Introduction to the PM Data Analysis Workbook

1
Introduction to the PM Data Analysis Workbook

• Objectives of the PM Monitoring Program
• Critical Issues for Data Uses and Interpretation
• Motivating Examples
• References

• Introduction
• Workbook Content
• PM2.5 Background
• PM2.5 Emission Sources
• Properties of PM
• PM Formation in the Atmosphere
• Atmospheric Transport of PM

The objective of the workbook is to guide
federal, state, and local agencies and other
interested people in using particulate matter
data to meet their objectives.
2
Introduction

• Particulate matter (PM) is a general term for a
  mixture of solid particles and liquid droplets
  found in the air.
• Scientific studies show a link between PM and a
  series of significant health effects.
• The new standards for particles lt2.5?m (PM2.5)
  are 15 ?g/m3 annual and 65 ?g/m3 24-hr.
• PM2.5, fine particles, result from sources such
  as combustion and from the transformation of
  gaseous emissions such as sulfur dioxide (SO2),
  nitrogen oxide (NOx), and volatile organic
  compounds (VOCs).

3
Introduction
Nature and sources of particulate matter (PM).
Particulate matter is the general term used for a
mixture of solid particles and liquid droplets
found in the air. These particles, which come in
a wide range of sizes, originate from many
different stationary and mobile sources as well
as from natural sources. They may be emitted
directly by a source or formed in the atmosphere
by the transformation of gaseous emissions. Their
chemical and physical compositions vary depending
on location, time of year, and meteorology. Healt
h and other effects of PM. Scientific studies
show a link between PM (alone or combined with
other pollutants in the air) and a series of
significant health effects. These health effects
include premature death, increased hospital
admissions and emergency room visits, increased
respiratory symptoms and disease, and decreased
lung function, and alterations in lung tissue and
structure and in respiratory tract defense
mechanisms. Sensitive groups that appear to be
at greater risk to such effects include the
elderly, individuals with cardiopulmonary disease
such as asthma, and children. In addition to
health problems, particulate matter is the major
cause of reduced visibility in many parts of the
United States. Airborne particles also can cause
soiling and damage to materials. New PM
standards. The primary (health-based) standards
were revised to add two new PM2.5 standards, set
at 15µg/m3 (annual) and 65 µg/m3 (24-hr), and to
change the form of the 24-hr PM10 standard. The
selected levels are based on the judgment that
public health will be protected with an adequate
margin of safety. The secondary (welfare-based)
standards were revised by making them identical
to the primary standards. In conjunction with
the Regional Haze Program, the secondary
standards will protect against major PM welfare
effects, such as visibility impairment, soiling,
and materials damage. PM2.5 composition. PM2.5
consists of those particles that are less than
2.5 micrometers in diameter. They are also
referred to as "fine" particles, while those
between 2.5 and 10 µ m are known as "coarse"
particles. Fine particles result from fuel
combustion from motor vehicles, power generation,
and industrial facilities and from residential
fireplaces and wood stoves. Fine particles can
also be formed in the atmosphere by the
transformation of gaseous emissions such as SO2,
NOx, and VOCs. Coarse particles are generally
emitted from sources such as vehicles traveling
on unpaved roads, materials handling, crushing
and grinding operations, and windblown dust.
Goals of PM2.5 monitoring. The goal of the
PM2.5 monitoring program is to provide ambient
data that support the nation's air quality
programs, including both mass measurements and
chemically resolved, or speciated, data. Data
from this program will be used for PM2.5 National
Ambient Air Quality Standard (NAAQS) comparisons,
development and tracking of implementation plans,
assessments for regional haze, assistance for
studies of health effects, and other ambient PM
research activities.
U.S. EPA, 1999a
4
PM Data Analysis Workbook Design Goals

• Relevant. The workbook should contain material
  that state PM data analysts need and omit
  material that they dont need.
• Technically sound. The workbook should be
  prepared and agreed upon by experienced PM
  analysts.
• Educational. The workbook content should be
  presented in a manner that enables state PM data
  analysts to learn relevant new PM analysis
  techniques.
• Practical. Beyond theory, the workbook should
  contain practical advice and access to examples,
  tools and methods.
• Gateway. The core workbook should be a gateway
  to additional on-line resources.
• Evolving. The on-line and hard copy workbooks
  should improve in time through feedback from the
  user and producer communities.

