Inference for stochastic processes in environmental science. V: Meteorological adjustment of air pollution data. Coworkers

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1 NRCSE Inference for stochastic processes in environmental science V: Meteorological adjustment of air pollution data Peter Guttorp NRCSE Coworkers Fadoua Balabdaoui, NRCSE Merlise Clyde, Duke Larry Cox, CDC Joel Reynolds, Alaska Fish & Game Paul Sampson, NRCSE Erin Sullivan, Kaiser Portland Mary Lou Thompson, NRCSE

2 Outline Why adjust? Four approaches regression extreme value temporal filtering space-time models Examples the impact of a large city on air quality the health effects of particulate matter Future directions Why adjust? Forecasting air quality Trend analysis temporal Spatial Estimating health effects Assessing regulatory compliance Scientific understanding

3 Regression methods Linear regression Contemporaneous Lagged forecasting SVD trend analysis Regression trees CART clusters weather types linear model within terminal nodes understanding of interactions Nonlinear regression Bloomfield et al., 1996 O Ê T t temp ˆ = Á m + T Ë 1 + u wspd 3 0 T ( 1+ r relhum)( 1+ qclcov)( 1+ wvis) T ( 1+ m wind)( 1+ tyear) + seasonal + error temp=(maxt, (maxt) 2, (maxt) 3, lag1 avet, lag2 avet) T wspd=(surf wspd, 700 hpa wspd, lag1 surf wspd) T relhum=(relhum, lag1 relhum) T wind=(mean.u, mean.v) T

4 Extreme value approaches Smith and Huang (1993): log p i 1 - p i T = b M i + e i p i = probability of exceedance on day i M i = meteorological values for day i (possibly also exceedance day i-1) Forecast exceedance given meteorology (forecast) Time series filtering X(t)=baseline(t)+weather(t) Rao-Zurbenko : iterated moving averages (of range a month) yield baseline; residual is weather Apply to both ozone and (lagged) temperature; linear regression both on weather and baseline Lag (baseline) explained by discrepancy between max solar angle and max surface temp; not stable when including other meteorological variables

5 Rao-Zurbenko Filter sum of residuals (range = 1 yr) to study trends in adjusted ozone Olt = a0 + a1mlt + elt Ost = b0 + b1mst + est Ot = Ost + Olt = a + b + a M + ( b - a ) M + e t 1 1 st t Adjusted linear model. Space-time models Separability of space and time depends on scales Heterogeneity of spatial covariance depends on geographic and meteorological features Data sets get moderately large Site-based meteorology vs. higher quality/fewer sites

6 Model assessment Issues: Variable selection science issues information criteria model uncertainty Trend models deterministic model-based nonparametric (GAM, wavelets) simultaneous confidence bands Paris data Main question: Can we detect the human contribution to air pollution from meteorologically adjusted data? Network: xx stations around Paris Data: NO x,o 3,hourly at network stations Wind, pressure, rel. humidity from airport 2 years (199?-9?)

7 Paris network Y(km) Roissy airport NO,NO2,O3 O3 only NO,NO2 only Coor.Paris$x/1000 X(km) Ozone data 235 mg/m mg/m mg/m 3 NW SE Paris 7eme Day

8 Wind directions Wind Direction Winds from NE Y(km) SW X(km) O3 daily max, Station:SW O3 daily max, Station:SW Wind direction

9 Winds from SE Y(km) NW X(km) O3 daily max, Station: NW O3 daily max, Station: NW Wind direction A comparison Station SE versus NW Y(km) O3 Daily max, Station=NW X(km) O3 Daily max, Station=SE Station NE versus SW Y(km) O3 Daily max, Station=SW X(km) O3 Daily max, Station=NE

10 Phoenix particulate matter and respiratory deaths Main question: Is respiratory deaths among elderly caused by particulate matter air pollution? Data: Single site PM 10,PM 2.5 5/95 6/98 Mortality Meteorology (temperature, specific humidity) Incl. baseline, lags 0-3, quadratic functions of met, total of 29 variables Bayesian model averaging BIC(m) = deviance(m) + dim(m) log(n) K a priori equally likely models Pm ( data) = K e K -BIC( m)/ 2 Â e i = 1 -BIC()/ i 2 E( b data) = ÂE( b data, i) P( i data) i = 1 K Var( b data) = Var( b data, i) P( i data) K i = 1 Â ( ) + E( b data, i) - E( b data) P( i data) i = 1 Â 2

11 BMA, cont. Uses all models considered, rather than the best model Often several models are nearly equally good Can use prior information about models Leaps and bounds algorithm to find best models of each size UNIFORM PM10 ELDERLY MORTALITY /1/1995 2/14/ /29/1996 9/14/1997 6/30/1998 DATE UNIFORM PM2.5 ELDERLY MORTALITY /1/1995 2/14/ /29/1996 9/14/1997 6/30/1998 DATE METRO AREA ELDERLY MORTALITY /1/1995 2/14/ /29/1996 9/14/1997 6/30/1998 DATE METRO AREA ACCIDENTAL MORTALITY /1/1995 2/14/ /29/1996 9/14/1997 6/30/1998 DATE NERL Platform PM COARSE /1/1995 2/14/ /29/1996 9/14/1997 DATE NERL Plaform PM FINE /1/1995 2/14/ /29/1996 9/14/1997 DATE

12 TOP 25 MODEL SPACE 95% PROBABILITY INTERVALS log(bayes FACTOR) Model Rank BASELINE TMAX TMAXLAG1 TMAXLAG2 TMAXLAG3 TMIN TMINLAG1 TMINLAG2 TMINLAG3 TMAXSQ TMAXSQLAG1 TMAXSQLAG2 TMAXSQLAG3 SH SHLAG1 SHLAG2 SHLAG3 SHSQ SHSQLAG1 SHSQLAG2 SHSQLAG3 PMC PMCLAG1 PMCLAG2 PMCLAG3 PM2.5 PM2.5LAG1 temp hum pm PM2.5LAG2 PM2.5LAG RELATIVE RISK Elderly Mortality Uniform PM10 Elderly Mortality Uniform PM Relative Risk Relative Risk Elderly Mortality Metro Area Accidental Mortality All Ages Relative Risk Relative Risk

13 Future directions Space-time extreme value models temporal and spatial done (Richard Smith) Wavelet approach to trend estimation temporal done (Peter Craigmile) Bayesian hierarchical models References Reviews of meteorological adjustment (of ozone) Porter, P. S., Rao, S. T., Zurbenko, I. G., Dunker, A. M. and Wolff, G. T. (2001): Ozone air quality over North America: Part II An analysis of trend detection and attribution techniques. Journal of the Air & Waste Management Association 51: Thompson, M. L., Reynolds, J., Cox, L. H., Guttorp, P. and Sampson, P. D. (2001): A review of statistical methods for the meteorological adjustment of tropospheric ozone. Atmospheric Environment 35: Wolff, G. T., Dunker, A. M., Rao, S. T., Porter, P. S. and Zurbenko, I. G. (2001): Ozone air quality over North America: Part I A review of reported trends. Journal of the Air & Waste Management Association 51: Health effects of air pollution Lipfert, F. W., and Wyzaga, R. I. (1995): Air pollution and mortality: issues and consideration. Journal of the Air & Waste Management Association 47: Bayesian model averaging Hoeting, J. A., Madigan, D., Raftery, A. E. and Volinsky, C. T. (1999): Bayesian model averaging: a tutorial. Statistical Science 14: