Correlation of noise levels and particulate matter concentrations near two major freeways in Los Angeles, California

Surrounding road density of child care centers in Australia

High surrounding road density could increase traffic-related air pollution, noise and the risk of traffic injuries, which are major public health concerns for children. We collected geographical data for all childcare centers (16,146) in Australia and provided the data on the road density surrounding them. The road density was represented by the child care center's nearest distance to main road and motorway, and the length of main road/motor way within 100~1000-meter buffer zone surrounding the child care center. We also got the data of PM_2.5 concentration from 2013 to 2018 and standard Normalized Difference Vegetation Index (NDVI) data from 2013 to 2019 according to the longitude and latitude of the child care centers. This data might help researchers to evaluate the health impacts of road density on child health, and help policy makers to make transportation, educational and environmental planning decisions to protect children from exposure to traffic-related hazards in Australia.

Characteristics and Extent of Particulate Matter Emissions of a Ropeway Public Mobility System in the City Center of Perugia (Central Italy)

Minimetrò (MM) is a ropeway public mobility system that has been in operation in the city of Perugia for about ten years to integrate with urban mobility and lighten vehicular traffic in the historic city center. The purpose of this work was to evaluate the impact of MM as a source of pollutants in the urban context, and the exposure of people in the cabins and the platforms along the MM line. These topics have been investigated by means of intensive measurement and sampling campaigns performed in February and June 2015 on three specific sites of the MM line representative of different sources and levels of urban pollution. Stationary and dynamic measurements of particle size distribution, nanoparticle and black carbon aerosol number and mass concentrations measurements were performed by means of different bench and portable instruments. Aerosol sampling was carried out using low volume and high-volume aerosol samplers, and the samples nalysed by off-line methods. Results show that MM is a considerable source of atmospheric particulate matter having characteristics very similar to those of the common urban road dust in Perugia. In the lack of clear indications on road dust effect, the contribution of MM to the aerosol in Perugia cannot be neglected.

Community daytime noise pollution and socioeconomic differences in Chicago, IL

Environmental noise may affect hearing and a variety of non-auditory disease processes. There is some evidence that, like other environmental hazards, noise may be differentially distributed across communities based on socioeconomic status. We aimed to a) predict daytime noise pollution levels and b) assess disparities in daytime noise exposure in Chicago, Illinois. We measured 5-minute daytime noise levels (L_{eq, 5-min}) at 75 randomly selected sites in Chicago in March, 2019. Geographically-based variables thought to be associated with noise were obtained, and used to fit a noise land-use regression model to estimate the daytime environmental noise level at the centroid of the census blocks. Demographic and socioeconomic data were obtained from the City of Chicago for the 77 community areas, and associations with daytime noise levels were assessed using spatial autoregressive models. Mean sampled noise level (L_{eq, 5-min}) was 60.6 dBA. The adjusted R² and root mean square error of the noise land use regression model and the validation model were 0.60 and 4.67 dBA and 0.51 and 5.90 dBA, respectively. Nearly 75% of city blocks and 85% of city communities have predicted daytime noise level higher than 55 dBA. Of the socioeconomic variables explored, only community per capita income was associated with mean community predicted noise levels, and was highest for communities with incomes in the 2^nd quartile. Both the noise measurements and land-use regression modeling demonstrate that Chicago has levels of environmental noise likely contributing to the total burden of environmental stressors. Noise is not uniformly distributed across Chicago; it is associated with proximity to roads and public transportation, and is higher among communities with mid-to-low incomes per capita, which highlights how socially and economically disadvantaged communities may be disproportionately impacted by this environmental exposure.

As the Wind Blows: The Effects of Long-Term Exposure to Air Pollution on Mortality

There is strong evidence that short-run fluctuations in air pollution negatively impact infant health and contemporaneous adult health, but there is less evidence on the causal link between long-term exposure to air pollution and increased adult mortality. This project estimates the impact of long-term exposure to air pollution on mortality by leveraging quasi-random variation in pollution levels generated by wind patterns near major highways. I combine geocoded data on the residence of every decedent in Los Angeles over three years, high-frequency wind data, and Census short form data. Using these data, I estimate the effect of downwind exposure to highway-generated pollutants on the age-specific mortality rate by using orientation to the nearest major highway as an instrument for pollution exposure. I find that doubling the percentage of time spent downwind of a highway increases mortality among individuals 75 or older by 3.8%-6.5%. These estimates are robust and imply significant loss of life years.

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.

