Linking urban spatial configuration, traffic, air pollution, and noise exposure: a scoping review towards a DPSIR framework

The objective of this paper is to establish a conceptual framework defining the relationship between traffic, noise, spatial conditions, and pollution (PM 2.5, PM 10, O 3, NO x) to move towards coping strategies and assessments of those strategies. A literature review is undertaken (n = 51) through the reading of different studies that combine pollution and noise to some extent. Different sources, approaches for modeling, and measurement are discussed, including the development of indexes that combine these measurements. The usage of these stressors together is discussed regarding effects on health and how they can be used for urban diagnosis and risk assessment. Mitigation strategies are also assessed, including nature-based solutions (NBS) as well as transportation shifts that are key for reducing emissions of noise and pollution in cities. The relationship between noise and pollution is very coincidental, and they occur in a tangible way on a very small scale close to the source of the pollution, specifically traffic. However, the literature shows that urban configuration heavily shapes this situation. A DPSIR framework is developed that relates the phenomena. Next steps in research include a methodology for diagnosing urban spaces and exploring whether noise spectral analysis could have a relationship with air quality.

The urban population around the world has been increasing over the years. The share of people living in agglomerations of more than 1 million inhabitants has risen from 20% in the year 2000 to 25% in the year 2024 (World Bank, 2025). The degree of urbanization (Dijkstra et al., 2021) has also been growing, with 79.8% of the population living in cities or towns in 2020, predicted to reach a total of 81.1% by 2030 (European Commission, 2023). This is particularly important in regions where the population is still growing, such as West Africa, where there is a clear relationship between urban population growth and population increase (Ofoezie et al., 2022).

One of the consequences of this growth in population and concentration is that emissions, such as carbon emissions, have increased irrespective of income level or development stage (Muhammad et al., 2020). One of the sources of these emissions is traffic, which has been a factor since its implementation in cities; the 20th century has even been described as the century of the car (Gilroy, 2001; Moll, 2024; BBC, 1999).

Traffic also contributes to pollution in cities by emitting CO 2, NOx, and PM; this is known as traffic-related air pollution (TRAP) (Khreis, 2020). Cars and car-related infrastructure are linked to various health problems, such as contributing to the Urban Heat Island (UHI), motor vehicle accidents, and sedentary behavior (Nieuwenhuijsen and Khreis, 2016). Urban expansions can, in some cases, alleviate air pollution by promoting public transport, but at the same time, they can worsen air pollution by exacerbating traffic congestion and increasing commuting time (Lu et al., 2021). Additionally, higher traffic density is related to increased pollution (Sharmilaa and Ilango, 2022). The consequences of pollution, apart from climate change, also contribute to local deaths through exposure to ambient pollution. Pollution, as late as 2019, contributed to one in six deaths worldwide (Fuller et al., 2022), and it is important to note that not only exhaust emissions are relevant; non-exhaust particulates also occur and are not as regulated as those derived from motors. They consist of particles generated by the wear of brakes, clutches, tires, and the road itself (OECD, 2020).

Road traffic has been identified as the main source of environmental noise, especially in densely populated urban areas (European Environment Agency, 2025), along with air traffic, railway traffic, and industries. Even if noise can be overlooked (Goines and Hagler, 2007) in contrast with air pollution, it still has consequences for public health, including auditory and non-auditory effects such as sleep disruption, hypertension, and cardiovascular disease (Basner et al., 2014), and it impairs cognitive performance (Thompson et al., 2022).

Air pollution (PM 2.5, NO 2) has been linked to physical health outcomes such as circulatory disease, respiratory disease, lung cancer, and acute lower respiratory infections with long-term exposure (WHO, 2025).

Different environmental rules have been applied not only to reduce health and wellbeing risks but also to mitigate environmental damage. For example, adequate implementation in the EU of environmental legislation could save about €55 billion every year in direct environmental and health costs (Brivio and Meder, 2019). In China, just PM2.5 could lead to losses of 210 billion Chinese Yuan in health expenses (Xie et al., 2019).

There have been efforts to reduce noise and ambient pollution in cities. Some of these initiatives focus on traffic, such as the deployment of 30 km/h zones in Europe and other regions, which also aim to reduce road crashes and injuries (Yannis and Michelaraki, 2025). Additionally, vegetation and car-free days have been successfully implemented (Masiol et al., 2014).

However, the ties between noise and ambient pollution have been understudied. Even if studies of the relationship between pollution and heat, through the Urban Heat Island, have been found (Ulpiani, 2021) or the relationship between the Urban Heat Island and noise mitigation (Kousis and Pisello, 2020). Using a systematic review (Page et al., 2021) is not feasible because a specific population would be required to apply a PICO framework. Since a systematic exploratory approach is needed, a scoping review (Tricco et al., 2018) is adequate.

Objectives

The objective of this paper is to establish an integrated conceptual framework defining the relationship between traffic, noise, spatial conditions, and air pollutants (PM 2.5, PM 10, NO x, O 3), discuss the relationship between both phenomena, and find gaps for future research in the context of improving urban health.

The structure of this paper is as follows: Inclusion criteria are described with their rationale, then a synthesis of the evidence found is presented that summarizes the main findings and the types of articles found. Then, a more in-depth analysis is conducted following the defined axes: different sources of pollution, different measurement approaches, spatial modeling, development of indexes, and uses of the measurements of air quality and noise pollution, grouped by effects on health, work and commute assessment, urban diagnosis, and mitigation strategies assessment. Finally, a discussion about the correlation between air quality and noise pollution is included. This structure is summarized in Figure 1.

Inclusion criteria

For this, an ad hoc method that will be described will be followed.

The conditions for being included in the review are as follows: From the last 20 years (2005–2025) to keep track of recent developments while providing good context.

Written in English to focus on international impact.

Referencing both noise AND ambient pollution directly.

Focused on journals to allow for in-depth review and established analysis.

Quality requirements: indexed in SCOPUS and published in peer-reviewed journals.

As a whole, it is important that a majority of the sources are Q1 or Q2. There was no restriction regarding the type of article (study or review), and for the search, the keywords “air pollution” and “noise” were used.

Study selection

The process used SCOPUS, which found a search result of 98 articles. The query was “TITLE-ABS-KEY (noise) AND TITLE-ABS-KEY (air pollution) AND TITLE-ABS-KEY (pm) AND (TITLE-ABS-KEY (urban) OR TITLE-ABS-KEY (traffic)) AND PUBYEAR > 2004 AND PUBYEAR < 2026 AND PUBYEAR > 2014 AND PUBYEAR < 2026 AND (LIMIT-TO (SRCTYPE,"j”)) AND (LIMIT-TO (PUBSTAGE,"final”)) AND (LIMIT-TO (LANGUAGE,"English”)).” This search was conducted on 4 August 2026, resulting in 132 records.

