Noise, air pollutants and traffic: continuous measurement and correlation at a high-traffic location in New York City

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.

Inequalities in noise will affect urban wildlife

Understanding how systemic biases influence local ecological communities is essential for developing just and equitable environmental practices that prioritize both human and wildlife well-being. With over 270 million residents inhabiting urban areas in the United States, the socioecological consequences of racially targeted zoning, such as redlining, need to be considered in urban planning. There is a growing body of literature documenting the relationships between redlining and the inequitable distribution of environmental harms and goods, green space cover and pollutant exposure. However, it remains unknown whether historical redlining affects the distribution of urban noise or whether inequitable noise drives an ecological change in urban environments. Here we conducted a spatial analysis of how urban noise corresponds to the distribution of redlining categories and a systematic literature review to summarize the effects of noise on wildlife in urban landscapes. We found strong evidence to indicate that noise is inequitably distributed in redlined urban communities across the United States, and that inequitable noise may drive complex biological responses across diverse urban wildlife, reinforcing the interrelatedness of socioecological outcomes. These findings lay a foundation for future research that advances relationships between acoustic and urban ecology through centring equity and challenging systems of oppression in wildlife studies.

Noise levels associated with urban land use types in Kigali, Rwanda

Noise pollution poses a serious threat to public health and continues to grow in extent, frequency, and severity due to the rapid population growth and urbanization, and this is of particular concern in developing countries such as Rwanda. However, data on noise pollution levels, noise laws and regulations are, however, lacking in Rwanda. We assessed the effect of land-use type during a two-month period at nine sites: three commercial sites, three passenger-car parking sites, two road junction sites, and one reference site (Car-Free Zone) in Rwanda. We collected data on noise pollution during weekdays (Monday, Wednesday, and Friday) and Weekends (Saturday and Sunday) in the morning (7h00-10h00), around noon (11h00-14h00), and in the evening (15h00-18h00). The mean noise levels were higher during weekdays (60-80) A-weighted decibels (dB) (A)) than during weekends (50-70 dB (A)). We recorded the lowest noise level at Kigali car-free zone in the morning (34.4 dB (A)) and the highest noise level at Nyabugogo passenger-car parks in the evening (111.2dB (A)). Spatial variation of noise levels interpolated for Kigali City shows higher noise levels (hotspot) in the outskirts of Kigali, Remera and Kimironko. Noise levels recorded in Kigali exceeded the World Health Organization permissible daytime limits during both weekdays and weekends at all land-use types except the car-free zone site. Our results indicate that Kigali residents are exposed to high levels of noise, and urgent development of noise pollution monitoring programs and control measures in Rwanda is required.

Correlating Traffic Data, Spectral Noise and Air Pollution Measurements: Retrospective Analysis of Simultaneous Measurements near a Highway in The Netherlands

Road traffic simultaneously emits noise and air pollution. This relation is primarily assessed by comparing A-weighted noise levels (LAeq) and various air pollutants. However, despite the common local traffic source, LAeq and the various sets of air pollution show a lower correlation than expected. Prior work, using simultaneous mobile noise and air pollution measurements, shows that the spectral content of the noise explains the complex and highly nonlinear relation between noise and air pollution significantly better. The spectral content distinguishes between traffic volume and traffic dynamics, two relevant modifiers explaining both the variability in noise and air pollution emissions of the local traffic flow. In May 2011, the environmental agency in the Netherlands performed noise and air pollutant measurements near a major highway and included spectral noise. In the resulting report, the analysis of the traffic, the noise and a wide set of air pollutants only showed a strong correlation between noise and NO. In this work, this dataset is re-evaluated using the noise-related covariates, engine noise and cruising noise, defined in prior work. The modeling approach proves valid for most of the measured air pollutants except for the large PM fractions. Conclusion: the prior established methodology explains the complex interaction between traffic dynamics, noise emission and air pollution emissions for a wide variety of air pollutants. The applicability of the ‘noise-as-a-traffic-proxy’ approach is extended.

Changes in exposure to ambient fine particulate matter after relocating and long term survival in Canada: quasi-experimental study

OBJECTIVE

To investigate the association between changes in long term residential exposure to ambient fine particulate matter (PM_2.5) and premature mortality in Canada.

DESIGN

Population based quasi-experimental study.

SETTING

Canada.

PARTICIPANTS

663 100 respondents to the 1996, 2001, and 2006 Canadian censuses aged 25-89 years who had consistently lived in areas with either high or low PM_2.5 levels over five years preceding census day and moved during the ensuing five years.

INTERVENTIONS

Changes in long term exposure to PM_2.5 arising from residential mobility.

MAIN OUTCOME MEASURES

The primary outcome was deaths from natural causes. Secondary outcomes were deaths from any cardiometabolic cause, any respiratory cause, and any cancer cause. All outcomes were obtained from the national vital statistics database.

RESULTS

Using a propensity score matching technique with numerous personal, socioeconomic, health, and environment related covariates, each participant who moved to a different PM_2.5 area was matched with up to three participants who moved within the same PM_2.5 area. In the matched groups that moved from high to intermediate or low PM_2.5 areas, residential mobility was associated with a decline in annual PM_2.5 exposure from 10.6 μg/m³ to 7.4 and 5.0 μg/m³, respectively. Conversely, in the matched groups that moved from low to intermediate or high PM_2.5 areas, annual PM_2.5 increased from 4.6 μg/m³ to 6.7 and 9.2 μg/m³. Five years after moving, individuals who experienced a reduction in exposure to PM_2.5 from high to intermediate levels showed a 6.8% (95% confidence interval 1.7% to 11.7%) reduction in mortality (2510 deaths in 56 025 v 4925 deaths in 101 960). A greater decline in mortality occurred among those exposed to a larger reduction in PM_2.5. Increased mortality was found with exposure to PM_2.5 from low to high levels, and to a lesser degree from low to intermediate levels. Furthermore, the decreases in PM_2.5 exposure were most strongly associated with reductions in cardiometabolic deaths, whereas the increases in PM_2.5 exposure were mostly related to respiratory deaths. No strong evidence was found for the changes in PM_2.5 exposure with cancer related deaths.

