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

Estimating near-roadway air pollution from multi-frequency noise measurements

Air pollution is a major environmental problem and its monitoring is essential for regulatory purposes, policy making, and protecting public health. However, dense networks of air quality monitoring equipment are prohibitively expensive due to equipment costs, labor requirements, and infrastructure needs. As a result, alternative lower-cost methods that reliably determine air quality levels near potent pollution sources such as freeways are desirable. We present an approach that couples noise frequency measurements with machine learning to estimate near-roadway particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) at 1-min temporal resolution. The models were based on data collected by co-located noise and air quality instruments near a busy freeway in Long Beach, California. Model performance was excellent for all three pollutants, e.g., NO2 predictions yielded Pearson's R = 0.87 with a root mean square error of 7.2 ppb; this error represents about 10 % of total morning rush hour concentrations. Among the best air pollutant predictors were noise frequencies at 40 Hz, 500 Hz, and 800 Hz, and meteorology, particularly wind direction. Overall, our method potentially provides a cost-effective and efficient approach to estimating and/or supplementing near-road air pollutant concentrations in urban areas at high temporal resolution.

Noise and silver nanoparticles induce hepatotoxicity via CYP450/NF-Kappa B 2 and p53 signaling pathways in a rat model

Co-exposure to noise and nanomaterials, such as silver nanoparticles (Silver-NPs), is a common occurrence in today's industries. This study aimed to investigate the effects of exposure to noise and the administration of silver-NPs on the liver tissue of rats. Thirty-six adult male albino Wistar rats were randomly divided into six groups: a control group (administered saline intraperitoneally), two groups administered different doses of Silver-NPs (50 mg/kg and 100 mg/kg, 5 days a week for 28 days), two groups exposed to noise in addition to Silver-NPs (at the same doses as mentioned before), and a group exposed only to noise (104 dB, 6 hours a day, 5 days a week for 4 weeks). Blood samples were taken to assess hepatic-functional alterations, such as serum ALP, ALT, and AST levels. Additionally, biochemical parameters (MDA, GPX, and CAT) and the silver concentration in the liver were measured. Histopathological analysis, mRNA expression (P53 and NF-κB), protein expression (CYP450), and liver weight changes in rats were also documented. The study found that the administration of Silver-NPs and exposure to noise resulted in elevated levels of ALP, ALT, AST, and MDA (p < .01). Conversely, GPX and CAT levels decreased in all groups compared with the control group (p < .0001). There was a significant increase (p < .05) in liver weight and silver concentration in the liver tissues of groups administered Silver-NPs (50 mg/kg) plus noise exposure, Silver-NPs (100 mg/kg), and Silver-NPs (100 mg/kg) plus noise exposure, respectively. The expression rate of P53, NF-κB, and cytochromes P450 (CYPs-450) was increased in the experimental groups (p < .05). These findings were further confirmed by histopathological changes. In conclusion, this study demonstrated that exposure to noise and the administration of Silver-NPs exacerbated liver damage by increasing protein and gene expression, causing hepatic necrosis, altering biochemical parameters, and affecting liver weight.

Mitigating air and noise pollution through highway capping: The Bundang-Suseo Highway Cap Project case study

Highways, while vital for transportation, often lead to heightened air and noise pollution, adversely affecting nearby communities. This study delves into the effectiveness of highway capping, a sustainable urban development strategy, in addressing these environmental challenges, with a specific focus on the Bundang-Suseo Highway in South Korea. This study employed a multifaceted approach, incorporating on-road monitoring, in situ measurements, and vertical assessments using UAVs. Following the cap's installation, the area experienced more stable pollutant levels, marking a notable shift from the previously fluctuating conditions heavily influenced by the highway. In-depth in situ monitoring near the cap revealed significant reductions in noise and pollutants like UFP and BC. Furthermore, UAV monitoring captured these changes in pollutant levels at different altitudes. Notably, the installation of the highway cap led to increased PM2.5, PM10, and NO2 levels at ground level, but a decrease above the cap, emphasizing the critical importance of intentional highway cap design in enhancing urban air quality and reducing exposure to harmful pollutants. This research yields invaluable insights for urban planners, health authorities, and policymakers, aiding the precise identification of pollution-prone areas and advocating for improved highway cap design to enhance urban environments.

Spatio-temporal variation of noise pollution in South Paris during and outside the COVID-19 lockdowns

Noise pollution is one of the major environmental problems of contemporary societies. In urban areas, road transport is the main source impacting the spatio-temporal variation of air quality. This work aims to analyse the spatio-temporal variability of the noise level in the 13th arrondissement during peak hours, focusing on the comparison of noise levels between the COVID-19 lockdown and non-lockdown days. This paper provides data that could be used to evaluate noise mitigation options. Mobile surveys, using EXTECH 4017764 sensors, were used to cover 272 points. In this work, the mobile measurements use the median of instant noise levels measured every 5 s over a 5-min period during the peak period. This study confirms that road traffic appears to be the determining factor in noise pollution. The noise levels calculated in 2020 show a strong spatio-temporal variability explained by the proximity of the emission sources, but also that the noise level decreases by 6-10 dB (A) during the lockdowns. Indeed, near the main roads, 57-63 dB (A) are recorded during the lockdowns, compared to 67-72 dB (A) outside the lockdowns. Mainly the number of vehicles contribute to a large part of the noise level, to which the noise of construction sites can occasionally be added as in the south-eastern part of the study area.

Impacts of climate change-induced heat stress on pig productivity in China

The economic impacts of climate-induced heat stress on pig farming have not been sufficiently investigated, despite the significant threat it poses to the industry. In this study, we coupled consecutive years of county-scale meteorological data with operational-level economic data to estimate the impact of climate-induced heat stress on the productivity of the Chinese pig industry using a stochastic frontier production function model. Based on model results, we projected the impact of heat stress on pig production in 2030 and 2050 with and without technological advances. Results showed that heat stress has a significant negative impact on pig production, with southern regions being more affected. However, considering technological advances, losses will decrease by 21 % and 52 %, respectively, compared with the scenario without technological advances. Simultaneously, we calculated the costs of climate change mitigation and adaptation and found that increasing production inputs to reduce production losses from heat stress costs one-fifth of forgoing production losses. Therefore, we recommend a positive climate change response strategy.

Beyond here and now: Evaluating pollution estimation across space and time from street view images with deep learning

Advances in computer vision, driven by deep learning, allows for the inference of environmental pollution and its potential sources from images. The spatial and temporal generalisability of image-based pollution models is crucial in their real-world application, but is currently understudied, particularly in low-income countries where infrastructure for measuring the complex patterns of pollution is limited and modelling may therefore provide the most utility. We employed convolutional neural networks (CNNs) for two complementary classification models, in both an end-to-end approach and as an interpretable feature extractor (object detection), to estimate spatially and temporally resolved fine particulate matter (PM2.5) and noise levels in Accra, Ghana. Data used for training the models were from a unique dataset of over 1.6 million images collected over 15 months at 145 representative locations across the city, paired with air and noise measurements. Both end-to-end CNN and object-based approaches surpassed null model benchmarks for predicting PM2.5 and noise at single locations, but performance deteriorated when applied to other locations. Model accuracy diminished when tested on images from locations unseen during training, but improved by sampling a greater number of locations during model training, even if the total quantity of data was reduced. The end-to-end models used characteristics of images associated with atmospheric visibility for predicting PM2.5, and specific objects such as vehicles and people for noise. The results demonstrate the potential and challenges of image-based, spatiotemporal air pollution and noise estimation, and that robust, environmental modelling with images requires integration with traditional sensor networks.

Changes in traffic congestion and air pollution due to major roadway infrastructure improvements in Texas

Traffic-related air pollution (TRAP) is an established health hazard, and roadway construction has the potential to affect TRAP by relieving congestion. The relationship between roadway construction and congestion is of policy importance, but few studies examine it using large samples of construction projects and detailed traffic and air pollution data. We create a dataset of construction projects in Texas and link them to data on air pollution and three variables operationalizing congestion: average annual daily traffic (AADT), AADT per lane, and delay in hours. We use difference-in-difference methods to estimate the effect of widening and intersection improvements on congestion and air pollution. On average over the period during construction, we find that widening increases delay by 42% (95% CI: 30, 56%), but intersection projects do not affect delay. On average and over the first three years post-construction, we find that widening reduces delay by 33% (95% CI: -41, -24%) and reduces NO2 levels within 500 m by 13% (95% CI: -22, -2%), and intersection projects reduce delay by 52% (95% CI: -65, -35%) and reduce NO2 levels within 500 m by 12% (95% CI: -18, -5%). These short-term impacts are relevant for understanding the impact of roadway construction on human health.

