California's zero-emission vehicle adoption brings air quality benefits yet equity gaps persist.

Zero-emission vehicle (ZEV) adoption is a key climate mitigation tool, but its environmental justice implications remain unclear. Here, we quantify ZEV adoption at the census tract level in California from 2015 to 2020 and project it to 2035 when all new passenger vehicles sold are expected to be ZEVs. We then apply an integrated traffic model together with a dispersion model to simulate air quality changes near roads in the Greater Los Angeles. We found that per capita ZEV ownership in non-disadvantaged communities (non-DACs) as defined by the state of California is 3.8 times of that in DACs. Racial and ethnic minorities owned fewer ZEVs regardless of DAC designation. While DAC residents receive 40% more pollutant reduction than non-DACs due to intercommunity ZEV trips in 2020, they remain disproportionately exposed to higher levels of traffic-related air pollution. With more ZEVs in 2035, the exposure disparity narrows. However, to further reduce disparities, the focus must include trucks, emphasizing the need for targeted ZEV policies that address persistent pollution burdens among DAC and racial and ethnic minority residents.

Environmental justice analysis of wildfire-related PM2.5 exposure using low-cost sensors in California.

The increasing number and severity of wildfires is negatively impacting air quality for millions of California residents each year. Community exposure to PM2.5 in two main population centers (San Francisco Bay area and Los Angeles County area) was assessed using the low-cost PurpleAir sensor network for the record-setting 2020 California wildfire season. Estimated PM2.5 concentrations in each study area were compared to census tract-level environmental justice vulnerability indicators, including environmental, health, and demographic data. Higher PM2.5 concentrations were positively correlated with poverty, cardiovascular emergency department visits, and housing inequities. Sensors within 30 km of actively burning wildfires showed statistically significant increases in indoor (~800 %) and outdoor (~540 %) PM2.5 during the fires. Results indicate that wildfire emissions may exacerbate existing health disparities as well as the burden of pollution in disadvantaged communities, suggesting a need to improve monitoring and adaptive capacity among vulnerable populations.

Aerosol Oxidative Potential in the Greater Los Angeles Area: Source Apportionment and Associations with Socioeconomic Position.

Oxidative potential (OP) has been proposed as a possible integrated metric for particles smaller than 2.5 µm in diameter (PM2.5) to evaluate adverse health outcomes associated with particulate air pollution exposure. Here, we investigate how OP depends on sources and chemical composition and how OP varies by land use type and neighborhood socioeconomic position in the Los Angeles area. We measured OH formation (OPOH), dithiothreitol loss (OPDTT), black carbon, and 52 metals and elements for 54 total PM2.5 samples collected in September 2019 and February 2020. The Positive Matrix Factorization source apportionment model identified four sources contributing to volume-normalized OPOH: vehicular exhaust, brake and tire wear, soil and road dust, and mixed secondary and marine. Exhaust emissions contributed 42% of OPOH, followed by 21% from brake and tire wear. Similar results were observed for the OPDTT source apportionment. Furthermore, by linking measured PM2.5 and OP with census tract level socioeconomic and health outcome data provided by CalEnviroScreen, we found that the most disadvantaged neighborhoods were exposed to both the most toxic particles and the highest particle concentrations. OPOH exhibited the largest inverse social gradients, followed by OPDTT and PM2.5 mass. Finally, OPOH was the metric most strongly correlated with adverse health outcome indicators.

Co-kriging with a low-cost sensor network to estimate spatial variation of brake and tire-wear metals and oxidative stress potential in Southern California.

Due to regulations and technological advancements reducing tailpipe emissions, an increasing proportion of emissions arise from brake and tire wear particulate matter (PM). PM from these non-tailpipe sources contains heavy metals capable of generating oxidative stress in the lung. Although important, these particles remain understudied because the high cost of actively collecting filter samples. Improvements in electrical engineering, internet connectivity, and an increased public concern over air pollution have led to a proliferation of dense low-cost air sensor networks such as the PurpleAir monitors, which primarily measure unspeciated fine particulate matter (PM2.5). In this study, we model the concentrations of Ba, Zn, black carbon, reactive oxygen species concentration in the epithelial lining fluid, dithiothreitol (DTT) loss, and OH formation. We use a co-kriging approach, incorporating data from the PurpleAir network as a secondary predictor variable and a land-use regression (LUR) as an external drift. For most pollutant species, co-kriging models produced more accurate predictions than an LUR model, which did not incorporate data from the PurpleAir monitors. This finding suggests that low-cost sensors can enhance predictions of pollutants that are costly to measure extensively in the field.

A systematic assessment of city-level climate change mitigation and air quality improvement in China.

