Debromination of PBDEs in Arkansas Water Bodies Analyzed by Positive Matrix Factorization.

A previously generated data set for polybrominated diphenyl ethers (PBDEs) in dated sediment cores of West Lake of El Dorado (AED), Calion Lake (ACL), and the lagoon of Magnolia Wastewater Treatment Facility (AMW) from Southern Arkansas is examined by a weighted chemical mass balance (CMB) model and positive matrix factorization (PMF) in order to quantify PBDE sources and debromination. DNA extraction and pyrosequencing were done on several core sections in order to investigate microbial debromination. CMB and PMF analyses indicate that deca technical mixtures are the dominant PBDE input (>99% in mole fraction in AED and ACL, and 94.7% in AMW). Minor contributions of penta and octa technical mixtures were found in all three water bodies (<1% in AED and ACL; and 1.1% and 4.1% in AMW, respectively). Results suggest that debromination takes place in all three lakes, but is more intense in AMW. In-situ microbial debromination was supported by the microorganism analysis. The PMF results are validated by PBDE manufacturing records, and the operating history of AMW. Despite the high PBDE concentrations in these sediments near former manufacturing facilities, the extent of debromination is limited, possibly due to sorption to natural organic matter of the sediment.

Graphene-based sensors for detection of heavy metals in water: a review.

Graphene (G) is attracting significant attention because of its unique physical and electronic properties. The production of graphene through the reduction of graphene oxide (GO) is a low-cost method. The reduction of GO can further lead to electrically conductive reduced GO. These graphene-based nanomaterials are attractive for high-performance water sensors due to their unique properties, such as high specific surface areas, high electron mobilities, and exceptionally low electronic noise. Because of potential risks to the environment and human health arising from heavy-metal pollution in water, G-/GO-based water sensors are being developed for rapid and sensitive detection of heavy-metal ions. In this review, a general introduction to graphene and GO properties, as well as their syntheses, is provided. Recent advances in optical, electrochemical, and electrical detection of heavy-metal ions using graphene or GO are then highlighted. Finally, challenges facing G/GO-based water sensor development and outlook for future research are discussed.

Polybromodiphenyl ethers and decabromodiphenyl ethane in aquatic sediments from southern and eastern Arkansas, United States.

South-central Arkansas (AR) is home to major manufacturing facilities for brominated flame retardant chemicals (BFRs) in the U.S. Unintended release during production may have caused accumulation of the BFRs in the local environment. In this work, sediment cores were collected from six water bodies in AR, including three located close to the BFR manufacturing facilities in El Dorado and Magnolia, to investigate past and recent deposition histories. A total of 49 polybromodiphenyl ethers (PBDEs) and decabromodiphenyl ethane (DBDPE) were detected, with concentrations as high as 57000 and 2400 ng/g dry weight for decabromodiphenyl ether (BDE209) and DBDPE, respectively. Log-log regression of BDE209 and DBDPE surface concentrations versus distance to known BFR manufacturing facilities fit the Gaussian Plume Dispersion model, and showed that, if the distance is shortened by half, concentrations of BDE209 and DBDPE would increase by 5-fold. The spatial distribution and temporal trend of the contamination indicate that the manufacturing of PBDEs and DBDPE is the primary source for these compounds in the environment of southern Arkansas. Interestingly, the occurrence of debromination of PBDEs in the sediments of a previously used wastewater sludge retention pond in Magnolia is indicated by the presence of congeners that had not been detected in any commercial PBDE mixtures and by increased fractions of lower brominated congeners relative to higher brominated congeners. Two unknown brominated compounds were detected in the sediments, and identified as nonabromodiphenyl ethanes.

Stormwater runoff characterized by GIS determined source areas and runoff volumes.

