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Objectives of the PM Monitoring Program Critical Issues for Data Uses and Interpretation Motivating Examples References. Introduction Workbook Content PM 2.5 Background Common PM 2.5 Emission Sources Properties of PM PM Formation in the Atmosphere Atmospheric Transport of PM.

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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

Common PM2.5 Emission Sources

Properties of PM

PM Formation in the Atmosphere

Atmospheric Transport of PM

Introduction to the PM Data Analysis Workbook

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.

PM Data Analysis Workbook: Introduction

slide2

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 <2.5 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).

PM Data Analysis Workbook: Introduction

introduction
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, area, 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.

Health 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

PM Data Analysis Workbook: Introduction

pm data analysis workbook design goals
PM Data Analysis Workbook: Design Goals
  • Relevant. The workbook should contain material that state PM data analysts need and omit material that they don’t 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 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!

PM Data Analysis Workbook: Introduction

why pm data analysis by individual states
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 quantities of complex new PM2.5 data,
    • 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, modelers, health researchers, and policymakers
    • multiple states, regions, nations, environmental groups and industrial stakeholders

Poirot, 1999

PM Data Analysis Workbook: Introduction

workbook content
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

PM Data Analysis Workbook: Introduction

workbook preparation strategy 1 of 2
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 an official 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.

PM Data Analysis Workbook: Introduction

workbook preparation strategy 2 of 2
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.

PM Data Analysis Workbook: Introduction

using the workbook
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

PM Data Analysis Workbook: Introduction

pm 2 5 background
Primary PM (directly emitted):

Suspended dust

Sea salt

Organic carbon

Elemental carbon

Metals from combustion

Small amounts of sulfate and nitrate

Gases that form PM in the atmosphere (secondary PM):

Sulfur dioxide (SO2): forms sulfates

Nitrogen oxides (NOx): forms nitrates

Ammonia (NH3): forms ammonium compounds

Volatile organic compounds (VOC): forms organic carbon compounds

PM2.5 Background

Emissions that Contribute to PM Mass

PM is composed of a mixture of primary and secondary compounds.

PM Data Analysis Workbook: Introduction

major pm 2 5 components
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 containing mainly carbon, hydrogen and oxygen.

Elemental carbon (EC): Composed of carbon without much hydrocarbon or oxygen. 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.

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.

Major PM2.5 Components

Most PM mass in urban and nonurban areas is composed of a combination of the following chemical components:

Chow and Watson, 1997

PM Data Analysis Workbook: Introduction

common pm 2 5 emission sources profiles
Common PM2.5 Emission Sources: Profiles

PM Data Analysis Workbook: Introduction

Fujita, 1998

properties of pm
Properties of PM
  • 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
  • Internal and External Mixtures
  • Optical Properties of PM

Husar, 1999

PM Data Analysis Workbook: Introduction

physical chemical and optical properties
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.

PM Data Analysis Workbook: Introduction

size range of particulate matter
Size Range of Particulate Matter
  • 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.

Husar, 1999

PM Data Analysis Workbook: Introduction

mass distribution of pm vs size pm 10 pm 2 5
Mass Distribution of PM vs. Size: PM10, PM2.5
  • 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.

Husar, 1999

Fine

Coarse

PM Data Analysis Workbook: Introduction

fine and coarse particles
Fine and Coarse Particles

Adapted from: Seinfeld and Pandis, 1998

PM Data Analysis Workbook: Introduction

fine particles pm 2 5
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.

PM Data Analysis Workbook: Introduction

coarse particle fraction pm 10 pm 2 5
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

PM Data Analysis Workbook: Introduction

relationship of pm 2 5 and pm 10
Relationship of PM2.5 and PM10
  • PM10 and PM2.5 are related to each other when most of the PM10 is contributed by PM2.5 (e.g., Northeast example above).
  • Different areas and/or different seasons may have different relationships between PM2.5 and PM10.
  • PM2.5 comprises a larger fraction of PM10 in the northeastern U.S. than in southern California.
  • PM2.5 seasonal patterns are similar to those for PM10 in the northeast; seasonal patterns of PM2.5 and PM10 differ in Southern California.

