Characterizing human exposure to traffic-related air pollution requires accounting for both the spatio-temporal variability of pollutant concentrations and the diversity of human activities. Agent-based mobility models coupled with synthetic populations offer promising opportunities to integrate these dimensions within a unified modelling framework. In this study, we propose an integrated modelling chain combining the EQASim synthetic population generator, the MATSim agent-based transport model, the COPERT emission model, the SIRANE dispersion model, and a dedicated exposure module. We show that dynamic mobility patterns can be integrated into exposure analyses to characterize exposure according to daily human activities. The proposed framework is applied to the Lyon metropolitan area and compared with a conventional static exposure approach. Results show that cumulative daily exposure indicators remain relatively similar between the static and agent-based approaches at the metropolitan scale. However, the dynamic framework provides additional information by capturing intra-day exposure variability, activity-specific exposure patterns, and the redistribution of populations throughout the day. Individual trajectories illustrate how work, education, and leisure activities modify exposure profiles in ways that cannot be represented by static approaches. Moreover, we find that many individuals experience high pollution levels at home and exposure follows daily traffic cycles. The proposed framework provides a basis for future developments integrating travel exposure, multi-day mobility patterns, more comprehensive assessments of environmental inequalities and other traffic-related nuisances, and coupling with well-established health impact assessment frameworks.

Noise impact assessments still largely rely on long-term averaged exposure indicators, which may overlook important temporal characteristics of environmental noise. Recent advances in noise modelling now allow the computation of more refined indicators, creating a need for objective methodologies to guide their selection according to the outcome of interest. This study proposes a methodological framework for comparing and selecting noise exposure indicators based on three criteria: validity, practical applicability, and transparency. Fifteen acoustic indicators, including conventional metrics and novel indicators capturing temporal patterns, restorative quiet periods, and noise peaks, are compared following the selection criteria. The framework is applied to early-life mental health assessment, an emerging research area with growing evidence of associations with noise exposure. Indicator validity is evaluated in two child populations: the longitudinal Amsterdam Born Children and their Development (ABCD) cohort and a cross-sectional Alpine sample. No single indicator consistently outperforms the others across all outcomes. Instead, complementary indicator sets appear necessary, with traditional metrics remaining relevant for their robustness and transparency, and advanced indicators providing added value for specific mechanisms particularly those related to sleep disturbance or restorativeness, at the cost of higher data and computational requirements. Beyond the mental health application, the proposed framework is transferable to other outcomes, populations, and exposure contexts.

Railway noise is a source of annoyance for populations living near railway infrastructure, particularly in the context of increasing high-speed train traffic. This study investigates short-term annoyance induced by individual high-speed train passages in everyday residential settings, with the aim of understanding the respective contributions of acoustic, situational, and personal factors. Short-term annoyance was assessed in situ using the experience-sampling method, which consists of repeated real-time ratings collected in daily life and associated with simultaneously recorded acoustic events. Participants living along three French high-speed railway lines provided short-term annoyance ratings following train passages, together with information on their activity, location and windows position, and indoor sound environment. A range of acoustic indicators, including conventional A-and C-weighted level indicators, loudness, and speed-related indicators, were examined using linear mixed-effects models. Results show that models based on LAeq,Tevt, LAmax, SELA, EA and loudness performed similarly and were statistically related to short-term annoyance but explained only a limited proportion of its variance. Indicators targeting low-frequency content or train speed did not provide any additional explanatory value in situ. In contrast, noise sensitivity and situational factors affecting perceived sound levels (participant location, windows position and indoor sound environment) played a dominant role and strongly influenced short-term annoyance ratings. The experience sampling method allowed us to study the short-term annoyance induced by high-speed trains noise in an ecological situation. With this method, we showed that considering the residents' real exposure is more important than focusing on subtle acoustic differences between train events, and the use of different facade-based indicators would not improve how residents' annoyance is considered.

This article presents a collaborative action research framework structured around a ”residents / city / researchers” triptych, supported by a third-party observer, for managing urban sound environments. It is illustrated through a collectively chosen action on aircraft noise in Rezé, France. The action unfolded through iterative workshops, bringing together the three parties to discuss issues and co-produce knowledge, alongside analyses conducted by researchers between workshops. The research mobilized multiple data sources, including participatory noise measurements, official monitoring station data, flight trajectories, resident interviews, and a citywide annoyance survey. Four complementary actions were implemented: an experience-based aircraft noise map (A1), an analysis of temporal variability of aircraft noise (A2), the production of locally grounded noise impact indicators (A3), and a citywide survey measuring resident annoyance (A4). Analyses confirmed residents’ perceptions, highlighted the most critical time periods, and produced complementary indicators to substantiate these perceptions and impacts. The aircraft noise action had tangible local impacts, enhancing residents’ technical understanding and their ability to engage in policy discussions. The research emphasizes the value of recognizing and leveraging citizen knowledge, ensuring free and open access to data and tools, and involving a third-party observer in governance. It shows how structured dialogue between scientific expertise and lived experience can strengthen local decision-making. The framework offers a methodology for integrating technical expertise and community knowledge in urban sound management, providing practical guidance for other cities seeking participatory approaches to environmental acoustics.

