Daytime variations of AOD and PM2 Daytime variations of AOD and PM2.5 relationship in the US: Implications for geostationary satellite data applications Mian Chin1, Alex Coy2, Qian Tan3,4, Tom Kucsera1,5, and Hongbin Yu1 1NASA Goddard Space Flight Center, MD, USA 2Montgomeray Blair High School, MD, USA 3NASA Ames Research Center, CA, USA 4Bay Area Environmental Research Inc., CA, USA 5Universities Space Research Association, MD, USA

Introduction Aerosol particles with diameter less then 2.5 μm (aka PM2.5) is a major pollutant that determines the air quality and affects human health In recent years, many studies have presented the use of satellite remote sensing of column aerosol optical depth (AOD) to estimate the surface PM2.5 concentrations, but they mostly involve using the polar-orbiting satellite data (once-a-day, “snapshot” observations). There is a lack of knowledge on how the column AOD and surface PM2.5 vary during the day for practical air quality applications This project examines the daytime variations of AOD and PM2.5 using AOD data from AERONET and surface PM2.5 data from the ground measurement networks at four nearly collocated AOD-PM2.5 sites in the U.S. Results have implications for using geostationary satellite AOD data over the U.S., e.g., GOES-16, TEMPO, to estimate daytime variations of PM2.5

Factors to be considered in interpreting AOD-PM2.5 relationship Aerosol vertical profile: AOD is an remote-sensing optical measurement for the entire atmospheric column, whereas PM2.5 is an in-situ measurements of mass at the surface. -> PM2.5 depends on aerosol vertical profile (including PBL or mixed layer height), while AOD corresponds to total aerosol amount in the atmospheric column Relative humidity (or water vapor): AOD value increases with the increase of RH, whereas PM2.5 usually refers to the aerosol dry mass -> AOD corresponds to RH but PM2.5 does not Aerosol composition and particle size: Different aerosol species and size have different mass extinction efficiency -> even under dry conditions, mass-to-extinction conversion depends on aerosol composition and particle size Due to the data limitation, we only examine the influence of water vapor. Other factors will be examined either using a reanalysis product or other proxies

AOD, PM2.5 and related data Data: Column 440 nm AOD: AERONET Column water vapor: AERONET Surface PM2.5: USA EPA AQS Hourly average Pair of AOD-PM2.5 sites selection criteria: AOD and PM measurement sites within 5 km in distance Near Washington DC: GSFC AOD and Prince George’s County PM2.5 (3.9 km apart), 2012, 2013, 2014 Fresno AOD and PM2.5 (0.1 km apart), 2012, 2013, 2014 St. Louis: St. Louis University AOD and St. Louis City PM2.5 (3.7 km apart), 2013, 2014, 2015 Houston: Univ. Houston AOD and Harris County PM2.5 (2.9 km apart), 2012

1. How closely are AOD and PM2 1. How closely are AOD and PM2.5 correlated each day in different locations?

Examples of daytime AOD-PM2 Examples of daytime AOD-PM2.5 correlation and AOD-water vapor Near Washington DC, 2012: “good” AOD-PM2.5 correlation days Dotted line: PM2.5 data without coincidental AOD data AOD-PM2.5 AOD-H2Og AOD varies with both PM2.5 and water vapor AOD varies with both PM2.5 and water vapor AOD varies mainly with PM2.5 AOD varies with PM2.5 but not water vapor

Examples of daytime AOD-PM2 Examples of daytime AOD-PM2.5 correlation and AOD-water vapor Near Washington DC, 2012: “bad” AOD-PM2.5 correlation days Dotted line: PM2.5 data without coincidental AOD data AOD-PM2.5 AOD-H2Og AOD does not vary with either PM2.5 or water vapor AOD varies with water vapor but not PM2.5 AOD varies with water vapor but not PM2.5 AOD does not vary with either PM2.5 or water vapor

Examples of daytime AOD-PM2 Examples of daytime AOD-PM2.5 correlation and AOD-water vapor over Fresno, CA, 2013: “good” AOD-PM2.5 correlation days Dotted line: PM2.5 data without coincidental AOD data AOD-PM2.5 AOD-H2Og AOD varies with both PM2.5 and water vapor AOD varies with both PM2.5 and water vapor AOD varies with PM2.5 but not water vapor AOD varies with both PM2.5 and water vapor

Examples of daytime AOD-PM2 Examples of daytime AOD-PM2.5 correlation and AOD-water vapor over Fresno, CA, 2013: “bad” AOD-PM2.5 correlation days Dotted line: PM2.5 data without coincidental AOD data AOD-PM2.5 AOD-H2Og AOD does not vary with either PM2.5 or water vapor AOD does not vary with PM2.5 but with water vapor AOD does not vary with PM2.5 but with water vapor AOD does not vary with PM2.5 but with water vapor

