Unmasking the sky: high-resolution PM2.5 prediction in Texas using machine learning techniques.

BACKGROUND: Although PM2.5 (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies. OBJECTIVE: This study aimed to predict PM2.5 concentrations at a fine spatial scale on a daily basis by using novel machine learning approaches and incorporating satellite-derived Aerosol Optical Depth (AOD) and a variety of weather and land use variables. METHODS: We compiled a comprehensive dataset in Texas from 2013 to 2017, including ground-level PM2.5 concentrations from regulatory monitors; AOD values at 1-km resolution based on images retrieved from the MODIS satellite; and weather, land-use, population density, among others. We built predictive models for each year separately to estimate PM2.5 concentrations using two machine learning approaches called gradient boosted trees and random forest. We evaluated the model prediction performance using in-sample and out-of-sample validations. RESULTS: Our predictive models demonstrate excellent in-sample model performance, as indicated by high R2 values generated from the gradient boosting models (0.94-0.97) and random forest models (0.81-0.90). However, the out-of-sample R2 values fall within a range of 0.52-0.75 for gradient boosting models and 0.44-0.69 for random forest models. Model performance varies slightly across years. A generally decreasing trend in predicted PM2.5 concentrations over time is observed in Eastern Texas. IMPACT STATEMENT: We utilized machine learning approaches to predict PM2.5 levels in Texas. Both gradient boosting and random forest models perform well. Gradient boosting models perform slightly better than random forest models. Our models showed excellent in-sample prediction performance (R2 > 0.9).

Development and demonstration of a data visualization platform of short-term guidelines for ambient air levels of benzene during disaster response in Houston, Texas.

Industrial disasters have caused hazardous air pollution and public health impacts. Response officials have developed limited exposure guidelines to direct them during the event; often, guidelines are outdated and may not represent relevant elevated-exposure periods. The 2019 Intercontinental Terminals Company (ITC) fire in Houston, Texas led to large-scale releases of benzene and presented a public health threat. This incident highlights the need for effective response and nimble, rapid public health communication. We developed a data-driven visualization tool to store, display, and interpret ambient benzene concentrations to assist health officials during environmental emergencies. Guidance values to interpret risk from acute exposure to benzene were updated using recent literature that also considers exposure periodicity. The visualization platform can process data from different sampling instruments and air monitors automatically, and displays information publicly in real time, along with the associated risk information and action recommendations. The protocol was validated by applying it retrospectively to the ITC event. The new guidance values are 6-30 times lower than those derived by the Texas regulatory agency. Fixed-site monitoring data, assessed using the protocol and revised thresholds, indicated that eight shelter-in-place and 17 air-quality alerts may have been considered. At least one of these shelter-in-place alerts corresponded to prolonged, elevated benzene concentrations (~1000 ppb). This new tool addresses essential gaps in the timely communication of air pollution measurements, provides context to understand potential health risks from exposure to benzene, and provides a clear protocol for local officials in responding to industrial air releases of benzene. Integr Environ Assess Manag 2024;20:533-546. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Characterizing vulnerabilities to climate change across the United States.

Climate change will cause a range of related risks, including increases in infectious and chronic disease, intensified social and economic stresses, and more frequent extreme weather events. Vulnerable groups will be disproportionately affected due to greater exposure to climate risks and lower ability to prepare, adapt, and recover from their effects. Better understanding of the intersection of vulnerability and climate change risks is required to identify the most important drivers of future climate risks and effectively build resilience and deploy targeted adaptation efforts. Incorporating community stakeholder input, we identified and integrated available public health, social, economic, environmental, and climate data in the United States (U.S.), comprising 184 indicators, to develop a Climate Vulnerability Index (CVI) composed of four baseline vulnerabilities (health, social/economic, infrastructure, and environment) and three climate change risks (health, social/economic, extreme events). We find that the vulnerability to and risks from climate change are highly heterogeneous across the U.S. at the census tract scale, and geospatially cluster into complementary areas with similar climate risks but differing baseline vulnerabilities. Our results therefore demonstrate that not only are climate change risks both broadly and variably distributed across the U.S., but also that existing disparities are often further exacerbated by climate change. The CVI thus lays a data-driven, scientific foundation for future research on the intersection of climate change risks with health and other inequalities, while also identifying health impacts of climate change as the greatest research gap. Moreover, given U.S. government initiatives surrounding climate and equity, the CVI can be instrumental in empowering communities and policymakers to better prioritize resources and target interventions, providing a template for addressing local-scale climate and environmental justice globally.

