Yu Ding recently joined the Sustainable URban Futures (SURF) Lab as a Ph.D. student in Meteorology. Welcome!
Before coming to OU, Yu Ding completed her master’s degree in Hydrology and Water Resources at Hohai University, China. Her previous research focused on improving the accuracy of satellite precipitation data and integrating bias correction and machine learning algorithms to enhance data precision. Yu has an interest in utilizing remote sensing techniques and hydrological modeling.
Her Ph.D. research will focus on developing an integrated high-resolution pollutant dispersion model over complex terrain (e.g., urban environments).
Bohong Li recently joined the Sustainable URban Futures (SURF) Lab as a visiting M.Sc. student. Welcome!
Bohong Li is an M.Sc. student majoring in Atmospheric Science from the University of Hamburg. He finished his B.Sc. degree in meteorology in the University of Hamburg. In his bachelor thesis, he analyzed steep temperature drops using data from the Hamburg Weather Mast. Bohong’s research interests include urban climate and emission, atmospheric chemistry, urban heat island, as well as health risk and public health due to changing urban climate. During his visit, his research will focus on urban effects on precipitation.
Bohong’s personal interests include everything about Taylor Swift, sports and esports, gaming, photography, and watching series and movies.
Our new paper, “Methane dynamics altered by reservoir operations in a typical tributary of the Three Gorges Reservoir“, is published in Water Research (IF: 11.4).
Authors: Jia Liu, Fei Xue, Xiaojuan Guo, Zhengjian Yang, Manchun Kang, Min Chen, Daobin Ji, Defu Liu, Shangbin Xiao, and Chenghao Wang
Abstract: Substantial nutrient inputs from reservoir impoundment typically increase sedimentation rate and primary production. This can greatly enhance methane (CH4) production, making reservoirs potentially significant sources of atmospheric CH4. Consequently, elucidating CH4 emissions from reservoirs is crucial for assessing their role in the global methane budget. Reservoir operations can also influence hydrodynamic and biogeochemical processes, potentially leading to pronounced spatiotemporal heterogeneity, especially in reservoirs with complex tributaries, such as the Three Gorges Reservoir (TGR). Although several studies have investigated the spatial and temporal variations in CH4 emissions in the TGR and its tributaries, considerable uncertainties remain regarding the impact of reservoir operations on CH4 dynamics. These uncertainties primarily arise from the limited spatial and temporal resolutions of previous measurements and the complex underlying mechanisms of CH4 dynamics in reservoirs. In this study, we employed a fast-response automated gas equilibrator to measure the spatial distribution and seasonal variations of dissolved CH4 concentrations in XXB, a representative area significantly impacted by TGR operations and known for severe algal blooms. Additionally, we measured CH4 production rates in sediments and diffusive CH4 flux in the surface water. Our multiple campaigns suggest substantial spatial and temporal variability in CH4 concentrations across XXB. Specifically, dissolved CH4 concentrations were generally higher upstream than downstream and exhibited a vertical stratification, with greater concentrations in bottom water compared to surface water. The peak dissolved CH4 concentration was observed in May during the drained period. Our results suggest that the interplay between aquatic organic matter, which promotes CH4 production, and the dilution process caused by intrusion flows from the mainstream primarily drives this spatiotemporal variability. Importantly, our study indicates the feasibility of using strategic reservoir operations to regulate these factors and mitigate CH4 emissions. This eco-environmental approach could also be a pivotal management strategy to reduce greenhouse gas emissions from other reservoirs.
Fig. 6. Conceptual diagram of CH4 dynamics in Xiangxi Bay under the operations of the Three Gorges Reservoir. The light blue area represents inflow from upstream of XXR, while the dark blue area represents flow from the mainstream. Orange and green circles along the riverbed represent terrestrial and aquatic OM, respectively. Solid green circles near the water surface represent algae.
Our new paper, “A modeling framework to assess fenceline monitoring and self-reported upset emissions of benzene from multiple oil refineries in Texas“, is published in Atmospheric Environment X (IF: 3.8).
Authors: Qi Li, Lauren Padilla, Tammy Thompson, Shuolin Xiao, Elizabeth Mohr, Xiaohe Zhou, Nino Kacharava, Yuanfeng Cui, and Chenghao Wang
Abstract: Benzene as one type of hazardous air pollutants (HAPs) is produced by industrial production processes and/or emitted during upset events caused by man-made or natural accidents. Although upset emissions of benzene can be a significant contributor to the total emission, it is still challenging to quantify. This study first develops a fast modeling framework using obstacle-resolving computational fluid dynamics modeling to compare the modeled within-facility-scale passive pollutant dispersion with the observed levels based on self-reported emissions for fourteen facilities in Texas, United States. Results of numerical simulations demonstrate that neglecting the obstacle effect can underpredict (overpredict) the near-(far-)field concentrations for a low source. For a source located above obstacles, underprediction occurs at all distances. The diagnostic framework is applied to 107 self-reported upset emission events for fourteen petroleum refineries in Texas from year 2019–2022. Considering different metrics across all events, it can be concluded that the modeled concentrations based on self-reported emissions likely underpredict the observed concentration increments. Depending on the possible source height, the median factor of underprediction ranges from 3 to 95 based on the average-plume metric. The agreement between model and observation is better for events characterized by high emission amounts and rates, which also correspond to high observed concentration increments. Overall, the research highlights the importance of considering obstacles and demonstrates the potential application of the current approach as an efficient diagnostic method for self-reported upset emissions using fenceline observations of HAPs.
Fig. 2. Normalized concentrations for cases without and with obstacles for horizontal plane at z = 2 m. The wind direction is zero degree, the obstacle geometry is sparse-low. The obstacles, i.e., the “white-bars” are 100 m × 100 m and the gap between, d, is 200 m in this example. The planar location of the source is indicated by the blue star in Fig. 1a; the top, middle, to bottom rows show cases with source heights corresponding to low, medium, and high as indicated in Fig. 1b. (a), (c), (e): C’LES for cases without obstacles with source heights zs low, medium and high. (b), (d), (f): C’LES for case with sparse-low obstacles with source heights zs low, medium and high.
Liam Thompson recently received the Bob Glahn Scholarship in Statistical Meteorology from the National Weather Association Foundation.
The Bob Glahn Scholarship in Statistical Meteorology was established by Dr. Bob (Harry R.) Glahn in 2012 to aid students in their final two years of undergraduate studies, enrolled in a program of meteorology or atmospheric science with a demonstrated interest in statistical meteorology.
