Structural Equation Models Suggest That On-Farm Noncrop Vegetation Removal Is Not Associated with Improved Food Safety Outcomes but Is Linked to Impaired Water Quality.

While growers have reported pressures to minimize wildlife intrusion into produce fields through noncrop vegetation (NCV) removal, NCV provides key ecosystem services. To model food safety and environmental tradeoffs associated with NCV removal, published and publicly available food safety and water quality data from the Northeastern United States were obtained. Because data on NCV removal are not widely available, forest-wetland cover was used as a proxy, consistent with previous studies. Structural equation models (SEMs) were used to quantify the effect of forest-wetland cover on (i) food safety outcomes (e.g., detecting pathogens in soil) and (ii) water quality (e.g., nutrient levels). Based on the SEMs, NCV was not associated with or had a protective effect on food safety outcomes (more NCV was associated with a reduced likelihood of pathogen detection). The probabilities of detecting Listeria spp. in soil (effect estimate [EE] = -0.17; P = 0.005) and enterohemorrhagic Escherichia coli in stream samples (EE = -0.27; P < 0.001) were negatively associated with the amount of NCV surrounding the sampling site. Larger amounts of NCV were also associated with lower nutrient, salinity, and sediment levels, and higher dissolved oxygen levels. Total phosphorous levels were negatively associated with the amount of NCV in the upstream watershed (EE = -0.27; P < 0.001). Similar negative associations (P < 0.05) were observed for other physicochemical parameters, such as nitrate (EE = -0.38). Our findings suggest that NCV should not be considered an inherent produce safety risk or result in farm audit demerits. This study also provides a framework for evaluating environmental tradeoffs associated with using specific preharvest food safety strategies. IMPORTANCE Currently, on-farm food safety decisions are typically made independently of conservation considerations, often with detrimental impacts on agroecosystems. Comanaging agricultural environments to simultaneously meet conservation and food safety aims is complicated because farms are closely linked to surrounding environments, and management decisions can have unexpected environmental, economic, and food safety consequences. Thus, there is a need for research on the conservation and food safety tradeoffs associated with implementing specific preharvest food safety practices. Understanding these tradeoffs is critical for developing adaptive comanagement strategies and ensuring the short- and long-term safety, sustainability, and profitability of agricultural systems. This study quantifies tradeoffs and synergies between food safety and environmental aims, and outlines a framework for modeling tradeoffs and synergies between management aims that can be used to support future comanagement research.

A machine learning approach to identify barriers in stream networks demonstrates high prevalence of unmapped riverine dams.

Restoring stream ecosystem integrity by removing unused or derelict dams has become a priority for watershed conservation globally. However, efforts to restore connectivity are constrained by the availability of accurate dam inventories which often overlook smaller unmapped riverine dams. Here we develop and test a machine learning approach to identify unmapped dams using a combination of publicly available topographic and geospatial habitat data. Specifically, we trained a random forest classification algorithm to identify unmapped dams using digitally engineered predictor variables and known dam sites for validation. We applied our algorithm to two subbasins in the Hudson River watershed, USA, and quantified connectivity impacts, as well as evaluated a range of predictor sets to examine tradeoffs between classification accuracy and model parameterization effort. The random forest classifier achieved high accuracy in predicting dam sites (true positive rate = 89%, false positive rate = 1.2%) using a subset of variables related to stream slope and presence of upstream lentic habitats. Unmapped dams were prevalent throughout the two test watersheds. In fact, existing dam inventories underestimated the true number of dams by ∼80-94%. Accounting for previously unmapped dams resulted in a 62-90% decrease in dendritic connectivity indices for migratory fishes. Unmapped dams may be pervasive and can dramatically bias stream connectivity information. However, we find that machine learning approaches can provide an accurate and scalable means of identifying unmapped dams that can guide efforts to develop accurate dam inventories, thereby informing and empowering efforts to better manage them.

Greenhouse Gas Emissions from Septic Systems in New York State.

