From the Cover: Health benefits of decreases in on-road transportation emissions in the United States from 2008 to 2017

Decades of air pollution regulation have yielded enormous benefits in the United States, but vehicle emissions remain a climate and public health issue. Studies have quantified the vehicle-related fine particulate matter (PM_2.5)-attributable mortality but lack the combination of proper counterfactual scenarios, latest epidemiological evidence, and detailed spatial resolution; all needed to assess the benefits of recent emission reductions. We use this combination to assess PM_2.5-attributable health benefits and also assess the climate benefits of on-road emission reductions between 2008 and 2017. We estimate total benefits of $270 (190 to 480) billion in 2017. Vehicle-related PM_2.5-attributable deaths decreased from 27,700 in 2008 to 19,800 in 2017; however, had per-mile emission factors remained at 2008 levels, 48,200 deaths would have occurred in 2017. The 74% increase from 27,700 to 48,200 PM_2.5-attributable deaths with the same emission factors is due to lower baseline PM_2.5 concentrations (+26%), more vehicle miles and fleet composition changes (+22%), higher baseline mortality (+13%), and interactions among these (+12%). Climate benefits were small (3 to 19% of the total). The percent reductions in emissions and PM_2.5-attributable deaths were similar despite an opportunity to achieve disproportionately large health benefits by reducing high-impact emissions of passenger light-duty vehicles in urban areas. Increasingly large vehicles and an aging population, increasing mortality, suggest large health benefits in urban areas require more stringent policies. Local policies can be effective because high-impact primary PM_2.5 and NH₃ emissions disperse little outside metropolitan areas. Complementary national-level policies for NO_x are merited because of its substantial impacts—with little spatial variability—and dispersion across states and metropolitan areas.

Health impacts of air pollution from transportation remain a major public health problem in the United States with several studies estimating roughly 17,000 to 20,000 deaths/year attributable to it in recent years, the vast majority from fine particulate matter (PM_2.5) (1–4). Researchers have used different methods to estimate this burden, limiting comparability among estimates, but those who have estimated attributable deaths in different years have shown this burden has decreased. Dedoussi et al. (3) estimate that they were cut in half in the 2005 to 2018 period, from 37,000 to 18,400 due to PM_2.5 and ozone, whereas Fann et al. (1) estimate just under 30,000 in 2005 and 19,300 in 2016, a decrease of about a third. These studies’ estimates for 2016 and 2018, however, rely on forecasts of emissions made years in advance.Transportation emissions also contribute to climate impacts. Transportation greenhouse gas (GHG) emissions have increased in recent years, and they were responsible for 28% of the US GHG emissions in 2018 (5). A total of 83% of transportation GHG emissions in 2018 came from vehicles, and 70% of vehicle GHG emissions came from light-duty vehicles (LDVs) (5). In recent years, LDV energy efficiency has increased and GHG emission factors per mile (EF) decreased, but their overall climate impacts have increased (5, 6). Increased market penetration of larger LDVs (6) and increased vehicle miles traveled (VMT) (7) have contributed to this overall increase.Decades of environmental regulation in the United States have drastically reduced emissions from vehicles by as much as 99% per vehicle for common pollutants since 1970 (8). Transportation emissions are one element of a substantial effort to reduce ambient PM_2.5 in recent decades (9, 10), following regulation of air pollution that has been cost-beneficial and has yielded substantial benefits. The US Environmental Protection Agency (EPA) (11) estimates that the Clean Air Act Amendments of 1990 have yielded $2 trillion/year (2006 US dollars) in benefits from all sectors in 2020, or 30 times its cost, with 90% of the benefits coming from reduced PM_2.5-attributable mortality. Fuel efficacy standards and vehicle emission controls have been responsible for a substantial part of these benefits.Benefits of recent reductions in vehicle emissions, on the other hand, are not well understood. Several studies have quantified mortality from on-road transportation in recent years (1–4, 12–15), some of them also assessing changes over time and showing decreases. To our knowledge, however, no study has carried out a fine-scale assessment relying on counterfactual scenarios that capture changes in fleet composition and VMT, population, age-specific baseline mortality rates, and lower ambient PM_2.5 concentrations at baseline. The latter is important because more recent epidemiological evidence from the Global Exposure Mortality Model (GEMM) (16) suggests a nonlinear function linking ambient PM_2.5 concentration to mortality. The GEMM concentration–response function (CRF) is concave, exhibiting higher marginal effects at lower concentrations. As ambient PM_2.5 concentrations in the United States have dropped in recent decades (10), this nonlinearity suggests marginal effects are increasing over time. The previously widely used Global Burden of Disease (GBD) Integrated Exposure-Response (IER) model (17, 18) also estimated a concave CRF, but GEMM estimates more than twice as many attributable deaths for the United States and Canada when compared to GBD IER. GEMM also includes more recent evidence from epidemiological studies of populations in the two countries that allow it to estimate mortality risks for exposures to very low ambient PM_2.5 concentrations—as low as 2.4 μg/m³, lower than previous models—that are relevant for policies in the United States.Vehicle impacts also exhibit large spatial variability across states and cities (19, 20). Metropolitan areas are especially important because previous research has suggested that impacts per mile of passenger vehicles driving in these areas are large (20), and passenger transportation is now responsible for more PM_2.5-attributable deaths in the United States than truck use (4). Spatial variability in impact suggests a potential for more stringent policies in metropolitan areas where impacts are higher, but considering local policies would require understanding local impacts versus those transported to and affecting populations in other areas. Previous research has shown that over a third of impacts caused by all vehicle emissions in the United States occur across state lines, mostly from NO_x emissions (3); nevertheless, transfers of impacts caused by vehicles in metropolitan areas are not well studied.This paper assesses benefits of recent emissions reductions of on-road transportation in the contiguous United States occurring between 2008 and 2017. We assess impacts on a fine scale using a nonlinear CRF from the most recent epidemiological evidence from GEMM (16). We combine 1-km–resolution baseline ambient PM_2.5 levels (21), fine-scale (1 km in densely populated areas) air pollution modeling (2, 22), and county-level age- and cause-specific mortality (23). We assess impacts in 2017 for four counterfactual emission scenarios (2008 EFs, 2011 EFs, 2014 EFs, and 2017 EFs), each using county-level EFs for each pollutant and 13 vehicle types from the respective year’s National Emissions Inventory (NEI) (24–27). Our combination of fine-scale modeling and counterfactual emission scenarios allows us to capture changes in demographics, fleet composition, and baseline ambient PM_2.5 levels. We estimate benefits from decreases in PM_2.5-attributable mortality due to reductions in on-road transportation emissions of primary PM_2.5, SO₂, NO_x, NH₃, and volatile organic compounds (VOCs) (air pollution) and climate benefits from reductions in on-road transportation emissions of CO₂, CH₄, and N₂O (GHGs). As passenger vehicles were previously estimated to be responsible for most of the burden, we present a spatially explicit analysis of passenger LDVs with a focus on 53 large metropolitan statistical areas (MSAs), which we define as those with population exceeding 1 million in 2017 according to the US Census Bureau (28). In 2017, these 53 MSAs accounted for 56% of the US population (29) and 50% of the US VMT from all road vehicles (27). We refer to these large MSAs simply as MSAs or metropolitan areas throughout the paper.     

Air pollution interacts with genetic risk to influence cortical networks implicated in depression

Air pollution is a reversible cause of significant global mortality and morbidity. Epidemiological evidence suggests associations between air pollution exposure and impaired cognition and increased risk for major depressive disorders. However, the neural bases of these associations have been unclear. Here, in healthy human subjects exposed to relatively high air pollution and controlling for socioeconomic, genomic, and other confounders, we examine across multiple levels of brain network function the extent to which particulate matter (PM_2.5) exposure influences putative genetic risk mechanisms associated with depression. Increased ambient PM_2.5 exposure was associated with poorer reasoning and problem solving and higher-trait anxiety/depression. Working memory and stress-related information transfer (effective connectivity) across cortical and subcortical brain networks were influenced by PM_2.5 exposure to differing extents depending on the polygenic risk for depression in gene-by-environment interactions. Effective connectivity patterns from individuals with higher polygenic risk for depression and higher exposures with PM_2.5, but not from those with lower genetic risk or lower exposures, correlated spatially with the coexpression of depression-associated genes across corresponding brain regions in the Allen Brain Atlas. These converging data suggest that PM_2.5 exposure affects brain network functions implicated in the genetic mechanisms of depression.

More than 90% of the world’s population live in places with atmospheric exposures exceeding World Health Organization air quality guidelines (1). Ambient air pollution is a major but potentially reversible cause of global morbidity and mortality (2). Empirical evidence suggests there may also be neurotoxic effects of air pollution, especially fine particulate matter (e.g., PM_2.5) (3) that, over exposure periods of several months or more, is associated with increased risk for major depressive disorders (4, 5). These exposures have also been observed to affect cognition (6). Moreover, gene-by-environment interactions are implicated in air pollution and genetic risk for neurodegenerative disorders, potentially involving inflammatory processes (7, 8). Air pollution and inflammation may both affect mood regulation in major depression (9, 10), and indeed, inflammatory processes are implicated in putative risk genes associated with depression (11). These observations raise the possibility that air pollution may interact with depression-associated genes in influencing stress-related brain network function. While these associations at multiple levels of in vivo brain network function have yet to be reported, familial vulnerability to depression may be influenced by genetic interactions with environmental stressors (12, 13).In this study, we examine the putative effects of recent months of relatively high air pollution exposures on cognitive (14) and emotional risk factors (trait anxiety/depression) of depressive illness (15–17) and, subsequently, pollution effects on underlying cognitive and stress-related brain network function in relation to genetic risk for major depression (11). In the latter, we focus on cognition during emotional stress as a paradigm to engage frontal and cortical–subcortical networks, which have been shown to be sensitive to disruption by stress and are also implicated in depressive disorders (18). Of note, the dysfunction of prefrontal and parietal cortex circuitry during working memory (WM) occurs in patients with depression and in healthy individuals with high polygenic risk for depression (14, 18, 19). Here, we aim to further define how exposure to recent months of air pollution may affect WM, social stress, and associated brain connectivity in the context of polygenic risk for depression.We examine the behavioral risk factors for depression and associated brain networks engaged during WM under varying social stress levels in a community sample from Beijing, who were exposed to relatively high levels of air pollution (e.g., PM_2.5). These exposures varied across the study period, during which pollution levels were moderated some 33% by policy interventions on industrial emissions (20), thus providing a unique opportunity to examine demographically and genomically well-matched individuals living in similar communities with varying (but still relatively high) pollution exposures. As poorer cognition was previously associated with higher levels of air pollution exposures over 3 mo to 3 y in an independent East Asian study (6) and adverse environments over recent months may affect emotional traits associated with depression (21) and indeed air pollution exposures over similar time periods have been associated with depression (4, 5), we examined the effects of recent 6 mo PM_2.5 exposure on these behavioral characteristics, the effects of exposure on brain connectivity networks, and their interaction with polygenic risk for depression (11). We then leveraged recent developments linking live functional brain network data with postmortem gene expression data across the same brain regions (22, 23), made possible by the unique Allen Brain Atlas resource that has densely sampled genome-wide expression at multiple brain regions. Here, we surveyed spatial coexpression of depression-related genes across the human brain in the Allen Brain Atlas and the extent to which the spatial correlation of coexpression with cognitive and stress-related connectivity may differ in individuals based on levels of depression genetic risk and PM_2.5 exposure.     

From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model

The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO₂, O₃, and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO₂ and particulate matter with aerodynamic diameters <2.5 μm by –30.1% and –17.5%, respectively, but a 5.7% increase in O₃. Heavy-duty truck emissions contribute primarily to these variations. Future traffic-emission controls are estimated to impose similar effects as observed during the COVID-19 lockdown, but with smaller magnitude. Vehicular electrification will achieve further alleviation of NO₂ levels.

