Statistics for Quality Assurance of Ambient Air Monitoring Data

Transcription

1 Statistics for Quality Assurance of Ambient Air Monitoring Data Prepared by Will Shuart, M.S. For the Mid-Atlantic Regional Air Management Association And the U.S. Environmental Protection Agency With the support of EPA Grant Agreement XA For presentation April 3-4, 2017 At the EPA Region 3 Office in Philadelphia, Pennsylvania

2 Acknowledgements Previous versions of this course were developed and presented by Mr. Shuart as part of EPA s course APTI 470, Quality Assurance for Air Pollution Measurement Systems, Mr. Jerry Winberry, lead instructor. Thanks to the following reviewers, who provided comments on drafts of this updated version of the statistics course: Mike Papp, EPA OAQPS Loretta Hayden, EPA Region 3 Stephanie McCarthy, EPA Region 4 Elizabeth Mannshardt, EPA OAQPS Donna Kenski, Lake Michigan Air Directors Consortium Betsy Frey, Delaware DNREC Namita Verma, Virginia DEQ Susan Wierman, MARAMA April

3 Statistics for Quality Assurance of Ambient Air Monitoring Data Lesson 1: Know Your Data 3

4 What We Will Cover In Lesson #1 Data Trends Seasonal/Diurnal Collocated data Data Completeness Data Aggregation Know Your Data Daily, Monthly, Annual Censored Data Method Detection Limit (MDL) Later: Knowing Your Data Through Statistics Descriptive Statistics Probability Models Analysis of Variance (ANOVA) Regression Analysis Process Control and Process Capability 4

5 Guidance Documents QA Handbook for Air Pollution Measurement Systems - Volume II Ambient Air Quality Monitoring Program Monitoring guidance appendix 2016 (link) Other resources: QA/G-3 Guidance on Assessing Quality Systems (2003) GA/G-7: Guidance on Technical Audits and Related Issues (2000) QA/G-8: Guidance on Environmental Data Verification and Validation (2002) 5

6 Guidance Documents (cont) Ambient Monitoring Technology Information Center (AMTIC) Data Assessment Statistical Calculator (DASC) - Software to assist those in calculating precision and bias statistics (MS Excel) 504 QA Data Assessment Tool (xlsm) (2016) Guideline on the Meaning and the Use of Precision and Bias Data Required by 40 CFR part 58 Appendix A - Version 1.1 (PDF) (2007) Companion Document for the Single Point Precision and Bias Graphics for Gaseous Criteria Pollutants Box-and-Whisker Plots (PDF) (2006) 6

7 Why monitor air quality? Regulatory Compliance Clean Air Act (1970) To assess the extent of pollution Provide air pollution data to the general public in a timely manner Support implementation of air quality goals or standards Evaluate the effectiveness of emissions control strategies Provide information on air quality trends Provide data for the evaluation of air quality models Support research (e.g., long-term studies of the health effects of air pollution). 7

8 Data Overview Before beginning data validation, it helps to know the typical patterns in data set Having this knowledge (a priori) helps the analyst set expectations for data patterns and identify data anomalies. Example: Diurnal and seasonal patterns help analysts understand possible impacts on data aggregations when some data are missing 8

9 National Ambient Air Quality Standards The Clean Air Act (1970) requires EPA to set National Ambient Air Quality Standards (40 CFR part 50) for pollutants considered harmful to public health and the environment iteria-airpollutants/naaqs-table Criteria Pollutants 9

10 Primary and Secondary Pollutants Primary A compound that is found in the same chemical form in which it was emitted into the atmosphere. Examples: CO, Particulate matter (PMx), some metals, benzene Secondary Created when a constituent undergoes a chemical reaction in the atmosphere Examples: ozone and formaldehyde This could happen far from the original source Primary & Secondary E.g. PM2.5 emitted and secondary formation 10

11 Current distribution EPA - AirData - ArcGIS Web Map 11

12 Factors that affect Criteria Pollutants Location Topography Point sources Interactions physical and chemical properties Time of day/year Weather and patterns, such as wind, temperature, air turbulence, air pressure, rainfall and cloud cover Anthropogenic or stochastic disturbances Fireworks or wild fires 12

