Urban stream syndrome in a small,lightly developed watershed: a statistical analysis of water chemistry parameters,land use patterns,and natural sources

The relationships among land use patterns, geology, soil, and major solute concentrations in stream water for eight tributaries of the Kayaderosseras Creek watershed in Saratoga County, NY, were investigated using Pearson correlation coefficients and multivariate regression analysis. Sub-watersheds corresponding to each sampling site were delineated, and land use patterns were determined for each of the eight sub-watersheds using GIS. Four land use categories (urban development, agriculture, forests, and wetlands) constituted more than 99 % of the land in the sub-watersheds. Eleven water chemistry parameters were highly and positively correlated with each other and urban development. Multivariate regression models indicated urban development was the most powerful predictor for the same eleven parameters (conductivity, TN, TP, NO $_{3}^-$ , Cl^?, HCO $_{3}^-$ , SO $_{4}^{2-}$ , Na⁺, K⁺, Ca²⁺, and Mg²⁺). Adjusted R ² values, ranging from 19 to 91 %, indicated that these models explained an average of 64 % of the variance in these 11 parameters across the samples and 70 % when Mg²⁺ was omitted. The more common R ², ranging from 29 to 92 %, averaged 68 % for these 11 parameters and 72 % when Mg²⁺ was omitted. Water quality improved most with forest coverage in stream watersheds. The strong associations between water quality variables and urban development indicated an urban source for these 11 water quality parameters at all eight sampling sites was likely, suggesting that urban stream syndrome can be detected even on a relatively small scale in a lightly developed area. Possible urban sources of Ca²⁺ and HCO $_{3}^-$ are suggested.     

Water quality time series for Big Melen stream (Turkey): its decomposition analysis and comparison to upstream

Big Melen stream is one of the major water resources providing 268 km³ year^???1 of drinking and municipal water for Istanbul. Monthly time series data between 1991 and 2004 for 25 chemical, biological, and physical water properties of Big Melen stream were separated into linear trend, seasonality, and error components using additive decomposition models. Water quality index (WQI) derived from 17 water quality variables were used to compare Aksu upstream and Big Melen downstream water quality. Twenty-six additive decomposition models of water quality time series data including WQI had R ² values ranging from 88% for log(water temperature) (P?≤?0.001) to 3% for log(total dissolved solids) (P?≤?0.026). Linear trend models revealed that total hardness, calcium concentration, and log(nitrite concentration) had the highest rate of increase over time. Tukey’s multiple comparison pointed to significant decreases in 17 water quality variables including WQI of Big Melen downstream relative to those of Aksu upstream (P?≤?0.001). Monitoring changes in water quality on the basis of watersheds through WQI and decomposition analysis of time series data paves the way for an adaptive management process of water resources that can be tailored in response to effectiveness and dynamics of management practices.     

Multi-scale landscape factors influencing stream water quality in the state of Oregon

Enterococci bacteria are used to indicate the presence of human and/or animal fecal materials in surface water. In addition to human influences on the quality of surface water, a cattle grazing is a widespread and persistent ecological stressor in the Western United States. Cattle may affect surface water quality directly by depositing nutrients and bacteria, and indirectly by damaging stream banks or removing vegetation cover, which may lead to increased sediment loads. This study used the State of Oregon surface water data to determine the likelihood of animal pathogen presence using enterococci and analyzed the spatial distribution and relationship of biotic (enterococci) and abiotic (nitrogen and phosphorous) surface water constituents to landscape metrics and others (e.g. human use, percent riparian cover, natural covers, grazing, etc.). We used a grazing potential index (GPI) based on proximity to water, land ownership and forage availability. Mean and variability of GPI, forage availability, stream density and length, and landscape metrics were related to enterococci and many forms of nitrogen and phosphorous in standard and logistic regression models. The GPI did not have a significant role in the models, but forage related variables had significant contribution. Urban land use within stream reach was the main driving factor when exceeding the threshold (> or =35 cfu/100 ml), agriculture was the driving force in elevating enterococci in sites where enterococci concentration was <35 cfu/100 ml. Landscape metrics related to amount of agriculture, wetlands and urban all contributed to increasing nutrients in surface water but at different scales. The probability of having sites with concentrations of enterococci above the threshold was much lower in areas of natural land cover and much higher in areas with higher urban land use within 60 m of stream. A 1% increase in natural land cover was associated with a 12% decrease in the predicted odds of having a site exceeding the threshold. Opposite to natural land cover, a one unit change in each of manmade barren and urban land use led to an increase of the likelihood of exceeding the threshold by 73%, and 11%, respectively. Change in urban land use had a higher influence on the likelihood of a site exceeding the threshold than that of natural land cover.     

