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Objectives: In China, news media are useful for educating the public about the health threats of air pollution. To explore the potential gaps between scientific findings and the public's understanding of them, the characteristics of news media articles and their corresponding scientific papers were analysed., Methods: We used 22 articles relating to the health outcomes of exposure to outdoor air pollution published on Baidu News over the past year. An assessment tool developed by Robinson et al was used to evaluate the quality scores of news articles. Pearson correlation coefficients were used to measure the relationship between news media reporting and the characteristics of scientific papers. Misleading reporting, interpretation, or extrapolation in headlines and text bodies of news articles were examined., Results: The quality scores of the news articles ranged from -4 to 8, with an overall median score of 3. Correlation results showed that the scientific papers citation in Twitter (r = .88, P < .001) and Facebook (r = .64, P < .01) were significantly and positively associated with their citations in news stories. Media misunderstanding of scientific findings was common: 15 news headlines were identified with at least one spin (misrepresentation of scientific results), and 12 news articles had seven types of spin in the body texts., Conclusion: Little media attention has been paid to scientific findings by Chinese researchers. Therefore, researchers and science journalists in China should make a better effort to engage in accurate and informative public discourse on domestic research., (© 2019 John Wiley & Sons, Ltd.)

Comprehensive surveys conducted at 5-yr intervals were used to estimate sulfur dioxide (SO,) and nitrogen oxides (NO.) emissions from U.S. pulp and paper mills for 1980, 1985, 1990, 1995, 2000, and 2005. Over the 25-yr period, paper production increased by 50%, whereas total SO, emissions declined by 60% to 340,000 short tons (t) and total NO, emissions decreased approximately 15% to 230,000 t. The downward emission trends resulted from a combination of factors, including reductions in oil and coal use, steadily declining fuel sulfur content, lower pulp and paper production in recent years, increased use of flue gas desulfurization systems on boilers, growing use of combustion modifications and add-on control systems to reduce boiler and gas turbine NO, emissions, and improvements in kraft recovery furnace operations.

Air emissions from the U.S. pulp and paper sector have been federally regulated since 1978; however, regulations are periodically reviewed and revised to improve efficiency and effectiveness of existing emission standards. The Industrial Sectors Integrated Solutions (ISIS) model for the pulp and paper sector is currently under development at the U.S. Environmental Protection Agency (EPA), and can be utilized to facilitate multi-pollutant, sector-based analyses that are performed in conjunction with regulatory development. The model utilizes a multi-sector, multi-product dynamic linear modeling framework that evaluates the economic impact of emission reduction strategies for multiple air pollutants. The ISIS model considers facility-level economic, environmental, and technical parameters, as well as sector-level market data, to estimate the impacts of environmental regulations on the pulp and paper industry. Specifically, the model can be used to estimate U.S. and global market impacts of new or more stringent air regulations, such as impacts on product price, exports and imports, market demands, capital investment, and mill closures. One major challenge to developing a representative model is the need for an extensive amount of data. This article discusses the collection and processing of data for use in the model, as well as the methods used for building the ISIS pulp and paper database that facilitates the required analyses to support the air quality management of the pulp and paper sector.

Changes in material use, energy use and emissions profiles of industry are the result of complex interrelationships among a multitude of technological and economic drivers. To better understand and guide such changes requires that attention is paid to the time-varying consequences that technology and economic influences have on an industry's choice of inputs and its associated (desired and undesired) outputs. This paper lays out an approach to improving our understanding of the dynamics of large industrial systems. The approach combines engineering and econometric analysis with a detailed representation of an industry's capital stock structure. A transparent dynamic computer modeling approach is chosen to integrate information from these analyses in ways that foster participation of stakeholders from industry and government agencies in all stages of the modeling process-from problem definition and determination of system boundaries to generation of scenarios and interpretation of results. Three case studies of industrial energy use in the USA are presented-one each for the iron and steel, pulp and paper, and ethylene industry. Dynamic models of these industries are described and then used to investigate alternative carbon emissions and investment-led policies. A comparison of results clearly points towards two key issues: the need for industry specific policy approaches in order to effectively influence industrial energy use, fuel mix and carbon emissions, and the need for longer time horizons than have typically been chosen for the analysis of industrial responses to climate change policies.

Steady improvements in ambient air quality in the USA over the past several decades, in part a result of public policy 1,2 , have led to public health benefits 1-4 . However, recent trends in ambient concentrations of particulate matter with diameters less than 2.5 μm (PM 2.5 ), a pollutant regulated under the Clean Air Act 1 , have stagnated or begun to reverse throughout much of the USA 5 . Here we use a combination of ground- and satellite-based air pollution data from 2000 to 2022 to quantify the contribution of wildfire smoke to these PM 2.5 trends. We find that since at least 2016, wildfire smoke has influenced trends in average annual PM 2.5 concentrations in nearly three-quarters of states in the contiguous USA, eroding about 25% of previous multi-decadal progress in reducing PM 2.5 concentrations on average in those states, equivalent to 4 years of air quality progress, and more than 50% in many western states. Smoke influence on trends in the number of days with extreme PM 2.5 concentrations is detectable by 2011, but the influence can be detected primarily in western and mid-western states. Wildfire-driven increases in ambient PM 2.5 concentrations are unregulated under current air pollution law 6 and, in the absence of further interventions, we show that the contribution of wildfire to regional and national air quality trends is likely to grow as the climate continues to warm., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

The climate change policy debate and ensuing discussions about industrial energy use and carbon emissions have highlighted the need to: (a) aggregate engineering information to a level relevant for economic policy analysis while maintaining sufficient detail so that results are meaningful for industry decision makers, (b) properly represent an industry’s capital vintage structure to better understand inertia associated with changes in aggregate industrial emissions profiles, and (c) identify policy instruments that leverage an industry’s potential for technological change such that carbon emissions can be noticeably reduced. This paper presents an econometric analysis of energy use and emissions profiles of the US Pulp and Paper Industry and uses the resulting set of equations to specify a dynamic model for the analysis of select climate change policies. Scenarios of cost of carbon, energy tax, and investment-led policies indicate that a combination of cost of carbon and investment-led policies can achieve the desired result of rapidly improving overall efficiency of the industry and promoting changes in fuel mix, which together can result in drastic reductions of carbon emissions. [Copyright &y& Elsevier]

