A Biotic Ligand Model was developed predicting the effect of cobalt on root growth of barley (Hordeum vulgare) in nutrient solutions. The extent to which Ca2+, Mg2+, Na+, K+ ions and pH independently affect cobalt toxicity to barley was studied. With increasing activities of Mg2+, and to a lesser extent also K+, the 4-d EC50Co2+ increased linearly, while Ca2+, Na+ and H+ activities did not affect Co2+ toxicity. Stability constants for the binding of Co2+, Mg2+ and K+ to the biotic ligand were obtained: log KCoBL = 5.14, log KMgBL = 3.86 and log KKBL = 2.50. Limited validation of the model with one standard artificial soil and one standard field soil showed that the 4-d EC50Co2+ could only be predicted within a factor of four from the observed values, indicating further refinement of the BLM is needed. Biotic Ligand Models are not only a useful tool to assess metal toxicity in aquatic systems but can also be used for terrestrial plants.

Concern regarding the impacts of continued nitrogen and sulfur deposition on ecosystem health has prompted the development of critical acid load assessments for forest soils. A critical acid load is a quantitative estimate of exposure to one or more pollutants at or above which harmful acidification-related effects on sensitive elements of the environment occur. A pollutant load in excess of a critical acid load is termed exceedance. This study combined a simple mass balance equation with national-scale databases to estimate critical acid load and exceedance for forest soils at a 1-km2 spatial resolution across the conterminous US. This study estimated that about 15% of US forest soils are in exceedance of their critical acid load by more than 250 eq ha-1 yr-1, including much of New England and West Virginia. Very few areas of exceedance were predicted in the western US. This simple mass balance equation estimated that 17% of US forest soils exceed their critical acid load by more than 250 eq ha-1 yr-1, and these areas are predominantly located in the northeastern US.

Multimedia mass balance models assume well-mixed homogeneous compartments. Particularly for soils, this does not correspond to reality, which results in potentially large uncertainties in estimates of transport fluxes from soils. A theoretically expected exponential decrease model of chemical concentrations with depth has been proposed, but hardly tested against empirical data. In this paper, we explored the correspondence between theoretically predicted soil concentration profiles and 84 field measured profiles. In most cases, chemical concentrations in soils appear to decline exponentially with depth, and values for the chemical specific soil penetration depth (dp) are predicted within one order of magnitude. Over all, the reliability of multimedia models will improve when they account for depth-dependent soil concentrations, so we recommend to take into account the described theoretical exponential decrease model of chemical concentrations with depth in chemical fate studies. In this model the dp-values should estimated be either based on local conditions or on a fixed dp-value, which we recommend to be 10 cm for chemicals with a log Kow > 3. Multimedia mass model predictions will improve when taking into account depth dependent soil concentrations.

Simple mass balance equations (SMBE) of critical acid loads (CAL) in forest soil were developed to assess potential risks of air pollutants to ecosystems. However, to apply SMBE reliably at large scales, SMBE must be tested for adequacy and uncertainty. Our goal was to provide a detailed analysis of uncertainty in SMBE so that sound strategies for scaling up CAL estimates to the national scale could be developed. Specifically, we wanted to quantify CAL uncertainty under natural variability in 17 model parameters, and determine their relative contributions in predicting CAL. Results indicated that uncertainty in CAL came primarily from components of base cation weathering (BCw; 49%) and acid neutralizing capacity (46%), whereas the most critical parameters were BCw base rate (62%), soil depth (20%), and soil temperature (11%). Thus, improvements in estimates of these factors are crucial to reducing uncertainty and successfully scaling up SMBE for national assessments of CAL.

Global climate change may have large impacts on water supplies, drought or flood frequencies and magnitudes in local and regional hydrologic systems. Water authorities therefore rely on computer models for quantitative impact prediction. In this study we present kernel-based learning machine river flow models for the Upper Gallego catchment of the Ebro basin. Different learning machines were calibrated using daily gauge data. The models posed two major challenges: (1) estimation of the rainfall-runoff transfer function from the available time series is complicated by anthropogenic regulation and mountainous terrain and (2) the river flow model is weak when only climate data are used, but additional antecedent flow data seemed to lead to delayed peak flow estimation. These types of models, together with the presented downscaled climate scenarios, can be used for climate change impact assessment in the Gallego, which is important for the future management of the system. Future climate change and data-based rainfall-runoff predictions are presented for the Upper Gallego.

Metal ions interact with biological materials and their decomposition products by ligation (coordination complex-formation with certain moieties containing O, N, S, etc.). The extent of this interaction depends on the identities of both ligand and metal ion and can be described by some equation derived from perturbation theory. Uptake of metal ions - including highly toxic ones - from soils is controlled by a competition between root exudate components and soil organic matter (SOM) for the ions. SOM consists of a variety of potential ligands which evolve during humification towards more efficient binding (retention) of metals such as Cu, Ni, Cr but also of toxicants like U, Cd. The actual way of interaction can be inferred from stoichiometry of the involved compounds and the C/N ratio in the soil, providing predictions as to which metals will be most efficiently shuttled into green plants or fungi, respectively. The latter, selective process is crucial for closing nutrient cycles and sensitively depends on C/N ratio and the extent of “forcing” by onfalling leaf or needle litter. Therefore, analytical data on the soil can be used to predict possible risks of exposition to toxic metals also for human consumption of plant parts. Degradation, amounts and evolution of N-free vs. nitrogenous SOM control transfer of essential and toxic metals from soil into plants, to be estimated from coordination (bio-)chemistry.

