served number of exceedances and the required number of scheduled samples (e.g. two observed exceedances with every other day sampling). Nonattainment of the annual standards can be demonstrated on the basis of quarterly mean concentrations developed from observed data combined with one-half the minimum detectable concentration substituted for missing values. In both cases, expected annual values must exceed the levels allowed by the standards.

2.4 Adjustment for Exceptional Events and Trends.

An exceptional event is an uncontrollable event caused by natural sources of particulate matter or an event that is not expected to recur at a given location. Inclusion of such a value in the computation of exceedances or averages could result in inappropriate estimates of their respective expected annual values. To reduce the effect of unusual events, more than 3 years of representative data may be used. Alternatively, other techniques, such as the use of statistical models or the use of historical data could be considered so that the event may be discounted or weighted according to the likelihood that it will recur. The use of such techniques is subject to the approval of the appropriate Regional Administrator in accordance with EPA guidance.

In cases where long-term trends in emissions and air quality are evident, mathematical techniques should be applied to account for the trends to ensure that the expected annual values are not inappropriately biased by unrepresentative data. In the simplest case, if 3 years of data are available under stable emission conditions, this data should be used. In the event of a trend or shift in emission patterns, either the most recent representative year(s) could be used or statistical techniques or models could be used in conjunction with previous years of data to adjust for trends. The use of less than 3 years of data, and any adjustments are subject to the approval of the appropriate Regional Administrator in accordance with EPA guidance.

3.0 Computational formulas for the 24-hour standards.

3.1 Estimating Exceedances for a year.

If PM10 sampling is scheduled less frequently than every day, or if some scheduled samples are missed, a PM10 value will not be available for each day of the year. To account for the possible effect of incomplete data, an adjustment must be made to the data collected at each monitoring location to estimate the number of exceedances in a calendar year. In this adjustment, the assumption is made that the fraction of missing values that would have exceeded the standard level is identical to the fraction of measured values above this level. This computation is to be made for all sites that are scheduled to monitor throughout the entire

e=the estimated number of exceedances for calendar quarter q,

V1=the observed number of exceedances for calendar quarter q,

N, the number of days in calendar quarter q, n=the number of days in calendar quarter q with PM10 data, and

q=the index for calendar quarter, q=1, 2, 3 or 4.

The estimated number of exceedances for a calendar quarter must be rounded to the nearest hundredth (fractional values equal to or greater than 0.005 must be rounded up).

The estimated number of exceedances for the year, e, is the sum of the estimates for each calendar quarter.

The estimated number of exceedances for a single year must be rounded to one decimal place (fractional values equal to or greater than 0.05 are to be rounded up). The expected number of exceedances is then estimated by averaging the individual annual estimates for the most recent 3 or more representative years of data. The expected number of exceedances must be rounded to one decimal place (fractional values equal to or greater than 0.05 are to be rounded up).

The adjustment for incomplete data will not be necessary for monitoring or modeling data which constitutes a complete record, 1.e., 365 days per year.

To reduce the potential for overestimating the number of expected exceedances, the correction for missing data will not be required for a calendar quarter in which the first observed exceedance has occurred if: (a) there was only one exceedance in the calendar quarter, (b) everyday sampling is subsequently initiated and maintained for 4 calendar quarters in accordance with 40 CFR 58.13 and (c) data capture of 75 percent is achieved during the required period of everyday sampling. In addition, if the first exceedance is observed in a calendar quarter in which the monitor is already sampling every day, no adjustment for missing data

In this example, everyday sampling was initiated following the first observed exceedance as required by 40 CFR 58.13. Accordingly, the first observed exceedance would not be adjusted for incomplete sampling. During the next three quarters, 1.2 exceedances were estimated. In this case, the estimated exceedances for the year would be 1.0+1.2+0.0+0.0 which equals 2.2. If, as before, no exceedances were observed for the two previous years, then the estimated exceedances for the 3-year period would then be (1/3)x(2.2+0.0+0.0)=0.7, and the monitoring site would not fail the attainment test.

