Air pollutant exposure field modeling using air quality model-data fusion methods and comparison with satellite AOD-derived fields: application over North Carolina, USA
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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.
KeywordsAmbient air pollution Spatiotemporal pollutant fields Data fusion CMAQ
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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Conflict of interest
The authors declare that they have no conflict of interest.
- Deming WE (1943) Statistical adjustment of dataGoogle Scholar
- Jathar SH, Cappa CD, Wexler AS et al (2016) Simulating secondary organic aerosol in a regional air quality model using the statistical oxidation model—part 1: assessing the influence of constrained multi-generational ageing. Atmos Chem Phys 16:2309–2322. https://doi.org/10.5194/acp-16-2309-2016 CrossRefGoogle Scholar
- Liu Y, Koutrakis P, Kahn R et al (2012) Estimating fine particulate matter component concentrations and size distributions using satellite-retrieved fractional aerosol optical depth: part 2—a case study. J Air Waste Manage Assoc 57:1360–1369Google Scholar
- Pleim J, Gilliam R, Appel W, Ran L (2016) Recent advances in modeling of the atmospheric boundary layer and land surface in the coupled WRF-CMAQ model. Springer International Publishing, pp 391–396Google Scholar