Using PM Data to Assess Visibility

1. Long-term Trends and Seasonal Patterns in Visibility Identifying Sources Contributing to Visibility Impairment Transport and Transformation of Atmospheric Particles and Gases Affecting Visibility Available Methods and Tools Available Visibility Data Summary References Using PM Data to Assess Visibility • Historical Perspective • Why is Visibility Information in the PM Workbook? • Why is Visibility Important? • What is the Definition of Visibility? • Visibility Impairment and Regional Haze • Visibility Measurements • Relationship Between Light Scattering and PM2.5 • What Pollutants Contribute to PM2.5 and Light Extinction on Days when Visibility is Impaired? PM Data Analysis Workbook: Visibility

2. Historical Perspective on Visibility • 1977: Congress amends the Clean Air Act to require visibility protection in areas of scenic importance, “Class I areas”. • 1980: EPA develops rule for 35 states to address source-by-source visibility impairment; defers action on regulating haze until science improves. • 1993: Publication of “Protecting Visibility in National Parks and Wilderness Areas”. • 1996: Publication of “Recommendations for Improving Western Vistas”. • 1997: EPA proposes regional haze rule. • 1999: EPA issues final rule on regional haze regulations. Adapted from <http://www.epa.gov/ttn/oarpg/t1/fr_notices/ruledevp.pdf> Last accessed January 2001 PM Data Analysis Workbook: Visibility

3. Why is Visibility Information in the PM Workbook? • In 1997 under the Clean Air Act, the EPA added two new PM2.5 standards. The Clean Air Act also sets forth a national goal for visibility. • In April 1999, the EPA announced a major effort to improve air quality in national parks and wilderness areas. The Regional Haze Rule calls for state and federal agencies to work together to improve visibility in 156 national parks and wilderness areas. The rule requires the states, in coordination with the EPA, the National Park Service, the U.S. Fish and Wildlife Service, the U.S. Forest Service, and other interested parties, to develop and implement air quality protection plans to reduce the pollution that causes visibility impairment. • The same particles (sulfates, nitrates, organic carbon, smoke, and soil dust) comprising PM2.5, which are linked to serious health effects and environmental effects (e.g., ecosystem damage), can also significantly degrade visual air quality. Thus, actions to reduce levels of visibility-impairing pollutants will benefit public health and reduce certain adverse effects to the environment. U.S. EPA, 1999b and <http://www.epa.gov/air/vis/> PM Data Analysis Workbook: Visibility

4. Why is Visibility Important? • Since most Americans reside in urban areas, the visibility conditions found in these areas best characterize the typical, daily experience of the average American. We cannot see ozone, for example, but we do notice when the sky is hazy. • Rural visibility measurements are representative of the visibility characteristics of the land mass of the United States. • Good visibility is valued by people throughout the country – in the places they live, work, and enjoy recreational activities. The regional haze program is designed to improve visibility and air quality in our most treasured natural areas so that these areas may be preserved and enjoyed by current and future generations. At the same time, control strategies designed to improve visibility in the national parks and wilderness areas will improve visibility over broad geographic areas, including other recreational sites, plus urban and rural areas. U.S. EPA, 1999b PM Data Analysis Workbook: Visibility

5. The Clean Air Act sets forth a national goal for visibility which is the prevention of any future, and the remedying of any existing, impairment of visibility in Class I areas in which impairment results from manmade air pollution. • There are 156 Class I areas across the United States. The map shows National Park Service Class I areas. <http://www2.nature.nps.gov/ard/parks/npsimage.html> PM Data Analysis Workbook: Visibility

6. What is the Definition of Visibility? • Visibility is a measurement of the ability to see and identify objects at different distances. • Visibility impairment is defined as any humanly perceptible change in visibility (i.e., changes in light extinction, visual range, image contrast, or image color). • Visual range is described as the farthest distance at which a large, black object can be distinguished against the horizon. • Factors which determine how far an observer can see through the atmosphere include the atmosphere’s optical properties, the amount and distribution of light, characteristics of the objects observed, and properties of the human eye. Visibility is reduced by absorption and scattering of light by both gases and particles. U.S. EPA, 1996 PM Data Analysis Workbook: Visibility

