Toxic effects of photochemical smog on ponderosa and Jeffrey pines in the San Bernardino Mountains were discovered in the 1950s. It was revealed that ozone is the main cause of foliar injury manifested as chlorotic mottle and premature needle senescence. Various morphological, physiological and biochemical alterations in the affected plants have been reported over a period of about 40 years of multidisciplinary research. Recently, the focus of research has shifted from studying the effects of ozone to multiple pollutant effects. Recent studies have indicated that the combination of ozone and nitrogen may alter biomass allocation in pines towards that of deciduous trees, accelerate litter accumulation and increase carbon sequestration rates in heavily polluted forests

Ambient air pollutants are well-known risk factors for childhood asthma and asthma exacerbation. It is unknown whether different air pollutants individually or jointly affect pathophysiological mechanisms of asthma. In this study, we aim to integrate transcriptome and untargeted metabolome to identify dysregulated genetic and metabolic pathways that are associated with exposures to a mixture of ambient and traffic-related air pollutants among adults with asthma history. In this cross-sectional study, 102 young adults with childhood asthma history were enrolled from southern California in 2012. Whole blood transcriptome was measured with 20,869 expression signatures, and serum untargeted metabolomics including 937 metabolites were analyzed by Metabolon, Inc. Participants’ exposures to regional air pollutants (NO₂, O₃, PM₁₀, PM₂.₅) and near-roadway air pollutants averaged at one month and one year before study visit were estimated based on residential addresses. xMWAS network analysis and joint-pathway analysis were performed to identify subnetworks and genetic and metabolic pathways that were associated with exposure to air pollutants adjusted for socio-characteristic covariates. Network analysis found that exposures to air pollutants mixture were connected to 357 gene markers and 92 metabolites. One-year and one-month averaged PM₂.₅ and NO₂ were associated with several amino acids related to serine, glycine, and beta-alanine metabolism. Lower serum levels of carnosine and aspartate, which are involved in the beta-alanine metabolic pathway, as well as choline were also associated with worse asthma control (p < 0.05). One-year and one-month averaged PM₁₀ and one-month averaged O₃ were associated with higher gene expression levels of HSPA5, LGMN, CTSL and HLA-DPB1, which are involved in antigen processing and presentation. These results indicate that exposures to various air pollutants are associated with altered genetic and metabolic pathways that affect anti-oxidative capacity and immune response and can potentially contribute to asthma-related pathophysiology.

The San Joaquin Valley (SJV) of California has suffered persistent particulate matter (PM) pollution despite many years of control efforts. To further understand the chemical drivers of this problem and to support the development of State Implementation Plan for PM, a time-of-flight aerosol chemical speciation monitor (ToF-ACSM) outfitted with a PM₂.₅ lens and a capture vaporizer has been deployed at the Fresno-Garland air monitoring site of the California Air Resource Board (CARB) since Oct. 2018. The instrument measured non-refractory species in PM₂.₅ continuously at 10-min resolution. In this study, the data acquired from Oct. 2018 to May 2019 were analyzed to investigate the chemical characteristics, sources and atmospheric processes of PM₂.₅ in the SJV. Comparisons of the ToF-ACSM measurement with various co-located aerosol instruments show good agreements. The inter-comparisons indicated that PM₂.₅ in Fresno was dominated by submicron particles during the winter whereas refractory species accounted for a major fraction of PM₂.₅ mass during the autumn associated with elevated PM₁₀ loadings. A rolling window positive matrix factorization analysis was applied to the organic aerosol (OA) mass spectra using the Multilinear Engine (ME-2) algorithm. Three distinct OA sources were identified, including vehicle emissions, local and regional biomass burning, and formation of oxygenated species. There were significant seasonal variations in PM₂.₅ composition and sources. During the winter, residential wood burning and oxidation of nitrogen oxides were major contributors to the occurrence of haze episodes with PM₂.₅ dominated by biomass burning OA and nitrate. In autumn, agricultural activities and wildfires were found to be the main cause of PM pollution. PM₂.₅ concentrations decreased significantly after spring and were dominated by oxygenated OA during March to May. Our results highlight the importance of using seasonally dependent control strategies to mitigate PM pollution in the SJV.

