Evaluating the Stormwater Treatment Performance of AbTech Industries Smart Sponge® Plus, Landry, N

The ability of AbTech’s Smart Sponge® Plus to remove fecal-borne bacteria from stormwater was evaluated in a storm drainage system located in Seabrook, New Hampshire. The Smart Sponge ® Plus was installed into a water quality inlet and samples were collected from influent (pre-treatment) and effluent (post-treatment) for analysis of bacterial concentrations and loadings during 15 storm events from September 3, 2003 to May 24, 2004, excluding winter months. The 15 storms included events with a range of rainfall intensities and amounts, as well as accompanying runoff volumes. Flow weighted composite samples were analyzed for fecal coliforms, Escherichia coli and enterococci to determine if concentrations were lowered as stormwater passed through the Smart Sponge® Plus material. In most cases, bacterial concentrations were reduced within the treatment system, but to varying degrees. The efficiency ratio based on reduction in event mean concentration for each bacterial indicator in the flow was calculated for each storm event. The values ranged most widely for fecal coliforms, whereas the range of ratios was narrower and the values were more consistent for enterococci. The overall load reductions for the bacterial indicators were 50.3% for fecal coliforms, 51.3% for Escherichia coli and 43.2% for enterococci. Relatively consistent pH values were observed in influent and effluent samples. The overall range of pH values was large, ranging from 5.21 units in influent from storm event #11 to 7.64 units in influent from storm event #1. Conductivity values were gr eater in the effluent in 14 of the 15 storm events, especially in storm events #12 and #13 when effluent conductivities were \u3e50% higher than influent values. Quality assurance/quality control procedures supported the methods and results of the study. Overall, the observed reductions in bacterial concentrations in post-treatment stormwater would still result in discharge of elevated bacterial levels that would continue to limit uses in receiving waters

Arkansas Bulletin of Water Research - Issue 2018

The Arkansas Bulletin of Water Research is a publication of the Arkansas Water Resources Center (AWRC). This bulletin is produced in an effort to share water research relevant to Arkansas water stakeholders in an easily searchable and aesthetically engaging way. This is the second publication of the bulletin and will be published annually. The submission of a paper to this bulletin is appropriate for topics at all related to water resources, by anyone conducting water research or investigations. This includes but is not limited to university researchers, consulting firms, watershed groups, and other agencies. Prospective authors should read the “Introduction to the Arkanasas Bulletin of Water Research” contained within this publication and should refer to the AWRC website for additional infromation. https://arkansas-water-center.uark.edu

A Memory-Efficient Sketch Method for Estimating High Similarities in Streaming Sets

Estimating set similarity and detecting highly similar sets are fundamental problems in areas such as databases, machine learning, and information retrieval. MinHash is a well-known technique for approximating Jaccard similarity of sets and has been successfully used for many applications such as similarity search and large scale learning. Its two compressed versions, b-bit MinHash and Odd Sketch, can significantly reduce the memory usage of the original MinHash method, especially for estimating high similarities (i.e., similarities around 1). Although MinHash can be applied to static sets as well as streaming sets, of which elements are given in a streaming fashion and cardinality is unknown or even infinite, unfortunately, b-bit MinHash and Odd Sketch fail to deal with streaming data. To solve this problem, we design a memory efficient sketch method, MaxLogHash, to accurately estimate Jaccard similarities in streaming sets. Compared to MinHash, our method uses smaller sized registers (each register consists of less than 7 bits) to build a compact sketch for each set. We also provide a simple yet accurate estimator for inferring Jaccard similarity from MaxLogHash sketches. In addition, we derive formulas for bounding the estimation error and determine the smallest necessary memory usage (i.e., the number of registers used for a MaxLogHash sketch) for the desired accuracy. We conduct experiments on a variety of datasets, and experimental results show that our method MaxLogHash is about 5 times more memory efficient than MinHash with the same accuracy and computational cost for estimating high similarities

EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets

This article introduces a new language-independent approach for creating a large-scale high-quality test collection of tweets that supports multiple information retrieval (IR) tasks without running a shared-task campaign. The adopted approach (demonstrated over Arabic tweets) designs the collection around significant (i.e., popular) events, which enables the development of topics that represent frequent information needs of Twitter users for which rich content exists. That inherently facilitates the support of multiple tasks that generally revolve around events, namely event detection, ad-hoc search, timeline generation, and real-time summarization. The key highlights of the approach include diversifying the judgment pool via interactive search and multiple manually-crafted queries per topic, collecting high-quality annotations via crowd-workers for relevancy and in-house annotators for novelty, filtering out low-agreement topics and inaccessible tweets, and providing multiple subsets of the collection for better availability. Applying our methodology on Arabic tweets resulted in EveTAR , the first freely-available tweet test collection for multiple IR tasks. EveTAR includes a crawl of 355M Arabic tweets and covers 50 significant events for which about 62K tweets were judged with substantial average inter-annotator agreement (Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating existing algorithms in the respective tasks. Results indicate that the new collection can support reliable ranking of IR systems that is comparable to similar TREC collections, while providing strong baseline results for future studies over Arabic tweets