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Open Access Publications from the University of California

This series is home to publications and data sets from the Center for Environmental Research and Technology at the University of California, Riverside.

Cover page of Overview of ICARUSA Curated, Open Access, Online Repository for Atmospheric Simulation Chamber Data

Overview of ICARUSA Curated, Open Access, Online Repository for Atmospheric Simulation Chamber Data

(2023)

Atmospheric simulation chambers continue to be indispensable tools for research in the atmospheric sciences. Insights from chamber studies are integrated into atmospheric chemical transport models, which are used for science-informed policy decisions. However, a centralized data management and access infrastructure for their scientific products had not been available in the United States and many parts of the world. ICARUS (Integrated Chamber Atmospheric data Repository for Unified Science) is an open access, searchable, web-based infrastructure for storing, sharing, discovering, and utilizing atmospheric chamber data [https://icarus.ucdavis.edu]. ICARUS has two parts: a data intake portal and a search and discovery portal. Data in ICARUS are curated, uniform, interactive, indexed on popular search engines, mirrored by other repositories, version-tracked, vocabulary-controlled, and citable. ICARUS hosts both legacy data and new data in compliance with open access data mandates. Targeted data discovery is available based on key experimental parameters, including organic reactants and mixtures that are managed using the PubChem chemical database, oxidant information, nitrogen oxide (NOx) content, alkylperoxy radical (RO2) fate, seed particle information, environmental conditions, and reaction categories. A discipline-specific repository such as ICARUS with high amounts of metadata works to support the evaluation and revision of atmospheric model mechanisms, intercomparison of data and models, and the development of new model frameworks that can have more predictive power in the current and future atmosphere. The open accessibility and interactive nature of ICARUS data may also be useful for teaching, data mining, and training machine learning models.

Cover page of Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal

Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal

(2023)

Low-cost air quality (LCAQ) sensors are increasingly being used for community air quality monitoring. However, data collected by low-cost sensors contain significant noise, and proper calibration of these sensors remains a widely discussed, but not yet fully addressed, area of concern. In this study, several LCAQ sensors measuring nitrogen dioxide (NO2) and ozone (O3) were deployed in six cities in the United States (Atlanta, GA; New York City, NY; Sacramento, CA; Riverside, CA; Portland, OR; Phoenix, AZ) to evaluate the impacts of different climatic and geographical conditions on their performance and calibration. Three calibration methods were applied, including regression via linear and polynomial models and random forest methods. When signals from carbon monoxide (CO) sensors were included in the calibration models for NO2 and O3 sensors, model performance generally increased, with pronounced improvements in selected cities such as Riverside and New York City. Such improvements may be due to (1) temporal co-variation between concentrations of CO and NO2 and/or between CO and O3; (2) different performance levels of low-cost CO, NO2, and O3 sensors; and (3) different impacts of environmental conditions on sensor performance. The results showed an innovative approach for improving the calibration of NO2 and O3 sensors by including CO sensor signals into the calibration models. Community users of LCAQ sensors may be able to apply these findings further to enhance the data quality of their deployed NO2 and O3 monitors.

Cover page of RAIM and Failure Mode Slope: Effects of Increased Number of Measurements and Number of Faults

RAIM and Failure Mode Slope: Effects of Increased Number of Measurements and Number of Faults

(2023)

This article provides a comprehensive analysis of the impact of the increasing number of measurements and the possible increase in the number of faults in multi-constellation Global Navigation Satellite System (GNSS) Receiver Autonomous Integrity Monitoring (RAIM). Residual-based fault detection and integrity monitoring techniques are ubiquitous in linear over-determined sensing systems. An important application is RAIM, as used in multi-constellation GNSS-based positioning. This is a field in which the number of measurements, m, available per epoch is rapidly increasing due to new satellite systems and modernization. Spoofing, multipath, and non-line of sight signals could potentially affect a large number of these signals. This article fully characterizes the impact of measurement faults on the estimation (i.e., position) error, the residual, and their ratio (i.e., the failure mode slope) by analyzing the range space of the measurement matrix and its orthogonal complement. For any fault scenario affecting h measurements, the eigenvalue problem that defines the worst-case fault is expressed and analyzed in terms of these orthogonal subspaces, which enables further analysis. For h>(m-n), where n is the number of estimated variables, it is known that there always exist faults that are undetectable from the residual vector, yielding an infinite value for the failure mode slope. This article uses the range space and its complement to explain: (1) why, for fixed h and n, the failure mode slope decreases with m; (2) why, for a fixed n and m, the failure mode slope increases toward infinity as h increases; (3) why a failure mode slope can become infinite for h≤(m-n). A set of examples demonstrate the results of the paper.