CONCLUSIONS

In Canada, decreases in PM_2.5 were associated with lower mortality, whereas increases in PM_2.5 were associated with higher mortality. These results were observed at PM_2.5 levels considerably lower than many other countries, providing support for continuously improving air quality.

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.

Assessment of noise pollution and associated subjective health complaints and psychological symptoms: analysis through structure equation model

Road traffic noise is affecting the exposed population through its detrimental effects. This study was conducted in urban zones of Peshawar, Khyber Pakhtunkhwa, Pakistan, to analyze causal relationship between noise and subjective health complaints with a special focus on psychological symptoms. A 12-h (LAeq) noise survey conducted at different locations (n = 57) indicated a noise range of 46.3-86.3 dB (A). A questionnaire survey was conducted from local residents (n = 500), students (n = 500), policemen (n = 500), shopkeepers (n = 500), and drivers (n = 500) exposed to road traffic noise and analyzed through structure equation modeling (SEM). Different models were prepared and a modified model obtained the acceptable model fit, i.e., chi-square 0.093, χ²/df 1.286, comparative fit index 0.986, goodness of fit index 0.966, normed fit index 0.943, Tucker-Lewis index 0.977, and root mean square error of approximation 0.034. The modified model gives not only the information about direct but also indirect effects of noise on the exposed population. Adding on, the model clearly indicates that sensitivity to noise has strong relationship with subjective health complaints (headache, exhaustion, and psychological symptoms such as annoyance, difficulty concentrating, ill temper, and anxiety) than profession, age, location, and gender. Duration of exposure to road traffic noise has an important role in increasing the frequency of subjective health issues. The model is important in depicting that sensitivity to noise may produce subjective health complaints (standardized parameter estimates of 0.12 and 0.29) but the mediator has much stronger positive path estimates (0.59). The modified model sought to discover and explicate the underlying mechanism of an observed relationship existing between the selected dependent and an independent variable through the identification of the mediator variables.

Opening a Large Delivery Service Warehouse in the South Bronx: Impacts on Traffic, Air Pollution, and Noise

Mott Haven, a low-income neighborhood in New York City, suffers from increased air pollution and accommodates several industrial facilities and interstates. In 2018, a large delivery service warehouse opened. Our objectives are to characterize black carbon (BC), fine particulate matter (PM_2.5), and noise in the community; model changes in traffic due to the facility opening; and estimate associated BC and noise changes. BC, PM_2.5, and noise were measured at eight sites pre-opening, and traffic counted continuously at two sites (June 2017–May 2019). An interrupted time series model was used to determine facility-related changes in traffic. Post-opening changes in traffic-related BC/noise were estimated from regressions of BC/noise with traffic flow. Mean (SD) pre-warehouse measures of BC and PM_2.5 were 1.33 µg/m³ (0.41) and 7.88 µg/m³ (1.24), respectively. At four sites, equivalent sound levels exceeded the EPA’s recommended 70 dBA limit. After the warehouse opening, traffic increased significantly, predominantly at night. At one site, the greatest change for trucks occurred 9PM-12AM: 31.7% (95%CI [23.4%, 40.6%]). Increased traffic translated into mean predicted increases of 0.003 µg/m³ (BC) and 0.06 dBA (noise). Though small, they negate the substantial decrease the community seeks. Our findings can help communities and policymakers better understand impacts of traffic-intensive facilities.

Low-Cost Sensors for Urban Noise Monitoring Networks-A Literature Review

Noise pollution reduction in the environment is a major challenge from a societal and health point of view. To implement strategies to improve sound environments, experts need information on existing noise. The first source of information is based on the elaboration of noise maps using software, but with limitations on the realism of the maps obtained, due to numerous calculation assumptions. The second is based on the use of measured data, in particular through professional measurement observatories, but in limited numbers for practical and financial reasons. More recently, numerous technical developments, such as the miniaturization of electronic components, the accessibility of low-cost computing processors and the improved performance of electric batteries, have opened up new prospects for the deployment of low-cost sensor networks for the assessment of sound environments. Over the past fifteen years, the literature has presented numerous experiments in this field, ranging from proof of concept to operational implementation. The purpose of this article is firstly to review the literature, and secondly, to identify the expected technical characteristics of the sensors to address the problem of noise pollution assessment. Lastly, the article will also put forward the challenges that are needed to respond to a massive deployment of low-cost noise sensors.

Road proximity, air pollution, noise, green space and neurologic disease incidence: a population-based cohort study

BACKGROUND

Emerging evidence links road proximity and air pollution with cognitive impairment. Joint effects of noise and greenness have not been evaluated. We investigated associations between road proximity and exposures to air pollution, and joint effects of noise and greenness, on non-Alzheimer's dementia, Parkinson's and Alzheimer's disease and multiple sclerosis within a population-based cohort.

METHODS

We assembled administrative health database cohorts of 45-84 year old residents (N ~ 678,000) of Metro Vancouver, Canada. Cox proportional hazards models were built to assess associations between exposures and non-Alzheimer's dementia and Parkinson's disease. Given reduced case numbers, associations with Alzheimer's disease and multiple sclerosis were evaluated in nested case-control analyses by conditional logistic regression.

RESULTS

Road proximity was associated with all outcomes (e.g. non-Alzheimer's dementia hazard ratio: 1.14, [95% confidence interval: 1.07-1.20], for living < 50 m from a major road or < 150 m from a highway). Air pollutants were associated with incidence of Parkinson's disease and non-Alzheimer's dementia (e.g. Parkinson's disease hazard ratios of 1.09 [1.02-1.16], 1.03 [0.97-1.08], 1.12 [1.05-1.20] per interquartile increase in fine particulate matter, Black Carbon, and nitrogen dioxide) but not Alzheimer's disease or multiple sclerosis. Noise was not associated with any outcomes while associations with greenness suggested protective effects for Parkinson's disease and non-Alzheimer's dementia.

CONCLUSIONS

Road proximity was associated with incidence of non-Alzheimer's dementia, Parkinson's disease, Alzheimer's disease and multiple sclerosis. This association may be partially mediated by air pollution, whereas noise exposure did not affect associations. There was some evidence of protective effects of greenness.

