1. AREP GURME Section 8 Pollutant Monitoring Monitor Types Monitor Density and Location Data Availability

2. AREP GURME 2 Section 8 – Pollutant Monitoring Data Sources for Forecasting Forecasting Data SourcesParametersTime ResolutionReal-time availability Cost/ Resources Written records of episodes  PM --Low Meteorological Data Visibility  PM Hourly Daily YesLow Reports of smoke/haze  PM VariesPossibleLow Satellite images  PM Hourly Daily YesLow Air Quality Data Surface ContinuousPM, O 3, CO, NO 2, NO x, SO 2, and moreHourlyPossibleModerate SamplersPM, O 3, CO, NO 2, NO x, SO 2, and moreTypically DailyNoModerate Passive samplersPM, O 3, CO, NO 2, NO x, SO 2, and moreIntegratedNoLow Upper-Air OzonesondeO3O3 PeriodicNoHigh AircraftPM, O 3, CO, NO 2, NO x, SO 2, and moreEpisodicNoVery high LIDARPM, O 3, CO, othersHourlyYesVery high

3. AREP GURME 3 Section 8 – Pollutant Monitoring Monitoring Monitor types –Gas monitors –Particle monitors and samplers –Other General issues –Uses of data –Variability among monitor types –Spatial representativeness Data availability

4. AREP GURME 4 Section 8 – Pollutant Monitoring Typical Continuous Gas Monitors ● Ambient air is continuously drawn into the monitor, pre-treated (e.g. particles, hydrocarbons, and/or H 2 S removed), and measured either directly or via chemical reaction using a spectroscopic method ● Direct measurement CO: gas filter correlation SO 2 : UV fluoresence O 3 : UV photometric ● After reaction with O 3 NO 2 : by difference (NO x -NO) using chemiluminescence and catalytic converter

5. AREP GURME 5 Section 8 – Pollutant Monitoring 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)

6. AREP GURME 6 Section 8 – Pollutant Monitoring Daily Filter Samplers Filter 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

7. AREP GURME 7 Section 8 – Pollutant Monitoring 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

8. AREP GURME 8 Section 8 – Pollutant Monitoring 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

9. AREP GURME 9 Section 8 – Pollutant Monitoring Visibility Sensors ● Nephelometers ● Transmissometer (weather visibility sensors) ● Measurements –Measures light scattering –Provides continuous data –Correlated with PM –Lower cost PM measurement

10. AREP GURME 10 Section 8 – Pollutant Monitoring 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.

11. AREP GURME 11 Section 8 – Pollutant Monitoring Nephelometers – Example Cool season hourly Nephelometer bsp versus BAM PM 2.5 at Angiola, California, USA stratified by relative humidity in the nephelometer (Alcorn et al., 2005).

12. AREP GURME 12 Section 8 – Pollutant Monitoring 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

13. AREP GURME 13 Section 8 – Pollutant Monitoring Transmissometer (2 of 2) Time series of hourly PM 2.5 and ASOS data for Minneapolis, Minnesota, USA from September 7 to September 15, 2003. PM 2.5 concentrations were measured at the Harding High School site and date and time are in Central Standard Time (CST) (Alcorn et al., 2003).

14. AREP GURME 14 Section 8 – Pollutant Monitoring 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

15. AREP GURME 15 Section 8 – Pollutant Monitoring Satellite (2 of 5) Visible satellite images Visible 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

16. AREP GURME 16 Section 8 – Pollutant Monitoring 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

17. AREP GURME 17 Section 8 – Pollutant Monitoring Satellite (4 of 5) Transport of smoke from wildfires on 18 July, 2004 Aerosol optical depth map Source: NOAA Visible satellite image Source: NASA/University of Wisconsin, Madison

18. AREP GURME 18 Section 8 – Pollutant Monitoring Satellite (5 of 5) July 18, 2004 Time-series plot of hourly observed PM 2.5 and daily derived aerosol optical depth Source: NASA Aerosol optical depth map overlaid with wind vectors and precipitation Source: NASA

19. AREP GURME 19 Section 8 – Pollutant Monitoring 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

20. AREP GURME 20 Section 8 – Pollutant Monitoring 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.

21. AREP GURME 21 Section 8 – Pollutant Monitoring 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

22. AREP GURME 22 Section 8 – Pollutant Monitoring Aircraft – Example 0 500 1000 1500 2000 2500 0102030405060708090 Concentration (ppb) Temperature ( o C) Altitude (m agl) NO y T O 3 Surface Layer Low Level Jet Residual Layer Free Atmosphere 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 0102030405060708090 Concentration (ppb) Temperature ( o C) Altitude (m agl) NO y T O 3 Surface Layer Low Level Jet Residual Layer Free Atmosphere 10 m/s North Aircraft measurement of ozone, NO y, 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.

23. AREP GURME 23 Section 8 – Pollutant Monitoring 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 wave­length is used, LIDAR can measure SO 2 concentration in the plume directly. An early transportable LIDAR from CSIRO on location during a plume tracking experiment.

24. AREP GURME 24 Section 8 – Pollutant Monitoring ● 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)

25. AREP GURME 25 Section 8 – Pollutant Monitoring 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 Monitor Density and Location (2 of 5)

26. AREP GURME 26 Section 8 – Pollutant Monitoring 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/m 3 based on 24-hr average sampler data Monitor Density and Location (3 of 5) Surface Wind Direction Fireworks 61.0 27.2 28.6 9.3 6.1 11.9 9.2 Portland, Oregon, USA site locations

27. AREP GURME 27 Section 8 – Pollutant Monitoring 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 Monitor Density and Location (4 of 5)

28. AREP GURME 28 Section 8 – Pollutant Monitoring 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/m 3 based on 24-hr average sampler data) between urban and rural sites 35.5 43.7 1.3 11.8 3.0 Monitor Density and Location (5 of 5) Las Vegas, Nevada, USA site locations

29. AREP GURME 29 Section 8 – Pollutant Monitoring ● 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 Location Analysis Techniques (1 of 3)

30. AREP GURME 30 Section 8 – Pollutant Monitoring Points Lines Population Elevation Input 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 Monitor Density and Location Analysis Techniques (2 of 3)

31. AREP GURME 31 Section 8 – Pollutant Monitoring Monitor Density and Location Analysis Techniques (3 of 3) Penfold et al., 2003

32. AREP GURME 32 Section 8 – Pollutant Monitoring 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?

33. AREP GURME 33 Section 8 – Pollutant Monitoring Monitor Representativeness (2 of 3) ● Variations between filter sampler and continuous monitors (TEOM) ● TEOMs underestimate filter sampler PM 2.5 concentrations by up to 50% due to cold conditions and volatilization caused by TEOM heating the air sample. Chow, 1998 Graham, 1999

34. AREP GURME 34 Section 8 – Pollutant Monitoring Scatter plots of PM 2.5 data from different sites Monitor Representativeness (3 of 3) 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

35. AREP GURME 35 Section 8 – Pollutant Monitoring ● 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 PM 2.5 AQI values from January 10, 1999 – January 23, 1999

36. AREP GURME 36 Section 8 – Pollutant Monitoring ● AQI is referenced to the National Air Quality Standards as a simple percentage. Data Availability Melbourne Australia, 26 May 2006

37. AREP GURME 37 Section 8 – Pollutant Monitoring 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