Then a process of manual screening for eligibility was undertaken, reviewing first the title and abstract and finally the full text. The main reasons for exclusions were not focusing on outdoor ambient noise, focusing only on air quality (usually, noise appeared not in the context of sound but as noise to signal), or noise itself. Studies focused on epidemiology were also excluded since effects on health had been previously studied by the authors. Another data charting was done by one of the authors by reading the articles again. After this screening, the number of remaining articles was 28.

Another 23 studies were added, known to the authors from previous searches in Scopus. For the articles citing those previously included, Scopus was used. The final list included 56 studies (Figure 2). After this process, a narrative/thematic classification was done, and during the writing, appropriate studies were added to the relevant sections. Full list of article can be found in the Supplementary Table 1.

Sources of noise and pollution

The starting point of this analysis is usually identifying the sources of noise and air pollution. In some cases, it is defined as traffic; in other cases, some temporal sources are identified either as the object of the study or as a covariant to work with.

When discussing sources, most of the studies have a clear objective or covariant identified. For example, in the work of Okokon et al. (2018), the Harmattan season is a period when north-easterly winds from the Sahara Desert blow southward across West Africa, negatively impacting air quality with characteristic dustiness. Other studies also discuss the influence of meteorological conditions, especially on air quality (Kostadinov et al., 2024; Weber and Litschke, 2008; Khan et al., 2018; Banzhaf et al., 2024; Silvaggio et al., 2020; Frederickson et al., 2024; Romero Cortés et al., 2025; Cohen et al., 2007; Shu et al., 2014; Gan et al., 2012).

Another contributor is biomass burning, either for heating, as found in Skopje, North Macedonia, by the study by Kostadinov et al. (2024). To arrive at that conclusion, they made the correlation between PM10 levels and temperature, finding that the lower the temperatures were, the higher the PM10 levels were. They supported this statement with the work of Bodor et al. (2020), which focused on the pollution correlation in Transylvania, Romania, explained by the use of heating systems. This burning was also detected in Khan et al. (2020) as part of why some of the correlations between air quality and noise were weak, also related to industrial fields in Roskilde, Denmark.

Another source of pollution for noise and PM is construction activity. In the study by King et al. (2016) about the High Line park in New York City, USA, they found that the active construction sites interfered with the measurements of PM and noise. In the study by Araújo et al. (2014), they identify the execution of reinforced concrete (columns, beams, and slabs) at a construction site in Salvador, Bahia, Brazil, as the most impactful in terms of air pollution. This is also discussed by Loo et al. (2023) in Hong Kong and by the team of Hafeez et al. (2013). The construction industry has the particularity that, instead of being produced in factories, it is very common to handcraft the production on the construction site. This leads to emissions into the air during construction and to high noise levels nearby. Another approach is studied by Brusselaers and Mommens (2022), emphasizing the environmental costs saved by using a logistics center for construction.

One specific source of pollution is attributed to firecrackers and fireworks. The studies by Kavitha et al. (2023) and Mandal et al. (2012) stress the issue of ambient pollution from firecrackers during the Diwali festival in India but also mention noise issues studied by Sahu et al. (2020). Other festivals, like Fallas in Valencia or Guy Fawkes celebrations in London, can also lead to problematic PM levels according to the study by Moreno et al. (2007). Even if they are temporary, they can be easily inhaled and pose a health risk. Other identified sources of pollution are airports (Cohen et al., 2007) and industrial activity (Chung et al., 2011).

However, one of the most common sources of noise and pollution is traffic (Davies et al., 2009; Frederickson et al., 2024; Shu et al., 2014; Gan et al., 2012). In the study by Paunović et al. (2009) in Belgrade, traffic was correlated with the annoyance perceived by dwellers, especially if they have windows in their bedrooms or living rooms facing noisy streets. In the study by the team of Okokon et al. (2018), one of the biggest exposures that should be addressed is commuting time. In their work, they studied the intake of noise and pollution from different types of commuting in Lagos during late 2015 and 2016 (car, micro-bus, and larger bus), and the results indicate that noise exposure was also related to problems with the transportation itself, particularly older buses that do not have air conditioning or are too noisy themselves. In Khan et al. (2020), there is a logarithmic correlation between noise and average daily traffic (AADT) as well as NO 2. However, the speed showed a decrease in the interval between 40 and 80 km/h, probably due to the variabilities in the speeds of NO x. In the study by Weber and Litschke (2008), the traffic of a busy road is clearly identified as a source of noise in a specific neighborhood of Essen, but PM distribution (especially PMcoarse and PM1) is not, particularly on windy days.

Nevertheless, the study by Saxena et al. (2024), which evaluates pollution in Lucknow, India, discusses the origins through different readings. To identify the sources, the study uses chemical markers. In this case: road dust re-entrainment, vehicular emissions (traffic), industrial emissions, and biomass burning. The difference between the seasons is the varying importance of biomass burning in Lucknow, with vehicular emissions and road dust re-entrainment responsible for more than 40% of the pollution in both seasons. Noise in this case was not analyzed in the same depth, but a pattern shows that the post-monsoon season is louder and exceeds the limits that should be.

Modeling

The next step of this analysis is identifying the approaches to find the specific values of air pollutants and noise, as well as other covariate variables. The approaches taken depend on the scope of the study and its spatial resolution. Some studies have an approach that models the data from different pre-existing models for noise (like CNOSSOS-EU or NMPB-Routes 96) or pollution models (for example, using Land Use Regression or Dispersion models).

Spatial modeling

Several models have been used to measure these phenomena, usually for epidemiology models to predict general exposure to noise, PM, or NO 2. The work of Shen et al. (2024) discusses that modeling of air pollution and noise requires detailed information on AADT loads on all roads. For this, they use a Random Forest implementation to show the non-linear relationships between predictor variables and AADT counts in grids of 5 × 5 meters. This is similar to the work of Khan et al. (2018). Another important input in these models is landscape metrics, which, according to the work of Weber et al. (2014), are also relevant for modeling. In their study, they input a specific division for Leipzig, Germany, derived from the previous study of Haase and Nuissl (2007), and this particular input resulted in good optimization of the prediction model. The study of Silva and Mendes (2012) uses spatial modeling to create a normalized index from previous models available, weighting them equally for noise and air quality (divided into 5 components: CO, NO 2, PM 10, C 6 H 6, O 3). To apply this index to Viana do Castelo, Portugal, a specific model was created to represent a pulp and paper mill located near the city.

