The relationships between chronic exposure to wildfire smoke PM2.5 (particulate matter with aerodynamic diameter of ≤2.5 μm) and mortality remain poorly understood, with causal evidence being particularly scarce. In this ecological study, we used a doubly robust method, incorporating flexible generalized propensity score estimation that captured potential nonlinearity and interactions among confounders and relaxed the distribution form assumption for exposure, to estimate the effects of annual exposure to wildfire smoke PM2.5 on all-cause and cause-specific mortality in the contiguous United States from 2006 to 2020. We found that wildfire smoke PM2.5 was associated with increased mortality rate for all studied outcomes, except for deaths from transport accidents or falls, which served as negative outcome controls. Wildfire smoke PM2.5 was responsible for ~24,100 all-cause deaths per year in the contiguous United States. The exposure-response curve for all-cause mortality increased monotonically, with no evidence of a "safe" threshold. Among the six cause-specific outcomes, mortality from neurological disease showed the greatest increase per 0.1 μg/m3 increase in smoke PM2.5 exposure. Our study provided robust evidence for the chronic effect of wildfire smoke PM2.5 on mortality, underscoring the urgent need for targeted measures to mitigate the substantial and escalating burden of wildfires.

BACKGROUND:Heat waves are increasing in frequency and intensity due to climate change. While acute mortality effects are well documented, the impact on annual mortality and the modification of this association by race, poverty level, and amount of green space at a national level are less understood. We aimed to quantify the association of heat waves with annual all-cause and cause-specific mortality among older adults in the contiguous USA. METHODS:We conducted a cohort study using an open cohort of 73 769 163 Medicare beneficiaries (aged ≥65 years) residing in 27 926 ZIP codes across the contiguous USA from 2000-18, with 668 448 618 person-years of follow-up. Heat waves were defined at the ZIP-code level as 2 or more consecutive days with a minimum temperature exceeding 2·5 SDs above the local 30-year summer mean. We used quasi-Poisson regression models at the ZIP-code-year level to examine the association between the annual number of heat waves and mortality counts, adjusting for individual and neighbourhood-level characteristics and secular trends. FINDINGS:An additional heat wave was associated with an increase in the annual mortality rate of 8·83 deaths per 10 000 person-years (95% CI 5·99 to 11·68). Across the contiguous USA from 2000-18, the 8307 observed heat waves were associated with an estimated 17 603 (95% CI 11 942 to 23 287) excess deaths. Were an additional heat wave to occur in every ZIP code each decade, we estimated 56 815 (95% CI 38 651 to 74 979) premature deaths would result. Increased heatwaves were associated with cardiovascular, respiratory, and neurological deaths. In subgroup analyses, the mortality increase was significantly greater for Black individuals (16·50 per 10 000 person-years, 95% CI 8·11 to 25·04) than for White individuals (5·85 per 10 000 person-years, 2·79 to 8·93). The association was stronger in high-poverty neighbourhoods (11·09, 4·00 to 18·29) and was protective in neighbourhoods with abundant green space (-13·51, -24·92 to -1·79). INTERPRETATION:Heat waves are associated with a substantial increase in annual mortality among older US adults, with disproportionate impacts on Black communities and communities on low incomes. Our findings suggest that climate change poses a significant chronic risk to public health, highlighting the need for urgent mitigation and targeted adaptation measures to address these inequities. FUNDING:None.

Ambient fine particulate matter (PM2.5) is a proven human lung carcinogen associated with lung cancer incidence. However, the relative toxicity of the various chemical components of PM2.5 and their joint association with all-cause mortality following lung cancer diagnosis remain unclear. We conducted a cohort study of 528,127 adults aged ≥65 years with lung cancer diagnosed between 2000 and 2019, derived from the SEER-Medicare database. Patients were followed annually from diagnosis until death, loss to follow-up, or end of the study in 2019. Two-year moving average exposures to 15 p.m.2.5 chemical components were estimated using high-resolution spatiotemporal models and linked to each patient based on residential ZIP code in each year. We used generalized weighted quantile sum regression with random holdouts to estimate both the joint association of PM2.5 component mixtures with all-cause mortality and the relative contribution of each component, adjusted for demographics, histological type, stage, first-course treatments, comorbidities, and neighborhood-level covariates. We found that joint exposure to PM2.5 component mixtures was associated with increased mortality, with relative risk of 1.011 (95% confidence interval [CI]: 1.010, 1.013) per decile increase in all components. Although differences in contributions were modest, silicon, nitrate, vanadium, zinc, and iron appeared to be more influential contributors, suggesting that controlling related sources, such as road dust, traffic emissions, fossil fuel combustion, and heavy fuel oil combustion, may obtain greater potential benefits. Exploratory subgroup analyses suggested that the joint association may be stronger among patients with non-small cell lung cancer, those with later-stage disease, and female patients.