Noise Indicators for Size Distributions of Airborne Particles and Traffic Activities in Urban Areas

The aim of this study was to explore the relationships among the particle number concentration (PNC), noise, and traffic conditions. Field measurements were conducted to measure the temporal variabilities of the noise levels and PNC over 24 h in a location adjacent to three main traffic roads in Seoul, Korea. The PNC was measured in the range of 0.3 to 10 µm. The noise data was measured by utilizing both the overall levels and spectral characteristics. Traffic data including volumes and speeds of vehicles on the roads were also collected. The results showed that the correlations among the three key parameters varied depending on changes in the noise frequency and particle size. The noise levels at 100–200 Hz were positively correlated with traffic volume and submicron particles. In contrast, they exhibited inverse correlations with the traffic speed and the number of larger particles (>2.5 µm). Compared to noise levels at 100–200 Hz, noise levels at 1–2 kHz exhibited reverse relationships between the traffic and PNC. Submicron particles (0.3–1.0 µm) tended to be more strongly associated with noise levels during the daytime, while those greater than 2.5 µm maintained relatively stable correlations with the noise throughout the day. The findings address the importance of temporal and spectral-specific monitoring of air and noise pollutants for a better understanding of the exposure of the community to air and noise pollution.

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.

Self-rated health and its association with perceived environmental hazards, the social environment, and cultural stressors in an environmental justice population

Background

Communities with large minority populations often are located near sources of pollution and have higher crime rates, which may work in combination with other factors to influence health. Poor self-rated health is related to chronic health conditions and premature mortality, with minority populations most likely to report poor health. To address how both resident perception of neighborhood environments and chronic health conditions individually and collectively influence health, we examined self-rated health and its association with multiple types of perceived environmental hazards in a majority-Hispanic urban population.

Methods

We conducted interviews with 354 residents of Chelsea, Massachusetts, US and asked about self-rated health, perceptions of their neighborhood, including participant-reported environmental hazards (e.g., air quality, odors and noise), aspects of the social environment (e.g., feeling safe, neighborhood crime, social cohesion), and culture-related stressors (e.g., immigration status, language stress, ethnic identity). Log-linear models examined the independent and multivariable associations between these factors and fair/poor self-rated health, controlling for socio-demographic characteristics and preexisting health conditions.

Results

Forty-one percent of participants reported fair/poor self-rated health. Participants frequently perceived environmental hazards such as problems with pests and regular noise disturbance as well as feeling unsafe. In a multivariable model, a greater number of reported noise disturbances (≥ 2 noise sources = 1.53 [1.04–2.26]) and reported insecurity with immigration status (1.66 [1.01–2.73]) were positively associated with fair/poor self-rated health. High social cohesion was inversely associated (0.74 [0.48–1.14]) with fair/poor self-rated health in the multivariable model.

Conclusions

Negative perceptions of environmental hazards and reported cultural stressors were significantly associated with fair/poor self-rated health among residents in a low-income majority-minority community, with social cohesion having a beneficial association with self-rated health. Efforts to improve health should recognize the importance of public perceptions of social and environmental hazards found in neighborhood environments, and benefits of strengthening community connections.

Electronic supplementary material

The online version of this article (10.1186/s12889-018-5797-7) contains supplementary material, which is available to authorized users.

Impact of transient truck and train traffic on ambient air and noise levels in underserved communities

Traffic-related air and noise pollution on or near major roadways have been examined but these pollutants have not been extensively investigated away from major roadways in residential communities, especially in the United States. To evaluate the impact of trucks and trains passing nearby on air and noise pollution in residential areas during non-rush hours, we simultaneously measured concentrations of size-resolved airborne particulate matter (PM) and sound pressure levels as A-weighted equivalent (dBA) with frequencies in three underserved communities adjacent to industrial facilities in Houston, TX. We found that median concentrations for PM1 (particle size ≤ 1 μm) and PM10 (particle size ≤ 10 μm) were highest when trucks passed by at sampling locations, followed by periods when trains passed by. PM1 and PM10 concentrations were lowest at background (defined when there was no truck or train traffic near the monitoring location). Median PM2.5 (particle size ≤ 2.5 μm) mass concentrations were 19.8 μg/m3 (trains), 16.5 μg/m3 (trucks), and 13.9 μg/m3 (background). Short-term increases in noise were attributed to trains and trucks passing nearby as well. The median noise levels were the highest when trains passed by (66.7 dBA) followed by periods when trucks were in the vicinity of the monitoring locations (62.5 dBA); background levels were 58.2 dBA. The overall Spearman correlation coefficients between air and noise pollution were between 0.09 and 0.46. Hence, we recommend that both air pollutant and noise levels be concurrently evaluated for accurate exposure assessment related to traffic sources in residential communities.

Cardiovascular benefits of reducing personal exposure to traffic-related noise and particulate air pollution: A randomized crossover study in the Beijing subway system

To assess the cardiovascular benefits of protecting against particulate air pollution and noise, we conducted a randomized crossover study with 40 young healthy college students from March to May 2017 in the underground subway, Beijing. Participants each received 4 treatments (no intervention phase [NIP], respirator intervention phase [RIP], headphone intervention phase [HIP], respirator plus headphone intervention phase [RHIP]) in a randomized order during 4 different study periods with 2-week washout intervals. We measured personal exposure to particulate matter (PM), noise and electrocardiogram (ECG) parameters (heart rate variability (HRV), heart rate (HR) and ST segment changes), ambulatory blood pressure (BP) continuously for 4 hours to investigate the cardiovascular effects. Compared with NIP, most of the HRV parameters increased, especially high frequency (HF) [21.1% (95% CI: 15.7%, 26.9%), 18.2% (95% CI: 12.8%, 23.9%), and 35.5% (95% CI: 29.3%, 42.0%) in RIP, HIP, and RHIP, respectively], whereas ST segment elevation and HR decreased for all 3 modes of interventions. However, no significant differences were observed in BP among the 4 treatments. In summary, short-term wearing of a respirator and/or headphone may be an effective way to minimize cardiovascular risk induced by air pollution in the subway by improving autonomic nervous function.