3.2 Adjustments for Non-Scheduled Sampling Days.

If a systematic sampling schedule is used and sampling is performed on days in addition to the days specified by the systematic sampling schedule, e.g., during episodes of high pollution, then an adjustment must be made in the formula for the estimation of exceedances. Such an adjustment is needed to eliminate the bias in the estimate of the quarterly and annual number of exceedances that would occur if the chance of an exceedance is different for scheduled than for non-scheduled days, as would be the case with episode sampling.

The required adjustment treats the systematic sampling schedule as a stratified sampling plan. If the period from one scheduled sample until the day preceding the next scheduled sample is defined as a sampling stratum, then there is one stratum for each scheduled sampling day. An average number of observed exceedances is computed for each of these sampling strata. With nonscheduled

A monitoring site samples according to a systematic sampling schedule of one sample every 6 days, for a total of 15 scheduled samples in a quarter out of a total of 92 possible samples. During one 6-day period, potential episode levels of PM10 were suspected, so 5 additional samples were taken. One of the regular scheduled samples was missed, so a total of 19 samples in 14 sampling strata were measured. The one 6-day sampling stratum with 6 samples recorded 2 exceedances. The remainder of the quarter with one sample per stratum recorded zero exceedances. Using formula [3], the estimated number of exceedances for the quarter is eq=(92/14)x(2/6+0+...+0)=2.19

4.0 Computational Formulas for Annual Standards.

4.1 Calculation of the Annual Arithmetic Mean.

An annual arithmetic mean value for PM10 is determined by averaging the quarterly means for the 4 calendar quarters of the year. The following formula is to be used for calculation of the mean for a calendar quar

i=the annual mean, and

-the mean for calendar quarter q.

[5]

The average of quarterly means must be rounded to the nearest tenth (fractional values of 0.05 should be rounded up).

The use of quarterly averages to compute the annual average will not be necessary for monitoring or modeling data which results in a complete record, i.e., 365 days per year.

The expected annual mean is estimated as the average of three or more annual means. This multi-year estimate, expressed in μg/m3, shall be rounded to the nearest integer for comparison with the annual standard (fractional values of 0.5 should be rounded up).

arithmetic average of the observed values as described by formula [4].

Example 5

During one calendar quarter, 9 observations were recorded. These samples were distributed among 7 sampling strata, with 3 observations in one stratum. The concentrations of the 3 observations in the single stratum were 202, 242, and 180 μg/m3. The remaining 6 observed concentrations were 55, 68, 73, 92, 120, and 155 μg/m3. Applying the weighting factors specified in formula [6], the quarterly mean is

= (1/7)×[(1/3)x(202+242+180)+

55+68+73+92+120+155]

ma kj

51.46 AQMA analysis: Projection of air quality concentrations.

where

-the quarterly mean concentration for quarter q, q=1, 2, 3, or 4,

I the ith concentration value recorded in stratum j,

k=the number of actual samples in stratum j, and

m, the number of strata with data in the quarter.

If one sample value is recorded in each stratum, formula [6] reduces to a simple

51.47 AQMA analysis: Description of data

ess.

51.408 Fiscal constraints for transportation plans and TIPS.

51.410 Criteria and procedures for determining conformity of transportation plans, programs, and projects: General. 51.412 Criteria and procedures: Latest planning assumptions.

51.414 Criteria and procedures: Latest emissions model.

51.416 Criteria and procedures: Consultation. 51.418 Criteria and procedures: Timely implementation of TCMs.

51.420 Criteria and procedures: Currently conforming transportation plan and TIP. 51.422 Criteria and procedures: Projects from a plan and TIP.

51.424 Criteria and procedures: Localized CO and PM10 violations (hot spots). 51.426 Criteria and procedures: Compliance with PM10 control measures. 51.428 Criteria and procedures: Motor vehicle emissions budget (transportation plan). 51.430 Criteria and procedures: Motor vehicle emissions budget (TIP).

51.432 Criteria and procedures: Motor vehicle emissions budget (project not from a plan and TIP).

51.434 Criteria and procedures: Localized CO violations (hot spots) in the interim period.

51.436 Criteria and procedures: Interim period reductions in ozone and CO areas (transportation plan).

51.438 Criteria and procedures: Interim period reductions in ozone and CO areas (TIP).