Background: Exposure to fine particulate matter (PM2.5) is an established risk factor for human mortality. However, previous US studies have been limited to select cities or regions or to population subsets (e.g., older adults)., Methods: Here, we demonstrate how to use the novel geostatistical method Bayesian maximum entropy to obtain estimates of PM2.5 concentrations in all contiguous US counties, 2000-2016. We then demonstrate how one could use these estimates in a traditional epidemiologic analysis examining the association between PM2.5 and rates of all-cause, cardiovascular, respiratory, and (as a negative control outcome) accidental mortality., Results: We estimated that, for a 1 log(μg/m3) increase in PM2.5 concentration, the conditional all-cause mortality incidence rate ratio (IRR) was 1.029 (95% confidence interval [CI]: 1.006, 1.053). This implies that the rate of all-cause mortality at 10 µg/m3 would be 1.020 times the rate at 5 µg/m3. IRRs were larger for cardiovascular mortality than for all-cause mortality in all gender and race-ethnicity groups. We observed larger IRRs for all-cause, nonaccidental, and respiratory mortality in Black non-Hispanic Americans than White non-Hispanic Americans. However, our negative control analysis indicated the possibility for unmeasured confounding., Conclusion: We used a novel method that allowed us to estimate PM2.5 concentrations in all contiguous US counties and obtained estimates of the association between PM2.5 and mortality comparable to previous studies. Our analysis provides one example of how Bayesian maximum entropy could be used in epidemiologic analyses; future work could explore other ways to use this approach to inform important public health questions., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Active transportation is defined as self-propelled, human-powered transportation modes, such as walking and bicycling. In this article, we review the evidence that reliance on gasoline-powered transportation is contributing to global climate change, air pollution, and physical inactivity and that this is harmful to human health. Global climate change poses a major threat to human health and in the future could offset the health gains achieved over the last 100 yr. Based on hundreds of scientific studies, there is strong evidence that human-caused greenhouse gas emissions are contributing to global climate change. Climate change is associated with increased severity of storms, flooding, rising sea levels, hotter climates, and drought, all leading to increased morbidity and mortality. Along with increases in atmospheric CO2, other pollutants such as nitrogen dioxide, ozone, and particulate matter (e.g., PM2.5) are released by combustion engines and industry, which can lead to pulmonary and cardiovascular diseases. Also, as car ownership and vehicle miles traveled have increased, the shift toward motorized transport has contributed to physical inactivity. Each of these global challenges has resulted in, or is projected to result in, millions of premature deaths each year. One of the ways that nations can mitigate the health consequences of climate change, air pollution, and chronic diseases is through the use of active transportation. Research indicates that populations that rely heavily on active transportation enjoy better health and increased longevity. In summary, active transportation has tremendous potential to simultaneously address three global public health challenges of the 21st century., (Copyright © 2021 by the American College of Sports Medicine.)

Exposure to outdoor particulate matter (PM 2.5 ) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM 2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM 2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM 2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM 2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Importance: Despite biological plausibility, very few epidemiologic studies have investigated the risks of clinically significant bleeding events due to particulate air pollution., Objective: To measure the independent and synergistic effects of PM 2.5 exposure and anticoagulant use on serious bleeding events., Design: Retrospective cohort study (2008-2016)., Setting: Nationwide Medicare population., Participants: A 50% random sample of Medicare Part D-eligible Fee-for-Service beneficiaries at high risk for cardiovascular and thromboembolic events., Exposures: Fine particulate matter (PM 2.5 ) and anticoagulant drugs (apixaban, dabigatran, edoxaban, rivaroxaban, or warfarin)., Main Outcomes and Measures: The outcomes were acute hospitalizations for gastrointestinal bleeding, intracranial bleeding, or epistaxis. Hazard ratios and 95% CIs for PM 2.5 exposure were estimated by fitting inverse probability weighted marginal structural Cox proportional hazards models. The relative excess risk due to interaction was used to assess additive-scale interaction between PM 2.5 exposure and anticoagulant use., Results: The study cohort included 1.86 million high-risk older adults (mean age 77, 60% male, 87% White, 8% Black, 30% anticoagulant users, mean PM 2.5 exposure 8.81 μg/m 3 ). A 10 μg/m 3 increase in PM 2.5 was associated with a 48% (95% CI: 45%-52%), 58% (95% CI: 49%-68%) and 55% (95% CI: 37%-76%) increased risk of gastrointestinal bleeding, intracranial bleeding, and epistaxis, respectively. Significant additive interaction between PM 2.5 exposure and anticoagulant use was observed for gastrointestinal and intracranial bleeding., Conclusions: Among older adults at high risk for cardiovascular and thromboembolic events, increasing PM 2.5 exposure was significantly associated with increased risk of gastrointestinal bleeding, intracranial bleeding, and epistaxis. In addition, PM 2.5 exposure and anticoagulant use may act together to increase risks of severe gastrointestinal and intracranial bleeding. Thus, clinicians may recommend that high-risk individuals limit their outdoor air pollution exposure during periods of increased PM 2.5 concentrations. Our findings may inform environmental policies to protect the health of vulnerable populations., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Rachel C. Nethery reports financial support was provided by National Institutes of Health. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Co-exposure to air pollution and neighborhood disadvantage may influence cognition decline. We tested these associations in the context of dementia risk., Methods: We leveraged a cohort of adults ≥65 years (n = 5397) enrolled from 2011 to 2018 in the National Health and Aging Trends Study (NHATS). Particulate matter (PM) ≤ 10 μm in diameter, PM ≤ 2.5 μm in diameter, carbon monoxide, nitric oxide, and nitrogen dioxide - and neighborhood disadvantage were tested for joint associations with dementia risk. Pollutant concentrations at the 2010 census tract level were assigned using the US Environmental Protection Agency's Community Multiscale Air Quality Modeling System. Neighborhood disadvantage was defined using the tract Social Deprivation Index (SDI). Dementia was determined through self- or proxy-report or scores indicative of "probable dementia" according to NHATS screening tools. Joint effects of air pollutants and SDI were tested using quantile g-computation Cox proportional hazards models. We also stratified joint air pollution effects across SDI tertiles. Analyses adjusted for age at enrollment, sex, education, partner status, urbanicity, income, race and ethnicity, years at residence, census segregation, and census region., Results: SDI score (aHR = 1.08; 95% CI 0.96, 1.22), joint air pollution (aHR = 1.03, 95% CI 0.92, 1.16) and joint SDI with air pollution (aHR = 1.04, 95% CI 0.89, 1.22) were not associated with dementia risk. After accounting for competing risk of death, joint SDI with air pollution was not associated with dementia risk (aHR = 1.06; 95% CI 0.87, 1.29). In stratified models, joint air pollution was associated with greater risk of dementia at high (aHR = 1.19; 95% CI 0.87, 1.63), but not at medium or low SDI., Conclusion: Air pollution was associated with greater dementia risk in disadvantaged areas after accounting for competing risks. Air pollution associations with dementia incidence may be attenuated when other risk factors are more prominent in disadvantaged neighborhoods., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Aisha S. Dickerson reports financial support was provided by National Institute of Environmental Health Sciences. Aisha S. Dickerson reports financial support was provided by Bloomberg American Health Initiative. Roland J. Thorpe, Jr. reports financial support was provided by National Institute on Aging. Roland J. Thorpe, Jr. reports financial support was provided by National Institute on Minority Health and Health Disparities. Cavin Ward-Caviness reports a relationship with Clock Foundation that includes: board membership. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Regardless of a country's income level, air pollution poses a significant environmental threat to human health. Long-term exposure to air pollution often triggers cardiovascular and respiratory diseases. Thus, air pollution significantly reduces life expectancy worldwide. The USA is one of the world's largest polluters of CO 2 emissions, often used to represent air pollution. In this context, the main objective of this study is to examine the relationship between air pollution and life expectancy in the USA. In doing so, we control for the role of medical innovation, health expenditures, economic complexity, and government effectiveness using data for the period 1995-2019. The results indicate the existence of a cointegration relationship in the proposed model. The long-run coefficients are statistically positive for medical innovation and negative for CO 2 emissions, economic complexity, and government effectiveness. On the other hand, health expenditures are ineffective in terms of life expectancy. Accordingly, medical innovation raises life expectancy, whereas CO 2 emissions, economic complexity, and government effectiveness decrease it. Higher economic prosperity and health expenditures are not always beneficial to life expectancy. Therefore, policymakers need to take action to reduce air pollution and increase the comprehensiveness of economic prosperity benefits and health expenditure efficiency., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Air pollution is a recognized risk factor for cardiovascular disease (CVD). Temperature is also linked to CVD, with a primary focus on acute effects. Despite the close relationship between air pollution and temperature, their health effects are often examined separately, potentially overlooking their synergistic effects. Moreover, fewer studies have performed mixture analysis for multiple co-exposures, essential for adjusting confounding effects among them and assessing both cumulative and individual effects., Methods: We obtained hospitalization records for residents of 14 U.S. states, spanning 2000-2016, from the Health Cost and Utilization Project State Inpatient Databases. We used a grouped weighted quantile sum regression, a novel approach for mixture analysis, to simultaneously evaluate cumulative and individual associations of annual exposures to four grouped mixtures: air pollutants (elemental carbon, ammonium, nitrate, organic carbon, sulfate, nitrogen dioxide, ozone), differences between summer and winter temperature means and their long-term averages during the entire study period (i.e., summer and winter temperature mean anomalies), differences between summer and winter temperature standard deviations (SD) and their long-term averages during the entire study period (i.e., summer and winter temperature SD anomalies), and interaction terms between air pollutants and summer and winter temperature mean anomalies. The outcomes are hospitalization rates for four prevalent CVD subtypes: ischemic heart disease, cerebrovascular disease, heart failure, and arrhythmia., Results: Chronic exposure to air pollutant mixtures was associated with increased hospitalization rates for all CVD subtypes, with heart failure being the most susceptible subtype. Sulfate, nitrate, nitrogen dioxide, and organic carbon posed the highest risks. Mixtures of the interaction terms between air pollutants and temperature mean anomalies were associated with increased hospitalization rates for all CVD subtypes., Conclusions: Our findings identified critical pollutants for targeted emission controls and suggested that abnormal temperature changes chronically affected cardiovascular health by interacting with air pollution, not directly., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: The relationship between long-term exposure to PM 2.5 and mortality is well-established; however, the role of individual species is less understood., Objectives: In this study, we assess the overall effect of long-term exposure to PM 2.5 as a mixture of species and identify the most harmful of those species while controlling for the others., Methods: We looked at changes in mortality among Medicare participants 65 years of age or older from 2000 to 2018 in response to changes in annual levels of 15 PM 2.5 components, namely: organic carbon, elemental carbon, nickel, lead, zinc, sulfate, potassium, vanadium, nitrate, silicon, copper, iron, ammonium, calcium, and bromine. Data on exposure were derived from high-resolution, spatio-temporal models which were then aggregated to ZIP code. We used the rate of deaths in each ZIP code per year as the outcome of interest. Covariates included demographic, temperature, socioeconomic, and access-to-care variables. We used a mixtures approach, a weighted quantile sum, to analyze the joint effects of PM 2.5 species on mortality. We further looked at the effects of the components when PM 2.5 mass levels were at concentrations below 8 μg/m 3 , and effect modification by sex, race, Medicaid status, and Census division., Results: We found that for each decile increase in the levels of the PM 2.5 mixture, the rate of all-cause mortality increased by 1.4% (95% CI: 1.3%-1.4%), the rate of cardiovascular mortality increased by 2.1% (95% CI: 2.0%-2.2%), and the rate of respiratory mortality increased by 1.7% (95% CI: 1.5%-1.9%). These effects estimates remained significant and slightly higher when we restricted to lower concentrations. The highest weights for harmful effects were due to organic carbon, nickel, zinc, sulfate, and vanadium., Conclusions: Long-term exposure to PM 2.5 species, as a mixture, increased the risk of all-cause, cardiovascular, and respiratory mortality., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Joel D Schwartz reports financial support was provided by National Institute of Environmental Health Sciences. Joel D Schwartz reports a relationship with US Department of Justice that includes: paid expert testimony. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Several epidemiological studies have provided evidence that long-term exposure to fine particulate matter (pm2.5) increases mortality rate. Furthermore, some population characteristics (e.g., age, race, and socioeconomic status) might play a crucial role in understanding vulnerability to air pollution. To inform policy, it is necessary to identify groups of the population that are more or less vulnerable to air pollution. In causal inference literature, the group average treatment effect (GATE) is a distinctive facet of the conditional average treatment effect. This widely employed metric serves to characterize the heterogeneity of a treatment effect based on some population characteristics. In this paper, we introduce a novel Confounder-Dependent Bayesian Mixture Model (CDBMM) to characterize causal effect heterogeneity. More specifically, our method leverages the flexibility of the dependent Dirichlet process to model the distribution of the potential outcomes conditionally to the covariates and the treatment levels, thus enabling us to: (i) identify heterogeneous and mutually exclusive population groups defined by similar GATEs in a data-driven way, and (ii) estimate and characterize the causal effects within each of the identified groups. Through simulations, we demonstrate the effectiveness of our method in uncovering key insights about treatment effects heterogeneity. We apply our method to claims data from Medicare enrollees in Texas. We found six mutually exclusive groups where the causal effects of pm2.5 on mortality rate are heterogeneous., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

