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Proposals for new data quality objectives to underpin ambient air quality monitoring networks

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Abstract

The data completeness requirements for the production of defensible annual averages of air quality parameters are discussed. Data quality objectives concerning time coverage and data capture specified by current legislation are critically examined, and the improved, combined metric of data coverage is proposed. Best practice proposals to ensure the representativeness of air quality data across the calendar year are presented. These are based on minimising any bias between the annual average calculated using the data acquired and the annual average that would have been calculated if data coverage had been 100 %. In all cases, the optimum solution is for air quality network operators to aim for 100 % data coverage.

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References

  1. Air Quality—Existing Legislation: http://ec.europa.eu/environment/air/quality/legislation/existing_leg.htm. Accessed July 2014

  2. European Commission (2005) Directive 2004/107/EC of the European Parliament and of the Council of 15 December 2004 relating to arsenic, cadmium, mercury, nickel and polycyclic aromatic hydrocarbons in ambient air. Off J Eur Union L Legis 23:3–16

    Google Scholar 

  3. European Commission (2008) Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe. Off J Eur Union L Legis 152:1–44

    Google Scholar 

  4. AQUILA Homepage: http://ies.jrc.ec.europa.eu/aquila-homepage.html. Accessed July 2014

  5. European Commission (2013) Guidance on the Commission Implementing Decision laying down rules for Directives 2004/107/EC and 2008/50/EC of the European Parliament and of the Council as regards the reciprocal exchange of information and reporting on ambient air (Decision 2011/850/EU): http://ec.europa.eu/environment/air/quality/legislation/pdf/IPR_guidance1.pdf. Accessed July 2014

  6. European Commission (2011) Commission Implementing Decision of 12 December 2011 laying down rules for Directives 2004/107/EC and 2008/50/EC of the European Parliament and of the Council as regards the reciprocal exchange of information and reporting on ambient air quality. Off J Eur Union L Legis 335:86–105

    Google Scholar 

  7. Personal communication (2014) Matthew Ross-Jones, Swedish Environment Protection Agency, Stockholm

  8. Mol M, van Hooydonk P (2013) ETC/ACM Technical Paper 2013/1: the European exchange of information in 2012. European Topic Centre on Air Pollution and Climate Change Mitigation, Bilthoven

    Google Scholar 

  9. European Environment Agency (2013) EEA Report No 9/2013: Air quality in Europe. European Environment Agency, Copenhagen

    Google Scholar 

  10. International Standards Organisation (2002) ISO 11222:2002 air quality—determination of the uncertainty of the time average of air quality measurements. ISO, Geneva

    Google Scholar 

  11. Brown RJC, Brown AS, Kim K-H (2013) A temperature-based approach to predicting lost data from highly seasonal pollutant data sets. Environ Sci Process Impacts 15:1256–1263

    Article  CAS  Google Scholar 

  12. Brown RJC (2013) Data loss from time series of pollutants in ambient air exhibiting seasonality: consequences and strategies for data prediction. Environ Sci Process Impacts 15:545–553

    Article  CAS  Google Scholar 

  13. Brown RJC, Harris PM, Cox MG (2014) Assessing the performance of standard methods to predict the standard uncertainty of air quality data having incomplete time coverage. Environ Sci Process Impacts 16:1700–1704

    Article  CAS  Google Scholar 

  14. Brown RJC, Woods PT (2012) Comparison of averaging techniques for the calculation of the ‘European Average Exposure Indicator’ for particulate matter. J Environ Monit 14:165–171

    Article  CAS  Google Scholar 

  15. Ballesta PP (2005) The uncertainty of averaging a time series of measurements and its use in environmental legislation. Atmos Environ 39:2003–2009

    Article  Google Scholar 

  16. Gómez-Carracedo MP, Andrade JM, López-Mahía P, Muniategui S, Prada D (2014) A practical comparison of single and multiple imputation methods to handle complex missing data in air quality datasets. Chemom Intell Lab Syst 134:23–33

    Article  Google Scholar 

  17. Gómez-Losada Á, Lozano-García A, Pino-Mejías R, Contreras-González J (2014) Finite mixture models to characterize and refine air quality monitoring networks. Sci Tot Environ 485:292–299

    Article  Google Scholar 

  18. Plaia A, Bondi AL (2006) Single imputation method of missing values in environmental pollution datasets. Atmos Environ 40:7316–7330

    Article  CAS  Google Scholar 

  19. Kao JJ, Huang SS (2000) Forecasts using neural network versus Box-Jenkins methodology for ambient air quality monitoring data. J Air Waste Manag Assoc 50:219–226

    Article  CAS  Google Scholar 

  20. Nosal M, Legge AH, Krupa SV (2000) Application of a stochastic, Weibull probability generator for replacing missing data on ambient concentrations of gaseous pollutants. Environ Pollut 108:439–446

    Article  CAS  Google Scholar 

  21. Sampson PD, Szpiro AA, Sheppard L, Lindstrom J, Kaufman JD (2011) Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data. Atmos Environ 45:6593–6606

    Article  CAS  Google Scholar 

  22. Junninen H, Niska H, Tuppurainen H, Ruuskanen J, Kolehmainen M (2004) Methods for imputation of missing values in air quality data sets. Atmos Environ 38:2895–2907

    Article  CAS  Google Scholar 

  23. Brown RJC, Harris PM, Cox MG (2013) Improved strategies for calculating annual averages of ambient air pollutants in cases of incomplete data coverage. Environ Sci Process Impacts 15:904–911

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors are grateful for very useful discussions with colleagues at AQUILA, particularly Matthew Ross-Jones (Swedish Environmental Protection Agency) and Frank de Leeuw (The National Institute for Public Health and the Environment, The Netherlands), and for funding from the UK National Measurement System’s Chemical and Biological Metrology and Innovation Research and Development Programmes by the UK Department for Business Innovation and Skills.

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Correspondence to Richard J. C. Brown.

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Brown, R.J.C., Woods, P.T. Proposals for new data quality objectives to underpin ambient air quality monitoring networks. Accred Qual Assur 19, 465–471 (2014). https://doi.org/10.1007/s00769-014-1085-0

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