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# INTRODUCTION

In order to show attainment with the 24-hour PM-10 National Ambient Air Quality Standard for the Maricopa County PM-10 nonattainment area un der high wind conditions, modeling was performed for both the PM-10 nonattainment area and a subset of the nonattainment area known as the Salt River area. The sections in th is chapter of the TSD provide a detailed discussion of the elements necessary to pe rform the attainment modeling and demonstrate that attainment is met in 2012; specifi cally including:

! The reasons for choosing distance-weighted rollbackas a conceptual model for the attainment demonstration;

! An analysis of the insights gained through operatio n of a short-term PM-10 temporary monitor saturation study;

! The application of a PM-10 emissions source weighti ng factor based upon distance from the exceeding monitor;

! An analysis of the non-anthropogenic low and high w ind background PM-10 concentrations in the Maricopa County nonattainmentarea;

! The process for selecting design days;

! The development of low and high wind modeling domai ns;

! The creation of model-ready low and high wind emiss ions inventories;

! Application of the benefits of the control measures ; and

! A demonstration of attainment in 2012.

An overview of the attainment modeling can be found in Chapter Five of the main plan document.

V - 1

# CONCEPTUAL MODEL

In December 2007, the Maricopa Association of Gover nments (MAG) submitted the “MAG Five Percent Plan for PM-10 for the Maricopa CountyNonattainment Area” (Five Percent Plan) to the U.S. Environmental Protection Agency ( EPA) in fulfillment of federal Clean Air Act requirements after a failure to attain the 24-h our PM-10 ambient air quality standard (standard) by the statutory deadline of December 31, 2006. The MAG 2007 Five Percent Plan for PM-10 included fifty-three control measures t hat demonstrated PM-10 emissions reductions of five percent per year and attainment of the PM -10 standard in 2010.

In 2008, there were a number of exceedances of the 24-hour PM-10 standard due to high winds in the Maricopa County nonattainment area. E PA indicated that it did not concur with four high wind exceptional events at the West 43 rd 

# Avenue monitor, which constituted a violation. The region then did not have the three y ears of clean data needed for attainment. In September 2010, EPA published a notice to propos e a partial approval and disapproval of the MAG 2007 Five Percent Plan for PM-10. The two maj or reasons for the proposed disapproval were inaccuracies in the 2005 baseline emissions inventory and EPA’s nonconcurrence with the four high wind exceptional events.

In January 2011, the Arizona Department of Environmental Quality (ADEQ) withdrew the MAG 2007 Five Percent Plan for PM-10 to address the technical approvability issues identified by EPA, and to include new information s uch as the new EPA equation for paved road dust emissions. EPA has indicated that the re vised plan needs to show how high wind emissions will be adequately mitigated.

Presented below is a review of the issues considere d in assessing available modeling concepts and the steps leading to the selection of distance-weighted rollback as the conceptual model.

Modeling Domain

Due to the uniform pattern and direction of wind fl ows upwind of the Salt River monitors during high wind events in the Spring, the use of m odeling domains extending along wind back-trajectories from these monitors focuses the are al extent of these modeling domains to the areas of significant emission contribution. Past analyses of back-trajectories related to exceptional event evaluations in the Spring have shown that many are from the west/southwest and lie along the Salt River channel due to prevailing synoptic wind fields and the topographic influence of the Estrella Mount ains lying directly south and running parallel to the river channel. Furthermore, past mode ling analyses indicate that the lands significantly contributing to elevated PM-10 concen trations recorded at a monitoring site lie generally within a mile of either side of the ba ck-trajectory path upwind of the monitor.

Since the areal extent impacting monitors is largel y a function of wind speed, it necessary to divide selected design days into low and high wi nd hours. High wind hours need to represent sources located along back trajectories a nd low wind hours need to represent

V - 2

# sources within a modeling domain surrounding the se lected monitoring site. A significant

amount of work was devoted to developing the Salt River area modeling domain in the withdrawn Five Percent Plan which is employed to as sess emissions impacting Salt River monitors during low wind hours. As part of a regio nal demonstration of attainment, other low wind modeling domains that represent similar di stributions of land use around modeled monitors are developed as necessary. The basis for distinguishing between low and high wind hours is the 12 mph aerodynamic entrainment th reshold (i.e., five-minute average) established in the 2008 PM-10 Periodic Emissions In ventory (Appendix A, Exhibit 1). Both, five-minute and hourly wind measurements at selecte d monitoring sites are available and can screen to determine which hours are “high wind” hours. The remaining hours are assumed to be “low wind” hours.

Modeling Inventory Development

The withdrawn Five Percent Plan expended considerab le resources in collecting sourcespecific activity data (e.g., traffic counts, aggre gate facility operating records, silt measurements, etc.) within the modeling domain to prov ide an accurate representation of emissions on the days selected for modeling. The focus of that effort, however, was on characterizing emissions under low wind stagnant co nditions. While emission estimates were also prepared for a high wind day, the model p erformance resulting from that inventory was considerably poorer than for the inve ntories developed for days with stagnant conditions. Given that experience, it suggests a different emissions inventory methodology is needed (i.e., one along the lines of quantifying upwind emissions along a back trajectory during high wind hours and low wind emissions withi n a modeling domain surrounding the selected monitoring site). Due to the limited con tribution of low wind hours to the 24- average values recorded on high wind days it is sug gested that design-day specific low wind inventories are not necessary to characterize low wind hour emissions within the Salt River modeling domain and the PM-10 nonattainment a rea as a whole. Instead, it is planned that existing annual emission estimates be employed through allocation of those emissions to the land uses within each of the model ing domains.

For high wind hours, the methodology employed in th e windblown dust emissions inventory developed for the 2008 PM-10 Periodic Emissions Inv entory is used to quantify fugitive dust emissions at upwind sources located along back-trajectory domains from each of the modeled monitors. One question that had not been previou sly researched, was how far back along a back-trajectory line the modeling doma in should extend. Insight into this question is provided in the discussion below of dat a collected at a temporary monitor upwind of the West 43 rd 

# Ave. monitoring site.

Temporary Monitoring Insights

An analysis of matched pair measurements between th e West 43 rd 

# Ave. long-term monitor and 43rd Ave. & Broadway temporary monitor (a distance of 0.42 miles) was prepared for the period between April 2010 and March 2011. The data were organized into five-degree increments so concentration differences could be co mputed as a function of wind direction

V - 3

# and wind speed. Measurements were organized into t hree categories of wind speed: 2-6

mph, 6-12 mph, and >12 mph (both monitors had to have wind speeds falling into these categories to be included in the analysis). Tabulati ons of the results for these monitors and other temporary monitors are presented in the subse quent section entitled Temporary Monitoring Insights. The results for these monitors show that at 240°, the angular direction traveling southwest from the West 43 rd 

