Measuring combined exposure to environmental pressures in urban areas: an air quality and noise pollution assessment approach

Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach

This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the fruit fly optimization algorithm (CatBoost-FOA), to spatially assess and map noise pollution prone areas in Tehran city, Iran. To spatially model areas susceptible to noise pollution, we established a comprehensive spatial database encompassing data for the annual average Leq (equivalent continuous sound level) from 2019 to 2022. This database was enriched with critical spatial criteria influencing noise pollution, including urban land use, traffic volume, population density, and normalized difference vegetation index (NDVI). Our study evaluated the predictive accuracy of these models using key performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and receiver operating characteristic (ROC) indices. The results demonstrated the superior performance of the CatBoost-FA algorithm, with RMSE and MAE values of 0.159 and 0.114 for the training data and 0.437 and 0.371 for the test data, outperforming both the CatBoost-FOA and CatBoost models. ROC analysis further confirmed the efficacy of the models, achieving an accuracy of 0.897, CatBoost-FOA with an accuracy of 0.871, and CatBoost with an accuracy of 0.846, highlighting their robust modeling capabilities. Additionally, we employed an explainable artificial intelligence (XAI) approach, utilizing the SHAP (Shapley Additive Explanations) method to interpret the underlying mechanisms of our models. The SHAP results revealed the significant influence of various factors on noise-pollution-prone areas, with airport, commercial, and administrative zones emerging as pivotal contributors.

Assessment of seasonal variations in particulate matter accumulation and elemental composition in urban tree species

Outdoor air pollution in urban areas, especially particulate matter (PM), is harmful to human health. Urban trees and shrubs provide crucial ecosystem services such as air pollution mitigation by acting as natural filters. However, urban greenery comprises a particular biodiversity, and different plant species vary in their capacity to accumulate PM. Twenty-two plant species were analyzed and selected according to their leaf traits, the different fractions of PM accumulated on the leaves (large - PML, coarse - PMC, and fine - PMF) and their chemical composition. The study was conducted in four city zones: urban traffic (UT), urban background (UB), industrial (IND), and rural (RUR), comparing winter (W) and summer (S) seasons. The average PM levels in the air and accumulated on the leaves were higher in W than in S season. During both seasons, the highest PM accumulated on the leaves was recorded at the UT zone. Nine species were selected as the most suitable for accumulating PML, seven as the most efficient for accumulating PMC, and six for accumulating PMF. The leaf area and leaf roundness were correlated negatively with PM accumulation. The evergreen species L. nobilis was indicated as suitable for dealing with air pollution based on PM10 and PM2.5 values recorded in the air. Regarding the PM element and metal composition, L. nobilis, Photinia x fraseri, Olea europaea, Quercus ilex and Nerium oleander were selected as species with notable elements and metal accumulation. In summary, the study identified species with higher PM accumulation capacity and assessed the seasonal PM accumulation patterns in different city zones, providing insights into the species interactions with PM and their potential for monitoring and coping with air pollution.

Built and social indices for hazards in Children's environments

Leveraging the capabilities of the Historical Spatial Data Infrastructure (HSDI) and composite indices we explore the importance of children's built and social environments on health. We apply contemporary GIS methods to a set of 2000 historical school records contextualized within an existing HSDI to establish seven variables measuring the relative quality of each child's built and social environments. We then combined these variables to create a composite index that assesses acute (short-term) health risks generated by their environments. Our results show that higher acute index values significantly correlated with higher presence of disease in the home. Further, higher income significantly correlated with lower acute index values, indicating that the relative quality of children's environments in our study area were constrained by familial wealth. This work demonstrates the importance of analyzing multiple activity spaces when assessing built and social environments, as well as the importance of spatial microdata.

