stacks.cdc.gov · Web viewTable S1. Multivariate neighborhood-specific and Boston-area land use regression models for ln(PNC). Table S 2. Statistics for model evaluation relative

Supporting Information for “Assessing the Suitability of Multiple Dispersion and Land Use Regression Models for Urban Traffic-Related Ultrafine Particles”

Allison P Patton*1,2, Chad Milando2,3, John L Durant2, Prashant Kumar4, 5

1 Environmental and Occupational Health Sciences Institute, Rutgers University, Piscataway, NJ, USA

2 Department of Civil and Environmental Engineering, Tufts University, Medford, MA, USA

3 Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann

Arbor, MI, USA

4 Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences

(FEPS), University of Surrey, Guildford GU2 7XH, Surrey, United Kingdom

5 Environmental Flow (EnFlo) Research Centre, FEPS, University of Surrey, Guildford GU2 7XH, United

Kingdom

Contents (24 Pages)

2 Supporting Text Sections

8 Figures

7 Tables

References

Page S1

Supporting Text

Section S1. Calculation of Emission Factors (2 pages)Section S2. Calculation of Comparative Performance Evaluation Statistics (1 page)

Supporting Figures

Figure S1. Scatter plots of PNC model predictions versus loess smoothed measurements in Somerville (span=0.2). The four scenarios are for wind directions relative to I-93 and hot or cold air temperatures, as listed in the panels and described in Table 2.

Figure S2. PNC-distance plots up to 200 m from the edge of I-90 in Chinatown predicted by CALINE4, R-LINE, AERMOD, QUIC, and the CAFEH regression model. The six scenarios are for wind directions relative to I-90 and hot or cold air temperatures, as listed in the panels and described in Table 2. For perpendicular wind directions, the descriptions include whether the wind was from the north (N) or south (S) across I-90. Smoothed near-road measurements (NR Measured) and background concentrations (B Measured) are shown for comparison.

Figure S3. Scatter plots of PNC model predictions versus loess smoothed measurements in Chinatown (span=0.2). The six scenarios are for wind directions relative to I-93 and hot or cold air temperatures, as listed in the panels and described in Table 2. For parallel wind directions, the descriptions include whether the wind was from the north (N) or south (S) across I-90.

Figure S4. PNC-distance plots of smoothed PNC measurements (loess span = 0.1, 0.2, and 0.5) up to 200 m from the edge of I-93 in Chinatown. The six scenarios are for wind directions relative to I-93 and hot or cold air temperatures, as listed in the panels and described in Table 2. For parallel wind directions, the descriptions include whether the wind was from the north (N) or south (S) across I-90.

Figure S5. PNC-distance plots of smoothed PNC measurements (loess span = 0.1, 0.2, and 0.5) up to 200 m from the edge of I-90 in Chinatown. The six scenarios are for wind directions relative to I-90 and hot or cold air temperatures, as listed in the panels and described in Table 2. For perpendicular wind directions, the descriptions include whether the wind was from the north (N) or south (S) across I-90.

Figure S6. Scatter plots of PNC model predictions versus loess smoothed measurements in Chinatown (span=0.2) using the 25th percentile of measurements as the background concentration in the dispersion models. The six scenarios are for wind directions relative to I-93 and hot or cold air temperatures, as listed in the panels and described in Table 2. For parallel wind directions, the descriptions include whether the wind was from the north (N) or south (S) across I-90.

Figure S7. Scatter plots of PNC model predictions versus loess smoothed measurements in Chinatown (span=0.2) at downwind receptors only. The six scenarios are for wind directions relative to I-93 and hot

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or cold air temperatures, as listed in the panels and described in Table 2. For parallel wind directions, the descriptions include whether the wind was from the north (N) or south (S) across I-90.

Figure S8. Wind velocity profiles from QUIC-URB at 2.9 m above ground level for Scenario CT-3 for wind components in the (a) x direction (U velocity, left to right), (b) y-direction (V velocity, bottom to top), and (c) z-direction (W velocity, upward), and (d) the velocity magnitude.

