Fundamentals of Air Quality Forecasting: PM2.5 Focus

1
Fundamentals of Air Quality Forecasting PM2.5
Focus
Prepared by Dianne S. Miller, Clinton P.
MacDonald, Adam N. Pasch, Patrick H. Zahn Sonoma
Technology, Inc. Petaluma, CA
Bill Ryan Pennsylvania State UniversityDepartmen
t of Meteorology State College, PA
Mike Gilroy Puget Sound Clean Air
Agency Seattle, WA
Clint Bowman Washington State Department of
Ecology Olympia, WA

• Presented to
• National Air Quality Conference
• Forecasters Training
• Dallas, TX
• March 2, 2009

Presenter
905501.09-3580
2
Agenda

• 1230 1235 Introduction
• 1235 1255 Air quality overview
• 1255 135 Forecasting tools overview
• 135 150 What controls air quality?
• 150 230 Meteorology and its influence on air
  quality (part 1)
• 230 300 Break
• 300 450 Meteorology and its influence on air
  quality (part 2)
• 450 500 Questions

3
Section 1 Air Quality Overview

• What is in our air?
• About PM2.5
• PM2.5 seasonal patterns
• PM2.5 lifecycles and trends
• PM2.5 formation and growth
• PM2.5 monitoring
• PM2.5 versus ozone

4
What Is In Our Air?

• Mixture of invisible gases, particles, and water
• Mostly nitrogen (78) and oxygen (21)
• Remaining 1 contains
• Argon
• Water vapor
• Carbon dioxide
• Ozone
• Particulate matter
• And much more

5
Air Pollution Major Pollutants
Pollutant Abbreviation Source
Carbon Monoxide CO Primary (emitted)
Sulfur Dioxide SO2 Primary
Ozone O3 Secondary (formed in the atmosphere)
Nitrogen Dioxide NO2 Secondary
Volatile Organic Compounds VOCs Primary Secondary
Particulate Matter PM Primary Secondary
6
About PM2.5

• A complex mixture of solid and liquid particles
• Both a primary and secondary pollutant
• Significant particle size variation
• Seasonal and regional differences
• Forms in many ways
• Clean-air levels are lt5 µg/m3
• U.S. concentrations range from 0 to 200 µg/m3
• Health concerns

Ultra-fine fly-ash or carbon soot
"Night at Noon." London's Piccadilly Circus at
midday during deadly smog episode in the winter
of 1955.Source When Smoke Ran Like Water,
Devra Davis, Perseus Books
24-hr average
7
Seasonal Patterns Regional/National (1 of 2)
Jan-Mar
Apr-Jun
Oct-Dec
July-Sept
8
Seasonal Patterns Regional/National (2 of 2)
Sulfate is important in the eastern U.S. while
carbon and nitrate are important in the western
U.S.
(organic carbon mass)
9
Seasonal Patterns Local
Seattle 5303300572002-2007
Cleveland 390350060 2000-2006

• OM is the largest contributor to PM2.5 in the
  western U.S. in all months, with the highest
  contributions in the winter months.
• Sulfates are a large contributor to PM2.5 in the
  eastern U.S. during the summer.
• Nitrates increase in the winter months, with the
  most dramatic increases observed in the eastern
  U.S.

10
Lifecycles and Trends

• Lifecycles are daily and episodic changes in
  pollution levels.
• Trends are long-term changes in air pollution
  that are caused by population and emission
  changes.
• Importance
• Daily forecasting (changes, evolution)
• Communication (message, exposure)
• Four time periods
• Day/night (diurnal) and multi-day
• Seasonal
• Yearly
• Long-term

11
Lifecycles

• In urban areas, during the afternoon, vertical
  mixing and horizontal transport tend to dilute
  concentrations. During the night and early
  morning, 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.

12
Trends Seasonal
Schichtel, 1999a

• In urban areas, winter mixing heights are low,
  trapping emissions. In the summer, intense
  vertical mixing raises mixing heights higher
  mixing heights, in turn, tend to dilute
  concentrations.

• PM primary and precursor emissions are dependent
  upon seasonal energy consumption for heating and
  cooling, occurrence of fires, etc. Many
  gas-to-particle transformation rates are
  photochemically driven and peak in the summer.

13
PM2.5 Formation and Growth
14
PM2.5 Monitoring
Hourly PM2.5 monitoring network

• Different PM2.5 monitors measure data at
  different time scales.
• Continuous monitors measure data hourly.
• Filter-based monitors measure data daily, every
  third day, or every sixth day.

15
PM2.5 versus Ozone

• In some parts of the country, PM2.5 and ozone
  concentrations can be high at the same time.
• In the Pacific Northwest, for example, PM2.5
  concentrations are highest in the winter where
  ozone concentrations are the lowest.

