Characterizing Ambient PM Concentrations and Processes

1
Characterizing Ambient PM Concentrations and
Processes

• Overview (pp. 1-4)
• Temporal Patterns of Primary and Secondary PM
  Components (pp. 5-22)
• Spatial Patterns (pp. 23-39)
• Compositional Patterns (pp. 40-42)
• Discerning Influences (pp. 43-53)
• Methods, tools, references (pp. 54-59)

• 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.

2
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
3
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.
4
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.
5
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.

6
Diurnal Patterns of PM

• Since the ambient PM standard is expressed as a
  daily average (65 ?g/m3), 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.

7
Diurnal Pattern of PM in an Urban Setting
In the winter, the PM2.5 appears to have small
peaks during the rush hours. In contrast, the
PM10 concentration doubles during the day due to
increases in coarse particle concentrations.
In the summer in Hartford, CT, the PM2.5
concentrations are nearly constant throughout the
day while the PM10 concentrations peak during the
day due to increases in the coarse particle
fraction.
Key reference Capita
8
Diurnal Pattern of PM in a Rural Setting
In the summer in Liberty, PA, the PM2.5 and PM10
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 PM2.5 and PM10 concentrations
show a mild diurnal fluctuation because there is
a smaller difference between daytime and
nighttime inversion heights.
Key reference Capita
9
Diurnal Pattern of PM in S. California
In Long Beach, CA, the PM10 concentration is low
at night and peaks at about 1400, 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 PM10
concentration peaks in the afternoon. Is this
increase caused by wind-blown dust? By
transported PM from the upwind urban area ?
Key reference
10
Some Causes of Diurnal PM Variation
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.
11
Diurnal Patterns Summary

• When PM measurements are made on a lt24-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?

12
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 PM10 but not for PM2.5.

13
Weekly Pattern of PM10 in Northeast
Within Boston, MA urban area, the daily average
PM10 concentration (?g/m3) 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.
At remote monitoring sites (e.g., Thomaston, ME),
the overall PM10 concentrations are lower than
urban sites and there is no discernable weekly
cycle.
Key reference
14
Weekly Pattern of PM10 in West
(?g/m3)
(?g/m3)
In some urban areas, such as Tacoma, WA, the
amplitude of the PM10 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.
Key reference
15
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.

16
Day-of-Week Summary

• At remote locations, the PM10 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 PM10
  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.

17
Episodic Patterns in PM
18
Seasonal Pattern of PM2.5

• 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 PM2.5 mass and
  its elemental constituents can provide insights
  into these causal factors.
• The season with the highest concentrations is a
  good candidate for PM2.5 control actions.

Key reference CAPITA
19
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.
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.
Key reference CAPITA
20
PM10-PM2.5 Relationship in the Northeast and
Southern California
In Southern California, the PM2.5 concentrations
peak in the winter with 2.5 times more mass than
during the spring and summer. The PM10 peaks in
the fall.
In the Northeast PM2.5 and PM10 concentrations
peak in the summer with approximately 30 more
mass in the summer than the winter.
Key reference CAPITA
21
Seasonal PM2.5 During 1988

• At Washington DC and Philadelphia, (Mid-Atlantic)
  the PM2.5 concentrations are 60 higher in summer
  than in winter.
• In the rural Appalachians, the summer PM2.5
  concentrations are a factor of three higher than
  during the winter.

• At urban Southwestern sites, PM2.5 concentrations
  in the winter are 50 higher than in the summer.
• At rural Southwestern sites, PM2.5 concentrations
  are 50 higher during June than January.

Key reference CAPITA
22
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.