The impact of wildfire smoke on air quality is a growing concern, contributing to air pollution through a complex mixture of chemical species with important implications for public health. Although previous studies have focused mainly on its association with total particulate matter (pm$_{2.5}$), the causal relationship between wildfire smoke and the chemical composition of pm$_{2.5}$ remains largely unexplored. To fill this gap, we propose a Bayesian causal regression factor model that estimates the multivariate causal effects of wildfire smoke on the concentration of 27 chemical species in pm$_{2.5}$ across the United States. Our approach introduces two key innovations: (i) a causal inference framework for multivariate potential outcomes, and (ii) a novel Bayesian factor model that employs a probit stick-breaking process as prior for treatment-specific factor scores. By focusing on factor scores, our method addresses the missing data challenge common to causal inference and enables a flexible, data-driven characterization of the latent factor structure, which is crucial to capturing the complex correlation between multivariate outcomes. Through Monte Carlo simulations, we show the accuracy of the model in estimating the causal effects in multivariate outcomes and characterizing the treatment-specific latent structure. Finally, we apply our method to US air quality data, estimating the causal effect of wildfire smoke on 27 chemical species in pm$_{2.5}$, providing a deeper understanding of their interdependencies.

Racialized and lower-income communities often face disproportionate health risks from air pollution. In central Texas, semiconductor fabrication plants (SFPs) are a major emissions source, but their health effects and impact on disparities remain unclear. This study examined 217 census tracts in Travis County, Texas, calculating an SFP exposure score based on facility-reported emissions, weighted by proximity. Using distance-adjusted propensity score matching, this study compared 30 tracts within 2 km of SFPs to similar nearby tracts, controlling for demographics and socioeconomic indicators. Results showed SFPs were located in tracts with higher proportions of Black (12

In semicompeting risks problems, nonterminal time-to-event outcomes such as time to hospital readmission are subject to truncation by death. These settings are often modeled with illness-death models for the hazards of the terminal and nonterminal events, but evaluating causal treatment effects with hazard models is problematic due to conditioning on survival (a post-treatment outcome) that is embedded in the definition of a hazard. Extending an existing survivor average causal effect (SACE) estimand, we frame the evaluation of treatment effects in the context of semicompeting risks with principal stratification and introduce two new causal estimands: the time-varying survivor average causal effect (TV-SACE) and the restricted mean survivor average causal effect (RM-SACE). These principal causal effects are defined among units that would survive regardless of assigned treatment. We adopt a Bayesian estimation procedure that parameterizes illness-death models for both treatment arms. We outline a frailty specification that can accommodate within-person correlation between nonterminal and terminal event times, and we discuss potential avenues for adding model flexibility. The method is demonstrated in the context of hospital readmission among late-stage pancreatic cancer patients.

Evaluating air quality interventions is confronted with the challenge of interference since interventions at a particular pollution source likely impact air quality and health at distant locations and air quality and health at any given location are likely impacted by interventions at many sources. The structure of interference in this context is dictated by complex atmospheric processes governing how pollution emitted from a particular source is transformed and transported across space, and can be cast with a bipartite structure reflecting the two distinct types of units: 1) interventional units on which treatments are applied or withheld to change pollution emissions; and 2) outcome units on which outcomes of primary interest are measured. We propose new estimands for bipartite causal inference with interference that construe two components of treatment: a "key-associated" (or "individual") treatment and an "upwind" (or "neighborhood") treatment. Estimation is carried out using a semi-parametric adjustment approach based on joint propensity scores. A reduced-complexity atmospheric model is deployed to characterize the structure of the interference network by modeling the movement of air parcels through time and space. The new methods are deployed to evaluate the effectiveness of installing flue-gas desulfurization scrubbers on 472 coal-burning power plants (the interventional units) in reducing Medicare hospitalizations among 21,577,552 Medicare beneficiaries residing across 25,553 ZIP codes in the United States (the outcome units).

