Abstract
Abstract. Satellite remote sensing is increasingly being used to monitor air quality over localized sources such as the Canadian oil sands. Following an initial study, significantly low biases have been identified in current NO2 and SO2 retrieval products from the Ozone Monitoring Instrument (OMI) satellite sensor over this location resulting from a combination of its rapid development and small spatial scale. Air mass factors (AMFs) used to convert line-of-sight "slant" columns to vertical columns were re-calculated for this region based on updated and higher resolution input information including absorber profiles from a regional-scale (15 km × 15 km resolution) air quality model, higher spatial and temporal resolution surface reflectivity, and an improved treatment of snow. The overall impact of these new Environment Canada (EC) AMFs led to substantial increases in the peak NO2 and SO2 average vertical column density (VCD), occurring over an area of intensive surface mining, by factors of 2 and 1.4, respectively, relative to estimates made with previous AMFs. Comparisons are made with long-term averages of NO2 and SO2 (2005–2011) from in situ surface monitors by using the air quality model to map the OMI VCDs to surface concentrations. This new OMI-EC product is able to capture the spatial distribution of the in situ instruments (slopes of 0.65 to 1.0, correlation coefficients of >0.9). The concentration absolute values from surface network observations were in reasonable agreement, with OMI-EC NO2 and SO2 biased low by roughly 30%. Several complications were addressed including correction for the interference effect in the surface NO2 instruments and smoothing and clear-sky biases in the OMI measurements. Overall these results highlight the importance of using input information that accounts for the spatial and temporal variability of the location of interest when performing retrievals.
Highlights
Space-based measurements of the near-surface atmospheric composition, or air quality, from near-UV-to-near-IR spectra have blossomed over the past two decades from relatively crude, research-grade products to refined, operational products suitable for monitoring and assimilation (e.g. Miyazaki et al, 2012)
NO2 are consistent with what might have been expected from the Air mass factors (AMFs): comparable spatial distributions with similar background values, but over the surface mines the Environment Canada (EC) vertical column density (VCD) are larger by a factor of up to 1.9
Significant low biases have been identified in current NO2 and SO2 retrieval products from the Ozone Monitoring Instrument (OMI) over the Canadian oil sands arising from a combination of its rapid development and small spatial scale
Summary
Space-based measurements of the near-surface atmospheric composition, or air quality, from near-UV-to-near-IR spectra have blossomed over the past two decades from relatively crude, research-grade products to refined, operational products suitable for monitoring and assimilation (e.g. Miyazaki et al, 2012). McLinden et al.: Improved satellite retrievals of NO2 and SO2 et al, 2013) are being derived from these less familiar vertically integrated quantities This category of sensor began with the GOME (Global Ozone Monitoring Experiment) instrument (Burrows et al, 1999) and continued with SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY, 2002–2012) (Bovensmann, 1999), OMI (Ozone Monitoring Instrument, 2004–present) (Levelt et al, 2006), and the operational GOME-2 (2006–present, 2012–present) instruments. Even among near point sources the oil sands represent a particular challenge to satellite remote sensing due to a combination of the rapidly changing landscape and emissions, its higher latitude (57◦ N), frequent snow cover, and higher boundary layer winds that disperse the emitted compounds more rapidly These factors conspire to make signals here more difficult to observe and, perhaps more importantly, the rapid industrial evolution of the region makes some of the assumptions in the current generation of retrieval algorithms suspect
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