Influence of urban vegetation on air pollution and noise exposure - A case study in Gothenburg, Sweden

Air pollution levels (NO₂, PAHs, O₃) were investigated, before (BLE) and after (ALE) leaf emergence, in the urban landscape of Gothenburg, Sweden. The aims were to study the 1) spatial and temporal variation in pollution levels between urban green areas, 2) effect of urban vegetation on air pollution levels at the same distance from a major emission source (traffic route), 3) improvement of urban air quality in urban parks compared to adjacent sites near traffic, 4) correlation between air pollution and noise in a park. O₃ varied little over the urban landscape. NO₂ varied strongly and was higher in situations strongly influenced by traffic. Four PAH variables were included: total PAH, total particle-bound PAH, the quantitatively important gaseous phenanthrene and the highly toxic particle-bound benzo(a)pyrene. The variation of PAHs was similar to NO₂, but for certain PAHs the difference between highly and less polluted sites was larger than for NO₂. At a vegetated site, NO₂ and particulate PAH levels were lower than at a non-vegetated site at a certain distance from a busy traffic route. This effect was significantly larger ALE compared to BLE for NO₂, indicating green leaf area to be highly significant factor for air quality improvement. For particulate PAHs, the effect was similar BLE and ALE, indicating that tree bark and branches also could be an important factor in reducing air pollution. Parks represented considerably cleaner local environments (park effect), which is likely to be a consequence of both a dilution (distance effect) and deposition. Noise and air pollution (NO₂ and PAH) levels were strongly correlated. Comparison of noise levels BLE and ALE also showed that the presence of leaves significantly reduced noise levels. Our results are evidence that urban green spaces are beneficial for urban environmental quality, which is important to consider in urban planning.

Spatial and temporal determinants of A-weighted and frequency specific sound levels-An elastic net approach

BACKGROUND

Urban sound levels are a ubiquitous environmental stressor and have been shown to be associated with a wide variety of health outcomes. While much is known about the predictors of A-weighted sound pressure levels in the urban environment, far less is known about other frequencies.

OBJECTIVE

To develop a series of spatial-temporal sound models to predict A-weighted sound pressure levels, low, mid, and high frequency sound for Boston, Massachusetts.

METHODS

Short-term sound levels were gathered at n = 400 sites from February 2015 - February 2016. Spatial and meteorological attributes at or near the sound monitoring site were obtained using publicly available data and a portable weather station. An elastic net variable selection technique was used to select predictors of A-weighted, low, mid, and high frequency sound.

RESULTS

The final models for low, mid, high, and A-weighted sound levels explained 59 - 69% of the variability in each measure. Similar to other A-weighted models, our sound models included transportation related variables such as length of roads and bus lines in the surrounding area; distance to road and rail lines; traffic volume, vehicle mix, residential and commercial land use. However, frequency specific models highlighted additional predictors not included in the A-weighted model including temperature, vegetation, impervious surfaces, vehicle mix, and density of entertainment establishments and restaurants.

CONCLUSIONS

Building spatial temporal models to characterize sound levels across the frequency spectrum using an elastic net approach can be a promising tool for noise exposure assessments within the urban soundscape. Models of sound's character may give us additional important sound exposure metrics to be utilized in epidemiological studies.

Race/Ethnicity, Socioeconomic Status, Residential Segregation, and Spatial Variation in Noise Exposure in the Contiguous United States

BACKGROUND

Prior research has reported disparities in environmental exposures in the United States, but, to our knowledge, no nationwide studies have assessed inequality in noise pollution.

OBJECTIVES

We aimed to a) assess racial/ethnic and socioeconomic inequalities in noise pollution in the contiguous United States; and b) consider the modifying role of metropolitan level racial residential segregation.

METHODS

We used a geospatial sound model to estimate census block group–level median (L₅₀) nighttime and daytime noise exposure and 90th percentile (L₁₀) daytime noise exposure. Block group variables from the 2006–2010 American Community Survey (ACS) included race/ethnicity, education, income, poverty, unemployment, homeownership, and linguistic isolation. We estimated associations using polynomial terms in spatial error models adjusted for total population and population density. We also evaluated the relationship between race/ethnicity and noise, stratified by levels of metropolitan area racial residential segregation, classified using a multigroup dissimilarity index.

RESULTS

Generally, estimated nighttime and daytime noise levels were higher for census block groups with higher proportions of nonwhite and lower-socioeconomic status (SES) residents. For example, estimated nighttime noise levels in urban block groups with 75% vs. 0% black residents were 46.3 A-weighted decibels (dBA) [interquartile range (IQR): 44.3–47.8 dBA] and 42.3 dBA (IQR: 40.4–45.5 dBA), respectively. In urban block groups with 50% vs. 0% of residents living below poverty, estimated nighttime noise levels were 46.9 dBA (IQR: 44.7–48.5 dBA) and 44.0 dBA (IQR: 42.2–45.5 dBA), respectively. Block groups with the highest metropolitan area segregation had the highest estimated noise exposures, regardless of racial composition. Results were generally consistent between urban and suburban/rural census block groups, and for daytime and nighttime noise and robust to different spatial weight and neighbor definitions.

CONCLUSIONS

We found evidence of racial/ethnic and socioeconomic differences in model-based estimates of noise exposure throughout the United States. Additional research is needed to determine if differences in noise exposure may contribute to health disparities in the United States. https://doi.org/10.1289/EHP898

Temporal and spatial variations in road traffic noise for different frequency components in metropolitan Taichung, Taiwan

