The Ecology of Fecal Indicators

Animal and human wastes introduce pathogens into rivers and streams, creating human health and economic burdens. While direct monitoring for pathogens is possible, it is impractical due to the sporadic distribution of pathogens, cost to identify, and health risks to laboratory workers. To overcome these issues, fecal indicator organisms are used to estimate the presence of pathogens. Although fecal indicators generally protect public health, they fall short in their utility because of difficulties in public health risk characterization, inconsistent correlations with pathogens, weak source identification, and their potential to persist in environments with no point sources of fecal pollution. This research focuses on characterizing the ecology of fecal indicators using both modeling and metabolic indicators to better understand the processes that drive fecal pollution. Fecal indicator impairment was modeled in Sinking Creek, a 303 (d) listed stream in Northeast Tennessee, using the ecological niche model, Maxent, for two different fecal indicators. While the use of Maxent has been well demonstrated at the macroscale, this study introduces its application to ecological niches at the microscale. Stream impairment seasonality was exhibited in two different indicators over multiple years and different resolutions (quarterly versus monthly sampling programs). This stresses the need for multiple year and month sampling to capture heterogeneity in fecal indicator concentrations. Although discharge is strongly associated with dissolved solutes, fecal indicator impairment was governed by other ecological factors such as populations of heterotrophic bacteria, enzyme activity, nutrient conditions, and other metabolic indicators. This research also incorporated metabolic indicators to characterize spatiotemporal variability in microbial community function, making connections to fecal and other pollution gradients. Communities differed in their ability to use a wide variety of substrates, and metabolic inhibition in sediments captured most of the interaction of aquatic and benthic communities. Sediment substrate activity was also indicative of degrees of pollution, suggesting that sediment is a potential reservoir for Escherichia coli in this stream, and there is possibility for resuspension, extended residence times, and increased duration for exposure. This research highlights the benefit of using models and other microbial indicators to better understand how environment shapes the niche of fecal indicators

Maxent Estimation of Aquatic Escherichia Coli Stream Impairment

Background.The leading cause of surface water impairment in United States’ rivers and streams is pathogen contamination. Although use of fecal indicators has reduced human health risk, current approaches to identify and reduce exposure can be improved. One important knowledge gap within exposure assessment is characterization of complex fate and transport processes of fecal pollution. Novel modeling processes can inform watershed decision-making to improve exposure assessment

