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Abstract: High-resolution maps of the mean concentrations of , NO, , , and in the period 2002–2004 were produced using half-hourly data from the local air quality monitoring network in Haifa Bay area, Israel. The network consists of 20 monitoring stations within an area of , which encompasses a major industrial and power generation centre in the midst of a population of about 500000. The pollutants’ spatial features agree well with their known sources and the expected dispersion by the prevailing meteorology. The ranking of their spatial variations agree with published observations on larger spatial scales. The high-resolution maps capture in a small spatial scale the and cycle relationships expected by theory, and previously observed by analyses of monitoring time series. High correlation was found between the spatial patterns of the , and , whereas the correlation between the spatial features of the and is low. This suggests that the traffic, a major source of , rather than industry, the major source of , is the main contributor to the anthropogenic in the study area. This inference is corroborated by the low sulphur to nitrogen ratio throughout the region, which is typical of traffic-dominated pollution. A general conclusion drawn from this study is that high-resolution monitoring and mapping can significantly contribute to air quality management programmes in terms of both pollution abatement and exposure and risk assessment. [Copyright &y& Elsevier]

Abstract: Spatial mapping of variables that vary in space and time is a common procedure in many research fields. Very often it is of interest to map the time-average or time-integration of the variable over the whole period of interest. Normally, such a map is produced by spatially interpolating the whole period averages of the observed data. An alternative option is to first spatially interpolate narrow time slice averages of the variable and then sum the resultant maps. This paper discusses the latter option, and the accuracy of the spatio-temporal variable interpolation as a function of the width of the time-averaging window. Theoretically, using a linear and data-value independent operator to interpolate a complete data set (i.e. without missing data), the accuracy is independent of the width of the time-averaging window. However, using a nonlinear or a data-value dependent interpolation operator, and/or in the presence of missing data, the accuracy of the interpolation can vary with the averaging window width. The concept is demonstrated using a set of half-hourly concentrations measured at 20 monitoring stations in Haifa Bay area, Israel, during the years 1996–2002. Cross-validated interpolation accuracy measures calculated for this data set vary significantly with the time-averaging window width, showing a clear minimum at daily averaging. The results and their general implications for the interpolation of spatio-temporal variables are discussed. [Copyright &y& Elsevier]

Abstract: Spatially detailed estimation of exposure to air pollutants in the urban environment is needed for many air pollution epidemiological studies. To benefit studies of acute effects of air pollution such exposure maps are required at high temporal resolution. This study introduces nonlinear optimisation framework that produces high resolution spatiotemporal exposure maps. An extensive traffic model output, serving as proxy for traffic emissions, is fitted via a nonlinear model embodying basic dispersion properties, to high temporal resolution routine observations of traffic-related air pollutant. An optimisation problem is formulated and solved at each time point to recover the unknown model parameters. These parameters are then used to produce a detailed concentration map of the pollutant for the whole area covered by the traffic model. Repeating the process for multiple time points results in the spatiotemporal concentration field. The exposure at any location and for any span of time can then be computed by temporal integration of the concentration time series at selected receptor locations for the durations of desired periods. The methodology is demonstrated for NO2 exposure using the output of a traffic model for the greater Tel Aviv area, Israel, and the half-hourly monitoring and meteorological data from the local air quality network. A leave-one-out cross-validation resulted in simulated half-hourly concentrations that are almost unbiased compared to the observations, with a mean error (ME) of 5.2 ppb, normalised mean error (NME) of 32%, 78% of the simulated values are within a factor of two (FAC2) of the observations, and the coefficient of determination (R 2) is 0.6. The whole study period integrated exposure estimations are also unbiased compared with their corresponding observations, with ME of 2.5 ppb, NME of 18%, FAC2 of 100% and R 2 that equals 0.62. [Copyright &y& Elsevier]

The Middle East military conflict of summer 2006 resulted in a few weeks in which the city of Haifa, Israel, and its environs experienced very profound variations in the commercial and personal activities. Large industrial plants continued almost normal operations but activities of small scale industry, shopping, and personal commuting were drastically reduced, leading to a dramatic decrease in the commercial and personal traffic volumes. This period of reduced activity serves as a real life experiment for assessment and demonstration of the impact that human activity, and mainly road traffic, may have on the air pollution levels in a bustling middle-sized city. The analysis is made especially sharp and reliable due to the abruptness of the beginning and the end of the reduced activity period, its length, and the stable summer meteorological conditions in the eastern Mediterranean region. The reduced traffic volumes resulted in lowered levels of NO2, hydrocarbons and particulate matter. The decrease in these pollutants’ mean concentration was significantly larger than the reduction in the mean traffic volume. Slightly higher mean O3 concentrations were observed during the reduced traffic period. [Copyright &y& Elsevier]