Assessment of the emissions and air quality impacts of biomass and biogas use in California

It is estimated that there is sufficient in-state “technically” recoverable biomass to support nearly 4000 MW of bioelectricity generation capacity. This study assesses the emissions of greenhouse gases and air pollutants and resulting air quality impacts of new and existing bioenergy capacity throughout the state of California, focusing on feedstocks and advanced technologies utilizing biomass resources predominant in each region. The options for bioresources include the production of bioelectricity and renewable natural gas (NG). Emissions of criteria pollutants and greenhouse gases are quantified for a set of scenarios that span the emission factors for power generation and the use of renewable natural gas for vehicle fueling. Emissions are input to the Community Multiscale Air Quality (CMAQ) model to predict regional and statewide temporal air quality impacts from the biopower scenarios. With current technology and at the emission levels of current installations, maximum bioelectricity production could increase nitrogen oxide (NO_x) emissions by 10% in 2020, which would cause increases in ozone and particulate matter concentrations in large areas of California. Technology upgrades would achieve the lowest criteria pollutant emissions. Conversion of biomass to compressed NG (CNG) for vehicles would achieve comparable emission reductions of criteria pollutants and minimize emissions of greenhouse gases (GHG). Air quality modeling of biomass scenarios suggest that applying technological changes and emission controls would minimize the air quality impacts of bioelectricity generation. And a shift from bioelectricity production to CNG production for vehicles would reduce air quality impacts further. From a co-benefits standpoint, CNG production for vehicles appears to provide the best benefits in terms of GHG emissions and air quality.

Implications:?This investigation provides a consistent analysis of air quality impacts and greenhouse gas emissions for scenarios examining increased biomass use. Further work involving economic assessment, seasonal or annual emissions and air quality modeling, and potential exposure analysis would help inform policy makers and industry with respect to further development and direction of biomass policy and bioenergy technology alternatives needed to meet energy and environmental goals in California.     

Assessment of the abiotic transformation of 17β-estradiol in the presence of vegetable matter--II: the role of molecular oxygen

This study characterizes the effect of oxygen in the abiotic transformation of estrogens when they are contacted with a surrogate of the vegetable wastes found in sewage. 17β-Estradiol (E2) and 17β-¹⁴C₄-estradiol (¹⁴C-E2) were utilized as model compounds. Batch experiments were run under both oxic and anoxic conditions. In order to accomplish an accurate mass balance of the target estrogen, two analyses were performed simultaneously: first, radioactivity counting, and second, quantitation of E2 and ¹⁴C-E2, as well as their transformation product estrone and ¹⁴C₄-estrone, by Liquid Chromatography tandem Mass Spectrometry. Under oxic conditions, the total concentration of ¹⁴C-E2 was found to decrease by 78% in 72 h (15% and 7% remained in the liquid and solid phases, respectively). Conversely, when the estrogens were contacted with the synthetic influent under anoxic conditions, E2 was quantitatively recovered after 72 h (70% and 22% in aqueous and solid matrices, correspondingly). These results suggest that when the concentration of dissolved oxygen is null or limited, catalysis through an oxidative coupling mechanism is halted. Moreover, it was confirmed that the catalytic reaction occurred solely in the presence of the solid phase of the model vegetable matter.     

Bioaerosols concentrations in working areas in biomethanization facilities

This study sought to fill the gap in information about the type and the concentration of bioaerosols present in the air of biomethanization facilities (BF). Evaluation of bioaerosol composition and concentration was achieved in two biomethanization facilities located in Eastern Canada, during summer and winter. In order to have a thorough understanding of the studied environment, the methodology combined culture of bacteria and molds, qualitiative polymerase chain reaction (qPCR) for specific microorganisms, endotoxin quantification, and next-generation sequencing (NGS) for bacterial diversity. Results revealed that workers in biomethanization facilities are exposed to bioaerosols and pathogenic microorganisms similar to those found in composting plants. However, human exposure levels to bioaerosols are lower in BF than in composting plants. Despite these differences, use of personal protective equipment is recommended to lower the risks of health problems.

Implications: Biomethanization is a new technology used in eastern Canada for waste management. In the next few years, it is expected that there will be an expansion of facilities in response of tight governmental regulations. Workers in biomethanization facilities are exposed to various amounts of bioaerosols composed of some harmful microorganisms. Therefore, monitoring this occupational exposure could be an interesting tool for improving worker’s health.     

