OPEN ACCESS
The accuracy of air quality modelling studies is signifi cantly infl uenced by the values adopted for background concentrations. In the absence of a reliable method of combining modelled and background concentrations, it has been common practice to sum the percentiles or annual means of each contribution to obtain a value for comparison with short-term limit values. This is often not appropriate as in many cases the meteorological conditions producing high concentrations from the source do not correspond to those resulting in high background concentrations. A novel method for predicting variable hourly background NO2 and PM10 concentrations based on diurnal and seasonal variations and variation with wind speed and direction has been developed and compared to a baseline method. The variable method has been compared to commonly applied methods such as the annual mean or percentile method. Furthermore, the validity of a number of equations derived in the UK to add background concentrations to modelled stack contributions has been examined for Irish conditions. The equations allow a total percentile concentration to be predicted at a given receptor based on an annual mean background concentration and hourly modelled concentrations. A theoretical line source was modelled using Caline4 and corresponding meteorological data, and the addition equations applied using monitored background NO2 and PM10 data. The methods were also tested for a point source, modelled using the Point source Gaussian plume equation. Baseline values were calculated by addition of the relevant hourly or daily background concentration to the modelled concentrations to produce a full year of total hourly or daily concentrations. Percentiles and annual mean values, and corresponding 95% confi dence limits were calculated directly from this data set and concentrations predicted by each method assessed for agreement. The variable method was found to produce the best results for both NO2 and PM10 when modelling a point and line source. Of the technical addition equations, the sum of squares method performed best for PM10 and NO2. The annual mean and the percentile methods performed poorly in all instances producing very large under- and overestimations highlighting the importance of this research. It is anticipated that this novel method will produce signifi cant improvements in the overall accuracy of local air quality modelling studies.
background concentrations, dispersion modelling, limit values, modelled concentrations, NO2, NOx, percentiles, PM10.
[1] Broderick, B., Budd, U., Misstear, B., Jennings, S. & Ceburnis, D., Validation of Air Pollution Dispersion Modelling for the Road Transport Sector under Irish Conditions, Project 2000-LS-6.3-M1, Environmental Protection Agency: Johnstown Castle, Co. Wexford, 2004.
[2] Vardoulakis, S., Valiantis, M., Milner, J. & ApSimon, H., Operational air pollution model-ling in the UK—Street canyon applications and challenges. Atmospheric Environment, 41(22), pp. 4622–4637, 2007. doi: http://dx.doi.org/10.1016/j.atmosenv.2007.03.039
[3] Stedman, J., Abbott, J., Willis, P. & Bower, J., Review of Background Air Quality Data and Methods to Combine these with Process Contributions: Technical Modelling Aspects, Environment Agency for England and Wales: Bristol, 2006.
[4] Stedman, J., Abbott, J., Willis, P. & Bower, J., Review of Background Air Quality Data and Methods to Combine these with Process Contributions, Environment Agency for England and Wales: Bristol, 2006.
[5] Abbott, J. & Downing, C., The Addition of Background Concentrations to Modelled Contributions from Discharge Stacks, Environment Agency for England and Wales: Bristol, 2000.
[6] Donnelly, A., Broderick, B. & Misstear, B., Assessing background air pollutant concentrations for modelling studies, evaluation of addition equations under Irish conditions. Air Pollution XVIII, p. 25, 2010.
[7] O’ Leary, B., Air Quality in Ireland 2007, Key indicators of Ambient Air Quality, Environmental Protection Agency, Johnstown Castle, Co. Wexford, 2008.
[8] Zannetti, P., Air Pollution Modeling: Theories, Computational Methods, and Available Software, Van Nostrand Reinhold, New York, 1990.
[9] Masters, G., Introduction to environmental engineering and science, Prentice Hall Englewood Cliffs: NJ, 1991.
[10] Ganguly, R. & Broderick, B., Development of models for predicting roadside nitrogen dioxide concentrations and their evaluation against datasets measured in Dublin, Ireland. Transportation Research Part D, 14(4). pp. 231–239, 2009. doi: http://dx.doi.org/10.1016/j.trd.2008.12.005 [11] Gilbert, R., Statistical Methods for Environmental Pollution Monitoring, Wiley, New York 1987.
[12] Donnelly, A., Misstear, B. & Broderick, B., Application of nonparametric regression methods to study the relationship between NO2 concentrations and local wind direction and speed at background sites, Science of the Total Environment, 409(6), pp. 1134–44, 2011. doi: http:// dx.doi.org/10.1016/j.scitotenv.2010.12.001