Onsite septic systems use microbial processes to eliminate organic wastes and nutrients such as nitrogen; these processes can contribute to air pollution through the release of greenhouse gases (GHGs). Current USEPA estimates for septic system GHG emissions are based on one study conducted in north-central California and are limited to methane; therefore, the contribution of these systems to the overall GHG emission budget is unclear. This study quantified and compared septic system GHG emissions from the soil over leach fields and the roof vent, which are the most likely locations for gas emissions during normal septic system operation. At each of eight septic systems, we measured fluxes of CH, CO, and NO using a static chamber method. The roof vent released the majority of septic system gas emissions. In addition, the leach field was a significant source of NO fluxes. Comparisons between leach field and vent emissions suggest that biological processes in the leach field soil may influence the type and quantity of gas released. Overall, our results suggest that (i) revisions are needed in USEPA guidance (e.g., septic systems are not currently listed as a source of NO emissions) and (ii) similar studies representing a wider range of climatic and geographic settings are needed. The total vent, sand filter, and leach field GHG emissions were 0.17, 0.045, and 0.050 t CO-equivalents capita yr, respectively. In total, this represents about 1.5% of the annual carbon footprint of an individual living in the United States.

A watershed-scale goals approach to assessing and funding wastewater infrastructure.

Capital needs during the next twenty years for public wastewater treatment, piping, combined sewer overflow correction, and storm-water management are estimated to be approximately $300 billion for the USA. Financing these needs is a significant challenge, as Federal funding for the Clean Water Act has been reduced by 70% during the last twenty years. There is an urgent need for new approaches to assist states and other decision makers to prioritize wastewater maintenance and improvements. We present a methodology for performing an integrated quantitative watershed-scale goals assessment for sustaining wastewater infrastructure. We applied this methodology to ten watersheds of the Hudson-Mohawk basin in New York State, USA that together are home to more than 2.7 million people, cover 3.5 million hectares, and contain more than 36,000 km of streams. We assembled data on 183 POTWs treating approximately 1.5 million m(3) of wastewater per day. For each watershed, we analyzed eight metrics: Growth Capacity, Capacity Density, Soil Suitability, Violations, Tributary Length Impacted, Tributary Capital Cost, Volume Capital Cost, and Population Capital Cost. These metrics were integrated into three goals for watershed-scale management: Tributary Protection, Urban Development, and Urban-Rural Integration. Our results demonstrate that the methodology can be implemented using widely available data, although some verification of data is required. Furthermore, we demonstrate substantial differences in character, need, and the appropriateness of different management strategies among the ten watersheds. These results suggest that it is feasible to perform watershed-scale goals assessment to augment existing approaches to wastewater infrastructure analysis and planning.

Wastewater management and Marcellus Shale gas development: trends, drivers, and planning implications.

Extraction of natural gas from tight shale formations has been made possible by recent technological advances, including hydraulic fracturing with horizontal drilling. Global shale gas development is seen as a potential energy and geopolitical "game-changer." However, widespread concern exists with respect to possible environmental consequences of this development, particularly impacts on water resources. In the United States, where the most shale gas extraction has occurred, the Marcellus Shale is now the largest natural gas producing play. To date, over 6,000,000 m(3) of wastewater has been generated in the process of extracting natural gas from this shale in the state of Pennsylvania (PA) alone. Here we examine wastewater management practices and trends for this shale play through analysis of industry-reported, publicly available data collected from the Pennsylvania Department of Environmental Protection Oil and Gas Reporting Website. We also analyze the tracking and transport of shale gas liquid waste streams originating in PA using a combination of web-based and GIS approaches. From 2008 to 2011 wastewater reuse increased, POTW use decreased, and data tracking became more complete, while the average distance traveled by wastewater decreased by over 30%. Likely factors influencing these trends include state regulations and policies, along with low natural gas prices. Regional differences in wastewater management are influenced by industrial treatment capacity, as well as proximity to injection disposal capacity. Using lessons from the Marcellus Shale, we suggest that nations, states, and regulatory agencies facing new unconventional shale development recognize that pace and scale of well drilling leads to commensurate wastewater management challenges. We also suggest they implement wastewater reporting and tracking systems, articulate a policy for adapting management to evolving data and development patterns, assess local and regional wastewater treatment infrastructure in terms of capacity and capability, promote well-regulated on-site treatment technologies, and review and update wastewater management regulations and policies.