Cover page of PowerMorph: QoS-Aware Server Power Reshaping for Data Center Regulation Service

PowerMorph: QoS-Aware Server Power Reshaping for Data Center Regulation Service

(2022)

Adoption of renewable energy in power grids introduces stability challenges in regulating the operation frequency of the electricity grid. Thus, electrical grid operators call for provisioning of frequency regulation services from end-user customers, such as data centers, to help balance the power grid’s stability by dynamically adjusting their energy consumption based on the power grid’s need. As renewable energy adoption grows, the average reward price of frequency regulation services has become much higher than that of the electricity cost. Therefore, there is a great cost incentive for data centers to provide frequency regulation service. Many existing techniques modulating data center power result in significant performance slowdown or provide a low amount of frequency regulation provision. We present PowerMorph , a tight QoS-aware data center power-reshaping framework, which enables commodity servers to provide practical frequency regulation service. The key behind PowerMorph  is using “complementary workload” as an additional knob to modulate server power, which provides high provision capacity while satisfying tight QoS constraints of latency-critical workloads. We achieve up to 58% improvement to TCO under common conditions, and in certain cases can even completely eliminate the data center electricity bill and provide a net profit.

Cover page of Observations and parameterization of the effects of barrier height and source-to-barrier distance on concentrations downwind of a roadway

Observations and parameterization of the effects of barrier height and source-to-barrier distance on concentrations downwind of a roadway

(2022)

New results are presented from wind tunnel studies performed at the United States Environmental Protection Agency (U.S. EPA), which include cases with solid roadside barriers of varying heights and cases with varying distances between the line source (roadway) and a 6-m-tall barrier. The Source-to-Barrier Distance cases include seven lanes of traffic with each lane acting as an independent source of continuous emissions along a line (i.e., line source). A mixed-wake algorithm that accounts for barrier effects within a steady-state air dispersion model was updated based on the recent wind tunnel studies. To study the effects of a solid roadside barrier, varying barrier heights and varying distances between the line source and barrier were modeled with the U.S. EPA regulatory air dispersion model AERMOD (v. 21112) using the line-source option that includes an experimental barrier option (RLINEXT). The mixed-wake algorithm reproduced the shape of the vertical concentration profiles observed in the wind tunnel data, including the uniform concentration profile from the ground vertically to a height somewhat greater than the height of the barrier. The algorithm responded appropriately to changes in barrier height and source-to-barrier distance, producing greater reductions in ground-level concentrations for taller barriers and for shorter source-to-barrier distances. Additionally, a rule of thumb that approximates the effect of a downwind barrier was formulated by converting an estimated vertical dispersion into an additional travel distance. The wind tunnel results, the update to the mixed-wake algorithm, and a comparison of the two data sets are described in this paper.

Cover page of Isotopic Signatures of Methane Emissions From Dairy Farms in California’s San Joaquin Valley

Isotopic Signatures of Methane Emissions From Dairy Farms in California’s San Joaquin Valley

(2022)

In this study, we present seasonal atmospheric measurements of δ13CCH4 from dairy farms in the San Joaquin Valley of California. We used δ13CCH4 to characterize emissions from enteric fermentation by measuring downwind of cattle housing (e.g., freestall barns, corrals) and from manure management areas (e.g., anaerobic manure lagoons) with a mobile platform equipped with cavity ring-down spectrometers. Across seasons, the δ13CCH4 from enteric fermentation source areas ranged from −69.7 ± 0.6 per mil (‰) to −51.6 ± 0.1‰ while the δ13CCH4 from manure lagoons ranged from −49.5 ± 0.1‰ to −40.5 ± 0.2‰. Measurements of δ13CCH4 of enteric CH4 suggest a greater than 10‰ difference between cattle production groups in accordance with diet. Isotopic signatures of CH4 were used to characterize enteric and manure CH4 from downwind plume sampling of dairies. Our findings show that δ13CCH4 measurements could improve the attribution of CH4 emissions from dairy sources at scales ranging from individual facilities to regions and help constrain the relative contributions from these different sources of emissions to the CH4 budget.