7. Light Extinction Light Scattering Light Absorption by particles by gases by particles by gases Light Extinction • The light extinction coefficient is a measure of the attenuation of light in the atmosphere caused by light scattering and light absorption. • The rate with respect to distance at which a beam of light loses energy is proportional to the intensity of the beam, and the proportionality constant is the light extinction coefficient. • bext for particle free air is 10 Mm-1 (Rayleigh scattering of gases). • bap is mostly due to black carbon. • bag is mostly caused by NO2. U.S. EPA, 1996 PM Data Analysis Workbook: Visibility

8. Visibility Impairment • For regulatory purposes, visibility impairment is classified into two principal forms: • “reasonably attributable” impairment, attributable to a single source or a small group of sources; and • regional haze, described as any perceivable change from natural conditions in visibility (i.e., light extinction, visual range, contrast, or coloration) that is caused predominantly by a combination of sources over a wide geographical area. • Visibility impairment is a function of pollutant properties, including size distribution, aerosol chemical composition, and relative humidity. • In the United States, visibility impairment is commonly caused by sulfate and nitrate particles, organic aerosols, carbon soot, and crustal dust. • Data from the existing visibility monitoring network show that visibility impairment caused by air pollution occurs virtually all the time at most national park and wilderness area monitoring stations. U.S. EPA, 1996; 1998 National Park Service PM Data Analysis Workbook: Visibility

9. Regional Haze (1 of 2) • Regional haze is visibility impairment that is produced by many sources and activities which emit fine particles and their precursors and which are located across a broad geographic area. • Pollutants come from a variety of natural (e.g., windblown dust, soot from wildfires) and manmade (e.g., motor vehicles, electric utility and industrial fuel burning) sources. Some haze-causing particles are directly emitted to the air while others are formed when gases emitted to the air form particles as they are transported. • Haze is caused when sunlight encounters tiny pollution particles in the air. Some light is absorbed by particles. Other light is scattered away before it reaches an observer. More pollutants mean more absorption and scattering of light, thus reducing the clarity and changing the color of what one sees. Some types of particles, such as sulfates, scatter more light than others, particularly during humid conditions. U.S. EPA, 1999a PM Data Analysis Workbook: Visibility

10. Regional Haze (2 of 2) • In most of the eastern United States, the average visual range is less than 30 km, or about one-fifth of the visual range that would exist under estimated natural conditions. Sulfates account for about two-thirds of the light extinction in the eastern United States. • Average visual range in many Class I areas in the western United States is 100 to 150 km, or about one-half to two-thirds of the visual range that would exist without manmade air pollution. In California, the primary cause of visibility effects is ambient nitrate. More haze Less haze Haze conditions vary across the country. Eastern U.S. areas have more haze due to higher pollutant and humidity levels. U.S. EPA, 1999a PM Data Analysis Workbook: Visibility

11. Visibility Measurements (1 of 3) • Human observations. The National Weather Service has historically recorded hourly visibility readings at all major airports in the United States based on human observation of the most distant targeted object’s perceptibility. Observations are dependent on the individual and the availability of a target and are poorly related to air quality. • Light extinction coefficient. The light extinction coefficient is directly measurable with a transmissometer or estimated by measuring the components of light extinction (scattering and absorption) and calculating the sum. The light extinction coefficient is linked to air quality. • Visual range. Visual range is calculated from the light extinction coefficient by assuming the atmosphere and the illumination over a sight path in the daytime is uniform and that the threshold contrast is 2%. Visual range (km)= 3.91/bext(Mm-1) U.S. EPA, 1996; <http://www.aqd.nps.gov/ard/vis/impnl/impr34.htm> PM Data Analysis Workbook: Visibility

12. Visibility Measurements (2 of 3) • Deciview Index. Changes in visual range are not proportional to human perception. A visibility index was developed which is linear with respect to perceived visual changes (analogous to the decibel scale for sound). The deciview scale starts near zero for a pristine (particle-free) atmosphere at 1.8 km elevation, and increases with increasing haziness. dv = 10 log10 (bext/10 Mm-1) U.S. EPA, 1996 PM Data Analysis Workbook: Visibility

13. Visibility Measurements (3 of 3) • Light scattering coefficient. The light scattering coefficient is linked to fine particle concentrations and is measurable with an open or enclosed integrating nephelometer or a forward scatter visibility monitor. The next slide illustrates the relationship between light scattering and PM2.5. • Discoloration. Discoloration is used as a quantitative measurement of atmospheric color changes in urban hazes (a component of plume visibility models). The color of haze depends primarily on the scene used and human vision. U.S. EPA, 1996 PM Data Analysis Workbook: Visibility

14. Relationship Between Light Scattering and PM2.5 • PM2.5 concentrations have a strong statistical correlation with total aerosol scattering because both the fine particle mass and the light scattering efficiency factor have a peak in the size range of 0.3 to 0.6 m. These examples show matching bscat and PM2.5 data for the period 1985-1995 (AIRS database) for Portland, Oregon, and Clayton, Missouri. • Exceptions to this relationship occur when the characteristic aerosol size is either smaller (such as primary automobile exhaust) or larger (wind blown dust) than this size range. Husar and Falke, 1996 PM Data Analysis Workbook: Visibility

15. Relative Humidity • Ambient particles contain water. As the relative humidity (RH) increases, a particle absorbs more water and increases in size and volume. This increase in particle size and volume acts to increase the light scattering properties of most particles. • The amount of increase in particle size with increasing RH is dependent on the particle composition. U.S. EPA, 1996 PM Data Analysis Workbook: Visibility

16. What Pollutants Contribute to PM2.5 on Days When Visibility is Impaired? (1 of 3) • Typical Rural Northeast. In the eastern United States, sulfates are the principal contributor to visibility impairment as illustrated by this figure for Acadia National Park in Maine. • This figure shows the average composition of PM2.5 speciated samples on days with the worst visibility impairment in 1997. <http://www.epa.gov/oar/vis/acad_p.html> Data source = IMPROVE last accessed 10/10/00 PM Data Analysis Workbook: Visibility

17. What Pollutants Contribute to PM2.5 on Days When Visibility is Impaired? (2 of 3) • Typical Rural Southwest. At the Grand Canyon National Park in Arizona, sulfates are still an important contributor to visibility impairment. However, note that crustal material is also an important contributor. • This figure shows the average composition of PM2.5 speciated samples on days with the worst visibility impairment in 1997. <http://www.epa.gov/oar/vis/grca_p.html> Data source = IMPROVE last accessed 10/10/00 PM Data Analysis Workbook: Visibility

18. What Pollutants Contribute to PM2.5 on Days When Visibility is Impaired? (3 of 3) • Typical Rural West. In California, nitrates and organic carbon are much more important contributors to visibility impairment than in other parts of the United States as illustrated by this figure for Yosemite National Park. • This figure shows the average composition of PM2.5 speciated samples on days with the worst visibility impairment in 1997. <http://www.epa.gov/oar/vis/yose_p.html> Data source = IMPROVE last accessed on 10/10/00 PM Data Analysis Workbook: Visibility

19. Reconstructing Light Extinction Using PM2.5 Speciated Data The influence of particles on visibility impairment is dependent on particle composition, solubility, and size. PM data can be used to reconstruct light extinction using the IMPROVE algorithm: bext (Mm-1) = bSO4 + bNO3 + bOC + bsoil + bCoarse + bap + bRay where bspecies = dry scattering efficiency*[species]*adjustment for RH bSO4 = 3 [(NH4)2SO4]*f(RH) bNO3 = 3 [NH4NO3]*f(RH) bOC = 4 [OC] bsoil = 1 [soil], where soil = 2.2*Al+2.49*Si+1.63*Ca+2.42*Fe+1.94*Ti bCoarse = 0.6 [Coarse mass] bap = 10 [EC] bRay = Rayleigh scattering = 10 Mm-1 [concentrations] are in g/m3; f(RH) is the relative humidity adjustment factor; and the coefficient in each equation is the dry scattering efficiencies in m2/g. All dry scattering efficiencies have been subject to debate. IMPROVE PM Data Analysis Workbook: Visibility

20. What Pollutants Contribute to Light Extinction on Days When Visibility is Impaired? (1 of 2) • This figure illustrates how pollutants contribute to the total light extinction at Acadia National Park in Maine on days when visibility is impaired. • Given the pollutant concentrations, relative humidity, and the equation for reconstructing light extinction, the analyst can estimate the relative importance of the PM2.5 constituents with respect to visibility impairment. • The biggest difference between this representation of the PM2.5 speciated data and the figure shown earlier for Acadia National Park is that elemental carbon (EC) is more important to light extinction than the soil component. Estimate of pollutant contributions to light extinction at Acadia National Park on days with low visibility in 1997. This figure was prepared assuming a PM2.5 concentration of 37 g/m3 and a relative humidity of 50%. WinHazesoftware was used to estimate the portion of light extinction attributable to the species using the IMPROVE algorithm. Coarse mass was not used in the equation. PM Data Analysis Workbook: Visibility

21. What Pollutants Contribute to Light Extinction on Days When Visibility is Impaired? (2 of 2) Pie charts showing the regional distribution of extinction conditions and contributions from principal aerosol species using IMPROVE data. Aerosol extinction at the sites shown is contributed to by sulfate extinction, nitrate extinction, extinction from organic species, absorption, and extinction from coarse mass plus fine soil. Note the large contribution from sulfates in the northeast compared to nitrates in southern California. Sisler and Malm, 1998 PM Data Analysis Workbook: Visibility

22. Western U.S. Eastern U.S. Long-term Trends in Visibility (1 of 5) Data source = IMPROVE U.S.EPA, 1998 • These figures illustrate western and eastern trends for total light extinction by quintile. Haziest days in the western United States are only slightly worse than the best days in the eastern United States. • In the eastern United States, the haziest days do not appear to be getting better. While it is noted in the reference that there appears to be steady visibility improvement in the western United States, it is difficult to see in the plot provided. PM Data Analysis Workbook: Visibility

23. Eastern U.S. Western U.S. Long-term Trends in Visibility (2 of 5) • Aerosol light extinction in western (left) and eastern (right) Class I areas for the haziest 20% of the days in the distribution from 1989 to 1998 (note differences in scale). • Sulfate is the largest contributor to visibility impairment in the eastern United States. In the western United States, sulfate is also the most significant single contributor to aerosol light extinction on the haziest days. However, organic carbon, crustal material, and nitrates play a more significant role (as a percentage of aerosol extinction) in western sites than eastern ones. Data source = IMPROVE U.S.EPA, 1998 PM Data Analysis Workbook: Visibility

24. Long-term Trends in Visibility (3 of 5) <http://www.epa.gov/oar/vis/acad_t.html> Data source = IMPROVE last accessed 10/10/00 • The visual range or distance one can see at Acadia National Park ranged from about 16 to 87 miles in the past ten years. Using this measure, a slight improvement in visibility is observed over the period plotted. • Visibility was evaluated by looking at each 20% segment of monitor data taken at a site. Trends presented here are in terms of the haziest ("worst") 20%, middle 20%, and the clearest ("best") 20% of data. For example the haziest ("worst") 20% represents the average of the 20% haziest days for that monitor site. The differences in visual range are due to the amount of air pollution in the form of haze that impairs visibility. PM Data Analysis Workbook: Visibility

25. Long-term Trends in Visibility (4 of 5) <http://www.epa.gov/oar/vis/grca_t.html> Data source = IMPROVE last accessed 10/10/00 • The visual range or distance one can see at Grand Canyon National Park ranged from about 61 to 145 miles in the past ten years. • Using this measure, a slight improvement in visibility is observed over the period plotted. PM Data Analysis Workbook: Visibility

26. Long-term Trends in Visibility (5 of 5) <http://www.epa.gov/oar/vis/yose_t.html> Data source = IMPROVE last accessed 10/10/00 • The visual range or distance one can see at Yosemite National Park ranged from about 34 to 157 miles in the past ten years. • Using this measure, visibility has remained relatively unchanged over the period plotted. PM Data Analysis Workbook: Visibility

27. Seasonal Patterns in Visibility (1 of 2) • Seasonal patterns in visibility are observed partly because of seasonal changes in the composition of PM2.5. For example, of the nonurban IMPROVE sites, Shenandoah shows the strongest seasonal trend in sulfate with the highest concentrations in the summer and the lowest concentrations in the winter. • Also at the nonurban IMPROVE sites, the highest PM2.5 concentrations occur in the summer, nitrate concentrations tend to be higher in the winter and spring than in summer and fall, and elemental carbon concentrations show little variation from season to season. Malm, 1999 PM Data Analysis Workbook: Visibility

28. Seasonal Patterns in Visibility (2 of 2) • This example shows the 90th percentile relative humidity- corrected light extinction data for St. Louis, Missouri. The daily noon-time visibility observations are plotted. Each year in the plot represents a three-year moving average value. • Prior to 1970, the highest extinction occurred during the winter due mainly to primary particulate matter. • After 1970, the highest extinction occurred during the summer due to secondary particulate matter. Falke, 2000. The processing of the visibility data is described in Husar et al. (undated). PM Data Analysis Workbook: Visibility

29. Identifying Sources Contributing to Visibility Impairment • Source apportionment methods are used to resolve the composition of PM (and visibility) into components related to emission sources. Several methods are available; it is useful to apply more than one method and look for consensus among results. Note that secondary sulfate and nitrate are significant contributors to visibility impairment, and it is considerably more difficult to relate ambient concentrations of secondary species to sources of precursor emissions than it is to identify the sources of primary particles. • Methods and tools discussed in the Source Apportionment section of this workbook include the following: • Spatial and temporal characteristics of data • Cluster, factor, and other multivariate statistical techniques • Positive matrix factorization (PMF) • UNMIX • Chemical mass balance (CMB) model • Trajectory approaches • Other multivariate receptor models and references are provided in the Source Apportionment section. PM Data Analysis Workbook: Visibility

30. Transport and Transformation of Atmospheric Particles and Gases Affecting Visibility • The role of regional transport of fine particles in contributing to high PM2.5 levels and regional haze impairment has been well-documented by many researchers and recognized as a significant issue by policymakers. • Meteorological factors including wind, cloud cover, rain, and temperature are important to pollutant transport and transformation processes. For example, • the conversion of SO2 to sulfate particles is a function of sunlight and the presence or absence of clouds; • the vertical temperature profile of the atmosphere determines whether the pollutants are well-mixed and diluted through the atmosphere or whether they are trapped under an inversion or in layers. Malm, 1999 PM Data Analysis Workbook: Visibility

31. Global Pattern of Haze Based on Visibility Data • A rough indicator of PM2.5 concentration is the extinction coefficient corrected for weather conditions and humidity. There are over 7000 qualified surface-based visibility stations in the world. • The June-August haze is most pronounced in southeast Asia and over sub-Saharan Africa where the seasonal average PM2.5 is estimated to be over 50 g/m3. • Interestingly, the industrial regions of the world such as eastern North America, Europe and China-Japan exhibit only moderate levels of haze during this time. Squares are scaled to average bext values. Husar, 1999 PM Data Analysis Workbook: Visibility

