This series is home to publications and data sets from the Center for Environmental Research and Technology at the University of California, Riverside.
Wildfire smoke is an increasingly significant contributor to air pollution in the western United States, posing serious health risks and complicating efforts to assess personal exposure, particularly indoors. The January 2025 Palisades and Eaton Fires in Los Angeles County caused elevated levels of PM2.5 in the downwind cities. This study leverages a high-resolution network of crowdsourced PurpleAir sensors to evaluate indoor and outdoor PM2.5 levels before, during, and after the wildfire smoke events. We matched indoor–outdoor sensors and analyzed disparities in smoke exposure across communities with different CalEnviroScreen (CES) vulnerability scores, ventilation types, and home values. Results indicate that outdoor PM2.5 increased substantially during smoke days, with the highest CES-burdened communities experiencing the greatest ambient concentrations. Indoor PM2.5 also increased across all neighborhoods but indoor/outdoor (I/O) ratios declined during the smoke period, indicating partial indoor protection and likely occupant behavior changes. Infiltrated PM2.5 increased during the smoke period and varied across the CES groups. Building attributes showed limited predictive power. These findings highlight the interplay between behavioral actions and neighborhood factors in shaping wildfire smoke exposure and underscore the need for targeted interventions to improve indoor air quality in vulnerable communities.
Industrial chemicals and untreated sewage have polluted the Tijuana River for decades, recently causing >1300 consecutive days of California beach closures. In summer 2024, wastewater flows surged to millions of gallons per day despite no rain, enhancing water-to-air transfer of hydrogen sulfide (H2S) and other toxic gases at a turbulent hotspot. High wastewater flows and low winds led to nighttime H2S peaks, reaching 4500 parts per billion (ppb)-exceeding typical urban levels of <1 ppb. H2S levels and community malodor reports were strongly correlated (correlation coefficient r = 0.92), validating long-dismissed community voices and highlighting an environmental injustice. This study demonstrates that poor water quality can substantially affect air quality-although rarely included in air quality models and health assessments-with far-reaching implications as polluted waterways increase globally.
Fires in the wildland-urban interface (WUI) are a global issue with growing importance. However, the impact of WUI fires on air quality and health is less understood compared to that of fires in wildland. We analyze WUI fire impacts on air quality and health at the global scale using a multi-scale atmospheric chemistry model-the Multi-Scale Infrastructure for Chemistry and Aerosols model (MUSICA). WUI fires have notable impacts on key air pollutants [e.g., carbon monoxide (CO), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3)]. The health impact of WUI fire emission is disproportionately large compared to wildland fires primarily because WUI fires are closer to human settlement. Globally, the fraction of WUI fire-caused annual premature deaths (APDs) to all fire-caused APDs is about three times of the fraction of WUI fire emissions to all fire emissions. The developed model framework can be applied to address critical needs in understanding and mitigating WUI fires and their impacts.
The Community Multiscale Air Quality (CMAQ) model simulates atmospheric phenomena, including advection, diffusion, gas-phase chemistry, aerosol physics and chemistry, and cloud processes. Gas-phase chemistry is often a major computational bottleneck due to its representation as large systems of coupled nonlinear stiff differential equations. We leverage the parallel computational performance of graphics processing unit (GPU) hardware to accelerate the numerical integration of these systems in CMAQ's CHEM module. Our implementation, dubbed CMAQ-CUDA, in reference to its use in the Compute Unified Device Architecture (CUDA) general purpose GPU (GPGPU) computing solution, migrates CMAQ's Rosenbrock solver from Fortran to CUDA Fortran. CMAQ-CUDA accelerates the Rosenbrock solver such that simulations using the chemical mechanisms RACM2, CB6R5, and SAPRC07 require only 51%, 50%, or 35% as much time, respectively, as CMAQv5.4 to complete a chemistry time step. Our results demonstrate that CMAQ is amenable to GPU acceleration and highlight a novel Rosenbrock solver implementation for reducing the computational burden imposed by the CHEM module.
Sulfur-containing volatile organic compounds emitted during wildfire events, such as dimethyl sulfide, are known to form secondary aerosols containing inorganic sulfate (SO4 2-) and surfactant-like organic compounds; however, little is known about the fate of sulfur in other emitted reduced organosulfur species. This study aimed to determine the sulfurous product distribution resulting from the nighttime oxidation of thiophene as a model system. Ion chromatography (IC) and aerosol mass spectrometry (a mini aerosol mass spectrometer, mAMS) were used to constrain the proportions of sulfurous compounds produced under wildfire-relevant conditions ([NO2]/[O3] = 0.1). With constraints from IC, results indicated that the sulfurous particle mass consisted of 30.3 ± 6.6% SO4 2-, while mAMS fractionation attributed 24.5 ± 1.6% of total sulfate signal to SO4 2-, 15.4 ± 1.9% to organosulfates, and 60.1 ± 0.9% to sulfonates. Empirical formulas of organosulfur products were identified as C1-C8 organosulfates and sulfonates using complementary mass spectrometry techniques. This study highlights the nighttime oxidation of thiophene and its derivatives as a source of SO4 2- and particulate organosulfur compounds, which have important implications for the atmospheric sulfur budget and aerosol/droplet physical and chemical properties.
