How data analysis can help to reduce noise pollution?

 

What is noise pollution?

Sound is a natural part of our surroundings. But when sounds become unwanted and loud, they can turn into noise pollution. Noise pollution is considered to be any unwanted or disturbing sound that affects the health and well-being of humans and other organisms.

Sound is measured in decibels. Noise is considered to be at an acceptable level if they are between 40 and 60 decibels, or match the ambient background noise — whichever is higher. Any sound above acceptable levels is generally considered noise pollution and sounds that reach 85 decibels or higher can harm a person’s ears.

Examples of typical noises include city traffic (70 decibels), lawnmowers (90 decibels), subway trains (90 to 115 decibels), and car horns (110 decibels). Other major contributors to urban noise include loud music and the construction of city streets and buildings.

How data analysis can help to reduce noise pollution?

Data analysis can play a crucial role in addressing and mitigating noise pollution. Here are several ways data analysis can help in saving noise pollution:

1.     Identifying noise sources: Data analysis can help identify the major sources of noise pollution in a given area. By analyzing data from various sensors and monitoring devices, such as sound level meters or noise monitoring networks, it becomes possible to pinpoint the specific locations, times, and activities that contribute to excessive noise levels.

2.     Real-time monitoring: Continuous monitoring of noise levels through sensors placed strategically in urban areas can provide real-time data. This data can be analyzed to detect patterns, peak noise times, and areas with consistently high noise levels. With this information, authorities can take immediate action to address the sources of noise and implement measures to reduce it.

3.     Noise mapping: Data analysis can be used to create noise maps, which visually represent noise levels across different regions. By collecting noise data from multiple sources and analyzing it, noise maps can be generated to identify areas with high noise exposure. This information can help urban planners, architects, and policymakers make informed decisions regarding the placement of buildings, transportation routes, or noise barriers to minimize noise impact on communities.

4.     Predictive modeling: Data analysis can enable the development of predictive models that forecast future noise levels based on various factors such as traffic patterns, construction activities, and population density. By simulating different scenarios and assessing their potential noise impacts, urban planners can proactively design noise control measures to prevent or minimize noise pollution before it occurs.

5.     Noise regulation enforcement: Data analysis can facilitate the enforcement of noise regulations by monitoring and analyzing noise data collected from various sources, such as citizen reports, sensors, or complaint hotlines. By identifying areas or individuals consistently violating noise regulations, authorities can take appropriate actions, such as issuing fines or implementing noise reduction measures.

6.     Public awareness and engagement: Data analysis can help raise public awareness about noise pollution by visualizing and presenting the collected data in accessible formats. By providing individuals and communities with information on noise levels, their sources, and the associated health impacts, people can make informed decisions and actively participate in noise reduction initiatives.

 

Conclusion:

Identification of noise sources, real-time monitoring, noise mapping, predictive modeling, execution of noise regulations, and public involvement are all made possible through data analysis. Policymakers, urban planners, and communities may take targeted efforts to reduce noise pollution and create calmer, healthier surroundings by utilizing data-driven insights.

 

References:

https://medium.com/cochl/tackling-noise-pollution-with-machine-listening-45660b3c5c42

Aniket Shukla

ISME Student Doing an internship with Hunnarvi under the guidance of nanobi data and analytics. Views are personal.

  # data analysis in noise# analytics #nanobi #hunnarvi

 

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