www.pnas.org/cgi/doi/10.1073/pnas.

1

Supplementary Information for

U.S. Particulate Matter Air Quality Improves Except in Wildfire Prone

Areas

Crystal D. McClure and Daniel A. Jaffe

Corresponding Author: Daniel A. Jaffe

Email: djaffe@uw.edu

This PDF file includes:

Figs. S1 to S5

Tables S1 to S2

1804353115

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Figure S1. Krige Uncertainty: PM2.5 In plot (a), GGS-interpolated results for the 98th quantile

PM2.5 are shown by the color ramp. This simulation provides a prediction of Krige-interpolated

values given site-specific trend uncertainty. Observed 98th quantile PM2.5 trends (calculated using

QR methods) from IMPROVE sites for 1988-2016 are shown by black dots with corresponding

values in µg/m3/yr. The solid black lines with arrows (indicating direction) show the boundary

where 90% of the GGS-interpolated PM2.5 trends within are positive or negative. In plot (b), the

standard error of the Krige prediction is shown. Black dots provide the location of each site used

in the original Krige product. Both plots correspond to Figure 1 in the text.

3

Figure S2. Krige Uncertainty: TC In plot (a), GGS-interpolated results for the 98th quantile TC

are shown by the color ramp. This simulation provides a prediction of Krige-interpolated values

given site-specific trend uncertainty. Observed 98th quantile TC trends (calculated using QR

methods) from IMPROVE sites for 1988-2016 are shown by black dots with corresponding

values in µg/m3/yr. The solid black lines with arrows (indicating direction) show the boundary

where 90% of the GGS-interpolated TC trends within are positive or negative. In plot (b), the

standard error of the Krige prediction is shown. Black dots provide the location of each site used

in the original Krige product. The higher standard error in Idaho and the southeast U.S. is due to

sparse spatial coverage and highly variable TC trends. Both plots correspond to Figure 5(a) in the

text.

4

Figure S3. Krige Uncertainty: SO4 In plot (a), GGS-interpolated results for the 98th quantile

SO4 are shown by the color ramp. This simulation provides a prediction of Krige-interpolated

values given site-specific trend uncertainty. Observed 98th quantile SO4 trends (calculated using

QR methods) from IMPROVE sites for 1988-2016 are shown by black dots with corresponding

values in µg/m3/yr. For all regions within the U.S., >90% of the GGS-interpolated SO4 trends are

negative. In plot (b), the standard error of the Krige prediction is shown. Black dots provide the

location of each site used in the original Krige product. Both plots correspond to Figure 5(b) in

the text.

5

Figure S4. 98th Quantile MODIS AQUA AOD Trends Krige-interpolated values are shown

for 2002-2017 observed 98th Quantile Regression AOD trends. Solid black lines with arrows

(indicating direction) show the boundary where 90% of the Krige-interpolated AOD trends within

are positive or negative.

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Figure S5. Krige Uncertainty: MODIS AOD In plot (a), GGS-interpolated results for the 98th

quantile MODIS AOD are shown by the color ramp. This simulation provides a prediction of

Krige-interpolated values given site-specific trend uncertainty. The solid black lines with arrows

(indicating direction) show the boundary where 90% of the GGS-interpolated MODIS AOD

trends within are positive or negative. In plot (b), the standard error of the Krige prediction is

shown. Both plots correspond to Figure S4 in the SI.

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Table S1. All 98th Quantile Trend Data PM2.5, TC, and SO4 98th quantile trend data (QR98)

and estimated trend standard error (SE) are included for all IMPROVE sites with greater than 10

years of data. NA values for some sites are due to less than 10 years for that specific parameter or

removed due to unrealistic OC mass values (for TC trends only). Sites in the continental U.S. and

Ontario, Canada, are listed. Bolded trend values have a statistical significance of p < 0.10.