Study on Sound-Insulation Performance of an Acoustic Metamaterial of Air-Permeable Multiple-Parallel-Connection Folding Chambers by Acoustic Finite Element Simulation

In order to achieve a balance between sound insulation and ventilation, a novel acoustic metamaterial of air-permeable multiple-parallel-connection folding chambers was proposed in this study that was based on Fano-like interference, and its sound-insulation performance was investigated through acoustic finite element simulation. Each layer of the multiple-parallel-connection folding chambers consisted of a square front panel with many apertures and a corresponding chamber with many cavities, which were able to extend both in the thickness direction and in the plane direction. Parametric analysis was conducted for the number of layers n_l and turns n_t, the thickness of each layer L₂, the inner side lengths of the helical chamber a₁, and the interval s among the various cavities. With the parameters of n_l = 10, n_t = 1, L₂ = 10 mm, a₁ = 28 mm, and s = 1 mm, there were 21 sound-transmission-loss peaks in the frequency range 200-1600 Hz, and the sound-transmission loss reached 26.05 dB, 26.85 dB, 27.03 dB, and 33.6 dB at the low frequencies 468 Hz, 525 Hz, 560 Hz, and 580 Hz, respectively. Meanwhile, the corresponding open area for air passage reached 55.18%, which yielded a capacity for both efficient ventilation and high selective-sound-insulation performance.

Integrated assessment of personal monitor applications for evaluating exposure to urban stressors: A scoping review

Urban stressors pose a health risk, and individual-level assessments provide necessary and fine-grained insight into exposure. An ever-increasing amount of research literature on individual-level exposure to urban stressors using data collected with personal monitors, has called for an integrated assessment approach to identify trends, gaps and needs, and provide recommendations for future research. To this end, a scoping review of the respective literature was performed, as part of the H2020 URBANOME project. Moreover, three specific aims were identified: (i) determine current state of research, (ii) analyse literature according with a waterfall methodological framework and identify gaps and needs, and (iii) provide recommendations for more integrated, inclusive and robust approaches. Knowledge and gaps were extracted based on a systematic approach, e.g., data extraction questionnaires, as well as through the expertise of the researchers performing the review. The findings were assessed through a waterfall methodology of delineating projects into four phases. Studies described in the papers vary in their scope, with most assessing exposure in a single macro domain, though a trend of moving towards multi-domain assessment is evident. Simultaneous measurements of multiple stressors are not common, and papers predominantly assess exposure to air pollution. As urban environments become more diverse, stakeholders from different groups are included in the study designs. Most frequently (per the quadruple helix model), civil society/NGO groups are involved, followed by government and policymakers, while business or private sector stakeholders are less frequently represented. Participants in general function as data collectors and are rarely involved in other phases of the research. While more active involvement is not necessary, more collaborative approaches show higher engagement and motivation of participants to alter their lifestyles based on the research results. The identified trends, gaps and needs can aid future exposure research and provide recommendations on addressing different urban communities and stakeholders.

Optimization design of the sound absorbing structure of double-layer porous metal material with air layer based on genetic algorithm

An acoustic absorption structure of a double-layer porous metal material with air layers is proposed. The Johnson-Champoux-Allard (JCA) model combined with the transfer matrix method (TMM) was used to establish the theoretical calculation model of the sound absorption coefficient (SAC). Meanwhile, the SAC between 500 and 6300 Hz were measured with an impedance tube. The errors between the theoretical and experimental values were compared to illustrate the good predictability of the theoretical model within the inverse estimations of the transport properties. The effects of the material placement order, material thickness, and cavity depth on the sound absorption performance from 200 to 5000 Hz were analyzed using the theoretical model. Further, a multi-objective function genetic algorithm was used to optimize the porous material's thickness and SAC to obtain an acoustic structure with a smaller thickness and higher sound absorption. A series of optimal solutions were obtained for acoustic structures with a total thickness of less than 70 mm. When the total thickness of the foam metal was 33.57 mm, the average SAC reached 0.853, which was significantly lower than the total thickness of the previous experiments. The multi-objective function genetic algorithm can provide a reliable solution for the optimal design of most sound-absorbing structures.

Drive less but exposed more? Exploring social injustice in vehicular air pollution exposure

Despite growing understanding of racial and class injustice in vehicular air pollution exposure, less is known about the relationship between people's exposure to vehicular air pollution and their contribution to it. Taking Los Angeles as a case study, this study examines the injustice in vehicular PM_2.5 exposure by developing an indicator that measures local populations' vehicular PM_2.5 exposure adjusted by their vehicle trip distances. This study applies random forest regression models to assess how travel behavior, demographic, and socioeconomic characteristics affect this indicator. The results indicate that census tracts of the periphery whose residents drive longer distances are exposed to less vehicular PM_2.5 pollution than tracts in the city center whose residents drive shorter distances. Ethnic minority and low-income tracts emit little vehicular PM_2.5 and are particularly exposed to it, while White and high-income tracts generate more vehicular PM_2.5 pollution but are less exposed.

Beyond Cleansing: Ecosystem Services Related to Phytoremediation

Phytotechnologies used for cleaning up urban and suburban polluted soils (i.e., brownfields) have shown some weakness in the excessive extent of the timeframe required for them to be effectively operating. This bottleneck is due to technical constraints, mainly related to both the nature of the pollutant itself (e.g., low bio-availability, high recalcitrance, etc.) and the plant (e.g., low pollution tolerance, low pollutant uptake rates, etc.). Despite the great efforts made in the last few decades to overcome these limitations, the technology is in many cases barely competitive compared with conventional remediation techniques. Here, we propose a new outlook on phytoremediation, where the main goal of decontaminating should be re-evaluated, considering additional ecosystem services (ESs) related to the establishment of a new vegetation cover on the site. The aim of this review is to raise awareness and stress the knowledge gap on the importance of ES associated with this technique, which can make phytoremediation a valuable tool to boost an actual green transition process in planning urban green spaces, thereby offering improved resilience to global climate change and a higher quality of life in cities. This review highlights that the reclamation of urban brownfields through phytoremediation may provide several regulating (i.e., urban hydrology, heat mitigation, noise reduction, biodiversity, and CO₂ sequestration), provisional (i.e., bioenergy and added-value chemicals), and cultural (i.e., aesthetic, social cohesion, and health) ESs. Although future research should specifically be addressed to better support these findings, acknowledging ES is crucial for an exhaustive evaluation of phytoremediation as a sustainable and resilient technology.

Cyclists' exposure to air pollution, noise, and greenery: a population-level spatial analysis approach

Urban travel exposes people to a range of environmental qualities with significant health and wellbeing impacts. Nevertheless, the understanding of travel-related environmental exposure has remained limited. Here, we present a novel approach for population-level assessment of multiple environmental exposure for active travel. It enables analyses of (1) urban scale exposure variation, (2) alternative routes' potential to improve exposure levels per exposure type, and (3) by combining multiple exposures. We demonstrate the approach's feasibility by analysing cyclists' air pollution, noise, and greenery exposure in Helsinki, Finland. We apply an in-house developed route-planning and exposure assessment software and integrate to the analysis 3.1 million cycling trips from the local bike-sharing system. We show that especially noise exposure from cycling exceeds healthy thresholds, but that cyclists can influence their exposure by route choice. The proposed approach enables planners and individual citizens to identify (un)healthy travel environments from the exposure perspective, and to compare areas in respect to how well their environmental quality supports active travel. Transferable open tools and data further support the implementation of the approach in other cities.

