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2004-09-23 PM Event Detection from Time Series
 

2004-09-23 PM Event Detection from Time Series

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    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
      • Aerosol species time series:
        • ammSO4f
        • OCf
        • ECf
        • SOILf
        • ammNO3f
        • RCFM
      Regions of Aggregation
    • Dust
      • Seasonal + spikes
      • East – west events are independent
      • East events occur several times a year, mostly in summer
      • West events are lest frequent, mostly in spring
      US West East
    • Dust
      • asgasgasfg
      Northeast Southwest Southeast
    • Dust
      • dfjdjdfjetyj
      Northwest S. California Great Plaines
    • Amm. Sulfate
      • wdthehreherh
      US West East
    • Amm. Sulfate
      • stheherheyju
      Northeast Southwest Southeast
    • Amm. Sulfate
      • shheherh
      Northwest S. California Great Plaines
    • Organic Carbon
      • sdhdfhefheryj
      US West East
    • Organic Carbon
      • sdheherh
      Northeast Southwest Southeast
    • Organic Carbon
      • erheryeyj
      Northwest S. California Great Plaines
    • Reconstructed Fine Mass
      • estrhertheryu
      US West East
    • Reconstructed Fine Mass
      • werty3rueru
      Northeast Southwest Southeast
    • Reconstructed Fine Mass
      • wthwrthwerhtr
      Northwest S. California Great Plaines