Urban Noise Pollution through Combined Analysis of Sound Levels

Introduction

Noise pollution has been consolidated as one of the main urban environmental risks, ranking in Europe as the second most serious environmental factor, surpassed only by air pollution (Arregi et al., 2024). On a global scale, sustained exposure to environmental noise represents a critical environmental factor for public health. The World Health Organization estimates that at least one million healthy life years (DALYs) are lost annually in Europe due to traffic noise, mainly from annoyance and sleep disturbances, followed by cardiovascular impacts and cognitive effects in children. In particular, this loss includes approximately 61,000 years due to ischemic heart disease, 903,000 years due to sleep disturbance, and 587,000 years due to noise-related annoyance (World Health Organization, 2011).

In recent epidemiological studies, exposure to high noise levels has been linked to a 34% greater cardiovascular risk and a 12% higher mortality from cardiac causes, according to research conducted by Chen et al. (2023). Likewise, the most recent evidence shows that high levels of noise annoyance, especially during sleep, can increase the prevalence of cardiovascular diseases up to 32.6%, compared to 25.4% in individuals without such annoyance (Hahad et al., 2024). In turn, Argou-Cardozo and Zeidán-Chuliá (2018) demonstrated that noise induces biological stress responses and endothelial dysfunction, increasing the prevalence of hypertension and coronary heart disease.

Despite the strong evidence, studies in Latin America are scarce and present methodological limitations. Lercher (2019) noted that exposure–response relationships derived from the European context cannot be extrapolated without adjustments to Latin American cities, due to differences in infrastructure, mobility patterns, and the absence of effective regulations. Moreover, most research in the region has been restricted to instrumental measurements, without integrating citizen perception, a key variable to understanding the social and psychological dimension of noise (Ferraioli & Ballart, 2023; Jiao et al., 2025). Gaps in the literature are identified regarding studies that simultaneously combine objective and subjective data, particularly in historical heritage contexts such as the Historic Center of Lima. This absence limits the design of evidence-based public policies aimed at mitigating acoustic impacts on health and urban well-being.

Abancay Avenue, located in the Historic Center of Lima, is a road corridor characterized by heavy traffic and continuous commercial activity, becoming one of the city’s critical points of noise pollution. The present study gains relevance because it seeks to generate localized scientific evidence through the integration of instrumental measurements and citizen perception surveys, thereby providing key inputs for the design of acoustic mitigation strategies and sustainable urban policies in a high-value heritage environment.

Therefore, the following research question is formulated: How does urban noise pollution influence sound levels on Abancay Avenue in the Historic Center of Lima? To answer this question, the study proposes to analyze the influence of urban noise pollution on sound levels in Abancay Avenue, establishing the relationship between the instrumental data obtained through acoustic monitoring and the experiences reported by residents and passersby, with the purpose of providing scientific evidence to support the formulation of public policies and mitigation strategies aimed at improving environmental quality and urban well-being.

Methods

The study was developed under a quantitative approach, as it was based on the collection and analysis of objective data measured in decibels, complemented by subjective information obtained through structured surveys. The type of research was applied, since it aimed to generate evidence to guide decision-making in public policy and urban management. A non-experimental, cross-sectional, and descriptive-explanatory design was adopted, as the phenomena were observed in their natural context without manipulating variables, and relationships between acoustic measurements and citizen perception were analyzed. The level of research was correlational-explanatory, as it allowed the establishment of the relationship between recorded environmental noise levels and the experiences reported by citizens.

The study population consisted of people who circulate and carry out activities in the Historic Center of Lima, specifically along the analyzed road corridor. The sample comprised 392 participants, selected through non-probabilistic convenience sampling, and included pedestrians, formal and informal vendors, buyers, tourists, and visitors, with representation from different age and gender groups. This diversity ensured the inclusion of heterogeneous perceptions regarding environmental noise in the area.

Instrumental data collection was conducted using a type I integrating sound level meter (BSWA-308), calibrated according to IEC 61672-1:2013, ANSI S1.4-1983, and ANSI S1.43-1997 standards. Thirteen monitoring points were strategically distributed along the corridor, where minimum (LAFmin), maximum (LAFmax), peak (LAPeak), and continuous equivalent A-weighted (LAeq) values were recorded. Measurements were taken in two daily sessions (7:00–9:30 h and 17:00–19:30 h) over 14 consecutive days, reaching a total of 208 records.

