidences, but the last category, the geometric mean fitness, has not well understood. Here we studied the geometric mean fitness by taking its logarithm, where the log growth rates become the fitness value. By further transforming the log growth rates, the fitness of log growth rates becomes its linear function. Therefore, a simple average of these distributions becomes the fitness measure across generations and consideration of variance discount or the entire probability distributions becomes unnecessary. We discuss some characteristic features of probabilistic optimization in general. Our view is considered a probabilistic view of natural selection, in contrast with the traditional statistical view of natural selection.     

Do credit constraints favor dirty production? Theory and plant-level evidence

This paper explores the effect of credit constraints on production-generated pollution emissions. I develop a theoretical model wherein polluting firms borrow externally to finance investment in various assets, subject to a credit constraint. The main insight of the model is that credit constraints distort the composition of assets towards over-investment in tangible assets, which can be pledged as collateral, thereby increasing the intensity of emissions. The predictions of the model are tested using a unique dataset consisting of plant-level measures of pollution emissions and creditworthiness. The empirical results indicate that credit constraints significantly increase pollution emissions (even after accounting for the scale effect), and the results withstand multiple robustness checks. Moreover, the effect of credit constraints is particularly acute in industries with greater reliance on external credit. Finally, I demonstrate that firm-level credit constraints distort the composition of assets and that the composition of assets influences pollution emissions.     

Recovery of forest structure and spectral properties after selective logging in lowland Bolivia.

Effective monitoring of selective logging from remotely sensed data requires an understanding of the spatial and temporal thresholds that constrain the utility of those data, as well as the structural and ecological characteristics of forest disturbances that are responsible for those constraints. Here we assess those thresholds and characteristics within the context of selective logging in the Bolivian Amazon. Our study combined field measurements of the spatial and temporal dynamics of felling gaps and skid trails ranging from <1 to 19 months following reduced-impact logging in a forest in lowland Bolivia with remote-sensing measurements from simultaneous monthly ASTER satellite overpasses. A probabilistic spectral mixture model (AutoMCU) was used to derive per-pixel fractional cover estimates of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil. Results were compared with the normalized difference in vegetation index (NDVI). The forest studied had considerably lower basal area and harvest volumes than logged sites in the Brazilian Amazon where similar remote-sensing analyses have been performed. Nonetheless, individual felling-gap area was positively correlated with canopy openness, percentage liana coverage, rates of vegetation regrowth, and height of remnant NPV. Both liana growth and NPV occurred primarily in the crown zone of the felling gap, whereas exposed soil was limited to the trunk zone of the gap. In felling gaps >400 m2, NDVI, and the PV and NPV fractions, were distinguishable from unlogged forest values for up to six months after logging; felling gaps <400 m2 were distinguishable for up to three months after harvest, but we were entirely unable to distinguish skid trails from our analysis of the spectral data.     

物种敏感度分布的非参数核密度估计模型

针对目前物种敏感度分布参数方法建模所存在的缺点,首次提出基于非参数核密度估计方法的物种敏感度分布模型,并提出相应的最优窗宽和检验方法。选用无机汞作为案例研究对象,利用非参数核密度估计方法和3种传统参数模型分别推导了保护我国水生生物的无机汞的急性水质基准值。结果表明,非参数核密度估计方法在推导无机汞水质基准中的稳健性和精确度都大大优于传统参数模型,能够更好地构建物种敏感度分布曲线。该方法的提出丰富了水质基准的理论方法学,为更好地保护水生生物提供了有力的支撑。     

The effects of direct and indirect constraints on biological communities

Human activities are expected to result in a diversity of directional or stochastic constraints that affect species either directly or by indirectly impacting their resources. However, there is no theoretical framework to predict the complex and various effects of these constraints on ecological communities. We developed a dynamic model that mimics the use of different resource types by a community of competing species. We investigated the effects of different environmental constraints (affecting either directly the growth rate of species or having indirect effects on their resources) on several biodiversity indicators. Our results indicate that (i) in realistic community models (assuming uneven resource requirements among species) the effects of perturbations are strongly buffered compared to neutral models; (ii) the species richness of communities can be maximized for intermediate levels of direct constraints (unimodal response), even in the absence of trade-off between competitive ability and tolerance to constraints; (iii) no such unimodal response occurs with indirect constraints; (iv) an increase in the environmental (e.g., climatic) variance may have different effects on community biomass and species richness.     