Ground-level ozone (O 3 ) is one of the major air pollutants. A large body of literature has linked O 3 air pollution to various adverse human health effects. The objective of this study is to attain a comprehensive and in-depth understanding of the progress and frontiers of research on O 3 and human health. We used bibliometric methods to summarize publications on O 3 air pollution and public health between 1990 and 2022 obtained from the Web of Science Core Collection database. VOSviewer and R software were used for bibliometric analysis and visualization. A total of 4501 relevant papers were included in the analysis. There has been a significant increase in the number of publications since 2013, with the USA being the major contributor, followed by China and England. Harvard University was the most prolific research institution, followed by the US Environmental Protection Agency and the University of North Carolina System. Professor Joel Schwartz was the most published author and has established a complex network of national and international collaborations. Co-occurrence analysis of keywords suggested evolving research hotspots, from toxicological studies to population-based epidemiological studies and from the respiratory system to the extra-pulmonary system. Research on O 3 and its human health effects has progressed rapidly over the past few decades, but academic disparities still persist between developed and developing countries. There is an urgent need to strengthen international cooperation to address the public health challenges posed by rising O 3 air pollution in the future., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background and Aims: Traffic-related exposures, such as air pollution and noise, have a detrimental impact on human health, especially in urban areas. However, there remains a critical research and knowledge gap in understanding the impact of community severance, a measure of the physical separation imposed by road infrastructure and motorized road traffic, limiting access to goods, services, or social connections, breaking down the social fabric and potentially also adversely impacting health. We aimed to robustly quantify a community severance metric in urban settings exemplified by its characterization in New York City (NYC)., Methods: We used geospatial location data and dimensionality reduction techniques to capture NYC community severance variation. We employed principal component pursuit, a pattern recognition algorithm, combined with factor analysis as a novel method to estimate the Community Severance Index. We used public data for the year 2019 at census block group (CBG) level on road infrastructure, road traffic activity, and pedestrian infrastructure. As a demonstrative application of the Community Severance Index, we investigated the association between community severance and traffic collisions, as a proxy for road safety, in 2019 in NYC at CBG level., Results: Our data revealed one multidimensional factor related to community severance explaining 74% of the data variation. In adjusted analyses, traffic collisions in general, and specifically those involving pedestrians or cyclists, were nonlinearly associated with an increasing level of Community Severance Index in NYC., Conclusion: We developed a high spatial-resolution Community Severance Index for NYC using data available nationwide, making it feasible for replication in other cities across the United States. Our findings suggest that increases in the Community Severance Index across CBG may be linked to increases in traffic collisions in NYC. The Community Severance Index, which provides a novel traffic-related exposure, may be used to inform equitable urban policies that mitigate health risks and enhance well-being., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Economic growth, air pollution, and forest fires in some states in the United States have increased the concentration of particulate matter with a diameter less than or equal to 2.5 μm (PM2.5). Although previous studies have tried to observe PM2.5 both spatially and temporally using aerosol remote sensing and geostatistical estimation, they were limited in accuracy by coarse resolution. In this paper, the performance of machine learning models on predicting PM2.5 is assessed with linear regression (LR), decision tree (DT), gradient boosting regression (GBR), AdaBoost regression (ABR), XGBoost (XGB), k‐nearest neighbors (K‐NN), long short‐term memory (LSTM), random forest (RF), and support vector machine (SVM) using PM2.5 station data from 2017 to 2021. To compare the accuracy of all the nine machine learning models, the coefficient of determination (R2), root mean square error (RMSE), Nash‐Sutcliffe efficiency (NSE), root mean square error ratio (RSR), and percent bias (PBIAS) were evaluated. Among all nine models, the RF (100 decision trees with a max depth of 20) and support vector regression (SVR; nonlinear kernel, degree 3 polynomial) models were the best for predicting PM2.5 concentrations. Additionally, comparison of the PM2.5 performance metrics displayed that the models had better predictive behavior in the western United States than that in the eastern United States. Key Points: We present the prediction of PM2.5 concentrations over the United States using various machine learning (ML) algorithmsWe show ML as a new approach for analyzing large data sets due to the computational speed and easy implementation for massive amounts of dataThe study is important for improving our understanding of the differences among ML algorithms for Earth Science research [ABSTRACT FROM AUTHOR]

