#
A Method to Estimate Students’ Exposure to Road Traffic Noise Events

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## Abstract

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_{Aeq}) and students’ cognitive impairment. Annoyance is frequently related to the effect of short-duration noise events characterized by high sound pressure levels, such as those due to aircraft fly-over and pass-by of buses, heavy trucks, motorcycles, or street sweepers. These noise events are often described, over specific measurement periods, in terms of maximum A-weighted sound pressure level, L

_{Amax}, or statistical levels, such as L

_{A1}or L

_{A10}. This aspect is not considered in the noise maps drawn in accordance with the European Environmental Noise Directive, as they provide the L

_{Aeq}only, determined over day, evening, and night periods. In this paper, students’ exposure to road traffic noise is analyzed by means of regression equations obtained by the authors between L

_{Aeq}and A-weighted maximum and statistical levels due to road traffic noise. The traffic noise of 28 urban streets was monitored during the opening period of Italian schools. A method is described to estimate students’ exposure to noise from data made available on noise maps by the municipalities of metropolitan areas. The application of this method to the case study of Florence shows that almost 60% of students from municipal primary and lower secondary schools could be exposed to the maximum sound pressure level (SPL) inside the classroom greater than 55 dB(A) every hour, probably exceeding the typical background noise in classrooms by more than 10 dB.

## 1. Introduction

_{Aeq}(or L

_{dn}), maximum (L

_{Amax}) or statistical levels (L

_{A1}or L

_{A10}) are related to each other. Lercher et al. [15] studied the reaction to noise in a group of primary school children selected from a large, representative sample of children living in the lower Inn Valley of Tyrol, Austria. The noise sources were rail and road traffic and one-half of the sample lived in quiet neighborhoods (L

_{A1}= 57 dB(A) and L

_{dn}= 46 dB(A)), while the remaining half lived in high noise areas (L

_{A1}= 74 dB(A) and L

_{dn}= 62 dB(A)). The most important conclusion was that even relatively modest-level exposures to noise may have detrimental effects on the cognitive systems of young children. Thus, the sound events characterized by short-duration high levels, such as the exposure to medium or high L

_{Aeq}levels in at-school and out-of-school environments, may be important.

_{Aeq}level equal to 42.5 dB(A), and further, language-based abilities, such as reading, understanding, and recalling, were the most vulnerable of the noise-sensitive cognitive functions.

_{Aeq}, L

_{Amax}, L

_{A90}levels and the score obtained by children in aptitude tests. They found that the children’s levels of annoyance were related to the maximum noise levels recorded outside the schools. Moreover, there was a hierarchy of sounds that was found to be annoying, since trains, motorbikes, trucks and sirens were rated as the most annoying, while trees were rated as the least annoying. They also found that younger primary school children are more affected by ambient and background levels of external noise, while the performance of older primary school children is more closely related to maximum noise levels. This suggests that the performance of older children (11 years old) is affected by the noise of individual events such as sirens, buses, street sweepers, lorries, or motorbikes passing the schools. Outdoor noise levels are related to indoor noise through facade sound insulation and indoor reverberation time, as described by the standard ISO 12354-3 [19]. In many countries, the acoustic performances of school facades are monitored very strictly to guarantee a comfortable acoustic indoor environment. An overview of the limit values for acoustic performances of school facades in some European and South American countries is given in [20].

_{AFmax}(interval) − L

_{Aeq}(interval) > 15 dB in the night period. Another study [31] shows that maximum levels due to vehicle pass-by (L

_{AFmax}) have a very broadened frequency distribution for each vehicle class (passenger cars, non-articulated trucks, articulated trucks, motorcycles). In particular, the standard deviation has its minimum value for cars passing at 80 km/h (sd 3.5 dB) and the maximum value for articulated trucks travelling at 100 km/h (sd 9.0 dB), with a significant overlapping of the distributions of each vehicle class.

## 2. Material and Methods

- selection of the appropriate noise indicators to describe the effect of the short-duration noise events (L
_{Amax}and L_{A1}); - analysis of the correlation between these indicators and the equivalent levels L
_{den}and L_{d}used in the Environmental Noise Directive (END) maps [32], available for each town with more than 100,000 inhabitants (2nd phase of END implementation); - analysis of the methodology used to calculate the noise levels in the END maps (with or without inclusion of sound reflection by the facades) and hypothesis for the calculation of noise levels at facades not directly exposed to traffic (internal courts, secondary roads, etc., for which facade level values are not shown on the maps);
- methodology to estimate the indoor sound levels in classrooms;
- methodology to determine the number of students involved for each school.

#### 2.1. Selection of Acoustic Indicators Used to Describe Short-Duration Noise Events

_{Amax}, and the statistical level, L

_{A1}, have been considered. Indeed, these two parameters are of interest for the analysis of acoustic annoyance in schools and also in other types of environment. For instance, L

_{Amax}was used in some studies to analyze the effect on children’s performances at school. Shield and Dockrell [9] found that L

_{Amax}showed the highest correlation with school performances (measured as standardized assessment test scores) of children aged 7 and 11 years. However, as L

_{Amax}represents the SPL of a single noise event that occurs once during the measurement interval, it could be inappropriate to correlate this descriptor with students’ performance and annoyance.

_{A1}is the SPL exceeded for 1% of the measurement time. With reference to time intervals of 1 hour, L

_{A1}is the SPL exceeded for a total duration of 36 s, and, therefore, it may be more properly associated with the acoustic annoyance than L

_{Amax}.

_{Amax}and L

_{A1}depend on the time interval of the measurement. Indeed, L

_{Amax}usually increases as the time interval of the measurement is larger, while the statistical level L

_{A1}is less dependent on this interval.

_{Amax}(a) and L

_{A1}(b) and L

_{Aeq}measured in a single street at different time intervals (1 h, 10 min and 1 min). L

_{A1}is slightly influenced by the time interval duration, especially for L

_{Aeq}values above 60 dB(A), while L

_{Amax}is strongly influenced by the time interval length and shows greater values when the time interval is longest (1 h).

- this time interval is the basic unit of lesson length;
- the typical sampling duration of road traffic noise measurements is 1 h;
- in Italy, the attention environmental noise values are expressed as L
_{Aeq}levels, referring to the time of 1 h [33]; - the highest value of correlation R
^{2}between L_{A1}and L_{Aeq}is for this time interval (Figure 1b).

#### 2.2. Noise Level at School Facades: Correlation between L_{den}, Day-Time L_{aeqd} Level and School-Time L_{aeq,8–17h} Level

_{Aeq}from about 60 to 75 dB(A).

_{Amax}, L

_{A1}, and L

_{Aeq}have been analyzed with reference to the day-time period as defined in Italy, from 6 to 20 h [34], in accordance with the European Environmental Noise Directive [32].

_{den}for road traffic noise. Thus, in order to use this large amount of data, it would be helpful to establish a relationship between L

_{den}and the hourly equivalent level, L

_{Aeqh}, used in this study.

_{Aeqh}, taken in this study, is formed of 768 values. To obtain a statistically more robust relationship, another larger dataset was referred to, formed of 26,808 hourly L

_{Aeqh}values (1117 time series of 24 h each) collected by continuous monitoring of urban road traffic noise in 38 Italian towns of different sizes.

_{Aeqd,6–20h}can be considered.

_{den}and L

_{Aeqd,6–20h}provides Equation (1) which gives an estimate of L

_{Aeqd}from L

_{den}with a standard deviation of the error of ±1.89 dB and a coefficient of determination R

^{2}= 0.900 as shown in Figure 2a, where the prediction bands at the 95% confidence level are also plotted (blue lines). The median value of the error is −0.1 dB and for 55.8% of the observations in the dataset, the error is within the range ±1.5 dB (orange lines in Figure 2a).

_{Aeq,8–17h}equivalent level from L

_{den}with a standard deviation of the error of ±2.05 dB and a coefficient of determination R

^{2}= 0.884, as shown in Figure 2b, where the prediction bands at the 95% confidence level are also plotted (blue lines). The median value of the error is −0.1 dB and for 53.8% of the observations in the dataset, the error is within the range ±1.5 dB (orange lines in Figure 2b).

_{den}is determined for the incident sound; that is, the reflection from the back facade is excluded.

_{aeqd}, available in some noise maps refers to the period 6–22 h and includes the sound reflection from the back facade. Thus, an additional equation has been determined by linear fitting in order to provide the estimate of the L

_{Aeq,8–17h}equivalent level from L

_{Aeqd,6–22h}. The results are plotted in Figure 2c and the equation is as follows:

_{Aeq,8–17h}with a standard deviation of the error of ±0.5 dB and a coefficient of determination R

^{2}= 0.993 as shown in Figure 2c, where the prediction bands at the 95% confidence level are also plotted (blue lines). The median value of the error is 0.0 dB and for 98.5% of the observations in the dataset, the error is within the range ±1.5 dB.

