Understanding how mercury (Hg) accumulates in the aquatic food web requires information on the factors driving methylmercury (MeHg) contamination. This paper employs data on MeHg in muscle tissue of three black bass species (Largemouth Bass, Spotted Bass, and Smallmouth Bass) sampled from 21 reservoirs in California. During a two-year period, reservoirs were sampled for total Hg in sediment, total Hg and MeHg in water, chlorophyll a, organic carbon, sulfate, dissolved oxygen, pH, conductivity, and temperature. These data, combined with land-use statistics and reservoir morphometry, were used to investigate relationships to size-normalized black bass MeHg concentrations. Significant correlations to black bass MeHg were observed for total Hg in sediment, total Hg and MeHg in surface water, and forested area. A multivariate statistical model predicted Largemouth Bass MeHg as a function of total Hg in sediment, MeHg in surface water, specific conductivity, total Hg in soils, and forested area. Comparison to historical reservoir sediment data suggested there has been no significant decline in sediment total Hg at five northern California reservoirs during the past 20 years. Overall, total Hg in sediment was indicated as the most influential factor associated with black bass MeHg contamination. The results of this study improve understanding of how MeHg varies in California reservoirs and the factors that correlate with fish MeHg contamination.

Autism spectrum disorder (ASD) affects more boys than girls. Recent animal studies found that early life exposure to ambient particles caused autism-like behaviors only in males. However, there has been little study of sex-specificity of effects on ASD in humans. We evaluated ASD risk associated with prenatal and first year of life exposures to particulate matter less than 2.5 μm in aerodynamic diameter (PM₂.₅) by child sex. This retrospective cohort study included 246,420 singleton children born in Kaiser Permanente Southern California (KPSC) hospitals between 1999 and 2009. The cohort was followed from birth through age five to identify 2471 ASD cases from the electronic medical record. Ambient PM₂.₅ and other regional air pollution measurements (PM less than 10 μm, ozone, nitrogen dioxide) from regulatory air monitoring stations were interpolated to estimate exposure during each trimester and first year of life at each geocoded birth address. Hazard ratios (HRs) were estimated using Cox regression models to adjust for birth year, KPSC medical center service areas, and relevant maternal and child characteristics. Adjusted HRs per 6.5 μg/m³ PM₂.₅ were elevated during entire pregnancy [1.17 (95% confidence interval (CI), 1.04–1.33)]; first trimester [1.10 (95% CI, 1.02–1.19)]; third trimester [1.08 (1.00–1.18)]; and first year of life [1.21 (95% CI, 1.05–1.40)]. Only the first trimester association remained robust to adjustment for other exposure windows, and was specific to boys only (HR = 1.18; 95% CI, 1.08–1.27); there was no association in girls (HR = 0.90; 95% CI, 0.76–1.07; interaction p-value 0.03). There were no statistically significant associations with other pollutants. PM₂.₅-associated ASD risk was stronger in boys, consistent with findings from recent animal studies. Further studies are needed to better understand these sexually dimorphic neurodevelopmental associations.

Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient. This study compares machine learning prediction models for ground-level ozone during wildfires, evaluating the predictive accuracy of ten algorithms on the daily 8-hour maximum average ozone during a 2008 wildfire event in northern California. Models were evaluated using a leave-one-location-out cross-validation (LOLO CV) procedure to account for the spatial and temporal dependence of the data and produce more realistic estimates of prediction error. LOLO CV avoids both the well-known overly optimistic bias of k-fold cross-validation on dependent data and the conservative bias of evaluating prediction error over a coarser spatial resolution via leave-k-locations-out CV. Gradient boosting was the most accurate of the ten machine learning algorithms with the lowest LOLO CV estimated root mean square error (0.228) and the highest LOLO CV Rˆ2 (0.677). Random forest was the second best performing algorithm with an LOLO CV Rˆ2 of 0.661. The LOLO CV estimates of predictive accuracy were less optimistic than 10-fold CV estimates for all ten models. The difference in estimated accuracy between the 10-fold CV and LOLO CV was greater for more flexible models like gradient boosting and random forest. The order of estimated model accuracy depended on the choice of evaluation metric, indicating that 10-fold CV and LOLO CV may select different models or sets of covariates as optimal, which calls into question the reliability of 10-fold CV for model (or variable) selection. These prediction models are designed for interpolating ozone exposure, and are not suited to inferring the effect of wildfires on ozone or extrapolating to predict ozone in other spatial or temporal domains. This is demonstrated by the inability of the best performing models to accurately predict ozone during 2007 southern California wildfires.

