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

[1]  Richard J C Brown,et al.  Comparison of averaging techniques for the calculation of the 'European average exposure indicator' for particulate matter. , 2012, Journal of environmental monitoring : JEM.

[2]  Rafael Pino-Mejías,et al.  Finite mixture models to characterize and refine air quality monitoring networks. , 2014, The Science of the total environment.

[3]  Richard J.C. Brown,et al.  Data loss from time series of pollutants in ambient air exhibiting seasonality: consequences and strategies for data prediction. , 2013, Environmental science. Processes & impacts.

[4]  M. P. Gómez-Carracedo,et al.  A practical comparison of single and multiple imputation methods to handle complex missing data in air quality datasets , 2014 .

[5]  S S Huang,et al.  Forecasts Using Neural Network versus Box-Jenkins Methodology for Ambient Air Quality Monitoring Data , 2000, Journal of the Air & Waste Management Association.

[6]  Andrew S. Brown,et al.  A temperature-based approach to predicting lost data from highly seasonal pollutant data sets. , 2013, Environmental science. Processes & impacts.

[7]  M. Cox,et al.  Improved strategies for calculating annual averages of ambient air pollutants in cases of incomplete data coverage. , 2013, Environmental science. Processes & impacts.

[8]  P. Ballesta The uncertainty of averaging a time series of measurements and its use in environmental legislation , 2005 .

[9]  Harri Niska,et al.  Methods for imputation of missing values in air quality data sets , 2004 .

[10]  A. Plaia,et al.  Single imputation method of missing values in environmental pollution data sets , 2006 .

[11]  P. Sampson,et al.  Pragmatic Estimation of a Spatio-Temporal Air Quality Model With Irregular Monitoring Data , 2011 .

[12]  S V Krupa,et al.  Application of a stochastic, Weibull probability generator for replacing missing data on ambient concentrations of gaseous pollutants. , 2000, Environmental pollution.

[13]  Maurice G Cox,et al.  Assessing the performance of standard methods to predict the standard uncertainty of air quality data having incomplete time coverage. , 2014, Environmental science. Processes & impacts.