Assessment of Mixed-Layer Height Estimation from Single-wavelength Ceilometer Profiles.

Differing boundary/mixed-layer height measurement methods were assessed in moderately-polluted and clean environments, with a focus on the Vaisala CL51 ceilometer. This intercomparison was performed as part of ongoing measurements at the Chemistry And Physics of the Atmospheric Boundary Layer Experiment (CAPABLE) site in Hampton, Virginia and during the 2014 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) field campaign that took place in and around Denver, Colorado. We analyzed CL51 data that were collected via two different methods (BLView software, which applied correction factors, and simple terminal emulation logging) to determine the impact of data collection methodology. Further, we evaluated the STRucture of the ATmosphere (STRAT) algorithm as an open-source alternative to BLView (note that the current work presents an evaluation of the BLView and STRAT algorithms and does not intend to act as a validation of either). Filtering criteria were defined according to the change in mixed-layer height (MLH) distributions for each instrument and algorithm and were applied throughout the analysis to remove high-frequency fluctuations from the MLH retrievals. Of primary interest was determining how the different data-collection methodologies and algorithms compare to each other and to radiosonde-derived boundary-layer heights when deployed as part of a larger instrument network. We determined that data-collection methodology is not as important as the processing algorithm and that much of the algorithm differences might be driven by impacts of local meteorology and precipitation events that pose algorithm difficulties. The results of this study show that a common processing algorithm is necessary for LIght Detection And Ranging (LIDAR)-based MLH intercomparisons, and ceilometer-network operation and that sonde-derived boundary layer heights are higher (10-15% at mid-day) than LIDAR-derived mixed-layer heights. We show that averaging the retrieved MLH to 1-hour resolution (an appropriate time scale for a priori data model initialization) significantly improved correlation between differing instruments and differing algorithms.

Aerosol particulate matter in the Baltimore metropolitan area: Temporal variation over a six-year period.

UNLABELLED: This study investigates the sources of fine particulate matter (aerodynamic diameter ≤2.5 µm; PM(2.5)) composition for the Baltimore, Maryland, metropolitan area, covering a 6-year period (2008-2013). Data obtained from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) were used for the identification of eight chemical speciation clusters (factors), which, as a percentage of the average concentration, were identified as secondary sulfate (31.9%), secondary nitrate (14.3%), gasoline (17.4%), diesel (10.1%), soil (4.0%), biomass burning (11%), marine aerosol (4.1%), and industrial processing (7.2%). The results show predominant influence from vehicle emissions transiting major highways I-695 and I-95 located in the vicinity of the sampling site. Strong influence on PM2.5 mass from biomass burning was found in the first 2 years (2008-2009) due to particulate matter remnants from forest fire events in North Carolina and a strong contribution in 2013 that was due mainly to wood burning during winter. Sulfate, nitrate, soil, and marine aerosol fractions registered very low variability over the 6-year period analyzed. In addition, this study shows a significant reduction in particulate matter from industrial origins after a major industrial source in Baltimore shut down. The results obtained from Baltimore were compared with those from the Beltsville, Maryland, sampling station located 25 miles south of Baltimore for 2011 and 2012, where good agreement was found for most of the factors. IMPLICATIONS: This paper presents the first long-term aerosol speciation analysis in a Mid-Atlantic United States metropolitan area, which is essential for the air quality management agencies in order to revise regulations and reduce human exposure to adverse air quality conditions. The results suggest that although a declining trend in the overall PM2.5 was observed, no significant tendency was observed in the identified sources besides exceptional events such as the impact of wildfires on local air quality and downward contribution from industrial fraction of PM(2.5) after the Steel Mill at Sparrows Point closure in 2012.

Monitoring the Impacts of Wildfires on Forest Ecosystems and Public Health in the Exo-Urban Environment Using High-Resolution Satellite Aerosol Products from the Visible Infrared Imaging Radiometer Suite (VIIRS).

