Polychlorinated dibenzo-p-dioxin (PCDD) and dibenzofuran (PCDF) isomer patterns from municipal waste combustion: formation mechanism fingerprints

Polychlorinated dibenzo-p-dioxin (PCDD) and dibenzofuran (PCDF) byproducts can be formed in combustion systems by a variety of mechanisms. While total PCDD/F emissions and, to a lesser extent, homologue distributions from incinerators have been found to vary widely depending on combustion conditions, PCDD/F isomer distributions do not. Formation mechanisms can be grouped into two general categories: condensation of precursors, such as chlorinated phenols, and formation from particulate carbon, termed de novo synthesis. In addition to these mechanisms, chlorination and dechlorination reactions may affect isomer patterns. In this work, isomer patterns from field and laboratory municipal waste combustion samples are compared with computed thermodynamic distributions and those from the following experimental investigations: both gas-phase and metal-catalyzed condensation of chlorinated phenols, chlorination of dibenzo-p-dioxin and dibenzofuran, and dechlorination of octachlorodibenzo-p-dioxin and octachlorodibenzofuran. PCDD/F isomer patterns produced by different formation mechanisms in controlled experiments are distinct and robust, largely unaffected by combustion conditions. PCDD isomer patterns from municipal waste combustion are most similar to those produced by CuCl(2)-catalyzed phenol condensation from 10 chlorinated phenols. PCDF isomer patterns are most similar to those produced by chlorination and dechlorination.     

Mid‐Range Streamflow Forecasts Based on Climate Modeling – Statistical Correction and Evaluation1

Abstract: Mid‐range streamflow predictions are extremely important for managing water resources. The ability to provide mid‐range (three to six months) streamflow forecasts enables considerable improvements in water resources system operations. The skill and economic value of such forecasts are of great interest. In this research, output from a general circulation model (GCM) is used to generate hydrologic input for mid‐range streamflow forecasts. Statistical procedures including: (1) transformation, (2) correction, (3) observation of ensemble average, (4) improvement of forecast, and (5) forecast skill test are conducted to minimize the error associated with different spatial resolution between the large‐scale GCM and the finer‐scale hydrologic model and to improve forecast skills. The accuracy of a streamflow forecast generated using a hydrologic model forced with GCM output for the basin was evaluated by forecast skill scores associated with the set of streamflow forecast values in a categorical forecast. Despite the generally low forecast skill score exhibited by the climate forecasting approach, precipitation forecast skill clearly improves when a conditional forecast is performed during the East Asia summer monsoon, June through August.     

Crustacean habitat potential mapping in a tidal flat using remote sensing and GIS

To make a macrofaunal (crustacean) habitat potential map, the spatial distribution of ecological variables in the Hwangdo tidal flat, Korea, was explored. Spatial variables were mapped using remote sensing and a geographic information system (GIS) combined with field observations. A frequency ratio (FR) and logistic regression (LR) model were employed to map the macrofauna potential area for the Ilyoplax dentimerosa, a crustacean species. Spatial variables affecting the tidal macrofauna distribution were selected based on abundance and biomass and used within a spatial database derived from remotely sensed data of various types of sensors. The spatial variables included the intertidal digital elevation model (DEM), slope, distance from a tidal channel, tidal channel density, surface sediment facies, spectral reflectance of the near infrared (NIR) bands and the tidal exposure duration. The relation between the I. dentimerosa and each spatial variable was calculated using the FR and LR. The species was randomly divided into a training set (70%) to analyse habitat potential using FR and LR and a test set (30%) to validate the predicted habitat potential map. The relations were overlaid to produce a habitat potential map with the species potential index (SPI) value for each pixel. The potential habitat maps were compared with the surveyed habitat locations such as validation data set. The comparison results showed that the LR model (accuracy is 85.28%) is better in prediction than the FR (accuracy is 78.96%) model. The performance of models gave satisfactory accuracies. The LR provides the quantitative influence of variables on a potential habitat of species; otherwise, the FR shows the quantitative influence of a class in each variable. The combination of a GIS-based frequency ratio and logistic regression models and remote sensing with field observations is an effective method to determine locations favorable for macrofaunal species occurrences in a tidal flat.     

Assessments of natural and anthropogenic influences on water chemistry in the upper Nakdong River,South Korea

Abstract

The purpose of this study is to evaluate what sources principally affect the chemical compositions in the catchment of the upper Nakdong River, South Korea where bedrock mostly consists of silicates as a natural factor. From October 7^th to 9^th, 2015, thirty-three water samples were collected, including samples from tributaries which run alongside mines. We analyzed the major anions, cations, trace elements and water isotopes of the samples and the analyzed data are divided into four groups by principle component analysis (PCA). We determined that most of the water samples are influenced by water-rock interactions when we consider the PCA results and the chemical weathering equation line. However, six samples have excess concentrations of sulfate and plot below the equation line. These samples were mostly from streams beside a zinc-cadmium smelter, which was also consistent with the PCA results. Samples near populated area could also be separated into a group via domestic pollution. Based on the PCA and chemical results, the chemical compositions of the river can be affected by two anthropogenic inputs: mine wastes and domestic contaminants.