Absolute quantification of Dehalococcoides proteins: enzyme bioindicators of chlorinated ethene dehalorespiration.

The quantification of trace proteins in complex environmental samples and mixed microbial communities would be a valuable monitoring tool in countless applications, including the bioremediation of groundwater contaminated with chlorinated solvents. Measuring the concentrations of specific proteins provides unique information about the activity and physiological state of organisms in a sample. We developed sensitive (< 5 fmol), selective bioindicator assays for the absolute quantification of select proteins used by Dehalococcoides spp. when reducing carbon atoms in the common pollutants trichloroethene (TCE) and tetrachloroethene (PCE). From complex whole-sample digests of two different dechlorinating mixed communities, we monitored the chromatographic peaks of selected tryptic peptides chosen to represent 19 specific Dehalococcoides proteins. This was accomplished using multiple-reaction monitoring (MRM) assays using nano-liquid chromatography-tandem mass spectrometry (nLC-MS/MS), which provided the selectivity, sensitivity and reproducibility required to quantify Dehalococcoides proteins in complex samples. We observed reproducible peak areas (average CV = 0.14 over 4 days, n = 3) and linear responses in standard curves (n = 5, R(2) > 0.98) using synthetic peptide standards spiked into a background matrix of sediment peptides. We detected and quantified TCE reductive dehalogenase (TceA) at 7.6 +/- 1.7 x 10(3) proteins cell(-1) in the KB1 bioaugmentation culture, previously thought to be lacking TceA. Fragmentation data from MS/MS shotgun proteomics experiments were helpful in developing the MRM targets. Similar shotgun proteomics data are emerging in labs around the world for many environmentally relevant microbial proteins, and these data are a valuable resource for the future development of MRM assays. We expect targeted peptide quantification in environmental samples to be a useful tool in environmental monitoring.

Is bigger better? Driving factors of POTW performance in New York.

Like many regions around the world, New York State, USA, faces challenges in meeting wastewater treatment quality standards because of aging infrastructure, limited funding, shifting demographics and increasingly stringent environmental regulations. In recent decades construction of new wastewater treatment and distribution infrastructure in NY has most often occurred in exurban communities and suburban developments that are less dense than traditional urban cores. Here, we examine the role of size and capacity utilization on wastewater treatment effectiveness with respect to critical effluent parameters, and additionally explore which common facility engineering controls influence water quality treatment using a unique dataset of descriptive information. Our results challenge conventional wisdom, suggesting that the largest facilities (>30,000 m3/d), not the smallest (<300 m3/d), discharge TSS, BOD, and coliform at significantly higher relative effluent concentrations (i.e., the ratio of discharged concentrations to allowable limits). Capacity utilization was also positively correlated to higher concentrations of TSS, BOD, and coliform effluent concentrations in larger facilities, though those concentrations were often within regulated limits. This implies that smaller-sized facilities may perform better in terms of environmental quality, but that the largest facilities demonstrate efficiency in the sense that they are not "over-treating" wastewater while avoiding violations. Results from NY suggest that medium sized facilities (300-30,000 m3/d) are sophisticated enough to incorporate appropriate unit processes, and employ operators with sufficient training and oversight, to reach treatment outcomes that are both reliable and of high quality.

Methane and nitrous oxide cycling microbial communities in soils above septic leach fields: Abundances with depth and correlations with net surface emissions.

