Hydrostratigraphy and hydrogeology of the western part of Maira area, Khyber Pakhtunkhwa, Pakistan: a case study by using electrical resistivity

Hydrostratigraphy and hydrogeology of the Maira vicinity is important for the characterization of aquifer system and developing numerical groundwater flow models to predict the future availability of the water resource. Conventionally, the aquifer parameters are obtained by the analysis of pumping tests data which provide limited spatial information and turn out to be costly and time consuming. Vertical electrical soundings and pump testing of boreholes were conducted to delineate the aquifer system at the western part of the Maira area, Khyber Pakhtun Khwa, Pakistan. Aquifer lithology in the eastern part of the study area is dominated by coarse sand and gravel whereas the western part is characterized by fine sand. An attempt has been made to estimate the hydraulic conductivity of the aquifer system by establishing a relationship between the pumping test results and vertical electrical soundings by using regression technique. The relationship is applied to the area along the resistivity profiles where boreholes are not drilled. Our findings show a good match between pumped hydraulic conductivity and estimated hydraulic conductivity. In case of sparse borehole data, regression technique is useful in estimating hydraulic properties for aquifers with varying lithology.     

Dependable Water Supplies from Valley Alluvium in Arid Regions

Reliable sources of high-quality water for domestic use are much needed in arid regions. Valley alluvium, coarse sand and gravel deposited by streams and rivers, provides an ideal storage medium for water in many regions of the world. However, river sediments will not accumulate in a valley without a natural or artificial barrier to slow the water. Sediments will deposit upstream of a barrier dam and form an alluvial deposit of relatively well-sorted material. The alluvium then acts as both an underground water-supply reservoir and a water filter, yielding a constant flow of high-quality water. Trap dams that store water in alluvial sediments and slowly release the filtered water represent an appropriate and inexpensive technology for combating desertification and mitigating the effects of drought at the community level. Small trap dams may be built as a community project using local materials and local labor.     

Statistical Simulation to Estimate Uncertain Behavioral Parameters of Hybrid Energy-Economy Models

In energy-economy modeling, new hybrid models attempt to combine the technological explicitness of bottom-up models with the macroeconomic feedbacks and statistically estimated behavioral parameters of top-down models. However, statistical estimation of behavioral parameters (portraying firm and household technology choices) with such models is challenged by the number of uncertain variables and the lack of historical data on technologies in terms of capital costs, operating costs, and market shares. Multiple combinations of parameter values might equally explain past technology choices. This paper reports on the application of a Bayesian statistical simulation approach for estimating the most likely values for these key behavioral parameters in order to best explain past technology choices and then simulate policies to influence future technology choices. The method included (1) data collection of key technology market shares, capital costs, and operating costs over the past; (2) backcasting a hybrid energy-economy model over a historical time period; and (3) the application of Markov chain Monte Carlo statistical simulation using the Metropolis–Hastings algorithm as a tool for estimating distributions for key parameters in the model. The results provide a means of indicating the uncertainty bounds around key behavioral parameters when generating forecasts of the effect of certain policies. However, the results also indicate that this approach may have limited applicability, given that future available technologies may differ substantially from past technologies and that it is difficult to separate the effects of parameter uncertainty from model structure uncertainty.     

Assessing Spatial, Temporal, and Analytical Variation of Groundwater Chemistry in a Large Nuclear Complex, USA

Statistical analyses were applied at the Hanford Site, USA, to assess groundwater contamination problems that included (1) determining local backgrounds to ascertain whether a facility is affecting the groundwater quality and (2) determining a ‘pre-Hanford' groundwater background to allow formulation of background-based cleanup standards. The primary purpose of this paper is to extend the random effects models for (1) assessing the spatial, temporal, and analytical variability of groundwater background measurements; (2) demonstrating that the usual variance estimate s ², which ignores the variance components, is a biased estimator; (3) providing formulas for calculating the amount of bias; and (4) recommending monitoring strategies to reduce the uncertainty in estimating the average background concentrations. A case study is provided. Results indicate that (1) without considering spatial and temporal variability, there is a high probability of false positives, resulting in unnecessary remediation and/or monitoring expenses; (2) the most effective way to reduce the uncertainty in estimating the average background, and enhance the power of the statistical tests in general, is to increase the number of background wells; and (3) background for a specific constituent should be considered as a statistical distribution, not as a single value or threshold. The methods and the related analysis of variance tables discussed in this paper can be used as diagnostic tools in documenting the extent of inherent spatial and/or temporal variation and to help select an appropriate statistical method for testing purposes.     