Runoff coefficients are usually considered in isolation for each drainage area with resulting large uncertainties in the areas and coefficients. Accurate areas and coefficients are obtained here by optimizing runoff coefficients for characteristic Geographic Information Systems (GIS) subareas within each drainage area so that the resulting runoff coefficients of each drainage area are consistent with those obtained from runoff and rainfall volumes. Lack of fit can indicate that the ArcGIS information is inaccurate or more likely, that the drainage area needs adjustment. Results for 18 drainage areas in Milwaukee, WI for 2000-2004 indicate runoff coefficients ranging from 0.123 for a mostly residential area to 0.679 for a freeway-related land, with a standard error of 0.047. Optimized runoff coefficients are necessary input parameters for monitoring, and for the analysis and design of in situ stormwater unit operations and processes for the control of both urban runoff quantity and quality.

Dose-response regressions for algal growth and similar continuous endpoints: calculation of effective concentrations.

We derive equations for the effective concentration giving 10% inhibition (EC10) with 95% confidence limits for probit (log-normal), Weibull, and logistic dose-response models on the basis of experimentally derived median effective concentrations (EC50s) and the curve slope at the central point (50% inhibition). For illustration, data from closed, freshwater algal assays are analyzed using the green alga Pseudokirchneriella subcapitata with growth rate as the response parameter. Dose-response regressions for four test chemicals (tetraethylammonium bromide, musculamine, benzonitrile, and 4-4-(trifluoromethyl)phenoxy-phenol) with ranges of representative slopes at 50% response (0.54-2.62) and EC50s (2.20-357 mg/L) were selected. Reference EC50s and EC10s with 95% confidence limits using probit or Weibull models are calculated by nonlinear regression on the whole dataset using a dose-response regression program with variance weighting and proper inverse estimation. The Weibull model provides the best fit to the data for all four chemicals. Predicted EC10s (95% confidence limits) from our derived equations are quite accurate; for example, with 4-4-(trifluoromethyl)phenoxy-phenol and the probit model, we obtain 1.40 (1.22-1.61) mg/L versus 1.40 (1.20- 1.64) mg/L obtained from the nonlinear regression program. The main advantage of the approach is that EC10 or ECx (where x = 1-99) can be predicted from well-determined responses around EC20 to EC80 without experimental data in the low- or high-response range. Problems with the estimation of confidence interval for EClow,x (concentration predicted to cause x% inhibition) from algal growth inhibition also are addressed. Large confidence intervals may be the result of experimental error and lack of a well-defined reference response value.

Poly- and per-fluoroalkyl compounds in sediments of the Laurentian Great Lakes: Loadings, temporal trends, and sources determined by positive matrix factorization.

A recent data set for 22 poly- and per-fluorinated compounds (PFASs) in Ponar grab samples of surface sediments and cores from the Great Lakes of North America was examined for concentrations, loads, correlations with geographical coordinates and depth (time), and for sources. Correlations were determined by multivariate regression analyses. Source apportionment of PFASs was carried out by positive matrix factorization (PMF) for two cores from Lake Ontario. For the five lakes together, the total load of PFASs in sediments was estimated to be 245 ±â€¯24 tonnes, which is about half the load for total PCBs. The recent annual loading was 1812 ±â€¯320 kg/yr. Concentrations and inventories of PFASs were greatest in Lakes Erie and Ontario. Since 1947, concentrations of perfluorooctane sulfonic acid (PFOS) in ten cores have increased exponentially as a function of time with doubling times between 10 and 54 yr and have leveled off in three cores since 2000. PMF demonstrated an effective grouping of two particle-associated factors, characterized mainly by longer-chain PFASs (C ≥ 8) and two other factors of mainly shorter-chain compounds (C ≤ 6). Two factors feature only one dominant compound: factor 1, PFOS, and factor 3, perfluorobutane sulfonic acid (PFBS). Of all factors, factor 3 with PFBS has the largest contribution (47.8%). Significant scores for perfluorohexane sulfonic acid (PFHxS) and PFBS, along with flat or decreasing PFOS contributions since 2003, indicate that the replacement of PFOS with these compounds is beginning to take effect in the environment.

Source apportionment of pollutants and flows of combined sewer wastewater.