Increasing development of exo-urban environments and the spread of urbanization into forested areas is making humans and forest ecosystems more susceptible to the risks associated with wildfires. Larger and more damaging wildfires are having a negative impact on forest ecosystem services, and smoke from wildfires adversely affects the public health of people living in exo-urban environments. Satellite aerosol measurements are valuable tools that can track the evolution of wildfires and monitor the transport of smoke plumes. Operational users, such as air quality forecasters and fire management officials, can use satellite observations to complement ground-based and aircraft measurements of wildfire activity. To date, wildfire applications of satellite aerosol products, such as aerosol optical depth (AOD), have been limited by the relatively coarse resolution of available AOD data. However, the new Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the Suomi National Polar-orbiting Partnership (S-NPP) satellite has high-resolution AOD that is ideally suited to monitoring wildfire impacts on the exo-urban scale. Two AOD products are available from VIIRS: the 750-m × 750-m nadir resolution Intermediate Product (IP) and the 6-km × 6-km resolution Environmental Data Record product, which is aggregated from IP measurements. True color (red, green, and blue [RGB]) imagery and a smoke mask at 750-m × 750-m resolution are also available from VIIRS as decision aids for wildfire applications; they serve as counterparts to AOD measurements by providing visible information about areas of smoke in the atmosphere. To meet the needs of operational users, who do not have time to process raw data files and need access to VIIRS products in near-real time (NRT), VIIRS AOD and RGB NRT imagery are available from the Infusing satellite Data into Environmental Applications (IDEA) web site. A key feature of IDEA is an interactive visualization tool that allows users to display tailored combinations of AOD and RGB imagery, as well as overlay the VIIRS smoke mask and fire hotspots at pixel resolution (~750-m × 750-m), and zoom into the county level. Two case studies of recent wildfires in the Western US are presented to show how operational users can access and display VIIRS aerosol products to monitor the transport of smoke plumes and evolution of fires in the exo-urban environment on the regional and county scales. The new National Oceanic and Atmospheric Administration (NOAA) Western Region Fire and Smoke Initiative is also discussed, which will enhance IDEA to allow visualization of VIIRS aerosol products down to the neighborhood scale. The new high-resolution VIIRS aerosol products can be used for NRT monitoring of human exposure to smoke, and they can be used to gauge the spread of fires and, thus, provide advanced warning for evacuations and fire suppression efforts, thereby reducing risks to human populations and forest ecosystems in the exo-urban environment.

An improved method for estimating surface fine particle concentrations using seasonally adjusted satellite aerosol optical depth.

Using satellite observations of aerosol optical depth (AOD) to estimate surface concentrations of fine particulate matter (PM2.5) is a well-established technique in the air quality community. In this study, the relationships between PM2.5 concentrations measured at five monitor locations in the Baltimore, MD/Washington, DC region and AOD from Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-Angle Imaging Spectroradiometer (MISR), and Geostationary Operational Environmental Satellite (GOES) were calculated for the summer of 2004 and all of 2005. Linear regression methods were used to determine the direct quantitative relationships between the satellite AOD values and PM2.5 concentration measurements. Results show that correlations between AOD and surface PM2.5 concentrations range from 0.46 to 0.84 for the analyzed time period. Correlations with AOD from MODIS and MISR were higher than those from GOES, likely because of variations in the algorithms used by the different instruments. To determine the relative usefulness of platform- and season-specific AOD PM2.5 regression analysis, the results from this study were used to estimate surface PM2.5 concentrations for two representative case studies. This analysis of case studies demonstrates that it is necessary to include season and satellite platform information for more accurate estimates of surface PM2.5 concentrations from satellite AOD data. Consequently, tools that currently use a constant relationship to estimate surface PM2.5 concentrations from satellite AOD data, such as the Infusing satellite Data into Environmental Applications (IDEA) website, may need to be revised to include parameters that allow the relationships to vary with season and satellite platform to provide more accurate results.

The relation between Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth and PM2.5 over the United States: a geographical comparison by U.S. Environmental Protection Agency regions.

Aerosol optical depth (AOD) acquired from satellite measurements demonstrates good correlation with particulate matter with diameters less than 2.5 microm (PM2.5) in some regions of the United States and has been used for monitoring and nowcasting air quality over the United States. This work investigates the relation between Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and PM2.5 over the 10 U.S. Environmental Protection Agency (EPA)-defined geographic regions in the United States on the basis of a 2-yr (2005-2006) match-up dataset of MODIS AOD and hourly PM2.5 measurements. The AOD retrievals demonstrate a geographical and seasonal variation in their relation with PM2.5. Good correlations are mostly observed over the eastern United States in summer and fall. The southeastern United States has the highest correlation coefficients at more than 0.6. The southwestern United States has the lowest correlation coefficient of approximately 0.2. The seasonal regression relations derived for each region are used to estimate the PM2.5 from AOD retrievals, and it is shown that the estimation using this method is more accurate than that using a fixed ratio between PM2.5 and AOD. Two versions of AOD from Terra (v4.0.1 and v5.2.6) are also compared in terms of the inversion methods and screening algorithms. The v5.2.6 AOD retrievals demonstrate better correlation with PM2.5 than v4.0.1 retrievals, but they have much less coverage because of the differences in the cloud-screening algorithm.

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.

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 Weblog [Smog Blog], Smog Stories, U.S. Environmental Protection Agency's AIRQuest 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.

Remote sensing of particulate pollution from space: have we reached the promised land?

