Risk-based evaluation of wastewater treatment projects: A case study in Niasar city,Iran

For sustainable wastewater management, it is essential to consider social, environmental, geological and technical features as well as economic feature in the decision-making process. A limitation of many of the earlier works on wastewater management is that they take into account only financial criteria to make a decision for a given problem. Moreover, the decision-makers’ (DMs) attitude to risk, or optimism degree, when faced with uncertainty is not considered. In this paper, we study the application of risk-based multi-attribute decision-making (RB-MADM) methods to achieve sustainable wastewater management. Consideration of uncertainty, value tradeoffs, and different risk attitudes of decision makers are the important features of the developed methodology. A case study on the Iranian city of Niasar is presented to illustrate how this methodology can be applied to select the most preferred alternative for wastewater management among a set of options. The most preferred option is selected with respect to nine attributes under different optimism/pessimism degrees, using six different MADM methods. The results show that the ranking of options is sensitive to the optimism degree of the DMs. The proposed approach may help policy makers to achieve sustainable wastewater management.     

Flexible interval mixed-integer bi-infinite programming for environmental systems management under uncertainty

A number of inexact programming methods have been developed for municipal solid waste management under uncertainty. However, most of them do not allow the parameters in the objective and constraints of a programming problem to be functional intervals (i.e., the lower and upper bounds of the intervals are functions of impact factors). In this study, a flexible interval mixed-integer bi-infinite programming (FIMIBIP) method is developed in response to the above concern. A case study is also conducted; the solutions are then compared with those obtained from interval mixed-integer bi-infinite programming (IMIBIP) and fuzzy interval mixed-integer programming (FIMIP) methods. It is indicated that the solutions through FIMIBIP can provide decision support for cost-effectively diverting municipal solid waste, and for sizing, timing and siting the facilities’ expansion during the entire planning horizon. These schemes are more flexible than those identified through IMIBIP since the tolerance intervals are introduced to measure the level of constraints satisfaction. The FIMIBIP schemes may also be robust since the solutions are “globally-optimal” under all scenarios caused by the fluctuation of gas/energy prices, while the conventional ones are merely “locally-optimal” under a certain scenario.     

Incorporating uncertainty in a chemical leaching assessment

Pesticide leaching models are being used to assist in the regulation and management of pesticides by indicating their potential for leaching to groundwater. Uncertainty in model input data is not, regrettably, included in most pesticide leaching assessments. In the work described here, we use logarithmic transformations of the attenuation factor (AF), a simple process-based index model, to represent uncertainty in a pesticide leaching assessment. Characterization of a wide range of pesticides as `leachers' or `non-leachers' for a specific Hawaii hydrogeological setting is facilitated by comparing the log-transformed AF, designated AFR, for each chemical with two reference chemicals for which leaching behavior in Hawaii is known. Defining a mean and uncertainty interval for the AFR index of each chemical being ranked provides a practical method of incorporating data uncertainty into a regulatory protocol.     

Inexact rough-interval two-stage stochastic programming for conjunctive water allocation problems

An inexact rough-interval two-stage stochastic programming (IRTSP) method is developed for conjunctive water allocation problems. Rough intervals (RIs), as a particular case of rough sets, are introduced into the modeling framework to tackle dual-layer information provided by decision makers. Through embeding upper and lower approximation intervals, rough intervals are capable of reflecting complex parameters with the most reliable and possible variation ranges being identified. An interactive solution method is also derived. A conjunctive water-allocation system is then structured for characterizing the proposed model. Solutions indicate a detailed optimal allocation scheme with a rough-interval form; a total of [[1048.83, 2078.29]:[1482.26, 2020.60]] would be obtained under the pre-regulated inputs. Comparisons of the proposed model to a conventional and an interval two-stage stochastic programming model are also conducted. The results indicate that the optimal objective function values of TSP and ITSP always fall into the range of , while they are sometimes out of the range of ; the optimal solutions of decision variables also present this feature. This implies the reliability of IRTSP in handling conjunctive water allocation problems.     

