Previous optimization-based watershed decision making approaches suffer two major limitations. First of all, these approaches generally do not provide a systematic way to prioritize the implementation schemes with consideration of uncertainties in the watershed systems and the optimization models. Furthermore, with adaptive management, both the decision environment and the uncertainty space evolve (1) during the implementation processes and (2) as new data become available. No efficient method exists to guide optimal adaptive decision making, particularly at a watershed scale. This paper presents a guided adaptive optimal (GAO) decision making approach to overcome the limitations of the previous methods for more efficient and reliable decision making at the watershed scale. The GAO approach is built upon a modeling framework that explicitly addresses system optimality and uncertainty in a time variable manner, hence mimicking the real-world decision environment where information availability and uncertainty evolve with time. The GAO approach consists of multiple components, including the risk explicit interval linear programming (REILP) modeling framework, the systematic method for prioritizing implementation schemes, and an iterative process for adapting the core optimization model for updated optimal solutions. The proposed approach was illustrated through a case study dealing with the uncertainty based optimal adaptive environmental management of the Lake Qionghai Watershed in China. The results demonstrated that the proposed GAO approach is able to (1) efficiently incorporate uncertainty into the formulation and solution of the optimization model, and (2) prioritize implementation schemes based on the risk and return tradeoff. As a result the GAO produces more reliable and efficient management outcomes than traditional non-adaptive optimization approaches. (C) 2011 Elsevier Ltd. All rights reserved.
Volatile Organic Compounds (VOCs) exposure can induce a range of adverse human health effects. To date, however, personal VOCs exposure and residential indoor and outdoor VOCs levels have not been well characterized in the mainland of China, less is known about health risk of personal exposure to VOCs. In this study, personal exposures for 12 participants as well as residential indoor/outdoor, workplace and in vehicle VOCs concentrations were measured simultaneously in Tianjin, China. All VOCs samples were collected using passive samplers for 5days and were analyzed using Thermal Desorption GC-MS method. U.S. Environmental Protect Agency's Inhalation Unit Risks were used to calculate the inhalation cancer health risk. To assess uncertainty of health risk estimate, Monte Carlo simulation and sensitivity analysis were implemented. Personal exposures were greater than residential indoor exposures as expected with the exception of carbon tetrachloride. Exposure assessment showed modeled and measured concentrations are statistically linearly correlated for all VOCs (P<0.01) except chloroform, confirming that estimated personal exposure using time-weighted model can provide reasonable estimate of personal inhalation exposure to VOCs. Indoor smoking and recent renovation were identified as two major factors influencing personal exposure based on the time-activity pattern and factor analysis. According to the cancer risk analysis of personal exposure, benzene, chloroform, carbon tetrachloride and 1,3-butadiene had median upper-bound lifetime cancer risks that exceeded the U.S. EPA benchmark of 1 per one million, and benzene presented the highest median risks at about 22 per one million population. The median cumulative cancer risk of personal exposure to 5 VOCs was approximately 44 per million, followed by indoor exposure (37 per million) and in vehicle exposure (36 per million). Sensitivity analysis suggested that improving the accuracy of exposure measurement in further research would advance the health risk assessment.
Three novel strains capable of heterotrophic nitrification-aerobic denitrification were isolated from the landfill leachate treatment system. Based on their phenotypic and phylogenetic characteristics, the isolates were identified as Agrobacterium sp. LAD9, Achromobacter sp. GAD3 and Comamonas sp. GAD4, respectively. Batch tests were carried out to evaluate the growth and the ammonia removal patterns. The maximum growth rates as determined from the growth curve were 0.286, 0.228, and 0.433 h(-1) for LAD9, GAD3 and GAD4, respectively. The maximum aerobic nitrification-denitrification rate was achieved by the strain GAD4 of 0.381 mmol/l h, followed by LAD9 of 0.374 mmol/l h and GAD3 of 0.346 mmol/l h. Moreover, hydroxylamine oxidase and periplasmic nitrate reductase were successfully expressed in all the isolates. The relationship between the enzyme activities and the aerobic nitrification-denitrification rates revealed that hydroxylamine oxidation may be the rate-limiting step in the heterotrophic nitrification-aerobic denitrification process. The study results are of great significance to the wastewater treatment systems where simultaneous removal of carbon and nitrogen is desired.