The on-line workbook and data analysis forum is
available at http//capita.wustl.edu/PMFine/.
Contributions to the workbook and site are
encouraged and welcome!
5
Why PM Data Analysis by Individual States?

• The new PM2.5 regulations will further increase
  the need to better understand the nature, causes,
  effects, and reduction strategies for PM.
• States collecting data have unique local
  perspectives on data quality, meteorology, and
  sources, and in articulating policy-relevant data
  analysis questions. States also face
• large, complex new PM2.5 data quantities,
• large uncertainties about causes and effects,
• immature nature and inherent complexity of
  analysis techniques,
• importance of both local and transport sources
  for PM2.5, and
• connections between PM2.5, visibility, ozone,
  climate change, and toxics.
• Collaborative data analysis is needed to develop
  and support linkages between
• data analysis experts, novices, and
  beginners
• data analysts and modelers, health researchers,
  and policymakers
• multiple states, regions, nations, environmental
  groups and industrial stakeholders

Poirot, 1999
6
Workbook Content

• Introduction
• Ensuring High Quality Data
• Quantifying PM NAAQS Attainment Status
• Characterizing Ambient PM Concentrations and
  Processes
• Quantifying Trends in PM and its Precursors
• Quantifying the Contribution of Important Sources
  to PM Concentrations
• Evaluating PM and Precursor Emission Inventories
• Identifying Control Strategies to Meet the NAAQS
  for PM2.5
• Using PM Data to Assess Visibility (to be added
  later)
• Glossary
• Workbook References

7
Workbook Preparation Strategy (1 of 2)

• This workbook was designed to
• Serve as a companion document to the PM2.5 Data
  Analysis Workshops.
• Reflect a snapshot in time of the workbook
  available on the website. By design, the website
  will have the most current information.
• Serve as an overview to the large topic of PM2.5
  data analysis (not a guidance document).
• For some topics, more information is provided by
  adding pages in 12 point font. A summary page in
  larger, presentation-friendly font is typically
  given to summarize these information-laden pages.

8
Workbook Preparation Strategy (2 of 2)

• Workshop presenters will use most, but not all,
  of the workbook pages in their presentations.
  The goal is that workshop attendees will walk
  away with all the presentation materials and
  more.
• The document was prepared in landscape format
  using a single software package to facilitate the
  presentation, HTML transfer, and printing of the
  hard copy document. Each topic area could be an
  entire workbook on its own.
• The web version of the workbook will eventually
  contain active links to methods, tools, data, and
  references.
• References are provided for readers who want more
  detail.

9
Using the Workbook

• Decision matrix to be used to identify
  example activities that will help the analyst
  meet policy-relevant objectives. To use the
  matrix, find your policy-relevant objective at
  the top left. Follow this line across to see
  which example activities will be useful to meet
  the objective. For each of these activities,
  look down the column to see which data and data
  analysis tools are useful for the activity.

Adapted from Main et al., 1998
10
PM2.5 Emission Sources
Most PM mass in urban and nonurban areas can be
explained by a combination of the following
chemical components

• Geological material suspended dust consists
  mainly of oxides of Al, Si, Ca, Ti, Fe, and other
  metal oxides.
• Sulfate results from conversion of SO2 gas to
  sulfate-containing particles.
• Nitrate results from a reversible gas/particle
  equilibrium between NH3, HNO3, and particulate
  ammonium nitrate.
• Ammonium ammonium bisulfate, sulfate, and
  nitrate are most common from the irreversible
  reaction between H2SO4 and NH3.

• NaCl Salt is found in PM near sea coasts, open
  playas, and after de-icing materials are
  applied.
• Organic carbon (OC) consists of hundreds of
  separate compounds that contain gtC20.
• Elemental carbon (EC) is black, often called
  soot.
• Liquid Water soluble nitrates, sulfates,
  ammonium, sodium, other inorganic ions, and some
  organic material absorb water vapor from the
  atmosphere.