A dynamic hydrological Monte Carlo simulation model to inform decision-making at Lake Toolibin,Western Australia

Lake Toolibin, an ephemeral lake in the agricultural zone of Western Australia, is under threat from secondary salinity due to land clearance throughout the catchment. The lake is extensively covered with native vegetation and is a Ramsar listed wetland, being one of the few remaining significant migratory bird habitats in the region. Currently, inflow with salinity greater than 1000 mg/L TDS is diverted from the lake in an effort to protect sensitive lakebed vegetation. However, this conservative threshold compromises the frequency and extent of lake inundation, which is essential for bird breeding. It is speculated that relaxing the threshold to 5000 mg/L may pose negligible additional risk to the condition of lakebed vegetation. To characterise the magnitude of improvement in the provision of bird breeding habitat that might be generated by relaxing the threshold, a dynamic water and salt balance model of the lake was developed and implemented using Monte Carlo simulation. Results from best estimate model inputs indicate that relaxation of the threshold increases the likelihood of satisfying habitat requirements by a factor of 9.7. A second-order Monte Carlo analysis incorporating incertitude generated plausible bounds of [2.6, 37.5] around the best estimate for the relative likelihood of satisfying habitat requirements. Parameter-specific sensitivity analyses suggest the availability of habitat is most sensitive to pan evaporation, lower than expected inflow volume, and higher than expected inflow salt concentration. The characterisation of uncertainty associated with environmental variation and incertitude allows managers to make informed risk-weighted decisions.     

Interval-parameter Fuzzy-stochastic Semi-infinite Mixed-integer Linear Programming for Waste Management under Uncertainty

An interval-parameter fuzzy-stochastic semi-infinite mixed-integer linear programming (IFSSIP) method is developed for waste management under uncertainties. The IFSSIP method integrates the fuzzy programming, chance-constrained programming, integer programming and interval semi-infinite programming within a general optimization framework. The model is applied to a waste management system with three disposal facilities, three municipalities, and three periods. Compared with the previous methods, IFSSIP have two major advantages. One is that it can help generate solutions for the stable ranges of the decision variables and objective function value under fuzzy satisfaction degree and different levels of probability of violating constraints, which are informative and flexible for solution users to interpret/justify. The other is that IFSSIP can not only handle uncertainties through constructing fuzzy and random parameter, but also reflect dynamic features of the system conditions through interval function of time over the planning horizon. By comparing IFSSIP with interval-parameter mixed-integer linear semi-infinite programming and parametric programming, the IFSSIP method is more reasonable than others.     

Probabilistic distribution coefficients (Kds) in freshwater for radioisotopes of Ag,Am, Ba,Be, Ce,Co, Cs,I, Mn,Pu, Ra,Ru, Sb,Sr and Th – implications for uncertainty analysis of models simulating the transport of radionuclides in rivers

The objective of this study was to provide operational probability density functions (PDFs) for distribution coefficients (K_ds) in freshwater, representing the partition of radionuclides between the particulate and the dissolved phases respectively. Accordingly, the K_d variability should be considered in uncertainty analysis of transport and risk assessment models. The construction of PDFs for 8 elements (Ag, Am, Co, Cs, I, Mn, Pu and Sr) was established according to the procedure already tested in Durrieu et al. [2006. A weighted bootstrap method for the determination of probability density functions of freshwater distribution coefficients (K_ds) of Co, Cs, Sr and I radioisotopes. Chemosphere 65 (8), 1308–1320]: (i) construction of a comprehensive database where K_ds values obtained under various environments and parametric conditions were collected; (ii) scoring procedure to account for the ‘quality’ of each datapoint (according to several criteria such as the presentation of data (e.g. raw data vs mean with or without replicates), contact time, pH, solid-to-liquid ratio, expert judgement) in the construction of the PDF; (iii) weighted bootstrapping procedure to build the PDFs, in order to give more importance to the most relevant datapoints. Two types of PDFs were constructed: (i) non-conditional, usable when no knowledge about the site of concern is available; (ii) conditional PDFs corresponding to a limited range of parameters such as pH or contact time; conditional PDFs can thus be used when some parametric information is known on the site under study. For 7 other radionuclides (Ba, Be, Ce, Ra, Ru, Sb and Th), a simplified procedure was adopted because of the scarcity of data: only non-conditional PDFs were built, without incorporating a scoring procedure.     