SiteCode

Max

Year

Min

Year

Years

of Data Latitude Longitude

PM QR98

(µg/m3/yr) PM SE

(µg/m3/yr)

TC QR98

(µg/m3/yr)

TC SE

(µg/m3/yr)

SO4

QR98

(µg/m3/yr)

SO4 SE

(µg/m3/yr)

ACAD1 2016 1988 29 44.4 -68.3 -0.67 0.10 -0.16 0.04 -0.39 0.06

ADPI1 2010 2001 10 42.1 -77.2 -2.46 0.49 -0.23 0.13 -1.51 0.30

AGTI1 2016 2000 17 33.5 -117.0 -0.51 0.09 -0.32 0.07 -0.27 0.02

AREN1 2010 2001 10 39.9 -77.3 -2.08 0.27 -0.15 0.13 -1.54 0.23

ATLA1 2016 2004 13 33.7 -84.3 NA NA -0.63 0.12 NA NA

BADL1 2016 1988 29 43.7 -101.9 -0.15 0.05 0.03 0.06 -0.09 0.01

BALD1 2016 2000 17 34.1 -109.4 -0.19 0.08 -0.31 0.19 -0.04 0.01

BAND1 2016 1988 29 35.8 -106.3 -0.02 0.05 -0.03 0.06 -0.03 0.01

BIBE1 2016 1988 29 29.3 -103.2 -0.10 0.14 -0.06 0.04 -0.05 0.02

BIRM1 2016 2004 13 33.6 -86.8 -2.10 0.27 -1.31 0.14 -1.08 0.06

BLIS1 2016 1990 27 39.0 -120.1 0.13 0.13 0.16 0.10 -0.02 0.01

BLMO1 2015 2002 14 43.7 -96.2 -0.64 0.24 0.12 0.13 -0.34 0.07

BOAP1 2016 2000 17 33.9 -106.9 0.21 0.15 -0.06 0.06 -0.05 0.02

BOND1 2016 2001 16 40.1 -88.4 -1.12 0.16 -0.25 0.06 -0.77 0.12

BOWA1 2016 1991 26 47.9 -91.5 -0.23 0.09 -0.01 0.10 -0.18 0.02

BRCA1 2016 1988 29 37.6 -112.2 0.03 0.05 0.13 0.09 -0.03 0.00

BRID1 2016 1988 29 43.0 -109.8 0.09 0.05 0.18 0.08 -0.02 0.00

BRIG1 2016 1991 26 39.5 -74.4 -0.93 0.10 -0.13 0.05 -0.72 0.05

BRLA1 2004 1993 12 41.4 -106.2 0.20 0.20 NA NA NA NA

BRMA1 2015 2001 15 44.1 -70.7 -0.86 0.20 -0.14 0.05 -0.63 0.10

CABA1 2016 2001 16 43.8 -70.1 -1.06 0.14 -0.29 0.08 -0.62 0.06

CABI1 2016 2000 17 48.0 -115.7 0.33 0.23 0.21 0.23 -0.05 0.01

CACO1 2016 2001 16 42.0 -70.0 -1.12 0.15 -0.28 0.07 -0.82 0.05

CACR1 2016 2000 17 34.5 -94.1 -0.65 0.15 -0.32 0.06 -0.54 0.04

CADI1 2010 2001 10 36.8 -87.9 -1.08 0.41 -0.08 0.15 -1.23 0.41

CANY1 2016 1988 29 38.5 -109.8 0.00 0.05 0.02 0.02 -0.04 0.01

CAPI1 2016 2000 17 38.3 -111.3 -0.13 0.09 -0.03 0.09 -0.04 0.01

CEBL1 2016 2002 15 38.8 -99.8 -0.44 0.10 -0.14 0.06 -0.28 0.05

CHAS1 2016 1993 24 28.7 -82.6 -0.61 0.11 -0.13 0.07 -0.35 0.04

8

CHIR1 2016 1988 29 32.0 -109.4 -0.12 0.06 -0.07 0.06 -0.05 0.01

CLPE1 2015 2002 14 44.3 -107.0 0.24 0.22 0.09 0.18 -0.02 0.01

COGO1 2011 1996 16 45.6 -122.2 -0.34 0.20 -0.25 0.19 -0.10 0.02

COHU1 2016 2000 17 34.8 -84.6 -1.22 0.21 -0.07 0.10 -0.93 0.07