Automated synoptic weather typing and robust orthogonal stepwise regression analysis (via principal components analysis) were applied together to develop within-weather-type air pollution prediction models for a variety of pollutants (specifically, carbon monoxide – CO, nitrogen dioxide – NO₂, ozone – O₃, sulphur dioxide – SO₂, and suspended particles – SP) for the period 1974–2000 in south-central Canada. The SAS robust regression procedure was used to limit the influence of outliers on air pollution prediction algorithms. Six-hourly Environment Canada surface observed meteorological data and 6-hourly US National Centers for Environmental Prediction (NCEP) reanalysis data of various weather elements were used in the analysis. The models were developed using two-thirds of the total years for meteorological and air pollution data; the remaining one-third (randomly selected) was used for model validation. Robust stepwise regression analysis was performed to analytically determine the meteorological variables that might be used to predict air pollution concentrations. There was a significant correlation between observed daily mean air pollution concentrations and model predictions. About 20, 50, and 80% of the 80 prediction models across the study area possessed R ² values ≥ 0.7, 0.6, and 0.5, respectively. The results of model validation were similar to those of model development, with slightly smaller model R ² values.

Extraction of natural gas from a confined coal aquifer requires the pumping of large amounts of groundwater, commonly referred to as produced water. Produced water from the extraction of coalbed natural gas is typically disposed into nearby constructed discharge ponds. The objective of this study was to collect produced water samples at outfalls and corresponding discharge ponds and monitor pH, electrical conductivity (EC), calcium (Ca), magnesium (Mg), sodium (Na), and alkalinity. Outfalls and corresponding discharge ponds were sampled from five different watersheds including Cheyenne River (CHR), Belle Fourche River (BFR), Little Powder River (LPR), Powder River (PR), and Tongue River (TR) within the Powder River Basin (PRB), Wyoming from 2003 to 2005. From Na, Ca, and Mg measurements, sodium adsorption ratios (SAR) were calculated, and used in a regression model. Results suggest that outfalls are chemically different from corresponding discharge ponds. Sodium, alkalinity, and pH all tend to increase, possibly due to environmental factors such as evaporation, while Ca decreased from outfalls to associated discharge ponds due to calcite precipitation. Watersheds examined in this study were chemically different form each other and most discharge ponds with in individual watersheds tended to increase in Na and SAR from 2003 to 2005. Since discharge pond water was chemically changing as a function of watershed chemistry, we predicted SAR of discharge pond water using a regression model. The predicted discharge pond water results suggested a high correlation (R ² = 0.83) to discharge well SAR. Overall, results of this study will be useful for landowners, water quality managers, and industry in properly managing produced water from the natural gas extraction.

Using within-weather-group air pollution prediction models developed in Part I of this research, this study estimates future air pollution levels for a variety of pollutants (specifically, carbon monoxide – CO, nitrogen dioxide – NO₂, ozone – O₃, sulphur dioxide – SO₂, and suspended particles – SP) under future climate scenarios for four cities in south-central Canada. A statistical downscaling method was used to downscale five general circulation model (GCM) scenarios to selected weather stations. Downscaled GCM scenarios were used to compare respective characteristics of the weather groups developed in Part I; discriminant function analysis was used to allocate future days from two windows of time (2040–2059 and 2070–2089) into one of four weather groups. In Part I, the four weather groups were characterised as hot, cold, air pollution-related, and other (defined as relatively good air quality and comfortable weather conditions). In estimating future daily air pollution concentrations, three future pollutant emission scenarios were considered: Scenario I – emissions decreasing 20% by 2050, Scenario II – future emissions remaining at the same level as at the end of the twentieth century, and Scenario III – emissions increasing 20% by 2050. The results showed that, due to increased temperatures, the average annual number of days with high O₃ levels in the four selected cities could increase by more than 40–100% by the 2050s and 70–200% by the 2080s (from the current areal average of 8 days) under the three pollutant emission scenarios. The corresponding number of low O₃ days could decrease by 4–10% and 5–15% (from the current areal average of 312 days). For the rest of the pollutants, future air pollution levels will depend on future pollutant emission levels. Under emission Scenarios II and III, the average annual number of high pollution days could increase 20–40% and 80–180%, respectively, by the middle and late part of this century. In contrast, under Scenario I, the average annual number of high pollution days could decrease by 10–65%.

Suspended particulate matter is significantly related to the degradation of air quality in urban agglomerations, generating adverse health effects. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important in order to improve public awareness and air quality management. This study aims at developing models using multiple regression and neural network (NN) methods that might produce accurate 24-h predictions of daily average (DA) value of PM10 concentration and at comparatively assessing the above mentioned techniques. Pollution and meteorological data were collected in the urban area of Volos, a medium-sized coastal city in central Greece, whose population and industrialization is continuously increasing. Both models utilize five variables as inputs, which incorporate meteorology (difference between daily maximum and minimum hourly value of ground temperature and DA value of wind speed), persistency in PM10 levels and weekly and annual variation of PM10 concentration. The validation of the models revealed that NN model showed slightly better skills in forecasting PM10 concentrations, as the regression and the NN model can forecast 55 and 61% of the variance of the data, respectively. In addition, several statistical indexes were calculated in order to verify the quality and reliability of the developed models. The results showed that their skill scores are satisfying, presenting minor differences. It was also found that both are capable of predicting the exceedances of the limit value of 50 μg/m³ at a satisfactory level.