Quantifying the influence of sea ice on ocean microseism using observations from the Bering Sea, Alaska

Microseism is potentially affected by all processes that alter ocean wave heights. Because strong sea ice prevents large ocean waves from forming, sea ice can therefore significantly affect microseism amplitudes. Here we show that this link between sea ice and microseism is not only a robust one but can be quantified. In particular, we show that 75–90% of the variability in microseism power in the Bering Sea can be predicted using a fairly crude model of microseism damping by sea ice. The success of this simple parameterization suggests that an even stronger link can be established between the mechanical strength of sea ice and microseism power, and that microseism can eventually be used to monitor the strength of sea ice, a quantity that is not as easily observed through other means

The Impact of Triclosan on the Spread of Antibiotic Resistance in the Environment

Triclosan (TCS) is a commonly used antimicrobial agent that enters wastewater treatment plants (WWTPs) and the environment. An estimated 1.1 × 105 to 4.2 × 105 kg of TCS are discharged from these WWTPs per year in the United States. The abundance of TCS along with its antimicrobial properties have given rise to concern regarding its impact on antibiotic resistance in the environment. The objective of this review is to assess the state of knowledge regarding the impact of TCS on multidrug resistance in environmental settings, including engineered environments such as anaerobic digesters. Pure culture studies are reviewed in this paper to gain insight into the substantially smaller body of research surrounding the impacts of TCS on environmental microbial communities. Pure culture studies, mainly on pathogenic strains of bacteria, demonstrate that TCS is often associated with multidrug resistance. Research is lacking to quantify the current impacts of TCS discharge to the environment, but it is known that resistance to TCS and multidrug resistance can increase in environmental microbial communities exposed to TCS. Research plans are proposed to quantitatively define the conditions under which TCS selects for multidrug resistance in the environment

Altered Antibiotic Tolerance in Anaerobic Digesters Acclimated to Triclosan Or Triclocarban

Bench-scale anaerobic digesters were amended to elevated steady-state concentrations of triclosan (850 mg/kg) and triclocarban (150 mg/kg) using a synthetic feed. After more than 9 solids retention time (SRT) values of acclimatization, biomass from each digester (and a control digester that received no antimicrobials) was used to assess the toxicity of three antibiotics. Methane production rate was measured as a surrogate for activity in microcosms that received doses of antibiotics ranging from no-antibiotic to inhibitory concentrations. Biomass amended with triclocarban was more sensitive to tetracycline compared to the control indicating synergistic inhibitory effects between this antibiotic and triclocarban. In contrast, biomass amended with triclosan was able to tolerate statistically higher levels of ciprofloxacin indicating that triclosan can induce functional resistance to ciprofloxacin in an anaerobic digester community

Autocatalytic Pyrolysis of Wastewater Biosolids for Product Upgrading

The main goals for sustainable water resource recovery include maximizing energy generation, minimizing adverse environmental impacts, and recovering beneficial resources. Wastewater biosolids pyrolysis is a promising technology that could help facilities reach these goals because it produces biochar that is a valuable soil amendment as well as bio-oil and pyrolysis gas (py-gas) that can be used for energy. The raw bio-oil, however, is corrosive; therefore, employing it as fuel is challenging using standard equipment. A novel pyrolysis process using wastewater biosolids-derived biochar (WB-biochar) as a catalyst was investigated to decrease bio-oil and increase py-gas yield for easier energy recovery. WB-biochar catalyst increased the py-gas yield nearly 2-fold, while decreasing bio-oil production. The catalyzed bio-oil also contained fewer constituents based on GC-MS and GC-FID analyses. The energy shifted from bio-oil to py-gas, indicating the potential for easier on-site energy recovery using the relatively clean py-gas. The metals contained in wastewater biosolids played an important role in upgrading pyrolysis products. The Ca and Fe in WB-biochar reduced bio-oil yield and increased py-gas yield. The py-gas energy increase may be especially useful at water resource recovery facilities that already combust anaerobic digester biogas for energy since it may be possible to blend biogas and py-gas for combined use

TAX INCENTIVES: AN EFFECTIVE DEVELOPMENT STRATEGY FOR RURAL COMMUNITIES?

