Ho-Wen Chen

Modeling the dioxin emission of a municipal solid waste incinerator using neural networks

Incineration is considered as an efficient approach in dealing with the increasing demand for municipal and industrial solid waste treatment, especially in areas without sufficient land resources. Facing the concern of health risk, the toxic pollutants emitted from incinerators have attracted much attention from environmentalists, even though this technology is capable of reducing solid waste volume and demand for landfill areas, together with plenty of energy generation. To reduce the negative impacts of toxic chemicals emitted from incinerators, various monitoring and control plans are made not only for use in facilities performance evaluation but also better control of operation for stable effluent quality. How to screen out the key variables from massive observed and control variables for modeling the dioxin emission has become an important issue in incinerator operation and pollution prevention. For these reasons, this study used 4-year monitoring data of an incinerator in Taiwan as a case study, and developed a prediction model based on an artificial neural network (ANN) to forecast the dioxin emission. By doing this, a simplified monitoring strategy for incinerators with regarding to dioxin emission control can be achieved. The result indicated that the prediction model based on a back-propagation neural network is a promising method to deal with complex and non-linear data with the help of statistics in screening out the useful variables for modeling. The suitable architecture of an ANN for using in the dioxin prediction consists of 5 input factors, 3 basic layers with 8 hidden nodes. The R(2) was found to equal 0.99 in both the training and testing steps. In addition, sensitivity analysis can identify the most significant variables for the dioxin emission. From the obtained results, the frequency of activated carbon injection showed as the factor of highest relative importance for the dioxin emission.

Accumulation of heavy metals and trace elements in fluvial sediments received effluents from traditional and semiconductor industries

Metal accumulation in sediments threatens adjacent ecosystems due to the potential of metal mobilization and the subsequent uptake into food webs. Here, contents of heavy metals (Cd, Cr, Cu, Ni, Pb, and Zn) and trace elements (Ga, In, Mo, and Se) were determined for river waters and bed sediments that received sewage discharged from traditional and semiconductor industries. We used principal component analysis (PCA) to determine the metal distribution in relation to environmental factors such as pH, EC, and organic matter (OM) contents in the river basin. While water PCA categorized discharged metals into three groups that implied potential origins of contamination, sediment PCA only indicated a correlation between metal accumulation and OM contents. Such discrepancy in metal distribution between river water and bed sediment highlighted the significance of physical-chemical properties of sediment, especially OM, in metal retention. Moreover, we used Se XANES as an example to test the species transformation during metal transportation from effluent outlets to bed sediments and found a portion of Se inventory shifted from less soluble elemental Se to the high soluble and toxic selenite and selenate. The consideration of environmental factors is required to develop pollution managements and assess environmental risks for bed sediments.

Biological toxicity of groundwater in a seashore area: Causal analysis and its spatial pollutant pattern

To ensure the safety of groundwater usage in a seashore area where seawater incursion and unexpected leakage are taking place, this paper utilizes the Microtox test to quantify the biological toxicity of groundwater and proposes an integrated data analysis procedure based on hierarchical cluster analysis (HCA) and principal component analysis (PCA) for determining the key environmental factors that may result in the biological toxicity, together with the spatial risk pattern associated with groundwater usage. For these reasons, this study selects the coastal area of Taichung city in Central Taiwan as an example and implements a monitoring program with 40 samples. The results indicate that the concentration of total arsenic in the coastal areas is about 0.23-270.4 μg L(-1), which is obviously higher than the interior of Taichung city. Moreover, the seawater incursion and organic pollution in the study area may be the key factors resulting in the incubation of toxic substances. The results also indicate that As(3+) is the main contributor to biological toxicity compared to other disinfection by-products. With the help of the visualized spatial pollutants pattern of groundwater, an advanced water quality control plan can be made.

Causal relationships among biological toxicity, geochemical conditions and derived DBPs in groundwater

Groundwater is indispensable water resource in coastal areas of Taiwan and is typically used following simple disinfection. Disinfection by-products (DBP), which are hazardous materials that are biologically toxic, are commonly produced. To elucidate the effect of environmental factors on the formulation of DBPs and arsenic species, and the effect of these factors on the bio-toxicity, data from a one-year monitoring program that was performed in a coastal area of central Taiwan were analyzed using the multivariate statistical method of redundancy analysis (RDA). The results reveal that the dominant DBP for trihalomethanes (THMs) was CHCl3 and for haloacetic acids (HAAs) was CHClBr2COOH (BDCAA). The formation of these compounds was most affected by the concentrations of humic substances and Br(-). As(5+) ions are abundant in the area close to the seashore and are the main source of biological toxicity. Cl(-), Br(-) and As(5+) concentrations were strongly correlated with biological toxicity as they promoted the formation of DBP. A geographic information system (GIS) and the above results revealed that the area near the seashore is rich in Br(-) wherever high As(5+) concentration and bio-toxicity are detected.

