August 1999PM Data Analysis Workbook: Characterizing PM1 Characterizing Ambient PM Concentrations and Processes What are the temporal, spatial, chemical, and size characteristics of suspended particles and precursor gases? By understanding these characteristics, we can begin to understand the sources, transport properties, formation and health effects of PM. Overview (pp. 1-4) Temporal Patterns of Primary and Secondary PM Components (pp. 5-22) Spatial Patterns (pp ) Compositional Patterns (pp ) Discerning Influences (pp ) Methods, tools, references (pp )

August 1999PM Data Analysis Workbook: Characterizing PM2 Overview Spatial and temporal analyses of PM data are the basis for improving our understanding of emissions and the dynamic atmospheric processes that influence particle formation and distribution. Goals of the data analyst performing these investigations can include: –identify possible important sources of PM and precursors –determine chemical and physical processes that lead to high PM concentrations –assess efficacy of existing control strategies Analyses help one to develop a conceptual model of processes affecting PM concentrations. Questions the analyst could be addressing with the data include the following: –What is the chemical composition of PM and how does the composition change with time and by site? –What are the statistical characteristics of pollutant concentrations and how do they change from site to site and from time to time? –How do different pollutant concentrations vary in space and time relative to each other? –What spatial and temporal scales are represented by pollutant measurements at each site? –What local or regional sources influence a given measurement site? –How did meteorology, nearby precursor and PM emissions, and natural events influence both spatial and temporal characteristics of the PM data? Many of the more detailed analyses discussed later in this workbook, such as source apportionment, are improved by a thorough understanding of spatial and temporal characteristics. For example, the analyst can point out key features in the data that need to be reproduced in modeling efforts used to assess control strategies or can identify key components of PM for source apportionment. Clearly, spatial and temporal characterization of the data is a fundamental part of all the workbook chapters. Key reference: Solomon, 1994

August 1999PM Data Analysis Workbook: Characterizing PM3 Decision Matrix for Spatial and Temporal Analyses Decision matrix to be used to select analysis objectives for the characterization of PM. To use the matrix, find your analysis objective across the top. Follow this column down to see which technical topic areas at the left illustrate analyses pertaining to the objective. For each of these analysis objectives, go to the next page to see which data and data analysis tools might be needed to meet the objective.

August 1999PM Data Analysis Workbook: Characterizing PM4 Decision Matrix for Spatial and Temporal Analyses For each of the analysis objectives that are of interest to you, follow down the column to see which data and data analysis tools might be needed.

August 1999PM Data Analysis Workbook: Characterizing PM5 Temporal Patterns Diurnal patterns: explore the daily cycle of PM and its relationship to emissions and meteorology. Day of week patterns: explore the weekly cycle of PM and its relationship to emissions. Episodic patterns: explore differences between episodes of high PM concentrations and non-episodes. Seasonal patterns: explore differences in seasonal PM concentrations and the causal factors.

August 1999PM Data Analysis Workbook: Characterizing PM6 Diurnal Patterns of PM Since the ambient PM standard is expressed as a daily average (65  g/m 3 ), most measurements of PM are 24-hr averages. Hourly values are not relevant for regulating compliance purposes. When measurements with shorter averaging times than 24-hr are available, analysts have observed a significant diurnal pattern of PM at most locations. The diurnal PM variation is due to the daily cycle of emissions, dispersion, and PM formation and removal processes. The diurnal variation of PM is not well understood, mostly because of data limitations. However, the limited data can be used to suggest possible influences.

August 1999PM Data Analysis Workbook: Characterizing PM7 In the summer in Hartford, CT, the PM 2.5 concentrations are nearly constant throughout the day while the PM 10 concentrations peak during the day due to increases in the coarse particle fraction. In the winter, the PM 2.5 appears to have small peaks during the rush hours. In contrast, the PM 10 concentration doubles during the day due to increases in coarse particle concentrations. Diurnal Pattern of PM in an Urban Setting Key reference: Capita

August 1999PM Data Analysis Workbook: Characterizing PM8 In the summer in Liberty, PA, the PM 2.5 and PM 10 concentrations peak at night and decrease during the daytime. The daily cycle of nighttime boundary layer formation and daytime mixing height growth appear to drive PM concentrations. In the winter, the PM 2.5 and PM 10 concentrations show a mild diurnal fluctuation because there is a smaller difference between daytime and nighttime inversion heights. Diurnal Pattern of PM in a Rural Setting Key reference: Capita

August 1999PM Data Analysis Workbook: Characterizing PM9 In Long Beach, CA, the PM 10 concentration is low at night and peaks at about 14:00, followed by a sharp drop with the arrival of the sea breeze. The sea breeze is composed of relatively clean, cool air that does not mix significantly with the more-polluted mixed layer. At Indio, CA, in the California desert, the PM 10 concentration peaks in the afternoon. Is this increase caused by wind-blown dust? By transported PM from the upwind urban area ? Diurnal Pattern of PM in S. California Key reference:

August 1999PM Data Analysis Workbook: Characterizing PM10 In urban areas, during the afternoon, vertical mixing and horizontal transport tend to dilute concentrations. During the night and early morning, the emissions are trapped by poor ventilation. In the afternoon, vertical mixing may carry pollutants above topographical barriers. During the night and early morning, dispersion may be hampered by topography. Some Causes of Diurnal PM Variation

August 1999PM Data Analysis Workbook: Characterizing PM11 Diurnal Patterns: Summary When PM measurements are made on a <24-hr time-scale, daily cycles in concentration are observed. These daily cycles are attributable to daily cycles in emissions, dispersion, and PM formation and removal processes. Meteorological information is critical to a complete understanding of daily cycles in the meteorological data, including mixing height, temperature, relative humidity, and wind speed and direction changes with time of day. Mountain barriers and large bodies of water are also factors to be considered. Add a motivating factor for performing hourly measurement??? Examples only deal with mass; however, composition changes with time of day - any examples of this? Brief statement of emissions, transport issues at urban and rural sites and how they might be observed in the diurnal patterns?