Chow and Watson, 1997
11
Common Emission Source Profiles
Example PM source profiles in development
12
Properties of Particulate Matter

• Physical, Chemical and Optical Properties
• Size Range of Particulate Matter (PM)
• Mass Distribution of PM vs. Size PM10, PM2.5
• Fine and Coarse Particles
• Fine Particles PM2.5
• Coarse Particle Fraction PM10-PM2.5
  Relationship of PM2.5 and PM10
• Chemical Composition of PM vs. Size
• Optical Properties of PM

Husar, 1999
13
Physical, Chemical and Optical Properties

• PM is characterized by its physical, chemical,
  and optical properties.
• Physical properties include particle size and
  shape. Particle size refers to particle diameter
  or equivalent diameter for odd-shaped
  particles. Particles may be liquid droplets,
  regular or irregular shaped crystals, or
  aggregates of odd shape.
• Particle chemical composition may vary including
  dilute water solutions of acids or salts, organic
  liquids, earth's crust materials (dust), soot
  (unburned carbon), and toxic metals.
• Optical properties determine the visual
  appearance of dust, smoke, and haze and include
  light extinction, scattering, and absorption.
  The optical properties are determined by the
  physical and chemical properties of the ambient
  PM.
• Each PM source type produces particles with a
  specific physical, chemical, and optical
  signature. Hence, PM may be viewed as several
  pollutants since each PM type has its own
  properties and sources and may require different
  controls.

14
Size Range of Particulate Matter
Husar, 1999

• The size of PM particles ranges from about tens
  of nanometers (nm) (which corresponds to
  molecular aggregates) to tens of microns (1 ?m ?
  the size of human hair).
• The smallest particles are generally more
  numerous, and the number distribution of
  particles generally peaks below 0.1 ?m. The size
  range below 0.1 ?m is also referred to as the
  ultrafine range.
• The largest particles (0.1-10 ?m) are small in
  number but contain most of the PM volume (mass).
  The volume (mass) distribution can have two or
  three peaks (modes). The bi-modal mass
  distribution has two peaks.
• The peak of the PM surface area distribution is
  always between the number and the volume peaks.

15
Mass Distribution of PM vs. Size PM10, PM2.5
Husar, 1999
Fine
Coarse

• The mass distribution tends to be bi-modal with
  the saddle in the 1-3 ?m size range.
• PM10 refers to the fraction of the PM mass less
  than 10 ?m in diameter.
• PM2.5, or fine mass, refers to the fraction of
  the PM mass less than 2.5 ?m in size.
• The difference between PM10 and PM2.5 constitutes
  the coarse fraction.
• The fine and coarse particles have different
  sources, properties, and effects. Many of the
  known environmental impacts (health, visibility,
  acid deposition) are attributed to PM2.5.
• There is a natural division of atmospheric
  particulates into Fine and Coarse fraction based
  on particle size.

16
Fine and Coarse Particles
Adapted from Seinfeld and Pandis, 1998
17
Fine Particles PM2.5

• Fine particles (? 2.5 ?m) result primarily from
  combustion of fossil fuels in industrial boilers,
  automobiles, and residential heating systems.
• A significant fraction of the PM2.5 mass over the
  U.S. is produced in the atmosphere through
  gas-particle conversion of precursor gases such
  as sulfur oxides, nitrogen oxides, organics, and
  ammonia. The resulting secondary PM products are
  sulfates, nitrates, organics, and ammonium.
• Some PM2.5 is emitted as primary emissions from
  industrial activities and motor vehicles,
  including soot (unburned carbon), trace metals,
  and oily residues.
• Fine particles are mostly droplets, except for
  soot which is in the form of chain aggregates.
• Over the industrialized regions of the U.S.,
  anthropogenic emissions from fossil fuel
  combustion contribute most of the PM2.5. In
  remote areas, biomass burning, windblown dust,
  and sea salt also contribute.
• Fine particles can remain suspended for long
  periods (days to weeks) and contribute to ambient
  PM levels hundreds of km away from where they are
  formed.

