Found 9889 publications. Showing page 179 of 396:
This report presents an evaluation of the ambient air pollution concentrations in Norwegian cities and agglomerations in the last 5 years up to 2014. It includes an evaluation criteria to support a possible revision of air pollution zones and presents a detailed evaluation of the existing monitoring network for air quality with respect to different pollution zone distributions. Recommendations for updates in the existing monitoring network to comply with current air quality legislation are provided in the report.
2014
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.
2014
Emissions inventory for Dhaka and Chittagong of pollutants PM10, PM2,5, NOx, SOx, and CO. NILU OR
The Bangladesh Air Pollution Studies (BAPS) project is being prepared for the Clean Air and Sustainability project at the Bangladesh Department of Environment (CASE/DoE). This report summarizes the results from Task 1: Emission inventories. A combined methodology of using bottom-up and top-down scaled input data has been used to populate the emission inventory for Dhaka and Chittagong. Sources of emissions of PM10, PM2.5, NOX, SOX, and CO have been investigated for the sectors Industry (including brick kilns separately), Road Traffic, non-road Traffic, Agriculture, Urban, and Fossil Fuel (energy and gas processing). The emissions inventory has been compiled in the NILU model AirQUIS, which can then process the dispersion modelling of the emissions as needed for Task 2 of this project.
2014
Area emissions for Oslo. NILU OR
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.
2014
2014
Dispersion calculations of NO2 emissions from a heating plant at Ranheim. NILU OR
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.
2014
2014
Bridge to Copernicus. Final project report. NILU OR
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.
2014
Background concentrations in Norway: Towards automated annual updates. NILU OR
A semi-automated technique was developed for performing annual updates of the dataset on background concentrations in Norway which was produced in previous years. The code is written in the Matlab programming language and large parts of the code base are included in the Appendix of this report.
The spatial component of the system was updated to include data from 2009 through 2011. Acquiring and preparing the input data for the spatial component still requires a relatively small amount of manual effort, however the majority of the remaining process has been automated to the largest extent possible, such that only the derivation of the emivariograms for the residual kriging step requires very brief interaction by an expert user.
The temporal component has been updated to version 8 of the European air quality database (AirBase), now including several additional years up to and including 2013. Entirely new anomaly matrices have been calculated from the updated data for all background stations in Norway.
Assuming that the availability and the format of the required input data remains unchanged, future annual updates of the system can be carried out within a very short time frame on the order of around 1-2 days.
2014
2014
2014
2014
2014
2014
2014
2014
2014
2014
2014
2014