Are Toronto’s streams sick? A look at the fish and benthic invertebrate communities in the Toronto region in relation to the urban stream syndrome

Impacts of urbanization on aquatic ecosystems are intensifying as urban sprawl spreads across the global land base. The urban stream syndrome (USS) identifies “symptoms” associated with urban development including changes in biotic communities, hydrology, water chemistry, and channel morphology. Direct relationships between road density (as surrogate of urbanization) and indicators of the USS were identified for streams in the Toronto region. Significant negative relationships were revealed between road density and biological (fish and benthic macroinvertebrate) richness, diversity, and fish Index of Biotic Integrity scores. Significant positive relationships were found between road density and tolerant fish/benthic macroinvertbrates, benthos Family Biotic Index scores, mean summer stream temperature, stream flashiness, and several water quality variables. Analysis of biological data showed that only four fish species and a reduced number of benthic macroinvertebrate families remained at the most urbanized sites. Road density was found to be a major determinant in both the fish and benthic macroinvertebrate community structure.     

Selecting Variables for Habitat Suitability of <Emphasis Type="Italic">Asellus</Emphasis> (Crustacea,Isopoda) by Applying Input Variable Contribution Methods to Artificial Neural Network Models

This study aimed to compare different methods to analyse the contribution of individual river characteristics to predict the abundance of Asellus (Crustacea, Isopoda). Six methods which provide the relative contribution and/or the contribution profile of the input variables of artificial neural network models were therefore compared: (1) the ‘partial derivatives’ method; (2) the ‘weights’ method; (3) the ‘perturb’ method; (4) the ‘profile’ method; (5) the ‘classical stepwise’ method; (6) the ‘improved stepwise’ method. Consequently, the key variables which affect the habitat preferences of Asellus could be identified. To evaluate the performance of the artificial neural network model, the model predictions were compared with the results of a multiple linear regression analysis. The dataset consisted of 179 samples, collected over a 3-year period in the Zwalm catchment in Flanders, Belgium. Twenty-four environmental variables as well as the log-transformed abundance of Asellus were used in this study. The different contribution methods seemed to give similar results concerning the order of importance of the input variables. Nevertheless, their diverse computation led to differences in sensitivity and stability of the methods and the derived outcomes on the habitat preferences. From an ecological point of view, the environmental variables describing the stream type (width, depth, stream order and distance to mouth) were the most significant variables for Asellus in the Zwalm catchment during the period 2000–2002 for all applied methods. Indirectly, one can conclude that the water quality is not the limiting factor for the survival of Asellus in the Zwalm catchment.     

The relative influence of nutrients and habitat on stream metabolism in agricultural streams

Stream metabolism was measured in 33 streams across a gradient of nutrient concentrations in four agricultural areas of the USA to determine the relative influence of nutrient concentrations and habitat on primary production (GPP) and respiration (CR-24). In conjunction with the stream metabolism estimates, water quality and algal biomass samples were collected, as was an assessment of habitat in the sampling reach. When data for all study areas were combined, there were no statistically significant relations between gross primary production or community respiration and any of the independent variables. However, significant regression models were developed for three study areas for GPP (r ² = 0.79–0.91) and CR-24 (r ² = 0.76–0.77). Various forms of nutrients (total phosphorus and area-weighted total nitrogen loading) were significant for predicting GPP in two study areas, with habitat variables important in seven significant models. Important physical variables included light availability, precipitation, basin area, and in-stream habitat cover. Both benthic and seston chlorophyll were not found to be important explanatory variables in any of the models; however, benthic ash-free dry weight was important in two models for GPP.     