Figure 1 shows the geographical location of the study area within the Historic Center of Lima, with emphasis on the Abancay Avenue Road corridor. The image illustrates, at the macro level, the position of Peru within the South American region; subsequently, the location of the city of Lima within the national context; and finally, the layout of the selected avenue, highlighted in red, which connects sectors of great commercial and urban transport relevance.

Figure 1. Location of the study area.

Avenida Abancay, Historic Center of Lima.

Figure 1 presents the geographical location of the study area at different spatial levels, from Peru’s position in South America to the detail of the Historic Center of Lima, where the analyzed avenue is outlined in red. This corridor constitutes a strategic urban axis as it connects residential and commercial sectors with high vehicular traffic density and pedestrian flow, which explains its critical condition in terms of noise pollution. Furthermore, its location within a heritage zone highlights the importance of the study, as it shows how the interaction between mobility, formal and informal commerce, and social dynamics generates sound levels that exceed environmental standards, shaping a representative scenario of contemporary urban challenges in Latin American cities.

Table 1 presents the characterization of the measurement points established along the analyzed road corridor, indicating the selected intersections and their geographic coordinates. The distribution of these points, spanning from Miguel Grau Avenue to Jirón Amazonas, made it possible to obtain a representative record of the spatial variability of noise levels across different sections of the roadway.

In Table 1, the location of the thirteen monitoring points is shown, which responded to technical and environmental criteria, prioritizing intersections with higher vehicular flow and the presence of formal and informal commerce, where the most intense acoustic activity is concentrated. Additionally, a strategic point was included in front of the Congress of the Republic, where an official environmental quality monitoring station is located, facilitating the validation and comparison of the data obtained. This selection ensures coverage of critical areas along the corridor and allows for the analysis of how urban dynamics influence the generation and propagation of noise.

Figure 2 shows the distribution of the thirteen noise measurement points established along the study corridor. The layout of these sites was strategically defined at intersections and sections with a high concentration of vehicular traffic and commercial activity, in order to obtain representative records of noise pollution in the area.

Figure 2. Distribution of the 13 noise measurement points along Avenida Abancay.

In Figure 2, the cartographic representation shows balanced coverage from the beginning of the corridor, at the intersection with Miguel Grau Avenue, to its final section near Jirón Amazonas. This distribution ensures data collection in critical areas characterized by the overlap of public transportation, formal and informal commerce, and high pedestrian flow. Likewise, the placement of points at both ends and in intermediate sectors allows for the comparison of sound pressure levels according to traffic intensity and the density of economic activities.

Regarding perceptual data collection, a structured questionnaire with five closed-ended questions was applied, aimed at identifying the perception of noise levels, the frequency of disturbances, the main sound sources, and the most critical times and days. The questionnaire was validated through expert judgment and demonstrated adequate internal consistency. It was administered in person and anonymously at the monitoring points, with the collaboration of the research team.

Finally, the data obtained were processed using SPSS statistical software version 26, applying descriptive analysis to characterize sound levels and population responses, as well as comparative analysis between objective records and perceptions. This methodological strategy enabled a comprehensive characterization of noise pollution, linking technical evidence with the social dimension of the phenomenon.

Results

The results obtained in the research allow for a comprehensive characterization of noise pollution in the study area. Based on instrumental measurements, it was evidenced that sound pressure levels exceeded the values established in current environmental regulations for commercial zones, with records reaching significantly high peaks during hours of heavier vehicular traffic. At the same time, survey data revealed that most participants perceive noise as a factor that affects quality of life, identifying motorized transport and informal commercial activity as the main sources of disturbance.

Figure 3 summarizes the main sources of urban noise identified in the study area, classified into six categories according to their origin: motorized transport, human movement, electromechanical sources, voice and instruments, other human activities, and natural sources.

Figure 3. Urban noise source categories in the study area.