# Ave. monitor to the West 43 rd 

# Ave. & Broadway temporary monitor, average concentrations at the West 43 rd 

# monitor are consistently higher than at the temporary site:

! At 2-6 mph, the difference (West 43 rd 

# - West 43 rd 

# & Broadway/West 43 rd 

# ) is 13.5% (6.38 ȝ g/m 3 

# );

! At 6-12 mph, the difference is 9% (3.2 ȝ g/m 3 

# ); and

! At >12 mph, the difference is 37.3% (39.78 ȝ g/m 3 

# )

The >12 mph differences increase as the angular direction increases from 240° - 290°, suggesting that fugitive dust from land located betw een those sites disproportionately impacted concentrations at the West 43 rd 

# Ave. monitoring site (particularly since emissions impacting the monitor at that hour are coming from a minimum distance of 12 miles and the distance between the monitors is 0.42 miles). This finding suggests that that any modeling analysis prepared to support an attainment demonstration needs to account for the distance between the location where fugitive dust is e mitted and the monitoring site impacted during high wind conditions (either throug h dispersion/deposition or some weighting function).

Available Modeling Concepts

Simple Rollback– Simple rollback analysis provides the least rigo rous approach to determining the levels of emission control needed to reduce ambient PM-10 concentrations. A fundamental assumption underlying this method is that pollutant concentrations are directly proportional to total emissions over the a rea of interest. This assumption acquires some validity if emissions from significant source categories are uniformly distributed throughout the modeling domain, and if control meas ures will reduce emissions equally from each parcel in a particular source category. The benefi ts of using simple rollback include a quick turnaround in computing emission re ductions necessary to meet a desired air quality improvement goal and the need for very limi ted emission inventory data to complete the calculation. The drawbacks of using th is approach include the loss of accuracy in forecasting air quality improvements if sources are not uniformly distributed and if control measures produce varying benefits on a p arcel-by-parcel basis. Simple rollback can be used with some success in modeling over microscale domains, but loses accuracy as the modeling domain grows because of the effects of particle dispersion and deposition over longer travel distances. Additionally, the mi croscale inventories surrounding each PM10 monitoring site are different, and one microscal e inventory cannot be used to assess inventory/air quality relationships at another site . Demonstrating that a control strategy is

V - 4

# effective at all sites recording exceedances requires the development of separate microinventories for each monitoring site. This approac h would also suffer accuracy losses if the modeling domains extended more than about one-half mile from the monitoring site.

Weighted Rollback– A weighted rollback approach applies a distance reduction factor to emissions of each source in the modeling domain. T he reduction factor is calculated on the basis of the distance between each source and t he impacted monitoring site. This method requires the separate analysis of distance reduction factors in addition to the development of modeling domain emission inventories u pwind of each impacted monitoring site. The selection of a mathematical form for the calcula tion of distance reduction factors, ranging from 1/distance to involved equation forms based on hourly meteorological data, requires testing to refine. Coordinates of the cen troid of each parcel of land within each back-trajectory swath would need to be extracted fro m land use GIS files to facilitate the calculation of parcel-specific weighting factors. The benefits of using this method also include a relatively quick turnaround in computing emission reductions and, hence, air quality improvements after inventory data are compi led and distance reduction factors are computed. This approach also takes advantage of th e limited-term saturation monitoring data collected in 2010 and early 2011 to inform the calculation of distance reduction factors at this site.

Principle Component Analysis – Principle Component Analysis (PCA) is a multivari ate statistical technique that uses eigenvectors to rep resent varied inter-relationships present in datasets. The latent root regression form extracts relationships among variables that include a dependent variable (here, PM-10) and its predictors (e.g., wind speed, wind direction, soil moisture levels, emissions). PCA i s an attractive alternative to photochemical and dispersion modeling when performance is poor du e to problems with secondary chemistry and/or meteorological modeling. PCA alsoprovides a framework for assessing the probability of attainment by contrasting meteorological conditions on design day(s) relative to an extended historical record. The dow nside of the methodology is that it requires assembly of relevant metrics for a long-te rm, multi-year meteorological record and preparation of an emissions inventory over multiple years so that vectors representing the bulk of the variance in the assembled dataset can b e extracted.

AERMOD – The use of dispersion models like AERMOD to quan tify the relationship between emissions and air quality can increase the accuracy of the reported results. Because of the complexities of the mathematical alg orithms coded into the models to quantify a number of dispersion components, the out put of dispersion models like AERMOD shows less linearity between emission strength and ambient pollutant concentration than is inherent in rollback models. These complexities require detailed assessments of the combinations of control measures converging to a solution that attains air quality targets and minimizes instabilities in the economies of regulated sources. Some sophisticated dispersion models, like ISCST3, conta in particle deposition algorithms that can be toggled to simulate this ambient removal mec hanism. AERMOD does not contain this capability, which is important in the modeling of windblown PM-10 emissions. An additional concern is that AERMOD provides no carry over of entrained emissions from one

V - 5

# hour to the next, which can lead to underestimates of concentrations under both stagnant

and high wind conditions.

Conclusion

Since high wind AERMOD model performance was poor i n the withdrawn Five Percent Plan, a weighted rollback approach was chosen to qua ntify the benefits of adopted controls on individual low and high wind hour concentrations for both the Salt River area domain and the regional nonattainment area demonstration. The 12 mph five-minute wind speed threshold was chosen to distinguish between low and high wind hours on selected design day(s). High wind hour emissions along the back trajectories are initially calculated based upon land use type at 2007 base levels and then cal culated again with the benefits of the control measures through 2012 as identified in the new Five Percent Plan. The difference in high wind emissions will provide the expected re duction benefit of the controls included in the new Five Percent Plan. The aggregate weight ed benefit computed for each high wind hour should be applied to the corresponding hi gh wind hour recorded concentration. Since there have been no stagnation exceedances of the PM-10 standard since 2007, it is assumed that control measures implemented since 2007 have been successful in eliminating exceedances during stagnant conditions. The successful elimination of stagnation exceedances dictate that new low wind micros cale, design day-specific inventories are not needed. As such, existing annu al low wind emissions are allocated by land use in each of the low wind modeling domains a s necessary. The 24-hour average of benefit-adjusted low and high wind concentration s in 2012 are lastly contrasted with the ambient 24-hour PM-10 standard to demonstrate attainm ent.

V - 6

# TEMPORARY MONITOR INSIGHTS

In an effort to better understand source contributi ons impacting the West 43 rd 

# Ave monitoring site during high wind events, Maricopa C ounty, with support from the Arizona Department of Environmental Quality (ADEQ) and the Maricopa Association of Governments (MAG) deployed a series of temporary PM -10 monitors along the dominant upwind trajectory in the spring of 2010. A total of five temporary monitors were deployed at the following sites: Arlington, 115 th 

# Avenue, 67th Avenue, 51 st

# Avenue, and West 43 rd 

# & Broadway. The locations of the five temporary moni tors and two nearby long-term monitors, Buckeye and West 43 rd 

# Ave., are displayed in Figure V-1.

Monitor Placement

The location of each of the temporary monitors is i dentified below, along with a brief description of the insight that it was expected to provide.