Exploring the background features of acidic and basic air pollutants around an industrial complex using data mining approach

Air pollution data around a monitored site are normally difficult to analyze due to highly inter-related meteorological and topographical factors on top of many complicated atmospheric chemical interactions occurred in local and regional wind fields. The challenge prompts this study to develop a comprehensive data-mining algorithm of cluster analysis followed by meteorological and interspecies correlations to mitigate the inherent data complexity and dissimilarity. This study investigated the background features of acidic and basic air pollutants around a high-tech industrial park in Taiwan. Monthly samplings were taken at 10 sites around the park in a year. The temporal distribution plots show a baseline with two characteristic groups of high and low peaks. Hierarchical cluster analysis confirms that high peaks were primarily associated with low speed south wind in summer for all the chemical species, except for F(-), Cl(-), NH(3) and HF. Crosschecking with the topographical map identifies several major external sources in south and southwest. Further meteorological correlation suggests that HCl is highly positively associated with humidity, while Cl(-) is highly negatively associated with temperature, both for most stations. Interestingly, HNO(3) is highly negatively associated with wind speed for most stations and the hotspot was found in summer and around the foothill of Da-Tu Mountain in the northwest, a stagnant pocket on the study site. However, F(-) is highly positively associated with wind speed at downwind stations to the prevailing north wind in winter, indicating an internal source from the north. The presence of NH(4)(+) stimulates the formation of NO(3)(-), SO(4)(-2) (R=0.7), and HNO(3), H(2)SO(4), NH(3) (R=0.3-0.4). As H(2)SO(4) could be elevated to a level as high as 40% of the regulated standard, species interactions may be a dominate mechanism responsible for the substantial increase in summer from external sources.

Effects of extracellular polymeric substances on the bioaccumulation of mercury and its toxicity toward the cyanobacterium Microcystis aeruginosa

This investigation examines how extracellular polymeric substances (EPSs) and environmental factors affect the bioaccumulation and toxicity of inorganic mercury (+2 oxidation state, Hg(II)) using a culture of Microcystis aeruginosa, which dominates eutrophic reservoir populations. The identified EPSs were classified as carbohydrates and proteins. Evaluation of the bioaccumulation of Hg(II) in cells by multiple regression analysis reveals that the concentration of EPSs in filtrate, the initial concentration of Hg(II) in medium, and the culture age significantly affected the amount of Hg(II) accumulated. Composition profiles revealed that the concentrations of soluble carbohydrates were significantly higher in Hg(II)-accumulated cells than in the control ones. Preliminary results based on scanning electron microscopic (SEM) map investigations suggest that most of the Hg(II) was accumulated in the cytoplasm (intracellular). Additionally, the effective concentrations (EC50) of Hg(II) that inhibit the growth of M. aeruginosa were 38.6 μg L−1 in the logarithmic phase and 17.5 μg L−1 in the stationary phase. As expected, the production of more EPSs in the logarithmic phase typically implies higher EC50 values because EPSs may be regarded as a protective barrier of cells against an external Hg(II) load, enabling them to be less influenced by Hg(II).

Adsorption characteristics of trace levels of bromate in drinking water by modified bamboo-based activated carbons

ABSTRACT This study was undertaken to investigate the adsorption kinetics and isotherms of bromate (BrO3−) on bamboo charcoals that are activated with nitrogen and water vapor. Bamboo-based activated carbon (AC) was dipped in acid and oxidized in a mixture of potassium permanganate and sulfuric acid. Oxidation treatment considerably improved the physicochemical properties of AC, including purity, pore structure and surface nature, significantly enhancing BrO3− adsorption capacity. AC with many oxygenated groups and a high mesopore volume exhibited a particularly favorable tendency for BrO3− adsorption. Its adsorption of BrO3− is best fitted using Langmuir isotherm, and forms a monolayer. A kinetic investigation revealed that the adsorption of BrO3− by the ACs involved chemical sorption and was controlled by intra-particle diffusion. The competitive effects of natural organic matter (NOM) on AC were evaluated, and found to reduce the capacity of carbon to adsorb BrO3−. Residual dissolved ozone reacted with AC, reducing its capacity to absorb BrO3−. Proper dosing and staging of the ozonation processes can balance the ozone treatment efficiency, BrO3− formation, and the subsequent removal of BrO3−.