This work explores the use of Raman micro-spectroscopy to determine sources of airborne particulate matter collected on PM₂.₅ air filters in Imperial Valley, California. The goal is to examine if nearby soil is a potential source of particles sampled on air filters deployed in an urbanized desert area during events of unusually high PM₂.₅ excursions. Particle specific composition information can be an indicator of potential origin. This can provide insights into the source of unexpectedly high proportion of large particles sampled on PM₂.₅ filters in the vicinity of Imperial Valley. The measured spectral correspondence between the filter and soil particles, in the size range of 2.5–10 μm, is consistent with windblown dust being a likely source of the larger (>2.5 μm) particles collected on the PM₂.₅ filters. Additionally, these particles were identified as components of commonly occurring crustal minerals in the vicinity of the sampling site, such as iron oxides, hydroxides, sulfides, titanium dioxides and aluminosilicates. A substantial portion of the analyzed filter particles displayed a strong broadband fluorescence signal, which is consistent with the presence of organic matter and has been recognized as a marker for soil related origin of the filter particles. Elemental carbon (soot) was found to be prevalent among the particles as well, suggesting the existence of combustion related sources. Comparison between a heavily loaded filter sample and a filter with a more typical, lower loading did not show any obvious difference in chemical compositions. In both cases the particles appeared to be of crustal origin with the prevalence of elemental carbon. The primary difference between these two filter samples appear to be their particle size distribution - the heavily loaded filter sample contained greater proportion of large particles (>2.5 μm), and was more consistent with spectral signature of soils analyzed from the region.

Two hundred sixty-three fine particulate matter (PM₂.₅) samples were collected over fourteen months in Fresno and Bakersfield, California. Samples were analyzed for organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC), and 160 organic molecular markers. Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) source apportionment models were applied to the results in order to understand monthly and seasonal source contributions to PM₂.₅ OC. Similar source categories were found from the results of the CMB and PMF models to PM₂.₅ OC across the sites. Six source categories with reasonably stable profiles, including biomass burning, mobile, food cooking, two different secondary organic aerosols (SOAs) (i.e., winter and summer), and forest fires were investigated. Both the CMB and the PMF models showed a strong seasonality in contributions of some sources, as well as dependence on wind transport for both sites. The overall relative source contributions to OC were 24% CMB wood smoke, 19% CMB mobile sources, 5% PMF food cooking, 2% CMB vegetative detritus, 17% PMF SOA summer, 22% PMF SOA winter, and 12% PMF forest fire. Back-trajectories using the Weather Research and Forecasting model combined with the FLEXible PARTicle dispersion model (WRF-FLEXPART) were used to further characterize wind transport. Clustering of the trajectories revealed dominant wind patterns associated with varying concentrations of the different source categories. The Comprehensive Air Quality Model with eXtensions (CAMx) was used to simulate aerosol transport from forest fires and thus confirm the impacts of individual fires, such as the Rough Fire, at the measurement sites.

Urban-use pesticides present a unique risk to non-target organisms in surface aquatic systems because impervious pavement facilitates runoff that may lead to serious contamination and ensuing aquatic toxicity. Fipronil is an insecticide used at high rates in urban environments, especially in regions such as California. This compound and its biologically active degradation products have been detected in urban runoff drainage and downstream surface water bodies at concentrations exceeding toxicity thresholds for sensitive aquatic invertebrates, necessitating a better understanding of the runoff sources and causes of this contamination at sites of application. In this study, we evaluated sorption of fipronil, fipronil desulfinyl, fipronil sulfide, and fipronil sulfone in urban dust, soil, and concrete, matrices commonly associated with the perimeter of a residential home. Samples were also collected from five single family homes treated with fipronil in Riverside, California, for five months to determine the occurrence of fipronil and its degradates in runoff water, urban dust, soil, and on concrete surfaces. Statistical analysis was performed to determine which urban matrices contributed more significantly to the contaminant levels in runoff water. Freundlich sorption coefficients for fipronil and its degradation products in dust were 3- to 9-fold greater than their values in soil. Fipronil and its degradates were detected in 100% of runoff samples and their presence was observed in dust, soil, and concrete wipe samples for 153 d after the treatment. Linear regression analysis showed that concrete surfaces were a primary source of all four compounds to runoff, and loose dust on concrete pavement also served as an important contributor. This study represents the first comprehensive investigation of the sources and causes for surface runoff contamination by fipronil and its degradation products. Findings highlight the importance to reduce fipronil residues on concrete surfaces through improved application methods and other mitigation practices.