Approaches for performing uncertainty analysis in large-scale energy/economic policy models

A number of key policy insights have emerged from the application of large-scale economic/energy models, such as integrated assessment models for climate change. These insights have been particularly powerful in those instances when they are shared by all or most of the existing models. On the other hand, some results and policy recommendations obtained from integrated assessment models vary widely from model to model. This can limit their usability for policy analysis. The differences between model results are mostly due to different underlying assumptions about exogenous processes, about endogenous processes and the dynamics among them, differences in value judgments, and different approaches for simplifying model structure for computational purposes. Uncertainty analyses should be performed for the dual purpose of clarifying the uncertainties inherent in model results and improving decision making under uncertainty. This paper develops a unifying framework for comparing the different types of uncertainty analyses through their objective functions, categorizes types of uncertainty analyses that can be performed on large models, and compares different approaches to uncertainty analysis by explaining underlying assumptions, suitability for different model types, and advantages and disadvantages. The appendix presents a summary of integrated assessment models for climate change that explicitly account for uncertainty.     

Analysing the sensitivity behaviour of two hydrology models

Recently, the New Morris Method has been presented as an effective sensitivity analysis tool for mathematical models. The New Morris Method estimates the sensitivity of an output parameter to a given set of input parameters (first-order effects) and the extent these parameters interact with each other (second-order effects). This method requires the specification of two parameters (runs and resolution) that control the sampling of the output parameter to determine its sensitivity to various inputs. The criteria for these parameters have been set on the analysis of a well-behaved analytical function (see Cropp and Braddock, Reliab. Eng. Syst. Saf. 78:77–83, 2002), which may not be applicable to other physical models that describe complex processes. This paper will investigate the appropriateness of the criteria from (Cropp and Braddock, 2002) and hence the effectiveness of the New Morris Method to determine the sensitivity behaviour of two hydrologic models: the Soil Erosion and Deposition System and Griffith University Representation of Urban Hydrology. In the first case, this paper will separately analyse the sensitivity of an output parameter on a set of input parameters (first- and second-order effects) for each model and discuss the physical meaning of these sensitivities. This will be followed by an investigation into the sampling criteria by exploring the convergence of the sensitivity behaviour for each model as the sampling of the parameter space is increased. By comparing these trends to the convergence behaviour from Cropp and Braddock (2002), we will determine how well the New Morris Method estimates the sensitivity for each model and whether the sampling criteria are appropriate for these models. It will be shown that the New Morris Method can provide additional insight into the functioning of these models, and that, under a different metric, the sensitivity behaviour of these models does converge confirming the sampling criteria set by Cropp and Braddock.     

A Satisficing Framework for Environmental Policy Under Model Uncertainty

We propose a novel framework for the economic assessment of environmental policy. Our main point of departure from existing work is the adoption of a satisficing, as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-a-vis some intertemporal objective function. Consistent to the nature of environmental policymaking, our model takes explicit account of model uncertainty. To this end, the decision criterion we propose is an analog of the well-known success-probability criterion adapted to settings characterized by model uncertainty. We apply our criterion to the climate-change context and the probability distributions constructed by Drouet et al. (2015) linking carbon budgets to future consumption. Insights from computational geometry facilitate computations considerably and allow for the efficient application of the model in high-dimensional settings.

     

Assessing different sources of uncertainty in hydrological projections of high and low flows: case study for Omerli Basin,Istanbul, Turkey

This study investigates the assessment of uncertainty contribution in projected changes of high and low flows from parameterization of a hydrological model and inputs of ensemble regional climate models (RCM). An ensemble of climate projections including 15 global circulation model (GCM)/RCM combinations and two bias corrections (change factor (CF) and bias correction in mean (BC)) was used to generate streamflow series for a reference and future period using the Hydrologiska Byråns Vattenbalansavdelning (HBV) model with the 25 best-fit parameter sets based on four objective functions. The occurrence time of high flows is also assessed through seasonality index calculation. Results indicated that the inputs of hydrological model from ensemble climate models accounts for greater contribution to the uncertainty related to projected changes in high flows comparing to the contribution from hydrological model parameterization. However, the uncertainty contribution is opposite for low flows, particularly for CF method. Both CF and BC increases the total mean variance of high and low flows. The variability in the occurrence time of high flows through RCMs is greater than the variability resulted from hydrological model parameters with and without statistical downscaling. The CF provides more accurate timing than BC and it shows the most pronounced changes in flood seasonality.     

Movement of petroleum hydrocarbons in sandy coastal soils

In a field trial, oiled beach sand was buried in a coastal dune system in south Wales. A monitoring programme was designed to assess the rate of leaching of inorganic ions and hydrocarbons from the deposit. Active breakdown of the weathered oil occurred within the oiled beach sand, but hydrocarbons from the original material, or arising as a result of degradation, did not follow the same leaching pattern as inorganic ions; they remained within the original deposit. The results suggest that weathered oil coming ashore from spills at sea can be mixed with sand and buried to degrade in coastal soils, without risk of groundwater contamination by hydrocarbons.     

Impact of model parameterization on predictive uncertainty of regional groundwater models in the context of environmental impact assessment

Parameterization strategy impacts models' outputs and the associated uncertainty. This is particularly true for transient regional groundwater models where parameters can only be weakly constrained by insufficient observations. However, this is rarely investigated under any particular model structure. This study bridges this gap using a regional groundwater model developed to understand the impact of coal seam gas extraction on groundwater systems in a probabilistic framework. Two different parameterization schemes were implemented for hydraulic conductivity and specific storage. The first method solely relies on the relationship between hydraulic properties and burial depths. The second more complex strategy allows more spatial variations of hydraulic parameters using pilot points. The study provides new insights and practical guidance on the application of groundwater modelling for environmental impact assessment. The results suggest that the choice of model parameterization has a significant influence on predictive uncertainty. The model using the simple parameterization provides predictions with a much wider range than the model with a more sophisticated parameterization. This is because that the lowly parameterized model tends to generate more extreme effective hydraulic parameter fields unless the parameterization simplification converts the inverse problem to a (close to) well-posed problem that rarely exists for applied regional groundwater modelling. The potential impact of model parameterization should be discussed explicitly in groundwater modelling applications to support decision making to avoid misinterpretation of the modelling results.     

Advanced REACH Tool (ART): calibration of the mechanistic model

The mechanistic model of the Advanced Reach Tool (ART) provides a relative ranking of exposure levels from different scenarios. The objectives of the calibration described in this paper are threefold: to study whether the mechanistic model scores are accurately ranked in relation to exposure measurements; to enable the mechanistic model to estimate actual exposure levels rather than relative scores; and to provide a method of quantifying model uncertainty. Stringent data quality guidelines were applied to the collated data. Linear mixed effects models were used to evaluate the association between relative ART model scores and measurements. A random scenario and company component of variance were introduced to reflect the model uncertainty. Stratified analyses were conducted for different forms of exposure (abrasive dust, dust, vapours and mists). In total more than 2000 good quality measurements were available for the calibration of the mechanistic model. The calibration showed that after calibration the mechanistic model of ART was able to estimate geometric mean (GM) exposure levels with 90% confidence for a given scenario to lie within a factor between two and six of the measured GM depending upon the form of exposure.     

A necessary distinction between spatial representativeness of an air quality monitoring station and the delimitation of exceedance areas

The European legislation on ambient air quality introduces the concepts of spatial representativeness of a monitoring station and spatial extent of an exceedance zone. Spatial representativeness is an essential macro-scale siting criterion which should be evaluated before the setting-up and during the life of a monitoring point. As for the exceedance area, it has to be defined each time an environmental objective is exceeded in an assessment zone. No specific approach is prescribed to delimit such areas. A probabilistic methodology is presented, based on a preliminary kriging estimation of atmospheric concentrations at each point of the domain. It is applied to NO₂ pollution on the urban scale. In the proposed approach, a point belongs to the area of representativeness of a station if its concentration differs from the station measurement by less than a given threshold. To take the estimation uncertainty into account, the standard deviation of the kriging error is used in a probabilistic framework. The choice of the criteria used to deal with overlapping areas is first tested on NO₂ annual mean concentration maps of France, built by combining surface monitoring observations and outputs from the CHIMERE chemistry transport model. At the local scale, data from passive sampling surveys and high -resolution auxiliary variables are used to provide a more precise estimation of the background pollution in different French cities. The traffic-related pollution can also be accounted for in the map by additional predictors such as distance to the road, and traffic-related NO_x emissions. Similarly, the proposed approach is implemented to identify the points, at a given statistical risk, where the NO₂ concentration is above the annual limit value.     