Pollutant Ozone Particles
Properties Invisible gas, three oxygen atoms Visible solids and liquids, many different compounds
Formation Reaction of precursor gases in sunlight Directly emitted and formed by multiple processes
Season Summer All seasons
Health effects Aggravates lung and respiratory diseases Aggravateslung and heart diseases
16
Summary

• PM2.5 is a complex mixture of chemicals from both
  primary emissions and secondary formation.
• Diurnal, seasonal, and annual trends in PM2.5 are
  related to emission sources (and their temporal
  and seasonal patterns) and meteorology.
• Forecasting PM2.5 is a challenge.

17
Section 2 Forecasting Tools

• Persistence, trend
• Climatology
• Criteria, thresholds
• Statistical
• Classification and Regression Tree (CART)
• Regression equations
• Numerical modeling
• Phenomenological and experience

Fewer resources to develop, lower accuracy
More resources to develop, potential for higher
accuracy
18
Persistence, Trend

• Persistence means to continue steadily in some
  physical state
• Tomorrows pollutant concentration will be the
  same as todays.
• Trend means to continue to change at the same
  rate
• Todays pollutant concentration increased 5
  µg/m3 from yesterdays tomorrows pollutant
  concentration will increase 5 µg/m3 from
  todays.
• Persistence and trend are used as starting points
  and guides for other forecasting methods.
• Persistence and trend should not be used as the
  only forecasting methods.

19
Climatology (1 of 2)

• Climatology is the study of average and extreme
  weather or air quality conditions at a given
  location.
• Climatology can help forecasters bound and guide
  their air quality predictions.
• All-time maximum pollutant concentrations (by
  month, by site)
• Duration of high pollutant episodes (number of
  consecutive days, hours of high pollutant
  concentrations each day)
• Average number of days with high pollutant levels
  by month and by week
• Day-of-week distribution of high pollutant
  concentrations
• Average and peak pollutant concentrations by
  holidays and non-holidays, weekends and weekdays

20
Climatology (2 of 2)
Sacramento, CA Daily record maximum PM2.5
concentration
Birmingham, AL
Day of week distribution of high PM2.5 days
(2001-2004)
Columbus, OHPM2.5 episode length (2003-2007)
Number of episodes
colors represent AQI levels
21
Criteria, Thresholds

• Use threshold values of meteorological or air
  quality variables to forecast pollutant
  concentrations
• If temperature gt 27C and wind speed lt 2 m/s,
  then ozone will be in the Unhealthy AQI
  category.
• Best suited to help forecast high pollution or
  low pollution events, or pollution in a
  particular AQI category range.
• Not ideal for an exact concentration forecast

22
Classification and Regression Tree (CART)

• A statistical procedure designed to classify data
  into distinct (or dissimilar) groups
• Enables a forecaster to use a decision tree to
  predict pollutant concentrations based on the
  values of predictor variables.

23
Numerical Modeling

• Mathematically represents the spatial and
  temporal evolution of processes that affect
  pollution using the laws of physics.
• Requires a system of models to simulate the
  emission, transport, diffusion, transformation,
  and removal of air pollution
• Numerical weather models
• Emissions models
• Air quality models

24
Numerical Weather Models

• Provide weather predictions forward in time,
  given an initial starting point (an
  initialization).

Initialization (t 0 hours)
Forecast (t 24 hours)
25
Numerical Weather Models Outputs
Graphical output (spatial images) used for daily
forecasting

• Surface
• Highs and lows
• Fronts
• Wind direction and speed
• Precipitation
• 850 mb
• Troughs and ridges
• Wind
• Temperature advection

• 700 mb
• Relative humidity
• Vertical velocity
• 500 mb
• Large-scale troughs or ridges
• Small-scale pressure waves

26
Numerical Weather Models Types (1 of 3)

• RUC (Rapid Update Cycle)
• NAM (North American Meso)
• GFS (Global Forecast System)
• Other models not discussed here are
• NGM (Nested Grid Modelwill be terminated Mar
  2009)
• UK Met
• ECMWF (European)
• Canadian

27
Numerical Weather Models Types (2 of 3)

• Rapid Update Cycle (RUC) model
• 13-km grid
• 50 vertical levels
• Runs hourly
• Forecasts out 9 hours in hourly increments
• North American Meso (NAM) model
• Driven by the Weather Research Forecast (WRF)
  model
• 12-km grid
• 60 vertical levels
• Runs 4 times per day
• Forecasts out 84 hours in 3-hr increments

28
Numerical Weather Models Types (3 of 3)

• Global Forecast System (GFS) model
• 0.5-1-degree grid (40-100 km)
• 64 vertical levels
• Runs 4 times per day
• Forecasts out to 16 days (384 hours) in 6-hr
  increments

29
Air Quality Models NOAA/EPA Air Quality
Forecasts

• Twice daily national ozone forecasts at 12-km
  resolution driven by Community Multiscale Air
  Quality (CMAQ) Modeling System
• National smoke predictionsat 12-km resolution
• PM2.5 prediction capability in developmental
  stage but still a few years from operational
  deployment

