Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring.

Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (<100 m) to regional (>10 km), and to assess consequences for outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resolution observations of spatially variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full-coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km2 and 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars equipped with the Aclima mobile platform. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2 (pollutants dominated by secondary contributions). Median concentrations of UFP, NO, and NO2 are, for Hispanic and Black populations, 8 to 30% higher than the population average; for White populations, average exposures to these pollutants are 9 to 14% lower than the population average. Systematic racial/ethnic disparities are influenced by regional concentration gradients due to sharp contrasts in demographic composition among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale.

Comparison of Mobile and Fixed-Site Black Carbon Measurements for High-Resolution Urban Pollution Mapping.

Urban concentrations of black carbon (BC) and other primary pollutants vary on small spatial scales (<100m). Mobile air pollution measurements can provide information on fine-scale spatial variation, thereby informing exposure assessment and mitigation efforts. However, the temporal sparsity of these measurements presents a challenge for estimating representative long-term concentrations. We evaluate the capabilities of mobile monitoring in the represention of time-stable spatial patterns by comparing against a large set of continuous fixed-site measurements from a sampling campaign in West Oakland, California. Custom-built, low-cost aerosol black carbon detectors (ABCDs) provided 100 days of continuous measurements at 97 near-road and 3 background fixed sites during summer 2017; two concurrently operated mobile laboratories collected over 300 h of in-motion measurements using a photoacoustic extinctiometer. The spatial coverage from mobile monitoring reveals patterns missed by the fixed-site network. Time-integrated measurements from mobile lab visits to fixed-site monitors reveal modest correlation (spatial R2 = 0.51) with medians of full daytime fixed-site measurements. Aggregation of mobile monitoring data in space and time can mitigate high levels of uncertainty associated with measurements at precise locations or points in time. However, concentrations estimated by mobile monitoring show a loss of spatial fidelity at spatial aggregations greater than 100 m.

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression.

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.

Gaining momentum: stem cell therapies for HIV cure.

PURPOSE OF REVIEW: Durable HIV-1 remission has been reported in a person who received allogeneic stem cell transplants (SCTs) involving CCR5 Δ32/Δ32 donor cells. Much of the reduction in HIV-1 burden following allogeneic SCT with or without donor cells inherently resistant to HIV-1 infection is likely due to cytotoxic graft-versus-host effects on residual recipient immune cells. Nonetheless, there has been growing momentum to develop and implement stem cell therapies that lead to durable long-term antiretroviral therapy (ART)-free remission without the need for SCT. RECENT FINDINGS: Most current research leverages gene editing techniques to modify hematopoietic stem cells which differentiate into immune cells capable of harboring HIV-1. Approaches include targeting genes that encode HIV-1 co-receptors using Zinc Finger Nucleases (ZFN) or CRISPR-Cas-9 to render a pool of adult or progenitor cells resistant to de-novo infection. Other strategies involve harnessing multipotent mesenchymal stromal cells to foster immune environments that can more efficiently recognize and target HIV-1 while promoting tissue homeostasis. SUMMARY: Many of these strategies are currently in a state of infancy or adolescence; nonetheless, promising preclinical and first-in-human studies have been performed, providing further rationale to focus resources on stem cell therapies.

Tissue-based T cell activation and viral RNA persist for up to 2 years after SARS-CoV-2 infection.

The mechanisms of postacute medical conditions and unexplained symptoms after SARS-CoV-2 infection [Long Covid (LC)] are incompletely understood. There is growing evidence that viral persistence, immune dysregulation, and T cell dysfunction may play major roles. We performed whole-body positron emission tomography imaging in a well-characterized cohort of 24 participants at time points ranging from 27 to 910 days after acute SARS-CoV-2 infection using the radiopharmaceutical agent [18F]F-AraG, a selective tracer that allows for anatomical quantitation of activated T lymphocytes. Tracer uptake in the postacute COVID-19 group, which included those with and without continuing symptoms, was higher compared with prepandemic controls in many regions, including the brain stem, spinal cord, bone marrow, nasopharyngeal and hilar lymphoid tissue, cardiopulmonary tissues, and gut wall. T cell activation in the spinal cord and gut wall was associated with the presence of LC symptoms. In addition, tracer uptake in lung tissue was higher in those with persistent pulmonary symptoms specifically. Increased T cell activation in these tissues was also observed in many individuals without LC. Given the high [18F]F-AraG uptake detected in the gut, we obtained colorectal tissue for in situ hybridization of SARS-CoV-2 RNA and immunohistochemical studies in a subset of five participants with LC symptoms. We identified intracellular SARS-CoV-2 single-stranded spike protein-encoding RNA in rectosigmoid lamina propria tissue in all five participants and double-stranded spike protein-encoding RNA in three participants up to 676 days after initial COVID-19, suggesting that tissue viral persistence could be associated with long-term immunologic perturbations.