Uncertainties in the assessment of “significant effect” on the Dutch Natura 2000 Wadden Sea site – The mussel seed fishery and powerboat race controversies

Natura 2000, the nature network based on the European Bird and Habitat Directives, is explicitly grounded on ecological science. To acquire a permit under the Dutch Nature Conservation Act, an appropriate assessment of significant effects must be conducted based on the best available scientific knowledge. In this way the scientific and policy world are directly linked. This article focuses on ‘significant effect’ as a boundary object to analyse how science–policy interactions shape the meaning and assessment of significant effect and how these interpretations influence the decision-making process. To this end, two conflicts over significant effect are investigated: the conflict over the 2006-spring permit for the mussel seed fishery, and the 2011 permit for the planned World Championship powerboat races. In both cases nature organisations started a court process against the government-granted permits in protest to the “no significant effect” claim, stating that there was insufficient certainty for this conclusion. These conflicts are approached as controversies between discourse coalitions with different interpretations of the ecological knowledge. We show how significant effect became a focal point in the controversies, limiting the debate to ecological arguments and science-based expertise, but also creating options for parties to advance their protest by articulating uncertainties. Only uncertainty of incomplete knowledge was explicitly addressed, excluding ambiguity of values and unpredictability of the actual ecosystem. We suggest that acknowledging the value aspect in disputes on significant effect would leave more space for effective solutions of the problems under debate.     

Multi-group approximation, scattering and calibration coefficients, uncertainty estimates and detection limits of a NaI(Tl)-based gamma spectrometry set-up for low-level activity analysis

This paper describes a quantitative radioactivity analysis method especially suitable for environmental samples with low-level activity. The method, consisting of a multi-group approximation based on total absorption and Compton spectra of gamma rays, is coherently formalized and a computer algorithm thereof designed to analyze low-level activity NaI(Tl) gamma ray spectra of environmental samples. Milk powder from 1988 was used as the example case. Included is a special analysis on the uncertainty estimation. Gamma sensitiveness is defined and numerically evaluated. The results reproduced the calibration data well, attesting to the reliability of the method. The special analysis shows that the uncertainty of the assessed activity is tied to that of the calibration activity data. More than 77% of measured 1461-keV photons of ⁴⁰K were counted in the range of clearly lower energies. Pile-up of single line photons (¹³⁷Cs) looks negligible compared to that of a two-line cascade (¹³⁴Cs). The detection limit varies with radionuclide and spectrum region and is related to the gamma sensitiveness of the detection system. The best detection limit always lies in a spectrum region holding a line of the radionuclide and the highest sensitiveness. The most radioactive milk powder sample showed a activity concentration of 21 ± 1 Bq g⁻¹for ¹³⁷Cs, 323 ± 13 Bq g⁻¹ for ⁴⁰K and no ¹³⁴Cs.     

The Effect of Jump on Evaluating Natural Resource Investments

Abstract

The evaluation of mining and other natural resource projects is made particularly difficult by the high degree of uncertainty attaching to output prices. It is shown that the techniques of continuous time arbitrage and stochastic control theory may be used not only to value such projects but also to determine the optimal policies for developing managing. This paper describes a model for evaluating natural resource investments under uncertainty from a new perspective. The previous works in this field mostly regard the movements of natural resource prices as a continuous GBM process, which pays few attentions to the shock of unexpected bad news. Our model provides the first theoretical method to analyze the impact of such “jump” on investment decisions. It concludes that the more frequently bad news happens, the earlier a project will be invested.