Road traffic noise exposure assessment typically relies on aggregated traffic flow data, which prevents the estimation of high-temporal-resolution noise indicators increasingly recognized as important for health impact studies. To bridge this gap, this research proposes two stochastic disaggregation methods that reconstruct refined vehicle kinematics from aggregated traffic flows, enabling 1-s resolution noise estimation comparable to computationally intensive microscopic traffic modelling chains. Using SUMO microscopic simulation as reference, the disaggregation methods are evaluated in a dense urban area, in Stockholm's S & ouml;dermalm Island. The resulting acoustic indicators calculated, including LAeq,1h, LA10,1h, LA1,1h, and LAeq,1s, show estimates comparable to those obtained from the microscopic traffic noise modelling chain. Robustness and sensitivity analyses show that the proposed methods maintain stable performance even with reduced input data granularity. The proposed methods offer a practical intermediate solution between static annual noise maps and detailed microscopic simulations, enabling cost-effective dynamic noise exposure assessment at an urban scale.

Identifying urban areas that concentrate critical noise exposures is a fundamental step in formulating noise action plans for protecting public health. Standard European assessments that guide this identification are based on residential exposure to noise, neglecting everyday mobility. There is growing interest in noise assessment using agent-based exposure models to address this limitation. This approach aims to simulate a daily activity plan for each individual in a modeled population to estimate their trajectories. Based on the mobility pattern of the population, transport-related noise emissions and propagation are modeled. Then, the exposure profile of each agent is estimated. This study uses this framework to investigate how mobility shapes noise exposure across activity contexts, social groups, and time of the day. A case study on Lyon and Villeurbanne cities shows that a residential exposure assessment can lead to a 10% underestimation of critical exposure occurrences compared to a mobility-based assessment. Beyond the total number of occurrences, activity contexts of exposure evolve during the day, with relevant critical exposure areas emerging concerning work and study activities. Regarding target group analysis, for young agents, exposure mitigation in educational settings has the potential to reduce critical exposure occurrences by nearly half. Further, the study analyzes the relationships between short-term and daily dose exposures and reviews the definition of critical exposure periods adapted to specific life rhythms. Future research should focus on noise impact assessments for context-specific exposures and the translation of critical area analysis into noise management strategies.

The individual exposure to environmental noise in cities is usually assessed at the residential neighbourhood level with static, year-averaged strategic maps. This representation may underestimate noise exposure, given the mobility of individuals within the city and proximate sources of exposure. Our study employs high-resolution sensor analysis to observe how personal noise exposure differs from modelled noise map metrics, identify socioeconomical and behavioural determinants of exposure, and explore the impact of reallocating certain behaviours to others on daily personal noise exposure (LAeq,24h). Data on daily activities of 259 participants of the MobiliSense cohort living in the metropolitan area of Paris were collected between 2018 and 2020. Participants were equipped of a personal monitor for sound pressure, and of a GPS receiver and an accelerometer. Modes of transport were collected during a mobility survey. Results showed that noise exposure based on personal monitoring during space-time behaviours differed from modelled noise levels at residence. Participants were exposed to values below the recommended critical value for health of 55 dB(A) in urban areas in only 36

During early childhood, children develop skills and vulnerabilities that can significantly influence their later life. To emphasize the complementarity of environmental exposures to genetic influences, the term "exposome" was coined. The exposome encompasses all exposures in the broadest sense, including the social environment in which a child develops. Sound and tranquility are integral components of the exposome. Given the complex interplay between these factors and other aspects of the exposome, traditional methods for establishing exposure-effect relationships may be inadequate. Measurable dimensions of the exposome are translated into indicators. Variants of these indicators may exist, for example, in the measurement or calculation of noise and the definition of tranquility. To refine the set of possible indicators, their validity on relevant outcomes should be assessed individually before integrating them into the overall exposome framework. Due to the strong interactions between exposome components, innovative approaches are required. One such approach involves combining data-driven identification of typical living environments through clustering with expert knowledge. This methodology will be illustrated with examples from the Equal Life project.

