Motor Vehicle Emissions as a Source of Indoor Particles

Morawska, Lidia

doi:10.1002/9783527610013.ch3g

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…Thus, in a setting where the number of variables is on the same order of magnitude as the number of samples, we have to utilize prior knowledge and enforce a special structure on the estimated coefficients. This study was done with emphasis on urban spaces, where two patterns stand out (Gurjar et al, 2004;Morawska, 2006;Saarikoski et al, 2008):…”

Section: Modelmentioning

confidence: 99%

Statistical-Physical Estimation of Pollution Emission

Barut¹,

Hwang²,

Yeo³

2018

STAT SINICA

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Air pollution is driven by non-local dynamics, in which air quality at a site is determined by transport of pollutants from distant pollution emission sources to the site by atmospheric processes. In order to understand the underlying nature of pollution generation, it is crucial to employ a physical knowledge to account for the pollution transport by wind. However, in most cases, it is not possible to utilize the physics models to obtain useful information, as it requires massive calibration and computation. In this paper, we propose a method to estimate the pollution emission from the domain of interest, by using both the physical knowledge and observed data. The proposed method uses an efficient optimization algorithm to estimate the emission from each of the spatial locations, while incorporating the physics knowledge. We demonstrate the effectiveness of the new method through a simulation study that mimics the real application.

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Section: Modelmentioning

confidence: 99%

Statistical-Physical Estimation of Pollution Emission

Barut¹,

Hwang²,

Yeo³

2018

STAT SINICA

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“…In a study of air pollution near major highways in the Netherlands, the NO concentration was found to be positively correlated with traffic density on the nearest highway and with the percentage of time downwind from the highway and negatively correlated with the distance from the nearest highway (24). Morawska, who conducted a review of particle dispersion measurements, concluded from the studies that although the number concentration decreased exponentially with distance, significantly elevated particle numbers compared with the background concentrations were observed within the first 100 m (25).…”

mentioning

confidence: 99%

Proposed Road Pollution Alert System Based on Lagrangian Wall Model

Marquez

Lilley

Cope

et al. 2006

Transportation Research Record

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Despite significant improvements in fuel and engine technology, road traffic remains a major source of air pollutants, such as oxides of nitrogen, CO, particulate matter, and volatile organic compounds. Many recent studies have demonstrated associations between traffic-related air pollution and the adverse effects of exposure to traffic-related air pollution in a wide variety of situations. This paper describes the components of a proposed road pollution modeling-and-alert system that can be deployed by local authorities on sections of roads that are under the continuous risk of exceeding thresholds of acceptable air quality. The system features the use of real-time traffic data from intelligent transportation systems and related sources; a power-based model for the calculation of emissions that takes into account the driving mode of the vehicle as well as characteristics of the road segment; a Lagrangian wall model (LWM) to simulate the dispersion of pollutants from different sources; and an alert-and-response system that will need further input from government authorities, industry, and the community. The ability to combine emissions estimates from a power-based motor vehicle emissions model with the LWM dispersion and chemical transformation capabilities significantly enhances the capacity to minimize population exposure and reduce health impacts of pollution from particular roads of interest. This paper discusses development of the LWM and summarizes outcomes of a verification study performed with the California Department of Transportation Highway 99 tracer dispersion data set. It also presents results of a case study performed to demonstrate capabilities of the LWM.

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