Microplastics are commonly found in marine ecosystems, but their distribution, prevalence, and impacts on resident fauna are still not well understood. Microplastics in coastal sediments expose invertebrate infauna to the risk of ingestion of plastic debris and associated toxicants. We assessed the prevalence of microplastics in beach sediments and ingested by Pacific mole crabs (Emerita analoga) at sandy beaches spanning >900 km of the California coast. Microplastics were present in sediments of every one of 51 beaches sampled. At a subset of 38 beaches Pacific mole crabs were collected and crabs at every beach had ingested microplastics. Across all beaches sampled, an average of 35% of Pacific mole crabs examined had microplastics in their guts. Our study demonstrates that microplastics are ubiquitous in sediments on California beaches and they are frequently consumed by a filter-feeding crustacean that is a common prey item in the diet of a wide variety of taxa, including fishes and birds.

Microplastics (MP) are detected in aquatic environments worldwide, yet detection is often limited to larger sized MP. To address this data gap, the abundance of MP 3–500 μm was assessed in the Los Angeles River, the San Gabriel River, and the Long Beach Harbor (CA, USA), three areas with highly urbanized surroundings. Whole surface water samples were taken, subjected to a hydrogen peroxide digestion and MP counts were compared between unstained visual examination and Nile Red staining identification techniques. The largest concentration of MP was found in the Los Angeles River, where 13,622 MP m−3 were found using unstained visual examination and 641,292 MP m−3 were found utilizing Nile Red staining. The protocol used to detect smaller sized MP is low cost, time efficient, and reproducible. This work highlights the need for more extensive sampling of smaller sized MP globally and universal testing and reporting standards for MP detection.

Identifying terrestrial sources of debris is essential to suppress the flow of plastic to the ocean. Here, we report a novel source of debris to the marine environment. From May 2016 to June 2018, we collected golf balls from coastal environments associated with five courses in Carmel, California. Our 75 collections recovered 39,602 balls from intertidal and nearshore environments adjacent to, or downriver from, the golf courses. Combining our collections with concurrent efforts of the Monterey Bay National Marine Sanctuary and the Pebble Beach Corporation, we report the retrieval of 50,681 balls, totaling approximately 2.5 tons of debris. We also examined decomposition patterns in the collected balls, which illustrate that degradation and loss of microplastic from golf balls to the marine environment may be of concern. Our findings will help to develop and direct mitigation procedures for this region and others with coastal golf courses.

The California Department of Pesticide Regulation conducted indoor air monitoring to estimate sulfury fluoride (SO₂F₂) emissions that bystanders around fumigated houses were potentially exposed to during structural fumigation of residential houses. Monitoring was conducted for 23 fumigations between September 2014 and March 2015. The SO₂F₂ indoor concentrations were measured from four locations inside the fumigated houses once an hour. The decreases of indoor concentrations are due to gas leakage (treatment period) and aeration (aeration period) from the structure to the outdoor environment. Analysis on the monitoring data showed considerable variability of half loss time (11–60 h) and mass loss (22–81%) during treatment periods. The decline of indoor concentrations followed the first-order kinetics; therefore, the hourly flux (g/m²s) of a treatment period can be calculated using initial SO₂F₂ concentration, treatment period mass loss, and house height. Although the California Aeration Plan requires a minimum 12–24 h of aeration after a treatment, the monitoring data showed that 93 ± 5% of the mass at the end of the treatment periods was emitted through the ducting system within the first 2 h. The average ratio of the loss amount in the first hour to the loss in the second was 6:1. These monitoring results provided the critical input for the computer modeling to estimate bystander exposure during structural fumigations of residential houses.