Environmental noise and health in low-middle-income-countries: A systematic review of epidemiological evidence

Evidence of the health impacts from environmental noise has largely been drawn from studies in high-income countries, which has then been used to inform development of noise guidelines. It is unclear whether findings in high-income countries can be readily translated into policy contexts in low-middle-income-countries (LMICs). We conducted this systematic review to summarise noise epidemiological studies in LMICs. We conducted a literature search of studies in Medline and Web of Science published during 2009-2021, supplemented with specialist journal hand searches. Screening, data extraction, assessment of risk of bias as well as overall quality and strength of evidence were conducted following established guidelines (e.g. Navigation Guide). 58 studies were identified, 53% of which were from India, China and Bulgaria. Most (92%) were cross-sectional studies. 53% of studies assessed noise exposure based on fixed-site measurements using sound level meters and 17% from propagation-based noise models. Mean noise exposure among all studies ranged from 48 to 120 dB (L_eq), with over half of the studies (52%) reporting the mean between 60 and 80 dB. The most studied health outcome was noise annoyance (43% of studies), followed by cardiovascular (17%) and mental health outcomes (17%). Studies generally reported a positive (i.e. adverse) relationship between noise exposure and annoyance. Some limited evidence based on only two studies showing that long-term noise exposure may be associated with higher prevalence of cardiovascular outcomes in adults. Findings on mental health outcomes were inconsistent across the studies. Overall, 4 studies (6%) had "probably low", 18 (31%) had "probably high" and 36 (62%) had "high" risk of bias. Quality of evidence was rated as 'low' for mental health outcomes and 'very low' for all other outcomes. Strength of evidence for each outcome was assessed as 'inadequate', highlighting high-quality epidemiological studies are urgently needed in LMICs to strengthen the evidence base.

Exploring the Combined Association between Road Traffic Noise and Air Quality Using QGIS

There is mounting evidence that exposure to air pollution and noise from transportation are linked to the risk of hypertension. Most studies have only looked at relationships between single exposures. To examine links between combined exposure to road traffic, air pollution, and road noise. A Casella CEL-63x instrument was used to monitor traffic noise on a number of locations in residential streets in Glasgow, UK during peak traffic hours. The spatial numerical modelling capability of Quantum GIS (abbreviated QGIS) was used to analyse the combined association of noise and air pollution. Based on geospatial mapping, data on residential environmental exposure was added using annual average air pollutant concentrations from local air quality monitoring network, including particulate matter (PM₁₀ and PM_2.5), nitrogen dioxide (NO₂), and road-traffic noise measurements at different component frequencies (Lden). The combined relationships between air pollution and traffic noise at different component frequencies were examined. Based on Moran I autocorrelation, geographically close values of a variable on a map typically have comparable values when there is a positive spatial autocorrelation. This means clustering on the map was influenced significantly by NO₂, PM₁₀ and PM_2.5, and Lden at the majority of monitoring locations. Studies that only consider one of these two related exposures may exaggerate the impact of the individual exposure while underestimating the combined impact of the two environmental exposures.

Emissions Production by Exhaust Gases of a Road Vehicle's Starting Depending on a Road Gradient

An increasing number of motor vehicles are connected with negative environmental impacts in relation to their operation. Among the main negative effects are exhaust gas emissions production. The annual increase in passenger cars and emissions from them deteriorates air quality daily. Traffic junctions also have a negative impact on increasing emissions production by exhaust gases. This situation may be caused by vehicle speed fluctuation, mainly when they get closer or leave. This study focuses on the emissions produced by exhaust gases after a road vehicle starts. The research was performed with a combustion engine vehicle on a route 30 m long. The vehicle was simulated in three different ways of starting (uphill, on ground level/plain and downhill). The values of carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbons (HC) and nitrogen oxides (NO_X) were observed, as well as the vehicle's operation performance during start-ups. The research results showed that the lowest emissions production is when the vehicle is starting downhill. There, the emissions increased up to a distance of 9.7 m from the start. After reaching this distance, the emissions decreased and the vehicle speed continued to increase. While the vehicle started uphill, the emissions increased up to the distance of 16.8 m. After reaching this distance, the emissions began decreasing. Due to this fact, this type of testing is assessed as "the worst" from the emissions production point of view. The research demonstrates the relations between a road gradient representing starting on a plain surface and a vehicle's emissions produced by the exhaust gases. It is known that exhaust emissions are higher predominantly at junctions. They depend considerably on vehicle speed and driving continuity on a route. This research helps to quantify all the data and, thus, to provide a possibility of further solutions in the future as a tool for emissions reduction in cities and close to traffic intersections.

The impacts from cold start and road grade on real-world emissions and fuel consumption of gasoline, diesel and hybrid-electric light-duty passenger vehicles

Progressively stringent regulations regarding vehicle emissions and fuel economy have spurred technology diversification in light-duty passenger vehicles (LDPVs). To assess the real-world emissions and fuel economy performances of hybrid electric vehicles (HEVs) compared to conventional internal combustion engine (ICE) vehicles, on-road measurements of ten gasoline, four diesel and six full hybrid LDPVs were performed using portable emissions measurement systems (PEMS) in Macao, China. The hot-running emission results indicate that the high emission risks of gasoline vehicles are associated with high mileage and old model years. Diesel vehicles are found to be the highest pollutant emitters in this study due to the intentional removal of aftertreatment systems. Under hot-running conditions, HEVs, as expected, could achieve carbon-reduction benefits of approximately 30 % (i.e., lower CO₂ emissions and fuel consumption) compared to their conventional gasoline counterparts, while no measurable reduction in pollutant emissions was observed except in NO_X (~70 % reduction). In contrast, the cold-start extra emissions (CSEEs) of CO₂ reached 120-364 g/start for these HEVs, even exceeding the maximum values of conventional gasoline vehicles. However, the higher CO₂ CSEEs of HEVs can be far offset by their hot-running emission reduction benefits. For tailpipe pollutants, the CSEEs of the HEVs were reduced by 21 %-68 % on average in comparison to those of conventional gasoline vehicles. Furthermore, strong correlations (R² values of 0.69-0.89) between the road grades and relative emissions were observed. These results can provide necessary information regarding the improvement of future LDPV emission models and inventories.

Prediction of the Sound Absorption Coefficient of Three-Layer Aluminum Foam by Hybrid Neural Network Optimization Algorithm

The combination of multilayer aluminum foam can have high sound absorption coefficients (SAC) at low and medium frequencies, and predicting its absorption coefficient can help the optimal structural design. In this study, a hybrid EO-GRNN model was proposed for predicting the sound absorption coefficient of the three-layer composite structure of the aluminum foam. The generalized regression neural network (GRNN) model was used to predict the sound absorption coefficient of three-layer composite structural aluminum foam due to its outstanding nonlinear problem-handling capability. An equilibrium optimization (EO) algorithm was used to determine the parameters in the neuronal network. The prediction results show that this method has good accuracy and high precision. The calculation result shows that this proposed hybrid model outperforms the single GRNN model, the GRNN model optimized by PSO (PSO-GRNN), and the GRNN model optimized by FOA(FOA-GRNN). The prediction results are expressed in terms of root mean square error (RMSE), absolute error, and relative error, and this method performs well with an average RMSE of only 0.011.

Estimating traffic noise over a large urban area: An evaluation of methods

Unlike air pollution, traffic-related noise remains unregulated and has been under-studied despite evidence of its deleterious health impacts. To characterize population exposure to traffic noise, both acoustic-based numerical models and data-driven statistical approaches can generate estimates over large urban areas. The aim of this work is to formally compare the performances of the most common traffic noise models by evaluating their estimates for different categories of roads and validating them against a unique dataset of measured noise in Long Beach, California. Specifically, a statistical land use regression model, an extreme gradient boosting machine learning model (XGB), and three numerical/acoustic traffic noise models: the US Noise Model (FHWA-TNM2.5), a commercial noise model (CadnaA), and an open-source European model (Harmonoise) were optimized and compared. The results demonstrate that XGB and CadnaA were the most effective models for estimating traffic noise, and they are particularly adept at differentiating noise levels on different categories of road.

