Noise Event Data:

ANEEM - Aircraft Noise Event Extraction Methodology: 
In January 2015, the Airports Authority became the first U.S. airport system to upgrade its noise monitoring software using a new and improved noise source classification methodology that accounts for quieter aircraft and higher background noise levels.  Descriptions of Noise Event Detection Methodologies are posted below.

• Noise Data Updates:  Approximately 30 days after the last day of each month.
• Available Data:            2015 - Present.

ANEEM Noise Event Classifications and Metrics:

Noise Classification Descriptions:

• Aircraft:          Noise events correlated with aircraft position data.
• Community:  Noise events without correlated aircraft position data.
• Mixed:            Noise events attributed to correlated aircraft and community noise.
• Background:  Environmental noise not associated with aircraft or community noise.

Noise Event Metric and Table Header Descriptions:

• Aircraft DNL:  Cumulative exposure metric that logarithmically calculates the average aircraft dB(A) level over a 24-hour period, with a 10 dB penalty** artificially applied to aircraft noise events occuring during nighttime hours (22:00 - 06:59).  (**10 dB penalty is equivalent to substituting '1' nighttime aircraft noise event with '10' nighttime aircraft noise events.)
   
• dB(A):  A-weighted decibel scale that adjusts (weights) low frequency ranges to model the response perceived by the human ear.
   
• Leq:  For Each Noise Classification:  A cumulative exposure metric that logarithmically averages the varying sound energy over a defined period of time (month), and states it as a constant (equivalent) level for each noise classification type.
   
• Lmax:  Single event noise metric that represents the maximum (peak) dB(A) level during a discrete noise event.  Monthly peak levels are represented as:
  • Min (lowest peak), Modal (most common 'whole #' peak), Max (highest peak).
     
• Total Leq:  For All Noise Classifications:  Cumulative exposure metric that logarithmically averages the varying sound energy over a defined period of time (month), as states it as a constant (equivalent) level for all noise classifications
   
• # Events:  Number of noise events during the month for each noise classification type.
   
• % Time Online:  Percent of the month that the noise monitor was operational.

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Noise Event Detection Methodologies:

Traditional Noise Event Detection Methodology:  Prior to 2015, the Airports Authority’s noise monitors detected a noise event when the noise level exceeded a noise threshold for a minimum duration.  During noise data post-processing, only detected noise events were correlated against aircraft position data.  The traditional methodology had technological challenges:

• Quieter aircraft commonly generated noise levels that did not satisfy the noise event threshold criteria.
• Aircraft noise events could easily be contaminated by community noise sources (traffic, sirens, lawn mowers, construction, community activities, nature – weather, animals).
• Lowering the noise event detection threshold did not account for increased background noise levels.

ANEEM - Aircraft Noise Event Extraction Methodology:  In 2015, the Airports Authority became the first U.S. airport system to implement ANEEM, developed by EMS Bruel & Kjaer, to identify and classify noise events.

• ANEEM does not solely rely on the noise monitor to detect a noise event.  During noise data post-processing, ANEEM cross-references databases to identify aircraft in the vicinity of the noise monitor when the noise level rises above the background level. 
• Aircraft-dominated noise events are identified and correlated by comparing aircraft position data with predicted noise levels for that aircraft, using FAA noise certification data.
• ANEEM provides a more accurate detection methodology for distinguishing aircraft noise from other noise sources experienced in neighboring communities.

It is very important to clarify that the “total noise” experienced at a noise monitor is unaffected by the choice of a noise event detection methodology.

ANEEM only improves the accuracy of the noise source classification process.  ANEEM noise event counts will be higher because ANEEM accounts for quieter aircraft and higher background noise levels.  However, the “total noise” experienced at the noise monitor is consistent with legacy software.