Model reduction via principal component truncation for the optimal design of atmospheric monitoring networks

A numerically efficient methodology for the optimal design of monitoring networks aiming at the surveillance of accidental atmospheric release is proposed in this paper. In a realistic context, the design of such a network requires the knowledge of a database of potential dispersion accidents occurring in the domain of the study. An objective function measures the ability of a potential network to provide measurements in order to reconstruct any accidental plume taken from the database. In the optimisation of such cost functions with respect to networks, most of the computational time is spent in the evaluation of the function, especially if the accidents database is large. In this paper we show how to optimally reduce this database and how this affects the design via a mathematical expansion in the cost function. We introduce methods based on principal component analysis, which are rigorous when the cost function is of least-squares type. These methods are then tested and validated with success on the design of a radionuclides monitoring network to be deployed over France. This is the so-called Descartes network which will be operated by the French Institute for Radiation and Nuclear Safety. These techniques are then applied on Descartes to solve several issues that are computationally demanding, but are also of more general interest, such as: how should one sequentially deploy the stations of the network? How is affected the optimal network when other European potential radiological sources are taken into account?     

Optimal reduction of the ozone monitoring network over France

Ozone is a harmful air pollutant at ground level, and its concentrations are measured with routine monitoring networks. Due to the heterogeneous nature of ozone fields, the spatial distribution of the ozone concentration measurements is very important. Therefore, the evaluation of distributed monitoring networks is of both theoretical and practical interests. In this study, we assess the efficiency of the ozone monitoring network over France (BDQA) by investigating a network reduction problem. We examine how well a subset of the BDQA network can represent the full network. The performance of a subnetwork is taken to be the root mean square error (rmse) of the hourly ozone mean concentration estimations over the whole network given the observations from that subnetwork. Spatial interpolations are conducted for the ozone estimation taking into account the spatial correlations. Several interpolation methods, namely ordinary kriging, simple kriging, kriging about the means, and consistent kriging about the means, are compared for a reliable estimation. Exponential models are employed for the spatial correlations. It is found that the statistical information about the means improves significantly the kriging results, and that it is necessary to consider the correlation model to be hourly-varying and daily stationary. The network reduction problem is solved using a simulated annealing algorithm. Significant improvements can be obtained through these optimizations. For instance, removing optimally half the stations leads to an estimation error of the order of the standard observational error (10 μg m^?3). The resulting optimal subnetworks are dense in urban agglomerations around Paris (Île-de-France) and Nice (Côte d’Azur), where high ozone concentrations and strong heterogeneity are observed. The optimal subnetworks are probably dense near frontiers because beyond these frontiers there is no observation to reduce the uncertainty of the ozone field. For large rural regions, the stations are uniformly distributed. The fractions between urban, suburban and rural stations are rather constant for optimal subnetworks of larger size (beyond 100 stations). By contrast, for smaller subnetworks, the urban stations dominate.     

Matrix diffusion coefficients in volcanic rocks at the Nevada test site: influence of matrix porosity, matrix permeability, and fracture coating minerals

Diffusion cell experiments were conducted to measure nonsorbing solute matrix diffusion coefficients in forty-seven different volcanic rock matrix samples from eight different locations (with multiple depth intervals represented at several locations) at the Nevada Test Site. The solutes used in the experiments included bromide, iodide, pentafluorobenzoate (PFBA), and tritiated water ((3)HHO). The porosity and saturated permeability of most of the diffusion cell samples were measured to evaluate the correlation of these two variables with tracer matrix diffusion coefficients divided by the free-water diffusion coefficient (D(m)/D*). To investigate the influence of fracture coating minerals on matrix diffusion, ten of the diffusion cells represented paired samples from the same depth interval in which one sample contained a fracture surface with mineral coatings and the other sample consisted of only pure matrix. The log of (D(m)/D*) was found to be positively correlated with both the matrix porosity and the log of matrix permeability. A multiple linear regression analysis indicated that both parameters contributed significantly to the regression at the 95% confidence level. However, the log of the matrix diffusion coefficient was more highly-correlated with the log of matrix permeability than with matrix porosity, which suggests that matrix diffusion coefficients, like matrix permeabilities, have a greater dependence on the interconnectedness of matrix porosity than on the matrix porosity itself. The regression equation for the volcanic rocks was found to provide satisfactory predictions of log(D(m)/D*) for other types of rocks with similar ranges of matrix porosity and permeability as the volcanic rocks, but it did a poorer job predicting log(D(m)/D*) for rocks with lower porosities and/or permeabilities. The presence of mineral coatings on fracture walls did not appear to have a significant effect on matrix diffusion in the ten paired diffusion cell experiments.     