August 1999PM Data Analysis Workbook: Characterizing PM12 Day-of-Week Patterns in PM There is a measurable weekly cycle of PM at most monitoring sites. The weekly periodicity of PM is explicitly attributable to the weekly cycling of anthropogenic emission sources and it is not influenced by weather. Hence, the weekly cycle can reveal features of PM emissions such as weekday peaks in concentration at industrial sites and weekend peaks at recreational sites. At this time, the weekly cycle has been analyzed for PM 10 but not for PM 2.5.

August 1999PM Data Analysis Workbook: Characterizing PM13 At remote monitoring sites (e.g., Thomaston, ME), the overall PM 10 concentrations are lower than urban sites and there is no discernable weekly cycle. Weekly Pattern of PM 10 in Northeast Within Boston, MA urban area, the daily average PM 10 concentration (  g/m 3 ) in the city center during the week is about 33 % higher than on weekends. At Boston suburban sites, daily average weekday PM10 concentrations are about 10-20% higher than on weekends. These patterns are consistent with weekly emission cycles. Key reference:

August 1999PM Data Analysis Workbook: Characterizing PM14 In some urban areas, such as Tacoma, WA, the amplitude of the PM 10 cycle may be up to 50% of the weekly average. At Yosemite NP, the highest concentrations occur on Sundays. This site is near major recreational facilities that experience a large weekend influx of visitors. Weekly Pattern of PM 10 in West (  g/m 3 ) Key reference:

August 1999PM Data Analysis Workbook: Characterizing PM15 Other Day-of-Week Issues Discuss minimum data requirements issues to assess day of week Show example of emissions day of week cycle and why we would expect a cycle in PM.

August 1999PM Data Analysis Workbook: Characterizing PM16 Day-of-Week: Summary At remote locations, the PM 10 concentration can be uniform during the entire week. Most urban centers have higher concentrations during the workweek and reduced values on weekends, consistent with activity patterns. At recreational locations, the PM 10 concentrations may peak during the weekend. The existence of a weekly cycle of PM in urban and at some rural areas is evidence that the PM concentrations are influenced by human activities. It is important to have a sufficient number of measurements on weekends versus weekdays to assess this issue. Also, activity patterns and emissions should be compared to the ambient data for corroboration.

August 1999PM Data Analysis Workbook: Characterizing PM17 Episodic Patterns in PM

August 1999PM Data Analysis Workbook: Characterizing PM18 Seasonal Pattern of PM 2.5 The seasonal cycle results from changes in PM background levels, emissions, atmospheric dilution, and chemical reaction, formation, and removal processes. Examining the seasonal cycles of PM 2.5 mass and its elemental constituents can provide insights into these causal factors. The season with the highest concentrations is a good candidate for PM 2.5 control actions. Key reference: CAPITA

August 1999PM Data Analysis Workbook: Characterizing PM19 In urban areas, the winter mixing heights are low, trapping emissions. In the summer, intense vertical mixing raises the mixing heights which, in turn, tends to dilute the concentrations. Some Causes of Seasonal PM Variation PM primary and precursor emissions are dependent on seasonal energy consumption for heating and cooling, occurrence of fires, etc. Many of the gas-to-particle transformation rates are photochemically driven and peak in the summer. Summer Daytime Winter Daytime Key reference: CAPITA

August 1999PM Data Analysis Workbook: Characterizing PM20 In the Northeast PM 2.5 and PM 10 concentrations peak in the summer with approximately 30% more mass in the summer than the winter. In Southern California, the PM 2.5 concentrations peak in the winter with 2.5 times more mass than during the spring and summer. The PM 10 peaks in the fall. PM 10 -PM 2.5 Relationship in the Northeast and Southern California Key reference: CAPITA

August 1999PM Data Analysis Workbook: Characterizing PM21 Seasonal PM 2.5 During 1988 At Washington DC and Philadelphia, (Mid-Atlantic) the PM 2.5 concentrations are 60% higher in summer than in winter. In the rural Appalachians, the summer PM 2.5 concentrations are a factor of three higher than during the winter. Key reference: CAPITA At urban Southwestern sites, PM 2.5 concentrations in the winter are 50% higher than in the summer. At rural Southwestern sites, PM 2.5 concentrations are 50% higher during June than January.

August 1999PM Data Analysis Workbook: Characterizing PM22 Seasonal Pattern: Summary Summertime photochemical production of secondary PM can be important at some sites. Summertime PM concentrations can be high because of dust events and secondary PM formation. Wintertime fog chemistry - not discussed here Wintertime PM concentrations can be high because of lower inversions and changes in emissions such as the use of wood-burning for home heating. Because of the potentially different sources of PM on a seasonal basis, different controls may be appropriate, depending on when PM exceedances are observed.