There is a growing body of economic literature focusing on the negative health effect of air pollution. This paper performs a data-driven analysis of 844 economic publications on air pollution and health in the last three decades. We provide a novel way to visualize the research patterns and features, including the numerical analysis, collaboration network analysis at macro-meso-micro levels, keyword co-occurrence network analysis, and co-citation connection analysis. Additionally, the comparison with the topic dendrogram in environmental science is supplemented to seek the potential research direction. The results reveal that the entire field is now experiencing the rapid development. United States has not only published the largest number of articles, but has also established the most extensive collaborations with other countries. Besides, there have developed a couple of stable cooperative teams consisting of the leading authors. Research topics are gradually leaning toward the developing countries, and research contents are turning to psychological health. More types of air pollutants are worthy of analysis in the future. [ABSTRACT FROM AUTHOR]

Transitioning to electric vehicles (EVs) is a prominent strategy for reducing greenhouse gas emissions. However, given the complexity of atmospheric chemistry, the nuanced implications on air quality are yet to be fully understood. Our study delved into changes in PM 2.5 , ozone, and their associated precursors in major US urban areas, considering various electrification and mitigation scenarios. In the full electrification (FullE) scenario, PM 2.5 reduction peaked at values between 0.34 and 2.29 μg.m -3 across distinct regions. Yet, certain areas in eastern Los Angeles exhibited a surprising uptick in PM 2.5 , reaching as much as 0.67 μg.m -3 . This phenomenon was linked to a surge in secondary organic aerosols (SOAs), resulting from shifting NOx/VOCs (volatile organic compounds) dynamics and a spike in hydroxyl radical (OH) concentrations. The FullE scenario ushered in marked reductions in both NOx and maximum daily average 8-h (MDA8) ozone concentrations, with maximum levels ranging from 14.00 to 32.34 ppb and 2.58-9.58 ppb, respectively. However, certain instances revealed growths in MDA8 ozone concentrations, underscoring the intricacies of air quality management. From a health perspective, in the FullE scenario, New York, Chicago, and Houston stand to potentially avert 796, 328, and 157 premature deaths/month, respectively. Los Angeles could prevent 104 premature deaths/month in the HighE-BL scenario, representing a 29 % EV share for light-duty vehicles. However, the FullE scenario led to a rise in mortality in Los Angeles due to increased PM 2.5 and MDA8 ozone levels. Economically, the FullE scenario projects health benefits amounting to 51-249 million $/day for New York, Chicago, and Houston. In contrast, Los Angeles may face economic downturns of up to 18 million $/day. In conclusion, while EV integration has the potential to improve urban air quality, offering substantial health and economic advantages, challenges persist. Our results emphasize the pivotal role of VOCs management, providing policymakers with insights for adaptable and efficient measures., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

At the present stage, collaborative control of particulate matter and ozone pollution has become a modern challenge. The atmospheric boundary layer height (ABLH) is an important meteorological parameter for the sources and sinks of air pollutants. It is generally recognized that the reduction of boundary layer is conducive to the accumulation of pollutants. However, in recent years, some studies have shown that the relationship between ABLH and ozone is not negatively correlated. Here, we analyzed the spatial distribution characteristics of PM 2.5 and ozone exceedance in China from 2015 to 2022. The relationships between particulate pollution and ozone pollution and boundary layer meteorology were discussed. The key to coordinated control is to control the PM 2.5 concentration in the winter and ozone in summer. Moreover, the two have different responses to meteorological factors, especially to the ABLH. Low temperature and low ABLH are conducive to the deterioration of particulate pollution, but high temperature and high ABLH are conducive to the occurrence and development of ozone pollution. The response of ozone to ABLH is contrary to previous studies in Europe and the United States. Moreover, an abnormal positive correlation was observed for PM 2.5 and ABLH in Southwest China, which was mainly due to the impact of biomass combustion in Southeast Asia., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Exposure to air pollutants and other environmental factors increases the risk of adverse pregnancy outcomes. There is growing evidence that adverse outcomes related to air pollution disproportionately affect racial and ethnic minorities. The objective of this paper is to explore the importance of race as a risk factor for air pollution-related poor pregnancy outcomes., Methods: Studies investigating the effects of exposure to air pollution on pregnancy outcomes by race were reviewed. A manual search was conducted to identify missing studies. Studies that did not compare pregnancy outcomes among two or more racial groups were excluded. Pregnancy outcomes included preterm births, small for gestational age, low birth weight, and stillbirths., Results: A total of 124 articles explored race and air pollution as risk factors for poor pregnancy outcome. Thirteen percent of these (n=16) specifically compared pregnancy outcomes among two or more racial groups. Findings across all reviewed articles showed more adverse pregnancy outcomes (preterm birth, small for gestational age, low birth weight, and stillbirths) related to exposure to air pollution among Blacks and Hispanics than among non-Hispanic Whites., Conclusion: Evidence support our general understanding of the impact of air pollution on birth outcomes and, specifically, of disparities in exposure to air pollution and birth outcomes for infants born to Black and Hispanic mothers. The factors driving these disparities are multifactorial, mostly social, and economic factors. Reducing or eliminating these disparities require interventions at individual, community, state, and national level., (© 2023. The Author(s).)

