Environmental Noise Research Articles

Integrating Public Perception and Quantitative Metrics for Improved Urban Park Planning in Isfahan, Iran

This study delves into the intricate realm of public perception regarding urban parks, aiming to uncover key factors influencing this perception and offer insights for urban planning and park management in Isfahan City, Central Iran. A comprehensive survey was conducted across 63 urban parks, gathering subjective feedback from park visitors on park size, vegetation, and exposure to external disturbances. Objective metrics were the area of the park, the mean and stdev of the Sentinel-2 NDVI as measures of vegetation density and diversity, respectively, distance to other parks and the park perimeter-to-area ratio. Linear and non-linear regression analysis between subjective feedback and objective metrics revealed that satisfaction peaks in parks with sizes of 15 ha or more. Higher vegetation density maximizes satisfaction. Parks with simpler, more regular shapes tend to offer a greater sense of freedom from external disruptions. According to the Generalized Additive Model model (R2= 0.737, explained deviance = 77.1%), lush and healthy vegetation is often associated with attractive and inviting parks, especially in parks with simpler, more regular shapes that minimize the exposure to (noise and visual) disturbances. Such green and circular park designs can guide urban planners and managers in creating inviting and user-friendly parks, aligning with public expectations.

Damping and acoustic properties of nanosilica filled poly(styrene-acrylate) interpenetrating polymer networks (IPNs): Effect of nanocomposite preparation method

Research on noise pollution reduction has focused on utilizing damping coatings and polymer nanocomposites to enhance sound absorption. Nanosilica/poly(styrene-acrylate) nanocomposites as a water-based coating were prepared through in-situ emulsion polymerization and direct mixing methods at varying concentrations of 1, 2, and 3 wt% of nanosilica. Fourier transform infrared (FTIR) spectroscopy revealed that the inclusion of surfactant-containing micelles and a more extended synthesis period in the in-situ synthesis technique enhanced the interaction between silica nanoparticles and polymer chains. Dynamic light scattering (DLS) analysis showed a unimodal distribution and an acceptable range of polydispersity index (PDI) for neat and nanocomposite latexes. The addition of silica nanoparticles enhanced the stability of latex particles, especially evident in the direct mixing method. The field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) images confirmed the better dispersion of nanoparticles within the polymer matrix in the in-situ method. The addition of nanosilica resulted in a significant increase in pseudoplastic behavior and viscosity, notably in the in-situ synthesis. In both the in-situ synthesis and direct mixing techniques, the addition of 1 wt% of nanosilica resulted in an increase in the damping factor of nanocomposite films to about 2. However, when the nanosilica content was further increased to 3 wt%, the damping factor decreased. Acoustic tests demonstrated improved sound absorption with silica nanoparticles, yielding noise reduction coefficient (NRC) values of 0.49 and 0.46 for in-situ synthesis and direct mixing. The direct mixing method notably enhanced tensile properties at all nanoparticle content levels. Dried nanocomposite films exhibited superior UV-blocking capabilities compared to neat polymer films.

A Comprehensive Method to Analyze Single Cell Vibrations

All living cells vibrate depending on metabolism. It has been hypothesized that vibrations are unique for a given phenotype and thereby suitable to diagnose cancer type, stage, and pre-assess the effectiveness of pharmaceutical treatments in real time. However, cells exhibit highly variable vibrational signals, can be subject to environmental noise, and may be challenging to differentiate, having so far limited the phenomenon’s applicability. Here, we combined the sensitive method of force-spectroscopy using optical tweezers (OT) with comprehensive statistical analysis. After data acquisition, the signal was decomposed into its spectral components via Fast Fourier Transform (FFT). Peaks were parameterized and subjected to Principal Component Analysis (PCA), to perform an unbiased multivariate statistical evaluation. This method, which we term Cell Vibrational Profiling (CVP), systematically assesses cellular vibrations. To validate CVP technique, we conducted experiments on five U251 glioblastoma (GBM) cells, using 8-10 μm polystyrene beads as a control for comparison. We collected raw data using OT, segmenting into 150+ five-second intervals. Each segment was converted into power spectra (PS) representing a frequency resolution of 10,000 Hz for both cells and controls. U251 GBM cells exhibited significant vibrations at 402.6, 1254.6, 1909.0, 2169.4, and 3462.8 Hz (p<0.0001). This method was further verified with PCA modelling, which revealed that in cell-cell comparisons using the selected frequencies, overlap frequently occurred, and clustering was difficult to discern. In contrast, comparison between cell-bead models showed that clustering was easily distinguishable. Our paper establishes CVP as an unbiased, comprehensive technique to analyze cell vibrations. This technique effectively differentiates between cell types and evaluates cellular responses to therapeutic interventions. Notably, CVP is a versatile, cell-agnostic technique requiring minimal sample preparation and no labelling or external interference. By enabling definitive phenotypic assessments, CVP holds promise as a diagnostic tool and could significantly enhance the evaluation of pharmaceutical treatments.

