## UNITED STATES ENVIRONMENTAL PROTECTION AGENCY REGION 4

Technical Support Document

For Ambient Monitoring

EPA’s Notice of Proposed Rulemaking For : Air Plan Approval and Air Quality Designation; GA; Redesignation of the Atlanta, GA, 1997 Annual Fine Particulate Matter Nonattainment Area to Attainment

June 20152

A. BACKGROUND

On July 18, 1997 (62 FR 36852), EPA established an annual PM 2.5 NAAQS at 15.0 micrograms per cubic meter ( μg/m 3 ) based on a 3

- year average of annual mean PM 2.5 concentrations. At that time, EPA also established a 24

- hour standard of 65 μg/m 3 (today’s action does not address the 24

- hour standard). See 40 CFR 50.7. On January 5, 2005 (70 FR 944), EPA published its air quality designations and classifications for the 1997 PM 2.5 NAAQS based upon air quality monitoring data for calendar years 20 01

– 2003. These designations became effective on April 5, 2005. The Atlanta Area ( c omprised of B a rr ow, B a rto w , Ca rroll, C h e rok e e , C l a y ton, Cobb, Cow e ta, De K a lb, Dou g las, F a y e t t e , F o r s y

th, F ul t on, G w innett, H a l l , H ea rd (partial), He n r y , N e wton, P a uld i n g , P utnam (partial), Ro c kd a le, Spalding a nd W a l t on Count i e s) was designated nonattainment for the 1997 PM 2.5 NAAQS. See 40 CFR 81. 3 01.

On October 17, 2006 (71 FR 61144), EPA retained the annual PM 2.5 NAAQS at 15.0 μg/m 3 based on a 3

- year average of annual mean PM 2.5 concentrations . On November 13, 2009, EPA clarified the designations promulgated in 2005 for the 1997 PM 2.5 NAAQS, stating that the Atlanta Area was designated as nonattainment for the annual standard and unclassifiable/attainment for the 24

- ho ur standard

. B. EPA REQUIREMENTS

A n area is deemed to have attaining data for the 1997 Annual PM 2.5 NAAQS when the design value a
meet s the requirements s tipulated in 40 CFR P art 50, section 50. 13 as follows:

The annual primary and secondary PM 2.5 standards are met when the annual arithmetic mean concentration, as determined in accordance with appendix N of this part, is less than or equal to 15.0 μg/m 3

. Upon determination by EPA t hat an area designated nonattainment for the PM 2.5 NAAQS has attained the standard, 40 CFR section 51.1004(c) provides that the requirement for such area to submit attainment demonstrations and associated reasonably available control measures, reasonable further progress plans, contingency measures, and other planning SIPs related to attainment of the PM 2.5 NAAQS shall b e susp ended until the area is redesignated to attainment, at which time the requirement no lon ger appl ies. However

, if EPA subsequently determines that the area has violated the PM 2.5 N AAQS, the requirement to submit such plans is reinstated.

Determinations of attainment are based on three years of complete, quality

- assured data. Appendix N does not provide for examining partial years of data because various seasons of the year reflect various influences on PM 2.5 concentrations, and a partial year’s data may no t be representative of values that would be determined from a full year’s data set. Nevertheless, any assessment should consider additional quality

- assured data to the extent that it exists.

a Design values are the metrics that are compared to the NAAQS levels to determine attainment. The annual design value is calculated as the average of three consecutive annual means. ( See 40 CFR part 50

, Appendix N.)3

C. EPA’S ANALYSIS OF MONITORED DATA

EPA’s determination tha t the Atlanta A rea has current attaining data for the 1997 Annual PM 2.5 NAAQS is based upon 2012

- 2014 data. This data has been quality assured, quality controlled and certified as required by 40 CFR Part 58

. The monitors and data used to determine the 2012

- 2014 A nnual PM 2.5 design value for the Atlanta A rea are includ ed in T able 1 below. All design values presented in this analysis are annual PM 2.5 design values and are calculated using the procedures provided in 40 CFR Part 50, Appendix N. Unless otherwise noted, all data presented in this analysis was retrieved from EPA’s Air Quality System Database and have been certified as accurate by the State of Georgia, as required by 40 CFR § 58.15.