Husar, 1999

PM Data Analysis Workbook: Introduction

chemical composition of pm vs size
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, <2.5 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, > 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

PM Data Analysis Workbook: Introduction

internal and external mixtures of particles
Internal and External Mixtures of Particles
  • During their multi-day atmospheric residence time, particles from different sources and with different compositions are mixed together by a range of atmospheric processes. The resulting particles can be either external or internal mixtures.
  • In an external mixture, the particle composition will be non-uniform because the components from different sources remain separate (e.g., a soot particle inside a sulfate droplet, as illustrated by the electron micrograph below).
  • In an internal mixture, the particle composition is uniform because the individual components are completely mixed.
  • The main process that produces internal mixtures is processing by water such as in fog and/or cloud scavenging and subsequent evaporation.

Electron micrograph of a PM2.5 droplet residue. Evidently, the droplet contained a solid particle, possibly soot.

Husar, 1999

PM Data Analysis Workbook: Introduction

optical properties of pm
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 < D < 1 m) scatter over ten times more than 10 g of coarse particles (D > 2.5 m)

Husar, 1999

PM Data Analysis Workbook: Introduction

slide24

PM Formation in the Atmosphere

Sulfate Formation in the Atmosphere

Sulfate Formation in Clouds

Seasonal SO2--to-Sulfate Transformation Rate

Residence Time of Sulfur and Organics

Nitrate Formation in the Atmosphere

Links to Ozone Formation, Health, and Visibility

PM Data Analysis Workbook: Introduction

sulfate formation in the atmosphere
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.

PM Data Analysis Workbook: Introduction

sulfate formation in clouds
Sulfate Formation in Clouds

Husar, 1999

  • 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.
  • 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.

PM Data Analysis Workbook: Introduction

season so 2 to sulfate transformation rate
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

PM Data Analysis Workbook: Introduction

residence time of sulfur and organics
Residence Time of Sulfur and Organics
  • 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.

Husar, 1999

PM Data Analysis Workbook: Introduction

nitrate formation and removal in the atmosphere
Nitrate Formation and Removal in the Atmosphere
  • NO2 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.
  • 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.
  • Thus, PM nitrate can be formed at night and during the day; daytime photochemistry also forms ozone.

PM Data Analysis Workbook: Introduction

pm and ozone 1 of 2
PM and Ozone (1 of 2)

The formation of a substantial fraction of secondary PM2.5 depends on photochemical 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 synoptic weather conditions.

NESCAUM, 1992

PM Data Analysis Workbook: Introduction

pm and ozone 2 of 2
PM and Ozone (2 of 2)
  • An illustration of some of the environmental factors that influence the production of ozone and secondary PM formation.
  • Meteorological (e.g., mixing heights, transport) and chemical conditions (e.g., emissions composition and intensity) affect the concentration of secondary PM and ozone precursors.

RRWG Policy Team, 1999

PM Data Analysis Workbook: Introduction

pm health and visibility
PM, Health, and Visibility
  • Human health research indicates that PM mass correlates with sickness and death. The components of PM that cause these health effects are not known.
  • Fine particles and/or coarse particles may contribute to these health effects.
  • Visibility, the distance one can distinguish a target, is influenced by lighting, contrast of the target to the background, and most importantly, the size, color, and concentration of the particles between the observer and the target.

Thus, we need to better understand the chemical and physical

characteristics and the formation of PM in order to identify the links

between and reduce the influence of PM on health and visibility.

PM Data Analysis Workbook: Introduction

summary of factors influencing pm concentrations meteorology
Summary of Factors Influencing PM Concentrations: Meteorology
  • Meteorological parameters important to PM concentration variations include: temperature, relative humidity, mixing heights, wind speed, and wind direction.
  • Seasonal changes in meteorology effect diurnal, seasonal, and chemical patterns of PM.

Chu and Cox, 1998

PM Data Analysis Workbook: Introduction

summary of factors influencing pm concentrations emissions
Summary of Factors Influencing PM Concentrations: Emissions
  • Time patterns of emissions
    • Diurnal patterns (e.g., traffic, biogenics)
    • Weekday/weekend patterns
  • Source type and location of emissions
    • Point vs. area vs. mobile source emissions
    • Height of emissions
  • Primary PM emissions vs. secondary PM
  • Chemical composition (e.g., Ni and V from oil, Se from coal, Na from sea salt or winter road salt)

Temporal, spatial, and chemical emissions characteristics influence PM concentrations and provide clues to source contributions.

PM Data Analysis Workbook: Introduction

atmospheric transport of pm
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

PM Data Analysis Workbook: Introduction

transport mechanisms
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.

PM Data Analysis Workbook: Introduction

influence of transport on source regions
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.