An instantaneous spatiotemporal model for predicting traffic-related ultrafine particle concentration through mobile noise measurements

People living near roadways are exposed to high concentrations of ultrafine particles (UFP, diameter < 100 nm). This can result in adverse health effects such as respiratory illness and cardiovascular diseases. However, accurately characterizing the UFP number concentration requires expensive sets of instruments. The development of an UFP surrogate with cheap and convenient measures is needed. In this study, we used a mobile measurement platform with a Fast Mobility Particle Sizer (FMPS) and sound level meter to investigate the spatiotemporal relations of noise and UFP and identify the hotspots of UFP. UFP concentration levels were significantly influenced by temporal and spatial variations (p < 0.001). We proposed a Generalized Additive Models to predict UFP number concentration in the study area. The model uses noise and meteorological covariates to predict the UFP number concentrations at an industrial site in Taichung, Taiwan. During the one year sampling campaign from fall 2013 to summer 2014, mobile measurements were performed at least one week for each season, both on weekdays and weekends. The proposed model can explain 80% of deviance and has coefficient of determination (R²) of 0.77. Moreover, the developed UFP model was able to adequately predict UFP concentrations, and can provide people with a convenient way to determine UFP levels. Finally, the results from this study could help facilitate the future development of noise mobile measurement.

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.

Spatial and temporal associations of road traffic noise and air pollution in London: Implications for epidemiological studies

Road traffic gives rise to noise and air pollution exposures, both of which are associated with adverse health effects especially for cardiovascular disease, but mechanisms may differ. Understanding the variability in correlations between these pollutants is essential to understand better their separate and joint effects on human health. We explored associations between modelled noise and air pollutants using different spatial units and area characteristics in London in 2003-2010. We modelled annual average exposures to road traffic noise (LAeq,24h, Lden, LAeq,16h, Lnight) for ~190,000 postcode centroids in London using the UK Calculation of Road Traffic Noise (CRTN) method. We used a dispersion model (KCLurban) to model nitrogen dioxide, nitrogen oxide, ozone, total and the traffic-only component of particulate matter ≤2.5μm and ≤10μm. We analysed noise and air pollution correlations at the postcode level (~50 people), postcodes stratified by London Boroughs (~240,000 people), neighbourhoods (Lower layer Super Output Areas) (~1600 people), 1km grid squares, air pollution tertiles, 50m, 100m and 200m in distance from major roads and by deprivation tertiles. Across all London postcodes, we observed overall moderate correlations between modelled noise and air pollution that were stable over time (Spearman's rho range: |0.34-0.55|). Correlations, however, varied considerably depending on the spatial unit: largest ranges were seen in neighbourhoods and 1km grid squares (both Spearman's rho range: |0.01-0.87|) and was less for Boroughs (Spearman's rho range: |0.21-0.78|). There was little difference in correlations between exposure tertiles, distance from road or deprivation tertiles. Associations between noise and air pollution at the relevant geographical unit of analysis need to be carefully considered in any epidemiological analysis, in particular in complex urban areas. Low correlations near roads, however, suggest that independent effects of road noise and traffic-related air pollution can be reliably determined within London.

Spatial and Temporal Distribution of PM2.5 Pollution in Xi'an City, China

The monitoring data of the 13 stations in Xi’an city for the whole years of 2013 and 2014 was counted and analyzed. Obtaining the spatial and temporal distribution characteristics of PM_2.5 was the goal. Cluster analysis and the wavelet transform were utilized to discuss the regional distribution characteristics of PM_2.5 concentration (ρ(PM_2.5)) and the main features of its yearly changes and sudden changes. Additionally, some relevant factors were taken into account to interpret the changes. The results show that ρ(PM_2.5) in Xi’an during 2013 was generally higher than in 2014, it is high in winter and low in summer, and the high PM_2.5 concentration centers are around the People’s Stadium and Caotan monitoring sites; For the regional PM_2.5 distribution, the 13 sites can be divided into three categories, in which Textile city is Cluster 1, and High-tech Western is Cluster 2, and Cluster 3 includes the remaining 11 monitoring sites; the coefficient of goodness of the cluster analysis is 0.6761, which indicates that the result is acceptable. As for the yearly change, apart from June and July, the average ρ(PM_2.5) concentration has been above the normal concentration criteria of Chinese National Standard (50 g/m³); cloudy weather and low winds are the major meteorological factors leading to the sudden changes of ρ(PM_2.5).

Proximity to busy highways and local resident perceptions of air quality

This study investigated variations in perceptions of air quality as a function of residential proximity to busy highways, across two suburbs of South Auckland, New Zealand. While plenty is known about the spatial gradients of highway emissions, very little is known about variation of lay understanding at the fine spatial scale and whether there are gradients in severity of concerns. One-hundred and four near-highway residents agreed to participate in a semi-structured interview on their knowledge and attitudes towards highway traffic emissions. Proximity to the highway edge varied within 5-380 m at the predominantly downwind side of the highway and 13-483 m at the upwind side. Likert-type ordered response questions were analysed using multivariate regression. Inverse linear relationships were identified for distance from highway and measures of concern for health impacts, as well as for noise (p<0.05). Positive linear relationships were identified for distance from highway and ratings of both outdoor and indoor air quality (p<0.05). Measures of level of income had no conclusive statistically significant effect on perceptions. Additional discussion was made surrounding participant's open-ended responses, within the context of limited international research. Findings indicate that there may be quantifiable psychological benefits of separating residents just a short distance (40 m+) from highways and that living within such close proximity can be detrimental to wellbeing by restricting local outdoor activity. This work lends additional rationale for a residential separation buffer of ~100 m alongside major highways in the interests of protecting human health.