Gabriela Meller,Willian Magalhães de Lourenço,Viviane Suzey Gomes de Melo,Giane de Campos Grigoletti
Use of noise prediction models for road noise mapping in locations that do not have a standardized model: a short systematic review
Noise pollution. Noise mapping. Traffic noise models. Acoustic. Environmental noise. Systematic review
Faced with the accelerated growth of cities and the consequent increase in the number of motor vehicles, urban noise levels caused by vehicular traffic have increased considerably. To assess noise levels in cities and implement noise control measures or identify the problem’s location in different urban areas, it is necessary to obtain the noise levels to which people are exposed. Noise maps are tools that have applications as they are cartographic representations of the noise level distribution in an area and over a period of time. This article aims to identify, select, evaluate, and synthesize information, through a systematic literature review, on using different road noise prediction models, in sound mapping computer programs in countries that do not have a standard noise prediction model. The analysis period was from 2018 to 2022. From a previous analysis of articles, the choice of topic was based on identifying various models for predicting road noise in countries without a standardized sound mapping model. The papers compiled by a systematic literature review showed that studies concentrated in China, Brazil, and Ecuador, the most used traffic noise prediction models, were the RLS-90 and the NMPB, and the most used mapping programs were SoundPLAN and ArcGIS with a grid size of 10 × 10 m. Most measurements were carried out during a 15-min period at a height from the ground level of 1.5 m. In addition, it was observed that research on noise maps in countries that do not have a local model has been increasing over time.
Use of noise prediction models for road noise mapping in locations that do not have a standardized model: a short systematic review

Faced with the accelerated growth of cities and the consequent increase in the number of motor vehicles, urban noise levels caused by vehicular traffic have increased considerably. To assess noise levels in cities and implement noise control measures or identify the problem’s location in different urban areas, it is necessary to obtain the noise levels to which people are exposed. Noise maps are tools that have applications as they are cartographic representations of the noise level distribution in an area and over a period of time. This article aims to identify, select, evaluate, and synthesize information, through a systematic literature review, on using different road noise prediction models, in sound mapping computer programs in countries that do not have a standard noise prediction model. The analysis period was from 2018 to 2022. From a previous analysis of articles, the choice of topic was based on identifying various models for predicting road noise in countries without a standardized sound mapping model. The papers compiled by a systematic literature review showed that studies concentrated in China, Brazil, and Ecuador, the most used traffic noise prediction models, were the RLS-90 and the NMPB, and the most used mapping programs were SoundPLAN and ArcGIS with a grid size of 10 × 10 m. Most measurements were carried out during a 15-min period at a height from the ground level of 1.5 m. In addition, it was observed that research on noise maps in countries that do not have a local model has been increasing over time.