Evidence derived primarily from physical models has identified saltwater disposal as the dominant causal factor that contributes to induced seismicity. To complement physical models, statistical/machine learning (ML) models are designed to measure associations from observational data, either with parametric regression models or more flexible ML models. However, it is often difficult to interpret the statistical significance of a parameter or the predicative power of a model as evidence of causation. We adapt a causal inference framework with the potential outcomes perspective to explicitly define what we meant by causal effect and declare necessary identification conditions to recover unbiased causal effect estimates. In particular, we illustrate the threat of time-varying confounding in observational longitudinal geoscience data through simulations and adapt established statistical methods for longitudinal analysis from the causal interference literature to estimate the effect of wastewater disposal on earthquakes in the Fort-Worth Basin of North Central Texas from 2013 to 2016.

Principal stratification analysis evaluates how causal effects of a treatment on a primary outcome vary across strata of units defined by their treatment effect on some intermediate quantity. This endeavor is substantially challenged when the intermediate variable is continuously scaled and there are infinitely many basic principal strata. We employ a Bayesian nonparametric approach to flexibly evaluate treatment effects across flexibly modeled principal strata. The approach uses Bayesian Causal Forests (BCF) to simultaneously specify 2 Bayesian Additive Regression Tree models; one for the principal stratum membership and one for the outcome, conditional on principal strata. We show how the capability of BCF for capturing treatment effect heterogeneity is particularly relevant for assessing how treatment effects vary across the surface defined by continuously scaled principal strata, in addition to other benefits relating to targeted selection and regularization-induced confounding. The capabilities of the proposed approach are illustrated with a simulation study, and the methodology is deployed to investigate how causal effects of power plant emissions control technologies on ambient particulate pollution vary as a function of the technologies' impact on sulfur dioxide emissions.

Background Household air pollution (HAP) is a major risk factor of non-communicable diseases, causing millions of premature deaths each year in developing nations. Populations living at high altitudes are particularly vulnerable to HAP and associated health outcomes. Objectives This study aims to explore the relationships between activity patterns, HAP, and an HAP biomarker among 100 Himalayan nomadic households during both cooking and heating-only periods. Methods Household CO was monitored in 100 rural homes in Qinghai, China, at 3500 m on the Himalayan Plateau among Himalayan nomads. Carboxyhemoglobin (COHb) was used as a biomarker to assess exposure among 100 male and 100 female heads of household. Linear mixed-effects models were used to explore the relationship between COHb and activity patterns. Results Cooking periods were associated with 7 times higher household CO concentrations compared with heating periods (94 ± 56 ppm and 13 ± 11 ppm, respectively). Over the three-day biomarker-monitoring period in each house, 99% of subjects had at least one COHb measurement exceeding the WHO safety level of 2%. Cooking was associated with a 32% increase in COHb ( p < 0.001). Impact statement This study on household air pollution (HAP) in high-altitude regions provides important insights into the exposure patterns of nomadic households in Qinghai, China. The study found that cooking is the primary factor influencing acute carbon monoxide (CO) exposure among women, while heating alone is sufficient to elevate CO exposure above WHO guidelines. The results suggest that cooking-only interventions have the potential to reduce HAP exposure among women, but solutions for both cooking and heating may be required to reduce COHb to below WHO guidelines. This study’s findings may inform future interventions for fuel and stove selection to reduce HAP and exposure among other populations.

BackgroundThe extent to which asthma-related ED visit incidence rates vary from neighborhood to neighborhood and predictors of neighborhood-level asthma ED visit burden are not well understood.ObjectiveTo describe the census tract-level spatial distribution of asthma-related emergency department visits in Central Texas and identify neighborhood-level characteristics that explain variability in neighborhood-level asthma ED visit rates.MethodsConditional autoregressive models were used to examine the spatial distribution of asthma-related ED visit incidence rates across Travis County, TX census tracts and to assess the contribution of census tract characteristics to their distribution.ResultsThere were distinct patterns in ED visit incidence rates at the census tract scale, which were largely unexplained by socioeconomic or selected built environment neighborhood characteristics. However, racial and ethnic composition explained 33% of the variability of ED visit incidence rates across census tracts. The census tract predictors of ED visit incidence rates differed by racial and ethnic group.ConclusionsVariability in asthma ED visit incidence rates are apparent at smaller spatial scales. The majority of the variability in census tract-level asthma ED visit rates in Central Texas is not explained by racial and ethnic composition or other neighborhood characteristics.

