Modeling the dispersion of drilling muds using the bblt model: the effects of settling velocity

The benthic boundary layer transport (bblt) model was widely used in the Atlantic Canadian offshore region to assess the potential impact zones from drilling wastes discharges from offshore oil and gas drilling. The current version of the bblt uses a single-class settling velocity scenario, which may affect its performance, as settling velocity is size, shape, and material dependent. In this study, the effects of settling velocity on bblt predictions were assessed by replacing this single-class settling velocity scenario with a multi-class size-dependent settling velocity scenario. The new scenario was used in a hypothetical study to simulate the dispersion of barite and fine-grained drilling cuttings. The study showed that the effects of settling velocity on bblt predictions are spatial, temporal, and material dependent.     

Assessment and prediction of the impacts of the Ok Tedi copper mine on fish catches in the Fly River system,Papua New Guinea

The Ok Tedi copper mine discharges overburden and ore residues into the Ok Tedi, a tributary of the Fly River. These discharges result in elevated suspended solids and dissolved and particulate associated copper. Analyses of covariance were performed to establish statistical model of the relationships between the mine discharges and fish catches between 1983 and 1988. These models were then extrapolated to predict the affects of future mine discharges on fish catches. The models predicted that if the observed effects were caused by particulate associated copper, the period of greatest impact will be between 1989 and 1993, following which catches should be close to 1988 levels for the remainder of mine life. Some additional catches not included in the data set used to derive the models were found to fit the model predictions well. As the predicted period of greatest impact is short and most species reproduce away from the river channel, the ability of the fish communities to undergo partial recovery after 1991 should be maintained.     

Geophysical and hydrological data assimilation to monitor water content dynamics in the rocky unsaturated zone

In recent years, geophysics is increasingly used to study the flow and transport processes in the vadose zone. Particularly, when the vadose zone is made up of rocks, it is difficult to install sensors in the subsurface to measure hydrological state variables directly. In these cases, the electrical resistivity tomography (ERT) represents a useful tool to monitor the hydrodynamics of the infiltration and to estimate hydraulic parameters and state variables, such as hydraulic conductivity and water content. We propose an integrated approach aimed at predicting water content dynamics in calcarenite, a sedimentary carbonatic porous rock. The uncoupled hydrogeophysical approach proposed consists in 4D ERT monitoring conducted during an infiltrometer test under falling head conditions. Capacitance probes were installed to measure water content at different depths to validate the estimations derived from ERT. A numerical procedure, based on a data assimilation technique, was accomplished by combining the model (i.e., Richards’ equation) with the observations in order to provide reliable water content estimations. We have used a new data assimilation method that is easy to implement, based on the ensemble Kalman filter coupled with Brownian bridges. This approach is particularly suitable for strongly non-linear models, such as Richards’ equation, in order to take into account both the model uncertainty and the observation errors. The proposed data assimilation approach was tested for the first time on field data. A reasonable agreement was found between observations and predictions confirming the ability of the integrated approach to predict water content dynamics in the rocky subsoil.     

Calibration and Validation of Far Field Dilution Models for Outfall at Worli, Mumbai

The city of Mumbai, India with a population of 15 million discharges about 2225 MLD of domestic wastewater after partial treatment to adjoining marine water body. Under the Mumbai Sewage Disposal Project Scheme, sewage is being disposed to the west coast at Worli and Bandra through 3.4 kms long submarine outfalls. A field study was conducted at recently commissioned outfall diffuser location at Worli, at the onset of neap flood tide to study the dispersion patterns and measure the far field dilutions using radio and dye tracers. Estimated dilutions using different tracers were compared with outputs from an empirical model (Brooks) and a 2D numerical model (DIVAST). Validation using parameters such as BOD and FC, indicated a good match for BOD in near field compared to FC. The radiotracer ⁸²Br and Rhodamine WT generally gave good correlation with Brooks' and DIVAST models for nearfield, however at further distances predictions were not accurate.     