Pollution modeling

If the sources are identified, using a pollution model is common, and from these, pollution can be assigned to specific places. For example, in the study of Grazuleviciene et al. (2022), the Land Use Regression (LUR) model was used for modeling pollution. In another approach, the study by the team of Tonne et al. (2016) utilized a specific modeling system for London called KCLUrban, which is based on the Atmospheric Dispersion Modelling System from the London Atmospheric Emissions Inventory. The study by Stucki et al. (2023) is based on Segersson et al. (2017), taking into consideration different layers of sources modeled and then averaged by year. In the study by Amundsen et al. (2008), a local dispersion model, EPISODE, whose present version is described in this work by Hamer et al. (2020), was adapted for studies in Oslo and Drammen, where they used Airquis, another model developed by the Norwegian Institute for Air Research (Sivertsen, 2004). Another dispersion model used by Ribeiro and Mendes (2013) and later by Ribeiro et al. (2022) is AUSTAL2000, which was utilized in the extension APL of the aforementioned CadnaA software, allowing for the modeling of PM 10, NO x, benzene, and SO 2 during different hours in the morning. Another approach used by Khan et al. (2020) employs the AirGIS system by the same research team (Khan et al., 2019). This system utilizes a Danish Eulerian Hemispheric Model (DEHM) (Christensen, 1997) that accounts for all sources, including power plants, in a grid of 5.6 × 5.6 km. Additionally, it uses the Operational Street Pollution Model (OSPM) described by Berkowicz (2000). In practical terms, this is a Postgres database with a spatial extension. The advantages of this semi-empirical parametric model are that it requires a relatively small number of measurements but relies on assumptions that are not always true. However, the OSPM model has limitations regarding the modeling of highways as contributors to pollution (Khan et al., 2020) and underestimates concentrations for narrow street canyons with proportions of less than 1:1 (Kakosimos et al., 2010; Murena et al., 2009). In the study of Perez et al. (2015), the methodology described in Keuken et al. (2014) was used, where background concentrations were obtained from a regional scale model. However, since they were not using dust and biogenic emissions (leading to an underestimation of PM 2.5 and PM 10), the results were corrected manually.

Noise modeling

For the case of noise modeling, it is more common to reuse open-data models, especially in Europe, where the implementation of the Environmental Noise Directive (END) (European Parliament, 2002) has had a profound effect, allowing these maps to be available for city planners and researchers. This has been utilized by Silva and Mendes (2012). In 2012, the EU also implemented the Common Noise Assessment Methods in the EU (CNOSSOS-EU) (Kephalopoulos et al., 2012). In 2015, the END was updated, and the member states of the EU are required to use CNOSSOS-EU after 2018. This has been confirmed by later studies (Crous-Bou et al., 2020; Khan et al., 2020). This framework, developed by Morley et al. (2015), allows different scaling of the model to various levels (A-F), ranging from more specific (A) to more general approaches with lower resolution (F). For example, the approach of Khan et al. (2020) uses scale D for the study of two Danish cities (Copenhagen and Roskilde). This process is not direct, however, and the study uses some coefficient adaptations that have been tested in other Nordic countries like Finland (Kokkonen et al., 2018).

Other models are also implemented; for example, in the work of Tonne et al. (2016), the study uses the TRANEX model based on the work of Gulliver et al. (2015), which models the roads near London, or in Vienneau et al. (2017), where they use information taken from the SiRENE project. Another approach is taken by the study of Stucki et al. (2023), which uses the Nordic prediction method described by Bendtsen (1999) that plans to use roads and distances to residences for modeling. Additionally, software is run for these purposes; some details are described in the study by Ribeiro and Mendes (2013) and later by Ribeiro et al. (2022) for modeling the study area from Braga, Portugal, using the software CadnaA, which integrates the NMPB-Routes 96 (Centre d’études sur les réseaux, les transports, l’urbanisme et les constructions publiques, 1997) as a model system. The advantage of this model is that it is viable to generate maps that are hour-dependent.

Green space modeling

One of the common descriptions for the studies mentioned is to describe the relationship of green areas as a covariate to the relationship with the stressors. One adjacent green space variable used is the Normalized Difference Vegetation Index (NDVI). It is used in Grazuleviciene et al. (2022), Pelgrims et al. (2021), Crous-Bou et al. (2020), Stucki et al. (2023), Vienneau et al. (2017), and Xu J. et al. (2024). This type of approach is used to define the proximity of people to green spaces. These values of NDVI are non-trivial to interpret, especially if there is a need to specify the type of vegetation (de la Iglesia Martínez and Labib, 2023).

Another approach for green space modeling is used in Crous-Bou et al. (2020). In this study, the green spaces were modeled alternatively using orthophotos and categorization using an urban atlas. In the analysis of the city of Nanjing, China, by Zhang et al. (2024), photogrammetry using unmanned aerial vehicles (UAV) in visible light was used for modeling. This is justified over LiDAR because photogrammetry is faster to process, models have a smaller size, and acquisition costs are reduced. They justified this instead of using NDVI since their approach includes 3D modeling through photogrammetry, which provides 3D information about the volumes. In the study by Ponce-Donoso et al. (2020), the percentage covered by the canopies was used instead of NDVI, divided into five discrete quintiles for measuring the effect of the coverage on PM. This is partly done to reflect the differences in the canopies over time, as indicated by Haase et al. (2014), since the scope of this study is the trees themselves. The approach from Banzhaf et al. (2024) actually uses LiDAR, as it was available to combine with orthophotos, taking advantage of this 3D property using an object-based image approach (OBIA) approach. In the study by Bottalico et al. (2017), for measuring the Urban Forest, definitions by FAO and European institutions were used. Based on orthophotos, they categorized different forest categories depending on the trees found. Individual trees were excluded due to the scope of the study.

Machine learning approaches

In some recent study cases (Kostadinov et al., 2024; El Mghouchi et al., 2024; Xu S. et al., 2024; Frederickson et al., 2024; Clark et al., 2020), methods from machine learning are used, as opposed to other more deterministic approaches, usually with some predictors. One of the ways for forecasting the presence of contamination is using Deep Learning, as the work by Kostadinov et al. (2024) suggests that simpler architectures with LSTM (Long Short Term Memory) units or SimpleRNN were more effective at predicting outcomes than combining them or using convolutional neural networks (CNN). The study by El Mghouchi et al. (2024) employs techniques with Feature Selection methods to identify the most relevant predictor variables, and after reviewing 10 other studies that have approaches for predicting pollution, it concludes that the approach should incorporate meteorological variables to determine which are best for predicting PM10 outcomes. In the study by Xu S. et al. (2024) in Toronto, the information collected utilized eXtreme Gradient Boosting, a machine learning package in R selected for handling sparse, continuous, and categorical features. The study by El Mghouchi et al. (2024) uses an ANN with a supervised learning method, taking information from a network of stations using Feature Selection specific to this situation, as it is based on the calculation of AQI indexes. In the review by Mihalakakou et al. (2023), several of these methods are discussed within the evaluation of green roofs. A couple of studies (Frederickson et al., 2024; Clark et al., 2020) use a machine learning approach to identify traffic during campaigns to infer correlations that are useful. The study by Clark et al. (2020) goes further, using audio recordings during the campaigns to identify specific sources. However, the transferability of these models needs to be better tested.