13 National Trends in Criteria Pollutants Source: EPA Criteria Air Pollutants - Data as of 1/20/

14 Carbon Monoxide (CO) CO is colorless, odorless gas that can be harmful when inhaled in large amounts. At very high levels, which are possible indoors or in other enclosed environments, CO can cause dizziness, confusion, unconsciousness and death. Type Averaging time Upper limit Decision primary 8 hours 9 ppm Not to be exceeded 1 hour 35 ppm more than once per year 008_08_co_naaqs_review_plan.pdf 14

15 Lead Major sources of lead in the air are ore and metals processing and piston-engine aircraft operating on leaded aviation fuel Previous major contributors was exhaust from motor vehicles using leaded gasoline added to soils and sediments through deposition Type primary and secondary Averaging time Rolling 3 month average Upper limit Decision 0.15 μg/m 3 (1) Not to be exceeded 15

16 Nitrogen Dioxide (NO 2 ) NO 2 is released from the burning of fuel. NO 2 forms from emissions from cars, trucks and buses, power plants, and off-road equipment. primary primary and secondary Type Averaging time Upper limit Decision 1 hour 100 ppb 98th percentile of 1- hour daily maximum concentrations, averaged over 3 years 1 year 53 ppb (2) Annual Mean 16

17 Ozone Ozone is not directly emitted into the atmosphere but forms when oxides of nitrogen (NO x ) and volatile organic compounds (VOCs) are in the air and sunlight is present for photochemical reactions. Sources of NOx and VOCs: vehicle exhaust, power plants, refineries, chemical plants, etc. Type Averaging time Upper limit Decision primary and secondary 8 hours ppm (3) daily maximum 8-hour concentration, Annual fourth-highest averaged over 3 years 17

18 Particulate Matter (PM) PM 10µm inhalable particle, with diameters that are 10µm and smaller PM 2.5µm fine inhalable particles (respirable), with diameters that are 2.5µm and smaller 40 CFR Part 50 Source: EPA Type Averaging time Upper limit Decision PM 2.5 secondary 1 year 15.0 μg/m 3 annual mean, averaged over 3 years primary 1 year 12.0 μg/m3 annual mean, averaged over 3 years primary and secondary 24 hours 35 μg/m 3 98th percentile, averaged over 3 years PM 10 primary and secondary 24 hours 150 μg/m 3 Not to be exceeded more than once per year on average over 3 years

19 Sulfur Dioxide The primary source of SO 2 is the combustion of sulfur-containing fuels (e.g., oil and coal). Type Averaging time Upper limit Decision primary 1 hour 75 ppb secondary 3 hours 0.5 ppm 99th percentile of 1- hour daily maximum concentrations, averaged over 3 years Not to be exceeded more than once per year 19

20 Understanding patterns and trends 20

21 Diurnal and Seasonal Ozone Correlates to NOx, VOC s and Sunlight 8- hour average > 0.070ppm effective October 1, 2015 APTI Course #470 21

22 Ozone Trends - Nationally 40 CFR Parts 52 22

23 Cautious analysis potential misleading conclusions/actions APTI Course #470 23

24 National Trends and Limits Source: EPA - Trends in PM 10 Source: EPA, PM 2.5 National Trends Type Averaging time Upper limit Decision PM 2.5 secondary 1 year 15.0 μg/m 3 annual mean, averaged over 3 years primary 1 year 12.0 μg/m3 annual mean, averaged over 3 years primary and secondary 24 hours 35 μg/m 3 98th percentile, averaged over 3 years PM 10 primary and secondary 24 hours 150 μg/m 3 Not to be exceeded more than once per year on average over 3 years 24

25 National Trends vs Local Trend in Nitrogen Dioxide Daily Max 1-Hour Average Mean National Mean Richmond (VADEQ*) National Standard National Trend in NO vs local Trend (Richmond, VA) *Yellow line indicates changes in sampler location or did not meet data completeness (2014) 25

26 Visualizations AirNow allows for current and historical data visualization for Ozone and PM 26

27 Know Your Data Replicate, Duplicate and Collocated A replicate sample is a single sample that is chemically analyzed multiple times. Example: Air sample that two independent labs measure Duplicate sample is collected simultaneously using one collection system with the same inlet, and analyzed separately 27

28 Know Your Data Replicate, Duplicate and Collocated In contrast, collocated samples are two samples collected at the same location and time by equivalent independent samplers and chemically analyzed by the same method. These samples provide a measure of the agreement of both sample collection and chemical analysis methods and equipment. 28

29 Know Your Data: Collocated Data At a site level, EPA requires regulatory agencies to investigate agreement between collocated data using scatter plots and linear regression lines. If collocated data agree: Slope will be close to 1 Intercept will be close to 0 R 2 value will be close to 1 29

30 Collocated Data PM 2.5, PM 10, Lead Vanderpool and Dillard 2005 Timeline of PM 10 concentrations measured by the two collocated PM 10 FRM samplers at the Leeds site during Timeline of PM 2.5 concentrations measured by the two collocated PM 2.5 FRM samplers at the Leeds site during

31 Know Your Data: Data Completeness If data are missing from a site because of an unforeseen event (e.g., a hurricane), sampling contamination, or other problems, or a site may always operate on an incomplete schedule (e.g., ozone monitoring in summer months only), data may not be representative of the period of interest. Metadata for process (from data exploration to analysis) 31

32 Know Your Data: Data Completeness When performing an analysis, it is important to ensure that data are comparable across sites, years, or other subsets of the data. Example: Comparing Ozone in May vs July Understand location, time of year, ambient environs, etc. Completeness criteria are necessary in creating valid aggregated values (e.g. annual averages) to verify that the distribution of measured values within the aggregation window is representative of that entire period. Example: Missing Data 32

33 Know Your Data: Data Completeness Data completeness is computed using the reported sampling frequency (when available) as a measure of how many samples should be collected in a given period versus the number of samples that were collected. When aggregating data, 75% completeness is required minimum value but 100% collection is the goal. 33

34 Know Your Data: Data Completeness Source: EPA Handbook Volume 2. (January 2017) 34

35 Know Your Data Data Completeness And Aggregation Example: Daily CO Summary 35

36 Know Your Data: Data Aggregation Creating Valid Quarterly and Annual Averages Annual averages are calculated by first computing valid quarterly averages Quarterly Averages Use valid 24-hr averages with 75% data completeness These criteria are shown in the table below. Frequency 75% Quarterly Completeness Cutoff Daily 68 Every 3rd Day 24 Every 6th Day 12 Every 12th Day 6 Unassigned 6 36

37 Know Your Data: Data Aggregation Creating Valid Quarterly and Annual Averages At least 58 days are suggested between the first and last sample in a quarter to ensure sampling represented the entire quarter. Unassigned frequencies mean that no frequency was reported with data and a frequency could not be easily determined. The completeness criteria then defaults to the minimum to preserve data, but should be identified for later QC if possible. Annual Averages four valid quarterly averages are required. 37

38 Know Your Data: Method Detection Limits The EPA Code of Federal Regulations (CFR), 40CFR136, Appendix B defines the MDL as: The minimum concentration of a substance that can be measured and reported with 99% confidence that the analyte concentration is greater than zero and is determined from analysis of a sample in a given matrix containing the analyte. The purpose of an MDL is to discriminate against false positives. Values reported below the MDL have much higher uncertainty but can provide insight into the lower concentration distribution. Proposed update* - See resource file 38

39 Know Your Data: Method Detection Limits In the illustration below, normally distributed results from a measured value of zero yields a 99% confidence value (3σ) at 3 ppb, which would be used as the MDL in this case. There is >99% confidence that values above 3 ppb are not false positives

40 Summary Potential patterns in seasonal and daily changes in constituents. Understand data aggregation Duplicate sample vs. Replicate Strive for 100% data completeness, knowing it may not happen Detection and concentration limit resources