Investigating noise exposure in urban school environments using noise monitoring and spatial-mapping approach

1.1 Critical assessment of existing literature

This section critically examines recent research on the impact of noise on educational environments. Studies consistently show that ambient noise, particularly from transportation sources, significantly affects both teaching and learning. Studies highlighted the adverse effects of traffic noise on roadside classroom activities, highlighting its detrimental impact on student concentration and teacher communication [16], 17]. Further, few studies have identified transportation noise as a major environmental stressor in schools, with low-middle income countries having developing economy facing particularly severe exposures [18], 19]. Recent studies indicate a possible correlation among exposure of noise to child with mental health implication [20], 21]. However, the studies of Adbi et al. (2025) and Fretes and Palau (2025) has shown a strong effect of noise on cognition and learning outcomes. It can lead to overall decline in cognition as it may prohibit the speech intelligibility and cause distraction [22], 23].

Furthermore, research on tire pavement noise has shown a possible contribution to noise-induced annoyance, emphasizing the need for precise noise reduction technologies in pavement-based noise reduction [6]. Murphy et al. (2018) adopted a comprehensive analytical approach to noise exposure estimation, stressing the critical need for accurate assessment methodologies that can inform more effective noise mitigation strategies [24].

Significant concerns have also been raised about the specific design features in urban infrastructure that exacerbate noise issues in low-middle-income countries. Study underscores the importance of considering urban design elements in noise pollution management [25].

Advances in noise mapping technology have also been significant. Lan et al. (2020) introduced improvements that enhance the utility of noise maps as tools for visualizing and understanding the distribution and intensity of noise pollution [26]. These maps are crucial for informing policy decisions and urban planning strategies. Concurrent studies by Li et al. (2020), Lan and Cai (2021), and Hong et al. (2023) have explored the physiological impacts of traffic noise and the dynamic updating of noise maps, respectively, highlighting the evolving sophistication of noise assessment tools and their potential to influence public health and urban policy effectively [27], 28].

Despite significant advancements in noise assessment and mapping technologies, their application within educational settings in densely populated urban areas, particularly in developing countries, remains limited. Most existing studies have concentrated on broader urban or residential environments, offering limited insight into how these methodologies can be adapted to address the unique acoustic challenges of schools. The reviewed literature highlights the complex and multifaceted nature of noise pollution, yet the specific impacts of transportation-related noise on learning environments are still underexplored.

In particular, the Indian context presents distinctive challenges due to highly heterogeneous traffic conditions and densely built environments, where schools are often situated near major roadways. The effects of such diverse noise exposures on children’s cognitive performance and well-being have not been comprehensively investigated. This gap underscores the need for targeted, context-specific research employing advanced GIS-based noise mapping and socio-acoustical assessments to evaluate and mitigate traffic noise impacts in schools. Addressing this issue is crucial for informing evidence-based noise management policies and improving educational and health outcomes in urban learning environments.

2.1 Study area

The study was conducted in Surat City, located in the state of Gujarat, India, a region experiencing rapid development and urbanization. Covering an area of 474.2 km², Surat had a population of approximately 4.65 million as per the 2011 census, with a population density of 10,052 individuals per km². The city is a major commercial hub for textiles and diamonds and plays a significant role in India’s economic landscape. With geographical coordinates of 210°12′00.00″N and 72°52′00.00″E, the city is situated on western part of India.

Ten roadside schools were selected for this study based on preliminary noise exposure measurements conducted using sound level meters. The schools represent a range of exposure conditions such as high, moderate, and low noise zones, and were coded S1 to S10 in random order. Figure 1 illustrates the geographical distribution of the selected schools. Students from grades 7, 8, 9, and 11, with an average of 40 students per class, participated in the study.

Figure 1:

Geographic overview of the study area showing (a) location of Gujarat state within India, (b) the location of Surat city within Gujarat, and (c) the spatial distribution of the ten sampling sites (S1–S10) across Surat city.

The selected schools (S1–S10) exhibit diverse architectural configurations and environmental characteristics that influence on-site noise propagation. Schools such as S1, S2, and S3, located along arterial or sub-arterial roads with continuous vehicular movement, experience elevated noise intrusion due to open ventilation grills/vents, poorly sealed windows, and limited surrounding vegetation. In contrast, S4 and S7 benefit from relatively larger buffer zones, including playgrounds and parking areas, which offer partial attenuation of external noise.

