Spatio-Temporal Analysis of Total Nitrate Concentrations using Dynamic Statistical Models*

Transcription

1 Spatio-Temporal Analysis of Total Nitrate Concentrations using Dynamic Statistical Models* Jerry M. Davis Senior Environmental Employee at the U.S. Environmental Protection Agency, Research Triangle Park, NC, USA Presented at the Air and Waste Management Association meeting in Xi an, China, May 2010 *Ghosh, S.K. Bhave, P.V., Davis, J.M. and Lee, H. (2010). Spatio-Temporal Analysis of Total Nitrate Concentrations Using Dynamic Statistical Models, Journal of the American Statistical Association (in press)

2 Outline of the Presentation Introduction CASTNet Data Model Variables Production of HNO 3 Covariate Selection A Simplified Model Results and Summary Future Work

3 Introduction 1. Nitrate is a major component of fine particulate matter (PM 2.5 ) in the U.S. 2. It is one of most difficult components to accurately simulate using air quality models. 3. Because of its semivolatile nature, nitrate partitions rapidly between gasphase nitric acid (HNO 3 ) and fine particulate nitrate (NO 3- ). Accurate simulation of particulate nitrate requires knowledge of the total nitrate (TNO 3 ) concentration as well as the partitioning behavior of TNO 3 into HNO 3 and NO Progress in the thermodynamics of inorganic aerosol systems has made it possible to accurately determine the partitioning behavior of TNO 3 5. Numerical simulation of TNO 3 remains a challenge because complex atmospheric formation pathways and loss mechanisms exist and some are highly uncertain. The goal of this research is to estimate the relative importance of these different pathways by identifying empirical relationships that exist between TNO 3 concentrations and a set of covariates that include chemical species and meteorology variables.

4 6. For effective air quality management, one needs to know the relative spatial/temporal importance of each TNO 3 production and loss pathway. Air quality models can provide this information, but are subject to large uncertainties in several of the modeling processes. 7. By developing a statistical model for total nitrate based on observed data, we were able to identify empirical relationships between TNO 3 and a set of predictors. The knowledge gained in this process will be useful in improving the simulation of TNO 3 in numerical air quality models. 8. To quantify these empirical relationships, we employ the Reparameterized Dynamic Space Time Models (RDSTM) developed by Lee and Ghosh (2008). These models allow one to estimate dynamical relationships that may very in time between TNO 3 and the predictors.

5 CASTNet Data 1. All data (including the meteorology data) came from the rural sites of the U.S. Clean Air Status and Trends Network (CASTNet). CASTNet data are widely used to evaluate the ability of deterministic air quality models (e.g., the U.S. EPA Community Multiscale Air Quality (CMAQ) model) to simulate the levels of key atmospheric pollutants. 2. Thirty-three locations in the eastern U.S. were used in this study, which covered the period from January 1997 to July 2004 (394 weeks). The CASTNet locations were selected to overlap spatially with major point sources of NO X emissions. 3. Considering missing data for TNO 3 and the covariates there was a maximum of 394 weeks of data available and a minimum of 361 weeks. On average across all locations only about 3% of the observations were missing.

6 Model Variables 1.The chemical species used in this study are nitric acid (HNO 3 ), particulate nitrate (NO 3 - ), sulfate (SO 4 2- ), ammonium (NH 4 + ), and ozone (O 3 ). 2. The maximum hourly O 3 (ppb) values on each day are averaged from Tuesday to Tuesday to get weekly values. 3. The other chemical species (µmol/m 3 ) are weekly integrated samples with every Tuesday being the beginning point. 4. Nitric acid and nitrate are summed to get TNO Residual ammonium is calculated as ResidNH 4 + = (NH SO 4 2- ). Residual ammonium provides an estimate of the amount of ammonium that is associated with fine particulate NO 3 -. The factor two is based on the implicit assumption that the preferred form of particulate NH 4 + is ammonium sulfate ((NH 4 ) 2 SO 4 ). 6. Meteorological variables that were considered for use in the statistical model were temperature (C), dew point temperature (C), relative humidity (%), solar radiation (W/m 2 ), wind speed (m/s), and precipitation (mm). 7. To conform to the weekly chemical measurements, precipitation data are summed over each week and the other meteorological variables are averaged to obtain weekly values. 8. Median values for the chemical species and the meteorology variables showed little yearto-year variation.