#### 2.3. Environmental Noise Directive Maps

_{den}is calculated at 4 m above the ground without including the reflection of the building facade, so as to consider only incident noise in agreement with the END. Therefore, the effective facade sound level, i.e., including the reflection, can be obtained by adding 3 dB to the values calculated with Equation (1) or Equation (2); this is the sound level that must be used with the standardized facade level difference to estimate the indoor sound level (see Section 2.4).

_{den}on the different facades of the school could be evaluated by interpolating the maps.

_{Aeqd,6–22h}) on the most exposed building facade (Figure 3). The latter is calculated at 4 m above the ground, as for L

_{den}, but it includes the facade reflection; therefore, Equation (3) can be directly used for this indicator without adding 3 dB as is the case of Equation (1) and Equation (2).

_{Aeqd,6–22h}values derived from the END map of Florence in Equation (3).

#### 2.4. Indoor Noise Level Estimation

_{2A}, due to sound coming from outdoors can be obtained by means of Equation (4), based on annex E of EN ISO 12354-3 [19].

_{1,2mA}is the A weighted outdoor sound pressure level in front of the facade (dB) as obtained from noise maps and referring to the time interval of 6–20 h or 8–17 h by means of Equation (1), Equation (2) or Equation (3);

_{2m,nT,w}is the standardized facade level difference of the school measured according to ISO 16283-3 [36] or estimated according to EN ISO 12354-3 [19] (dB);

_{tr}is the spectrum adaptation term for traffic noise as described by ISO 717-1 [37] (dB);

_{0}is the reference reverberation time (0.5 s).

_{2}) may be considered as the unoccupied ambient noise level due to traffic noise.

_{2m,nT,w}= 31.1 (dB); c

_{tr}= −2.1 (dB); T

_{(500–1000 Hz)}= 1.3 (s)

_{(500–1000 Hz)}is the average value of the reverberation time between the octave bands of 500 and 1000 Hz.

#### 2.5. Determination of the Number of Students Potentially Involved for Each School

## 3. Results

- determination of the correlation both between L
_{den}and day-time equivalent level L_{Aeq,6–22h}and between L_{den}and the hourly values of the selected descriptors of noise events (L_{Amax}and L_{A1}) due to traffic noise; - determination of the number of students exposed to specific intervals of noise levels; these results, affected by a greater uncertainty, refer to the case study of the municipality of Florence and must be considered as an application example of the proposed methodology.

#### 3.1. Correlation between Different Indicators

_{Amax}and L

_{A1}versus the corresponding values of L

_{den}for the 28 urban streets considered (data matrix formed of three variables (columns) and 28 roads × 14 h = 392 rows).

_{A1}and L

_{den}is higher (R

^{2}= 0.661) than that observed for L

_{Amax}(R

^{2}= 0.207) due to a greater dispersion of data for the latter. Indeed, L

_{Amax}measured in a time interval of 1 h may be influenced by unusual events, such as very noisy vehicles or sirens. However, these events are often present in traffic noise and it would be inappropriate to exclude them from the analysis. The difference between the regression slopes of L

_{Amax}and L

_{A1}is statistically not significant at the 95% confidence level (p-value = 0.725).

_{Amax}and and ±2.1 dB for L

_{A1}, whereas the median value of the error is 0.3 and 0.2 dB, respectively. The percentage of errors (predicted–observed values) within the range ±1.5 dB is 21.4% for L

_{Amax}and 54.3% for L

_{A1}. The range of ±1.5 dB has been chosen because its width (3 dB) corresponds to a doubling of sound energy.

_{Amax}and L

_{A1}versus the corresponding values of L

_{Aeq,6–22h}(data matrix formed of three columns and 28 roads × 16 h = 448 rows), as shown in Figure 7. The correlation between hourly L

_{A1}values and L

_{Aeq,6–22h}is much higher (R

^{2}= 0.688) than that observed for L

_{Amax}(R

^{2}= 0.223), while the difference between the regression slopes of L

_{Amax}and L

_{A1}is statistically not significant at the 95% confidence level (p-value = 0.523).