30
Air Quality Models Real-time BlueSky Gateway
Modeling System

• Experimental predictions of PM2.5 resulting from
  fire and non-fire sources on a national scale
• Smoke prediction enabled by the USFS BlueSky
  Framework
• Work supported by NASA Decision Support, USFS,
  and the Joint Fire Science Program

http//www.getbluesky.org/bluesky/sti
31
Air Quality Models Canadian Air Quality
Forecasts

• CHRONOS (Canadian Hemispheric and Regional Ozone
  and NOx System)
• Once daily (00Z) PM2.5 and ozone forecasts over
  U.S. and Canada at 21-km resolution
• To be replaced by 15-km GEM-MACH operational
  model (2009?)

http//www.weatheroffice.gc.ca/chronos/index_e.htm
l
32
Air Quality Observational Tools IDEA

• IDEA Infusing Satellite Data into Environmental
  Applications
• NASA-NOAA-EPA partnership to improve air quality
  assessment, management, and prediction
• MODIS aerosol optical depth retrievals fused into
  EPA/NOAA analyses for public benefit
• Tutorials on product interpretation available on
  website

http//www.star.nesdis.noaa.gov/smcd/spb/aq/index.
php
33
Phenomenological and Experience

• Involves analyzing and conceptually processing
  air quality and meteorological information to
  formulate an air quality prediction
• Heavily relies on the experience provided by a
  meteorologist or air quality scientist who
  understands the phenomena that influence
  pollution
• Balances some of the limitations of objective
  prediction methods

34
Phenomenological Guidelines Example

• Knowledge can be documented as forecast rules

35
Phenomenological Forecast Worksheets
Example forecast worksheets for PM2.5
35
40
20
PM2.5 Forecast (µg/m3) Phenomenological Objective Tool Final
Today 40 25 30
Tomorrow 20 25 23
36
Summary

• Many types of forecast tools can be used in a
  forecast program.
• Many national tools are already available
• Custom, city-specific tools can be developed
• Tools range from simple to complex
• Tools can be subjective or objective
• A suite of forecast tools will improve accuracy.

37
Section 3 What Controls Air Quality?

• Emissions
• Chemistry
• Weather

38
Emissions What Are They?

• Man-made sources (anthropogenic)
• NOx through combustion
• VOCs and particulate carbon through combustion
  and numerous other sources
• SO2 through combustion of coal and oil that
  contains sulfur
• Metals through industrial processes
• PM through combustion, mechanical processes
  (e.g., wind blown dust through human activity)
• Natural sources (biogenic)
• VOCs from trees/vegetation
• NOx from soils (fertilizer)

39
Emissions Impact

• Pollutant concentration depends on
• Emissions source (ES) location, source density,
  and source strength
• Meteorology

Courtesy of New Jersey Department of
Environmental Protection
40
PM2.5 Chemistry (1 of 2)
PM2.5 is composed of a mixture of primary and
secondary compounds.

• Primary PM2.5 (directly emitted)
• Ca, Si, Fe, Al (dust)
• Sea salt (Na, Cl)
• Organic matter (OM)
• Elemental carbon (EC)
• Zn, Fe, Pb (industrial)
• Small amounts of sulfate and nitrate

• Secondary PM2.5 (precursor gases that form PM2.5
  in the atmosphere)
• Sulfur dioxide (SO2) forms sulfates
• Nitrogen oxides (NOx) forms nitrates
• Ammonia (NH3) forms ammonium compounds
• Volatile organic compounds (VOCs) contribute to
  OM

41
PM2.5 Chemistry (2 of 2)

• Sulfate formation in the atmosphere
• Photochemical oxidation of sulfur dioxide (SO2)
  in the daytime forms sulfates (SO4)
• Condensation of sulfuric acid (H2SO4) results in
  the accumulation and growth of sulfate particles
  (NH4)2SO4
• Sulfate formation in clouds
• SO2 is scavenged by water droplets and oxidizes
  to form sulfate
• Cloud droplets evaporate, leaving sulfate residue
• Nitrate formation
• Photochemical reaction of nitrogen dioxide (NO2)
  with hydroxyl radicals (OH) will form nitric acid
  (HNO3) in the daytime
• NO2 will react with ozone at night to form HNO3
• HNO3 reacts with ammonia to form ammonium nitrate
  particles (NH4NO3)

Summer
Winter
42
PM2.5 Weather

• Processes that influence air quality
• Sunlight
• Horizontal dispersion
• Vertical mixing
• Transport
• Clouds and precipitation
• Large scale to local scale
• Global 4,000 km 20,000 km
• Synoptic 400 km 4,000 km
• Meso 10 km 400 km
• Urban 5 km 50 km
• Neighborhood 50 m 5 km

Local Scale
43
Summary

• Forecasters need to understand
• PM and PM precursor emissions
• Atmospheric chemistry related to PM formation and
  removal
• Weathers contributions to pollutant chemistry,
  dispersion, and transport

44
Section 4 Meteorological Phenomena

• What phenomena are important for air quality
  forecasting?
• Meteorological variables
• Temperature
• Water vapor
• Pressure
• Meteorological phenomena
• Surface pressure patterns and regional/local
  winds
• Aloft pressure patterns
• Fronts and air masses
• Stability and mixing
• Clouds, fog, and precipitation