Figure 3 illustrates a comprehensive systematization of the urban noise sources identified in the study area, grouped into six main categories: motorized transport, human movement, electromechanical sources, voice and instruments, other human activities, and natural sources. This typology reflects the complexity of the urban soundscape in densely populated contexts with heavy vehicular load. Motorized transport stands out as the predominant noise source, which is supported by scientific evidence. According to Miner et al. (2024), vehicles are the primary source of noise pollution in modern urban environments. Furthermore, McAlexander et al. (2015) demonstrated that street-level noise, mainly generated by vehicular traffic, contributes significantly estimated at 4% to individuals’ annual noise exposure.

The presence of advertising messages and commercial announcements, incorporated as an additional category in the diagram, highlights an emerging noise source impacting heritage and commercial areas. Recent studies in Ethiopia show that sound advertising amplifies the perception of annoyance in historic urban contexts with high commercial intensity (Bogale et al., 2022). Likewise, unamplified human expressions such as conversations, singing, or laughter form part of the everyday acoustic landscape and enrich urban vitality. However, their coexistence with dominant noise sources may contribute to perceptual acoustic saturation, aggravating pedestrians’ auditory experience. Mitchell et al. (2022) found that the presence of vehicular sources within a sound environment significantly increases the perception of annoyance, especially as the complexity of the soundscape intensifies.

Subsequently, Figure 4 shows the behavior of equivalent (LAeq), maximum (LAeqMax), and minimum (LAeqMin) environmental noise levels compared to the Environmental Quality Standard (EQS) for commercial zones during morning sessions across the different measurement points. The graphical representation makes it possible to identify variations between weekdays and weekends, evidencing the persistence of noise levels that exceed regulatory limits in all cases.

Figure 4. Variation of noise levels with respect to the environmental standard in the morning session.

Figure 4 summarizes the morning behavior (7:00–9:30 h) of equivalent (LAeq), maximum (LAeqMax), and minimum (LAeqMin) noise levels across the 13 monitoring points, compared with the environmental standard for commercial zones (EQS). On all days analyzed (Tuesday, Thursday, Saturday, and Sunday), a systematic exceedance of LAeq over the EQS was observed, with LAeqMax peaks close to or above 90 dB(A), indicating high and persistent exposure regardless of whether it was a weekday or weekend. This pattern is consistent with reports from road corridors in Latin America and Asia, where daytime LAeq values in commercial areas frequently exceed the thresholds recommended for health protection (Huang et al., 2021; Puyana-Romero et al., 2022).

In spatial terms, variability among points suggests the influence of intersections, bus stops, and traffic congestion hotspots; at the micro-urban level, these conditions explain abrupt increases in LAeqMax and the elevation of the baseline (LAeq). Street-level exposure studies demonstrate that traffic intensity and proximity to vehicular flows account for much of personal noise load, and they document expected differences between weekdays and weekends, consistent with the slight modulation observed in panels c–d (McAlexander et al., 2015). From the perspective of social response, the persistence of equivalent levels above the standard favors high annoyance and the risk of conflicts in road “hot-spots”; community assessments in Quito show that sections with elevated LAeq concentrate higher probabilities of annoyance and are prioritized for control. This finding supports that, even with hourly variations, corridors of high commercial activity sustain acoustic pressures capable of affecting perceived well-being (Puyana-Romero et al., 2022).

The public health relevance of these exceedances is consistent with robust epidemiological evidence: chronic exposure to transportation noise is associated with increased cardiovascular risk through mechanisms of oxidative stress, endothelial dysfunction, and autonomic activation; moreover, several observational studies and meta-analyses have linked traffic noise to higher prevalence of hypertension. The magnitude and continuity of the levels observed in the figure fall within the range of concern described by these reviews, reinforcing the need for mitigation measures (Babisch et al., 2014; Münzel et al., 2014).

Figure 5 shows the variation of environmental noise levels during the evening-night session (5:00–7:30 p.m.) at the thirteen monitoring points, considering equivalent (LAeq), maximum (LAeqMax), and minimum (LAeqMin) values in comparison with the environmental quality standard for commercial zones (EQS). The graphical representation highlights the persistence of elevated levels on both weekdays and weekends, with peaks that significantly exceed regulatory values.

Figure 5. Variation of noise levels in the evening-night session with respect to the environmental standard.

