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The research in this study was carried out to quantify real-time personal exposure to PM10 in a large number of individuals residing in the Dublin area. This was carried out in order to highlight activities and locations with high personal exposure concentrations of particulate matter. The main subpopulation of individuals chosen for inclusion in the personal exposure study was office workers, with volunteers sampling for 24-hour periods. They used a real-time nephelometer device, which recorded concentrations at two minute intervals, and were required to complete activity diaries and carry a GPS device during sampling.
Personal exposure data was collected for 255 separate 24-hour periods by 59 volunteers. The gathered dataset was comprehensively analysed using various statistical methods. The study population was found to spend over 90% of their time indoors. The mean 24- hour PM10 personal exposure for the study population was found to below the EPA daily limit value. However, a number of indoor microenvironments were highlighted in which high concentrations could be encountered, and these included cafes, public houses, and recreation facilities such as gyms. The lowest personal concentrations were consistently found when the subjects were sleeping.
Research was carried out into the relative exposure of commuters to air pollutants in Dublin between four modes of transport. These differences were determined experimentally by simultaneously sampling the personal exposure of commuters to VOCs and PM2.5 in cars, buses, on bicycles and on foot. Over 400 samples were recorded along two different commuter routes and the resulting dataset revealed statistically significant differences between exposure concentrations in the modes of transport.
The Car commuter was found to have the highest exposure to VOCs followed by the bus, cyclist and pedestrian, while the bus had the highest exposure to PM2.5 followed by the car, cyclist and pedestrian. Using a numerical lung model to predict the internal deposition and absorption of these harmful pollutants revealed that for PM2.5 the cyclists had the highest uptake due to their elevated breathing rates, followed by the bus, pedestrian and car. For VOCs the car was found to have the highest uptake, owing to its high exposure concentration and long duration of exposure, followed by the cyclist, pedestrian and bus. Samples were recorded using mobile sampling equipment and analysed using gas chromatography for VOCs and gravimetric analysis for PM2.5.
The values adopted for the background air pollutant concentrations in local air quality dispersion modelling studies have a significant effect on the accuracy of the overall result. Current practice regarding the data sources and assumptions on background concentrations, and subsequent addition to modelled concentrations, varies widely amongst modellers within Ireland and worldwide.
Generally, the aim of a modelling study is to produce total concentration values, often for EIA, and comparison to EL) limit values. Currently in Ireland nitrogen dioxide (NO2) and particulate matter of less than 10 micro grams diameter (PM10) are the two pollutants at most risk of exceeding these values and they are thus the focus of this thesis.
Previous research has focused on the validation of air quality models to Irish conditions or on the study of air quality in heavily polluted areas such as kerbside or urban centre locations. However, these studies generally neglect the background concentration or conclude that it is indeed an important area and so attribute much of the observed error to this source. No detailed study specific to background concentrations in Ireland has been carried out previously. This research is based on pre-existing data and the collection of additional hourly NO2 and PM10 concentration data at a unique background site.
There are many good reasons to encourage urban cycling such as the reductions in the social costs of air and noise pollution and the promotion of active and healthy lifestyles. However, there are also risks associated with urban cycling such as traffic collisions and increased inhalation of air pollutants. In order to mitigate the risks of cycling, the factors affecting the variability in these risks also need to be understood so that they can be mitigated.
Volunteer cyclists cycled through Dublin while collecting data about the pollution and noise they were exposed to. These data were analysed along with time-resolved information about the cycling facilities they used, the vehicle traffic volumes they interacted with and the weather conditions. The analysis produced new insights such as the observation that while segregated cycle lanes decreased pollution exposures, roadside cycle lanes and bus lanes actually increased exposures.
The final contribution of this thesis was a tool for systematically designing a cycle network in order to optimise the resulting net impacts to the network users and society. This Network Design Problem (NDP) is formulated as an MPEC and a solution approach is presented which is uses a genetic algorithm (GA) to find the optimal solution. The problem formulation and solution algorithm are tested using a numerical example and the GA algorithm was shown to efficiently converge to a near-optimal solution for the cycle network design.
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The United Nations urban environmental unit associates up to 1 million premature deaths annually to urban air pollution (United Nations Environment Programme, 2010). Much research on indoor environments has focused on residential buildings with commercial buildings being often overlooked.
This research project aimed to improve the understanding of the Indoor/Outdoor air quality relationship at urban commercial buildings in Dublin, Ireland. In order to gain the required understanding, data was required for Dublin sites, therefore monitoring was conducted.
Real-time concentrations of PM2.5 and NO2 were recorded at 10 commercial buildings (including shops, offices and gallery spaces) all located in heavily trafficked parts of the city centre.