Table 1: 2012

- 2014 Annual Average PM 2.5 Concentrations for Monitors in the Atlanta, Georgia , Nonattainment Area

Location Site Number 2012 Annual Average (μg/m 3 ) 2013

Annual Average (μg/m 3 ) 2014

Annual Average (μg/m 3 ) Annual

Design Value (μg/m 3 ) Maximum

Bootstrapped Annual Design Value (μg/m 3 ) Georgia DOT 13

- 063

- 0091 10.8 9.7 10.6 10.4

- GA Nat ’ l Guard 13

- 067

- 0003 10.1 9.6 10.3 10.0

- South DeKalb 13

- 089

- 0002 10.2 9.3 10.1 9.9

- Fire Station #8 13

- 121

- 0039 11.3 10.4 11.2 11.0* 11.1
Gwinnett Tech 13

- 135

- 0002 10.3 8.9 9.3 9.5

- Gainesville 13

- 139

- 0003 9.3 8.5 8.9 8.9

- Yorkville 13

- 223

- 0003 8.8 8.5 8.7 8.7

-

* Fire Station #8 d id not produce a valid design value for 2012

- 2014 due to incomplete data.

All of the sites in the approved monitoring network recorded valid, attaining design values during 2012

- 2014 except for the Fire Station #8 site . This site had incomplete data during the 3 rd quarter of 2012 and is not eligible for the high value substitution test in 40 CFR Part 50 Appendix N because the data completeness in this quarter is below 50 percent . The site has incomplete data for this period because the PM 2.5 sampler failed a monthl y one

- point flow rate verification (required by 40 CFR Part 58

, Appendix A
, Section 3.2.3) during this period, and several samples were invalidated as a result. An analysis of the data completeness for all sites in the area from 2010

- 2014 is shown in Figu re 1. EPA approved three sites (Macland Aquatic Center, 13

- 067

- 0004; Doraville, 13

- 089

- 2001; and E. Rivers School, 13

- 121

- 0032) to shut down at the end of 2012

, and they are not included in this analysis. These sites are not considered incomplete because they are no longer part of the approved PM 2.5 monitoring network for the area. The GA National Guard site collected incomplete data in 3 rd quarter of 2014, but this site has a valid, attaining design value after passing the high value substitution test in 40 CFR Part 50

, Appendix N.4

Figure 1 : Atlanta Data Completeness by Site, 2010

- 2014

Fire Station #8 Site: Analysis of Incomplete Data

Since the Fire Station #8 site has historically recorded the highest annual design value in the Atlanta area, EPA conducted additional statistical analysis which indicates, as a weight of evidence, that the site would likely have attained the 1997 annual PM 2.5 NAAQS during 2012

- 2014 had it collected complete data. This analysis estimates the miss ing samples at the incomplete site using a linear regression model and uses bootstrapping to evaluate the model and approximate the probable 2012

- 2014 design value at Fire Station #8. A similar approach has been applied to areas with incomplete data in th e past

. 1

, 2 This approach was determined to be appropriate for this area for the reasons discussed below, but it may or may not be suitable for other areas with incomplete data. EPA will evaluate the a ppropriateness of this analytical approach on a case

- by

- case basis for determinations regarding each area with incomplete data.

