5 Forecasting PM10AEAT-3844
An important new task following the introduction of the new air pollution bandings in November 1997 is the forecasting of episodes of elevated concentrations of airborne particles, specifically PM
10. The forecasting of PM10 episodes is a challenge because of the range of sources that contribute to ambient particle concentrations. The relative contributions from these sources must be understood before a reliable forecasting strategy can be implemented. The sources of PM10 in the UK have been reviewed by the Quality of Urban Air Review Group (QUARG, 1996) and source attribution methods involving the combination of data from several DETR monitoring networks has recently been developed at NETCEN (Stedman, 1998). Ambient PM10 can be considered to be made up from three distinct types of particles:

We have used an empirical regression method to disentangle the different origins of measured daily PM
10 particle concentrations. An equation of the following form can be obtained:

[measured PM
10] = A.[measured black smoke] + B.[measured sulphate] + C

with these three terms representing, primary combustion, secondary and coarse particle concentrations, all in
mgm-2. This method was originally applied (Stedman, 1998) on a network mean basis (mean of all sites on a daily basis) and regression coefficients have subsequently been derived for the cities listed in Table 5.1.

Table 5.1. Regression coefficients for PM
10 data
  Smoke coefficient, A SO4 coefficient, B Intercept, C r2
London Bloomsbury 0.64 2.26 10.96 0.78
Birmingham Centre 0.59 2.41 8.30 0.71
Bristol Centre 1.03 2.35 10.83 0.70
Manchester Piccadilly 0.60 2.46 9.77 0.74
Newcastle Centre 0.66 3.13 7.73 0.84
Belfast Centre 0.71 2.30 9.21 0.79
Cardiff Centre 0.86 1.71 13.07 0.73
Leeds Centre 1.00 2.58 4.56 0.84
Network Mean 1.00 3.00 5.00 0.84

The forecasting of episodes of elevated PM
10 concentrations therefore requires the use of appropriate models to forecast these different contributions on a daily basis. The regression method described by Stedman (1998) significantly simplifies the derivation and testing of the required models because the individual models can be compared with measured black smoke and sulphate data. The models that have been developed for forecasting primary combustion and secondary PM10 are discussed below. This is followed by an investigation of the reliability of the combination of these two models during a 10 week period during the winter of 1997/98.

Fixed daily mean concentrations have been considered here in order to simplify the analysis because black smoke and sulphate concentrations have been measured on a daily basis. It is recognised that the daily maximum of running 24-hour averages can be higher than fixed daily means. The methods described here represent the initial work that has been undertaken to provide a method for forecasting PM
10. There is a continuing research programme to refine these techniques.

Primary Combustion PM
Primary combustion derived PM10 originates, in most UK cities, from the same emission sources as oxides of nitrogen (NOx), including vehicle exhausts. High concentrations of both of these species tend to be associated with poor dispersion of low level emissions caused by light winds and low mixing heights. The box model that is used to forecast hourly NOx concentrations can therefore be adapted to provide forecasts of primary combustion PM10. The following multi-stage analysis was carried out using daily monitored and forecast pollutant concentrations for the one year period from April 1995 to March 1996. This period was chosen because it includes the major secondary particle episodes in early 1996 and several primary particle episodes in late 1995.