Effects of geomorphology, habitat, and spatial location on fish assemblages in a watershed in Ohio, USA

In this paper, we evaluate relationships between in-stream habitat, water chemistry, spatial distribution within a predominantly agricultural Midwestern watershed and geomorphic features and fish assemblage attributes and abundances. Our specific objectives were to: (1) identify and quantify key environmental variables at reach and system wide (watershed) scales; and (2) evaluate the relative influence of those environmental factors in structuring and explaining fish assemblage attributes at reach scales to help prioritize stream monitoring efforts and better incorporate all factors that influence aquatic biology in watershed management programs. The original combined data set consisted of 31 variables measured at 32 sites, which was reduced to 9 variables through correlation and linear regression analysis: stream order, percent wooded riparian zone, drainage area, in-stream cover quality, substrate quality, gradient, cross-sectional area, width of the flood prone area, and average substrate size. Canonical correspondence analysis (CCA) and variance partitioning were used to relate environmental variables to fish species abundance and assemblage attributes. Fish assemblages and abundances were explained best by stream size, gradient, substrate size and quality, and percent wooded riparian zone. Further data are needed to investigate why water chemistry variables had insignificant relationships with IBI scores. Results suggest that more quantifiable variables and consideration of spatial location of a stream reach within a watershed system should be standard data incorporated into stream monitoring programs to identify impairments that, while biologically limiting, are not fully captured or elucidated using current bioassessment methods.     

Use of fuzzy logic models for prediction of taste and odor compounds in algal bloom-affected inland water bodies

Mechanistic modeling of how algal species produce metabolites (e.g., taste and odor compounds geosmin and 2-methyl isoborneol (2-MIB)) as a biological response is currently not well understood. However, water managers and water utilities using these reservoirs often need methods for predicting metabolite production, so that appropriate water treatment procedures can be implemented. In this research, a heuristic approach using Adaptive Network-based Fuzzy Inference System (ANFIS) was developed to determine the underlying nonlinear and uncertain quantitative relationship between observed cyanobacterial metabolites (2-MIB and geosmin), various algal species, and physical and chemical variables. The model is proposed to be used in conjunction with numerical water quality models that can predict spatial–temporal distribution of flows, velocities, water quality parameters, and algal functional groups. The coupling of the proposed metabolite model with the numerical water quality models would assist various utilities which use mechanistic water quality models to also be able to predict distribution of taste and odor metabolites, especially when monitoring of metabolites is limited. The proposed metabolite model was developed and tested for the Eagle Creek Reservoir in Indiana (USA) using observations over a 3-year period (2008–2010). Results show that the developed models performed well for geosmin (R ²?=?0.83 for all training data and R ²?=?0.78 for validation of all 10 data points in the validation dataset) and reasonably well for the 2-MIB (R ²?=?0.82 for all training data and R ²?=?0.70 for 7 out of 10 data points in the validation dataset).     

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl^?), calcium (Ca²⁺), total alkalinity, total hardness, nitrate nitrogen (NO₃-N), and ammonical nitrogen (NH₄-N). The ANN structure with 14 hidden neurons obtained the best selection. By making comparison between the results of the ANN model and the measured data on the basis of correlation coefficient (r) and root mean square error (RMSE), a good model-fitting DO values indicated the effectiveness of neural network model. It is found that the coefficient of correlation (r) values for the training, validation, and test sets were 0.9654, 0.9841, and 0.9680, respectively, and the respective values of RMSE for the training, validation, and test sets were 0.4272, 0.3667, and 0.4570, respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable. The most effective inputs were determined as pH, NO₃-N, NH₄-N, and Ca²⁺. Cl^? was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.     

The influence of suburban land use on habitat and biotic integrity of coastal Rhode Island streams

Watershed land use in suburban areas can affect stream biota through degradation of instream habitat, water quality, and riparian vegetation. By monitoring stream biotic communities in various geographic regions, we can better understand and conserve our watershed ecosystems. The objective of this study was to examine the relationship between watershed land use and the integrity of benthic invertebrate communities in eight streams that were assessed over a 3-year period (2001-2003). Sites were selected from coastal Rhode Island watersheds along a residential land-use gradient (4-59%). Using the rapid bioassessment protocol, we collected biological, physicochemical, habitat, and nutrient data from wadeable stream reaches and compared metrics of structure and integrity. Principal component analyses showed significant negative correlation of indicators for stream physicochemical, habitat, and instream biodiversity with increasing residential land use (RLU) in the watershed. The physicochemical variables that were most responsive to percent RLU were conductivity, instream habitat, nitrate, and dissolved inorganic nitrogen (DIN). The positive correlation of DIN with percent RLU indicated an anthropogenic source of pollution affecting the streams. The biotic composition of the streams shifted from sensitive to insensitive taxa as percent RLU increased; the most responsive biological variables were percent Ephemeroptera, percent Scrapers, percent Insects, and the Hilsenhoff biotic index. These data show the importance of land management and conservation at the watershed scale to sustaining the biotic integrity of coastal stream ecosystems.     

