Characterizing methane and total non-methane hydrocarbon levels in Los Angeles communities with oil and gas facilities using air quality monitors

Cardiovascular health and proximity to urban oil drilling in Los Angeles, California

BACKGROUND

Although ~18 million people live within a mile from active oil and gas development (OGD) sites in the United States, epidemiological research on how OGD affects the health of nearby urban residents is sparse. Thousands of OGD sites are spread across Los Angeles (LA) County, California, home to the largest urban oil production in the country. Air pollution and noise from OGD may contribute to cardiovascular morbidity.

OBJECTIVE

We examined the association between proximity to OGD and blood pressure in a diverse cohort of residents in LA.

METHODS

We recruited residents in South LA who lived <1 km from an OGD site. We collected three blood pressure measurements for each participant and used the second and third measurements to calculate averages for systolic blood pressure (SBP) and diastolic blood pressure (DBP) separately. We conducted multivariable linear regression to examine the relationship between distance to OGD sites and continuous SBP and DBP, adjusting for BMI, smoking status, distance to freeway, sex, age, and use of antihypertension medications, with a random effect for household. We examined effect measure modification by BMI category and smoking category.

RESULTS

Among the 623 adult participants, we found that for every 100 meter increase in distance from the OGD site, DBP was reduced by an average of 0.73 mmHg (95% CI: -1.26, -0.21) in this population. We observed stronger effects of proximity to OGD site on DBP among never smokers and among participants with a healthy BMI. The associations observed between proximity to OGD site and SBP were weaker but followed the same patterns as those for DBP.

IMPACT

Our study suggests that living near urban oil drilling sites is significantly associated with greater diastolic blood pressure in urban Los Angeles communities. This research improves understanding of impacts from living nearby drilling operations on the health and welfare of this community, which is critical to inform public health relevant strategies.

Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models

The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4. Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH4 and its related intensity in the affected areas. Various meteorological characteristics, including wind speed, temperature, pressure, relative humidity, water vapor, and heat flux, were included in the simulation. We used the ensemble learning method to determine the best-performing weighted ensemble ML models built upon several weaker lower-layer ML models to (i) detect the presence of CH4 as a classification problem and (ii) predict the intensity of CH4 as a regression problem. The classification model performance for CH4 detection was evaluated using accuracy, F1 score, Matthew's Correlation Coefficient (MCC), and the area under the receiver operating characteristic curve (AUC ROC), with the top-performing model being 97.2%, 0.972, 0.945 and 0.995, respectively. The R 2 score was used to evaluate the regression model performance for CH4 intensity prediction, with the R 2 score of the best-performing model being 0.858. The ML models developed in this study for fugitive CH4 detection and intensity prediction can be used with fixed environmental sensors deployed on the ground or with sensors mounted on unmanned aerial vehicles (UAVs) for mobile detection.

VOC source apportionment: How monitoring characteristics influence positive matrix factorization (PMF) solutions

Positive matrix factorization (PMF) can be used to develop more targeted air quality mitigation strategies by identifying major sources of a pollutant in an area. This technique is dependent, however, on the ability of PMF to resolve factors that accurately represent all sources of that pollutant in an area. We investigated how the accuracy of PMF solutions might be influenced by monitoring data characteristics, such as temporal resolution, monitoring location, and species composition, to better inform the use of PMF in VOC mitigation strategies. We applied PMF to five VOC monitoring programs collected within a four-year period in Colorado and found generally consistent factors, which we identified as oil extraction, processing, and evaporation; natural gas; vehicle exhaust; and liquid gasoline/short-lived oil and gas. The main determinant influencing whether or not a dataset resolved each of these sources was whether the dataset had a comprehensive list of VOC species covering key species of each source. Pollution spikes were not well-modeled in any of the solutions. Hyperlocal and volatile chemical product factors expected to be resolved in the industrialized, urban location were also missing, highlighting three limitations of PMF analysis. Wind direction dependence and diurnal trends aided in source identification, suggesting that high-time resolution data is important for developing actionable PMF results. Based on these findings, we recommend that air monitoring for PMF-informed VOC mitigation efforts include high temporal resolution and a comprehensive array of VOC species.

