Integration of air quality modelling and monitoring methods: review and applications Science Report – SC060037/SR1 Science Report - Integration of air quality modelling and monitoring methods: review and applications i SCHO0308BOAE-E-P The Environment Agency is the leading public body protecting and improving the environment in England and Wales. It’s our job to make sure that air, land and water are looked after by everyone in today’s society, so that tomorrow’s generations inherit a cleaner, healthier world. Our work includes tackling flooding and pollution incidents, reducing industry’s impacts on the environment, cleaning up rivers, coastal waters and contaminated land, and improving wildlife habitats. This report is the result of research commissioned and funded by the Environment Agency’s Science Programme. Published by: Author(s): Environment Agency, Rio House, Waterside Drive, Ball, Angela, Hill, Richard & Jenkinson, Peter Aztec West, Almondsbury, Bristol, BS32 4UD Tel: 01454 624400 Fax: 01454 624409 Dissemination Status: www.environment-agency.gov.uk Released to all regions Publicly available ISBN: 978-1-84432-896-3 Keywords: © Environment Agency – May 2008 Integrating, modelling, monitoring, network, design, air quality, assessment, data assimilation All rights reserved. This document may be reproduced with prior permission of the Environment Agency. Research Contractor: Westlakes Scientific Consulting Ltd The views and statements expressed in this report are Westlakes Science & Technology Park those of the author alone. The views or statements Moor Row expressed in this publication do not necessarily Cumbria represent the views of the Environment Agency and the CA24 3LN Environment Agency cannot accept any responsibility for such views or statements. Environment Agency’s Project Manager: Dr. Hope Brett, This report is printed on Cyclus Print, a 100% recycled Environment Agency, stock, which is 100% post consumer waste and is totally Lower Bristol Road, chlorine free. Water used is treated and in most cases Bath returned to source in better condition than removed. BA2 9ES Further copies of this report are available from: Collaborator(s): None The Environment Agency’s National Customer Contact Centre by emailing: Science Project Number: [email protected] SC060037 or by telephoning 08708 506506. Product Code: SCHO0308BOAE-E-P Science Report - Integration of air quality modelling and monitoring methods: review and applications ii Science at the Environment Agency Science underpins the work of the Environment Agency. It provides an up-to-date understanding of the world about us and helps us to develop monitoring tools and techniques to manage our environment as efficiently and effectively as possible. The work of the Environment Agency’s Science Department is a key ingredient in the partnership between research, policy and operations that enables the Environment Agency to protect and restore our environment. The science programme focuses on five main areas of activity: • Setting the agenda, by identifying where strategic science can inform our evidence-based policies, advisory and regulatory roles; • Funding science, by supporting programmes, projects and people in response to long-term strategic needs, medium-term policy priorities and shorter-term operational requirements; • Managing science, by ensuring that our programmes and projects are fit for purpose and executed according to international scientific standards; • Carrying out science, by undertaking research – either by contracting it out to research organisations and consultancies or by doing it ourselves; • Delivering information, advice, tools and techniques, by making appropriate products available to our policy and operations staff. Steve Killeen Head of Science Science Report - Integration of air quality modelling and monitoring methods: review and applications iii Executive summary The Environment Agency is responsible for the regulation of Part A(1) industrial activities in England and Wales. Ambient monitoring and air quality modelling are the tools most frequently used to assess the impact of industrial emissions to air and ultimately provide evidence upon which regulatory decisions are based. However, the use of either modelling or monitoring alone may introduce a high degree of uncertainty into air quality impact assessment. In reality, monitoring and modelling are often used independently of one another. However, it is anticipated that by applying monitoring and modelling integration techniques, the strengths of both technologies can be complemented and the uncertainties associated with each technique reduced. The purpose of this report is to: i. Bring the Environment Agency up-to-date with recent developments in the field of monitoring and modelling integration methods; ii. Inform the Environment Agency of methods that could be used to resolve challenges to regulatory decisions, through the application of monitoring and modelling integration techniques; iii. Review the possibility of economic savings through more efficient use of the existing monitoring resources in England and Wales. To achieve effective integration, it may be necessary to increase investment in monitoring resources and ensure the optimal placement of monitoring sites. Existing monitoring networks such as the AURN are of limited use as they are often located far from industrial sources and the potential to attribute pollutant concentrations to such sources may be difficult due to the interference of pollution from roads and urban areas. It is essential that any monitoring resource must be optimised in terms of the number and placement of monitors, both to ensure cost effectiveness and to optimise the potential for effective integration of monitoring and modelling data. This can be achieved with the adoption of decision-support tools, which are able to prioritise multiple objectives such as population protection, ecosystem protection or targeting specific air quality metrics and are invaluable in designing an effective monitoring network. Improving the accuracy of modelled data is also crucial to the effective integration of modelling and monitoring data. This may be achieved through the use of more representative meteorological data and the application of more appropriate background correction factors. Data assimilation (model calibration) methods applied to the Kincaid SO2 validation data set indicated that the optimum number of monitoring sites necessary to maximise the accuracy of data assimilation methods is between 10 and 15. However, the success of the more complex integration techniques, e.g. kriging, is limited. Of the simple techniques, the simple ratio method provided the best calibration using both the Kincaid data set and the Aire Valley data set. The linear regression method performed on a similar level to the simple ratio method when using a higher number of data points, but the performance of this method decreased dramatically when less than three monitoring points were used to calibrate the modelled data. It should be noted that calibration is designed to reduce uncertainty in model outputs caused by systematic errors and is of limited use when uncertainty is due to random errors. The application of modelling and monitoring integration methods to improve the extrapolation of short-term monitoring campaigns, currently calculated using the Environment Agency pro-rata extrapolation method, proved of limited value. The Science Report - Integration of air quality modelling and monitoring methods: review and applications iv Integration Scaling Method showed an improvement in the consistency of measured and predicted exceedences compared to the pro-rata method, although overall uncertainty was still high. A full set of recommendations for the Environment Agency stemming from this review is listed in Section 8 of this report. Science Report - Integration of air quality modelling and monitoring methods: review and applications v Contents 1 Introduction 1 2 Monitoring ambient air quality 2 2.1 The history of air quality monitoring in the UK 2 2.2 Legislation and air quality monitoring 6 2.3 Industrial processes and air quality monitoring 10 2.4 Uncertainties in air quality monitoring 18 3 Modelling ambient air quality 21 3.1 Modelling ambient air quality in the UK 21 3.2 Uncertainties in air quality modelling 21 4 Summary: uncertainty in monitoring and modelling 32 5 Integration of modelling and monitoring 33 5.1 Model validation 33 5.2 Data assimilation 38 5.3 Inverse modelling 54 5.4 Optimisation of monitoring network design 54 5.5 Emerging technologies 61 6 Application of integration techniques: The extrapolation of short-term monitoring data 62 6.1 Testing the Environment Agency pro-rata extrapolation method 62 6.2 Integration Scaling Method (ISM) 65 6.3 Meteorology-based extrapolation method 71 7 Application of integration techniques: Exploring integration methods for model calibration 74 7.1 Methodology 75 7.2 Results 79 7.3 Discussion 85 8 Recommendations 87 8.1 Towards more effective integration of monitoring and modelling data 87 8.2 Expansion of the monitoring infrastructure 87 8.3 Optimisation of the monitoring infrastructure 87 8.4 Determination of effective calibration methods 88 8.5 Application of suitable background correction factors 89 8.6 Extrapolating from short-term monitoring campaigns 89 References 91 Science Report - Integration of air quality modelling and monitoring methods: review and applications vi Appendices 97 Appendix