Numerical modelling of the groundwater inflow to an advancing open pit mine: Kolahdarvazeh pit,Central Iran

The groundwater inflow into a mine during its life and after ceasing operations is one of the most important concerns of the mining industry. This paper presents a hydrogeological assessment of the Irankuh Zn-Pb mine at 20 km south of Esfahan and 1 km northeast of Abnil in west-Central Iran. During mine excavation, the upper impervious bed of a confined aquifer was broken and water at high-pressure flowed into an open pit mine associated with the Kolahdarvazeh deposit. The inflow rates were 6.7 and 1.4 m³/s at the maximum and minimum quantities, respectively. Permeability, storage coefficient, thickness and initial head of the fully saturated confined aquifer were 3.5?×?10^?4 m/s, 0.2, 30 m and 60 m, respectively. The hydraulic heads as a function of time were monitored at four observation wells in the vicinity of the pit over 19 weeks and at an observation well near a test well over 21 h. In addition, by measuring the rate of pumping out from the pit sump, at a constant head (usually equal to height of the pit floor), the real inflow rates to the pit were monitored. The main innovations of this work were to make comparison between numerical modelling using a finite element software called SEEP/W and actual data related to inflow and extend the applicability of the numerical model. This model was further used to estimate the hydraulic heads at the observation wells around the pit over 19 weeks during mining operations. Data from a pump-out test and observation wells were used for model calibration and verification. In order to evaluate the model efficiency, the modelling results of inflow quantity and hydraulic heads were compared to those from analytical solutions, as well as the field data. The mean percent error in relation to field data for the inflow quantity was 0.108. It varied between 1.16 and 1.46 for hydraulic head predictions, which are much lower values than the mean percent errors resulted from the analytical solutions (from 1.8 to 5.3 for inflow and from 2.16 to 3.5 for hydraulic head predictions). The analytical solutions underestimated the inflow compared to the numerical model for the time period of 2–19 weeks. The results presented in this paper can be used for developing an effective dewatering program.     

Development of the concept of spatial–temporal mask for testing effects of discharge from well-drilling activities on biological communities

This paper describes the use of Bayesian spatial models to develop the concept of a spatial–temporal mask for the purpose of identifying regions in which before and after drilling effects are most clearly defined and from which the consequences of exposure of macrofauna and meiofauna to the release of drilling discharges can be evaluated over time. To determine the effects of drilling fluids and drill-cuttings on the marine benthic community, it is essential to know not only where discharged materials ended up within the possible impact area, but also the chemical concentrations to which biota were exposed during and after drilling. Barium and light hydrocarbons were used as chemical tracers for water-based and non-aqueous-based fluids in a shallow water site in the Campos Basin, off the coast of Brazil. Since the site showed evidence of exposure to waste material from earlier drilling, the analysis needed to take into account the background concentrations of these compounds. Using the Bayesian models, concentrations at unsampled sites were predicted and regions altered and previously contaminated were identified.     

Aggregated Versus Individual Land-Use Models: Modeling Spatial Autocorrelation to Increase Predictive Accuracy

The objective of this paper is to compare the predictive accuracy of individual and aggregated econometric models of land-use choices. We argue that modeling spatial autocorrelation is a comparative advantage of aggregated models due to the smaller number of observation and the linearity of the outcome. The question is whether modeling spatial autocorrelation in aggregated models is able to provide better predictions than individual ones. We consider a complete partition of space with four land-use classes: arable, pasture, forest, and urban. We estimate and compare the predictive accuracies of individual models at the plot level (514,074 observations) and of aggregated models at a regular 12 × 12 km grid level (3,767 observations). Our results show that modeling spatial autocorrelation allows to obtain more accurate predictions at the aggregated level when the appropriate predictors are used.     