Utilization of a Low-Cost Sensor Array for Mobile Methane Monitoring

The use of low-cost sensors (LCSs) for the mobile monitoring of oil and gas emissions is an understudied application of low-cost air quality monitoring devices. To assess the efficacy of low-cost sensors as a screening tool for the mobile monitoring of fugitive methane emissions stemming from well sites in eastern Colorado, we colocated an array of low-cost sensors (XPOD) with a reference grade methane monitor (Aeris Ultra) on a mobile monitoring vehicle from 15 August through 27 September 2023. Fitting our low-cost sensor data with a bootstrap and aggregated random forest model, we found a high correlation between the reference and XPOD CH4 concentrations (r = 0.719) and a low experimental error (RMSD = 0.3673 ppm). Other calibration models, including multilinear regression and artificial neural networks (ANN), were either unable to distinguish individual methane spikes above baseline or had a significantly elevated error (RMSDANN = 0.4669 ppm) when compared to the random forest model. Using out-of-bag predictor permutations, we found that sensors that showed the highest correlation with methane displayed the greatest significance in our random forest model. As we reduced the percentage of colocation data employed in the random forest model, errors did not significantly increase until a specific threshold (50 percent of total calibration data). Using a peakfinding algorithm, we found that our model was able to predict 80 percent of methane spikes above 2.5 ppm throughout the duration of our field campaign, with a false response rate of 35 percent.

Social and Environmental Stressors of Urban Oil and Gas Facilities in Los Angeles County, California, 2020

Objectives. To examine patterns of cumulative environmental injustice with respect to operations of urban oil and gas development in Los Angeles County, California. Methods. Using CalEnviroScreen (CES) 4.0, oil and gas data permit records, and US census data, we examined the association between CES score (grouped into equal quintiles, with the lowest representing low cumulative burden) and oil and gas development (presence or absence of an oil and gas production well) within 1 kilometer of a census block centroid. Results. Among census blocks in the highest quintile of CES score, we observed 94% increased odds of being within 1 kilometer of a well compared with census blocks in the lowest quintile of CES score (odds ratio = 1.94; 95% confidence interval = 1.83, 2.10). In our multivariable model, the proportion of Black residents and higher quintiles of CES score were also associated with increased odds of a nearby oil and gas well. Conclusions. These findings suggest that oil and gas facilities are operating in neighborhoods already cumulatively burdened and with higher proportions of Black residents. (Am J Public Health. 2023;113(11):1182-1190. https://doi.org/10.2105/AJPH.2023.307360).

Study on Spatiotemporal Evolution Features and Affecting Factors of Collaborative Governance of Pollution Reduction and Carbon Abatement in Urban Agglomerations of the Yellow River Basin

Exploring spatiotemporal evolution features and factors affecting pollution reduction and carbon abatement on the urban agglomeration scale is helpful to better understand the interaction between ecological environment and economic development in urban agglomerations. In this study, we constructed an evaluation index system for collaborative governance of pollution reduction and carbon abatement in urban agglomerations. In addition, we employed the correlation coefficient matrix, the composite system synergy model, the Gini coefficient, and the Theil index to evaluate the level of and regional differences in collaborative governance of pollution reduction and carbon abatement in seven urban agglomerations in the Yellow River Basin from 2006 to 2020. Moreover, we explored the factors affecting collaborative governance of pollution reduction and carbon abatement in urban agglomerations in the basin. The following findings were obtained: (1) the order degree of collaborative governance of pollution reduction and carbon abatement in the seven urban agglomerations exhibited a significant growing trend, representing a spatial evolution feature of "high in the west and low in the east"; (2) the internal differences in collaborative governance synergy of pollution reduction and carbon abatement decreased in Lanzhou-Xining Urban Agglomeration, Hohhot-Baotou-Ordos-Yulin Urban Agglomeration, Central Shanxi Urban Agglomeration, Zhongyuan Urban Agglomeration, and Shandong Peninsula Urban Agglomeration, while the internal differences basically remained stable in Guanzhong Urban Agglomeration and the Urban Agglomeration along the Yellow River in Ningxia; (3) the variances in environmental regulation and industrial structure among urban agglomerations had a significant positive effect on collaborative governance of pollution reduction and carbon abatement in urban agglomerations in the basin, and the variances in economic growth had a significant inhibitory effect. In addition, the variances in energy consumption, greening construction, and opening-up had an inhibitory impact on collaborative governance of pollution reduction, but the impact was not significant. Finally, this study proposes various recommendations to improve collaborative governance for pollution reduction and carbon abatement in urban agglomerations in the basin in terms of promoting industrial structure upgrading, strengthening regional cooperation, and reducing regional differences. This paper represents an empirical reference for formulating differentiated collaborative governance strategies for pollution reduction and carbon abatement, comprehensive green and low-carbon economic and social transformation programs, and high-quality green development paths in urban agglomerations, which is of certain theoretical and practical significance.

