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Lignocellulose comprises a significant portion of municipal solid waste (MSW) - 40-70% in developed countries, including paper, wood, and yard waste. Cellulose and hemicellulose are often shielded by lignin, posing a barrier to waste decomposition and landfill gas generation. Unfortunately, lignin is resistant to microbial degradation under low-oxygen conditions that normally occur in MSW landfills. The bacterium strain TAV5, microaerophilic and member of phylum Verrucomicrobia, isolated from the hindgut of the Reticulitermes flavipes termite, the most widely distributed subterranean termite in North America. Its genome contains genes associated with methylotrophic competency which code for enzymes that structurally modify lignin. The overall goal of this research was to use TAV5 to modify lignin and boost methane production from MSW. Batch-scale reactors (125 mL) were filled with paper, yard, or wood waste, and four ratios of mixed of waste. Reactors were seeded with different ratios of TAV5 to anaerobic digester (AD) microorganisms (representing landfill anaerobic microorganisms). Based on batch tests, optimal ratios of TAV5 to AD microorganisms were used to seed wastes (mixed, yard, and wood) in 6-gallon reactors. Addition of TAV5 increased methane production from mixed waste, yard waste, and wood, by 49%, 34%, and 297%, respectively. TAV5 decreased acid soluble lignin by 7-39%, depending on waste type. TAV5 grown under aerobic conditions and room temperature (not requiring a heated anaerobic chamber) was found to remain viable and increase methane production under low-level oxygen conditions (1-2%). This finding will potentially lessen costs for growing large volumes of it for seeding landfills.

ABSTRACT Propylene and butylene are highly reactive volatile organic compounds (HRVOCs) in terms of ground-level ozone formation. This study examined the effectiveness of biofiltration in removing propylene and butylene as [...]

ABSTRACT Carbonyl sulfide (COS) is an odor-causing compound and hazardous air pollutant emitted frequently from wastewater treatment facilities and chemical and primary metals industries. This study examined the effectiveness of [...]

ABSTRACT Wastewater treatment plant odors are caused by compounds such as hydrogen sulfide ([H.sub.2]S), methyl mercaptans, and carbonyl sulfide (COS). One of the most efficient odor control processes is activated [...]

ABSTRACT Chemical mass balance receptor models (CMBs) use measured pollutant concentrations, along with source information, to apportion the contributions of primary sources to the measured concentrations. CMBs can be used [...]

ABSTRACT The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates [...]

ABSTRACT In designing a photocatalytic oxidation (PCO) system for a given air pollution source, destruction rates for volatile organic compounds (VOCs) are required. The objective of this research was to [...]

Information regarding air emissions from shale gas extraction and production is critically important given production is occurring in highly urbanized areas across the United States. Objectives of this exploratory study were to collect ambient air samples in residential areas within 61 m (200 feet) of shale gas extraction/production and determine whether a “fingerprint” of chemicals can be associated with shale gas activity. Statistical analyses correlating fingerprint chemicals with methane, equipment, and processes of extraction/production were performed. Ambient air sampling in residential areas of shale gas extraction and production was conducted at six counties in the Dallas/Fort Worth (DFW) Metroplex from 2008 to 2010. The 39 locations tested were identified by clients that requested monitoring. Seven sites were sampled on 2 days (typically months later in another season), and two sites were sampled on 3 days, resulting in 50 sets of monitoring data. Twenty-four-hour passive samples were collected using summa canisters. Gas chromatography/mass spectrometer analysis was used to identify organic compounds present. Methane was present in concentrations above laboratory detection limits in 49 out of 50 sampling data sets. Most of the areas investigated had atmospheric methane concentrations considerably higher than reported urban background concentrations (1.8–2.0 ppmv). Other chemical constituents were found to be correlated with presence of methane. A principal components analysis (PCA) identified multivariate patterns of concentrations that potentially constitute signatures of emissions from different phases of operation at natural gas sites. The first factor identified through the PCA proved most informative. Extreme negative values were strongly and statistically associated with the presence of compressors at sample sites. The seven chemicals strongly associated with this factor (o-xylene, ethylbenzene, 1,2,4-trimethylbenzene,m- andp-xylene, 1,3,5-trimethylbenzene, toluene, and benzene) thus constitute a potential fingerprint of emissions associated with compression. Implications:Information regarding air emissions from shale gas development and production is critically important given production is now occurring in highly urbanized areas across the United States. Methane, the primary shale gas constituent, contributes substantially to climate change; other natural gas constituents are known to have adverse health effects. This study goes beyond previous Barnett Shale field studies by encompassing a wider variety of production equipment (wells, tanks, compressors, and separators) and a wider geographical region. The principal components analysis, unique to this study, provides valuable information regarding the ability to anticipate associated shale gas chemical constituents. [ABSTRACT FROM PUBLISHER]

