An investigation of the influence of daily activities & location on personal exposure to air pollution in Dublin: measurement, analysis, modelling & application
Over the last few decades the body of evidence for the serious health consequences from exposure to air pollution has been growing. As a result of this the need for better air quality has been recognised and efforts have been made internationally in establishing minimum quality standards for the ambient air. However, these minimum air quality standards are based on background or regional ambient air quality monitoring. Owing to the exposures encountered due to certain activities and microenvironments, background concentration may in many instances be an unsatisfactory basis on which to solely investigate the impacts of air pollution on human populations. A more relevant measure for individual subjects of an air pollutant health impact assessment would be personal exposure. This can be quantified by recording personal exposure data over 24-hour periods on a real-time basis incorporating different activities (e.g. commuting, shopping, working, sleeping, etc.) and different locations.
The current research study aims to gather sufficient data of this nature to be used in the development of predictive modelling tools. Data has been collected for one such pollutant, particulate matter (PM), with the use of a real-time Aerocet-531 nephelometer monitor. Different statistical methods will be applied in order to investigate a number of novel approaches to the prediction of 24 hour personal exposure concentrations for PM10, as well as dose. The successful implementation of this approach will lead to a personal exposure model on a macroscopic scale which could be used to investigate the health impacts of personal exposure to air pollution in a more extensive and comprehensive manner than using background concentrations. In addition, as it is impractical to monitor personal exposure on a long term basis and due to the fact the monitoring of background data is a legal requirement in many countries, it is also the aim of this research project to develop methods of predicting personal exposure from background monitoring data. This will be achieved through the investigation of background monitoring data located across the Dublin region in conjunction with GPS data on locations of subjects. ("A model for evaluating and disseminating personal exposure to air pollution”, Sponsored by the Irish Environmental Protection Agency under the STRIVE Programme 2007-2013).
Personal exposure of commuters to air pollution in the urban environment
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 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 dataset comprised the exposure concentration of 5 VOCs and PM2.5 as well as number of meteorological and traffic variables. The dataset was comprehensively analysed using statistical methods. Trends were revealed such as increases in concentrations during evening rush hour compared to morning rush hour; higher VOC emissions with cold temperatures compared to warm or mild conditions; the degree of difference between the modes of transport was generally greater on Route 2 than on Route 1; evidence of non-transport sources contributing to the PM2.5 concentration etc.
Investigations of intra-mode variability were also carried out whereby the importance of roadside positioning was found for the pedestrian, with lower exposures found for increased distances from the traffic. Reduced exposure for car commuters who maintain a larger inter-vehicle spacing of 2m compared to 1m in idling traffic was found. Lower exposure for car commuters and cyclist in suburban areas were found compared to inner city commuting.
Numerical analysis of the data using computational fluid dynamics (CFD) was successfully carried out using the commercial CFD software Fluent 6.2. 3D models of Route 1 were constructed which allows the accurate prediction of the personal exposure of commuters exposure to air pollutants based on the meteorological conditions of wind speed, wind direction and temperature. Models of intra-mode variability for the pedestrian and car commuter were also constructed in 3D which provided insightful graphical findings on the dispersion of air pollutants in each of these microenvironments.
Quantifying air pollution exposure and relative cardiac health effects in commuters
This project evaluates the temporal relationship between air pollution exposure and altered cardiac function, as measured by heart rate variability. Commuters comprising cyclists, pedestrians, bus users, train users and private car users have differing physiological states and are compared in terms of exposure, dose using a numerical model of the respiratory tract, fitness and cardiovascular health. This research examines commuting from an environmental health perspective and may have implications for commuter modal choice and policy.
Background concentrations monitoring for monitoring air pollution
Global environmental monitoring and health impacts
Project coordinator: Prof. Brian Broderick