Report

Emissions, dispersion and deposition of SO2 from Nikel and Zapolyarny. A WRD-Chem model study. NILU OR

Engdahl, B.J.; Velken, A.V.S.; Berglen, T.F.; Hodnebrog, Ø.; Stordal, F.

The WRF-Chem model has been applied to study two specific episodes of pollution from the Nikel and Zapolyarny facilities, the summer episode 2007 and winter 2010/11. Meteorological input data from WRF compare well with data from ECMWF. The model underestimates concentrations of SO2 in specific episodes, a possible explanation for this is that the flue gas plume is levelled out in the model, in addition little is known about the emissions. A budget for the different processes show that chemical loss and wet deposition are most important in summer time, while wet and dry deposition are most important in winter time. WRF-Chem is computational demanding and most suited to study processes and specific episodes.

Modelled concentrations of criteria air pollutants in Dhaka and Chittagong, PM10, PM2,5, NOx, and SOx for 2013 and 2020. NILU OR

Randall, S.; Sivertsen, B.; Ahammad, Sk. S.; Cruz, N.D.

Air pollution concentrations were estimated the dispersion models as well as the emissions inventories compiled in AirQUIS for Dhaka and Chittagong. Meteorological data were generated from TAPM. Concentration plots for PM10, PM2.5 and SO2 and NO2 were presented for both cities. A scenario for 2020 was developed based on a combination of projected mitigation measures and sector growth based on GDP and population growth rates. In addition, health impacts were assessed based on methodologies from previous studies performed in Asia.
Results show that in general the PM2.5 and PM10 concentration distributions are closely linked to the emissions from brick kilns in the Dhaka area, while in Chittagong the impacts are more spread between the urban sources, brick, and industry and traffic sources. Results also show that PM10 and PM2.5 concentrations exceeds annual limit values, and that the dry season is most critical when it comes to high concentrations of PM10 and PM2.5.

Area emissions for Oslo. NILU OR

Sundvor, I.

Area source emissions used in dispersion calculations for Oslo have been changed based on available information and trends. As a results the emission data contain more source categories and the total emissions have increased.

Dispersion calculations of NO2 emissions from a heating plant at Ranheim. NILU OR

Haugsbakk, I.

Dispersion calculations have been carried out for emissions from a heating plant at Ranheim. Contribution to NO2- and SO2-concentrations from the facility will be acceptable with recommended stack dimensions.

Bridge to Copernicus. Final project report. NILU OR

Stebel, K.; Fjæraa, A.M.; Schneider, P.; Svendby, T.

NILU has a mandate to monitor air quality and particularly its changes over time, both nationally through Miljødirektoratet (MD) and internationally through the European Monitoring and Evaluation Programme (EMEP). Satellite data related to atmospheric composition are increasingly used for monitoring as they provide long time series of spatially continuous observations. It is therefore essential for NILU to begin preparing for the upcoming Copernicus missions. Here, we evaluate methane products from AIRS, TES, TANSO-FTS and SCIAMACHY as added value for GHG monitoring in Norway and Svalbard. As expected, due to the low sensitivity of the sensors to ground-level Artic large deviations are seen when comparing to in situ data from Birkenes and Ny-Ålesund. Higher level products (L4), combining satellite and ground-based information, seem more appropriate for future reporting purposes. Further, we investigated the usability of the current set of long-term operational ground-based MAX-DOAS stations worldwide for inter-comparing their NO2 observations to those of satellite-based instruments, in particular OMI and GOME-2A. The two data sources agree very well for sites located in rural, non-polluted regions. For sites located in polluted areas we found strong systematic biases, large random errors, or slightly shifting systematic biases. The systematic biases can be explained primarily by the strong spatial gradients in NO2 levels in urban areas in conjunction with the large differences in the spatial representativity of the measurements. We evaluated the possibility to use the now relatively long time series of MAX-DOAS observations to fit a statistical trend model and to directly compare the resulting trends to those obtained for the satellite-based time series for the same area and time period. It was found that the sites with approximately 50 months of valid data for both data sources showed quite similar long-term trends and that sites with fewer than 30 months of valid data exhibited significant discrepancies in the resulting trends.