arep gurme section 8 pollutant monitoring monitor types monitor density and location data...

AREPGURME

Section 8Pollutant Monitoring

Monitor TypesMonitor Density and Location

Data Availability

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Section 8 – Pollutant Monitoring

Data Sources for Forecasting

Forecasting Data Sources Parameters Time Resolution Real-time availability

Cost/Resources

Written records of episodes PM - - Low

Meteorological Data

Visibility PM HourlyDaily

Yes Low

Reports of smoke/haze PM Varies Possible Low

Satellite images PM HourlyDaily

Yes Low

Air Quality Data

Surface

Continuous PM, O3, CO, NO2, NOx, SO2, and more Hourly Possible Moderate

Samplers PM, O3, CO, NO2, NOx, SO2, and more Typically Daily No Moderate

Passive samplers PM, O3, CO, NO2, NOx, SO2, and more Integrated No Low

Upper-Air

Ozonesonde O3 Periodic No High

Aircraft PM, O3, CO, NO2, NOx, SO2, and more Episodic No Very high

LIDAR PM, O3, CO, others Hourly Yes Very high

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Monitoring

• Monitor types– Gas monitors– Particle monitors and samplers– Other

• General issues– Uses of data– Variability among monitor types– Spatial representativeness

• Data availability

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Typical Continuous Gas Monitors● Ambient air is continuously drawn into the monitor, pre-treated

(e.g. particles, hydrocarbons, and/or H2S removed), and measured either directly or via chemical reaction using a spectroscopic method

● Direct measurement

CO: gas filter correlation

SO2: UV fluoresence

O3: UV photometric

● After reaction with O3

NO2: by difference (NOx-NO) using chemiluminescence and catalytic converter

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Typical Particle Monitoring

● Filter sampler for 24-hr (daily) PM mass– Single channel or sequential

● Continuous (hourly) monitors– Tapered Element Oscillating Microbalance

(TEOM)– Beta Attenuation Monitors (BAM)

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Daily Filter SamplersFilter Sampler

• Ambient air is drawn through inlet (to remove larger particles)

• Material collects on the filter; filter is later analyzed for mass or chemical species

• Problems– Potential loss of volatile material– Not available for short time intervals– Not available in real time

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Continuous Particle Monitors (1 of 2)

Tapered Element Oscillating Microbalance (TEOM)• Determines mass by variation

in frequency of filter element on an oscillating arm

• Differential mass for each hour• Problems

– Negative mass values due to volatilization

– Volatilizing nitrates and carbon species, particularly during cold weather or high humidity causes underestimation of PM mass

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Continuous Particle Monitors (2 of 2)

Beta Attenuation Monitor (BAM)• Mass of PM collects on a filter and is

exposed to beta ray, the attenuation of which is proportional to the mass on the filter

• Problems– PM species attenuate beta rays differently

– Relative humidity (RH) can influence calculation of PM mass from attenuation data causing under or overestimation of PM mass during periods of fluctuating RH values

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Visibility Sensors

● Nephelometers● Transmissometer (weather

visibility sensors)● Measurements

– Measures light scattering– Provides continuous data– Correlated with PM– Lower cost PM measurement

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Nephelometers● Nephelometers

– Measure light scatter from particles

– Sensitive to aerosols < 2.5 µm

– Heater dries air (removes affect of humidity)

– Not sensitive to flow rate

– Lower maintenance costs

● Problems– Sensitive to humidity

– Carbon can absorb light and bias data

The relationship of the components of light extinction.

Light Extinctionbext

Light Absorption babs

Light Scattering bscat

Light Scatteringby Gases

bsg

Light Scatteringby Particles

bsp

Light Absorptionby Particles

bap

Light Absorptionby Particles

bag

Light Scatteringby Coarse Particles

bscp

Light Scatteringby Fine Particles

bsfp

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Nephelometers – Example

Cool season hourly Nephelometer bsp versus BAM PM2.5 at Angiola, California, USA stratified by relative humidity in the nephelometer (Alcorn et al., 2005).

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Transmissometer (1 of 2)

● Transmissometer– Automated Surface Observing Systems

(ASOS)– Measures light scattering– Provides continuous data

● Problems– Sensitive to relative humidity (must adjust

data for humidity)– Affected by fog, clouds, and precipitation

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Transmissometer (2 of 2)

0

10

20

30

40

50

60

70

0

100

200

300

400

500

600

PM2.5

ASOS

Minneapolis, MN

Time series of hourly PM2.5 and ASOS data for Minneapolis, Minnesota, USA from September 7 to September 15, 2003. PM2.5 concentrations were measured at the Harding High School site and date and time are in Central Standard Time (CST) (Alcorn et al., 2003).