Measurement

The other approach involves empirical measurements. These measurements include different types of sensing. This also addresses not only the objective measurements of dB(A) or PM 2.5 but also the subjective perspective of people regarding the perception of noise pollution, air quality, and annoyance. Measuring the air quality and sound of a city can take three main approaches: either a campaign is undertaken with sensors, or open data is used [which utilizes previous campaigns or already available open data or model information like the CALIOPE model (Benavides et al., 2019)].

In summary, Image data processing can accurately count how many vehicles transit a given spot (using machine learning).

Sound analysis is sufficient to identify noise sources during a campaign (using machine learning).

GIS models are competent with or without machine learning approaches.

Low-cost sensors, despite their limitations, can be used to gain a better understanding of the spatial distribution of noise and pollution.

Surveys can detect areas of noise annoyance as well as pollution annoyance.

Use of fixed sensors

One common approach for these studies is to conduct several campaigns of discrete measurements with fixed sensors (El Mghouchi et al., 2024; Titos et al., 2015; Silvaggio et al., 2020; Davies et al., 2009; Clark et al., 2020; Puscasiu et al., 2019). In the case of the study by Silvaggio et al. (2020), this leads to a spatially specific model using generalized additive models (GAM). These fixed sensors have the advantage of allowing information collection for long campaigns (by weeks, months, or even years). However, they still require human hours to plan them properly, as stated by Clark et al. (2020), for preparation, maintenance, data download, and management.

Use of wearable sensors

In Helbig et al. (2021), a literature overview of personal exposure focusing on wearable sensors for heat, air pollution, and noise is undertaken. These sensors have the advantage over weather stations or fixed points of being able to measure individual exposure and have the ability to record perception and track behavior while being low in cost. However, these methods require taking into account more factors (besides weather) and have lower precision. Specific cases of use are described in Okokon et al. (2018), Okokon et al. (2017), Liu et al. (2015), McKercher and Vanos (2018), Apparicio et al. (2018), and Ueberham et al. (2019). In the case of McKercher and Vanos (2018), it is an attempt to address the lack of public sensors in the city of Lubbock, USA, but they state the limitations in terms of accuracy for this type of sensor for that purpose. However, other studies (Okokon et al., 2018; Okokon et al., 2017; Ueberham et al., 2019) prove that wearable sensors are a good way to assess personal exposure of citizens.

Use of mobile sensors on cars

In the study by Xu J. et al. (2024) and Xu S. et al. (2024) in Toronto, Canada, they used mobile sensing methodologies layered by Chen et al. (2022) and described in Xu et al. (2022). This method consists of using cars that, following certain routes, take several measurements at specific locations. The work of Chen et al. (2022), discusses how many times a measurement needs to be repeated in order to obtain a representative air quality pattern. This approach was also used by Park and Lee (2024) to understand the general picture of the construction of a cap over a highway in Seongnam, South Korea, spanning measurements from July 2020 to October 2022.

Use of unmanned aerial vehicles

In the work by Liu et al. (2021) investigating the effects of a noise barrier, unmanned aerial vehicle (UAV) measurements are carried out, providing information about the different PM distribution at various heights. This type of measurement using UAVs was also undertaken previously by Li et al. (2025). A similar method was used by Park and Lee (2024) to capture pollutant levels at different altitudes to evaluate the effectiveness of a highway capping in Seongnam, South Korea. A good practice highlighted in this last study is to operate at speeds below 30 km/h to avoid interactions with other vehicles and minimize potential emissions from the monitoring vehicle itself.

Subjective perspectives

In some studies, such as Xu J. et al. (2024) and Honold et al. (2012), a specific subjective perception of noise pollution (using a Likert scale ranging from 1 to 5) is employed. This has the advantage of evaluating the specific subjectivity of the population and their annoyance. Additionally, in Ueberham et al. (2019), Xu J. et al. (2024), and Honold et al. (2012), as well as Mueller et al. (2020), a Likert scale (ranging from 1 to 5) is used to discuss the population’s subjective perception of pollution. However, since the effects of PM or air pollution are not always directly perceived, it should be accompanied by other objective measurements.

As pointed out by Helbig et al. (2021), the subjective experience is difficult to capture solely with objective measures like the systems described in the EU directives (CNOSSOS-EU), since personal subjectivity can lead to different levels of annoyance in the same objective location, as described by Aletta et al. (2018) in their study in Antwerp, Belgium. These limitations were also shown in the studies by Verbeek (2018) and Von Szombathely et al. (2018), which are not conclusive and are quite location-specific. In this sense, a combination with local specifications of the environment seems reasonable, as well as combining objective measurements with a more specific subjective approach.

Another approach, noted by Helbig et al. (2021), is the study by Ueberham et al. (2019), which uses surveys in addition to wearable sensors. In their methodology, they indicate that the questionnaire needs to address not only existing European travel patterns but also protection motivation theory (Rippetoe and Rogers, 1987). To compare them with the objective information from their wearable devices, some thresholds were created to align with the Likert scale of the subjective perspectives of the cyclists. In this sense, the conclusion was that noise and ambient pollution were consistently underestimated. Another objective for surveys is to understand the perceived benefits and preferences for policies like low-emission zones (LEZs), as shown in the study by Jiménez-Espada et al. (2023).

Use of open data

Open Data (or “smart city tools”) refers to all the data that is already available to the public from various layers of administration. This has significant value as it saves effort in sensing (or modeling) and adds more information to these studies.

