Browsing by Author "Actkinson, Blake"

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    Detecting plumes in mobile air quality monitoring time series with density-based spatial clustering of applications with noise
    (Copernicus Publications, 2023) Actkinson, Blake; Griffin, Robert J.
    Mobile monitoring is becoming an increasingly popular technique to assess air pollution on fine spatial scales, but methods to determine specific source contributions to measured pollutants are sorely needed. One approach is to isolate plumes from mobile monitoring time series and analyze them separately, but methods that are suitable for large mobile monitoring time series are lacking. Here we discuss a novel method used to detect and isolate plumes from an extensive mobile monitoring data set. The new method relies on density-based spatial clustering of applications with noise (DBSCAN), an unsupervised machine learning technique. The new method systematically runs DBSCAN on mobile monitoring time series by day and identifies a subset of points as anomalies for further analysis. When applied to a mobile monitoring data set collected in Houston, Texas, analyzed anomalies reveal patterns associated with different types of vehicle emission profiles. We observe spatial differences in these patterns and reveal striking disparities by census tract. These results can be used to inform stakeholders of spatial variations in emission profiles not obvious using data from stationary monitors alone.
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    Improving and Developing Statistical Methods for Analyzing Air Pollution Measurements
    (2022-04-19) Actkinson, Blake; Griffin, Robert
    With the advent of a big data revolution in air pollution research comes a necessity for refining existing statistical techniques and developing new ones that are robust, scalable, and informative. This work discusses the development of novel statistical methods applied to air pollution and can be broken into two parts: improving an existing method to analyze air pollution measurements taken at stationary monitors and developing new methods to analyze novel measurements taken with a mobile platform. In the first main chapter of this thesis, Dynamic Principal Component Analysis (DPCA) is discussed as an alternative to Principal Component Analysis (PCA) for analyzing time-dependent relationships between air pollutant variables measured at stationary monitors. DPCA is shown to offer several advantages over PCA, in that it generates a set of statistics which are time dependent and uncovers a wealth of information not readily apparent in a conventional static application. The second part of this work discusses development of new statistical methods to analyze a burgeoning and increasingly important method of measuring air pollution, which is through mobile monitoring. Mobile monitoring offers excellent spatial coverage at the expense of temporal coverage, making many conventional statistical techniques frequently employed by the atmospheric sciences community ill-suited for analysis of these measurements. Subsequent chapters discuss mobile monitoring as a measurement strategy, a novel algorithm to derive and quantify the background in mobile monitoring measurements, and another novel algorithm to identify and analyze detected plumes in time series. Results from these algorithms showcase the power mobile measurements have to inform stakeholders about the spatial variability of air pollution not obvious through the use of stationary monitors alone. It is hoped that through the refinement and development of these techniques and others that an important and exciting era of air pollution research can be ushered and its long-term mysteries solved.
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    SIBaR: a new method for background quantification and removal from mobile air pollution measurements
    (European Geosciences Union, 2021) Actkinson, Blake; Ensor, Katherine; Griffin, Robert J.
    Mobile monitoring is becoming increasingly popular for characterizing air pollution on fine spatial scales. In identifying local source contributions to measured pollutant concentrations, the detection and quantification of background are key steps in many mobile monitoring studies, but the methodology to do so requires further development to improve replicability. Here we discuss a new method for quantifying and removing background in mobile monitoring studies, State-Informed Background Removal (SIBaR). The method employs hidden Markov models (HMMs), a popular modeling technique that detects regime changes in time series. We discuss the development of SIBaR and assess its performance on an external dataset. We find 83 % agreement between the predictions made by SIBaR and the predetermined allocation of background and non-background data points. We then assess its application to a dataset collected in Houston by mapping the fraction of points designated as background and comparing source contributions to those derived using other published background detection and removal techniques. The presented results suggest that the SIBaR-modeled source contributions contain source influences left undetected by other techniques, but that they are prone to unrealistic source contribution estimates when they extrapolate. Results suggest that SIBaR could serve as a framework for improved background quantification and removal in future mobile monitoring studies while ensuring that cases of extrapolation are appropriately addressed.