Economic and mental health impacts of multiple adverse events: Hurricane Harvey, other flooding events, and the COVID-19 pandemic.

OBJECTIVES: To assess the economic and mental health impacts of COVID-19 in the presence of previous exposure to flooding events. METHODS: Starting in April 2018, the Texas Flood Registry (TFR) invited residents to complete an online survey regarding their experiences with Hurricane Harvey and subsequent flooding events. Starting in April 2020, participants nationwide were invited to complete a brief online survey on their experiences during the pandemic. This study includes participants in the TFR (N = 20,754) and the COVID-19 Registry (N = 8568) through October 2020 (joint N = 2929). Logistic regression and generalized estimating equations were used to examine the relationship between exposure to flooding events and the economic and mental health impacts of COVID-19. RESULTS: Among COVID-19 registrants, 21% experienced moderate to severe anxiety during the pandemic, and 7% and 12% of households had difficulty paying rent and bills, respectively. Approximately 17% of Black and 15% of Hispanic households had difficulty paying rent, compared to 5% of non-Hispanic white households. The odds of COVID-19 income loss are 1.20 (1.02, 1.40) times higher for those who previously had storm-related home damage compared to those who did not and 3.84 (3.25-4.55) times higher for those who experienced Harvey income loss compared to those who did not. For registrants for whom Harvey was a severe impact event, the odds of having more severe anxiety during the pandemic are 5.14 (4.02, 6.58) times higher than among registrants for whom Harvey was a no meaningful impact event. CONCLUSIONS: Multiple crises can jointly and cumulatively shape health and wellbeing outcomes. This knowledge can help craft emergency preparation and intervention programs.

Chemical explosion, COVID-19, and environmental justice: Insights from low-cost air quality sensors.

OBJECTIVES: To examine the impact of the Intercontinental Terminals Company (ITC) fire and COVID-19 on airborne particulate matter (PM) concentrations and the PM disproportionally affecting communities in Houston using low-cost sensors. METHODS: We compared measurements from a network of low-cost sensors with a separate network of monitors from the Environmental Protection Agency (EPA) in the Houston metropolitan area from Mar 18, 2019, to Dec 31, 2020. Further, we examined the associations between neighborhood-level sociodemographic status and air pollution patterns by linking the low-cost sensor data to EPA environmental justice screening and mapping systems. FINDINGS: We found increased PM levels during ITC fire and pre-COVID-19, and lower PM levels after the COVID-19 lockdown, comparable to observations from the regulatory monitors, with higher variations and a greater number of locations with high PM levels detected. In addition, the environmental justice analysis showed positive associations between higher PM levels and the percentage of minority, low-income population, and demographic index. IMPLICATION: Our study indicates that low-cost sensors provide pollutant measures with higher spatial variations and a better ability to identify hot spots and high peak concentrations. These advantages provide critical information for disaster response and environmental justice studies. SYNOPSIS: We used measurements from a low-cost sensor network for air pollution monitoring and environmental justice analysis to examine the impact of anthropogenic and natural disasters.

Responding to Natural and Industrial Disasters: Partnerships and Lessons Learned.

OBJECTIVES: The aim of this study was to provide insights learned from disaster research response (DR2) efforts following Hurricane Harvey in 2017 to launch DR2 activities following the Intercontinental Terminals Company (ITC) fire in Deer Park, Texas, in 2019. METHODS: A multidisciplinary group of academic, community, and government partners launched a myriad of DR2 activities. RESULTS: The DR2 response to Hurricane Harvey focused on enhancing environmental health literacy around clean-up efforts, measuring environmental contaminants in soil and water in impacted neighborhoods, and launching studies to evaluate the health impact of the disaster. The lessons learned after Harvey enabled rapid DR2 activities following the ITC fire, including air monitoring and administering surveys and in-depth interviews with affected residents. CONCLUSIONS: Embedding DR2 activities at academic institutions can enable rapid deployment of lessons learned from one disaster to enhance the response to subsequent disasters, even when those disasters are different. Our experience demonstrates the importance of academic institutions working with governmental and community partners to support timely disaster response efforts. Efforts enabled by such experience include providing health and safety training and consistent and reliable messaging, collecting time-sensitive and critical data in the wake of the event, and launching research to understand health impacts and improve resiliency.

