Percentile-level examination of ten acoustic indicators pinpoints urban noise factors affecting noise effects on pedestrians

Percentile-Level Examination of Ten Acoustic Indicators 1 Pinpoints Urban Noise Factors Affecting Noise Effects on Pedestrians 2 Juan Miguel Barrigón Morillasa, David Montes Gonzáleza,*, Rosendo Vílchez-Gómeza, 3 Guillermo Rey-Gozaloa 4 aLaboratorio de Acústica (Lambda), Departamento de Física Aplicada, Instituto 5 Universitario de Investigación para el Desarrollo Territorial Sostenible (INTERRA), 6 Escuela Politécnica, Universidad de Extremadura, Avda. de la Universidad, s/n, 10003 7 Cáceres, Spain. 8 *Corresponding author. Tel.: +34 927257195; fax: +34 927257203; E-mail address: 9 [email protected] (D. Montes González) 10 11 ABSTRACT 12 This study examines how different temporal acoustic percentiles can enhance the analysis 13 of urban noise effects on pedestrians. Traditional noise indicators, typically focusing on 14 energy averages, are expanded by considering statistical temporal structures to better 15 correlate with subjective noise effects. Data was gathered via simultaneous noise 16 measurements and pedestrian surveys across streets with different traffic conditions. 17 Noise indicators, including LAF, LAeq, LZeq, LCpeak, LAFmax, etc., were analysed at multiple 18 percentiles (L1, L5, L10, L50, L90, L95, L99). 19 Results indicated that temporal percentiles substantially improved the predictive power 20 for various noise effects. Among all percentiles analysed, L50 emerged as universally 21 effective, closely correlating with subjective measures such as irritability, conversation 22 interruption, and overall noise perception. Specific effects, such as startle and auditory 23 annoyance, correlated more strongly with background noise levels (LAF90, LAF95, LAF99). 24 Additionally, high-level noise indicators (LCpeak and LAFmax) showed improved correlation 25 with noise effects when considered through percentiles rather than conventional values. 26 The study suggests that temporal statistical analysis offers critical insight beyond 27 traditional energy-based noise measures. It further highlights the significance of 28 frequency weighting in noise assessment, advocating the use of Z or C weightings rather 29 than the common A weighting, especially for predicting irritability or startle. Ultimately, 30 incorporating temporal acoustic percentiles can substantially enhance the accuracy of 31 predicting how urban noise affects people's daily experiences. 32 33 Keywords: effects of noise, noise perception, noise annoyance, irritability, startle, 34 disturbance in spoken communication, environmental noise. 35 36 1. Introduction 37 Environmental noise is considered a major pollutant due to its negative impact, especially 38 on the population in urban environments (Fu et al., 2022) (Hahad et al., 2022) (Jimenez 39 et al., 2023) (Starke et al., 2023) and on both wildlife and territory in natural areas 40 (Shannon et al., 2016) (Kight and Swaddle, 2011) (Sánchez-Fernández et al., 2022) 41 (Bruschi et al., 2015). The fight against noise pollution is a major challenge for 42 contemporary society due to various noise sources, such as transportation, industrial 43 areas, construction, and leisure activities (Garg, 2022) (Montes González et al., 2024) 44 (Silva et al., 2021) (Ballesteros et al., 2014) and whose predominance depends on the type 45 of environment evaluated. Public administrations face complexity in decision-making 46 when adopting action plans for noise abatement, since it is necessary to harmonize the 47 different interests and uses of public space that may exist. In addition, it is essential to 48 take into consideration the characteristics and conditions of the environment in which the 49 noise mitigation measures would be implemented (Licitra et al., 2017) (Paschalidou et 50 al., 2019), as well as the different factors on which each noise source depends (Thompson, 51 2024) (Zhao et al., 2020) (Sánchez-Fernández et al., 2021). 52 The assessment of environmental noise exposure in the area where the action plans need 53 to be implemented seems to be the first logical step to take to determine the baseline 54 acoustic situation. In this regard, in situ measurements are a methodology that provides 55 fairly accurate and reliable results of the existing noise levels (Zagubień and Wolniewicz, 56 2024) (Vílchez-Gómez et al., 2023) (Quintero et al., 2021). However, if the area under 57 evaluation is large, such as an entire city, the costs associated with this methodology may 58 be a factor to be considered. It is common in this type of scenarios to use methodologies 59 that combine both measurements and acoustic simulations by means of computational 60 models, which with proper planning allow obtaining results of noise levels with a limited 61 uncertainty (Law et al., 2011) (Licitra and Memoli, 2008) (Barrigón Morillas et al., 2021). 