PM Data Analysis Workbook: Introduction

plume transport
Plume Transport

Much of the man-made PM2.5 in the eastern U.S. is from SO2 emitted by power plants.

  • 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.

Husar, 1999

PM Data Analysis Workbook: Introduction

long range transport
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

PM Data Analysis Workbook: Introduction

atmospheric residence time and spatial scales
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.

PM Data Analysis Workbook: Introduction

Husar, 1999

residence time dependence on height
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.

PM Data Analysis Workbook: Introduction

range of transport
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

PM Data Analysis Workbook: Introduction

objectives of the pm monitoring program
Objectives of the PM Monitoring Program
  • The primary objective of the PM monitoring program is to provide ambient data that support the nation’s 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

PM Data Analysis Workbook: Introduction

overview of national pm 2 5 network
Overview of National PM2.5 Network

Homolya et al., 1998

PM Data Analysis Workbook: Introduction

slide45

PM2.5 Implementation Update

  • The bulk of all compliance and continuous monitoring sites are to be established by December 31, 1999.
  • The first 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.
  • Operation of the Super-sites began with 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

PM Data Analysis Workbook: Introduction

slide46

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 schedule.

Byrd, 1999

PM Data Analysis Workbook: Introduction

site types
Site Types

Homolya et al., 1998

The larger check marks reflect the primary use of the data.

PM Data Analysis Workbook: Introduction

data collected
Data Collected

Homolya et al., 1998

PM Data Analysis Workbook: Introduction

sampling artifacts and interferences 1 of 2
Sampling Artifacts and Interferences(1 of 2)

Homolya et al., 1998

PM Data Analysis Workbook: Introduction

sampling artifacts and interferences 2 of 2
Sampling Artifacts and Interferences(2 of 2)
  • Organic gas adsorption (positive bias) comprised up to 50% of the organic carbon measured on quartz-fiber filters in southern California (Turpin et al., 1994). These studies also indicated that adsorption was much more important than organic particle volatilization (negative bias).
  • 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).

PM Data Analysis Workbook: Introduction

critical issues for data interpretation
Critical Issues for Data Interpretation

Issues to be considered when planning and performing data interpretation:

  • Data availability (mass, ions, metals, organic carbon, speciated organic carbon, etc.)
  • Data quality (standard operating procedures, audits, accuracy and precision, data validation)
  • Sampling artifacts and interferences (organic carbon volatilization, nitrate volatilization, moisture)
  • Data representativeness for planned analysis (nearby sources vs. regional background)
  • Sampling duration (use of 24-hr data to investigate diurnal changes in photochemistry, emissions and meteorology)
  • Sampling frequency (use of 1-in-6 day data to investigate many episodes of high PM)
  • Availability of complementary data (PM precursor, meteorological, and visibility data)

Use the decision matrix to proceed from policy-relevant objectives,

to data analysis activities, to applicable data and tools.

PM Data Analysis Workbook: Introduction

motivating examples
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.

PM Data Analysis Workbook: Introduction

data validation continues during data analysis
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

PM Data Analysis Workbook: Introduction

annual standards calculation
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

PM Data Analysis Workbook: Introduction

episodic patterns in pm
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

PM Data Analysis Workbook: Introduction

day of week cycle in pm emissions
Day-of-Week Cycle in PM Emissions

Chicago

80 samples

1990-1991

  • 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.

Lin et al., 1993

PM Data Analysis Workbook: Introduction

seasonal pattern of pm 2 5
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

PM Data Analysis Workbook: Introduction

seasonal pm 2 5 dependence on elevation in the appalachian mountains
Seasonal PM2.5 Dependence on Elevation in the Appalachian Mountains

Monitor locations and topography

  • 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.

Schichtel, 1999a

PM Data Analysis Workbook: Introduction

seasonal maps of pm 2 5 1994 1996
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.

Seasonal Maps of PM2.5 (1994-1996)

Falke, 1999

PM Data Analysis Workbook: Introduction

pm 10 in the u s during the central american smoke event
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

PM Data Analysis Workbook: Introduction

combining spatial and temporal trends
Combining Spatial and Temporal Trends
  • 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

PM Data Analysis Workbook: Introduction

discerning natural vs anthropogenic sources using spatial and temporal analyses
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 K:Fe 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.

PM Data Analysis Workbook: Introduction

air mass history analysis
Air Mass History Analysis

Upwind probabilities for high aerosol arsenic at three Champlain Basin sites

  • 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.