The Texas flood registry: a flexible tool for environmental and public health practitioners and researchers.

BACKGROUND: Making landfall in Rockport, Texas in August 2017, Hurricane Harvey resulted in unprecedented flooding, displacing tens of thousands of people, and creating environmental hazards and exposures for many more. OBJECTIVE: We describe a collaborative project to establish the Texas Flood Registry to track the health and housing impacts of major flooding events. METHODS: Those who enroll in the registry answer retrospective questions regarding the impact of storms on their health and housing status. We recruit both those who did and did not flood during storm events to enable key comparisons. We leverage partnerships with multiple local health departments, community groups, and media outlets to recruit broadly. We performed a preliminary analysis using multivariable logistic regression and a binomial Bayesian conditional autoregressive (CAR) spatial model. RESULTS: We find that those whose homes flooded, or who came into direct skin contact with flood water, are more likely to experience a series of self-reported health effects. Median household income is inversely related to adverse health effects, and spatial analysis provides important insights within the modeling approach. SIGNIFICANCE: Global climate change is likely to increase the number and intensity of rainfall events, resulting in additional health burdens. Population-level data on the health and housing impacts of major flooding events is imperative in preparing for our planet's future.

HGBEnviroScreen: Enabling Community Action through Data Integration in the Houston-Galveston-Brazoria Region.

The Houston-Galveston-Brazoria (HGB) region faces numerous environmental and public health challenges from both natural disasters and industrial activity, but the historically disadvantaged communities most often impacted by such risks have limited ability to access and utilize big data for advocacy efforts. We developed HGBEnviroScreen to identify and prioritize regions of heightened vulnerability, in part to assist communities in understanding risk factors and developing environmental justice action plans. While similar in objectives to existing environmental justice tools, HGBEnviroScreen is unique in its ability to integrate and visualize national and local data to address regional concerns. For the 1090 census tracts in the HGB region, we accrued data into five domains: (i) social vulnerability, (ii) baseline health, (iii) environmental exposures and risks, (iv) environmental sources, and (v) flooding. We then integrated and visualized these data using the Toxicological Prioritization Index (ToxPi). We found that the highest vulnerability census tracts have multifactorial risk factors, with common drivers being flooding, social vulnerability, and proximity to environmental sources. Thus, HGBEnviroScreen is not only helping identify communities of greatest overall vulnerability but is also providing insights into which domains would most benefit from improved planning, policy, and action in order to reduce future vulnerability.

Characterizing Elevated Urban Air Pollutant Spatial Patterns with Mobile Monitoring in Houston, Texas.

Diverse urban air pollution sources contribute to spatially variable atmospheric concentrations, with important public health implications. Mobile monitoring shows promise for understanding spatial pollutant patterns, yet it is unclear whether uncertainties associated with temporally sparse sampling and instrument performance limit our ability to identify locations of elevated pollution. To address this question, we analyze 9 months of repeated weekday daytime on-road mobile measurements of black carbon (BC), particle number (PN), and nitrogen oxide (NO, NO2) concentrations within 24 census tracts across Houston, Texas. We quantify persistently elevated, intermittent, and extreme concentration behaviors at 50 m road segments on surface streets and 90 m segments on highways relative to median statistics across the entire sampling domain. We find elevated concentrations above uncertainty levels (±40%) within portions of every census tract, with median concentration increases ranging from 2 to 3× for NO2, and >9× for NO. In contrast, PN exhibits elevated concentrations of 1.5-2× the domain-wide median and distinct spatial patterns relative to other pollutants. Co-located elevated concentrations of primary combustion tracers (BC and NOx) near 30% of metal recycling and concrete batch plant facilities within our sampled census tracts are comparable to those measured within 200 m of highways. Our results demonstrate how extensive mobile monitoring across multiple census tracts can quantitatively characterize urban air pollution source patterns and are applicable to developing effective source mitigation policies.

An integrative method for identification and prioritization of constituents of concern in produced water from onshore oil and gas extraction.