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Satellite (1 of 5)

● Polar-orbiting or geostationary

● Visible imagery● Aerosol optical depth

Images courtesy of University of Wisconsin, Madison

● Advantage – Data available from around the world

● Disadvantage – No direct pollutant measurements– Only works during daylight and when

skies are cloud-free– No vertical resolution

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Satellite (2 of 5)

Visible satellite imagesVisible satellite imagery can be used to track aerosols (haze, dust, or smoke)

Saharan dust over the Canary Islands August 25, 2004 Victoria, Australia, wildfire

January 24, 2006

Images from NASA

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Satellite (3 of 5)

Aerosol optical depth• A measure of light extinction through the atmosphere• Proportional to the number of particles in the

atmospheric column

Source: NASA

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Satellite (4 of 5)

Transport of smoke from wildfires on 18 July, 2004

Aerosol optical depth map Source: NOAA

Visible satellite imageSource: NASA/University of Wisconsin, Madison

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Satellite (5 of 5)

July 18, 2004

Time-series plot of hourly observed PM2.5 and daily derived aerosol optical depthSource: NASAAerosol optical depth map overlaid

with wind vectors and precipitationSource: NASA

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Ozonesonde

● Sensor attached to a radiosonde● Measures vertical profile of ozone● Examines aloft ozone conditions● Problems for forecasting

– Expensive

– Very sparse, non-routine networks

Courtesy of T&B systems

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Ozonesonde – Example

Morning (6 a.m.) and afternoon (4 p.m.) ozonezonde data showing ozone concentration (black line), temperature (red), dew point temperature (blue), and winds from Las Vegas, Nevada, USA on July 1, 2005. Courtesy of T&B systems.

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Aircraft

● Provide multi-pollutant monitoring● Able to fly in area of concern● Useful for monitoring aloft carryover, transport

of pollution, and mixing processes● Morning flights profile useful forecasting

information● Problems

– Expensive– Data not available in real-time

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Aircraft – Example

0

500

1000

1500

2000

2500

0 10 20 30 40 50 60 70 80 90

Concentration (ppb)

Temperature (oC)

Alt

itu

de

(m

ag

l)

NOy T O3

SurfaceLayer

LowLevelJet

ResidualLayer

FreeAtmosphere

10 m/s North

Aircraft Spiral and Upper-Air Winds at Gettysburg, PA

(0600 EST on August 1, 1995)

0

500

1000

1500

2000

2500

0 10 20 30 40 50 60 70 80 90

Concentration (ppb)

Temperature ( oC)

Alt

itu

de

(m

ag

l)

NOy T O3

SurfaceLayer

LowLevelJet

ResidualLayer

FreeAtmosphere

10 m/s North

Aircraft measurement of ozone, NOy, temperature, and winds provide aloft information about the air quality conditions in the nocturnal low-level jet. In this example, aircraft data collected near Gettysburg, Pennsylvania, USA on August 1, 1995, at 0600 EST showed a nocturnal jet that transported air pollution over several hundred kilometers during the overnight hours. This aloft pollution mixed to the surface during the late morning hours.

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Lidar

Cross section of a plume displayed during data acquisition obtained by LIDAR (located to the left), showing the height of the mixing layer (red) and structure in the plume (blue). If an appropriate wave length is used, LIDAR can measure SO2 concentration in the plume directly.

An early transportable LIDAR from CSIRO on location during a plume tracking experiment.

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● Identify the purpose of the monitoring network– Air quality regulations, scientific studies, model verification,

trends and historical analysis, etc.

– Forecasting● Historical analysis● Real-time monitoring● Forecast verification

● Issues to consider– Historical data set (how many years?)