To reduce the combined sewer overflows (CSOs) and improve surface water quality in the Greater Milwaukee area, construction of a 19.5-mile (31.4 km) inline storage system (ISS) was completed in 1994 to capture and convey sanitary sewage and stormwater for treatment. However, one to six overflows per year still occur. Chemical mass balance (CMB) and positive matrix factorization (PMF) modeling was done in order to find origins of flows and pollutants in CSOs. Based on overflow events from 2000 to 2006 (CMB) and 2004 to 2006 (PMF), we found that between 27% and 56% of the total overflow is from sanitary sewage and most of the remaining from stormwater with possible minor contribution (< or = 8%) from groundwater. Most total suspended solids and metals (Cd, Cr, Cu, Pb, Ni, Hg, and Zn) are from stormwater, while sanitary sewage carries large contributions (> or = 28%) of BOD5, NH3, and total phosphorus. The fraction of NH3 is especially high, i.e., > or = 58%. Implications for a possible future sewer separation in combined sewer areas into separate sanitary and storm sewers are discussed.

A washoff model for stormwater pollutants.

A washoff rate equation for stormwater pollutants is presented based on linear buildup of pollutant mass in the watershed. The expression was applied to residential and open land areas that have not previously been considered even though these areas are major sources of metals, nutrients, and bacteria in stormwater runoff. The transport coefficient c was determined from the nearly constant portion of the hydrograph at the peak of each runoff event. Values of c (cm(-1)) for seven metals, i.e., Zn, Cu, Cd, Ni, Pb, Hg, and Ag (3.03+/-1.34-6.10+/-2.55 cm(-1)), TSS, BOD(5), total phosphorus (TP), E. coli, and fecal coliform (2.12+/-0.907-7.16+/-2.72 cm(-1)) were estimated using this equation. The washoff rate was developed to reflect mixed land uses based on mass deposition and effective area. The order of transport coefficients for metals is Pb>Ag>Zn>Cu>Ni>Hg>Cd reflecting decreasing particle association. For all parameters this order is TSS>fecal coliform>E. coli>Pb>BOD(5) BOD(5)>Ag>Zn>Cu>Ni>Hg>Cd>TP. The washoff rate and a related pollutant concentration model are expected to be useful for the development of best management practices for stormwater.

A load model based on antecedent dry periods for pollutants in stormwater.

A load model for stormwater constituents is proposed that describes mass retained on the drainage area after previous storms, as a function of a weighted sum of previous antecedent dry periods. It was used to compute the event load for 14 constituents--zinc, copper, cadmium, nickel, chromium, lead, mercury, silver, total suspended solids (TSS), E. coli, fecal coliform, total soluble phosphorus, total phosphorus, and 5-day biochemical oxygen demand. The results show acceptable fits for most constituents based on over 400 rain events (2000 to 2004) in Milwaukee, Wisconsin. Considering the retained pollutant mass after one previous storm rather than assuming the area to be clean after the previous storm gives more accurate results for all constituents, except TSS and silver. The model can be used for mixed land-use areas. The ratios of deposition fluxes of zinc and individual metals were determined at a major parking lot producing a profile characteristic of automobile emissions.

Anthropogenic and geogenic mass input of trace elements to moss and natural surface soil in Norway.

Data sets for concentrations of up to 22 elements including Pb, Cd, Ag, As, and Hg and several rare earth elements (REEs) in moss and surface soil from all over mainland Norway are analyzed by positive matrix factorization (PMF) and principal component analysis (PCA) with centered log-ratio transformation. Moss and soil samples collected in 2010 and 2005, respectively, show both a distinct long-range atmospheric transport PMF factor including a dominant Pb loading along with smaller loadings of Mo, Cd, Sb, and As, and a geogenic factor dominated by Ce, La, and Y. Other PMF factors for moss and soil are mainly anthropogenic except for two soil factors, a Cr, Co, Ce dominated factor, and an Eu factor. The source area of Eu is mainly inland consistent with its divalent oxidation state. A significant advantage of PMF factors is that their loadings are scaled according to their relative factor contributions. The PMF air pollution factor F3 in moss with average contribution of 7.11µg/g produces an air pollution input of 5.0µg/g which compares well with literature values for the total Pb concentration in moss for 2005 in southern Norway. PCA has the advantage that it can separate factors with very low element concentrations. To support sources of elements identified by PCA it is recommended to include calculation of factor scores to identify significant source areas.

Source apportionment of PAHs in sediments using factor analysis by time records: application to Lake Michigan, USA.

Apportionment of sources of polycyclic aromatic hydrocarbons (PAHs) by a factor analysis model based on time records for seven sediment cores from central Lake Michigan is reconsidered to provide a more quantitative treatment, and one additional factor (source). The effect of mixing depth was observed on the unmixed fluxes, and a computer program was developed for estimation of the average PAH fluxes from the reconstructed PAH records. The new factor analysis model with nonnegative constraints includes not only scaling but also back scaling which improved the source apportionment significantly. One core, CLM-A, was an outlier and was omitted from the analysis. Wood burning was identified as a third major source of PAHs in the area (20% of the total PAH flux). The other two sources petroleum (traffic), 45%, and coal (coke oven emissions), 35%, became more apparent, where the contributions are based on the sum of concentrations of 16 PAH compounds. A reduced mixing depth (1.0-1.5cm) was found to be more appropriate than the originally estimated mixing depth (1.5-2.0cm).

PAHs in sediments of the Black River and the Ashtabula River, Ohio: source apportionment by factor analysis.

Sources of polycyclic aromatic hydrocarbons (PAHs) in the Black River and Ashtabula River sediments were apportioned using a factor analysis (FA) model with nonnegative constraints. Source profiles, contributions (microg/g) and percent contributions are determined with no prior knowledge of sources. The FA model includes scaling and backscaling of data with average PAH concentrations, without sample normalization. The present work is a follow-up to studies that used a chemical mass balance model to apportion sources to the same data sets. Literature source profiles, modified based on gas/particle partitioning of individual PAHs, from nine PAH sources were considered for comparison. FA results for a three-source solution indicate traffic (58%), coke oven (26%), and wood burning/coal tar (16%) are the primary PAH sources to Black River sediments. The primary PAH sources to the Ashtabula River sediments are traffic (51%), coke oven (44%), and wood burning (5%). This work supports the previous studies of Black River and Ashtabula River PAHs by CMB model. In addition, FA provides a more realistic fit to Ashtabula River model data by eliminating 100% contributions from one source.

Individual and combined toxicity of nitriles and aldehydes to Raphidocelis subcapitata.

A closed-system algal toxicity test with no headspace was applied to evaluate the toxicity of 11 aldehydes and two nitriles to Raphidocelis subcapitata. Algal growth rate and dissolved oxygen (DO) production were used as the test endpoints. Compared to literature data, our test results based on the endpoint of DO production is 2.5 to 257 times more sensitive than the conventional algal batch tests. In addition, our analyses show that different relative toxicity relationships may be observed when different test methods or different endpoints are applied. The test alga was found to be quite sensitive to aldehydes, and a quantitative structure-activity relationship (QSAR) relationship was established based on the n-octanol/water partition coefficient (log Kow). This study also evaluated the combined effects of aldehydes and nitriles. Four synergistic joint actions related to malononitrile were identified. On the other hand, most of the combined effects between aldehydes and acetonitrile were antagonistic. In general, we find that greater-than-additive effects mainly are associated with toxicants displaying flat dose-response curves, and less-than-additive effects may be related to certain steep-slope chemicals. Model analyses show that the above mixture toxicity behavior may be due to response addition or response multiplication joint action modes.

Problems in the fingerprints based polycyclic aromatic hydrocarbons source apportionment analysis and a practical solution.

This work intended to explain the challenges of the fingerprints based source apportionment method for polycyclic aromatic hydrocarbons (PAH) in the aquatic environment, and to illustrate a practical and robust solution. The PAH data detected in the sediment cores from the Illinois River provide the basis of this study. Principal component analysis (PCA) separates PAH compounds into two groups reflecting their possible airborne transport patterns; but it is not able to suggest specific sources. Not all positive matrix factorization (PMF) determined sources are distinguishable due to the variability of source fingerprints. However, they constitute useful suggestions for inputs for a Bayesian chemical mass balance (CMB) analysis. The Bayesian CMB analysis takes into account the measurement errors as well as the variations of source fingerprints, and provides a credible source apportionment. Major PAH sources for Illinois River sediments are traffic (35%), coke oven (24%), coal combustion (18%), and wood combustion (14%).