In the United States, onshore oil and gas extraction operations generate an estimated 900 billion gallons of produced water annually, making it the largest waste stream associated with upstream development of petroleum hydrocarbons. Management and disposal practices of produced water vary from deep well injection to reuse of produced water in agricultural settings. However, there is relatively little information with regard to the chemical or toxicological characteristics of produced water. A comprehensive literature review was performed, screening nearly 16,000 published articles, and identifying 129 papers that included data on chemicals detected in produced water. Searches for information on the potential ecotoxicological or mammalian toxicity of these chemicals revealed that the majority (56%) of these compounds have not been a subject of safety evaluation or mechanistic toxicology studies and 86% lack data to be used to complete a risk assessment, which underscores the lack of toxicological information for the majority of chemical constituents in produced water. The objective of this study was to develop a framework to identify potential constituents of concern in produced water, based on available and predicted toxicological hazard data, to prioritize these chemicals for monitoring, treatment, and research. In order to integrate available evidence to address gaps in toxicological hazard on the chemicals in produced water, we have catalogued available information from ecological toxicity studies, toxicity screening databases, and predicted toxicity values. A Toxicological Priority Index (ToxPi) approach was applied to integrate these various data sources. This research will inform stakeholders and decision-makers on the potential hazards in produced water. In addition, this work presents a method to prioritize compounds that, based on hazard and potential exposure, may be considered during various produced water reuse strategies to reduce possible human health risks and environmental impacts.

Validating and Refining EPA's Traffic Exposure Screening Measure.

Exposure to high air pollutant concentrations results in significant health risks. Many communities of color and low-income communities face disproportionately higher levels of air pollution exposure. Environmental justice (EJ) screening tools play a critical role in focusing early attention on areas with a high likelihood of disparate health impacts. In 2015, the United States Environmental Protection Agency (US EPA) released EJScreen, a screening tool with indicators of a range of pollution burdens across the US. However, little is known about the accuracy of the screening estimates of pollution exposure. This study compares EJScreen's traffic proximity air quality metric to dispersion modeling results. Using the area around the Houston Ship Channel, we conduct fine-grained air pollution dispersion modeling to evaluate how closely EJScreen's indicator approximates estimated roadway air pollution concentrations. We find low correlation between modeled concentrations and the EJScreen roadway air pollution indicator. We extend EJScreen's roadway air pollution screening method in three ways: (1) using a smaller unit of analysis, (2) accounting for the length of each road segment, and (3) accounting for wind direction. Using the Houston region, we use two of the methods and show that the proposed extensions provide a more accurate transportation air pollution screening assessment at the regional and local level.

Evaluating narrow windows of maternal exposure to ozone and preterm birth in a large urban area in Southeast Texas.

The association between O3 exposure and preterm birth (PTB) remains unclear. We evaluated associations for three categories of PTB and O3 in Harris County, Texas, during narrow periods of gestation. We computed two sets of exposure metrics during every 4 weeks of pregnancy for 152,214 mothers who delivered singleton, live-born infants in 2005-2007, accounting first for temporal variability and then for temporal and spatial sources of variability in ambient O3 levels. Associations were assessed using multiple logistic regression. We also examined the potential for a fixed cohort bias. In the bias-corrected cohort where associations were somewhat stronger, elevated odds ratios (ORs) per 10 parts per billion increase in O3 exposure (county-level metric) were detected for the fifth (OR=1.08, 95% confidence interval (CI): 1.04-1.12), sixth (OR=1.05, 95% CI=1.01-1.09), and seventh (OR=1.07, 95% CI=1.03-1.10) 4-week periods of pregnancy for late PTB (33-36 completed weeks gestation), the fifth (OR=1.13, 95% CI=1.02-1.25) and seventh (OR=1.15, 95% CI=1.04-1.27) 4-week periods of pregnancy for moderate PTB (29-32 completed weeks gestation), and the fifth (OR=1.21, 95% CI=1.08-1.36) 4-week period of pregnancy for severe PTB (20-28 completed weeks gestation). Conversely, decreased odds were found in the first 4-week period of pregnancy for severe PTB (OR=0.83, 95% CI=0.74-0.94). Associations were slightly attenuated using the spatially interpolated (kriged) metrics, and for women who did not work outside of the home. Our analyses confirm reports in other parts of the United States and elsewhere with findings that suggest that maternal exposure to ambient levels of O3 is associated with PTB.