32. Available Methods and Tools • The CAPITA Monte Carlo model was developed in the 1980's to provide quantification of regional atmospheric transport, transformation, and removal processes governing the source receptor relationship. Information about the model is available at <http://capita.wustl.edu/CAPITA/CapitaReports/MonteCarloDescr/mc_pcim0.html#monte>. • Information about the use of airmass history models and techniques for source attribution is available at <http://capita.wustl.edu/capita/capitareports/airmasshist/EPASrcAtt_jul17/index.htm>. • Information about the NOAA trajectory cluster model is available at <http://www.arl.noaa.gov/slides/ready/index.html>. • For discussion of back trajectory receptor models and back trajectory receptor models as applied to visibility, see Malm (1999). Other source apportionment tools are listed in the Source Apportionment section of this workbook. • WinHaze Air Quality Modeler isa computer-imaging software program that simulates visual air quality differences of various scenes. The software is free from Air Resource Specialists Inc. and is available at <http://www.air-resource.com/whats_new.asp>. The software allows analysts to view visual air quality scenarios on their desktop computer as a supplement to air quality monitoring. Users can select from 30 wilderness and urban scenes. Users can then model scenes using different optical parameters or aerosol species to simulate effects they have on the scenes. PM Data Analysis Workbook: Visibility

33. Available Visibility Data • Automated observing systems: Automated Surface Observing System (ASOS) and the Automated Weather Observing System (AWOS) are placed at airports around the country. The visibility sensor measures the clarity of the air using a forward-scatter visibility meter. See <http://www.nws.noaa.gov/asos/>. • Interagency Monitoring of Protected Visual Environments (IMPROVE): This monitoring network includes both visibility and air quality measurements. For more information, see <http://www2.nature.nps.gov/ard/impr/> and, for data, see <http://capita.wustl.edu/CAPITA/DataSets/IMPROVE/impnes.html>. • EPA PM2.5 Monitoring Network: This national monitoring network established throughout the United States includes air quality measurements and is coordinated with visibility monitoring efforts such as IMPROVE. See http://www.epa.gov/ttn/amtic/amticpm.html>. • Data sets including National Park Service, MOHAVE, PREVENT, WHITEX, NAPS, and others are described by Schichtel et al. (1999) at <http://capita.wustl.edu/CAPITA/CapitaReports/Awma99/NamPM/NAMPMdata.htm>. • Real-time visibility information is available at <http://www.hazecam.net/>. PM Data Analysis Workbook: Visibility

34. Summary • Particulate matter contributes to visibility impairment. Thus, data collected as a part of the national PM2.5 monitoring network can be used in visibility data analyses including trends, source apportionment, and spatial/temporal characterization. • Historical data from the IMPROVE network show that the visibility in the eastern United States is worse than in the western United States and that the haziest days in the east do not appear to be getting better. • Visibility data are available from several sources and have been used in past analyses to augment PM2.5 data or vice versa. PM Data Analysis Workbook: Visibility

