PM 2.5 Synoptic Climatology St. Louis, MO

1. PM2.5 Synoptic ClimatologySt. Louis, MO Bret Anderson USEPA Region 7 St. Louis Joint Workgroup Meeting September 26, 2006

2. Acknowledgements • Dr. Jay Turner, Washington University for meteorological monitoring data and hourly ion data streams. • Michael Davis, USEPA Region 7 for AQ summaries for St. Louis

3. Synoptic Climatology in a Nutshell • Different statistical methods can be employed to describe air quality climatology. Each technique provides complimentary information regarding climatology. • Preference is to use recognized synoptic climatological techniques to categorize meteorological patterns according to their meteorological characteristics, as opposed to air quality characteristics, and then use descriptive statistics to infer air quality characteristics. • Principal components analysis (PCA), cluster analysis are two primary statistical methods for synoptic climatological analysis.

4. Rotated Principal Components Analysis (RPCA)

5. IMPROVE Sites Utilized

6. Principal Components Methodology • S-mode (multiple stations over time) decomposition (rotated principle components analysis) was applied to the 20,496 (84 sites  244 observations) observations The principal components analysis (PCA) begins with the construction of a 84  84 correlation matrix for the site-to-site correlations. The goal of the PCA is to explain the correlation structure through a few orthogonal linear combinations of the original PM2.5 concentrations. • A large proportion of the variability can be explained by retaining a small number of principal components. Then, the original “large” dataset can be replaced by the reduced dataset that consists of these principal components. To this end, the correlation matrix is decomposed into a set of 84 eigenvector‑eigenvalue pairs (from which the principal components are derived) with the eigenvectors representing the mutually orthogonal linear combinations of the PM2.5 concentrations (i.e., the principal components) and the eigenvalues representing the amount of variance explained by each of the eigenvectors. • Component loadings can be spatially mapped to delineate areas of homogenous PM2.5 component behavior (airsheds). The air quality in these areas behave similarly, either indicating similar emission sources impact the area or unique climatological characteristics. It can also be deduced that monitors within each region will respond similarly to future control scenarios. • The time series of each region can be graphed to depict the long-term temporal structure of PM2.5 and its components, providing analysts a quick method for selecting potential periods for modeling episodes.

7. SO4 Regional Analysis

8. Time Series of Sulfate Expansion Coefficients September 2002 Episode

9. NO3 Regional Analysis

10. Dec 2002 Episode Time Series of Nitrate Expansion Coefficients Alternative Episode

11. PCA-Two Stage Cluster Analysis

12. Multivariate Statistical Method – Synoptic Climatology • Meteorological datasets with multiple observations designed to provide complete diurnal characterization will be both voluminous and will likely exhibit a high degree of collinearity between the same parameter at different times of the day. It is very important to eliminate this collinearity in the dataset prior to clustering in order to reduce the potential impact of highly correlated variables contaminating the solution. Principle component analysis (PCA) can be employed to eliminate the collinearity. PCA also reduces overall size of the original dataset through a linear transformation series of orthogonal components which are independent in nature. Each successive component explains a lesser amount of the overall amount of variance present in the original dataset.

13. Cluster Analysis • The second step in the classification procedure involves the delineation of natural groupings of days which share similar meteorological characteristics. This classification is handled through the use of two-stage clustering technique of the daily scores of the principle components retained from the PCA analysis. The first cluster stage is referred to as hierarchical clustering. Hierarhical clustering calculates the Euclidean distance between all observations in the dataset and then the two closest observations are merged to form a new cluster. Distances between the new cluster and others is recalculated, with this process repeated until all observations are eventually merged into one cluster. Therefore, each day in the study period as represented by its five factor scores starts as its own cluster, with similar days being merged. This process continues until all days are finally merged into one cluster. • The optimal number of clusters is determined through the use of the pseudo-F and pseudo-t2 scores to determine cluster cohesiveness. Guidance for the selection of the final number of clusters is to observe a local maximum of the pseudo-F followed by a local maximum of the pseudo-t2 as the number of clusters decrease. The point where the largest drop in R2 occurs where the local maximums of pseudo-F and pseudo-t2 is the considered to be the optimal number of clusters. • Once the number of clusters is determined, the mean principal component scores for each of the clusters is calculated to serve as a “seed” value for the nonhierarchical convergent k means clustering technique. This method uses an iterative approach the allows for reclassification of study days after an initial grouping, allowing for continual refinement of the solution. The result is the definition of well defined, homogenous meteorological regimes group in “clusters.”

14. Meteorological Variables • Meteorological data obtained from Midwest Supersite on-site meteorological monitor provided by Dr. Jay Turner, Washington U. • Surface Variables: U – V Scalar Wind Components, Temperature, Relative Humidity, PG Stability Class, Total Daily Insolation • Upper Air Variables (850mb, 700mb, and 500mb): U-V Scalar Wind Components, Temperature, Dew Point

15. Frequency of Synoptic Regimes • Summer months dominated by Summer Anticyclonic pattern (C1) and secondary maximum of the stationary front pattern (C4). • Winter months dominated by frontal approach (C3) and Winter Anitcyclonic Pattern (C2), but near equal frequency of other transitional patterns, indicating frequent air mass shifts during autumn and winter months.

16. C1 Synoptic Pattern – Summer Anticyclonic • Characterized by strong anticyclonic system dominating eastern US. • Surface flow in St. Louis typically on return flow side of anticyclone, marked by south-southeast winds. • Highest frequency for periods of high sulfate concentrations.

17. C5 Synoptic Pattern – Late Autumn anticyclone, north-central frontal transition • C5 Pattern characterized by anticyclonic system over northeast US and cyclonic system transitioning across north central US. • Highest frequency during autumn, secondary maximum during spring months • Frequency of synoptic pattern usually associated with late autumn-early winter nitrate episodes (December 2002).

18. Cluster Summary Air Quality Characteristics

19. Monthly Air Quality Characteristics - Sulfate

20. Monthly Air Quality Characteristics - Nitrate

21. Cluster Summary Air Quality Characteristics – Deviation Statistics Sulfate Nitrate

22. Patterns in Atmospheric Transport History (PATH) Trajectory Cluster Analysis

23. Trajectory Climatology – PATH Analysis • Methodology described in journal article “Harvard Forest regional-scale air mass composition by Patterns in Atmospheric Transport History (PATH)”, Journal of Geophysical Research (1998). • PATH is unique among all of the clustering methodologies in that PATH does not use a pre-defined number of clusters in its analysis. • Grouping of trajectories is achieved by choosing a radius of proximity. This differs from most other clustering techniques by allowing the user to determine the radius of influence for development of clusters. Once a radius of proximity is selected, each trajectory is used as a center and is recursively compared against all other trajectories in the dataset. This establishes a distance matrix between all trajectories in the dataset. If a trajectory is determined to be within the radius of proximity of another trajectory (initial cluster seed), it is assigned to that cluster. However, membership in a cluster is not exclusive, as a trajectory can be determined to be in the radius of proximity for a number of different initial clusters. Once the cluster with the largest membership is determined (initial trajectory cluster), this is determined to be the first cluster and the trajectories within its radius of proximity are assigned to that cluster and are removed from the analysis. Distances between the remaining trajectories are recalculated and new cluster memberships are determined. Again, the initial cluster with the largest number of trajectories is designated as a final cluster, and trajectories in its membership are removed from the analysis. The process of designating pattern centers continues until a center is determined to have less than 2.5% of the original number of trajectories within its radius of proximity. • At this stage, all remaining unassigned trajectories are analyzed again to determine their proximity to the defined flow features identified in the first phase of this analysis. The central tendency (cluster mean trajectory) is calculated for each identified flow pattern, and the distance from each flow feature center to each unassigned trajectory is determined. If an unassigned trajectory is within twice the distance of radius of proximity previously defined by the analyst, it is assigned to that flow feature. Any trajectory falling outside this distance from these flow feature centers is classified as an outlier.

24. Trajectory Clusters (PATH)

25. Cluster Probabilities (PATH)

26. PATH Frequencies by Month

27. Summary Air Quality Characteristics by PATH – Deviation Statistics Sulfate Nitrate

28. Conclusions • Two-stage cluster analysis and trajectory PATH analysis provide complimentary information to elucidate air quality characteristics for St. Louis meteorological patterns. The RPCA analysis provides analysts a quick, quantitative view of the time series behavior of PM2.5 components, allowing for easy identification of periods for further examination. The two-stage cluster analysis provides quantitative analysis of meteorological conditions and identification of actual meteorological patterns, whereas trajectory analysis provides geographic context to meteorological patterns by showing air mass origins. • Clusters C1 and C4 (summer anticyclone and stationary front) correspond with trajectory patterns PATH1 (summer anticyclone, slow flow), PATH4 (Ohio Valley centric anticyclone), and PATH6 (southeast US centric anticyclone) for elevated sulfate. • Cluster C5 (autumn anticyclone northeast US, north-central US frontal transition) corresponds well with trajectory pattern PATH2 (north central slow flow) for periods of elevated nitrate.