Bioaccumulation of environmental contaminants in mammalian predators can serve as an indicator of ecosystem health. We examined mercury concentrations of raccoons (Procyon lotor; n = 37 individuals) and striped skunks (Mephitis mephitis; n = 87 individuals) in Suisun Marsh, California, a large brackish marsh that is characterized by contiguous tracts of tidal marsh and seasonally impounded wetlands. Mean (standard error; range) total mercury concentrations in adult hair grown from 2015 to 2018 were 28.50 μg/g dw (3.05 μg/g dw; range: 4.46–81.01 μg/g dw) in raccoons and 4.85 μg/g dw (0.54 μg/g dw; range: 1.53–27.02 μg/g dw) in striped skunks. We reviewed mammalian hair mercury concentrations in the literature and raccoon mercury concentrations in Suisun Marsh were among the highest observed for wild mammals. Although striped skunk hair mercury concentrations were 83% lower than raccoons, they were higher than proposed background levels for mercury in mesopredator hair (1–5 μg/g). Hair mercury concentrations in skunks and raccoons were not related to animal size, but mercury concentrations were higher in skunks in poorer body condition. Large inter-annual differences in hair mercury concentrations suggest that methylmercury exposure to mammalian predators varied among years. Mercury concentrations of raccoon hair grown in 2017 were 2.7 times greater than hair grown in 2015, 1.7 times greater than hair grown in 2016, and 1.6 times greater than hair grown in 2018. Annual mean raccoon and skunk hair mercury concentrations increased with wetland habitat area. Furthermore, during 2017, raccoon hair mercury concentrations increased with the proportion of raccoon home ranges that was wetted habitat, as quantified using global positioning system (GPS) collars. The elevated mercury concentrations we observed in raccoons and skunks suggest that other wildlife at similar or higher trophic positions may also be exposed to elevated methylmercury bioaccumulation in brackish marshes.

Epidemiological research on the adverse health outcomes due to PM₂.₅ exposure frequently relies on measurements from regulatory air quality monitors to provide ambient exposure estimates, whereas personal PM₂.₅ exposure may deviate from ambient concentrations due to outdoor infiltration and contributions from indoor sources. Research in quantifying infiltration factors (Fᵢₙf), the fraction of outdoor PM₂.₅ that infiltrates indoors, has been historically limited in space and time due to the high costs of monitor deployment and maintenance. Recently, the growth of openly accessible, citizen-based PM₂.₅ measurements provides an unprecedented opportunity to characterize Fᵢₙf at large spatiotemporal scales. In this analysis, 91 consumer-grade PurpleAir indoor/outdoor monitor pairs were identified in California (41 residential houses and 50 public/commercial buildings) during a 20-month period with around 650000 h of paired PM₂.₅ measurements. An empirical method was developed based on local polynomial regression to estimate site-specific Fᵢₙf. The estimated site-specific Fᵢₙf had a mean of 0.26 (25ᵗʰ, 75ᵗʰ percentiles: [0.15, 0.34]) with a mean bootstrap standard deviation of 0.04. The Fᵢₙf estimates were toward the lower end of those reported previously. A threshold of ambient PM₂.₅ concentration, approximately 30 μg/m³, below which indoor sources contributed substantially to personal exposures, was also identified. The quantified relationship between indoor source contributions and ambient PM₂.₅ concentrations could serve as a metric of exposure errors when using outdoor monitors as an exposure proxy (without considering indoor-generated PM₂.₅), which may be of interest to epidemiological research. The proposed method can be generalized to larger geographical areas to better quantify PM₂.₅ outdoor infiltration and personal exposure.

High concentrations of ground-level ozone affect human health, plants, and animals. Reducing ozone pollution in rural regions, where local emissions are already low, poses challenge. We use meteorological back-trajectories, air quality model sensitivity analysis, and satellite remote sensing data to investigate the ozone sources in Yuma, Arizona and find strong international influences from Northern Mexico on 12 out of 16 ozone exceedance days. We find that such exceedances could not be mitigated by reducing emissions in Arizona; complete removal of state emissions would reduce the maximum daily 8-h average (MDA8) ozone in Yuma by only 0.7% on exceeding days. In contrast, emissions in Mexico are estimated to contribute to 11% of the ozone during these exceedances, and their reduction would reduce MDA8 ozone in Yuma to below the standard. Using satellite-based remote sensing measurements, we find that emissions of nitrogen oxides (NOₓ, a key photochemical precursor of ozone) increase slightly in Mexico from 2005 to 2016, opposite to decreases shown in the bottom-up inventory. In comparison, a decrease of NOₓ emissions in the US and meteorological factors lead to an overall of summer mean and annual MDA8 ozone in Yuma (by ∼1–4% and ∼3%, respectively). Analysis of meteorological back-trajectories also shows similar transboundary transport of ozone at the US-Mexico border in California and New Mexico, where strong influences from Northern Mexico coincide with 11 out of 17 and 6 out of 8 ozone exceedances. 2020 is the final year of the U.S.-Mexico Border 2020 Program, which aimed to reduce pollution at border regions of the US and Mexico. Our results indicate the importance of sustaining a substantial cooperative program to improve air quality at the border area.

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.

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.

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.