Humans are exposed to complex mixtures of environmental pollutants rather than single chemicals, necessitating methods to quantify the health effects of such mixtures. Research on environmental mixtures provides insights into realistic exposure scenarios, informing regulatory policies that better protect public health. However, statistical challenges, including complex correlations among pollutants and nonlinear multivariate exposure-response relationships, complicate such analyses. A popular Bayesian semi-parametric Gaussian process regression framework addresses these challenges by modeling exposure-response functions with Gaussian processes and performing feature selection to manage high-dimensional exposures while accounting for confounders. Originally designed for small to moderate-sized cohort studies, this framework does not scale well to massive datasets. To address this, we propose a divide-and-conquer strategy, partitioning data, computing posterior distributions in parallel, and combining results using the generalized median. While we focus on Gaussian process models for environmental mixtures, the proposed distributed computing strategy is broadly applicable to other Bayesian models with computationally prohibitive full-sample Markov Chain Monte Carlo fitting. We apply this method to estimate associations between a mixture of ambient air pollutants and 650 000 birthweights recorded in Massachusetts during 2001-2012. Our results reveal negative associations between birthweight and traffic pollution markers, including elemental and organic carbon and PM$_{2.5}$, and positive associations with ozone and vegetation greenness.

Bitcoin mines-massive computing clusters generating cryptocurrency tokens-consume vast amounts of electricity. The amount of fine particle (PM2.5) air pollution created because of their electricity consumption, and its effect on environmental health, is unknown. In this study, we located the 34 largest mines in the United States in 2022, identified the electricity-generating plants that responded to them, and pinpointed communities most harmed by Bitcoin mine-attributable air pollution. From mid-2022 to mid-2023, the 34 mines consumed 32.3 terawatt-hours of electricity-33% more than Los Angeles-85% of which came from fossil fuels. We estimated that 1.9 million Americans were exposed to ≥0.1 μg/m3 of additional PM2.5 pollution from Bitcoin mines, which were often hundreds of miles away from communities they affected. Americans living in four regions-including New York City and near Houston-were exposed to the highest Bitcoin mine-attributable PM2.5 concentrations (≥0.5 μg/m3) with the greatest health risks.

BACKGROUND:Many studies have reported associations of fine particulate matter with aerodynamic diameter ≤2.5μm (PM2.5)with mortality but fewer at low concentrations and even fewer using causal modeling or correcting for exposure error bias. None have corrected for the nonrepresentativeness of monitoring locations. OBJECTIVES:We examined the association of PM2.5 with all-cause mortality in the Medicare cohort using a combination of causal modeling, flexible concentration-response modeling, and bias correction for exposure error, while controlling for NO2 and O3 as well as standard confounders. METHODS:Using monitors not used to fit our PM2.5 model, we fitted 72 regression calibration models stratified by season, region, and elevation in the US. We fitted a B-spline with 4 degrees of freedom to the calibrated PM2.5 and fitted separate generalized propensity score models for each spline component using gradient boosting. We also used inverse probability weights to account for the nonrepresentativeness of monitoring locations. Using the generalized propensity scores and the B-splines, we fitted quasi-Poisson models to counts of deaths in each ZIP code-year stratified by race, Medicaid status, and gender. Separate models were fit for participants identifying as black and as white and for ZIP codes with higher and lower poverty rates. We fit a model using the original exposure to estimate the extent of exposure error bias. RESULTS:The propensity score analysis achieved good balance for all covariates. Controlling for the propensity scores, we found a concentration-response curve with no evidence of a threshold and whose confidence interval did not include the null from 4 μg/m3 and upward. There were 223,666,531 person-years of follow-up between the current US Environmental Protection Agency (EPA) standard of 9 μg/m3 and the World Health Organization (WHO) guideline of 5 μg/m3, and the rate ratio between them was 1.088 [95% confidence interval (CI): 1.064, 1.113]. Using the original exposure, the rate ratio was 1.076 (95% CI: 1.070, 1.083). Hence, effects continue below the EPA standard, and calibrated estimates of effect were 16% higher. Effects were larger from 8 μg/m3 among participants identifying as black. DISCUSSION:The concentration-response curve between air pollution and mortality remains after adjustment for exposure error and using causal models and continues to concentrations below current US EPA and EU standards and even below WHO guidelines. Exposure error in the original exposure resulted in noticeable downward bias at low concentrations. Persons identifying as black are more susceptible. https://doi.org/10.1289/EHP15238.