Longitudinal variability in streamwater chemistry and carbon and nitrogen fluxes in restored and degraded urban stream networks

Stream restoration has increasingly been used as a best management practice for improving water quality in urbanizing watersheds, yet few data exist to assess restoration effectiveness. This study examined the longitudinal patterns in carbon and nitrogen concentrations and mass balance in two restored (Minebank Run and Spring Branch) and two unrestored (Powder Mill Run and Dead Run) stream networks in Baltimore, Maryland, USA. Longitudinal synoptic sampling showed that there was considerable reach-scale variability in biogeochemistry (e.g., total dissolved nitrogen (TDN), dissolved organic carbon (DOC), cations, pH, oxidation/reduction potential, dissolved oxygen, and temperature). TDN concentrations were typically higher than DOC in restored streams, but the opposite pattern was observed in unrestored streams. Mass balances in restored stream networks showed net uptake of TDN across subreaches (mean ± standard error net uptake rate of TDN across sampling dates for Minebank Run and Spring Branch was 420.3 ± 312.2 and 821.8 ± 570.3 mg m(-2) d(-1), respectively). There was net release of DOC in the restored streams (1344 ± 1063 and 1017 ± 944.5 mg m(-2) d(-1) for Minebank Run and Spring Branch, respectively). Conversely, degraded streams, Powder Mill Run and Dead Run showed mean net release of TDN across sampling dates (629.2 ± 167.5 and 327.1 ± 134.5 mg m(-2) d(-1), respectively) and net uptake of DOC (1642 ± 505.0 and 233.7 ± 125.1 mg m(-2) d(-1), respectively). There can be substantial C and N transformations in stream networks with hydrologically connected floodplain and pond features. Assessment of restoration effectiveness depends strongly on where monitoring is conducted along the stream network. Monitoring beyond the stream-reach scale is recommended for a complete perspective of evaluation of biogeochemical function in restored and degraded urban streams.     

Agriculture and elevation are the main factors for Pampasic stream habitat and water quality

Streams of the Pampasic plain in Southeastern South America are ecosystems affected by both water pollution and habitat alteration mainly due to agricultural activity. Water quality is influenced by the quality of habitats and both depend on land use and watershed morphology. The objective of this study was to determine the relationship between the variables of four factors: (1) the morphology of the watershed, (2) land use in the watershed, (3) river habitat, and (4) water quality of wadeable streams in Uruguay, as well as to determine the most representative variables to quantify such factors. We studied 28 watersheds grouped into three ecoregions and four principal activities, which generated seven zones with three to five streams each. Correlations between the variables of each factor allowed reducing the total number of variables from 57 to 32 to perform principal component analyses (PCA) by factor, reducing the number of variables to 18 for a general PCA. The first component was associated with water quality and elevation. The second was associated with the stream and watershed size, the third with habitat quality, and the fourth to the use of neighboring soils and objects in the channel. Our results indicate that agricultural intensity and elevation are the main factors associated with the habitat and water quality of these lowland streams. These factors must be especially considered in the development of water quality monitoring programs.     