_{Amax}and ±2.0 dB for L

_{A1}, whereas the median value of the error is 0.3 and 0.2 dB respectively. The percentage of errors (predicted–observed values) within the range ±1.5 dB is 19.6% for L

_{Amax}and 60.5% for L

_{A1}.

_{Amax}and L

_{A1}versus the corresponding values of L

_{Aeq,6–22h}(data matrix formed of three columns and 28 roads × 9 h = 252 rows). Again, the correlation between hourly L

_{A1}values and L

_{Aeq,6–22h}is much higher (R

^{2}= 0.737) than that observed for L

_{Amax}(R

^{2}= 0.261), while the difference between the regression slopes of L

_{Amax}and L

_{A1}is statistically not significant at the 95% confidence level (p-value = 0.607). The standard deviation of the errors (predicted–observed values) is 5.2 dB for L

_{Amax}and ±1.7 dB for L

_{A1}, whereas the median value of the error is 0.1 and 0.2 dB, respectively. The percentage of errors within the range ±1.5 dB is 21.4% for L

_{Amax}and 61.1% for L

_{A1}.

_{A1}values within the opening hours of schools (8–17 h) from the day-time L

_{Aeq}, in the period 6–22 h according to the Italian legislation, shows the best performance, with 61.1% being the percentage of errors within the range ±1.5 dB. This percentage is reduced to 21.4% for hourly L

_{Amax}values due to greater variability of data, and it is equal to that observed for the regression between L

_{den}and the hourly L

_{Amax}values in the day-time period 6–20 h.

#### 3.2. Determination of the Students’ Exposure to Road Traffic Noise: Application to the Case Study

_{Aeqd,6–22h}) for each facade of all the schools has been converted into the equivalent SPL corresponding to the school hours (L

_{Aeq,8–17h}) by means of Equation (3). Then, these values have been associated with the number of students of each school and divided into intervals of 5 dB, from 55 to 80 dB(A) of the facade SPL, according to the SPL intervals of noise maps. From the facade SPL given by Equation (3), the indoor value of the SPL has been calculated by means of Equation (4), considering the same facade properties and reverberation time for each school by means of Equation (5). This procedure clearly produces an approximation, but it was not possible to obtain the actual value of facade sound insulation and of reverberation time for each classroom. Indeed, the period of construction was known for each school and a tentative investigation was carried out to correlate the period of construction to the predicted facade sound insulation. In any case, the actual facade sound insulation given by a facade is due to many factors, such as the maintenance of the junctions and the sealings of the windows, that can vary not only from one school to another, but also from one classroom to the other.

_{Aeq,8–17h}) and of the indoor SPL for all the students of the primary and lower secondary schools of Florence.

_{Amax}due to road traffic noise inside the classroom greater than 55 dB(A).

_{A1}descriptor, about 45% of students are exposed to hourly levels greater than 45 dB(A).

## 4. Discussion

_{Amax}, or by means of statistical descriptors, such as L

_{A1}. Even so, without the use of long-term measurements, the values of these descriptors are not known for a specific street, since the noise maps drawn by the municipalities for the urban agglomerations show only the values of L

_{den}(or also the facade L

_{Aeqd,6–22h}level in the case of some Italian maps) for the road traffic noise. Thus, with the aim of better characterizing the acoustic annoyance at school, it is important to define a correlation between the values of L

_{den}and the corresponding values of L

_{Amax}and L

_{A1}.

_{den}and L

_{Aeq,6–22h}has been performed versus the hourly values of L

_{Amax}and L

_{A1}for the periods 6–20 h (L

_{den}), 6–22 h, and school opening hours 8–17 h (L

_{Aeq,6–22h}). At 28 urban roads, road traffic noise was monitored for at least one day and hourly values of equivalent, maximum, and statistical levels were measured for each of them. The time interval of one hour has been chosen for the analysis of each descriptor because it corresponds to the length of one lesson in schools and to the typical sampling duration of road traffic noise measurements. Moreover, the better correlation between L

_{A1}and L

_{Aeq}has been obtained for this time interval.

_{den}, it was necessary to establish a relationship between this indicator and the hourly equivalent level, L

_{Aeqh}, used in this study. This has been carried out by means of a dataset of 26,808 hourly values of L

_{Aeqh}collected by continuous monitoring of 312 urban roads in 38 different Italian towns.

