Identifying source information after river chemical spill occurrences is critical for emergency responses. However, the inverse uncertainty characteristics of this kind of pollution source inversion problem have not yet been clearly elucidated. To fill this gap, stochastic analysis approaches, including a regional sensitivity analysis method, identifiability plot and perturbation methods, were employed to conduct an empirical investigation on generic inverse uncertainty characteristics under a well-accepted uncertainty analysis framework. Case studies based on field tracer experiments and synthetic numerical tracer experiments revealed several new rules. For example, the release load can be most easily inverted, and the source location is responsible for the largest uncertainty among the source parameters. The diffusion and convection processes are more sensitive than the dilution and pollutant attenuation processes to the optimization of objective functions in terms of structural uncertainty. The differences among the different objective functions are smaller for instantaneous release than for continuous release cases. Small monitoring errors affect the inversion results only slightly, which can be ignored in practice. Interestingly, the estimated values of the release location and time negatively deviate from the real values, and the extent is positively correlated with the relative size of the mixing zone to the objective river reach. These new findings improve decision making in emergency responses to sudden water pollution and guide the monitoring network design.
GOAL, SCOPE, AND BACKGROUND: Diesel exhaust is believed to consist of thousands of organic constituents and is a major cause of urban pollution. We recently reported that a systematic separation procedure involving successive solvent extractions, followed by repeated column chromatography, resulted in the isolation of vasodilatory active nitrophenols. These findings indicated that the estimation of the amount of nitrophenols in the environment is important to evaluate their effect on human health. The isolation procedure, however, involved successive solvent extractions followed by tedious, repeated chromatography, resulting in poor fractionation and in a significant loss of accuracy and reliability. Therefore, it was crucial to develop an alternative, efficient, and reliable analytical method. Here, we describe a facile and efficient acid-base extraction procedure for the analysis of nitrophenols. MATERIALS AND METHODS: Diesel exhaust particles (DEP) were collected from the exhaust of a 4JB1-type engine (ISUZU Automobile Co., Tokyo, Japan). Gas chromatography-mass spectrometry (GC-MS) analysis was performed with a GCMS-QP2010 instrument (Shimadzu, Kyoto, Japan). RESULTS: A solution of DEP in 1-butanol was extracted with aqueous NaOH to afford a nitrophenol-rich oily extract. The resulting oil was methylated with trimethylsilyldiazomethane and subsequently subjected to GC-MS analysis, revealing that 4-nitrophenol, 3-methyl-4-nitrophenol, 2-methyl-4-nitrophenol, and 4-nitro-3-phenylphenol were present in significantly higher concentrations than those reported previously. DISCUSSION: Simple acid-base extraction followed by the direct analysis of the resulting extract by GC-MS gave only broad peaks of nitrophenols with a poor detection limit, while the GC-MS analysis of the sample pretreated with (trimethylsilyl)diazomethane gave satisfactorily clear chromatograms with sharp peaks and with a significantly lowered detection limit (0.5 ng/ml, approximately 100 times). CONCLUSION: The present method involving an acid-base extraction, in situ derivatization, and GC-MS analysis has shown to be a simple, efficient, and reliable method for the isolation and identification of the chemical substances in DEP. 相似文献
A study of the decolorization of reactive brilliant blue in an aqueous solution using Fe-Mn-sepiolite as a heterogeneous Fenton-like catalyst has been performed. The Fourier transform infrared (FTIR) spectra of the catalyst showed bending vibrations of the Fe-O. The X-ray diffraction (XRD) patterns of the catalyst showed characteristic diffraction peaks of α-Fe2O3, γ-Fe2O3 and MnO. A four factor central composite design (CCD) coupled with response surface methodology (RSM) was applied to evaluate and optimize the important variables (catalyst addition, hydrogen peroxide dosage, initial pH value and initial dye concentration). When the reaction conditions were catalyst dosage= 0.4 g, [H2O2]= 0.3 mL, pH= 2.5, [reactive brilliant blue]o = 50 mg·L−1, and volume of solution= 500 mL at room temperature, the decolorization efficiency of reactive brilliant blue was 91.98% within 60 min. Moreover, the Fe-Mn-sepiolite catalyst had good stability for the degradation of reactive brilliant blue even after six cycles. Leaching of iron ions (<0.4 mg·L−1) was observed. The decoloring process was reactive brilliant blue specific via a redox reaction. The benzene ring and naphthalene ring were first oxidized to open ring; these were then oxidized to the alcohol and carboxylic acid. The reactive brilliant blue was decomposed mainly by the attack of ·OH radicals including surface-bound ·OH radicals generated on the catalyst surface. 相似文献
As bioenergy production expands and new bioenergy-based technologies emerge, there is a growing concern regarding the sustainability of their ecological and socioeconomic impacts. Comprehensive sustainability assessments are needed to address this concern and to assure that the development of the bioenergy industry meets sustainability goals. Most sustainability assessments for bioenergy are expert-driven, broad, and largely motivated by an interest in optimizing international trade in bioenergy. As a result, social and cultural sustainability targets are vague or underdeveloped. In this study, we developed a sustainability assessment for a regional bioenergy production industry in Upper Michigan, using stakeholder participation. Semi-structured interviews and focus group meetings were used to elicit participants’ concerns and opinion. These concerns were translated into sustainability Criteria and Indicators (C&Is), many of which could be supported with available science. Some of the C&Is identified by participants were unique to the region. Sustainability C&Is were broadly categorized into economic (C = 5; I = 22), environmental (C = 6; I = 12), social (C = 3; I = 9), policy and regulations (C = 3; I = 13), and institutional capacity (C = 4; I = 13). While participants could identify indicators for most of the criteria (many of which are also supported by existing literature), further research and validation will be necessary to identify measurable, practical, and bias-free indicators for all criteria. 相似文献