Importance:Given the increasing wildfire activity in the US, assessment of the health impacts of wildfire-specific fine particulate matter (PM2.5), a growing source of surface air pollution, and its relative toxicity compared to non-wildfire PM2.5 is needed to support mitigation strategies. Objective:To investigate associations of long-term exposure of wildfire-specific and non-wildfire PM2.5 with cardiopulmonary hospitalization risks. Design Setting and Participants:We obtained over 89 million cardiopulmonary hospitalizations for residents across 20 US states from 2006 to 2019 from the State Inpatient Databases. We assigned estimated 2-year average concentrations of wildfire-specific and non-wildfire PM2.5 to each hospitalization based on residential ZIP codes to characterize exposure levels. We used a self-controlled design, which is robust to unmeasured confounding, to assess the associations. Exposures:2-year moving average exposures to wildfire-specific and non-wildfire PM2.5 from the year of hospitalization to the prior year. Main Outcomes and Measures:The hospitalizations for cardiovascular (ischemic heart disease, cerebrovascular disease, heart failure, arrhythmia, other cardiovascular diseases) and pulmonary diseases (acute respiratory infections, pneumonia, chronic obstructive pulmonary disease [COPD], asthma, other respiratory diseases) were identified based on the first 3 diagnosis codes at discharge. Results:Wildfire-specific PM2.5 had stronger effects than non-wildfire PM2.5. Specifically, each 1-μg/m3 increase in 2-year wildfire-specific PM2.5 was significantly associated with increased hospitalization risks for all cardiopulmonary diseases, with relative risk ranging from 1.100 (95% CI: 1.091, 1.108) for heart failure to 1.160 (95% CI: 1.142, 1.178) for asthma. In comparison, a 1 μg/m3 increase in non-wildfire PM2.5 was associated with increased hospitalization risks for all cardiopulmonary diseases, but with relative risks ranging from 1.047 (95% CI: 1.042, 1.051) for COPD to 1.085 (95% CI: 1.082, 1.088) for hypertension. Stronger effects of both wildfire-specific and non-wildfire PM2.5 were observed among minorities, individuals with obesity or diabetes, and those living in metropolitan areas, those with fewer years of education, and more deprived communities. Conclusions:Long-term exposure to wildfire-specific PM2.5 poses a greater risk of cardiopulmonary hospitalization than PM2.5 from non-wildfire sources. Greater effort should be placed on wildfire management, with particular focus on strategies to reduce smoke in addition to traditional air quality control strategies.

The extent and robustness of the interaction between exposures to heat and ambient PM2.5 is unclear and little is known of the interaction between exposures to cold and ambient PM2.5. Clarifying these interactions, if any, is crucial due to the omnipresence of PM2.5 in the atmosphere and increasing scope and frequency of extreme temperature events. To investigate both of these interactions, we merged 6 073 575 individual-level mortality records from thirteen states spanning seventeen years with 1 km daily PM2.5 predictions from sophisticated prediction model and 1 km meteorology from Daymet V4. A time-stratified, bidirectional case-crossover design was used to control for confounding by individual-level, long-term and cyclic weekly characteristics. We fitted conditional logistic regressions with an interaction term between PM2.5 and extreme temperature events to investigate the potential interactive effects on mortality. Ambient PM2.5 exposure has the greatest effect on mortality by all internal causes in the 2 d moving average exposure window. Additionally, we found consistently synergistic interactions between a 10 mu g m-3 increase in the 2 d moving average of PM2.5 and extreme heat with interaction odds ratios of 1.013 (95% CI: 1.000, 1.026), 1.024 (95% CI: 1.002, 1.046), and 1.033 (95% CI: 0.991, 1.077) for deaths by all internal causes, circulatory causes, and respiratory causes, respectively, which represent 75%, 156%, and 214% increases in the coefficient estimates for PM2.5 on those days. We also found evidence of interactions on the additive scale with corresponding relative excess risks due to interaction (RERIs) of 0.013 (95% CI: 0.003, 0.021), 0.020 (95% CI: 0.008, 0.031), and 0.017 (95% CI: -0.015, 0.036). Interactions with other PM2.5 exposure windows were more pronounced. For extreme cold, our results were suggestive of an antagonistic relationship. These results suggest that ambient PM2.5 interacts synergistically with exposure to extreme heat, yielding greater risks for mortality than only either exposure alone.

