Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed.This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables.The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills.Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.     

WC/TiO2纳米复合界面光催化剂制备及其光催化降解酚类污染物研究

设计并制备了新型WC/TiO2纳米复合界面光催化剂应用于酚类污染物的光催化降解反应中.采用X射线衍射(XRD)和扫描电子显微镜(SEM)技术分析了WC/TiO2纳米复合界面光催化剂的晶型和表面形貌.结果显示锐钛矿型TiO2纳米颗粒均匀地分散在WC纳米球表面并很好地构筑了WC/TiO2界面.研究了不同WC负载比例的WC/TiO2光催化剂在模拟太阳光照射下降解苯酚的光催化性能.结果表明:WC/TiO2复合界面的形成可以有效地提高TiO2光催化降解性能;其中,3%WC/TiO2(质量分数)光催化降解苯酚的活性最高.利用紫外-可见光谱(UV-Vis)和高效液相色谱-质谱联用技术(HPLC-MS)分析了WC/TiO2纳米复合界面光催化剂降解苯酚的中间产物,提出了苯酚在WC/TiO2界面上可能的降解机理.     

The use of state workers' compensation administrative data to identify injury scenarios and quantify costs of work-related traumatic brain injuries

PROBLEM: Traumatic brain injury (TBI) is a public health problem but little is known about the nature of that problem in the working population. METHOD: The author used a national definition to identify cases in Washington State from workers' compensation (WC) hospital billing data, quantified the cost of WC insurance benefits using actuarial cost estimates, and identified high risk industries using ANSI Z16.2 typology. RESULTS: There were 928 cases of TBI with a lifetime claim cost of $159 million from the Washington State Fund (1994-2001). Sixty percent of injuries resulted in death or disability. The highest risks of TBI are concentrated in 16 industrial insurance risk classes and the highest costs in 19 North American Industry Classification codes. Injury scenarios were identified for nine industrial insurance risk classes. CONCLUSIONS: TBI is a disabling and costly workplace injury in the state of Washington, affecting even teenagers and seniors who are not generally considered to be part of the workforce. Injury typology codes provide useful information for improving workplace safety. IMPACT ON INDUSTRY: This research provides industry with quantitative information regarding the cost of work-related traumatic brain injury and the usefulness of using workers' compensation claims data to reduce the burden of workplace injury.