arXiv subjects

Bora Jin

Publications and source records attributed to Bora Jin.

5 recordsLinked to original sources

arXiv2025

Source apportionment of air pollution burden using geometric non-negative matrix factorization and high-throughput multi-pollutant air sensor data in Curtis Bay, Baltimore, USA

Air sensor networks provide hyperlocal, high-frequency data on multiple pollutants, but unlike speciated particulate matter (PM) measurements, they lack direct chemical signatures for source identification. High temporal resolution and multiple spatial locations nonetheless create new opportunities to interpret latent sources through their relationships with spatial proximity to known origins, temporal patterns, and meteorology. We analyze 451946 one-minute air sensor records from Curtis Bay (Baltimore, USA; October 2022 - June 2023), covering size-resolved PM, black carbon (BC), carbon monoxide (CO), nitric oxide (NO), and nitrogen dioxide (NO2), using a geometric non-negative matrix factorization (NMF) approach that scales to large datasets and yields provably unique source attribution percentages. Three stable latent sources emerge with converging evidence toward recognizable source categories: Source 1 explains $>$ 70% of fine and coarse PM and $\sim$30% of BC; Source 2 dominates CO and contributes $\sim$70% of BC, NO, and NO2; Source 3 is specific to the larger PM fractions, PM10 to PM40. Regression analyses and a case study on a known bulldozer incident link Sources 1 and 3 to a nearby coal terminal. Extreme-intensity episodes from Sources 1 and 3 averaged $\sim$33 and $\sim$24 minutes per day at the site nearest the terminal, attenuating with distance. Source 2 reflects diurnal traffic patterns. Together, these results show that dense air sensor networks paired with the geometric NMF method can move community air monitoring beyond pollution detection toward identifying likely source categories and informing actionable mitigation strategies.

stat.AP↗

arXiv2025

Identification and consistent estimation in source apportionment using geometry

Source apportionment, the attribution of observed multipollutant concentrations to underlying sources, can be cast as a non-negative matrix factorization (NMF) problem. Because NMF is non-unique, source apportionment imposes additional, often unverifiable, constraints such as sparsity. Geometric approaches offer an alternative route to identification, but many of them still rely on source profiles with arbitrary scalings, make strong structural assumptions including exact separability, and lack a statistical framework for consistent estimation. In this manuscript, we address these limitations. We introduce the source attribution matrix that is scale-invariant and establish its identifiability under a stochastic framework that replaces hard separability constraints with soft probabilistic relaxations. We then present a scalable geometric algorithm to estimate the source attribution matrix and prove its consistency. To our knowledge, this is the first consistency result for estimating the source attribution matrix that requires no exact sparsity, makes no parametric distributional assumptions, and accommodates spatio-temporal dependence in data. Numerical experiments confirm the theory.

math.ST↗

arXiv2022

Bayesian Matrix Completion for Hypothesis Testing

We aim to infer bioactivity of each chemical by assay endpoint combination, addressing sparsity of toxicology data. We propose a Bayesian hierarchical framework which borrows information across different chemicals and assay endpoints, facilitates out-of-sample prediction of activity for chemicals not yet assayed, quantifies uncertainty of predicted activity, and adjusts for multiplicity in hypothesis testing. Furthermore, this paper makes a novel attempt in toxicology to simultaneously model heteroscedastic errors and a nonparametric mean function, leading to a broader definition of activity whose need has been suggested by toxicologists. Real application identifies chemicals most likely active for neurodevelopmental disorders and obesity.

stat.AP↗

arXiv2022

Spatial predictions on physically constrained domains: Applications to Arctic sea salinity data

In this paper we predict sea surface salinity (SSS) in the Arctic Ocean based on satellite measurements. SSS is a crucial indicator for ongoing changes in the Arctic Ocean and can offer important insights about climate change. We particularly focus on areas of water mistakenly flagged as ice by satellite algorithms. To remove bias in the retrieval of salinity near sea ice, the algorithms use conservative ice masks, which result in considerable loss of data. We aim to produce realistic SSS values for such regions to obtain more complete understanding about the SSS surface over the Arctic Ocean and benefit future applications that may require SSS measurements near edges of sea ice or coasts. We propose a class of scalable nonstationary processes that can handle large data from satellite products and complex geometries of the Arctic Ocean. Barrier overlap-removal acyclic directed graph GP (BORA-GP) constructs sparse directed acyclic graphs (DAGs) with neighbors conforming to barriers and boundaries, enabling characterization of dependence in constrained domains. The BORA-GP models produce more sensible SSS values in regions without satellite measurements and show improved performance in various constrained domains in simulation studies compared to state-of-the-art alternatives. An R package is available at https://github.com/jinbora0720/boraGP.

stat.AP↗

arXiv2021

Bag of DAGs: Inferring Directional Dependence in Spatiotemporal Processes

We propose a class of nonstationary processes to characterize space- and time-varying directional associations in point-referenced data. We are motivated by spatiotemporal modeling of air pollutants in which local wind patterns are key determinants of the pollutant spread, but information regarding prevailing wind directions may be missing or unreliable. We propose to map a discrete set of wind directions to edges in a sparse directed acyclic graph (DAG), accounting for uncertainty in directional correlation patterns across a domain. The resulting Bag of DAGs processes (BAGs) lead to interpretable nonstationarity and scalability for large data due to sparsity of DAGs in the bag. We outline Bayesian hierarchical models using BAGs and illustrate inferential and performance gains of our methods compared to other state-of-the-art alternatives. We analyze fine particulate matter using high-resolution data from low-cost air quality sensors in California during the 2020 wildfire season. An R package is available on GitHub.

stat.ME↗