Supporting Tables

Table S1. Multivariate neighborhood-specific and Boston-area land use regression models for ln(PNC).

Table S2. Statistics for model evaluation relative to loess smoothed PNC measurements (span=0.2) for different days in Somerville.

Table S3. Pearson correlations of model predictions in Somerville.

Table S4. Statistics for model evaluation relative to loess smoothed PNC measurements (span=0.2) for different days in Chinatown.

Table S5. Statistics for model evaluation relative to loess smoothed PNC measurements for different days in Chinatown. The loess smooth span was 0.2 and the 25th percentile of measurements was used for the background concentration in the dispersion models.

Table S6. Statistics for model evaluation relative to loess smoothed PNC measurements (span=0.2) for different days in Chinatown at only receptors downwind of I-90.

Table S7. Pearson correlations of model predictions in Chinatown.

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Calculation of Emission Factors

Estimate the particle number emission factor EFPN (units in brackets) for a given temperature in °C, T,

based on a log-linear relationship between PNC and temperature in Perkins et al.61

EFPN [ particlesveh∗mile ]=1014.261−(0.0116∗T) [ particlesveh∗km ]∗1.6[ kmmile ]

Convert the number emission factor to a mass emission factor for use in the dispersion models.

Theoretically the number emissions could be used in the models. However, there would be buffer

overflows in the output for some of the dispersion models (e.g., CALINE4, which cannot report output

concentrations > 999 μg/m3). Because the models assumed inert particles, conversions between mass

concentration and PNC were not affected by the size, shape, or density of particles. Calculate the

volume and mass per particle, assuming a spherical particle with d = 100 nm diameter and density = 1

g/cm3.

Volume [ m3

particle ]=π3∗( d [nm]

2 )3

∗(10−9 [ mnm ])3

Mass [ gparticle ]=ρ [ g

m3 ]∗Volume [m3 ]

Convert EFPN from number concentration to mass concentration in units required by the models.

CALINE4 EFPN [ gveh∗mile ]=EFPN [ particlesveh∗mile ]∗Mass [ g

particle ]QUIC

Page S4

EFPN [mgs ]=EFPN [ gveh∗mile ]∗LinkLength [miles ]∗TrafficCount

[ vehhr ]∗hr

3600 s∗1000mg

g

AERMOD EFPN [ gs∗m2 ]=

EFPN [mgs ]LinkLength [m ]∗L inkWidth[m ]

R-LINE EFPN [ gs∗m ]=

EFPN [mgs ]LinkLength [m ]

∗[ g1000mg ]

Run the dispersion models with EFPN = 1 (units depend on the model). Scale by Model EFPN (as calculated

for each model) to adjust output to “real” mass concentrations.

MassConcentration[ μgm3 ]=ModelOutput∗Model EFPN

Convert mass concentration to number concentration.

PNC [ particlescm3 ]=

MassConcentration[ μgm3 ]Mass [ g

particle ]∗ gcm3

1012 μgm3

Page S5

Calculation of Comparative Performance Evaluation Statistics

All statistics were calculated using the custom stats function in R. In the calculations, meas = measured

PNC and mod = PNC predicted by the models

library(rgp)stats <- function(mod,meas){

# percent within factor of 2 fac2 <- sum(meas > 0.5*mod & meas < 2*mod,na.rm=T)/length(meas)*100

# percent within factor of 1.5 fac1.5 <- sum(meas > mod/1.5 & meas < 1.5*mod,na.rm=T)/length(meas)*100

# R-squared r2 <- round(summary(lm(mod~meas))$r.squared,2)

# Normalized mean square error nmse <- round(nmse(meas,mod),3)

# fractional bias Cm.mean = mean(meas) Cp.mean = mean(mod) FB = round(2*(Cp.mean-Cm.mean)/(Cm.mean+Cp.mean),2)

}

Usage at command prompt:stats(modeled_values_here, measured_values_here)

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Figures

Figure S1. Scatter plots of PNC model predictions versus loess smoothed measurements in Somerville (span=0.2). The four scenarios are for wind directions relative to I-93 and hot or cold air temperatures, as listed in the panels and described in Table 2.