Understanding the interaction between human activities and physical health under extreme heat environment in Phoenix, Arizona

Long-term community resilience, which privileges a long view look at chronic issues influencing communities, has begun to draw more attention from city planners, researchers and policymakers. In Phoenix, resilience to heat is both a necessity and a way of life. In this paper, we attempt to understand how residents living in Phoenix experience and behave in an extreme heat environment. To achieve this goal, we introduced a smartphone application (ActivityLog) to study spatio-temporal dynamics of human interaction with urban environments. Compared with traditional paper activity log results we have in this study, the smartphone-based activity log has higher data quality in terms of total number of logs, response rates, accuracy, and connection with GPS and temperature sensors. The research results show that low-income residents in Phoenix mostly stay home during the summer but experience a relatively high indoor temperature due to the lack/low efficiency of air-conditioning (AC) equipment or lack of funds to run AC frequently. Middle-class residents have a better living experience in Phoenix with better mobility with automobiles and good quality of AC. The research results help us better understand user behaviors for daily log activities and how human activities interact with the urban thermal environment, informing further planning policy development. The ActivityLog smartphone application is also presented as an open-source prototype to design a similar urban climate citizen science program in the future.

From Theory to Praxis: 'Go Sustainable Living' Survey for Exploring Individuals Consciousness Level of Decision-Making and Action-Taking in Daily Life Towards a Green Citizenship

This study aims at embedding sustainability practices by exploring sustainable actions of individuals consisting the educated workforce of Greece. A tailored questionnaire was created and sent via e-mails to 500 respondents, to identify a snapshot of participants daily buying and consuming actions. 483 responses received and analyzed using statistical tools. They respond to recommendations for enhancing sustainability consciousness at individual level, inspiring people to buy sustainable, creating new consumption attitudes that are key factors for moving towards a sustainable citizenship. The findings will further provide information for a second paper on developing the ‘Go Sustainable Living’ digital application to be uploaded in individuals’ mobile phones, for rewarding users with points that correspond to each sustainable action and can later be used for discounts in all participating stores. The analysis showed that <30% of consumers are considered sustainability-conscious, 57.6% are in a transition phase, while 13% fell into the category of non-conscious. To make sustainable decisions and actions in every daily life, individuals need to have knowledge of sustainability, awareness, consciousness of their actions, and be active citizens. An educated workforce armed with sustainability perceptions and competencies is an asset for societies and businesses poised to respond to the sustainability call. Sustainability should not be only an ‘utopia’ in our societies but an ‘eutopia’ entailing a life with ecological and social health and prosperity at a local, regional, and global level.

Association between exposure to road traffic noise and hearing impairment: a case-control study

PURPOSE

Noise pollution in urban areas is increasing steadily, and the study of road traffic noises and their effects on the auditory system was rare. This study investigated the potential effects of road traffic noise on auditory systems and hearing.

METHODS

A case-control study recruited outpatients from the Otolaryngology department. The case group (n = 41) had binaural hearing loss (HL) of standard pure-tone average(PTA)  ≥ 25 dB or high frequency PTA ≥ 25 dB, while the control group (n = 39) had binaural hearing level of any frequency < 25 dB. Detailed otologic evaluations were performed. Between-group data were evaluated using logistic regression analysis. Case or control group was identified based on the audiogram.

RESULTS

A total of 80 subjects were recruited, including 41 with hearing impairment and 39 as control. The mean exposure level of road traffic noise was significantly higher in the case group than the control group (p = 0.005). A crude OR of 5.78 showed an increased risk of greater than 70 dB of road traffic noise on hearing impairment and tinnitus (p < 0.001). The aOR of 9.24 (p = 0.002) from a multiple variate analysis suggested that road traffic noise levels greater than 70 dB may have a damaging effect on hearing. Damaging effects on hearing persisted even after adjusting for confounders in the full multivariate model (aOR of 9.24 [95% CI: 2.198-38.869]; p = 0.002).

CONCLUSIONS

Exposing to road traffic noise greater than 70 dB showed an increased risk of damage to the auditory system. These results might help public health administrators and physicians to develop programs that address the health dangers of noise.