In this paper, an intelligent decision-making framework (DMF) is developed to help decision makers identify cost-effective ozone control policies. High concentrations of ozone at the ground level continue to be a serious problem in numerous U.S. cities. Our DMF searches for dynamic and targeted control policies that require a lower total reduction of emissions than current control strategies based on the "trial and error" approach typically employed by state government decision makers. Our DMF utilizes a rigorous stochastic dynamic programming (SDP) formulation and incorporates an atmospheric chemistry module to model how ozone concentrations change over time. Within the atmospheric chemistry module, methods from design and analysis of computer experiments are employed to create SDP state transition equation metamodels, and critical dimensionality reduction is conducted to reduce the state-space dimension in solving our SDP problem. Results are presented from a prototype DMF for the Atlanta metropolitan region. [ABSTRACT FROM AUTHOR]

Chemical mass balance receptor models (CMBs) use measured pollutant concentrations, along with source information, to apportion the contributions of primary sources to the measured concentrations. CMBs can be used to evaluate the accuracy of the emission inventories that underlie State Implementation Plan (SIP) modeling, by providing an allocation of emissions to individual source categories. CMBs, however, traditionally have not accounted for the chemical reaction and differential deposition or fractionation that occur between the source and receptor. This means that they have historically had severe limitations in apportioning secondary particulate matter (PM), which is an especially important component of fine PM (PM2.5). Stafford and Liljestrand developed a method to account for fractionation in CMBs using depletion factors based on a solution of the steady-state advection-dispersion equation, including gravitational settling, dry deposition, and first-order chemical reaction. In the research presented here, the method of Stafford and Liljestrand was tested using gaseous and PM ambient concentration data from the Los Angeles, CA, air shed, along with traffic source profiles specific to Los Angeles and the CMB7 receptor model of the U.S. Environment Protection Agency. Including fractionation increased nitrate apportioned from 5% and 6% to 83% and 86% for Claremont, CA, and Long Beach, CA, respectively. This is significant, because CMBs have historically had difficulty apportioning nitrate. Including fractionation increased the ammonium apportioned by a factor of 7. The method could be used in future case studies to apportion secondary organic carbon as well. [ABSTRACT FROM AUTHOR]

Accurately estimating landfill methane emissions is important for quantifying a landfill’s greenhouse gas emissions and power generation potential. Current models, including LandGEM and IPCC, often greatly simplify treatment of factors like rainfall and ambient temperature, which can substantially impact gas production. The newly developed Capturing Landfill Emissions for Energy Needs (CLEEN) model aims to improve landfill methane generation estimates, but still require inputs that are fairly easy to obtain: waste composition, annual rainfall, and ambient temperature. To develop the model, methane generation was measured from 27 laboratory scale landfill reactors, with varying waste compositions (ranging from 0% to 100%); average rainfall rates of 2, 6, and 12 mm/day; and temperatures of 20, 30, and 37 °C, according to a statistical experimental design. Refuse components considered were the major biodegradable wastes, food, paper, yard/wood, and textile, as well as inert inorganic waste. Based on the data collected, a multiple linear regression equation ( R 2 = 0.75) was developed to predict first-order methane generation rate constant values k as functions of waste composition, annual rainfall, and temperature. Because, laboratory methane generation rates exceed field rates, a second scale-up regression equation for k was developed using actual gas-recovery data from 11 landfills in high-income countries with conventional operation. The Capturing Landfill Emissions for Energy Needs (CLEEN) model was developed by incorporating both regression equations into the first-order decay based model for estimating methane generation rates from landfills. CLEEN model values were compared to actual field data from 6 US landfills, and to estimates from LandGEM and IPCC. For 4 of the 6 cases, CLEEN model estimates were the closest to actual. [ABSTRACT FROM AUTHOR]