1. Workshop on Air Quality Data Analysis and Interpretation Photochemical Assessment Monitoring Stations (PAMS) – US Approach

2. PAMS Data Uses  Corroborate precursor emission inventories  Assess changes in emissions; corroborate emissions reductions (control strategy evaluation)  Assess ozone and precursor trends  Provide input to models; evaluate models  Evaluate population exposure

3. PAMS Sampling Sites  Type 1: Upwind and background characterization  Type 2: Maximum ozone precursor emissions impact  Type 3: Maximum ozone concentration  Type 4: Extreme downwind monitoring

4. PAMS Sampling Sites Schematic

5. PAMS Sampling Considerations  Site Location (Types 1-4)  Number of Sites Ozone and Precursors Upper-Air Meteorology  Sampling Frequency Hydrocarbons Carbonyl Compounds Upper-Air Meteorology

6. Ozone and Precursor Measurements Continuous measurements  Ozone  Nitrogen Oxides  Total Non-Methane Organic Compounds Time integrated sampling  Speciated NMOC’s  Carbonyl Compounds

7. PAMS Target VOCs COMPOUNDAIRS codeCAS code 1. Ethylene 43203 748512. Acetylene 43206 74862 3. Ethane 43202 748404. Propylene 43205 115071 5. Propane 43204 749866. Isobutane 43214 75285 7. 1-Butene1 43280 1069898. n-Butane 43212 106978 9. t-2-Butene 43216 62464610. c-2-Butene 43217 590181 11. Isopentane 43221 7878412. 1-Pentene 43224 109671 13. n-Pentane 43220 10966014. Isoprene 43243 78795 15. t-2-Pentene 43226 64604816. c-2-Pentene 43227 627203 17. 2,2-Dimethylbutane 43244 7583218. Cyclopentane 43242 287923 19. 2,3-Dimethylbutane 43284 7929820. 2-Methylpentane 43285 107835 21. 3-Methylpentane 43230 9614022. n-Hexane 43231 110543 23. Methylcyclopentane43262 9637724. 2,4-Dimethylpentane 43247 108087 25. Benzene 45201 7143226. Cyclohexane 43248 110827 27. 2-Methylhexane 43263 59176428. 2,3-Dimethylpentane 43291 565593 29. 3-Methylhexane 43249 58934430. 2,2,4-Trimethylpentane 43250 540841 31. n-Heptane 43232 14282532. Methylcyclohexane 43261 108872 33. 2,3,4-Trimethylpentane 43252 56575334. Toluene 45202 108883 35. 2-Methylheptane 43960 59227836. 3-Methylheptane 43253 589811 37. n-Octane 43233 11165938. Ethylbenzene 45203 100414 39. m & p-Xylene 45109 108383/10642340. Styrene 45220 100425 41. o-Xylene 45204 9547642. n-Nonane 43235 111842 43. Isopropylbenzene 45210 9882844. n-Propylbenzene 45209 103651 45. m-Ethyltoluene 45212 62014446. p-Ethyltoluene 45213 622968 47. 1,3,5-Trimethylbenzene 45207 10867848. o-Ethyltoluene 45211 611143 49. 1,2,4-Trimethylbenzene 45208 9563650. n-Decane 43238 124185 51. 1,2,3-Trimethylbenzene 45225 52673852. m-Diethylbenzene3 45218 141935 53. p-Diethylbenzene 45219 10505554. n-Undecane 43954 1120214

8. Collection and Analysis of Speciated NMOCs  Automated Approach – Automated Field GC analysis of Sorbent tube samples Whole-air samples  Manual Approach – Laboratory GC analysis of Sequential sampler for Sorbent tube samples Whole-air samples

9. PAMs Target Carbonyls Compounds Formaldehyde Acetaldehyde Acetone Propionaldehyde Crotonaldehyde Butyr/isobutyraldehyde Benzaldehyde Isovaleraldehyde Valeraldehyde Tolualdehydes Hexaldehyde 2,5-dimethylbenzaldehyde (Acrolein)

10. Collection and Analysis of Carbonyl Compounds  Sequential Sampler collecting carbonyl compounds as DNPH derivatives  Laboratory analysis of samples by HPLC

11. PAMS Sampling Frequency (during O 3 season)  Type 1 - Background 8 – 3 hr ave SNMOC samples every 3 rd day and 1 – 24 hr ave SNMOC sample every 6 th day.  Type 2 - Max. Emissions 8 – 3 hr ave SNMOC samples every 3 rd day and 1 – 24 hr ave SNMOC sample every 6 th day. 8 – 3 hr ave carbonyl samples on the 5 peak O 3 days plus each previous day and 8 – 3 hr samples every sixth day.  Type 3 – Max. Ozone and Type 4 - Downwind 8 – 3 hr ave SNMOC samples every 3 rd day and 1 – 24 hr ave SNMOC sample every 6 th day.

12. PAMS Surface Meteorological Monitoring  At each monitoring site Wind direction Wind speed Ambient temperature Humidity (e.g., dew point or relative humidity)  At least one network site Solar radiation Ultraviolet radiation Barometric pressure Precipitation

13. Capabilities and Limitations of Vertical Profiling Systems VariableTowerSodarMini- Sodar RADARRADAR with RASS Radio- sonde Tether- sonde Wind speed 10-100 m 50- 600m 10- 300m 100- 2500m 10m- 10km 10- 1000m Wind direction 10-100 m 50- 600m 10- 300m 100- 2500m 10m- 10km 10- 1000m Wind sigmas 10-100 m 50- 600m 10- 300m 100- 2500m Rel. Hum. 10-100 m 10m- 10km 10- 1000m Temp.10-100 m 100- 1200m 10m- 10km 10- 1000m

14. Example flow chart for data analysis

15. Purpose of Data Validation  Definition "The purpose of data validation is to detect and then verify any data values that may not represent actual air quality conditions at the sampling station. Effective data validation procedures usually are handled completely independently from the procedures of initial data collection. Moreover, it is advisable that the individuals responsible for data validation not be directly involved with data collection." (U.S. EPA, 1984, Sec. 2.0.3, p.10)  Why is Data Validation Important? Data validation is necessary to identify data with errors, biases, and physically unrealistic values before they are used for identification of exceedances, for analysis, or for modeling.

16. Data Validation Definitions  Outliers Data physically, spatially, or temporally inconsistent.  Level 0 Data Validation Conversion of instrument output voltages to their scaled scientific units using nominal calibrations. May incorporate data logger inserted flags.  Level 1 Data Validation Observations have received quantitative and qualitative reviews for accuracy, completeness, and internal consistency. Final audit reviews required.  Level 2 Data Validation Measurements are compared for external consistency against other independent data sets (e.g., comparing surface ozone concentrations with ozone concentrations from nearby aircraft flights, intercomparing radionsonde and radar profiler winds, etc.).  Level 3 Data Validation Continuing evaluation of the data as part of the data interpretation process.

17. Example of Quality Control Flags FlagDescriptionExplanation 0Valid Observations judged accurate within the performance limits of the instruments. 1Estimated Observations required additional processing because original values were suspect, invalid, or missing. 7Suspect Values judged to be in error because they violate reasonable physical criteria or do not exhibit reasonable consistency, but a specific cause of the problem is not identified. 8Invalid Values judged to be inaccurate or in error, known cause of the inaccuracy or error. 9Missing Observations not collected. Values assigned -999.

18. Data Validation Procedures  Assemble Level I database.  Place data in a common data format with descriptive information concerning variables, validation level, QC codes, and standard units.  Ensure that results of and suggestions from final audit reports have been incorporated into the database.  Review simple statistics for unrealistic maxima or minima and for consistency with nearby stations (still Level I)  Perform spatial and temporal comparisons of the data (begin Level II).  Perform intercomparisons of the data (e.g., from two different instruments). Data now Level III.

19. VOC Data Validation Tools & Tips  Overall Total VOC --> Species groups --> Individual species Inspect every species  Time Series Inspect time series for the following: Large "jumps" or "dips" in the concentrations Periodicity of peaks, calibration carryover Expected diurnal behavior (i.e., isoprene) Expected relationships among species High single-hour concentrations of less abundant species

20. VOC Data Validation Tools & Tips (continued)  Scatter Plots Prepare scatter plots of the following: Total NMOC vs. species group totals, vs. individual species Benzene vs. Toluene, Acetylene, Ethane Species that elute close together Isomers Other  Fingerprints Prepare and inspect fingerprint plots for the following: Identify calibration data. Investigate hours surrounding suspect and invalid data. Obtain overall view of diurnal changes.

21. VOC Data Validation Tools & Tips (continued)  Additional Data To further investigate outliers, use: Wind direction data Other air quality data (e.g., ozone, NO x ) Subsets of data (e.g., high ozone days only) Industrial or agricultural operating schedules Traffic patterns Other