The United States and Canada currently use exposure-based metrics to protect vegetation from O3. Using 5 years (1999-2003) of co-measured O3, meteorology and growth response, we have developed exposure-based regression models that predict Populus tremuloides growth change within the North American ambient air quality context. The models comprised growing season fourth-highest daily maximum 8-h average O3 concentration, growing degree days, and wind speed. They had high statistical significance, high goodness of fit, include 95% confidence intervals for tree growth change, and are simple to use. Averaged across a wide range of clonal sensitivity, historical 2001-2003 growth change over most of the 26 M ha P. tremuloides distribution was estimated to have ranged from no impact (0%) to strong negative impacts (-31%). With four aspen clones responding negatively (one responded positively) to O3, the growing season fourth-highest daily maximum 8-h average O3 concentration performed much better than growing season SUM06, AOT40 or maximum 1 h average O3 concentration metrics as a single indicator of aspen stem cross-sectional area growth. A new exposure-based metric approach to predict O3 risk to North American aspen forests has been developed.

A geologic classification scheme was combined with elevation to test hypotheses regarding watershed sensitivity to acidic deposition using available regional spatial data and to delimit a high-interest area for streamwater acidification sensitivity within the Southern Appalachian Mountains region. It covered only 28% of the region, and yet included almost all known streams that have low acid neutralizing capacity (ANC ≤20 μeq l⁻¹) or that are acidic (ANC ≤0). The five-class geologic classification scheme was developed based on recent lithologic maps and streamwater chemistry data for 909 sites. The vast majority of the sampled streams that had ANC ≤20 μeq l⁻¹ and that were totally underlainby a single geologic sensitivity class occurred in the siliceous class, which is represented by such lithologies as sandstone and quartzite. Streamwater acid-base chemistry throughout the region was also found to be associated with a number of watershed features that were mapped for the entire region, in addition to lithology and elevation, including ecoregion, physiographic province, soils type, forest type and watershed area. Logistic regression was used to model the presence/absence of acid-sensitive streams throughout the region.

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.

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.

This paper presents an application of high resolution satellite remote sensing data for mapping water quality in the Goldon Horn, Istanbul. It is an applied research emphasizing the present water quality conditions in this region for water quality parameters; secchi disc depth (SDD), chlorophyl-a (chl-a) and total suspended sediment (TSS) concentration. The study also examines the retrievals of these parameters through high resolution IKONOS multispectral data supported by in situ measurements. Image processing procedure involving radiometric correction is carried out for conversion from digital numbers (DNs) to spectral radiance to correlate water quality parameters and satellite data by using multiple regression technique. The retrieved and verified results show that the measured and estimated values of water quality parameters in good agreement (R ² > 0.97). The spatial distribution maps are developed by using multiple regression algorithm belonging to water quality parameters. These maps present apparent spatial variations of selected parameters and inform the decision makers of water quality variations in a large water region in the Istanbul metropolitan area.

Nitrogen (N) budget was estimated with dissolved inorganic N (DIN) and dissolved organic N (DON) in a forested mountainous watershed in Tsukui, Kanagawa Prefecture, about 50 km west of Tokyo in Central Japan. The forest vegetation in the watershed was dominant by Konara oak (Quercus serrata) and Korean hornbeam (Carpinus tschonoskii), and Japanese cedar (Cryptomeria japonica). Nitrate (NO₃ -) concentration in the watershed streamwater was averagely high (98.0 ±± 19 (±± SD, n = 36) μmol L-¹) during 2001-2003. There was no seasonal and annual changes in the stream NO- ₃ concentration even though the highest N uptake rate presumably occurred during the spring of plant growing season, a fact indicating that N availability was in excess of biotic demands. The DON deposition rates (DON input rates) in open area and forest area were estimated as one of the main N sources, accounting for about 32% of total dissolved N (TDN). It was estimated that a part of the DON input rate contributed to the excessive stream NO- ₃ output rate under the condition of the rapid mineralization and nitrification rates, which annual DON deposition rates were positively correlated with the stream NO₃ - output rates. The DON retention rate in the DON budget had a potential capacity, which contributed to the excessive stream NO- ₃ output rate without other N contributions (e.g. forest floor N or soil N).