Exposure to ambient air pollution is associated with a wide range of adverse health effects such as respiratory symptoms, cardiovascular events, and premature mortality. Canada and the United States (US) have worked collaboratively for decades to address transboundary air pollution and its impacts across their shared border. To inform transboundary air quality considerations, we conducted modelling to attribute health impacts from ambient PM 2.5 and O 3 exposure in Canada to Canadian and US emission sources. We employed emissions, chemical transport, and health impacts modelling for 2015, 2025, and 2035 using a brute-force modelling approach whereby anthropogenic domestic and US emissions were reduced separately by 20 % or 100 %, and the resulting changes in health impacts were estimated across Canada. We find that transboundary PM 2.5 and O 3 related health impacts vary widely by region, with >80 % of impacts occurring in Central Canada, and most health impacts occurring within 200-300 km of the Canada-US border. The relative contribution of US sources to O 3 in Canada is larger than for PM 2.5 , yet we find that the health impacts from transboundary PM 2.5 exceeded those from transboundary O 3 . Nationally, we estimate that roughly one in five PM 2.5 deaths in Canada is attributable to US sources (2000 deaths in 2015) and more than one in two O 3 deaths are attributable to US sources (roughly 800 to 1200 deaths in 2015). We project health impacts from domestic and US sources to increase from 2025 to 2035 in Canada. Our results suggest that there are substantial benefits to be gained by domestic and international strategies to reduce PM 2.5 in the Canada-US transboundary region., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Air pollution is responsible worldwide for 9-12 million deaths annually. The major contributor to air pollution is particulate matter ≤2.5 µg per cubic meter of air (PM2.5) from vehicles, industrial emissions, and wildfire smoke. United States ambient air standards recommend annual average PM2.5 concentrations of ≤12 μg/m³ while European standards allow an average annual PM2.5 concentration of ≤20 μg/m 3 . However, significant PM2.5 cardiovascular and pulmonary health risks exist below these concentrations. Chronic PM2.5 exposure significantly increases major cardiovascular and pulmonary event risks in Americans by 8 to more than 20% for each 10-μg/m 3 increase in PM2.5. PM2.5-induced increases in lipid peroxidation, induction of vascular inflammation and endothelial cell injury initiate and propagate respiratory diseases, coronary and carotid atherosclerosis. PM2.5 can cause atherosclerotic vascular plaque rupture and myocardial infarction and stroke by activating metalloproteinases. This article discusses PM2.5 effects on the cardiovascular and pulmonary systems, specific PM2.5 pathophysiologic mechanisms contributing to cardiopulmonary disease, and preventive measures to limit the cardiovascular and pulmonary effects of PM2.5., Competing Interests: Declaration of Competing Interest The author declare that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Pregnancy loss, a major health issue that affects human sustainability, has been linked to short-term exposure to ground-surface ozone (O 3 ). However, the association is inconsistent, possibly because of the co-occurrence of O 3 and heat episodes, as increased temperature is a risk factor for pregnancy loss. To explain this inconsistency, the effect of O 3 on pregnancy loss needs to be examined jointly with that of high temperature., Methods: A total of 247,305 pregnancy losses during the warm season were extracted from fetal death certificates from the 386 counties in contiguous United States from 1989 to 2005. We assessed environmental exposure based on the daily maximum 8 h average of O 3 from Air Quality System monitors and the 24 h average temperature from the North American Regional Reanalysis product. We conducted a bidirectional, time-stratified case-crossover study of the association between pregnancy loss and exposures to O 3 and temperature and their multiplicative interaction. The main time window for the exposure assessment was the day of case occurrence and the preceding 3 days. To estimate the association, we used conditional logistic regression with adjustment for relative humidity, height of the planetary boundary layer, and holidays. Sensitivity analyses were performed on the lagged structure, nonlinearity, and between-subpopulation heterogeneity of the estimated joint effect., Results: The joint effect was first estimated by the regression against categorical exposure by tertile. Compared to the low-low exposure group (O 3  ≤ 78 μg/m 3 and temperature ≤ 18 °C), the odds of pregnancy loss was significantly higher by 6.0 % (95 % confidence interval [CI] 2.4-9.7 %), 9.8 % (6.1-13.8 %), and 7.5 % (4.7-10.3 %) in the high-low (>104 μg/m 3 and ≤18 °C), low-high (≤78 μg/m 3 and >23 °C), and high-high (>104 μg/m 3 and >23 °C) groups. The model of linear exposure and the multiplicative interaction yielded similar results. Each increment of 10 μg/m 3 in O 3 and 1 °C in temperature was associated with a 3.0 % (2.0 %-4.0 %) and 3.9 % (3.5 %-4.3 %), respectively, increase in the odds of pregnancy loss. A decrease in odds of 0.2 % (0.1 %-0.2 %) was associated with the temperature × O 3 interaction. The finding of an antagonistic interaction between temperature and O 3 was confirmed by models parametrizing the joint exposure as alternative nonlinear terms (i.e., a two-dimensional spline term or a varying-coefficient term) and was robust to a variety of exposure lags and stratifications. Therefore, the marginal effect of O 3 was estimated to vary by climate zone. A significant association between O 3 and pregnancy loss was observed in the northern, but not southern, United States., Conclusion: Joint exposure to O 3 and high temperature can increase the risk for pregnancy loss. The adverse effect of O 3 is potentially modified by ambient temperature. In high-latitude cities, controlling for O 3 pollution could protect maternal health., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Air pollution from residential wood heating poses a significant public health risk and is a primary cause of PM nonattainment in some areas of the United States. Those emissions also play a role in regional haze and climate change. While regulatory programs have focused on emissions reductions from large facilities, the residential heating sector has received limited attention. The failure to develop effective programs to address this emission source hampers the ability of state and local air quality programs to meet clean air goals. An updated New Source Performance Standard (NSPS) for Residential Wood Heaters was promulgated in 2015, which includes more stringent emissions standards for wood stoves and broadens its scope to regulate additional types of wood heating appliances. However, weaknesses in the test methods and programs used to certify compliance with the NSPS limits hamper the efficacy of those requirements. Current emissions certification tests measure stove performance under defined laboratory conditions that (1) do not adequately reflect operation and performance of appliances in homes, (2) are not sufficiently repeatable to allow for comparison of emissions of different appliances, and (3) allow manufacturers leeway to modify critical test fueling and operating parameters which can significantly impact performance outcomes. These foundational regulatory issues present substantial challenges to promoting the cleanest and most efficient wood heating systems. This paper provides an overview of the air quality and public health impacts of residential wood heating and discusses the weaknesses in the current emission certification approaches and work by the Northeast States for Coordinated Air Use Management (NESCAUM) and the New York State Energy Research and Development Authority to develop improved testing methods. Other articles in this issue discuss the development and testing of those methods in detail. Implications: Air pollution from residential wood heating poses a significant public health risk and is a primary cause of PM nonattainment in some areas of the United States. Those emissions also play a role in regional haze and climate change. While regulatory programs have focused on emissions reductions from large facilities, the residential heating sector has received limited attention. The failure to develop effective programs to address this emission source hampers the ability of state and local air quality programs to meet clean air goals. This paper provides an overview of the issue. [ABSTRACT FROM AUTHOR]