West 43 rd 

# Avenue and Broadway (Temp #1) – The objective was to obtain measurements at the western-most edge of the industrialized area located between the West 43 rd 

# Ave. monitor and the river bed (roughly 0.4 miles west of t he monitor). Differences between monitored values at this location and the West 43 rd 

# Ave monitor will provide a basis to assess contributions coming from nearby sources whe n the wind is from the west/southwest.

V - 7

# 51 st

# Avenue and W. Roeser Road (Temp #2) – The goal was to locate monitors on the

eastern side of 51 st

# Ave. Differences between measurements at this site and Temp #1 would provide insight into contributions from aggre gate facilities located between 51 st

# Ave. and 43 rd 

# Ave (a distance of roughly 1 mile).

67th Avenue and Southern Avenue (Temp #3) – The goal was to locate monitors on the eastern side of 67 th 

# Ave. between the river bed and Southern Ave. Differences between measurements at this site and Temp #2 would provide insight into emissions from facilities located within the 2 mile area located between 67 th 

# Ave. and 51 st

# Ave.

115 th 

# Avenue – South side of PIR Bridge (Temp #4) – The purpose of this site was to obtain information on particulate mass being transported b y drainage flow down the Agua Fria River during morning commutes. Concern had been ex pressed that drainage mass accumulates on the river bed and surrounding area o ver time and is subsequently reentrained under high wind conditions and impactin g the West 43 rd 

# Ave. monitor. This location also has the advantage of providing a roug h mid-point measurement of PM-10 concentrations between the Buckeye and West 43 rd 

# Ave. monitors (it is located roughly 10 miles southwest of the West 43 rd 

# Ave. monitor).

Arlington School (Temp #5) – The 5% Plan relied on measurements collected at Org an Pipe National Monument to obtain estimates of backg round concentrations being transported off the desert. The goal was to place a monitor beyond the western boundary of agricultural fields located to the west of the Bu ckeye monitor to gain a true measurement of background concentrations being transported off the desertinto the Salt River area. Initially, it was thought that ADEQ could utilize t he former monitoring site at Palo Verde; subsequent investigation, however, determined this site was not available.

Analysis

Presented below are comparisons of pairs of measure ments collected between the fixed and temporary sites moving west from the West 43 rd 

# Ave. monitor, highlighted in the box displayed in Figure V-1. Comparisons between the A rlington and Buckeye sites are presented in a separate section addressing Backgrou nd Conditions. To aid the comparison of site measurements, the data were orga nized into five-degree increments so concentration differences could be computed as a function of wind direction and wind speed. Comparisons between site measurements were adjusted to account for time it takes air mass to travel between the two sites. Th is was done by lagging the upwind measurement from the downwind measurement by the appro priate number of five-minute intervals (the increment in which the data were col lected) needed to account for the time required for air mass to traverse the distance betwee n the two sites along the angular increment between the two sites. Thus, if the angl e between the two sites is 260 degrees, the average wind speed computed for that increment was used to adjust all of the angular increments between 180 and 360 degrees. The reason i s that the most accurate comparison is along that angle connecting the two s ites. Comparisons at other increments reflect the impact of sources not located directly a long the line/angle between

V - 8

# the two sites. As the difference between the angul ar increment and the angle between the

two sites grows, the effects of these sources and dispers ion become greater. Similarly, as the distance between the two sites grows, the ef fects of nonaligned sources, dispersion, and deposition continue to grow and the co mparisons provide less insight. Fortunately, the sites are located to the southwestof the West 43 rd 

# Avenue monitor, which is the dominant direction for high winds impacting that site. Comparisons along the arc between 230 to 290 degrees generally have the large st measurement count and provide the most relevant insight for the analysis. To simplify the presentation, comparisons of winds coming from the east (i.e., 0 to 175 degree incremen ts) are not included for any of the lagged data sets for each site pair and wind sp eed category. All tables are presented at the end of this section for ease of review.

West 43 rd 

# Avenue versus West 43 rd 

# Avenue & Broadway (Temp #1) – An analysis of matched pair measurements between the West 43 rd 

# Ave. long-term monitor and 43rd Ave. & Broadway temporary monitor was prepared for the p eriod between April 2010 and March 2011. The data were organized into five-degree inc rements so concentration differences could be computed as a function of wind direction a nd wind speed. Since the distance between the two sites is 0.4 miles, no lags were in troduced into the data comparisons. With the comparisons all on a consistent time basis, resu lts are presented for winds coming from all directions not just the west.

Measurements were organized into three categories o f wind speed: 2-6 mph (Tables V-5 & V-6), 6-12 mph (Tables V-3 & V-4), and >12 mph (T ables V-1 & V-2). To ensure matched pair comparisons, both monitors had to have wind speeds falling into these categories to be included in the analysis. The res ults show that at 240 degrees, the angular direction traveling southwest from the West43 rd 

# Ave. monitor to the West 43 rd 

# Ave. & Broadway temporary monitor, average concentrations at the West 43 rd 

# monitor are consistently higher than at the temporary site:

! At 2-6 mph, the difference (West 43 rd 

# - West 43 rd 

# & Broadway/West 43 rd 

# ) is 13.5% (6.38 ȝ g/m 3 

# );

! At 6-12 mph, the difference is 9% (3.2 ȝ g/m 3 

# ); and

! At >12 mph, the difference is 37.3% (39.78 ȝ g/m 3 

# ).

The >12 mph differences increase as the angular direction increases from 240 – 290 degrees, suggesting that fugitive dust from land lo cated between those sites disproportionately impacted concentrations at the West 43 rd 

# Ave. monitoring site (particularly since emissions impacting the monitor at that hour are coming from a minimum distance of 12 miles and the distance betwe en the monitors is 0.4 miles). This finding suggests that any modeling analysis prepare d to support an attainment demonstration needs to account for distance between the location where fugitive dust is emitted and the monitoring site impacted during hig h wind conditions (either through dispersion/deposition or some weighting function).

V - 9

# West 43 rd 

# Avenue & Broadway (Temp #1) versus 51 st

# Avenue & W. Roeser Road (Temp

#2)– An analysis of matched pair measurements between these sites was also prepared for the period between April 2010 and March 2011. Only the high wind category (>12 mph), was analyzed from this pair to get the insights of the emissions between these two monitors. Low wind conditions (< 12 mph) were excl uded because of the relatively long distances of the sources to the West 43 rd 

# monitor and the great wind variability during low wind conditions. To account for the time it takes wind to travel the 1-mile distance between the two sites, the Temp #2 site measurementwas lagged by five minutes relative to the Temp 1 measurement (for winds coming from the west). Tabulations of the high wind results are presented in Table V-7. The resul ts show that at 250 degrees, the angular direction when wind is traveling from the sou thwest from Temp #2 to Temp #1, average concentrations at Temp #1 were consistently high er than at Temp #2:

! At >12 mph, 250 degrees, the difference is 33.8% (2 8.5 ȝ g/m 3 

# ).

A similar relationship was observed between the twosites when the wind was coming from a broader arc from southwest (200 to 270 degrees), average concentrations at Temp #1 were consistently higher than they were at Temp #2. Win d directions between 270 and 300 degrees produced the opposite relationship, wit h Temp #2 concentrations consistently higher than Temp #1. This indicates that when the w ind is coming from the southwest, fugitive dust from disturbed land located between th e two monitors is contributing to elevated concentrations at West 43 rd 

# & Broadway and therefore impacting concentrations recorded at the West 43 rd 

# Avenue monitoring site. It also suggests that there is considerably less disturbed land located in the 270 and 300 degree arc between the two sites.

51 st

# Avenue & W. Roeser Road (Temp #2) versus 67th Aven ue & Southern Avenue (Temp #3)– Similar to the previous analysis, comparisons be tween the April 2010 and March 2011 data set were prepared only for high wind cond itions. The data were lagged by ten minutes to account for the time required for air mass to tra nsit the 2-mile distance between the two sites (i.e., Temp #3 measurements were lagg ed 10 minutes relative to Temp #2 measurements). A summary of the comparisons for wi nds coming from the west is presented in Table V-8.

The results show that at 255 degrees, the angular direction traveling southwest from the Temp 2 to Temp 3 monitor, average concentrations at the 51 st Ave. monitor are lower than at the 67th Ave monitor:

! At >12 mph, 255 degrees, the difference is -7.4% (- 4.6 μg/m 3 

# ).

This pattern of higher upwind values is seen consis tently in an arc ranging from 240 to 300 degrees. It is also opposite of the patterns seen in the previous site comparisons. This suggests that upwind sources impacting the 67 th 

# Ave. monitor had a significantly greater impact than sources located between the two sites h ad on the 51 st

# Ave monitor. Another factor to consider is that the greater distance betwe en these sites also allows more

V - 10

# time/distance for dispersion and deposition relativ e to the previous site comparisons.

Given the limitations of the data, it is not possible to know which effect is stronger (i.e., the influence of nearby upwind sources at both sites ve rsus dispersion/deposition). Nevertheless, it suggests some form of distance wei ghting should be incorporated into the modeling because the greatest impacts at the West 4 3 rd 

# Avenue monitor appear to be coming from nearby upwind land.

67 th 

# Avenue & Southern (Temp#3) versus 115 th 

# Avenue (Temp #4) – Similar to the previous analysis, comparisons between the April 2010 and Ma rch 2011 data set were only prepared for high wind conditions. The data w ere lagged by 25 minutes to account for the time required for air mass to transit the 6-mil e distance between the two sites (i.e., Temp #4 measurements were lagged 25 minutes relativ e to Temp #3 measurements). A summary of the comparisons for winds coming from the west is presented in Table V-9.

The results show that at 255 degrees, the angular d irection traveling southwest from the Temp 2 to Temp 3 monitor, average concentrations atthe 51st Ave. monitor are consistently lower than at the 67th Ave monitor:

! At >12 mph, 255 degrees, the difference is -48.0% (-33. 1 μg/m 3 

# ).

This pattern is consistent with that observed in th e previous comparison between Temp #2 and #3, but inconsistent with the pattern seen i n the sites closer to the West 43 rd 

# Avenue monitor. The differences observed at 255 de grees between Temp #3 and #4, however, are not generally seen over the broader arc between 230 to 290 degrees. Generally, the pattern is the opposite—higher value s are recorded at the 67 th 

# Avenue monitor, suggesting land located between the two si tes, but not directly along the angle between them, is contributing to emissions impacting that site.

115 th 

# Avenue (Temp #4) versus Buckeye – Due to the 18-mile distance between the sites, no comparisons were prepared.

Summary

Analysis of data collected along a line of fixed an d temporary PM-10 monitors located on a southwest trajectory from the West 43 rd 

# Avenue monitor between April 2010 and March 2011 shows a pattern of elevated impacts from nearby land s. Similar impacts were observed in comparisons between concentrations reco rded at the next set of upwind sites. Subsequent site comparisons produced inconsistent an d contradictory results, indicating a lessening impact of lands located between the mon itored pairs. These findings suggest that lands with the greatest impacts on concentration s recorded at the West 43 rd 

# Avenue site are nearby and diminish with distance. The trajectory modeling used to estimate concentrations during high wind hours should incorp orate a form of distance weighting or dispersion to account for this effect.

V - 11

# WD Sector

(Degree) Measurement

Count 43rd & Broadway

(μg/m 3 

# ) West 43 rd



# (μg/m 3 

# ) Difference

(μg/m 3 

# ) Difference

(Percent) 0 21 116.39 113.54 -2.85 -2.5% 5 40 80.27 89.04 8.77 9.8% 10 36 75.15 86.64 11.49 13.3% 15 38 52.07 41.29 -10.78 -26.1% 20 38 43.94 29.64 -14.31 -48.3% 25 38 58.74 27.68 -31.07 -112.3% 30 28 128.18 70.54 -57.64 -81.7% 35 11 167.49 72.14 -95.35 -132.2% 40 11 96.82 45.57 -51.25 -112.4% 45 8 122.26 50.31 -71.95 -143.0% 50 8 66.86 31.31 -35.55 -113.5% 55 1 94.40 47.90 -46.50 -97.1% 60 7 58.56 68.64 10.09 14.7% 65 8 52.56 40.15 -12.41 -30.9% 70 14 50.38 44.15 -6.23 -14.1% 75 27 50.50 46.56 -3.94 -8.5% 80 27 64.63 63.34 -1.29 -2.0% 85 25 56.80 50.55 -6.25 -12.4% 90 19 68.35 62.76 -5.58 -8.9% 95 22 49.96 50.95 0.98 1.9% 100 53 79.18 67.96 -11.22 -16.5% 105 35 141.95 101.19 -40.77 -40.3% 110 21 150.54 104.44 -46.10 -44.1% 115 12 22.78 40.67 17.89 44.0% 120 13 248.30 153.05 -95.25 -62.2% 125 15 166.12 87.01 -79.11 -90.9% 130 23 296.25 139.17 -157.07 -112.9% 135 15 418.20 318.69 -99.51 -31.2% 140 14 177.39 148.76 -28.62 -19.2% 145 12 123.76 118.34 -5.42 -4.6% 150 8 234.20 173.84 -60.36 -34.7% 155 6 796.67 1013.33 216.67 21.4% 160 8 529.34 601.58 72.24 12.0% 165 13 764.37 365.05 -399.32 -109.4% 170 4 273.75 398.73 124.98 31.3% 175 16 630.31 305.08 -325.24 -106.6%