Road traffic noise is one of the biggest polluters in urban centers (Graziuso et al., 2022; Wen et al., 2019). It is, therefore, important to know how to diagnose and quantify the noise to which people are exposed. Noise maps provide detailed information on the sound environment and are an efficient tool for managers to monitor the sound quality of the current environment and for sound prediction purposes (Dintrans & Préndez, 2013; Pinto & Mardones, 2009). Several studies confirm the importance of strategic simulations and environmental impact assessments to optimize urban planning (Cai et al., 2019; Manohare & Rajasekar, 2018; Tsai et al., 2009; Wang et al., 2018; Zytoon, 2016). Noise maps can be produced through measurement or modeling and are divided into static and dynamic maps (Estévez-Mauriz & Forssén, 2018). Static noise maps do not change over time and exhibit the general characteristics of spatial distributions over a long period (Lan & Cai, 2021). Dynamic noise maps are adjusted in real-time or in short periods of time (Estévez-Mauriz & Forssén, 2018; Li et al., 2017). In this case, these maps can reflect the noise distributions in the time and space dimensions (Lan & Cai, 2021; Li et al., 2017). The methodology for producing maps based on measurements can involve several acoustic monitoring sensors and spatial interpolations, which can be arduous and costly, especially on a macroscale. In contrast, another methodology uses traffic noise prediction models (TNM) to implement them, the advantage of which is the ease of implementation. Noise prediction models are vital tools for urbanists looking to reduce and control noise, as it is practically unfeasible to use the sound pressure level (SPL) at all points. This technique was mainly promoted after the publication of the European Directive 2002/49/EC, in which noise maps were recommended for the determination of exposure to environmental noise. The models, which were empirical, were developed, validated, and integrated into the geographic information system (GIS) interface in the respective countries and brought into regular use (Garg & Maji, 2014). As a result, several countries have developed their own TNM. The main traffic noise models used worldwide are shown in Table 1. All current noise emission prediction models follow similar principles, in which they require source characterization using advanced numerical methods, including wave equations and continuity equations. These methods are employed to solve the effects of sound propagation and use high-speed processing computers and skilled operators to achieve the final goals (Can & Aumond, 2018; Garg & Maji, 2014). Thus, it is essential to know all the variables to choose from to develop a model that best suits the study place to be mapped. In countries that do not have a TNM standard, the main difficulty they face is that the methods for simulating the propagation of environmental noise are based on the reality of other countries (Bakowski et al., 2019; Pozzer et al., 2018). The input data used in the programs, such as the typology of vehicles and road surfaces, are from TNM adapted from other countries. Furthermore, the input data in software (open or commercial) differ in several aspects: use of standard software data instead of real data; mode of attribution of receivers on the facades of buildings; integrity, accuracy, and reliability of the spatial data used (Garg & Maji, 2014; Kumar et al., 2020; Murphy & King, 2010). This variation in the input data makes it difficult to compare results for noise monitoring purposes. Noise prediction models consider different parameters in their mathematical models and present different limitations. Therefore, the choice of the ideal model must be carried out following the requirements of the current regulation of the place of study or, if there is none, it should be based on the one that best suits the local reality or based on the amount of data available for its modeling. The choice of topic was based on a previous analysis of articles that identified the use of different road TNMs in countries that do not have a standardized model for application in noise maps. As a result, public managers and designers may have difficulty preparing future sound maps because the methodology needs to be standardized. A systematic literature review (SLR) was undertaken for a deeper analysis. The SLR has the function of mapping, critically evaluating, and compiling results of primary studies to identify trends and gaps in research (Ruschel et al., 2017). For this, a pre-established methodology is applied in order to avoid a direction of the research results. Therefore, this article adopts the methodology of Kitchenham (Kitchenham, 2004). This procedure is divided into three stages: planning (identification of the need for a review and elaboration of the protocol with the research questions), review direction (selection of studies, application of inclusion and exclusion criteria), and analysis of results (compilation and discussion of results). Urban noise can come from different sound sources such as aeronautical (Grigolato et al., 2018; Lee et al., 2021a), road (Morillas et al., 2021; Cai et al., 2019; Yang et al., 2020), railway (Lee et al., 2021b; Wosniacki & Zannin, 2021), industrial (Bozkurt & Demirkale, 2017), ports (Fredianelli et al., 2022), and mining (Lokhande et al., 2018). Thus, this paper aims to identify, select, evaluate, and synthesize information on the use of different road TNMs and the different noise mapping software used in countries that do not have a local TNM. We also identify which mapping method is the most used, in addition to evaluating and compiling measurement parameters (time measurement and height of the sound pressure level (SPL) meter in relation to the ground) and parameters used in the software (high and grid size) commonly used in the elaboration of maps.

The systematic review of the literature was carried out using the StArt software (Universidade Federal de São Carlos, 2022). The protocol planning stage followed was to answer two main questions: What is the noise prediction model most used by countries that do not have a local model? What software is most used by countries with no local model? The databases were Science Direct and Scopus. The first stage was a review of conference papers, research articles, and short communications published in the last five years (2018 to 2022) in English. The keyword and synonyms of the string were (road OR traffic) AND noise mapping. The selection criteria for the papers included are countries that do not have a local noise prediction model; papers in which a traffic noise map was created and the exclusion criteria are countries that already have a local noise prediction model; papers that do not have an abstract; papers that did not include a traffic noise map. After defining the protocol, the second stage was to search. Science Direct database was collected on May 30th, 2022, and the search string used was (road OR traffic) AND "noise mapping." The Scopus database used the search string (road OR traffic) AND ("noise mapping") AND (LIMIT-TO (OA, "all")) AND (LIMIT-TO (PUBYEAR, 2022) OR LIMIT-TO (PUBYEAR, 2021) OR LIMIT-TO (PUBYEAR, 2020) OR LIMIT-TO (PUBYEAR, 2019) OR LIMIT-TO (PUBYEAR, 2018)) AND (LIMIT-TO (DOCTYPE, "air") OR LIMIT-TO (DOCTYPE, "cp")) AND (LIMIT-TO (LANGUAGE, "English")) AND (LIMIT-TO (SRCTYPE, "j") OR LIMIT-TO (SRCTYPE, "p")). Duplicate papers were removed. The third stage was the selection of studies based on the inclusion and exclusion criteria. After that, the fourth stage was the complete reading of the papers, and papers that already had a local noise prediction model were excluded, and the data from the remaining papers were collected. In the end, the data obtained from the two databases were summarized. The papers selected after these stages presented the following information: (a) location of the study, (b) noise prediction models, (c) software used in noise mapping, (d) duration of measurements, (e) time of measurements, and (f) information about the height from the ground of the sound pressure level meter and grid size used in the computer program. Figure 1 summarizes the literature review stages.