PCB sources, transformations, and contributions in recent Fox River, Wisconsin sediments determined from receptor modeling.

The PCB contamination in lower Fox River sediments was investigated in order to identify possible PCB sources, contributions, and transformations, using two receptor models. Congener specific sediment PCB data from sites immediately upstream of DePere dam to Green Bay that had been gathered for the Green Bay/Fox River Mass Balance Study, were used in this analysis. The first receptor model is a self training factor analysis (FA) model with non-negative constraints that was applied to identify the PCB sources and significant congener patterns. The second is a chemical mass balance model (CMB) in which published Aroclor sources, inferred from our FA model, were used to apportion these Aroclors to each sample. The FA model indicated two significant factors, the major one being Aroclor 1242 and the other, a profile dominated by low chlorinated congeners, indicating a possible PCB alteration profile. This profile had significant contributions to samples at or around sites with total PCB concentrations higher than 50 ppm, indicating a potential anaerobic dechlorination activity. It was also deduced from the FA model that very small contributions of more highly chlorinated Aroclors may be present in the system. The results from the CMB model confirmed that the system is dominated by Aroclor 1242. Its average contribution was 95%, with small amounts of Aroclor 1254 (2%) and 1260 (1%). Two of the samples, located in the vicinity of point sources, showed high contributions of Aroclor 1016 by the CMB model. This is interpreted as an altered Aroclor profile resembling the less chlorinated Aroclor 1016. Contributions obtained form the CMB and FA models show similar patterns.

Source apportionment of PAHs in dated sediments from the Black River, Ohio.

Black River, OH, is contaminated with polycyclic aromatic hydrocarbons (PAHs) from coke ovens of a US Steel Corp. Plant. Closing of a coking plant in 1983 and environmental dredging of the sediments during 1989 and 1990 has reduced the PAH levels significantly. This study quantifies the decrease, and consider source apportionment of PAHs in Black River sediments using chemical mass balance modeling. Five vibra cores collected in 1998 and dated using 210Pb and 137Cs, were analyzed for 18 PAHs. The cores had total PAH concentrations between 250 and 0.10 ppm. PAH maxima occur in 1949, 1969, in accordance with regional historical inputs (core BR4), and in 1991 due to remediation (BR6). Coke oven emissions (CO), highway dust (HWY), and wood burning (WB) are likely sources. The CO source (6-92% of total PAHs) is maximal in 1954, and again in 1992-1994 due to the exposure and redistribution of older contaminated sediments during dredging, and decreases thereafter. There is minimal CO content in 1985 (BR4), 2 yr after closure of the coking plant. The HWY contribution (2-86%) is high during 1969-1988, and increases again after 1993. The WB source is less than 23%, and exhibits a minimum (2%) around 1979 (BR4). There is evidence of aerobic biodegradation or photolysis in the sediment of phenanthrene (PhA) at PhA concentrations >500 ppb.

Modeling polychlorinated biphenyl congener patterns and dechlorination in dated sediments from the Ashtabula River, Ohio, USA.

Polychlorinated biphenyl (PCB) congeners were analyzed in four deep, dated sediment cores from the Ashtabula River (OH, USA), for the purpose of identifying relevant PCB sources and congener patterns. The time span for three of the cores is from the mid 1960s to 1998, whereas the fourth has a time span of six years. The total PCB concentrations are in the range of 0.4 to 6.8 microg/g dry weight, with the highest concentrations observed in samples from the 1970s. A factor analysis (FA) model with nonnegative constraints was used to investigate the sources and patterns of PCBs. Additionally, a new model, based on a least squares method, was developed to identify possible patterns of anaerobic dechlorination of PCBs in the sediments, and to quantify the relevant dechlorination pathways. Both models were validated successfully either by artificially created data sets (FA model) or by using laboratory data from the literature (dechlorination model). The FA model revealed two significant sources. The first was identified as a slightly altered Aroclor 1248. The second did not resemble any Aroclor closely, but was very similar to the overall average congener profile of all samples. Simulation of anaerobic dechlorination on an Aroclor 1248 profile from the literature, according to dechlorination activities H/H', as defined in the literature, yielded a congener profile very similar to that of the second pattern. This indicates the likelihood of anaerobic dechlorination of PCBs in Ashtabula River sediments.