62 However, studies based only on acoustic measurements have an important advantage over 63 simulations, such as the possibility of obtaining different types of more specific acoustic 64 variables such as indicators related to the maximum and minimum sound energy (Lmax, 65 Lmin), temporal noise indicators (LN), indicators related to the frequency spectrum (LZeq, 66 LAeq, LCeq, Leq20–200Hz, SIL, PSIL) (Maristany et al., 2016) (Swain et al., 2023) (Barrigón 67 Morillas et al., 2024a). 68 If a study of this type is to be carried out in an urban environment, it should be taken into 69 account in the design of the working methodology that, although there is a diversity of 70 noise sources, road traffic is the one to which a higher percentage of the population is 71 usually exposed (EEA, 2020) (Rey-Gozalo et al., 2022). Measurements to conduct a fully 72 experimental study or to validate the computational calculation model should be carried 73 out using a sampling method representative of the spatial and temporal variability of the 74 sound source (Quintero et al., 2019) (Brambilla et al., 2023) (Barrigón Morillas et al., 75 2024b). The multiple factors on which road traffic noise depends, such as vehicle flow, 76 speed, type of pavement, urban and architectural characteristics of the city and 77 environmental conditions, should also be considered in the planning of the study 78 (Guarnaccia et al., 2024) (Barrigón Morillas et al., 2022) (Ascari et al., 2023) (Sanchez 79 et al., 2016) (Barros et al., 2023) (Deng et al., 2025). In addition to the aforementioned 80 aspects, it is also important in the planning of the study to select the acoustic indicators 81 that need to be considered according to the objectives to be achieved. In this sense, sound 82 variables that allow considering the temporal, frequency and energy aspects of urban 83 noise can be useful. Some indicators commonly used in the scientific literature in acoustic 84 studies in cities are Leq, Lmin, Lmax, LN and Lpeak, to some of which frequency (A, C) and 85 time (S, F, I) weightings are applied (Tao et al., 2020) (Song et al., 2024) (Barrigon 86 Morillas et al., 2024a) (Paszkowski et al., 2018). Kasess et al. use certain percentiles (the 87 median and the value that was exceeded 5 % of the time) of some acoustical and 88 psychoacoustical indicators to explain the annoyance produced by noise generated by 89 heat pumps (Kasess et al., 2020). Chang et al. (2024), analysing the relationship between 90 acoustic indicators and the perception of noisiness in a hospital environment, found that 91 the L10 percentile of LAeq was the best predictor of perceived noisiness in waiting areas 92 used by staff in a children's hospital. Additionally, Fiebig and Sottek (2015) investigated 93 the relationship between perceived loudness and certain temporal characteristics of 94 sound. In their study, both peak and background loudness levels were found to 95 significantly contribute to overall loudness assessments. These studies highlight the need 96 to further explore the relationship between noise effects and the temporal characteristics 97 of the sound environment. Öhrström et al. (2006) used the LAeq,24h indicator to examine 98 the relationship between road traffic noise and general annoyance, indoor daytime 99 activities (such as communication, relaxation, concentration, and listening to radio or 100 television), and several health and well-being variables (e.g., feeling irritated or angry, 101 worried, and nervous). 102 In addition to conducting a study of urban noise by means of objective indicators, for 103 making decisions regarding noise mitigation plans it is also useful to assess the perception 104 of the effects of noise pollution by the inhabitants of the city (Li et al., 2025) (Lin et al., 105 2025) (Barros et al., 2024) (Morihara et al., 2022). This allows understanding of the 106 subjective perceptions of residents regarding noise impacts on their quality of life, 107 assessing aspects such as annoyance, satisfaction with the sound environment and the 108 possible disturbances of this pollutant agent in their everyday activities (Pasanen et al., 109 2025) (Köklü et al., 2025) (Li et al., 2022) (Myllyntausta et al., 2020). Although there are 110 different survey methodologies, conducting on-site surveys to pedestrians together with 111 the acoustic measurements in that street can be a very interesting technique, as the noise 112 indicators closely reflect the acoustic conditions in the time frame in which people answer 113 the questions. 