The optimal design of a proportional-integral-derivative controller with two cascaded first-order low-pass filters (PID-FF) for non-ideal buck converters faces significant challenges, including effective disturbance rejection, robustness to parameter variations, and the mitigation of high-frequency signal noise, with existing approaches often struggling and leading to suboptimal performance in practical applications. This study addresses these challenges by introducing a constraint on the open-loop crossover frequency to mitigate high-frequency noise and ensuring the controller prioritizes maintaining constant output voltage and robust responsiveness to input voltage and load current variations. This study also introduces an innovative metaheuristic algorithm, the opposition-based snake optimizer with pattern search (OSOPS), designed to address these limitations. OSOPS enhances the Snake Optimizer (SO) by integrating opposition-based learning (OBL) and Pattern Search (PS), thereby improving its exploration and exploitation capabilities. The proposed algorithm design includes a crossover frequency constraint aimed at counteracting high-frequency noise and ensuring robust performance under diverse disturbances. The efficacy of the OSOPS algorithm is demonstrated through rigorous statistical box plot analysis and convergence response comparisons with the original SO algorithm. Additionally, we systematically compare the performance of the OSOPS-based PID-FF-controlled non-ideal buck converter system against systems utilizing the original SO algorithm and the classical pole placement (PP) method. This evaluation encompasses transient and frequency responses, disturbance rejection, and robustness analysis. The results reveal that the OSOPS-based system outperforms the SO- and PP-based systems with 14.21 % and 32.10 % faster rise times, along with 15.38 % and 84.95 % faster settling times, respectively. The OSOPS and SO systems also exhibit higher bandwidths, exceeding the PP-based system by 18.74 % and 17.03 %, respectively. By addressing the key challenges in PID-FF controller design for non-ideal buck converters, this study provides a substantial advancement in control strategy, promising enhanced performance in practical applications.
Wildfires are a significant threat to human health, in part through degraded air quality. Prescribed burning can reduce wildfire severity but can also lead to an increase in air pollution. The complexities of fires and atmospheric processes lead to uncertainties when predicting the air quality impacts of fire and make it difficult to fully assess the costs and benefits of an expansion of prescribed fire. By modeling differences in emissions, surface conditions, and meteorology between wildfire and prescribed burns, we present a novel comparison of the air quality impacts of these fire types under specific scenarios. One wildfire and two prescribed burn scenarios were considered, with one prescribed burn scenario optimized for potential smoke exposure. We found that PM2.5 emissions were reduced by 52%, from 0.27 to 0.14 Tg, when fires burned under prescribed burn conditions, considerably reducing PM2.5 concentrations. Excess short-term mortality from PM2.5 exposure was 40 deaths for fires under wildfire conditions and 39 and 15 deaths for fires under the default and optimized prescribed burn scenarios, respectively. Our findings suggest prescribed burns, particularly when planned during conditions that minimize smoke exposure, could be a net benefit for the impacts of wildfires on air quality and health.
Warming climate in the Arctic is leading to an increase in isoprene emission from ecosystems. We assessed the influence of temperature on isoprene emission from Arctic willows with laboratory and field measurements. Our findings indicate that the hourly temperature response curve of Salix spp., the dominant isoprene emitting shrub in the Arctic, aligns with that of temperate plants. In contrast, the isoprene capacity of willows exhibited a more substantial than expected response to the mean ambient temperature of the previous day, which is much stronger than the daily temperature response predicted by the current version of the Model of Emissions of Gases and Aerosols from Nature (MEGAN). With a modified algorithm from this study, MEGAN predicts 66% higher isoprene emissions for Arctic willows during an Arctic heatwave. However, despite these findings, we are still unable to fully explain the high temperature sensitivity of isoprene emissions from high latitude ecosystems.
It has been widely reported that isoprene emissions from the Arctic ecosystem have a strong temperature response. Here we identify sedges (Carex spp. and Eriophorum spp.) as key contributors to this high sensitivity using plant chamber experiments. We observe that sedges exhibit a markedly stronger temperature response compared to that of other isoprene emitters and predictions by the widely accepted isoprene emission model, the Model of Emissions of Gases and Aerosols from Nature (MEGAN). MEGAN is able to reproduce eddy-covariance flux observations at three high-latitude sites by integrating our findings. Furthermore, the omission of the strong temperature responses of Arctic isoprene emitters causes a 20% underestimation of isoprene emissions for the high-latitude regions of the Northern Hemisphere during 2000-2009 in the Community Land Model with the MEGAN scheme. We also find that the existing model had underestimated the long-term trend of isoprene emissions from 1960 to 2009 by 55% for the high-latitude regions.
