While these metrics can be used to estimate the magnitude, frequency, and relative

amount of ozone or PM2.5 reductions from any given future emissions scenario, there are no

threshold quantities of these metrics that can directly translate to an attainment determination.

Generally, a large reduction in the frequency, magnitude, and relative amount of 8-hour ozone

nonattainment (i.e., >= 85 ppb) or PM2.5 nonattainment (24-hour and/or annual) is consistent

with a conclusion that a proposed strategy would meet the NAAQS. In the context of a weight

of evidence determination, these metrics could be used to suggest that a particular location may

be “stiff” or relatively unresponsive to emissions controls, while the rest of the modeling

domain/nonattainment area is projected to experience widespread reductions. If a sound

technical argument can be made for why atypically high RRFs at any particular location are not

reasonable, then these types of supplemental modeling metrics would suggest that attainment is

more likely to be achieved than the modeling analysis alone would indicate.

As discussed in section 3.4, an unmonitored area analysis may provide evidence that the

area may not achieve timely attainment, even if modeling suggests that attainment will occur at

all monitoring locations. In such cases, assessment of metrics concerning the frequency,

magnitude, and relative amount of nonattainment may help supplement the information from the

unmonitored area analysis. If application of the unmonitored area test indicates that most (if not

all) of the unmonitored areas will be in attainment, then that information would be evidence that

future attainment may be likely.

Uncertainty estimates associated with the spatial interpolation technique can also be

considered when reviewing and interpreting the results of an unmonitored area analysis. When

making a decision on whether attainment is likely to occur, areas with very high uncertainty

estimates for interpolated design values should be given less weight than areas with low

uncertainty estimates88.

The overall modeling analyses can also be evaluated to determine how appropriate the

modeling systems are for making regulatory decisions. Roth (2005) has proposed an “idealized

evaluation framework” for judging the quality of modeling applications. The paper lists a series

of twenty questions which can be used to judge the overall model application. These questions

provide an objective way to compare the quality, and identify deficiencies in modeling

applications. The absence of major deficiencies may provide a strong basis for acceptance of

model results.

Trends in Ambient Air Quality and Emissions: Generally, air quality models are regarded as

the most appropriate tools for assessing the expected impacts of a change in emissions.

However, it may also be possible to extrapolate future trends in ozone or PM2.5 based on

measured historical trends of air quality and emissions. There are several elements to this

analysis that are difficult to quantify. First, in most cases, the ambient data trends are best

 

 

n701 - n702 - n703 - n704 - n705 - n706 - n707 - n707 - n709 - n710 - n711 - n712 - n713 - n714 - n715 - n716 - n717 - n718 - n719 - n720 - n721 - n722 - n723 - n724 - n725 - n726 - n727 - n728 - n729 - n730 - n731 - n732 - n733 - n734 - n735 - n736 - n737 - n738 - n739 - n740 - n741 - n742 - n743 - n744 - n745 - n746 - n747 - n748 - n749 - n750

 

   Flag of Portugal 

 english:

 castellano: DISPER CUSTIC DESCAR RADIA    italiano:     

 

 français:    português:  

 

deutsch:

 

 

 

deutsch: DIS CUS  DES  RAD

castellano: DIS CUS DES  RAD   english: DIS CUS DES RAD  

 

 português: DIS CUS DES RAD   italiano:   DIS CUS  DES RAD

 

français:  DIS CUS DES RAD