Maxent Estimation of Aquatic Escherichia Coli Stream Impairment

Background: The leading cause of surface water impairment in United States’ rivers and streams is pathogen contamination. Although use of fecal indicators has reduced human health risk, current approaches to identify and reduce exposure can be improved. One important knowledge gap within exposure assessment is characterization of complex fate and transport processes of fecal pollution. Novel modeling processes can inform watershed decision-making to improve exposure assessment. Methods: We used the ecological model, Maxent, and the fecal indicator bacterium Escherichia coli to identify environmental factors associated with surface water impairment. Samples were collected August, November, February, and May for 8 years on Sinking Creek in Northeast Tennessee and analyzed for 10 water quality parameters and E. coli concentrations. Univariate and multivariate models estimated probability of impairment given the water quality parameters. Model performance was assessed using area under the receiving operating characteristic (AUC) and prediction accuracy, defined as the model’s ability to predict both true positives (impairment) and true negatives (compliance). Univariate models generated action values, or environmental thresholds, to indicate potential E. coli impairment based on a single parameter. Multivariate models predicted probability of impairment given a suite of environmental variables, and jack-knife sensitivity analysis removed unresponsive variables to elicit a set of the most responsive parameters. Results: Water temperature univariate models performed best as indicated by AUC, but alkalinity models were the most accurate at correctly classifying impairment. Sensitivity analysis revealed that models were most sensitive to removal of specific conductance. Other sensitive variables included water temperature, dissolved oxygen, discharge, and NO3. The removal of dissolved oxygen improved model performance based on testing AUC, justifying development of two optimized multivariate models; a 5-variable model including all sensitive parameters, and a 4-variable model that excluded dissolved oxygen. Discussion: Results suggest that E. coli impairment in Sinking Creek is influenced by seasonality and agricultural run-off, stressing the need for multi-month sampling along a stream continuum. Although discharge was not predictive of E. coli impairment alone, its interactive effect stresses the importance of both flow dependent and independent processes associated with E. coli impairment. This research also highlights the interactions between nutrient and fecal pollution, a key consideration for watersheds with multiple synergistic impairments. Although one indicator cannot mimic the plethora of existing pathogens in water, incorporating modeling can fine tune an indicator’s utility, providing information concerning fate, transport, and source of fecal pollution while prioritizing resources and increasing confidence in decision making. Methods We used the ecological model, Maxent, and the fecal indicator bacterium Escherichia coli to identify environmental factors associated with surface water impairment. Samples were collected August, November, February, and May for 8 years on Sinking Creek in Northeast Tennessee and analyzed for 10 water quality parameters and E. coli concentrations. Univariate and multivariate models estimated probability of impairment given the water quality parameters. Model performance was assessed using area under the receiving operating characteristic (AUC) and prediction accuracy, defined as the model’s ability to predict both true positives (impairment) and true negatives (compliance). Univariate models generated action values, or environmental thresholds, to indicate potential E. coli impairment based on a single parameter. Multivariate models predicted probability of impairment given a suite of environmental variables, and jack-knife sensitivity analysis removed unresponsive variables to elicit a set of the most responsive parameters. Results Water temperature univariate models performed best as indicated by AUC, but alkalinity models were the most accurate at correctly classifying impairment. Sensitivity analysis revealed that models were most sensitive to removal of specific conductance. Other sensitive variables included water temperature, dissolved oxygen, discharge, and NO3. The removal of dissolved oxygen improved model performance based on testing AUC, justifying development of two optimized multivariate models; a 5-variable model including all sensitive parameters, and a 4-variable model that excluded dissolved oxygen. Discussion Results suggest that E. coli impairment in Sinking Creek is influenced by seasonality and agricultural run-off, stressing the need for multi-month sampling along a stream continuum. Although discharge was not predictive of E. coli impairment alone, its interactive effect stresses the importance of both flow dependent and independent processes associated with E. coli impairment. This research also highlights the interactions between nutrient and fecal pollution, a key consideration for watersheds with multiple synergistic impairments. Although one indicator cannot mimic theplethora of existing pathogens in water, incorporating modeling can fine tune an indicator’s utility, providing information concerning fate, transport, and source of fecal pollution while prioritizing resources and increasing confidence in decision making

Dissolved noble gases and stable isotopes as tracers of preferential fluid flow along faults in the Lower Rhine Embayment, Germany

Groundwater in shallow unconsolidated sedimentary aquifers close to the Bornheim fault in the Lower Rhine Embayment (LRE), Germany, has relatively low δ2H and δ18O values in comparison to regional modern groundwater recharge, and 4He concentrations up to 1.7 × 10−4 cm3 (STP) g–1 ± 2.2 % which is approximately four orders of magnitude higher than expected due to solubility equilibrium with the atmosphere. Groundwater age dating based on estimated in situ production and terrigenic flux of helium provides a groundwater residence time of ∼107 years. Although fluid exchange between the deep basal aquifer system and the upper aquifer layers is generally impeded by confining clay layers and lignite, this study’s geochemical data suggest, for the first time, that deep circulating fluids penetrate shallow aquifers in the locality of fault zones, implying  that sub-vertical fluid flow occurs along faults in the LRE. However, large hydraulic-head gradients observed across many faults suggest that they act as barriers to lateral groundwater flow. Therefore, the geochemical data reported here also substantiate a conduit-barrier model of fault-zone hydrogeology in unconsolidated sedimentary deposits, as well as corroborating the concept that faults in unconsolidated aquifer systems can act as loci for hydraulic connectivity between deep and shallow aquifers. The implications of fluid flow along faults in sedimentary basins worldwide are far reaching and of particular concern for carbon capture and storage (CCS) programmes, impacts of deep shale gas recovery for shallow groundwater aquifers, and nuclear waste storage sites where fault zones could act as potential leakage pathways for hazardous fluids

People and Things on the Move: Domestic Material Culture, Poverty and Mobility in Victorian London