CORI1 2016 1993 24 45.7 -121.0 -0.20 0.09 -0.12 0.07 -0.07 0.02

CRES1 2015 2002 14 41.8 -102.4 0.04 0.13 0.17 0.06 -0.10 0.03

CRLA1 2016 1988 29 42.9 -122.1 0.11 0.10 0.28 0.11 0.00 0.00

CRMO1 2016 1992 25 43.5 -113.6 0.17 0.10 0.27 0.25 -0.03 0.01

DETR1 2016 2003 14 42.2 -83.2 NA NA -0.32 0.11 NA NA

DEVA1 2013 1993 21 36.5 -116.8 0.31 0.11 -0.22 0.12 -0.03 0.01

DOME1 2016 2000 17 35.7 -118.1 0.02 0.14 -0.05 0.19 -0.07 0.02

DOSO1 2016 1991 26 39.1 -79.4 -1.25 0.11 -0.22 0.05 -0.82 0.07

DOUG1 2015 2004 12 31.3 -109.5 -0.38 0.26 -0.05 0.06 -0.04 0.04

EGBE1 2016 2005 12 44.2 -79.8 -0.80 0.26 -0.17 0.11 -0.75 0.10

ELDO1 2015 2002 14 37.7 -94.0 -0.81 0.18 -0.22 0.12 -0.65 0.06

ELLI1 2015 2002 14 36.1 -99.9 -0.44 0.15 -0.09 0.08 -0.30 0.06

EVER1 2016 1988 29 25.4 -80.7 -0.49 0.10 -0.43 0.29 -0.18 0.02

FLAT1 2016 2002 15 47.8 -114.3 0.13 0.42 0.06 0.46 -0.04 0.01

FOPE1 2016 2002 15 48.3 -105.1 0.26 0.22 -0.02 0.24 -0.06 0.03

FRES1 2016 2004 13 36.8 -119.8 0.27 0.83 -1.46 0.27 -0.13 0.03

FRRE1 2016 2004 13 39.7 -79.0 -1.37 0.15 -0.14 0.07 -0.98 0.06

GAMO1 2016 2000 17 46.8 -111.7 0.12 0.23 -0.26 0.36 -0.03 0.02

GICL1 2016 1994 23 33.2 -108.2 -0.24 0.12 NA NA -0.07 0.01

GLAC1 2016 1988 29 48.5 -114.0 -0.05 0.09 0.07 0.17 -0.05 0.01

GRBA1 2016 1992 25 39.0 -114.2 0.01 0.05 0.02 0.04 -0.03 0.01

GRCA2 2016 1996 21 36.0 -112.0 0.01 0.09 0.05 0.10 -0.03 0.01

GRGU1 2016 1995 22 44.3 -71.2 -0.90 0.11 -0.19 0.07 -0.58 0.08

GRRI1 2016 2002 15 43.9 -91.4 -0.91 0.25 -0.21 0.07 -0.46 0.07

GRSA1 2016 1988 29 37.7 -105.5 -0.01 0.11 0.06 0.02 -0.03 0.00

GRSM1 2016 1988 29 35.6 -83.9 -1.18 0.08 -0.12 0.08 -0.68 0.03

GUMO1 2016 1988 29 31.8 -104.8 0.03 0.10 -0.02 0.01 -0.07 0.02

HECA1 2016 2000 17 45.0 -116.8 0.17 0.30 0.02 0.28 -0.03 0.01

HEGL1 2016 2001 16 36.6 -92.9 -0.93 0.16 -0.29 0.10 -0.60 0.06

HOOV1 2016 2001 16 38.1 -119.2 -0.09 0.22 -0.01 0.18 -0.03 0.01

IKBA1 2016 2000 17 34.3 -111.7 -0.18 0.11 -0.08 0.08 -0.05 0.02

INGA1 2013 1989 25 36.1 -112.1 0.02 0.03 0.04 0.07 -0.04 0.01

9

ISLE1 2016 1999 18 47.5 -88.1 -0.40 0.09 -0.04 0.07 -0.34 0.04

JARB1 2016 1988 29 41.9 -115.4 0.12 0.06 0.15 0.06 -0.01 0.00

JARI1 2016 2000 17 37.6 -79.5 -1.30 0.13 -0.21 0.08 -0.81 0.05

