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Open Access Publications from the University of California

Scholarly Works (14 results)

  • Article
  • Peer Reviewed
LBL Publications (2019)
Estimating the spatial distribution of precipitation is important for understanding ecohydrological processes at catchment scales. However, this estimation is difficult in mountainous areas because ground-based observation stations are often sparsely located and do not represent the spatial variability of precipitation. In this study, we develop a Bayesian assimilation method based on data collected on the Tibetan Plateau from 1980 to 2014 to estimate monthly and daily precipitation. To accomplish this, point-scale ground meteorological observations are combined with large-scale precipitation data such as satellite observations or reanalysis data. First, we remove the terrain effects from ground observations by fitting the precipitation data as functions of elevation, and then we spatially interpolate the residuals to 5-km-resolution grids to obtain monthly and daily precipitation. Additionally, we use Tropical Rainfall Measuring Mission (TRMM) satellite observations and ERA-Interim reanalysis data. Cross-validation methods are used to evaluate our method; the results show that our method not only captures the change in precipitation with terrain but also significantly reduces the associated uncertainty. The improvements are more evident in the main river source areas on the edge of the Tibetan Plateau, where elevation changes dramatically, and in high-altitude areas, where the ground gauges are sparse compared with those in low-altitude areas. Our assimilation method is applicable to other regions and is particularly useful for mountainous watersheds where ground meteorological stations are sparse and precipitation is considerably influenced by terrain.
    • Thesis
    • Peer Reviewed
    UC Berkeley Electronic Theses and Dissertations (2024)

    Photochemical air pollution is a common problem at urban and regional scales, with ground-level ozone (O3) being a key constituent. Exposure to ozone can lead to adverse effects on human health, crop yields and ecosystems. As a secondary pollutant, ozone is formed by photochemical reactions involving its precursors: nitrogen oxides (NOx) and volatile organic compounds (VOC). Driven by the complex nonlinear chemistry, ozone responses to precursor reductions can vary in magnitude and sign, depending on where and when emissions of which precursor are reduced.

    To develop effective air quality management plans, atmospheric chemistry and transport models are commonly used to simulate the physical and chemical processes involved and to demonstrate the adequacy of proposed emission control plans in a brute-force manner. Integrated online sensitivity analysis techniques can better resolve relationships between air quality responses and changes in precursor emission sources, but these techniques are less commonly used. Forward (source-oriented) sensitivity analysis techniques are typically used to compute sensitivities of many air quality responses to a specified model input perturbation. A complementary technique involving the use of the adjoint method is particularly effective and well-suited for delineating impacts on air quality of changes in many input parameters (e.g., spatially and temporally resolved emission rates). As a backward (receptor-oriented) technique, the adjoint sensitivity analysis can efficiently compute sensitivities of specific air quality response of interest to a large number of (actually all) input parameters in a single model run.

    In this dissertation, I use the adjoint of Community Multiscale Air Quality model (CMAQ adjoint) to study the control of photochemical air pollution in California’s San Joaquin Valley (SJV). Air quality metrics of interest are defined to represent urban- and regional-scale odd oxygen (Ox ≡ O3 + NO2) responses. By employing the adjoint modeling tool at 4 km spatial and hourly temporal resolution, I examine how air quality responds to spatially and temporally resolved changes in precursor emissions. The spatiotemporal variations in chemical regimes that limit ozone formation are thereby illustrated. Synergies and tradeoffs in achieving different urban- and regional air quality objectives are discussed. I also investigate dependences of first-order source-receptor relationships on prevailing emissions and meteorological conditions, which are effectively second-order sensitivity analysis questions. Emission reductions on decadal time scales are considered by including anthropogenic emission scenarios corresponding to years 2000, 2012, and 2022. The benefits of location-specific NOx control are evaluated in detail, and demonstrate increasing efficacy over time. Three summer season meteorological regimes – baseline, low-wind, and high-T – are identified from a cluster analysis, with all of these regimes conducive to high ozone levels in the SJV. By comparing sensitivity results, I separately evaluate the effects of stagnation and increased temperature on ozone formation. High-priority source locations to target for emission reductions that are consistent across meteorological regimes are also pinpointed.

    Contributions to odd oxygen responses in the SJV originate from precursor sources located within the SJV itself and also from upwind air basins. Under 2012 conditions, upwind contributions from the San Francisco Bay (23–35%) and Sacramento Valley (20–28%) combined are of comparable magnitude to within-valley contributions (36–48%), for three urban receptors located in the northern, middle, and southern portions of the SJV. Upwind impacts are mainly attributable to emissions released on prior days. Sensitivities to same-day emissions are mostly associated with local SJV sources. Depending on source location, time, and receptor of interest, the preferred precursor to control can be either NOx or VOC. At local source locations, the most influential emissions occur during early afternoon (1–3 pm) and morning (9–11 am) hours for NOx and VOC, respectively. A consistent diurnal evolution toward more NOx-sensitive (or less VOC-sensitive) conditions is found across receptors. Opportunities to control pollution through shifts in precursor source location and timing are discussed.

    Large reductions in ozone precursor emissions have been achieved over the past two decades, and a transition from VOC- to NOx-sensitive ozone chemistry occurred in the SJV between 2000 and 2022. As a result, the efficacy of NOx-focused emission control programs increases, and areas with negative sensitivities to NOx vanish. The preferred source locations to target NOx control have shifted over decadal timescales. Under present-day (2022) conditions, the identified high-priority locations are associated with 28% of the total SJV NOx emissions, but account for 60% of the impacts on its population-weighted odd oxygen response. These high-priority source locations are found to differ for individual city-level versus regionwide receptors of interest.

    Meteorological impacts on air pollution were found to be as influential as the effects of decadal-scale emission changes. Under stagnant (low-wind) weather conditions, local emission contributions to SJV Ox increase to 62-71%, compared to a 32-48% contribution under baseline meteorological conditions. Influential emission source locations become more widespread spatially under stagnant conditions, due to enhanced within-valley recirculation. The chemistry shifts towards a more VOC-sensitive (or less NOx-sensitive) regime. Impacts of increased temperatures (high-T scenario), on the other hand, lead to overall increases in Ox sensitivities to both precursors. Exceptions occur near major cities and busy traffic corridors, where high-T conditions drive sensitivities to NOx and VOC to change in opposite directions.

    This dissertation features a comprehensive analysis of source-receptor relationships for urban- and regional-scale air pollution. The combination of both spatial and temporal dimensions in the analysis reveals the coupled spatiotemporal complexity in source-receptor relationships in great detail. The consideration of decadal emission changes and multiple meteorological regimes allows me to capture higher-order dynamics and interactions, going beyond static representations of first-order source-receptor relationships. From a regulatory perspective, my research identifies new opportunities for optimizing the design of air pollution control strategies through spatial and temporal targeting of mandated emission reductions. The approach described in this research can also be applied to other vexing air pollution problems such as airborne fine particulate matter (PM2.5), or for multi-pollutant control.

      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2022)
      As solar energy contributes an increasing share of total electricity generation, solar forecasting errors become important relative to overall load uncertainty and can add costs to electricity systems. We investigated the costs of day-ahead solar forecast errors across 667 existing solar power plants in the United States (years 2012 through 2019). Our analysis was based on hourly real-time and day-ahead nodal prices. We analyzed two types of solar forecasts: persistence forecasts, a simple approach to forecasting, and a numerical weather prediction forecast, the North American Mesoscale Model (NAM), an improvement over persistence forecasts based on public data and modelling software. We modeled hourly energy forecasts using meteorological forecasts and plant specific characteristics. Hourly plant generation was modeled and debiased with multiple sources of generation records. NAM forecast errors had relatively low costs on average, at no more than $1/MWh in all years except 2016, when costs rose to $1.5/MWh. Even after these error costs, the value of solar was marginally higher when simulating solar participation in day-ahead markets versus participation only in real-time markets. On average, the premium for participating in the day-ahead market, based on NAM forecasts, ranged from −0.5 to 5.2 $/MWh across years. Average error costs were higher in regions with higher solar penetration (i.e., California and New England) compared to regions with low solar penetration. However, California and New England had similar error costs despite higher solar penetration in California, indicating that error costs to date have been only loosely correlated with solar penetration levels.
        Cover page: The cost of day-ahead solar forecasting errors in the United States
        • Article
        • Peer Reviewed
        LBL Publications (2017)
        The Yellow River source region is located in the transition region between permafrost and seasonally frozen ground on the northeastern Qinghai-Tibet Plateau. The region has experienced severe climate change, especially air temperature increases, in past decades. In this study, we employed a geomorphology-based eco-hydrological model (GBEHM) to assess the impacts of climate change on the frozen ground and eco-hydrological processes in the region. Based on a long-term simulation from 1981 to 2015, we found that the areal mean maximum thickness of seasonally frozen ground ranged from 1.1-1.8m and decreased by 1.2cm per year. Additionally, the ratio of the permafrost area to the total area decreased by 1.1% per year. These decreasing trends are faster than the average in China because the study area is on the sensitive margin of the Qinghai-Tibet Plateau. The annual runoff exhibited variations similar to those of the annual precipitation (R2=0.85), although the annual evapotranspiration (ET) exhibited an increasing trend (14.3mm/10a) similar to that of the annual mean air temperature (0.66°C/10a). The runoff coefficient (annual runoff divided by annual precipitation) displayed a decreasing trend because of the increasing ET, and the vegetation responses to climate warming and permafrost degradation were manifested as increases in the leaf area index (LAI) and ET at the start of the growing season. Furthermore, the results showed that changes to the frozen ground depth affected vegetation growth. Notably, a rapid decrease in the frozen ground depth (< -3.0cm/a) decreased the topsoil moisture and then decreased the LAI. This study showed that the eco-hydrological processes in the headwater area of the Yellow River have changed because of permafrost degradation, and these changes could further influence the water resources availability in the middle and lower reaches of the basin.
          Cover page: Impacts of climate warming on the frozen ground and eco-hydrology in the Yellow River source region, China
          • Article
          • Peer Reviewed
          UC Riverside Previously Published Works (2022)
          Superoxide and other reactive oxygen species (ROS) shape microbial communities and drive the transformation of metals and inorganic/organic matter. Taxonomically diverse bacteria and phytoplankton produce extracellular superoxide during laboratory cultivation. Understanding the physiological reasons for extracellular superoxide production by aerobes in the environment is a crucial question yet not fully solved. Here, we showed that iron-starving Arthrobacter sp. QXT-31 (A. QXT-31) secreted a type of siderophore [deferoxamine (DFO)], which provoked extracellular superoxide production by A. QXT-31 during carbon sources-level fluctuation. Several other siderophores also demonstrated similar effects to A. QXT-31. RNA-Seq data hinted that DFO stripped iron from iron-bearing proteins in electron transfer chain (ETC) of metabolically active A. QXT-31, resulting in electron leakage from the electron-rich (resulting from carbon sources metabolism by A. QXT-31) ETC and superoxide production. Considering that most aerobes secrete siderophore(s) and undergo carbon sources-level fluctuation, the superoxide-generation pathway is likely a common pathway by which aerobes produce extracellular superoxide in the environment, thus influencing the microbial community and cycling of elements. Our results pointed that the ubiquitous siderophore might be the potential driving force for the microbial generation of superoxide and other ROS and revealed the important role of iron physiology in microbial ROS generation.
            Cover page: Siderophores provoke extracellular superoxide production by Arthrobacter strains during carbon sources‐level fluctuation
            • Article
            LBL Publications (2021)
            With continued deployment of solar across the United States, assessing the interactions of solar with the power system is an increasingly important complement to studies tracking the cost and performance of solar plants. This project focuses on the historical contribution to reliability, trends in market value, and impacts on the bulk power system of solar deployed in the U.S. through the end of 2019. The scope of this analysis includes the seven organized U.S. wholesale power markets and is based on historical hourly solar generation profiles for each individual plant larger than 1 MW or county-level aggregate profiles for smaller solar. In addition, we present a limited set of results for ten utilities that are outside of the independent system operator (ISO)/regional transmission organization (RTO) markets. Highlights include: -Solar Generation: Solar deployment in the California Independent System Operator (CAISO), where solar generation was equivalent to 18.7% of annual load in 2019, far exceeds the level in other ISOs. The New England Independent System Operator (ISO-NE) has the second-highest penetration, with solar generation equivalent to 4.3% of annual load in 2019. All other ISOs have annual solar generation shares at or below 2%. -Reliability Contribution: Solar’s contribution to the overall resource adequacy of the power system is measured by its “capacity credit”. While calculation methods vary across ISO/RTOs, summer capacity credits for solar in 2019 range 45-76% of a system’s nameplate capacity. Capacity credits have remained largely stable over the past years except in CAISO where solar’s capacity credit steeply declined in 2018. -Market Value: The market value of solar, defined here as the sum of the energy and capacity values, primarily varies across regions and years because of variations in average energy prices and capacity market prices. The energy value, based