# V - 12

# WD Sector

(Degree) Measurement

Count 43rd & Broadway

(μg/m 3 

# ) West 43 rd



# (μg/m 3 

# ) Difference

(μg/m 3 

# ) Difference

(Percent) 180 15 720.92 320.11 -400.81 -125.2% 185 3 310.43 156.47 -153.97 -98.4% 190 3 46.30 38.80 -7.50 -19.3% 195 2 105.20 94.25 -10.95 -11.6% 200 5 147.90 152.32 4.42 2.9% 205 3 646.00 774.00 128.00 16.5% 210 14 144.09 266.19 122.10 45.9% 215 16 373.00 419.85 46.85 11.2% 220 34 130.21 150.99 20.79 13.8% 225 32 66.88 109.71 42.82 39.0% 230 57 69.08 103.92 34.84 33.5% 235 104 63.30 102.79 39.50 38.4% F

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# 245 158 88.30 132.58 44.29 33.4% 250 153 78.77 134.13 55.36 41.3% 255 182 68.66 129.96 61.30 47.2% 260 246 68.12 130.17 62.05 47.7% 265 271 48.46 91.98 43.53 47.3% 270 205 51.11 110.52 59.40 53.7% 275 141 64.33 124.81 60.48 48.5% 280 94 62.54 150.43 87.89 58.4% 285 51 62.39 124.07 61.68 49.7% 290 20 85.40 195.19 109.79 56.2% 295 24 51.48 74.85 23.37 31.2% 300 7 87.66 164.27 76.61 46.6% 305 8 45.71 46.89 1.18 2.5% 310 12 53.38 64.98 11.61 17.9% 315 15 52.79 53.25 0.45 0.9% 320 13 67.18 84.66 17.48 20.6% 325 8 62.76 56.28 -6.49 -11.5% 330 14 32.36 24.97 -7.39 -29.6% 335 23 57.46 49.74 -7.72 -15.5% 340 41 81.66 79.40 -2.26 -2.8% 345 49 49.45 43.17 -6.28 -14.6% 350 20 141.52 142.74 1.22 0.9% 355 30 126.71 105.82 -20.89 -19.7% 360 3 20.53 25.37 4.83 19.1%



# V - 13

# WD Sector

(Degree) Measurement

Count 43rd & Broadway

(μg/m 3 

# ) West 43 rd



# (μg/m 3 

# ) Difference

(μg/m 3 

# ) Difference

(Percent) 0 61 20.32 16.66 -3.66 -22.0% 5 47 27.47 22.41 -5.06 -22.6% 10 45 31.10 26.73 -4.37 -16.3% 15 53 24.12 20.18 -3.94 -19.5% 20 55 20.65 20.55 -0.10 -0.5% 25 49 25.99 20.67 -5.33 -25.8% 30 41 29.32 22.95 -6.37 -27.8% 35 46 30.43 22.58 -7.85 -34.8% 40 57 27.96 20.81 -7.15 -34.3% 45 59 29.15 21.02 -8.13 -38.7% 50 66 28.94 21.84 -7.09 -32.5% 55 104 29.37 25.18 -4.19 -16.7% 60 141 36.94 29.59 -7.35 -24.8% 65 157 35.87 29.32 -6.55 -22.3% 70 264 37.52 59.90 22.38 37.4% 75 330 35.36 86.99 51.63 59.4% 80 279 36.85 64.04 27.18 42.5% 85 256 34.53 47.96 13.43 28.0% 90 319 30.11 31.37 1.26 4.0% 95 301 30.76 43.93 13.17 30.0% 100 275 32.54 34.14 1.59 4.7% 105 221 33.60 51.44 17.84 34.7% 110 206 26.63 24.19 -2.45 -10.1% 115 189 29.78 27.39 -2.39 -8.7% 120 156 30.39 26.72 -3.68 -13.8% 125 129 51.33 46.01 -5.33 -11.6% 130 125 30.62 24.80 -5.82 -23.5% 135 117 40.18 35.45 -4.73 -13.3% 140 110 39.70 39.48 -0.23 -0.6% 145 94 52.01 37.78 -14.24 -37.7% 150 109 56.90 59.36 2.47 4.2% 155 115 62.44 68.93 6.50 9.4% 160 105 49.32 41.58 -7.74 -18.6% 165 116 67.84 66.32 -1.53 -2.3% 170 82 50.22 66.74 16.52 24.8% 175 74 43.92 49.20 5.28 10.7%



# V - 14

# WD Sector

(Degree) Measurement

Count 43rd & Broadway

(μg/m 3 

# ) West 43 rd



# (μg/m 3 

# ) Difference

(μg/m 3 

# ) Difference

(Percent) 180 63 35.47 51.12 15.65 30.6% 185 57 26.91 27.85 0.95 3.4% 190 50 26.95 25.52 -1.43 -5.6% 195 74 28.78 38.80 10.02 25.8% 200 54 26.78 28.81 2.03 7.1% 205 65 28.55 30.41 1.86 6.1% 210 78 29.71 32.21 2.50 7.8% 215 93 25.50 36.73 11.23 30.6% 220 133 29.03 33.54 4.51 13.4% 225 219 32.26 34.84 2.58 7.4% 230 375 30.65 33.38 2.73 8.2% 235 389 30.89 34.32 3.43 10.0% F

GH GF M J

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# 245 514 33.45 42.23 8.78 20.8% 250 478 33.18 42.14 8.96 21.3% 255 629 35.81 41.11 5.30 12.9% 260 880 33.41 38.89 5.48 14.1% 265 951 32.05 37.10 5.05 13.6% 270 838 32.56 36.81 4.25 11.6% 275 763 32.53 37.56 5.03 13.4% 280 651 30.68 35.33 4.65 13.1% 285 509 27.87 30.98 3.11 10.0% 290 390 27.76 28.06 0.29 1.0% 295 228 27.45 29.60 2.15 7.3% 300 149 27.40 24.79 -2.62 -10.6% 305 96 32.92 27.19 -5.73 -21.1% 310 70 22.01 19.45 -2.56 -13.2% 315 60 35.80 26.73 -9.07 -33.9% 320 60 26.63 22.79 -3.84 -16.8% 325 58 23.16 22.42 -0.74 -3.3% 330 64 23.18 22.59 -0.58 -2.6% 335 50 23.22 16.96 -6.26 -36.9% 340 45 22.04 15.34 -6.70 -43.7% 345 55 25.49 15.47 -10.02 -64.8% 350 70 18.35 14.71 -3.64 -24.8% 355 64 20.18 19.90 -0.28 -1.4% 360 7 20.13 17.17 -2.96 -17.2%