Figure 5 presents the evening-night variation of environmental noise (5:00–7:30 p.m.) at the thirteen measured points, showing average (LAeq), maximum (LAeqMax), and minimum (LAeqMin) values compared with the Environmental Quality Standard (EQS) for commercial zones. On all days, equivalent levels fluctuated above the regulatory limit, while maximum peaks reached values close to or exceeding 95 dB(A), indicating persistent and elevated exposure. This behavior is consistent with international records showing that equivalent noise levels during the late afternoon and evening in dense urban environments with high commercial and vehicular activity exceed recommended thresholds, even in the absence of heavy traffic (Quader et al., 2024), underscoring the importance of also addressing the effects of noise pollution outside typical working hours.

Furthermore, the literature establishes that nighttime noise levels below regulatory standards can still impact health, particularly by interfering with sleep and rest. Studies recommend nighttime limits of 40–55 dB(A), well below the observed records, noting that levels above 55 dB(A) are associated with a significant increase in the incidence of sleep disorders and chronic stress (Halonen et al., 2012; Nadine Nascimento, 2024). From a health perspective, the non-auditory effects of environmental noise include not only sleep disturbances but also increases in blood pressure, cognitive decline, and emotional disorders. Chronic exposure to noise outside working hours is associated with a higher disease burden and reduced overall well-being (Mucci et al., 2020).

Figure 6 shows the comparison of equivalent environmental noise levels (LAeq) recorded at the thirteen monitoring points during the morning (7:00–9:30 a.m.) and evening-night (5:00–7:30 p.m.) sessions, differentiated by day of the week. The graphical representation makes it possible to observe daily and hourly fluctuations, as well as the persistence of elevated values in both periods, evidencing the continuity of acoustic pressure in the study area.

Figure 6. Hourly and daily variation of equivalent noise levels.

Figure 6 shows the hourly and daily variation of equivalent noise levels (LAeq) at the monitoring points during the morning (7:00–9:30 a.m.) and evening-night (5:00–7:30 p.m.) sessions. The results reveal that, although fluctuations between days are observed, the levels remain persistently above the values recommended by environmental standards, indicating chronic exposure to urban noise in the study area. The persistence of these levels is consistent with the findings of Thompson et al. (2022), who demonstrated that daily exposure to urban noise is associated with cognitive decline and a higher risk of psychological stress, even when daily averages do not exceed critical limits.

From a cardiovascular perspective, D’Souza et al. (2021) confirmed in a cohort study that prolonged exposure to environmental noise is related to increased blood pressure and higher prevalence of resistant hypertension, reinforcing the importance of analyzing the temporal variability of noise and not only its peak values. Additionally, a recent meta-analysis by Chan et al. (2024) showed that personal exposure to noise at different times of the day directly affects sleep quality and increases the prevalence of fatigue, demonstrating that the accumulation of daily exposures generates a significant adverse effect on physical and mental well-being.

Figure 7 presents the comparison of equivalent environmental noise levels (LAeq) obtained during the morning (7:00–9:30 a.m.) and evening-night (5:00–7:30 p.m.) sessions across the thirteen monitoring points. The graph includes the linear regression adjustment for each period, allowing the identification of differentiated trends in the magnitude and distribution of sound pressure between both timeframes, evidencing that noise exposure is persistent and varies according to the urban dynamics of the day.

Figure 7. Comparison of equivalent noise levels between morning and evening-night sessions.

Figure 7 compares the equivalent noise levels (LAeq) between the morning and evening-night sessions across the 13 measurement points. Both series show a decreasing trend in sound levels along the route (approximate slopes of −0.20 dB per point), although evening-night values generally remain above morning values, revealing persistent exposure during hours of greater commercial activity and traffic. Evening-night dispersion is wider, with local minima (e.g., point 11) consistent with micro-environments of lower congestion or localized traffic control effects. This temporal pattern is consistent with high-impact evidence linking chronic exposure to transport noise with cardiovascular events and highlighting the critical role of the evening-night period due to its interference with rest and autonomic homeostasis. Large cohort studies show that road traffic noise is associated with a higher incidence of acute myocardial infarction and heart failure, even after adjusting for air pollutants, suggesting that noise acts as an independent risk factor in dense urban environments (Bai et al., 2020). Likewise, a national cohort study in healthcare professionals documented that long-term exposure to road traffic noise is associated with incident heart failure, reinforcing the relevance of sustained exposures such as those observed in the evening-night curve (Lim et al., 2021).