35. References (1 of 2) Falke S. (2000) Seasonal haze trends 1948-1994. Available at <http://capita.wustl.edu/StLSuperSite/images/StlHazeTrend.htm> (last accessed 10/17/00). Federal Register 40 CFR Part 51. Regional Haze Regulations; Final Rule. July 1, 2000. Friedlander S.K. (1977) Smoke, Dust, and Haze: Fundamentals of Aerosol Behavior. John Wiley and Sons, New York. Grand Canyon Visibility Transport Commission Public Advisory Committee (1996) Proposed recommendations. May. Approved by the Commissioners at the Grand Canyon, June 10. Available at <http://www.nmia.com/gcvtc/final.html> Husar R.B., Elkins J. B., and Wilson W.E. (undated) U.S. visibility trends, 1960-1992. Available at <http://capita.wustl.edu/CAPITA/CapitaReports/USVisiTrend/usvstrd0.html> (last accessed 10/17/00). Husar R. and S. Falke (1996) The relationship between aerosol light scattering and fine mass. Available at <http://capita.wustl.edu/CAPITA/CapitaReports/BScatFMRelation/BSCATFM.html> (last accessed 10/17/00). IMPROVE Newsletter (1996) Available at <http://www.aqd.nps.gov/ard/vis/impnl/impr34.htm> (last accessed 11/3/00). Malm W.C. (1999) Introduction to Visibility. Available at <http://www2.nature.nps.gov/ard/vis/intro_to_visibility.pdf> (last accessed 10/2/00). Map of National Park Service Class I areas available at <http://www2.nature.nps.gov/ard/parks/npsimage.html> (last accessed 11/3/00). National Park Service Air Quality Public Service Awareness Program at <http://www2.nature.nps.gov/ard/pubedhp.html> (last accessed 10/2/00). National Research Council (1993) Protecting visibility in national parks and wilderness areas. Committee on Haze in National Parks and Wilderness Areas, Board on Environmental Studies and Toxicology, Commission on Geosciences, Environment, and Resources. National Academy Press, Washington, D.C. Richards L.W. (1984) Suggested units for quantities related to visibility in the atmosphere. J. Air Pollut. Control Assoc. 34, 378-379. Richards L.W. (1988) Sight path measurements for visibility monitoring and research. J. Air Pollut Control Assoc. 38, 784-791. Richards L.W. (1997) Use of the deciview haze index as an indicator of regional haze. J. Air & Waste Manag. Assoc. 49, 1230-1237 (STI 1758). PM Data Analysis Workbook: Visibility

36. References (2 of 2) Richards L.W., Bergstrom R.W., and Ackerman T.P. (1986) The optical effects of fine-particle carbon on urban atmospheres. Atmos. Environ. 20, 387-396. Richards L.W., Stoelting M., and Hammarstrand R.G. (1989) Photographic method for visibility monitoring. Environ. Sci. & Technol. 23, 182-186. Richards L.W., Alcorn S.H., McDade C., Couture T., Lowenthal D., Chow J.C., and Watson J.G. (1999) Optical properties of the San Joaquin Valley aerosol collected during the 1995 integrated monitoring study. Atmos. Environ. 33, 4787-4795 (STI-1834). Schichtel B., Falke S., and Husar R. (1999) North American integrated fine particle data set. Available at <http://capita.wustl.edu/CAPITA/CapitaReports/AWMA99/NamPM/NAMPMdata.htm> (last accessed 10/18/00). Seinfeld J.H. and Pandis S. N. (1998) Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. John Wiley and Sons, Inc., New York. Sisler J.F. and Malm W.C. (2000) Interpretation of trends of PM2.5 and reconstructed visibility from the IMPROVE network. J. Air & Waste Manag. Assoc.50, 775-789. Sloane C.S., Watson J., Chow J., Pritchett L., and Richards L.W. (1991) Size-segregated fine particle measurements by chemical species and their impact on visibility impairment in Denver. Atmos. Environ. 25A, 1013-1024. U.S. Environmental Protection Agency (1996) PM Criteria Document available at <http://www.epa.gov/ttncaaa1/t1cd.html> (last accessed October 2, 2000). U.S. Environmental Protection Agency (1998) National air quality and emissions trends report, 1997. Prepared by the Office of Air Quality, Planning and Standards, Research Triangle Park, NC. Available at <http://www.epa.gov/oar/aqtrnd98/> (last accessed 10/5/00) U.S. Environmental Protection Agency (1999a) Regional haze and visibility protection: clearing the air and improving the view. Office of Air Quality Planning and Standards. September. Available at <http://www.epa.gov/air/vis/epahaze/default.html> (last accessed 10/4/00) U.S. Environmental Protection Agency (1999b) Regional haze regulations: final rule. 40 CFR Part 51, July 1. Available at <http://www.epa.gov/air/vis/facts.pdf> (last accessed 11/3/00). U.S. Environmental Protection Agency (2000) Approval and promulgation of implementation plans: revision of the visibility FIP for Nevada. Proposed Rule, 65 CFR 140, 45003-45013, July 20. PM Data Analysis Workbook: Visibility