7 Production of HNO 3 During the day TNO 3 is produced mainly by the following reaction: NO 2 + OH HNO 3 (R1) At night, TNO 3 is produced by a series of reactions: NO 2 + O 3 NO 3 + O 2 (R2a) NO 2 + NO 3 N 2 O 5 (R2b) N 2 O 5 + H 2 O 2HNO 3 (R2c) 1. The N 2 O 5 hydrolysis reaction (R2c) occurs in the gas phase and on particle surfaces but the rate is highly variable and uncertain. 2. In general, TNO 3 may be removed from the atmosphere by wet and dry deposition. 3. Wet deposition rates are strongly dependent on precipitation, whereas dry deposition depends on the partitioning of TNO 3 between the gas and particle phases because the dry deposition velocity of HNO 3 is significantly greater than that of NO 3 -

8 The Hydroxyl Radical (Daytime) O + hv O( D) + O O D H O OH 1 ( ) + 2 2

9 Covariate Selection Based on this figure ln(tno 3 ) was used as the response variable. We also linearly transformed the chemical species and the meteorological variables such that the covariates have mean zero and a variance of 1.

10 Based on the Spearman rank correlation coefficients and the stepwise covariate selection procedure, five covariates were selected: ResidNH 4, O 3, WS, RH, and P. ResidNH 4 is an indicator of the gas/particle partitioning behavior of TNO 3, and is thus a surrogate for dry deposition. Ozone serves as a surrogate for the hydroxyl radical which plays a major role in the daytime production of TNO 3. Wind speed has an impact on the dry deposition of TNO 3 in both the gaseous and particle phase. Relative humidity may play an important chemical role both at night and during the day. High daytime RH favors partitioning of TNO 3 to the particle phase. At night, high RH enhances the formation TNO 3 via N 2 O 5 hydrolysis. Precipitation acts as an scavenging agent for TNO 3 and is thus a surrogate for wet deposition.

11 A Simplified RDSTM A simplified model was developed because of the problems encountered interpreting the results from the full model. The main problems were: a. The intercept term exhibits a pronounced seasonal cycle that peaks in the winter and reaches a minimum value every summer. b. The spatial term is quite large and swamps the summed contributions from all observable covariates at many CASTNet locations. In the simplified model we assume that the TNO 3 observations are spatially independent. We use a site specific intercept to capture spatial differences in the mean concentrations. The intercept term is fixed in time.

12 Results I For every covariate, k, and week, t, the RDSTM provides posterior estimates (e.g., posterior medians) of the dynamic regression coefficient, β kt Weekly β values from the RDSTM are binned by month-of-year to obtain the boxplot distributions The relative contributions of each covariate to TNO 3 during each time step are computed as β kt X itk / (Σ β kt X ikt + ν it ) The contributions calculated in this manner lie between -1 and +1. The site-specific intercept term has been omitted from this equation to allow comparisons of the covariate contributions across all sites. Comparison across months is facilitated by the fact that the denominator in this equation exhibits no discernable seasonal cycle.

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14 Results II: Site Specific Analysis The solid black line represents the intercept term. The contributions of each covariate to ln(tno 3 ) during the time steps are shown as red (O 3 ), orange (RH), yellow (WS), green (ResidNH 4 ), and blue (P) patches. The colored parches represent the absolute contributions from different covariates. These contributions are computed by the product β kt X itk Model error is shown as gray patches. Colored patches are plotted above the black line if the product is positive, and below the black line if otherwise. For visualization purposes, model results are averaged into 4-week intervals.

15 Ann Arbor (MI) and Georgia Station (GA) At the Ann Arbor (MI) site the two most important covariates are ResidNH 4 and O 3 ; their contributions dominate during winter and summer, respectively. Wind speeds make a small negative contribution each summer at the Ann Arbor site. Results at the Georgia Station (GA) are representative of those at other southeastern CASTNet locations. The O 3 covariate at these sites makes a substantial positive contribution throughout the year, while the contribution from ResidNH 4 and WS are smaller in magnitude and their sign is more variable. O 3 is important because of the enhanced photolysis of ozone in the southeast due to higher daily integrated total solar radiation values. Low ResidNH 4 contributions are due to lower ammonia emissions in central Georgia as compared to the Midwest.

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18 Conclusions The RDSTM results indicate that the ResidNH 4, O 3 and WS covariates have the greatest impact on TNO 3 concentrations. The monthly contributions of these three covariates to TNO 3 match qualitatively with expectations based on known production and loss pathways. The relative humidity and precipitation covariates have a smaller net effect on ambient TNO 3, which is also an informative result.

19 Leapfroging Opportunities for Air Quality Improvement Bayesian Spatial/Temporal Processes