Ensemble Techniques to Improve Air Quality Assessment: Focus on O3 and PM

Five air quality models were applied over Portugal for July 2006 with an ensemble purpose. These models were used, with their own meteorology, parameterizations, boundary conditions and chemical mechanisms, but with the same emission data. The validation of the individual models and its ensemble for ozone (O₃) and particulate matter was performed using monitoring data from 22 background stations over Portugal. After removing the bias from each model, different ensemble techniques were applied and compared. Besides the median, several weighted ensemble approaches were tested and intercompared: static (SLR) and dynamic (DLR) multiple linear regressions (using less-square optimization method) and the Bayesian Model Averaging (BMA) methodology. The goal of the comparison is to estimate to what extent the ensemble analysis is an improvement with respect to the single model results. The obtained results revealed that no one of the 4 tested ensembles clearly outperforms the others on the basis of statistical parameters and probabilistic analysis (reliability and resolution properties). Nevertheless, statistical results have shown that the application of the weights slightly improves ensemble performance when compared to those obtained from the median ensemble. The same statistical analysis together with the probabilistic measures demonstrates that the SLR and BMA methods are the best performers amongst the assessed methodologies.     

Distribution of mercury and organic matter in particle-size classes in sediments contaminated by a waste water treatment plant: Vidy Bay, Lake Geneva, Switzerland

In Lake Geneva, Switzerland, the most Hg-contaminated sediments have been found in the Vidy Bay where high Hg contents largely exceeds the background levels of Lake Geneva sediments. This contamination has been attributed to the discharge of a waste water treatment plant (WWTP). Total Hg (THg) and monomethylmercury (MMHg) were determined in bulk sediment and in three different grain size fractions (i: clay and silt, ii: fine-coarse sand, iii: and very coarse sand and gravel) collected close to the outlet pipe of a WWTP in order to verify whether the standardized procedures of sediment treatment is adequate for this setting and, by extension, for similar contaminated sites. THg was homogeneously distributed in the different grain size fractions and was correlated to organic matter content (R(2) = 0.6). MMHg was homogeneously distributed in the two finer grain fractions (Φ < 0.063 mm; 0.063 mm < Φ <1 mm). The results of this study suggested that the analysis of the bulk sediment seems to be more appropriate for the assessment of the content and spatial distribution of Hg in freshwater sediments contaminated by WWTPs.     

Development of an Ozone Forecasting Model for Non-Attainment Areas in the State of Ohio

Ground level ozone is responsible for the formation ofsmog, and for a variety of adverse effects on bothhuman and plant life. High concentrations of groundlevel ozone occur during the summer months. This paperdescribes the development of a model to forecast themaximum daily concentration of ozone as a function ofthe maximum surface temperature, for ozonenon-attainment regions in Ohio. The model wasdeveloped by statistical analysis of existing data.Site-specific models were developed initially. Theverification and evaluation of the performancecriteria of the model at each site were explored bycomparing the model with an independent datasetcollected from that site. A generalized statewidemodel was developed from the site-specific models. Theperformance criteria of this model were verified andevaluated by employing the same independent datasetsemployed for the site-specific models. An exceedencemodel to predict the occurrence of ozone exceedencesover 100 ppb has also been presented.     

Uncertainty in Integrated Assessment Models of Climate Change: Alternative Analytical Approaches

Uncertainty plays a key role in the economics of climate change, and research on this topic has led to a substantial body of literature. However, the discussion on the policy implications of uncertainty is still far from being settled, partly because the uncertainty of climate change comes from a variety of sources and takes diverse forms. To reflect the multifaceted nature of climate change uncertainty better, an increasing number of analytical approaches have been used in the studies of integrated assessment models of climate change. The employed approaches could be seen as complements rather than as substitutes, each of which possesses distinctive strength for addressing a particular type of problems. We review these approaches—specifically, the non-recursive stochastic programming, the real option analysis, and the stochastic dynamic programming—their corresponding literatures and their respective policy implications. We also identify the current research gaps associated with the need for further developments of new analytical approaches.     

A Bayesian approach for incorporating uncertainty and data worth in environmental projects

A data worth model is presented for the analysis of alternative sampling schemes in a special project where decisions have to be made under uncertainty. This model is part of a comprehensive risk analysis algorthm with the acronym BUDA. The statistical framework in BUDA is Bayesian in nature and incorporates both parameter uncertainty and natural variability. In BUDA a project iterates among the analyst, the decision maker, and the field work. As part of the analysis, a data worth model calculates the value of a data campaign before the actual field work, thereby allowing the identification of an optimum data collection scheme. A goal function which depicts the objectives of a project is used to discriminate among different alternatives. A Latin hypercube sampling scheme is used to propagate parameter uncertainties to the goal function. In our example the uncertain parameters are the parameters which describe the geostatistical properties of saturated hydraulic conductivity in a Molasse environment. Our results indicated that failing to account for parameter uncertainty produces unrealistically optimistic results, while ignoring the spatial structure can lead to an inefficient use of the existing data.     