Evaluating the effects of maternal exposure to benzene, toluene, ethyl benzene, and xylene on oral clefts among offspring in Texas: 1999-2008.

BACKGROUND: There is evidence from previous studies that maternal occupational exposure to hazardous air pollutants (HAPs) is positively associated with oral clefts; however, studies evaluating the association between residential exposure to these toxicants and oral clefts are lacking. Therefore, our goal was to conduct a case-control study examining the association between estimated maternal residential exposure to benzene, toluene, ethyl benzene, and xylene (BTEX) and the risk of oral clefts among offspring. METHODS: Data on 6045 nonsyndromic isolated oral cleft cases (3915 cleft lip with or without cleft palate [CL ± P] and 2130 nonsyndromic isolated cleft palate [CP] cases) delivered between 1999 and 2008 were obtained from the Texas Birth Defects Registry. The control group was a sample of unaffected live births, frequency matched to cases on year of birth. Census tract-level estimates of annual average exposures were obtained from the U.S. Environmental Protection Agency 2005 Hazardous Air Pollutant Exposure Model (HAPEM5) for each pollutant and assigned to each subject based on maternal residence during pregnancy. Logistic regression was used to assess the relationship between estimated maternal exposure to each pollutant (BTEX) separately and the risk of oral clefts in offspring. RESULTS: High estimated maternal exposure to benzene was not associated with oral clefts, compared with low estimated exposure (CL ± P adjusted OR = 0.95; 95% CI = 0.81 - 1.12; CP adjusted OR = 0.85; 95% CI = 0.67 - 1.09). Similar results were seen for the other pollutants. CONCLUSION: In our study, there was no evidence that maternal exposure to environmental levels of BTEX was associated with oral clefts.

Prioritizing environmental issues around the world: opinions from an international Central and Eastern European environmental health conference.

BACKGROUND: As the next generation of scientists enters the field of environmental health, it is imperative that they view their contributions in the context of global environmental stewardship. In this commentary, a group of international graduate students facilitated by three experienced environmental health scientists present their views on what they consider to be the global environmental health concerns of today. This group convened initially in October 2004 at an international health conference in Prague, Czech Republic. OBJECTIVES: In this report we identify perceived environmental health concerns that exist around the world, with a focus on Central and Eastern Europe. Additionally, we address these perceived problems and offers some potential solutions. DISCUSSION: At the meeting, students were invited to participate in two panel discussions. One group of young international scientists identified several significant global environmental health concerns, including air pollution, occupational hazards, and risk factors that may exacerbate current environmental health issues. The second panel determined that communication, education, and regulation were the mechanisms for addressing current environmental challenges. CONCLUSIONS: In this commentary we expand on the views presented at the meeting and represent the concerns of young investigators from nine different countries. We provide ideas about and support the exchange of information between developed and developing countries on how to handle the environmental health challenges that face the world today.

Thyroid-hormone-disrupting chemicals: evidence for dose-dependent additivity or synergism.

Endocrine disruption from environmental contaminants has been linked to a broad spectrum of adverse outcomes. One concern about endocrine-disrupting xenobiotics is the potential for additive or synergistic (i.e., greater-than-additive) effects of mixtures. A short-term dosing model to examine the effects of environmental mixtures on thyroid homeostasis has been developed. Prototypic thyroid-disrupting chemicals (TDCs) such as dioxins, polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers have been shown to alter thyroid hormone homeostasis in this model primarily by up-regulating hepatic catabolism of thyroid hormones via at least two mechanisms. Our present effort tested the hypothesis that a mixture of TDCs will affect serum total thyroxine (T4) concentrations in a dose-additive manner. Young female Long-Evans rats were dosed via gavage with 18 different polyhalogenated aromatic hydrocarbons [2 dioxins, 4 dibenzofurans, and 12 PCBs, including dioxin-like and non-dioxin-like PCBs] for 4 consecutive days. Serum total T4 was measured via radioimmunoassay in samples collected 24 hr after the last dose. Extensive dose-response functions (based on seven to nine doses per chemical) were determined for individual chemicals. A mixture was custom synthesized with the ratio of chemicals based on environmental concentrations. Serial dilutions of this mixture ranged from approximately background levels to 100-fold greater than background human daily intakes. Six serial dilutions of the mixture were tested in the same 4-day assay. Doses of individual chemicals that were associated with a 30% TH decrease from control (ED30), as well as predicted mixture outcomes were calculated using a flexible single-chemical-required method applicable to chemicals with differing dose thresholds and maximum-effect asymptotes. The single-chemical data were modeled without and with the mixture data to determine, respectively, the expected mixture response (the additivity model) and the experimentally observed mixture response (the empirical model). A likelihood-ratio test revealed statistically significant departure from dose additivity. There was no deviation from additivity at the lowest doses of the mixture, but there was a greater-than-additive effect at the three highest mixtures doses. At high doses the additivity model underpredicted the empirical effects by 2- to 3-fold. These are the first results to suggest dose-dependent additivity and synergism in TDCs that may act via different mechanisms in a complex mixture. The results imply that cumulative risk approaches be considered when assessing the risk of exposure to chemical mixtures that contain TDCs.

