Urban Greenery and Perceived Noise | #UrbanNoise #GreenSpaces #RealTimeData #NoisePerception #InterpretableAI #EnvironmentalPsychology #SustainableCities

Evaluating the Restorative, Masking, and Congruency Effects of Greenery on Noise Perception across Urban Contexts

1. Introduction: Why Greenery and Noise Matter in Cities

Noise pollution is a critical urban challenge, affecting physical and mental health through disrupted sleep, increased stress, and reduced cognitive performance. At the same time, urban greenery is increasingly recognized not just for aesthetic and ecological benefits but also for its potential to buffer noise. However, research findings on this are inconsistent, suggesting that context may be the key to understanding the relationship between greenery and noise perception.


2. Two Competing Mechanisms: Restorative vs. Audio-Visual Congruency Effects

a) Restorative and Masking Effects

According to theories like Attention Restoration Theory (ART) and Stress Recovery Theory (SRT), natural environments—including greenery—can reduce stress and enhance focus. Greenery also contributes directly to sound masking, either by adding desirable sounds (e.g., rustling leaves, birdsong) or diffusing sound waves through its physical structure.

b) Audio-Visual Congruency Effect

Sometimes, greenery may increase noise annoyance, especially in settings where it creates mismatches between visual expectations and auditory realities. For example, people may expect peace in green areas and feel more disturbed by traffic noise that breaks this expectation. This incongruence can heighten noise sensitivity, especially if sound sources are hidden.

3. The Uncertain Geographic Context Problem (UGCoP)

The inconsistent findings in past studies may be due to the UGCoP, which highlights how people's reactions to environments depend on the varying places they occupy throughout the day—homes, workplaces, parks, and transit zones. Each of these settings comes with different expectations and tolerances for noise and greenery.

4. A Mobility-Based Solution: Real-Time Sensing and EMA

To address this, the study employed real-time environmental sensing and Ecological Momentary Assessment (EMA) to capture people’s experiences across different times and locations. Participants wore portable noise meters and GPS-enabled devices and reported their noise perceptions throughout the day. This approach allowed researchers to link objective exposure data (e.g., LAeq, NDVI) with subjective perception in dynamic geographic contexts.

5. Machine Learning Insights: Global vs. Local Effects

Using XGBoost and interpretable machine learning tools like Partial Dependence Plots (PDPs) and LIME, the study identified:

  • Global patterns, such as the nonlinear moderation of greenery on noise perception

  • Local variations, where the dominant mechanism (restorative or congruency) shifted depending on urban land use (e.g., commercial, industrial, residential zones)

6. Implications for Urban Design and Policy

  • Targeted greenery planning is needed: Not all green is good everywhere. Context matters.

  • In transport hubs, greenery may buffer sound better with minimal expectation mismatch.

  • In residential areas, visual access to noise sources might be more critical than planting more trees.

  • Policy should promote audio-visual harmony in urban space design, integrating natural sounds and green infrastructure thoughtfully.

7. Conclusion

This study confirms that the effect of greenery on noise perception is not one-size-fits-all. While restorative and masking effects are powerful, they can be overridden by contextual mismatches in expectations and environmental congruency. Through real-time, mobility-based methods and interpretable AI, the research bridges the gap between lab-based theory and real-world application—offering valuable insights for urban planners, public health officials, and environmental designers.

#UrbanNoise #GreenSpaces #RealTimeData #NoisePerception #InterpretableAI #EnvironmentalPsychology #SustainableCities

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