18
Coarse Particle Fraction PM10-PM2.5

• Coarse particles (2.5 to 10 ?m) are generated by
  mechanical processes that break down crustal
  material into dust that can be suspended by the
  wind, agricultural practices, and vehicular
  traffic on unpaved roads.
• Coarse particles are primary in that they are
  emitted as windblown dust and sea spray in
  coastal areas. Anthropogenic coarse particle
  sources include flyash from coal combustion and
  road dust from automobiles.
• The chemical composition of the coarse particle
  fraction is similar to that of the earth's crust
  or the sea, but sometimes coarse particles also
  carry trace metals and nitrates.
• Coarse particles are removed from the atmosphere
  by gravitational settling, impaction to surfaces,
  and scavenging by precipitation. Their
  atmospheric residence time is generally less than
  a day, and their typical transport distance is
  below a few hundred km. Some dust storms tend to
  lift the dust to several km altitude, which
  increases the transport distance to many thousand
  km.

Albritton and Greenbaum, 1998
19
Relationship of PM2.5 and PM10
Husar, 1999

• The historical PM2.5 network is sparse thus, it
  is difficult to assess PM2.5 concentrations over
  the U.S.
• In many areas of the country, PM10 and PM2.5 are
  related because most of the PM10 is contributed
  by PM2.5. Evaluating the relationship between
  the two measurements provides information on
  PM2.5 concentrations in areas not monitored for
  PM2.5.
• PM2.5 comprises a larger fraction and has a more
  similar seasonal pattern in the northeastern U.S.
  than in southern California.

20
Chemical Composition of PM vs. Size

• The chemical species that make up the PM occur at
  different sizes.
• For example in Los Angeles, ammonium and sulfate
  occur in the fine mode, lt2.5 ?m in diameter.
  Carbonaceous soot, organic compounds, and trace
  metals tend to be in the fine particle mode.
• The sea salt components, sodium and chloride,
  occur in the coarse fraction, gt 2.5 ?m.
  Wind-blown and fugitive dust are also found
  mainly in the coarse mode.
• Nitrates may occur in fine and coarse modes.

Husar, 1999
21
Optical Properties of PM

• Particles effectively scatter and absorb solar
  radiation.
• The scattering efficiency per PM mass is highest
  at about 0.5 ?m. This is why, for example, 10 ?g
  of fine particles (0.2 lt D lt 1 ?m) scatter over
  ten times more than 10 ?g of coarse particles
  (D gt 2.5 ?m)

Husar, 1999
22
PM Formation in the Atmosphere

• Sulfate Formation in the Atmosphere
• Sulfate Formation in Clouds
• Season SO2--to-Sulfate Transformation Rate
• Residence Time of Sulfur and Organics
• Nitrate Formation in the Atmosphere

23
Sulfate Formation in the Atmosphere

• Sulfates constitute about half of the PM2.5 in
  the eastern U.S. Virtually all the ambient
  sulfate (99) is secondary, formed within the
  atmosphere from SO2.
• About half of the SO2 oxidation to sulfate occurs
  in the gas phase through photochemical oxidation
  in the daytime. NOx and hydrocarbon emissions
  tend to enhance the photochemical oxidation rate.

Husar, 1999

• The condensation of H2SO4 molecules results in
  the accumulation and growth of particles in the
  0.1-1.0 ?m size range hence the name
  accumulation-mode particles.

24
Sulfate Formation in Clouds

• At least half of the SO2 oxidation takes place in
  cloud droplets as air molecules pass through
  convective clouds at least once every summer day.
• Within clouds, the soluble pollutant gases, such
  as SO2, get scavenged by the water droplets and
  rapidly oxidize to sulfate.

Husar, 1999

• Only a small fraction of the cloud droplets rain
  out most droplets evaporate at night and leave a
  sulfate residue or convective debris. Most
  elevated layers above the mixing layer are
  pancake-like cloud residues.
• Such cloud processing is responsible for
  internally mixing PM particles from many
  different sources. It is also believed that such
  wet processes are significant in the formation
  of the organic fraction of PM2.5.