Step 1

– Organize data and perform linear regression between incomplete site and candidate sites

The first step in this analysis was to develop a linear regression model for the incomplete site (Fire Station #8) using historical data from the last five years collected at other sites in the area. Linear regressions were developed using any sites with at least 20 sample pairs (histor ical samples collected at the predictor site and the incomplete site on the same day) in each calendar quarter (1 st , 2 nd , 3 rd , and 4 th quarter) over the five

- year period, and at least 100 total sample pairs. In this analysis, a Theil

- Sen linear regression model 3

, 4 was used. The Theil

- Sen regress ion is a nonparametric linear model and is typically less likely to be influenced by outliers than a least squares linear regression.5

Step 2

– Select the best candidate site as the predictor site

For each model, the Spearman correlation coefficient was eva luated to determine the correlation of the incomplete site and the predictor site. To evaluate the prediction error of the model, a k

- folds cross

validation 5 was performed. This method randomly removes subsets of the original dataset and predicts these known values using the same model method (in this case the Theil

- Sen regression) to determine the model error on the dataset. In this analysis, 10 folds were used and a standard error estimate, or root mean squared error (RMSE)

, was calculated from the k

- folds results.

The models using different predictor sites were ranked using the Spearman correlation and the RMSE to determine the best model to use for predi cting the incomplete sample values. Summary statistics about the models using each of the potential predictor sites are shown in Table 2, ranked by the Spearman correlation. The selected predictor site must also have collected enough samples during the ca lendar quarter(s) that are incomplete to generate at least 75 percent data completeness in each quarter in the most recent three

- year period after modeling the missing samples. Based on the above criteria, the correlation and cross

- validation results indic ated that the South DeKalb site ( Spearman correlation ρ = 0.91, RMSE = 2.09 μ g/m 3 ) is the best predictor for concentrations at Fire Station #8. Overall, these results indicate a strong linear relationship between the South De K alb and Fire Station #8 sites . A plot of the Theil

- Sen regression model and the paired data from both sites is shown in Figure 2.

Table 2: Linear Regression Model Comparison for Candidate Predictor Sites
Incomplete Site Predictor

Site n

Sample Pairs n pairs

Q1 n pairs

Q2 n pairs

Q3 n pairs

Q4 Minimum

Pairs Met

13

- 121

- 0039 13

- 089

- 0002 519 124 135 121 139 Yes
13

- 121

- 0039 13

- 063

- 0091 497 121 122 118 136 Yes
13

- 121

- 0039 13

- 135

- 0002 474 110 128 112 124 Yes
13

- 121

- 0039 13

- 139

- 0003 462 113 114 113 122 Yes
13

- 121

- 0039 13

- 223

- 0003 477 111 125 112 129 Yes
Incomplete Site Predictor

Site Theil

Slope Theil

Intercept Spearman

ρ Theil RMSE (μg/m 3 ) 13

- 121

- 0039 13

- 089

- 0002 1.030 0.676 0.910 2.095
13

- 121

- 0039 13

- 063

- 0091 1.000 0.449 0.894 2.519
13

- 121

- 0039 13

- 135

- 0002 1.007 1.243 0.885 2.712
13

- 121

- 0039 13

- 139

- 0003 0.965 2.600 0.759 3.744
13

- 121

- 0039 13

- 223

- 0003 0.984 2.218 0.737 3.5556

Figure 2: Correlation plot and Theil

- Sen regression between the South DeKalb (13

- 089

- 0002) and Fire Station #8 (13

- 121

- 0039) sites, using PM 2.5 data ( μ g/m 3 ) from 2010

- 2014

The missing samples at Fire Station #8 in any quarter with data completeness below 75 percent were estimated using the Theil

- Sen regression model. Missing samples were not estimated in quarters with data completeness of 75 percent or greater.
Step 3

– Calculate a test design value

Next, a test 2012

- 2014 design value was calculated using the combination of the collected samples and the estimated samples. This procedure produced a test design value of 10.9 μ g/m 3 , below the 1997 PM 2.5 NAAQS of 15 μ g/m 3

. Step 4

- Bootstrapping Analysis

In order to estimate the variance of the predicted design value, a bootstrapping analysis was conducted. Bootstrapping is a statistical procedure that uses resampling of a dataset to estimate t he variance of a statistic

. 6 In this analysis, the following bootstrapping procedure was used:

a. Residuals were calculated from the Theil

- Sen regressi on model b. For each missing sample that was estimated in Step 2, a regression residual was randomly selected with replacement from the pool of residuals and added (or subtracted, in the case of a negative residual) to the estimated value.

c. Repeat (b) 10,000 times

. d. For each of the 10,000 runs, the predicted 3

- year design value was calculated as in Step 3.