Regional data refine local predictions: modeling the distribution of plant species abundance on a portion of the central plains

Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Similarly, the process of using regional data to inform local prediction for species distribution models has not been adequately evaluated. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents and associated with using regional data for predictions at a local extent for a native and non-native plant species across the northeastern central plains of Colorado. Our objectives were to compare model results and accuracy between those developed locally and extrapolated regionally, those developed regionally and extrapolated locally, and to evaluate extending species distribution modeling from predicting the probability of presence to predicting abundance. We developed models to predict the spatial distribution of plant species abundance using topographic, remotely sensed, land cover and soil taxonomic predictor variables. We compared model predicted mean and range abundance values to observed values between local and regional. We also evaluated model prediction performance based on Pearson's correlation coefficient. We show that: (1) extrapolating local models to regional extents may restrict predictions, (2) regional data can help refine and improve local predictions, and (3) boosted regression trees can be useful to model and predict plant species abundance. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network may improve our ability to monitor changes in local species abundance.     

Prediction models of CO, SPM and SO2 concentrations in the Campo de Gibraltar Region, Spain: a multiple comparison strategy

The 'Campo de Gibraltar' region is a very industrialized area where very few air pollution studies have been carried out. Up to date, no model has been developed in order to predict air pollutant levels in the different towns spread in the region. Carbon monoxide (CO), Sulphur dioxide (SO(2)) and suspended particulate matter (SPM) series have been investigated (years 1999-2000-2001). Multilayer perceptron models (MLPs) with backpropagation learning rule have been used. A resampling strategy with two-fold crossvalidation allowed the statistical comparison of the different models considered in this study. Artificial neural networks (ANN) models were compared with Persistence and ARIMA models and also with models based on standard Multiple Linear Regression (MLR) over test sets with data that had not been used in the training stage. The models based on ANNs showed better capability of generalization than those based on MLR. The designed procedure of random resampling permits an adequate and robust multiple comparison of the tested models. Principal component analysis (PCA) is used to reduce the dimensionality of data and to transform exogenous variables into significant and independent components. Short-term predictions were better than medium-term predictions in the case of CO and SO(2) series. Conversely, medium-term predictions were better in the case of SPM concentrations. The predictions are significantly promising (e.g., d (SPM 24-ahead) = 0.906, d (CO 1-ahead) = 0.891, d (SO2 1-ahead) = 0.851).     

Predicting the biological condition of streams: use of geospatial indicators of natural and anthropogenic characteristics of watersheds

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments.     

Estimating Field Volatility of Soil Fumigants Using CHAIN_2D: Mitigation Methods and Comparison Against Chloropicrin and 1,3-Dichloropropene Field Observations

Academic, government, and industrial field researchers have generated a significant database of field studies of the volatility of soil applied fumigants. However, limited work exists in validating physical models against field volatility data sets and fully exploring the volatility parametric response surface. Field studies quantifying atmospheric flux for soil fumigants 1,3-dichloropropene and chloropicrin are validated against the United States Department of Agriculture (USDA Salinity Laboratory) soil physics model CHAIN_2D that was modified specifically for agronomic uses of soil fumigants. Comparison between model predictions and field observations for six unique field trials in five different states indicate that CHAIN_2D effectively captures the magnitude and duration of fumigant emission from soil observed experimentally with r ²  ∼ 0.14–0.96 (avg. 0.66) for peak emission, and r ²  ∼ 0.76–1.0 (avg. 0.91) for cumulative emissions (r equals the Pearson moment correlation coefficient). Correct prediction magnitudes suggest that CHAIN_2D is a useful tool for extrapolating flux predictions to diverse scenarios not addressed by field trials. Examples of mitigation strategies such as the use of agricultural films (tarps), increased soil injection depth, and management of soil water content, under near semi-infinite parameter combinations of soil, meteorological, and agronomic properties are discussed.     

Predicting Water Quality Impaired Stream Segments using Landscape-Scale Data and a Regional Geostatistical Model: A Case Study in Maryland

