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Airports are vital infrastructures that drive economic growth and connectivity, yet their environmental footprint, particularly in terms of noise pollution, poses persistent challenges for nearby communities. Aircraft noise has been linked to a range of negative outcomes, including sleep disturbance, elevated stress levels, and long-term cardiovascular effects. In rapidly urbanizing regions such as Singapore, where residential areas coexist in close proximity to major runways, understanding the spatial variation of environmental noise is essential for informed urban planning and community health management.
This study aims to evaluate the environmental noise disparity between a runway-adjacent zone and a nearby residential area using Smart Citizen Kit (SCK) sensors. By leveraging real-time environmental monitoring, we sought to (1) quantify differences in ambient sound levels during flight and non-flight periods, (2) assess the degree of continuous versus intermittent noise exposure, and (3) provide empirical data that can inform future discussions on sustainable airport expansion and residential zoning.
ProcedureThe first step of the project involved designing and fabricating a protective case for the Smart Citizen Kit to ensure it could remain outdoors for multiple days without damage. The case was split into a top shell and a bottom shell. The top shell includes ventilation and sensor exposure holes, allowing air, light, and sound to reach the sensors unobstructed, while the bottom shell features integrated hinge mounts for easy assembly and maintenance. The parts were exported as .stl files and 3D-printed using PLA or PETG, with standard layer height and moderate infill to ensure durability. After printing, the Smart Citizen Kit was inserted into the bottom shell, cables were threaded through the designated pass-through holes, and the top shell was securely attached using the hinges. This setup allowed for stable outdoor deployment while keeping the sensors fully exposed to environmental conditions.
Once the device was prepared, two measurement sites were identified: Nicole Drive, located near the runways serving Terminals 5 and 6, and Changi Village Hawker Centre, a well-known community hub representing residential conditions. These locations were chosen strategically to provide a contrast between direct runway-adjacent exposure and a more distant residential zone.
The Smart Citizen Kit was then deployed at each site and set to record data at five-minute intervals for approximately three days, slightly extended beyond 72 hours to ensure data authenticity. Each time window included decibel measurements as well as a categorical marker noting whether a flight was incoming, outgoing, or absent.After preparing the device, I identified two measurement sites: Nicole Drive, located near the runways serving Terminals 5 and 6, and Changi Village Hawker Centre, a well-known community hub representing residential conditions. These locations were chosen strategically to provide a contrast between direct runway-adjacent exposure and a more distant residential zone.Once deployed, the Smart Citizen Kit was set to record data at five-minute intervals for approximately three days, slightly extended beyond 72 hours to ensure data authenticity. Each time window included decibel measurements as well as a categorical marker noting whether a flight was incoming, outgoing, or absent.
From the various parameters collected, the dataset was organized primarily by timestamp and decibel levels. A categorical variable, flight_status, was subsequently added by cross-referencing measurement times with the official flight schedule. This allowed for clear differentiation between baseline environmental noise and flight-associated events. To systematically process and visualize the data, a Python script was used. The script first loaded the two CSV datasets, runway_noise.csv for Nicole Drive and residential_noise.csv for Changi Village. It then classified each measurement as either flight or non-flight based on the flight_status column. Average decibel levels were computed for each condition at both locations and combined into a single dataset for comparison. Finally, the script produced a bar chart illustrating the contrast between flight and non-flight periods for the runway-adjacent and residential areas.
The organized dataset revealed marked differences between the two sites. At Nicole Drive, noise levels rarely dropped below 60 dB due to continuous road traffic, with non-flight readings clustering between 90 and 100 dB. During aircraft takeoffs or landings, noise levels spiked dramatically, averaging around 120 dB and occasionally peaking higher. By contrast, Changi Village Hawker Centre presented a much quieter baseline environment, typically between 40 and 50 dB during non-flight periods. Occasional outliers occurred when community-generated sounds such as hawker stall operations or crowd activity raised readings into the 60–70 dB range. However, during flight events, decibel levels consistently climbed to around 80 dB, a sharp increase compared to the baseline, though still significantly lower than runway-adjacent conditions.
To reproduce these results, users simply need to ensure that the CSV files are in the same directory as the Python script, and that Python along with the pandas and matplotlib libraries are installed. Running the script will automatically generate the bar chart that highlights the average decibel levels during flight versus non-flight periods, clearly demonstrating the differences in noise exposure between the runway-adjacent and residential sites.
AnalysisTo systematically interpret these findings, the dataset was separated into flight and non-flight conditions, and the average decibel levels were compared. The bar graph produced from this analysis highlighted striking contrasts. At Nicole Drive, non-flight periods were already loud, averaging close to 95 dB, but flight events pushed the mean to approximately 120 dB. This confirms that areas adjacent to the runway experience extreme noise levels not only from aircraft but also from the background traffic that supports airport operations. At Changi Village, the difference between flight and non-flight conditions was proportionally larger. Although the baseline of 45 dB was relatively quiet, flights increased average sound levels to around 80 dB, nearly doubling perceived loudness. This suggests that while residential zones are not constantly exposed to high-intensity noise, flight events produce acute disturbances that may disrupt community life. The comparison of both sites demonstrates that the runway area suffers from chronic high noise exposure, whereas the residential area experiences intermittent but still significant spikes.
ConclusionThis study demonstrates the effectiveness of using low-cost sensors such as the Smart Citizen Kit to measure and analyze environmental noise around airports. The findings confirm that runway-adjacent areas endure extreme and continuous noise levels, while residential zones, though generally quieter, are not insulated from the disruptive effects of aircraft activity. The deployment of the SCK in both Nicole Drive and Changi Village highlighted not only the direct impact of aircraft noise but also the contribution of other sources such as road traffic and community activity. These results carry important implications for urban planning, public health, and airport management, especially as Changi Airport continues to expand with new runways and terminals. Future research could expand the monitoring duration, include additional residential neighborhoods, and incorporate surveys of community perception to connect objective noise measurements with lived experience.
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