45
Meteorological Variables

• These variables
• Define the state of the atmosphere
• Are predicted by all weather models
• Directly or indirectly affect chemical reactions,
  emissions, dispersion, transport, and deposition
• Different formulations/analyses of these
  variables let forecasters predict different
  weather phenomena

46
Temperature

• Temperature is an important variable that affects
  PM2.5 formation, emissions, dispersion, and
  transport
• At lower temperatures, there is more home heating
  (residential wood burning)
• Higher temperatures promote more volatilization
  of VOCs and increased biogenic emissions
• Forecasters are interested in changes in
  temperature vertically (e.g., inversions),
  spatially (e.g., fronts), and temporally (e.g.,
  diurnal mixing heights)

47
Water Vapor

• Water vapor (a.k.a. moisture) can increase
• Production of secondary PM2.5 including sulfates
  and nitrates in some areas
• Deposition in the form of precipitation
• Forecasters are interested in changes in water
  vapor content temporally (e.g., diurnal relative
  humidity changes) and vertically (e.g., cloud
  formation potential).

48
Water Vapor Parameters

• Dew Point Temperature
• The temperature to which a given parcel of air
  must be cooled in order for saturation to occur
• Saturation is the point at which water vapor
  condenses to a liquid
• Relative Humidity (RH)
• A measure of how close the atmosphere is to being
  saturated
• Derived from temperature (T) and dew point
  temperature Td
• When dew point temperature equals the air
  temperature, saturation is reached and RH100

49
Relative Humidity vs. Temperature

• Dew point temperature (actual water vapor
  content) is relatively constant throughout the
  day
• RH is normally highest in the early morning when
  temperature is coldest
• RH is normally lowest in the afternoon when
  temperature is warmest

50
Pressure

• Atmospheric pressure the weight of air above a
  point.
• Spatial differences in pressure control wind
  speed and direction, which influence pollutant
  dispersion.
• Large spatial pressure differences lead to
  stronger wind and increased dispersion (i.e.,
  lower PM2.5 concentrations)
• Small spatial pressure differences contribute to
  stagnant conditions
• Forecasters are interested in changes in pressure
  spatially (e.g., wind) and temporally (e.g.,
  fronts).

51
Pressure and Altitude
Mt. Rainier
Seattle

• The pressure at station 2 (p2) is lower than the
  pressure at station 1 (p1).

52
Pressure Changes

• Larger difference in pressure with altitude than
  with horizontal distance
• Horizontal Changes 1 mb over 6000 meters
• Vertical 1 mb over 10 meters (600 X greater)
• Vertical motions are more important than
  horizontal motions for weather system development
• Forecasters need to understand what is happening
  at different pressure levels aloft to diagnose
  vertical motions. Common levels used
• 500 mb
• 700 mb
• 850 mb
• 1,000 mb (approximately sea level)

53
Station Pressure vs. Sea Level Pressure
Converting station pressure (e.g., p2 on Mount
Rainier) to sea level pressure (e.g., p1 in
Seattle) allows for comparison of pressures on an
equal basis around the world despite differences
in elevation.
Isobars lines of equal pressure
54
Surface Pressure Maps

• Altitude-adjusted surface station pressures are
  used to construct sea level pressure contours.

55
How Do Pressure Differences Develop?

• The following example demonstrates how
  temperature differences cause changes in pressure
  vertically and spatially and initiate air
  movement.
• Conceptually consider a cool coastal site and a
  warmer inland site (e.g., sea breeze development).

56
Pressure Example (1 of 5)

• Two columns of air same temperature and same
  distribution of mass.

500-mb level
1,000 mb
1,000 mb
57
Pressure Example (2 of 5)

• Cool the left column warm the right column.

58
Pressure Example (3 of 5)

• The level of the 500-mb surface changes the
  surface pressure remains unchanged.

The 500-mb surface is displaced upward in
the warmer column and downward in the
cooler column. The surface pressure remains the
same since both columns still contain the same
mass of air however, the density in the two
columns is different.
59
Pressure Example (4 of 5)

• Air moves from high to low pressure in the middle
  of the column, causing surface pressure to change.

60
Pressure Example (5 of 5)

• Air moves from high to low pressure at the surface

61
Summary of Meteorological Variables

• Temperature affects PM2.5 formation, emissions,
  dispersion, and transport
• Lower temperatures result in more home heating
  (residential wood burning)
• Higher temperatures promote more volatilization
  of VOCs and nitrates and increased biogenic
  emissions
• Pressure differences control wind speed and
  direction, which influence pollutant dispersion
  and transport
• Large pressure differences lead to stronger winds
  and increased dispersion
• Small spatial pressure differences contribute to
  stagnant conditions and limited dispersion
• High relative humidity can increase production of
  secondary PM2.5 including sulfates and nitrates

62
Meteorological Phenomena

• Surface pressure patterns and regional/local
  winds
• Aloft pressure patterns
• Fronts and air masses
• Stability and mixing
• Clouds, fog, and precipitation