Estimating stepwise debromination pathways of polybrominated diphenyl ethers with an analogue Markov Chain Monte Carlo algorithm.

A stochastic process was developed to simulate the stepwise debromination pathways for polybrominated diphenyl ethers (PBDEs). The stochastic process uses an analogue Markov Chain Monte Carlo (AMCMC) algorithm to generate PBDE debromination profiles. The acceptance or rejection of the randomly drawn stepwise debromination reactions was determined by a maximum likelihood function. The experimental observations at certain time points were used as target profiles; therefore, the stochastic processes are capable of presenting the effects of reaction conditions on the selection of debromination pathways. The application of the model is illustrated by adopting the experimental results of decabromodiphenyl ether (BDE209) in hexane exposed to sunlight. Inferences that were not obvious from experimental data were suggested by model simulations. For example, BDE206 has much higher accumulation at the first 30 min of sunlight exposure. By contrast, model simulation suggests that, BDE206 and BDE207 had comparable yields from BDE209. The reason for the higher BDE206 level is that BDE207 has the highest depletion in producing octa products. Compared to a previous version of the stochastic model based on stochastic reaction sequences (SRS), the AMCMC approach was determined to be more efficient and robust. Due to the feature of only requiring experimental observations as input, the AMCMC model is expected to be applicable to a wide range of PBDE debromination processes, e.g. microbial, photolytic, or joint effects in natural environments.

Photolytic debromination pathway of polybrominated diphenyl ethers in hexane by sunlight.

The objective of this work is to identify the photolytic debromination pathways of polybrominated diphenyl ethers (PBDEs). Thirteen PBDEs (BDEs 209, 208, 207, 206, 196, 183, 154, 153, 100, 99, 85, 47 and 28) in hexane were individually exposed to sunlight for up to 64 h. A total of 180 PBDEs were screened and 74 BDE debromination products were detected. The disappearance rate constant increased exponentially with increasing number of bromines. While no evident difference in debromination preference among ortho, meta and para bromines was found for heavier congeners, the vulnerability rank order was meta ≥ ortho > para for the lighter congeners (≤8 Br). The total molar mass of PBDEs continuously decreased during sunlight exposure, indicating PBDEs were transformed to non-PBDE compounds. A stochastic least squares debromination pathway model was developed to simulate the reactions and determine the yields to extend the results beyond the experimental observations.

Selecting an appropriate multivariate source apportionment model result.

Two easily available multivariate source apportionment models, Unmix and positive matrix factorization (PMF), often produce nearly the same source apportionment. However, this paper gives two examples in which this is not the case: a simulated air pollution data set of 8 species and 200 samples and a water quality data set of 32 PCB congeners and 106 sediment core samples from Sheboygan River Inner Harbor, WI. In the first case, a basic form of PMF fails primarily because the source compositions do not have any species with zero or near zero concentrations. Unmix produces source compositions and contributions that are much closer to the true values. A version of PMF with an adjustable parameter also gives good results. In the second case, each model found 5 sources for the Sheboygan PCB sediment data. PMF determined sources compositions were consistent with the original 50/50% Aroclor 1248/1254 mixture, a previously determined prominent dechlorination profile (processes H' + M), and three other partially dechlorinated profiles. The Unmix determined source compositions were not as successful as the Unmix results depended heavily on just three data points. Source apportionment results favor Unmix when edges in the data are well-defined and PMF when several zeros are present in the loading and score matrices. Since both models are seen to have potential weaknesses, both should be applied in all cases. If the two methods do not produce similar results the methods given in the paper can be used to select the model result most likely to be closest to the truth.