Urban soundscapes significantly influence public health, with sound quality affecting well-being and social value. While traditional noise control has emphasized harm reduction, soundscape studies propose that managing sound environments can promote health benefits. This study explores the complex relationships between soundscape quality and public health using a systems thinking approach. In a participatory workshop with 21 experts from fields such as urban planning, environmental psychology, and acoustics, a causal loop diagram (CLD) was developed to illustrate the interactions between soundscape quality and public health variables. The CLD revealed key feedback loops and intervention points, organized around themes of socio-economic impact, environmental justice, biodiversity, and soundscape design. Findings highlight that while soundscape quality can enhance community well-being, increased economic value may drive gentrification, altering the social structure and reducing sound source diversity. Additionally, the role of soundscape quality in biodiversity suggests both co-benefits and ecological risks. This study demonstrates the potential of systems thinking to guide interdisciplinary approaches in soundscape management, identifying strategic pathways to inform future research and policy development for equitable and health-promoting urban environments.

Environmental noise is an issue of concern in urban environments. Strategic noise mapping based on numerical models provides a visualization and quantification of environmental noise conditions with the aim of identifying individuals living in areas with excessive noise and designating noise protected areas, commonly referred to as quiet areas. The noise assessment method adopted by the European Environmental Noise Directive has three main limitations: the location of individuals is static, exposure is estimated based on long-term noise doses, and the socio-economic assessment of affected groups is not systematized. Therefore, agent-based models represent a methodological improvement to promote a detailed spatio-temporal study of the impact of excessive noise levels on the population and its accessibility to quiet areas. These large-scale models simulate the everyday mobility of each individual of a population. This article utilizes an agent-based, open source, open data framework applied to the Lyon metropolitan area to jointly analyze the impacts and opportunities related to environmental noise. The flexibility of the approach, with the potential for analysis according to different times of day, regions of the city, activity contexts and specific subpopulations, is confronted with the challenges of modeling at the scale of the individual.

Standard European approaches to assessing environmental noise focus on individuals exposed to critical noise levels. However, there are complementary approaches that question the accessibility of the population to quiet areas, highlighting the restorative properties of natural and quiet spaces for human health. In this regard, from an agent-based model, this study proposes a spatio-temporal methodology to assess accessibility to quiet areas in agglomerations, integrating everyday mobility into the analysis of place effects and opportunities. The two primary objectives are to identify current quiet areas that are accessible in order to preserve them acoustically and to identify green spaces with the greatest potential for accessibility in order to improve them acoustically. Green spaces of Lyon and Villeurbanne (France) are assessed during the lunch break period using an open-source framework. The results indicate that on average about 30% of agents have access to a quiet area. Further, green spaces in courtyards represent the current quiet areas with the greatest accessibility. Concerning spaces with great potential for accessibility, linear green spaces along rivers and small squares near high-attended urban centers represent the greatest potential gain in accessibility to quiet areas. Improvements pertain to the utilization of in-situ surveys to integrate human perception and place attendance evaluations in the formulation of action plans.

Annoyance caused by high-speed trains (HST, >250 km/h) seems to be not completely modelled by current indicators (e.g., LDEN). For example, the suddenness, spectral content, temporal fluctuations of pass-by noises, and the density of peaks seem to play a significant role that is not fully understood [1]. To better understand these effects, we report on the results of a pilot study aiming to test experimental protocol that allows for a detailed analysis of short-term annoyance due to high-speed trains. We used an experience-sampling method (ESM): volunteer participants living nearby French high-speed lines were instructed to report their annoyance level right after a train pass-by with a remote device. This method has the potential to combine ecological validity with a precise control of the sound experienced by participants. As this method has never been used for railway noise assessment, residents were recruited to take part in the study and received two protocols in two different periods, to determine which ESM parameters are the most efficient.

The transition from light internal combustion engine (ICE) vehicles, such as cars and vans, to light electric vehicles (EVs) presents an opportunity to reduce road traffic noise exposure in urban environments, which still needs to be quantified. Although the noise emissions of light ICE vehicles are generally well understood, including during acceleration and deceleration, the noise emitted by light EVs has so far not been studied as thoroughly, in particular during acceleration. This study thus proposes a correction model for the noise emissions of light EVs during acceleration, based on pass-by measurements under reference conditions. Data were collected for 6 vehicle models at both steady speed and full acceleration. The difference in noise levels between these two conditions was analysed to develop the correction model. This correction model accounts for both speed and acceleration at an octave-band level. The resulting model shows that acceleration has no impact on the noise emissions of light EVs in the 63 and 125 Hz octave bands, and that acceleration may increase the overall A-weighted emissions of a light EV by up to 5 dBA, at 20 km/h. Furthermore, the analysis suggests that deceleration does not increase noise emissions for light EVs. This contribution paves the way for the integration of EV-specific noise emissions into noise exposure assessment frameworks, enabling a more comprehensive understanding of the potential benefits associated with the transition towards EVs.

