The monitoring units were installed and left in place at each location for at least 8 weeks between April and November 2015 to ensure that a full 6-month period was assessed. Table 1 shows the measurement periods in which the monitors were installed, as well as the area of the city where they were placed, the number of units, and the number of weeks of measurement. A minimum of 9 monitors continuously made measurements throughout the areas. Measurement efforts were progressively increased to reach 15 monitors operating simultaneously during the summer period (July to September) because the leisure activities increase in number during this period, as does the number of tourists in the city. Subsequently, we kept the additional monitors in place until the end of the assessment period (November).

The monitoring unit measurements could be accessed through the web in real time to promote engagement with the public and to improve trust and transparency. We decided that the instruments had to measure the A-weighted equivalent sound pressure levels at 1 s intervals (L Aeq,1s ), because this is a dynamic indicator that reacts easily to changes in noise levels and because this measurement can be understood effortlessly by nonexperts. Each unit sent these second measurements every 5 min to a server to be preprocessed before displaying them on the visualization platform. The measurements were shown on the web platform with a delay of 20 min to prevent people from deliberately making noises with the intention of seeing changes in real time on the web. In addition to a time–history graph of these results, the webpage contained a map showing the location of the monitor, a brief description of the location, and its noise descriptors (L d , L e , L n , L den ). The system could also create reports for these noise descriptors based on user queries.

The measurements were adjusted according to the methods specified in ISO 1996-2 [ 45 ]; therefore, all the results can be considered to be descriptive of the incident sound, which is the reference condition in ISO 1996-2. For practical reasons, the microphones had to be situated at different heights between 4 and 8 m (to be considered for future analysis). As an example, Figure 2 shows the installation of one of the monitors.

Therefore, after a preliminary field study of possible areas, we selected 40 locations at which to install the monitors. Most of the selected locations were in the city center, with 35 monitors installed in this area, while the other five were in Teatinos. We placed the monitors in the subareas that had the highest rates of leisure activities and numbers of noise complaints. The distribution of the monitors is imbalanced, because while in the city center, the leisure activities extend over the entire area, the discos in Teatinos are concentrated over a smaller area, and hence, the number of necessary sound level meters is smaller. Figure 1 shows the position of each unit in the selected areas. All the locations were chosen to be representative of the predominant leisure noise in the area while also keeping basic security, safety, and accessibility constraints in mind. Each noise monitor consisted of a Type 1 certified sound level meter and was installed on a street lamp; hence, they would have a power supply to charge the batteries at night and can be camouflaged to a certain extent. From a technical perspective, the measurement units were equipped with an outdoor kit for both equipment and microphones, a data processing and storage system, a 3G-internet data connection, a rechargeable battery, and a solar panel.

In view of the project goals, both assessment and engagement, it was decided (a) to perform the noise assessment through actual noise monitoring rather than through simulation tools; (b) to measure the noise in a large number of different locations, observing the noise spatial variability within the large study area in such a way that the bulk of the citizens would perceive the noise measurements as representative of the acoustic climate in their homes; and (c) to provision the noise monitors with real-time web access.

These types of results can be very useful for both noise managers and authorities, allowing them to analyze data and extract conclusions, but they are not intended for communication to the public because they do not provide a proper description of the noise level at any given instant, and they cannot be compared easily with the AQT regulation levels. Therefore, we used simple line graphs in the public reports, with one line for each day of the week.

In general, the noise levels started to increase in the late hours on Thursdays and remained high during weekend evenings and nights. The maximum noise levels were observed during the evening and nighttime periods on Saturdays. The noise level (L) was quite high throughout the night in most of the locations. The noise level exceeded 70 dBA for several hours on weekend nights at 83% of the locations, 75 dBA at 15%, and 80 dBA at 10%. During weekdays, 88% of the locations had a quiet period (below 55 dBA) for 2–5 h, but this period decreased to 1–3 h during the weekends or even disappeared in 75% of the locations. An example of this hourly noise-level evolution can be seen in Figure 6 for one of the units in the city center, but all of them followed the same pattern with a time displacement of between 1 and 3 h.

We processed the noise levels at one-hour intervals (L Aeq,1h ) from the collected measurements to obtain noise patterns over both a full day and a full week. The goal was to gather information related to the typical periods of activity, identify noisy days, and assess the duration of the quiet periods at night.

According to the described criteria, the D-AQT were met at none of the monitoring locations during the nighttime period, at only 6 monitoring sites during the daytime period, and at only 2 locations during the evening period. In addition, during the night period, 100% of the days did not meet the D-AQT criteria in 38 of the 40 locations, which shows the severe problem of night noise present in the measurement locations.

In Figure 5 , a fully green bar means that the sound level remained below the AQT every day. When a yellow section is present, it means that some days tolerably exceeded the target (by up to 3 dBA and in up to 3% of the days assessed). The red sections signify that the D-AQT was not met, either because the noise levels were over the target by more than 3 dBA or because the tolerable exceedance occurred on more than 3% of the days assessed.

The histograms of the daily indicators confirmed that the noise levels frequently exceed the targets. We presented this information for each location as cumulative bar graphs, such as those shown in Figure 5

To be in compliance with the daily AQT (D-AQT) regulations [ 46 ], the daily noise level (for any reference interval: Day, evening, or night) may exceed the targets by more than 3 dBA only in less than 3% of the days assessed.

Noise levels in an area are not consistent from one day to another. It seems reasonable that—even in a quiet area—some days are especially noisy. However, when these noisy episodes become too frequent or too loud, the health and welfare of the residents in that area may be compromised. Moreover, using only long-term descriptors could mask these episodes in otherwise quiet areas. Therefore, it is necessary to identify noisy episodes on a daily basis.

Additionally, we presented the results as a map, as shown in Figure 4 , where the color labels are linked to the sound levels: Green means below the target and red means 10 dBA above the target. This type of presentation is similar to those used in many other noise-monitoring platforms [ 48 50 ].

With the goal of making the results easier for nonexperts to interpret, we reported them in the form of graphs, as seen in Figure 3 , using a color gradient background similar to the one used in the Harmonica Project [ 47 ]. A horizontal dashed line shows the AQT that applies to each period. Each point in the graph is labeled with the result for that location. A line connects the points to make the presentation clearer, although each result is independent from the others and the connecting line has no real meaning. The idea behind this presentation is to give residents an overall way to see the results at a glance.

As seen in Figure 3 , the variability between locations was greater at night than during the day and evening periods, and 35% of the locations had a higher noise level during this period (both the maximum and the quartiles Q2 and Q3 were higher for the nighttime period).

The situation degenerated during the evening periods. The mean value in the evenings increased to 65.5 dBA (with a similar standard deviation, 2.8 dBA), and only 39% of the locations had a value lower than 65 dBA (L e ). However, during the nighttime period, the situation became even noisier, not only because the noise levels (L n ) are higher in 43% of the locations, but also because the target for this period is 10 dBA lower (55 dBA). During the nighttime period, half the locations had an L n value ranging between 63 and 68 dBA, and 95% of the locations showed noise levels 5 dBA over the target (55 dBA). Half of those were over 66 dBA. The mean value for L n was 66.0 dBA, and the standard deviation was 4.0 dBA.

In general, the acoustic situation in these areas during the daytime period is similar to that of other leisure areas in the city (noise map). The arithmetic mean value of L d among the locations was 64.0 dBA, with a standard deviation of 2.9 dBA. In 78% of the locations, the noise levels (L d ) were in the range of 60–65 dBA; only 22% of the locations showed a cumulative level over the target (65 dBA).

Because leisure activities in Málaga are not sporadic, it is important to obtain a full description of the cumulative noise pollution over the long term. This “long term” should be a full year, although in this case, with the assumption that the samples are sufficiently representative, the duration was reduced to the length of the measurement effort at each location (at least 8 weeks at each location; depending on the operative restrictions for the installation or uninstallation of the monitors, the measurement period in some of the locations was extended to over 20 weeks). Long-term equivalent sound pressure levels were assessed in three different reference intervals: Daytime (L), from 7 a.m. to 7 p.m.; evening (L), from 7 p.m. to 11 p.m.; and nighttime (Ln), from 11 p.m. to 7 a.m. These indicators were compared to the acoustic quality targets (AQT) established by Spanish regulations; for residential areas, these targets are 65 dBA during the day and evening periods and 55 dBA at night [ 46 ].