63
Large-Scale Meteorology

• Large-scale weather events (400 to 4,000 km)
• Affect meso (10 to 400 km) and local-scale (5 to
  50 km) weather and air quality conditions
• Drive major changes in meteorological variables
• Models tend to predict large-scale features well
• Forecasters need to downscale predicted
  large-scale weather conditions to understand the
  influence on local air quality phenomena
• Large-scale meteorology is often evaluated
  through review of surface and aloft pressure
  patterns

64
Surface Pressure Patterns and Winds

• The strength of the surface pressure gradient
  determines the strength of the wind (e.g., larger
  gradients lead to stronger winds).
• Wind strength contributes to PM2.5
  accumulation/dispersion (e.g., light winds lead
  to increased accumulation).
• Wind direction and strength influence pollutant
  transport (e.g., winds from the direction of
  major emissions sources will transport more
  pollutants).
• Forecasters use surface pressure charts to
  analyze weather patterns and wind speed and
  direction.

65
Winds Affect Pollution

• Winds disperse pollutants by spreading
  atmospheric constituents.
• Dispersion is a dilution process.
• Atmospheric turbulence
• Mechanical
• Shear
• Buoyancy (convective)

66
Surface Pressure and Winds Example

• Winds flow counterclockwise around and in toward
  the center of the low pressure system
• Winds flow clockwise around and outward from the
  center of the high pressure system

67
Wind Patterns Examples

• Winds driven by large-scale high and low pressure
  patterns
• Winter storms
• Post-frontal northwesterly winds
• Winds driven by local forcing mechanisms
• Sea breeze
• Land breeze
• Mountain/valley winds

68
Large-scale Wind Patterns Example

• Surface pressure chart showing isobars, fronts,
  and wind barbs
• Isobars black lines
• Fronts colored lines
• Wind wind barbs
• L center of surface low pressure system

69
Local-Scale Winds Sea Breeze

• Sea breezes
• Differential heating/cooling of adjacent land and
  water surfaces
• Development
• Land warms and creates rising air and a thermal
  low
• Cooler (denser) sea air moves in to replace
  rising air
• Air sinks over the water in response to surface
  air movement, producing return circulation
  (land-to-sea breeze) aloft

70
Local-Scale Winds Mountain/Valley Flow

• Sunlight heats mountain slopes during the day and
  they cool by radiation at night
• Air in contact with surface is heated/cooled in
  response
• A difference in air density is produced between
  air next to the mountainside and air at the same
  altitude away from the mountain
• Density difference produces upslope (day) or
  downslope (night) flow
• Daily upslope/downslope wind cycle is strongest
  in clear summer weather when prevailing winds are
  light

71
Winds and Air Quality

• Weak synoptic pressure gradients allow local
  forcing to control winds
• Light or calm winds overnight lead PM2.5
  accumulation (e.g., wood smoke on a cold, still
  night)
• Local-scale forcing can cause recirculation of
  pollution (e.g., weak upslope flow during the day
  and drainage flow at night).

72
Aloft Pressure

• At any given height in the atmosphere, the
  pressure can be measured. Conversely, at any
  given pressure in the atmosphere, the height can
  be measured.
• Forecasters use heights at constant pressure
  levels to analyze upper-air weather, which
  influences surface weather patterns and
  large-scale vertical mixing.

73
Aloft Pressure Surface (1 of 2)

• An aloft pressure surface is the surface of equal
  pressure
• An aloft pressure surface is at a lower altitude
  in the cold column than in the warm column
• High heights (e.g., Height 2) are analogous to
  aloft high pressure

• Cold air (blue column) is denser than warm air
  (pink column)

74
Aloft Pressure Surface (2 of 2)

• To create a contour map of an aloft pressure
  surface (e.g., 500 mb), physical measurements of
  the height of the pressure surface at many
  locations are compiled and mapped.

75
Aloft Ridges and Troughs
Ridges are mountains of warm air and troughs are
valleys of cool air
76
Aloft Pressure Surface Example
Contours are lines of constant height at 500 mb
77
Generation of Divergence Aloft (1 of 2)

• Upper-air divergence causes air to rise
  (increases vertical mixing)
• Upper-air convergence causes air to sink (limits
  vertical mixing)
• Winds generally travel faster around ridges and
  slower around troughs
• Patterns create areas of aloft convergence and
  divergence

Wind slows convergence (net gain of mass)
Wind speeds up divergence (net loss of mass)
78
Generation of Divergence Aloft (2 of 2)

• Aloft divergence removes mass from the column of
  air, lowering surface pressure vice versa for
  aloft convergence
• These motions create areas of rising and sinking
  air

79
Aloft Ridges and Troughs Air Pollution Impact

• Sinking motion
• Creates stable conditions
• Reduces vertical mixing
• Associated with greater air pollution
• Leads to clear skies
• Rising motion
• Creates unstable conditions
• Increases mixing
• Associated with less air pollution
• Causes cloud cover

