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RD83479901 Evaluation and Comparison OF Methods to Construct Air Quality Fields for Exposure Assessment haofei yu, jim mulholland, howard chang, ran huang,

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Presentation on theme: "RD83479901 Evaluation and Comparison OF Methods to Construct Air Quality Fields for Exposure Assessment haofei yu, jim mulholland, howard chang, ran huang,"— Presentation transcript:

1 RD Evaluation and Comparison OF Methods to Construct Air Quality Fields for Exposure Assessment haofei yu, jim mulholland, howard chang, ran huang, sunni ivey, ted russell, and a cast of many from git and emory

2 Acknowledgements EPRI Emory Georgia Tech ARA and Southern Company
Funding, discussions Emory Paige Tolbert, Jeremy Sarnat, Stefanie Sarnat, Matt Strickland, Lindsey Darrow, Yang Liu, and many others Georgia Tech Rodney Weber, Yongtao Hu, Talat Odman, many additional graduate students and post-docs ARA and Southern Company A great set of data, discussions, support Eric Edgerton & John Jansen US EPA Funding for SCAPE and other research NASA and others All of the views expressed are my own, and do not reflect those of any agency or organization.

3 Objectives Better understand the implications of using various exposure modeling methods Many methods are used Study outcomes reflect methods used 13 methods applied in Atlanta, GA region Urban level application of observation-, air quality model-, model-data fused- and satellite-based methods Comprehensive comparison and evaluation

4 Atlanta Study Region 8 pollutants: 20 county region Year 2011
CO, NO2, SO2, O3 PM2.5, EC, OC & SO4 20 county region Center in Atlanta Year 2011 Detailed performance evaluated at: JST (urban) YRK (rural)

5 Methods Central Site Monitor (CM) Spatial Average (SA):
Obs. data from one monitor (SDK) Spatial Average (SA): Average all obs. data in domain 3 spatial interpolation methods (4km): Use obs. within 100 km buffer of the region Inverse Distance Weighting (IDW) Tessellation (TESS) Cut area into polygons, each with one monitor, assign monitor data to each polygon Kriging (KRIG) SDK

6 Methods (cont’d) RLINE (250m): CMAQ (12km): CMAQ-krig adjust (12km):
Dispersion model, only on-road vehicular emissions CO, PM2.5 CMAQ (12km): CMAQ Chemical transport model CMAQ-krig adjust (12km): Adjust CMAQ concentration using spatially kriged CMAQ/observation ratios at monitors

7 Methods (cont’d) CMAQ-data-fusion (12 km)
Statistically blend spatial structure of CMAQ with temporal variations of obs. (Friberg et al., 2015) Friberg, M. D., Zhai, X., Holmes, H. A., Chang, H. H., Strickland, M. J., Sarnat, S. E., ... & Mulholland, J. A. (2016). Method for Fusing Observational Data and Chemical Transport Model Simulations To Estimate Spatiotemporally Resolved Ambient Air Pollution. Environmental science & technology, 50(7), Hu, Y., Balachandran, S., Pachon, J. E., Baek, J., Ivey, C., Holmes, H., ... & Russell, A. G. (2014). Fine particulate matter source apportionment using a hybrid chemical transport and receptor model approach. Atmospheric Chemistry and Physics, 14(11), Ivey, C. E., Holmes, H. A., Hu, Y. T., Mulholland, J. A., & Russell, A. G. (2015). Development of PM 2.5 source impact spatial fields using a hybrid source apportionment air quality model. Geoscientific Model Development, 8(7),

8 CMAQ-DF-RLINE (or CMAQ-RLINE-obs)
Used calibrated RLINE (line source dispersion model) to provide 250 m resolution mobile source impacts in Atlanta Used in birth cohort study with detailed address information RLINE independently evaluated and fused with observations Daily 12 km CMAQ-datafusion Annual avg. 12 km RLINE 12 km species “without” on-road Interpolate to 250m Daily 250m PM2.5 field Ann. Avg. 250m RLINE 250m smoothed

9 Methods (cont’d) Satellite aerosol optical depth (AOD) method using two-stage model (PM2.5 only, 1 km) (Chang et al., 2015) Land use regression (PM2.5 only, 1 km) CMAQ-hybrid (36 km) CMAQ-CMB for source impact fields Adjust CMAQ fields using hybrid source apportionment optimization (Hu et al., 2014; Ivey et al., 2015)

10 Spatial fields of EC

11 Spatial fields of EC Vary significantly and lack spatial information
1 in 3/6 sampling; few monitors; SA can include very low or high locations

12 Spatial fields of EC Similar spatial pattern
Abrupt concentration changes for tessellation

13 Spatial fields of EC CMAQ: Reasonable spatial pattern
Significant biases and error CMAQ-krig and data fusion Preserved CMAQ spatial pattern Corrected CMAQ biases and errors

14 Spatial fields of EC Different spatial pattern for 12 and 36 km CMAQ
Different emission inventory CMAQ-hybrid also corrected some CMAQ (36km) biases and errors

15 Spatial fields of CO

16 Spatial fields of CO RLINE captured high concentration near roads
Misses regional impacts CMAQ-RLINE-obs incorporated regional impact 250 m resolution vs 12 km for CMAQ High resolution desired for exposure analysis

17 Spatial fields of PM2.5

18 Spatial fields of PM2.5 LUR simulates similar fields as kriging
None of the land use variables here have sufficient predicting power due to limited monitors AOD field resembles some features of data fusion methods, though more flat

19 Method PM2.5 temporal R2 at all monitors
L1O: leave-one-out cross validation Observation based methods show similar performance Use nearby measurement to infer unknown locations

20 Method PM2.5 temporal R2 at all monitors
RLINE and CMAQ perform worst Data fusion substantially improved performance CMAQ-data-fusion performs better than CMAQ-krig L1O: leave-one-out cross validation

21 Method PM2.5 temporal R2 at all monitors
LUR & AOD performs well CMAQ-data-fusion performs slightly better or on-par with LUR and AOD L1O: leave-one-out cross validation

22 Spatial correlation of PM2.5
Purely observation based methods: negative spatial correlation You might note that CM is zero (it must be). CMAQ and data-integrated methods: positive correlation *leave-one-out cross validation results

23 Conclusions Methods lead can lead to different estimated levels of exposure Interpolation methods (IDW and kriging) lead to similar patterns Lack much spatial variation (and temporally-limited) Model-based methods Raw model outputs can be very biased and have large errors Great caution should be taken Integrating observations dramatically improves performance Can readily integrate models of different scales Attractive because they allow range of pollutants and scales and provide source impacts AOD and model-data fusion methods led to similar concentration fields AOD limited to a few species, has missing data Insufficient number of monitors available in area to adequately apply LUR approach LUR+AOD also lacked spatial texture Change “Should not be used” to “Great caution should be taken”. You can note that not only are the spatial and temporal correlations low, but the interspecies correlations and autocorrelations (if you have that as well) can be very different when using the model results directly. You can note that the spatial R2 would suggest caution even when used for long term exposure studies.

24 Thanks

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