Observational insights into aerosol formation from isoprene.

Atmospheric photooxidation of isoprene is an important source of secondary organic aerosol (SOA) and there is increasing evidence that anthropogenic oxidant emissions can enhance this SOA formation. In this work, we use ambient observations of organosulfates formed from isoprene epoxydiols (IEPOX) and methacrylic acid epoxide (MAE) and a broad suite of chemical measurements to investigate the relative importance of nitrogen oxide (NO/NO2) and hydroperoxyl (HO2) SOA formation pathways from isoprene at a forested site in California. In contrast to IEPOX, the calculated production rate of MAE was observed to be independent of temperature. This is the result of the very fast thermolysis of MPAN at high temperatures that affects the distribution of the MPAN reservoir (MPAN / MPA radical) reducing the fraction that can react with OH to form MAE and subsequently SOA (F(MAE formation)). The strong temperature dependence of F(MAE formation) helps to explain our observations of similar concentrations of IEPOX-derived organosulfates (IEPOX-OS; ~1 ng m(-3)) and MAE-derived organosulfates (MAE-OS; ~1 ng m(-3)) under cooler conditions (lower isoprene concentrations) and much higher IEPOX-OS (~20 ng m(-3)) relative to MAE-OS (<0.0005 ng m(-3)) at higher temperatures (higher isoprene concentrations). A kinetic model of IEPOX and MAE loss showed that MAE forms 10-100 times more ring-opening products than IEPOX and that both are strongly dependent on aerosol water content when aerosol pH is constant. However, the higher fraction of MAE ring opening products does not compensate for the lower MAE production under warmer conditions (higher isoprene concentrations) resulting in lower formation of MAE-derived products relative to IEPOX at the surface. In regions of high NOx, high isoprene emissions and strong vertical mixing the slower MPAN thermolysis rate aloft could increase the fraction of MPAN that forms MAE resulting in a vertically varying isoprene SOA source.

Multimodal Molecular Imaging Reveals Tissue-Based T Cell Activation and Viral RNA Persistence for Up to 2 Years Following COVID-19.

The etiologic mechanisms of post-acute medical morbidities and unexplained symptoms (Long COVID) following SARS-CoV-2 infection are incompletely understood. There is growing evidence that viral persistence and immune dysregulation may play a major role. We performed whole-body positron emission tomography (PET) imaging in a cohort of 24 participants at time points ranging from 27 to 910 days following acute SARS-CoV-2 infection using a novel radiopharmaceutical agent, [18F]F-AraG, a highly selective tracer that allows for anatomical quantitation of activated T lymphocytes. Tracer uptake in the post-acute COVID group, which included those with and without Long COVID symptoms, was significantly higher compared to pre-pandemic controls in many anatomical regions, including the brain stem, spinal cord, bone marrow, nasopharyngeal and hilar lymphoid tissue, cardiopulmonary tissues, and gut wall. Although T cell activation tended to be higher in participants imaged closer to the time of the acute illness, tracer uptake was increased in participants imaged up to 2.5 years following SARS-CoV-2 infection. We observed that T cell activation in spinal cord and gut wall was associated with the presence of Long COVID symptoms. In addition, tracer uptake in lung tissue was higher in those with persistent pulmonary symptoms. Notably, increased T cell activation in these tissues was also observed in many individuals without Long COVID. Given the high [18F]F-AraG uptake detected in the gut, we obtained colorectal tissue for in situ hybridization SARS-CoV-2 RNA and immunohistochemical studies in a subset of participants with Long COVID symptoms. We identified cellular SARS-CoV-2 RNA in rectosigmoid lamina propria tissue in all these participants, ranging from 158 to 676 days following initial COVID-19 illness, suggesting that tissue viral persistence could be associated with long-term immunological perturbations.

