Satellite mapping of PM2.5 episodes in the wintertime San Joaquin Valley: A "static" model using column water vapor

The use of satellite aerosol optical thickness (AOT) from imaging spectrometers has been successful in quantifying and mapping high-<span classCombining double low line"inline-formula">PM2.5</span> (particulate matter with a mass <span classCombining double low line"inline-formula">&lt;2.5</span>&thinsp;<span classCombining double low line"inline-formula">μ</span>m diameter) episodes for pollution abatement and health studies. However, some regions have high <span classCombining double low line"inline-formula">PM2.5</span> but poor estimation success. The challenges in using AOT from imaging spectrometers to characterize <span classCombining double low line"inline-formula">PM2.5</span> worldwide was especially evident in the wintertime San Joaquin Valley (SJV). The SJV's attendant difficulties of high-albedo surfaces and very shallow, variable vertical mixing also occur in other significantly polluted regions around the world. We report on more accurate <span classCombining double low line"inline-formula">PM2.5</span> maps (where cloudiness permits) for the whole winter period in the SJV (19 November 2012-18 February 2013). Intensive measurements by including NASA aircraft were made for several weeks in that winter, the DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) California mission.

We found success with a relatively simple method based on calibration and checking with surface monitors and a characterization of vertical mixing, and incorporating specific understanding of the region's climatology. We estimate <span classCombining double low line"inline-formula">PM2.5</span> to within <span classCombining double low line"inline-formula">ĝ&circ;1/47</span>&thinsp;<span classCombining double low line"inline-formula">μ</span>g&thinsp;m<span classCombining double low line"inline-formula">-3</span> root mean square error (RMSE) and with <span classCombining double low line"inline-formula">R</span> values of <span classCombining double low line"inline-formula">ĝ&circ;1/40.9</span>, based on remotely sensed multi-angle implementation of atmospheric correction (MAIAC) observations, and certain further work will improve that accuracy. Mapping is at 1&thinsp;km resolution. This allows a time sequence of mapped aerosols at 1&thinsp;km for cloud-free days. We describe our technique as a "static estimation." Estimation procedures like this one, not dependent on well-mapped source strengths or on transport error, should help full source-driven simulations by deconstructing processes. They also provide a rapid method to create a long-term climatology.

Essential features of the technique are (a) daily calibration of the AOT to <span classCombining double low line"inline-formula">PM2.5</span> using available surface monitors, and (b) characterization of mixed layer dilution using column water vapor (CWV, otherwise "precipitable water"). We noted that on multi-day timescales both water vapor and particles share near-surface sources and both fall to very low values with altitude; indeed, both are largely removed by precipitation. The existence of layers of <span classCombining double low line"inline-formula">H2O</span> or aerosol not within the mixed layer adds complexity, but mixed-effects statistical regression captures essential proportionality of <span classCombining double low line"inline-formula">PM2.5</span> and the ratio variable (AOT&thinsp;<span classCombining double low line"inline-formula">ĝ&circ;•</span>&thinsp;CWV). Accuracy is much higher than previous statistical models and can be extended to the whole Aqua satellite data record. The maps and time series we show suggest a repeated pattern for large valleys like the SJV - progressive stabilization of the mixing height after frontal passages: <span classCombining double low line"inline-formula">PM2.5</span> is somewhat more determined by day-by-day changes in mixing than it is by the progressive accumulation of pollutants (revealed as increasing AOT).

.