A growing literature within the field of air pollution exposure assessment addresses the issue of environmental justice. Leveraging the increasing availability of exposure datasets with broad spatial coverage and high spatial resolution, a number of works have assessed inequalities in exposure across racial/ethnic and other socioeconomic groupings. However, environmental justice research presents the additional need to evaluate exposure inequity—inequality that is systematic, unfair, and avoidable—which may be framed in several ways. We discuss these framings and describe inequality and inequity conclusions provided from several contrasting approaches drawn from recent work. We recommend that future work addressing environmental justice interventions include complementary “Exposure-driven” and “Socially weighted” metrics, taking an intersectional view of areas and social groups that are both disproportionately impacted by pollution and are impacted by additional health risks resulting from structural racism and consider implications for environmental justice beyond distributional equity.

Causal inference for air pollution mixtures is an increasingly important issue with appreciable challenges. When the exposure is a multivariate mixture, there are many exposure contrasts that may be of nominal interest for causal effect estimation, but the complex joint mixture distribution often renders observed data extremely limited in their ability to inform estimates of many commonly defined causal effects. We use potential outcomes to (1) define causal effects of air pollution mixtures, (2) formalize the key assumption of mixture positivity required for estimation, and (3) offer diagnostic metrics for positivity violations in the mixture setting that allow researchers to assess the extent to which data can actually support estimation of mixture effects of interest. For settings where there is limited empirical support, we redefine causal estimands that apportion causal effects according to whether they can be directly informed by observed data versus rely entirely on model extrapolation, isolating key sources of information on the causal effect of an air pollution mixture. The ideas are deployed to assess the ability of a national US data set on the chemical components of ambient particulate matter air pollution to support estimation of a variety of causal mixture effects. This article is part of a Special Collection on Environmental Epidemiology.

BackgroundAsthma exacerbations are an important cause of emergency department visits but much remains unknown about the role of environmental triggers including viruses and allergenic pollen. A better understanding of spatio-temporal variation in exposure and risk posed by viruses and pollen types could help prioritize public health interventions.ObjectiveHere we quantify the effects of regionally important Cupressaceae pollen, tree pollen, other pollen types, rhinovirus, seasonal coronavirus, respiratory syncytial virus, and influenza on asthma-related emergency department visits for people living near eight pollen monitoring stations in Texas.MethodsWe used age stratified Poisson regression analyses to quantify the effects of allergenic pollen and viruses on asthma-related emergency department visits.ResultsYoung children (< 5 years of age) had high asthma-related emergency department rates (24.1 visits/1,000,000 person-days), which were mainly attributed to viruses (51.2%). School-aged children also had high rates (20.7 visits/1,000,000 person-days), which were attributed to viruses (57.0%), Cupressaceae pollen (0.7%), and tree pollen (2.8%). Adults had lower rates (8.1 visits/1,000,000 person-days) which were attributed to viruses (25.4%), Cupressaceae pollen (0.8%), and tree pollen (2.3%). This risk was spread unevenly across space and time; for example, during peak Cuppressaceae season, this pollen accounted for 8.2% of adult emergency department visits near Austin where these plants are abundant, but 0.4% in cities like Houston where they are not; results for other age groups were similar.ConclusionsAlthough viruses are a major contributor to asthma-related emergency department visits, airborne pollen can explain a meaningful portion of visits during peak pollen season and this risk varies over both time and space because of differences in plant composition.

