Integrated cost and environmental impact assessment of management options for dredged sediment.

Large quantities of sediment must be dredged regularly to enable marine transport and trade. The sediments are often polluted, with e.g. metals, which limits the management options. The aim of this study has been to assess costs and environmental impacts (impact on climate, marine organisms, etc.) of different management options for polluted dredged sediment, by combining life-cycle assessment (LCA) of the climate impact, scoring of other environmental aspects and a cost evaluation. This approach has been used to study both traditional and new management alternatives for a real port case. The studied options include landfilling, deep-sea disposal, construction of a port area using a stabilization and solidification (S/S) method, and a combination of the aforementioned methods with the innovative option of metal recovery through sediment electrolysis. The LCA showed that deep-sea disposal had the lowest climate impact. The assessment of the other environmental impacts showed that the result varied depending on the pollution level and the time perspective used (short or long-term). Using sediment for construction had the highest climate impact, although other environmental impacts were comparably low. Electrolysis was found to be suitable for highly polluted sediments, as it left the sediment cleaner and enabled recovery of precious metals, however the costs were high. The results highlight the complexity of comparing different environmental impacts and the benefits of using integrated assessments to provide clarity, and to evaluate both the synergetic and counteracting effects associated with the investigated scenarios and may aid early-stage decision making.

Can dispersion modeling of air pollution be improved by land-use regression? An example from Stockholm, Sweden.

Both dispersion modeling (DM) and land-use regression modeling (LUR) are often used for assessment of long-term air pollution exposure in epidemiological studies, but seldom in combination. We developed a hybrid DM-LUR model using 93 biweekly observations of NOx at 31 sites in greater Stockholm (Sweden). The DM was based on spatially resolved topographic, physiographic and emission data, and hourly meteorological data from a diagnostic wind model. Other data were from land use, meteorology and routine monitoring of NOx. We built a linear regression model for NOx, using a stepwise forward selection of covariates. The resulting model predicted observed NOx (R2=0.89) better than the DM without covariates (R2=0.68, P-interaction <0.001) and with minimal apparent bias. The model included (in descending order of importance) DM, traffic intensity on the nearest street, population (number of inhabitants) within 100 m radius, global radiation (direct sunlight plus diffuse or scattered light) and urban contribution to NOx levels (routine urban NOx, less routine rural NOx). Our results indicate that there is a potential for improving estimates of air pollutant concentrations based on DM, by incorporating further spatial characteristics of the immediate surroundings, possibly accounting for imperfections in the emission data.