25
Season SO2-to-Sulfate Transformation Rate
SO2-to-sulfate transformation rates peak in the
summer due to enhanced summertime photochemical
oxidation and SO2 oxidation in clouds.
Transformation rates derived from the CAPITA
Monte Carlo Model, Schichtel and Husar (1997).
Husar, 1999
26
Residence Time of Sulfur and Organics.
Husar, 1999

• SO2 is depleted mostly by dry deposition
  (2-3/hr) and also by conversion to sulfate (up
  to 1/hr). This gives SO2 an atmospheric
  residence time of only 1 to 1.5 days.
• It takes about a day to form the sulfate PM.
  Once formed, sulfate is removed mostly by wet
  deposition at a rate of 1-2 /hr yielding a
  residence time of 3 to 5 days.
• Overall, sulfur as SO2 and sulfate is removed at
  a rate of 2-3/hr, which corresponds to a
  residence time of 2-4 days.
• These processes have at least a factor of two
  seasonal and geographic variation.
• It is believed that the organics in PM2.5 have a
  similar conversion rate, removal rate, and
  atmospheric residence time.

27
Nitrate Formation in the Atmosphere

• The NO2 portion of NOx can be converted to nitric
  acid (HNO3) by reaction with hydroxyl radicals
  (OH) during the day.
• The reaction of OH with NO2 is about 10 times
  faster than the OH reaction with SO2.
• The peak daytime conversion rate of NO2 to HNO3
  in the gas phase is about 10 to 50 per hour.
• During the nighttime, NO2 is converted into HNO3
  by a series of reactions involving ozone and the
  nitrate radical (NO3).
• HNO3 reacts with ammonia to form particulate
  ammonium nitrate (NH4NO3).
• About 1/3 of anthropogenic NOx emissions in the
  U.S. are estimated to be removed by wet
  deposition.

28
PM, Ozone, and Other Pollutants (1 of 2)

• The formation of a substantial fraction of fine
  particles can depend on the gas phase reactions
  which also produce ozone.
• Concentrations of OH radicals, ozone, and
  hydrogen peroxide (H2O2), formed by gas phase
  reactions involving VOCs and NOx, depend on the
  concentrations of the reactants and on
  meteorological conditions including temperature,
  solar radiation, wind speed, mixing volume, and
  passage of high pressure systems.

NESCAUM, 1992
29
PM, Ozone, and Other Pollutants (2 of 2)

• Bullets discussing PM link to health, acid
  precipitation, visibility in development

30
Summary of Factors Influencing PM Concentrations
(1 of 2)
Chu and Cox, 1998

• Meteorology
• Meteorological parameters important to PM
  concentration fluctuations include temperature,
  relative humidity, mixing heights, wind speed,
  and wind direction.
• Seasonal changes in meteorology affect diurnal,
  seasonal, and chemical patterns of PM.

31
Summary of Factors Influencing PM Concentrations
(2 of 2)

• Bullets on Emissions in development
• Hot spots
• Brief overview of how some PM species are tied to
  sources (e.g., Na marine when near coast but
  also road salt in winter.
• Ni, V oil combustion

32
Atmospheric Transport of PM

• Transport Mechanisms
• Influence of Transport on Source Regions
• Plume Transport
• Long-range Transport
• Atmospheric Residence Time and Spatial Scales
• Residence Time Dependence on Height
• Range of Transport

33
Transport Mechanisms

• Pollutants are transported by the atmospheric
  flow field which consists of the mean flow and
  the fluctuating turbulent flow.