Tropopause
convergence
divergence
Sinking
Rising
top of boundary layer
convergence
divergence
High Low
Stull (2000)
80
Using Aloft Pressure Charts

• The following slides provide an overview of what
  to look for on aloft pressure charts at the
  standard levels used for air quality forecasting.
  Levels include
• 500 mb
• 700 mb
• 850 mb

81
500-mb Charts

• Common variables plotted
• Wind barbs wind direction and speed
• Isoheights lines of constant height above
  ground level used to identify troughs and ridges
• Isotherms lines of constant temperature used
  to identify temperature advection (i.e., movement
  of cold or warm air)
• Key features to look for
• Ridges tend to produce conditions conducive for
  accumulation of PM2.5
• Troughs tend to produce conditions conducive for
  dispersion of PM2.5

82
500-mb Charts Example

• 500-mb heights, temperatures, and winds
• Heights black lines
• Temperatures color contours
• Winds wind barbs

83
700-mb Charts

• Common variables plotted
• Isoheights lines of constant height above
  ground level used to identify troughs and ridges
• Isotherms lines of constant temperature used
  to identify temperature advection
• Wind barbs wind direction and speed
• Isohumes lines of constant relative humidity
  used to identify areas of potential cloud
  development (typically gt 70)
• Vertical velocity direction and magnitude of
  vertical air motion used to identify areas of
  sinking and rising air
• Key features to look for
• Sinking motion acts to warm the atmosphere and
  decrease mixing, which increases PM2.5
• Rising motion acts to cool the atmosphere and
  increase mixing, which decreases PM2.5

84
700-mb Chart Example

• 700-mb chart showing heights, vertical velocity,
  and winds
• Heights black lines
• Vertical velocity color contours
• Winds wind barbs

85
850-mb Charts

• Common variables plotted
• Isoheights lines of constant height above
  ground level used to identify troughs and ridges
• Isotherms lines of constant temperature used
  to identify temperature advection
• Wind barbs wind direction and speed
• Isohumes lines of constant relative humidity
  used to identify areas of potential cloud
  development (typically gt 70)
• Key features to look for
• Warming temperatures increase stability and
  decrease mixing, which increases PM2.5
• Cooling temperatures decrease stability and
  increase mixing, which decreases PM2.5

86
850-mb Charts Example

• 850-mb heights, temperature, and winds
• Heights black lines
• Temperature color contours
• Wind wind barbs

87
Summary Example
500 mb
700 mb
850 mb

• Note common features at all levels

88
Aloft Pressure Summary

• 500 mb
• Ridges tend to produce conditions conducive to
  accumulation of PM2.5
• Troughs tend to produce conditions conducive to
  dispersion of PM2.5
• 700 mb
• Sinking motion acts to warm the atmosphere and
  decrease mixing, which increases PM2.5
• Rising motion acts to cool the atmosphere and
  increase mixing, which decreases PM2.5
• 850 mb
• Warming temperatures increase stability and
  decrease mixing, which increases PM2.5
• Cooling temperatures decrease stability and
  increase mixing, which decreases PM2.5

89
Air Masses and Fronts

• An air mass is a three-dimensional, large body of
  air with similar temperature and moisture
  properties throughout.
• A front is a boundary or transition zone between
  different air masses.
• Forecasters look for fronts because a change in
  air mass leads to a change in air quality
  conditions.

90
Air Mass Source Regions
91
Fronts

• A front is the boundary or transition zone
  between different air masses. Below is an
  example of a low pressure system, its fronts, and
  the associated air masses.

92
Front Symbology
93
Cold Front

• A cold front is a boundary that moves in such a
  way that the colder (more dense) air advances and
  displaces the warmer (less dense) air.

94
Warm Front

• A warm front is a boundary that moves in such a
  way that the colder (denser) air retreats and is
  replaced by the warmer (less dense) air
• Warm air rides up over colder air at the surface
  slope is not usually very steep
• Clouds and precipitation well in advance of
  boundary

95
Stationary Front

• Shows little movement
• Not truly stationary
• Rapid weather changes can occur if the boundary
  is near and moves slightly
• Can be associated with high PM2.5 in some parts
  of the country

96
Fronts, Air Masses, and Air Quality (1 of 2)

• A moist air mass is conducive to secondary PM2.5
  formation, particularly for nitrates (cool
  temperatures) and sulfates (warm temperatures).
• Approaching weak fronts
• Associated stagnation can lead to high PM2.5
• Can slide over valleys and basins with strong
  inversions without ventilating the area, also
  leading to high PM2.5
• Approaching strong fronts
• Southwesterly winds in advance of moderate to
  strong cold fronts can lead to lower PM2.5

97
Fronts, Air Masses, and Air Quality (2 of 2)

• Timing of frontal passage
• Prior to frontal passage, concentrations are
  often higher than after frontal passage
• Strength of northwest winds behind front
• Moderate to strong winds can lead to very good
  air quality conditions
• Light or stagnant conditions and a cold airmass
  can lead to rapid increase in PM2.5
  concentrations during overnight hours
• Even after frontal passage, a cold, dry air mass
  tends to develop stable conditions at night,
  allowing PM2.5 to accumulate near the ground.