For example, in the work by Lebrusán and Toutouh (2020), data from existing public portals is used to access data using the approach of “smart city tools”. In this case, they use the existing sensor network of Madrid and the Open Data Portal (ODP) of Madrid City Council for air pollution and noise. In the studies by Tobollik et al. (2016), Shkembi et al. (2024), Garkov et al. (2024), Yang N. et al. (2020), Yang W. et al. (2020), Romero Cortés et al. (2025), and Perez et al. (2015), open data for their respective cities is also used but is not mentioned as a “smart city tool”, even though they use Google Traffic data (Romero Cortés et al., 2025). In the study by Bottalico et al. (2017), the use of orthophotos was taken from online public sources (Toscana region, in Italy). In the case of large scopes, like in the study of Beijing, China, by Xu J. et al. (2024), publicly available data is used, specifically datasets already available from the Canadian Atmospheric Composition Analysis Group (CACAG) that have a resolution of 0.1° (around 1.2 km by 1.2 km) (Van Donkelaar et al., 2021).

In some cases, previous research studies provide information for the next steps. This is the case of the study by the team of Crous-Bou et al. (2020), which used the measurements collected during the monitoring campaign of the European Study of Cohorts for Air Pollution Effects (ESCAPE) (Beelen et al., 2013). In the case of the study by Vienneau et al. (2017), they use the methodology from the SiRENE project.

Measurements during the COVID-19 lockdown period

In some of these studies, one of the measurements was taken to observe the behavior of cities during the COVID-19 lockdown. In the study by Kostadinov et al. (2024), they measured PM10 and noise levels, and they found that indeed noise was around 5–7 dB lower during the time of lockdown, but no significant difference in PM 10 pollution was observed, probably due to the heating systems in place in the city of Skopje. During the COVID lockdown, as has been studied by Kavitha et al. (2023), there was a positive impact on air quality; this is consistent with the findings in Lucknow, India, by the team of Kumar et al. (2023).

Development of indexes

For risks and evaluation, the use of specific indexes has been described. These indexes are usually tailor-made for analyzing a specific environment and summarize different sets of measurements. Even with a local scope (to some specific study), these indexes are useful for summarizing a complex reality in just one number.

For evaluating specific urban interventions, this is done by Lebrusán and Toutouh (2020) as well as King et al. (2016). Other studies are more widespread and evaluate the whole urban area (Silva and Mendes, 2012; Xu S. et al., 2024; Rocha et al., 2025), usually based on the specific legislation of the studied areas and including other factors relevant to the study. Another study was created to summarize personal exposure of workers (Yang N. et al., 2020; Yang W. et al., 2020). However, these indexes tend to be very purpose-specific and difficult to apply outside the scope of their respective papers.

Effects on health

Effects on mental health

One of the examples of study is the prevalence of mental health issues related to pollution (air or noise pollution). The studies included in this review focus on both of these effects; however, there is more extensive documentation separately. The study by Zijlema et al. (2024) discusses mental health at the intersection of the built environment, green spaces, air pollution, and noise for the city of Barcelona, Spain. In this case, noise and pollution are only part of the environment, where the distance to the nearest green space and population density are other variables taken into account. Another study by Grazuleviciene et al. (2022) used a similar approach, examining the spatial impact of green spaces and the quality of the built neighborhood. In this case, the focus was the difference in gender that was not found, as well as the differences only in environmental exposures, but only in socio-demographic variables and the prevalence of chronic diseases in self-rated health. In the study by Pelgrims et al. (2021), they found a correlation between high levels of black carbon, NO2, and PM10, which were correlated with higher odds of depressive disorders. However, the association between urban greenness, noise, and building morphology could have been identified. In the study by Bakolis et al. (2021), pollution levels (PM 10, PM 2.5, and NO x) were linked even after adjusting for road-traffic noise. The study by Crous-Bou et al. (2020), which is more centered on Alzheimer’s disease, found associations between PM 10, NO 2, and some precursors of Alzheimer’s disease. However, no relevant associations were observed in relation to exposure to green space or noise. Additionally, in the study by Zijlema et al. (2024), it was found that population density is negatively correlated with poor mental health if the pollution levels are low; in this study, this was because, at the moment, some streets in the city center had been pedestrianized, which caused the pollution levels to drop. This is consistent with the results from Titos et al. (2015) in the city of Ljubljana, Slovenia.

Effects on physical health

The study by Tonne et al. (2016) found that there was a connection between noise and pollution to mortality and hospital readmission among myocardial infarction survivors in London, United Kingdom. In this study, the Hazard Ratio for non-exhaust PM10 was found to be positive. They also found that the relationship between noise is only slightly related when air pollution is excluded as a variable. The approach by Liu et al. (2015) discusses heart rate variability (HRV) as a quantitative biological measure that is related to the risk of mortality and can show associations between traffic-related air pollution and traffic-induced cardiovascular effects. HRV reduction has been associated with higher mortality and with air pollution (Zanobetti et al., 2010). In some cases, this difference in health due to pollution is caused by social exclusion, such as the redlining discussed by Shkembi et al. (2024).

Risk assessment and diagnosis

All this information can also be used for making risk assessments for workers or specific environments and for urban diagnosis to identify points where actions should be taken.

Urban diagnosis

In the case of the work by Silva and Mendes, (2012) s the idea is to find several specific priority areas for mitigation and monitoring using the index proposed in the article. This is a similar approach to that of Xu J. et al. (2024) and Xu S. et al. (2024); however, in this approach, Xu J. et al. (2024) and Xu S. et al. (2024), included other parameters such as the availability of public transportation and socioeconomic status.

There are some studies (Rocha et al., 2025; Silva and Mendes, 2012; Xu S. et al., 2024; Shkembi et al., 2024; Romero Cortés et al., 2025; Temkov et al., 2025) whose focus is to discuss the state of a particular city or areas within it (Han et al., 2018). In these cases, measuring both noise and air pollution leads to more information about the environmental quality of the cities, either for making a diagnosis for improvements (Rocha et al., 2025; Silva and Mendes, 2012) or for conducting a social analysis (Xu S. et al., 2024; Shkembi et al., 2024).

Referenced by the study of Khan et al. (2020), the publication of the European Commission (2016) links air quality and noise using socioeconomic status, meaning that the poorer the neighborhood, the higher the chances that old infrastructure and conditions are nearby. This multi-variable approach has been helpful in both respects. Additionally, another study by Yang N. et al. (2020) and Yang W. et al. (2020) focused on the evaluation of exposure of workers for delivery but also concluded with an urban diagnosis of the most polluted places in the city studied (Wuhan, China).

Work and commute assessment

Several studies have discussed the risks of commuting and the risks faced by workers in urban areas (Yang N. et al., 2020; Yang W. et al., 2020; Salmon et al., 2025; Apparicio et al., 2018; Okokon et al., 2018; Okokon et al., 2017; King et al., 2016). These studies conclude that public transport and active transportation, like biking and walking, imply more exposure to pollutants, which is consistent with the discussion by Xu J. et al. (2024) and Xu S. et al. (2024), where an endogenous problem with traffic congestion is described. When there is a perception that the air is polluted, using a private vehicle is preferred, leading to more congestion and pollution in a vicious cycle. This is consistent with a previous study by Liu et al. (2015), which found that walking to commute leads to more exposure to PM 2.5 compared to subway travel.