# V - 15

# WD Sector

(Degree) Measurement

Count 43rd & Broadway

(μg/m 3 

# ) West 43 rd



# (μg/m 3 

# ) Difference*

(μg/m 3 

# ) Difference

(Percent) 0 518 46.53 40.66 -5.86 -14.4% 5 536 45.97 45.20 -0.77 -1.7% 10 601 52.61 50.22 -2.39 -4.8% 15 609 52.82 45.26 -7.56 -16.7% 20 638 47.23 43.22 -4.01 -9.3% 25 639 49.91 43.46 -6.45 -14.8% 30 636 51.64 42.89 -8.75 -20.4% 35 723 50.86 45.73 -5.13 -11.2% 40 708 48.95 41.29 -7.66 -18.6% 45 803 48.89 41.39 -7.50 -18.1% 50 859 46.98 41.00 -5.98 -14.6% 55 961 46.18 43.81 -2.38 -5.4% 60 1,113 47.28 43.02 -4.26 -9.9% 65 1,159 46.63 45.47 -1.16 -2.6% 70 1,373 44.75 50.10 5.35 10.7% 75 1,424 43.21 54.39 11.18 20.6% 80 1,316 43.92 49.60 5.68 11.5% 85 1,264 42.95 47.16 4.21 8.9% 90 1,341 40.62 42.41 1.78 4.2% 95 1,361 40.10 46.95 6.85 14.6% 100 1,340 41.59 40.95 -0.64 -1.6% 105 1,240 44.05 45.02 0.96 2.1% 110 1,078 43.69 40.22 -3.47 -8.6% 115 1,014 41.27 39.82 -1.45 -3.6% 120 978 45.21 42.08 -3.12 -7.4% 125 982 50.38 45.33 -5.04 -11.1% 130 958 52.45 48.14 -4.31 -8.9% 135 819 56.34 47.72 -8.63 -18.1% 140 874 54.13 47.03 -7.10 -15.1% 145 825 53.75 46.78 -6.97 -14.9% 150 796 55.61 48.67 -6.94 -14.3% 155 833 59.45 56.67 -2.78 -4.9% 160 790 57.46 52.65 -4.81 -9.1% 165 828 67.32 55.33 -11.99 -21.7% 170 841 52.88 51.87 -1.01 -1.9% 175 870 63.00 52.76 -10.24 -19.4%



# V - 16

# WD Sector

(Degree) Measurement

Count 43rd & Broadway

(μg/m 3 

# ) West 43 rd



# (μg/m 3 

# ) Difference

(μg/m 3 

# ) Difference

(Percent) 180 913 63.96 53.33 -10.64 -19.9% 185 952 52.75 48.40 -4.35 -9.0% 190 954 50.78 50.93 0.15 0.3% 195 1,014 54.49 56.53 2.04 3.6% 200 1,007 52.77 58.15 5.38 9.3% 205 1,035 48.48 50.77 2.28 4.5% 210 1,080 47.35 51.14 3.79 7.4% 215 1,043 49.25 57.43 8.18 14.3% 220 1,126 43.43 47.65 4.23 8.9% 225 1,234 40.96 46.57 5.62 12.1% 230 1,479 38.28 45.15 6.87 15.2% 235 1,564 39.60 46.77 7.17 15.3% F

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L Q

P 

# 245 2,115 42.33 51.05 8.72 17.1% 250 2,154 43.65 56.51 12.86 22.8% 255 2,514 43.70 48.68 4.99 10.2% 260 2,795 44.17 51.20 7.04 13.7% 265 2,907 41.10 49.44 8.33 16.9% 270 2,755 41.13 45.52 4.39 9.7% 275 2,646 41.49 45.26 3.76 8.3% 280 2,515 39.15 42.85 3.70 8.6% 285 2,360 37.96 43.91 5.94 13.5% 290 1,964 37.29 35.66 -1.63 -4.6% 295 1,542 37.81 41.53 3.72 9.0% 300 1,243 39.00 41.53 2.53 6.1% 305 1,012 39.05 35.26 -3.79 -10.7% 310 929 40.86 36.68 -4.18 -11.4% 315 823 42.02 36.28 -5.74 -15.8% 320 769 39.93 38.25 -1.68 -4.4% 325 726 41.51 36.75 -4.76 -13.0% 330 639 43.14 38.31 -4.83 -12.6% 335 644 45.94 37.05 -8.89 -24.0% 340 587 45.38 40.66 -4.73 -11.6% 345 626 43.47 39.32 -4.15 -10.6% 350 624 47.85 40.69 -7.17 -17.6% 355 617 46.02 41.83 -4.19 -10.0% 360 52 37.66 41.61 3.95 9.5



# V - 17

# WD Sector

(Degree) Measurement

Count 51st Avenue

(μg/m 3 

# ) 43rd & Broadway

(μg/m 3 

# ) Difference

(μg/m 3 

# ) Difference

(Percent) 180 9 274.6 839.6 565.0 67.3% 185 14 171.2 442.3 271.1 61.3% 190 3 27.4 83.1 55.7 67.0% 195 2 38.2 102.5 64.3 62.8% 200 NA NA NA NA NA 205 1 32.3 82.3 50.0 60.8% 210 4 34.3 154.3 120.0 77.8% 215 6 59.6 294.4 234.9 79.8% 220 8 182.0 278.3 96.4 34.6% 225 8 197.8 428.8 231.0 53.9% 230 14 219.7 298.2 78.5 26.3% 235 11 135.9 132.7 -3.3 -2.5% 240 18 38.8 78.8 39.9 50.7% 245 24 48.3 64.7 16.5 25.4% F

Q

H J

G Q

Q

L N O

G

L F F

O

L Q J

J

L O

P 

# 255 45 60.0 85.0 25.0 29.4% 260 52 37.7 57.5 19.8 34.4% 265 68 61.8 72.8 10.9 15.0% 270 76 49.4 62.2 12.8 20.5% 275 95 51.8 52.0 0.2 0.4% 280 90 43.9 39.8 -4.1 -10.3% 285 62 49.9 36.6 -13.4 -36.6% 290 35 47.0 29.7 -17.3 -58.2% 295 12 35.6 26.7 -8.8 -33.1% 300 6 49.8 46.8 -3.1 -6.6% 305 4 47.4 58.7 11.4 19.3% 310 5 37.5 51.7 14.2 27.5% 315 10 36.5 40.2 3.7 9.2% 320 13 60.9 73.3 12.4 16.9% 325 11 88.7 91.9 3.3 3.6% 330 4 21.0 21.8 0.8 3.6% 335 9 186.6 118.6 -68.0 -57.3% 340 40 50.2 55.9 5.7 10.2% 345 32 73.9 55.1 -18.8 -34.2% 350 15 111.3 86.0 -25.3 -29.4% 355 18 286.0 231.6 -54.4 -23.5%