Schools S5 showed elevated indoor noise levels due to its minimal setback from the road, combined with cemented flooring, metallic furniture, and reflective wall surfaces that increase reverberation. S6, situated close to a high-speed flyover and a signalized intersection, is exposed to higher tire-pavement noise, engine noise, and making it one of the more acoustically vulnerable sites. Schools S9 and S10, situated within large institutional campuses, benefit from substantial vegetation, restricted vehicle access, and greater separation between classroom blocks and nearby roads, resulting in comparatively lower ambient noise levels. S8 represents an intermediate condition, where moderate noise exposure persists despite the presence of a playground buffer.

Overall, the variations observed among schools highlight the critical influence of architectural form, material selection, and site layout on the transmission, reflection, and attenuation of traffic noise within educational environments.

2.2 Methodology

To comprehensively assess the impact of traffic noise on student well-being, this study employs a two-fold approach, a) Noise monitoring and mapping, conducted both inside classrooms and outside school boundaries to assess traffic noise propagation from adjacent roadways; and b) A socio-acoustical survey, administered using a 5-point Likert scale questionnaire. Prior consent was obtained from school authorities, students, and teachers, and participation was voluntary at all stages of survey. The questionnaire consisted of 10 items targeting specific health and behavioural parameters associated with noise exposure, including speech intelligibility, cognition, tinnitus, hearing impairment, annoyance, loss of concentration, mental distress, decrease in work efficiency, mind wandering, and psychological stress.

A-weighted sound levels were measured because A-weighting corresponds closely to human auditory perception. Although L_Aeq resembles L_eq, it incorporates a frequency weighting that aligns with the ear’s sensitivity across different frequencies. Sound level measurements were carried out using Kimo-dB300 (class-II) sound level meters (SLMs) with a 1-s logging interval. Each SLM was mounted on a tripod to ensure stability, programmed to record L_Aeq, L_Amax, L_Amin L₉₀, L₅₀, and L₁₀. Noise measurements were conducted simultaneously at the roadside along the school boundary and inside operational classrooms. All SLM adhered to ISO acoustic measurement guidelines. Instruments were positioned at an average ear height of seated students inside classrooms and oriented towards the dominant noise source while avoiding proximity to reflective surfaces.

The SLMs were placed 0.5–1 m from the adjacent walls and 1 m from the windows. Figure 2 illustrate all measurement points: ‘RT’ denotes roadside traffic noise monitoring locations, while C7, C8, C9, and C11 represent indoor classroom noise measurement positions. Indoor measurements were taken at a height of 1.2 m above the floor level, corresponding to the average ear height of a seated students, while RT measurements were performed at 1.5 m above ground and approximately 1.5 m from the school facade to capture direct traffic noise exposure.

Figure 2:

Schematic representation of sound level measurement locations at school S1 (a) plan view showing indoor classroom monitoring points (C7, C8, C9, C11) and the outdoor roadside traffic (RT) noise monitoring location (b) Side-view diagram illustrating microphone heights and horizontal distances from the school boundary wall and classroom facades. A similar measurement methodology was adopted for all surveyed schools.

The questionnaire survey focused on the perceived physical and psychological effects of acute and chronic noise exposure. Responses were collected on a five-point scale ranging from ’always’ to ‘never.’ Blank, unreadable, or incomplete responses were excluded from the analysis. Data were analysed using relative percentage frequency, with additional assessment of individual response patterns. Statistical analysis was conducted using SPSS software and noise-propagation maps were developed using SoundPLAN8.2.

Noise mapping was used to analyse traffic-noise propagation from the road to classroom areas. This helped to identify locations where noise exceeded acceptable limits and informed potential mitigation strategies such as optimized classroom placement, vegetative buffers, or installation of sound-absorbing materials. For the preparation of noise maps, the following assumptions were taken a) building’s reflection loss (dB): 1.00 (default value), b) walls were taken as 3 m in height, c) reflection loss = absorption loss = 1.00 dB, d) interpolation at 1 m height. Further, the CNOSSOS-EU (2021/2015) traffic noise prediction model was adopted for noise estimation.

3.1 Sound level analysis (inside and outside of the school campus)

The measured outdoor and indoor noise levels for all schools are presented in Figure 3 and Figure 4. In most of the cases, both roadside and classroom L_Aeq values exceeded WHO-recommended limits (50 dB for silence zones and 35 dB for classrooms). Roadside percent exceedance ranged from 15 % (S5) to 47 % (S4), while indoor exceedance ranged from 78 % (S4) to 107 % (S6). Noise Climate (NC), derived from L₁₀ and L₉₀, was highest at S4 (roadside) and S1 (classrooms), indicating substantial short-term fluctuations driven by honking and acceleration events. High TNI and NC values at S2, S4, and S6 further confirm the dominance of intermittent impulsive noise near main arterial roads. Conversely, lower NC values in S9 and S10 correspond to stable acoustic conditions associated with limited traffic movement and extensive vegetative cover.