_{den}and L

_{Aeq,6–22h}) and L

_{A1}is good, whereas L

_{Amax}shows a greater dispersion of data: about 60% of errors of the estimation of L

_{A1}are in the range ±1.5 dB, while only about 20% of errors of L

_{Amax}(hourly values) are in the same range. This is due to the greater variability of L

_{Amax}data. Moreover, values of L

_{Amax}are strongly influenced by the time interval length with greater values when this time interval is longer. For these reasons, it could be more reliable to use the statistical level L

_{A1}when performing an analysis of the effect of noise levels due to traffic noise on the attention and cognitive impairment at school. This can be considered the main finding of this study at this stage.

## 5. Conclusions

_{den}and L

_{Aeq,6–22h}versus the corresponding hourly values of L

_{Amax}and L

_{A1}shows that the distribution of the maximum SPLs is very large. Indeed, this descriptor may be influenced by unusual events, such as the pass-by of very noisy vehicles or sirens. In particular, the correlation between L

_{A1}and L

_{den}is much higher (R

^{2}= 0.66) than that observed for L

_{Amax}(R

^{2}= 0.21). Moreover, these correlations are higher when referred to L

_{Aeq,6–22h}instead of to L

_{den}(R

^{2}= 0.74 for the correlation with L

_{A1}and R

^{2}= 0.26 for L

_{Amax}, hourly values in the period 8–17 h).

_{A1}seems more suitable than L

_{Amax}for studying the relationship between acoustic 8–17 h annoyance and occurrence of short-duration noise events. This conclusion could be useful for future studies on this topic and also for the definition of guidelines for the protection of schools from traffic noise.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**Figure 1.**Linear regression between L

_{Amax}and L

_{Aeq}(

**a**) and L

_{A1}and L

_{Aeq}(

**b**) measured in a street at different time intervals (1 h, 10 min and 1 min).

**Figure 2.**Linear regression between L

_{den}and L

_{Aeqd,6–20h}(

**a**), L

_{den}and L

_{Aeq,8–17h}(

**b**), L

_{Aeqd,6–22h}and L

_{Aeq,8–17h}(

**c**).

**Figure 3.**Road traffic noise map of the center of Florence; the colors of the map refer to values of L

_{den}, while values in the box refer to the SPL in the facade of the buildings, expressed with the Italian indicators for day-time and night-time (L

_{Aeqd,6–22h}and L

_{Aeqn,22–6h}, respectively).

**Figure 5.**Typical distribution of traffic noise on the facades of a school located near a busy road.

**Figure 6.**Linear regressions between hourly values of both L

_{Amax}and L

_{A1}versus L

_{den}for all the streets.

**Figure 7.**Linear regressions between hourly values in the interval 6–22 h of both L

_{Amax}and L

_{A1}versus L

_{Aeq,6–22h}for all the streets.

**Figure 8.**Linear regressions between hourly values in the interval 8–17 h of both L

_{Amax}and L

_{A1}versus L

_{Aeq,6–22h}for all the streets.

**Figure 9.**Distribution of the primary and secondary schools of Florence (in blue) in the noise map of the Municipality.

**Figure 10.**Distribution of the facade SPL (L

_{Aeqd,8–17h}) and of the indoor SPL (L

_{Aeqd,8–17h}) to which the students of primary and lower secondary schools of Florence were exposed.

**Figure 11.**Distribution of the maximum (L

_{Amax}) and statistical (L

_{A1}) SPL inside the classrooms estimated for the students of primary and lower secondary schools of Florence.

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Secchi, S.; Brambilla, G.; Casini, D.; Cellai, G. A Method to Estimate Students’ Exposure to Road Traffic Noise Events

. *Environments* **2018**, *5*, 39.
https://doi.org/10.3390/environments5030039

**AMA Style**

Secchi S, Brambilla G, Casini D, Cellai G. A Method to Estimate Students’ Exposure to Road Traffic Noise Events

. *Environments*. 2018; 5(3):39.
https://doi.org/10.3390/environments5030039

**Chicago/Turabian Style**

Secchi, Simone, Giovanni Brambilla, David Casini, and Gianfranco Cellai. 2018. "A Method to Estimate Students’ Exposure to Road Traffic Noise Events

" *Environments* 5, no. 3: 39.
https://doi.org/10.3390/environments5030039