In the urban environment, vehicular traffic is a principal source of air pollutants, including nitrogen oxides (NO_x = NO + NO₂), carbon monoxide (CO), and carbonaceous particles. Secondary ozone (O₃) and particulate matter (PM) have adverse impacts on human health (1) by inducing dysfunction and deterioration of cardiovascular, respiratory, and immune systems (2). The COVID-19 pandemic led to unprecedented decreases in traffic-related emissions in megacities worldwide (3–5). Owing to the short chemical lifetime of NO_x and the pandemic-induced emission changes, the well-defined and abrupt decrease in NO₂ has been captured by satellites as well as ground-based observations (6–8). However, changes in secondary pollutants like O₃ and a major portion of PM_2.5 (PM with aerodynamic diameters <2.5 μm) during the pandemic were diverse in different regions (7, 9), for which the major drivers remain unclear. Atmospheric chemical reactions serve as essential nonlinear links between emissions and atmospheric composition. Moreover, local meteorological factors, such as air temperature, humidity, radiation, and clouds, also strongly regulate photochemical formation of ozone and multiphase chemistry of secondary PM (6, 9–11). The response of secondary pollutants to COVID-19–induced emission changes remains poorly understood; existing studies provide limited insight into the consequent chemistry (7). Here, we disentangle the complex factors involving emissions, chemical reactions, pollutant transport, and meteorology to evaluate the effect of pandemic-induced or other dramatic emission changes on air quality.Los Angeles (LA) has long been one of the most polluted cities in the United States (12). Surrounded by mountains on three sides and bounded by the Pacific Ocean, ideal conditions exist for pollutant buildup over the LA Basin and downwind areas (13, 14). Owing to the strict sulfur oxides (SO_x) emission control program established in 1978 and major improvements of motor vehicle engines, SO₂ and black carbon levels have significantly declined (15). However, organic aerosol concentrations, contributing to more than half of PM_2.5, have not declined as significantly as primary emissions (16, 17). The COVID-19–induced variability of air quality provides an opportunity to evaluate the efficacy of traffic mitigation strategies.Diesel-powered heavy-duty vehicles and medium-duty vehicles, such as trucks and buses, comprise only a modest fraction of the total numbers of the on-road fleet in LA but disproportionately contribute to a large fraction of overall vehicle emissions (17, 18, 19). Even with installation of diesel particle filters and selective catalytic reduction (SCR) systems, unusually high emissions of NO_x and lower SCR efficiency are still reported (20). In 2017, The California Air Resources Board (CARB) adopted a series of regulations including reduction of NO_x emissions by 90% for new heavy-duty diesel trucks (21), requiring truck manufacturers to transition from diesel trucks and vans to electric zero-emission trucks beginning in 2024, aiming for an all-zero-emission short-haul drayage fleet in ports and railyards by 2035 and zero-emission “last-mile” delivery trucks and vans by 2040 (22). An assessment of the air-quality-related benefit of the zero-emission delivery truck plan is lacking.Atmospheric chemical transport models have been widely used to examine the response of air pollutant concentrations to the changes of emissions and meteorological conditions. However, the challenge in preparing high-temporal-resolution emission profiles in a timely manner has limited a dynamic analysis of air-quality impacts resulting from the abrupt emission changes through the pandemic period. Recent studies have demonstrated the capability of predictive machine-learning (ML) models to capture the timing, magnitude, and major factors influencing real-time atmospheric responses to emission control measures (23–25). Compared with traditional chemical transport modeling, the ML technique has more flexibility in leveraging real-world data and possesses higher computational efficiency. Here, real-time data including traffic information from the California Department of Transportation (Caltrans), in situ surface-level pollutant concentrations and meteorology from the CARB, and population density and points of interest (physical location of compressed natural gas stations, power plants, landfills, etc.) at the city level are used within an ML framework to develop a model that can directly link atmospheric composition with societal factors. A supervised ML algorithm, the random-forest (RF) model, is employed to account for the nonlinear interactions between different input parameters without specifying any form of their relationships. We use this model to assess the sensitivity of NO₂, O₃, and PM_2.5 in the LA Basin to traffic emission changes at different stages of the COVID-19 lockdown by comparing predicted concentrations under different traffic emission scenarios. Moreover, by considering future climate changes and traffic emissions, we assess the possible benefits of future traffic evolution, including vehicular electrification, in 2035 and 2050.     

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.

Air pollution varies in complex patterns across the urban landscape, arising from the interplay of emissions source locations and atmospheric transport and transformation. Gradients exist at multiple spatial scales, reflecting regional, city-level, and neighborhood-level phenomena, including highly localized peaks near major sources (1–3). The uneven distribution of sources has been shown in the United States to cause systematically higher outdoor concentrations for people of color and communities facing disproportionate socioeconomic and environmental stressors (4–10). Increased air pollution exposure is associated with premature mortality and a multitude of chronic health problems, as well as increased vulnerability to extreme events such as wildfire pollution episodes and COVID-19 (11–13). Measurement and analysis of this disparity in outdoor concentrations are vital for understanding how the causes of air pollution (e.g., city zoning, infrastructure development, emissions sources) affect differential health outcomes. This understanding can aid in designing effective environmental justice measures and tracking the effects of the evolving urban landscape on population-wide and community exposure. Here, we use mobile monitoring (in-motion measurements by vehicle-mounted instruments) to observe highly localized air pollution patterns in a variety of urban settings and consider the implications for the measurement and mitigation of pollution exposure and environmental inequity.The full complexity of multiscale patterns of air pollution is largely unknown in most urban areas despite great advances in measurement and modeling methods over the past few decades. Regulatory monitoring sites are sparsely distributed and generally do not measure unregulated pollutants of health concern, such as black carbon (BC) and ultrafine particles (UFP). While satellite remote sensing provides nearly global spatial coverage, most conventional products are limited in resolution to 1 to 5 km² and do not include all pollutants of interest (14, 15). Mechanistic models predict concentrations over broad domains but are limited by computational constraints and data gaps (16). Recent statistical models provide both high spatial resolution and geographic coverage, but concentration predictions reflect generalized patterns, tend to predict central tendencies better than extremes, and may miss local idiosyncrasies, especially for models with broad (e.g., national) domains (17). Despite the growing sophistication of these technologies, in many cases they are best suited to depict patterns of pollutants that are predominantly secondary, such as ozone and PM_2.5, which tend to vary more over the regional than neighborhood scale (17). In contrast, fine-scale gradients dominate spatial variability for directly emitted (primary) pollutants like BC and for pollutants with highly localized transformation dynamics like UFP and NO (1, 18–22). Both BC and UFP are suspected to cause distinct health impacts (23–25), but these effects—and the potential racial/ethnic and socioeconomic health disparities—will remain poorly understood until higher-resolution measurements are more widespread (25, 26).Mobile monitoring and low-cost sensors are increasingly used to detect fine-scale pollution gradients with applications ranging from new risk estimates for cardiovascular disease to the identification of unexpected sources of exposure disparity (24, 27). Low-cost sensors are used to supplement the density of existing regulatory networks, but appropriate sensors are not available for all pollutants and operation is limited by the capacity for consistent calibration and maintenance (28). Mobile monitoring has been used to measure multipollutant gradients in a wide range of urban contexts (29–31). Example applications of mobile monitoring include studies of short-range multipollutant variation attributable to highway traffic (31–35), infrastructure geometry, such as street canyons and near-road barriers (22, 36–38), and a variety of specific local sources (37, 39–41). While mobile monitoring is a flexible method, it is also labor intensive, requiring many repeated visits to collect enough localized measurements to capture the full temporal variation in conditions.Because of the large resource requirement for long-term mobile monitoring, only a small number of campaigns have measured multiyear, high-resolution patterns over multiple neighborhoods (19, 42). Few have attempted comprehensive coverage of all major and residential streets of several contiguous neighborhoods (21, 41, 43). This study presents the results of 32 mo of mobile monitoring along every street of 13 cities, towns, and urban districts (93 km²) distributed through four counties of the San Francisco Bay Area, providing over 2,100 h of sampling of four pollutants at ∼0.01 km² (i.e., ∼100 × 100 m²). We employ two custom-equipped Google Street View cars to repeatedly measure city block air quality, providing estimates of outdoor air pollution for a year-2010 population of ∼450,000 individuals and an opportunity to characterize, quantify, and analyze multiscale gradients across the urban landscape. We specifically consider implications for racial and ethnic exposure disparities. We find that dense, urban neighborhoods exhibit peaks that vary by pollutant in both location and magnitude, reflecting complex interactions among diverse emission sources and urban microenvironments. Despite high-magnitude hyperlocal peaks, we find that concentration differences between sampling areas (i.e., among distinct neighborhoods and cities) cause greater average concentrations for people of color. These findings demonstrate the need to consider mitigation policies at multiple urban scales to address environmental inequity.     

From the Cover: Mapping pollution exposure and chemistry during an extreme air quality event (the 2018 Kīlauea eruption) using a low-cost sensor network

Extreme air quality episodes represent a major threat to human health worldwide but are highly dynamic and exceedingly challenging to monitor. The 2018 Kīlauea Lower East Rift Zone eruption (May to August 2018) blanketed much of Hawai‘i Island in “vog” (volcanic smog), a mixture of primary volcanic sulfur dioxide (SO₂) gas and secondary particulate matter (PM). This episode was captured by several monitoring platforms, including a low-cost sensor (LCS) network consisting of 30 nodes designed and deployed specifically to monitor PM and SO₂ during the event. Downwind of the eruption, network stations measured peak hourly PM_2.5 and SO₂ concentrations that exceeded 75 μg m⁻³ and 1,200 parts per billion (ppb), respectively. The LCS network’s high spatial density enabled highly granular estimates of human exposure to both pollutants during the eruption, which was not possible using preexisting air quality measurements. Because of overlaps in population distribution and plume dynamics, a much larger proportion of the island’s population was exposed to elevated levels of fine PM than to SO₂. Additionally, the spatially distributed network was able to resolve the volcanic plume’s chemical evolution downwind of the eruption. Measurements find a mean SO₂ conversion time of ∼36 h, demonstrating the ability of distributed LCS networks to observe reaction kinetics and quantify chemical transformations of air pollutants in a real-world setting. This work also highlights the utility of LCS networks for emergency response during extreme episodes to complement existing air quality monitoring approaches.

Outdoor air pollution leads to the deaths of millions of people per year, representing the single largest environmental risk factor for premature mortality worldwide (1). Air quality (AQ) monitoring is critical to understand and ultimately minimize people’s exposure to harmful air pollutants; however, surface-based measurements remain relatively sparse in much of the world (2). Moreover, a substantial (though poorly quantified) fraction of humans’ exposure to air pollutants occurs during extreme AQ events in which pollution levels are dramatically elevated relative to mean levels. Examples include the 1948 Donora, Pennsylvania smog event (3), the London Fog of 1952 (4), the Great Smog of Delhi (5), the Beijing “Airpocalypse” in 2013 (6), and recent severe wildfires in North America and Australia (7, 8). During such events, hazardous pollutants such as particulate matter (PM) can be primary (emitted directly) or secondary (formed via atmospheric reactions) (9). The variable confluence of primary emissions, meteorological transport dynamics, and complex secondary chemical processes makes AQ monitoring during extreme episodes exceptionally challenging. There is currently no established approach or strategy to monitor pollutant distribution or human exposure during these episodes.Here, we track and characterize a recent extreme AQ event, the 2018 lower East Rift Zone (LERZ) eruption of Kīlauea Volcano (Island of Hawai‘i, USA, May to August 2018), using a low-cost air quality sensor network. Prior to this event, Kīlauea had been continuously erupting since 1983 (10). As the nearly constant northeasterly trade winds transported the plume downwind around the southern coast of the Island, the primary sulfur dioxide (SO₂) emissions oxidized to form sulfuric acid, leading to elevated levels of fine PM on the island’s downwind western side (the Kona coast) (11). The SO₂ and PM (collectively known as “vog,” for “volcanic smog”) from this effusive eruption had for decades been recognized as a local AQ nuisance and health hazard (12, 13) for the island’s ∼175,000 residents and even for residents in neighboring islands. Prior to May 2018, both satellite measurements and a regulatory network (five stations operated by Hawai‘i Department of Health) showed levels of SO₂ and PM that were substantially elevated above those of background (marine) air (Fig. 1).Open in a separate windowFig. 1.Satellite- and ground-based monitoring of air quality before, during, and after the LERZ eruption. (A and B) Satellite observations of column integrated SO₂ and aerosol optical depth (AOD) from May to July, comparing the average of 3 y prior to the eruption (2015 to 2017), the year of the eruption (2018), and the year following the eruption (2019). SO₂ (shown in Dobson Units [DU]) and AOD measurements are taken from the Ozone Mapping and Profiler Suite (OMPS) instrument aboard Suomi National Polar-orbiting Partnership (NPP) (50 km product, Version 2) and the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Aqua platform (10 km product, Collection 6.1), respectively. Daily satellite observations are gridded and averaged at 0.5° × 0.5° horizontal resolution. (C and D) Concentrations of SO₂ and PM_2.5 for all of 2018, as measured by the Hawai‘i Department of Health ground-level regulatory station at Ocean View.In May 2018, the eruption entered an intense phase with Kīlauea experiencing its largest rift eruption in more than 200 y (14). On May 3, eruptive fissures opened in a residential neighborhood in the LERZ, pumping out lava and emitting substantially elevated amounts of SO₂ (>50,000 tons a day⁻¹) (14) directly into a populated area. During the course of the 3-mo–long eruption, lava covered 35.5 km² of land, more than 700 homes were destroyed, and thousands of residents were displaced. The elevated SO₂ emissions led to exceedingly poor AQ not only in the immediate vicinity of the eruption but also across the wider region. The order-of-magnitude step change in SO₂ emissions and resulting secondary PM was clearly visible from space (Fig. 1 A and B) and was also measured by the ground-based regulatory network (Fig. 1 C and D).The measurements in Fig. 1 established the LERZ eruption as an extreme AQ event, and recent studies have used satellite and in situ measurements (15–17) to explore air quality implications and plume dynamics during the eruption. For example, analysis of regulatory network data found that 24-h average PM_2.5 concentrations exceeded US Environmental Protection Agency (EPA) AQ thresholds eight times during the eruption in certain locations, compared to zero times during the previous 8 y (17). However, such measurements are typically designed to monitor regional-scale AQ and therefore provide limited details about the fine-scale spatiotemporal distribution of air pollutants. Satellite measurements are limited both spatially and temporally (because of overpass intervals of 1 to 3 d, pixel sizes of ∼tens of km, cloud cover, and limited vertical resolution). Ground-based regulatory measurements provide improved temporal resolution and networks are strategically placed to monitor ambient AQ in populated regions but are generally not designed to monitor fine-scale exposure from dynamic plumes during extreme events. On the Island of Hawai‘i, the average resident lived ∼17 km from the nearest regulatory AQ station (closer than the United States average of 22 km), and while this network provides continuous, high-quality measurements at key locations, this is too sparse for high-resolution estimates of residents’ pollutant exposure given the high temporal and spatial variability of the volcanic plume.     

Elucidating severe urban haze formation in China

As the world’s second largest economy, China has experienced severe haze pollution, with fine particulate matter (PM) recently reaching unprecedentedly high levels across many cities, and an understanding of the PM formation mechanism is critical in the development of efficient mediation policies to minimize its regional to global impacts. We demonstrate a periodic cycle of PM episodes in Beijing that is governed by meteorological conditions and characterized by two distinct aerosol formation processes of nucleation and growth, but with a small contribution from primary emissions and regional transport of particles. Nucleation consistently precedes a polluted period, producing a high number concentration of nano-sized particles under clean conditions. Accumulation of the particle mass concentration exceeding several hundred micrograms per cubic meter is accompanied by a continuous size growth from the nucleation-mode particles over multiple days to yield numerous larger particles, distinctive from the aerosol formation typically observed in other regions worldwide. The particle compositions in Beijing, on the other hand, exhibit a similarity to those commonly measured in many global areas, consistent with the chemical constituents dominated by secondary aerosol formation. Our results highlight that regulatory controls of gaseous emissions for volatile organic compounds and nitrogen oxides from local transportation and sulfur dioxide from regional industrial sources represent the key steps to reduce the urban PM level in China.Fine particulate matter smaller than 2.5 μm (PM_2.5) represents a major environmental problem, degrading visibility, negatively affecting human health, and directly and indirectly impacting weather and climate (1–5). Aerosols can be directly emitted (primary) or formed through the gas-to-particle conversion process (secondary) in the atmosphere (6, 7). The primary aerosol sources include emissions from combustion, road or wind-blown dust, and plants, while the secondary formation processes include nucleation and growth by multiphase chemical processes. Also, primary and secondary particles undergo chemical and physical transformations and are subjected to cloud processing and transport in the atmosphere (8, 9). The formation mechanisms leading to severe haze episodes with exceedingly high PM_2.5 levels in China remain highly uncertain, and the abundance and chemical constituents of PM_2.5 vary considerably, depending on complex interplay between meteorology, pollution sources, and atmospheric chemical processes (10–16). For example, on the basis of ambient measurements and receptor model analysis, the contribution to the annual mean PM_2.5 in Beijing has been suggested to be mainly from industrial pollution and secondary inorganic aerosol formation, but negligibly from traffic emissions (14). In addition, meteorological conditions may govern regional and long-range transport of air pollutants (17, 18).     

Wildfire smoke impacts on indoor air quality assessed using crowdsourced data in California

Wildfires have become an important source of particulate matter (PM_2.5 < 2.5-µm diameter), leading to unhealthy air quality index occurrences in the western United States. Since people mainly shelter indoors during wildfire smoke events, the infiltration of wildfire PM_2.5 into indoor environments is a key determinant of human exposure and is potentially controllable with appropriate awareness, infrastructure investment, and public education. Using time-resolved observations outside and inside more than 1,400 buildings from the crowdsourced PurpleAir sensor network in California, we found that the geometric mean infiltration ratios (indoor PM_2.5 of outdoor origin/outdoor PM_2.5) were reduced from 0.4 during non-fire days to 0.2 during wildfire days. Even with reduced infiltration, the mean indoor concentration of PM_2.5 nearly tripled during wildfire events, with a lower infiltration in newer buildings and those utilizing air conditioning or filtration.

Fine particulate matter (PM_2.5) air pollution is the single largest environmental risk factor for human health and death in the United States (1). Wildfires are a major source of PM_2.5 and are documented to cause adverse respiratory health effects and increased mortality (2). Toxicological and epidemiological studies suggest that PM_2.5 from wildfires is more harmful to the respiratory system than equal doses of non-wildfire PM_2.5 (3, 4). The number and magnitude of wildfires in the western United States has increased in recent decades due to climate change and land management (5–7). Although the annual mean level of PM_2.5 has substantially declined over this period following the implementation of extensive air quality policies to reduce emissions from controllable sources, the frequency and severity of smoke episodes with PM_2.5 exceedances has increased sharply due to wildfires in the Pacific Northwest and California (8, 9). The annual mean PM_2.5 in Northern California has increased since 2015 (SI Appendix, Fig. S1) due to massive seasonal fire events, and these events have become the dominant cause of PM_2.5 exceedances.People in the United States spend 87% of their time indoors (10). However, the protection against air pollutants of outdoor origin provided by buildings is commonly overlooked in air quality, epidemiologic, and risk assessment studies (11). To accurately characterize and reduce population exposures to wildfire PM_2.5, it is necessary to understand how buildings are used by their occupants to mitigate exposure. Previous estimations of indoor particles of outdoor origin typically relied on measurements from a limited number of buildings and extrapolation of these measurements to other buildings based on the empirical infiltration and removal parameters (12, 13). However, such extrapolation is not applicable to wildfire events because it does not take into account the distribution of protection provided by buildings (including natural and mechanical ventilation) due to lack of data measuring infiltration under representative conditions. The infiltration of outdoor particles is dependent on people’s behavior (11, 14, 15), which changes during wildfires (and in 2020 during the COVID-19 pandemic). Pollution levels during wildfire events, and knowledge of those pollution levels through available air quality data, directly impact human responses aimed at controlling the infiltration of outdoor PM_2.5 including reducing ventilation, using air conditioning, and using active filtration. Statistically robust observations of the variability of PM_2.5 infiltration during actual wildfire events across a broad cross-section of normally occupied residences provides the opportunity to understand the distribution of real infiltration rates affecting human exposure and the factors controlling them, potentially informing guidance toward improvement.Here, we exploit a recent trend in air quality sensing—public data from a network of ubiquitous crowdsourced low-cost PM_2.5 sensors—to characterize how indoor air quality during wildfire episodes is affected by buildings and their occupants. We demonstrate that buildings provide substantial protection against wildfire PM_2.5 and that behavioral responses of building occupants contribute to effective mitigation of wildfire smoke exposure. Real-time PM_2.5 sensors based on aerosol light scattering have proliferated as easy to use and low-cost consumer devices in recent years, providing a novel opportunity to explore the indoor intrusion of wildfire PM_2.5. Among various networks of devices, the crowdsourced PurpleAir network is the most extensive public-facing network currently available. As of June 2, 2021, there are 15,885 publicly accessible active PurpleAir sensors reporting data from across the earth; 76% are outdoor (12,088), and 24% are indoor (3,797). Of these PurpleAir sensors, 57% are installed in California (9,072), split into 69% outdoor (6,273) and 31% indoor (2,799). As shown in Fig. 1, California accounts for 74% of all indoor PurpleAir sensors in the United States, with adoption increasing most rapidly following individual wildfire episodes as noted by prior work (16). We focus here on analyzing the data from these sensors deployed across the metropolitan regions of San Francisco and Los Angeles, California, where the public adoption of indoor and outdoor PurpleAir sensors is especially high, at least partially in response to the high frequency of recent wildfire events. Analyses are presented for the wildfire season in the San Francisco Bay Area of Northern California (NC) during August to September 2020 (denoted NC 2020) and November 2018 (NC 2018) and for the Los Angeles area of Southern California (SC) in August to September 2020 (SC 2020). Maps of the measurement regions are provided in SI Appendix, Figs. S2 and S3. We analyzed the data from over 1,400 indoor sensors and their outdoor counterparts to characterize levels of and dynamics of indoor PM_2.5 and the fraction of outdoor PM_2.5 that entered buildings, comparing wildfire and non-fire periods. The vast majority (>87%) of sensors in our dataset are in buildings that are unambiguously identified as residential. We mainly focus on residential buildings, which is facilitated by linking individual PurpleAir sensor locations with a dataset of detailed home property characteristics (Zillow).Open in a separate windowFig. 1.Number of publicly accessible indoor PurpleAir sensors in the United States and California. The shadings show major wildfire periods (start date to containment date of fires with >50,000 total acres burned) in California. Wildfire periods are from the Cal Fire website (https://www.fire.ca.gov/incidents/).     

Coulomb interaction,phonons, and superconductivity in twisted bilayer graphene

The polarizability of twisted bilayer graphene, due to the combined effect of electron–hole pairs, plasmons, and acoustic phonons, is analyzed. The screened Coulomb interaction allows for the formation of Cooper pairs and superconductivity in a significant range of twist angles and fillings. The tendency toward superconductivity is enhanced by the coupling between longitudinal phonons and electron–hole pairs. Scattering processes involving large momentum transfers, Umklapp processes, play a crucial role in the formation of Cooper pairs. The magnitude of the superconducting gap changes among the different pockets of the Fermi surface.

Twisted bilayer graphene (TBG) shows a complex phase diagram which combines superconducting and insulating phases (1, 2) and resembles strongly correlated materials previously encountered in condensed matter physics (3–6). On the other hand, superconductivity seems more prevalent in TBG (7–11), while in other strongly correlated materials magnetic phases are dominant.The pairing interaction responsible for superconductivity in TBG has been intensively studied. Among other possible pairing mechanisms, the effect of phonons (12–19) (see also ref. 20), the proximity of the chemical potential to a van Hove singularity in the density of states (DOS) (21–25) and excitations of insulating phases (26–28) (see also refs. 29–31), and the role of electronic screening (32–35) have been considered.In the following, we analyze how the screened Coulomb interaction induces pairing in TBG. The calculation is based on the Kohn–Luttinger formalism (36) for the study of anisotropic superconductivity via repulsive interactions. The screening includes electron–hole pairs (37), plasmons (38), and phonons (note that acoustic phonons overlap with the electron–hole continuum in TBG). Our results show that the repulsive Coulomb interaction, screened by plasmons and electron–hole pairs only, leads to anisotropic superconductivity, although with critical temperatures of order T_c ∼ 10⁻³ to 10⁻² K. The inclusion of phonons in the screening function substantially enhances the critical temperature, to T_c ∼ 1 to 10 K.     

Whole-blood metabolomics of dementia patients reveal classes of disease-linked metabolites

Dementia is caused by factors that damage neurons. We quantified small molecular markers in whole blood of dementia patients, using nontargeted liquid chromatography–mass spectroscopy (LC-MS). Thirty-three metabolites, classified into five groups (A to E), differed significantly in dementia patients, compared with healthy elderly subjects. Seven A metabolites present in plasma, including quinolinic acid, kynurenine, and indoxyl-sulfate, increased. Possibly they act as neurotoxins in the central nervous system (CNS). The remaining 26 compounds (B to E) decreased, possibly causing a loss of support or protection of the brain in dementia. Six B metabolites, normally enriched in red blood cells (RBCs), all contain trimethylated ammonium moieties. These metabolites include ergothioneine and structurally related compounds that have scarcely been investigated as dementia markers, validating the examination of RBC metabolites. Ergothioneine, a potent antioxidant, is significantly decreased in various cognition-related disorders, such as mild cognitive impairment and frailty. C compounds also include some oxidoreductants and are normally abundant in RBCs (NADP⁺, glutathione, adenosine triphosphate, pantothenate, S-adenosyl-methionine, and gluconate). Their decreased levels in dementia patients may also contribute to depressed brain function. Twelve D metabolites contains plasma compounds, such as amino acids, glycerophosphocholine, dodecanoyl-carnitine, and 2-hydroxybutyrate, which normally protect the brain, but their diminution in dementia may reduce that protection. Seven D compounds have been identified previously as dementia markers. B to E compounds may be critical to maintain the CNS by acting directly or indirectly. How RBC metabolites act in the CNS and why they diminish significantly in dementia remain to be determined.

“Dementia” is a collective term to describe various symptoms of cognitive impairment in a condition in which intelligence is irreversibly diminished due to acquired organic disorders of the brain, characterized by deterioration of memory, thinking, behavior, and the ability to perform daily activities (1, 2). Though a common cause is Alzheimer’s disease (AD), a neurodegenerative disease in which memory is rapidly impaired due to hippocampal atrophy, multiple types of dementia, known as “mixed dementia,” can coexist (3, 4). Mental and physical exercise and avoidance of obesity may reduce the risk of dementia (5–7). No medications or supplements have been definitively shown to decrease risk (8, 9). Dementia most often begins in people over 65 y of age, and about 6% of seniors are afflicted with it. It is one of the most costly diseases in developed countries (10).In this study, we conducted nontargeted, comprehensive analysis of blood metabolites in dementia patients. Thorough metabolomic evaluation can supply complete information about metabolite abundance in each subject. While nontargeted analysis is far more laborious than targeted analysis, the effort expended in this “no assumptions” approach is often recompensed by identification of diagnostic compounds overlooked by targeted analysis. A wealth of metabolite information may provide clues to understanding the profound metabolic changes occurring in dementia. Liquid chromatography–mass spectroscopy (LC-MS) was employed for whole-blood metabolite profiling of dementia patients, and we found metabolic compounds not previously known to be related to dementia. Metabolomics of blood cells have scarcely been investigated, particularly in relation to diseases, despite the fact that red blood cells (RBCs) account for about 40% of all blood metabolites (11–13). Thus, metabolomic information from RBCs also provides crucial information on health and disease (14–16). Here we identified 33 dementia-linked markers (12 of which are RBC-enriched) and validated them by principal component analysis (PCA), correlation, and heatmap analyses, confirming that these markers actually are involved in development of dementia. Our results suggest that detailed molecular diagnosis of dementia is now possible. Somewhat unexpectedly, markers deduced from dementia only partially overlap with amino acid markers obtained from frailty patients with cognitive defects (16), so that frailty and dementia partly share the diminished cognitive markers. We also show that an antioxidant, ergothioneine (ET), an RBC component involved in human cognitive ability (16, 17), and two related compounds are reduced in dementia.     

Passage of exogeneous fine particles from the lung into the brain in humans and animals

There are still significant knowledge gaps in understanding the intrusion and retention of exogeneous particles into the central nervous system (CNS). Here, we uncovered various exogeneous fine particles in human cerebrospinal fluids (CSFs) and identified the ambient environmental or occupational exposure sources of these particles, including commonly found particles (e.g., Fe- and Ca-containing ones) and other compositions that have not been reported previously (such as malayaite and anatase TiO₂), by mapping their chemical and structural fingerprints. Furthermore, using mouse and in vitro models, we unveiled a possible translocation pathway of various inhaled fine particles from the lung to the brain through blood circulation (via dedicated biodistribution and mechanistic studies). Importantly, with the aid of isotope labeling, we obtained the retention kinetics of inhaled fine particles in mice, indicating a much slower clearance rate of localized exogenous particles from the brain than from other main metabolic organs. Collectively, our results provide a piece of evidence on the intrusion of exogeneous particles into the CNS and support the association between the inhalation of exogenous particles and their transport into the brain tissues. This work thus provides additional insights for the continued investigation of the adverse effects of air pollution on the brain.

Air pollution is still a great threat to public health globally. In the past decades, the lung and cardiovascular systems have been considered the predominant organs afflicted by air pollution as evidenced by numerous epidemiological and experimental findings (1–3). Meanwhile, recent evidence recognizes the likely injuries in the brain arising from extended exposure to polluted air and the consequent neurodegenerative diseases and behavioral disorder symptoms (4, 5). For instance, observations revealed a strong link between high levels of air pollution and marked neuroinflammation, Alzheimer''s-like changes, and cognitive problems in older people and even in children (6–12). Maher et al. (13) observed the presence of magnetic particles with a size less than 200 nm in the human frontal cortex and proposed the entry of particles into the brain via the olfactory bulb. More recently, Calderón-Garcidueñas et al. (14) identified the existence of exogenous metal-rich and magnetic fine particles and Ti-rich nanorods in the human brain and also showed evidence of entry via axonal transport, namely from the gastrointestinal tract, as the key entry portal and subsequent transport to the brain stem via the vagus nerves. In support of these findings in humans, experimental studies obtained similar observations in different animal models (15–18). However, much less is understood regarding the impact of ambient fine particles on the brain compared with their impact on the lung and heart. For instance, there is a lack of evidence on the intrusion of exogeneous fine particles from environmental or occupational sources into the brain as well as a lack of evidence regarding the exposure route and transport process of exogeneous particles, particle-induced injuries to the surrounding brain tissue, and the accumulation and retention kinetics of particles in the brain. To understand the scale of the issue, it would be of great significance to fill these knowledge gaps on airborne fine particles and their impacts on the brain.Air pollutants are a mixture of many toxic components. Of various pollutants, particulate matters (PMs), especially ambient fine particles (such as PM_2.5 and PM_0.1), are thought to be the most concerning in terms of their induction of detrimental health effects (19). Many physicochemical properties have been documented to dictate the toxicity of PMs, and the ambient particle size appears to be the most decisive one (20). Hence, PMs, in particular ultrafine particles, bear an intrinsic capability to escape the recent monitoring techniques and the body’s protective systems, including the sentinel immune cells and the biological barriers (21–24). In fact, inhaled ambient fine particles or exogeneous engineered nanoparticles (NPs) have been observed in human serum (25), pleural effusion (25), heart tissue (26), vascular inflammation (27), and even the placenta (28). Unlike the liver, spleen, and other organs with defenseless large-volume blood circulation, the brain is naturally protected by a stringent defense line, namely the blood–brain barrier (BBB), which prevents the crossing of both soluble and particulate foreign entities or offenders (29, 30). Under this premise, it is rather difficult for PMs to penetrate the BBB; however, crossing may occur when the integrity of the BBB is impaired or if the PMs per se damage the BBB (5, 30). To date, multiple pathways have been proposed for the direct intrusion of PMs into the brain (4, 5, 31). One pathway bypasses the BBB, typically through the nose–olfactory route, and this has been confirmed for some synthesized (engineered) NPs (21, 22, 31, 32). The second pathway is in the capacity of nanoscale particles to bind specific proteins, such as apolipoprotein E from plasma, which interact with cellular receptors in the BBB and thus, actively aid the transportation of the particles into the brain via transcytosis without damaging the BBB (33, 34). Meanwhile, the third pathway (i.e., through the inhalation–circulation–BBB route) relies on damage of the BBB, but it is still under debate (4, 5). Moreover, an additional pathway (i.e., the neuroenteric pathway) was indicated to translocate fine particles to the brain from the digestive system, as it is through the neurons of the enteric system to the brain (stem) (14). Since significant BBB impairments were found in animals responding to heavy PM exposure (15, 16), whether the inhaled atmospheric PMs could translocate from blood to the brain ventricle through damage to the BBB needs to be elucidated. To this end, these passive and active pathways for PMs through the BBB warrant detailed investigations.In the current study, we aimed to investigate the intrusion and retention of exogenous fine particles into the brain using in vitro and in vivo models with the aid of ultrastructural microscopy and isotope-labeled particles and to use this knowledge to scrutinize the retention of exogeneous particles in human brain tissues from hospitalized patient samples. Collectively, our combined results revealed the presence of exogeneous fine particles of various compositions in human cerebrospinal fluid (CSF) and added experimental proof (in vivo in mice and in vitro) for the transport of inhaled fine particles into the brain. This work provides further evidence for a potential pathway by which air pollution may exert effects on the brain.     

Pairing facts with imagined consequences improves pandemic-related risk perception

The COVID-19 pandemic reached staggering new peaks during a global resurgence more than a year after the crisis began. Although public health guidelines initially helped to slow the spread of disease, widespread pandemic fatigue and prolonged harm to financial stability and mental well-being contributed to this resurgence. In the late stage of the pandemic, it became clear that new interventions were needed to support long-term behavior change. Here, we examined subjective perceived risk about COVID-19 and the relationship between perceived risk and engagement in risky behaviors. In study 1 (n = 303), we found that subjective perceived risk was likely inaccurate but predicted compliance with public health guidelines. In study 2 (n = 735), we developed a multifaceted intervention designed to realign perceived risk with actual risk. Participants completed an episodic simulation task; we expected that imagining a COVID-related scenario would increase the salience of risk information and enhance behavior change. Immediately following the episodic simulation, participants completed a risk estimation task with individualized feedback about local viral prevalence. We found that information prediction error, a measure of surprise, drove beneficial change in perceived risk and willingness to engage in risky activities. Imagining a COVID-related scenario beforehand enhanced the effect of prediction error on learning. Importantly, our intervention produced lasting effects that persisted after a 1- to 3-wk delay. Overall, we describe a fast and feasible online intervention that effectively changed beliefs and intentions about risky behaviors.