Appendix 1 provides information from all papers compiled in the SLR (Table 2).

Upon completing the fourth stage of the systematic review, 63 papers were selected, the majority (75%) from the Scopus database (n = 576) and 25% from Science Direct (n = 191). Among the selected papers, it was observed that 17% of the papers were published in the journals Applied Acoustics (n = 11), 11% (n = 7) in Noise Mapping, 6% (n = 4) in Sustainability (Switzerland) and Transportation Research Part D: Transport and Environment, 5% (n = 3) in the IOP Conference Series: Earth and Environmental Science and Science of The Total Environment, 3% (n = 2) in Civil and Environmental Engineering, Environment International, Environmental Research, Environmental Science and Pollution Research and ISPRS Archives. The remaining 2% in each journal (n = 1) were from the journals Building and Environment, Cartographic Journal, Case Studies on Transport Policy, Complexity, Energies, Environment, Development and Sustainability, Environmental Challenges, Environmental Pollution, Global Health Journal, IEEE Access, International Journal of Environmental Research and Public Health, International Journal of Sustainable Development and Planning, Journal of Environmental Health Science and Engineering, Journal of Environmental Management, Journal of Environmental Planning and Management, Journal of Urban and Environmental Engineering, Land Use Policy, PLoS ONE, Proceedings of Meetings on Acoustics, and Sustainable Cities and Society. The results indicate the broad application of noise maps in the most diverse fields of science. There were publications in journals (n = 57) and conference papers (n = 6). Thus, even without a local noise prediction model, research and elaboration of maps were encouraged in publications. One of the exclusion criteria was to remove studies in countries that already had a local TNM. Most of the articles excluded in the extraction phase (n = 85) were from studies carried out in places that already had a TNM, e.g., European countries, Japan, and the USA. After this exclusion phase, 63 papers remained. Among the papers collected (n = 63), it was identified that the countries that performed the most studies with noise maps were China (n = 12), India (n = 9), Brazil (n = 7), Turkey (n = 5), Ecuador (n = 3), Malaysia (n = 3), Singapore (n = 3), Canada (n = 2), Colombia (n = 2), Iran (n = 2), South Korea (n = 2) Taiwan (n = 2), Australia, Bangladesh, Chile, Czech Republic, Ghana, Indonesia, Japan, Jordan, Korea, Nigeria, Slovakia, and Thailand, (each with 1 study) (see Fig. 2). Also, one study was performed at a generic place so it could be replicated in any country, including those that do not have a local TNM (Forssén et al., 2022). One of the reasons why there have been few studies that include traffic noise maps is the difficulty of finding a noise prediction model that represents the local reality (Bakowski et al., 2019; Pozzer et al., 2018). In addition, there is difficult to make noise maps in places with no normative requirement, such as the European Noise Directive (The European Parliament and the Council of the European Union, 2002). It is also challenging to obtain data in underdeveloped countries with no continuous noise monitoring data, vehicle count data, or even local topography (Adulaimi et al., 2021; Alam et al., 2020; Bocher et al., 2019; Raess et al., 2021).