Another conclusion of these studies is that better public transportation (Okokon et al., 2018; Okokon et al., 2017) and public policies improve the conditions of exposure. Also cited by Apparicio et al. (2018), the health benefits of using active transportation modes are greater than the risks (Cepeda et al., 2017).

When combined, these types of assessments of specific exposures and wearable sensors are examples (though not mentioned in the aforementioned studies) of geographically explicit ecological momentary assessment (GEMA), defined by Kirchner and Shiffman (2016). GEMA is an extension of ecological momentary assessment (EMA), also discussed by Chaix (2018), that includes explicit locations, emphasizing the measurement of mental health and wellbeing. However, one of the studies by Ueberham et al. (2019) explicitly uses this GEMA approach.

Mitigation strategies assessment

Nature-based solutions

A relatively recent approach for addressing challenges associated with health in urban areas focuses on nature-based solutions (NBS). NBS can be defined as “solutions that are inspired and supported by nature, which are cost-effective, simultaneously provide environmental, social, and economic benefits, and help build resilience” (European Commission, 2025). This approach has been supported by the European Union through different policies and funds. This approach, which brings together ecosystem-based approaches and social benefits (Raymond et al., 2017), creates increased attention to urban vegetation due to this lens (Ode Sang et al., 2022).

The review by Cardinali et al. (2023) focuses on green walls as this approach of NBS, which, even if promising, lacks in-field studies for both air quality and acoustic behavior. The taxonomy of Green Walls (or Vertical Greenery Systems) is discussed in detail by Bustami et al. (2018).

In one of the studies discussed by Cardinali et al. (2023), the one conducted by Wong et al. (2010) uses an experiment finding that the absorption of green walls is better with more plants, and that they perform well for isolation compared to other construction materials (except acoustic isolators), especially at higher frequencies. The traffic spectrum includes a part that indicates these green walls are more effective at absorbing frequencies over 400 Hz; however, there is a partial mismatch with typical traffic noise (Gjestland, 2008), which, apart from a peak over 500 Hz, has another peak in lower frequencies around 63 Hz, difficult to overcome given the length of the sound wave. This is consistent with the findings of Paull et al. (2020). According to their findings, external factors specific to each project may modify sound reflection or absorption. These factors include the soil substrate, leaf area and density, and atmospheric conditions such as wind (Van Renterghem et al., 2015). Another influence that can affect this performance is the shape of the city itself. In the study pointed out by Bustami et al. (2018) and Ismail (2013) notes that the noise absorption by green walls is greater in narrower streets. Regarding air pollution, one of the studies cited by Cardinali et al. (2023), finds that green walls have the capacity for pollutant removal, but this depends on the species, and it is usually more effective for PM than for gaseous pollutants (Tomson et al., 2021). The mechanisms discussed by Cardinali et al. (2023) indicate that PM is temporarily deposited on leaf surfaces and may also be dispersed with the help of rainfall. This is also affected by the geometry of the street and the canyon effect (Ysebaert et al., 2021). On a smaller scale, this is also discussed in Abhijith et al. (2025), where a small structure implemented in a school effectively addresses pollution but only addresses noise when it is part of a gate.

Another approach, even if it is very building-scale, is green roofs. The literature review by Mihalakakou et al. (2023) provides a comprehensive overview of other literature not only about the benefits of green roofs for the buildings where they are installed but also for the city, as they can improve water absorption and reduce the urban heat island (UHI) effect (Tomson et al., 2021; Pugh et al., 2012). They become part of the urban ecosystem, renaturing cities, and since they are part of the green spaces network of the cities (to some extent), they contribute to the wellbeing of citizens (Williams et al., 2019). Another interesting review has been conducted by Ode Sang et al. (2022), which discusses green roofs and green walls from an ecosystem perspective, with air quality and pollution being a fragment of this study. In their work, reduction can be improved with proper design, taking into account the pollutant source and the main winds of the location. An example of optimal pollutant filtering is a heterogeneous topography of the vegetation layer with different key properties (Leaf Area Index (LAI), transpiration, stomatal conductance, photosynthetic capacity). This is similar to the benefits found by Moniuszko et al. (2025).

Different studies have covered the advantages of using green spaces (Zhang et al., 2024; Guidi Nissim and Labrecque, 2021; Vienneau et al., 2017; Ponce-Donoso et al., 2020; Bottalico et al., 2017; King et al., 2016; McNabola et al., 2008; Abhijith et al., 2025; Garkov et al., 2024). Several of these studies include covariates such as noise and pollution. In the work by Zhang et al. (2024), the relationship between vegetation coverage and sleep quality was discussed using several variables, where covariates included noise and pollution. In this case, it was found that vegetation coverage (calculated through photogrammetry and assigned to a specific community) had an indirect correlation with sleep quality through noise pollution (p = 0.012) and mental stress (p = 0.016). However, there were no relevant correlations with PM and sleep quality. In this sense, the work by Vienneau et al. (2017) discusses that the health benefits of green spaces, measured using NDVI in a Swiss cohort, were quite independent of PM pollution or transportation noise, which were studied as mediators responsible for only 8% and 2–6% of the green space’s contribution to preventing deaths, respectively. This is coherent with the findings by Garkov et al. (2024). The approach by Ponce-Donoso et al. (2020) aimed to find if different types of canopies influenced noise and air quality, among other factors. Actual canopy coverage shows a decreasing tendency with particulate matter, and maximum noise levels are attenuated. In the study by Bottalico et al. (2017), a significant contribution to attenuating PM 10 and O 3 in the city due to urban forests was estimated; however, in relation to the pollution of the city itself (Florence, Italy), particularly in some areas, this contribution is rather modest. In the study by King et al., (2016) about the High Line in New York City, USA, it was found that limiting pedestrian traffic through a green area elevated from direct contact with barriers (vegetation, other buildings) to the road is beneficial for air quality, as the distance from the source is greater for exposed pedestrians, as discussed by McNabola et al. (2008).

Other constructive solutions

In the study by Liu et al. (2021), which focused on PM distribution with a highway as the main source (without other nearby sources, like biomass burning or industrial emissions), it is recommended that designs avoid street canyons near pedestrians, especially on roads that are perpendicular to the main winds. This is consistent with the study by Li and Managi (2021), which suggests that pollutants like PM 2.5 can extend up to 300 m from noise barriers. Another option is capping highways completely. Findings by Park and Lee (2024) from a highway capping project in Seongnam, South Korea, show that highway caps play a crucial role in mitigating traffic noise and primary pollutants.