Road traffic noise exposure has been associated with auditory and non-auditory health effects, but few studies report noise characteristics. This study determines 24-h noise levels and analyzes their frequency components to investigate associations between seasons, meteorology, land-use types, and traffic. We set up 50 monitoring stations covering ten different land-use types and conducted measurements at three times of the year to obtain 24-h-average A-weighted equivalent noise levels (L_Aeq,_24h) and frequency analyses from 2013 to 2014 in Taichung, Taiwan. Information on land-use types, road parameters, traffic flow rates, and meteorological variables was also collected for analysis with the annual averages of road traffic noise and its frequency components. The annual average L_Aeq,_24h in Taichung was 66.4 ± 4.7 A-weighed decibels (dBA). Significant differences in L_Aeq,_24h and frequency components were observed between land-use types (all p-values < 0.001), but not between seasons, with the highest two noise levels of 71.2 ± 1.0 dBA and 70.0 ± 2.6 dBA measured in stream-channel and commercial areas, with the highest component being 61.4 ± 5.3 dBA at 1000 Hz. Road width, traffic flow rates, and land-use types were significantly associated with annual average L_Aeq,_24h (all p-values < 0.050). Noise levels at 125 Hz had the highest correlation with total traffic (Spearman's coefficient = 0.795) and the highest prediction in the multiple linear regression (R² = 0.803; adjusted R² = 0.765). These findings reveal the spatial variation in road traffic noise exposure in Taichung. The highest correlation and predictive capacity was observed between this variation and noise levels at 125 Hz. We recommend that governmental agencies should take actions to reduce noise levels from traffic vehicles.

The Covariance between Air Pollution Annoyance and Noise Annoyance, and Its Relationship with Health-Related Quality of Life

Air pollution originating from road traffic is a known risk factor of respiratory and cardiovascular disease (both in terms of chronic and acute effects). While adverse effects on cardiovascular health have also been linked with noise (after controlling for air pollution), noise exposure has been commonly linked to sleep impairment and negative emotional reactions. Health is multi-faceted, both conceptually and operationally; Health-Related Quality of Life (HRQOL) is one of many measures capable of probing health. In this study, we examine pre-collected data from postal surveys probing HRQOL obtained from a variety of urban, suburban, and rural contexts across the North Island of New Zealand. Analyses focus on the covariance between air pollution annoyance and noise annoyances, and their independent and combined effects on HRQOL. Results indicate that the highest ratings of air pollution annoyance and noise annoyances were for residents living close to the motorway, while the lowest were for rural residents. Most of the city samples indicated no significant difference between air pollution- and noise-annoyance ratings, and of all of the correlations between air pollution- and noise-annoyance, the highest were found in the city samples. These findings suggest that annoyance is driven by exposure to environmental factors and not personality characteristics. Analysis of HRQOL indicated that air pollution annoyance predicts greater variability in the physical HRQOL domain while noise annoyance predicts greater variability in the psychological, social and environmental domains. The lack of an interaction effect between air pollution annoyance and noise annoyance suggests that air pollution and noise impact on health independently. These results echo those obtained from objective measures of health and suggest that mitigation of traffic effects should address both air and noise pollution.

Do outdoor environmental noise and atmospheric NO2 levels spatially overlap in urban areas?

The urban environment holds numerous emission sources for air and noise pollution, creating optimum conditions for environmental multi-exposure situations. Evaluation of the joint-exposure levels is the main obstacle for multi-exposure studies and one of the biggest challenges of the next decade. The present study aims to describe the noise/NO2 multi-exposure situations in the urban environment by exploring the possible discordant and concordant situations of both exposures. Fine-scale diffusion models were developed in the European medium-sized city of Besançon (France), and a classification method was used to evaluate the multi-exposure situations in the façade perimeter of 10,825 buildings. Although correlated (Pearson's r = 0.64, p < 0.01), urban spatial distributions of the noise and NO2 around buildings do not overlap, and 30% of the buildings were considered to be discordant in terms of the noise and NO2 exposure levels. This discrepancy is spatially structured and associated with variables describing the building's environment. Our results support the presence of several co-existing, multi-exposure situations across the city impacted by both the urban morphology and the emission and diffusion/propagation phases of each pollutant. Identifying the mechanisms of discrepancy and convergence of multi-exposure situations could help improve the health risk assessment and public health.

From street canyon microclimate to indoor environmental quality in naturally ventilated urban buildings: Issues and possibilities for improvement

Many buildings in urban areas are more or less naturally ventilated. A good understanding of the current status and issues of indoor environmental quality (IEQ) in naturally ventilated urban buildings and the association with urban microclimate is fundamental for improving their IEQ. This paper reviews past studies on (a) the microclimate in urban street canyons, (b) the potential influence of such microclimate on IEQ of nearby naturally ventilated buildings, and (c) the real-life IEQ status in these buildings. The review focuses mainly on studies conducted by on-site measurements. The microclimate in urban street canyons is characterized by low wind speed, high surface temperature difference, high pollutant concentration, and high noise level. Insufficient ventilation rates and excessive penetration of outdoor pollutants are two key risks involved in naturally ventilated urban buildings. Existing knowledge suggests that reasonable urban planning and careful building envelope design are the primary methods to ensure acceptable IEQ and maximize the utilization of natural ventilation. However, quantitative studies of both microclimate in street canyons and IEQ in buildings are still highly insufficient in many aspects, which make cross comparison and influencing factors analysis currently impossible. Based on the limitations of previous studies and the current issues of naturally ventilated urban buildings, suggestions are made for future studies to better understand and improve IEQ in naturally ventilated urban buildings.

Childhood cancer and residential exposure to highways: a nationwide cohort study

Children living near highways are exposed to higher concentrations of traffic-related carcinogenic pollutants. Several studies reported an increased risk of childhood cancer associated with traffic exposure, but the published evidence is inconclusive. We investigated whether cancer risk is associated with proximity of residence to highways in a nation-wide cohort study including all children aged <16 years from Swiss national censuses in 1990 and 2000. Cancer incidence was investigated in time to event analyses (1990-2008) using Cox proportional hazards models and incidence density analyses (1985-2008) using Poisson regression. Adjustments were made for socio-economic factors, ionising background radiation and electromagnetic fields. In time to event analysis based on 532 cases the adjusted hazard ratio for leukaemia comparing children living <100 m from a highway with unexposed children (≥500 m) was 1.43 (95 % CI 0.79, 2.61). Results were similar in incidence density analysis including 1367 leukaemia cases (incidence rate ratio (IRR) 1.57; 95 % CI 1.09, 2.25). Associations were similar for acute lymphoblastic leukaemia (IRR 1.64; 95 % CI 1.10, 2.43) and stronger for leukaemia in children aged <5 years (IRR 1.92; 95 % CI 1.22, 3.04). Little evidence of association was found for other tumours. Our study suggests that young children living close to highways are at increased risk of developing leukaemia.