Using Convolutional Neural Networks for Image Semantic Segmentation and Object Detection

Convolutional neural networks are widely used for feature extraction in the fields of object detection and image segmentation. However, traditional CNN models often struggle to ensure accuracy in high noise environments. A study proposes an enhanced CNN model to improve its ability to recognize targets of different scales. This model combines multi-scale perceptual aggregation and feature alignment (MPAFA) mechanisms. This new method effectively combines low-level and high-level features, which helps to better identify objects of different sizes. The experimental results show that the proposed model achieved a segmentation accuracy of 99.6% on the Cityscapes dataset, and maintained an accuracy of 97.3% even with increased noise. Further experiments have shown that the model outperforms existing methods in terms of accuracy and recall. The experimental results show that the model exhibits excellent performance in object detection and segmentation tasks. This study provides a more effective strategy for processing complex images by optimizing network structure and enhancing feature fusion.

Techno-Economic Analysis of Hybrid Pv-Battery-Diesel Power System for Rural Electrification: A Case Study of Ibudo Ora, Ogbomoso

Fossil-fueled generators and electrical grid extensions are the most popular energy sources for supplying electricity to rural areas. However, the high cost of running and maintenance, noise pollution, and a need for decarbonization necessitate the hybridization of different energy sources as a viable solution. Using the conventional technique for the optimal design of a Hybrid Power System (HPS), such as the Hybrid of Multiple Energy Resources (HOMER), is inefficient regarding electricity cost and carbon emission reduction. Hence, this research conducted a technical and economic analysis of an optimal photovoltaic (PV), battery, and diesel generator-based power system to electrify Ibudo Ora, a rural community in Ogbomoso. A feasibility study on electricity demand was conducted on the Ibudo Ora community by conducting an onsite survey, and the community load profile was estimated. Mathematical modeling of the HPS component was formulated. A multi-objective function was developed to minimize the Net Present Cost (NPC), Levelized Cost of Electricity (LCOE), and Total Carbon Emission (TCE) and maximize the System Reliability (SR) of the proposed HPS and optimized using the Energy Valley Optimizer (EVO). MATLAB R2021a was used to simulate the developed model. The performance of the developed EVO-based HPS was evaluated using NPC, LCOE, TCE, and SR as metrics and compared with HOMER, which was used for the same purpose. The NPC of the developed EVO-based HPS and HOMER-based HPS were $998702.87 and $1011984.27, respectively, while the LCOE were $0.4889 and $0.4954. The loss of power supply probabilities of the developed EVO-based and HOMER-based HPS was zero, while in terms of TCE, EVO-based HPS and HOMER-based HPS had 775958.15 kg and 832912.49 kg, respectively. The results showed an appreciable reduction in the NPC, LCOE, and TCE using EVO compared with HOMER for an optimized HPS. This research will assist the government, investors, and policymakers in making decisions on rural electrification using HPS.

The Effect of Combined Foot Massage and Nature Music on the Sleep Quality of Critically Ill Patients

Aims: Critical patients in ICU and HCU often experience poor sleep due to excessive stress and various symptoms like environmental factors, pain, noise, anxiety, and physical discomfort. Foot massage and natural music interventions can help meet their sleep needs, providing a relaxing effect and improving physical and psychological conditions. The purpose of this study is to determine the effect of providing combination therapy of foot massage and natural music therapy in Dr. Hospital's peacock room Salamun 2023. Methods: This type of research is pre-experimental with a pre-post test of one group and uses the Richard Champbell Sleep Questionnaire (RCSQ). Then the data was processed using a paired sample T-test. The sample in this study is 16 respondents. Results: The results of the statistical test obtained a p-value of 0.01, meaning that there was an effect between the pretest and posttest of the combination of foot massage and natural music therapy on the quality of sleep in critical patients in the peacock room at Dr. Salamun Hospital, Bandung City, 2023. Conclusions : the conclusion of this study is that there is an effect of foot massage therapy and natural music on the sleep quality of critical patients as shown by the results of measuring the quality of critical patients before and after consecutive interventions with massage of both feet combined with natural music. For 10 minutes it has been proven to be able to improve sleep quality in critically ill patients so that this intervention can be applied as a therapy to improve sleep quality.

