Author: Chenghao Wang Page 1 of 4

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).

The paper and its supplement can be downloaded at https://www.sciencedirect.com/science/article/pii/S0043135424010625.

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.

DOI: https://doi.org/10.1016/j.watres.2024.122163

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).

The paper and its supplement can be downloaded at https://www.sciencedirect.com/science/article/pii/S2590162124000480.

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.

DOI: https://doi.org/10.1016/j.aeaoa.2024.100281

The School of Meteorology and the Center for Analysis and Prediction of Storms at the University of Oklahoma invite applications for two fully funded full-time Postdoctoral Researcher positions focused on the modeling of air quality and/or greenhouse gases (GHGs) in the urban environment. Areas of focus include but are not limited to:

• Development of new or improved numerical parameterization schemes
• Spatial and temporal characterization of air pollution (e.g., ozone and particulate matter) and GHGs
• Assessment of mitigation strategies to reduce air pollutants and GHG emissions

Both positions are based in Norman, OK, and will be working with Dr. Chenghao Wang and Dr. Xiao-Ming Hu.

Salary will be commensurate with the applicant’s experience. Full-time employment comes with OU research staff benefits, including generous paid leave, health insurance, and retirement savings plans. The successful candidates will work in the National Weather Center, with numerous opportunities to collaborate with world-leading academic and operational partners both on and off campus, such as the Center for Analysis and Prediction of Storms (CAPS) and National Center for Atmospheric Research (NCAR). The University of Oklahoma and City of Norman offer a vibrant college town atmosphere with numerous recreational and cultural activities. Norman is just 20 miles away from Oklahoma City, which provides all the amenities of a larger city. Norman also has a low cost of living compared to most cities in the U.S.

Qualifications: Applicants must have earned a Ph.D. in Atmospheric Sciences, Engineering, Earth Science, Computer Science, or a closely related field by the time of appointment. Candidates should have demonstrated experience with numerical 3D air quality models, such as WRF-Chem, CMAQ, HYSPLIT, GEOS-Chem, and LES models, be proficient in programming languages commonly used in models (Fortran) and data analytics (MATLAB, Python, R, or NCL), and possess strong oral and written communication skills, evidenced by their publication record and presentations at scientific meetings.

Application Instructions

Applicants are encouraged to apply as soon as possible. To apply, interested individuals should submit electronically:

Please submit your application through http://apply.interfolio.com/150611 by Sep 30, 2024. Applications will be reviewed as received and will continue until the positions are filled. For questions regarding these two positions, please contact Dr. Chenghao Wang (chenghao.wang@ou.edu) or Dr. Xiao-Ming Hu (xhu@ou.edu).

Dr. Wang was recently awarded the NASA Early Career Investigator Grant titled “Compound Heat and Ozone Pollution Episodes in the Urban Environment: Dynamics, Mechanism, and Mitigation with Nature-Based Solutions”.

See OU News here: https://www.ou.edu/news/articles/2024/july/researcher-receives-nasa-funding-to-study-ozone-pollution.

Our new paper, “The potential of urban irrigation for counteracting carbon-climate feedback“, is published in Nature Communications (IF: 16.6).

The paper and its supplement can be downloaded at https://www.nature.com/articles/s41467-024-46826-3.

Authors: Peiyuan Li, Zhi-Hua Wang, and Chenghao Wang

Abstract: Global climate changes, especially the rise of global mean temperature due to the increased carbon dioxide (CO2) concentration, can, in turn, result in higher anthropogenic and biogenic greenhouse gas emissions. This potentially leads to a positive loop of climate–carbon feedback in the Earth’s climate system, which calls for sustainable environmental strategies that can mitigate both heat and carbon emissions, such as urban greening. In this study, we investigate the impact of urban irrigation over green spaces on ambient temperatures and CO2 exchange across major cities in the contiguous United States. Our modeling results indicate that the carbon release from urban ecosystem respiration is reduced by evaporative cooling in humid climate, but promoted in arid/semi-arid regions due to increased soil moisture. The irrigation-induced environmental co-benefit in heat and carbon mitigation is, in general, positively correlated with urban greening fraction and has the potential to help counteract climate–carbon feedback in the built environment.

DOI: https://doi.org/10.1038/s41467-024-46826-3

Our new paper, “Enhanced observations from an optimized soil-canopy-photosynthesis and energy flux model revealed evapotranspiration-shading cooling dynamics of urban vegetation during extreme heat“, is published in Remote Sensing of Environment (IF: 13.5).

The paper and its supplement can be downloaded at https://www.sciencedirect.com/science/article/pii/S0034425724001093. The Share Link to download a copy is https://authors.elsevier.com/c/1ikad7qzT3Dj5 (valid through Apr 30, 2024).

Authors: Zhaowu Yu, Jiaqi Chen, Jike Chen, Wenfeng Zhan, Chenghao Wang, Wenjuan Ma, Xihan Yao, Siqi Zhou, Kai Zhu, and Ranhao Sun

Abstract: Previous studies on the cooling of urban vegetation mainly focused on its transpiration or shading effect separately, neglecting to explore the combined evapotranspiration-shading cooling. Further, accurate quantification of evapotranspiration-shading cooling remains challenging due to heterogeneity of urban landscapes, which limits understanding of its high-resolution spatiotemporal patterns. Here, we integrate high-precision remote sensing data and the Soil-Canopy-Observations of Photosynthesis and Energy Fluxes (SCOPE) model to propose an optimized quantitative approach. The approach was used to investigate changes in evapotranspiration-shade cooling during extreme heat. Taking Shanghai metropolitan as case, the results show: (1) The cooling capacity of urban vegetation in nighttime (18:00–6:00) is enhanced during extreme heat, which is attributed to accumulated effect of shading and enhanced evapotranspiration due to elevated vapor-pressure deficit. (2) In densely built-up areas with limited vegetation, there is a significant lack of thermal regulation, especially in the early morning (7:00) and late evening (17:00), thus exacerbating thermal stress. (3) At midday (11:00–13:00) there was a slight decrease in evaporative cooling, probably caused by the behaviour of the stomatal closure at high temperatures. Concurrently, high radiation causes the shading effect of vegetation to become more prominent, amplifying the cooling contrast between areas with dense and sparse vegetation cover. Moreover, the study also highlights that grassland with >50% cover can provide cooling effects similar to that of forest land. Overall, our study not only enhances the understanding of urban vegetation’s cooling effects but also underscores the importance of strategic urban vegetation planning in mitigating urban heat, particularly under the escalating frequency and intensity of heat waves.

DOI: https://doi.org/10.1016/j.rse.2024.114098