Biological Integrity in Mid-Atlantic Coastal Plains Headwater Streams

The objective of this study was to assess the applicability of using landscape variables in conjunction with water quality and benthic data to efficiently estimate stream condition of select headwater streams in the Mid-Atlantic Coastal Plains. Eighty-two streams with riffle sites were selected from eight-two independent watersheds across the region for sampling and analyses. Clustering of the watersheds by landscape resulted in three distinct groups (forest, crop, and urban) which coincided with watersheds dominant land cover or use. We used non-parametric analyses to test differences in benthos and water chemistry between groups, and used regression analyses to evaluate responses of benthic communities to water chemistry within each of the landscape groups. We found that typical water chemistry measures associated with urban runoff such as specific conductance and dissolved chloride were significantly higher in the urban group. In the crop group, we found variables commonly associated with farming such as nutrients and pesticides significantly greater than in the other two groups. Regression analyses demonstrated that the numbers of tolerant and facultative macroinvertebrates increased significantly in forested watersheds with small shifts in pollutants, while in human use dominated watersheds the intolerant macroinvertebrates were more sensitive to shifts in chemicals present at lower concentrations. The results from this study suggest that landscape based clustering can be used to link upstream landscape characteristics, water chemistry and biotic integrity in order to assess stream condition and likely cause of degradation without the use of reference sites. Notice: Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.     

Water quality trend analysis for the Karoon River in Iran

The Karoon River basin, with a basin area of 67,000 km², is located in the southern part of Iran. Monthly measurements of the discharge and the water quality variables have been monitored at the Gatvand and Khorramshahr stations of the Karoon River on a monthly basis for the period 1967–2005 and 1969–2005 for Gatvand and Khorramshahr stations, respectively. In this paper the time series of monthly values of water quality parameters and the discharge were analyzed using statistical methods and the existence of trends and the evaluation of the best fitted models were performed. The Kolmogorov–Smirnov test was used to select the theoretical distribution which best fitted the data. Simple regression was used to examine the concentration-time relationships. The concentration-time relationships showed better correlation in Khorramshahr station than that of Gatvand station. The exponential model expresses better concentration – time relationships in Khorramshahr station, but in Gatvand station the logarithmic model is more fitted. The correlation coefficients are positive for all of the variables in Khorramshahr station also in Gatvand station all of the variables are positive except magnesium (Mg²⁺), bicarbonates () and temporary hardness which shows a decreasing relationship. The logarithmic and the exponential models describe better the concentration-time relationships for two stations.     

Seasonal variation of some heavy metal pollution with environmental and microbiological parameters in sub-basin of Kocabas Stream (Biga, Canakkale, Turkey) by ICP-AES

Waste water pollution in industrial areas is one of the most important environmental problems. Heavy metal pollution, especially chromium species in waste water sources from tannery affects our lives. Kocabas Stream is located in south-west Marmara region and Biga town is positioned in the sub basin on the stream. This water source functions as the water for irrigation in agriculture, drinking water for animals and for human use. Thus, this study is of great importance. Waste water pollution can affect all ecosystems and human health by directly or indirectly as in food chain. The concentration of heavy metals (Pb, Cd, Cu, Zn and Cr) were pre-analysed by ICP-AES method in water samples taken from sub-basin of Kocabas stream. In the results of these analyses, concentrations of the metals except chromium were founded at the limit value. But the total concentration of the Cr was found at high levels of between 0.0082 +/- 0.0001 and 5.7231 +/- 0.0921 mg l(-1) over the limit value (0.05 mg l(-1); WHO, EPA, TSE 266 and inland water quality classification) at sampling points very close to tannery factories. Also physicochemical and microbiological parameters of Kocabas Stream were determined. The effects of the experimental results on environment were investigated.     

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.     

Upstream to downstream: stormwater quality in Mayagüez, Puerto Rico

The focus of this research was upon consequences of urban stormwater runoff entering two streams in Mayagüez, Puerto Rico. Mayagüez is the largest urban area of the western side of the island of Puerto Rico and provides an excellent point of reference to monitor the affects of urban development on water quality in a tropical climate. The two monitored streams were Quebrada del Oro and Cano Majagual. The research hypothesis asks, "Does stormwater runoff from urban development measurably affect the water quality of downstream receiving water by raising the conductivity, temperature, and flow quantity characteristics during storm events in comparison to upstream water quality?" In essence, the results for Quebrada del Oro agreed with the hypothesis of this project, while Cano Majagual produced results different from the hypothesis primarily due to the absence of non-urbanized land use for both upstream and downstream sections as well as the buffering capacity of a large wetland just upstream of the downstream instrument location of Cano Majagual. Both streams showed signs of stream impairment according to the temperature criteria (32°C or 90°F) set by the Junta de Calidad Ambiental and the US Environmental Protection Agency. Dissolved oxygen levels of the streams were severely affected by water temperature and oxygen-consuming matter within these stream systems, making dissolved oxygen and temperature important water quality parameters for tropical climates.     