114 This paper presents experimental research involving simultaneous on-site surveys and 115 noise measurements to study the relationships between some subjective variables 116 associated with effects of urban noise in pedestrians and some objective indicators related 117 to the temporal characteristics of sound environment. The primary scientific novelty of 118 this study is the introduction of a new approach using percentile levels of some traditional 119 sound indicators to study the relationship with noise effects. In Section 2, the 120 methodology used for simultaneous acoustic measurements and pedestrian surveys in 121 diverse urban environments is detailed. Section 3 presents and discusses the significant 122 findings related to the correlations between subjective noise effects and various acoustic 123 temporal percentiles, highlighting the effectiveness of non-standard acoustic indicators. 124 Finally, Section 4 summarizes the key conclusions derived from the analysis, 125 emphasizing the relevance of temporal noise characteristics and their implications for 126 predicting noise effects in urban settings. 127 128 2. Method 129 A study was conducted based on simultaneous in situ surveys and noise measurements in 130 the streets of Cáceres (Spain). A previous categorization of urban streets based on their 131 traffic circulation functionality (Barrigón Morillas et al., 2021) was used as a reference to 132 design the sampling method, ensuring variability in urban characteristics and traffic 133 flows. Objective and subjective variables were collected through measurements and 134 surveys at 29 randomly selected sampling points (Fig. 1) on urban streets classified into 135 different categories during daytime working hours. A random sample stratified by age 136 group and gender was taken with the aim of obtaining a sample with an age structure and 137 gender ratio similar to that of the city being evaluated (Barrigón Morillas et al., 2024a). 138 139 140 Figure 1. Sampling points in Cáceres (from Google Earth) 141 142 In situ noise measurements of 15-minute duration were conducted simultaneously with 143 the surveys, ensuring sufficient spatial separation between measurement and survey 144 points to prevent interference with recorded noise levels. A class 1 sound level meter-145 analyser was utilized to measure objective acoustic variables, alongside a class 1 sound 146 calibrator for verifying calibration accuracy before and after each measurement series. 147 Noise measurements adhered to the general guidelines provided by the ISO 1996-2 148 standard (ISO 1996-2, 2017) and methodologies described in recent literature (Zagubień 149 and Wolniewicz, 2021) (Montes González et al., 2020) (Mateus et al., 2015). The 150 microphone was positioned 1.5 m above ground level, 2 m from the nearest reflective 151 façades, and similarly distanced from the closest point of the primary noise source (road 152 traffic). 153 The survey comprised nine questions, each rated on an 11-point numerical scale ranging 154 from 0 (nothing or never) to 10 (totally or always), addressing the effects of urban noise 155 on pedestrians. The survey comprised nine questions, each rated on an 11-point numerical 156 scale ranging from 0 (nothing or never) to 10 (totally or always), addressing the effects 157 of urban noise on pedestrians. More detailed information on the survey is included in 158 Supplementary Material. In cases where signs of hearing impairment were identified 159 during the interview, the respondent’s data was excluded from the analysis. This survey 160 has been designed, pretested, and validated in previous studies (Rey Gozalo et al., 2017, 161 2018). The internal consistency of the items in this study shows a high Cronbach's alpha 162 (0.93). In addition, in the validation of the construct through factor analysis, a KMO of 163 0.97 and a principal component encompassing the different items with a percentage of 164 explained variance greater than 65% were obtained. 