Mercury in the Barents region – River fluxes, sources, and environmental concentrations

Arctic rivers are receiving increased attention for their contributing of mercury (Hg) to the Arctic Ocean. Despite this, the knowledge on both the terrestrial release sources and the levels of Hg in the rivers are limited. Within the Arctic, the Barents region has a high industrial development, including multiple potential Hg release sources. This study presents the first overview of potential Hg release sources on Norwegian and Russian mainland draining to the Barents Sea. Source categories cover mining and metallurgy industry; historical pulp and paper production; municipal and industrial solid waste handling; fossil fuel combustion; and past military activities. Available data on Hg in freshwater bodies near the identified potential release sources are reviewed. Levels of Hg were occasionally exceeding the national pollution control limits, thereby posing concern to the local human population and wildlife. However, the studies were sparse and often unsystematic. Finally, we present new data of Hg measured in five Barents rivers. These data reveal strong seasonality in the Hg levels, with a total annual flux constituting 2% of the panarctic total. With this new insight we aspire to contribute to the international efforts of reducing Hg pollution, such as through the effective implementation of the Minamata Convention. Future studies documenting Hg in exposed Barents freshwater bodies are warranted.publishedVersio

Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth

Aerosol optical depth (AOD) is one of the basic characteristics of atmospheric aerosol. A global ground-based network of sun and sky photometers, the Aerosol Robotic Network (AERONET) provides AOD data with low uncertainty. However, AERONET observations are sparse in space and time. To improve data density, we merged AERONET observations with a GEOS-Chem chemical transport model prediction using an optimal interpolation (OI) method. According to OI, we estimated AOD as a linear combination of observational data and a model forecast, with weighting coefficients chosen to minimize a mean-square error in the calculation, assuming a negligible error of AERONET AOD observations. To obtain weight coefficients, we used correlations between model errors in different grid points. In contrast with classical OI, where only spatial correlations are considered, we developed the spatial-temporal optimal interpolation (STOI) technique for atmospheric applications with the use of spatial and temporal correlation functions. Using STOI, we obtained estimates of the daily mean AOD distribution over Europe. To validate the results, we compared daily mean AOD estimated by STOI with independent AERONET observations for two months and three sites. Compared with the GEOS-Chem model results, the averaged reduction of the root-mean-square error of the AOD estimate based on the STOI method is about 25%. The study shows that STOI provides a significant improvement in AOD estimates

Spatio-Temporal Optimal Interpolation of Aerosol Optical Depth Observations Using a Chemical Transport Model

To estimate the spatial and temporal distribution of aerosol optical depth (AOD), we used the optimal interpolation (OI). In OI, observational data and a model forecast are linearly combined according to their relative accuracies. Weight coefficients are chosen to minimize the mean-square error in the estimate. To obtain weight coefficients, correlations between model errors in the different grid points are used. In classical OI, only spatial correlations are considered. We used spatial and temporal correlation functions. To obtain error statistics, we used observations from European stations of ground-based sun photometers, the Aerosol Robotic Network (AERONET), and simulations by a chemical transport model GEOS-Chem, assuming a negligible error of AERONET AOD observations. The estimates of the daily mean AOD distribution over Europe are obtained. The reduction of the root-mean-square error of the AOD estimate based on the OI method in comparison with the GEOS-Chem model results is discussed