Page S7

Figure S2. PNC-distance plots up to 200 m from the edge of I-90 in Chinatown predicted by CALINE4, R-LINE, AERMOD, QUIC, and the CAFEH regression model. The six scenarios are for wind directions relative to I-90 and hot or cold air temperatures, as listed in the panels and described in Table 2. For perpendicular wind directions, the descriptions include whether the wind was from the north (N) or south (S) across I-90. Smoothed near-road measurements (NR Measured) and background concentrations (B Measured) are shown for comparison.

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Figure S3. Scatter plots of PNC model predictions versus loess smoothed measurements in Chinatown (span=0.2). The six scenarios are for wind directions relative to I-93 and hot or cold air temperatures, as listed in the panels and described in Table 2. For parallel wind directions, the descriptions include whether the wind was from the north (N) or south (S) across I-90.

Page S9

Figure S4. PNC-distance plots of smoothed PNC measurements (loess span = 0.1, 0.2, and 0.5) up to 200 m from the edge of I-93 in Chinatown. The six scenarios are for wind directions relative to I-93 and hot or cold air temperatures, as listed in the panels and described in Table 2. For parallel wind directions, the descriptions include whether the wind was from the north (N) or south (S) across I-90.

Page S10

Figure S5. PNC-distance plots of smoothed PNC measurements (loess span = 0.1, 0.2, and 0.5) up to 200 m from the edge of I-90 in Chinatown. The six scenarios are for wind directions relative to I-90 and hot or cold air temperatures, as listed in the panels and described in Table 2. For perpendicular wind directions, the descriptions include whether the wind was from the north (N) or south (S) across I-90.

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Figure S6. Scatter plots of PNC model predictions versus loess smoothed measurements in Chinatown (span=0.2) using the 25 th percentile of measurements as the background concentration in the dispersion models. The six scenarios are for wind directions relative to I-93 and hot or cold air temperatures, as listed in the panels and described in Table 2. For parallel wind directions, the descriptions include whether the wind was from the north (N) or south (S) across I-90.

Page S12

Figure S7. Scatter plots of PNC model predictions versus loess smoothed measurements in Chinatown (span=0.2) at downwind receptors only. The six scenarios are for wind directions relative to I-93 and hot or cold air temperatures, as listed in the panels and described in Table 2. For parallel wind directions, the descriptions include whether the wind was from the north (N) or south (S) across I-90.

Page S13

Figure S8. Wind velocity profiles from QUIC-URB at 2.9 m above ground level for Scenario CT-3 for wind components in the (a) x direction (U velocity, left to right), (b) y-direction (V velocity, bottom to top), and (c) z-direction (W velocity, upward), and (d) the velocity magnitude.

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(a) (b)

(d)(c)

Tables

Page S15

Table S1. Multivariate neighborhood-specific and Boston-area land use regression models for ln(PNC).a

Modified from Patton et al, 2015.1

Somerville ChinatownModel Adjusted R2 0.42 0.23

Variable coeff b SE c coeff b SE c

(Intercept) 10.677 0.011 10.209 0.014Spatial Variables

within highway corridor d 0.244 0.006 NA NAon a major road d 0.208 0.005 NA NAupwind of I-93 d -0.192 0.005 NA NAupwind of nearest major road d NA NA NA NAdistance upwind of I-93, km -0.213 0.006 NA NAdistance downwind of I-93, km -0.464 0.007 -0.373 0.014distance from nearest major road, km -0.230 0.014 NA NAdistance from major intersection, km e NA NA -0.964 0.020