Assessing the Current Integration of Multiple Personalised Wearable Sensors for Environment and Health Monitoring

The ever-growing development of sensor technology brings new opportunities to investigate impacts of the outdoor environment on human health at the individual level. However, there is limited literature on the use of multiple personalized sensors in urban environments. This review paper focuses on examining how multiple personalized sensors have been integrated to enhance the monitoring of co-exposures and health effects in the city. Following PRISMA guidelines, two reviewers screened 4898 studies from Scopus, Web of Science, ProQuest, Embase, and PubMed databases published from January 2010 to April 2021. In this case, 39 articles met the eligibility criteria. The review begins by examining the characteristics of the reviewed papers to assess the current situation of integrating multiple sensors for health and environment monitoring. Two main challenges were identified from the quality assessment: choosing sensors and integrating data. Lastly, we propose a checklist with feasible measures to improve the integration of multiple sensors for future studies.

Associations of co-exposures to air pollution and noise with psychological stress in space and time: A case study in Beijing, China

Air pollution and noise are both ubiquitous environmental stressors that pose great threats to public health. Emerging evidence has noticed the combined health risks caused by the coexistence of traffic-related air pollutants and noise in the residential context. However, less is known about how mobile individuals are simultaneously exposed to multiple sources of air pollution and noise, and thus respond with more acute psychological responses beyond the residence. This study examines the co-exposures to fine particles (PM_2.5) and noise across spatiotemporal contexts where the concurrent exposures are jointly associated with momentary psychological stress. An innovative research protocol, including GPS-equipped activity-travel diaries, air pollutant and noise sensors, and ecological momentary assessment, was adopted to collect real-time data from a sample of residents in Beijing, China. The results showed a minor correlation between PM_2.5 and noise exposures after accounting for individual mobility and the spatiotemporal dynamics of these two environmental pollutants. Further, exposure to PM_2.5 was more associated with momentary psychological stress given the insignificant independent effect and the weak moderating effect of noise exposure. Three specific spatiotemporal contexts involving the health risks of co-exposures were delineated, including morning rush hours and traveling by public transits with intensified stress risks caused by combined exposures to air pollution and noise, workplaces with counteracting stress effect of both exposures, and evening time at home with stress-induced air pollution and stress-relieving social noise. In conclusion, the mobility-based and context-aware analysis provides a more nuanced understanding of the associations of co-exposures to environmental pollution and synchronous psychological stress in space and time.

Wearable sensors for multifactorial personal exposure measurements - A ranking study

Individuals are simultaneously exposed to multiple environmental stressors during their daily life. Studies of adverse health effects and their etiology as well as recommendations for a healthier life style demand for an assessment of multifactorial personal exposure, according to the exposome concept. A challenge is to record exposure while people are moving in heterogeneous urban environments. Therefore wearable sensor technologies are becoming a promising way to measure personal exposure continuously: indoors, outdoors and even on the move. So far, studies which test the accuracy and usability of wearable sensors for multiple stressors are lacking. Performance evaluations are important and should take place beforehand, especially to ensure the success of citizens-oriented studies. For the first time we rigorously examined the accuracy and application suitability of wearable sensors for acoustic noise, heat (temp), particle number counts (PNC) and geo-location (GPS) in different environments. We present an extensive device inter-comparison and a ranking of the sensors based on performance measures, Taylor diagrams, Bland-Altman plots, and ease-of-use aspects. The sensors showed moderate to high correlations with precision reference devices (r = 0.4-0.99). Differences between errors outdoors and indoors suggest that environmental conditions have impact upon the accuracy of the sensors. Reaction time, recording interval, and sensor ventilation are features that play a crucial role for both ease-of-use and accuracy. We conclude with a final performance () ranking: (GPS) >  (noise) >  (temp) >  (PNC). The results are relevant for future epidemiological studies of multifactorial exposure of individuals and their health and should guide the selection of wearables when persons are involved that are technically untaught. Inferences from multifactorial data are based on the performance of all sensors and the weakest chain links are PNC and temp sensors for which our article recommends urgent improvements.