Existing studies on the health effects of smoke fine particulate matters (PM2.5), a primary emission from wildfires, have often lacked comparison with other air pollutants, focused primarily on acute exposures, and not applied causal methods. In this study, we obtained county-level, three-year average cardiovascular hospitalization rates for Medicare beneficiaries across the contiguous US between 2006 and 2016 from the Centers for Disease Control and Prevention. These data were linked with spatio-temporal estimates of smoke PM2.5, non-smoke PM2.5, nitrogen dioxide (NO2), ozone, and county-level confounders. We used a difference-in-differences method to evaluate simultaneous causal effects of three-year moving average exposures (lag 0-2, 1-3, 2-4, or 3-5 year) to the four pollutants on hospitalization rates for total cardiovascular disease (CVD) and its two major subtypes: heart disease and stroke. We found that, for total CVD, the absolute change in hospitalization rate associated with smoke PM2.5 increased with longer lag periods: from -0.879 (95 % confidence interval [CI]: -2.528, 0.771) at lag 0-2 to 7.538 (95 % CI: 4.594, 10.481) at lag 3-5 per 1 μg/m3 increase in exposure per 1000 people. The effect of non-smoke PM2.5 was smaller and diminished over time. NO2 and ozone had even smaller effects per 1 part per billion increases in exposure. Similar patterns were seen for heart disease. For stroke, all pollutants had minimal and mostly non-significant effects. More rural and lower-income counties experienced greater risks. These findings suggested the need to prioritize wildfire management in addition to traditional air quality control strategies.

BACKGROUND:Although emerging studies link air pollution to mortality in patients with breast cancer, large-scale evidence remains limited. We aimed to evaluate associations between chronic exposures to three key regulated air pollutants, fine particulate matter, ozone, and nitrogen dioxide, and mortality in a nationwide cohort of older patients with breast cancer. METHODS:We constructed a cohort of patients with primary diagnosis of breast cancer aged 65 years or older between 2000 and 2016 using the Surveillance, Epidemiology, and End Results-Medicare database. High-resolution ambient concentrations of annual fine particulate matter, warm-season ozone, and annual nitrogen dioxide were estimated using hybrid models and linked to patients' residential zip codes as proxy exposures. A 3-pollutant Cox model was fitted to estimate hazard ratios for mortality associated with each pollutant, adjusting for demographics, tumor characteristics, cancer treatments, comorbidities, lifestyle factors, meteorological variables, and neighborhood-level characteristics. RESULTS:Among 593 333 patients with breast cancer, a 1-µg/m3 increase in annual fine particulate matter, a 1-part per billion increase in warm-season ozone, and a 1-part per billion increase in annual nitrogen dioxide were associated with hazard ratios for mortality of 1.0048 (95% CI = 1.0026 to 1.0070), 1.0021 (95% CI = 1.0013 to 1.0029), and 1.0022 (95% CI = 1.0014 to 1.0030), respectively. This finding translated to 49 annual excess deaths attributable to fine particulate matter, 21 to ozone, and 22 to nitrogen dioxide within the cohort. Effects were substantially larger at low exposure levels. Fine particulate matter and nitrogen dioxide had greater effects in younger patients, individuals who received chemotherapy or radiation, and individuals with disease diagnosed at later stages. CONCLUSION:Our findings identified air pollution as a risk factor for mortality in older patients with breast cancer. Protective measures and air pollution control strategies may help reduce exposure and improve outcomes.