Stress and the City: Mental Health in Urbanized vs. Rural Areas in Salzburg, Austria

Background: Living in the city is associated with a higher risk of suffering from stress, anxiety, and depression. Due to an increase of migration to the city, the association between mental health and city life is highly relevant to society. Methods: We analyzed data of 9573 participants (Ø 55.3 years, SD = 7.4) of the Paracelsus 10,000 study (Salzburg, Austria) who were classified into having or not having depressive symptoms. Population density, green space, and noise around the home address of the participants were collected and tested for correlations with mental health defined by depressive symptoms. We additionally tested whether migration status influenced the effect of urbanization on mental health. Results: There is a positive correlation between degree of urbanization and the probability of suffering from depressive symptoms (p = 0.011), yet this effect is independent of the migration background (p = 0.581). Participants in areas with high residential density were significantly more likely to suffer from poor mental health (p = 0.006 unadjusted). No significant association could be found between mental health and noise (p = 0.126 unadjusted) or green space neither regarding distance to closest green space (p = 0.549 unadjusted), nor size of green space (p = 0.549 unadjusted). Conclusions: In the Paracelsus 10,000 cohort, living in the city is associated with lower mental health, especially in participants with a high population density in the direct neighborhood. This might be due to social stress yet does not reflect minority stress in migrants. However, the influence of noise pollution and green space on mental health is limited in this cohort.

Surface Defect Identification of Strip Steel Using ViT‐RepVGG

In the production of strip steel, surface defect identification is crucial for improving product quality and ensuring smooth subsequent processes. Existing technologies face challenges such as low detection efficiency and susceptibility to environmental noise. This article employs an automated deep learning method without requiring consideration of complex environmental changes and proposes an improved RepVGG (ViT‐RepVGG) model for surface defect identification. The model is based on the RepVGG architecture, and the study explores the impact of incorporating the self‐attention mechanism of ViT under various addition strategies on model performance. A comparison is made between the optimized model and classic network models, as well as recently published models, in terms of identification performance. The research also examines the performance variations of the model under different hyperparameter settings and its identification performance for six types of defects. The results indicate that adding the ViT module to stage 3 of the A1‐type RepVGG, with a learning rate, optimizer, and activation function set to 0.0001, Adam, and Gelu, respectively, yields the optimal ViT‐RepVGG model performance. These findings demonstrate the feasibility of enhancing classification performance by incorporating the self‐attention mechanism into neural networks, providing an effective foundation for the online identification of strip steel surface defects.

Filtering Algorithm using Fuzzy Weights based on Noise Types in Mixed Noise Environments

Filtering Algorithm using Fuzzy Weights based on Noise Types in Mixed Noise Environments

Protection of the quiet and nocturnal landscape

Motives: We associate landscape primarily with the presence of vistas that change with the time of day, season and weather conditions. Landscape is shaped by light and darkness, sound and silence, all of which influence its perception. Quiet and nocturnal landscapes, however, remain on the margins of scientific interest in traditional landscape studies.Aim: The main aim of the study was to identify the younger generation’s awareness of the importance of nocturnal landscapes, light and noise pollution, and their preferences for protection against these threats. For this purpose, a diagnostic survey method was used. The results of the survey were referred to the 2015 TNS Poland report prepared for the Ministry of the Environment. In addition, the legal conditions of landscape protection in Poland were identified, with reference to protection against noise and light pollution. National and international experiences in the protection of quiet and nocturnal landscapes were also presented.Results: The survey demonstrated that public awareness of light pollution is increasing. A nocturnal (and quiet) landscape is of high value to the respondents. Light pollution causes, among other things, energy waste and landscape degradation (change of landscape characteristics), while noise negatively affects human and animal health. Therefore, the nocturnal (and quiet) landscape should be protected by law. Currently, there are no direct instruments for the protection of nocturnal landscapes in Poland. On the other hand, in the case of quiet landscapes, there are some instruments (e.g. quiet zones), but they have not been used yet. It is encouraging that public awareness of light and noise pollution is increasing. It is necessary to link landscape protection with the protection of the night sky and quiet zones, for example through advertising resolutions and the establishment of cultural parks. It is also important to educate the public about the risks involved.