Poirot et al. (1998)

Shaded areas show 20%, 40%, and 60% of upwind probability on highest concentration day

PM Data Analysis Workbook: Introduction

unmix analysis
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)

PM Data Analysis Workbook: Introduction

pmf analysis
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.

PM Data Analysis Workbook: Introduction

case study top down emissions evaluation

Primary PM10/NOx

Ambient Ratio

Emission Inventory Ratio

Comparison of the ambient- and emissions-derived PM10/NOx ratios in two cities are quite different. It appears as though PM10 is overestimated in the emission inventory by approximately a factor of two.

Recommendation: the PM10 portion of the inventory should be investigated from the bottom-up.

City #1

City #2

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

PM Data Analysis Workbook: Introduction

case study using cmb to assess emission estimates and source apportionment

Comparison of CMB modeling results and emission inventory source apportionment are very different. The results of CMB modeling show that mobile sources are responsible for a much larger percentage of PM2.5 in the ambient air while the emission inventory data shows dust being the main contributor to PM2.5. These types of discrepancies are important to consider prior to control strategy development.

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

PM Data Analysis Workbook: Introduction

model performance evaluation

Sulfate (g/m3)

Bias (g/m3)

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)

PM Data Analysis Workbook: Introduction

summary
Summary

PM2.5 data can be used to meet a wide range of objectives.

This workbook will serve as a companion document to the PM2.5 Data Analysis Workshop, will reflect a snapshot in time of the workbook available on the website, and will serve as an overview to the large topic of PM2.5 data analysis.

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!

PM Data Analysis Workbook: Introduction

references
References

Albritton D.L. and Greenbaum D.S. (1998) Atmospheric observations: Helping build the scientific basis for decisions related to airborne particulate matter.

Chow J.C. (1995) Measurement methods to determine compliance with ambient air quality standards for suspended particles. J. Air & Waste Manage., 45, pp. 320-382.

Chow J.C. and Watson J.G. (1997) Guideline on speciated particulate monitoring. Report prepared by Desert Research Institute and available at http://www.epa.gov/ttn/amtic/files/ambient/pm25/spec/drispec.pdf

Chu S. and W. Cox (1998) Relationship of PM fine to Ozone and Meteorology. Paper 98-RA90A.03 presented at the Air & Waste Management Association\'s 91st Annual Meeting & Exhibition, June 14-18, 1998, San Diego, California.

Falke S. (1999) PM2.5 topic summaries available at: http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/UrbanSpatialPattern/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/NationalSpatialPattern/sld001.htm

Fitz-Simmons T. (1999) How to calculate the particulate NAAQS. Paper presented at the National AIRS conference, San Francisco, May.

Frechtel P., Eberly S., Cox W. (1999) PM-Fine Trends at Long-Term IMPROVE Sites. Paper available at http://capita.wustl.edu/PMFine/Workgroup/Status&Trends/Reports/Completed/LongTermIMPROVE/LongTermIMPROVE.html

Fujita E.M. (1998) MAG Brown Cloud Study Source Attribution of PM2.5. Final report prepared by Desert Research Institute for Maricopa Assoc. of Governments, Phoenix, AZ. December.

Haste T.L., Chinkin L.R., Kumar N., Lurmann F.W., and Hurwitt, S.B. (1998) Use of ambient data collected during IMS95 to evaluate a regional emission inventory for the San Joaquin Valley. Final report prepared for the San Joaquin Valleywide Air Pollution Study Agency and the California Air Resources Board, Sacramento, CA by Sonoma Technology, Inc., Petaluma, CA, STI-997211-1800-FR, July.

Hering S. and Cass G. (1999) the magnitude of bias in the measurement of PM2.5 arising from volatilization of particulate nitrate from Teflon filters. J. Air & Waste Manage. Assoc., 49, pp. 725-733.

Homolya J.B., Rice J., Scheffe R.D. (1998) PM2.5 speciation - objectives, requirements, and approach. Presentation. September.

PM Data Analysis Workbook: Introduction

references71
References

Husar, R. (1999) Draft PM2.5 topic summaries available at http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PMProperties/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/Pm25Formation/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PMTransport/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PMOrigin/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PM10PM25Relationship/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PMAnalysis/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/Pm25TransportROI/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/DiurnalPattern/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/WeeklyPattern/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PMGlobalContPattern/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/NaturalEvents/sld001.htm

Lin J. Scheff P.A., and Wadden R.A. (1993) Development of a two-phase receptor model for VOC and PM10 air pollution sources in Chicago. Paper 93-A487 presented at the 86th annual meeting of the Air & Waste Management Assoc., Denver, June.

