BAM Monitor Performance. Seasonal and Geographic Variation in NC

Transcription

1 BAM Monitor Performance Seasonal and Geographic Variation in NC

2 2 Presenter Wayne Cornelius

3 3 Introduction Unsuccessful ARM tests in 2007 and 2009, using a configuration of R&P TEOM monitor. Acquired the first met-one BAM in Dec Set up in Raleigh (the #2 ARM site). Began reporting data to AQS June Second BAM set up in Bryson City also began reporting data to AQS June 2009.

4 4 Introduction Third BAM set up in Castle Hayne began reporting data to AQS January Mecklenburg County Air Quality program set up a BAM in Charlotte, reporting data to AQS in non-fem configuration January 2010.

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6 6 Sampling Methods All 4 sites have a Reference Method monitor (R&P 2025 Sequential) and a VSCC-50º TEOM in addition to the BAM.

7 7 Statistical Methods Time series, showing seasonal variations of the 24-hour averages Time series, showing BAM-TEOM seasonal variations Diurnal profiles, showing mean 1-hour averages within calendar quarters

8 8 Statistical Methods Scatterplots (BAM vs. RM) Linear Regression models Quadratic Regression models (when regression diagnostics support them) Hypothesis tests (visual comparisons)

9 9 Comparisons Between Sites BAM TEOM time series EPA hexagon (linear regression coefficients)

10 10 Time Series Results: Raleigh Time series, showing seasonal variations of the 24-hour averages (quarterly) BAM-TEOM series

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17 17 Time Series: Bryson City Time series, showing seasonal variations of the 24-hour averages BAM-TEOM series

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20 20 Time Series: Castle Hayne Time series, showing seasonal variations of the 24-hour averages BAM-TEOM series

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23 23 Time Series: Charlotte Multivariate series, showing seasonal variations of the 24-hour averages BAM-TEOM series

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26 26 Time Series: All Sites BAM-TEOM series smooths

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28 28 Regression Analyses Scatterplots (BAM vs. RM) Linear regression models Quadratic Regression models (when regression diagnostics support them) Hypothesis tests (visual comparisons)

29 29 Regression Analyses: Raleigh Scatterplot (BAM vs. RM) Linear Regression model

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31 31 BAM on FRM linear model fit Call: lm(formula = BAM ~ FRM, data = bam.ml.df2) Residuals: Min 1Q Median 3Q Max Coefficients: Value Std. Error t value Pr(> t ) (Intercept) FRM (FRM-1) Residual standard error: on 466 degrees of freedom Multiple R-Squared:

32 32 Regression Analyses: Raleigh Diagnostic plot of residuals against fitted values Recommends fitting quadratic regression model!

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35 35 BAM on FRM quadratic model fit Call: lm(formula = BAM ~ FRM + FRM^2, data = bam.ml.df2) Residuals: Min 1Q Median 3Q Max Coefficients: Value Std. Error t value Pr(> t ) (Intercept) FRM I(FRM^2) Residual standard error: on 465 degrees of freedom Multiple R-Squared:

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37 37 Regression Analyses Hypothesis tests: Are the BAM data equivalent to the RM data (TEOM data)?

38 38 Hypothesis Tests EPA acceptance criteria for Class III FEM (40 CFR 58 App. C 2.4): additive & multiplicative bias, equivalently the intercept and slope of linear regression Statistical criteria: compare the BAM regression line confidence interval to the RM expectation (or to the TEOM regression line)

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41 41 Regression Analyses: Bryson City Scatterplot (BAM vs. RM) Linear regression model Quadratic regression model Hypothesis test graphs

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44 44 BAM on FRM linear model fit Call: lm(formula = BAM ~ FRM, data = bam.by1.df) Residuals: Min 1Q Median 3Q Max Coefficients: Value Std. Error t value Pr(> t ) (Intercept) FRM (FRM-1) Residual standard error: on 146 degrees of freedom Multiple R-Squared:

45 45 BAM on FRM quadratic model fit Call: lm(formula = BAM ~ FRM + FRM^2, data = bam.by1.df) Residuals: Min 1Q Median 3Q Max Coefficients: Value Std. Error t value Pr(> t ) (Intercept) FRM I(FRM^2) Residual standard error: on 145 degrees of freedom Multiple R-Squared:

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48 48 Regression Analyses: Castle Hayne Scatterplot (BAM vs. RM) Linear regression model Hypothesis test graph

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50 50 BAM on FRM linear model fit Call: lm(formula = BAM ~ FRM, data = bam.ch2.df[ - c(3, 39), ]) Residuals: Min 1Q Median 3Q Max Coefficients: Value Std. Error t value Pr(> t ) (Intercept) FRM (FRM-1) Residual standard error: on 59 degrees of freedom Multiple R-Squared:

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52 52 Regression Analyses: Charlotte Scatterplot (BAM vs. RM) Linear regression model Quadratic regression model Hypothesis test graphs

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55 55 BAM on FRM linear model fit Call: lm(formula = BAM ~ FRM, data = bam.gr.df) Residuals: Min 1Q Median 3Q Max Coefficients: Value Std. Error t value Pr(> t ) (Intercept) FRM (FRM-1) Residual standard error: on 273 degrees of freedom Multiple R-Squared:

56 56 BAM on FRM quadratic model fit Call: lm(formula = BAM ~ FRM + FRM^2, data = bam.gr.df) Residuals: Min 1Q Median 3Q Max Coefficients: Value Std. Error t value Pr(> t ) (Intercept) FRM I(FRM^2) Residual standard error: on 272 degrees of freedom Multiple R-Squared:

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59 Regression Analyses, EPA style 59

60 60 Other NC Results (not presented herein) Diurnal Cycles for each monitor in each calendar quarter Some episodic diurnal profiles for Raleigh and Charlotte More at index on ftp server (for now): ftp://ftp.ncdenr.org/aq/pub/ambient/bamstudy/index.ht m

61 61 Other BAM Results Felton, Dirk; P. Fine, M.A. Heindorf, and A. Kelley (2010). Beta Attenuation Monitors. National Monitoring Steering Committee Meeting, Research Triangle Park NC, July Frey, Betsy (2008). Delaware's Experience with Continuous PM2.5 Monitoring [ThermoAnderson BAM and SHARP (Synchronized Hybrid Ambient Real-Time Particulate) monitor, July 2007-June 2008]. MARAMA, 2008 Annual Monitoring Meeting, Richmond VA. Hart, Dennis (2009). BAM-1020 Ambient Particulate Mass Monitor: Keys to Obtaining High Quality Particulate Data Using a PM 2.5 Federal Equivalent Method. National Ambient Air Monitoring Conference, Nashville TN. Krask, David and John Haus (2008). BAM FEM vs FRM Comparison [by Maryland DOE, July-November 2008]. MARAMA, 2008 Annual Monitoring Meeting, Richmond VA. BAMM_Mon08.pdf

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