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

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

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

PAMS Sampling Sites Schematic

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

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

PAMS Target VOCs COMPOUNDAIRS codeCAS code 1. Ethylene Acetylene Ethane Propylene Propane Isobutane Butene n-Butane t-2-Butene c-2-Butene Isopentane Pentene n-Pentane Isoprene t-2-Pentene c-2-Pentene ,2-Dimethylbutane Cyclopentane ,3-Dimethylbutane Methylpentane Methylpentane n-Hexane Methylcyclopentane ,4-Dimethylpentane Benzene Cyclohexane Methylhexane ,3-Dimethylpentane Methylhexane ,2,4-Trimethylpentane n-Heptane Methylcyclohexane ,3,4-Trimethylpentane Toluene Methylheptane Methylheptane n-Octane Ethylbenzene m & p-Xylene / Styrene o-Xylene n-Nonane Isopropylbenzene n-Propylbenzene m-Ethyltoluene p-Ethyltoluene ,3,5-Trimethylbenzene o-Ethyltoluene ,2,4-Trimethylbenzene n-Decane ,2,3-Trimethylbenzene m-Diethylbenzene p-Diethylbenzene n-Undecane

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

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

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

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.

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

Capabilities and Limitations of Vertical Profiling Systems VariableTowerSodarMini- Sodar RADARRADAR with RASS Radio- sonde Tether- sonde Wind speed m m m m 10m- 10km m Wind direction m m m m 10m- 10km m Wind sigmas m m m m Rel. Hum m 10m- 10km m Temp m m 10m- 10km m

Example flow chart for data analysis

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 , 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.

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.

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.

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.

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

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.

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