Husar, 1999
The three major airmass source regions that
influence North America are the northern Pacific,
the Arctic, and the tropical Atlantic. During
the summer, the eastern U.S. is influenced by the
tropical airmass from the Gulf of Mexico.
The three transport processes that shape regional
dispersion are wind shear, veer, and eddy motion.
Homogeneous hazy airmasses are created through
shear and veer at night followed by vigorous
vertical mixing during the day.
34
Influence of Transport on Source Regions
Horizontal Dilution
Vertical Dilution
Husar, 1999
Low wind speeds over a source region allows for
pollutants to accumulate. High wind speeds
ventilate a source region preventing local
emissions from accumulating.
In urban areas, during the night and early
morning, the emissions are trapped by poor
ventilation. In the afternoon, vertical mixing
and horizontal transport tend to dilute the
concentrations.
35
Plume Transport
Much of the man-made PM2.5 in the eastern U.S. is
from SO2 emitted by power plants.
Husar, 1999

• Plume transport varies diurnally from a
  ribbon-like layer near the surface at night to a
  well-mixed plume during the daytime.
• Even during the daytime mixing, individual power
  plant plumes remain coherent and have been
  tracked for 300 km from the source.
• Most of the plume mixing is due to nighttime
  lateral dispersion followed by daytime vertical
  mixing.

36
Long-range Transport

• In many remote areas of the U.S., high
  concentrations of PM2.5 have been observed. Such
  events have been attributed to long-range
  transport.
• Long-range transport events occur when there is
  an airmass stagnation over a source region, such
  as the Ohio River Valley, and the PM2.5
  accumulates. Following the accumulation, the
  hazy airmass is transported to the receptor
  areas.
• Satellite and surface observations of fine
  particles in hazy airmasses provide a clear
  manifestation of long-range pollutant transport
  over eastern North America.

Husar, 1999
37
Atmospheric Residence Time and Spatial Scales

• PM2.5 sulfates reside 3 to 5 days in the
  atmosphere.
• Ultrafine 0.1 ?m coagulate while coarse particles
  above 10 ?m settle out more rapidly.
• PM in the 0.1-1.0 ?m size range has the longest
  residence time because it neither settles nor
  coagulates.

• Atmospheric residence time and transport distance
  are related by the average wind speed, about 5
  m/s.
• Residence time of several days yields long-
  range transport and more uniform spatial
  pattern.
• On average, PM2.5 particles are transported 1000
  or more km from the source of their precursor
  gases.

Husar, 1999
38
Residence Time Dependence on Height
Husar, 1999

• The PM2.5 residence time increased with height.
• Within the atmospheric boundary layer (the lowest
  1-2 km), the residence time is3 to 5 days.
• If aerosols are lifted to 1-10 km in the
  troposphere, they are transported for weeks and
  many thousand miles before removal.
• The lifting of boundary layer air into the free
  troposphere occurs by deep convective clouds and
  by converging airmasses near weather fronts.

39
Range of Transport

• The residence time determines the range of
  transport. For example, given a residence time
  of 4 days (100 hrs) and a mean transport speed
  of 10 mph, the transport distance is about 1000
  miles.
• The range of transport determines the region of
  influence of specific sources.

Husar, 1999
40
Objectives of the PM Monitoring Program

• The primary objective of the PM monitoring
  program is to provide ambient data that support
  the nations air quality program objectives. At
  a minimum, this includes
• Determine whether health and welfare standards
  (NAAQS) are met.
• Assess annual and seasonal spatial
  characterization of PM.
• Track progress of the nation and specific areas
  in meeting Clean Air Act requirements (provided,
  for example, through national trends analyses).
• Develop emission control strategies.

Homolya et al., 1998
41
Overview of National PM2.5 Network
Homolya et al., 1998
42
PM2.5 Implementation Update

• The bulk of all compliance and continuous
  monitoring sites are to be established by
  December 31, 1999.
• The chemical speciation sites will begin
  operation by November 1999, and installations
  will continue through December 31, 2000.
• The IMPROVE sites were to have been deployed by
  December 31, 1999 however, this schedule has
  been delayed.
• The super sites began in Atlanta in August 1999
  the site in Fresno will be next, followed by the
  remaining areas (to be announced once grants are
  awarded).

Byrd, 1999
43
PM2.5 Sampling Schedule

• Compliance sites those with federal reference
  method samples (FRMS) will operate largely on an
  everyday or one-in-three-day schedule. Some
  sites will operate on a one-in-six-day schedule.
• Continuous sites will operate every day.
• Fifty-four speciation sites will operate on a
  one-in-three-day schedule.
• The remaining sites will operate on a
  one-in-six-day or episodic schedule depending on
  data needs.
• The IMPROVE sampling schedule will ultimately
  match a one-in-three-day regime.

