Exposure assessment is a critical step in air quality-related epidemiological studies. Accurate estimates of exposure within urban areas are a vital input to models that aim to assess the health effects of air quality among populations of interest. In this study, I have derived and applied a novel approach for capturing the distribution of air quality in Arlington, VA and Washington DC using mobile monitoring. The main objectives of this study are:
1. Deploy a year-long sampling campaign in the Washington DC region to capture the within-city variability of air quality for Particle Number Concentration (PNC), fine particulate matter (PM2.5), and Black Carbon (BC) using mobile monitoring.
2. Derive a method for selecting the best representative mobile monitoring routes to capture within-city spatial patterns of air quality. The end-use of the monitoring campaign described here is as an input for Land Use Regression (LUR) models.
3. Collect unconventional data to characterize the built environment, e.g., videos, sound, etc., that could be employed to improve the LUR models beyond conventional approaches.
This study describes the data collection effort that was deployed for a year to characterize annual average concentrations at different locations across the Washington DC region. My thesis describes the challenges experienced and lessons learned during the data collection phase. The goal of this thesis is to describe the data collected and the methods used to sample the DC region. This effort is a component of a larger project that will later use these observations in LUR models.
The central site used for measurement of background concentration had a lower concentration median when compared with the median concentration measured on bike. The median PM2.5 concentration at the central site was observed to be 5.2 μg/m3 and the median PNC at the central site was observed to be 6,365 #/cm3. The Arlington PM2.5 concentration was 1 μg/m3 and the Washington DC concentration PM2.5 was 0.3 μg/m3 higher than the background median concentration. Also, the Particle Number Concentration (PNC) was 222 #/cm3 more and the Washington DC PNC was 2,139 #/cm3 higher than the median background concentration. / Many studies have shown that living in polluted air has long-term negative impacts on human health. These negative impacts include premature death, lung disease, heart disease, blood disease, and other complications. Due to these impacts, it is critical to know the level of air pollution within cities to identify areas that have elevated concentrations.
The measurement of air quality is challenging because of the low number of monitors available due to cost. Reference grade air quality monitors are often very costly. In this study, I have developed an approach for using a bike to collect mobile measurements of particulate air pollutants in the Washington DC area. I collected one year of data at a fixed site in Arlington and four seasons of data from the bike mobile monitoring campaign. After analyzing the data, I observed that the fixed station showed lower concentration when compared with the data collected by bicycle. I have also suggested improvements in the mobile monitoring method and developed an approach for joining these data with outputs from computer vision models to describe the built environment.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/113383 |
| Date | 23 January 2023 |
| Creators | Dixit, Kuldeep Kumar |
| Contributors | Urban and Regional Planning |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Coverage | Washington, D.C. |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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