Onsite septic systems use soil microbial communities to treat wastewater, in the process creating potent greenhouse gases (GHGs): methane (CH4) and nitrous oxide (N2O). Subsurface soil dispersal systems of septic tank overflow, known as leach fields, are an important part of wastewater treatment and have the potential to contribute significantly to GHG cycling. This study aimed to characterize soil microbial communities associated with leach field systems and quantify the abundance and distribution of microbial populations involved in CH4 and N2O cycling. Functional genes were used to target populations producing and consuming GHGs, specifically methyl coenzyme M reductase (mcrA) and particulate methane monooxygenase (pmoA) for CH4 and nitric oxide reductase (cnorB) and nitrous oxide reductase (nosZ) for N2O. All biomarker genes were found in all soil samples regardless of treatment (leach field, sand filter, or control) or depth (surface or subsurface). In general, biomarker genes were more abundant in surface soils than subsurface soils suggesting the majority of GHG cycling is occurring in near-surface soils. Ratios of production to consumption gene abundances showed a positive relationship with CH4 emissions (mcrA:pmoA, p < 0.001) but not with N2O emission (cnorB:nosZ, p > 0.05). Of the three measured soil parameters (volumetric water content (VWC), temperature, and conductivity), only VWC was significantly correlated to a biomarker gene, mcrA (p = 0.0398) but not pmoA or either of the N2O cycling genes (p > 0.05 for cnorB and nosZ). 16S rRNA amplicon library sequencing results revealed soil VWC, CH4 flux and N2O flux together explained 64% of the microbial community diversity between samples. Sequencing of mcrA and pmoA amplicon libraries revealed treatment had little effect on diversity of CH4 cycling organisms. Overall, these results suggest GHG cycling occurs in all soils regardless of whether or not they are associated with a leach field system.

Evolving shale gas management: water resource risks, impacts, and lessons learned.

Unconventional shale gas development promises to significantly alter energy portfolios and economies around the world. It also poses a variety of environmental risks, particularly with respect to the management of water resources. We review current scientific understanding of risks associated with the following: water withdrawals for hydraulic fracturing; wastewater treatment, discharge and disposal; methane and fluid migration in the subsurface; and spills and erosion at the surface. Some of these risks are relatively unique to shale gas development, while others are variations of risks that we already face from a variety of industries and activities. All of these risks depend largely on the pace and scale of development that occurs within a particular region. We focus on the United States, where the shale gas boom has been on-going for several years, paying particular attention to the Marcellus Shale, where a majority of peer-reviewed study has taken place. Governments, regulatory agencies, industry, and other stakeholders are challenged with responding to these risks, and we discuss policies and practices that have been adopted or considered by these various groups. Adaptive Management, a structured framework for addressing complex environmental issues, is discussed as a way to reduce polarization of important discussions on risk, and to more formally engage science in policy-making, along with other economic, social and value considerations. Data suggests that some risks can be substantially reduced through policy and best practice, but also that significant uncertainty persists regarding other risks. We suggest that monitoring and data collection related to water resource risks be established as part of planning for shale gas development before activity begins, and that resources are allocated to provide for appropriate oversight at various levels of governance.

Correlation of respiratory gene expression levels and pseudo-steady-state PCE respiration rates in Dehalococcoides ethenogenes.

DNA and RNA transcripts, particularly of genes of functional importance in the reductively dechlorinating microbe Dehalococcoides, are increasingly being studied as potential molecular bioindicators of reductive dechlorination. Ideally, mRNA bioindicators would be informative both qualitatively (with respect to dechlorination end point and substrate range) and quantitatively (with respectto activity rates). Here, we examined pseudo-steady-state mRNA levels in Dehalococcoides-containing microcosms continuously fed PCE at various loading rates. We characterized gene transcript abundance of potential Dehalococcoides bioindicators of reductive dechlorination, including 16S rRNA, and genes encoding an annotated formate dehydrogenase (Fdh), the hydrogenase (H2ase) Hup, and the reductive dehalogenases (RDases) TceA, DET1559, PceA, and DET1545. Increases in steady PCE loading rate led to corresponding increases in PCE respiration rate (1.5 +/- 0.1, 2.5 +/- 0.3, 4.8 +/- 0.1, and 9.2 +/- 0.5 micromol/L/hr). We also observed that pseudo-steady-state expression levels of most functional targets increase linearly over PCE respiration rates of 1.5-4.8 micromol/L/hr, with Fdh, Hup, and TceA transcripts increasing by approximately 2 x 10(10) copies per mL of culture for every micromol/L/hr increase in chloroethene respiration rate, and DET1559 and PceAtranscripts increasing by approximately9 x 10(9) copies per mL of culture, butthat increased respiration rates of 9.2 micromol/L/hr did not necessarily lead to corresponding increases in transcript levels.

