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Hains, Jennifer C.

Publications and source records attributed to Hains, Jennifer C..

An Elevated Reservoir of Air Pollutants over the Mid-Atlantic States During the 2011 DISCOVER-AQ Campaign: Airborne Measurements and Numerical Simulations

During a classic heat wave with record high temperatures and poor air quality from July 18 to 23, 2011, an elevated reservoir of air pollutants was observed over and downwind of Baltimore, MD, with relatively clean conditions near the surface. Aircraft and ozonesonde measurements detected approximately 120 parts per billion by volume ozone at 800 meters altitude, but approximately 80 parts per billion by volume ozone near the surface. High concentrations of other pollutants were also observed around the ozone peak: approximately 300 parts per billion by volume CO at 1200 meters, approximately 2 parts per billion by volume NO2 at 800 meters, approximately 5 parts per billion by volume SO2 at 600 meters, and strong aerosol optical scattering (2 x 10 (sup 4) per meter) at 600 meters. These results suggest that the elevated reservoir is a mixture of automobile exhaust (high concentrations of O3, CO, and NO2) and power plant emissions (high SO2 and aerosols). Back trajectory calculations show a local stagnation event before the formation of this elevated reservoir. Forward trajectories suggest an influence on downwind air quality, supported by surface ozone observations on the next day over the downwind PA, NJ and NY area. Meteorological observations from aircraft and ozonesondes show a dramatic veering of wind direction from south to north within the lowest 5000 meters, implying that the development of the elevated reservoir was caused in part by the Chesapeake Bay breeze. Based on in situ observations, Community Air Quality Multi-scale Model (CMAQ) forecast simulations with 12 kilometers resolution overestimated surface ozone concentrations and failed to predict this elevated reservoir; however, CMAQ research simulations with 4 kilometers and 1.33 kilometers resolution more successfully reproduced this event. These results show that high resolution is essential for resolving coastal effects and predicting air quality for cities near major bodies of water such as Baltimore on the Chesapeake Bay and downwind areas in the Northeast.

ozone↗

SO2 Emissions and Lifetimes: Estimates from Inverse Modeling Using In Situ and Global, Space-Based (SCIAMACHY and OMI) Observations

Top-down constraints on global sulfur dioxide (SO2) emissions are inferred through inverse modeling using SO2 column observations from two satellite instruments (SCIAMACHY and OMI). We first evaluated the S02 column observations with surface SO2 measurements by applying local scaling factors from a global chemical transport model (GEOS-Chem) to SO2 columns retrieved from the satellite instruments. The resulting annual mean surface SO2 mixing ratios for 2006 exhibit a significant spatial correlation (r=0.86, slope=0.91 for SCIAMACHY and r=0.80, slope = 0.79 for OMI) with coincident in situ measurements from monitoring networks throughout the United States and Canada. We evaluate the GEOS-Chem simulation of the SO2 lifetime with that inferred from in situ measurements to verity the applicability of GEOS-Chem for inversion of SO2 columns to emissions. The seasonal mean SO2 lifetime calculated with the GEOS-Chem model over the eastern United States is 13 h in summer and 48 h in winter, compared to lifetimes inferred from in situ measurements of 19 +/- 7 h in summer and 58 +/- 20 h in winter. We apply SO2 columns from SCIAMACHY and OMI to derive a top-down anthropogenic SO2 emission inventory over land by using the local GEOS-Chem relationship between SO2 columns and emissions. There is little seasonal variation in the top-down emissions (<15%) over most major industrial regions providing some confidence in the method. Our global estimate for annual land surface anthropogenic SO2 emissions (52.4 Tg S/yr from SCIAMACHY and 49.9 Tg S / yr from OMI) closely agrees with the bottom-up emissions (54.6 Tg S/yr) in the GEOS-Chem model and exhibits consistency in global distributions with the bottom-up emissions (r = 0.78 for SCIAMACHY, and r = 0.77 for OMI). However, there are significant regional differences.

Lee, Chulkyu↗