Daily AOD-PM2.5 correlation coefficients in the four locations Near Washington DC, 2012 Near Washington DC, 2012: Total available days: 207 16% ( 33): R ≥ 0.7 15% ( 32): 0.5 ≤ R < 0.7 13% ( 26): 0.3 ≤ R < 0.5 14% ( 30): 0.0 ≤ R < 0.3 42% ( 86): R < 0.0 R (AOD-PM2.5) Fresno, CA, 2013 Fresno, 2013: Total available days: 225 19% ( 42): R ≥ 0.7 19% ( 42): 0.5 ≤ R < 0.7 11% ( 26): 0.3 ≤ R < 0.5 20% ( 46): 0.0 ≤ R < 0.3 31% ( 69): R < 0.0 R (AOD-PM2.5) St Louis, MO, 2014 St. Louis, 2014: Total available days: 217 17% ( 38): R ≥ 0.7 11% ( 23): 0.5 ≤ R < 0.7 12% ( 26): 0.3 ≤ R < 0.5 14% ( 31): 0.0 ≤ R < 0.3 46% ( 99): R < 0.0 R (AOD-PM2.5) Houston, TX, 2012 Houston, 2012: Total available days: 181 15% ( 27): R ≥ 0.7 7% ( 13): 0.5 ≤ R < 0.7 8% ( 14): 0.3 ≤ R < 0.5 21% ( 38): 0.0 ≤ R < 0.3 49% ( 89): R < 0.0 R (AOD-PM2.5)

Daily AOD-water vapor correlation coefficients Near Washington DC, 2012 Near Washington DC, 2012: Total available days: 220 52% (114): R ≥ 0.7 11% ( 24): 0.5 ≤ R < 0.7 9% ( 19): 0.3 ≤ R < 0.5 8% ( 17): 0.0 ≤ R < 0.3 20% ( 45): R < 0.0 R (AOD-H2Og) Fresno, CA, 2013 Fresno, 2013: Total available days: 228 31% ( 71): R ≥ 0.7 11% ( 24): 0.5 ≤ R < 0.7 12% ( 28): 0.3 ≤ R < 0.5 16% ( 36): 0.0 ≤ R < 0.3 30% ( 69): R < 0.0 R (AOD-H2Og) St Louis, MO, 2014 St. Louis, 2014: Total available days: 224 39% ( 88): R ≥ 0.7 15% ( 34): 0.5 ≤ R < 0.7 10% ( 22): 0.3 ≤ R < 0.5 11% ( 25): 0.0 ≤ R < 0.3 25% ( 55): R < 0.0 R (AOD-H2Og) Houston, TX, 2012 Houston, 2012: Total available days: 198 55% (108): R ≥ 0.7 11% ( 22): 0.5 ≤ R < 0.7 9% ( 17): 0.3 ≤ R < 0.5 9% ( 17): 0.0 ≤ R < 0.3 17% ( 34): R < 0.0 R (AOD-H2Og)

Summary of R between AOD and PM2.5 for multiple years over the 4 sites Location Year Total days with ≥ 3 hours obs. % days (days) with R ≥ 0.7 % days (days) with 0.5 ≤ R < 0.7 % days (days) with 0.3 ≤ R < 0.5 % days (days) with 0 ≤ R < 0.3 % days (days) with R < 0 Near Washington DC (GSFC-PG County, 3.9 km apart) 2012 207 16% (33) 15% (32) 14% (28) 14% (30) 42% (86) 2013 183 13% (24) 11% (20) 11% (21) 19% (34) 46% (84) 2014 170 15% (26) 14% (24) 14% (23) 16% (28) 41% (69) Fresno (Fresno_2-Fresno, 0.1 km apart) 213 20% (42) 17% (37) 12% (25) 22% (46) 30% (63) 227 19% (42) 11% (26) 20% (46) 31% (69) 169 22% (37) 17% (28) 34% (58) St. Louis (St. Louis U.-St. Louis City, 3.7 km apart) 113 11% (12) 11% (13) 57% (64) 217 17% (38) 11% (23) 12% (26) 14% (31) 46% (99) 2015 137 14% (19) 9% (13) 13% (18) 30% (41) 34% (46) Houston (U. Houston-Harris, 2.9 km apart) 181 15% (27) 7% (13) 8% (14) 21% (38) 49% (89) Only 13-22% days when daytime AOD and PM2.5 are correlated with R ≥ 0.7 But 30-57% days when daytime AOD and PM2.5 are un-correlated with R < 0