© 2016, The Author(s). The development of what Mayne and Lawrence (Urban History 26: 325–48, 1999) termed “ethnographic” approaches to studying nineteenth-century households and urban communities has gathered momentum in recent years. As such research agendas have taken hold and been applied to new contexts, so critiques, methodological developments, and new intellectual and theoretical currents, have provided opportunities to enhance and develop approaches. This article contributes to this on-going process. Drawing upon household archaeological research on Limehouse, a poor neighborhood in Victorian London, and inspired by the theoretical insights provided by the “new mobilities paradigm,” it aims to place “mobility” as a central and enabling intellectual framework for understanding the relationships between people, place, and poverty. Poor communities in nineteenth-century cities were undeniably mobile and transient. Historians and archaeologists have often regarded this mobility as an obstacle to studying everyday life in such contexts. However, examining temporal routines and geographical movements across a variety of time frames and geographical scales, this article argues that mobility is actually key to understanding urban life and an important mechanism for interpreting the fragmented material and documentary traces left by poor households in the nineteenth-century metropolis.We are grateful to the UK’s Arts and Humanities Research Council who funded the research upon which this paper is based (Grant Reference AH/E002285/1): ‘Living in Victorian London: Towards a Material History of Everyday Domestic Life in the Nineteenth-Century Metropolis

Global carbon budget 2019

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFF) are based on energy statistics and cement production data, while emissions from land use change (ELUC), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), EFF was 9.5±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.9±0.02 GtC yr−1 (2.3±0.01 ppm yr−1), SOCEAN 2.5±0.6 GtC yr−1, and SLAND 3.2±0.6 GtC yr−1, with a budget imbalance BIM of 0.4 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in EFF was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr−1, reaching 10 GtC yr−1 for the first time in history, ELUC was 1.5±0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5±0.9 GtC yr−1 (42.5±3.3 GtCO2). Also for 2018, GATM was 5.1±0.2 GtC yr−1 (2.4±0.1 ppm yr−1), SOCEAN was 2.6±0.6 GtC yr−1, and SLAND was 3.5±0.7 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated by this work are available at https://doi.org/10.18160/gcp-2019 (Friedlingstein et al., 2019)

A Comparative Analysis of Carbon Emissions from Countries of Varying Fossil Fuel Dependence

The objective of this research is to compare the true drivers of carbon emissions in different types of countries and compare these to the policies currently in place to abate emissions. The Carbon Dioxide Information Analysis Center (CDIAC) data will be used to collect data on total emissions, liquid emissions, solid emissions, and gas emissions. These will be compared to socioeconomic factors for 10 countries in a canonical correlation analysis to determine which factors are most predictive of different types of fossil fuel usage. These factors will then be deconstructed by the Kaya Identity for each country, and compared to that country’s climate policy to determine if the policy is considering the most critical factors. Previous research covered in this research- in-progress paper considers the reasoning between choosing the selected 10 countries, previous emissions modeling, and projected current trend models into 2100. This will serve as a guide to what exactly countries like the types of countries explored here need to focus on to limit emissions

Canonical Variable Selection for Ecological Modeling of Fecal Indicators

More than 270,000 km of rivers and streams are impaired due to fecal pathogens, creating an economic and public health burden. Fecal indicator organisms such as Escherichia coli are used to determine if surface waters are pathogen impaired, but they fail to identify human health risks, provide source information, or have unique fate and transport processes. Statistical and machine learning models can be used to overcome some of these weaknesses, including identifying ecological mechanisms influencing fecal pollution. In this study, canonical correlation analysis (CCorA) was performed to select parameters for the machine learning model, Maxent, to identify how chemical and microbial parameters can predict E. coli impairment and F+-somatic bacteriophage detections. Models were validated using a bootstrapping cross-validation. Three suites of models were developed; initial models using all parameters, models using parameters identified in CCorA, and optimized models after further sensitivity analysis. Canonical correlation analysis reduced the number of parameters needed to achieve the same degree of accuracy in the initial E. coli model (84.7%), and sensitivity analysis improved accuracy to 86.1%. Bacteriophage model accuracies were 79.2, 70.8, and 69.4% for the initial, CCorA, and optimized models, respectively; this suggests complex ecological interactions of bacteriophages are not captured by CCorA. Results indicate distinct ecological drivers of impairment depending on the fecal indicator organism used. Escherichia coli impairment is driven by increased hardness and microbial activity, whereas bacteriophage detection is inhibited by high levels of coliforms in sediment. Both indicators were influenced by organic pollution and phosphorus limitation