The recent literature on satellite remote sensing of air quality is reviewed. 2009 is the 50th anniversary of the first satellite atmospheric observations. For the first 40 of those years, atmospheric composition measurements, meteorology, and atmospheric structure and dynamics dominated the missions launched. Since 1995, 42 instruments relevant to air quality measurements have been put into orbit. Trace gases such as ozone, nitric oxide, nitrogen dioxide, water, oxygen/tetraoxygen, bromine oxide, sulfur dioxide, formaldehyde, glyoxal, chlorine dioxide, chlorine monoxide, and nitrate radical have been measured in the stratosphere and troposphere in column measurements. Aerosol optical depth (AOD) is a focus of this review and a significant body of literature exists that shows that ground-level fine particulate matter (PM2.5) can be estimated from columnar AOD. Precision of the measurement of AOD is +/-20% and the prediction of PM2.5 from AOD is order +/-30% in the most careful studies. The air quality needs that can use such predictions are examined. Satellite measurements are important to event detection, transport and model prediction, and emission estimation. It is suggested that ground-based measurements, models, and satellite measurements should be viewed as a system, each component of which is necessary to better understand air quality.

Validation of the Raman lidar algorithm for quantifying aerosol extinction.

To calculate aerosol extinction from Raman lidar data, it is necessary to evaluate the derivative of a molecular Raman signal with respect to range. The typical approach taken in the lidar community is to make an a priori assumption about the functional behavior of the data to calculate the derivative. It has previously been shown that the use of the chi-squared technique to determine the most likely functional behavior of the data prior to actually calculating the derivative eliminates the need for making a priori assumptions. Here that technique is validated through numerical simulation and by application to a significant body of Raman lidar measurements. In general, we show that the chi-squared approach for evaluating extinction yields lower extinction uncertainty than traditional techniques. We also use the technique to study the feasibility of developing a general characterization of the extinction uncertainty that could permit the uncertainty in Raman lidar aerosol extinction measurements to be estimated accurately without the need of the chi-squared technique.

Application of satellite remote-sensing data for source analysis of fine particulate matter transport events.

Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative imagery and quantitative data, such as aerosol optical depth. Yet there has been limited application of these new datasets in the study of air pollutant sources relevant to public policy. One promising approach to more directly link satellite sensor data to air quality policy is to integrate satellite sensor data with air quality parameters and models. This paper presents a visualization technique to integrate satellite sensor data, ground-based data, and back trajectory analysis relevant to a new rule concerning the transport of particulate matter across state boundaries. Overlaying satellite aerosol optical depth data and back trajectories in the days leading up to a known fine particulate matter with an aerodynamic diameter of <2.5 microm (PM2.5) event may indicate whether transport or local sources appear to be most responsible for high PM2.5 levels in a certain location at a certain time. Events in five cities in the United States are presented as case studies. This type of analysis can be used to help understand the source locations of pollutants during specific events and to support regulatory compliance decisions in cases of long distance transport.

Recommendations on the use of satellite remote-sensing data for urban air quality.

In the last 5 yr, the capabilities of earth-observing satellites and the technological tools to share and use satellite data have advanced sufficiently to consider using satellite imagery in conjunction with ground-based data for urban-scale air quality monitoring. Satellite data can add synoptic and geospatial information to ground-based air quality data and modeling. An assessment of the integrated use of ground-based and satellite data for air quality monitoring, including several short case studies, was conducted. Findings identified current U.S. satellites with potential for air quality applications, with others available internationally and several more to be launched within the next 5 yr; several of these sensors are described in this paper as illustrations. However, use of these data for air quality applications has been hindered by historical lack of collaboration between air quality and satellite scientists, difficulty accessing and understanding new data, limited resources and agency priorities to develop new techniques, ill-defined needs, and poor understanding of the potential and limitations of the data. Specialization in organizations and funding sources has limited the resources for cross-disciplinary projects. To successfully use these new data sets requires increased collaboration between organizations, streamlined access to data, and resources for project implementation.

Source-Receptor Modeling Using Trace Metals in Aerosols Collected at Three Rural Canadian Great Lakes Sampling Stations.

High-volume air samplers were used to collect aerosol samples on Whatman 41 air filters at the Canadian air sampling stations Burnt Island, Egbert, and Point Petre. The samples were analyzed for trace elements by neutron activation analysis. Air concentrations of over 30 trace elements were determined. Factor analysis, elemental ratios, and enrichment factor analysis were used to determine source-receptor relationships at the three different sites. Factor analysis exhibited trends that indicate oil and coal combustion, road salt, mining, incineration, and smelting as anthropogenic sources to aerosols of the rural Great Lakes. Elemental ratios showed that the Na to Cl ratio in the Great Lakes aerosol is similar to that found in sea water. Enrichment factor analysis revealed elements with non-crustal sources including the elements Ag, As, Br, Cl, Cu, I, In, Sb, Se, Sn, W, and Zn.