Noise tolerance of algorithms for estimating chlorophyll a concentration in turbid waters

The accuracy and noise tolerance of 13 global models and 5 Case II chlorophyll a (chl a) retrieval models were evaluated using three dataset. It was found that if 5 % input noise related to atmospheric correction is considered, then the uncertainty associated with noise tolerance varied from 5.5 % to 55.6 %, and these uncertainties generally accounts for 15.63 % to 24.75 % of the total uncertainty. This observation suggests that an optimal algorithm not only should have a strong chl a concentration prediction ability but also should possess high insensitivity to the noise of remote-sensing imagery. The accuracy evaluations of chl a models were based on comparisons of chl a predicted models with chl a concentration measured analytically for field measurements. The results indicate that none of the selected chl a estimation algorithms provide accurate retrievals of chl a in turbid waters. This may be attributed to the strong optical influence of organic and inorganic matter at the blue green range, and the non-negligible of non-organic matter absorption at the red and near-infrared ranges. In order to solve this problem, the chl a concentration retrieval models must be further optimized. After being optimized using the empirical optimized method constructed in this paper, a single parameterized NDCI (normalized difference chl a index) model produces accurate retrievals in the Yellow River Estuary, Taihu Lake and Chesapeake Bay. If 5 % input noise associated with residual uncertainty 0of atmospheric correction is taken into account, the model produces only 29.96 % uncertainty for the remote sensing of chl a concentration in these three turbid waters.     

Evaluation of Three Air Dispersion Models: ISCST2, ISCLT2, and Screen2 for Mercury Emissions in an Urban Area

The goal of this paper is to compare and evaluate the performance of three air quality regulatory models for mercury releases. The models include Industrial Source Complex Short Term model (ISCST2), Industrial Source Complex Long Term model (ISCLT2), and SCREEN2. The evaluation is conducted in multiple point source urban environment using meteorological data, emission inventory and monitoring data for eight stations for the year 1990 to 1992. The performance of the models is evaluated using eight statistical parameters. The comparison of models results for both quarterly and annual averaging periods shows that ISCST2 predictions qualitatively match the observed concentrations; whereas SCREEN2 predicts highest concentrations and ISCLT2 the lowest concentrations. The summary of statistical analysis obtained by using three different methods of observed concentration (Co) and predicted concentrations (Cp) comparison show that the ISCST2 has a better overall performance than ISCLT2 and SCREEN2 models. However, none of the models met the criteria for a reasonable model. Summaries of 95% confidence limits on normalize mean square error (NMSE), geometric mean variance (VG) and geometric mean bias (MG) for each and among model indicate that of the three models, ISCST2 has the best overall performance indicators. Improved model performance may be achieved by incorporating different types of mercury forms into emission rate and air dispersion calculations.     

Portable XRF analysis of occupational air filter samples from different workplaces using different samplers: final results, summary and conclusions

This paper concludes a five-year program on research into the use of a portable X-ray fluorescence (XRF) analyzer for analyzing lead in air sampling filters from different industrial environments, including mining, manufacturing and recycling. The results from four of these environments have already been reported. The results from two additional metal processes are presented here. At both of these sites, lead was a minor component of the total airborne metals and interferences from other elements were minimal. Nevertheless, only results from the three sites where lead was the most abundant metal were used in the overall calculation of method accuracy. The XRF analyzer was used to interrogate the filters, which were then subjected to acid digestion and analysis by inductively-coupled plasma optical-emission spectroscopy (ICP-OES). The filter samples were collected using different filter-holders or "samplers" where the size (diameter), depth and homogeneity of aerosol deposit varied from sampler to sampler. The aerosol collection efficiencies of the samplers were expected to differ, especially for larger particles. The distribution of particles once having entered the sampler was also expected to differ between samplers. Samplers were paired to allow the between-sampler variability to be addressed, and, in some cases, internal sampler wall deposits were evaluated and compared to the filter catch. It was found, rather surprisingly, that analysis of the filter deposits (by ICP-OES) of all the samplers gave equivalent results. It was also found that deposits on some of the sampler walls, which in some protocols are considered part of the sample, could be significant in comparison to the filter deposit. If it is concluded that wall-deposits should be analyzed, then XRF analysis of the filter can only give a minimum estimate of the concentration. Techniques for the statistical analysis of field data were also developed as part of this program and have been reported elsewhere. The results, based on data from the three workplaces where lead was the major element present in the samples, are summarized here. A limit of detection and a limit of quantitation are provided. Analysis of some samples using a second analyzer with a different X-ray source technology indicated reasonable agreement for some metals (but this was not evaluated for lead). Provided it is only necessary to analyze the filters, most personal samplers will provide acceptable results when used with portable XRF analysis for lead around applicable limit values.