Byrd, 1999
44
Critical Issues for Data Uses and Interpretation

• Sampling losses on the order of 30 of the annual
  federal standard for PM2.5 may be expected due to
  volatilization of ammonium nitrate in those areas
  of the country where nitrate is a significant
  contributor to the fine particle mass and where
  ambient temperatures tend to be warm (Hering and
  Cass, 1999).
• Add bullet on organic carbon losses.
• Discuss how these issues relate to data
  interpretation and can affect uses of the data.
• Some analyses require data collected on a less
  than 24-hr basis because of the changes in
  photochemistry, emissions and meteorology that
  occur during a 24-hr period.

45
Site Types
Homolya et al., 1998
The larger check marks reflect the primary use of
the data.
46
Data Collected
Homolya et al., 1998
47
Sampling Artifacts, Interferences, and Limitations
Homolya et al., 1998
48
Motivating Examples

• The following pages are excerpts from other
  chapters in this workbook. These examples
  illustrate key PM data analysis and validation
  issues.
• Meaningful data analyses
• Begin with the collection and reporting of valid
  data.
• Proceed through an understanding of the chemical
  and physical processes related to PM formation,
  transport, and removal.
• Evolve as more analysis techniques are applied to
  the data to obtain a consensus view of attainment
  and control issues.

49
Data Validation Continues During Data Analysis

• Two source apportionment models were applied to
  PM2.5 data collected in Vermont, and the results
  of the models were compared.
• Excellent agreement for the selenium source was
  observed for part of the data while the rest of
  the results did not agree well.
• Further investigation showed that the period of
  good agreement coincided with a change in
  laboratory analysis (with an accompanying change
  in detection limit and measurement uncertainty -
  the two models treat these quantities
  differently.)

Poirot, 1999b
50
Annual Standards Calculation
A PM2.5 network with annual means calculated from
quarterly means

• Annual means are averaged across sites (spatial
  mean) before averaging across years.
• This calculation assumes the site with 38 data
  completeness (Site 3, year 2) had less than 11
  samples in each quarter. Thus, the 15.2 ?g/m3
  annual mean was left out of the spatial mean
  calculation.
• If we also assume that the site with 50 data
  completeness (Site 4, year 4) resulted in all
  quarters with at least 11 samples, then the 16.9
  ?g/m3 annual mean at that site is included in the
  spatial mean.
• The 3-yr mean rounds to 14.4 ?g/m3 which is less
  than the level of the standard of 15.0 ?g/m3.

Fitz-Simmons, 1999
51
Episodic Patterns in PM

• Investigations of episodes of high PM
  concentrations are necessary in order to
  understand the meteorological conditions and
  possible PM and precursor sources that lead to
  the high concentrations.
• Unlike ozone episodes which typically occur
  during the summer, episodes of high PM2.5
  concentrations can occur during any time of year
  (e.g., winter wood smoke, summer photochemical
  event, etc.).

Poirot et al., 1999
52
Day-of-Week Cycle in PM Emissions

• Example day of week pattern of diesel engine
  emissions in Chicago, Illinois as determined by
  chemical mass balance model. Though the CMB fit
  was performed using PM10 and nonmethane organic
  gas (NMOG) data, diesel emissions in this case
  were nearly 100 particulate matter.
• Note that Saturday and Sunday diesel emissions
  are statistically significantly lower than Monday
  through Friday.

Chicago 80 samples 1990-1991
Lin et al., 1993
53
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.

Schichtel, 1999a
54
Seasonal PM2.5 Dependence on Elevation in the
Appalachian Mountains
Monitor locations and topography
Schichtel, 1999a

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

55
Seasonal Maps of PM2.5 (1994-1996)
Falk, 1999

• These maps illustrate the regional differences in
  PM. The same control strategies may not be
  effective if applied on a national scale.
• The PM2.5 concentrations peak during the summer
  (Q3) in the eastern U.S. The PM2.5
  concentrations peak in the winter (Q1) in
  populated regions of the Southwest and in the San
  Joaquin Valley in California.