Dehalococcoides' gene transcripts as quantitative bioindicators of tetrachloroethene, trichloroethene, and cis-1,2-dichloroethene dehalorespiration rates.

Gene transcripts corresponding to 16S rRNA, the hydrogenase (H2ase) Hup, a sequence annotated asformate dehydrogenase (Fdh) and reductive dehalogenases (RDases) TceA, PceA, DET1559, and DET1545 in Dehalococcoides ethenogenes strain 195 (DET) hold promise as potential bioindicators of the dehalorespiration of chlorinated ethenes. Here, we present quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) data taken from DET-containing mixed culture microcosms (4.4 x 10(8) DET 16S rRNA gene copies/mL) operated under continuous-feed conditions, with the aim of clarifying the relationships between pseudosteady-state abundances of bioindicator transcripts and respiration rate of various substrates: tetrachloroethene (PCE), trichloroethene (TCE), and cis-1,2-dichloroethene (cDCE). Results from PCE-fed microcosms suggested an induction threshold for transcription of some bioindicator genes between chloroethene respiration rates of 2.1 and 9.5 microeeq/L/hr. Putative RDase genes DET1559 and DET1545, however, were up-regulated at low PCE respiration rates and may be functionally significant when substrate levels are low. Data from PCE-fed microcosms operated at saturation kinetics indicated that a high respiration rate was not necessarily associated with a correspondingly high bioindicator transcript abundance. From these microcosms we calculated an approximate yield value of 1.6 x 10(8) 16S rRNA gene copies (cells) per micromol Cl- released and estimated a kmax of PCE respiration of 3 x 10(-9) micromol Cl- per 16S rRNA gene copy per day. TCE- and cDCE-fed microcosm studies indicated that Fdh, Hup, and TceA were the most abundant transcripts and could make suitable choices as bioindicators of activity for these substrates. Hup transcripts could be positively correlated to respiration rate (between approximately 8 and 45 microeeq/L/hr) regardless of chloroethene substrate, with transcript levels predicted to increase by 1.8 x 10(9) copies/mL culture for every/eeq/ L/hr increase in respiration rate (R2 = 0.90). Although RDase transcripts may provide information on substrate range, H2ase transcripts may be better indicators of per cell respiration rates.

Temporal expression of respiratory genes in an enrichment culture containing Dehalococcoides ethenogenes.

Multiple reductive dehalogenase (RDase), hydrogenase (H2ase), and other respiration-associated (RA) oxidoreductase genes have been identified in cultured representatives of Dehalococcoides. Although their products are likely to play key roles in the environmentally important process of reductive dechlorination, very little information is available about their regulation and specific functions. Here we show increased expression and temporal variability in the expression of five RDase genes and in the expression of genes for a putative formate dehydrogenase (Fdh) and two H2ases, including a periplasmic [Ni/Fe] H2ase (Hup) and a cytoplasmic [Fe] H2ase (Vhu). mRNA transcripts extracted from tetrachloroethene-dechlorinating mixed cultures corresponding to Fdh, the H2ase Hup, and the RDase targets TceA and DET0162 were expressed most highly, with average levels 34 (+/- 7.5)-, 23 (+/- 6.7)-, 16 (+/- 3.3)-, and 13 (+/- 3.3)-fold higher, respectively, than that for RNA polymerase (RpoB). H2ase and RA transcripts reached their respective expression maxima within the first 2 h after feeding. RDase transcripts, however, were most highly expressed after 3 h and exhibited greater temporal variability than other transcripts. Comparison with D. ethenogenes strain 195 pure culture expression levels indicated that RDase DET1545 was more highly expressed in mixed cultures, where, on average, its transcript level was sixfold higher than that of RpoB. While the specific functions of several of these gene products remain elusive, the high expression levels and temporal variability reported here suggest that these groups of enzymes are metabolically important for the respiration of chlorinated ethenes in mixed cultures containing Dehalococcoides.

