Abstract
Abstract. There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the United States motivates the need for advanced statistical models to predict air quality metrics, such as PM2.5, at unobserved sites. Remote sensing technologies have the potential to expand our knowledge of PM2.5 spatial patterns beyond what we can predict from current PM2.5 monitoring networks. Data from satellites have an additional advantage in not requiring extensive emission inventories necessary for most atmospheric models that have been used in earlier data fusion models for air pollution. Statistical models combining monitoring station data with satellite-obtained aerosol optical thickness (AOT), also referred to as aerosol optical depth (AOD), have been proposed in the literature with varying levels of success in predicting PM2.5. The benefit of using AOT is that satellites provide complete gridded spatial coverage. However, the challenges involved with using it in fusion models are (1) the correlation between the two data sources varies both in time and in space, (2) the data sources are temporally and spatially misaligned, and (3) there is extensive missingness in the monitoring data and also in the satellite data due to cloud cover. We propose a hierarchical autoregressive spatially varying coefficients model to jointly model the two data sources, which addresses the foregoing challenges. Additionally, we offer formal model comparison for competing models in terms of model fit and out of sample prediction of PM2.5. The models are applied to daily observations of PM2.5 and AOT in the summer months of 2013 across the conterminous United States. Most notably, during this time period, we find small in-sample improvement incorporating AOT into our autoregressive model but little out-of-sample predictive improvement.
Highlights
Particulate matter (PM) in the atmosphere poses a dangerous public health risk worldwide with effects ranging from reduced vision to respiratory and cardiovascular problems (e.g., US Environmental Protection Agency, 2004; Pope III and Dockery, 2006; Miller et al, 2007; Valavanidis et al, 2008)
We propose a daily hierarchical autoregressive spatially varying coefficients model to jointly predict, at point level, daily average PM2.5 for consecutive days across the contiguous United States, using aerosol optical thickness (AOT) obtained from Visible Infrared Imaging Radiometer Suite (VIIRS)
The VIIRS AOT data product used for this study is the validated stage 2 level maturity Environmental Data Record (EDR) AOT available at the National Oceanic and Atmospheric (NOAA) Comprehensive Large Array-data Stewardship System (CLASS)5
Summary
Particulate matter (PM) in the atmosphere poses a dangerous public health risk worldwide with effects ranging from reduced vision to respiratory and cardiovascular problems (e.g., US Environmental Protection Agency, 2004; Pope III and Dockery, 2006; Miller et al, 2007; Valavanidis et al, 2008). Van Donkelaar et al (2012) improved upon the prediction of daily ground-level PM2.5 using AOT and dailygenerated scale factors from the GEOS-Chem chemical transport model That is, they apply a linearly interpolated simulation ratio of ground-level PM2.5 and AOT from GEOS-Chem to the observed MODIS and MISR AOT to get a satellite-driven PM2.5 estimate followed by a climatological ground-based regional bias correction factor derived from a comparison with surface-based PM2.5 monitoring data. They apply a linearly interpolated simulation ratio of ground-level PM2.5 and AOT from GEOS-Chem to the observed MODIS and MISR AOT to get a satellite-driven PM2.5 estimate followed by a climatological ground-based regional bias correction factor derived from a comparison with surface-based PM2.5 monitoring data Their model is fitted to data obtained between the years 2004 and 2009 across North America. For the summer months of 2013, between 4 and 8 % of monitoring stations are missing observations on a given day
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More From: Advances in Statistical Climatology, Meteorology and Oceanography
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