The gridded model output (absolute) concentrations can also be used to examine

unmonitored area concentrations. The model provides an hourly concentration for every grid

cell. The concentrations can be analyzed to determine unmonitored areas where the model

predicts high values. But the absolute predictions from the model may not be entirely accurate.

The model output is only as accurate as the emissions and meteorological input. But unlike the

interpolated ambient data, the model output explicitly accounts for emissions, chemistry, and

meteorology over the entire domain.

Both the interpolated ambient data and the model outputs have major weaknesses. But

they also both have strengths. We can take advantage of the strengths of each dataset by

combining the two types of data. The interpolated spatial fields of ambient data provide a strong

basis for estimating accurate pollutant concentrations at monitors and near monitors. Given that

information, the model outputs can be used to adjust the interpolated spatial fields (either up or

down) so that more accurate estimates can be derived in the unmonitored areas21. The best way

to use the model to adjust the spatial fields is to use modeled gradients. It is preferable to

assume that the model is predicting areas of generally high or low ozone or PM, as compared to

assuming that the absolute predictions from the model are correct. For example, in areas where

the model predicts relatively high ozone or PM concentrations, the spatial fields can be adjusted

upward. In areas where the model predicts relatively low ozone or PM concentrations, the

spatial fields can be adjusted downward. In this way, it may be possible to predict unmonitored

areas that may have high concentrations. At the same time, concentrations in rural areas, (which

may be overly influenced by high monitored concentrations near urban areas), may be adjusted

downward. It may also be possible to predict high PM2.5 concentrations in unmonitored areas

which contain large sources of primary PM emissions. The combination of interpolated spatial

fields and modeled output will be referred to as “gradient adjusted spatial fields”

 

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