Background: Despite strong evidence of the association of fine particulate matter (PM 2.5 ) exposure with an increased risk of lung cancer mortality, few studies had investigated associations of multiple pollutants simultaneously, or with incidence, or using causal methods. Disparities were also understudied., Objectives: We investigated long-term effects of PM 2.5 , nitrogen dioxide (NO 2 ), warm-season ozone, and particle radioactivity (PR) exposures on lung cancer incidence in a nationwide cohort., Methods: We conducted a cohort study with Medicare beneficiaries (aged ≥ 65 years) continuously enrolled in the fee-for-service program in the contiguous US from 2001 to 2016. Air pollution exposure was averaged across three years and assigned based on ZIP code of residence. We fitted Cox proportional hazards models to estimate the hazard ratio (HR) for lung cancer incidence, adjusted for individual- and neighborhood-level confounders. As a sensitivity analysis, we evaluated the causal relationships using inverse probability weights. We further assessed effect modifications by individual- and neighborhood-level covariates., Results: We identified 166,860 lung cancer cases of 12,429,951 studied beneficiaries. In the multi-pollutant model, PM 2.5 and NO 2 exposures were statistically significantly associated with increased lung cancer incidence, while PR was marginally significantly associated. Specifically, the HR was 1.008 (95% confidence interval [CI]: 1.005, 1.011) per 1-μg/m 3 increase in PM 2.5 , 1.013 (95% CI: 1.012, 1.013) per 1-ppb increase in NO 2 , and 1.005 (0.999, 1.012) per 1-mBq/m 3 increase in PR. At low exposure levels, all pollutants were associated with increased lung cancer incidence. Men, older individuals, Blacks, and residents of low-income neighborhoods experienced larger effects of PM 2.5 and PR., Discussion: Long-term PM 2.5 , NO 2 , and PR exposures were independently associated with increased lung cancer incidence among the national elderly population. Low-exposure analysis indicated that current national standards for PM 2.5 and NO 2 were not restrictive enough to protect public health, underscoring the need for more stringent air quality regulations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Although air quality has gradually improved in recent years, as shown by the decrease in PM 2.5 concentration, the problem of rising ambient ozone has become increasingly serious. To reduce hazards to human health and environmental welfare exposure to ozone, scientists and government regulators have developed ozone guidelines and standards. These answer the questions of which levels of exposure are hazardous to human health and the environment, and how can ambient ozone exposure be guaranteed, respectively. So what are the basis for the ozone guidelines and standards? This paper reviews in detail the process of revising ozone guidelines and standards by the World Health Organization (WHO) and the United States Environmental Protection Agency (EPA). The present study attempts to explore and analyze the scientific basis and empirical methods for updating guidelines and standards, in a view to guide the future revision process and provide directions for further scientific research. We found many epidemiological and toxicological studies and exposure-response relationships provided strong support for developing and revising the ozone guidelines. When setting standards, ozone exposure has been effectively considered, and the economic costs, health, and indirect economic benefits of standard compliance were reasonably estimated. Accordingly, epidemiological and toxicological studies and the establishment of exposure-response relationships, as well as exposure and risk assessment and benefit-cost estimates of standards compliance should be strengthened for the further update of guidelines and standards. In addition, with the increasing prominence of combined air pollution led by ozone and PM 2.5 , more joint exposure scientific research related to ozone guidelines and standards should be undertaken., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Xue and Zhang.)

From the ensemble of stations that monitor surface air quality over the United States and Europe, we identify extreme ozone pollution events and find that they occur predominantly in clustered, multi-day episodes with spatial extents of more than 1000 km. Such scales are amenable to forecasting with current global atmospheric chemistry models. We develop an objective mapping algorithm that uses the heterogeneous observations of the individual surface sites to calculate surface ozone averaged over 1° by 1° grid cells, matching the resolution of a global model. Air quality extreme (AQX) events are identified locally as statistical extremes of the ozone climatology and not as air quality exceedances. With the University of California, Irvine chemistry-transport model (CTM) we find there is skill in hindcasting these extreme episodes, and thus identify a new diagnostic using global chemistry-climate models (CCM) to identify changes in the characteristics of extreme pollution episodes in a warming climate. [ABSTRACT FROM AUTHOR]