Causal inference with spatial environmental data is often challenging due to the presence of interference: outcomes for observational units depend on some combination of local and nonlocal treatment. This is especially relevant when estimating the effect of power plant emissions controls on population health, as pollution exposure is dictated by: (i) the location of point-source emissions as well as (ii) the transport of pollutants across space via dynamic physical-chemical processes. In this work we estimate the effectiveness of air quality interventions at coal-fired power plants in reducing two adverse health outcomes in Texas in 2016: pediatric asthma ED visits and Medicare all-cause mortality. We develop methods for causal inference with interference when the underlying network structure is not known with certainty and instead must be estimated from ancillary data. Notably, uncertainty in the interference structure is propagated to the resulting causal effect estimates. We offer a Bayesian, spatial mechanistic model for the interference mapping, which we combine with a flexible nonparametric outcome model to marginalize estimates of causal effects over uncertainty in the structure of interference. our analysis finds some evidence that emissions controls at upwind power plants reduce asthma ED visits and all-cause mortality; however, accounting for uncertainty in the interference renders the results largely inconclusive.

Rationale: The share of Black or Latinx residents in a census tract remains associated with asthma-related emergency department (ED) visit rates after controlling for socioeconomic factors. The extent to which evident disparities relate to the within-city heterogeneity of long-term air pollution exposure remains unclear. Objectives: To investigate the role of intraurban spatial variability of air pollution in asthma acute care use disparity. Methods: An administrative database was used to define census tract population-based incidence rates of asthma-related ED visits. We estimate the associations between census tract incidence rates and 1) average fine and coarse particulate matter, nitrogen dioxide (NO2), and sulfur dioxide (SO2), and 2) racial and ethnic composition using generalized linear models controlling for socioeconomic and housing covariates. We also examine for the attenuation of incidence risk ratios (IRRs) associated with race/ethnicity when controlling for air pollution exposure. Measurements and Main Results: Fine and coarse particulate matter and SO2 are all associated with census tract-level incidence rates of asthma-related ED visits, and multipollutant models show evidence of independent risk associated with coarse particulate matter and SO2. The association between census tract incidence rate and Black resident share (IRR, 1.51 [credible interval (CI), 1.48-1.54]) is attenuated by 24% when accounting for air pollution (IRR, 1.39 [CI, 1.35-1.42]), and the association with Latinx resident share (IRR, 1.11 [CI, 1.09-1.13]) is attenuated by 32% (IRR, 1.08 [CI, 1.06-1.10]). Conclusions: Neighborhood-level rates of asthma acute care use are associated with local air pollution. Controlling for air pollution attenuates associations with census tract racial/ethnic composition, suggesting that intracity variability in air pollution could contribute to neighborhood-to-neighborhood asthma morbidity disparities.

Asthma exacerbations cause many emergency department visits but much remains unknown about the role of seasonal environmental triggers including viruses and allergenic pollen. A better understanding of these triggers and the role of regionally-important pollen types such as 'mountain cedar' (in the Cupressaceae family) in Texas could help prioritize public health interventions. We acquired data on all emergency department visits in Texas from 2015–2020 where asthma was the primary ICD diagnosis code. Emergency department visit rates were compared to concentrations of several pollen types (Cupressaceae, trees, and other plants), and to test positivity of viruses (rhinovirus, coronavirus, RSV, and influenza) among populations living near eight pollen monitoring stations. We analyzed these relationships with age-stratified Poisson regression analyses in a distributed lag framework. Young children had high asthma-related emergency department rates (22.4 visits/1,000,000 person-days), which were explained by viruses (70.4±2.6%), Cupressaceae pollen (0.4±0.1%), and tree pollen (0.9±0.4%). School-aged children also had high rates (19.7 visits/1,000,000 person-days), which were attributed to viruses (61±2%), Cupressaceae pollen (0.5±%) and tree pollen (1.5±0.1%). Adults had lower rates (7.6 visits/1,000,000 people/day) which were attributed to viruses (38±2%), Cupressaceae pollen (0.5±0.1%) and tree pollen (2.5±0.2%). Rates attributed to pollen exhibited spatio-temporal patterns; for example, Cupressaceae accounted for 7% of adult cases in January in Austin, but <1% in Houston. Similarly, for adults in Houston tree pollen accounted for 2% of cases annually but 19% in March. These findings demonstrate the consequences of regionally important allergenic plants to respiratory health and spatio-temporal patterns of emergency department visits.