Reducing children's exposure to traffic-related noise and air pollution in urban areas is a critical challenge. Therefore, evaluating traffic management strategies in a computational environment offers a practical tool for planners, policymakers, and researchers. However, a key research gap remains: most studies evaluate traffic strategies on air pollution, noise, or traffic separately. Few quantify their combined impacts in a single framework. To address this, we propose a multi-criteria evaluation approach and apply it by comparing four scenarios against a baseline. Through a comprehensive panel of statistical, spatial, and temporal analyses of traffic conditions, air pollutant concentrations, and noise levels, we find that: (1) restricting vehicle access during student arrival times significantly reduces exposure to both noise and air pollution; and (2) speed limit reductions have only limited effects on noise and may, under certain conditions, increase air pollution levels.

Traffic-related air pollutants pose major health risks in metropolitan areas, with Nitrogen Oxides (NOx) being particularly harmful. Reducing road traffic emissions through mobility management is crucial for achieving sustainable urban living. To assess such strategies, reliable modeling chains are essential. Traditionally, these chains couple traffic and pollutant emission models. However, this coupling can introduce significant uncertainty, particularly when traffic and pollution models are developed at different spatial scales. This paper addresses the challenge of examining the effects of spatio-temporal representation of traffic data on emission models. To this end, we compare emissions derived from two widely used European models, COPERT and HBEFA, using traffic data from the microscopic traffic model Symuvia and the agent-based model MATSim. Traffic outputs are aggregated at the trip, street, and square-cell grid levels. Despite significant variations in speed estimation due to mean speed calculation methodologies, emissions estimated at the trip level are generally closely aligned. At the street level, Symuvia is better suited for emission estimations, as it more accurately captures local variations and congested traffic conditions. Emissions estimated at the cell-grid level reveal high spatial variability, influenced by the street network form: higher values are often observed in city centers with MATSim, while Symuvia tends to show higher values on the ring road. Our findings indicate that MATSim+COPERT and Symuvia+HBEFA yield more consistent overall results, primarily due to the route assignment algorithm and the sensitivity of emission models to variations in traffic conditions. Emissions estimated with HBEFA, particularly when coupled with MATSim, tend to be higher than those from COPERT, largely due to differences in how stop-and-go traffic is represented. On the other hand, COPERT estimates are generally lower, especially when coupled with Symuvia, mainly due to the challenges of capturing congested conditions and speed variations with the mean speed representation.

The goal of this article is to report on the development of a study of the acoustic factors driving the short-term annoyance experienced by neighbors of high-speed train lines.In fact, annoyance caused by high-speed trains (> 250 km/h) is not completely modeled by current indicators (e.g., LDEN).For example, the suddenness, spectral content, temporal fluctuations of pass-by noises, and the density of train passages seem to significantly impact annoyance in a way that is not fully understood [1].To investigate these aspects, we first review different approaches used to study annoyance caused by transportation noise.For railway noise, two main approaches are reported in the literature: insitu social surveys exploring the contributions of acoustic and non-acoustic factors to long-term annoyance; laboratory experiments, in which controlled stimuli (individual pass-by noises) are played back to participants who rate their annoyance.Another approach is also used in transportation noise studies, but not yet for railway noise: diary, or experience sampling methods, whereby neighbors report their annoyance, at home, at different times over a longer time span (usually several weeks) while noise exposure is simultaneously recorded [2].Such in situ approach allows experimenters to focus on the precise characteristics of each pass-by noise and consider annoyance in the context of participants' real environment

This study introduces an agent-specific assessment method of traffic noise exposure in agent mobility simulations. The assessment is achieved through a combination of an energy-based noise exposure impact assessment using noise exposure cost, and the state-of-the-art traffic noise prediction tool NoiseModelling coupled with the activity-based agent mobility simulation software MATSim. The agent-specific noise exposure cost is a measure to evaluate how the noise emissions from the transport of agents relate to the noise-related impact on other agents performing stationary activities. By introducing an agent-specific level, each agent's individual responsibility for the noise exposure may be estimated. The potential of the agent-specific noise exposure cost concept, combined with the MATSim-NoiseModelling framework, is illustrated through a case study, applying activity-based agent mobility simulations across Nantes, France. The results of the case study highlight, among other considerations, the insights that an agent-specific, activity-based noise exposure cost approach provides by visualizing the noise exposure "footprint" resulting from an agent's transportation activities.