Correlations are not the only thing; slopes and intercepts of AOD-PM2 Correlations are not the only thing; slopes and intercepts of AOD-PM2.5 show large variations on the days when AOD-PM2.5 are correlated at R ≥ 0.7 – Example 1: Near Washington DC, 2012 R (AOD-PM2.5) Slope (b) of linear fitting PM2.5 = a + b AOD when R ≥ 0.7 Intercept (a) of linear fitting PM2.5 = a + b AOD when R ≥ 0.7 R (AOD-PM2.5) Slopes vary by a factor near 20 from the lowest to the highest slopes No clear seasonal pattern Slope Intercepts vary with a range of [-45, +10] No clear seasonal pattern Intercept Days in 2012

Correlations are not the only thing; slopes and intercepts of AOD-PM2 Correlations are not the only thing; slopes and intercepts of AOD-PM2.5 show large variations on the days when AOD-PM2.5 are correlated at R ≥ 0.7 – Example 2: Fresno CA, 2013 R (AOD-PM2.5) Slope (b) of linear fitting PM2.5 = a + b AOD when R ≥ 0.7 Intercept (a) of linear fitting PM2.5 = a + b AOD when R ≥ 0.7 Days in 2013 R (AOD-PM2.5) Slopes vary by a factor near 30 from the lowest to the highest slopes No clear seasonal pattern Slope Intercepts vary with a range of [-170, +25] No clear seasonal pattern Intercept

Conclusions #1 Daytime variations of AOD and PM2.5 are not always correlated. From the four sites we examined: Only about 10-20% days when daytime AOD and PM2.5 are correlated at R ≥ 0.7 In comparison, about 30-60% days when daytime AOD and PM2.5 are un-correlated with R < 0 Even in the “good correlation” days when R ≥ 0.7, the linear fitting of PM2.5 = a + b*AOD exhibits significant day-to-day differences in slopes and intercepts, making quantitative estimates of PM2.5 from AOD observations difficult AOD is much more frequently correlated with the column water vapor than with PM2.5, indicating that the humidification effects on AOD (not on PM2.5) complicate the interpretation of AOD-PM2.5 relationship Bottom line: Satellite AOD will provide information on possible pollution levels at the surface, but direct use for quantitative estimation of PM2.5 is difficult

Example over Washington DC in 2012 and Fresno in 2013 2. What are the errors in estimated daytime PM2.5 based on observed AOD, if we use the monthly statistics of AOD-PM2.5 relationship? Example over Washington DC in 2012 and Fresno in 2013

Near Washington DC: Hourly AOD-PM2.5 relationship in each month 2012 Annual average of co-existing AOD and PM2.5: coincidental AOD_440nm =0.19, PM2.5=10.7 μg m-3 (avg all=11.4 μg m-3) AOD and PM2.5 are positively correlated on monthly statistics but with large variations (R = 0.33-0.84, slope = 15-78, intercept = -0.3-8.4) Errors in hourly PM2.5 using PM2.5 = a + b・AOD for each month If we use the monthly linear fitting of AOD and PM2.5 from above, the error in estimated hourly PM2.5 can be as high as >20 μg m-3 for near Washington DC in 2012, although the 80% of the data are within 10 μg m-3

Fresno: Hourly AOD-PM2.5 relationship in each month 2013 Annual average of co-existing AOD and PM2.5: coincidental AOD_440nm =0.16, PM2.5=15.4 μg m-3 (avg all=19.3 μg m-3) AOD and PM2.5 have weak correlations for all months (R = -0.09-0.60) Errors in hourly PM2.5 using PM2.5 = a + b・AOD for each month If we use the monthly linear fitting of AOD and PM2.5 from above, the error in estimated hourly PM2.5 can be as high as >80 μg m-3 for near Fresno in 2013, even though 80% of the data in most months are within 15 μg m-3.

Conclusions #2 On monthly basis, the hourly AOD and PM2.5 are mostly positively correlated near Washington DC, but R, slope, and intercept for the monthly data also have large variations (slope and intercept varying > 4x) from month to month. Correlations are much weaker in Fresno Using the linear fit statistics within a month, the errors in estimated hourly PM2.5 can be as high as > 20 μg m-3 near Washington DC in 2012. For Fresno, the monthly correlations are worse; although most errors are within 20 μg m-3, the maximum can be 80 μg m-3. Using the same statistics for a different year would likely result in larger errors.

