James J. Szykman

IMPROVING NATIONAL AIR QUALITY FORECASTS WITH SATELLITE AEROSOL OBSERVATIONS

Accurate air quality forecasts can allow for mitigation of the health risks associated with high levels of air pollution. During September 2003, a team of NASA, NOAA, and EPA researchers demonstrated a prototype tool for improving fine particulate matter (PM2.5) air quality forecasts using satellite aerosol observations. Daily forecast products were generated from a near-real-time fusion of multiple input data products, including aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS)/Earth Observing System (EOS) instrument on the NASA Terra satellite, PM2.5 concentration from over 300 state/local/national surface monitoring stations, meteorological fields from the NOAA/NCEP Eta Model, and fire locations from the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) product. The products were disseminated via a Web interface to a small g...

Intercomparison of near-real-time biomass burning emissions estimates constrained by satellite fire data

We compare biomass burning emissions estimates from four different techniques that use satellite based fire products to determine area burned over regional to global domains. Three of the techniques use active fire detections from polar-orbiting MODIS sensors and one uses detections and instantaneous fire size estimates from geostationary GOES sensors. Each technique uses a different approach for estimating trace gas and particulate emissions from active fires. Here we evaluate monthly area burned and CO emission estimates for most of 2006 over the contiguous United States domain common to all four techniques. Two techniques provide global estimates and these are also compared. Overall we find consistency in temporal evolution and spatial patterns but differences in these monthly estimates can be as large as a factor of 10. One set of emission estimates is evaluated by comparing model CO predictions with satellite observations over regions where biomass burning is significant. These emissions are consistent with observations over the US but have a high bias in three out of four regions of large tropical burning. The large-scale evaluations of the magnitudes and characteristics of the differences presented here are a necessary first step toward an ultimate goal of reducing the large uncertainties in biomass burning emission estimates, thereby enhancing environmental monitoring and prediction capabilities.

Improving the accuracy of daily satellite-derived ground-level fine aerosol concentration estimates for North America.

We improve the accuracy of daily ground-level fine particulate matter concentrations (PM(2.5)) derived from satellite observations (MODIS and MISR) of aerosol optical depth (AOD) and chemical transport model (GEOS-Chem) calculations of the relationship between AOD and PM(2.5). This improvement is achieved by (1) applying climatological ground-based regional bias-correction factors based upon comparison with in situ PM(2.5), and (2) applying spatial smoothing to reduce random uncertainty and extend coverage. Initial daily 1-σ mean uncertainties are reduced across the United States and southern Canada from ± (1 μg/m(3) + 67%) to ± (1 μg/m(3) + 54%) by applying the climatological ground-based regional scaling factors. Spatial interpolation increases the coverage of satellite-derived PM(2.5) estimates without increased uncertainty when in close proximity to direct AOD retrievals. Spatial smoothing further reduces the daily 1-σ uncertainty to ±(1 μg/m(3) + 42%) by limiting the random component of uncertainty. We additionally find similar performance for climatological relationships of AOD to PM(2.5) as compared to day-specific relationships.

Air Quality Forecast Verification Using Satellite Data

Abstract NOAA’s operational geostationary satellite retrievals of aerosol optical depths (AODs) were used to verify National Weather Service developmental (research mode) particulate matter (PM2.5) predictions tested during the summer 2004 International Consortium for Atmospheric Research on Transport and Transformation/New England Air Quality Study (ICARTT/NEAQS) field campaign. The forecast period included long-range transport of smoke from fires burning in Canada and Alaska and a regional-scale sulfate event over the Gulf of Mexico and the eastern United States. Over the 30-day time period for which daytime hourly forecasts were compared with observations, the categorical (exceedance defined as AOD > 0.55) forecast accuracy was between 0% and 20%. Hourly normalized mean bias (forecasts − observations) ranged between −50% and +50% with forecasts being positively biased when observed AODs were small and negatively biased when observed AODs were high. Normalized mean errors are between 50% and 100% with t...