The analysis of the measurements at each location was performed according to three different criteria, which are described in the following subsections. As opposed to the measurements, which could be accessed in real time, these results were not presented during the monitoring sessions; instead, they were presented in a specific report at the end of the project. The report was formatted for presentation online and designed to optimize the communication of the technical issues to the public. In addition to the measurement results, the following sections provide brief descriptions of the contents and formats used in the report.

3.3. Discussion of the Measurement Results and Prediction Models

We collected a large number of measurements in this project and processed them to determine to what extent the models proposed by Ballesteros [ 26 ] may be valid for the wide range of leisure activities present in Málaga. These models try to describe the noise levels in an area using the number of leisure venues in it. The conclusions may be useful for future noise assessment not only in this city, but also in many other cities throughout southern Europe.

The independent variable in the basic Ballesteros model is the number of leisure venues (N) within a distance of 40 m—within a 20 m radius from the monitor in our case. An optimized version of this model splits the independent variable by considering the type of leisure venue (B for bars, P for pubs, D for discotheques, and R for restaurants). A third model adds the width (W) of the street and the mean height (H) of the surrounding buildings. There is also a fourth model that considers the number of people present, but we were unable to accurately determine a value for this variable in the monitoring areas; therefore, only the first three models were tested.

The three models proposed by Ballesteros describe the equivalent sound pressure levels spatially averaged over a “soundwalk” with a length of 40 m (L Aeq,40m ) measured by a microphone at a 1.5 m height. Our monitors were placed higher and in fixed locations, and they recorded the variability in noise levels at each area all day long. In an attempt to find a value that could be compared to the soundwalk (L Aeq,40m ), we decided to use the mean L Aeq,1h of the measurements made during the noisiest hour on Saturdays (L saturday ; each Saturday night, we consider the maximum L Aeq,1h and calculate the mean value for each location). Although these indicators are obviously not equivalent, we consider the comparison acceptable because the noise levels during this period are quite steady. Moreover, we assume that spatial averaging is not performed in the monitoring case.

As mentioned by Ballesteros, the behavior of noise is different in squares and wide avenues; therefore, we have excluded data recorded in both types of locations from the analysis. We also excluded some locations where the noise from nighttime leisure activities was not clearly dominant. Consequently, only 28 of the 40 locations were included in this analysis.

In general terms, we can conclude that the models proposed by Ballesteros fit the noise levels observed in the leisure streets of Málaga, as Table 2 shows. The noise measurements were, on average, about one decibel higher than the predictions, and the standard error values in this case were similar to those reported by the authors along with the measurements used to create the model. However, we must conclude that the results are quite uncertain because the coverage interval extends up to 14 dBA. Similar results could have been obtained in Málaga with a simple Gaussian model in which the number of leisure venues was not considered at all, but in this case, the model was created after a long economic and technical effort of monitoring. Therefore, these models could be useful depending on the goals intended to be achieved. For instance, they could be very useful in a planning stage, since no further information is available, and some assessment could be necessary to establish noise limits, land use, or restrictions to activities. By contrast, to prioritize an action plan to reduce noise levels, the results provided have a very large uncertainty, and a measurement-based assessment could be more reliable.

d , L e and L n ). We can create an unbiased Gaussian model for these long-term indicators ( The main disadvantage of the previous models is that they do not consider the temporal patterns of noisy activity, nor do they predict indicators that can be compared to regulations (L, Land L). We can create an unbiased Gaussian model for these long-term indicators ( Table 3 ), but, although the mean error is 0 dB, the resulting coverage intervals are still similar to those presented in Table 1 , and they can be too uncertain for any practical use.

We also analyzed the differences between the operating noise levels (L saturday ) and the long-term L n indicators. This correction factor is, on average, 5.1 dBA, but its variability is too high (standard error, 4.8 dBA); therefore, it is not sufficiently precise and cannot be used to infer long-term indicators from the Ballesteros models because the new variability contribution must be added to that derived from the initial model.

In consequence, although the discussed models can be useful tools during the planning stages, the uncertainty derived from each model is quite high. This limits their application in prioritizing or action planning because those activities involve regulations, and such a large uncertainty factor could lead to incorrect decisions. Moreover, these models have some additional drawbacks: