Drought impacts on the electricity system, emissions, and air quality in the western United States.

The western United States has experienced severe drought in recent decades, and climate models project increased drought risk in the future. This increased drying could have important implications for the region's interconnected, hydropower-dependent electricity systems. Using power-plant level generation and emissions data from 2001 to 2021, we quantify the impacts of drought on the operation of fossil fuel plants and the associated impacts on greenhouse gas (GHG) emissions, air quality, and human health. We find that under extreme drought, electricity generation from individual fossil fuel plants can increase up to 65% relative to average conditions, mainly due to the need to substitute for reduced hydropower. Over 54% of this drought-induced generation is transboundary, with drought in one electricity region leading to net imports of electricity and thus increased pollutant emissions from power plants in other regions. These drought-induced emission increases have detectable impacts on local air quality, as measured by proximate pollution monitors. We estimate that the monetized costs of excess mortality and GHG emissions from drought-induced fossil generation are 1.2 to 2.5x the reported direct economic costs from lost hydro production and increased demand. Combining climate model estimates of future drying with stylized energy-transition scenarios suggests that these drought-induced impacts are likely to remain large even under aggressive renewables expansion, suggesting that more ambitious and targeted measures are needed to mitigate the emissions and health burden from the electricity sector during drought.

Aerosol-into-liquid capture and detection of atmospheric soluble metals across the gas-liquid interface using Janus-membrane electrodes.

The soluble fraction of atmospheric transition metals is particularly associated with health effects such as reactive oxygen species compared to total metals. However, direct measurements of the soluble fraction are restricted to sampling and detection units in sequence burdened with a compromise between time resolution and system bulkiness. Here, we propose the concept of aerosol-into-liquid capture and detection, which allowed one-step particle capture and detection via the Janus-membrane electrode at the gas-liquid interface, enabling active enrichment and enhanced mass transport of metal ions. The integrated aerodynamic/electrochemical system was capable of capturing airborne particles with a cutoff size down to 50 nm and detecting Pb(II) with a limit of detection of 95.7 ng. The proposed concept can pave the way for cost-effective and miniaturized systems, for the capture and detection of airborne soluble metals in air quality monitoring, especially for abrupt air pollution events with high airborne metal concentrations (e.g., wildfires and fireworks).

Association of improved air quality with lower dementia risk in older women.

Late-life ambient air pollution is a risk factor for brain aging, but it remains unknown if improved air quality (AQ) lowers dementia risk. We studied a geographically diverse cohort of older women dementia free at baseline in 2008 to 2012 (n = 2,239, aged 74 to 92). Incident dementia was centrally adjudicated annually. Yearly mean concentrations of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) were estimated using regionalized national universal kriging models and averaged over the 3-y period before baseline (recent exposure) and 10 y earlier (remote exposure). Reduction from remote to recent exposures was used as the indicator of improved AQ. Cox proportional hazard ratios (HRs) for dementia risk associated with AQ measures were estimated, adjusting for sociodemographic, lifestyle, and clinical characteristics. We identified 398 dementia cases during follow up (median = 6.1 y). PM2.5 and NO2 reduced significantly over the 10 y before baseline. Larger AQ improvement was associated with reduced dementia risks (HRPM2.5 0.80 per 1.78 µg/m3, 95% CI 0.71-0.91; HRNO2 0.80 per 3.91 parts per billion, 95% CI 0.71-0.90), equivalent to the lower risk observed in women 2.4 y younger at baseline. Higher PM2.5 at baseline was associated with higher dementia risk (HRPM2.5 1.16 per 2.90 µg/m3, 95% CI 0.98-1.38), but the lower dementia risk associated with improved AQ remained after further adjusting for recent exposure. The observed associations did not substantially differ by age, education, geographic region, Apolipoprotein E e4 genotypes, or cardiovascular risk factors. Long-term AQ improvement in late life was associated with lower dementia risk in older women.

Environmental inequality in the neighborhood networks of urban mobility in US cities.

Research has made clear that neighborhoods impact the health and well-being of their residents. A related strand of research shows that neighborhood disadvantage is geographically clustered. Because the neighborhoods of low-income and minority populations tend to be more disadvantaged, neighborhood conditions help explain racial and socioeconomic inequalities. These strands of research restrict processes of neighborhood influence to operate only within and between geographically contiguous neighbors. However, we are underestimating the role of neighborhood conditions in explaining inequality if disadvantage extends beyond the residential and extralocal environments into a network of neighborhoods spanning the urban landscape based on where residents move within a city. I use anonymized mobile phone data to measure exposure to air pollution among residents of poor and minority neighborhoods in 88 of the most populous US cities. I find that residents from minority and poor neighborhoods travel to neighborhoods that have greater air pollution levels than the neighborhoods that residents from White and nonpoor neighborhoods visit. Hispanic neighborhoods exhibit the greatest overall pollution burden, Black/White and Asian/White disparities are greatest at the network than residential scale, and the socioeconomic advantage of lower risk exposure is highest for residents from White neighborhoods. These inequalities are notable given recent declines in segregation and air pollution levels in American cities.

Tripling of western US particulate pollution from wildfires in a warming climate.

SignificanceRecord-setting fires in the western United States over the last decade caused severe air pollution, loss of human life, and property damage. Enhanced drought and increased biomass in a warmer climate may fuel larger and more frequent wildfires in the coming decades. Applying an empirical statistical model to fires projected by Earth System Models including climate-ecosystem-socioeconomic interactions, we show that fine particulate pollution over the US Pacific Northwest could double to triple during late summer to fall by the late 21st century under intermediate- and low-mitigation scenarios. The historic fires and resulting pollution extremes of 2017-2020 could occur every 3 to 5 y under 21st-century climate change, posing challenges for air quality management and threatening public health.

Location-specific strategies for eliminating US national racial-ethnic [Formula: see text] exposure inequality.

Air pollution levels in the United States have decreased dramatically over the past decades, yet national racial-ethnic exposure disparities persist. For ambient fine particulate matter ([Formula: see text]), we investigate three emission-reduction approaches and compare their optimal ability to address two goals: 1) reduce the overall population average exposure ("overall average") and 2) reduce the difference in the average exposure for the most exposed racial-ethnic group versus for the overall population ("national inequalities"). We show that national inequalities in exposure can be eliminated with minor emission reductions (optimal: ~1% of total emissions) if they target specific locations. In contrast, achieving that outcome using existing regulatory strategies would require eliminating essentially all emissions (if targeting specific economic sectors) or is not possible (if requiring urban regions to meet concentration standards). Lastly, we do not find a trade-off between the two goals (i.e., reducing overall average and reducing national inequalities); rather, the approach that does the best for reducing national inequalities (i.e., location-specific strategies) also does as well as or better than the other two approaches (i.e., sector-specific and meeting concentration standards) for reducing overall averages. Overall, our findings suggest that incorporating location-specific emissions reductions into the US air quality regulatory framework 1) is crucial for eliminating long-standing national average exposure disparities by race-ethnicity and 2) can benefit overall average exposures as much as or more than the sector-specific and concentration-standards approaches.

US Embassy air-quality tweets led to global health benefits.

The World Health Organization estimates that over 90% of the world's population is exposed to hazardous levels of local air pollution. Air pollution is markedly worse in low- and middle-income countries, yet air-quality monitoring is typically sparse. In 2008, the US Embassy in Beijing began tweeting hourly air-quality information from a newly installed pollution monitor, dramatically improving the information on air quality available to Beijing residents. Since then, the United States has installed over 50 monitors around the world, tweeting real-time reports on air quality in those locations. Using spatially granular measurements of local air pollution from satellite data that span the globe, we employ variation in whether and when US embassies installed monitors to evaluate the impact of air-quality information on pollution. We estimate that embassy monitors led to reductions in fine particulate concentration levels in host countries of 2 to 4 µg/m3. Our central estimate of the annual monetized benefit of the decrease in premature mortality due to this reduction in pollution is $127 million for the median city in 2019. Our findings point to the substantial benefits of improving the availability and salience of air-quality information in low- and middle-income countries.

Increasing Awareness Regarding the Relationship between Environmental Exposures and Allergic Disease.

This review highlights studies from the past 3 years that add to the understanding of the impact of environmental exposures on allergic disease. These include aeroallergens, air quality, prenatal or early life exposures, and occupational exposures. Recent studies focused on the relationship between the environment, the microbiome, and allergic disease as well as new therapeutic options are also reviewed. Lastly, there has been significant recent research that improves our knowledge of the link between health disparities and environmental exposures. These scientific advances have resulted in a better understanding that sets the foundation for current and future research dedicated to improving health outcomes by modifying environmental exposures.

Cleaning Indoor Air- What Works for Respiratory Health: An Updated Literature Review and Recommendations.

Indoor air pollution is a growing public health concern globally and is associated with increased respiratory symptoms and morbidity. Individuals spend most of their time indoors, and pollutant-related health effects are often driven by the indoor environment. Understanding effective interventions to improve indoor air quality (IAQ) and their impact on respiratory outcomes is key to decreasing the burden of air pollution for high-risk populations across the lifespan. This review applies a hierarchy of interventions framework specific to respiratory health effects and focuses on recent studies of interventions to improve IAQ among high-risk populations with chronic respiratory disease published in the past three years. While policy and source control interventions are likely the most effective and equitable approaches to improve IAQ and benefit population health, these were less extensively investigated. Engineering interventions, such as air cleaner interventions, were the most widely studied. Several studies, including those focused on asthma and COPD, demonstrated improvement in symptoms and medication use with interventions in both home and school-based settings. Combined multilevel interventions with engineering and behavioral interventions led to improved respiratory outcomes in some, but not all, studies. Placing the recent work in the context of the broader literature, we identify gaps in research. Further research is needed to understand intervention effectiveness over time and an increased focus on policy and source control interventions that can mitigate risk in vulnerable populations.

Atomically thin-layered WS2based resistive sensors for detection of CO and NO2at room temperature.

The number of layers present in a two-dimensional (2D) nanomaterial plays a critical role in applications that involve surface interaction, for example, gas sensing. This paper reports the synthesis of 2D WS2nanoflakes using the facile liquid exfoliation technique. The nanoflakes were exfoliated using bath sonication (BS-WS2) and probe sonication (PS-WS2). The thickness of the BS-WS2was found to range between 70 and 200 nm, and that of PS-WS2varied from 0.6 to 80 nm, indicating the presence of single to few layers of WS2when characterized using atomic force microscope. All the WS2samples were thoroughly characterized using electron microscopes, x-ray diffractometer, Raman spectroscopy, UV-Visible spectroscopy, Fourier transform infrared spectroscope, and thermogravimetric analyser. Both the nanostructured samples were exposed to 2 ppm of NO2at room temperature. Interestingly, BS-WS2which comprises of a greater number of WS2layers exhibited -14.2% response as against -3.4% response of PS-WS2, the atomically thin sample. The BS-WS2sample was found to be highly selective towards NO2but was slower (with incomplete recovery) as compared to PS-WS2. The PS-WS2sample was observed to exhibit -11.9% to -27.4% response to 2-10 ppm of CO and -3.4%-35.2% response to 2-10 ppm of NO2at room temperature, thereby exhibiting the potential to detect two gases simultaneously. These gases could be accurately predicted and quantified if the response times of the PS-WS2sample were considered. The atomically thin WS2-based sensor exhibited a limit of detection of 131 and 81 ppb for CO and NO2, respectively.

Designing Retirement Strategies for Coal-Fired Power Plants To Mitigate Air Pollution and Health Impacts.

Retiring coal power plants can reduce air pollution and health damages. However, the spatial distribution of those impacts remains unclear due to complex power system operations and pollution chemistry and transport. Focusing on coal retirements in Pennsylvania (PA), we analyze six counterfactual scenarios for 2019 that differ in retirement targets (e.g., reducing 50% of coal-based installed capacity vs generation) and priorities (e.g., closing plants with higher cost, closer to Environmental Justice Areas, or with higher CO2 emissions). Using a power system model of the PJM Interconnection, we find that coal retirements in PA shift power generation across PA and Rest of PJM, leading to scenario-varying changes in the plant-level release of air pollutants. Considering pollution transport and the size of the exposed population, these emissions changes, in turn, give rise to a reduction of 6-136 PM2.5-attributable deaths in PJM across the six scenarios, with most reductions occurring in PA. Among our designed scenarios, those that reduce more coal power generation yield greater aggregate health benefits due to air quality improvements in PA and adjacent downwind regions. In addition, comparing across the six scenarios evaluated in this study, vulnerable populations─in both PA and Rest of PJM─benefit most in scenarios that prioritize plant closures near Environmental Justice Areas in PA. These results demonstrate the importance of considering cross-regional linkages and sociodemographics in designing equitable retirement strategies.

Air Pollution Inequality in the Denver Metroplex and its Relationship to Historical Redlining.

Prior studies have shown that people of color (POC) in the United States are exposed to higher levels of pollution than non-Hispanic White people. We show that the city of Denver, Colorado, displays similar race- and ethnicity-based air pollution disparities by using a combination of high-resolution satellite data, air pollution modeling, historical demographic information, and areal apportionment techniques. TROPOMI NO2 columns and modeled PM2.5 concentrations from 2019 are higher in communities subject to redlining. We calculated and compared Spearman coefficients for pollutants and race at the census tract level for every city that underwent redlining to contextualize the disparities in Denver. We find that the location of polluting infrastructure leads to higher populations of POC living near point sources, including 40% higher Hispanic and Latino populations. This influences pollution distribution, with annual average PM2.5 surface concentrations of 6.5 µg m-3 in census tracts with 0-5% Hispanic and Latino populations and 7.5 µg m-3 in census tracts with 60-65% Hispanic and Latino populations. Traffic analysis and emission inventory data show that POC are more likely to live near busy highways. Unequal spatial distribution of pollution sources and POC have allowed for pollution disparities to persist despite attempts by the city to rectify them. Finally, we identify the core causes of the pollution disparities to provide direction for remediation.

Land-Use Decisions Have Substantial Air Quality Health Effects.

Understanding how best to use limited land without compromising food security, health, and beneficial ecosystem functions is a critical challenge of our time. Ecosystem service assessments increasingly inform land-use decisions but seldom include the effects of land use on air quality, the largest environmental health risk. Here, we estimate and value the air quality health effects of potential land-use policies and projected trends in the United States, alongside carbon sequestration and economic returns to land, until 2051. We show that air quality health effects are of first-order importance in land-use decisions, often larger in value than carbon sequestration and economic returns combined. When air quality is properly accounted for, policies that appeared beneficial are shown to be detrimental and vice versa. Land-use-driven air quality impacts are largely from agricultural emissions and biogenic forest emissions, although incentives for reduced deforestation remain beneficial overall. Without evaluating air quality, we are unable to determine whether land-use decisions make us better or worse off.

Unraveling Cross-Organ Impacts of Airborne Pollutants: A Multiomics Study on Respiratory Exposure and Gastrointestinal Health.

Poor air quality is increasingly linked to gastrointestinal diseases, suggesting a potential correlation with human intestine health. However, this relationship remains largely unexplored due to limited research. This study used a controlled mouse model exposed to cooking oil fumes (COFs) and metagenomics, transcriptomics, and metabolomics to elucidate interactions between intestine microbiota and host metabolism under environmental stress. Our findings reveal that short-term COF inhalation induces pulmonary inflammation within 3 days and leads to gastrointestinal disturbances, elucidating a pathway connecting respiratory exposure to intestinal dysfunction. The exposure intensity significantly correlates with changes in intestinal tissue integrity, microbial composition, and metabolic function. Extended exposure of 7 days disrupts intestine microbiota and alters tryptophan metabolism, with further changes observed after 14 days, highlighting an adaptive response. These results highlight the vulnerability of intestinal health to airborne pollutants and suggest a pathway through which inhaled pollutants may affect distant organ systems.

Indoor Emissions Contributed the Majority of Ultrafine Particles in Chinese Urban Residences.

Ultrafine particle (UFP) pollution should be controlled to reduce its effects on health. The design of control measures is limited owing to the uncertainty of source contributions in Chinese residences, where indoor UFP pollution is more severe than in Western residences. Herein, a source-specific, time-dependent UFP concentration model was developed by applying an infiltration factor model incorporating coagulation effects. A Monte Carlo framework with the UFP concentration model was employed to estimate the probabilistic distribution of source contributions in Chinese residences. The input parameter distributions were determined based on our survey and previous studies. The annually averaged indoor UFP concentration was estimated at (2.75 ± 1.71) × 104 #/cm3, ranging from 2.35 × 103 to 1.27 × 105 #/cm3 outside the kitchen, and at (5.48 ± 3.08) × 104 #/cm3, ranging from 2.90 × 103 to 1.94 × 105 #/cm3 in the kitchen. Indoor sources contributed more to indoor UFPs, accounting for 61% in the nonkitchen and 80% in the kitchen, surpassing their contribution to indoor PM2.5 in Chinese residences. Meanwhile, the indoor UFP emission contributions were higher than those in the United States, Canada, and Germany, owing to higher emissions from cooking and cigarette smoking. These results will aid in elucidating human exposure to UFPs and in designing more targeted control measures.

Quantifying HONO Production from Nitrate Photolysis in a Polluted Atmosphere.

The photolysis of particulate nitrate (pNO3-) has been suggested to be an important source of nitrous acid (HONO) in the troposphere. However, determining the photolysis rate constant of pNO3- (jpNO3-) suffers from high uncertainty. Prior laboratory measurements of jpNO3- using aerosol filters have been complicated by the "shadow effect"─a phenomenon of light extinction within aerosol layers that potentially skews these measurements. We developed a method to correct the shadow effect on the photolysis rate constant of pNO3- for HONO production (jpNO3- â†’ HONO) using aerosol filters with identical chemical compositions but different aerosol loadings. We applied the method to quantify jpNO3- â†’ HONO over the North China Plain (NCP) during the winter haze period. After correcting for the shadow effect, the normalized average jpNO3- â†’ HONO at 5 °C increased from 5.89 × 10-6 s-1 to 1.72 × 10-5 s-1. The jpNO3- â†’ HONO decreased with increasing pH and nitrate proportions in PM2.5 and had no correlation with nitrate concentrations. A parametrization for jpNO3- â†’ HONO was developed for model simulation of HONO production in NCP and similar environments.

Formation and Transport of Secondary Contaminants Associated with Germicidal Ultraviolet Light Systems in an Occupied Classroom.

Germicidal ultraviolet light (GUV) systems are designed to control airborne pathogen transmission in buildings. However, it is important to acknowledge that certain conditions and system configurations may lead GUV systems to produce air contaminants including oxidants and secondary organic aerosols (SOA). In this study, we modeled the formation and dispersion of oxidants and secondary contaminants generated by the operation of GUV systems employing ultraviolet C 254 and 222 nm. Using a three-dimensional computational fluid dynamics model, we examined the breathing zone concentrations of chemical species in an occupied classroom. Our findings indicate that operating GUV 222 leads to an approximate increase of 10 ppb in O3 concentration and 5.2 µg·m-3 in SOA concentration compared to a condition without GUV operation, while GUV 254 increases the SOA concentration by about 1.2 µg·m-3, with a minimal impact on the O3 concentration. Furthermore, increasing the UV fluence rate of GUV 222 from 1 to 5 µW·cm-2 results in up to 80% increase in the oxidants and SOA concentrations. For GUV 254, elevating the UV fluence rate from 30 to 50 µW·cm-2 or doubling the radiating volume results in up to 50% increase in the SOA concentration. Note that indoor airflow patterns, particularly buoyancy-driven airflow (or displacement ventilation), lead to 15-45% lower SOA concentrations in the breathing zone compared to well-mixed airflow. The results also reveal that when the ventilation rate is below 2 h-1, operating GUV 254 has a smaller impact on human exposure to secondary contaminants than GUV 222. However, GUV 254 may generate more contaminants than GUV 222 when operating at high indoor O3 levels (>15 ppb). These results suggest that the design of GUV systems should consider indoor O3 levels and room ventilation conditions.

Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality.

Urban air pollution can vary sharply in space and time. However, few monitoring strategies can concurrently resolve spatial and temporal variation at fine scales. Here, we present a new measurement-driven spatiotemporal modeling approach that transcends the individual limitations of two complementary sampling paradigms: mobile monitoring and fixed-site sensor networks. We develop, validate, and apply this model to predict black carbon (BC) using data from an intensive, 100-day field study in West Oakland, CA. Our spatiotemporal model exploits coherent spatial patterns derived from a multipollutant mobile monitoring campaign to fill spatial gaps in time-complete BC data from a low-cost sensor network. Our model performs well in reconstructing patterns at fine spatial and temporal resolution (30 m, 15 min), demonstrating strong out-of-sample correlations for both mobile (Pearson's R ∼ 0.77) and fixed-site measurements (R ∼ 0.95) while revealing features that are not effectively captured by a single monitoring approach in isolation. The model reveals sharp concentration gradients near major emission sources while capturing their temporal variability, offering valuable insights into pollution sources and dynamics.

High-Resolution Modeling of Summertime Biogenic Isoprene Emissions in New York City.

As cities strive for ambitious increases in tree canopy cover and reductions in anthropogenic volatile organic compound (AVOC) emissions, accurate assessments of the impacts of biogenic VOCs (BVOCs) on air quality become more important. In this study, we aim to quantify the impact of future urban greening on ozone production. BVOC emissions in dense urban areas are often coarsely represented in regional models. We set up a high-resolution (30 m) MEGAN (The Model of Emissions of Gases and Aerosols from Nature version 3.2) to estimate summertime biogenic isoprene emissions in the New York City metro area (NYC-MEGAN). Coupling an observation-constrained box model with NYC-MEGAN isoprene emissions successfully reproduced the observed isoprene concentrations in the city core. We then estimated future isoprene emissions from likely urban greening scenarios and evaluated the potential impact on future ozone production. NYC-MEGAN predicts up to twice as much isoprene emissions in NYC as the coarse-resolution (1.33 km) Biogenic Emission Inventory System version 3.61 (BEIS) on hot summer days. We find that BVOCs drive ozone production on hot summer days, even in the city core, despite large AVOC emissions. If high isoprene emitting species (e.g., oak trees) are planted, future isoprene emissions could increase by 1.4-2.2 times in the city core, which would result in 8-19 ppbv increases in peak ozone on ozone exceedance days with current NOx concentrations. We recommend planting non- or low-isoprene emitting trees in cities with high NOx concentrations to avoid an increase in the frequency and severity of future ozone exceedance events.

High-Precision Microscale Particulate Matter Prediction in Diverse Environments Using a Long Short-Term Memory Neural Network and Street View Imagery.

In this study, we propose a novel long short-term memory (LSTM) neural network model that leverages color features (HSV: hue, saturation, value) extracted from street images to estimate air quality with particulate matter (PM) in four typical European environments: urban, suburban, villages, and the harbor. To evaluate its performance, we utilize concentration data for eight parameters of ambient PM (PM1.0, PM2.5, and PM10, particle number concentration, lung-deposited surface area, equivalent mass concentrations of ultraviolet PM, black carbon, and brown carbon) collected from a mobile monitoring platform during the nonheating season in downtown Augsburg, Germany, along with synchronized street view images. Experimental comparisons were conducted between the LSTM model and other deep learning models (recurrent neural network and gated recurrent unit). The results clearly demonstrate a better performance of the LSTM model compared with other statistically based models. The LSTM-HSV model achieved impressive interpretability rates above 80%, for the eight PM metrics mentioned above, indicating the expected performance of the proposed model. Moreover, the successful application of the LSTM-HSV model in other seasons of Augsburg city and various environments (suburbs, villages, and harbor cities) demonstrates its satisfactory generalization capabilities in both temporal and spatial dimensions. The successful application of the LSTM-HSV model underscores its potential as a versatile tool for the estimation of air pollution after presampling of the studied area, with broad implications for urban planning and public health initiatives.