– Location of sites relative to● Population● Emissions sources● Region of maximum pollution levels

● Several methods to evaluate monitoring network

Monitor Density and Location (1 of 5)

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Mountain Versus Valley ● Diurnal variation of concentration due to monitor siting

– Mountain versus valley

– Urban versus rural● Different diurnal pattern and concentrations

– Use caution when applying forecasting rules developed for rural sites to urban sites


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Local Source/Topography

● July 5, 2000

● Local source – fireworks on the river

● Topographic interaction – wind channeling by river/valley topography

● Combination of topographic channeling and local source produced inter-area variation in PM of 54.9 μg/m3 based on 24-hr average sampler data


Surface Wind Direction

Fireworks

61.0

27.2

28.6

9.36.111.9

9.2

Portland, Oregon, USA site locations

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Urban Versus Rural ● Diurnal variation of concentration due to monitor siting

– Mountain versus valley– Urban versus rural

● Different diurnal pattern and concentrations– Use caution when applying forecasting rules developed for rural sites to urban

sites


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Regional Representativeness/Urban and Rural Difference

• December 27, 2001

• Regional representativeness of specific sites

• Meteorological conditions favored high PM levels, but rural sites only measured low concentrations

• Indicates vast difference (40.7 μg/m3 based on 24-hr average sampler data) between urban and rural sites

35.5

43.7

1.3

11.8

3.0


Las Vegas, Nevada, USA site locations

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● Suitability modeling = identify monitoring locations based on specific criteria

● Analysis method– Use geographic map of emissions sources– Determine the relative importance of each

geographic data set based on its emissions contribution, and combine to produce a suitability map using a geographic information system (GIS)

– Incorporate predominant wind direction and population to the suitability model to assess population exposure

Monitor Density and LocationAnalysis Techniques (1 of 3)

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Points Lines Population ElevationInput Data: Point, line, or polygon geographic data

Gridded Data: Create distance contours or density plots from the data sets

Reclassified Data: Reclassify them to create a common scale

Weight and combine datasets

Output suitability model

High Suitability

Low Suitability


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Penfold et al., 2003

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Monitor Representativeness (1 of 3)

● Representativeness– Do monitors measure the prevailing conditions at

site, location, or region?– Do collocated monitors measure similar conditions

(variations can exist among monitoring techniques)

– Do closely located monitors measure similar conditions?

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● Variations between filter sampler and continuous monitors (TEOM)

● TEOMs underestimate filter sampler PM2.5 concentrations by up to 50% due to cold conditions and volatilization caused by TEOM heating the air sample.

SSI 1 ( µg/m3 )

0 50 100 150 200

TE

OM

( µ

g/m

3 )

0

50

100

150

200BakersfieldOct. - Feb.

SSI 1 ( µg/m3 )

0 50 100 150 2000

50

100

150

200SacramentoOct. - Feb.

Collocated ComparisonSSI 1 and TEOM (Winter/Fall)

Slope = 0.61Intercept = 7.4r = 0.82N = 14

Slope = 0.65Intercept = 3.0r = 0.84N = 58

Chow, 1998

Graham, 1999

PM2.5 Mass measurements in New Haven, CT 1998

0

10

20

30

40

50

60

70

07/0

1/19

98

07/1

5/19

98

07/2

9/19

98

08/1

2/19

98

08/2

6/19

98

09/0

9/19

98

09/2

3/19

98

10/0

7/19

98

10/2

1/19

98

11/0

4/19

98

11/1

8/19

98

12/0

2/19

98

12/1

6/19

98

12/3

0/19

98

g/m

3

TEOM

IMPROVE

FRM

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Scatter plots of PM2.5 data from different sites


0.66 0.60

0.83 0.79

0.85 0.87

0.84 0.79

0.91 0.90

0.93 0.86

0.70 0.64

1 2

2

1

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● Regional maximum differences from evolving and changing networks– Adding or removing sites from a network can affect overall network

monitoring results– The same is true for sites removed from the network

● Data availability– Filter sampling may measure PM on different schedules (daily or every third

or sixth day), which makes analysis more difficult

Data Availability

Atlanta, Georgia, USA PM2.5 AQI values from January 10, 1999 – January 23, 1999

Date 130630091 130670003130890002 Monitor #1

130890002 Monitor #2

130892001 131210032 131210039 131211001

1/10/1999 541/11/19991/12/1999 51 42 44 441/13/1999 60 561/14/1999 76 611/15/1999 47 118 35 351/16/1999 153 631/17/1999 701/18/1999 41 44 3 681/19/1999 361/20/19991/21/1999 60 59 58 62 631/22/1999 44 51 451/23/1999 16 18 20

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● AQI is referenced to the National Air Quality Standards as a simple percentage.

Data Availability

Melbourne Australia, 26 May 2006

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Summary● Monitor types

– Gas monitors– Particle samplers and monitors– Continuous – real-time, good for forecasting– Samplers – not real-time– Other

● Visibility sensors● Satellite measurements● Ozonesondes

● General issues– Uses of data– Variability among monitor types– Spatial representativeness– Data availability

arep gurme section 8 pollutant monitoring monitor types monitor density and location data...

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