Regarding the construction industry: A study in particular, the study by Hafeez et al. (2013), on sprinkling water for road construction is useful for reducing PM 10 and indirectly reduces noise due to lesser machinery efforts. In the study by Loo et al. (2023), a comparison was made between Modular Integrated Construction and cast- in situ methods, revealing that air quality was better using modular construction, and the periods of noise over 70 dB were 81.25 times shorter than those using in situ techniques.

Transportation shift

Since one of the main contributors to both noise and air pollution is traffic, various ideas are discussed regarding transport in cities, from the implementation of electric vehicles (EVs) to the establishment of low-emission zones (LEZs).

The growth of electric cars specifically and electric vehicles (EVs) in general is evident, even though the total global share is still less than 5% of total cars, with peaks such as in Norway with over 30% and China with over 10% of the total car share (IEA, 2025). The study of the impact of electric cars usually focuses on their positive CO2 impact rather than their sound effects. However, a study cited by Sabel et al. (2016) as part of the same project, Urban Reduction of Greenhouse Gas Emissions in China and Europe (URGENCHE), conducted by Keuken et al. (2014), concluded using a model developed in 2010 that substituting half of the cars with electric cars would impact NO 2 exposure in the city, but it would not have substantial effects in the cities studied. This projected result is consistent with another study, also part of URGENCHE, by Perez et al. (2015) in the city of Basel, Switzerland; however, in this study, the impact of EVs on the noise environment is quite positive.

One way of reducing heavy traffic, and thus its emissions, is through the employment of Cargo Bikes (Zimmermann and Palgan, 2024). However, this study has yet to provide quantitative data about the specific impact on air quality and noise pollution. Early adoptions have occurred in Germany and Austria, where the state provides support for systems organizing Cargo Bike sharing. The cited studies have limited information about the impact, but the survey results (n = 930) from Becker and Rudolf (2018) showed that more than half of the trips from cargo-bike sharing systems, in their absence, would be made by car or not at all. This infers that it is a good approach for a modal shift towards cargo bikes since they can change not only individual daily commutes but also the transportation of children to school or carrying products (Carracedo and Mostofi, 2022).

The study by Ribeiro et al. (2022) focuses on finding if it is viable to generate healthier routes for pedestrians and cyclists. Using an algorithm, it calculates different possible routes that have less pollution, and they conclude that it is, indeed, possible to calculate and incorporate into different algorithms. This study followed the work by Ribeiro and Mendes (2013), which focused only on school trips where the trips were not automatically calculated. Taking this into account is also consistent with the study by Ueberham et al. (2019), where green spaces and low traffic volume had a high or very high importance.

Some studies focus on the consequences of public transportation, either for the users themselves or for the city as a whole. According to the work of Okokon et al. (2018), urban transportation planning should also take into consideration the exposure of commuters to air and noise pollution. The impact of a good transportation system that is up to date and has good planning behind it is effective in lowering the pollutants in the nearby area, as shown in the study by Titos et al. (2015) in the city of Granada, Spain. In Xu J. et al. (2024) and Xu S. et al. (2024), it was studied that in cities with subways, congestion was less prominent and therefore, pollution was reduced.

Additionally, in Xu J. et al. (2024) and Xu S. et al. (2024), one of the main conclusions was that holiday time in China (in this case, next to the New Year celebrations) was relevant in reducing theCongestion Delay Index (CDI) considerably in all cities studied. This suggests that a different relationship with work could lead to improvements in congestion and, therefore, pollution and noise.

Low-emission zones and their related approaches, like zero-emission zones or noise low-emission zones, are urban areas where the administration imposes some constraints on how vehicles are permitted in specific parts of the city in order to reduce emissions. They are several throughout Europe, but others have been undertaken also in Latin America (Carvalho Neto et al., 2026), and similar restrictions have also been implemented in Asia (Park and In Lim, 2025).

The study by Lebrusán and Toutouh (2020) analyzes the pollution and noise improvements in Madrid and finds that NO2 was reduced significantly, as well as noise, which was reduced slightly (background noise from traffic). It was also concluded that no border effects were observed; this means that traffic remains the same but is re-routed, increasing pollution and noise at the border of the LEZ. This is consistent with other previous studies, such as the case analyzed by Panteliadis et al. (2014) in Amsterdam, where the implementation of the LEZ resulted in significant decreases in traffic-related air pollutants. In Titos et al. (2015), changes in two European cities (Liubliana, Slovenia, and Granada, Spain) that had undertaken transformations in their city centers were evaluated, and the conclusion was that pollution improved, but not throughout the whole city—only in the local area affected. In the same study, they discuss other LEZ analyses that were not widespread by the time of the study, as these areas are implemented with other measures. A recent study by Delgado-Lindeman et al. (2025) also contributes to this topic. In the study by Silvaggio et al. (2020), there was an analysis of a specific intervention in a Noise LEZ in Monza, Italy, which consisted of the substitution of a low-noise-emitting pavement that proved efficient in terms of noise (especially during the night); however, given the specifics of the measurements (close to the houses), it could not determine any improvements in air quality. Another study that emphasized calming traffic, without being specific about the implementation of LEZ, is the work of Zefreh and Torok (2021).

Correlation between noise and pollution

The correlation between noise and pollution has been discussed in several studies (Kostadinov et al., 2024; Han et al., 2018; Khan et al., 2018; Khan et al., 2020; Frederickson et al., 2024; Weber and Litschke, 2008; Ribeiro et al., 2022; Weber et al., 2014; Davies et al., 2009; Ponce-Donoso et al., 2020; Amundsen et al., 2008; Silvaggio et al., 2020; Dekoninck and Severijnen, 2022; Romero Cortés et al., 2025), and the conclusion is that they correlate under very specific circumstances of weather and origin. The relationship follows these ideas: Traffic still produces a great deal of air pollution; specifically, NO 2, SO x, and PM are generated due to internal combustion engines, and all vehicles produce PM due to tire wear, especially at higher speeds. Traffic, whether from electric or combustion vehicles, has non-exhaust emissions from particles eroded from the tires, the road, or abraded from brakes, as well as road dust (OECD, 2020). According to Beddows and Harrison (2021), in the case of electric vehicles, PM emissions arerelatively similar to those of combustion vehicles.

Weather conditions (especially wind) alter traffic pollution. Specific weather events can add PM to the ambient (Okokon et al., 2018). Noise is less affected by weather (Khan et al., 2018).