Molecular characterization of microbial populations at two sites with differing reductive dechlorination abilities.

This study compares three molecular techniques, including terminal restriction fragment length polymorphism (T-RFLP), RFLP analysis with clone sequencing, and quantitative PCR (Q-PCR) for surveying differences in microbial communities at two contaminated field sites that exhibit dissimilar chlorinated solvent degradation activities. At the Idaho National Engineering and Environmental Laboratory (INEEL), trichloroethene (TCE) was completely converted to ethene during biostimulation with lactate. At Seal Beach, California, perchloroethene (PCE) was degraded only to cis-dichloroethene (cDCE) during biostimulation but was degraded to ethene after bioaugmentation with a dechlorinating culture containing Dehalococcoides strains. T-RFLP analysis showed that microbial community composition differed significantly between the two sites, but was similar within each site among wells that had low or no electron donor exposure. Analysis of INEEL clone libraries by RFLP with clone sequencing revealed a complex microbial population but did not identify any Dehalococcoides strains. Q-PCR targeting the 16S rRNA gene of Dehalococcoides strains - known for their unique capability to dechlorinate solvents completely to ethene - revealed a significant population at INEEL, but no detectable population at Seal Beach prior to bioaugmentation. Detection of Dehalococcoides by Q-PCR correlated with observed dechlorination activity and ethene production at both sites. Q-PCR showed that Dehalococcoides was present in even the pristine well at INEEL, suggesting that the difference in dechlorination ability at the two sites was due to the initial absence of this genus at Seal Beach. Of the techniques tested, Q-PCR quantification of specific dechlorinating species provided the most effective and direct prediction of community dechlorinating potential.

Phylogenetic analysis of TCE-dechlorinating consortia enriched on a variety of electron donors.

Two rapidly fermented electron donors, lactate and methanol, and two slowly fermented electron donors, propionate and butyrate, were selected for enrichment studies to evaluate the characteristics of anaerobic microbial consortia that reductively dechlorinate TCE to ethene. Each electron donor enrichment subculture demonstrated the ability to dechlorinate TCE to ethene through several serial transfers. Microbial community analyses based upon 16S rDNA, including terminal restriction fragment length polymorphism (T-RFLP) and clone library/sequencing, were performed to assess major changes in microbial community structure associated with electron donors capable of stimulating reductive dechlorination. Results demonstrated that five phylogenic subgroups or genera of bacteria were present in all consortia, including Dehalococcoides sp., low G+C Gram-positives (mostly Clostridium and Eubacterium sp.), Bacteroides sp., Citrobacter sp., and delta Proteobacteria (mostly Desulfovibrio sp.). Phylogenetic association indicates that only minor shifts in the microbial community structure occurred between the four alternate electron donor enrichments and the parent consortium. Inconsistent detection of Dehalococcoides spp. in clone libraries and T-RFLP of enrichment subcultures was resolved using quantitative polymerase chain reaction (Q-PCR). Q-PCR with primers specific to Dehalococcoides 16S rDNA resulted in positive detection of this species in all enrichments. Our results suggest that TCE-dechlorinating consortia can be stably maintained on a variety of electron donors and that quantities of Dehalococcoides cells detected with Dehalococcoides specific 16S rDNA primer/probe sets do not necessarily correlate well with solvent degradation rates.