JOSH1 2016 2000 17 34.1 -116.4 -0.36 0.11 -0.22 0.06 -0.07 0.02

KAIS1 2016 2000 17 37.2 -119.2 0.17 0.30 NA NA -0.04 0.01

KALM1 2016 2000 17 42.6 -124.1 -0.18 0.12 -0.43 0.21 -0.04 0.01

LABE1 2016 2000 17 41.7 -121.5 0.49 0.23 NA NA -0.02 0.01

LASU2 2016 2004 13 40.7 -92.0 -1.17 0.22 -0.24 0.09 -0.67 0.09

LAVO1 2016 1988 29 40.5 -121.6 0.24 0.44 NA NA -0.01 0.00

LIGO1 2016 2000 17 36.0 -81.9 -1.22 0.14 -0.18 0.11 -0.86 0.04

LIVO1 2010 2001 10 38.5 -86.3 -1.16 0.44 -0.03 0.14 -1.51 0.31

LOST1 2016 1999 18 48.6 -102.4 -0.09 0.21 -0.18 0.16 -0.04 0.04

LYBR1 2012 1991 22 43.1 -73.1 -0.62 0.19 -0.05 0.04 -0.35 0.08

MACA1 2016 1991 26 37.1 -86.1 -0.99 0.09 -0.20 0.03 -0.74 0.07

MAVI1 2016 2002 15 41.3 -70.8 -1.03 0.23 -0.21 0.08 -0.83 0.09

MEAD1 2016 1991 26 36.0 -114.1 -0.06 0.05 -0.06 0.07 -0.06 0.02

MELA1 2016 1999 18 48.5 -104.5 0.28 0.17 0.12 0.27 -0.06 0.03

MEVE1 2016 1988 29 37.2 -108.5 -0.02 0.04 0.03 0.04 -0.04 0.00

MING1 2016 2000 17 37.0 -90.1 -0.92 0.16 -0.53 0.14 -0.77 0.09

MKGO1 2010 2001 10 41.4 -80.1 -1.44 0.50 -0.12 0.09 -1.19 0.22

MOHO1 2016 2000 17 45.3 -121.8 0.26 0.48 0.17 0.13 -0.05 0.01

MOMO1 2016 2001 16 41.8 -73.3 -1.29 0.15 -0.34 0.09 -0.88 0.09

MONT1 2016 2000 17 47.1 -113.2 -0.20 0.42 NA NA -0.04 0.01

MOOS1 2016 1994 23 45.1 -67.3 -0.49 0.18 -0.18 0.05 -0.30 0.05

MORA1 2016 1988 29 46.8 -122.1 -0.29 0.06 -0.22 0.03 -0.11 0.01

MOZI1 2016 1994 23 40.5 -106.7 -0.03 0.07 0.04 0.05 -0.04 0.00

NEBR1 2016 2002 15 41.9 -100.3 -0.32 0.13 -0.05 0.12 -0.23 0.04

NOAB1 2016 2000 17 44.7 -109.4 0.22 0.16 -0.02 0.21 -0.03 0.01

NOCA1 2016 1997 20 48.7 -121.1 -0.28 0.11 -0.21 0.12 -0.08 0.01

NOCH1 2016 2002 15 45.6 -106.6 0.47 0.55 0.49 0.57 -0.07 0.02

OKEF1 2016 1991 26 30.7 -82.1 -0.62 0.17 NA NA -0.43 0.05

OLYM1 2016 2001 16 48.0 -123.0 -0.16 0.04 -0.14 0.06 -0.06 0.01

ORPI1 2016 2002 15 32.0 -112.8 -0.13 0.14 -0.15 0.03 -0.06 0.01

PACK1 2016 2007 10 42.9 -71.9 -0.58 0.22 -0.06 0.13 -0.43 0.07

PASA1 2016 2000 17 48.4 -119.9 0.21 0.34 NA NA -0.03 0.01

PEFO1 2016 1988 29 35.1 -109.8 -0.01 0.10 -0.04 0.07 -0.04 0.01

10

PENO1 2016 2006 11 44.9 -68.6 -0.50 0.21 -0.19 0.21 -0.39 0.04

PHOE1 2016 2001 16 33.5 -112.1 -0.58 0.13 -0.80 0.15 -0.03 0.01

PHOE5 2016 2005 12 33.5 -112.1 -0.39 0.27 -0.65 0.27 -0.04 0.02

