Remote sensing and in-situ characterisation of atmospheric aerosol pollution
MetadataShow full item record
This item's downloads: 227 (view details)
This work focuses on characterising various aspects of clean-background and polluted aerosol, mainly focusing on the North East Atlantic and Europe, using a range of in-situ and remote sensing instrumentation. Prior to achieving the objectives of aerosol characterisation, effort was invested in characterising a new, near-real-time satellite aerosol profiling products. The near-real-time Level 1.5 Cloud-Aerosol Light Detection and Ranging (lidar) with Orthogonal Polarization (CALIOP) products were evaluated against data from ground-based European Aerosol Research Lidar Network (EARLINET). A statistical study was performed on 48 CALIOP overpasses with ground tracks within a 100 km distance from operating stations over three years period. For the whole data set, the correlation coefficient (R) was 0.86. The correlation was reduced somewhat to R=0.6 when the analysis was repeated for the planetary boundary layer (PBL) on its own. Further filtering to remove free troposphere (FT) layers with high attenuated backscatter did not improve the agreement either and suggests that the considerable variability across the data set, leading to the low correlation, is due to spatial inhomogeneities in the PBL. Additional evaluation between CALIOP and ground-based lidar close to Atlanta (United States (US)) also showed poor agreement. This poor agreement led us to consider the whole feasibility of a Calibration & Validation (CALVAL) exercise using a ground-based reference stations for space-borne CALVAL platforms. The required accuracy necessitates uncertainties of the order of 2% to be meaningful. To achieve this level of uncertainty with a polar-orbiting satellite would require averaging the ground-based lidar profiles along the ground track of CALIOP for distances of at least 1,500 km, which, clearly, cannot be achieved with ground-based lidars. Satellite remote sensing is useful for characterising sources and extent of long range aerosol transport and, in particular, quantifying its contribution to transboundary air pollution. Using a threshold Aerosol Optical Depth (AOD) value of 0.5 for extremely polluted conditions, we analysed the frequency of occurrence of such events at the Mace Head Global Atmosphere Watch station and found that over a period of 6 years, a total of 17 extremely-polluted cases were identified. Such events were associated with continental pollution outflow from Europe, Sahara Dust outflow, or a combination of both. In addition, volcanic ash eruptions and forest fires in Canada also contributed. Analysis of a 35 year record of sulphur air pollution revealed a 75% reduction in pollution associated with reductions in emissions following intervention and establishment of the European Monitoring and Evaluation Programme under the United Nations Economic Commission for Europe (UNECE) Convention. Over the last 6 years, the pollution levels over the Mace Head region of the North East Atlantic have levelled off, providing us with the opportunity to conduct a detailed statistical analysis on the cleanliness of North Atlantic air and the frequency of occurrence on clean and polluted air at Mace Head. The unique dataset comprising 6-years online measurements of black carbon, aerosol size distributions and aerosol chemistry, including organic aerosol, revealed that air masses arriving at Mace Head were clean to pristine for 65% of the time, moderately polluted to polluted for 35% of the time, and extremely polluted for 1.5% of the time in a 6-year period. In clean-to-pristine air, sulphate mass dominates over organics, sea-salt and nitrates, contributing 42% to the total, while carbonaceous aerosol (of which more than 90% comprises organics and less than 10% black carbon) mass dominates with a 60% contribution in polluted air, increasing to 90% in the most polluted. Worryingly, organic aerosol mass, which is the single largest contributor to particulate mass pollution, is not measured in regulatory networks.
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. Please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.
The following license files are associated with this item: