Impact assessment of river dust on regional air quality through integrated remote sensing and air quality modeling.

Sand and dust storms in arid and semiarid regions deteriorate regional air quality and threaten public health security. To quantify the negative effects of river dust on regional air quality, this study selected the estuary areas located in central Taiwan as a case study and proposed an integrated framework to measure the fugitive emission of dust from riverbeds with the aid of satellite remote sensing and wind tunnel test, together with the concentrations of particulate matter with a diameter of <10 µm (PM10) around the river system by using The Air Pollution Model. Additionally, the effects of 25 types of meteorological conditions on the health risk due to exposure to dust were evaluated near the estuary areas. The results reveal landscape changes in the downstream areas of Da'an and Dajia rivers, with an increase of 370,820 m2 and 1,554,850 m2 of bare land areas in the dry season compared with the wet season in Da'an and Dajia rivers, respectively. On the basis of the maximum emission of river dust, PM10 concentration increases considerably during both wet and dry seasons near the two rivers. Among 25 different types of weather conditions, frontal surface transit, outer-region circulation from tropical depression system, weak northeast monsoons, and anticyclonic outflow have considerable influence on PM10 diffusion. In particular, weak northeast monsoons cause the highest health risk in the areas between Da'an and Dajia rivers, which is the densely populated Taichung City. Future studies should attempt to elucidate the environmental impact of dust in different weather conditions and understand the spatial risks to human health due to PM10 concentration. Facing the increasing threat of climate and landscape changes, governments are strongly encouraged to begin multimedia assessments in environmental management and propose a long-term and systematic framework in resources planning.

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

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-.

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.

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.