Analysis of observational studies increasingly confronts the challenge of determining which of a possibly high-dimensional set of available covariates are required to satisfy the assumption of ignorable treatment assignment for estimation of causal effects. We propose a Bayesian nonparametric approach that simultaneously 1) prioritizes inclusion of adjustment variables in accordance with existing principles of confounder selection; 2) estimates causal effects in a manner that permits complex relationships among confounders, exposures, and outcomes; and 3) provides causal estimates that account for uncertainty in the nature of confounding. The proposal relies on specification of multiple Bayesian Additive Regression Trees models, linked together with a common prior distribution that accrues posterior selection probability to covariates on the basis of association with both the exposure and the outcome of interest. A set of extensive simulation studies demonstrates that the proposed method performs well relative to similarly-motivated methodologies in a variety of scenarios. We deploy the method to investigate the causal effect of emissions from coal-fired power plants on ambient air pollution concentrations, where the prospect of confounding due to local and regional meteorological factors introduces uncertainty around the confounding role of a high-dimensional set of measured variables. Ultimately, we show that the proposed method produces more efficient and more consistent results across adjacent years than alternative methods, lending strength to the evidence of the causal relationship between SO2 emissions and ambient particulate pollution.

Policy-makers seeking to limit the impact of coal electricity-generating units (EGUs, also known as power plants) on air quality and climate justify regulations by quantifying the health burden attributable to exposure from these sources. We defined "coal PM2.5" as fine particulate matter associated with coal EGU sulfur dioxide emissions and estimated annual exposure to coal PM2.5 from 480 EGUs in the US. We estimated the number of deaths attributable to coal PM2.5 from 1999 to 2020 using individual-level Medicare death records representing 650 million person-years. Exposure to coal PM2.5 was associated with 2.1 times greater mortality risk than exposure to PM2.5 from all sources. A total of 460,000 deaths were attributable to coal PM2.5, representing 25% of all PM2.5-related Medicare deaths before 2009 and 7% after 2012. Here, we quantify and visualize the contribution of individual EGUs to mortality.

Estimating the causal effects of a spatially-varying intervention on a spatially-varying outcome may be subject to non-local confounding (NLC), a phenomenon that can bias estimates when the treatments and outcomes of a given unit are dictated in part by the covariates of other nearby units. In particular, NLC is a challenge for evaluating the effects of environmental policies and climate events on health-related outcomes such as air pollution exposure. This paper first formalizes NLC using the potential outcomes framework, providing a comparison with the related phenomenon of causal interference. Then, it proposes a broadly applicable framework, termed weather2vec, that uses the theory of balancing scores to learn representations of non-local information into a scalar or vector defined for each observational unit, which is subsequently used to adjust for confounding in conjunction with causal inference methods. The framework is evaluated in a simulation study and two case studies on air pollution where the weather is an (inherently regional) known confounder.

To analyze changes in water quality conditions in water distribution systems (WDS), such as disinfectant byproduct formation, chlorine residual, and biofilm growth, water utilities can use coupled hydraulic and chemical models. Model-based operational decisions are complicated by uncertainties in model input parameters, which propagate through the model resulting in uncertain model predictions. Previous works have focused on conducting sensitivity analyses of input parameters in various water quality reactions and the interactions with hydraulic factors, but have overlooked the specific contribution of hydraulic and chemical parameter uncertainty. In this work, a framework for assessing the hydraulic and chemical uncertainty in water quality models is presented and demonstrated using monochloramine decay in WDSs supplied by gravity and pumping systems. In the first step, a sensitivity analysis is conducted to determine the influential and non-influential chemical parameters which govern the rate of monochloramine decay. In the second step, Monte Carlo simulations are used to explore the individual and combined effects of uncertainty in hydraulic and chemical parameters. Results show that uncertainty in modeled monochloramine concentration increases with water age and higher reaction rates, varies throughout the course of a day, and heavily depends on hydraulic variability, emphasizing the need to account for input uncertainty. The computational tool developed in this work can be extended to other reaction mechanisms, water quality parameters, and distribution systems for case-specific conditions, and used to evaluate system-wide effects of uncertainty on water quality.