Association between ambient air pollution and diabetes mellitus in Europe and North America: systematic review and meta-analysis

BACKGROUND

Air pollution is hypothesized to be a risk factor for diabetes. Epidemiological evidence is inconsistent and has not been systematically evaluated.

OBJECTIVES

We systematically reviewed epidemiological evidence on the association between air pollution and diabetes, and synthesized results of studies on type 2 diabetes mellitus (T2DM).

METHODS

We systematically searched electronic literature databases (last search, 29 April 2014) for studies reporting the association between air pollution (particle concentration or traffic exposure) and diabetes (type 1, type 2, or gestational). We systematically evaluated risk of bias and role of potential confounders in all studies. We synthesized reported associations with T2DM in meta-analyses using random-effects models and conducted various sensitivity analyses.

RESULTS

We included 13 studies (8 on T2DM, 2 on type 1, 3 on gestational diabetes), all conducted in Europe or North America. Five studies were longitudinal, 5 cross-sectional, 2 case-control, and 1 ecologic. Risk of bias, air pollution assessment, and confounder control varied across studies. Dose-response effects were not reported. Meta-analyses of 3 studies on PM2.5 (particulate matter ≤ 2.5 μm in diameter) and 4 studies on NO2 (nitrogen dioxide) showed increased risk of T2DM by 8-10% per 10-μg/m3 increase in exposure [PM2.5: 1.10 (95% CI: 1.02, 1.18); NO2: 1.08 (95% CI: 1.00, 1.17)]. Associations were stronger in females. Sensitivity analyses showed similar results.

CONCLUSION

Existing evidence indicates a positive association of air pollution and T2DM risk, albeit there is high risk of bias. High-quality studies assessing dose-response effects are needed. Research should be expanded to developing countries where outdoor and indoor air pollution are high.

Assessment of ultrafine particles and noise measurements using fuzzy logic and data mining techniques

This study focuses on correlations between total number concentrations, road traffic emissions and noise levels in an urban area in the southwest of Spain during the winter and summer of 2009. The high temporal correlation between sound pressure levels, traffic intensity, particle number concentrations related to traffic, black carbon and NOx concentrations suggests that noise is linked to traffic emissions as a main source of pollution in urban areas. First, the association of these different variables was studied using PreFuRGe, a computational tool based on data mining and fuzzy logic. The results showed a clear association between noise levels and road-traffic intensity for non-extremely high wind speed levels. This behaviour points, therefore, to vehicular emissions being the main source of urban noise. An analysis for estimating the total number concentration from noise levels is also proposed in the study. The high linearity observed between particle number concentrations linked to traffic and noise levels with road traffic intensity can be used to calculate traffic related particle number concentrations experimentally. At low wind speeds, there are increases in noise levels of 1 dB for every 100 vehicles in circulation. This is equivalent to 2000 cm(-3) per vehicle in winter and 500 cm(-3) in summer. At high wind speeds, wind speed could be taken into account. This methodology allows low cost sensors to be used as a proxy for total number concentration monitoring in urban air quality networks.

Socioeconomic status and environmental noise exposure in Montreal, Canada

Background

This study’s objective was to determine whether socioeconomically deprived populations are exposed to greater levels of environmental noise.

Methods

Indicators of socioeconomic status were correlated with LAeq24h noise levels estimated with a land-use regression model at a small geographic scale.

Results

We found that noise exposure was associated with all socioeconomic indicators, with the strongest correlations found for median household income, proportion of people who spend over 30% of their income on housing, proportion of people below the low income boundary and with a social deprivation index combining several socio-economic variables.

Conclusion

Our results were inconsistent with a number of studies performed elsewhere, indicating that locally conducted studies are imperative to assessing whether this double burden of noise exposure and low socioeconomic status exists in other contexts. The primary implication of our study is that noise exposure represents an environmental injustice in Montreal, which is an issue that merits both investigation and concern.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-015-1571-2) contains supplementary material, which is available to authorized users.

Street-level noise in an urban setting: assessment and contribution to personal exposure

BACKGROUND

The urban soundscape, which represents the totality of noise in the urban setting, is formed from a wide range of sources. One of the most ubiquitous and least studied of these is street-level (i.e., sidewalk) noise. Mainly associated with vehicular traffic, street level noise is hard to ignore and hard to escape. It is also potentially dangerous, as excessive noise from any source is an important risk factor for adverse health effects. This study was conducted to better characterize the urban soundscape and the role of street level noise on overall personal noise exposure in an urban setting.

METHODS

Street-level noise measures were obtained at 99 street sites located throughout New York City (NYC), along with data on time, location, and sources of environmental noise. The relationship between street-level noise measures and potential predictors of noise was analyzed using linear and logistic regression models, and geospatial modeling was used to evaluate spatial trends in noise. Daily durations of street-level activities (time spent standing, sitting, walking and running on streets) were estimated via survey from a sample of NYC community members recruited at NYC street fairs. Street-level noise measurements were then combined with daily exposure durations for each member of the sample to estimate exposure to street noise, as well as exposure to other sources of noise.

RESULTS

The mean street noise level was 73.4 dBA, with substantial spatial variation (range 55.8-95.0 dBA). Density of vehicular (road) traffic was significantly associated with excessive street level noise levels. Exposure duration data for street-level noise and other common sources of noise were collected from 1894 NYC community members. Based on individual street-level exposure estimates, and in consideration of all other sources of noise exposure in an urban population, we estimated that street noise exposure contributes approximately 4% to an average individual's annual noise dose.

CONCLUSIONS

Street-level noise exposure is a potentially important source of overall noise exposure, and the reduction of environmental sources of excessive street- level noise should be a priority for public health and urban planning.

Social stressors and air pollution across New York City communities: a spatial approach for assessing correlations among multiple exposures

BACKGROUND

Recent toxicological and epidemiological evidence suggests that chronic psychosocial stress may modify pollution effects on health. Thus, there is increasing interest in refined methods for assessing and incorporating non-chemical exposures, including social stressors, into environmental health research, towards identifying whether and how psychosocial stress interacts with chemical exposures to influence health and health disparities. We present a flexible, GIS-based approach for examining spatial patterns within and among a range of social stressors, and their spatial relationships with air pollution, across New York City, towards understanding their combined effects on health.