Other sources can also add more pollutants to the air. To be precise, industrial activities, depending on their nature, and heating systems during winter (Kostadinov et al., 2024; Bodor et al., 2020; Khan et al., 2020). These sources can be identified either by using outdoor temperature as a proxy to know when heating systems will be operative.

In the case of street canyons, the relationship is more linear, especially when there is little turbulent atmosphere (Weber et al., 2014).

When using modeling, noise and air pollution can appear decoupled (Ribeiro et al., 2022).

In this case, the main hypothesis is that given traffic conditions (AADT) and the geometrical disposition of the city (landscape metrics), a model can be implemented to simulate both noise and air quality. Elements that would affect this coupling include The presence of other air or noise pollutants different from traffic, such as festivals, heating systems based on combustion, industrial activity, and weather events.

The geometry of the city; narrow streets (street canyon effect) are more prone to have this coupling than wide avenues.

The presence of greenery would affect differently since noise reduction and the absorption of pollutants depend on different processes.

Another hypothesis, based on the studies of urban diagnosis mentioned in 4.6.1, states that poorer neighborhoods would have worse environmental conditions since property values also depend on the surrounding conditions, with one devaluating factor being the proximity to high-traffic roads.

Another factor complicating this relationship is perceived annoyance. This has been studied by Amundsen et al. (2008) and in Silvaggio et al. (2020). Ambient pollution and noise pollution surveys can be implemented to diagnose areas where attention is needed. The limitation is usually restricted to models that discuss annoyance over a constant period of time and should be implemented alongside objective measurements.

Relevance of urban spatial configuration DSPIR framework

One of the modulating factors in this relationship is urban spatial configuration. Urban spatial configuration incorporates the road design of a city and the distribution of buildings, their density, and the green spaces. This affects traffic, configuring the routes taken by the citizens. Traffic, whether from electric or combustion vehicles, has non-exhaust emissions due to particles eroded from the tires, the road, or abraded from brakes, as well as road dust (OECD, 2020). According to Beddows and Harrison (2021), in the case of electric vehicles, the PM emission is relatively similar to that of combustion vehicles.

These emissions of noise are also modulated by urban spatial configuration. In the case of both air pollution and noise, this is influenced by the street canyon effect and different possible design barriers already mentioned. In the case of noise, it is also determined by the sky view factor used by Silva et al. (2018). This relationship is synthesized in Figure 8. The DSPIR is a format used by the European Environment Agency (2016) for reporting and is well known (Carnohan et al., 2023). Applied to the findings in this scoping review, it would be the following.

Drivers would be the cities and the economic activity that are closely linked, as well as climate change, which generates the movement of all the elements.

Pressures will be land use, which is determined by cities and conditions the transport of commuting and logistics. There are three reasons for transportation: leisure, commuting, and logistics, two of which are heavily affected by economic activity. All three determine which travel needs to be done, depending on the different types of vehicles available in the vehicle pool. This conditions the road traffic, the last pressure.

The states are the weather conditions, which modulate air pollution from traffic, the urban geometry that is affected by land use, and conditions directly noise and, less directly, air pollution. The pressure of road traffic, modulated by the aforementioned conditions, creates noise and air pollution. However, this is a step before noise and air pollution exposure (see Figure 8).

Finally, the impacts are mental health and physical health. Noise exposure affects mental health more significantly, while traffic air pollution primarily affects physical health.

To address this, several responses can be deployed, mainly via urban planning and green infrastructure (which are directly intertwined). Urban planning allows for bike and walking alternatives, as well as public transport alternatives. These affect how many routes are taken by car. Urban planning also affects urban geometry (street canyon effect), so pollution and noise differ. Additionally, urban planning influences traffic restrictions. Green infrastructure (parks, green walls, trees on the street) also generates phytoremediation, reducing air pollution. It also promotes an active lifestyle and allows for bike and walking alternatives, both of which reduce health impacts. Other responses include telework and holidays, which reduce the necessity of commuting, as well as EV standardization and fleet updates that improve the vehicle pool. Low-noise paving reduces noise, and better routing optimizes both commuting and logistics routes.

Research gaps

In several of these case studies, the study of noise is not as consistent and systematic compared to air quality. This is a clear gap where research can still improve and yield better results.

Additionally, more research needs to be done combining different modeling and measurement approaches.

Several research gaps can be identified. For instance, Cardinali et al. (2023) note that mitigation lacks in-field studies where specific measures are taken. In the work of Titos et al. (2015), black carbon was identified as a proxy identifier of pollution caused by traffic.

A line of investigation could determine if noise, using spectral analysis (Yang N. et al., 2020; Yang W. et al., 2020), or sound energy, as studied by Brambilla et al. (2020), could also be used as a proxy identifier for specific traffic assessments and air quality.

Urban spatial configuration (Weber et al., 2014) is also key for these factors, and a study that undertakes the exploration of both variables within the configuration of the city in an integrated context would help understand this complexity and develop better and more cohesive interventions.

Another development that can be undertaken is to develop a methodology that, using wearable or low-cost sensors, could indicate interventions that need to be implemented. This information could also be combined with the availability of open data that different cities may have at public disposal.

Using surveys could also uncover leads if done properly to find intervention points. However, incorporating these surveys into a methodology for diagnosing city interventions pre and post facto is a research gap that could be addressed, especially in an integrated context where air quality and noise are evaluated together.

Limitations

This study has been limited by the large number of search hits in previous searches, which made narrowing the search necessary. Some studies had to be added since they had been studied by the authors previously. In the end, this search was hindered by the volume of previous literature that did not appear in the search.

Furthermore, the limitation to only journal papers means that new insights and approaches can be missed since no public conferences were included in this search.

Another limitation of this DPSIR framework is that it analyzes the phenomena but does not discuss how they are measured, which is addressed in the corresponding section.

One of the common points of these studies is that research on public health requires detailed and location-specific knowledge of the occurrence and distribution of environmental exposures (Stucki et al., 2023). The impact of different measures, like green spaces, can vary in their effects on noise annoyance and air quality. The most effective measures involve reducing traffic as much as possible within cities. Transitioning to high-quality public transportation or zero-emission transportation is key. However, a one-size-fits-all solution is not viable.

The relationship between noise and pollution is very coincidental, and both occur in a tangible way on a very small scale close to the source of pollution, specifically traffic. However, analyzing air quality and noise together in urban contexts provides more dimensional details about the complex physical reality of cities. The framework of embracing this complex reality through different lenses and using various sources of information (modeling, measurements, subjective perspectives) allows for a better understanding of complexity and proper evaluation of interventions. This review is a step toward untangling the complex reality of urban environments to improve the quality of life for their inhabitants.