Spatial modelling and inequalities of environmental noise in Accra, Ghana

Noise pollution is a growing environmental health concern in rapidly urbanizing sub-Saharan African (SSA) cities. However, limited city-wide data constitutes a major barrier to investigating health impacts as well as implementing environmental policy in this growing population. As such, in this first of its kind study in West Africa, we measured, modelled and predicted environmental noise across the Greater Accra Metropolitan Area (GAMA) in Ghana, and evaluated inequalities in exposures by socioeconomic factors. Specifically, we measured environmental noise at 146 locations with weekly (n = 136 locations) and yearlong monitoring (n = 10 locations). We combined these data with geospatial and meteorological predictor variables to develop high-resolution land use regression (LUR) models to predict annual average noise levels (LAeq24hr, Lden, Lday, Lnight). The final LUR models were selected with a forward stepwise procedure and performance was evaluated with cross-validation. We spatially joined model predictions with national census data to estimate population levels of, and potential socioeconomic inequalities in, noise levels at the census enumeration-area level. Variables representing road-traffic and vegetation explained the most variation in noise levels at each site. Predicted day-evening-night (Lden) noise levels were highest in the city-center (Accra Metropolis) (median: 64.0 dBA) and near major roads (median: 68.5 dBA). In the Accra Metropolis, almost the entire population lived in areas where predicted Lden and night-time noise (Lnight) surpassed World Health Organization guidelines for road-traffic noise (Lden <53; and Lnight <45). The poorest areas in Accra also had significantly higher median Lden and Lnight compared with the wealthiest ones, with a difference of ∼5 dBA. The models can support environmental epidemiological studies, burden of disease assessments, and policies and interventions that address underlying causes of noise exposure inequalities within Accra.

Development of transferable neighborhood land use regression models for predicting intra-urban ambient nitrogen dioxide (NO2) spatial variations

Land use regression (LUR) models have been extensively used to predict air pollution exposure in epidemiological and environmental studies. The lack of dense routine monitoring networks in big cities places increased emphasis on the need for LUR models to be developed using purpose-designed neighborhood-scale monitoring data. However, the unsatisfactory model transferability limits these neighborhood LUR models to be then applied to other intra-urban areas in predicting air pollution exposure. In this study, we tackled this issue by proposing a method to develop transferable neighborhood NO₂ LUR models with comparable predictive power based on only micro-scale predictor variables for modeling intra-urban ambient air pollution exposure. Taking Auckland metropolis, New Zealand, as a case study, the proposed method was applied to three neighborhoods (urban, central business district, and dominion road) and compared with the corresponding counterpart models developed using pools of (a) only macro-scale predictor variables and (b) a mixture of both micro- and macro-scale predictor variables (traditional method). The results showed that the models using only macro-scale variables achieved the lowest accuracy (R²: 0.388-0.484) and had the worst direct (R²: 0.0001-0.349) and indirect transferability (R²: 0.07-0.352). Those models using the traditional method had the highest model fitting R² (0.629-0.966) with lower cross-validation R² (0.495-0.941) and slightly better direct transferability (R²: 0.0003-0.386) but suffered poor model interpretability when indirectly transferred to new locations. Our proposed models had comparable model fitting R² (0.601-0.966) and the best cross-validation R² (0.514-0.941). They also had the strongest direct transferability (R²: 0.006-0.590) and moderate-to-good indirect transferability (R²: 0.072-0.850) with much better model interpretability. This study advances our knowledge of developing transferable LUR models for the very first time from the perspective of the scale of the predictor variables used in the model development and will significantly benefit the wider application of LUR approaches in epidemiological and environmental studies.

An Optimized Neural Network Acoustic Model for Porous Hemp Plastic Composite Sound-Absorbing Board

Current acoustic modeling methods face problems such as complex processes or inaccurate sound absorption coefficients, etc. Therefore, this paper studies the topic. Firstly, the material samples were prepared, and standing wave tube method experiments were conducted. Material acoustic data were obtained, while a model using improved genetic algorithm and neural network was subsequently proposed. Secondly, the acoustic data obtained from the experiment were analyzed; a neural network structure was designed; and the training, verification, and test data were all divided. In order to facilitate data processing, a symmetrical method was used to inversely normalize all the data. Thirdly, by the design of real coding scheme, fitness function, crossover, and mutation operators, an improved genetic algorithm was proposed to obtain the optimal solution, as the initial weight and threshold, which were then input into the neural network along with the training and verification data. Finally, the test data were input into the trained neural network in order to test the model. The test results and statistical analysis showed that compared with other algorithms, the proposed model has the lower root mean squared error (RMSE) value, the maximum coefficient of determination (R2) value, and shorter convergence time.

A systematic review of the mental health risks and resilience among pollution-exposed adolescents

Pollution is harmful to human physical health and wellbeing. What is less well established is the relationship between adolescent mental health - a growing public health concern - and pollution. In response, we systematically reviewed studies documenting associations between pollution and mental health in adolescents. We searched Africa Wide, Medline, PsycArticles, PsycInfo, PubMed, CINAHL, ERIC, SciELO, Scopus, and Web of Science Core Collection for studies published up to 10 April 2020 that investigated exposure to any pollutant and symptoms of anxiety; depression; disruptive, impulse-control, and conduct disorders; neurodevelopmental disorders; psychosis; or substance abuse in 10-24-year-olds (i.e., adolescents as per expanded and more inclusive definition of adolescence). This identified 2291 records and we assessed 128 papers for inclusion. We used a narrative synthesis to coalesce the studies' findings. This review is registered on PROSPERO, CRD42020176664. Seventeen studies from Asia, Europe, the Middle East, and North America were included. Air and water pollution exposure was associated with elevated symptoms of depression, generalised anxiety, psychosis, and/or disruptive, impulse control and conduct disorder. Exposure to lead and solvents was associated with neurodevelopmental impairments. Most studies neglected factors that could have supported the mental health resilience of adolescents exposed to pollution. Notwithstanding the limited quality of most reviewed studies, results suggest that pollution exposure is a risk to adolescent mental health. High-quality research is urgently required, including the factors and processes that protect the mental health of pollution-exposed adolescents. Studies with adolescents living in low- and lower middle-income countries and the southern hemisphere must be prioritized.

Noise and air pollution during Covid-19 lockdown easing around a school site

During the Covid-19 pandemic and resulting lockdowns, road traffic volumes reduced significantly leading to reduced pollutant concentrations and noise levels. Noise and the air pollution data during the lockdown period and loosening of restrictions through five phases in 2021 are examined for a school site in the United Kingdom. Hourly and daily average noise level as well as the average over each phase, correlations between noise and air pollutants, variations between pollutants, and underlying reasons explaining the temporal variations are explored. Some strong linear correlations were identified between a number of traffic-sourced air pollutants, especially between the differently sized particulates PM₁, PM_2.5, and PM₁₀ (0.70 < r <0.98) in all phases and an expected inverse correlation between nitrogen dioxide (NO₂) and ground-level ozone (O₃) (-0.68 < r < -0.78) as NO₂ is a precursor of O₃. Noise levels exhibit a weak correlation with the measured air pollutants and moderate correlation with meteorological factors, including wind direction, temperature, and relative humidity. There was a consistent and significant increase in noise levels (p < 0.01) of up to 3 dB with initial easing, and this was maintained through the remaining phases.

Understanding temporal and spatial changes of O3 or NO2 concentrations combining multivariate data analysis methods and air quality transport models

The application of the multivariate curve resolution method to the analysis of temporal and spatial data variability of hourly measured O₃ and NO₂ concentrations at nineteen air quality monitoring stations across Catalonia, Spain, during 2015 is shown. Data analyzed included ground-based experimental measurements and predicted concentrations by the CALIOPE air quality modelling system at three horizontal resolutions (Europe at 12 × 12 km², Iberian Peninsula at 4 × 4 km² and Catalonia at 1 × 1 km²). Results obtained in the analysis of these different data sets allowed a better understanding of O₃ and NO₂ concentration changes as a sum of a small number of different contributions related to daily sunlight radiation, seasonal dynamics, traffic emission patterns, and local station environments (urban, suburban and rural). The evaluation of O₃ and NO₂ concentrations predicted by the CALIOPE system revealed some differences among data sets at different spatial resolutions. NO₂ predictions, showed in general a better performance than O₃ predictions for the three model resolutions, specially at urban stations. Our results confirmed that the application of the trilinearity constraint during the multivariate curve resolution factor analysis decomposition of the analyzed data sets is a useful tool to facilitate the understanding of the resolved variability sources.