Applications of the three-dimensional air quality system to western U.S. air quality: IDEA, smog blog, smog stories, airquest, and the remote sensing information gateway.

Abstract A system has been developed to combine remote sensing and ground-based measurements of aerosol concentration and aerosol light scattering parameters into a three-dimensional view of the atmosphere over the United States. Utilizing passive and active remote sensors from space and the ground, the system provides tools to visualize particulate air pollution in near real time and archive the results for retrospective analyses. The main components of the system (Infusing satellite Data into Environmental Applications [IDEA], the U.S. Air Quality Web log [Smog Blog], Smog Stories, U.S. Environmental Protection Agency’s AIR Quest decision support system, and the Remote Sensing Information Gateway [RSIG]) are described, and the relationship of how data move from one system to another is outlined. To provide examples of how the results can be used to analyze specific pollution episodes, three events (two fires and one wintertime low planetary boundary layer haze) are discussed. Not all tools are useful at all times, and the limitations, including the sparsity of some data, the interference caused by overlying clouds, etc., are shown. Nevertheless, multiple sources of data help a state, local, or regional air quality analyst construct a more thorough picture of a daily air pollution situation than what one would obtain with only surface-based sensors.

Large vertical gradient of reactive nitrogen oxides in the boundary layer: Modeling analysis of DISCOVER-AQ 2011 observations

An often used assumption in air pollution studies is a well-mixed boundary layer (BL), where pollutants are evenly distributed. Because of the difficulty in obtaining vertically resolved measurements, the validity of the assumption has not been thoroughly evaluated. In this study, we usemore than 200 vertical profiles observed in the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) aircraft campaign in July 2011 to examine the vertical distributions of pollutants over the Washington-Baltimore area. While many long-lived species are well mixed in daytime, the observed average vertical profile of NOx shows a large negative gradient with increasing altitude in the BL. Our analysis suggests that the magnitude of the NOx gradient is highly sensitive to atmospheric stability. We investigate how parameterizations of the BL and land-surface processes impact vertical profiles in a 1-D chemical transport model, using three BL schemes (Asymmetric Convective Model version 2 (ACM2), Yonsei University (YSU), and Mellor-Yamada-Janjic (MYJ)) and two land-surface schemes (Noah and Rapid Update Cycle (RUC)). The model reasonably reproduces the median vertical profiles of NOx under different BL stability conditions within 30% of observations, classified based on potential temperature gradient and BL height. Comparisons with NOx observations for individual vertical profiles reveal that while YSU performs better in the turbulent and deep BL case, in general, ACM2 (RMSE=2.0ppbv) outperforms YSU (RMSE=2.5ppbv) and MYJ (RMSE=2.2ppbv). Results also indicate that the land-surface schemes in the Weather Research and Forecasting (WRF) model have a small impact on the NOx gradient. Usingmodel simulations, we analyze the impact of BL NOx gradient on the calculation of the ozone production rate and satellite NO2 retrieval. We show that using surface measurements and the well-mixed BL assumption causes a~45%highbias in the estimated BL ozoneproduction rate and that the variability of NO2 vertical profiles is responsible for 5–10% variability in the retrieved NO2 tropospheric vertical columns.

Assessing Satellite-Based Fire Data for use in the National Emissions Inventory

Biomass burning is significant to emission estimates because: (1) it is a major contributor of particulate matter and other pollutants; (2) it is one of the most poorly documented of all sources; (3) it can adversely affect human health; and (4) it has been identified as a significant contributor to climate change through feedbacks with the radiation budget. Additionally, biomass burning can be a significant contributor to a regions inability to achieve the National Ambient Air Quality Standards for PM 2.5 and ozone, particularly on the top 20% worst air quality days. The United States does not have a standard methodology to track fire occurrence or area burned, which are essential components to estimating fire emissions. Satellite imagery is available almost instantaneously and has great potential to enhance emission estimates and their timeliness. This investigation compares satellite-derived fire data to ground-based data to assign statistical error and helps provide confidence in these data. The largest fires are identified by all satellites and their spatial domain is accurately sensed. MODIS provides enhanced spatial and temporal information, and GOES ABBA data are able to capture more small agricultural fires. A methodology is presented that combines these satellite data in Near-Real-Time to produce a product that captures 81 to 92% of the total area burned by wildfire, prescribed, agricultural and rangeland burning. Each satellite possesses distinct temporal and spatial capabilities that permit the detection of unique fires that could be omitted if using data from only one satellite.