BACKGROUND:Wildfire activity in the United States has increased substantially in recent decades. Smoke fine particulate matter (PM 2.5 ), a primary wildfire emission, can remain in the air for months after a wildfire begins, yet large-scale evidence of its health effects remains limited. METHODS:We obtained hospitalization records for the residents of 15 states between 2006 and 2016 from the State Inpatient Databases. We used existing daily smoke PM 2.5 estimations at 10-km 2 grid cells across the contiguous United States and aggregated them to ZIP codes to match the spatial resolution of hospitalization records. We extended the traditional case-crossover design, a self-controlled design originally developed for studying acute effects, to examine associations between 3-month average exposure to smoke PM 2.5 and hospitalization risks for a comprehensive range of cardiovascular (ischemic heart disease, cerebrovascular disease, heart failure, arrhythmia, hypertension, and other cardiovascular diseases) and respiratory diseases (acute respiratory infections, pneumonia, chronic obstructive pulmonary disease, asthma, and other respiratory diseases). RESULTS:We found that 3-month exposure to smoke PM 2.5 was associated or marginally associated with increased hospitalization risks for most cardiorespiratory diseases. Hypertension showed the greatest susceptibility, with the highest hospitalization risk associated with 0.1 µg/m 3 increase in 3-month smoke PM 2.5 exposure (relative risk: 1.0051; 95% confidence interval = 1.0035, 1.0067). Results for single-month lagged exposures suggested that estimated effects persisted up to 3 months after exposure. Subgroup analyses estimated larger effects in neighborhoods with higher deprivation level or more vegetation, as well as among ever-smokers. CONCLUSIONS:Our findings provided unique insights into medium-term cardiorespiratory effects of smoke PM 2.5 , which can persist for months, even after a wildfire has ended.

Background Investigations into long-term fine particulate matter (PM2.5 ) exposure's impact on nonaccidental and cardiovascular (CVD) deaths primarily involve nonrepresentative adult populations at concentrations above the new Environmental Protection Agency annual PM2.5 standard. Methods Using generalized linear models, we studied PM2.5 exposure on rates of five mortality outcomes (all nonaccidental, CVD, myocardial infarction, stroke, and congestive heart failure) in 12 US states from 2000 to 2016. We aggregated predicted annual PM2.5 exposures from a validated ensemble exposure model, ambient temperature from Daymet predictions, and mortality rates to all census tract-years within the states. We obtained covariates from the decennial Census and the American Community Surveys and assessed effect measure modification by race and education with stratification. Results For each 1-mu g/m(3) increase in annual PM2.5 , we found positive associations with all five mortality outcomes: all nonaccidental (1.08%; 95% confidence interval [CI]: 0.96%, 1.20%), all CVD (1.27%; 95% CI: 1.14%, 1.41%), myocardial infarction (1.89%; 95% CI: 1.67%, 2.11%), stroke (1.08%; 95% CI: 0.87%, 1.30%), and congestive heart failure (2.20%; 95% CI: 1.97%, 2.44%). Positive associations persisted at <8 g/m(3) PM2.5 levels and among populations with only under 65. In our study, race, but not education, modifies associations. High-educated Black had a 2.90% larger increased risk of CVD mortality (95% CI: 2.42%, 3.39%) compared with low-educated non-Black. Conclusion Long-term PM2.5 exposure is associated with nonaccidental and CVD mortality in 12 states, below the new Environmental Protection Agency standard, for both low PM2.5 regions and the general population. Vulnerability to CVD mortality persists among Black individuals regardless of education level.

Humans are exposed to complex mixtures of environmental pollutants rather than single chemicals, necessitating methods to quantify the health effects of such mixtures. Research on environmental mixtures provides insights into realistic exposure scenarios, informing regulatory policies that better protect public health. However, statistical challenges, including complex correlations among pollutants and nonlinear multivariate exposure-response relationships, complicate such analyses. A popular Bayesian semi-parametric Gaussian process regression framework (Coull et al., 2015) addresses these challenges by modeling exposure-response functions with Gaussian processes and performing feature selection to manage high-dimensional exposures while accounting for confounders. Originally designed for small to moderate-sized cohort studies, this framework does not scale well to massive datasets. To address this, we propose a divide-and-conquer strategy, partitioning data, computing posterior distributions in parallel, and combining results using the generalized median. While we focus on Gaussian process models for environmental mixtures, the proposed distributed computing strategy is broadly applicable to other Bayesian models with computationally prohibitive full-sample Markov Chain Monte Carlo fitting. We provide theoretical guarantees for the convergence of the proposed posterior distributions to those derived from the full sample. We apply this method to estimate associations between a mixture of ambient air pollutants and ~650,000 birthweights recorded in Massachusetts during 2001-2012. Our results reveal negative associations between birthweight and traffic pollution markers, including elemental and organic carbon and PM2.5, and positive associations with ozone and vegetation greenness.