In the United States, probability-based water quality surveys are typically used to meet the requirements of Section 305(b) of the Clean Water Act. The survey design allows an inference to be generated concerning regional stream condition, but it cannot be used to identify water quality impaired stream segments. Therefore, a rapid and cost-efficient method is needed to locate potentially impaired stream segments throughout large areas. We fit a set of geostatistical models to 312 samples of dissolved organic carbon (DOC) collected in 1996 for the Maryland Biological Stream Survey using coarse-scale watershed characteristics. The models were developed using two distance measures, straight-line distance (SLD) and weighted asymmetric hydrologic distance (WAHD). We used the Corrected Spatial Akaike Information Criterion and the mean square prediction error to compare models. The SLD models predicted more variability in DOC than models based on WAHD for every autocovariance model except the spherical model. The SLD model based on the Mariah autocovariance model showed the best fit (r² = 0.72). DOC demonstrated a positive relationship with the watershed attributes percent water, percent wetlands, and mean minimum temperature, but was negatively correlated to percent felsic rock type. We used universal kriging to generate predictions and prediction variances for 3083 stream segments throughout Maryland. The model predicted that 90.2% of stream kilometers had DOC values less than 5 mg/l, 6.7% were between 5 and 8 mg/l, and 3.1% of streams produced values greater than 8 mg/l. The geostatistical model generated more accurate DOC predictions than previous models, but did not fit the data equally well throughout the state. Consequently, it may be necessary to develop more than one geostatistical model to predict stream DOC throughout Maryland. Our methodology is an improvement over previous methods because additional field sampling is not necessary, inferences about regional stream condition can be made, and it can be used to locate potentially impaired stream segments. Further, the model results can be displayed visually, which allows results to be presented to a wide variety of audiences easily.     

Evaluation of Numerical Modeling Methods for the Management of Produced Water Discharges in the Coastal Region with a Canadian Case Study

This study aims at evaluating two numerical methods for 3D simulation of marine pollutant dispersion problems: the random walk particle tracking (RWPT) method and an explicit second-order finite difference method (FDM) for assessing produced water discharges from offshore oil platform. Test cases in a steady flow field were used to evaluate the efficiency and accuracy of simulating pollutant concentration profiles obtained using both the FDM and RWPT method in comparison with an analytical solution. Additionally, a field study was conducted to simulate the lead concentration distribution of produced water discharged from an oil platform off Canada’s east coast, based on the Princeton Ocean Model modeling the ocean flow in the study area with field verifications. Results indicate that, with proper configuration of grid resolution and particle resolution, both FDM and RWPT method can provide accurate results with reasonable computational costs for complex field cases. Particularly, the satisfactory 3D simulations of marine pollutant dispersion in the far field by both FDM and RWPT numerical methods enable effective assessment and management of offshore waste discharges.     

Introduction of Relative Abundance Distribution (RAD) Indices, Estimated from the Rank-Frequency Diagrams (RFD), to Assess Changes in Community Diversity

Quantifying a relative abundance distribution based on thesampling of a set of species is a widespread problem in ecology.A number of diversity indices have been proposed and used in numerous works in spite of a lack of statistical characteristics and tests of comparison. The relative abundancedistribution can also be described using rank-frequency diagrams but fitting these diagrams to mathematical models such as the Zipf-Mandelbrot model remains problematic. Strong correlation between the Zipf-Mandelbrot model parameters prevent their estimation by optimization algorithm. In light of this, new indices of sampled communities are introduced here. These indices are two linear regression slopes estimated from rank-frequency diagrams. The numerous statistical studies that have been carried out on linear regression models are used to compare sampled communities. These new indices possesscharacteristic properties with an ecological meaning.Correlations between these indices, the Zipf-Mandelbrot modelparameters and an evenness diversity index are examined. Anecological application is made using entomological data as anexample. This example consists of a transect from the edge of apond to a dry forest along which 60,000 insects were sampledfrom six different sampling stations. Using the new indicesdescribed here, station C, located at the edge of that areasubject to influence from the pond, is differentiated from theother stations. This station would seem to present the lowestdegree of niche diversity and the lowest evenness, and recent observations confirm the deterioration of this station.     