Selected Aspects of Sustainable Mobility Reveals Implementable Approaches and Conceivable Actions

The transportation sector plays a prominent role in driving the economy of any given nation. However, with the recent tensions arising in and around the transportation sector, sustainable mobility concepts have evolved. However, it is quite unclear whether sustainable mobility is feasible and exhibits economic returns, environmental benefits, and societal advantages. Hence, taking into account the environmental, economic, and social impact, and technical possibilities, this study intends to analyse sustainable mobility in relation to economic returns, environmental benefits and societal advantages using bibliometric analysis. For this study, we considered two decades of research, from 2001 to 2021. An in-depth search was performed on articles generated in the last two decades to assess the state of the literature on sustainable mobility. The most reverent, frequently referenced papers and influential journals in the field of sustainable mobility were identified. The acquired findings highlight the most prominent publications, journals, and authors who have made significant contributions to sustainable mobility studies, as well as the sub-areas or themes linked to sustainable mobility. Overall, the analysis discovered current paradigms, significant research topics, and a relationship between the domains of sustainable mobility studies. Meanwhile, this study also demonstrates advancements in the primary themes and sub-areas during the previous 20 years and alterations in future research fields. In addition, this study identified the promotion of rapid-reliable-safe-convenient (RRSC) transportation services, reduction in urban car traffic, and support to low transportation demand as the critical steps that require immediate attention in order to build a sustainable mobility future. We also observed that hydrogen would be a promising fuel and potential technology for the future mobility sector in the post-COVID era.

On the Dependence of Acoustic Pore Shape Factors on Porous Asphalt Volumetrics

The sound absorption of a road pavement depends not only on geometric and volumetric factors but also on pore shape factors. In turn, pore shape factors mainly refer to thermal and viscous factors (i.e., thermal and viscous effects that usually occur inside porous materials). Despite the presence of a number of studies and researches, there is a lack of information about how to predict or estimate pore shape factors. This greatly affects mixture design, where a physical-based or correlation-based link between volumetrics and acoustics is vital and plays an important role also during quality assurance and quality control (QA/QC) procedures. Based on the above, the objective of this study is to link mixture volumetrics and pore shape factors. In particular, 10 samples of a porous asphalt concrete were tested in order to estimate their thickness, air voids content (vacuum-sealing method, ASTM D6857/D6857M), sound absorption coefficient (Kundt’s tube, ISO 10354-2), airflow resistivity (ISO 9053-2), and permeability (ASTM PS 129). Subsequently, two models (herein called STIN and JCAL) were used to derive both volumetrics and pore shape factors from the estimated parameters listed above, and statistical analysis was carried out to define correlations among the parameters and models performance. Results confirm the complexity of the tasks and point out that estimates of the pore shape factors can be derived based on mixture volumetrics. Results can benefit researchers (in acoustic and pavement mixtures) and practitioners involved in mix design and pavement acceptance processes.

Urban noise assessment and its nonauditory health effects on the residents of Chiniot and Jhang, Punjab, Pakistan

Elevated noise level is an emerging global problem. Therefore, the present work is conducted that can improve, increase, and integrate the already known issue in literature with new information coming from an emerging country such as Pakistan. The objectives of this study were (i) to assess the urban noise levels and traffic density of Chiniot and Jhang and (ii) to determine nonauditory health effects of noise levels on the residents of both cities. Noise levels were examined from 181 locations (103 from Jhang and 78 from Chiniot) and categorized into hospitals, educational, religious and recreational, residential, industrial areas, and traffic intersections. A-weighted noise level measurements were taken using an integrated sound level meter which recorded short-term road traffic noise continuously for 15 min at each location (LA_eq15). The urban noise data showed 82% of the sites in Jhang (LA_max = 103 dB) and 95% in Chiniot (LA_max = 120 dB) exceeded the noise limits set by the National Environment Quality Standard of Pakistan (NEQS-Pak) and World Health Organization (WHO). Moreover, higher intensity of noise levels (LA_eq15 ≥ 100 dB) was recorded in Chiniot (17 sites) than in Jhang (1 site). Regression analysis showed a relatively strong relationship of traffic density with noise at Chiniot (R² = 0.48) compared to Jhang (R² = 0.31). However, spatial variability of noise with traffic density was observed in both cities. Survey study revealed that all the respondents in Jhang and Chiniot suffered from many noise-related health problems such as annoyance (53 and 51%), depression (45 and 47%), dizziness (61 and 65%), headache (67 and 64%), hypertension (71 and 56%), hearing loss (53 and 56%), physiological stress (65 and 65%), sleeplessness (81 and 84%), and tinnitus (70 and 62%) due to noise, respectively. We conclude that noise levels are higher in Chiniot primarily due to high road traffic and secondarily due to high population density. It is recommended that vehicle maintenance and family and urban planning could be effective measures to reduce urban noise levels.

Stakeholder Management and Project Sustainability—A Throw of the Dice

The aim of this study was to analyze the strategies that external stakeholders apply to influence sustainable projects. In order to investigate this phenomenon, we employed a qualitative case study approach considering the Serbian small hydro power plant project. For the purpose of this research, we developed a theoretical framework consisting of four types of different influence strategies, which were identified and then applied to our case. The results show that external stakeholders utilized all four strategies interchangeably, with the goal to influence the project and decision-makers. The case of the small hydro power plant project revealed certain relationships between influence strategies, as well as the intensity and direction of these relationships. It also revealed that external stakeholders were highly motivated by negative impacts on the environment. Five propositions were derived as a result of our research. This paper contributes not just to the project and stakeholder management literature but also to the practical knowledge of project managers. Understanding stakeholder actions and influence is essential to achieving project goals.

Oxidative Stress Biomarkers in the Relationship between Type 2 Diabetes and Air Pollution

The incidence and prevalence of type 2 diabetes have increased in the last decades and are expected to further grow in the coming years. Chronic hyperglycemia triggers free radical generation and causes increased oxidative stress, affecting a number of molecular mechanisms and cellular pathways, including the generation of advanced glycation end products, proinflammatory and procoagulant effects, induction of apoptosis, vascular smooth-muscle cell proliferation, endothelial and mitochondrial dysfunction, reduction of nitric oxide release, and activation of protein kinase C. Among type 2 diabetes determinants, many data have documented the adverse effects of environmental factors (e.g., air pollutants) through multiple exposure-induced mechanisms (e.g., systemic inflammation and oxidative stress, hypercoagulability, and endothelial and immune responses). Therefore, here we discuss the role of air pollution in oxidative stress-related damage to glycemic metabolism homeostasis, with a particular focus on its impact on health. In this context, the improvement of new advanced tools (e.g., omic techniques and the study of epigenetic changes) may provide a substantial contribution, helping in the evaluation of the individual in his biological totality, and offer a comprehensive assessment of the molecular, clinical, environmental, and epidemiological aspects.

Mobile surveys and machine learning can improve urban noise mapping: Beyond A-weighted measurements of exposure

Urban noise pollution is a major environmental issue, second only to fine particulate matter in its impacts on physical and mental health. To identify who is affected and where to prioritise actions, noise maps derived from traffic flows and propagation algorithms are widely used. These may not reflect true levels of exposure because they fail to consider noise from all sources and may leave gaps where roads or traffic data are absent. We present an improved approach to overcome these limitations. Using walking surveys, we recorded 52,366 audio clips of 10 s each along 733 km of routes throughout the port city of Southampton. We extracted power levels in low (11 to 177 Hz), mid (177 Hz to 5.68 kHz), high (5.68 to 22.72 kHz) and A-weighted frequencies and then built machine-learning (ML) models to predict noise levels at 30 m resolution across the entire city, driven by urban form. Model performance (r²) ranged from 0.41 (low frequencies) to 0.61 (mid frequencies) with mean absolute errors of 4.05 to 4.75 dB. The main predictors of noise were related to modes of transport (road, air, rail and water) but for low frequencies, port activities were also important. When mapped to the city scale, A-weighted frequencies produced a similar spatial pattern to mid-frequencies, but did not capture the major sources of low frequency noise from the port or scattered hotspots of high frequencies. We question whether A-weighted noise mapping is adequate for health and wellbeing impact assessments. We conclude that mobile surveys combined with ML offer an alternative way to map noise from all sources and at fine resolution across entire cities that may more accurately reflect true exposures. Our approach is suitable for noise data gathered by citizen scientists, or from a network of sensors, as well as from structured surveys.