Depleted and natural uranium: chemistry and toxicological effects.

Depleted uranium (DU) is a by-product from the chemical enrichment of naturally occurring uranium. Natural uranium is comprised of three radioactive isotopes: (238)U, (235)U, and (234)U. This enrichment process reduces the radioactivity of DU to roughly 30% of that of natural uranium. Nonmilitary uses of DU include counterweights in airplanes, shields against radiation in medical radiotherapy units and transport of radioactive isotopes. DU has also been used during wartime in heavy tank armor, armor-piercing bullets, and missiles, due to its desirable chemical properties coupled with its decreased radioactivity. DU weapons are used unreservedly by the armed forces. Chemically and toxicologically, DU behaves similarly to natural uranium metal. Although the effects of DU on human health are not easily discerned, they may be produced by both its chemical and radiological properties. DU can be toxic to many bodily systems, as presented in this review. Most importantly, normal functioning of the kidney, brain, liver, and heart can be affected by DU exposure. Numerous other systems can also be affected by DU exposure, and these are also reviewed. Despite the prevalence of DU usage in many applications, limited data exist regarding the toxicological consequences on human health. This review focuses on the chemistry, pharmacokinetics, and toxicological effects of depleted and natural uranium on several systems in the mammalian body. A section on risk assessment concludes the review.

Comparative responsiveness of hypothyroxinemia and hepatic enzyme induction in Long-Evans rats versus C57BL/6J mice exposed to TCDD-like and phenobarbital-like polychlorinated biphenyl congeners.

Numerous mechanisms have been postulated to explain how polyhalogenated aromatic hydrocarbons alter thyroid homeostasis with almost all data derived from studies using the rat. This study compared the sensitivity of rats and mice to polychlorinated biphenyl (PCB)-induced hypothyroxemia. Male and female C57BL/6J mice and Long-Evans rats were dosed orally for 4 consecutive days with either PCB126 (0.03-300.0 microg/kg/day) or PCB153 (0.3-300.0 mg/kg/day). Trunk blood and livers were collected 24 h after the last dose and used to determine total serum thyroxine (T(4)) and hepatic microsomal T(4) glucuronidation activity. Hepatic microsomal ethoxyresorufin-O-deethylase (EROD) and pentoxyresorufin-O-deethylase (PROD) activities were also determined as markers for Ah receptor or phenobarbital response unit activation, respectively. PCB126 did not affect T(4) in the mouse but decreased T(4) (up to 50%) in the rat. PCB153 decreased T(4) (up to 80%) in both the rat and the mouse. PCB126 increased EROD in both rats (12- to 22-fold) and mice (15- to 20-fold). PCB153 induced hepatic PROD activity in both rats (30-fold) and mice (4-fold). T(4) glucuronidation was increased approximately 2- to 3-fold in both rats and mice treated with PCB153. PCB126 increased T(4) glucuronidation 13-fold in rats but only marginally (20%) in mice at the highest doses. Western blot analysis confirmed the PCB126-induced changes in expression of UGT1A in rats and the minimal increase in mice. These data suggest that species differences in response to chemicals that induce hypothyroxinemia are due to differential induction of hepatic UGT enzymes.