165 The subjective variables obtained from surveys and the objective variables recorded from 166 acoustic measurements are listed respectively in Table 1 and Table 2. Regarding 167 subjective variables, respondents were first asked to indicate the extent or frequency with 168 which environmental noise on the street caused them: a) irritability; b) startle; c) 169 annoyance in the ears; d) interrupting a conversation with someone nearby; e) raising the 170 volume of their voice to speak with someone nearby; f) interrupting a phone conversation; 171 and g) raising the volume of their voice on a phone conversation. Participants 172 subsequently rated (h) acoustic perception of the environment on that street, and (i) the 173 degree to which the noise annoyed them during the survey. The simultaneous timing of 174 the sound measurements and surveys, together with the interviewers' experience, were 175 decisive in controlling for confounding factors. 176 177 Table 1. Subjective variables registered in during the survey. 178 Subjective variables Meaning a) Irritability b) Startle c) Annoyance ears d) Interrupting conversation e) Raising volume f) Interrupting phone g) Raising phone h) Noisy street i) Annoyance 179 Table 2. Objective variables registered during the measurements and their percentiles 180 Objective variables Percentiles of objective variables LAF LAF1 LAF5 LAF10 LAF50 LAF90 LAF95 LAF99 LZeq LZeq1 LZeq5 LZeq10 LZeq50 LZeq90 LZeq95 LZeq99 LAeq LAeq1 LAeq5 LAeq10 LAeq50 LAeq90 LAeq95 LAeq99 LCeq LCeq1 LCeq5 LCeq10 LCeq50 LCeq90 LCeq95 LCeq99 LAIeq LAIeq1 LAIeq5 LAIeq10 LAIeq50 LAIeq90 LAIeq95 LAIeq99 Loudness Loudness1 Loudness5 Loudness10 Loudness50 Loudness90 Loudness95 Loudness99 Loudness level Loudness level1 Loudness level5 Loudness level10 Loudness level50 Loudness level90 Loudness level95 Loudness level99 LCpeak LCpeak1 LCpeak5 LCpeak10 LCpeak50 LCpeak90 LCpeak95 LCpeak99 LAFmax LAFmax1 LAFmax5 LAFmax10 LAFmax50 LAFmax90 LAFmax95 LAFmax99 LAFmin LAFmin1 LAFmin5 LAFmin10 LAFmin50 LAFmin90 LAFmin95 LAFmin99 181 An analysis was conducted to investigate the relationships between subjective variables 182 related to the perceived effects of noise on pedestrians (Table 1) and acoustic variables 183 reflecting the statistical temporal characteristics of urban noise environments (Table 2). 184 For this study, acoustic variables were divided into two groups. The first group included 185 standard acoustic variables, encompassing the percentiles traditionally employed in 186 environmental acoustics derived from instantaneous sound pressure levels with A and F 187 frequency weightings. The second group comprised what were called non-standard 188 acoustic variables. These were calculated as typical percentiles (L1, L5, L10, L50, L90, L95, 189 Notably, the correlation between the percentile structure for (b) startle and those for (c) 324 annoyance ears (p < 0.01), and especially (f) interrupting phone (p < 0.001), suggests that 325 these effects may be triggered by similar temporal characteristics of the urban noise 326 environment. This may indicate that the temporal patterns of noise causing startle also 327 contribute to ear discomfort or interfere with phone conversations. This finding is 328 consistent with the statistically significant relationship reported in Table 4 between ear 329 annoyance and phone call interruption (p < 0.05). 330 Remarkably, the correlation structure for the annoyance effect (i) shows no significant 331 similarity with those of the other effects, except for a single negative correlation with (c) 332 annoyance ears (p < 0.05). Two key conclusions can be drawn from this finding. First, it 333 suggests a substantial difference between the objective temporal features of the sound 334 environment responsible for the annoyance effect and those underlying the other eight 335 effects. Second, it may indicate that opposing temporal structures of the sound 336 environment underlie two distinct responses: the more physiological ear annoyance and 337 the more psychological perception of general annoyance from ambient noise. 338 339 3.2. Non-standard acoustic variables related to the statistical time structure of sound 340 This section introduces a novel approach to analysing the relationships between perceived 341 noise effects and sound indicators related to the statistical temporal variability of sound 342 levels. In urban noise studies, percentiles calculated from instantaneous Aand F-343 weighted sound pressure levels are typically used to characterize statistical temporal 344 variability. The new method proposed calculates standard percentiles (L1, L5, L10, L50, L90, 345 L95, L99) based on second-by-second recordings of several conventional acoustic 346 indicators: LZeq, LCeq, LAeq, LAIeq, Loudness, Loudness Level, LCpeak, LAFmax, and LAFmin. To 347 assess the relevance of statistical temporal variability in noise effect prediction, Pearson 348 correlation coefficients for these percentiles were compared with those obtained from the 349 indicators themselves (Barrigón Morillas et al., 2024a) and with standard Aand F-350 weighted percentiles (see Section 3.1). 