Performance Evaluation and Community Application of Low-Cost Sensors for Ozone and Nitrogen Dioxide

This study reports on the performance of electrochemical-based low-cost sensors and their use in a community application. CairClip sensors were collocated with federal reference and equivalent methods and operated in a network of sites by citizen scientists (community members) in Houston, Texas and Denver, Colorado, under the umbrella of the NASA-led DISCOVER-AQ Earth Venture Mission. Measurements were focused on ozone (O3) and nitrogen dioxide (NO2). The performance evaluation showed that the CairClip O3/NO2 sensor provided a consistent measurement response to that of reference monitors (r2 = 0.79 in Houston; r2 = 0.72 in Denver) whereas the CairClip NO2 sensor measurements showed no agreement to reference measurements. The CairClip O3/NO2 sensor data from the citizen science sites compared favorably to measurements at nearby reference monitoring sites. This study provides important information on data quality from low-cost sensor technologies and is one of few studies that reports sensor data collected directly by citizen scientists.

Trophic status, ecological condition, and cyanobacteria risk of New England lakes and ponds based on aircraft remote sensing

Abstract. Aircraft remote sensing of freshwater ecosystems offers federal and state monitoring agencies an ability to meet their assessment requirements by rapidly acquiring information on ecosystem responses to environmental change for water bodies that are below the resolution of space-based platforms. During this study, hyperspectral data were collected over a two-day period from glacial lakes, ponds, and man-made reservoirs in New Hampshire, Massachusetts, Connecticut, and Rhode Island. These lakes ranged from five to greater than 1600 hectares and oligotrophic-mesotrophic to eutrophic and hypereutrophic conditions. Water samples were collected by several New England state agencies coincident with the airborne remote-sensing flights to provide ground reference data for algorithm development and testing. Using an inverse modeling approach remotely sensed reflectances from the near-infrared to red portion of the spectrum were used to develop an empirical model to estimate chlorophyll a concentrations. The accuracy of the algorithm was assessed from the RSM error of predicted and measured chlorophyll values for all lakes sampled. Results showed a strong statistical relationship between measured and predicted values. The predicted chlorophyll concentrations were used to assess the biological condition, trophic status, and recreational risk to human health for the New England lakes and ponds surveyed.

Application of satellite data for three-dimensional monitoring of PM2.5 formation and transport in San Joaquin Valley, California

High resolution (5x5 km2 horizontal resolution) retrievals of aerosol optical depth (AOD) from the MODerate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASAs Aqua and Terra satellite platforms have been examined. These data products have been compared to coincident, hourly measurements of ground-based PM-2.5 routinely obtained by the San Joaquin Valley Air Pollution Control District (SJV APCD) and California Air Resources Board (CARB) and to airborne light detection and ranging (lidar) aerosol scattering measurements obtained by NASA in July 2003 in San Joaquin Valley (SJV). Comparison of MODIS AOD to ground based PM-2.5 measurement shows significant improvement for the higher resolution MODIS AOD. Lidar aerosol scattering measurements correspond well to MODIS AOD during a variety of atmospheric conditions, and throughout the SJV. Future lidar measurements are proposed to establish a high resolution vertical link between satellite and ground-based measurements during the winter. With the data from these two episodes, we plan to characterize the horizontal, vertical, and temporal distribution of PM-2.5 in SJV and evaluate the need for future intensive ground-based measurement and modeling studies in SJV.