A comparison of speciated atmospheric mercury at an urban center and an upwind rural location

Gaseous elemental mercury (GEM), particulate mercury (PHg) and reactive gaseous mercury (RGM) were measured every other hour at a rural location in south central Wisconsin (Devil's Lake State Park, WI, USA) between April 2003 and March 2004, and at a predominantly downwind urban site in southeastern Wisconsin (Milwaukee, WI, USA) between June 2004 and May 2005. Annual averages of GEM, PHg, and RGM at the urban site were statistically higher than those measured at the rural site. Pollution roses of GEM and reactive mercury (RM; sum of PHg and RGM) at the rural and urban sites revealed the influences of point source emissions in surrounding counties that were consistent with the US EPA 1999 National Emission Inventory and the 2003-2005 US EPA Toxics Release Inventory. Source-receptor relationships at both sites were studied by quantifying the impacts of point sources on mercury concentrations. Time series of GEM, PHg, and RGM concentrations were sorted into two categories; time periods dominated by impacts from point sources, and time periods dominated by mercury from non-point sources. The analysis revealed average point source contributions to GEM, PHg, and RGM concentration measurements to be significant over the year long studies. At the rural site, contributions to annual average concentrations were: GEM (2%; 0.04 ng m(-3)); and, RM (48%; 5.7 pg m(-3)). At the urban site, contributions to annual average concentrations were: GEM (33%; 0.81 ng m(-3)); and, RM (64%; 13.8 pg m(-3)).     

Machine learning for radioxenon event classification for the Comprehensive Nuclear-Test-Ban Treaty

A method of weapon detection for the Comprehensive nuclear-Test-Ban-Treaty (CTBT) consists of monitoring the amount of radioxenon in the atmosphere by measuring and sampling the activity concentration of ^131mXe, ¹³³Xe, ^133mXe, and ¹³⁵Xe by radionuclide monitoring. Several explosion samples were simulated based on real data since the measured data of this type is quite rare. These data sets consisted of different circumstances of a nuclear explosion, and are used as training data sets to establish an effective classification model employing state-of-the-art technologies in machine learning. A study was conducted involving classic induction algorithms in machine learning including Naïve Bayes, Neural Networks, Decision Trees, k-Nearest Neighbors, and Support Vector Machines, that revealed that they can successfully be used in this practical application. In particular, our studies show that many induction algorithms in machine learning outperform a simple linear discriminator when a signal is found in a high radioxenon background environment.     

Nematode species at risk--a metric to assess pollution in soft sediments of freshwaters

Soft sediments are often highly polluted as many of the toxic chemicals introduced into surface waters bind to settling particles. The resulting accumulation of pollutants in the sediments poses a risk for benthic communities. However, pollution induced changes in benthic communities have been difficult to determine when using macro-invertebrates as bioindicators, as these organisms are often absent in soft sediment. The present study therefore examined the ability of meiofaunal organisms, specifically, nematodes, to assess the ecological status of soft sediments. Over a 9-year period, nematode communities present in sediments collected from large rivers and lake Constance in Germany were studied. These sediments showed a large range of physico-chemical properties and anthropogenic contamination. After the degree of metal and organic contamination was translated into ecotoxicologically more relevant toxic units (TUs), multivariate methods were used to classify nematode taxa in species at risk (NemaSPEAR) or not at risk (NemaSPE(not)AR). This approach clearly distinguished the influence of sediment texture from that of the toxic potential of the samples and thus allowed classification of the nematode species according to their sensitivity to or tolerance of toxic stress. Two indices, expressing the proportion of species at risk within a sample (NemaSPEAR[%](metal), NemaSPEAR[%](organic)), were calculated from independent data sets obtained in field and experimental studies and showed good correlations with the toxic potential (field data) or chemical concentrations (microcosm data). NemaSPEAR[%] indices for metal and organic pollution were therefore judged to be suitable for assessing the impact of chemical contamination of freshwater soft sediments.