Testing the MAGIC acid rain model in highly organic,low-conductivity waters using multiple calibrations

Accurate predictions of acid precipitation effects on water resources are important in order to allow a better understanding of various pollution control strategy outcomes. Dynamic geochemical models have been developed to address this need, but have to be tested under a variety of environmental conditions to provide confidence in their predictions. The most commonly used aquatic acidification model in North America and Europe is the model of acidification of groundwater in catchments (MAGIC). Though extensively used, MAGIC has never been tested in catchments with extremely low ionic strength water and high in natural organic acids (NOAs) from wetlands, two conditions which are common in large parts of Canada. We calibrated the model for two catchments located in Nova Scotia, Canada, which had some of the most dilute freshwaters reported in the literature and very high NOA. We also evaluated the variability inherent in calibration data sets by using five separate data sets collected over a 15-year period at the same sites. We show good model simulations for the main cations and anions in catchment waters. However, modeling pH is more difficult in the highly organic waters and requires modification to the acid dissociation constants. Calculated acid neutralization capacity can also be more difficult to model due to the low ion content making small errors more important. In theory, multiple calibrations of a model at a same site should produce identical hindcasts and predictions. In reality, the multiple calibrations produced a series of similar, but not identical outcomes which give a probable range of past values and future outcomes. We feel that this practical approach to validation is a useful addition to the arsenal of model testing tools.     

Estimating the aquatic emissions and fate of perfluorooctane sulfonate (PFOS) into the river Rhine

The sources, distribution, levels and sinks of perfluorooctane sulfonate (PFOS) estimated to be released from areas of high population density, have been explored using the river Rhine as a case study. A comparison between modelled and measured data is presented, along with analysis of the importance of PFOS sorption in riverine systems. PFOS releases into the Rhine were estimated to be 325-690 kg/yr based on per capita emission rates of 27-57 μg day(-1) from a population of 33 million living within a 50 km zone either side of the river. Sorption of PFOS to suspended particles and sediments may alter its fate in the aquatic environment. Therefore available measured and modelled partitioning data was assessed, and K(d) values (sorption coefficient) of 7.5 and 20 were selected. This resulted in sediment-water ratios of 23-76 : 1, which are similar to ratios reported in the literature, and resulted in modelled estimates that <20% of the total PFOS entering the Rhine binds to sediments or suspended particles. The calculated discharge from the Rhine to the North Sea based on measured data was 420-2200 kg/yr; our model predictions are in good agreement with these estimates. Emission trends were accurately predicted, suggesting population density can be effectively used as a surrogate for diffuse PFOS emissions from product use, while predicted concentrations were a factor of 2-4 below measured data showing the importance of other sources. Transfer of PFOS to sediment is estimated to be minimal, and consequently discharges to the North Sea are roughly equal to PFOS releases to river water.     

Flow Field and Pollution Dispersion in a Central London Street

Urban pollution due to roadways is perceived as a major obstacle to implementing low-energy ventilation design strategies in urban non-domestic buildings. As part of a project to evaluate the use of a computational fluid flow model as an environmental design tool for urban buildings, this paper seeks to address the impact of pollution from roadways on buildings in areas of restricted topography and assess dominant influencing factors and other requirements for testing the flow model predictions. Vertical profiles of carbon monoxide (CO) and temperature at the facade of a building in a Central London street, in addition to above-roof wind speed and direction, were measured over a period of three months. The street has a height-to-width (h/W) ratio of 0.6 and is of asymmetric horizontal alignment. The air flows in the area surrounding the building were modelled using a computational fluid flow model for two orthogonal wind directions. CO concentrations were calculated from the steady-state flow field in order to place point measurements in the context of the flow field, identify persistent features in the measured data attributable to the flow structure and, by comparison with measurements, identify further testing requirements.Some qualitative and quantitative agreement between measured and modelled data was obtained. Measured CO levels at the building facade and vertical variations of CO were small, as predicted by the model. A wake-interference type flow was predicted by the model for wind speeds >2ms-1 with formation of a vortex cell occurring for roof-level wind speeds >5ms-1 for the cross-wind direction, which was reflected in the measured CO levels and facade gradients. A direction-dependent inverse relationship was noted, both in the model and measurements, between above-roof wind speed and facade CO levels although statistical correlations in the time series were poor. CO concentrations at the facade were found to increase with height frequently, as well as decrease, especially for parallel winds. It is expected that mechanical turbulence due to vehicles was largely responsible. In comparison, thermal stratification appeared to play only a minor role in controlling vertical mixing in the street, under low wind speed conditions.     