PINN1 2016 1988 29 36.5 -121.2 -0.17 0.04 -0.10 0.06 -0.05 0.01

PITT1 2016 2004 13 40.5 -80.0 NA NA -0.32 0.12 NA NA

PMRF1 2016 1993 24 44.5 -72.9 -0.89 0.13 -0.23 0.06 -0.77 0.09

PORE1 2016 1988 29 38.1 -122.9 -0.14 0.08 -0.26 0.05 -0.07 0.01

PRIS1 2016 2001 16 46.7 -68.0 -0.65 0.12 -0.22 0.07 -0.43 0.07

PUSO1 2016 1996 21 47.6 -122.3 -0.62 0.11 -0.72 0.09 -0.13 0.01

QUCI1 2016 2001 16 39.9 -81.3 -1.36 0.15 -0.19 0.09 -1.11 0.12

QURE1 2015 2001 15 42.3 -72.3 -1.30 0.20 -0.21 0.07 -0.80 0.07

QUVA1 2015 2001 15 33.3 -111.3 -0.28 0.16 -0.14 0.03 -0.02 0.01

RAFA1 2016 2000 17 34.7 -120.0 -0.14 0.13 -0.10 0.31 -0.14 0.02

REDW1 2016 1988 29 41.6 -124.1 -0.05 0.05 -0.14 0.06 -0.04 0.00

ROMA1 2016 1994 23 32.9 -79.7 -0.67 0.12 -0.08 0.11 -0.60 0.04

ROMO1 2016 1990 27 40.3 -105.5 0.03 0.07 0.07 0.05 -0.05 0.01

SACR1 2016 2000 17 33.5 -104.4 -0.21 0.16 -0.04 0.04 -0.10 0.04

SAFO1 2011 2002 10 40.0 -95.6 -0.61 0.39 -0.21 0.19 -0.52 0.25

SAGA1 2016 2001 16 34.3 -118.0 -0.28 0.09 -0.25 0.10 -0.10 0.02

SAGO1 2016 1988 29 34.2 -116.9 -0.58 0.11 -0.18 0.07 -0.07 0.01

SAGU1 2016 1988 29 32.2 -110.7 -0.02 0.04 -0.08 0.02 -0.04 0.01

SAMA1 2016 2000 17 30.1 -84.2 -0.90 0.16 -0.19 0.28 -0.60 0.04

SAPE1 2016 2000 17 36.0 -106.8 -0.09 0.11 -0.15 0.16 -0.02 0.02

SAWE1 2016 2001 16 32.2 -111.2 -0.50 0.22 -0.12 0.03 -0.04 0.01

SAWT1 2016 1993 24 44.2 -114.9 0.97 0.22 1.70 0.46 0.01 0.01

SENE1 2016 1999 18 46.3 -86.0 -0.61 0.13 -0.19 0.05 -0.40 0.09

SEQU1 2016 1992 25 36.5 -118.8 -0.72 0.40 -0.34 0.18 -0.06 0.01

SHEN1 2016 1988 29 38.5 -78.4 -0.94 0.10 -0.21 0.03 -0.71 0.05

SHMI1 2016 2004 13 37.3 -107.5 0.37 0.24 0.27 0.14 -0.05 0.01

SHRO1 2016 1994 23 35.4 -82.8 -1.05 0.15 -0.14 0.04 -0.73 0.05

SIAN1 2016 2000 17 34.1 -110.9 0.02 0.19 NA NA -0.05 0.01

SIKE1 2010 2001 10 32.1 -92.4 -1.49 0.32 -0.52 0.23 -0.83 0.14

SIPS1 2016 1992 25 34.3 -87.3 -1.04 0.11 -0.24 0.06 -0.75 0.05

SNPA1 2016 1993 24 47.4 -121.4 -0.12 0.07 0.01 0.09 -0.08 0.01

STAR1 2016 2000 17 45.2 -118.5 -0.40 0.14 -0.53 0.22 -0.03 0.01

SULA1 2016 1994 23 45.9 -114.0 0.68 0.42 1.45 0.88 -0.01 0.01

11

SWAN1 2016 2000 17 35.5 -76.2 -0.77 0.22 0.01 0.17 -0.71 0.05

SYCA1 2015 1991 25 35.1 -112.0 -0.37 0.20 0.00 0.06 -0.02 0.01

TALL1 2016 2002 15 38.4 -96.6 -0.33 0.37 -0.27 0.41 -0.42 0.05