# V - 18

# WD Sector

(Degree) Measurement

Count 67th Avenue

(μg/m 3 

# ) 51 st

# Avenue

(μg/m 3 

# ) Difference

(μg/m 3 

# ) Difference

(Percent) 180 10 517.4 181.8 -335.5 -184.5% 185 15 402.3 132.5 -269.8 -203.6% 190 6 64.5 71.0 6.5 9.2% 195 3 29.0 38.8 9.9 25.4% 200 NA NA NA NA NA 205 1 93.4 15.7 -77.7 -494.9% 210 1 221.5 320.7 99.2 30.9% 215 4 59.0 144.7 85.7 59.2% 220 8 106.2 164.9 58.7 35.6% 225 7 124.4 131.8 7.4 5.6% 230 13 154.1 226.6 72.5 32.0% 235 9 102.3 191.1 88.8 46.5% 240 25 55.7 37.9 -17.8 -46.9% 245 24 99.1 68.1 -31.1 -45.7% 250 37 88.3 53.5 -34.8 -65.1% F

Q

Q Q

F K

K

L K K

F

L H S G L K S N L GP 

# 260 63 60.3 45.2 -15.0 -33.2% 265 88 80.8 62.7 -18.2 -29.0% 270 93 72.3 55.0 -17.3 -31.4% 275 106 62.1 55.9 -6.2 -11.0% 280 102 53.6 62.1 8.5 13.7% 285 84 62.9 44.9 -18.0 -40.0% 290 58 148.4 73.2 -75.2 -102.7% 295 18 184.1 95.7 -88.5 -92.5% 300 13 114.0 51.8 -62.2 -120.0% 305 9 69.6 40.4 -29.2 -72.2% 310 18 70.9 71.7 0.8 1.1% 315 23 34.3 17.8 -16.4 -92.0% 320 16 97.7 49.3 -48.4 -98.3% 325 12 164.6 74.9 -89.8 -119.9% 330 3 27.6 20.9 -6.7 -32.3% 335 11 109.9 92.8 -17.1 -18.4% 340 36 47.5 40.7 -6.8 -16.7% 345 28 50.0 70.7 20.7 29.3% 350 16 85.3 84.4 -1.0 -1.1% 355 12 114.2 208.8 94.6 45.3%



# V - 19

# WD Sector

(Degree) Measurement

Count Avondale

(μg/m 3 

# ) 67th Avenue

(μg/m 3 

# ) Difference

(μg/m 3 

# ) Difference

(Percent) 180 NA NA NA NA NA 185 NA NA NA NA NA 190 2 59.7 499.5 439.9 88.1% 195 3 45.2 140.4 95.2 67.8% 200 3 27.7 303.8 276.1 90.9% 205 6 16.1 28.3 12.2 43.1% 210 11 56.3 58.4 2.1 3.6% 215 4 55.8 42.2 -13.6 -32.2% 220 11 125.2 164.7 39.5 24.0% 225 16 71.9 55.4 -16.5 -29.8% 230 22 68.5 158.7 90.2 56.8% 235 19 38.3 81.3 43.0 52.9% 240 33 50.8 56.5 5.8 10.2% 245 47 37.5 80.2 42.7 53.2% 250 84 42.4 113.7 71.3 62.7% 255 72 36.2 74.5 38.3 51.4% F

K

H O

N I

H

F

L J K

M

L I S J

J

L I S GO L H

P 

# 265 61 75.7 63.2 -12.5 -19.8% 270 86 38.2 44.2 5.9 13.4% 275 58 51.2 68.9 17.7 25.6% 280 56 50.5 88.3 37.8 42.8% 285 39 83.7 130.9 47.2 36.1% 290 30 112.5 177.6 65.1 36.7% 295 8 26.4 45.7 19.3 42.2% 300 4 25.9 81.5 55.7 68.3% 305 6 38.3 75.5 37.2 49.3% 310 2 25.4 66.0 40.7 61.6% 315 8 52.2 80.5 28.4 35.2% 320 25 36.8 66.3 29.5 44.5% 325 27 60.4 95.9 35.5 37.0% 330 5 140.7 35.9 -104.8 -291.8% 335 3 100.0 25.2 -74.8 -297.2% 340 19 25.9 37.6 11.7 31.1% 345 16 63.3 40.3 -23.0 -57.1% 350 9 173.8 79.1 -94.7 -119.6% 355 NA NA NA NA NA



# V - 20

# SOURCE WEIGHTING ANALYSIS

A modified rollback analysis is used to assess the benefits of controls to reduce windblown dust emissions on disturbed soil areas. Although t hese benefits can be quantified in terms of emissions, the corresponding reductions in ambie nt PM-10 concentrations at fixed monitoring sites cannot be determined without addit ional analysis of the relationships between emissions reductions and reductions in downwin d concentrations. This relationship is typically quantified through use of disp ersion modeling, and dispersion modeling is used in a screening mode here to inform mo difications to the basic rollback process.

Rollback analysis basically assumes a linear relati onship between levels of pollutant emissions and levels of resulting pollutant concent rations in downwind ambient air. Under this approach, a fractional reduction in emissions is assumed to produce an equivalent reduction in downwind ambient concentrations after backing out the contribution of background air quality. In the case of entrained P M-10 generated by high wind events, however, we know that two primary factors invalidate this principle of linearity: (1) the settling and deposition from the airstream of entraine d particulate with increasing time and distance from the point of entrainment, and (2) the di spersion of particulate both laterally and vertically within the airstream due to increase d boundary layer turbulence at higher wind speeds. Both of these phenomena cause PM-10 co ncentrations to drop dramatically as air parcels holding these concentrations continue movin g downwind from the soils generating windblown dust during high wind events.

Fortunately, quantitative estimates of the impacts of these two phenomena can be derived from the use of steady state, Gaussian-dispersion m odels configured to site-specific conditions found in the Salt River area. One of th ese models, AERMOD, was used to quantify source-receptor relationships in the previ ous version of the Maricopa Area Five Percent Plan. For this reason, AERMOD was used to generate PM-10 concentrations downwind of a hypothetical disturbed soil parcel producing windblown dust to assess the rate at which the combination of particle depositio n and dispersion reduced concentrations at varying distances downwind of the source.

The hypothetical disturbed soil parcel was configured to represent such parcels typically found in the windblown dust corridor straddling the Salt River. The area of the parcel was specified to be 40 acres, representative of larger cultivated parcels in the area. The parcel was assumed to be square with sides oriented north- south and east-west and measuring 401.54 meters each. The plume release height was s et at 0.5 meters, the ambient temperature at 297.9 °K (76.6 °F), and Monin-Obukhov le ngths—relating to atmospheric stability—were set at -304.2 meters. These ambientconditions were the same as those that occurred at the W. 43rd monitoring station on th e high wind hour of February 15, 2006, the single high wind day evaluated in the Tec hnical Support Document to the initial Five Percent Plan. The urban dispersion toggle was set to represent albedo from a population center of 1.5 million. Both source and receptor elevations were set to zero to generally represent the topographical gradient alon g the Salt River. The surface

V - 21

# roughness was set at 0.04 meters based on Nickling and Gillies 1986 wind tunnel testing

in the Salt River area. The particle size ranges o f loose soil particles entrained by high wind conditions from this parcel were assumed to be the same as the size distribution used in the initial Five Percent Plan modeling of hig h wind conditions. This distribution, as adapted from ambient air monitoring conducted byT&B Systems in the Salt River area in 2006, is shown in Table V-10.

