Growth and yields of dechlorinators, acetogens, and methanogens during reductive dechlorination of chlorinated ethenes and dihaloelimination of 1 ,2-dichloroethane

The population dynamics of a mixed microbial culture dechlorinating trichloroethene (TCE), cis-1,2-dichloroethene (cDCE), 1,2-dichloroethane (1,2-DCA), and vinyl chloride (VC) to ethene were studied. Quantitative PCR revealed that Dehalococcoides, Geobacter, Sporomusa, Spirochaetes, and Methanomicrobiales phylotypes grew in short-term experiments. Both Geobacter and Dehalococcoides populations grew during TCE dechlorination to cDCE, but only Dehalococcoides populations grew during further dechlorination to ethene. The cell yields for Dehalococcoides determined in this study were similar on an electron equivalent basis regardless of the chlorinated compound transformed: (0.9+/-0.3) x 10(8)16S rRNA gene copies/microelectron equivalent (microeeq) ethene produced during cDCE dechlorination, (1.5 +/-0.3) x 10(8) copies/microeeq ethene produced during VC dechlorination, and (1.6+/-0.8) x 10(8) copies/ u,eeq ethene produced during 1,2-DCA dihaloelimination. The yield for the Geobacter population on TCE was estimated to be (1+/-0.5) x 10(8) copies/microeeq cDCE produced. Calculations showed that the Geobacter population was likely responsible for approximately 80% of the TCE dechlorinated to cDCE in this experiment. Acetogenesis by a Sporomusa population was the main competition to dechlorination for reducing equivalents. Sporomusa did not transform any chlorinated substrates tested, but was capable of converting methanol to acetate and hydrogen for dechlorination. Understanding the functions of various populations in mixed communities may explain why Dehalococcoides spp. are active at some sites and not others, and may also assist in optimizing the growth of bioaugmentation cultures, both in the laboratory and in the field.     

The strong network orientation problem

This study presents models and heuristics for solving the strong network orientation problem (SNOP), which can model several tactical optimization problems of setting directions in urban networks. The objective is to set an orientation for each edge in an undirected graph such that the resulting digraph is strongly connected and the total travel distance between any pair of nodes is minimized (or maximized). Investigating tactical optimization problems such as SNOP is motivated by several challenges in urban networks due to the growth of population in urban areas, large number of daily trips, increasing price of maintaining urban networks, and the need to reduce air pollution and passive noise. Thus, a new trend is to utilize the urban networks better. In this context, we first use a multicommodity flow formulation to model the minimization problem. The maximization version is modeled by using the dual formulation of the shortest path problem. Then, scalable heuristic strategies for solving SNOP are investigated. For such purpose, we first propose basic components such as constructive heuristics, perturbations and local searches. They are combined into several metaheuristics based on local searches, multi-start and evolutionary schemes, i.e. Multistart Local Search, Iterated Local Search (ILS), Relaxed ILS, Evolutionary Local Search (ELS), Relaxed ELS, and Variable Neighborhood Search. Computational experiments have been performed to analyze the proposed methods in terms of efficiency and quality of solutions, using grid instances and a graph from downtown Clermont-Ferrand in France.