Emissions into the Atmosphere Due to the Ceramic Salt Glazing Process

The techniques of Principal Component Analysis (PCA) and subsequent regression analysis were used in an attempt to describe local and upwind chemical and physical factors which affect the variability of SO₄ ^–2 concentrations observed in a rural area of the northeastern U.S. The data used in the analyses included upwind and local O₃ concentrations, temperature, relative humidity and other climatological information, SO₂, and meteorological information associated with backward trajectories. The investigation identified five principal components, three major (eigenvalues >1) and two minor (eigenvalues < one), which accounted for 52% (r = 0.72) of the variability in the SO₄ ^–2 regression model. These components can be described as representing local and upwind photochemistry, droplet growth, SO₂ emissions, and air mass characteristics. The study also indicated that in future studies it will be necessary to a priori select air pollution and meteorological variables for measurement to potentially increase the sensitivity of this type of receptor model.     

Comparison of Predicted and Measured Concentrations for 58 Alternative models of Plume Transport in Complex Terrain

The purpose of this study was to evaluate alternative prediction models for the SO₂ concentrations produced in the vicinity of the Ohio Edison Company Sammis Power Plant. The plant is situated in the northeastern portion of the Ohio River Valley in complex terrain. Comparisons of the 16 highest predicted and measured short-term SO₂ concentrations were conducted for a one year period for 58 alternative models. Several models were found to predict reasonably accurately the 16 highest measured 24-hour SO₂ concentrations. Each of these models requires an upward adjustment in the plume centerline location as the plume is transported downwind in rising terrain. These same models overpredict by substantial margins the 16 highest measured 3-hour SO₂ concentrations. Improvements in emissions inventory data and improvements in the prediction models used are believed necessary to increase prediction accuracy further.     

Characterisation and fingerprinting of PCBs in flue gas and ash from waste incineration and in technical mixtures

Congener patterns of mono- to deca-chlorinated biphenyls (PC1-10B) were evaluated in (a) waste incineration flue gases collected in the post-combustion zone of a laboratory-scale fluidized-bed reactor, (b) ashes from two different MSW incineration plants, and (c) published data of eight Aroclor formulations. The congener patterns of the flue gases, ashes, and Aroclor mixtures clearly differed from each other, likely reflecting differences in formation pathways. The flue gas congener patterns were largely dominated by the least chlorinated congeners, whereas the ashes displayed more evenly distributed patterns. The most abundant congeners indicated a preference for 3,3',4,4'-oriented substitution, which may be related to de novo-type formation involving perylene. Principal component analysis confirmed that congener patterns differed among the three matrices and also distinguished flue gases collected at 200 °C from those collected at 300 °C and 450 °C. This distinction could be partly explained by the degree of chlorination, although the substitution status of the ortho-position, and substitution in the 3,3',4,4'-positions also seemed to be influential. Injecting biphenyl into the post-combustion zone of the reactor did not alter the patterns, indicating that availability of the backbone structure is not a limiting factor for PCB formation.     

Reductive debromination of nonabrominated diphenyl ethers by sodium borohydride and identification of octabrominated diphenyl ether products

A method was developed to study reductive transformation of highly brominated diphenyl ethers (BDEs). The method development is a part of a broader project where it will be used to determine the susceptibility of environmental pollutants to reductive conditions, in an attempt to create a scheme for determination of chemical’s persistence. This paper focuses on identification of octabrominated diphenyl ether transformation products from reductive debromination of the three nonabrominated diphenyl congeners (nonaBDE), BDE-206, -207 and -208. Sodium borohydride was used to explore the reductive debromination of the nonaBDEs. The transformation products were collected at two time-points and identified products were quantified by GC-MS. The reduction of the nonaBDEs lead primarily to debrominated products, mainly octaBDEs. The three nonabrominated DEs gave isomer-related transformation product patterns. BDE-207 and BDE-208 showed a propensity for ortho-debromination in the initial reaction step, while no discrimination between initial debromination positions was seen for BDE-206. All three nonabrominated DEs displayed a preferred initial debromination on the fully brominated DE ring.     

Pollutants and the health of green sea turtles resident to an urbanized estuary in San Diego, CA

Rapid expansion of coastal anthropogenic development means that critical foraging and developmental habitats often occur near highly polluted and urbanized environments. Although coastal contamination is widespread, the impact this has on long-lived vertebrates like the green turtle (Chelonia mydas) is unclear because traditional experimental methods cannot be applied. We coupled minimally invasive sampling techniques with health assessments to quantify contaminant patterns in a population of green turtles resident to San Diego Bay, CA, a highly urbanized and contaminated estuary. Several chemicals were correlated with turtle size, suggesting possible differences in physiological processes or habitat utilization between life stages. With the exception of mercury, higher concentrations of carapace metals as well as 4,4′-dichlorodiphenyldichloroethylene (DDE) and γ chlordane in blood plasma relative to other sea turtle studies raises important questions about the chemical risks to turtles resident to San Diego Bay. Mercury concentrations exceeded immune function no-effects thresholds and increased carapace metal loads were correlated with higher levels of multiple health markers. These results indicate immunological and physiological effects studies are needed in this population. Our results give insight into the potential conservation risk contaminants pose to sea turtles inhabiting this contaminated coastal habitat, and highlight the need to better manage and mitigate contaminant exposure in San Diego Bay.     