Telemetric System for Hydrology and Water Quality Monitoring in Watersheds of Northern New Mexico, Usa

This study utilized telemetric systems to sample microbes and pathogens in forest, burned forest, rangeland, and urban watersheds to assess surface water quality in northern New Mexico. Four sites included remote mountainous watersheds, prairie rangelands, and a small urban area. The telemetric system was linked to dataloggers with automated event monitoring equipment to monitor discharge, turbidity, electrical conductivity, water temperature, and rainfall during base flow and storm events. Site data stored in dataloggers was uploaded to one of three types of telemetry: 1) radio in rangeland and urban settings; 2) a conventional phone/modem system with a modem positioned at the urban/forest interface; and 3) a satellite system used in a remote mountainous burned forest watershed. The major variables affecting selection of each system were site access, distance, technology, and cost. The systems were compared based on operation and cost. Utilization of telecommunications systems in this varied geographic area facilitated the gathering of hydrologic and water quality data on a timely basis.     

Atmospheric aerosol concentration level and chemical characteristics of water-soluble ionic species in wintertime in Beijing,China

Total suspended particulate (TSP) samples were collected during wintertime from November 24, 1998 to February 12, 1999 in Beijing. Ionic species including Cl-, NO3(-), SO4(2-), Na+, NH4(+), K+, Mg2+ and Ca2+ were determined by Ion Chromatography (IC). The sum average concentration of all the determined ions accounted for 18.9% of the TSP concentration, and SO4(2-) appeared the dominant ion with an average concentration of 30.84 microg m(-3); the sum mass concentration of SO4(2-), NO3(-), Ca2+ and NH4(+) accounted for about 83.2% of all the eight ions measured. The study indicated that the chemical form of sulfate and ammonium varies with TSP concentration levels. During heavy pollution periods, the average TSP concentration was 0.66 mg m(-3), and the NH4(+)/SO4(2-) molar ratio was low (0.58). It indicated that sulfate may present as CaSO4 and (NH4)2SO4 x CaSO4 x 2H2O. When TSP concentration (average 0.186 mg m(-3)) was relatively low, the NH4(+)/SO4(2-) molar ratio was 1.94, close to the theoretical ratio of 2 of (NH4)2SO4. Under this condition (NH4)2SO4 is expected to exist as the major form of sulfate. When the TSP concentration level was medium (average 0.35 mg m(-3)), the NH4+/SO4(2-) molar ratio appeared an average value (1.27), (NH4)2SO4, (NH4)2SO4 x CaSO4 x 2H2O and CaSO4 are expected to be present in those aerosol particles. Meteorological conditions including wind speed and wind direction were related to the TSP concentration level.     

Monitoring the species of arsenic, chromium and nickel in milled coal, bottom ash and fly ash from a pulverized coal-fired power plant in western Canada

The concentration of As, Cr and Ni and their speciation (As3+;5+, Cr3+;6+ and Ni0;2+) in milled coal, bottom ash and ash collected by electrostatic precipitator (ESP) from a coal fired-power plant in western Canada were determined using HGAAS, ICP-AES and XANES. The chemical fractionation of these elements was also determined by a sequential leaching procedure, using deionized water, NH4OAC and HCI as extracting agents. The leachate was analyzed by ICP-AES. Arsenic in the milled coal is mostly associated with organic matter, and 67% of this arsenic is removed by ammonium acetate. This element is totally removed from milled coal after extraction with HCI. Arsenic occurs in both the As3+ and the As5+ oxidation states in the milled coal, while virtually all (>90%) of the arsenic in bottom ash and fly ash appears to be in the less toxic arsenate (As5+) form. Both Ni and Cr in the milled coal are extracted by HCI, indicating that water can mobilize Ni and Cr in an acidic environment. The chromium is leached by water from fly ash as a result of the high pH of the water, which is induced during the leaching. Ammonium acetate removes Ni from bottom ash through an ion exchange process. Chromium in milled coal is present entirely as Cr3+, which is an essential human trace nutrient. The Cr speciation in bottom ash is a more accentuated version of the milled coal and consists mostly of the Cr3+ species. Chromium in fly ash is mostly Cr3+, with significant contamination by stainless-steel from the installation itself.