Mobile-Platform Measurement of Air Pollutant Concentrations in California: Performance Assessment, Statistical Methods for Evaluating Spatial Variations, and Spatial Representativeness.

Mobile platform measurements provide new opportunities for characterizing spatial variations of air pollution within urban areas, identifying emission sources, and enhancing knowledge of atmospheric processes. The Aclima, Inc. mobile measurement and data acquisition platform was used to equip four Google Street View cars with research-grade instruments, two of which were available for the duration of this study. On-road measurements of air quality were made during a series of sampling campaigns between May 2016 and September 2017 at high (i.e., 1-second [s]) temporal and spatial resolution at several California locations: Los Angeles, San Francisco, and the northern San Joaquin Valley (including non-urban roads and the cities of Tracy, Stockton, Manteca, Merced, Modesto, and Turlock). The results demonstrate that the approach is effective for quantifying spatial variations of air pollutant concentrations over measurement periods as short as two weeks. Measurement accuracy and precision are evaluated using results of weekly performance checks and periodic audits conducted through the sampler inlets, which show that research instruments located within stationary vehicles are capable of reliably measuring nitric oxide (NO), nitrogen dioxide (NO2), ozone (O3), methane (CH4) black carbon (BC), and particle number (PN) concentration with bias and precision ranging from <10 % for gases to <25 % for BC and PN at 1-s time resolution. The quality of the mobile measurements in the ambient environment is examined by comparisons with data from an adjacent (< 9 m) stationary regulatory air quality monitoring site and by paired collocated vehicle comparisons, both stationary and driving. The mobile measurements indicate that U.S. EPA classifications of two Los Angeles stationary regulatory monitors' scales of representation are appropriate. Paired time-synchronous mobile measurements are used to characterize the spatial scales of concentration variations when vehicles were separated by <1 to 10 kilometers (km). A data analysis approach is developed to characterize spatial variations while limiting the confounding influence of diurnal variability. The approach is illustrated using data from San Francisco, revealing 1-km scale differences in mean NO2 and O3 concentrations up to 117 % and 46 %, respectively, of mean values during a two-week sampling period. In San Francisco and Los Angeles, spatial variations up to factors of 6 to 8 occur at sampling scales of 100 - 300m, corresponding to 1-minute averages.

Photoelectron resonance capture ionization (PERCI): a novel technique for the soft-ionization of organic compounds.

Photoelectron resonance capture ionization (PERCI) is demonstrated as a sensitive ionization technique involving minimal fragmentation of organic molecules. PERCI has been used successfully to softly and efficiently ionize both strongly UV absorbing and non-absorbing molecules. Tunable low energy (<1 eV) electrons are generated by focusing a pulsed UV laser on an aluminum photocathode in the presence of gas phase analyte. Negative ions are formed through a resonance electron capture process. Mass analysis is done using a reflectron time-of-flight mass spectrometer. PERCI is demonstrated for a number of gas phase compounds and simple mixtures, including sulfur hexafluoride, nitrobenzene, nitrophenol, 2-pentanone, hexanal, heptanal, and octanal. In all cases the molecular ion (or [M - H](-)) was observed to be the dominant peak. The 1sigma limit of detection was estimated to be on the order of 10(6) molecules in the ionization region.

Photoelectron resonance capture ionization mass spectrometry: a soft ionization source for mass spectrometry of particle-phase organic compounds.

Photoelectron resonance capture ionization (PERCI) is a soft and sensitive ionization method, based on the attachment of low-energy (<1 eV) photoelectrons to organic analyte molecules. PERCI has been developed in our laboratory for the real-time analysis of organic particles by mass spectrometry, and is employed here to monitor the heterogeneous reaction of ozone with oleic acid. Simplified identification of the reaction products is possible as a result of the soft nature of PERCI, giving predominantly the [M--H](-) ions. The major particle-phase products are identified as: 1-nonanal, nonanoic acid, 9-oxononanoic acid, and azelaic acid, consistent with proposed mechanisms. New insight into this well-studied heterogeneous reaction is gained as additional minor particle-phase products, consistent with the Criegee mechanism, are readily detected.