Forward and inverse transport of particulate matter and gaseous pollutants affecting the region bordering the English Channel

The ATTMA "Aerosol Transport in the Trans-Manche Atmosphere" project investigates the transportation and dispersion of air pollutants across the English Channel, in collaboration with local authorities and other Universities in Southern England and Northern France. The research is concerned with both forward and inverse (receptor based) tracking. Two alternative dispersion simulation methods are used: (a) Lagrangian Particle Dispersion (LPD) models, (b) Eulerian Finite Volume type models. This paper is concerned with part (a), the simulations based on LPD models. Two widely applied LPD models are used and compared. Since in many observed episodes the source of pollution is traced outside the region of interest, long range, trans-continental transport is also investigated.

Modelling high particulate matter and ozone episodes in the Trans-Manche region

There is concern in the Cross-Channel region of Nord-Pas-de-Calais (France) and Kent (Great Britain), regarding the extent of atmospheric pollution detected in the area from emitted gaseous (VOC, NOx, S02)and particulate substances. In particular, the air quality of the Cross-Channel or "Trans-Manche" region is highly affected by the heavily industrial area of Dunkerque, in addition to transportation sources linked to cross-channel traffic in Kent and Calais, posing threats to the environment and human health. In the framework of the cross-border EU Interreg IIIA activity, the joint Anglo-French project, ATTMA, has been commissioned to study Aerosol Transport in the Trans-Manche Atmosphere. Using ground monitoring data from UK and French networks and with the assistance of satellite images the project aims to determine dispersion patterns. and identify sources responsible for the pollutants. The findings of this study will increase awareness and have a bearing on future air quality policy in the region. Public interest is evident by the presence of local authorities on both sides of the English Channel as collaborators. The research is based on pollution transport simulations using (a) Lagrangian Particle Dispersion (LPD) models, (b) an Eulerian Receptor Based model. This paper is concerned with part (a), the LPD Models. Lagrangian Particle Dispersion (LPD) models are often used to numerically simulate the dispersion of a passive tracer in the planetary boundary layer by calculating the Lagrangian trajectories of thousands of notional particles. In this contribution, the project investigated the use of two widely used particle dispersion models: the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the model FLEXPART. In both models forward tracking and inverse (or·. receptor-based) modes are possible. Certain distinct pollution episodes have been selected from the monitor database EXPER/PF and from UK monitoring stations, and their likely trajectory predicted using prevailing weather data. Global meteorological datasets were downloaded from the ECMWF MARS archive. Part of the difficulty in identifying pollution sources arises from the fact that much of the pollution outside the monitoring area. For example heightened particulate concentrations are to originate from sand storms in the Sahara, or volcanic activity in Iceland or the Caribbean work identifies such long range influences. The output of the simulations shows that there are notable differences between the formulations of and Hysplit, although both models used the same meteorological data and source input, suggesting that the identification of the primary emissions during air pollution episodes may be rather uncertain.

Effect of lesion morphology on microwave signature in 2-D ultra-wideband breast imaging

This paper studies the possibility of distinguishing between benign and malignant masses by exploiting the morphology-dependent temporal and spectral characteristics of their microwave backscatter response in ultra-wideband breast cancer detection. The spiculated border profiles of 2-D breast masses are generated by modifying the baseline elliptical rings based upon the irregularity of their peripheries. Furthermore, the single- and multilayer lesion models are used to characterize a distinct mass region followed by a sharp transition to background, and a blurred mass border exhibiting a gradual transition to background, respectively. Subsequently, the complex natural resonances (CNRs) of the backscatter microwave signature can be derived from the late-time target response and reveal diagnostically useful information. The fractional sequence CLEAN algorithm is proposed to estimate the lesions' delay intervals and identify the late-time responses. Finally, it is shown through numerical examples that the locations of dominant CNRs are dependent on the lesion morphologies, where 2-D computational breast phantoms with single and multiple lesions are investigated. The analysis is of potential use for discrimination between benign and malignant lesions, where the former usually possesses a better-defined, more compact shape as opposed to the latter.