[1] Traditional analysis of aquifer tests uses the observed drawdown at one well, induced by pumping at another well, for estimating the transmissivity (T) and storage coefficient (S) of an aquifer. The analysis relies on Theis' solution or Jacob's approximate solution, which assumes aquifer homogeneity. Aquifers are inherently heterogeneous at different scales. If the observation well is screened in a low-permeability zone while the pumping well is located in a high-permeability zone, the resulting situation contradicts the homogeneity assumption in the traditional analysis. As a result, what does the traditional interpretation of the aquifer test tell us? Using numerical experiments and a first-order correlation analysis, we investigate this question. Results of the investigation suggest that the effective T and S for an equivalent homogeneous aquifer of Gaussian random T and S fields vary with time as well as the principal directions of the effective T. The effective T and S converge to the geometric and arithmetic means, respectively, at large times. Analysis of the estimated T and S, using drawdown from a single observation well, shows that at early time both estimates vary with time. The estimated S stabilizes rapidly to the value dominated by the storage coefficient heterogeneity in between the pumping and the observation wells. At late time the estimated T approaches but does not equal the effective T. It represents an average value over the cone of depression but influenced by the location, size, and degree of heterogeneity as the cone of depression evolves.

Although portable x-ray fluorescence (XRF) technology is widely accepted for environmental use in field screening test regarding the analytical approach, it needs to be evaluated with sufficient data and meet its performance characteristics to be employable for decision making purposes. Usually, for an XRF sample, the most interesting query is: How reliable is the XRF technique in detecting different targeted metals in soil? This study presents pairwise comparisons between the XRF and inductively coupled plasma atomic emission spectrometer (ICP-AES) results for individual elements of Ni, Cu, Zn, Pb, Cd, Cr, Hg, and As. The portable XRF analyzer was used to estimate the concentration levels of eight heavy metal elements, and then pairwise comparisons were made between the XRF and ICP-AES results. Results presented in this paper suggest that the use of XRF testing is highly reliable as a screening technique for the first sample group of metal element (Pb, Zn, Ni, and Cu) concentrations well in excess of the pollution threshold limits (PTLs). The order of reliability of the XRF measurements is Pb > Zn > Ni > Cu, and their relative proximity (RP) ranges from 85%-35%. In contrast, the results of another group of metal elements that include Hg, Cd, Cr, and As show poor correlation. Their RP ranges from 25%-2.3%.Keywords: x-ray fluorescence (XRF), heavy metal, inductively coupled plasma atomic emission spectrometer (ICP-AES), soil pollution, relative proximityDuring the year of 1970, industry was booming and great quantities of industrial wastes were dumped along the Erren River in Taiwan. Electronic waste recyclers and metal smelters accounted for approximately 80% of all illegal dumping activity along the Erren River. Since 2001, restoration of the Erren River has been ongoing and the Environmental Protection Agency (EPA) of Taiwan has spent NT$ 50-60 million (Taiwan dollars) to clean up sites along the river even though funding for the clean-up effort has been difficult to secure. In 2007, a huge amount of hazardous contaminants of electronic wastes, which included stripped electronic circuit boards, plastic-coated metals, and unknown composites were found embedded in the subsurface soil on both sides of the riverbanks during a riverbank construction project along a 3-kilometer stretch down-

Abstract:In subsurface porous media, the soil water retention curve (WRC) and unsaturated hydraulic conductivity curve (UHC) are two important soil hydraulic property curves. Spatial heterogeneity is ubiquitous in nature, which may significantly affect soil hydraulic property curves. The main theme of this paper is to investigate how spatial heterogeneities, including their arrangements and amounts in soil flumes, affect soil hydraulic property curves. This paper uses a two-dimensional variably saturated flow and solute transport finite element model to simulate variations of pressure and moisture content in soil flumes under a constant head boundary condition. To investigate the behavior of soil hydraulic property curves owing to variations of heterogeneities and their arrangements as well, cases with different proportions of heterogeneities are carried out. A quantitative evaluation of parameter variations in the van Genuchten model (VG model) resulting from heterogeneity is presented. Results show that the soil hydraulic properties are strongly affected by variations of heterogeneities and their arrangements. If the pressure head remains at a specific value, the soil moisture increases when heterogeneities increase in the soil flumes. On the other hand, the unsaturated hydraulic conductivity decreases when heterogeneities increase in the soil flumes under a constant pressure head. Moreover, results reveal that parameters estimated from both WRC and UHC also are affected by shapes of heterogeneity; this indicates that the parameters obtained from the WRC are not suitable for predicting the UHC of different shapes in heterogeneous media.

Typhoons and storms have often brought heavy rainfalls and induced floods that have frequently caused severe damage and loss of life in Taiwan. Our ability to predict sewer discharge and forecast floods in advance during storm seasons plays an important role in flood warning and flood hazard mitigation. In this paper, we develop an integrated model (TFMBPN) for forecasting sewer discharge that combines two traditional models: a transfer function model and a back propagation neural network. We evaluated the integrated model and the two traditional models by applying them to a sewer system of Taipei metropolis during three past typhoon events (NARI, SINLAKU, and NAKR). The performances of the models were evaluated by using predictions of a total of 6 h of sewer flow stages, and six different evaluation indices of the predictions. Finally, an overall performance index was determined to assess the overall performance of each model. Based on these evaluation indices, our analysis shows that TFMBNP yields accurate results that surpass the two traditional models. Thus, TFMBNP appears to be a promising tool for flood forecasting for the Taipei metropolis sewer system.