In terms of mechanisms, environmental noise, especially in the late afternoon and night, is described as triggering sympathetic activation, oxidative stress, endothelial dysfunction, and sleep disruption—plausible pathways linking continued exposure to hypertension, ischemic heart disease, and cerebrovascular events. The systematic difference between the two sessions in the figure is consistent with such time-of-day–dependent mechanisms (Münzel et al., 2014). Complementarily, longitudinal meta-analyses estimating cardiovascular risk increases per dB increment in traffic noise emphasize the need for targeted mitigation during periods of higher exposure (Münzel et al., 2024). Furthermore, studies conducted around airports confirm that areas with high noise levels present higher hospitalization and mortality rates from cardiovascular disease, supporting a preventive approach in urban corridors with elevated acoustic loads (Hansell et al., 2013).

Figure 8 summarizes the results of the survey applied to the population regarding the perception of environmental noise on the avenue studied. It represents men’s and women’s responses concerning the perceived intensity of noise, the frequency of annoyance, the main identified sources, and the times of greatest impact, allowing the variability of perceptions according to gender and exposure experience to be evidenced.

Figure 8. Population perception of environmental noise on the avenue.

Figure 8 reveals detailed patterns of noise perception among respondents, segmented by gender and key themes: noise intensity (from mild to extremely strong), frequency of annoyance, recognition of noise sources, and the most impactful time periods. A notable proportion of the population—more evident among women described noise as “very intense” or “extremely strong” and reported a high frequency of annoyance (“sometimes” or “always”), particularly due to the presence of motorized traffic as the dominant source. This finding aligns with high-impact studies: research anticipates that circadian rhythms and gender-related emotional regulation influence noise perception. Beheshti et al. (2019) documented that personality traits and gender affect intolerance to environmental noise, with women reporting greater annoyance at comparable auditory stimuli, especially when these interfere with sleep or low-arousal activities.

In addition, the elevated perception of annoyance suggests that noise exerts not only a physical burden but also a psychophysiological one. A study by Paiva Vianna et al. (2015), highly recognized in the field, found that environments with multiple urban noise sources generate high levels of annoyance and greater deterioration of well-being among the exposed population; traffic and commercial activity were the main drivers of this perception. From an occupational perspective, gender differences are also reinforced: according to Liu et al. (2025), occupational noise exposure is increasing for women in upper-middle-income regions, many of whom are entering traditionally male-dominated roles with higher environmental noise, which may intensify perceived annoyance and accumulated auditory burden.

Finally, the explicit recognition of traffic as the primary source of annoyance is supported by epidemiological studies that directly link residential vehicular noise with higher levels of self-reported annoyance, especially among women, as well as with adverse effects on quality of life. Banerjee (2013) reported that, in a population in India, annoyance levels increased significantly among women exposed to residential noise above 65 dB(A), with an odds ratio higher for women than for men.

Conclusions

The study fulfilled its objective of characterizing noise pollution on Abancay Avenue by integrating instrumental measurements and citizen perception. The results directly address the research question, demonstrating that noise levels systematically exceed the environmental standards established for commercial zones, both in the morning and in the evening-night sessions, and that the exposed population perceives this phenomenon as a nuisance factor that affects their quality of life.

Among the main findings, vehicular traffic and informal commercial activity were identified as the predominant sources of noise, with intensities reaching critical values during peak hours. Likewise, it was evidenced that population perception aligns with instrumental records, indicating greater impact during afternoons and evenings, and recognizing noise as an element that interferes with social interaction and urban well-being.

However, the research presented limitations associated with the temporal restriction of monitoring and the non-probabilistic selection of the population sample, which may limit the generalization of results to other sectors of the city. In addition, the lack of a longitudinal assessment prevented the analysis of the phenomenon’s evolution over longer periods and its cumulative impact on population health.

Based on these considerations, it is recommended that future research incorporate long-term measurements, personal exposure analyses using portable devices, and epidemiological studies that allow for more precise links between noise pollution and public health. Likewise, a future line of work is proposed through the application of predictive models that integrate urban, social, and environmental variables to guide mitigation strategies adapted to heritage and high-density urban contexts.