Satellite data of atmospheric pollutants are becoming more widely used in the decision-making and environmental management activities of public, private sector and non-profit organizations. They are employed for estimating emissions, tracking pollutant plumes, supporting air quality forecasting activities, providing evidence for “exceptional event” declarations, monitoring regional long-term trends, and evaluating air quality model output. However, many air quality managers are not taking full advantage of the data for these applications nor has the full potential of satellite data for air quality applications been realized. A key barrier is the inherent difficulties associated with accessing, processing, and properly interpreting observational data. A degree of technical skill is required on the part of the data end-user, which is often problematic for air quality agencies with limited resources. Therefore, we 1) review the primary uses of satellite data for air quality applications, 2) provide some background information on satellite capabilities for measuring pollutants, 3) discuss the many resources available to the end-user for accessing, processing, and visualizing the data, and 4) provide answers to common questions in plain language

A lightning-nitrogen oxide (NO) algorithm is implemented in the Community Multiscale Air Quality Model (CMAQ) and used to evaluate the impact of lightning-NO emissions (LNO_x) on tropospheric photochemistry over the United States during the summer of 2006. For a 500 mole per flash lightning-NO source, the mean summertime tropospheric NO₂ column agrees with satellite-retrieved columns to within −5 to +13%. Temporal fluctuations in the column are moderately well simulated; however, the addition of LNO_x does not lead to a better simulation of day-to-day variability. The contribution of lightning-NO to the model column ranges from ∼10% in the northern US to >45% in the south-central and southeastern US. Lightning-NO adds up to 20 ppbv to upper tropospheric model ozone and 1.5–4.5 ppbv to 8-h maximum surface layer ozone, although, on average, the contribution of LNO_x to model surface ozone is 1–2 ppbv less on poor air quality days. LNO_x increases wet deposition of oxidized nitrogen by 43% and total deposition of nitrogen by 10%. This additional deposition reduces the mean magnitude of the CMAQ low-bias in nitrate wet deposition with respect to National Atmospheric Deposition monitors to near zero. Differences in urban/rural biases between model and satellite-retrieved NO₂ columns were examined to identify possible problems in model chemistry and/or transport. CMAQ columns were too large over urban areas. Biases at other locations were minor after accounting for the impacts of lightning-NO emissions and the averaging kernel on model columns. In order to obtain an upper bound on the contribution of uncertainties in NO_y chemistry to upper tropospheric NO_x low biases, sensitivity calculations with updated chemistry were run for the time period of the Intercontinental Chemical Transport Experiment (INTEX-A) field campaign (summer 2004). After adjusting for possible interferences in NO₂ measurements and averaging over the entire campaign, these updates reduced 7–9 km biases from 32 to 17% and 9–12 km biases from 57 to 46%. While these changes lead to better agreement, a considerable unexplained NO₂ low-bias remains in the uppermost troposphere.

Frequent observations of aerosol over land are desirable for aviation, air pollution and health applications. Thus, a method is proposed here to correct surface effects and retrieve aerosol optical depth using visible reflectance measurements from the Geostationary Operational Environmental Satellite (GOES). The surface contribution is determined from temporal compositing of visible imagery, where darker pixels correspond to less atmospheric attenuation and surface reflectance is deduced from the composite using radiative transfer. The method is applied to GOES‐8 imagery over the eastern US. Retrieved surface reflectance is compared with separate retrievals using a priori ground‐based observations of aerosol optical depth. The results suggest that surface reflectances can be determined to within ±0.04. The composite‐derived surface reflectance is further analysed by retrieving aerosol optical depth and validating retrievals with Aerosol Robotic Network (AERONET) observations. This analysis indicates that the retrieved optical depth is least biased, hence the surface reflectance is most accurate, when the composite time period varies seasonally. Aerosol optical depth retrievals from this validation are within ±0.13 of AERONET observations and have a correlation coefficient of 0.72. While aerosol optical depth retrieval noise at low optical depths may be limiting, the retrieval accuracy is adequate for monitoring large outbreaks of aerosol events.