351 352 For this analysis, the percentile with the highest regression performance was selected 353 (optimal percentile). Additionally, based on previous results from Section 3.1, the L50 354 percentile was used as a reference, since it is the one with which the best results are 355 generally achieved. Figure 3 presents correlation coefficients between various noise 356 effects (a-i) and the traditional indicators, including comparisons with the L50 percentile 357 and the optimal percentile. Table 5 summarizes explicitly which percentile is optimal for 358 each effect-indicator pair. 359 A detailed examination of the results presented in Figure 3 reveals several significant 360 findings: 361 - For the majority of the 81 studied effect-indicator pairs, at least one percentile 362 outperforms the traditional acoustic indicator. Only five pairs show no 363 improvement, and even then, differences are minimal (under 3%). These 364 exceptions involve the "noisy street" (effect h) with LZeq and LCeq, and 365 "annoyance" (effect i) with LAeq, LAIeq, and Loudness Level. These results 366 emphasize the importance of considering the temporal variability of sound in 367 urban noise assessment. 368 - Another noteworthy result concerns indicators associated with extreme sound 369 levels (LCpeak, LAFmax, LAFmin) which showed notably stronger correlations when 370 analysed through percentiles, compared to using the indicators directly, even for 371 those effects where the base indicators do not show significant relationships. 372 - The L50 percentile generally surpasses the base indicator and performed close to 373 optimal across indicators and effects, particularly for high-level indicators (LCpeak 374 and LAFmax) and for A-weighted or level-dependent weighted indicators. Although 375 for specific nose effects, like "annoyance" (effect i) or “annoyance ears” (effect 376 c), it performed slightly below the best percentile. These results support the use 377 of L50 as a reliable, generalized reference percentile. Studying other sound aspects, 378 similar results to those obtained in the present work on L50 primacy have been 379 published by several authors (Can and Gauvreau, 2015; Nilsson et al., 2007). 380 Other authors (Chang et al., 2024) find that L10 (not L50) may be a better indicator 381 to explain how noisy is perceived the waiting areas in a Chinese children's 382 hospital. In this case, it is not possible to assert the existence of discrepancies with 383 respect to the findings reported in this study, since Chang et al. (2024) does not 384 analyse L50 and in our work, for the LAF indicator, the L10 percentile is better 385 than the L90 percentile, as occurs in the work of Chang et al. (2024). 386 - Additionally, for all effects, except those related to telephone communication (f 387 and g), the best correlations are generally found with the percentiles associated to 388 Zor C-weighted indicators. This observation could be particularly significant 389 given the widespread use of A-weighted indicators in urban noise assessments. 390 This finding and the well-known limitation of telephone systems in reproducing 391 low-frequency sounds effectively (Jiang et al., 2022), show a coherence of the 392 results and reinforce their quality. Note that even for annoyance (i), which is the 393 only effect for which a clear improvement in the value of r was found in those 394 indicators not weighted Z or C when using traditional energy based indicators 395 (Barrigón Morillas et al., 2024a), when it is now analysed on the basis of the 396 percentiles of all the indicators, the highest correlation of annoyance with the Z 397 or C weighted indicator percentiles is achieved using LCpeak (r = 0.80), similar to 398 the best previous value obtained with LAIeq (r = 0.81) (Barrigón Morillas et al., 399 2024a).400 401 Figure 3. Correlation coefficients between the noise effects (a-i) with respect to the noise indicators (Barrigón Morillas et al., 2024a) and their 402 percentiles L50 and LXX (percentile with the higher value of r coefficient). 