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Errors arise when using conventional radar reflectivity, Z, to estimate rainfall rate, R, and these can be particularly severe during severe convective storms; the very events when accurate estimates are needed so that action can be taken to mitigate the effects of flooding. Concentration is on three problems associated with heavy rainfall: hail, attenuation and absolute calibration of the radar, and consider how polarisation radar parameters, differential reflectivity, ZDR, and specific differential phase shift KDP, might lead to their alleviation. It is essential to consider the fundamental limits to the accuracy with which these parameters can be estimated. If ZDR can be measured to an accuracy of 0.2 dB, then it provides a measure of mean raindrop shape which is sufficiently precise to improve rainrate estimates. This can be achieved at S-band (10 cm), but seems very difficult for operational C-band (5 cm) radars; differential attenuation by the heavy rain introduces a negative bias into ZDR which increases with range. However, the magnitude of this bias at C-band can then be used to correct for the total attenuation of Z. Differential phase, KDP has the advantage that it is a phase measurement and so is unaffected by attenuation. It only responds to the rainfall and is unaffected by the hail, but KDP is a noisy parameter and is only useful for heavy rainfall above 30 – 60 mm hr-1. Fortuitously, KDP and ZDR are not independent and one use of KDP and ZDR may well be to exploit this redundancy to identify rain areas as opposed to hail, and in rainfall to use the redundancy to provide an automatic calibration of the absolute reflectivity, Z, to 0.5 dB (12%). Finally, the noisy character of both ZDR and KDP together with the low level of the co-polar correlation coefficient provide the first reliable means of detecting and removing anomalous propagation which is a major operational problem for all weather radars. Keywords: polarisation radar, rainfall calibration, attenuation, hail, anomalous propagation

We address the problem of accurately locating buried utility segments by fusing data from multiple sensors using a novel Marching-Cross-Section (MCS) algorithm. Five types of sensors are used in this work: Ground Penetrating Radar (GPR), Passive Magnetic Fields (PMF), Magnetic Gradiometer (MG), Low Frequency Electromagnetic Fields (LFEM) and Vibro-Acoustics (VA). As part of the MCS algorithm, a novel formulation of the extended Kalman Filter (EKF) is proposed for marching existing utility tracks from a scan cross-section (scs) to the next one; novel rules for initializing utilities based on hypothesized detections on the first scs and for associating predicted utility tracks with hypothesized detections in the following scss are introduced. Algorithms are proposed for generating virtual scan lines based on given hypothesized detections when different sensors do not share common scan lines, or when only the coordinates of the hypothesized detections are provided without any information of the actual survey scan lines. The performance of the proposed system is evaluated with both synthetic data and real data. The experimental results in this work demonstrate that the proposed MCS algorithm can locate multiple buried utility segments simultaneously, including both straight and curved utilities, and can separate intersecting segments. By using the probabilities of a hypothesized detection being a pipe or a cable together with its 3D coordinates, the MCS algorithm is able to discriminate a pipe and a cable close to each other. The MCS algorithm can be used for both post- and on-site processing. When it is used on site, the detected tracks on the current scs can help to determine the location and direction of the next scan line. The proposed “multi-utility multi-sensor” system has no limit to the number of buried utilities or the number of sensors, and the more sensor data used, the more buried utility segments can be detected with more accurate location and orientation.

The successful operation of buried infrastructure within urban environments is fundamental to the conservation of modern living standards. Open-cut methods are predominantly used, in preference to trenchless technology, to effect a repair, replace or install a new section of the network. This is, in part, due to the inability to determine the position of all utilities below the carriageway, making open-cut methods desirable in terms of dealing with uncertainty since the buried infrastructure is progressively exposed during excavation. However, open-cut methods damage the carriageway and disrupt society's functions. This paper describes the progress of a research project that aims to develop a multi-sensor geophysical platform that can improve the probability of complete detection of the infrastructure buried beneath the carriageway. The multi-sensor platform is being developed in conjunction with a knowledge-based system that aims to provide information on how the properties of the ground might affect the sensing technologies being deployed. The fusion of data sources (sensor data and utilities record data) is also being researched to maximize the probability of location. This paper describes the outcome of the initial phase of testing along with the development of the knowledge-based system and the fusing of data to produce utility maps.

The Oxford Transport Strategy (OTS) was a major traffic intervention implemented in June 1999. It involved a wide range of permanent changes focused primarily on the city centre of Oxford (England). We aimed to assess the impact of OTS on peak expiratory flow (PEF) and respiratory symptoms among city schoolchildren. Between 1998 and 2000, 1389 children aged 6–10 years were visited 2–3 times a year for 5-day periods. On each day of each visit, we measured their PEF and enquired about respiratory symptoms including wheeze (n = 19260 child-days of data). Exposure to traffic pre and post-OTS was estimated by the change in modelled traffic on the street nearest the home. Pre-OTS traffic levels were relatively low compared to other major cities, but post-OTS changes in children's exposure varied considerably; from a drop of 11184 to an increase of 9240 vehicles/24hrs. Adjusted regression analyses showed a statistically significant improvement in PEF (beta = 5.71 L·min–1, 95% CI (3.28, 8.18)) and wheeze (OR = 0.80, 95% CI (0.69, 0.92)) post-OTS. The impact of OTS on PEF was greater among children living near roads where traffic decreased compared to those living where there had been an increase. This association was only found among children currently receiving treatment for asthma and those in socio-economic classes III–V. These results suggest that even in cities with relatively low traffic levels interventions can result in improvements in the respiratory health of children. These benefits may be especially pertinent to children with pre-existing respiratory problems and those from less affluent socio-economic backgrounds.