August 1999 PM Data Analysis Workbook: Characterizing PM 1

Spatial Patterns

• Urban spatial patterns: explore PM concentrations in urban settings.

• Urban/Rural spatial patterns: explore the differences between urban and rural PM concentrations.

• Elevational patterns: explore PM concentrations as a function of elevation (e.g., on a mountain) and altitude (e.g., measured above ground by aircraft).

• Regional patterns: explore regional PM concentrations.

• National/International patterns: explore PM concentrations across the nation, internationally, and assess important transport phenomena.

August 1999 PM Data Analysis Workbook: Characterizing PM 2

Urban PM Concentration Spatial Pattern

• Urban areas contain sources of PM that increase PM concentrations and cause “hot spots” (areas with concentrations in excess of background concentrations) in the PM spatial pattern

• Urban PM concentrations vary greatly from day to day

• Knowledge of urban concentrations aids in the siting of monitors

Key reference: Capita

PM10 concentration isopleths in the Los Angeles Basin during...

August 1999 PM Data Analysis Workbook: Characterizing PM 3

PM10 concentrations in Philadelphia exhibit large differences among sites from day to day.

Background concentrations on July 8, 1995 were 30 to 38 μg/m3. High concentrations (above 79 μg/m3) were only observed at one urban site.

On July 20, 1995, the background PM10 concentrations were 30-46 μg/m3 and multiple locations experienced high concentrations.

Daily Average PM10 Concentrations Philadelphia, July 1995

Key reference: Capita

Monitor Locations

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August 1999 PM Data Analysis Workbook: Characterizing PM 4

The monthly and seasonal spatial pattern varies greatly in Philadelphia although the variation is generally less than that of the daily concentrations.

Local source-influenced sites are represented as “hotspots” in the spatial concentration maps.

The summer average concentrations tend to show less spatial variation than average concentrations for the single month of July.

Monthly & Seasonal Average PM10 Philadelphia, Summer 1995

Key reference: Capita

Monitor Locations

August 1999 PM Data Analysis Workbook: Characterizing PM 5

Urban/Rural Patterns

• highlight differences between urban and rural

August 1999 PM Data Analysis Workbook: Characterizing PM 6

Dependence of PM on Elevation

• The dependence of PM2.5 on elevation is needed to assess the representativeness of a monitoring site to its surrounding areas. For example, a high elevation site outside the haze layer is not representative of nearby valley concentrations.

• PM2.5 dependence on elevation is the result of the limited extent and intensity of vertical mixing, source elevation, and changes in the chemical and physical removal processes with height.

• These causal factors vary both seasonally and diurnally; therefore, the PM2.5 dependence on elevation should also vary with season and time of day.

Key reference: capita

August 1999 PM Data Analysis Workbook: Characterizing PM 7

PM Elevation Dependence: Influence of the Seasonal Variation in Mixing Heights

• During the summer, the afternoon mixing heights typically reach 1-3 km and PM is evenly distributed throughout this layer.

• During the winter, mixing heights are lower, so the PM is distributed within only the first several hundred meters.

• Above the mixing height, the PM concentrations normally decrease with altitude.

Key reference:

August 1999 PM Data Analysis Workbook: Characterizing PM 8

Vertical Profile of the Light Scattering Coefficient in the Los Angeles Basin

• The light scattering coefficient, bscat, is largely dependent on particle concentrations between 0.1 - 1 m.

• The bscat is highest in the mixed layer, fairly uniform through the layer, and drops to low levels above the mixing layer.

Mean morning and afternoon summer light scattering profiles

Key reference: Capita

August 1999 PM Data Analysis Workbook: Characterizing PM 9

Seasonal PM2.5 Dependence on Elevation in Appalachian Mountains

• During August, the PM2.5 concentrations are independent of elevation to at least 1200 m. Above 1200 m, PM2.5 concentrations decrease.

• During January, PM2.5 concentrations decrease between sites at 300 and 800 m by about 50% . PM2.5 concentrations are approximately constant from 800 m to 1200 m and decrease another ~50% from 1200 to 1700 m.