Road traffic air and noise pollution exposure assessment - A review of tools and techniques

Road traffic induces air and noise pollution in urban environments having negative impacts on human health. Thus, estimating exposure to road traffic air and noise pollution (hereafter, air and noise pollution) is important in order to improve the understanding of human health outcomes in epidemiological studies. The aims of this review are (i) to summarize current practices of modelling and exposure assessment techniques for road traffic air and noise pollution (ii) to highlight the potential of existing tools and techniques for their combined exposure assessment for air and noise together with associated challenges, research gaps and priorities. The study reviews literature about air and noise pollution from urban road traffic, including other relevant characteristics such as the employed dispersion models, Geographic Information System (GIS)-based tool, spatial scale of exposure assessment, study location, sample size, type of traffic data and building geometry information. Deterministic modelling is the most frequently used assessment technique for both air and noise pollution of short-term and long-term exposure. We observed a larger variety among air pollution models as compared to the applied noise models. Correlations between air and noise pollution vary significantly (0.05-0.74) and are affected by several parameters such as traffic attributes, building attributes and meteorology etc. Buildings act as screens for the dispersion of pollution, but the reduction effect is much larger for noise than for air pollution. While, meteorology has a greater influence on air pollution levels as compared to noise, although also important for noise pollution. There is a significant potential for developing a standard tool to assess combined exposure of traffic related air and noise pollution to facilitate health related studies. GIS, due to its geographic nature, is well established and has a significant capability to simultaneously address both exposures.

Assessing the Value of Systematic Cycling in a Polluted Urban Environment

The positive health effects of systematic cycling are weighted against the negative effects due to higher pollutant inhalation in the actual case of the city of Milan in northern Italy. The paper first evaluates the actual use of bikes in the city, and then considers why and how much such an active mobility style can be expanded. Two models are used to compare the outcome of cycling on the specific population sample with the equivalent path travelled by car. The first model computes the long term effects of the physical activity, and the second evaluates the exacerbation of some relevant diseases due to the exposure to high levels of pollutants, in the case at hand, mainly particulate matter with diameter smaller than 10 μm (PM10). According to these two models, the overall balance for public health is always in favour of systematic biking. Even the current level of biking, low in comparison to other European cities, allows a considerable economic advantage on the order of tens of millions euros per year. This may increase to hundreds of millions if the biking level of more bike-friendly cities is reached. Despite being much less relevant from the economic viewpoint, the study also estimates the reduction of pollution and greenhouse gas emissions corresponding to the assumed biking levels.

The temporal structure of pollution levels in developed cities

Currently, the need for mobility can cause significant pollution levels in cities, with important effects on health and quality of life. Any approach to the study of urban pollution and its effects requires an analysis of spatial distribution and temporal variability. It is a crucial dilemma to obtain proven methodologies that allow an increase in the quality of the prediction and the saving of resources in the spatial and temporal sampling. This work proposes a new analytical methodology in the study of temporal structure. As a result, a model for estimating annual levels of urban traffic noise was proposed. The average errors are less than one decibel in all acoustics indicators. A new working methodology of urban noise has begun. Additionally, a general application can be found for the study of the impacts of pollution associated with traffic, with implications for urban design and possibly in economic and sociological aspects.

A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods

The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)).

Incidência e prevalência de perda auditiva induzida por ruído em trabalhadores de uma indústria metalúrgica, Manaus - AM, Brasil

OBJETIVO: estimar a incidência e a prevalência de déficit auditivo sugestivo de Perda Auditiva Induzida por Ruído e sua associação com idade e tempo de serviço em trabalhadores de uma indústria metalúrgica do pólo industrial de Manaus.MÉTODOS: estudo transversal descritivo em trabalhadores que se submeteram a exame audiométrico periódico no ano de 2012, totalizando 1499 sujeitos. Para estimativa da incidência foram selecionadas audiometrias de 763 trabalhadores com audição dentro da normalidade no exame de referência e comparados com exame atual. Realizou-se análise estatística por meio de medidas de tendência central, dispersão e distribuições de frequência. Para verificação de diferenças estatisticamente significantes utilizou-se o teste qui-quadrado, com nível de significância (p≤0,05).RESULTADOS: a prevalência de perda auditiva foi de 44,23% sendo 28,89% sugestivo de PAIR. Houve maior prevalência de perda auditiva nos trabalhadores com faixa etária acima de 45 anos e com tempo de serviço superior a 21 anos. Apenas 11,1% dos trabalhadores acima dos 21 anos de serviço apresentaram audição normal, e 61,9% perda auditiva sugestiva de Perda Auditiva Induzida por Ruído. A classificação de Não Sugestivo de Perda Auditiva Induzida por Ruído permanece estável nos indivíduos abaixo de 20 anos de exposição laboral (14,9%) e nas pessoas expostas com mais de 20 anos aumenta para 27%. A incidência de perda auditiva foi de 28% e desse total 19,7% sugestiva de Perda Auditiva Induzida por Ruído. Houve maior prevalência de perda auditiva grau leve.Conclusão:a prevalência e a incidência de perda auditiva aumentaram com a idade e tempo de serviço. As empresas devem se empenhar na implementação do Programa de Conservação Auditiva a fim de minimizar essas perdas.