Most of the papers compiled in the first stage of the selection were from research in places with a local TNM because these countries encourage and even require the elaboration of noise maps. It can be seen that without a regional model, it is necessary to choose the model that best fits the region. Furthermore, the TNM may often require a considerable amount of input data in the model and, depending on the location, and such data are scarce because the country is underdeveloped or there is no or little available geoprocessing data, few noise monitoring stations or a lack of vehicle counting technology, which can make the elaboration of noise maps difficult or even impossible. The most used noise prediction models, which can be adapted to the local reality, were RLS-90 (n = 9), NMPB (n = 9), FHWA (n = 7), ISO 9613 (n = 6), CoRTN (n = 4), CNOSSOS-EU (n = 3), Nort Test Method (n = 2), ISO 1996 (n = 2), and STL-86 (n = 1). It is noteworthy that the Chinese studies (n = 3) that used the emission model proposed in The Technical Guidelines for Environmental Impact Assessment — HJ 2.4 2009 (Ministry of Environment Protection, 2009) based on Specifications for Environmental Impact Assessment of Highways × JTG B03-2006 (Ministry of Transportation, 2006), which has a series of correction parameters based on the FHWA model (Zou et al., 2019), were accounted for in studies that used the FHWA model. Several of the selected papers used the interpolation method, such as the Empirical Bayesian Kriging (EBK), which is used for the Inversion of the distribution of traffic noise, or the inverse distance weighting (IDW) technique (n = 14) that is based on GIS modeling. Land use regression (LUR) modeling was also used (n = 3). To develop a traffic noise prediction model representing the local reality, eight studies proposed their model. Thus, Fig. 3 presents the noise prediction models used as references in the selected studies. The papers in red are from standard TNM. In most studies (n = 36), the authors adapted a prediction model to the local reality, while 25 papers used some interpolation method, IDW technique, or even proposed a model. Two studies did not mention the TNM used in the noise map (Chan et al., 2019; Raess et al., 2022). Among the noise prediction models that were most used in the studies (RLS-90, NMPB, and FHWA, respectively), it is essential to observe the input data in the programs. For the NMPB, each vehicle category’s speed and traffic flow type, road platform surface category, and road gradient are entered (Besnard & LeDuc, 2009; Garg & Maji, 2014). For the RLS-90, the following are required: data on the type of traffic, speed, topography information, number of light and heavy vehicles, vehicle flow, road surface data, and also, parking information when available (DER BUNDESMINISTER FÜR VERKEHR 1990; Garg & Maji, 2014). For the FHWA, data on the type of traffic, vehicle flow, speed, and road surface data are entered (Barry & Reagan, 1978; Garg & Maji, 2014). An important fact is that although the RLS-90 was updated in 2019 to the RLS-19, no studies published from that year used the current model (DER BUNDESMINISTER FÜR VERKEHR, 2019). One reason these TNMs are the most used may be the smaller amount of input data required in the models. An example of a TNM requiring a large amount of specific data is the CNOSSOS-EU model (Kephalopoulos et al., 2012), which is more complex. In this model, more categories of vehicles can be accounted for, which can be a hindrance when collecting data in countries that do not require the obligatory elaboration of noise maps. Furthermore, maps that use interpolation methods or the IDW technique exhibited a lower level of graphic detail in the noise maps. Among the papers selected, some studies analyzed more than one TNM or in more than one place (Bravo-Moncayo et al., 2019a; Gozalo & Escobar, 2021; Liu et al., 2021; Sonaviya & Tandel, 2019, 2020; Wosniacki & Zannin, 2021). Appendix 2 presents the TNM used in each country (Fig. 8). Most of the papers compiled were from China (n = 13). Consequently, this was where the most different noise prediction models were used. Among them, FHWA was the most used TNM, and there was also the same number of papers that used noise prediction models proposed by the authors themselves. India was the country that most used different TNMs: CoRTN (n = 4), ISO 9613, RSL-90, EBK interpolation method (n = 2, each), and ISO 1996–1/2 (n = 1). It was observed that each country used different TNMs or proposed its model. From the SLR, it was found that the models can be adapted or created for the local reality. However, for monitoring sound quality and noise prediction, it is important to have a country standard to standardize the modeling of noise maps. Therefore, it is important that countries develop standardized methods and criteria for modeling environmental noise levels in their territories, as is done by the European Directive 2002/49/EC (The European Parliament and the Council of the European Union, 2002), for example. These models should be designed to reflect local conditions and take into consideration local factors of topography, building density, road types, and traffic patterns.