The COVID-19 pandemic has brought unprecedented global challenges, affecting both physical health and mental well-being (1–8). Public health experts have promoted restrictions to mitigate the spread of disease, including social distancing (i.e., physical distancing) and closing nonessential businesses (7). Despite rapid progress in preventative and palliative care, widespread global vaccination will require an extended period of time, and social/physical distancing continues to be crucial for protecting vulnerable individuals and limiting the spread of viral variants (9). Severe outbreaks will limit the success of vaccine implementation, underscoring the need for behavioral interventions that reduce the spread of disease (10). Given the exponential rate of virus transmission (9, 11), encouraging even a single individual to comply with public health guidelines could have significant and widespread downstream effects (12–16).To make adaptive decisions during the pandemic, individuals should balance conflicting needs, which might include avoiding exposure to the virus, earning an income, supporting local businesses, or seeking social support to bolster mental health (1–3, 5–7). Accurately assessing the risks associated with behavioral options is fundamental to adaptive decision making in any context (17–19), especially under chronic stress (20–22). Nonetheless, risk misestimation is common, especially for low-probability events (23–26), and low quantitative literacy is linked to poor health decision making and outcomes (27, 28). During the pandemic, risk underestimation could lead to risky behaviors that harm individuals and society at large, but risk overestimation could increase distress and anxiety while reducing mental well-being (29, 30).Encouraging large-scale, long-term behavior change during the COVID-19 pandemic has proven difficult: widespread “pandemic fatigue” and prolonged economic hardship contributed to a deadly global resurgence of the virus during late 2020 and early 2021 (7, 9). Empowering individuals to accurately assess local risk levels can support more informed decision making, bolstering sustainable compliance with public health recommendations. Although recent studies have found that subjective perceived risk relates to demographic variables, attitudes, and risky behaviors during the pandemic (3, 29, 31–36), past studies have not evaluated the accuracy of perceived risk or intervened to change perceived risk. Local risk levels can change rapidly over time (11, 37); an intervention that is fast, low effort, and easy to administer could realign perceived risk with actual risk.Prior interventions on risk estimation have shown some success, although effect sizes are typically small and weaken over time (38, 39). A separate line of research has demonstrated that episodic simulation, or imagination, of the downstream outcomes of choices can enhance decision making, including self-regulation (40–44). The rich, personalized mental imagery generated during episodic simulation may drive these effects by increasing the salience of an intervention (44–46) and supporting the formation of “gist” representations that persist over time (47). Furthermore, thinking concretely about outcomes increases perceived risk and estimation accuracy for common adverse events (48). Other studies have shown that increasing the salience of an intervention can enhance initial behavioral outcomes and also boost long-term effects (49, 50). Risk perception is influenced by the availability of information about outcomes (51–53); anecdotes tend to be more vivid and easily recalled, and can exert greater influence on risk perception than statistics (54–56). Crucially, combining statistical information with an imagined narrative could create a synergistic effect that enhances learning (57).Other studies have explored how individuals update beliefs and knowledge in response to feedback (58–60). Information prediction error (i.e., surprise) describes the discrepancy between expectation and reality; the valence (better or worse than expected) and magnitude of this surprise signal drive learning. Larger prediction errors lead to more successful belief revision (58–61). A prior study found that prediction error allowed beliefs about risk to be updated, but participants tended to resist using bad news to learn about future adverse events (62). Likewise, another study found a valence bias in belief updating (particularly in youths), such that negative information about risk tended to be discounted (63). Overall, presenting surprising risk information may change beliefs and improve the accuracy of risk perception. However, combining prediction error with another psychological intervention—such as an episodic simulation—could enhance learning, particularly if people tend to resist updating beliefs about adverse events.Here, we report the results of an efficient and accessible intervention designed to reduce risk misestimation and realign individual behavior with public health guidelines. Using a large, nationally representative sample of US residents, we first showed that perceived risk was not aligned with actual risk (study 1). To remedy this misalignment, we developed an intervention that combined an episodic simulation with a risk estimation exercise that provided accuracy feedback (study 2). In this preregistered experiment, we found that a simple 10-min intervention helped realign perceived risk with actual risk and reduced willingness to engage in potentially risky activities. The magnitude of the information prediction error experienced during a prevalence-based risk estimation exercise drove change in the perceived risk of engaging in a variety of everyday risky activities; this effect of surprise on learning was enhanced when the intervention included an episodic simulation about the possible outcomes of risky decisions.     

Characterization of T cell receptors reactive to HCRTNH2, pHA273-287, and NP17-31 in control and narcolepsy patients

Narcolepsy type 1 (NT1), a disorder caused by hypocretin/orexin (HCRT) cell loss, is associated with human leukocyte antigen (HLA)-DQ0602 (98%) and T cell receptor (TCR) polymorphisms. Increased CD4⁺ T cell reactivity to HCRT, especially DQ0602-presented amidated C-terminal HCRT (HCRT_NH2), has been reported, and homology with pHA_273–287 flu antigens from pandemic 2009 H1N1, an established trigger of the disease, suggests molecular mimicry. In this work, we extended DQ0602 tetramer and dextramer data to 77 cases and 44 controls, replicating our prior finding and testing 709 TCRs in Jurkat 76 T cells for functional activation. We found that fewer TCRs isolated with HCRT_NH2 (∼11%) versus pHA_273–287 or NP_17–31 antigens (∼50%) were activated by their ligand. Single-cell characterization did not reveal phenotype differences in influenza versus HCRT_NH2-reactive T cells, and analysis of TCR CDR3αβ sequences showed TCR clustering by responses to antigens but no cross-peptide class reactivity. Our results do not support the existence of molecular mimicry between HCRT and pHA_273–287 or NP_17–31.

Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin (HCRT) neurons in the mediolateral hypothalamus (1–3), with recent data suggesting reversion of the human and animal phenotype with orexin agonists. The disease is strongly associated with human leukocyte antigen (HLA) DQB1*06:02/DQA1*01:02 (98% vs. 25%) (DQ0602) and displays weaker genetic associations with other immune loci, thus suggesting autoimmunity (4–9), although not meeting all criteria for being classified as an autoimmune disease (10). Like other autoimmune diseases, NT1 presents with increased comorbidity with other autoimmune conditions and asthma (11–13).Onset of NT1 is often abrupt and seasonal, and association with both Streptococcus pyogenes (14, 15) and influenza A infections (16) suggests that it may be triggered by winter infections. Most strikingly, prevalence of NT1 increased several folds in mainland China and Taiwan following the 2009 to 2010 “swine flu” H1N1 influenza pandemic (pH1N1) (4, 17, 18), although association with the pandemic is less clear in other countries (19). Vaccination with the pH1N1 vaccine Pandemrix has also been associated with an elevated relative risk for developing narcolepsy of 5- to 14-fold in children and adolescents and 2- to 7-fold in adults (18, 20–22). As Pandemrix is an AS03-adjuvanted vaccine containing the artificially produced reassortant strain X-179A, a mix of A/Puerto Rico/8/1934 (PR8), an old H1N1 strain derived from pre-2009 seasonal H1N1, and the key H1N1 2009 surface proteins hemagglutinin (HA) and neuraminidase (NA) (23), flu proteins are likely critically involved in triggering NT1. Evidence showing that HLA and T cell receptor (TCR) genetic associations are universal (9, 24–27) is also consistent with a flu trigger, as influenza A infections occur on a global basis (28). Importantly, however, even with Pandemrix vaccination in Europe, only ∼1 in 16,000 vaccinated children developed NT1, thus demanding the consideration of additional factors to fully explain the initiation of NT1 (29).Unlike in other autoimmune diseases, autoantibodies against HCRT cell proteins, HCRT itself (30–32), or other targets such as TRIB2 (33, 34) or HCRT receptor 2 (35–38) have not been consistently found. This has led to the suggestion that HCRT cell loss may be primarily T cell mediated, with limited or no involvement of autoantibodies. Consistent with this hypothesis, mounting evidence suggests involvement of CD4⁺ T cell reactivity to HCRT in NT1 (39–41), notably toward amidated fragments of the secreted, mature peptide (HCRT_54–66-NH2 and HCRT_86–97-NH2, homologous peptides collectively denoted as HCRT_NH2) (42), as critical factors in the development of the disease. Furthermore, CD8⁺ mediation of HCRT cell death has also been shown to cause NT1 in an animal model (43) and Pedersen and colleagues (44) recently highlighted the presence of CD8⁺ T cell responses against intracellular proteins contained in HCRT neurons in narcolepsy patients. Of additional interest is the observation that the TCR polymorphisms associated with NT1 are quantitative trait loci for TRAJ24 (decreasing), TRAJ28, and TRBV4-2 (increasing) usage in peripheral T cells in both controls and patients (29). A significant L to F coding polymorphism located within the antigen-binding complementarity-determining region (CDR) 3 loop of TRAJ24 expressing TCRs is also associated with NT1. Altogether, this suggests that T cell responses involving TRAJ24- or TRAJ28- and TRBV4-2–bearing TCRs may be bottleneck responses in a causative autoimmune T cell response, leading to HCRT cell death (4, 14, 17–19, 45).Based on the evidence provided above, our group hypothesized that a CD4⁺ T cell–mediated response directed against specific flu epitopes could lead to molecular mimicry with HCRT itself, potentially HCRT_NH2, subsequently recruiting CD8⁺ cytotoxic T cells and leading to HCRT cell death. To test this hypothesis, we screened 135 DQ0602 tetramers binding peptides originating from Pandemrix, wild-type 2009 H1N1, and two autoantigens (HCRT and RFX4) for the presence of antigen-restricted CD4⁺ T cells (42). After this systematic survey, it was established that CD4⁺ T cell populations recognizing influenza pHA_273–287 (pH1N1 specific) and PR8 (H1N1 pre-2009 and H2N2)-restricted NP_17–31 epitopes were increased in NT1 versus DQ0602 controls. Supporting this finding, this difference was also present in post-Pandemrix cases versus controls and was stronger in recent onset cases (42). Additionally, studies of single cells recognizing these peptides revealed that TCR clones carrying TRBV4-2 and TRAJ24 were retrieved from both HCRT_NH2 and pHA_273–287 tetramers (42), suggesting involvement of these clones in molecular mimicry and disease pathophysiology. Similarly, Jiang et al. (39) isolated TRAJ24-positive cells recognizing DQ0602 bound to HCRT_87–100 tetramer, many of which expressed perforin and granzyme-B, suggesting a terminally differentiated effector T cell (T_EMRA) phenotype. In one case, a TRAJ24 clone isolated from a narcoleptic patient showed elevated TCR reactivity toward HCRT_87–97-NH2 when transfected in Jurkat 76 (J76) cells, thus implying a role for TRAJ24 reactivity toward DQ0602-HCRT in narcolepsy autoimmunity (39).Here, we extend prior work from our group by doubling the number of patients and controls and increasing the representation of TRAJ24F narcolepsy susceptibility–associated alleles in these subjects. Results validated an increased frequency of pHA_273–287 and HCRT_54–66-NH2 tetramer-positive CD4⁺ T cells in NT1, while also testing isolated T cell clones for potential activation by their cognate ligands when expressed in J76 cells. Importantly, we also analyzed TCR CDR3αβ sequences in this larger dataset and conducted expression profiling of the corresponding T cells, providing insights into T cell characteristics in narcolepsy.     

Two-dimensional electronic–vibrational sum frequency spectroscopy for interactions of electronic and nuclear motions at interfaces

Interactions of electronic and vibrational degrees of freedom are essential for understanding excited-states relaxation pathways of molecular systems at interfaces and surfaces. Here, we present the development of interface-specific two-dimensional electronic–vibrational sum frequency generation (2D-EVSFG) spectroscopy for electronic–vibrational couplings for excited states at interfaces and surfaces. We demonstrate this 2D-EVSFG technique by investigating photoexcited interface-active (E)-4-((4-(dihexylamino) phenyl)diazinyl)-1-methylpyridin-1- lum (AP3) molecules at the air–water interface as an example. Our 2D-EVSFG experiments show strong vibronic couplings of interfacial AP3 molecules upon photoexcitation and subsequent relaxation of a locally excited (LE) state. Time-dependent 2D-EVSFG experiments indicate that the relaxation of the LE state, S₂, is strongly coupled with two high-frequency modes of 1,529.1 and 1,568.1 cm⁻¹. Quantum chemistry calculations further verify that the strong vibronic couplings of the two vibrations promote the transition from the S₂ state to the lower excited state S₁. We believe that this development of 2D-EVSFG opens up an avenue of understanding excited-state dynamics related to interfaces and surfaces.