In this study, we developed a practical approach to augment elemental carbon (EC) emissions to improve the reproducibility of the most recent air quality with photochemical grid modeling in support of source-receptor relationship analysis. We demonstrated the usefulness of this approach with a series of simulations for EC concentrations over Northeast Asia during the 2016 Korea-United States Air Quality study. Considering the difficulty of acquiring EC observational data in foreign countries, our approach takes two steps: (1) augmenting upwind EC emissions based on simulated upwind contributions and observational data at a downwind EC monitor considered as the most representative monitor for upwind influences and (2) adjusting downwind EC emissions based on simulated downwind contributions, including the effects of updated upwind emissions from the first step and observational data at the downwind EC monitors. The emission adjustment approach resulted in EC emissions 2.5 times higher than the original emissions in the modeling domain. The EC concentration in the downwind area was observed to be 1.0 μg m -3 during the study period, while the simulated EC concentration was 0.5 μg m -3 before the emission adjustment. After the adjustment, the normalized mean error of the daily mean EC concentration decreased from 48 % to 22 % at ground monitor locations. We found that the EC simulation results were improved at high altitudes, and the contribution of the upwind areas was greater than that of the downwind areas for EC concentrations downwind with or without emission adjustment. This implies that collaborating with upwind regions is essential to alleviate high EC concentrations in downwind areas. The developed emission adjustment approach can be used for any upwind or downwind area when transboundary air pollution mitigation is needed because it provides better reproducibility of the most recent air quality through modeling with improved emission data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Air pollution exposure is associated with cardiovascular morbidity and mortality. Although exposure to air pollution early in life may represent a critical window for development of cardiovascular disease risk factors, few studies have examined associations of long-term air pollution exposure with markers of cardiovascular and metabolic health in young adults., Objectives: By combining health data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) with air pollution data from the Fused Air Quality Surface using Downscaling (FAQSD) archive, we: (1) calculated multi-year estimates of exposure to ozone (O 3 ) and particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM 2.5 ) for Add Health participants; and (2) estimated associations between air pollution exposures and multiple markers of cardiometabolic health., Methods: Add Health is a nationally representative longitudinal cohort study of over 20,000 adolescents aged 12-19 in the United States (US) in 1994-95 (Wave I). Participants have been followed through adolescence and into adulthood with five in-home interviews. Estimated daily concentrations of O 3 and PM 2.5 at census tracts were obtained from the FAQSD archive and used to generate tract-level annual averages of O 3 and PM 2.5 concentrations. We estimated associations between average O 3 and PM 2.5 exposures from 2002 to 2007 and markers of cardiometabolic health measured at Wave IV (2008-09), including hypertension, hyperlipidemia, body mass index (BMI), diabetes, C-reactive protein, and metabolic syndrome., Results: The final sample size was 11,259 individual participants. The average age of participants at Wave IV was 28.4 years (range: 24-34 years). In models adjusting for age, race/ethnicity, and sex, long-term O 3 exposure (2002-07) was associated with elevated odds of hypertension, with an odds ratio (OR) of 1.015 (95% confidence interval [CI]: 1.011, 1.029); obesity (1.022 [1.004, 1.040]); diabetes (1.032 [1.009,1.054]); and metabolic syndrome (1.028 [1.014, 1.041]); PM 2.5 exposure (2002-07) was associated with elevated odds of hypertension (1.022 [1.001, 1.045])., Conclusion: Findings suggest that long-term ambient air pollution exposure, particularly O 3 exposure, is associated with cardiometabolic health in early adulthood., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Some studies suggest that ambient particulate air pollution is associated with cognitive decline. However, the findings are mixed, and there is no relevant research examining the influences of ultrafine particles (UFP), which may have more toxicity than larger particles. We therefore conducted this study to investigate whether residential UFP exposure is associated with cognitive decline using data from the Alzheimer's Disease Research Centers in the United States., Methods: This is a longitudinal study of participants who were aged 65 years and older and had normal cognitive status at baseline. Residential UFP exposure, expressed as particle number concentrations (PNC), was assessed in 2016-2017 using a nationwide land use regression model, and was assigned to each participant using their 3-digit residential ZIP codes. Cognitive functions including memory, attention, language, executive function, and global function were assessed annually using 15 neuropsychological tests from March 2015 to February 2022. Linear mixed-effects models were used to examine the associations after adjustment for covariates including baseline age, sex, APOE ε4 status, race, education, smoking status, history of diabetes, quartiles of neighborhood median household income, and interaction terms of follow-up time with each covariate., Results: This study included 5646 participants (mean age 76 years, 65% female). On average, each participant had 4 annual visits. When PNC was treated as a continuous variable, there were no statistically or clinically significant changes in annual decline of each cognitive function in relation to an interquartile range elevation in PNC (4026 particles/cm 3 ). Similarly, when PNC was treated as a categorical variable including five exposure groups, there were no linear exposure-response trends in annual decline of each cognitive function across the five exposure groups., Conclusions: This study found no meaningful associations between residential UFP exposure and cognitive decline in global and domain-specific functions. There is a need for further research that assigns UFP exposure at a finer geographic scale., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Transportation network companies (TNCs), such as Uber and Lyft, have pledged to fully electrify their ridesourcing vehicle fleets by 2030 in the United States. In this paper, we introduce AgentX, a novel agent-based model built in Julia for simulating ridesourcing services with high geospatial and temporal resolution. We then instantiate this model to estimate the life cycle air pollution, greenhouse gas, and traffic externality benefits and costs of serving rides based on Chicago TNC trip data from 2019 to 2022 with fully electric vehicles. We estimate that electrification reduces life cycle greenhouse gas emissions by 40-45% (9-10¢ per trip) but increases life cycle externalities from criteria air pollutants by 6-11% (1-2¢ per trip) on average across our simulations, which represent demand patterns on weekdays and weekends across seasons during prepandemic, pandemic, and post-vaccination periods. A novel finding of our work, enabled by our high resolution simulation, is that electrification may increase deadheading for TNCs due to additional travel to and from charging stations. This extra vehicle travel increases estimated congestion, crash risk, and noise externalities by 2-3% (2-3¢ per trip). Overall, electrification reduces net external costs to society by 3-11% (5-24¢ per trip), depending on the assumed social cost of carbon.

The limited number of ozone monitoring stations imposes uncertainty in various applications, calling for accurate approaches to capturing ozone values in all regions, particularly those with no in-situ measurements. This study uses deep learning (DL) to accurately estimate daily maximum 8-hr average (MDA8) ozone and examines the spatial contribution of several factors on ozone levels over the contiguous U.S. (CONUS) in 2019. A comparison between in-situ observations and DL-estimated MDA8 ozone values shows a correlation coefficient (R) of 0.95, an index of agreement (IOA) of 0.97, and a mean absolute bias (MAB) of 2.79 ppb, highlighting the promising performance of the deep convolutional neural network (Deep-CNN) at estimating surface MDA8 ozone. Spatial cross-validation also confirms the high spatial accuracy of the model, which obtains an R of 0.91, and IOA of 0.96 and an MAB of 3.46 ppb when it is trained and tested on separate stations. To interpret the black-box nature of our DL model, we use Shapley additive explanations (SHAP) to generate a spatial feature contribution map (SFCM), the results of which confirm an advanced ability of Deep-CNN to capture the interactions between most predictor variables and ozone. For instance, the model shows that solar radiation (SRad) SFCM, with higher values, enhances the formation of ozone, particularly in the south and southwestern CONUS. As SRad triggers ozone precursors to produce ozone via photochemical reactions, it increases ozone concentrations. The model also shows that humidity, with its low values, increases ozone concentrations in the western mountainous regions. The negative correlation between humidity and ozone levels can be attributed to factors such as higher ozone decomposition resulting from increased levels of humidity and OH radicals. This study is the first to introduce the SFCM to investigate the spatial role of predictor variables on changes in estimated MDA8 ozone levels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

The article offers information on the 140 intercomparisons of data collected during U.S. National Aeronautics and Space Administration's (NASA) Intercontinental Chemical Transport Experiment-B (INTEX-B) mission. It says that the INTEX-B, part of the NASA-led INTEX-NA mission, was the second major airborne field mission aiming to investigate the transport and transportation of pollution over the North American continent. It notes that it operates in coordination with MILAGRO and IMPEX missions.

Numerous studies have investigated the associations between COVID-19 risks and long-term exposure to air pollutants, revealing considerable heterogeneity and even contradictory regional results. Studying the spatial heterogeneity of the associations is essential for developing region-specific and cost-effective air-pollutant-related public health policies for the prevention and control of COVID-19. However, few studies have investigated this issue. Using the USA as an example, we constructed single/two-pollutant conditional autoregressions with random coefficients and random intercepts to map the associations between five air pollutants (PM 2.5 , O 3 , SO 2 , NO 2 , and CO) and two COVID-19 outcomes (incidence and mortality) at the state level. The attributed cases and deaths were then mapped at the county level. This study included 3108 counties from 49 states within the continental USA. The county-level air pollutant concentrations from 2017 to 2019 were used as long-term exposures, and the county-level cumulative COVID-19 cases and deaths through May 13, 2022, were used as outcomes. Results showed that considerably heterogeneous associations and attributable COVID-19 burdens were found in the USA. The COVID-19 outcomes in the western and northeastern states appeared to be unaffected by any of the five pollutants. The east of the USA bore the greatest COVID-19 burdens attributable to air pollution because of its high pollutant concentrations and significantly positive associations. PM 2.5 and CO were significantly positively associated with COVID-19 incidence in 49 states on average, whereas NO 2 and SO 2 were significantly positively associated with COVID-19 mortality. The remaining associations between air pollutants and COVID-19 outcomes were not statistically significant. Our study provided implications regarding where a major concern should be placed on a specific air pollutant for COVID-19 control and prevention, as well as where and how to conduct additional individual-based validation research in a cost-effective manner., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Industrial and agricultural off-gas streams are comprised of numerous volatile compounds, many of which have substantially different odorous properties. State-of-the-art waste-gas treatment includes the characterization of these molecules and is directed at, if possible, either the avoidance of such odorants during processing or the use of existing standardized air purification techniques like bioscrubbing or afterburning, which however, often show low efficiency under ecological and economical regards. Selective odor separation from the off-gas streams could ease many of these disadvantages but is not yet widely applicable. Thus, the aim of this paper is to identify possible model substances in selective odor separation research from 155 volatile molecules mainly originating from livestock facilities, fat refineries, and cocoa and coffee production by knowledge-based methods. All compounds are examined with regard to their structure and information-content using topological and information-theoretical indices. Resulting data are fitted in an observation matrix, and similarities between the substances are computed. Principal component analysis and k-means cluster analysis are conducted showing that clustering of indices data can depict odor information correlating well to molecular composition and molecular shape. Quantitative molecule describtion along with the application of such statistical means therefore provide a good classification tool of malodorant structure properties with no thermodynamic data needed. The approximate look-alike shape of odorous compounds within the clusters suggests a fair choice of possible model molecules. [ABSTRACT FROM AUTHOR]