Spatial extent of road pollution: A national analysis

Roads form vast, pervasive and growing networks across the Earth, causing negative environmental impacts that spill out into a 'road-effect zone'. Previous research has estimated the regional and global extent of these zones using arbitrary distances, ignoring the spatial distribution and distance-dependent attenuation of different forms of road environmental impact. With Great Britain as a study area, we used mapping of roads and realistic estimates of how pollution levels decay with distance to project the spatial distribution of road pollution. We found that 25% of land was less than 79 m from a road, 50% of land was less than 216 m and 75% of land was less than 527 m. Roadless areas were scarce, and confined almost exclusively to the uplands (mean elevation 391 m), with only ca 12% of land in Great Britain more than 1 km from roads and <4% of land more than 2.5 km from roads. Using light, noise, heavy metals, NO₂, and particulate matter PM_2.5 and PM₁₀ as examples, we estimate that roads have a zone of influence that extends across >70% of the land area. Potentially less than 6% of land escapes any impact, resulting in nearly ubiquitously elevated pollution levels. Generalising from this, we find that, whilst the greatest levels of road pollution are relatively localised around the busiest roads, low levels of road pollution (which may be ecologically significant) are pervasive. Our findings demonstrate the importance of incorporating greater realism into road-effect zones and considering the ubiquity of road pollution in global environmental issues. We used Great Britain as a study area, but the findings likely apply to other densely populated regions at present, and to many additional regions in the future due to the predicted rapid expansion of the global road network.

Assessing the effectiveness of vehicle emission regulations on improving perinatal health: a population-based accountability study

BACKGROUND

Since the 1990s, extensive regulations to reduce traffic-related air pollution (TRAP) have been implemented, yet the effectiveness of these regulations has not been assessed with respect to improving infant health. In this study, we evaluate how infant health risks associated with maternal residences near highways during pregnancy have changed over time.

METHODS

We created a population-based retrospective birth cohort with geocoded residential addresses in Texan metropolitan areas from 1996 through 2009 (n = 2 259 411). We compared term birthweight (37-42 weeks of gestation) among maternal residences <300 m from a highway (high TRAP exposure) (n = 394 346) and 500-3500 m from a highway (comparison group) (n = 1 865 065). We implemented linear regressions to evaluate interactions between high TRAP exposure and birth year, adjusting for demographics, socioeconomic status and neighbourhood context. In addition, we used propensity score matching to further reduce residual confounding.

RESULTS

From 1996 to 2009, outdoor NO2 decreased by 51.3%, based on regulatory monitoring data in Texas. Among pregnant women who resided in the high TRAP zone during pregnancy, interaction terms between residential location and birth year show that birthweight increased by 1.1 g [95% confidence interval CI): 0.7, 1.5) in unadjusted models and 0.3 g (95% CI: 0.0, 0.6) in matched models. Time-stratified models also show decreasing impacts of living in high TRAP areas on birthweight when comparing infants born in 1996-97 with 2008-09. Sensitivity analyses with alternative exposure and control groups show consistent results.

CONCLUSIONS

Infant health risks associated with maternal residence near highways have reduced over time, paralleling regulatory measures to improve exhaust pipe emissions.

A community noise survey in Southwest Detroit and the value of supplemental metrics for truck noise

Noise exposure can affect sleep, health and cognitive performance, and it disproportionately affects communities of color. This study has the objective of evaluating both conventional and supplemental noise metrics in a community noise survey examining Southwest Detroit, Michigan, a densely populated and industrialized area with extensive truck traffic on residential streets. Sound pressure level (SPL) monitors were deployed at 21 residential sites within 900 m of a major interstate highway. With assistance from youth volunteers, continuous SPL measurements were obtained for 1.5-7 days at each site, and short-term vehicle counts on local roads were recorded. We calculated conventional noise metrics, including the day-evening-night average sound level L_DEN and the 90th percentile 1-hr maximum L₁₀(h), and evaluated the effect of distance from highways, traffic volume, time-of-day, and other factors. Supplemental metrics potentially appropriate for intermittent traffic noise were calculated, including fraction of time over specific SPL thresholds using a new metric called F_DEN, which is the fraction of time over 60, 65 and 70 dB during night, evening and daytime periods, respectively, and a peak noise metric called L_2P(h), which utilizes the 98th percentile SPL within time blocks to increase robustness. The conventional metrics indicated five sites that exceeded 70 dB, and the highest noise levels were found within ~50 m of truck routes, arterials and freeway ramps. The estimated impact of truck traffic ranged up to 17 dB for hourly averages and to 33 dB for 1-s peaks. The conventional metrics did not always capture short-term noise exposures, which may be especially important to annoyance and sleep issues. In addition to showing widespread exposure to traffic noise in the study community that warrants consideration of noise abatement strategies, the study demonstrates the benefits of supplemental noise metrics and community engagement in noise assessment.

Associations between residential greenness and self-reported heart disease in Sri Lankan men: A cross-sectional study

Cardiovascular diseases (CVDs) are major contributors to morbidity and mortality in lower-middle-income countries (LMICs). Features of the natural environment, such as greenness, are a potential, modifiable determinant of CVD, yet there is a lack of evidence, particularly in LMICs. Our study investigated associations between residential greenness, measured using the Normalized Difference Vegetation Index (NDVI), and self-reported heart disease in 5268 Sri Lankan men aged 34 to 55 years. Multivariable logistic regression models were fitted to examine associations between mean NDVI within 100 m, 400 m, 800 m, 1600 m, and 2000 m of the residential address, adjusting for age, marital status, income, education, alcohol consumption, smoking and road length. Fully adjusted models showed that a 0.1 increase in mean NDVI was associated with lower odds of heart disease when using the 400 m (OR: 0.80; 95% CI: 0.64, 1.00), 800 m (OR: 0.85; 95% CI: 0.63, 1.14), and 2000 m (OR: 0.74; 95% CI: 0.48, 1.13) buffers. Further research in different contexts, and with improved outcome measures, is needed to confirm relationships between residential greenness and heart disease in rural areas and in LMICs.

Space-time characterization of community noise and sound sources in Accra, Ghana

Urban noise pollution is an emerging public health concern in growing cities in sub-Saharan Africa (SSA), but the sound environment in SSA cities is understudied. We leveraged a large-scale measurement campaign to characterize the spatial and temporal patterns of measured sound levels and sound sources in Accra, Ghana. We measured sound levels and recorded audio clips at 146 representative locations, involving 7-days (136 locations) and 1-year measurements between 2019 and 2020. We calculated metrics of noise levels and intermittency and analyzed audio recordings using a pre-trained neural network to identify sources. Commercial, business, and industrial areas and areas near major roads had the highest median daily sound levels (LAeq_24hr: 69 dBA and 72 dBA) and the lowest percentage of intermittent sound; the vice-versa was found for peri urban areas. Road-transport sounds dominated the overall sound environment but mixtures of other sound sources, including animals, human speech, and outdoor music, dominated in various locations and at different times. Environmental noise levels in Accra exceeded both international and national health-based guidelines. Detailed information on the acoustical environmental quality (including sound levels and types) in Accra may guide environmental policy formulation and evaluation to improve the health of urban residents.

Environmental exposure during travel: A research review and suggestions forward

Daily travel through the urban fabric exposes urban dwellers to a range of environmental conditions that may have an impact on their health and wellbeing. Knowledge about exposures during travel, their associations with travel behavior, and their social and health outcomes are still limited. In our review, we aim to explain how the current environmental exposure research addresses the interactions between human and environmental systems during travel through their spatial, temporal and contextual dimensions. Based on the 104 selected studies, we identify significant recent advances in addressing the spatiotemporal dynamics of exposure during travel. However, the conceptual and methodological framework for understanding the role of multiple environmental exposures in travel environments is still in an early phase, and the health and wellbeing impacts at individual or population level are not well known. Further research with greater geographical balance is needed to fill the gaps in the empirical evidence, and linking environmental exposures during travel with the causal health and wellbeing outcomes. These advancements can enable evidence-based urban and transport planning to take the next step in advancing urban livability.

Cycling in one of the most polluted cities in the world: Exposure to noise and air pollution and potential adverse health impacts in Delhi

Background

In India, many cities struggle with extreme levels of air pollution and noise. Delhi, in particular, has the notorious reputation of being one of the most polluted cities in the world. Cyclists constitute a particularly exposed population, since they cycle among motor vehicles without any protection. This paper modeled the cyclists’ exposure to nitrogen dioxide (NO₂) and noise in Delhi, India.

Methods

Using primary data collected on 1,229 kms of roads in Delhi, Generalized Additive Mixed Models with Auto-Regressive terms (GAMMAR) are constructed for noise exposure, NO₂ exposure and NO₂ inhalation doses.

Results

Results show that cyclists are exposed to 47 µg/m³ of NO₂ and 3.3 dB(A) more when cycling on a primary road than on a residential street. Using WHO guideline values for noise and air pollution, we assessed how many minutes of inhaling doses and noise doses become potentially harmful to cyclists’ health in Delhi. Such thresholds are quickly exceeded: after cycling one hour in an area with moderate predicted values of noise and air pollution, the noise dose and inhaled dose of NO₂ will reach 212% and 403 µg on residential streets, and 459% and 482 µg on primary roads, respectively.

Conclusion

Policy makers should take these results into account to minimize cyclists’ exposure, especially for the most deprived people.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12942-021-00272-2.

Environmental noise in hospitals: a systematic review

Environmental noise has been growing in recent years, causing numerous health problems. Highly sensitive environments such as hospitals deserve special attention, since noise can aggravate patients' health issues and impair the performance of healthcare professionals. This work consists of a systematic review of scientific articles describing environmental noise measurements taken in hospitals between the years 2015 and 2020. The researchers started with a consultation of three databases, namely, Scopus, Web of Science, and ScienceDirect. The results indicate that for the most part, these studies are published in journals in the fields of medicine, engineering, environmental sciences, acoustics, and nursing and that most of their authors work in the fields of architecture, engineering, medicine, and nursing. These studies, which are concentrated in Europe, the Americas, and Asia, use as reference values sound levels recommended by the World Health Organization. L_eq measured in hospital environments showed daytime values ranging from 37 to 88.6 dB (A) and nighttime values of 38.7 to 68.8 dB (A). L_eq values for outdoor noise were 74.3 and 56.6 dB (A) for daytime and nighttime, respectively. The measurements were taken mainly inside hospitals, prioritizing more sensitive departments such as intensive care units. There is a potential for growth in work carried out in this area, but research should also include discussions about guidelines for improvement measures aimed at reducing noise in hospitals.

Towards environmental sustainability in the local community: Future insights for managing the hazardous pollutants at construction sites

Although various technologies are being developed in the construction industry, management technologies for achieving environmental sustainability in the local community are still lacking. As such, this study suggests future insights for the development of an automated intelligent environment management system for the promotion of environmental sustainability in the local community, through a systematic review of 1,707 relevant literature. The systematic review was conducted in two steps: (i) quantitative review: keyword co-occurrence and trend analysis; and (ii) qualitative review: a review on monitoring, evaluation, and improvement technologies. As a result, the research level related to the local-level pollutants (noise, vibration, and dust) was found to be quantitatively insufficient, and the limitations of the existing technologies for these pollutants were presented. Eventually, to overcome these limitations, new technologies and application strategies that can be applied to construction sites as future research roadmap to effectively manage the hazardous pollutants were proposed. Furthermore, an intelligent management system should be developed, and the management of environmental complaints is also necessary for environmental sustainability at the local level in the construction industry. As a fundamental study, this study could become a benchmark for future researches dealing with environmental sustainability and hazardous pollutants in the construction industry.

Development and evaluation of a mobile laboratory for collecting short-duration near-road fine and coarse ambient particle and road dust samples

This study used fine and coarse PM concentrator technology in a Mobile Particle Concentrator Platform (MPCP) designed and built to allow the collection of large amounts of ambient PM, enabling time-resolved speciation analysis, which would not be feasible using conventional methods. One hour of sampling yielded sufficient sample loading for trace elemental analysis using X-Ray Fluorescence (XRF). In addition, we developed a novel Road Dust Aerosolizer (RDA) sampler in order to collect PM_2.5 and PM₁₀ surface road dust in situ. This sampler aerosolizes dust from the road surface, simulating ambient road dust resuspension, resulting in measured PM composition and size more appropriately (and less labor-intensive) than those obtained from studies using bulk road dust sieved and re-suspended in the laboratory. Overall, our modified fine and coarse particle concentrators yielded good reproducibility between co-located samples and sufficient loading for trace elemental analysis. For particle mass concentration, we observed a relative error of 3% and 4% among pairs of filters for fine and coarse concentrators, respectively; confirming that the mass collected on an unweighted quartz filter in parallel with a Teflon filter will have the same PM mass as the weighed Teflon filter. For samples with elements that are well above the LOD, relative uncertainty values were between 5% and 10% for the fine and 3% and 10% for the coarse. Our results show that the RDA system has an excellent precision for mass and elements as well. The relative error for mass is 7% for PM₁₀ and 3% for PM_2.5 within pairs and ranged from 2 to10% for elements. In conclusion, we developed a method for collecting PM₁₀ and PM_2.5 near-road air and surface road dust for short durations, which allows investigation of the composition of direct (airborne) and indirect (re-suspended road dust) non-tailpipe vehicular emissions. Implications: The methods we developed in this study allow the collection of one-hour PM10-2.5 and PM2.5-0.2 samples from near-road ambient air at several distances from the same roadway in 1 day, and collection of road dust directly from the road surface, with sufficient loading for trace elemental analysis. This will allow investigation of the composition of direct (airborne) and indirect (re-suspended road dust) vehicular emissions.

Four Decades of United States Mobile Source Pollutants: Spatial-Temporal Trends Assessed by Ground-Based Monitors, Air Quality Models, and Satellites

On-road emissions sources degrade air quality, and these sources have been highly regulated. Epidemiological and environmental justice studies often use road proximity as a proxy for traffic-related air pollution (TRAP) exposure, and other studies employ air quality models or satellite observations. To assess these metrics' abilities to reproduce observed near-road concentration gradients and changes over time, we apply a hierarchical linear regression to ground-based observations, long-term air quality model simulations using Community Multiscale Air Quality (CMAQ), and satellite products. Across 1980-2019, observed TRAP concentrations decreased, and road proximity was positively correlated with TRAP. For all pollutants, concentrations decreased fastest at locations with higher road proximity, resulting in "flatter" concentration fields in recent years. This flattening unfolded at a relatively constant rate for NO_x, whereas the flattening of CO concentration fields has slowed. CMAQ largely captures observed spatial-temporal NO₂ trends across 2002-2010 but overstates the relationships between CO and elemental carbon fine particulate matter (EC) road proximity. Satellite NO_x measures overstate concentration reductions near roads. We show how this perspective provides evidence that California's on-road vehicle regulations led to substantial decreases in NO₂, NO_x, and EC in California, with other states that adopted California's light-duty automobile standards showing mixed benefits over states that did not adopt these standards.

Assessing schoolchildren's exposure to air pollution during the daily commute - A systematic review

Air pollution is mostly caused by emissions from human activities, and exposure to air pollution is linked with numerous adverse human health outcomes. Recent studies have identified that although people only spend a small proportion of time on their daily commutes, the commuter microenvironment is a significant contributor to their total daily air pollution exposure. Schoolchildren are a particularly vulnerable cohort of the population, and their exposure to air pollution at home or school has been documented in a number of case studies. A few studies have identified that schoolchildren's exposure during commutes is linked with adverse cognitive outcomes and severe wheeze in asthmatic children. However, the determinants of total exposure, such as route choice and commute mode, and their subsequent health impacts on schoolchildren are still not well-understood. The aim of this paper is to review and synthesize recent studies on assessing schoolchildren's exposure to various air pollutants during the daily commute. Through reviewing 31 relevant studies published between 2004 and 2020, we tried to identify consistent patterns, trends, and underlying causal factors in the results. These studies were carried out across 10 commute modes and 12 different air pollutants. Air pollution in cities is highly heterogeneous in time and space, and commuting schoolchildren move through the urban area in complex ways. Measurements from fixed monitoring stations (FMSs), personal monitoring, and air quality modeling are the three most common approaches to determining exposure to ambient air pollutant concentrations. The time-activity diary (TAD), GPS tracker, online route collection app, and GIS-based route simulation are four widely used methods to determine schoolchildren's daily commuting routes. We found that route choices exerted a determining impact on schoolchildren's exposure. It is challenging to rank commute modes in order of exposure, as each scenario has numerous uncontrollable determinants, and there are notable research gaps. We suggest that future studies should concentrate on examining exposure patterns of schoolchildren in developing countries, exposure in the subway and trains, investigating the reliability of current simulation methods, exploring the environmental justice issue, and identifying the health impacts during commuting. It is recommended that three promising tools of smartphones, data fusion, and GIS should be widely used to overcome the challenges encountered in scaling up commuter exposure studies to population scales.