Peroxidase-mediated removal of endocrine disrupting compound mixtures from water

Several classes of oxidative enzymes have shown promise for efficient removal of endocrine disrupting compounds (EDCs) that are resistant to conventional wastewater treatments. Although the kinetics of reactions between individual EDCs and selected oxidative enzymes are well documented in the literature, there has been little investigation of reactions with EDC mixtures. This makes it impossible to predict how enzyme-mediated treatment systems will perform since wastewater effluents generally contain multiple EDCs. This paper reports pseudo-first order rate constants for a model oxidative enzyme, horseradish peroxidase (HRP), during single-substrate (k₁) and mixed-substrate (k_1-MIX) reactions. Measured values are compared with literature values of three Michaelis-Menten parameters: half-saturation constant (K_M), enzyme turnover number (k_CAT), and the ratio k_CAT/K_M. Published reports had suggested that each of these could be correlated with HRP reactivity towards EDCs in mixtures, and empirical results from this study show that K_M can be used to predict the sequence of EDC removal reactions within a particular mixture. We also observed that k_1-MIX values were generally greater than k₁ values and that compounds exhibiting greatest estrogenic toxicities reacted most rapidly in a given mixture. Finally, because K_M may be tedious to measure for every EDC of interest, we have constructed a quantitative structure-activity relationship (QSAR) model to predict these values. This model predicts K_M quite accurately (R² = 89%) based on two molecular characteristics: molecular volume and hydration energy. Its accuracy makes this QSAR a useful tool for predicting which EDCs will be removed most efficiently during enzyme treatment of EDC mixtures.     

Application of a combined measurement and modeling method to quantify windblown dust emissions from the exposed playa at Mono Lake, California

Particulate matter < or =10 microm (PM10) emissions due to wind erosion can vary dramatically with changing surface conditions. Crust formation, mechanical disturbance, soil texture, moisture, and chemical content of the soil can affect the amount of dust emitted during a wind event. A refined method of quantifying windblown dust emissions was applied at Mono Lake, CA, to account for changing surface conditions. This method used a combination of real-time sand flux monitoring, ambient PM10 monitoring, and dispersion modeling to estimate dust emissions and their downwind impact. The method identified periods with high emissions and periods when the surface was stable (no sand flux), even though winds may have been high. A network of 25 Cox sand catchers (CSCs) was used to measure the mass of saltating particles to estimate sand flux rates across a 2-km2 area. Two electronic sensors (Sensits) were used to time-resolve the CSC sand mass to estimate hourly sand flux rates, and a perimeter tapered element oscillating microbalance (TEOM) monitor measured hourly PM10 concentrations. Hourly sand flux rates were related by dispersion modeling to hourly PM10 concentrations to back-calculate the ratio of vertical PM10 flux to horizontal sand flux (K-factors). Geometric mean K-factor values (K(f)) were found to change seasonally, ranging from 1.3 x 10(-5) to 5.1 x 10(-5) for sand flux measured at 15 cm above the surface (q15). Hourly PM10 emissions, F, were calculated by applying seasonal K-factors to sand flux measurements (F = K(f) x q15). The maximum hourly PM10 emission rate from the study area was 76 g/m2 x hr (10-m wind speed = 23.5 m/sec). Maximum daily PM10 emissions were estimated at 450 g/m2 x day, and annual emissions at 1095 g/m2 x yr. Hourly PM10 emissions were used by the U.S. Environmental Protection Agency (EPA) guideline AERMOD dispersion model to estimate downwind ambient impacts. Model predictions compared well with monitor concentrations, with hourly PM10 ranging from 16 to over 60,000 microg/m3 (slope = 0.89, R2 = 0.77).     

A Critique of Estimating Future Costs Using Most Likely Value

ASTM E2137 (Standard Guide for Estimating Monetary Costs and Liability for Environmental Matters, or E2137) is the guidance for developing estimates of future environmental costs. E2137 provides a hierarchy of cost estimation approaches, and expresses an explicit preference for the use of probabilistic cost analysis to develop expected values for environmental costs. Dr. Ram and his colleagues have published an article (Remediation Journal, Autumn 2013) which rejects the use of EV analysis, arguing that while “good in principle” it has little practical value because it is nearly impossible to develop supportable probabilities. The E2137 principles and processes have been used for more than a decade in the context of addressing future environmental costs, yet their view of E2137 renders the standard meaningless. We conclude that the deficiency is not in the ASTM standard, and that when properly constructed, probabilistic analyses can be used to develop expected values with supportable probabilities. ©2015 Wiley Periodicals, Inc.     

Integrating the Social Dimension in Remediation Decision‐Making: State of the Practice and Way Forward

Sustainable remediation guidance, frameworks, and case studies have been published at an international level illustrating established sustainability assessment methodologies and successful implementation. Though the terminology and indicators evaluated may differ, one common theme among international organizations and regulatory bodies is more comprehensive and transparent methods are needed to evaluate the social sphere of sustainable remediation. Based on a literature review and stakeholder input, this paper focused on three main areas: (1) status quo of how the social element of sustainable remediation is assessed among various countries and organizations; (2) methodologies to quantitatively and qualitatively evaluate societal impacts; and (3) findings from this research, including challenges, obstacles, and a path forward. In conclusion, several existing social impact assessment techniques are readily available for use by the remediation community, including rating and scoring system evaluations, enhanced cost benefit analysis, surveys/interviews, social network analysis, and multicriteria decision analysis. In addition, a list of 10 main social indicator categories were developed: health and safety, economic stimulation, stakeholder collaboration, benefits community at large, alleviate undesirable community impacts, equality issues, value of ecosystem services and natural resources, risk‐based land management and remedial solutions, regional and global societal impacts, and contributions to other policies. Evaluation of the social element of remedial activities is not without challenges and knowledge gaps. Identification of obstacles and gaps during the project planning process is essential to defining sustainability objectives and choosing the appropriate tool and methodology to conduct an assessment. Challenges identified include meaningful stakeholder engagement, risk perception of stakeholders, and trade‐offs among the various triple bottom line dimensions. ©2015 Wiley Periodicals, Inc.