Time constraints mediate predator-induced plasticity in immune function, condition, and life history

The simultaneous presence of predators and a limited time for development imposes a conflict: accelerating growth under time constraints comes at the cost of higher predation risk mediated by increased foraging. The few studies that have addressed this tradeoff have dealt only with life history traits such as age and size at maturity. Physiological traits have largely been ignored in studies assessing the impact of environmental stressors, and it is largely unknown whether they respond independently of life history traits. Here, we studied the simultaneous effects of time constraints, i.e., as imposed by seasonality, and predation risk on immune defense, energy storage, and life history in lestid damselflies. As predicted by theory, larvae accelerated growth and development under time constraints while the opposite occurred under predation risk. The activity of phenoloxidase, an important component of insect immunity, and investment in fat storage were reduced both under time constraints and in the presence of predators. These reductions were smaller when time constraints and predation risk were combined. This indicates that predators can induce sublethal costs linked to both life history and physiology in their prey, and that time constraints can independently reduce the impact of predator-induced changes in life history and physiology.     

Energy exploration in the Australian arid zone: Constraints and environmental consequences

The main constraints on energy exploration in the Australian arid zone are logistic, with limited additional constraints from Aboriginal reserves. The main consequence is increased access, with possible introduction of weeds and expansion of tourism and cattle grazing in consequence.     

A simple economic model of endangered species preservation in the United States

Concern for the acceleration in threats to species diversity in the United States led to the passage of the Endangered Species Act of 1973. In this paper, this legislation is examined in an economic context. A simple model is developed where management and constraints on economic activity are substitutes in the production of species stocks. Whether economic growth constraints (the primary policy tool of the 1973 Act) are justified for preservation purposes depends on technical substitution possibilities, relative cost ratios, and the existence of budget constraints for species management. Since the same combination of these factors will not occur for all species, it is argued that more flexibility in endangered species policy may preserve the same levels of species stocks at lower costs to society.     

Probabilistische Schätzung der inhalativen Exposition vs. Punktschätzung, dargestellt am Beispiel der Freisetzung von Xylol aus Farben

Estimations of consumer exposure by inhalation have been calculated taking point estimates and were compared to estimates from a probabilistic approach using Monte Carlo analysis. The calculation is based on a model in which the liberation of the highly volatile substance, xylene is only limited by the velocity of administration. The loss of the substance is limited by the air exchange rate. It is also assumed that the substance is used in a single room. Other compartments were not considered. The distribution of xylene in solvent based paints was evaluated by probability analysis of the BgVV product data base. In the products there were xylene concentrations in a range between ～1 and 44% with a median concentration of 11,2%. For “normal-case” assumptions the exposure estimate differed between single-point and probabilistic calculations: The peak concentration in a room was 0,99 g/m³ taking the single-point and 0,49 g/m³ taking the distribution. The average concentration during duration of use was 0,16 g/m³ and 0,083 g/m³, respectively. Interestingly, comparison with calculations taking the EPA-SCIES programme revealed similar concentrations showing that models are comparable.     

Line intersect sampling: Ell-shaped transects and multiple intersections

The probability of selecting a population element under line intersect sampling depends on the width of the particle in the direction perpendicular to the transect, as is well known. The consequence of this when using ell-shaped transects rather than straight-line transects are explicated, and modifications that preserve design-unbiasedness of Kaiser's (1983) conditional and unconditional estimators are presented. A case against treating multiple intersections as multiple probabilistic events is argued on the basis, also, of preserving design-unbiased estimation.     

Ecosystems emerging. 5: Constraints

Ecosystem constraints are both ontic and epistemic. They limit activity, and as problems to be solved they drive organization, which is our hypothesis:

The driver of organization is constraint.

Solutions proliferate further constraints in an unending spiral of problem (constraint) generation and solution. As constraints proliferate, behavior narrows, and species diversify to compensate (paradox of constraint). Resource enrichment reduces constraints, releases behavior, and reduction of challenges decreases diversity (paradox of enrichment)—high diversity is expressed in low-resource environments and low diversity in high-resource environments. A three-part model of constraints is formulated for non-living systems, and also for goal-directed, problem-solving biota. Mode 1: dynamical means behavior is co-determined by internal states and external inputs. Mode 2: cybernetic employs negative feedback to keep dynamics within goal-oriented operating limits. Mode 3: model-making entails ability to represent (model) physical reality and respond to both phenomenal (modeled) and physical inputs; this property distinguishes living from nonliving systems. Principal sections of the paper elaborate dynamical constraints (three classes), boundary constraints (expressed in edge effects and trophic dynamics), physical constraints (space, time, temperature), chemical constraints (environment fitness, ecological stoichiometry, chemical evolution, limiting factors), coding constraints (environmental vs. genetic coding), network and pathway constraints (connectivity), and natural selection constraints (fitting to the biosphere). Consideration of how the world would look without constraints suggests how fundamental these are in ecosystem emergence, and how the next property in this series, differentiation, would be unmotivated without them. We conclude that constraints as a category are under-studied in ecology, poorly understood in ecological phenomenology, and (our hypothesis) comprise a ubiquitous organizing force in nature.