An analysis was performed on the compiled works to identify which mapping method was mostly used in studies that did not have a local TNM. Murphy and King (2010) emphasize the importance of the competent authorities specifying the spatial interpolation method to be used in the states to compile strategic noise maps. The results of the papers compiled showed that the most used mapping method (software) was SoundPLAN (n = 14), followed by ArcGIS (n = 13), CadnaA (n = 9), GIS (n = 7), Predictor LimA (n = 5), q-GIS (n = 3), E-map (n = 2), NoiseModelling (n = 2), DISIA and SurferTM (together), ERDAS Imagine, MATLAB, NoiseExplorer app and NoiseSystem (n = 1, each). Two studies did not mention the mapping method used in the research. The mapping method used to create the noise maps can be seen in Fig. 4. Most studies used specific software for the elaboration of noise maps, such as SoundPLAN, Cadna-A, Predictor Lima, NoiseModelling, DISIA, NoiseExplorer, or NoiseSystem. In addition to the interface aimed at making the maps, some software allows a choice of the TNM to be used and also allows the mapping of noise in 3 dimensions, which provides the level of noise exposure also in the Z direction (e.g., facades). Used in residential areas, this option can assess the number of people affected by noise in high buildings (Zhao et al., 2017). Several studies used the geographic information system (GIS), either in commercial programs such as ArcGIS or EDAS Imagine, or in free software with open-source code such as q-GIS. The advantage of using this for the elaboration of noise maps is in the customization of the source code and in the way to interpolate the data. Most papers based on GIS modeling did not mention sound maps in 3D. This is the case in the study by Alam et al. (2020), which also identified that most researchers used GIS to develop a 2D noise map. Thus, although there is software that also performs an analysis in the Z direction, 2D maps have been used extensively in environmental impact studies such as air pollution, soil pollution, and noise in the existing environment (Alam et al., 2020).

The duration and measurement times were analyzed in this research. Given that there is no local regulation in these countries, analyzing the period of the day and the duration of the measurement for the vehicular traffic noise data collected to realize maps is essential. Thus, most studies performed measurements from 7:00 to 17:00 h for daytime, 18:00 to 20:00 h for the evening, and 23:00 to 5:00 h for nighttime. The highest incidence of times cited by papers that performed measurements at peak times was from 7:00 to 9:00 h, 10:00 to 12:00 h, 16:00 to 18:00 h for daytime; 18:00 to 19:00 h for the evening; and 23:00 to 00:00 h for nighttime. The European Directive 2002/49/EC (The European Parliament and the Council of the European Union, 2002), a guideline for large-scale strategic noise mapping, determines that the noise indicators collected should be Lden and Lnight. However, it is essential to note that in underdeveloped countries, collecting data in the evening and night is difficult due to high crime. Similarly, data for noise monitoring is difficult to obtain due to the lack of monitoring stations in the cities. When analyzing the measurement time, most studies performed measurements during 15 min (n = 11) and 10 min (n = 10) (Fig. 5). Among measurements that took less than 1 h, the average measurement time was 17.82 min. Various parameters can be adopted when creating a noise map. For monitoring noise over time, the parameters adopted in the modeling must be the same. Among them is grid spacing (or mesh size), and the size of the rectangles will determine the accuracy of the map. The computational processing time increases as the grid size is reduced to reduce the interpolation errors. Thus, optimizing mesh spacing involves balancing accuracy in sound mapping and processing time (The European Parliament and the Council of the European Union, 2002). Figure 6a presents the different grid sizes adopted in the papers.

Among the papers that cited the grid spacing adopted (n = 27), most adopted the size of 10 × 10 m (n = 11), followed by 5 × 5 m (n = 4), 20 × 20 m (n = 3), 30 × 30 m (n = 2), and 200 × 200 m (n = 3). The grid sizes of 1 × 1 m, 3 × 3 m, 8 × 8 m, 125 × 125 m, and 140 × 140 m were used once in each paper. It is important to emphasize that the dimension of the evaluation grid always results from a convergence study for each specific work. Therefore, depending on the scale of the noise map (macro or micro scale) and also to reduce the interpolation errors, the grid size can be changed. Another parameter used is the height of the noise map, which is often prepared at the same height in relation to the SPL meter for calibration purposes (noise maps validation). The European Parliament and the Council of the European Union (2002) suggest that assessment points should be 4.0 ± 0.2 m (3.8 to 4.2 m) above ground for strategic noise mapping purposes. Regarding noise exposure in and around buildings, other heights can be chosen. Still, they must never be less than 1.5 m above the ground, and the results must be corrected for an equivalent height of 4.0 m (The European Parliament and the Council of the European Union, 2002). For countries that do not have a TNM standard, the papers that mentioned the height from the ground level (n = 39 of 63), most used 1.5 m (n = 13), 1.2 m (n = 9), 4.0 m (n = 7), 2.5–3.0 m (n = 3), 4.5 m (n = 2), and 2.0 m (n = 1) (see Fig. 6b). It is important to note that heights of 1.5 m and 1.2 m are commonly used because of the height of the SPL meter mounting tripod. In addition, two papers performed measurements at the height of 1.2 m from the ground level and adjusted the grid to a height of 4.0 m (Cai et al., 2019; Paiva et al., 2019). Three papers only reported a grid height of 4.0 m (Beran et al., 2021; Bravo-Moncayo et al., 2019a, b), and the study of Beran et al. (2021) tested the heights of 4, 8, 16, and 20 m above the terrain. In summary, to model the noise maps, the grid size of 10 × 10 m and the height of the SPL meter of 1.5 m were the most adopted parameters.