METHODS

We identified a wide suite of administrative indicators of community-level social stressors (2008-2010), and applied simultaneous autoregressive models and factor analysis to characterize spatial correlations among social stressors, and between social stressors and air pollutants, using New York City Community Air Survey (NYCCAS) data (2008-2009). Finally, we provide an exploratory ecologic analysis evaluating possible modification of the relationship between nitrogen dioxide (NO2) and childhood asthma Emergency Department (ED) visit rates by social stressors, to demonstrate how the methods used to assess stressor exposure (and/or consequent psychosocial stress) may alter model results.

RESULTS

Administrative indicators of a range of social stressors (e.g., high crime rate, residential crowding rate) were not consistently correlated (rho = - 0.44 to 0.89), nor were they consistently correlated with indicators of socioeconomic position (rho = - 0.54 to 0.89). Factor analysis using 26 stressor indicators suggested geographically distinct patterns of social stressors, characterized by three factors: violent crime and physical disorder, crowding and poor access to resources, and noise disruption and property crimes. In an exploratory ecologic analysis, these factors were differentially associated with area-average NO2 and childhood asthma ED visits. For example, only the 'violent crime and disorder' factor was significantly associated with asthma ED visits, and only the 'crowding and resource access' factor modified the association between area-level NO2 and asthma ED visits.

CONCLUSIONS

This spatial approach enabled quantification of complex spatial patterning and confounding between chemical and non-chemical exposures, and can inform study design for epidemiological studies of separate and combined effects of multiple urban exposures.

Spatial variation in environmental noise and air pollution in New York City

Exposure to environmental noise from traffic is common in urban areas and has been linked to increased risks of adverse health effects including cardiovascular disease. Because traffic sources also produce air pollutants that increase the risk of cardiovascular morbidity, associations between traffic exposures and health outcomes may involve confounding and/or synergisms between air pollution and noise. While prior studies have characterized intraurban spatial variation in air pollution in New York City (NYC), limited data exists on the levels and spatial variation in noise levels. We measured 1-week equivalent continuous sound pressure levels (Leq) at 56 sites during the fall of 2012 across NYC locations with varying traffic intensity and building density that are routinely monitored for combustion-related air pollutants. We evaluated correlations among several noise metrics used to characterize noise exposures, including Leq during different time periods (night, day, weekday, weekend), Ldn (day-night noise), and measures of intermittent noise defined as the ratio of peak levels to median and background levels. We also examined correlations between sound pressure levels and co-located simultaneous measures of nitric oxide (NO), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and black carbon (BC) as well as estimates of traffic and building density around the monitoring sites. Noise levels varied widely across the 56 monitoring sites; 1-week Leq varied by 21.6 dBA (range 59.1-80.7 dBA) with the highest levels observed during the weekday, daytime hours. Indices of average noise were well correlated with each other (r > 0.83), while indices of intermittent noise were not well correlated with average noise levels (r < 0.41). One-week Leq correlated well with NO, NO2, and EC levels (r = 0.61 to 0.68) and less so with PM2.5 levels (r = 0.45). We observed associations between 1-week noise levels and traffic intensity within 100 m of the monitoring sites (r = 0.58). The high levels of noise observed in NYC often exceed recommended guidelines for outdoor and personal exposures, suggesting unhealthy levels in many locations. Associations between noise, traffic, and combustion air pollutants suggest the possibility for confounding and/or synergism in intraurban epidemiological studies of traffic-related health effects. The different spatial pattern of intermittent noise compared to average noise level may suggest different sources.

A 2.5-dimensional method for the prediction of structure-borne low-frequency noise from concrete rail transit bridges

Predicting structure-borne noise from bridges subjected to moving trains using the three-dimensional (3D) boundary element method (BEM) is a time consuming process. This paper presents a two-and-a-half dimensional (2.5D) BEM-based procedure for simulating bridge-borne low-frequency noise with higher efficiency, yet no loss of accuracy. The two-dimensional (2D) BEM of a bridge with a constant cross section along the track direction is adopted to calculate the spatial modal acoustic transfer vectors (MATVs) of the bridge using the space-wave number transforms of its 3D modal shapes. The MATVs calculated using the 2.5D method are then validated by those computed using the 3D BEM. The bridge-borne noise is finally obtained through the MATVs and modal coordinate responses of the bridge, considering time-varying vehicle-track-bridge dynamic interaction. The presented procedure is applied to predict the sound pressure radiating from a U-shaped concrete bridge, and the computed results are compared with those obtained from field tests on Shanghai rail transit line 8. The numerical results match well with the measured results in both time and frequency domains at near-field points. Nevertheless, the computed results are smaller than the measured ones for far-field points, mainly due to the sound radiation from adjacent spans neglected in the current model.

Spatial association between ambient fine particulate matter and incident hypertension

BACKGROUND

Laboratory studies suggest that exposure to fine particulate matter (≤2.5 μm in diameter) (PM2.5) can trigger a combination of pathophysiological responses that may induce the development of hypertension. However, epidemiological evidence relating PM2.5 and hypertension is sparse. We thus conducted a population-based cohort study to determine whether exposure to ambient PM2.5 is associated with incident hypertension.

METHODS AND RESULTS

We assembled a cohort of 35 303 nonhypertensive adults from Ontario, Canada, who responded to 1 of 4 population-based health surveys between 1996 and 2005 and were followed up until December 31, 2010. Incident diagnoses of hypertension were ascertained from the Ontario Hypertension Database, a validated registry of persons diagnosed with hypertension in Ontario (sensitivity=72%, specificity=95%). Estimates of long-term exposure to PM2.5 at participants' postal-code residences were derived from satellite observations. We used Cox proportional hazards models, adjusting for various individual and contextual risk factors including body mass index, smoking, physical activity, and neighbourhood-level unemployment rates. We conducted various sensitivity analyses to assess the robustness of the effect estimate, such as investigating several time windows of exposure and controlling for potential changes in the risk of hypertension over time. Between 1996 and 2010, we identified 8649 incident cases of hypertension and 2296 deaths. For every 10-µg/m(3) increase of PM2.5, the adjusted hazard ratio of incident hypertension was 1.13 (95% confidence interval, 1.05-1.22). Estimated associations were comparable among all sensitivity analyses.