Study on need of monitoring sound pollution at developing urban localities

Environmental pollution is a very serious issue around the world due to development of humankind towards well-being and modernization. It not only impacts health but also affects over all biodiversity. Sound does nothing till it is up to certain limit, as it creates pressure on the particles of the medium, when the limit of pressure is increased, it becomes nuisance. Sound pollution is the one important agent out of other factors that disrupts the living of humans and its ecosystem. It is related with physical, psychological or emotional states. Sound pollution adversely affects the humankind and animals leading to irritation, loss of concentration, and loss of hearing and abnormal behaviour of animals. The present work emphasizes the need to monitor and control the sound pollution in the developing cities to tackle the related future problems, to investigate the status and validity of ambient noise standards in the Indian situation, also to propose need of noise control based on the available knowledge on noise policies and regulations. The study is an approach towards importance of monitoring the sound pollution, its sources and its effects on the human activities or the ecosystem with respect to degrees of exposure in the newly developing urban localities with moderate commercial activities. The impact of sound pollution will increase if appropriate measures are not taken.

Smart noise pollution monitoring system

Increasing pollution poses serious health risks, necessitating urgent control measures for a healthier future for all. Among various pollution types, sound pollution significantly impacts quality of life and well-being, especially in densely populated urban areas. This proposal describes the creation and deployment of an Internet of Things (IoT)-based noise pollution monitoring system intended to track sound levels in certain regions in real time. The core of this system comprises advanced sensors strategically placed to measure sound intensity accurately. These sensors can detect even minor variations in noise levels, providing precise and reliable data. After being gathered, the data is sent to a microcontroller, which effectively processes it. This microcontroller ensures the data is accurately captured and sent over the internet to an online server. Once the data reaches the online server, it becomes accessible for analysis and monitoring by authorities. The system's real-time data capability is crucial for timely intervention. Authorities can continuously monitor noise levels and identify trends or spikes in sound pollution, allowing them to address issues promptly. This feature is particularly beneficial for sensitive areas such as schools, hospitals, and residential zones, areas with heavy traffic, library, Where excessive noise can have severe health effects, including stress, sleep disturbances, and hearing loss. The system includes an alert mechanism that notifies relevant authorities when noise levels exceed predefined thresholds. These alerts enable quick responses to mitigate noise pollution. For instance, in a school, reducing noise levels can enhance the learning atmosphere and improve students' concentration. In hospitals, maintaining a quiet environment is crucial for patient recovery. The integration of IoT technology in this noise pollution monitoring system not only enhances efficiency but also provides a scalable solution adaptable to various urban and rural settings. This scalability ensures the system can be expanded to cover larger areas or multiple locations as needed.

MosquitoSong+: A noise-robust deep learning model for mosquito classification from wingbeat sounds.

In order to assess risk of mosquito-vector borne disease and to effectively target and monitor vector control efforts, accurate information about mosquito vector population densities is needed. The traditional and still most common approach to this involves the use of traps along with manual counting and classification of mosquito species, but the costly and labor-intensive nature of this approach limits its widespread use. Numerous previous studies have sought to address this problem by developing machine learning models to automatically identify species and sex of mosquitoes based on their wingbeat sounds. Yet little work has addressed the issue of robust classification in the presence of environmental background noise, which is essential to making the approach practical. In this paper, we propose a new deep learning model, MosquitoSong+, to identify the species and sex of mosquitoes from raw wingbeat sounds so that it is robust to the environmental noise and the relative volume of the mosquito's flight tone. The proposed model extends the existing 1D-CNN model by adjusting its architecture and introducing two data augmentation techniques during model training: noise augmentation and wingbeat volume variation. Experiments show that the new model has very good generalizability, with species classification accuracy above 80% on several wingbeat datasets with various background noise. It also has an accuracy of 93.3% for species and sex classification on wingbeat sounds overlaid with various background noises. These results suggest that the proposed approach may be a practical means to develop classification models that can perform well in the field.

Comparison of current and proposed travel modes for students considering travelling and sustainable indicators

In the twenty-first century, global warming has become one of the severe challenges. Around one-fourth of the total carbon footprint is contributed by transport vehicles. It is crucial to provide alternative solutions to unsustainable transportation systems. This study compares the present travel modes of students of the Capital University of Science and Technology, Islamabad, with the proposed sustainable travel mode. A campus survey (N = 565) was conducted to understand travel choices, travel times, origin and destinations, and student preferences, considering university buses and private vehicles. A comparison was made between the travel time analysis of university bus users and non-bus users. A sustainability analysis has been conducted to evaluate the effects of the present travel modes on students and the proposed university bus transport system. Five main factors were compared: fuel consumption and associated fuel costs, carbon and vehicular emissions, traffic noise pollution, traffic congestion, and accident ratio. The results show that 38% of students commute by private cars and 18% by motorbikes. Around 25% of the students avoid university buses due to long travel times, and 30% prefer because their parents ask them. On average, university buses take 56.8 min, and non-bus transports take 38.8 min for one-way travel. If all students travel by university busses, they could be 86% more sustainable in fuel costs and carbon emissions than non-bus travel modes. These results give useful policy implications for providing sustainable travel alternatives to educational institutions.

Enhancing the MCL-SLAM algorithm to overcome the issue of illumination variation, non-static environment and kidnapping to present the NIK-SLAM

AbstractSimultaneous Localization and Mapping (SLAM) is a major challenge that has been comprehensively investigated by scholars. SLAM’s ability to support autonomous navigation helps the robot to move from one place to another without being controlled by humans. This achievement has attracted the attention of scholars towards focusing on this area. Over the decades, countless procedures have been developed with extraordinary accomplishments. However, some problems can degrade the efficiency of SLAM (Simultaneous Localization and Mapping) technique, and examples of such problems include environmental noises (light intensity and shadow), non-static environment, and kidnap robots. These problems create inconsistency in measurement that can lead to erroneous navigation for an unmanned vehicle. In minimizing the effect of these problems, a novel SLAM technique based on several re-modifications of MCL is presented in this paper. This innovative technique is known as NIK-SLAM. The NIK-SLAM is equipped with filters alongside some modifications of the original Monte-Carlo algorithm (MCL) to maximize its performance. The filters were employed to overcome the effect of shadow and light intensity while the re-modification of MCL is used to overcome the limitation of kidnapping and non-static objects. A publicly available dataset (TUM-RGBD) and a private dataset were used for evaluation. Matlab was used for simulation while assessments were based on quantitative and qualitative validation. The results demonstrated a better performance of the NIK-SLAM, attaining a lower trajectory error for most experiments when compared to the original MCL and other SLAM algorithms found in the literature, but at the expense of delayed processing time. Furthermore, this accomplishment supports pathway planning, and exploration into unstable environments while it decreases accident rates/human fatalities, and in the future study, the issue of delayed processing time will be addressed.

Weak Compound Fault Identification of Gearboxes Based on Improved Symplectic Geometric Mode Decomposition and Optimized Cyclic Kurtosis Deconvolution

Abstract Given the complex and harsh operating conditions of gear transmission systems, gearboxes are prone to compound faults. These faults, involving multiple types, often couple together, causing the weak fault pulse characteristics to be entirely masked by strong environmental noise. This significantly complicates the extraction of relevant fault information from the gearbox. To address these issues, this paper proposes a method for extracting compound fault features based on improved symplectic geometric mode decomposition (SGMD) and optimized cyclic bispectrum deconvolution (CYCBD). Firstly, considering the periodic impact characteristics of different fault types, morphological envelope cyclic bispectrum is proposed to cluster the initial components obtained from SGMD decomposition, thereby adaptively separating different fault features contained in the compound fault signals. An adaptive filter length search strategy is subsequently introduced to optimize the CYCBD by deconvolving each initial fault component, thereby eliminating the interference caused by complex transmission paths and substantial environmental noise, which, in turn, enhances weak periodic fault pulses. Following this, the enhanced signals are subjected to envelope demodulation to extract fault characteristic frequencies, enabling the identification of various types of faults. The effectiveness and feasibility of the proposed method are demonstrated through both simulation signals and actual experimental data related to gearbox compound faults. Compared with existing methods, the proposed method demonstrates superior performance in identifying weak compound faults under strong environmental noise.&amp;#xD;

AI-Powered Disinfection Force: Harnessing 5G for Intelligent Distributed Object Recognition and Sanitation Fleet

With the increasing prevalence of embedded cameras in devices such as phones, robots, and vehicles, there’s a growing need to enhance their perception capabilities through advanced deep learning techniques. These systems, however, often struggle under suboptimal conditions like environmental noise. The paper addresses these challenges by proposing a 5G-enabled solution that uses a fleet of unmanned ground vehicles (UGVs) for coordinated disinfection tasks in hospital environments. Utilizing high-bandwidth, low-latency 5G networks, these UGVs can share high-quality images for distributed object recognition tasks and execute precise disinfection routines using onboard UV lamps. The proposed system includes an optimal formation algorithm for UGVs to maintain effective positioning and coordination. By leveraging 5G connectivity, the fleet can efficiently exchange sensor data and perform real-time, innovative object recognition and disinfection, presenting a significant advancement in intelligent sanitation technology.

Bluetooth Active Noise Cancellation (ANC) Technology: Principles, Integration, Applications, and Performance Evaluation

Thanks to the development of wireless audio transmission technology, Bluetooth technology made it possible to give users a seamless and convenient connection experience. However, in a noisy environment, preserving the audio quality is still difficult, which motivated people to explore the technology of Active Noise Cancellation (ANC). This paper aims to analyze integration of ANC technology and the potential of Bluetooth audio transmitting to improve the audio effect, especially in application scenarios where clear audio is required. At the beginning of this paper, how Bluetooth and ANC are evolved is reviewed for the preparatory works. Then, the technology mechanism of the ANC and the advantages in particular application scenarios such as Industrial automation and autonomous vehicles are described in detail. In addition, this paper give experimental data to illustrate that the ANC systems is effective in reducing ambient noise. The central technology of ANC system is using a microphone to detect environmental noise information and then creates a sound wave phase-inverted to the original noise to cancel. Furthermore, by connecting artificial intelligence technique, the ANC system dynamically fits from different acoustic environment to improve the audio quality. Finally, the experimental results that are quoted reveal that the advanced ANC system can reduce the noise level in a wide range of environments, cutting down the sound pressure level to 65 dB. It is clearly demonstrated that the ANC technology is widely applicable and effective to enhance the audio experience.

Development of Low-Stress Double-Pass Filter Device for Methane and Ethane Flammable Gas Detection System

To meet the needs of industrial and environmental monitoring of methane and ethane flammable gases, a low-stress filter device for detecting methane and ethane simultaneously is developed based on the absorption spectra of methane and ethane and the thin-film design theory. In order to improve the sensitivity of the detection system and reduce the influence of environmental noise, the information of methane and ethane gas is extracted simultaneously by using a filter with double bandpass and wide cut-off in the middle-infrared and far-infrared bands. The transmittance of the filter is 91.3%, 93.1%, and 93.02% at the wavelengths of 3.31 μm, 6.71 μm, and 7.67 μm, respectively. The cut-off depth in the cut-off interval is above OD3, which meets the system’s requirements.

8.E. Skills building seminar: Methods for measuring the true health cost of pollution

Abstract Healthy environments are essential for physical and mental wellbeing. Air pollution is considered to be one of the most significant environmental risk factors for ill health and premature death within the European Union. Poor air and noise quality have been linked to the exacerbation of symptoms, hospital admissions, and even premature deaths, especially for those with cardiorespiratory diseases. The associated medical costs and productivity losses therefore also impose a large economic cost. Exposure to environmental stressors disproportionately affects socially disadvantaged groups, and therefore contributes to the large socioeconomic inequities in health observed persistently in Europe. The EU has prioritised action on causes of pollution. For example, its Green Deal outlines ambitious plans to address climate challenges such as pollution and align air quality ambitions closer to the World Health Organization guidelines. Likewise, the European Environmental Agency routinely monitors the mortality burden associated with air pollution, while the impact of environmental noise on European citizens is examined through the Environmental Noise Directive. While in the political scene there is interest to advance the reduction of pollution, efforts are hindered by the lack of an harmonized methodology to quantify the socioeconomic cost of environmental stressors. Currently, researchers working with the burden of disease framework face many challenges in its application in the context of environmental stressors. Among others, these challenges include unmet data needs and major lack of transparency in methodological developments. In this skills-building seminar, we will outline an integrated approach for measuring the true health cost of pollution. This approach is currently being trialled in the EU-funded BEST-COST project, which sets out to improve methodologies for understanding the socioeconomic cost of environmental stressors, focusing on air and noise pollution. The project brings together a consortium of 17 partners from Europe and the USA, and is led by Sciensano, the Belgian institute for health. Through a synergy of theoretical outlines and real-life examples, seminar participants will gain methodological insights in the three key components of this integrated approach - i.e., the quantification of the health burden of environmental stressors via comparative risk assessment, the monetization of Disability-Adjusted Life Years to assess economic impact, and the quantification of environmental health inequalities via a novel area-level based framework. Key messages • There are significant unmet needs with respect to both data and methodologies for quantifying the socioeconomic cost of environmental stressors. • We outline an integrated approach for measuring the true health cost of pollution, combining health burden, economic burden, and environmental health inequalities.