AIRMoN Technical Support Document Comments

            Annapolis, MD, 24-27 October 1994

                    Roland R. Draxler




To confirm as quickly as possible that a reduction in sulfur dioxide emissions results in an improvement of environmental quality as evidenced by a measured reduction of sulfur deposition.





The daily, seasonal, and even annual variations (give some numbers and references) in measurements of air quality and deposition are so large that the small emissions reductions (10%) mandated by the Clean Air Act may be obscured by the natural variability.


The primary component of the variability in the measurements is due to temporal variations in meteorological conditions (daily and seasonal cycles) as well as the more complicated meteorological variations that are introduced as a pollutant is transported from its source to the measurement location.





Although the chemical processes may be complex, meteorological variability can be modelled with sufficient accuracy to be able to use a model prediction to sort the measurement data into smaller data groups that have less variability within each group.  Differences in measurement means between groups can the be explained with greater precision.  For example measurements may indicate that over a 5 year period there was a 20% reduction in measured pollutant levels.  However if the measurement data are segregated by upwind transport direction, one of the most simple model predictors, we may find that due to climatic reasons there was an slight increase over the 5 year period in the number of days that the airflow was from cleaner source regions. When pollutant measurements are compared for only those days when the flow was from polluted regions, we may find that there was no decrease overall pollutant levels.  Essentially this is the methodology we propose to determine if Clean Air Act reductions are improving the environmental quality and why it is insufficient to rely only on measurement data to make this determination.





The example given above was only a very simple approach, in fact we plan to test several methods at reducing the variance in grouped measurement data.  These will be called Sectorial Sampling, Prediction Differencing, and Source Fingerprinting.  Each method differs in complexity and approach.  At this stage we do not know which is best.  Regardless of which method is selected, all approaches require the best and most detailed meteorological data base from which these calculations can be performed.  In conjunction with NMC, we plan to archive the analysis, forecast, and assimilation data fields from the most advanced primitive equation models that are operationally available, with a resolution of at least every 2h and 40km.  Additional meteorological measurements taken at the monitioring sites will also be incorporated.  Local meteorological measurements may explain some of the variance introduced into the measurements not evident from regional meteorological data. 


Sectorial Sampling -  This is the approach as noted in the example where it is presumed that local concentration measurements will be higher when the airflow is from pollutant source regions.  Although these methods have been used in many studies (Ref .... ) and have worked reasonably well, it may not be sufficient to reduce the group measurement variance.  More objective methods, such as cluster analysis (Ref  ), can reduce the positional variance of the upwind source region, over more simpler sectorial approaches, however there may still be a significant contribution to the variance in the measurements due to differences in meteorogical processes, although the flow is still within the same sector. (Are there any references or numbers to indicate intra-sector variability?)  This problem can be reduced to a large extent by using a source-receptor matrix approach, in which weighting factors (similar to a normalized concentration) are computed for all the source-receptor combinations.  These weights are then used to group the measurement data.  For example comparing two different measurements, one high and one low, although the model indicates are from the same source region, one may have experienced significant precipitation prior to arrival at the measurement site, and hence has a lower weight.


Prediction Differencing -  In this method we use a meteorological model, which includes a chemical model component, to actually compute the air concentrations and deposition corresponding to each measurement.  The model uses the best known emission inventory in its calculation.  The model emissions remain constant, while the real world is subjected to the reduction.  Will the differences between the model prediction and measurements reflect the emission reduction by showing a larger difference?  The time series of prediction-measured differences in theory will have much of the meteorological variability removed.  Therefore it is only necessary to show that the mean of the differences prior to reduction is significantly different than the mean of the differences after the emission reduction.  The following data sample can be used to show that we need at least 225 independent measurement points to reduce the standard error of the sample mean to less than 0.05.  However this is assuming that the standard deviation will remain at 0.60 to show a pH reduction of 0.15.  As we accumulate more data points the sample deviation will go up.  This approach is simple in concept, however it needs further investigation.



Source Fingerprinting - The above approaches relied upon meteorological modeling techniques to group measurement data.  However there are other approaches that can be used to aggregate time series and spatial observations.  The purpose of the groping remains the same, to reduce variance within the group.  One approach is the use Principal Components to identify spatial and temporal coherence in the measurements.  The principal component approach can be used to show the spatial elements that have the greatest contribution to the measured variance.  The pollutant source regions should show a reduction to the variance contribution with time.  However it is not clear how many sampling stations are required for this approach to prove satisfactory.  A variation of this option is develop time series analytical techniques at individual stations (such as wavelet transforms) in conjunction with standard meteorological measurements to deduce which meteorlogical variances are contributing to the variance in the measurements.  Another independent method would be identify source regions through an additonal conservative pollutant species, that is not subjected to a reduction.  Ratios of reduction/non-reduction pollutants in conjunction with multiple regression methods can then be used to provide a source region weighting factor to the measurements for grouping.



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