Browsing by Author "Li, Jiaqi"

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    Database of snow holograms collected from 2019 to 2022 for machine learning training or other purposes
    (2022-10-06) Li, Jiaqi; Guala, Michele; Hong, Jiarong; li001334@umn.edu; Li, Jiaqi; University of Minnesota Flow Field Imaging Lab
    This dataset includes the original combined snow holograms and holograms with image augmentation (rotation, exposure, blur, noise) for YOLOv5 model training to detect and classify snow particles. The individual snow particles are cropped and combined to enrich the particle numbers in each image for the ease of manual labeling. The snow particles are classified into six categories, including aggregate/irregular (I), dendrite (P2), graupel/rime (R), plate (P1), needle/column (N/C), and small particles/germ (G).
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    Ndma Formation During Chloramination: Temporospatial Distribution Of Ndma Precursors, The Effect Of Lime Softening Treatment, And Precursor Identification Via Non-Targeted Analysis
    (2023-08) Li, Jiaqi
    N-nitrosodimethylamine (NDMA), a byproduct formed in drinking water during chloramination disinfection, is notorious both for its potent carcinogenicity and toxicity. NDMA and other nitrosamine precursors are compounds containing secondary, tertiary, and quaternary amines, which can be released from diverse sources into the surface waters. There is still much to be understood, however, about the fate of these precursors in natural water and engineered water system. This work mainly addressed the seasonal variations and geographical occurrence of NDMA precursors in rivers, the effect of lime softening treatment on NDMA formation, and the identification of NDMA precursors via nontargeted mass spectrometric analysis. The seasonal and spatial variations in NDMA precursor levels in Crow River was assessed by approximately monthly sampling at twelve locations over 18 months. NDMA precursor concentrations was assessed as NDMA formation potential under Uniform Formation Test (UFC) conditions, which represent the average and realistic disinfection conditions. River water samples were lime softened in parallel. Raw water NDMAUFC concentrations (2.2 to 128 ng/L) exhibited substantial temporal variation but relatively little spatial variation. Lime-softening treatment typically resulted in an increase in NDMA formation using the UFC protocol likely due to the decrease in competition between precursors and NOM for chloramines and possibly reduced interactions of precursors with NOM. The geospatial distribution of NDMA precursors in watersheds in Minnesota and its association with anthropogenic activities was investigated via geostatistical analysis. River water samples were collected from major watersheds in Minnesota in summer 2022. Representative regional fluorescence from excitation-emission matrix (EEM) spectroscopy was explored for its correlation with NDMAUFC. The results show the primary source of NDMA precursors is animal operations, followed by domestic wastewater discharge. The spearman correlation analysis between organic matter fluorescence and NDMAUFC suggests region IV is indicative of contributors to NDMA formation while organic matter in region V may serve as competitor for available chloramines during UFC testing. The advancement of analytical techniques with higher mass resolution and accuracy facilitates the identification of NDMA precursors via a non-targeted approach. River water samples collected from Crow river were processed and loaded into mix-bed cartridges containing five different sorbents, which demonstrated effective extraction of NDMA precursors. Extracted samples were analyzed by liquid chromatograph quadrupole time-of-flight mass spectrometry. Following a non-targeted analysis workflow established on a pre/post chloramination sample comparison approach, twenty-eight compounds containing primary, secondary, tertiary, or quaternary amines were identified. Thirteen of these compounds were validated as nitrosamine precursors.
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    Snow properties, trajectories, and turbulence data collected during field deployments from 2022 to 2023 at Eolos research station
    (2024-07-29) Li, Jiaqi; Guala, Michele; Hong, Jiarong; li001334@umn.edu; Li, Jiaqi
    The data contains the three-dimensional trajectories, snow particle properties, and raw meteorological data of four datasets (with dominant snow types, including aggregates, dendrites, graupels, and needles). The datasets are collected during three deployments, on April 7th, 2022, December 19th, 2022, and January 2nd, 2023, at the Eolos Wind Research Field Station in Rosemount, MN.
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    Unraveling snow settling dynamics: a field study on the effects of snow morphology and atmospheric turbulence
    (2024-08) Li, Jiaqi
    Accurate modeling of ground snow accumulation is important for applications such as snow hazard warnings, hydrology, and traffic regulation during snow events. However, current predictions often fall short due to an incomplete understanding of how snow particle morphology, density, and atmospheric turbulence influence snow settling dynamics. While laboratory experiments and numerical simulations have explored the impact of turbulence on particles with simple geometries, few studies have addressed the complexities of real-world conditions, where a wide range of turbulence scales and intricate snow particle shapes come into play. To bridge this gap, field investigations are essential for enhancing the understanding of snow settling behavior in the atmosphere and improving predictive models for ground snow accumulation. This study investigates the influence of snow morphology and atmospheric turbulence on snow particle settling behavior, including settling velocity and spatial distribution. A snow particle analyzer, incorporating a digital inline holography system and a high-precision scale, is used to simultaneously measure snow particle morphology (i.e., size, shape, type) and density, which are critical for predicting the aerodynamic properties and terminal velocity of snow particles. Additionally, planar and three-dimensional imaging techniques, such as super-large-scale particle image velocimetry (SLPIV), large-scale particle tracking velocimetry (LSPTV), and 3D particle tracking velocimetry (3D PTV), are employed to visualize snow settling dynamics and capture atmospheric turbulence in the field. These methods were deployed during selected snow events at the EOLOS field research station in Rosemount, MN, alongside measurements of atmospheric turbulence by a meteorological tower. The field experiments yield significant insights into how morphology and turbulence affect snow settling dynamics. Specifically, measurements from the snow particle analyzer have contributed to the understanding of the relationship between snow particle morphology and density, which is later used for the estimation of snow aerodynamic properties and terminal velocity. In addition, simultaneous planar measurement techniques (SLPIV and LSPTV) have provided direct evidence of preferential sweeping of snow particles by atmospheric turbulence, with higher concentration and enhanced settling velocity observed on the downward side of both prograde and retrograde vortices. Furthermore, the 3D PTV study under weak turbulence has demonstrated a significant impact of snow particle morphology on their meandering behavior (magnitude and frequency), which in turn influences their settling velocity. Last but not least, 3D PTV measurements under moderate and high turbulence have offered deeper insights into how turbulence affects the settling velocity of snow particles with varying morphologies. These findings contribute valuable information for enhancing the accuracy of snow settling velocity models in winter weather forecasting.