Electronic and vibrational degrees of freedom are the most important physical quantities in molecular systems at interfaces and surfaces. Knowledge of interactions between electronic and vibrational motions, namely electronic–vibrational couplings, is essential to understanding excited-states relaxation pathways of molecular systems at interfaces and surfaces. Many excited-states relaxation processes occur at interfaces and surfaces, including charge transfer, energy transfer, proton transfer, proton-coupled electron transfer, configurational dynamics, and so on (1–11). These relaxation processes are intimately related to the electronic–vibrational couplings at interfaces and surfaces. Strong electronic–vibrational couplings could promote nonadiabatic evolution of excited potential energy and thus, facilitate chemical reactions or intramolecular structural changes of interfacial molecules (10, 12, 13). Furthermore, these interactions of electronic and vibrational degrees of freedom are subject to solvent environments (e.g., interfaces/surfaces with a restricted environment of unique physical and chemical properties) (9, 14, 15). Despite the importance of interactions of electronic and vibrational motions, little is known about excited-state electronic–vibrational couplings at interfaces and surfaces.Interface-specific electronic and vibrational spectroscopies enable us to characterize the electronic and vibrational structures separately. As interface-specific tools, second-order electronic sum frequency generation (ESFG) and vibrational sum frequency generation (VSFG) spectroscopies have been utilized for investigating molecular structure, orientational configurations, chemical reactions, chirality, static potential, environmental issues, and biological systems at interfaces and surfaces (16–52). Recently, structural dynamics at interfaces and surfaces have been explored using time-resolved ESFG and time-resolved VSFG with a visible pump or an infrared (IR) pump thanks to the development of ultrafast lasers (6–9, 13–15, 49, 53–61). Doubly resonant sum frequency generation (SFG) has been demonstrated to probe both electronic and vibration transitions of interfacial molecular monolayer (15, 62–71). This frequency-domain two-dimensional (2D) interface/surface spectroscopy could provide information regarding electronic–vibrational coupling of interfacial molecules. However, contributions from excited states are too weak to be probed due to large damping rates of vibrational states in excited states (62, 63). As such, the frequency-domain doubly resonant SFG is used only for electronic–vibrational coupling of electronic ground states. Ultrafast interface-specific electronic–vibrational spectroscopy could allow us to gain insights into how specific nuclear motions drive the relaxation of electronic excited states. Therefore, development of interface-specific electronic–vibrational spectroscopy for excited states is needed.In this work, we integrate the specificity of interfaces and surfaces into the capabilities of ultrafast 2D spectroscopy for dynamical electronic–vibrational couplings in excited states of molecules; 2D interface-specific spectroscopies are analogous to those 2D spectra in bulk that spread the information contained in a pump−probe spectrum over two frequency axes. Thus, one can better interpret congested one-dimensional signals. Two-dimensional vibrational sum frequency generation (2D-VSFG) spectroscopy was demonstrated a few year ago (72–74). Furthermore, heterodyne 2D-VSFG spectroscopy using middle infrared (mid-IR) pulse shaping and noncollinear geometry 2D-VSFG experiments have also been developed to study vibrational structures and dynamics at interfaces (31, 75–78). Recently, two-dimensional electronic sum frequency generation (2D-ESFG) spectroscopy has also been demonstrated for surfaces and interfaces (79). On the other hand, bulk two-dimensional electronic–vibrational (2D-EV) spectroscopy has been extensively used to investigate the electronic relaxation and energy transfer dynamics of molecules, biological systems, and nanomaterials (80–90). The 2D-EV technique not only provides electronic and vibrational interactions between excitons or different excited electronic states of systems but also, identifies fast nonradiative transitions through nuclear motions in molecules, aggregations, and nanomaterials. However, an interface-specific technique for two-dimensional electronic–vibrational sum frequency generation (2D-EVSFG) spectroscopy has yet to be developed.Here, we present the development of 2D-EVSFG spectroscopy for the couplings of electronic and nucleic motions at interfaces and surfaces. The purpose of developing 2D-EVSFG spectroscopy is to bridge the gap between the visible and IR regions to reveal how structural dynamics for photoexcited electronic states are coupled with vibrations at interfaces and surfaces. As an example, we applied this 2D-EVSFG experimental method to time evolution of electronic–vibrational couplings at excited states of interface-active molecules at the air–water interface.     

Confinement effects facilitate low-concentration carbon dioxide capture with zeolites

Engineered systems designed to remove CO₂ from the atmosphere need better adsorbents. Here, we report on zeolite-based adsorbents for the capture of low-concentration CO₂. Synthetic zeolites with the mordenite (MOR)-type framework topology physisorb CO₂ from low concentrations with fast kinetics, low heat of adsorption, and high capacity. The MOR-type zeolites can have a CO₂ capacity of up to 1.15 and 1.05 mmol/g for adsorption from 400 ppm CO₂ at 30 °C, measured by volumetric and gravimetric methods, respectively. A structure–performance study demonstrates that Na⁺ cations in the O33 site located in the side-pocket of the MOR-type framework, that is accessed through a ring of eight tetrahedral atoms (either Si⁴⁺ or Al³⁺: eight-membered ring [8MR]), is the primary site for the CO₂ uptake at low concentrations. The presence of N₂ and O₂ shows negligible impact on CO₂ adsorption in MOR-type zeolites, and the capacity increases to ∼2.0 mmol/g at subambient temperatures. By using a series of zeolites with variable topologies, we found the size of the confining pore space to be important for the adsorption of trace CO₂. The results obtained here show that the MOR-type zeolites have a number of desirable features for the capture of CO₂ at low concentrations.

The anthropogenic emission of CO₂ is associated with the continuous increase in global temperature since the industrial revolution. Therefore, many CO₂ mitigation strategies have been under investigation in the past years with the goal of net-zero emission by 2050. Point-source capture provides solutions for the sustainable operation of steel industries, cement plants, coal-based power stations, and the like. Bioenergy with carbon capture and storage (BECCS) and direct air capture (DAC) are being investigated for the direct removal of CO₂ from air to address the emissions from mobile sources such as automobiles, airplanes, and cargo ships (1, 2). BECCS and DAC provide opportunities for net-negative emissions.The search for CO₂ adsorbents that are affordable, effective, energy efficient, environmentally friendly, and safe for large-scale applications poses a serious challenge for the implementation of carbon capture technologies (3–5). This is particularly significant for DAC because of the low diffusion rates and low CO₂ capacities resulting from trace concentrations of CO₂ (5). It is generally believed that physisorbents are not applicable to the DAC system due to the their weak affinity for CO₂ (6). Thus, chemisorbents have been extensively investigated for trace CO₂ capture (7). Liquid amine and alkaline adsorbents as well as other types of chemisorbents (e.g., ionic liquids (8) and electric based membranes (9)) have been investigated for the capture of CO₂ from low-concentration environments. However, the intensive energy requirements for desorption (60–120 kJ/mol) and slow kinetics may be problematic for processing the large quantities needed for the implementation of DAC with chemisorbents. Amines supported on porous materials have also been studied for DAC to increase surface exposure and diffusion kinetics as compared to the liquid analogs (1, 10–14). The time-dependent oxidative degradation and evaporation of amines are intrinsic challenges to the use of these methods (15). The development of new physisorbents that display high stability, low energy cost for desorption, fast sorption kinetics, and high capacity, as well as that possess moderate adsorption heat of 30–60 kJ/mol and fully reversible physisorption properties, could be enabling adsorbents for large-scale DAC (16).The most widely studied physisorbents for DAC are metal–organic frameworks (MOFs) (5). Promising CO₂ capture performances are reported for hybrid ultramicroporous materials, a subgroup of MOFs (17–20). The high performance of these solids originates from the strongly electronegative, fluorine-based adsorption centers as well as the well-controlled pore size (20). However, it is still a challenging task to obtain a MOF sorbent for DAC with cost-effective scalability, high performance, and long-term stability (18, 20). Zeolites are another type of microporous materials with vast structural and physicochemical properties and high stability. They can be synthesized in large quantities in cost-effective processes and have a long history in the industry for catalysis and adsorption (21). For carbon capture, they often possess faster kinetics than supported amines (22). These merits have made zeolites interesting candidates for the capture of high-concentration CO₂ (23–25). The known challenges for the application of zeolites for DAC are the detrimental effect of water as well as capacities for low-concentration CO₂ capture. The former issue can be addressed by engineering multibed systems with a desiccant bed upstream of the zeolitic adsorbents (26). For DAC applications, the capture of atmospheric water could be a useful endeavor to complement the removal of CO₂ in areas where fresh water is badly needed. To address the later issue, recent research has been focused on maximizing the number (22, 27) of adsorption sites by using low-silica zeolites or adjusting the type of extraframework cations (28–30) by incorporating Zn²⁺ and Ca²⁺ ions. These approaches have given high capacities of 0.4 and 0.87 mmol/g for Na-FAU zeolites with Si/Al = 1.2 (zeolite X) and 1.0 (low-silica zeolite X), respectively, as well as 0.67 mmol/g for Zn-CHA with Si/Al = ∼2.0 and 1.8 mmol/g for Ca-LTA with Si/Al = 1 (the three letter codes of zeolites are assigned by the Interantional Zeolite Association for indicating their framework topologies). However, the low thermostability for low-silica zeolites and irreversible adsorption of CO₂ in Ca²⁺-containing zeolites will probably present challenges for commercial-scale implementation of DAC. Therefore, there remains a need for a zeolitic adsorbent with competitive capacity, high stability, and cyclability (31, 32).In this work, we report that the confinement effect in zeolites greatly affects the adsorption of low-concentration CO₂. MOR-type zeolites with eight-membered ring (8MR) side-pockets synthesized with or without (33) organic structure-directing agents (OSDAs) can give a CO₂ capacity of ∼1.15 mmol/g, among the highest reported for physisorbents for DAC. This capacity results in approximately an order of magnitude improvement of adsorption efficiency (i.e., CO₂ per adsorption site), compared to the standard 13X zeolite adsorbent. The O33 site in the 8MR side-pocket is the primary adsorption site of MOR-type zeolites, and the size of the confined space for the adsorption site in zeolites dictates their properties for the adsorption of 400 ppm CO₂.     