China is facing dual challenges of air pollution and climate change. By using city-level data, we comprehensively assessed air quality and CO2 emission changes from 2015 to 2019 for 335 Chinese cities. We selected important regions for air pollution control and categorized all cities into different classes according to their development levels. Our novel approach revealed new insights on different patterns of changes of PM2.5, O3, and CO2 by region and city class. We found that PM2.5 concentrations decreased remarkably due to mandatory city-level reduction targets, especially in the Beijing-Tianjin-Hebei (-27%) region. Nonetheless, O3 concentrations and CO2 emissions increased in 91% and 69% of Chinese cities, respectively. Observed CO2 emission reductions in more developed cities were mainly due to prominent energy intensity reduction and energy structure improvement. Our study indicates a lack of synergy in air pollution control and CO2 mitigation under current policies in China. To address both challenges holistically, we suggest setting mandatory city-level CO2 emission reduction targets and reinforcing clean energy and energy efficiency measures.

Long-term evaluation of a low-cost air sensor network for monitoring indoor and outdoor air quality at the community scale.

Given the growing interest in community air quality monitoring using low-cost sensors, 30 PurpleAir II sensors (12 outdoor and 18 indoor) were deployed in partnership with community members living adjacent to a major interstate freeway from December 2017- June 2019. Established quality assurance/quality control techniques for data processing were used and sensor data quality was evaluated by calculating data completeness and summarizing PM2.5 measurements. To evaluate outdoor sensor performance, correlation coefficients (r) and coefficients of divergence (CoD) were used to assess temporal and spatial variability of PM2.5 between sensors. PM2.5 concentrations were also compared to traffic levels to assess the sensors' ability to detect traffic pollution. To evaluate indoor sensors, indoor/outdoor (I/O) ratios during resident-reported activities were calculated and compared, and a linear mixed-effects regression model was developed to quantify the impacts of ambient air quality, microclimatic factors, and indoor human activities on indoor PM2.5. In general, indoor sensors performed more reliably than outdoor sensors (completeness: 73% versus 54%). All outdoor sensors were highly temporally correlated (r > 0.98) and spatially homogeneous (CoD<0.06). The observed I/O ratios were consistent with existing literature, and the mixed-effects model explains >85% of the variation in indoor PM2.5 levels, indicating that indoor sensors detected PM2.5 from various sources. Overall, this study finds that community-maintained sensors can effectively monitor PM2.5, with main data quality concerns resulting from outdoor sensor data incompleteness.

Linkage between human population and trace elements in soils of the Pearl River Delta: Implications for source identification and risk assessment.

The human population is both an emitter and receptor of metals. This study aims to clarify how the relationship of metals and metalloids to human populations influences their source characterization and health risk, based on metal concentrations in 298 soil samples in the Pearl River Delta (PRD) and the corresponding zip-code level population. Nickel (Ni), copper (Cu), zinc (Zn), cadmium (Cd), mercury (Hg), and lead (Pb), but not chromium (Cr) and arsenic (As), were significantly correlated with population (p<0.01), suggesting potential anthropogenic sources. A principal component analysis (PCA) revealed three factors (i.e., F1, F2, and F3) contributing to metal levels in the PRD: (1) metal transport from rivers (F1), which explained the high levels of Cr, Ni, Cu, Zn, As, and Cd in downstream areas; (2) industrial sources (F2), mainly contributing to Ni, Cu, Zn, Cd, Hg, and Pb; and (3) natural and agricultural sources (F3), mainly contributing to As and Pb. F2 was significantly correlated with population, while F3 was not, indicating that an analysis of the correlation with population could be used to identify industrial sources of metals. Compared with directly calculated risks, the population-weighted non-carcinogenic and carcinogenic risks were increased by 4.2-4.9% and 7.7-9.2%, respectively. A unit increase in the concentration of industrial metals led to higher extra risks than a corresponding increase in natural metals due to the proximity to human populations.

Gender differences in cerebral regional homogeneity of adult healthy volunteers: a resting-state FMRI study.

OBJECTIVE: We sought to use the regional homogeneity (ReHo) approach as an index in the resting-state functional MRI to investigate the gender differences of spontaneous brain activity within cerebral cortex and resting-state networks (RSNs) in young adult healthy volunteers. METHODS: One hundred and twelve healthy volunteers (56 males, 56 females) participated in the resting-state fMRI scan. The ReHo mappings in the cerebral cortex and twelve RSNs of the male and female groups were compared. RESULTS: We found statistically significant gender differences in the primary visual network (PVN) (P < 0.004, with Bonferroni correction) and left attention network (LAtN), default mode network (DMN), sensorimotor network (SMN), executive network (EN), and dorsal medial prefrontal network (DMPFC) as well (P < 0.05, uncorrected). The male group showed higher ReHo in the left precuneus, while the female group showed higher ReHo in the right middle cingulate gyrus, fusiform gyrus, left inferior parietal lobule, precentral gyrus, supramarginal gyrus, and postcentral gyrus. CONCLUSIONS: Our results suggested that men and women had regional specific differences during the resting-state. The findings may improve our understanding of the gender differences in behavior and cognition from the perspective of resting-state brain function.