Sustainable Mobility: A Review of Possible Actions and Policies

In this paper, a review of the main actions and policies that can be implemented to promote sustainable mobility is proposed. The work aims to provide a broad, albeit necessarily not exhaustive, analysis of the main studies and research that from different points of view have focused on sustainable mobility. The structure of the paper enables the reader to easily identify the topics covered and the studies related to them, so as to guide him/her to the related in-depth studies. In the first part of the paper, there is a preliminary analysis of the concept of sustainable mobility, the main transport policies implemented by the European Union and the USA, and the main statistical data useful to analyze the problem. Next, the main policies that can promote sustainable mobility are examined, classifying them into three topics: Environmental, socio-economic, and technological. Many of the policies and actions examined could be classified into more than one of the three categories used; for each of them, there is a description and the main literature work on which the topic can be analyzed in more detail. The paper concludes with a discussion on the results obtained and the prospects for research.

High-resolution spatiotemporal measurement of air and environmental noise pollution in Sub-Saharan African cities: Pathways to Equitable Health Cities Study protocol for Accra, Ghana

INTRODUCTION

Air and noise pollution are emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data are a barrier to the formulation and evaluation of policies to reduce air and noise pollution.

METHODS AND ANALYSIS

We designed a year-long measurement campaign to characterise air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area (GAMA), Ghana. Our design uses a combination of fixed (year-long, n=10) and rotating (week-long, n =~130) sites, selected to represent a range of land uses and source influences (eg, background, road traffic, commercial, industrial and residential areas, and various neighbourhood socioeconomic classes). We will collect data on fine particulate matter (PM_2.5), nitrogen oxides (NO_x), weather variables, sound (noise level and audio) along with street-level time-lapse images. We deploy low-cost, low-power, lightweight monitoring devices that are robust, socially unobtrusive, and able to function in Sub-Saharan African (SSA) climate. We will use state-of-the-art methods, including spatial statistics, deep/machine learning, and processed-based emissions modelling, to capture highly resolved temporal and spatial variations in pollution levels across the GAMA and to identify their potential sources. This protocol can serve as a prototype for other SSA cities.

ETHICS AND DISSEMINATION

This environmental study was deemed exempt from full ethics review at Imperial College London and the University of Massachusetts Amherst; it was approved by the University of Ghana Ethics Committee (ECH 149/18-19). This protocol is designed to be implementable in SSA cities to map environmental pollution to inform urban planning decisions to reduce health harming exposures to air and noise pollution. It will be disseminated through local stakeholder engagement (public and private sectors), peer-reviewed publications, contribution to policy documents, media, and conference presentations.

Monitoring road traffic participants' exposure to PM10 using a low-cost system

The paper presents the application of a low-cost system for monitoring the current level of road traffic participants' exposure to PM₁₀ air pollution. The research was carried out from the end of August 2017 to the beginning of October 2017 on the central section of one of the main roads in Bielsko-Biała, Poland. In the analysed period, significant changes in the daily distribution of road traffic both into and out of the city centre were observed. The average travel time depended on the direction of traffic, and the difference between directions being almost 50%. The PM₁₀ urban background concentration was also subject to daily changes, and in the fifth week of observation, it reached a value more than twice as high as in the first week of observation. The maximum level of road traffic participants' exposure was observed at a relatively low urban background PM₁₀ concentration. It was observed that a significant slowdown in traffic in conditions of acceptable urban air quality led to a comparable level of exposure to that of standard traffic conditions and poor urban air quality. It was also found that the slowdown in traffic increased the exposure time of traffic participants travelling towards the city centre by an average of 24% and, for those travelling in the opposite direction, by as much as 50%. In an extreme case of traffic delay, exposure to PM₁₀ concentration in the vicinity of the road was two and a half times as long.

Crohn's disease and environmental contamination: Current challenges and perspectives in exposure evaluation

Although the incidence of Crohn's disease has increased worldwide over the past 30 years, the disorder's exact causes and physiological mechanisms have yet to be determined. Given that genetic determinants alone do not explain the development of Crohn's disease, there is growing interest in "environmental" determinants. In medical science, the term "environment" refers to both the ecological and social surroundings; however, most published studies have focused on the latter. In environmental and exposure sciences, the term "environment" mostly relates to contamination of the biotope. There are many unanswered questions on how environmental hazards might contribute to the pathogenesis of Crohn's disease. Which pollutants should be considered? Which mechanisms are involved? And how should environmental contamination and exposure be evaluated? The objective was to perform a systematic review of the literature on Crohn's disease and environmental contamination. We searched the PubMed, Google Scholar, Scopus, ISI Web of Science and Prospero databases. We considered all field studies previous to April 2019 conducted on human health indicators, and evaluating exposure to all type of physical, biological and chemical contamination of the environment. The lack of clear answers to date can be ascribed to the small total number of field studies (n = 16 of 39 publications, most of which were conducted by pioneering medical scientists), methodological differences, and the small number of contaminants evaluated. This make it impossible to conduct a coherent and efficient meta-analysis. Based on individual analysis of available studies, we formulated five recommendations on improving future research: (i) follow up the currently identified leads - especially metals and endocrine disruptors; (ii) explore soil contamination; (iii) gain a better knowledge of exposure mechanisms by developing transdisciplinary studies; (iv) identify the most plausible contaminants by developing approaches based on the source-to-target distance; and (v) develop registries and cohort-based analyses.

The spatial relationship between traffic-related air pollution and noise in two Danish cities: Implications for health-related studies

Air pollution and noise originating from urban road traffic have been linked to the adverse health effects e.g. cardiovascular disease (CVD), although their generation and propagation mechanisms vary. We aimed to (i) develop a tool to model exposures to air pollution and noise using harmonized inputs based on similar geographical structure (ii) explore the relationship (using Spearman's rank correlation) of both pollutions at residential exposure level (iii) investigate the influence of traffic speed and Annual Average Daily Traffic (AADT) on air-noise relationship. The annual average (2005) air pollution (NO_x, NO₂, PM₁₀, PM_2.5) and noise levels (L_day, L_eve, L_night, L_den, L_Aeq,24h) are modelled at address locations in Copenhagen and Roskilde (N = 11,000 and 1500). The new AirGIS system together with the Operational Street Pollution Model (OSPM®) is used to produce air pollution estimates. Whereas, noise is estimated using Common Noise Assessment Methods in the EU (CNOSSOS-EU, hereafter CNOSSOS) with relatively coarser inputs (100 m CORINE land cover, simplified vehicle composition). In addition, noise estimates (L_day, L_eve, L_night) from CNOSSOS are also compared with noise estimates from Road Traffic Noise 1996 (RTN-96, one of the Nordic noise prediction standards). The overall air-noise correlation structure varied significantly in the range |r_S| = 0.01-0.42, which was mainly affected by the background concentrations of air pollution as well as non-traffic emission sources. Moreover, neither AADT nor traffic speed showed substantial influence on the air-noise relationship. The noise levels estimated by CNOSSOS were substantially lower, and showed much lower variation than levels obtained by RTN-96. CNOSSOS, therefore, needs to be further evaluated using more detailed inputs (e.g. 10 m land cover polygons) to assess its feasibility for epidemiological noise exposure studies in Denmark. Lower to moderate air-noise correlations point towards significant potential to determine the independent health effects of air pollution and noise.

Association between exposure to air pollution and prefrontal cortical volume in adults: A cross-sectional study from the UK biobank

Associated with numerous cognitive and behavioral functions and with several diseases, the prefrontal cortex is vulnerable to environmental insult. Among other factors, toxins in air pollution have been associated with damage to the prefrontal cortex in children and older adults. We used data from the UK Biobank to assess further associations between an array of toxins in air pollution and gray matter in the prefrontal cortex including the left and right frontal poles, left and right superior frontal gyri, left and right frontal medial cortex, left and right orbitofrontal cortex, and left and right frontal opercula, using multivariate models adjusted for covariates that possibly could confound the association between air pollution and volume of prefrontal gray matter. The results showed inverse associations between PM 2.5, PM 10, and nitrogen oxides and prefrontal volume in models adjusted for age, sex, education, socioeconomic status, race-ethnicity, self-rated overall health, body mass index, total brain volume, smoking status, and alcohol use frequency. Education appeared to moderate the association between air pollution and prefrontal volume. The data in these analyses came from regions whose mean PM 2.5 was near the upper limit and whose mean PM 10 was under those recommended by the World Health Organization. These findings suggest that comparatively low levels of air pollution might be associated with reduced volume of the prefrontal cortex.