The Performance of Dispersion Modelling for the Prediction of Nitrogen Dioxide in the UK Review and Assessment Review and Assessment Process Dr Michael Bull – Ove Arup and Partners Ltd
UK Review and Assessment Process • In 1997 the Environment Act created a process where local authorities were required to carry out a regular assessment of air quality in their areas, these must be regularly updated • Intended to identify whether “air quality objectives” would be met by their relevant target years • Air quality objectives mirror the EU Limit Values but • Air quality objectives mirror the EU Limit Values but generally their target years are before those of the EU • Overall guidance has been produced by the UK’s National Government although this allows for many different approaches to be used for the assessments
UK Review and Assessment Process • Most assessments are carried out using dispersion modelling • Selection of dispersion models are used • ADMS • Caline • Airviro • Airviro • Some bespoke models • Most of these assessments report the model’s ability to predict nitrogen dioxide concentrations • Provides us with a large database of results that we can use to assess model performance
Performance of Dispersion Models • Collation of the results allows assessment of model performance that can include both user and input data errors • Provides a “Real World” assessment of model performance • Allows assessment of the risk of an exceedance of an air quality standard/limit value • Nearly 60 model validation studies were available containing 623 and 349 validation points for nitrogen dioxide and nitrogen oxides respectively
Use of Models in the UK Model Name Number of Studies AAQUIRE 7 ADMS (version not specified) 2 ADMS -Roads 22 ADMS-Urban ADMS-Urban 12 12 Airviro 3 Caline 6 Kings College ERG Model 3 AEA Model LADS 10
Purpose of the Study • Intended not as an assessment of individual model performance • Intended as an assessment of the overall ability of a community of model users to predict nitrogen dioxide concentrations • Model has concentrated on nitrogen dioxide rather than nitrogen oxides • Where nitrogen oxides have been examined many of the studies have estimated NOx concentrations from NO 2 diffusion tubes measurements • Introduces significant errors
Comparison of Predicted and Measured NOx Concentrations 450 400 350 300 ured NOx µg/m3 250 200 Measur 150 100 50 0 0 50 100 150 200 250 300 Predicted NOx µg/m3
Raw results – nitrogen dioxide 100 90 80 70 centrations µg/m3 60 50 Measured Conce 40 30 20 10 0 0 10 20 30 40 50 60 70 80 90 100 Predicted Concentrations µg/m3
Results – Nitrogen Dioxide • Some evidence of a trend in under-prediction of concentrations • 67% of modelled values lower than measured (limited NOx results suggest similar) • Analysis using Boot software confirms • Analysis using Boot software confirms underprediction Data Mean Standard Bias Corr Fractional Deviation Bias Measured 39.95 12.59 NA NA NA Predicted 35.84 11 4.11 0.688 0.108
Further analysis of NO2 results • Can “bin” data into concentration ranges • Results placed into 5µg/m 3 bins of predicted values • So for example all results where a concentration of between 35-40 µg/m 3 were analysed to examine mean and standard deviation within each bin mean and standard deviation within each bin • Allows an assessment of the spread of results within each predicted range of concentrations
Binned NO2 data 70 60 50 entration µg/m3 40 Measured Concen 30 20 10 0 <20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 Predicted Concentration µg/m3
Results from “Binning” data • Tendency for under-prediction is evident • On average the measured value is 4.5 µg/m 3 higher within each concentration bin • Standard deviation is typically some 25% of the median value median value • Can examine further the spread of results within each concentration bin
Predicted Concentration 35+40.g/m 3 35 30 25 of Observations 20 Observed Number of 15 10 5 0 20 25 30 35 40 45 50 55 60 65 70 75 Measured Concentration
Analysis of data • In practise a narrow 5µg/m 3 range in predicted concentrations is represented by a very wide range of measured concentrations • Possible to use results to assess the probability of an exceedance of an objective/limit value rather than interpreting results as absolute concentrations than interpreting results as absolute concentrations • Can compare results with theoretical distributions derived from mean/standard deviations of observed data • In this case a normal distribution has been used
Compare with normal distribution 35 30 25 of Observations 20 Observed Theoretical Number of 15 10 5 0 20 25 30 35 40 45 50 55 60 65 70 75 Measured Concentration
Predicted concentration 40+45 .g/m 3
Compare with Normal Distribution
Assessing risk of exceedance of limit value • If a normal distribution is assumed then for a predicted concentration, it is possible to calculate the probability that the actual measured concentration will be above a particular value • So – for each predicted 5µg/m 3 range in concentration the probability the limit value of concentration the probability the limit value of 40µg/m 3 will be exceeded can be calculated
Probability of exceedance of 40.g/m 3 120% 100% concentration >40µg/m 3 80% 60% Probability that measured co 40% 20% 0% <20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 75-80 80-85 85-90 Predicted NO2 concentration
Conclusions • The prediction of nitrogen dioxide concentrations is subject to considerable uncertainty although on average, there is reasonable agreement between modelled and measured values although with some evidence of under-prediction • Analysis of the results by “binning” the data into 5µg/m 3 concentration ranges allows for further examination of the data • Analysis demonstrates of model usage by a wide pool of • Analysis demonstrates of model usage by a wide pool of model users suggests a considerable range in model performance • This range can be taken into account using a risk based approach for interpreting the results • Approach can be used by regulators to consider the uncertainties in the results of dispersion modelling
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