98
Frontal Passage Example 1
Animation showing air quality (colored dots) and
cold front passage in the Great Lakes region
99
Frontal Passage Example 2
Minneapolis- St. Paul, Minnesota, January
27-February 9, 2005
100
Stability (1 of 2)

• Stability is a measure of the atmospheres
  ability to resist vertical motion
• Greater stability more resistance (weaker
  vertical motion)
• Lower stability less resistance (stronger
  vertical motion)
• Forecasters need to understand stability because
  a stable atmosphere limits vertical mixing and
  dispersion

101
Stability (2 of 2)

• If an air parcel is colder (heavier) than its
  environment, then the atmospheric condition is
  stable
• If an air parcel is warmer (lighter) than its
  environment, then the atmospheric condition is
  unstable

102
Stability and Vertical Temperature

• Evaluate stability by looking at the vertical
  temperature profile

Cool Aloft
Height
Height
Warm layer
Warm Surface
Cool Surface
Temperature
Temperature
A normal temperature profile gets colder with
altitude
A stable temperature profile gets warmer with
altitude
103
Stability and Diurnal Temperature (1 of 2)
12 AM
6 AM
9 AM
Height
Height
Height
Warm
Warm
Cool
Warm layer
Cool
Cool Surface
Warming
Warming
Temperature
Temperature
Temperature
Sunset
3 PM
12 NOON
Height
Height
Height
Maximum
Warming
Cooling
Temperature
Temperature
Temperature
104
Stability and Diurnal Temperature (2 of 2)
Example of stable and unstable conditions
influence on a chimney plume (APM, Latrobe
Valley, Victoria, Australia)
Stable well-defined plume
Paper Mill plume after sunrise
Unstable plume is dispersing
Paper Mill plume at dawn
105
Stability and Inversions

• An inversion is a layer characterized by an
  increase of temperature with height
• Inversions indicate a very stable atmosphere
• Four types
• Subsidence
• Radiation
• Frontal
• Photochemical (tropopause)
• Forecasters are interested in inversions because
  they indicate a very stable atmosphere, which
  will limit vertical mixing and dispersion of PM2.5

106
Subsidence Inversion (1 of 2)

• Produced by the compressional warming of a layer
  of subsiding (sinking) air
• Usually found under high pressure areas where
  aloft convergence causes air to sink

107
Subsidence Inversion (2 of 2)
108
Radiation Inversion

• Produced by nighttime radiational cooling of the
  earths surface
• Cooling causes the lowest layer of air to be
  substantially cooler than the overlying air
• Usually forms on clear, calm nights
• Found in lowest layers (generally lowest 5 to 200
  m)
• Often humid with fog present at the surface

109
Stability, Inversions, and Mixing (1 of 2)

• Mixing height The vertical extent to which
  pollutants emitted at the surface mix
• Weak inversion Pollutants mix into large volume
  resulting in low pollution levels

110
Stability, Inversions, and Mixing (2 of 2)

• Strong inversion Pollutants mix into smaller
  volume resulting in higher pollution levels

111
Vertical Motion and Lapse Rates

• Adiabatic Lapse Rate the rate at which an
  unsaturated air parcel cools as it rises 1C
  per 100 m
• Moist Adiabatic Lapse Rate the rate at which a
  saturated air parcel cools as it rises
• It varies with the original air temperature of
  the parcel
• A commonly used value is0.6C/100 m

28C
Cold
200 m
Parcel sinks
Parcels are same temperature when they reach 100 m
29C
100 m
Parcel rises
30C
Ground
Warm
112
Vertical Motion and Lapse Rates Stable
Atmosphere
113
Vertical Motion and Lapse Rates Unstable
Atmosphere
114
Evaluating Stability Rawinsondes

• To evaluate stability, meteorologists analyze
  vertical profiles of temperature and moisture
• Different chart types
• Skew-T Most common (temperature grid is skewed)
• Non-skewed Somewhat common in air quality

115
Rawinsondes Vertical Profile Charts (1 of 4)

• Non-skewed vs. Skewed

116
Rawinsondes Vertical Profile Charts (2 of 4)

• Dry Adiabats
• Cooling or warming rate of dry air as it is
  forced upward or downward
• Curved lines (solid green lines in examples on
  next slides)
• -1C/100 m as parcel moves upward
• Drawn in 10C increments

117
Rawinsondes Vertical Profile Charts (3 of 4)

• Moist adiabats
• Cooling or warming rate of saturated air as it
  moves up or down
• Curved, broken lines

118
Rawinsondes Vertical Profile Charts (4 of 4)
Winds
Temperature
Temperature grid
Dry adiabat grid rate at which an unsaturated
air parcel cools as it rises.
Dew point temperature
Moist adiabat grid rate at which a saturated air
parcel cools as it rises.
Pressure scale (mb)
Ground level
Pressure level grid
Temperature scale (C)
More information can be found at
http//www.theweatherprediction.com/thermo/skewt/
119
Interpreting Rawinsondes