Meteorologytemperature, °C -0.037 0.000 -0.008 0.000humidity, % NA NA 0.002 0.000wind speed (U), m/s -0.182 0.002 -0.071 0.001cosine of wind direction relative to I-93 -0.029 0.003 NA NAsquare of cosine of wind direction relative to southeast 0.820 0.007 NA NAsine of wind direction NA NA 0.347 0.004wind direction ±15° from airport and downtown Bostond NA NA NA NA

Traffic and day of the weeklow traffic (<7000 vph) d -0.103 0.006 NA NAcongestion (<64 km/hr) d 0.181 0.005 NA NAvolume on I-93, 1000 vph NA NA 0.012 0.001Monday d 0.398 0.010 0.496 0.008Tuesday d 0.569 0.008 0.373 0.006Wednesday d 0.530 0.008 0.379 0.006Thursday d 0.579 0.008 0.773 0.006Friday d 0.239 0.011 0.793 0.006Saturday d 0.504 0.008 0.018 0.006aVariables in the model are statistically significant (p≤0.001). Temporal variables are input on an hourly basis. NA = not applicable for this model. bCoeff is the coefficient estimate. The full model is the intercept plus the sum of products of the coefficients and their variable values. cSE is the standard error in the coefficient estimate. dThese variables are categorical variables. The reference for day of week is Sunday when all days are included individually or weekend days when only weekday vs weekend is included. All other variables are linear variables. eMajor intersections are defined as intersections with average vehicle delay of 20 or more seconds.

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Table S2. Statistics for model evaluation relative to loess smoothed PNC measurements (span=0.2) for different days in Somerville.

Model and Scenario FAC2, % a FAC1.5, % b R2 c NMSE d FB e

CALINE4Scenario SV-1 100 100 0.62 0.05 -0.26Scenario SV-2 64 0 0.02 0.20 -0.63Scenario SV-3 45 0 0.93 0.01 -0.67Scenario SV-4 100 91 0.92 0.01 -0.25

Overall 77 48 0.54 0.06 -0.49R-LINE

Scenario SV-1 100 82 0.50 0.07 -0.28Scenario SV-2 64 9 0.00 0.16 -0.66Scenario SV-3 82 0 0.94 0.01 -0.61Scenario SV-4 100 91 0.94 0.01 -0.20

Overall 86 45 0.58 0.13 -0.49AERMOD


Overall 86 48 0.57 0.11 -0.51QUIC

Scenario SV-1 100 100 0.59 0.05 -0.18Scenario SV-2 64 9 0.52 ---f -0.69Scenario SV-3 100 100 0.43 0.08 -0.22Scenario SV-4 91 55 0.82 0.04 0.39

Overall 89 66 0.42 0.14 -0.33Regression


Overall 61 45 0.42 0.16 -0.53The model statistics are (a) Fac2, fraction of predictions within a factor of 2 of the measurements; (b) Fac1.5, fraction of predictions within a factor of 1.5 of the measurements; (c) R2, simple linear regression correlation coefficient; (d) NMSE, normalized mean square error; and (e) FB, fractional bias. Values are bold if they meet the criteria of R2 > 0.9, NMSE ≤ 0.25, absolute value of FB ≤ 0.25, and FAC2 >70 %68. (f) No estimate of NMSE was possible because QUIC predicted no highway influence on PNC under this test case.

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Table S3. Pearson correlations of model predictions in Somerville.

CALINE4 R-LINE AERMOD QUICScenario SV-1

R-LINE 0.99AERMOD 0.98 0.99

QUIC 0.94 0.91 0.91Regression 0.98 0.97 0.94 0.92

Scenario SV-2R-LINE 0.98

AERMOD 1.00 0.97QUIC --- a --- a --- a

Regression 0.63 0.75 0.58 --- a

Scenario SV-3R-LINE 1.00

AERMOD 1.00 1.00QUIC 0.78 0.79 0.80

Regression 0.97 0.98 0.96 0.78Scenario SV-4


QUIC 0.96 0.95 0.92Regression 0.93 0.90 0.85 0.94

OverallR-LINE 0.99

AERMOD 0.99 1.00QUIC 0.85 0.88 0.89

Regression 0.94 0.95 0.93 0.82(a) QUIC predicted no highway influence (i.e., all concentrations equal to background concentration) on PNC under this test case.