56
PM10 in the U.S. During the Central American
Smoke Event
24-hr PM10 concentrations in ?g/m3 are shown for
several cities. The likely smoke impact on these
cities is highlighted.
The vertical line is at 65 ?g/m3 in each figure.
Husar, 1999
57
IMPROVE Network PM2.5 Trends 1988-1997

• The map shows the annual trends in overall PM2.5
  concentration for 1988-1997, at 34 monitoring
  sites in the continental U.S. which have been
  recording PM2.5 concentrations for over six
  years.
• The site labels are the annual trends of PM2.5
  concentrations at each site. The data were
  deseasonalized to "take out" the seasonal cycle
  of PM2.5.

Frechtel et al., 1999
58
Discerning Natural vs. Anthropogenic Sources
Using Spatial and Temporal Analyses
Concentrations of PM2.5 iron with silicon,
aluminum, and potassium at Chiricahua National
Park in Arizona.

• Fe and Al concentrations strongly correlate,
  suggesting a common source influence. Ratios
  are consistent with soil.
• Fe and K concentrations do not correlate as well.
  The lower KFe ratio of 0.6 is indicative of
  soil. Higher ratios are consistent with
  woodsmoke.
• Data corresponding to the July 4th weekend are
  highlighted.

Poirot, (1998)
Microsoft Excel used to prepare scatter plot and
calculate regression coefficients.
59
Air Mass History Analysis
Upwind probabilities for high aerosol arsenic at
three Champlain Basin sites
Poirot et al. (1998)
Shaded areas show 20, 40, and 60 of upwind
probability on highest concentration day

• Upwind probability plots for high arsenic
  concentrations have a strong NW orientation at
  all three sites, pointing directly toward a
  smelter region.
• The location of several large smelters are also
  identified in the plots, with the smelter
  identified as a green dot appearing to be the
  most likely contributor (the yellow dot is the
  receptor location).
• High arsenic levels paper to be excellent tracers
  for influence in the Lake Champlain Basin from
  the smelter region.

60
UNMIX Analysis

• UNMIX was applied to PM2.5 data collected at
  Underhill, VT, during 1988-1995.
• Six sources were identified using mass (MF),
  particle absorption (BABS), arsenic (As), calcium
  (Ca), iron (Fe), nickel (Ni), selenium (Se),
  silicon (Si), total sulfur (S), and non-soil
  potassium (KNON).
• The sources were further investigated by
  performing back trajectories and investigating
  time series.
• The smelter (smelt) source, oil combustion, and
  winter coal combustion source trajectories are
  consistent with known emission patterns.

Values represent the of the element accounted
for by the source.
Poirot (1999)
61
PMF Analysis

• The highest average PM2.5 concentration at the
  Bering Land Bridge site (BELA) may be due to the
  strong influence of aerosol emissions from local
  pollution sources in nearby Nome plus PM
  transported into the region.
• Note the large seasonal difference in the forest
  fire factor at Gates of the Arctic (GAAR).

Polissar et al., 1998 Stacked bar plots
prepared using a spreadsheet program.
62
Case Study Top-Down Emissions Evaluation
Top-down comparison of ambient- and
emissions-derived primary PM10/NOx in two cities.
Note that this example corresponds to PM10 a
similar comparison could be made for PM2.5
Haste et. al., 1998
63
Case Study Using CMB to Assess Emission
Estimates and Source Apportionment
Emission Inventory PM2.5 Source Apportionment
CMB PM2.5 Source Apportionment
Lurmann et. al., 1999 Watson et. al., 1998
64
Model Performance Evaluation

• Mean daily variation in sulfate predictions and
  observations in this example show that the model
  predictions were greater than the ambient
  observations during most of the year.
• The largest over-predictions occurred on Julian
  days 200-250 (mid- to late summer).
• There are some occurrences when the model
  under-predicts.
• The tendency for over-prediction is most easily
  seen in the bias display.

Adapted from Wayland (1998)
65
References

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