Next steps Examine AOD-PM2.5 relationship in other locations (e.g., DISCOVER-AQ data over US, and KORUS-AQ data over Korea) Examine AOD-PM2.5 relationship at longer time scales (e.g., daily mean, monthly mean) Explain physical reasons of AOD-PM2.5 relationships in addition to the humidification effects on AOD (including mixing layer depth, aerosol vertical profile, aerosol composition and particle size) with a model and/or with available meteorological data Acknowledgment: NASA GEO-CAPE study funding NASA Space Club internship funding AERONET site managers and calibration teams EPA AQS site managers and calibration teams

Bonus: AOD-PM2.5 relationship in Beijing, China Beijing’s pollution level is much higher than US’ by a factor of ~8 on annual average High PM2.5 and high AOD are often observed together Sites: Beijing 1: CAMS AOD and Guan Yuan PM2.5 (1 km apart), 2014, 2015, 2016 Beijing 2: Beijing AOD and Olympic Center PM2.5 (1.5 km apart), 2014, 2015, 2016 (Surface PM2.5 data from China Environmental Monitoring Center)

Examples of “good” AOD-PM2.5 correlation days in Beijing, 2015 Dashed line/open circles: PM2.5 data with no coincidental AOD data AOD varies with PM2.5 but not with water vapor

Examples of “bad” AOD-PM2.5 correlation days in Beijing, 2015 Dotted line: PM2.5 data without coincidental AOD data AOD varies with water vapor but not with PM2.5

Daily correlation coefficients in Beijing, China, 2015 Daily AOD-PM2.5 vapor correlation coefficients, 2015 Beijing, China, 2015 Beijing, AOD-PM2.5, 2015: Total available days: 212 36% ( 92): R ≥ 0.7 14% ( 35): 0.5 ≤ R < 0.7 10% ( 25): 0.3 ≤ R < 0.5 15% ( 38): 0.0 ≤ R < 0.3 25% ( 64): R < 0.0 R (AOD-PM2.5) Daily AOD-water vapor correlation coefficients, 2015 Beijing, China, 2015 Beijing, AOD-water, 2015: Total available days: 256 50% (127): R ≥ 0.7 11% ( 29): 0.5 ≤ R < 0.7 6% ( 16): 0.3 ≤ R < 0.5 10% ( 26): 0.0 ≤ R < 0.3 22% ( 58): R < 0.0 R (AOD-H2Og) For all three years (2014-2016) and two pairs of Beijing sites, 30-40% days AOD and PM2.5 are correlated with R ≥ 0.7 and 25-33% days R < 0 – better correlations than sites in the US For AOD-water vapor, 40-60% days they are correlated with R ≥ 0.7 and 20-25% days R < 0 – Comparable the sites in the US

Summary of R between AOD and PM2.5 for multiple years in Beijing Location Year Total days with ≥ 3 hours obs. % days (days) with R ≥ 0.7 % days (days) with 0.5 ≤ R < 0.7 % days (days) with 0.3 ≤ R < 0.5 % days (days) with 0 ≤ R < 0.3 % days (days) with R < 0 Beijing 1 (CAMS-Guan Yuan, 1.0 km apart) 2014 158 31% (49) 17% (27) 10% (16) 14% (22) 28% (44) 2015 254 36% (92) 14% (35) 10% (25) 15% (38) 25% (64) 2016 198 28% (55) 13% (26) 11% (21) 15% (30) 33% (66) Beijing 2 (Beijing-Olympic Ctr., 1.5 km apart) 197 34% (67) 10% (20) 9% (18) 16% (31) 31% (61) 212 40% (84) 9% (19) 10% (22) 32% (68) 140 31% (44) 12% (17) 14% (19) 14% (20) 29% (40) 28-40% days when daytime AOD and PM2.5 are correlated with R ≥ 0.7 25-33% days when daytime AOD and PM2.5 are un-correlated with R < 0

slopes and intercepts of AOD-PM2 slopes and intercepts of AOD-PM2.5 also show large variations on the days when AOD-PM2.5 are correlated at R ≥ 0.7 R (AOD-PM2.5) Slope (b) of linear fitting PM2.5 = a + b AOD when R ≥ 0.7 Intercept (a) of linear fitting PM2.5 = a + b AOD when R ≥ 0.7 R (AOD-PM2.5) Slopes vary by a factor near 90 from the lowest to the highest slopes Slope Intercepts vary with a range of [-600, +100] Intercept Days in 2015

Beijing: Hourly AOD-PM2.5 relationship in each month 2015 Annual average of co-existing AOD and PM2.5: coincidental AOD_440nm =0.65, PM2.5=65.3 μg m-3 (avg all=82.6 μg m-3) AOD and PM2.5 are positively correlated on monthly statistics but with large variations (R = 0.73-0.96, slope = 35-146, intercept = 0.6-26) Errors in hourly PM2.5 using PM2.5 = a + b・AOD for each month If we use the monthly linear fitting of AOD and PM2.5 from above, the error in estimated hourly PM2.5 can be as high as >300 μg m-3 for Beijing in 2015, although 80% of the data are within 70 μg m-3