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Table S4. Statistics for model evaluation relative to loess smoothed PNC measurements (span=0.2) for different days in Chinatown.


CALINE4Scenario CT-1 70 16 0.04 0.11 -0.58Scenario CT-2 5 1 0.37 0.09 -0.97Scenario CT-3 29 10 0.07 0.22 -0.85Scenario CT-4 35 4 0.10 0.26 -0.72Scenario CT-5 48 11 0.27 0.18 -0.73Scenario CT-6 5 4 0.01 0.20 -1.14

Overall 32 8 0.78 0.02 -0.81R-LINE

Scenario CT-1 58 9 0.09 0.08 -0.65Scenario CT-2 4 0 0.45 0.09 -0.99Scenario CT-3 29 9 0.12 0.18 -0.85Scenario CT-4 31 1 0.02 0.22 -0.73Scenario CT-5 25 12 0.07 0.15 -0.88Scenario CT-6 5 4 0.05 0.13 -1.14

Overall 25 6 0.81 0.02 -0.84AERMOD


Overall 25 6 0.81 0.02 -0.84QUIC

Scenario CT-1 56 12 0.13 0.17 -0.65Scenario CT-2 40 24 0.01 0.15 0.68Scenario CT-3 31 20 0.03 0.21 0.17Scenario CT-4 49 10 0.03 0.20 -0.65Scenario CT-5 70 49 0.06 0.16 -0.34Scenario CT-6 8 5 0.00 0.24 -1.00

Overall 43 20 0.52 0.05 -0.57Regression

Scenario CT-1 0 0 0.37 0.07 -0.93Scenario CT-2 89 51 0.00 0.15 0.34Scenario CT-3 95 68 0.00 0.12 -0.10Scenario CT-4 0 0 0.26 0.05 -1.18

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Scenario CT-5 94 45 0.43 0.05 -0.36Scenario CT-6 5 3 0.36 0.04 -1.21

Overall 47 28 0.82 0.02 -0.91The model statistics are (a) Fac2, fraction of predictions within a factor of 2 of the measurements; (b) Fac1.5, fraction of predictions within a factor of 1.5 of the measurements; (c) R2, simple linear regression correlation coefficient; (d) NMSE, normalized mean square error; and (e) FB, fractional bias. Values are bold if they meet the criteria of R2 > 0.9, NMSE ≤ 0.25, absolute value of FB ≤ 0.25, and FAC2 >70%.

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Table S5. Statistics for model evaluation relative to loess smoothed PNC measurements for different days in Chinatown. The loess smooth span was 0.2 and the 25th percentile of measurements was used for the background concentration in the dispersion models.


CALINE4Scenario CT-1 96 59 0.04 0.11 -0.37Scenario CT-2 76 29 0.37 0.09 -0.56Scenario CT-3 79 38 0.07 0.22 -0.47Scenario CT-4 100 81 0.10 0.26 -0.27Scenario CT-5 76 46 0.27 0.18 -0.47Scenario CT-6 60 6 0.01 0.20 -0.64

Overall 81 43 0.85 0.01 -0.41R-LINE


Overall 77 39 0.86 0.01 -0.43AERMOD


Overall 75 38 0.86 0.02 -0.44QUIC

Scenario CT-1 85 49 0.13 0.17 -0.42Scenario CT-2 40 20 0.01 0.15 0.77Scenario CT-3 45 31 0.03 0.21 0.33Scenario CT-4 100 92 0.03 0.20 -0.22Scenario CT-5 79 55 0.06 0.16 -0.14Scenario CT-6 71 22 0.00 0.24 -0.54

Overall 70 45 0.71 0.03 -0.24The model statistics are (a) Fac2, fraction of predictions within a factor of 2 of the measurements; (b) Fac1.5, fraction of predictions within a factor of 1.5 of the measurements; (c) R2, simple linear regression correlation coefficient; (d) NMSE, normalized mean square error; and (e) FB, fractional bias. Values are bold if they meet the criteria of R2 > 0.9, NMSE ≤ 0.25, absolute value of FB ≤ 0.25, and FAC2 >70%.