CONCLUSIONS

This study supports an association between PM2.5 and incident hypertension.

The impacts of short-term exposure to noise and traffic-related air pollution on heart rate variability in young healthy adults

Traffic-related air pollution and noise are associated with cardiovascular diseases, and alternation of heart rate variability (HRV), which reflects cardiac autonomic function, is one of the mechanisms. However, few studies considered the impacts of noise when exploring associations between air pollution and HRV. We explored whether noise modifies associations between short-term exposure to traffic-related air pollution and HRV in young healthy adults. In this randomized, crossover study, 40 young healthy adults stayed for 2 h in a traffic center and, on a separate occasion, in a park. Personal exposure to traffic-related air pollutants and noise were measured and ambulatory electrocardiogram was performed. Effects were estimated using mixed-effects regression models. Traffic-related air pollution and noise were both associated with HRV, and effects of air pollutants were amplified at high noise level (>65.6 A-weighted decibels (dB[A])) compared with low noise level (≤ 65.6 dB[A]). High frequency (HF) decreased by -4.61% (95% confidence interval, -6.75% to-2.42%) per 10 μg/m(3) increment in fine particle (PM2.5) at 5-min moving average, but effects became insignificant at low noise level (P>0.05). Similar effects modification was observed for black carbon (BC) and carbon monoxide (CO). We conclude that noise is an important factor influencing the effects of air pollution on HRV.

Exposure of highway maintenance workers to fine particulate matter and noise

In this study, we assessed the mixed exposure of highway maintenance workers to airborne particles, noise, and gaseous co-pollutants. The aim was to provide a better understanding of the workers' exposure to facilitate the evaluation of short-term effects on cardiovascular health endpoints. To quantify the workers' exposure, we monitored 18 subjects during 50 non-consecutive work shifts. Exposure assessment was based on personal and work site measurements and included fine particulate matter (PM2.5), particle number concentration (PNC), noise (Leq), and the gaseous co-pollutants: carbon monoxide, nitrogen dioxide, and ozone. Mean work shift PM2.5 concentrations (gravimetric measurements) ranged from 20.3 to 321 μg m(-3) (mean 62 μg m(-3)) and PNC were between 1.6×10(4) and 4.1×10(5) particles cm(-3) (8.9×10(4) particles cm(-3)). Noise levels were generally high with Leq over work shifts from 73.3 to 96.0 dB(A); the averaged Leq over all work shifts was 87.2 dB(A). The highest exposure to fine and ultrafine particles was measured during grass mowing and lumbering when motorized brush cutters and chain saws were used. Highest noise levels, caused by pneumatic hammers, were measured during paving and guardrail repair. We found moderate Spearman correlations between PNC and PM2.5 (r = 0.56); PNC, PM2.5, and CO (r = 0.60 and r = 0.50) as well as PNC and noise (r = 0.50). Variability and correlation of parameters were influenced by work activities that included equipment causing combined air pollutant and noise emissions (e.g. brush cutters and chain saws). We conclude that highway maintenance workers are frequently exposed to elevated airborne particle and noise levels compared with the average population. This elevated exposure is a consequence of the permanent proximity to highway traffic with additional peak exposures caused by emissions of the work-related equipment.

Air-Pollution and Cardiometabolic Diseases (AIRCMD): a prospective study investigating the impact of air pollution exposure and propensity for type II diabetes

There is a paucity of prospective cohort studies investigating the impact of environmental factors on the development of cardiometabolic (CM) disorders like type II diabetes (T2DM). The objective of the Air-Pollution and Cardiometabolic Diseases (AIRCMD) study is to investigate the impact of personal level air pollution measures [personal black carbon (BC)/sulfate measures] and ambient fine particulate matter [(PM2.5)/NO2] levels on propensity to type II diabetes in Beijing, China. Subjects with metabolic syndrome will undergo four repeated study visits within each season over a one year period following an initial screening visit. At each study visit, subjects will be monitored for sub-acute exposure to personal and ambient measures of air-pollution exposure and will undergo a series of functional CM outcomes. The primary endpoints include independent associations between integrated 5-day mean exposure to PM2.5 and BC and homeostasis model assessment of insulin resistance (HOMA-IR) measures, 24-hour mean diastolic and mean arterial pressure and endothelial-dependent vasodilatation. The secondary endpoints will explore the mechanistic explanation for a causal relationship between exposures and propensity for type II diabetes and will include additional functional outcomes such as arterial compliance, heart rate variability and plasma adipokines. The novel aspects of the study include the launch of infrastructure for future translational investigations in highly polluted urbanized environments and the creation of novel methodologies for linking personalized exposure measurements with functional CM outcomes. We believe that AIRCMD will allow for unprecedented new investigations into the association between environmental risk factors and CM disorders.

Some insights into the relationship between urban air pollution and noise levels

The relationship between noise and air pollution was investigated in eight different districts across Seoul, Korea, between September and November 2010. The noise levels in each district were measured at both roadside and non-roadside locations. It was found that the maximum levels of noise were generally at frequencies of around 1000 Hz. The equivalent noise levels (L(eq)), over all districts, averaged 61.4 ± 7.36 dB which is slightly lower than the noise guidelines set by the World Health Organization (WHO) of 70 dB for industrial, commercial, traffic, and outdoor areas. Comparison of L(eq) levels in each district consistently indicates that noise levels are higher at roadside sites than non-roadside sites. In addition the relative dominance of noise during daytime as compared to nighttime was also apparent. Moreover, the results of an analysis relating sound levels with air pollutant levels indicate strongly that the correlation between these two parameters is the strongest at roadside sites (relative to non-roadside sites) and during nighttime (relative to daytime). The results of our data analysis point to a positive, but complex, correlation between noise levels and air pollution.