Edward Ming-Yang Wu

Size distribution and dry deposition of road dust PAHs

Abstract The size distribution of polycyclic aromatic hydrocarbons (PAHs) for road dust and for the engine exhaust of both gasoline-powered cars and motorcycles was investigated. In addition, by using the measured size distribution data, monitoring and modeling the PAH dry deposition, the contribution fraction of road dust on the dry deposition materials was also studied. Twenty-one PAHs were analyzed primarily by using a gas chromatograph/mass spectrometer (GC/MS). The mass median diameters (MMDs) of 21 individual PAHs for resuspendable road dust (cut size

Influence of memory effect caused by aged bag filters on the stack PCDD/F emissions

To our best knowledge, this study is the first research which investigates whether the PCDD/F memory effect could also be caused by bag filters. In this study, the PCDD/F characteristics of the flue gases in front of and behind the bag filters of one municipal solid waste incinerator (MSWI) and two electric arc furnaces (EAFs) were investigated to clarify the memory effect of PCDD/Fs and their influence on emissions. After the bag filters were operated for over four years, the PCDD/F concentrations in the stack flue gases were all higher than those prior to the aged bag filter, rendering a negative PCDD/F removal. This memory effect is because gaseous phase PCDD/Fs are released from the contaminated filters of the aged bag filters. The release of 2,3,7,8-TeCDF, 1,2,3,7,8-PeCDF and 2,3,4,7,8-PeCDF from the contaminated filters of the EAFs increased the PCDD/F concentration and their fractions in the stack flue gases. In contrast, the MSWI exhibited increasing fractions of 1,2,3,4,6,7,8-HpCDD, OCDD after flue gases passed through its aged bag filter. The activated carbon of the MSWI adsorbed more lower chlorinated-substituted PCDD/Fs in the raw flue gases, and the remaining higher chlorinated congeners gradually accumulated on the aged filters, and were released in lower PCDD/F concentration conditions.

The Application of Water Quality Monitoring Data in a Reservoir Watershed Using AMOS Confirmatory Factor Analyses

This study investigates six water quality monitoring stations in the watershed of the Feitsui Reservoir. It uses nine parameters of water quality collected in an interval of two and half years for factor analyses, which was first conducted to determine four types of factors, respectively, those for organic pollution, eutrophication, seasonal influence, and sediment pollution. The analysis results effectively help to determine water quality in the watershed of the reservoir. The authors reutilize analysis of moment structures (AMOS) to acquire further results in order to confirm the goodness of fit of the previous factor analysis model. During the confirmation, we examine the hypothesized orthogonal results as well as utilize oblique rotation to explore the goodness of fit of the reflective indicators of the orthogonal rotation. As shown in the algorithm results, as long as the covariance curve is included in the four factors, no related issues are detected in the goodness of fit of reflective indicators and interior and external quality is reported with excellence. The orthogonal model, thus, stands. Additionally, when the analysis of structural equation modeling (SEM) is conducted, sample data mismatches the hypotheses of multivariate normality. Therefore, this study adopts the generalized least square (GLS) for an algorithm. Research results of this study have been submitted to the reservoir management authorities in Taiwan for the improvement of statistical application and strategic evaluation of water quality monitoring data in order to strengthen the managerial effectiveness of water quality in watersheds.

Stochastic Integer Programming Analysis For Wastewater Treatment Plant Design

It is the purpose of this paper to present a methodology of analysis which identifies optimum types and combination of unit treatment processes from the range of available alternatives. The objective is to identify the optimum combination and efficiencies of various unit processes in multistage plant; then meet design criteria. Optimality, as used herein, is defined as meeting a given treatment requirement (treatment efficiency, allowable stream loading, etc) at minimum total annual cost. Stochastic Integer Programming is the main principle used to develop the optimal model for the liquid waste treatment plant process design.