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Table S6. Statistics for model evaluation relative to loess smoothed PNC measurements (span=0.2) for different days in Chinatown at only receptors downwind of I-90.


CALINE4Scenario CT-2 13 3 0.00 0.20 -0.88Scenario CT-3 37 3 0.07 0.15 -0.78Scenario CT-5 64 12 0.56 0.06 -0.60Scenario CT-6 0 0 0.01 0.12 -1.13

R-LINEScenario CT-2 10 0 0.03 0.16 -0.93Scenario CT-3 33 0 0.11 0.14 -0.79Scenario CT-5 28 12 0.09 0.12 -0.83Scenario CT-6 0 0 0.11 0.09 -1.13

AERMODScenario CT-2 0 0 0.10 0.13 -0.98Scenario CT-3 30 0 0.04 0.16 -0.85Scenario CT-5 30 10 0.11 0.16 -0.83Scenario CT-6 0 0 0.14 0.10 -1.14

QUICScenario CT-2 53 33 0.05 0.26 -0.13Scenario CT-3 10 7 0.03 0.28 -0.91Scenario CT-5 98 72 0.07 0.11 -0.10Scenario CT-6 4 2 0.03 0.16 -0.91

RegressionScenario CT-2 100 93 0.02 0.14 0.11Scenario CT-3 100 73 0.13 0.21 -0.22Scenario CT-5 96 46 0.44 0.05 -0.37Scenario CT-6 0 0 0.17 0.13 -1.21

These comparisons had 30 receptors for Scenarios 2 and 3, 50 receptors for Scenario 5, and 47 receptors for Scenario 6. The model statistics are (a) Fac2, fraction of predictions within a factor of 2 of the measurements; (b) Fac1.5, fraction of predictions within a factor of 1.5 of the measurements; (c) R2, simple linear regression correlation coefficient; (d) NMSE, normalized mean square error; and (e) FB, fractional bias. Values are bold if they meet the criteria of R2 > 0.9, NMSE ≤ 0.25, absolute value of FB ≤ 0.25, and FAC2 >70%.

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Table S7. Pearson correlations of model predictions in Chinatown.

CALINE4 R-LINE AERMOD QUICScenario CT-1


QUIC −0.61 −0.57 −0.07Regression 0.08 0.34 0.32 0.04

Scenario CT-2R-LINE 0.97

AERMOD 0.90 0.96QUIC −0.46 −0.49 −0.50

Regression −0.12 −0.22 −0.24 0.32Scenario CT-3


QUIC −0.66 −0.75 −0.74Regression −0.11 −0.15 −0.14 0.51


AERMOD 0.93 0.89QUIC 0.30 0.33 0.14

Regression 0.11 0.23 0.19 0.02Scenario CT-5


QUIC 0.73 0.92 0.87Regression 0.53 0.56 0.51 0.51


AERMOD 0.79 0.91QUIC 0.36 0.40 0.30

Regression 0.55 0.53 0.45 0.18Overall


QUIC 0.80 0.81 0.81Regression 0.93 0.94 0.93 0.79

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References

1. Patton, A. P.; Zamore, W.; Naumova, E. N.; Levy, J. I.; Brugge, D.; Durant, J. L., Transferability and generalizability of regression models of ultrafine particles in urban neighborhoods in the Boston area. Environ. Sci. Technol. 2015, 49, (10), 6051-60.

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Documents

stacks.cdc.gov · Web viewTable S1. Multivariate neighborhood-specific and Boston-area land use regression models for ln(PNC). Table S 2. Statistics for model evaluation relative