Understanding Excess Emissions from Industrial Facilities: Evidence from Texas

An equity and environmental justice assessment of anti-science actions during the Trump administration

In the United States, science shapes federal health and safety protections, but political officials can and do politicize federal science and science-based safeguards. Many presidential administrations have politicized science, but under the administration of President Trump, these attacks on science-such as buried research, censored scientists, halted data collection-increased in number to unprecedented levels. Underserved communities bore the brunt of the harms. Such attacks disproportionately harm Black, Indigenous, low-income communities, and communities of color, all of whom have long been burdened by pollution exposure and other stressors. We analyze the effects on underserved communities of the Trump administration's anti-science environmental and public health policy actions and offer policy recommendations for current and future administrations. Our goal is to strengthen scientific integrity, prioritize health disparity research, and meaningfully engage affected communities in federal rulemaking.

Multivariate Statistical Analysis for the Detection of Air Pollution Episodes in Chemical Industry Parks

Air pollution episodes (APEs) caused by excessive emissions from chemical industry parks (CIPs) have resulted in severe environmental damage in recent years. Therefore, it is of great importance to detect APEs timely and effectively using contaminant measurements from the air quality monitoring network (AQMN) in the CIP. Traditionally, APE can be detected by determining whether the contaminant concentration at any ambient monitoring station exceeds the national environmental standard. However, the environmental standards used are unified in various ambient monitoring stations, which ignores the source–receptor relationship in the CIP and challenges the effective detection of excessive emissions in some scenarios. In this paper, an approach based on a multivariate statistical analysis (MSA) method is proposed to detect the APEs caused by excessive emissions from CIPs. Using principal component analysis (PCA), the spatial relationships hidden among the historical environmental monitoring data are extracted, and the high-dimensional data are projected into only two subspaces. Then, two monitoring indices, T2 and Q, which represent the variability in these subspaces, are utilized to monitor the pollution status and detect the potential APEs in the CIP. In addition, the concept of APE detectability is also defined, and the condition for APE detectability is derived, which explains when the APEs can be detectable. A simulated case for a CIP in Zhejiang province of China is studied to evaluate the performance of this approach. The study indicates that the method can have an almost 100% APE detection rate. The real-world measurements of Total Volatile Organic Compounds (TVOC) at a 10-min time interval from 3 December 2020∼12 December 2020 are also analyzed, and 64 APEs caused by excessive TVOC emissions are detected in a total of 1440 time points.

Spatial and Temporal Analysis of Impacts of Hurricane Florence on Criteria Air Pollutants and Air Toxics in Eastern North Carolina

Natural and anthropogenic disasters are associated with air quality concerns due to the potential redistribution of pollutants in the environment. Our objective was to conduct a spatiotemporal analysis of air concentrations of benzene, toluene, ethylbenzne, and xylene (BTEX) and criteria air pollutants in North Carolina during and after Hurricane Florence. Three sampling campaigns were carried out immediately after the storm (September 2018) and at four-month intervals. BTEX were measured along major roads. Concurrent criteria air pollutant concentrations were predicted from modeling. Correlation between air pollutants and possible point sources was conducted using spatial regression. Exceedances of ambient air criteria were observed for benzene (in all sampling periods) and PM2.5 (mostly immediately after Florence). For both, there was an association between higher concentrations and fueling stations, particularly immediately after Florence. For other pollutants, concentrations were generally below levels of regulatory concern. Through characterization of air quality under both disaster and "normal" conditions, this study demonstrates spatial and temporal variation in air pollutants. We found that only benzene and PM2.5 were present at levels of potential concern, and there were localized increases immediately after the hurricane. These substances warrant particular attention in future disaster response research (DR2) investigations.

Resident perspectives of environmental health risk exposures after Hurricane Harvey

This study examines what visitors to urban parks in Houston, TX, know about environmental health risks resulting from Hurricane Harvey, a category 4 storm that made landfall in August 2017 and dropped over 60 in. of rain in 8 days making it the most significant rainfall event in US history. Interviews were conducted with adult Houstonians using purposive sampling. In total, 27 interviews were conducted with 36 different participants. Interviews were audio-recorded, transcribed verbatim, and analyzed qualitatively using a phronetic iterative approach. This study found that park visitors lack sufficient knowledge about environmental health risks, yet they have strong desires to learn more about such risks. In particular, participants have clear opinions on what the content of the messages (i.e., concise, manageable, not fear-inducing) should be and how they would like to receive the information (i.e., conveniently accessible, from trusted local sources). Implications for health campaign interventions utilizing uncertainty theories are discussed.

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 NO_x) 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.

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.

Racial, ethnic, and income disparities in air pollution: A study of excess emissions in Texas

Objective

Excess emissions are pollutant releases that occur during periods of startups, shutdowns or malfunctions and are considered violations of the U.S. Clean Air Act. They are an important, but understudied and under-regulated, category of pollution releases given their frequency and magnitude. In this paper, we examine the demographic correlates of excess emissions, using data from industrial sources in Texas.

Methods

We conduct two complementary sets of analyses: one at the census tract level and one at the facility level. At the census tract level, we use a multinomial logit model to examine the relationships between racial, ethnic, and income characteristics and the incidence of excess emissions. At the facility level, we first estimate a logit model to examine whether these characteristics are associated with facilities that emit excess emissions, and then, conditional on the presence of excess emissions, we use ordinary least square regression to estimate their correlation with the magnitude of releases.

Results

Across our analyses, we find that the percentage of Black population and median household income are positively associated with excess emissions; percentage of college graduate, population density, median housing value, and percentage of owner-occupied housing unit are negatively associated with excess emissions. We, however, have not found a clear and significant relationship between the percentage of Hispanic population and excess emissions.

Development of Rhodococcus opacus as a chassis for lignin valorization and bioproduction of high-value compounds

The current extraction and use of fossil fuels has been linked to extensive negative health and environmental outcomes. Lignocellulosic biomass-derived biofuels and bioproducts are being actively considered as renewable alternatives to the fuels, chemicals, and materials produced from fossil fuels. A major challenge limiting large-scale, economic deployment of second-generation biorefineries is the insufficient product yield, diversity, and value that current conversion technologies can extract from lignocellulose, in particular from the underutilized lignin fraction. Rhodococcus opacus PD630 is an oleaginous gram-positive bacterium with innate catabolic pathways and tolerance mechanisms for the inhibitory aromatic compounds found in depolymerized lignin, as well as native or engineered pathways for hexose and pentose sugars found in the carbohydrate fractions of biomass. As a result, R. opacus holds potential as a biological chassis for the conversion of lignocellulosic biomass into biodiesel precursors and other value-added products. This review begins by examining the important role that lignin utilization will play in the future of biorefineries and by providing a concise survey of the current lignin conversion technologies. The genetic machinery and capabilities of R. opacus that allow the bacterium to tolerate and metabolize aromatic compounds and depolymerized lignin are also discussed, along with a synopsis of the genetic toolbox and synthetic biology methods now available for engineering this organism. Finally, we summarize the different feedstocks that R. opacus has been demonstrated to consume, and the high-value products that it has been shown to produce. Engineered R. opacus will enable lignin valorization over the coming years, leading to cost-effective conversion of lignocellulose into fuels, chemicals, and materials.