Contact efflorescence as a pathway for crystallization of atmospherically relevant particles

Inadequate knowledge of the phase state of atmospheric particles represents a source of uncertainty in global climate and air quality models. Hygroscopic aqueous inorganic particles are often assumed to remain liquid throughout their atmospheric lifetime or only (re)crystallize at low relative humidity (RH) due to the kinetic limitations of efflorescence (salt crystal nucleation and growth from an aqueous solution). Here we present experimental observations of a previously unexplored heterogeneous nucleation pathway that we have termed “contact efflorescence,” which describes efflorescence initiated by an externally located solid particle coming into contact with the surface of a metastable aqueous microdroplet. This study demonstrates that upon a single collision, contact efflorescence is a pathway for crystallization of atmospherically relevant aqueous particles at high ambient RH (≤80%). Soluble inorganic crystalline particles were used as contact nuclei to induce efflorescence of aqueous ammonium sulfate [(NH₄)₂SO₄], sodium chloride (NaCl), and ammonium nitrate (NH₄NO₃), with efflorescence being observed in several cases close to their deliquescence RH values (80%, 75%, and 62%, respectively). To our knowledge, these observations represent the highest reported efflorescence RH values for microdroplets of these salts. These results are particularly important for considering the phase state of NH₄NO₃, where the contact efflorescence RH (∼20–60%) is in stark contrast to the observation that NH₄NO₃ microdroplets do not homogeneously effloresce, even when exposed to extremely arid conditions (<1% RH). Considering the occurrence of particle collisions in the atmosphere (i.e., coagulation), these observations of contact efflorescence challenge many assumptions made about the phase state of inorganic aerosol.Nucleation of the solid phase from a liquid solution (crystallization) is an important process in pharmaceuticals, manufacturing, and atmospheric science (1). In the atmosphere, the phase state and water content of particulate matter influences both heterogeneous chemistry and the aerosol direct and indirect effect on climate (2–6). Despite its importance, there is no comprehensive understanding of the phase state of atmospheric particulate, and the aerosol radiative forcing remains one of the largest uncertainties in climate predictions (6).A significant fraction of aqueous atmospheric aerosol contains soluble inorganics such as chlorides, sulfates, and nitrates that can undergo efflorescence, i.e., the process of salt crystallization and water evaporation. Efflorescence often occurs at a significantly lower relative humidity (RH) than the reverse process of deliquescence (2, 7–12). A potent example of this hydration hysteresis is demonstrated with ammonium nitrate (NH₄NO₃), a hygroscopic component of atmospheric aerosol (9–11). NH₄NO_3(s) crystals will deliquesce to form an aqueous droplet at ∼62% RH (T = 295 K) (13). However, NH₄NO_3(aq) droplets do not homogeneously effloresce even at extremely low RH (∼0%) but instead remain in a metastable liquid state (i.e., supersaturated with respect to the aqueous solute) (2, 9, 10). Thus, many atmospheric chemistry and climate models assume that fully deliquesced inorganic particles such as NH₄NO₃ always remain in the aqueous state or only (re)crystallize at a very low ambient RH (<35%) (7, 14, 15).Although the deliquescence RH (DRH) and efflorescence RH (ERH) for inorganic salts are well-characterized (2), heterogeneous efflorescence has been less extensively studied. Heterogeneous efflorescence occurs when a solid particle acts as a surface upon which nuclei can form, lowering the overall activation barrier for nucleation (2, 8–10). Analogous to heterogeneous ice formation, the solid particles can either be immersed inside the droplet (immersion mode of nucleation) or may come into contact with the exterior of the droplet (contact mode of nucleation) (16, 17). Past research has shown that immersion of solid particles such as mineral dust can raise the ERH of some salts relative to homogeneous efflorescence, causing the particles to be solid under a wider range of atmospherically relevant RH conditions (8–10). Similar experiments have not been performed to explore contact-mode efflorescence, and it has yet to be established whether contact would have a different effect on efflorescence than immersion.Although experimental studies of contact efflorescence have been lacking, a recent modeling study (11) considered phase changes of metastable liquid particles upon coagulation with dry solid particles (i.e., contact efflorescence). That study found an appreciable difference in the particle-phase concentrations of semivolatile inorganic species (e.g., NH₄NO₃) and total aerosol liquid water content when contact-induced phase changes were included in the simulation. Those simulation results point toward the potential real-world importance of contact efflorescence. Indeed, metastable liquid particles and crystalline particles of greatly different compositions can coexist simultaneously in the same air parcel (3, 7, 11, 18, 19). Although immersion efflorescence requires the heterogeneous nuclei to be insoluble or sparingly soluble, there is no such inherent limitation for contact efflorescence. Thus, even highly soluble solid particles can potentially serve as contact nuclei (CN). Alternatively, soluble particles may dissolve upon contact. The contrasting effects of dissolution upon contact compared with efflorescence are illustrated in Fig. 1. Although efflorescence is accompanied by crystallization and water loss, dissolution would be accompanied by droplet growth and water uptake to establish an equilibrium water activity. Reconciling which process preferentially occurs upon contact is thus necessary to predict the phase state and water content of atmospheric particles.Open in a separate windowFig. 1.The potential outcomes of an externally located solid soluble inorganic aerosol coming into contact with a metastable aqueous inorganic microdroplet. In process A, dissolution of the solid particle accompanied by uptake of water. In process B, efflorescence accompanied by evaporation of water.Here, we report on well-controlled experimental observations of contact efflorescence and a discussion of potential implications. To our knowledge, these experiments represent the first observations of contact efflorescence beyond our previous study (12) of aqueous ammonium sulfate [(NH₄)₂SO₄] microdroplets seeded with (NH₄)₂SO₄ microcrystals. In the present study, highly soluble inorganic salts of atmospheric relevance were used as CN. Due to their high solubility, these CN salts are not typically considered as effective heterogeneous nuclei. We probe the heterogeneous ability of these soluble CN by observing contact efflorescence of aqueous sodium chloride (NaCl), (NH₄)₂SO₄, and NH₄NO₃, three atmospherically abundant and important compounds (2–15). The highest single-collision contact efflorescence relative humidity (CERH) was measured for single optically levitated droplets exposed to single collisions with CN. We demonstrate that contact efflorescence is a pathway for crystallization of aqueous inorganic particles at the highest RH at which efflorescence is thermodynamically possible (near their respective DRH).     

Fenton chemistry at aqueous interfaces

In a fundamental process throughout nature, reduced iron unleashes the oxidative power of hydrogen peroxide into reactive intermediates. However, notwithstanding much work, the mechanism by which Fe²⁺ catalyzes H₂O₂ oxidations and the identity of the participating intermediates remain controversial. Here we report the prompt formation of O=Fe^IVCl₃⁻ and chloride-bridged di-iron O=Fe^IV·Cl·Fe^IICl₄⁻ and O=Fe^IV·Cl·Fe^IIICl₅⁻ ferryl species, in addition to Fe^IIICl₄⁻, on the surface of aqueous FeCl₂ microjets exposed to gaseous H₂O₂ or O₃ beams for <50 μs. The unambiguous identification of such species in situ via online electrospray mass spectrometry let us investigate their individual dependences on Fe²⁺, H₂O₂, O₃, and H⁺ concentrations, and their responses to tert-butanol (an ·OH scavenger) and DMSO (an O-atom acceptor) cosolutes. We found that (i) mass spectra are not affected by excess tert-butanol, i.e., the detected species are primary products whose formation does not involve ·OH radicals, and (ii) the di-iron ferryls, but not O=Fe^IVCl₃⁻, can be fully quenched by DMSO under present conditions. We infer that interfacial Fe(H₂O)_n²⁺ ions react with H₂O₂ and O₃ >10³ times faster than Fe(H₂O)₆²⁺ in bulk water via a process that favors inner-sphere two-electron O-atom over outer-sphere one-electron transfers. The higher reactivity of di-iron ferryls vs. O=Fe^IVCl₃⁻ as O-atom donors implicates the electronic coupling of mixed-valence iron centers in the weakening of the Fe^IV–O bond in poly-iron ferryl species.High-valent Fe^IV=O (ferryl) species participate in a wide range of key chemical and biological oxidations (1–4). Such species, along with ·OH radicals, have long been deemed putative intermediates in the oxidation of Fe^II by H₂O₂ (Fenton’s reaction) (5, 6), O₃, or HOCl (7, 8). The widespread availability of Fe^II and peroxides in vivo (9–12), in natural waters and soils (13), and in the atmosphere (14–18) makes Fenton chemistry and Fe^IV=O groups ubiquitous features in diverse systems (19). A lingering issue regarding Fenton’s reaction is how the relative yields of ferryls vs. ·OH radicals depend on the medium. For example, by assuming unitary ·OH radical yields, some estimates suggest that Fenton’s reaction might account for ∼30% of the ·OH radical production in fog droplets (20). Conversely, if Fenton’s reaction mostly led to Fe^IV=O species, atmospheric chemistry models predict that their steady-state concentrations would be ∼10⁴ times larger than [·OH], thereby drastically affecting the rates and course of oxidative chemistry in such media (20). Fe^IV=O centers are responsible for the versatility of the family of cytochrome P450 enzymes in catalyzing the oxidative degradation of a vast range of xenobiotics in vivo (21–28), and the selective functionalization of saturated hydrocarbons (29). The bactericidal action of antibiotics has been linked to their ability to induce Fenton chemistry in vivo (9, 30–34). Oxidative damage from exogenous Fenton chemistry likely is responsible for acute and chronic pathologies of the respiratory tract (35–38).Despite its obvious importance, the mechanism of Fenton’s reaction is not fully understood. What is at stake is how the coordination sphere of Fe²⁺ (39–46) under specific conditions affects the competition between the one-electron transfer producing ·OH radicals (the Haber–Weiss mechanism) (47), reaction R1, and the two-electron oxidation via O-atom transfer (the Bray–Gorin mechanism) into Fe^IVO²⁺, reaction R2 (6, 23, 26, 27, 45, 48–51):Ozone reacts with Fe²⁺ via analogous pathways leading to (formally) the same intermediates, reactions R3a, R3b, and R4 (8, 49, 52, 53):At present, experimental evidence about these reactions is indirect, being largely based on the analysis of reaction products in bulk water in conjunction with various assumptions. Given the complex speciation of aqueous Fe²⁺/Fe³⁺ solutions, which includes diverse poly-iron species both as reagents and products, it is not surprising that classical studies based on the identification of reaction intermediates and products via UV-absorption spectra and the use of specific scavengers have fallen short of fully unraveling the mechanism of Fenton’s reaction. Herein we address these issues, focusing particularly on the critically important interfacial Fenton chemistry that takes place at boundaries between aqueous and hydrophobic media, such as those present in atmospheric clouds (16), living tissues, biomembranes, bio-microenvironments (38, 54, 55), and nanoparticles (56, 57).We exploited the high sensitivity, surface selectivity, and unambiguous identification capabilities of a newly developed instrument based on online electrospray mass spectrometry (ES-MS) (58–62) to identify the primary products of reactions R1–R4 on aqueous FeCl₂ microjets exposed to gaseous H₂O₂ and O₃ beams under ambient conditions [in N₂(g) at 1 atm at 293 ± 2 K]. Our experiments are conducted by intersecting the continuously refreshed, uncontaminated surfaces of free-flowing aqueous microjets with reactive gas beams for τ ∼10–50 μs, immediately followed (within 100 μs; see below) by in situ detection of primary interfacial anionic products and intermediates via ES-MS (Methods, SI Text, and Figs. S1 and S2). We have previously demonstrated that online mass spectrometric sampling of liquid microjets under ambient conditions is a surface-sensitive technique (58, 62–67).     

From the Cover: Genomic evidence for inbreeding depression and purging of deleterious genetic variation in Indian tigers

Increasing habitat fragmentation leads to wild populations becoming small, isolated, and threatened by inbreeding depression. However, small populations may be able to purge recessive deleterious alleles as they become expressed in homozygotes, thus reducing inbreeding depression and increasing population viability. We used whole-genome sequences from 57 tigers to estimate individual inbreeding and mutation load in a small–isolated and two large–connected populations in India. As expected, the small–isolated population had substantially higher average genomic inbreeding (F_ROH = 0.57) than the large–connected (F_ROH = 0.35 and F_ROH = 0.46) populations. The small–isolated population had the lowest loss-of-function mutation load, likely due to purging of highly deleterious recessive mutations. The large populations had lower missense mutation loads than the small–isolated population, but were not identical, possibly due to different demographic histories. While the number of the loss-of-function alleles in the small–isolated population was lower, these alleles were at higher frequencies and homozygosity than in the large populations. Together, our data and analyses provide evidence of 1) high mutation load, 2) purging, and 3) the highest predicted inbreeding depression, despite purging, in the small–isolated population. Frequency distributions of damaging and neutral alleles uncover genomic evidence that purifying selection has removed part of the mutation load across Indian tiger populations. These results provide genomic evidence for purifying selection in both small and large populations, but also suggest that the remaining deleterious alleles may have inbreeding-associated fitness costs. We suggest that genetic rescue from sources selected based on genome-wide differentiation could offset any possible impacts of inbreeding depression.