Diameter (micron) 1.229 3.263 4.949 6.802 8.724

Mass Fraction 0.189 0.27 0.231 0.206 0.105 Density (g/cm 3 

# ) 2.5 2.5 2.5 2.5 2.5



# The emission rate for the 40-acre parcel was set at 1,000 pounds of PM-10 per hour, or 7.815x10 -4

# g/sec-m 2 

# , which is consistent with emission rates published in the 2008 Periodic Emissions Inventory for PM-10 (Appendix A, Ex hibit 1).

Receptor sites were located at regular intervals do wnwind of the trailing edge of the area source. Receptor spacing was set at 100 meters between 0 and 1,000 meters; at 200 meters between 1,000 and 3,000 meters; and then at 500 meters between 3,000 and 30,000 meters. All receptors were configured to lie on a radius pointing due east from the

source.

Meteorological conditions for this screening run in cluded winds blowing from due west at a series of elevated wind speeds. Hourly average w ind velocities ranged from 12 to 24 mph in 1-mph increments.

The resulting hourly-average PM-10 concentrations reported by the AERMOD screening run are shown in Appendix A. These results show co ncentrations varying by three orders of magnitude over the downwind distance span of 0 t o 30,000 meters. At the threshold of high wind conditions, 12 mph, concentrations dro p by a factor of 10 between 0 and 500 meters, between 500 and 2,800 meters, and between 2 ,800 and 30,000 meters. In other words, emissions from sources proximate to or withi n 500 meters upwind of a monitoring station will overwhelm the emission contributions of s ources more than 500 meters upwind.

The trends of modeling results were analyzed using the curve-fitting functions of Excel. In the first analytical series, plots of PM-10 conc entrations vs. downwind distance were mapped for the wind speeds of 12, 16, 20, and 24 mp h. These plots are shown in Figure V-2.

V - 2212 MPH16 MPH24 MPH20 MPHV - 23

# This progression of plots includes the best fit curve statistics for each AERMOD wind

speed. Because of the rapidity at which concentrat ions decline with increasing downwind distance, a power curve was found to provide the be st equation fit to these data. From observation, the correlation coefficients (r2 

# ) of these plots are very high—exceeding 0.96 in all cases. Similarly, the exponent of x in each best fit power curve hovers near -1.9, suggesting that concentrations roughly decline with downwind distances on a 1/x2

# basis.

Further analysis of the modeling data, however, sugg ests a somewhat different picture. In each of the plots in Figure V-2, modeling results straddle the best fit curve in a sinusoidal fashion. The first plot in Figure V-2, that of the 12 mph modeling results, was disaggregated into separate sinusoid cycles to dete rmine whether the power equation exponent remained constant over each block of downw ind receptors contained within a cycle. The sinusoid cycles were chosen to encompas s the receptor intervals of 0 to 1,000 meters; 1,000 to 3,000 meters; 3,000 to 15,000 meters; an d 15,000 to 30,000 meters. The resulting plots of PM-10 concentration vs. distance are presented in Figure V-3.

The plots in Figure V-3 show similarly high r2

# values, but the power exponents vary substantially from -0.2 to -1.2. Also, the trend in po wer exponents is somewhat linear, starting at the high end of -1.233 for receptors in the 0 t o 1,000 meter range, and steadily decreasing to -0.173 in the 15,000 to 30,000 meter range. These results point dramatically to a concentration-distance relationsh ip much closer to 1/x than 1/x 2



# , especially in the critical zone of nearest receptors, from 0 to 1,000 meters, in which source impacts significantly overwhelm the contributions o f sources farther upwind of a receptor.

A similar, but less dramatic, result is seen when t he modeling data are evaluated over different portions of the right tail (i.e., toward increasing distances) of these plots in Figure V-2. This analysis was conducted to determine the effect of removing the highest—and most dominant—concentrations reported for near-downwind recept ors from the analysis in order to study that portion of the curve with the flatter gradient. In this analysis, power equation curves were fitted to the distributions of PM-10 concentrations (1) over the full range of receptors, (2) over the range of receptors equal to or greater than 500 meters downwind of the source, (3) equal to or greater than 1 ,000 meters downwind, (4) equal to or greater than 2,000 meters downwind, and (5) equa l to or greater than 4,000 meters downwind. The plots of these ranges are shown in F igure V-4.

The power curve statistics of plots in Figure V-4, like those in Figure V-3, show a declining trend in negative power curve exponents with increa sing distance, but also stable and very high values of r2

# with increasing distance. Over the full range of receptors, the power curve exponent is -1.803, again suggesting a roughl y 1/x2

# relationship of PM-10 concentration with increasing downwind distance. E xcluding the receptors within 500 meters of the source, however, causes the exponent to decline to -1.391. Removing receptors within 1,000 meters, 2,000 meters, and 4 ,000 meters of the source causes the power curve exponent to drop to -1.098, -0.825, and -0.504, respectively. These exponents conversely suggest rough relationships be tween PM 10

# concentration and distance of 1/x to 1/x.

V - 2412 MPH, 0-1000 M12 MPH, 1000-3000 M12 MPH, 3000-15000M12 MPH, 15000-30000MV - 2512 MPH, RECEPTORS > 0M 12 MPH, RECEPTORS > 500M12 MPH, RECEPTORS > 4000M12 MPH, RECEPTORS > 2000M12 MPH, RECEPTORS > 1000MV - 26

# Without question, receptors close to an upwind source will be much more dramatically

impacted by emissions from that source—emission rat es being equal—than by similar sources at increasing distances upwind of receptors . As a result, the power curve exponents produced by the modeling of impacts in th e near downwind field (0 to 1,000 meters) from this modeling analysis must be given g reatest weight because of the exponentially smaller impacts at receptors at great er downwind distances. The first curve in Figure V-3 represents this receptor interval, and re ports a power curve exponent of - 1.233. Because the remainder of the plots report exp onents straddling this value, this exponent represents the best singular value for the a djustment of downwind concentrations with increasing distance. Also, bec ause of the range of exponents reported in these plots, an exponent of -1.0—equivalent to 1 /x—is reasonably equivalent and should be used to adjust windblown dust emission im pacts with increasing downwind distance in any modified rollback analysis in the S alt River area.

V - 27