Cense: a tool to assess combined exposure to environmental health stressors in urban areas

This paper describes the structure of the Combined Environmental Stressors' Exposure (CENSE) tool. Individuals are exposed to several environmental stressors simultaneously. Combined exposure represents a more serious hazard to public health. Consequently, there is a need to address co-exposure in a holistic way. Rather than viewing chemical and physical health stressors separately for decision making and environmental sustainability considerations, the possibility of an easy-to-comprehend co-exposure assessment is herein considered. Towards this aim, the CENSE tool is developed in the programming environment of Delphi. The graphical user's interface facilitates its tractable application. Studying different scenarios is easy since the execution time required is negligible. The tool incorporates co-exposure indicators and takes into account the potential dose of each chemical stressor by considering the physical activities of each citizen in an urban (micro)environment. The capabilities of the CENSE tool are demonstrated through its application for the case of Thessaloniki, Greece. The test case highlights usability and validation insights and incorporates health stressors and local characteristics of the area considered into a well identified user/decision maker interface. The main conclusion of the work reported is that a decision maker can trust CENSE for urban planning and environmental sustainability considerations, since it supports a holistic assessment of the combined potential damage attributed to multiple health stressors. CENSE abandons the traditional approach of viewing chemical and physical stressors separately, which represents the most commonly adopted strategy in real life decision support cases.

Bridging the gap between traffic generated health stressors in urban areas: predicting xylene levels in EU cities

Many citizens live, work, commute, or visit traffic intensive spaces and are exposed to high levels of chemical health stressors. However, urban conurbations worldwide present monitoring "shortage" - due to economical and/or practical constraints - for toxic stressors such as xylene isomers, which can pose human health risks. This "shortage" may be covered by the establishment of associations between rarely monitored substances such as xylenes and more frequently monitored (i.e. benzene) or usually monitored (i.e. CO). Regression analysis is used and strong statistical relationships are detected. The adopted models are applied to EU cities and comparison between measurements and predictions depicts their representativeness. The analysis provides transferability insights in an effort to bridge the gap between traffic-related stressors. Strong associations between substances of the air pollution mixture may be influential to interpret the complexity of the causal chain, especially if a synergetic exposure assessment in traffic intensive spaces is considered.

Building statistical associations to forecast ethylbenzene levels in European urban-traffic environments

Emission from road traffic has become the most important source of local air pollution in numerous European cities. Epidemiological research community has established consistent associations between traffic-related substances and various health outcomes. Nevertheless, the vast majority of urban areas are characterised by infrastructure's absence to routinely monitor chemical health stressors, such as ethylbenzene. This paper aims at developing and presenting a tractable approach to reliably - and inexpensively - predict ethylbenzene trends in EU urban environments. The establishment of empirical relationships between rarely monitored pollutants such as ethylbenzene and more frequently or usually monitored, such as benzene and CO respectively, may cover the infrastructure's absence and support decision-making. Multiple regression analysis is adopted and the resulting statistical associations are applied to EU cities with available data for validation purposes. The results demonstrate that this approach is capable of capturing ethylbenzene concentration trends and should be considered as complementary to air quality monitoring.