Dehalobium species implicated in 2,3,7,8-tetrachloro-p-dioxin dechlorination in the contaminated sediments of Sydney Harbour Estuary

Polychlorinated dibenzo-p-dioxins and furans (PCDD/F) are some of the most environmentally recalcitrant and toxic compounds. They are naturally occurring and by-products of anthropogenic activity. Sydney Harbour Estuary (Sydney, Australia), is heavily contaminated with PCDD/F. Analysis of sediment cores revealed that the contamination source in Homebush Bay continues to have one of the highest levels of PCDD/F contamination in the world (5207 pg WHO-TEQ g-1) with >50% of the toxicity attributed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD) the most toxic and concerning of the PCDD/F congeners. Comparison of congener profiles at the contamination source with surrounding bays and historical data provided evidence for the attenuation of 2,3,7,8-TCDD and other congeners at the source. This finding was supported by the detection of di-, mono- and unchlorinated dibenzo-p-dioxin. Microbial community analysis of sediments by 16S amplicon sequencing revealed an abundance of lineages from the class Dehalococcoidia (up to 15% of the community), including the genus Dehalobium (up to 0.5%). Anaerobic seawater enrichment cultures using perchloroethene as a more amenable growth substrate enriched only the Dehalobium population by more than six-fold. The enrichment culture then proved capable of reductively dechlorinating 2,3,7,8-TCDD to 2,3,7-TCDD and octachlorodibenzo-p-dibenzodioxin to hepta and hexa congeners. This work is the first to show microbial reductive dehalogenation of 2,3,7,8-TCDD with a bacterium from outside the Dehalococcoides genus, and one of only a few that demonstrates PCDD/F degradation in a marine environment.

Does the Zero Carry Essential Information for Artificial Neural Network learning to simulate the contaminant transport in Urban Areas?

The release of hazardous materials in urbanized areas is a considerable threat to human health and the environment. Therefore, it is vital to detect the contamination source quickly to limit the damage. In systems localizing the contamination source based on the measured concentrations, the dispersion models are used to compare the simulated and registered point concentrations. These models are run tens of thousands of times to find their parameters, giving the model output’s best fit to the registration. Artificial Neural Networks (ANN) can replace in localization systems the dispersion models, but first, they need to be trained on a large, diverse set of data. However, providing an ANN with a fully informative training data set leads to some computational challenges. For example, a single simulation of airborne toxin dispersion in an urban area might contain over 90% of zero concentration in the positions of the sensors. This leads to the situation when the ANN target includes a few percent positive values and many zeros. As a result, the neural network focuses on the more significant part of the set - zeros, leading to the non-adaptation of the neural network to the studied problem. Furthermore, considering the zero value of concentration in the training data set, we have to face many questions: how to include zero, scale a given interval to hide the zero in the set, and include zero values at all; or limit their number? This paper will try to answer the above questions and investigate to what extend zero carries essential information for the ANN in the contamination dispersion simulation in urban areas. For this purpose, as a testing domain, the center of London is used as in the DAPPLE experiment. Training data is generated by the Quick Urban & Industrial Complex (QUIC) Dispersion Modeling System.

TEMPORAL VARIATION OF GROUNDWATER QUALITY IN SLEMAN, YOGYAKARTA, INDONESIA

<span id="docs-internal-guid-45763421-7fff-0266-2084-cf670bd943dc"><span>Groundwater quality evaluation is important to gain an insight of contamination source. It can later be utilized to review the implementation of a water resource management policy in a specific region. </span><span>Aim: </span><span>This study evaluate the short-term temporal variation of groundwater quality and its possible contamination source in Sleman, Yogyakarta. </span><span>Methodology and Results: </span><span>the statistical approach was utilized using boxplot, principle component analysis (PCA) and correlation matrices, to the data for 50 sampling sites. The data of groundwater quality are available from the local environmental authority of Environmental Agency Sleman.</span><span>The box plots revealed that groundwater quality might largely influenced by rainfall in the area. The factor loading of PCA presented that the ratio of concentration of both chloride and TDS are the most varied of all samples, and the less variable parameter is fluoride. The pair of groundwater quality parameter which had strong correlation were varied in each year, except for TDS and chloride that showed strong correlation in all three years. Nitrite had strong correlation with iron in 2017 and, nitrite also had strong correlation with both manganese and fluoride in 2019. The existence of fluoride in correlation with other parameter might give an insight of contamination from livestock wastes, where in the study area there are many poultry and cow farms, and small scale chicken slaughter industries. </span><span>Conclusion, significant and impact study: </span><span>This study gives preliminary understanding on temporal variation of groundwater quality, for further research on groundwater quality in Sleman, Yogyakarta.</span></span>