403 - Finally, it can be seen in Figure 3 that there are two groups of behaviours. A first 404 group for the Zor C-weighted indicators, and a second group for the rest of the 405 indicators. In each of these two groups, it can be said that the way in which the 406 indicator is weighted is more important than the indicator used, whether it is 407 obtained by means of an energy average or by means of a maximum or minimum 408 energy value. This finding may indicate that the ability of statistical time 409 indicators to explain the effects of noise is conditioned to a greater extent by the 410 weighting used than by whether the indicator is derived from an energy average 411 or is an indicator of maximum or minimum values. 412 Table 5. Number indicating the best percentile for each effect (a-i)/indicator pair. 413 Effect LAFx LAFMax LAFMin LCpeak LAIeq LAeq LCeq Loudness LoudLevel LZeq a) 10 10 50 50 10 50 50 5 50 50 b) 90 90 90 90 90 90 90 90 50 90 c) 95 95 95 95 95 95 95 95 95 95 d) 50 50 50 5 50 50 5 50 50 5 e) 50 50 50 10 50 50 5 50 50 5 f) 50 50 50 10 50 50 5 90 50 5 g) 50 50 50 10 50 50 5 50 50 5 h) 50 50 50 50 50 50 50 50 50 5 i) 5 1 1 5 1 5 5 1 1 5 414 Now, an analyse of results showed in table 5 reveals that: 415 - The noise effect being analysed is more influential than the indicator itself in 416 determining the most relevant percentile. For example, “startle” (effect c) is best 417 explained by L95 across all indicators; “annoyance ears” (effect b) by L90 (except 418 for Loudness Level, where L50 is better). In contrast, no single indicator 419 consistently determines the best percentile. The most stable combination is 420 Loudness Level with the L50 percentile. 421 - For indicators not Zor C-weighting, the best percentile for predicting the impact 422 of noise on spoken communication in urban settings (effects d to g) is L50. This 423 pattern is also observed when assessing perceived street noisiness also including 424 C-weighted indicators. Thus, across nine indicators, the results suggest a strong 425 link between the perceived noisiness of a street and the interference that noise 426 causes in verbal communication. 427 Table 5 and Fig. 3 also supports the conclusions drawn from Figure 2 and Table 2. This 428 is particularly significant as earlier conclusions (from Section 3.1) were based on a single 429 sound indicator whereas the current findings are supported by percentiles across nine 430 indicators. It can be observed that: 431 - For all indicators, the effects (b) startle and (c) annoyance ears are best explained 432 by percentiles associated with background noise levels. Several studies have 433 examined various aspects related to the startle effect, either in animals (Ison, J.R., 434 1979; Hoffman, H.S., et al., 1964; Flaten M.A. et al., 2005) or in humans (May, 435 D.N., 1971). The experimental conditions of these studies (laboratory settings that 436 do not include percentile-based acoustic indicators and that do not consider a 437 single general acoustic environment, but rather distinguish between a target noise 438 and a separate background noise) differ significantly from those adopted in the 439 present study. It is considered that there is complementarity between the results 440 shown in this work and those obtained in the cited bibliography, and it is believed 441 that, given the complex nature of psychoacoustic mechanisms and the different 442 factors that may be associated with noise perception, new research, with 443 multidisciplinary teams of specialists in acoustic engineering, neuropsychology 444 and otorhinolaryngology, are necessary to understand the relationship between 445 physical noise characteristics and noise effects occurrence. 446 - A notable difference is observed between the temporal characteristics associated 447 with effect (i) annoyance and those associated with the other effects with the 448 largest difference with effect (c) annoyance ears. 449 - Depending on the specific noise effect, the optimal percentile varies, and this 450 variation is consistently observed across all nine indicators. 451 - The 50th percentile appears most frequently and the Pearson correlation values 452 for L50 are generally close to those of the best percentile —with the largest 453 differences seen in effects (c) annoyance ears and (i) annoyance. These findings 454 (now with ten different indicators) allow to conclude that if one percentile must 455 be chosen to evaluate all effects, L50 is the most reliable. 