Lurmann F.W., et. al., (1999) Personal communication.

Main H.H., Chinkin L.R., and Roberts P.T. (1998) PAMS data analysis workshops: illustrating the use of PAMS data to support ozone control programs. Web page prepared for the U.S. Environmental Protection Agency, Research Triangle Park, NC by Sonoma Technology, Inc., Petaluma, CA, <http://www.epa.gov/oar/oaqps/pams/analysis> STI-997280-1824, June.

NESCAUM (1992) 1992 Regional Ozone Concentrations in the Northeastern United States. Paper available at http://capita.wustl.edu/neardat/reports/TechnicalReports/NEozone92/avoztitl.html

Polissar A.V., Hopke P.K., Paatero P., Malm W.C., Sisler J.F. (1998) Atmospheric aerosol over Alaska 2. Elemental composition and sources. J. Geophysical Research, Vol. 103, No. D15, pp. 19045-19057.

Poirot R., A. Leston, C. Michaelsen (1999) August 1995 forest fire impacts in New England and Atlantic Canada. Report available at http://capita.wustl.edu/NEARDAT/Reports/TechnicalReports/smoke895/895smoke.htm

PM Data Analysis Workbook: Introduction

references72
References

Poirot R., P. Wishinski, B. Schichtel, and P. Girton (1998) Air trajectory pollution climatology for the Lake Champlain Basin. Draft paper presented at 1998 symposium of the Lake Champlain Research Consortium. Available at http://capita.wustl.edu/neardat/Reports/TechnicalReports/lakchamp/lchmpair.htm

Poirot R. (1998) Tracers of opportunity: Potassium. Paper available at http://capita.wustl.edu/PMFine/Workgroup/SourceAttribution/Reports/In-progress/Potass/ktext.html

Poirot, R. (1999) Draft PM2.5 topic summary available athttp://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PMAnlysisByStates/sld001.htm

Poirot R. (1999b) personal communication

Reactivity Research Work Group Policy Team (1999) VOC Reactivity Policy White Paper. Prepared for the Reactivity Research Work Group, October.

Schichtel B. and Husar R. (1995) Regional simulation of atmospheric pollutants with the Capita Monte Carlo Model. Prepared by the Center for air Pollution and Trend Analysis, Washington University, St. Louis, MO. September. Available at http://capita.wustl.edu/CAPITA/CapitaReports/MonteCarlo/MonteCarlo.html

Schichtel B. and Husar R. (1997) Derivation of SO2 – SO42- Transformation and Deposition Rate Coefficients Over The Eastern US using a Semi-Empirical Approach. Paper available at http://capita.wustl.edu/capita/capitareports/mcarlokinetics/mcrateco4_AWMAPres.html

Schichtel B.A. (1999a) PM2.5 topic summaries available at: http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/SeasonalPattern/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/ElevationDep/sld001.htm http://capita.wustl.edu/CAPITA/CapitaReports/USVisiTrend/80_95/USVistrnd80_95/index.htm http://capita.wustl.edu/Central-America/reports/SmokeSum/SmokeSumApr99/index.htm

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, New York.

Turpin B.J., Huntzicker J.J., and Hering S.V. (1994) Investigation of organic aerosol sampling artifacts in the Los Angeles basin. Atmos. Environ., 28, pp. 3061-3071.

PM Data Analysis Workbook: Introduction

references73
References

U.S. EPA (1999a) Particulate matter (PM2.5) speciation guidance document. Available at http://www.epa.gov/ttn/amtic/files/ambient/pm25/spec/specpln3.pdf

U.S. EPA (1999b) General Information regarding PM2.5 data analysis posted on the EPA Internet web site http://www.epa.gov/oar/oaqps/pm25/general.html

U.S. EPA (1998) Fact sheet on PM data handling available at http://ttnwww.rtpnc.epa.gov/naaqsfin/fs122398.htm

Watson J.G., Fujita E.M., Chow J.C., Richards L.W., Neff W., and Dietrich D. (1998) Northern Front Range Air Quality Study. Final report prepared for Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO by Desert Research Institute, Reno, NV.

Wayland R.J. (1999) REMSAD - 1990 Base case simulation: model performance evaluation. Draft report prepared by USEPA OAQPS, Research Triangle Park, NC, March.

PM Data Analysis Workbook: Introduction

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