Figure 3:

Comparison of observed roadside noise levels at ten schools (S1–S10) against the World Health Organization (WHO) recommended limit.

Figure 4:

Comparison of observed classroom noise levels at 10 schools (S1–S10) against the World Health Organization (WHO) recommended limit.

3.2 Noise maps

Figure 5 presents the noise propagation maps for each school. Schools S8, S9, and S10 exhibit a comparatively better acoustic environment than the others. S8 contains acoustic elements such as thick curtains and double-glazed window, while S9 and S10 are situated within well-established, low-traffic township areas. Lower population density, extensive vegetation acting as natural sound absorbers, and restrictions on vehicular entry likely contribute to the reduced noise levels around these schools [29], 30].

Figure 5:

Spatial noise propagation maps illustrating the predicted sound contours (45–75 dB) indicating noise levels around the school periphery for (a) S1 (b) S2 (c) S3 (d) S4 (e) S5 (f) S6 (g) S7 (h) S8 (i) S9 and (j) S10. Lower bands were omitted from the visualization to emphasize the observed noise gradients.

In contrast, S4 and S6 are located along the city’s main arterial roads and are exposed to consistently high noise levels. Their proximity to signalized intersections, commercial markets, and roadside vendors further amplifies the impact of engine idling, honking, and acceleration noise during traffic signal changes. Nearby shopkeepers also reported pronounced impulsive noise during the start-and-go phases of traffic.

Although S2 and S5 experience lower roadside noise, their indoor environments are comparatively boisterous. Indoor playground activity, high reverberation due to acoustically reflective surfaces, and poor classroom acoustics contribute to elevated indoor noise levels. Open vents on classroom walls and broken windowpanes also increase external noise intrusion [31], 32].

Overall, the comparative analysis shows that variations in noise exposure across schools are strongly influenced by architectural configuration and the surrounding environment. Schools adjacent to major arterial roads, lacking vegetative buffers, or having open vents and reflective interior materials (e.g., cemented floors, metallic furniture) recorded the highest noise levels. In contrast, schools with greater setback distances, enclosed facades, and dense greenery demonstrated substantially lower exposure. The noise-propagation maps further show that the geometry of the boundary wall and positioning of classroom blocks affect acoustic shielding. For instance, S1 and S3 show partial screening due to angled boundary walls and intermediate playground structures, whereas schools aligned parallel to the roadway with minimal facade offsets experience more direct noise transmission into classrooms.

3.3 Socio-acoustical survey

The socio-acoustical survey was conducted on 1,524 students, and the results are presented in Figure 6. More than 50 % of students reported feeling annoyed by external noise. Schools located along the main arterial roads experienced particularly high exposure to traffic noise, which contributes substantially to this annoyance. Noise-induced annoyance has been associated with difficulties in learning and concentration, impaired communication, emotional and psychological stress, and reduced academic performance [33], [34], [35], [36].

Figure 6:

Distribution of subjective responses from the socio-acoustical survey across the 10 considered schools. Each bar represents a survey question and the segments indicate the percentage of responses across different categories.

Nearly 80 % of the students reported experiencing disturbance, and more than 65 % reported a loss of concentration due to noise entering the classroom. Traffic-related noise was identified as the primary source, while indoor noise from other students and adjacent classrooms (as noted during verbal interviews) was considered comparatively less disruptive. Students also reported frequent problems related to speech intelligibility and work efficiency [37], 38]. In many cases, the teacher’s voice was not clearly audible, requiring repeated questioning and clarification, which lowered both student engagement and overall classroom efficiency.

Despite thorough investigation, this study has inherent limitations that highlight clear directions for future research. A uniform building reflection loss of 1.0 dB was applied within the acoustic model to maintain consistency across sites and enable planning-level comparisons. While suitable for our comparative objectives, this assumption may slightly overestimate reflected sound; therefore, future studies should consider integrating detailed, site-specific façade and material data. Moreover, the present study primarily addresses external traffic noise and does not evaluate internal acoustic parameters such as classroom reverberation time or the practical performance of specific noise-mitigation measures. Nonetheless, the findings establish an essential baseline for understanding noise conditions in school environments and provide a foundation for future research exploring architectural acoustics, material properties, and their influence on learning outcomes.