Comparison of different packing materials for the biofiltration of air toxics

Four different biofilter packing materials (two porous ceramics, perlite, and open pore polyurethane foam) were compared for the removal of toluene vapors. The focus was on evaluating performance at relatively short gas retention time (13.5 and 27 sec). The reactors were initially operated as biotrickling filters with continuous feeding and trickling of a nutrient solution. After significant plugging of the biotrickling filter beds with biomass was observed, the operation mode was switched to biofiltration with only periodic supply of mineral nutrients. This resulted in stable conditions, which allowed detailed investigations over > 6 months. The reactor packed with cattle bone Porcelite (CBP), a ceramic material containing some macronutrients and micronutrients, exhibited the highest performance. The critical load (i.e., load at which 95% removal occurred) was 29 g m(-3) hr(-1) at a gas retention time of 13.5 sec and 66 g m(-3) hr(-1) at a gas retention time of 27 sec. After the long-term experiment, the packing materials were taken from the reactors and examined. The reactors were divided into three sections, top, middle, and bottom, to determine whether spatial differentiation of biomass occurred. The assays included a double-staining technique to count total and live microorganisms and determination of moisture, protein, and dry weight contents. Microbial community analysis was also conducted by denaturing gradient gel electrophoresis. The results showed that most reactors had a significant fraction of inactive biomass. Comparatively, the CBP biofilter held significantly higher densities of active biomass, which may be the reason for the higher toluene removal performance. The analyses suggest that favorable material properties and the nutrients slowly released by the CBP provided better environmental conditions for the process culture.     

Enhanced coagulation with polyaluminum chlorides: role of pH/alkalinity and speciation

Enhanced coagulation is considered to be among the best available techniques (BAT) for disinfection by-product (DBP) precursor removal in water treatment. Improving existing understanding requires further consideration of nuances of chemical speciation relative to source water chemistry. In this paper, the effect of alkalinity/pH and speciation on inorganic polymer flocculants, polyaluminum chlorides (PACls) for enhanced particle and natural organic matter (NOM) removal was investigated. Three kinds of well-characterized typical source waters in China with low, moderate, and high alkalinity were selected. Performance of coagulants is controlled not only by preformed species but also by those formed in situ. At neutral and basic pH values, PACls with higher basicity (ratio of OH(-)/Al), which have more stable preformed Alb (the rapid reacted species as in ferron assay), are more efficient for turbidity and NOM removal. At slightly acidic pH, PACls with lower basicity are more efficient since more Alb can be formed in situ. Optimal NOM removal was achieved at pH 5.5-6.5 for all PACls. Basicity, speciation, and dosage of coagulant should be optimized based on raw water alkalinity to enhance the removal efficiency of NOM.     

Combining target analysis with sum parameters—a comprehensive approach to determine sediment contamination with PFAS and further fluorinated substances

Recent studies aiming at a fluorine mass balance analysis in sediments combined the determination of extractable organic fluorine (EOF) with target analysis. They reported high fractions of unidentified organic fluorine (UOF) compounds, as the target analysis covers only a limited number of per- and polyfluoroalkyl substances (PFAS). For this reason, in this study, a comprehensive approach was used combining target analysis with an extended PFAS spectrum, the EOF and a modified total oxidisable precursor (TOP) assay, which includes trifluoroacetic acid, to determine the PFAS contamination in sediments (n=41) and suspended solids (n=1) from water bodies in Northern Germany (Lower Saxony). PFAS are ubiquitous in the sediments (detected in 83% of the samples). Perfluorinated carboxylic acids (PFCAs) were found in 64% of the samples; perfluorinated sulfonic acids (PFSAs) were detected less frequently (21%), with the highest concentration observed for perfluorooctanesulfonic acid (PFOS). Levels of precursors and substitutes were lower. Applying the TOP assay resulted in an increase in PFCAs in 43% of the samples analysed. In most cases, target analysis and the TOP assay could not account for the EOF concentrations measured. However, as the fraction of UOF decreased significantly, the application of the TOP assay in fluorine mass balance analysis proved to be an important tool in characterising the PFAS contamination of riverine sediments.

     

In situ exposures using caged organisms: a multi-compartment approach to detect aquatic toxicity and bioaccumulation

An in situ toxicity and bioaccumulation assessment approach is described to assess stressor exposure and effects in surface waters (low and high flow), the sediment-water interface, surficial sediments and pore waters (including groundwater upwellings). This approach can be used for exposing species, representing major functional and taxonomic groups. Pimephales promelas, Daphnia magna, Ceriodaphnia dubia, Hyalella azteca, Hyalella sp., Chironomus tentans, Lumbriculus variegatus, Hydra attenuatta, Hexagenia sp. and Baetis tibialis were successfully used to measure effects on survival, growth, feeding, and/or uptake. Stressors identified included chemical toxicants, suspended solids, photo-induced toxicity, indigenous predators, and flow. Responses varied between laboratory and in situ exposures in many cases and were attributed to differing exposure dynamics and sample-processing artifacts. These in situ exposure approaches provide unique assessment information that is complementary to traditional laboratory-based toxicity and bioaccumulation testing and reduce the uncertainties of extrapolating from the laboratory to field responses.