THBA1 2016 2002 15 44.7 -105.3 0.23 0.14 0.15 0.24 -0.10 0.02

THRO1 2016 1999 18 46.9 -103.4 0.01 0.12 -0.02 0.15 -0.11 0.03

THSI1 2016 1993 24 44.3 -122.0 0.07 0.10 0.14 0.11 -0.05 0.01

TONT1 2016 1988 29 33.7 -111.1 -0.08 0.04 -0.15 0.05 -0.04 0.01

TRIN1 2015 2000 16 40.8 -122.8 0.08 0.51 NA NA -0.01 0.02

ULBE1 2016 2000 17 47.6 -108.7 0.11 0.23 -0.13 0.29 -0.02 0.02

UPBU1 2016 1991 26 35.8 -93.2 -0.59 0.10 -0.08 0.09 -0.47 0.05

VILA1 2016 2002 15 41.0 -95.0 -0.69 0.14 -0.26 0.09 -0.48 0.05

VOYA2 2016 1988 29 48.4 -92.8 -0.13 0.13 0.03 0.10 -0.19 0.04

WASH1 2015 1988 28 38.9 -77.0 -0.88 0.09 -0.42 0.07 -0.59 0.07

WEMI1 2016 1988 29 37.7 -107.8 -0.02 0.09 0.04 0.09 -0.03 0.00

WHIT1 2016 2001 16 33.5 -105.5 0.19 0.18 -0.04 0.06 -0.10 0.04

WHPA1 2016 2000 17 46.6 -121.4 0.25 0.15 0.25 0.12 -0.04 0.01

WHPE1 2016 2000 17 36.6 -105.5 -0.20 0.08 0.04 0.08 -0.03 0.01

WHRI1 2016 1993 24 39.2 -106.8 0.00 0.09 -0.13 0.08 -0.04 0.01

WICA1 2016 1999 18 43.6 -103.5 -0.03 0.18 -0.07 0.15 -0.11 0.02

WIMO1 2016 2001 16 34.7 -98.7 -0.43 0.12 -0.12 0.10 -0.41 0.06

YELL2 2016 1988 29 44.6 -110.4 0.05 0.12 0.16 0.20 -0.01 0.01

YOSE1 2016 1988 29 37.7 -119.7 -0.03 0.20 NA NA -0.04 0.01

ZICA1 2016 2002 15 37.2 -113.2 -0.27 0.12 -0.27 0.11 -0.06 0.01

12

Table S2. Kriging Validation Summary Multiple Kriging model validation values using 98th

quantile PM2.5 data are provided. To determine the lowest error model, we look for Kriging

Standard Error near zero and close to the root mean squared error (RMSE). We also expect

standardized RMSE to be close to one. Based on these results, the highlighted model provided the

best prediction and is used for all Kriging products.

Maximum

Neighbors

Minimum

Neighbors Sectors

Kriging

Standard

Error

Standardized

RMSE RMSE

25 15 8 0.283 1.03 0.270

15 5 8 0.282 1.03 0.269

15 5 4 (45°) 0.283 1.03 0.270

15 5 4 0.283 1.03 0.270

15 5 1 0.284 1.03 0.270

10 5 8 0.283 1.03 0.270

10 5 4 (45°) 0.283 1.04 0.272

10 5 4 0.283 1.04 0.272

10 5 1 0.287 1.04 0.273

8 3 8 0.283 1.03 0.271

8 3 4 (45°) 0.283 1.03 0.270

8 3 4 0.283 1.04 0.271

8 3 1 0.289 1.03 0.272

5 2 8 0.283 1.04 0.271

5 2 4 (45°) 0.284 1.03 0.269

5 2 4 0.284 1.03 0.268

5 2 1 0.296 1.00 0.275