Development and Comparison of Air Pollution Exposure Surfaces Derived from On-Road Mobile Monitoring and Short-Term Stationary Sidewalk Measurements

Minet, Laura; Liu, Rick; Valois, Marie‐France; Xu, Junshi; Weichenthal, Scott; Hatzopoulou, Marianne

doi:10.1021/acs.est.7b05059

Cited by 68 publications

(69 citation statements)

References 27 publications

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“…First, road traffic might be measured as real time traffic recorded specifically during the time span of the study [32,33], estimations of daily traffic volume [34][35][36], or the type of roads taken by the cyclist [19,28,29]. In all cases, road traffic is associated with cyclists' greater exposure to air and noise pollution.…”

Section: Conceptual Framework For Modelling Cyclists' Multi-exposurementioning

confidence: 99%

Modelling Cyclists’ Multi-Exposure to Air and Noise Pollution with Low-Cost Sensors—The Case of Paris

Gelb

Apparicio

2020

Atmosphere

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Cyclists are particularly exposed to air and noise pollution because of their higher ventilation rate and their proximity to traffic. However, few studies have investigated their multi-exposure and have taken into account its real complexity in building statistical models (nonlinearity, pseudo replication, autocorrelation, etc.). We propose here to model cyclists’ exposure to air and noise pollution simultaneously in Paris (France). Specifically, the purpose of this study is to develop a methodology based on an extensive mobile data collection using low-cost sensors to determine which factors of the urban micro-scale environment contribute to cyclists’ multi-exposure and to what extent. To this end, we developed a conceptual framework to define cyclists’ multi-exposure and applied it to a multivariate generalized additive model with mixed effects and temporal autocorrelation. The results show that it is possible to reduce cyclists’ multi-exposure by adapting the planning and development practices of cycling infrastructure, and that this reduction can be substantial for noise exposure.

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Section: Conceptual Framework For Modelling Cyclists' Multi-exposurementioning

confidence: 99%

Modelling Cyclists’ Multi-Exposure to Air and Noise Pollution with Low-Cost Sensors—The Case of Paris

Gelb

Apparicio

2020

Atmosphere

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“…Nieuwenhuijsen MJ et al (2015) [34] found a lower correlation between LUR estimates at home and personal BC values during commuting (r = 0.32) than the one we found (r = 0.74). Furthermore, Minet L et al (2018) [37] compared BC LUR derived surfaces (100 × 100 m grid) to personal exposure measures finding that the latter (median = 1764 ng/m 3 ) were underestimated by the model (median = 1469 ng/m 3 ). This result is comparable with our findings showing that short-term high exposure events are very hard to catch with a LUR approach.…”

Section: Discussionmentioning

confidence: 99%

Is a Land Use Regression Model Capable of Predicting the Cleanest Route to School?

et al. 2019

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Land Use Regression (LUR) modeling is a widely used technique to model the spatial variability of air pollutants in epidemiology. In this study, we explore whether a LUR model can predict home-to-school commuting exposure to black carbon (BC). During January and February 2019, 43 children walking to school were involved in a personal monitoring campaign measuring exposure to BC and tracking their home-to-school routes. At the same time, a previously developed LUR model for the study area was applied to estimate BC exposure on points along the route. Personal BC exposure varied widely with mean ± SD of 9003 ± 4864 ng/m3. The comparison between the two methods showed good agreement (Pearson's r = 0.74, Lin's Concordance Correlation Coefficient = 0.6), suggesting that LUR estimates are capable of catching differences among routes and predicting the cleanest route. However, the model tends to underestimate absolute concentrations by 29% on average. A LUR model can be useful in predicting personal exposure and can help urban planners in Milan to build a healthier city for schoolchildren by promoting less polluted home-to-school routes.

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“…A recent effort to outfit Google street cars across several US cities has also been successful in mapping fine-scale urban pollution gradients [24,25,28,29]. The importance of combining these mobile platforms with fixed-site platforms to derive highly resolved spatial pollution maps and related pollutant exposure metrics is a very active area of research [30][31][32]. In many cases, mobile air pollution monitoring studies describe short-term or seasonal campaigns.…”

Section: Overview Of Recent Mobile Urban Air Quality Studiesmentioning

confidence: 99%

“…Identifying the distribution of emission sources, in conjunction with monitoring the spatial distribution of pollutant exposure, is a necessary first step towards assessing potential health outcomes for vulnerable populations. research [30][31][32]. In many cases, mobile air pollution monitoring studies describe short-term or seasonal campaigns.…”

Section: Sociodemographic and Emission Characteristicsmentioning

confidence: 99%

The TRAX Light-Rail Train Air Quality Observation Project

et al. 2019

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Observing air quality from sensors onboard light rail cars in Salt Lake County, Utah began as a pilot study in 2014 and has now evolved into a five-year, state-funded program. This metropolitan region suffers from both elevated ozone levels during summer and high PM 2.5 events during winter. Pollution episodes result predominantly from local anthropogenic emissions but are also impacted by regional transport of dust, chemical precursors to ozone, and wildfire smoke, as well as being exacerbated by the topographical features surrounding the city. Two electric light-rail train cars from the Utah Transit Authority light-rail Transit Express ("TRAX") system were outfitted with PM 2.5 and ozone sensors to measure air quality at high spatial and temporal resolutions in this region. Pollutant concentration data underwent quality control procedures to determine whether the train motion affected the readings and how the sensors compared against regulatory sensors. Quality assurance results from data obtained over the past year show that TRAX Observation Project sensors are reliable, which corroborates earlier preliminary validation work. Three case studies from summer 2019 are presented to illustrate the strength of the finely-resolved air quality observations: (1) an elevated ozone event, (2) elevated particulate pollution resulting from 4th of July fireworks, and (3) elevated particle pollution during a winter time inversion event. The mobile observations were able to capture spatial gradients, as well as pollutant hotspots, during both of these episodes. Sensors have been recently added to a third light rail train car, which travels on a north-south oriented rail line, where air quality was unable to be monitored previously. The TRAX Observation

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Development and Comparison of Air Pollution Exposure Surfaces Derived from On-Road Mobile Monitoring and Short-Term Stationary Sidewalk Measurements

Cited by 68 publications

References 27 publications

Modelling Cyclists’ Multi-Exposure to Air and Noise Pollution with Low-Cost Sensors—The Case of Paris

Modelling Cyclists’ Multi-Exposure to Air and Noise Pollution with Low-Cost Sensors—The Case of Paris

Is a Land Use Regression Model Capable of Predicting the Cleanest Route to School?

The TRAX Light-Rail Train Air Quality Observation Project

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