Release of chlorinated, brominated and mixed halogenated dioxin-related compounds to soils from open burning of e-waste in Agbogbloshie (Accra, Ghana)

Although complex mixtures of dioxin-related compounds (DRCs) can be released from informal e-waste recycling, DRC contamination in African e-waste recycling sites has not been investigated. This study examined the concentrations of DRCs including chlorinated, brominated, mixed halogenated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs, PBDD/Fs, PXDD/Fs) and dioxin-like polychlorinated biphenyls (DL-PCBs) in surface soil samples from the Agbogbloshie e-waste recycling site in Ghana. PCDD/F and PBDD/F concentrations in open burning areas (18-520 and 83-3800 ng/g dry, respectively) were among the highest reported in soils from informal e-waste sites. The concentrations of PCDFs and PBDFs were higher than those of the respective dibenzo-p-dioxins, suggesting combustion and PBDE-containing plastics as principal sources. PXDFs were found as more abundant than PCDFs, and higher brominated analogues occurred at higher concentrations. The median total WHO toxic equivalent (TEQ) concentration in open burning soils was 7 times higher than the U.S. action level (1000 pg/g), with TEQ contributors in the order of PBDFs>>PCDD/Fs>PXDFs. DRC emission to soils over the e-waste site as of 2010 was estimated, from surface soil lightness based on the correlations between concentrations and lightness, at 200mg (95% confidence interval 93-540 mg) WHO-TEQ over three years. People living in Agbogbloshie are potentially exposed to high levels of not only chlorinated but also brominated DRCs, and human health implications need to be assessed in future studies.

Iterative ensemble Kalman filter for atmospheric dispersion in nuclear accidents: An application to Kincaid tracer experiment

Information about atmospheric dispersion of radionuclides is vitally important for planning effective countermeasures during nuclear accidents. Results of dispersion models have high spatial and temporal resolutions, but they are not accurate enough due to the uncertain source term and the errors in meteorological data. Environmental measurements are more reliable, but they are scarce and unable to give forecasts. In this study, our newly proposed iterative ensemble Kalman filter (EnKF) data assimilation scheme is used to combine model results and environmental measurements. The system is thoroughly validated against the observations in the Kincaid tracer experiment. The initial first-guess emissions are assumed to be six magnitudes underestimated. The iterative EnKF system rapidly corrects the errors in the emission rate and wind data, thereby significantly improving the model results (>80% reduction of the normalized mean square error, r=0.71). Sensitivity tests are conducted to investigate the influence of meteorological parameters. The results indicate that the system is sensitive to boundary layer height. When the heights from the numerical weather prediction model are used, only 62.5% of reconstructed emission rates are within a factor two of the actual emissions. This increases to 87.5% when the heights derived from the on-site observations are used.

Ensemble-based simultaneous emission estimates and improved forecast of radioactive pollution from nuclear power plant accidents: application to ETEX tracer experiment

The accidental release of radioactive materials from nuclear power plant leads to radioactive pollution. We apply an augmented ensemble Kalman filter (EnKF) with a chemical transport model to jointly estimate the emissions of Perfluoromethylcyclohexane (PMCH), a tracer substitute for radionuclides, from a point source during the European Tracer Experiment, and to improve the forecast of its dispersion downwind. We perturb wind fields to account for meteorological uncertainties. We expand the state vector of PMCH concentrations through continuously adding an a priori emission rate for each succeeding assimilation cycle. We adopt a time-correlated red noise to simulate the temporal emission fluctuation. The improved EnKF system rapidly updates (and reduces) the excessively large initial first-guess emissions, thereby significantly improves subsequent forecasts (r = 0.83, p < 0.001). It retrieves 94% of the total PMCH released and substantially reduces transport error (>80% average reduction of the normalized mean square error).