The resulting 10,000 predicted design values are interpreted as an approximate probability distribution of the design value at the incomplete site, had it collected complete data. The distribution of the resulting bootstrapped design values is shown in Fig ure 3. The results of the 10,000 bootstrapped design7

values are summarized in Table 3. Since none of the 10,000 bootstrapping runs resulted in a design value above the NAAQS, the Fire Station #8 site is considered to have met the NAAQS.

Figure 3: Distribu tion of design value bootstrap results for Fire Station #8

Table 3: Summary of Fire Station #8 design value bootstrap results
Incomplete Site Predictor Site Bootstrap Runs Predicted

DV (μg/m 3 ) 95% Confidence

Interval (μg/m 3 ) Max Bootstrapped

DV (μg/m 3 )

13

- 121

- 0039 13

- 089

- 0002 10
, 000 10.9 [10. 9

– 11.0 ] 11.0

Additional Analysis of Seasonal Model Performance

The bootstrapping method described above assumes that the underlying dataset is independent and identically distributed (i.i.d.). This assumption can be inappropriate for time series data, and several alternative bootstrap methods (e.g.

, a block bootstrap) have been developed for these types of data

. 7 PM 2.5 concentrations often exhibit a strong seasonal pattern. In this analysis, the general bootstrap method could overpredict or underpredict concentrations in certain seasons if the relationship between the incomplete site and the predictor site changes seasonally. To investigate this possibility, the concentration difference between Fire Station #8 and South DeKalb was plotted over time (Figure 4).8

The Theil

- Sen model RMSE and median model residual value (to identify a positive or negative
pre diction bias) were also calculated for each calendar quarter. The quarterly RMSE is shown in Figure 5, and the quarterly median residual is shown in Figure 6. These analyses do not show a seasonal pattern in the relationship between the two sites or in th e predictive accuracy of the linear model. Therefore, the general bootstrap method was considered appropriate for this dataset, and a time series bootstrap was not conducted.

Figure 4: PM 2.5 concentration difference time series for each sample pair collected at Fire Station #8 and South DeKalb, 2010

- 2014

Figure 5: Theil

- Sen regression model standard error by year and calendar quarter9

Figure 6: Theil

- Sen regression model median residual value by year and calendar quarter Air Quality Trend for the Nonattainment Area

In the years since the Atlanta area was designated nonattainment in 2005 for the 1997 Annual PM 2.5 NAAQS, all of the monitors in the area have shown improvements in annual average ambient PM 2.5 concentrations. The annual PM 2.5 desig n values for all sites in the area from 2001

- 2003 (the period used for the original nonattainment designation) to 2012

- 2014 (the period used in this action) are shown in Figure 7.

Figure 7: Atlanta Annual PM2.5 Design Values, 2003

– 2014

Invalid Design Value due to Incomplete Data10

D . CONCLUSIONS AND RECOMMENDED AGENCY ACTION

The monitors in the Atlanta Area that collected complete data for the period 2012

- 2014 have average PM 2.5 concentrations that are all in attainment with the 1997 Annual PM 2.5 NAAQS. The Fire Station #8 site had incomplete data during the 3 rd quarter of 2012 and is not eligible for the high value substitution test in 40 CFR Part 50, Appendix N. However, based upon the analysis described in this Technical Support Document, EPA has determined that the upper end of the range of probable 2012

- 2014 design values at the Fire Station #8 site did not exceed the NAAQS . In addition , the GA National Guard site collected incomplete data in 3 rd quarter of 2014 but has a valid, attaining design value after passing the high value substitution test. On the basis of this review, EPA has concluded that the Atlanta Area continues to meet the 1997 Annual PM 2.5 NAAQS of 15.0 μg/m 3 for the period 2012

- 2014, the most recent 3

- year period of certified data availability

.11

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