As national and local economies become more globalized, many rural areas are going to find it more difficult to compete for private capital investments. A traditional tool, modifications to tax policy, of state and local governments will not be as effective (for many communities it has never been effective) in the future. These communities will need to seek other avenues of growth. However, for many rural communities even alternative avenues will not lead to enhanced economic opportunity.agglomeration, rural development, tax policy, Community/Rural/Urban Development, R51, R58, O21, O23, R11, R38,

Pyrolysis of Wastewater Biosolids Significantly Reduces Estrogenicity

Most wastewater treatment processes are not specifically designed to remove micropollutants. Many micropollutants are hydrophobic so they remain in the biosolids and are discharged to the environment through land-application of biosolids. Micropollutants encompass a broad range of organic chemicals, including estrogenic compounds (natural and synthetic) that reside in the environment, a.k.a. environmental estrogens. Public concern over land application of biosolids stemming from the occurrence of micropollutants hampers the value of biosolids which are important to wastewater treatment plants as a valuable by-product. This research evaluated pyrolysis, the partial decomposition of organic material in an oxygen-deprived system under high temperatures, as a biosolids treatment process that could remove estrogenic compounds from solids while producing a less hormonally active biochar for soil amendment. The estrogenicity, measured in estradiol equivalents (EEQ) by the yeast estrogen screen (YES) assay, of pyrolyzed biosolids was compared to primary and anaerobically digested biosolids. The estrogenic responses from primary solids and anaerobically digested solids were not statistically significantly different, but pyrolysis of anaerobically digested solids resulted in a significant reduction in EEQ; increasing pyrolysis temperature from 100 °C to 500 °C increased the removal of EEQ with greater than 95% removal occurring at or above 400 °C. This research demonstrates that biosolids treatment with pyrolysis would substantially decrease (removal \u3e 95%) the estrogens associated with this biosolids product. Thus, pyrolysis of biosolids can be used to produce a valuable soil amendment product, biochar, that minimizes discharge of estrogens to the environment

Learning Provably Useful Representations, with Applications to Fairness

Representation learning involves transforming data so that it is useful for solving a particular supervised learning problem. The aim is to learn a representation function which maps inputs to some representation space, and an hypothesis which maps the representation space to targets. It is possible to learn a representation function using unlabeled data or data from a probability distribution other than that of the main problem of interest, which is helpful if labeled data is scarce. This approach has been successfully applied in practice, for example through pre-trained neural networks in computer vision and word embeddings in natural language processing. This thesis explores when it is possible to learn representations that are provably useful. We consider learning a representation function from unlabeled data, and propose an approach to identifying conditions where this technique will be useful for a subsequent supervised learning task. The approach requires shared structure in the labeled and unlabeled distributions, as well as a compatible representation function class and hypothesis class. We provide an example where representation learning can exploit cluster structure present in the data. We also consider learning a representation function from a source task distribution and re-using it on a target task of interest, and again propose conditions where this approach will be successful. In this case the conditions depend on shared structure between source and target task distributions. We provide an example involving the transfer of weights in a two-layer feedforward neural network. Representation learning can be applied to another topic of interest: fairness in machine learning. The issue of fairness arises when machine learning systems make or provide advice on decisions about people. A common approach to defining fairness is measuring differences in decisions made by an algorithm for one demographic group compared to another. One approach to preventing discrimination against particular groups is to learn a representation of the data from which it is not possible for an adversary to determine an individual's group membership, but which preserves other useful information. We quantify the costs and benefits of such an approach with respect to several possible fairness definitions. We also examine the relationships between different definitions of fairness and show cases where they cannot simultaneously be satisfied. We explore the use of representation learning for fairness through two case studies: predicting domestic violence recidivism while avoiding discrimination on the basis of race, and predicting student outcomes at university while avoiding discrimination on the basis of gender. Our case studies reveal both the utility of fair representation learning and the trade-offs between accuracy and the definitions of fairness considered