456 As demonstrated by the results of this study, the use of indicators that define the temporal 457 statistical structure of sound environments generally improves the ability of sound 458 indicators to explain the effects of urban noise on pedestrians. Among them, the L50 459 indicator shows, on average, better performance than the rest of the percentiles. In 460 addition, in other studies, researchers have shown that L50 aligns more closely with 461 subjective assessments of sound quality and pleasantness, especially in urban contexts 462 (Can & Gauvreau, 2015; Can et al., 2016; Ricciardi et al., 2015). Its potential to better 463 reflect the typical auditory experience, suggests that L50 could be a useful indicator for 464 spatial characterisation and perceptual evaluation of acoustics environments, overcoming 465 to LAeq. Other authors have also shown that L50 may serve as a reliable indicator for the 466 continuous assessment of noise, due to its reduced sensitivity to anomalous events, an 467 aspect of particular importance given the growing relevance of dynamic noise mapping. 468 Therefore, the use of L50 in noise policy possibly can contribute to a more targeted and 469 effective noise management strategies. 470 All these results may indicate that, in addition to the energetic aspects normally 471 considered when relating noise effects to received doses, the statistical temporal structure 472 of noise may also play a critical role in the occurrence of such effects. Consequently, this 473 structure may be significant for predicting the effects that urban noise environments may 474 have on people. 475 Considering these findings, may be of interest that urban planners and policy makers 476 review current noise assessment protocols and mitigation strategies by incorporating 477 statistical temporal structures of acoustic indicators, such as temporal percentiles, into 478 both the diagnosis and monitoring phases. By integrating these acoustic descriptors into 479 urban noise models and action plans, decision-makers can more effectively target 480 interventions to enhance acoustic comfort and public well-being in diverse urban 481 environments. Finally, public communication strategies can be enhanced using 482 percentile-based noise maps that reflect actual perceptual impacts rather than solely 483 energetic averages, promoting more transparent and targeted decision-making. 484 485 4. Conclusions 486 This study demonstrates the significant role of the statistical temporal structure of urban 487 noise in explaining and predicting its effects on pedestrians. The following conclusions 488 are supported by the results obtained with both the percentiles of the traditional LAF sound 489 indicator and the percentiles of nine other indicators that are not traditionally used with 490 percentiles. The optimal percentile for describing the impact of noise varies depending 491 on the specific effect examined. Nevertheless, the L50 percentile consistently proved to be 492 the most robust overall indicator, displaying strong correlations across all subjective 493 variables and supporting the use of L50 as a reliable, generalized reference percentile. In 494 particular, it effectively captured the influence of noise on communication-related effects, 495 such as the interruption of conversations and the need to raise one’s voice, as well as the 496 general perception of a street being noisy. These findings suggest a close relationship 497 between perceived street noisiness and the degradation of spoken communication quality 498 due to urban noise exposure. 499 The results further indicate that there is a large difference between the objective 500 characteristics of the sound environment that cause the annoyance effect and those that 501 cause the other eight effects analysed. In fact, the discomfort in the ears, likely more 502 physiological, and the feeling of ambient annoyance, possibly more psychological, seem 503 to be caused by reversed temporal noise structures. These distinctions highlight the 504 complexity of human responses to urban noise and emphasize the limitations of 505 traditional energy-based indicators. In this regard, the dynamic variation of sound levels 506 over time emerges as a key explanatory factor for the manifestation and intensity of noise 507 effects. 508 Complementary analysis using non-standard acoustic indicators supports and extends 509 these conclusions. Percentiles derived from second-by-second recordings of nine 510 conventional indicators (spanning average energy, peak levels, and minimum levels) 511 yielded improved predictive capacity compared to the indicators themselves. This was 512 particularly evident for high-level indicators such as LCpeak and LAFmax, whose percentiles 513 showed markedly stronger correlations with all studied effects, even when the original 514 indicators were not significantly related. 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