TUGAI: An Integrated Simulation Tool for Ecological Assessment of Alternative Water Management Strategies in a Degraded River Delta

The development of ecologically sound water allocation strategies that account for the needs of riverine ecosystems is a pressing issue, especially in semiarid river basins. In the Aral Sea Basin, a search for strategies to mitigate ecological and socioeconomic deterioration has been in process since the early 1990s. The Geographic Information System–based simulation tool TUGAI has been developed to support the policy determination process by providing a simple, problem-oriented method to assess ecological effects of alternative water management strategies for the Amudarya River. It combines a multiobjective water allocation model with simple, spatially explicit statistical and rule-based models of landscape dynamics. Changes in environmental conditions are evaluated by a fuzzy habitat suitability index for Populus euphratica, which is the dominant species of the characteristic riverine Tugai forests. Water management scenarios can be developed by altering spatiotemporal water distribution in the delta area or the amount of water inflow into the delta. Outcomes of scenario analysis are qualitative comparisons of the ecological effects of different options for a time period of up to 28 years. The given approach utilizes different types of knowledge, from quantitative hydrological data to qualitative local expert knowledge. The main purpose of the tool is to integrate the knowledge in a comprehensive way to make it available for discussions on alternative policies in moderated workshops with stakeholders. In this article, the modules of the tool, their integration, and three hypothetical scenarios are presented. Based on the experience gained when developing the TUGAI tool, we propose that the general framework can be transferred to other areas where tradeoffs in water allocation between the environment and other water users are of major concern. The potential for a simulation tool to structure and inform a complex resource management situation by involving local experts and stakeholders in the development of possible future scenarios will become increasingly valuable for transparent and participatory resource management.     

Contribution of tobacco smoke to environmental benzene exposure in Germany

The concentrations of environmental tobacco smoke (ETS) constituents including benzene were measured in the living rooms of 10 nonsmoking households and 20 households with at least one smoker situated in the city and suburbs of Munich. In the city, the median benzene levels during the evening, when all household members were at home, were 8.1 and 10.4 μg/m³ in nonsmoking and smoking homes, respectively. The corresponding levels of 3.5 and 4.6 μg/m³ were considerably lower in the suburbs. Median time-integrated 1-week benzene concentrations in the city were 10.6 μg/m³ in nonsmoking homes and 13.1 μg/m³ in smoking homes. In the suburbs, the corresponding values were 3.2 and 5.6 μg/m³. While the benzene concentrations in nonsmoking homes located in the city were significantly higher (p < 0.05) than in suburban nonsmoking households, no difference was found between smoking and nonsmoking households located either in the city or in the suburbs. Individual exposures to benzene and to specific markers for tobacco smoke of all household members (82 nonsmokers and 32 smokers) were determined by questionnaire, personal monitoring, and biomonitoring. Within the city, the benzene exposure determined by personal samplers was 11.8 μg/m³ for nonsmokers living in nonsmoking homes and 13.3 μg/m³ for nonsmokers in smoking homes. The corresponding values for nonsmokers living in the suburbs were 5.9 and 6.9 μg/m³, respectively. Neither difference was statistically significant. Nonsmokers living in nonsmoking households in the city had significantly higher exposure to benzene compared to their counterparts living in the suburbs (personal samplers: 11.8 vs 5.9 μg/m³, p < 0.001; benzene in exhalate: 2.4 vs. 1.1 μg/m³, p < 0.05; trans,trans-muconic acid excretion in urine: 92 vs. 54 μg/g creatinine, p < 0.05). Nonsmokers from all households with smokers were significantly more exposed to benzene than nonsmokers living in the nonsmoking households (personal samplers: 13.2 vs. 7.0 μg/m³, p < 0.05; benzene in exhalate: 2.6 vs. 1.8 μg/m³, p < 0.01; trans,trans-muconic acid excretion in urine: 73 vs. 62 μg/g creatinine), but the contribution of ETS to the total benzene exposure was relatively low compared to that from other sources. Analysis of variance showed that at most 15% of the benzene exposure of nonsmokers living in smoking homes was attributable to ETS. For nonsmokers living in nonsmoking households benzene exposure from ETS was insignificant.