A large proportion of Earth’s biodiversity persists in small and isolated populations in today’s anthropogenically modified world (1). Such populations may suffer from decreased genetic variation and increased inbreeding (2), which together lead to decreased fitness and increased extinction risk (3). Several theoretical (4–7), experimental (8), and empirical studies (9) reveal that species surviving in small and isolated populations are at the greatest risk of extinction.While species exist in nature along a continuum from small to large populations with different levels of isolation, populations of endangered species often tend to be small and isolated. African wild dog, Ethiopian wolf, and great Indian bustard are examples of species where all populations are small and isolated (10–12). The “small population paradigm” of conservation biology suggests that such smaller and more isolated populations are at a higher risk of extinction due to inbreeding depression and demographic stochasticity (13–15).Inbred individuals express deleterious, partially recessive alleles that are inherited identically by descent (IBD) from related parents, leading to inbreeding depression (16). Such inbreeding depression can reduce the average fitness of a population, eventually leading to reduced population size and possibly extinction (17). A commonly adopted strategy to conserve inbred populations is genetic rescue (18), which aims to increase average fitness by decreasing the frequency of deleterious mutations and increase heterozygosity at loci harboring deleterious alleles, via translocations of individuals from genetically differentiated populations. A meta-analysis of empirical data from wild populations showed broadly consistent positive effects of genetic rescue on fitness (15, 19).Population genetic theory (20–22) predicts that purifying selection can reduce inbreeding depression by purging deleterious alleles from inbred populations in the absence of immigration. Whether isolated populations are likely to purge a substantial fraction of the mutation load has been of longstanding interest in evolutionary biology and conservation. Early empirical data from pedigreed captive populations suggested that purging either was absent or resulted only in slight decreases in inbreeding depression (23, 24). However, several experimental studies based on model organisms reveal substantial purging and significant reduction in inbreeding depression in small populations (25–27). Recent molecular and population genetic studies have found genomic evidence for purging in wild populations (28–30). Despite broad empirical support for the efficacy of genetic rescue (15, 19), genomic evidence for purging and the long-term persistence of some small–isolated populations have been cited to question the small population paradigm and to argue that standard genetic rescue practices are likely to be counterproductive (29, 31–33). Whether purging removes enough deleterious alleles to improve the viability of small, isolated populations (contradicting the small population paradigm) remains an open question. We address this question by contrasting genomic inbreeding and mutation load in small–isolated versus large–connected populations of wild tigers.We use Bengal tigers (Panthera tigris tigris) from India as a model to investigate levels of inbreeding and relative mutation loads in small–isolated and large–connected populations and examine the potential for genetic rescue. Tigers are large, endangered carnivores, but Bengal tigers have high genetic variation compared to other subspecies, with some subpopulations showing high inbreeding indicating isolation (34). All Bengal tiger populations have been through historic bottlenecks, but inbreeding and genetic variation vary among populations. Also, some populations are relatively large and connected while others are small and isolated from both genetic (35) and demographic perspectives (36, 37), making them an ideal system to investigate inbreeding, mutation load, and possible genetic rescue strategies.We use genomic data to measure the impact of historically declining population sizes and connectivity on inbreeding and mutation load in three wild Bengal tiger (P. tigris tigris) populations that are small–isolated (SI) and large–connected (LC). The small–isolated population is from northwestern India and the large–connected populations are from southern India (s-LC) and central India (c-LC). We expected large–connected populations to be the least inbred and to have the lowest mutation load. Alternatively, purging could result in lower mutation load in this small–isolated population. We also explore strategies for genetic rescue that might effectively decrease inbreeding depression. For tigers, we specifically suggest strategies for identifying populations that may benefit from genetic rescue and how such strategies may be effectively implemented.     

Herpesvirus-mediated stabilization of ICP0 expression neutralizes restriction by TRIM23

Herpes simplex virus (HSV) infection relies on immediate early proteins that initiate viral replication. Among them, ICP0 is known, for many years, to facilitate the onset of viral gene expression and reactivation from latency. However, how ICP0 itself is regulated remains elusive. Through genetic analyses, we identify that the viral γ₁34.5 protein, an HSV virulence factor, interacts with and prevents ICP0 from proteasomal degradation. Furthermore, we show that the host E3 ligase TRIM23, recently shown to restrict the replication of HSV-1 (and certain other viruses) by inducing autophagy, triggers the proteasomal degradation of ICP0 via K11- and K48-linked ubiquitination. Functional analyses reveal that the γ₁34.5 protein binds to and inactivates TRIM23 through blockade of K27-linked TRIM23 autoubiquitination. Deletion of γ₁34.5 or ICP0 in a recombinant HSV-1 impairs viral replication, whereas ablation of TRIM23 markedly rescues viral growth. Herein, we show that TRIM23, apart from its role in autophagy-mediated HSV-1 restriction, down-regulates ICP0, whereas viral γ₁34.5 functions to disable TRIM23. Together, these results demonstrate that posttranslational regulation of ICP0 by virus and host factors determines the outcome of HSV-1 infection.

Herpes simplex viruses (HSV) are human pathogens that switch between lytic and latent infections intermittently (1, 2). This is a lifelong source of infectious viruses (1, 2), in which immediate early proteins drive the onset of HSV replication. Among them, ICP0 enables viral gene expression or reactivation from latency (2–4), which involves chromatin remodeling of the HSV genome, resulting in de novo virus production. In this process, the accessory factor γ₁34.5 of HSV is thought to govern viral protein synthesis (5, 6). It has long been known that γ₁34.5 precludes translation arrest mediated by double-stranded RNA–dependent protein kinase PKR (7–9). The γ₁34.5 protein has also been shown to dampen intracellular nucleic acid sensing, inhibit autophagy, and facilitate virus nuclear egress (10–17). In experimental animal models, wild-type HSV, but not HSV that lacks the γ₁34.5 gene, replicates competently, penetrates from the peripheral tissues to the nervous system and reactivates from latency (18–23). Despite these observations, active HSV replication or reactivation from latency is not readily reconciled by the currently known functions of the γ₁34.5 protein (8–13, 16, 17).Several lines of work demonstrate that tripartite motif (TRIM) proteins regulate innate immune signaling and cell intrinsic resistance to virus infections (24, 25). These host factors typically work as E3 ubiquitin ligases that can synthesize degradative or nondegradative ubiquitination on viral or host proteins. A number of TRIM proteins, for example TRIM5α, TRIM19, TRIM21, TRIM22, and TRIM43, act at different steps of virus replication and subsequently inhibit viral production (26–32). Recent evidence indicates that TRIM23 limits the replication of certain RNA viruses and DNA viruses, including HSV-1 (33). In doing so, TRIM23 recruits TANK-binding kinase 1 (TBK1) to autophagosomes, thus promoting TBK1-mediated phosphorylation and activation of the autophagy receptor p62 and ultimately leading to autophagy. It is unknown whether TRIM23 plays an additional role(s) in HSV infection.Here, we report that ICP0 expression is regulated by the γ₁34.5 protein and TRIM23 during HSV-1 infection. We show that TRIM23 facilitates the proteasomal degradation of ICP0, whereas viral γ₁34.5 maintains steady-state ICP0 expression by preventing K27-linked TRIM23 autoubiquitination that is required for TRIM23 activation. The γ₁34.5 protein also interacts with and stabilizes ICP0, enabling productive infection. Furthermore, we provide evidence that TRIM23 binds to ICP0 and induces its K11-linked polyubiquitination, which triggers K48-linked polyubiquitin-dependent proteasomal degradation of ICP0. These insights establish a model of posttranslational networks in which virus- and host-mediated mechanisms regulate immediate early protein ICP0 stability and thereby lytic HSV replication.     

Luminescence color switching of supramolecular assemblies of discrete molecular decanuclear gold(I) sulfido complexes

A series of discrete decanuclear gold(I) μ₃-sulfido complexes with alkyl chains of various lengths on the aminodiphosphine ligands, [Au₁₀{Ph₂PN(C_nH_2n+1)PPh₂}₄(μ₃-S)₄](ClO₄)₂, has been synthesized and characterized. These complexes have been shown to form supramolecular nanoaggregate assemblies upon solvent modulation. The photoluminescence (PL) colors of the nanoaggregates can be switched from green to yellow to red by varying the solvent systems from which they are formed. The PL color variation was investigated and correlated with the nanostructured morphological transformation from the spherical shape to the cube as observed by transmission electron microscopy and scanning electron microscopy. Such variations in PL colors have not been observed in their analogous complexes with short alkyl chains, suggesting that the long alkyl chains would play a key role in governing the supramolecular nanoaggregate assembly and the emission properties of the decanuclear gold(I) sulfido complexes. The long hydrophobic alkyl chains are believed to induce the formation of supramolecular nanoaggregate assemblies with different morphologies and packing densities under different solvent systems, leading to a change in the extent of Au(I)–Au(I) interactions, rigidity, and emission properties.Gold(I) complexes are one of the fascinating classes of complexes that reveal photophysical properties that are highly sensitive to the nuclearity of the metal centers and the metal–metal distances (1–59). In a certain sense, they bear an analogy or resemblance to the interesting classes of metal nanoparticles (NPs) (60–69) and quantum dots (QDs) (70–76) in that the properties of the nanostructured materials also show a strong dependence on their sizes and shapes. Interestingly, while the optical and spectroscopic properties of metal NPs and QDs show a strong dependence on the interparticle distances, those of polynuclear gold(I) complexes are known to mainly depend on the nuclearity and the internuclear separations of gold(I) centers within the individual molecular complexes or clusters, with influence of the intermolecular interactions between discrete polynuclear molecular complexes relatively less explored (34–38), and those of polynuclear gold(I) clusters not reported. Moreover, while studies on polynuclear gold(I) complexes or clusters are known (34–54), less is explored of their hierarchical assembly and nanostructures as well as the influence of intercluster aggregation on the optical properties (34–38). Among the gold(I) complexes, polynuclear gold(I) chalcogenido complexes represent an important and interesting class (44–51). While directed supramolecular assembly of discrete Au₁₂ (52), Au₁₆ (53), Au₁₈ (51), and Au₃₆ (54) metallomacrocycles as well as trinuclear gold(I) columnar stacks (34–38) have been reported, there have been no corresponding studies on the supramolecular hierarchical assembly of polynuclear gold(I) chalcogenido clusters.Based on our interests and experience in the study of gold(I) chalcogenido clusters (44–46, 51), it is believed that nanoaggegrates with interesting luminescence properties and morphology could be prepared by the judicious design of the gold(I) chalcogenido clusters. As demonstrated by our previous studies on the aggregation behavior of square-planar platinum(II) complexes (77–80) where an enhancement of the solubility of the metal complexes via introduction of solubilizing groups on the ligands and the fine control between solvophobicity and solvophilicity of the complexes would have a crucial influence on the factors governing supramolecular assembly and the formation of aggregates (80), introduction of long alkyl chains as solubilizing groups in the gold(I) sulfido clusters may serve as an effective way to enhance the solubility of the gold(I) clusters for the construction of supramolecular assemblies of novel luminescent nanoaggegrates.Herein, we report the preparation and tunable spectroscopic properties of a series of decanuclear gold(I) μ₃-sulfido complexes with alkyl chains of different lengths on the aminophosphine ligands, [Au₁₀{Ph₂PN(C_nH_2n+1)PPh₂}₄(μ₃-S)₄](ClO₄)₂ [n = 8 (1), 12 (2), 14 (3), 18 (4)] and their supramolecular assembly to form nanoaggregates. The emission colors of the nanoaggregates of 2−4 can be switched from green to yellow to red by varying the solvent systems from which they are formed. These results have been compared with their short alkyl chain-containing counterparts, 1 and a related [Au₁₀{Ph₂PN(C₃H₇)PPh₂}₄(μ₃-S)₄](ClO₄)₂ (45). The present work demonstrates that polynuclear gold(I) chalcogenides, with the introduction of appropriate functional groups, can serve as building blocks for the construction of novel hierarchical nanostructured materials with environment-responsive properties, and it represents a rare example in which nanoaggregates have been assembled with the use of discrete molecular metal clusters as building blocks.