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.

Background Redlining has been associated with worse health outcomes and various environmental disparities, separately, but little is known of the interaction between these two factors, if any. We aimed to estimate whether living in a historically-redlined area modifies the effects of exposures to ambient PM 2.5 and extreme heat on mortality by non-external causes. Methods We merged 8,884,733 adult mortality records from thirteen state departments of public health with scanned and georeferenced Home Owners Loan Corporation (HOLC) maps from the University of Richmond, daily average PM 2.5 from a sophisticated prediction model on a 1-km grid, and daily temperature and vapor pressure from the Daymet V4 1-km grid. A case-crossover approach was used to assess modification of the effects of ambient PM 2.5 and extreme heat exposures by redlining and control for all fixed and slow-varying factors by design. Multiple moving averages of PM 2.5 and duration-aware analyses of extreme heat were used to assess the most vulnerable time windows. Results We found significant statistical interactions between living in a redlined area and exposures to both ambient PM 2.5 and extreme heat. Individuals who lived in redlined areas had an interaction odds ratio for mortality of 1.0093 (95% confidence interval [CI]: 1.0084, 1.0101) for each 10 µg m −3 increase in same-day ambient PM 2.5 compared to individuals who did not live in redlined areas. For extreme heat, the interaction odds ratio was 1.0218 (95% CI 1.0031, 1.0408). Conclusions Living in areas that were historically-redlined in the 1930’s increases the effects of exposures to both PM 2.5 and extreme heat on mortality by non-external causes, suggesting that interventions to reduce environmental health disparities can be more effective by also considering the social context of an area and how to reduce disparities there. Further study is required to ascertain the specific pathways through which this effect modification operates and to develop interventions that can contribute to health equity for individuals living in these areas.

Transportation Network Company (TNC) services have become a prominent factor in urban transportation in recent years, and there is an ongoing debate regarding their relationship with public transit. While many argue that TNCs draw passengers away from public transportation, others believe the two modes complement each other. However, due to the inadequate sample size of rider surveys as primary data sources, our understanding of how riders choose between these two modalities remains limited. This study uses nine months of trip planning data generated by the Transit App, which captures how travelers engage with multiple options in real time, including TNC and public transit services. We extract measures from Transit describing the travel options and the habits of each individual user for sessions in which the user “tapped” on one of these two modes, indicating consideration of it as an option. Machine learning models predict the likelihood of a rider tapping TNC based on features of the available public transit options and other contextual factors (e.g., time of day, weather conditions). The models find that these taps are driven by factors that highlight the convenience of TNC, such as the waiting time, walking distance, and the number of transfers for public transportation trips. We also find that the majority of TNC trips tapped by app users combine the two modes when using the Transit App, with TNC acting as a connection to or from public transit. These results provide detailed additional evidence for current arguments for both competition and complementarity between TNC and public transit from a population that uses an app to navigate public transit.

BACKGROUND:The relationship between long-term exposure to PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 species on mortality. We further looked at the effects of the components when PM2.5 mass levels were at concentrations below 8 μg/m3, and effect modification by sex, race, Medicaid status, and Census division. RESULTS:We found that for each decile increase in the levels of the PM2.5 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 PM2.5 species, as a mixture, increased the risk of all-cause, cardiovascular, and respiratory mortality.

Exposure to fine particulate matter (PM2.5) is linked to increased health risks and disproportionately affects minority and low-income communities. Current research lacks assessment of exposure disparities to PM2.5 components and effective communication of these disparities. We developed an interactive, web-based air pollution mapper, combining high-resolution predictions of PM2.5 components for 2010 across the contiguous United States with U.S. Census demographic data. The interface, hosted at https://disparitiesmapper.github.io/, allows users to visualize the relationship between variables, highlighting the racial, socioeconomic, and geographic disparities in exposure. By mapping individual PM2.5 components, it provides insights into specific sources driving PM2.5 concentrations, aiding in designing targeted reduction programs. This tool makes air quality data more accessible and informs policy outcomes.