• Temperature
• Inversions
• Stability
• Mixing height
• Winds
• Clouds

120
Interpreting Rawinsondes Temperature

• Look for warming aloft temperatures because they
  lead to stable conditions and poor air quality
• Determine the relationship for your area by
  season by correlating aloft temperatures with air
  quality conditions

850-mb temperature
10.5C
121
Interpreting Rawinsondes Inversions

• Look for
• Subsidence inversion
• Created by sinking air associated with ridges
• Can limit daytime mixing depth and plays
  important role in daytime pollutant
  concentrations
• Nocturnal inversion
• Created by cooling ground at night
• Strongest with clear skies, light winds, and long
  nights
• Can trap emissions, released during the overnight
  hours, close to the ground
• Height and strength
• Lower and stronger higher PM2.5 concentration

122
Interpreting Rawinsondes Estimating Mixing
Heights (1 of 3)

• Holtzworth Method Starting at the forecasted
  maximum temperature, follow the dry adiabat
  (dashed line) until it crosses the morning
  sounding. This is the estimated peak mixing
  height for the day.

2,000 m
2,000 m
Estimated mixing height
T
1,500 m
1,500 m
Estimated peak mixing height
1,000 m
1,000 m
500 m
500 m
Forecasted max. temp.
Forecasted max. temp.
123
Interpreting Rawinsondes Estimating Mixing
Heights (2 of 3)

• Considerations
• Will the predicted maximum temperature be
  reached?
• How long will the inversion be open?
• How strong are the winds above the inversion?
  Will they be efficient at dispersing pollutants?
• Note that there can be several days in a row with
  a strong nocturnal inversion but daytime mixing
  will keep pollution levels low.

Forecasted max. temp.
124
Interpreting Rawinsondes Estimating Mixing
Heights (3 of 3)
Look for low mixing heights in the afternoon
Estimated mixing height 1800 m or about 815 mb
125
Rawinsonde Example
850-mb temperature 3C Forecasted afternoon
mixing Height 630 mb or 3800 m
Actual afternoon mixing height 600 mb or 4205
m
Inversions
126
Summary

• Stability is a measure of the atmospheres
  ability to resist vertical motion
• Greater stability more resistance high PM2.5
• Lower stability less resistance low PM2.5
• Inversions indicate a very stable atmosphere
• Low and strong inversions are associated with
  high PM2.5 concentrations

127
Clouds, Fog, and Precipitation

• Clouds, fog, and precipitation can affect PM2.5
  formation, chemistry, and dispersion.
• PM2.5 can form through aqueous reactions
• Clouds and fog impact diurnal temperatures and
  vertical mixing
• Forecasters look for clouds and fog because they
  influence the diurnal temperature pattern and the
  resultant vertical mixing.

128
Clouds Satellite Observations (1 of 2)

• Visible satellite imagery
• High resolution (1 km)
• Available only during daylight hours
• Particularly useful for identifying extent of fog
  and low clouds

129
Clouds Satellite Observations (2 of 2)

• Infrared satellite imagery
• Coarser resolution (4 km)
• Available 24-hours a day
• Can infer cloud altitude by the temperature
• White cold high cloud tops
• Gray warm low cloud tops

130
Fog

• Fog slows heating of the ground
• Most fog indicates limited vertical mixing

2001 Brooks/Cole-Thomson Learning
131
Precipitation

• Falling precipitation can remove some pollutants
  from the air mostly larger particles (PM10)
• Falling precipitation is often associated with
  vertical motion in the atmosphere, which
  increases dilution
• Precipitation can increase humidity, which can
  increase secondary PM2.5 formation

132
What To Look For

• Fog can lead to enhanced secondary PM2.5
  formation and can impact vertical mixing and
  PM2.5 dispersion
• Clouds during the day leads to decreased
  vertical mixing and PM2.5 dispersion
• Precipitation if heavy enough, decreases PM2.5
  due to increased vertical mixing and deposition

133
Course Summary

• Daily variations in air quality are controlled by
  emissions, meteorology, and chemistry.
• Understanding these processes and their impacts
  in your area are important.
• Meteorology is the key component in the
  day-to-day variability of air quality therefore,
  a detailed evaluation of the current and forecast
  meteorological phenomena is vital to forecasting
  air quality.

134
Resources

• This presentation has been complied from the
  following sources
• C. Donald Ahrens, Essentials of Meteorology
• Jon Nese, The Weather Channel
• Jan Null, San Francisco State University lecture
  notes
• U.S. Environmental Protection Agency (EPA) 2002,
  2003 National Air Quality Conference Short
  Courses
• U.S. EPA Regional PM2.5 Training Workshops
• University of Washington Department of
  Atmospheric Sciences
• World Meteorological Organization Course on Air
  Quality Forecasting
• Dianne Miller and Clinton MacDonald, Santa Rosa
  Junior College lecture notes