Abstract
In order to generate air-pollutant exposure fields for health studies, a data fusion (DF) approach is developed that combines observations from ambient monitors and simulated data from the Community Multiscale Air Quality (CMAQ) model. These resulting fields capture the spatiotemporal information provided by the air quality model, as well as the finer temporal scale variations from the pollutant observations and decrease model biases. Here, the approach is applied to develop daily concentration fields for PM2.5 total mass, five major particulate species (OC, EC, SO4 2−, NO3 −, and NH4 +), and three gaseous pollutants (CO, NO x , and NO2) from 2006 to 2008 over North Carolina (USA). Several data withholding methods are then conducted to evaluate the data fusion method, and the results suggest that typical approaches may overestimate the ability of spatiotemporal estimation methods to capture pollutant concentrations in areas with limited or no monitors. The results show improvements in capturing spatial and temporal variability compared with CMAQ results. Evaluation tests for PM2.5 led to an R 2 of 0.95 (no withholding) and 0.82 when using 10% random data withholding. If spatially based data withholding is used, the R 2 is 0.73. Comparisons of DF-developed PM2.5 total mass concentration with the spatiotemporal fields derived from two other methods (both use satellite aerosol optical depth (AOD) data) find that, in this case, the data fusion fields have slightly less overall error, with an RMSE of 1.28 compared with 3.06 μg/m3 (two-stage statistical model) and 2.74 (neural network-based hybrid model). Applying the Integrated Mobile Source Indicator (IMSI) method shows that the data fusion fields can be used to estimate mobile source impacts. Overall, the growing availability of chemically detailed air quality model fields and the accuracy of the DF field, suggest that this approach is better able to provide spatiotemporal pollutant fields for gaseous and speciated particulate pollutants for health and planning studies.
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Acknowledgments
We gratefully acknowledge the USEPA, especially Valerie Garcia and K. Wyat Appel, for supplying CMAQ modeling results. The work of X. Hu and Y. Liu was supported by NASA Applied Sciences Program (grant numbers NNX11AI53G and NNX14AG01G, principal investigator: Liu). This publication was funded, in part, by USEPA grant number R834799. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the US government. Further, the US government does not endorse the purchase of any commercial products or services mentioned in the publication. We also acknowledge the Southern Company and the Electric Power Research Institute (EPRI) for their support.
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Electronic supplementary material
ESM 1
(DOC 35 kb)
Table S1
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Fig. S1
PM2.5 monitor site (each color represents a spatially removed group) (GIF 111 kb)
Fig. S2
Probability density distribution of all species from 2006 to 2008 (GIF 208 kb)
Fig. S3a
Annual average spatial distributions fields from data fusion, 2006 (GIF 659 kb)
Fig. S3b
Annual average spatial distributions fields from data fusion, 2007 (GIF 656 kb)
Fig. S4
Normalized monthly average concentration for all species from 2006 to 2008 (GIF 161 kb)
Fig. S5
Annual trends of IMSIEB, IMSIEB, GV, and IMSIEB, DV from 2006 to 2008 (unitless) (GIF 21 kb)
Fig. S6
Annual IMSIEB, IMSIEB, GV, and IMSIEB, DV from 2006 to 2008 (GIF 219 kb)
Fig. S7
Temporal correlations between IMSI and PM2.5 concentrations from 2006 to 2008 (GIF 86 kb)
Fig. S8
Temporal correlations between PM2.5 and EC, CO, and NO x from 2006 to 2008 (GIF 207 kb)
Fig. S9
Comparison of R 2 between observations and simulated datasets (CMAQ, data fusion and 10% data-withheld data fusion) for 2006–2008 (GIF 84 kb)
Fig. S10
Linear regression between observation (OBS) and simulations (CO, data fusion) (GIF 488 kb)
Fig. S11
Linear regression between observation (OBS) and simulations (NO2) (GIF 465 kb)
Fig. S12
Comparison of RMSE between observations and simulated datasets (CMAQ, data fusion, and 10% data-withheld data fusion) for 2006–2008 (μg/m3: PM25, EC, OC, NH4 +, NO3 −, SO4 2−; ppb: NO2, NO x , CO) (GIF 27 kb)
Fig. S13a
Maximum RMSD between leave-out randomly (first time) and data fusion for all randomly leave 10% monitor-out from 2006 (left) to 2008 (right). (GIF 80 kb)
Fig. S13b
Maximum RMSD between leave-out randomly (second time) and data fusion among all randomly leave 10% monitor-out groups from 2006 (left) to 2008 (right). (GIF 80 kb)
Fig. S14
Maximum RMSD between leave-out spatially and data fusion among all spatially leave-out groups from 2006 (left) to 2008 (right) (GIF 81 kb)
Fig. S15
Annual average spatial distributions fields from ordinary kriging (2006, 2007, 2008) (GIF 101 kb)
Fig. S16a
Linear regression between OBS and ordinary kriging (PM2.5, up: total data; done: leave-monitor-out results) (GIF 143 kb)
Fig. S16b
Linear regression between OBS and ordinary kriging (CO, left: total data; right: leave-one-out results) (GIF 106 kb)
Fig. S17
Linear regression between observation (OBS) and neural network-based hybrid model (hybrid) (GIF 63 kb)
Fig. S18
Annual average spatial distributions fields from neural network-based hybrid model for PM2.5, 2006–2008 (12 km) (GIF 130 kb)
Fig. S19
Annual average spatial distributions fields from two-stage statistical model for PM2.5, 2006–2008 (12 km) (GIF 167 kb)
Fig. S20
Annual average spatial distributions fields from data fusion for PM2.5, 2006–2008 (12 km) (GIF 161 kb)
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Huang, R., Zhai, X., Ivey, C.E. et al. Air pollutant exposure field modeling using air quality model-data fusion methods and comparison with satellite AOD-derived fields: application over North Carolina, USA. Air Qual Atmos Health 11, 11–22 (2018). https://doi.org/10.1007/s11869-017-0511-y
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DOI: https://doi.org/10.1007/s11869-017-0511-y