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Understanding the Spatial Variations of Pollutant Concentrations in Near-Road Environments.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Understanding the Spatial Variations of Pollutant Concentrations in Near-Road Environments./
作者:	Ranasinghe, Dilhara Roshini.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	112 p.
附註:	Source: Dissertations Abstracts International, Volume: 79-12, Section: B.
Contained By:	Dissertations Abstracts International79-12B.
標題:	Atmospheric Chemistry. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10826609
ISBN:	9780438021334

Understanding the Spatial Variations of Pollutant Concentrations in Near-Road Environments.
Ranasinghe, Dilhara Roshini.

Understanding the Spatial Variations of Pollutant Concentrations in Near-Road Environments. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 112 p.

Source: Dissertations Abstracts International, Volume: 79-12, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2018.

This item must not be sold to any third party vendors.

Many epidemiological studies have associated elevated concentrations of air pollutants found on and near roadways with a variety of adverse health outcomes. Concentrations of freshly emitted pollutants in urban areas exhibit a high degree of spatial variability, which makes pollutant exposures, and potentially their resulting health effects both very location dependent and difficult to estimate. Mobile air pollution monitoring offers an opportunity to map pollutants with much higher spatial resolution than sparse stationary monitors. In the first study, we developed a framework to address the challenges and constraints to developing higher spatial resolution maps from mobile data. For 1 s time resolution data collected at normal city driving speeds, we showed that concentration maps of 5 m spatial resolution can be obtained, by including up to 21% interpolated values. We estimated the minimum number of sampling runs needed to make a representative concentration map with a specific spatial resolution, and found that generally between 15 to 21 repeats of a particular route under similar traffic and meteorological conditions is sufficient. The concentration maps can afford insights into factors influencing pollutant concentrations at the city block and sub block scale; information that is useful in urban planning strategies to reduce pollution exposure. Methodical analysis of mobile monitoring data facilitate meaningful comparison of concentration maps of different routes/studies. Solid sound walls and vegetation barriers are commonly used to mitigate noise but they also help to reduce near-road air pollution. In the second study, we assessed the effectiveness of adding vegetation to sound walls (combination barriers) and vegetation-only barriers in reducing pollution concentrations downwind of roads. Using field measurements collected with a mobile monitoring platform, we developed concentration decay profiles of ultrafine particles, fine particles, oxides of nitrogen (NO and NO2) and carbon monoxide downwind of two roads in California with different solid barrier-vegetation barrier configurations and meteorological conditions. Generally, when winds were blowing approximately perpendicular to the road, both vegetation and combination barriers were effective in reducing near road air pollution. Under calm and stable atmospheric conditions (wind speed < 0.6 m/s); a taller and denser vegetation-only barrier was more effective than a combination barrier. For ultrafine particles and gas pollutants, the additional reduction by vegetation-only barrier ranged from 10-24 %, in the first 160 m from the barrier. Under light winds (wind speed< 3 m/s), in both unstable and stable atmospheric conditions, combination barriers with moderately dense vegetation that is taller than the solid barrier were more effective relative to the sound wall or the taller and higher and denser vegetation barrier alone. The additional reduction by combination barriers ranged 6-33% in the first 160 m from the barrier. Our results are consistent with the notion that at low wind speeds vegetation act as effective barriers, and mean particle size data suggests a strong contribution of deposition in reducing ultrafine particles downwind of vegetation barriers. At higher wind speeds, the importance of the barrier effect diminishes and their windbreak effect becomes more important. Overall, adding vegetation alone or to an existing solid barrier resulted in lower downwind pollution concentrations, especially under low wind speeds when concentrations are higher. In the third study, we used a modified dispersion model "Quick Urban & Industrial Complex" (QUIC) together with field measurements to assess factors controlling the effectiveness of vegetation and combination barriers in reducing near-road pollution concentrations. Two study sites with different building morphologies and configurations of barriers were modeled using QUIC. QUIC simulations in general captured the effect of barriers on pollution dispersion and the complex flow in near-road urban environments. Improvements to handle characteristics of vegetation are needed to capture the wind speed dependent effects of vegetation barriers. The QUIC model showed promise as a useful tool to optimize the characteristics of barriers to mitigate near-road air pollution exposure.

ISBN: 9780438021334Subjects--Topical Terms:

1669583
Atmospheric Chemistry.
Subjects--Index Terms:

Air pollution

Understanding the Spatial Variations of Pollutant Concentrations in Near-Road Environments.
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