Dealing with heterogeneous regression slopes in analysis of covariance: new methodology applied to environmental effects monitoring fish survey data

Analysis of covariance (ANCOVA) is a powerful statistical method which incorporates one or more covariates into the analysis to reduce error associated with measurement. ANCOVA (modeling response as a function of fish size) is frequently used to analyze environmental effects monitoring (EEM) fish survey data. In approximately 12% of fish survey data sets taken from cycles 1 to 3 of Environment Canada’s EEM database for pulp and paper mills, the standard assumption of parallel regression slopes is not met. For the first three cycles of the EEM program, these data sets were classified as indicating a mill effect, but for the most part were excluded from subsequent analyses aimed at quantifying the effect. We present two different methods for initially dealing with data sets that exhibit heterogeneous slopes so that they can be analyzed using the parallel slope model. The first method identifies data sets where heterogeneous slopes are forced by a few high-influence observations. The second approach identifies data sets where a model with heterogeneous slopes is statistically, but not practically, significant: with a high coefficient of determination for the parallel slope model. These new methodologies are applied to EEM pulp and paper data sets and about 55% of cases with heterogeneous slopes can be described by a parallel slope model. We also discuss a third method that can be used to describe mill effects when regression slopes remain heterogeneous even after applying the above two methods, enabling comparison with a critical effect size. These new methodologies could benefit the EEM program by enabling more data sets to be incorporated into meta-analyses and be used to make more equitable mill monitoring decisions in the future.     

Patterns of Spatial Autocorrelation in Stream Water Chemistry

Geostatistical models are typically based on symmetric straight-line distance, which fails to represent the spatial configuration, connectivity, directionality, and relative position of sites in a stream network. Freshwater ecologists have explored spatial patterns in stream networks using hydrologic distance measures and new geostatistical methodologies have recently been developed that enable directional hydrologic distance measures to be considered. The purpose of this study was to quantify patterns of spatial correlation in stream water chemistry using three distance measures: straight-line distance, symmetric hydrologic distance, and weighted asymmetric hydrologic distance. We used a dataset collected in Maryland, USA to develop both general linear models and geostatistical models (based on the three distance measures) for acid neutralizing capacity, conductivity, pH, nitrate, sulfate, temperature, dissolved oxygen, and dissolved organic carbon. The spatial AICC methodology allowed us to fit the autocorrelation and covariate parameters simultaneously and to select the model with the most support in the data. We used the universal kriging algorithm to generate geostatistical model predictions. We found that spatial correlation exists in stream chemistry data at a relatively coarse scale and that geostatistical models consistently improved the accuracy of model predictions. More than one distance measure performed well for most chemical response variables, but straight-line distance appears to be the most suitable distance measure for regional geostatistical modeling. It may be necessary to develop new survey designs that more fully capture spatial correlation at a variety of scales to improve the use of weighted asymmetric hydrologic distance measures in regional geostatistical models.     

Comparative analysis of regional water quality in Canada using the Water Quality Index

The Canadian Council of Ministers for the Environment (CCME) has developed a Water Quality Index (WQI) to simplify the reporting of complex water quality data. This science-based communication tool tests multi-variable water data against numeric water quality guidelines and/or objectives to produce a single unit-less number that represents overall water quality. The CCME WQI has been used to rate overall water quality in spatial and temporal comparisons of site(s). However, it has not been used in a comparative-analysis of exposure sites to reference sites downstream of point source discharges. This study evaluated the ability of the CCME WQI to differentiate water quality from metal mines across Canada at exposure sites from reference sites using two different types of numeric water quality objectives: (1) the water quality guidelines (WQG) for the protection of freshwater aquatic life and (2) water quality objectives determined using regional reference data termed Region-Specific Objectives (RSO). The application of WQG to the CCME WQI was found to be a good tool to assess absolute water quality as it relates to national water quality guidelines for the protection of aquatic life, but had more limited use when evaluating spatial changes in water quality downstream of point source discharges. The application of the RSO to the CCME WQI resulted in assessment of spatial changes in water quality downstream of point source discharges relative to upstream reference conditions.