Key reference:

Monitor Locations and topography

August 1999 PM Data Analysis Workbook: Characterizing PM 10

Topographical Influence on PM

• Mountains can restrict the horizontal flow of particles while the mixing height restricts the vertical mixing of particles.

• Pollutants can be “trapped” in valleys depending upon the height of the surrounding mountains and the height of the mixed layer. When the mixed layer is lower than the mountain top site, the elevated site may have low concentrations.

• The analyst needs to know the physical and meteorological properties of the mountain sites in order to assess the data collected at that site.

Key reference: Capita

August 1999 PM Data Analysis Workbook: Characterizing PM 11

Because topography can significantly affect PM concentrations, it should be considered in preparation of spatial contour maps of PM concentrations. As an example of the mountain-valley effect, the concentrations in the San Joaquin Valley and South Coast Basin are much higher than in the Sierra Nevada Mountains.

Incorporating Barriers in Mapping PM10Topography Spatial contouring of PM10 concentrations using topography

Sierra Nevada

San Joaquin Valley

South Coast Basin

Key reference: Capita

August 1999 PM Data Analysis Workbook: Characterizing PM 12

Regional Spatial Patterns

August 1999 PM Data Analysis Workbook: Characterizing PM 13

National and International Spatial Patterns

• Annual PM2.5 concentration maps are useful in identifying potential non-attainment areas of the PM2.5 NAAQS (annual average of 15 μg/m3 )

• Monitoring data are used to estimate PM2.5 maps.

• The limited number of PM2.5 monitoring data requires the application of surrogate data (i.e., PM10 and visibility) in the mapping process.

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SO2Note: Incomplete data, not classified, and Section 185(a) areas are not shown.* Ozone nonattainment areas on map are based on the pre-existing ozone standard.Nonattainment designations based on the revised 8-hour ozone standard will not be designated until 2000.** PM-10 nonattainment areas on map are based on the pre-existing PM-10 standards.Nonattainment designations based on the revised PM-10 standards have not yet been made.Source: U.S. EPA, National Air Quality and Emissions Trends Report, 1997.

Location of nonattainment areas for criteria pollutants, September 1998.

Key reference: US EPA, 1997

August 1999 PM Data Analysis Workbook: Characterizing PM 14

Visibility-Aided PM2.5 PM10-Aided PM2.5

Annual average PM2.5 concentrations are above 15 μg/m3 in the San Joaquin Valley and South Coast Basin of California. Annual average PM2.5 concentrations in Pittsburgh, St. Louis, Roanoke, and an area stretching from New York City to Washington D.C. are above 15 μg/m3 in both maps. The visibility-aided estimates indicate a larger region above 15 μg/m3 along the eastern seaboard. Additional areas above 15 μg/m3 are shown with PM10-aided estimates including Atlanta and eastern Tennessee.

Annual Average PM2.5 Concentrations (1994-1996)

Key reference: capita

August 1999 PM Data Analysis Workbook: Characterizing PM 15

• PM10-and visibility-aided PM2.5 maps have similar overall patterns but contain distinct differences in some areas. For instance, the visibility-aided PM2.5 concentrations are more than 5 μg/m3 higher than PM10-aided estimates in Texas, Michigan, and the eastern seaboard.

• The differences between the two maps is one indication of the uncertainty in the estimation of PM2.5 concentrations. Where the two maps are similar, the PM2.5 concentration estimates are more certain than in areas of large differences.

Differences between visibility- and PM10-aided annual estimates

PM2.5 Map Uncertainty

Key reference: capita

August 1999 PM Data Analysis Workbook: Characterizing PM 16

Seasonal Maps of PM2.5 (1994-1996)

• The PM2.5 concentrations peak during the summer, Q3, in the Eastern US. The PM2.5 concentrations peak in the winter, Q1, in populated regions of the Southwest and in the San Joaquin Valley in California.

• These maps illustrate the regional differences in PM. The same control strategies may not be effective if applied on a national scale.

Key reference: capita

August 1999 PM Data Analysis Workbook: Characterizing PM 17

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 over Eastern North America, Europe and China-Japan exhibit only moderate levels of haze during this time.

Squares are scaled to average bext values.

Key reference: capita