Early life exposure to air pollutants during pregnancy is one of the leading causes of different health outcomes. However, few studies have provided an overview of this area of research. The aim of this study was to look at the key trends in the research on prenatal exposure to air pollution. Data were retrieved from Web of Science, and the search was conducted based on the paper title, abstract, and keywords. The relevant literature searched was from 1994 to 2022, and 952 English documents were obtained. Of the total documents, 438 documents were included in the review and 83% ( n = 365) of the documents were journal articles. Type of document, annual distribution of publications, and distribution of prenatal exposure by countries were extracted. Co-authorship and keywords co-occurrence analyses were also carried out. Of all the countries that published in this field, the United States of America. had the highest number of publications, followed by China. Among the different health and environmental disciplines, 62% ( n = 273) of papers came from environmental science. There were limited collaborations among researchers from different countries and institutions. In conclusion, there should be more collaboration among the researchers in this field regarding institutions, countries, and disciplines.

Alzheimer's disease and related dementias (ADRD) poses substantial health challenges among an aging population. One of the primary challenges in studying ADRD is that biological processes underlying these ailments begin decades prior to diagnosis. Previous studies indicate a relationship between ADRD and air pollution exposure to both fine particulate matter (PM 2.5 ) and nitrogen dioxide (NO 2 ) but are limited in their interpretation because they consider exposure measurements at a single time point. Our retrospective cohort study considered 27 + million Medicare enrollees in the United States followed up to 17 years and matched with highly accurate annual air pollution exposure measurements for PM 2.5 , NO 2 , and summer ozone. We applied distributed lag models and estimated the lagged associations between air pollution and odds of first hospitalization with ADRD. We found significantly increased odds due to overall PM 2.5 and NO 2 exposure and time-lagged exposure 10 and 8 years prior to admission, respectively. Furthermore, we found the connection between air pollution exposure and increased odds of first hospitalization with ADRD exists at air pollution levels below current National Ambient Air Quality Standards set by the US Environmental Protection Agency, with the steepest increase in odds occurring at low concentrations of PM 2.5 . Our findings are the first to show that air pollution exposures from as many as 10 years prior to the admission are related to increased odds of hospitalizations with ADRD. As there are no clear treatments available for ADRD, identifying modifiable risk factors such as air pollution exposure may make significant contributions towards prevention or delayed disease progression., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The Chengyu Metropolitan Area (CYMA), located in the Sichuan Basin, is an unevenly developed region with high PM 2.5 concentrations and a population of approximately 100 million. Although exposure inequality in air pollution has received increasing concern, no related research has been carried out in the CYMA to date. In this work, we used the concentration index to assess inequality of PM 2.5 population-weighted exposure in the CYMA among different subgroups, including age, education, gender, occupation and GDP per capita in the city of residence. Our findings revealed that the non-disadvantaged subgroups (people aged 15-64, people with senior and higher education, people with high-income occupations and residents of cities with high GDP per capita) had a higher PM 2.5 exposure in the CYMA, with the concentration indices of -0.03 (95% CI: 0.064, -0.001), -0.14 (95% CI: 0.221, -0.059), -0.15 (95% CI: 0.238, -0.056) and -0.27 (95% CI: 0.556, 0.012), opposite to previous studies in developed countries such as the United States and France. In addition, exposure differences among cities were much larger than those among populations in the CYMA. These findings may benefit the government in identifying disproportionately exposed subgroups in developing regions, and suggest that related measures should initially be carried out for cities exposed to high PM 2.5 concentrations rather than for populations exposed to high PM 2.5 concentrations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Previous studies on the impact of measurement error for PM 2.5 were mostly simulation studies, did not control for other pollutants, or used a single regression calibration model to correct for measurement error. However, the relationship between actual and error-prone PM 2.5 concentration may vary by time and region. We aim to correct the measurement error of PM 2.5 predictions using stratified regression calibration and investigate how the measurement error biases the association between PM 2.5 and mortality in the Medicare Cohort., Methods: The "gold-standard" measurements of PM 2.5 were defined as daily monitoring data. We regressed daily monitoring PM 2.5 on modeled PM 2.5 using the simple linear regression by strata of season, elevation, census division and time period. Calibrated PM 2.5 was calculated with stratum-specific calibration parameters β 0 (intercept) and β 1 (slope) for each strata and aggregated to annual level. Associations between calibrated and error-prone annual PM 2.5 and all-cause mortality among Medicare beneficiaries were estimated with Quasi-Poisson regression models., Results: Across 208 strata, the median of β 0 and β 1 were 0.62 (25% 0.0.20, 75% 1.06) and 0.93 (25% 0.87, 75% 0.99). From calibrated and error-prone PM 2.5 data, we estimated that each 10 μg/m 3 increase in PM 2.5 was respectively associated with 4.9% (95%CI 4.6-5.2) and 4.6% (95%CI 4.4-4.9) increases in the mortality rate among Medicare beneficiaries, conditional on confounders., Conclusions: Regression calibration parameters of PM 2.5 varied by time and region. Using error-prone measures of PM 2.5 underestimated the association between PM 2.5 and all-cause mortality. Modern exposure models produce relatively small bias., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Though fine particulate matter (PM 2.5 ) has decreased in the United States (U.S.) in the past two decades, the increasing frequency, duration, and severity of wildfires significantly (though episodically) impairs air quality in wildfire-prone regions and beyond. Increasing PM 2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM 2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM 2.5 concentrations in unmonitored areas often rely on big data, such as satellite-derived aerosol properties and meteorological variables, apply computationally-intensive deterministic modeling, and do not distinguish wildfire-specific PM 2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM 2.5 presents a challenge since measurements of the smoke contribution to PM 2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM 2.5 from other sources of emissions. Our study presents an ensemble model that optimally combines multiple machine learning algorithms (including gradient boosting machine, random forest and deep learning), and a large set of explanatory variables to, first, estimate daily PM 2.5 concentrations at the ZIP code level, a relevant spatiotemporal resolution for epidemiological studies. Subsequently, we propose a novel implementation of an imputation approach to estimate the wildfire-specific PM 2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM 2.5 prediction while avoiding processing larger, computationally intensive datasets. Our study is the first to apply a suite of statistical models using readily available datasets to provide daily wildfire-specific PM 2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)