As mentioned, noise maps are a diagnostic tool used to pursue control strategies, inform the public, and make stakeholders aware of noise-related issues. Therefore, the importance of creating noise maps has been growing over time (Khomenko et al., 2022; Yang et al., 2020). In addition, with the growth of cities and consequently the population and the number of vehicles, traffic noise has become a pollutant that must be constantly monitored. It is important to highlight that from the year 2020 onwards, and there was the impact of the pandemic (COVID-19), which became one of the biggest global health crises in recent decades and influenced the behavior of many people. During this period, many inhabitants changed their habits, especially regarding the use of public transport, preferring to travel in private vehicles (Arimura et al., 2020; Benita, 2021). Thus, there has been an increase in the number of studies regarding vehicular traffic changes and how this impacts noise even after the pandemic (Arimura et al., 2020; Benita, 2021; Lokhande et al., 2021; Xu et al., 2022). Figure 7 shows the number of papers compiled by year of publication. It is important to note that this SLR was carried out until mid-2022 (May 30th), and despite this, the number of compiled papers has already surpassed the publications of 2018 and, practically, equaled the number of papers of 2020.

The computational modeling stage of a scenario can be performed in several ways. The criteria used will depend on the level of fidelity the planners want and the information about the study area. This article reviewed papers that developed road traffic noise maps in countries that do not have a standard noise prediction model. This systematic literature review addresses a topic that has expanded due to the increase in vehicles and, consequently, the noise caused by them. It is the first time that this type of review has been carried out to find a pattern and analyze the trend that countries are using in elaborating noise maps. The most used traffic noise models are discussed and identified as either a model adapted from another country or proposed by authors. The method of elaboration of the noise maps, the parameters used in the software, and the measurement time most used for sound mapping are also reviewed. In addition, the countries and places of publication of these papers are identified. The number of publications on the subject has been growing every year, confirming the importance of this study. One of the main challenges of this review was to identify the most relevant criteria in the elaboration of maps and to find a trend that can help governments to create a standard in the elaboration of noise maps to be able to compare them over time, increasing their efficiency. In general, it was observed that countries that do not have a local model usually adapt the TNM of another country (n = 43), use some interpolation method based on GIS modeling (n = 17), or create their model (minority, n = 8). The results identified that the most used traffic noise prediction models were the RLS-90 and the NMPB. The most used modeling methods were the SoundPLAN and ArcGIS software. The 10 × 10 m grid was the most used in the map modeling stage. To calibrate the noise maps, sound pressure level measurements were performed locally. Most of the measurements were performed during the time of 15 min at the height of 1.50 m in relation to the ground level. A tendency was identified for most noise maps to be calculated at the same height as the tripod used in the field measurement. Most of the noise maps for strategic purposes were carried out at the times with the highest vehicular concentration: 7:00–9:00 h; 10:00–12:00 h; 16:00–18:00 h, for daytime; 18:00–19:00 h for the evening; and 23:00–00:00 h for nighttime. The literature review observed that most studies focused on China, Brazil, and Ecuador. Furthermore, research on noise maps in places that do not have a local model has been increasing over time. The choice of model used in a particular country may depend on factors such as the availability of data, the level of technical expertise, and the country’s specific needs. Thus, some countries have adopted TNM from other countries, whose validations are adjusted according to the purpose of use. In the short term, these may be viable alternatives. However, each country must standardize and creates its own TNM to create strategic noise maps that can be evaluated over time. In this way, it will be possible to produce maps showing the distribution of noise levels in their territories and update them periodically to perform a good acoustical planning analysis and noise zoning.