Sounding Out Community Noise Complaints – Construction & Planning

Understanding the complexities of reported noise disturbances
may require a multifaceted scientific approach

From construction sites to pickleball courts to traffic congestion, community
noise complaints arising from everyday sources create concerns over
the effect of noise on well-being and health, the loss
of sleep and recovery, and adverse
effects on the development of children.

For municipalities, construction companies, and other
large-scale noise generators, it’s best practice to develop a
comprehensive noise monitoring plan before launching
sound-producing activities, especially given the rise of
noise-detecting technologies and public awareness of the impacts of
noise exposures. Smart cities are turning up the volume on noise
complaints by installing cameras and sound meters to capture vehicle
noise violations while apps like the Airnoise app allow residents to
register airplane noise complaints with the click of a button.

Despite their best efforts, stakeholders — ranging from
construction sites and flight path operators to schools and
community recreation centers — may find themselves facing
community noise complaints, which can carry the risk of litigation,
reputation damage, and frayed public relations if not handled
correctly. Determining the legitimacy of these noise complaints
requires a suite of tools and a data-driven approach to analyze the
alleged sources of the complaint, establish the significance of the
disturbance, and predict community response while proactively
working to prevent noise complaints in the future.

The varying human perception of noise

Analyzing noise complaints requires knowing what data to
collect, but this is complicated by the fact that everyone
perceives sound differently. An individual’s response to sound
— or psychoacoustic perception of sound — varies based
on cultural factors, sensitivity to
noise, what they were doing at the time of exposure, and
more.

For example, sources of noise such as football games,
children’s recreation areas, and concerts often elicit very
different responses from those nearby, ranging from fun and playful
to a minor annoyance to the cause of significant disruption. In
2019, for instance, the volume of noise at Kentucky’s Bourbon
& Beyond festival made headlines, but while some residents were
angered, others described a very loud Foo Fighters set as a
“nice lullaby” for their
children. There’s also always the possibility that a
noise-producing venture becomes a source of litigation:
Northwestern University has recently been involved in a lawsuit
related to its Ryan Field stadium and proposal to host summer
concerts to help pay for the facility, which has been met with outrage from residents over the
potential disturbances.

Because people perceive sound differently, data from sound-level
meters and laboratory microphones using essential acoustic metrics
(e.g., A-weighted decibels expressed in units dBA or dB(A) and
Zwicker parameters) is critical because it helps characterize how
“noisy” a sound may have seemed to a particular
group.

Measuring sound to quantify noise perceptions

As our ears and brains interpret sound differently, we hear some
frequencies as louder than others. A-weighted decibel units (dBA)
used to measure data from sound-level meter readings are designed
to account for variations in the human perception of loudness by
modifying the decibel (dB) reading according to how sensitive the
average human ear is to different frequencies of sound (the
A-weighting).

For example, a change in sound level of ±5 dBA is
considered clearly noticeable, whereas people would categorize a
change in sound level of ±10 dBA as twice or half as loud.
But if the sound level changes only by ±3 dBA, many people
may not even notice the increase or decrease. As a result, an
absolute change in sound level must be interpreted carefully in
light of human perception variations.

While basic sound level meter readings provide important
information about human perception through loudness measures, the
time-dependent data collected by a laboratory microphone or an
advanced sound level meter with data recording capabilities
provides detailed insights, especially regarding annoyance. We can
quantify how “annoying” a noise was by processing
microphone pressure data for noise metrics known as Zwicker
parameters, which include sharpness, fluctuation strength, and
roughness.

For example, when Zwicker parameters show that the sharpness and
roughness characteristics of a sound are especially high, they
uncover particular insights, such as why a fingernail scratch on a
blackboard might draw cringes even when heard from a hundred feet
away. Zwicker parameters also quantitatively explain why sirens and
alarm clocks work well to alert people to critical information.

Putting community noise complaints in context

In addition to the human perception of sound, examining the
human context of sound can help reveal how a community may have
perceived a given sound:

• When was the noise created? Was it during the workday when
  residents were away from their homes?




• What produced the noise? Was the noise created by a diesel
  generator powering construction equipment all night long?




• Was the noise impulsive and periodic in nature, like pickleball
  or pile-driving, or was it continuous, like an engine idling?




• How long did the noise last: for a minute or for hours?




• Where is the neighborhood? Is the community already exposed to
  other noise sources?

For instance, community noise complaints are often generated by
noise sources that are not native to the community. This is why
construction projects near neighborhoods, recreational activities
adjacent to residential housing, and traffic near homes often
generate noise complaints, whereas noisy lawnmowing in the morning
and children who play and shout late into the evening often do
not.

Consequently, if absolute sound levels from recordings suggests
there is no cause for a noise concern, it is possible a community
has risen up against an unfamiliar noise source that temporarily
upset the acoustic norms of the community.

Gathering sound data

Knowing how to best collect data is as important as knowing what
data to collect. Here are some best practices for gathering data
about sound issues and noise complaints:

Know your codes:

• Recording noise: Municipal codes often dictate how to
  record noise and often recommend the use of sound level meters to
  record a noise source in the community.




• Distance from noise: While local codes may require
  measuring the sound level a certain distance from the noise source,
  community noise complaints often originate from a resident who is
  at a different distance from the noise. For example, the codes may
  require that construction companies monitor the sound level of
  their activities at a distance of 50 feet, but neighborhood
  complaints may be collected from residents hundreds of feet away
  from the construction. In other cases, sound may travel a shorter
  distance than codes indicate due to natural barriers obscuring the
  noise, such as hills, dense foliage, or other commercial and
  industrial buildings. As a result, it’s good practice to
  collect acoustic measurements not only from the required locations
  according to code but also from where the complaints
  originated.

Measure sound in real-time:

• Continuous acoustic data: Because basic sound level
  meters often only save the overall sound level per increment of
  time, such as one-second increments, it’s wise to complement
  averaged sound-level meter readings with continuous acoustic data
  collected using traditional laboratory microphones or advanced
  sound level meters, which collect the actual sounds as heard in
  time. Stakeholders can use this real-time data to analyze the human
  perception-based characteristics of noise, including Zwicker
  parameters, that sound-level meters do not capture.

If your community noise complaint is delivered long after the
noise has ceased, making it difficult to collect new acoustic
recordings, it is possible to work with data collected during the
noise-generating activities — or even to work with no
acoustic data at all. For example, if a construction site only had
vibration data to work with, that could be leveraged to confirm
sound levels and model the likely propagation of sound over a
specific distance.

Science meets psychology: interpreting community noise
complaints

With sufficient data gathered — the contextual information
about the noise generation along with the loudness recorded by
sound level meters and lab microphones — we can assess the
legitimacy of the complaint by building a predictive model to
analyze community reaction to a given acoustic disturbance. These
models account for the duration, location, frequency, and loudness
of the noise, as well as the location of the community, to
approximate the seriousness of potential community reactions.

Predictive models play an essential role in analyzing community
noise complaints because the complaint may be driven by a small
number of highly annoyed residents. It’s often best practice to
de-escalate community reactions by acknowledging how the noise
exposures may have affected their quality of life while
interpreting such impacts considering the factual data.

The content of this article is intended to provide a general
guide to the subject matter. Specialist advice should be sought
about your specific circumstances.