2004-09-23 PM Event Detection from Time Series

2004-09-23 PM Event Detection from Time Series - Presentation Transcript

PM Event Detection from Time Series

Contributed by the FASNET Community, Sep. 2004

Correspondence to R Husar , R Poirot

Coordination Support by

Inter-RPO WG Fast Aerosol Sensing Tools for Natural Event Tracking, FASTNET

NSF Collaboration Support for Aerosol Event Analysis

NASA REASON Coop

EPA -OAQPS

Event : Deviation > x*percentile

Temporal Analysis

The time series for typical monitoring data are ‘messy’; the signal variation occurs at various scales and the time pattern at each scale is different

Inherently, aerosol events are spikes in the time series of monitoring data but extracting the spikes from the noisy data is a challenging endeavor

The temporal signal can be meaningfully decomposed into a

Seasonal component with stable periodic pattern

Random variation with ‘white noise’ pattern

Spikes or events that are more random in frequency and magnitude

Each signal component is caused by different combination of the key processes: emission, transport, transformations and removal

Typical time series of daily AIRNOW PM25 over the Northeastern US

Temporal Signal Decomposition and Event Detection

First, the median and average is obtained over a region for each hour/day (thin blue line)

Next, the data are temporally smoothed by a 30 day moving window (spatial median - red line; spatial mean – heavy blue line). These determine the seasonal pattern.

EUS Daily Average 50%-ile, 30 day 50%-ile smoothing Deviation from %-ile Event : Deviation > x*percentile Median Seasonal Conc. Mean Seasonal Conc. Average Median

Finally, the hourly/daily deviation from the the smooth median is used to determine the noise (blue) and event (red) components

Seasonal PM25 by Region

The 30-day smoothing average shows the seasonality by region

The Feb/Mar PM25 peak is evident for the Northeast, Great Lakes and Great Plains

This secondary peak is absent in the South and West

Northeast – Southeast Comparison

Northeast and Southeast differ in the pattern of seasonal and event variation

Northeast has two seasonal peaks and more events–values well above the median

Southeast peaks in September and has few values much above the noise

Northeast Southeast

Causes of Temporal Variation by Region

The temporal signal variation is decomposable into seasonal, meteorological noise and events

Assuming statistical independence, the three components are additive:

V 2 Total = V 2 Season + V 2 MetNoise + V 2 Event

The signal components have been determined for each region to assess the differences

Northeast exhibits the largest coeff. variation (56%); seasonal, noise and events each at 30% Southeast is the least variable region (35%), with virtually no contribution from events Southwest, Northwest, S. Cal. and Great Lakes/Plains show 40-50% coeff. variation mostly, due to seasonal and meteorological noise. Interestingly, the noise is about 30% in all regions, while the events vary much more, 5-30%

‘ Composition’ of Eastern US Events

The bar-graph shows the various combinations of species-events that produce Reconstructed Fine Mass (RCFM) events

‘ Composition’ is defined in terms of co-occurrence of multi-species events (not by average mass composition)

The largest EUS RCFM events are simultaneously ‘events’ (spikes) in sulfate, organics and soil!

Some EUS RCFM events are events in single species, e.g. 7-Jul-97 (OC), 21-Jun-97 (Soil)

Based on VIEWS data

Northeast

Great Lakes

Great Lakes-Plains

Northeast

Great Plains

NorthWest

S. California

Southeast

Southwest

Event Definition: Time Series Approach

Eastern US aggregate time series

Sulfate EUS Daily Average 50%-ile, 30 day 50%-ile smoothing Deviation from %-ile Event – Deviation > percentile value Median Seasonal Conc. Mean Seasonal Conc.

Reconstructed Fine Mass RCFM

Organic Carbon

Eelemental Carbon

SOIL

Nitrate

Temporal Pattern Regional Speciated Analysis - VIEWS