on the hourly solar generation and real-time power prices at pricing nodes near each solar plant, is the largest component of the market value across ISOs. In 2019, the average energy value spanned from $24/MWh in CAISO to $60/MWh in ERCOT. The capacity value of solar is based on the capacity credit of solar and the capacity price. The capacity value in 2019 was highest in the Southwest Power Pool (SPP; $25/MWh), though capacity prices there are based on estimates of bilateral capacity transactions rather than transparent organized capacity market prices, and lowest in the Midcontinent Independent System Operator (MISO; below $1/MWh). -Market Value Decline: The market value of solar in CAISO declined between 2012 and 2019, both overall and relative to annual average energy and capacity prices. In 2012, solar’s market value in CAISO was 40% higher than the value of a flat block of power (representing the market value of a generator that operates at full nameplate capacity in all hours of the year). By 2019, however, solar’s value was 31% lower than a flat block of power’s value because of a solar-induced shift in the timing of high and low energy prices and a reduction in solar’s capacity credit. In contrast, the market values of solar in regions where solar penetrations were low did not decline relative to average prices. -Overall Competitiveness: As solar’s market value declined in CAISO, its cost—as measured by levelized power purchase agreement (PPA) prices—declined at a similar pace, thus maintaining solar’s overall competitiveness. Solar was more competitive in PJM and the non-ISO utilities where the market value in 2019 exceeded the levelized PPA price of contracts signed in 2019. Solar was more competitive in ERCOT in 2018, and the energy value of solar was significantly higher in 2019 than 2018, but data to report an average PPA price were insufficient in 2019. -System Impact: In CAISO, the net load has shifted to resemble the “duck curve,” with particularly low net load during spring days and high ramps as the sun sets in the evening. Similar patterns emerge in real-time prices, with lower prices during the day and higher prices in the early evening. Ancillary service requirements, particularly regulation reserves, have increased during the day, as have regulation prices. Negative real-time prices during low net load days in the spring suggest growing challenges with providing flexibility. However, broader shifts in the system—including growing participation of western utilities in the Western Energy Imbalance Market and variations in hydropower levels—appear to have mitigated some challenges in 2019 relative to 2017, even with greater solar deployment in 2019. Impacts to the net load shape from solar are similarly evident in a number of non-ISO utilities in the Western U.S., for example Arizona and Nevada. With much less solar deployment in the other ISOs, solar impacts on the bulk power system are much less obvious.
              Cover page: Solar-to-Grid: Trends in System Impacts, Reliability, and Market Value in the United States with Data Through 2019
              • Article
              • Peer Reviewed
              LBL Publications (2023)
              Macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. Understanding network-wide traffic through MFDs can optimally allocate demand to existing networks, improving performance by maximizing network production and avoiding congestion. However, due to historical data limitations, empirically derived MFD models are sparse in the literature, especially for the U.S. cities. Leveraging a large-scale and granular census-tract-level flow and density derived from vehicle probe data, this research is the first to develop a machine learning approach to both derive MFD models and interpret their underlying difference among urban networks across the entire United States. Among the four machine learning methods tested here XGBoost is found to deliver the best performance to predict the network traffic flow for given vehicular density and location attributes. Interaction Shapley Additive explanation (SHAP) values are used to interpret the factors, such as land use, transportation infrastructure, and network topology, that influence the flow-density relationships among locations. The analysis framework developed in this work can generate datadriven MFDs and a deeper understanding of their shape dependence on network, infrastructure, and land use characteristics, which can be used by transportation authorities to derive and optimize location-specific MFDs facilitating more informed management and planning decisions at the network level.
                Cover page: Leveraging Probe Data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities
                • Article
                • Peer Reviewed
                LBL Publications (2025)
                The Simons Observatory (SO) is a cosmic microwave background (CMB) experiment located in the Atacama Desert in Chile that will make precise temperature and polarization measurements over six spectral bands ranging from 27 to 285 GHz. Three small aperture telescopes (SATs) and one large aperture telescope (LAT) will house ∼ 60,000 detectors and cover angular scales between one arcminute and tens of degrees. We present the performance of the dichroic, low-frequency (LF) lenslet-coupled sinuous antenna transition-edge sensor (TES) bolometer arrays with bands centered at 27 and 39 GHz. The LF focal plane will primarily characterize Galactic synchrotron emission as a critical part of foreground subtraction from CMB data. We will discuss the design, optimization, and current testing status of these pixels.
                  Cover page: The Simons Observatory: Design, Optimization, and Performance of Low-Frequency Detectors
                  • Article
                  • Peer Reviewed
                  LBL Publications (2024)
                  This paper documents the design and application of ATLAS (Automobile and Technology Lifecycle-Based ASsignment), a comprehensive household vehicle transaction and technology adoption micro-simulator in the San Francisco Bay Area. ATLAS evolves the fleet mix of individual households by simulating the vehicle transaction and choice decisions in response to co-evolving demographics, land use, and vehicle technology simulations. While most existing literature has focused on the aggregate clean vehicle uptake, this paper differentiates distributional effects and decomposes the underlying mechanisms across heterogeneous sub-populations of households. Using scenarios and sensitivity simulations that vary vehicle technology and policy assumptions, we find that Zero Emission Vehicles (ZEVs) penetrate into higher income groups at a faster rate than into lower income groups, which is intuitive and aligns with expectations. Interestingly, the relative income disparity in ZEV ownership shrinks over time across all scenarios, with a ZEV mandate coupled with declining battery cost leading to the greatest reduction in disparity of ZEV ownership by 2050. Federal, state, and local financial incentives influence the redistribution of ZEV uptake across income groups and contribute to narrowing income disparity. Vehicle transaction frequency and new versus used market dynamics are found to be important factors contributing to the income disparity.
                    Cover page: Technology progress and clean vehicle policies on fleet turnover and equity: insights from household vehicle fleet micro-simulations with ATLAS
                    • Article
                    • Peer Reviewed
                    LBL Publications (2024)
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