Upstream oil and gas production and ambient air pollution in California

BACKGROUND

Prior studies have found that residential proximity to upstream oil and gas production is associated with increased risk of adverse health outcomes. Emissions of ambient air pollutants from oil and gas wells in the preproduction and production stages have been proposed as conferring risk of adverse health effects, but the extent of air pollutant emissions and resulting nearby pollution concentrations from wells is not clear.

OBJECTIVES

We examined the effects of upstream oil and gas preproduction (count of drilling sites) and production (total volume of oil and gas) activities on concentrations of five ambient air pollutants in California.

METHODS

We obtained data on approximately 1 million daily observations from 314 monitors in the EPA Air Quality System, 2006-2019, including daily concentrations of five routinely monitored ambient air pollutants: PM_2.5, CO, NO₂, O₃, and VOCs. We obtained data on preproduction and production operations from Enverus and the California Geographic Energy Management Division (CalGEM) for all wells in the state. For each monitor and each day, we assessed exposure to upwind preproduction wells and total oil and gas production volume within 10 km. We used a panel regression approach in the analysis and fit adjusted fixed effects linear regression models for each pollutant, controlling for geographic, seasonal, temporal, and meteorological factors.

RESULTS

We observed higher concentrations of PM_2.5 and CO at monitors within 3 km of preproduction wells, NO₂ at monitors at 1-2 km, and O₃ at 2-4 km from the wells. Monitors with proximity to increased production volume observed higher concentrations of PM_2.5, NO₂, and VOCs within 1 km and higher O₃ concentrations at 1-2 km. Results were robust to sensitivity analyses.

CONCLUSION

Adjusting for geographic, meteorological, seasonal, and time-trending factors, we observed higher concentrations of ambient air pollutants at air quality monitors in proximity to preproduction wells within 4 km and producing wells within 2 km.

High-Performance NMHC Detection Enabled by a Perylene Bisimide-Cored Metallacycle Complex-Based Fluorescent Film Sensor

Non-methane hydrocarbons (NMHCs) can serve as precursors of ozone and photochemical smog, and hence their highly efficient detection is of great importance for air quality monitoring. Here, we synthesized a new fluorescent perylene bisimide (PBI)-cored metallacycle complex through coordination-driven self-assembly and used it for the production of a fluorescent film sensor. The unique rectangular structure of the developed fluorophore endows the sensor with enhanced sensing performance and discriminability to n-alkanes (C_5-10). Specifically, the experimental detection limits for n-pentane, n-hexane, and n-decane are 39, 7, and 1.4 mg/m³, respectively, and the corresponding linear ranges are from 39 to 2546, 7 to 1745, and 1.4 to 85 mg/m³, respectively. Moreover, the sensing is fully reversible. In tandem with a gas chromatographic separation system, the film sensor showed comparable detection ability for the n-alkanes with a commercial flame ionization detector (FID), while the film sensor needs no hydrogen; it occupies a much smaller size (30 × 30 × 44 mm³) and consumes less energy (0.215 W). Further studies demonstrated that the developed sensor can be used for on-site and real-time quantification of NHMCs, laying the foundation for developing into a portable detector.

Changes in neighborhood air quality after idling of an urban oil production site

Oil and gas development is occurring in urban, densely populated neighborhoods; however, the impacts of these operations on neighborhood air quality are not well characterized. In this research, we leveraged ambient air monitoring adjacent to an oil and gas production site in Los Angeles, California during active and idle periods. This study analyzed continuous methane (CH4) and non-methane hydrocarbon (NMHC) measurements, together with triggered grab samples and 24 hour integrated canister samples collected by the South Coast Air Quality Management District. Ambient air pollutant levels and trends were evaluated during active and idle well operations to assess changes in neighborhood air quality after the suspension of oil and gas production. We find that mean concentrations of methane, NMHC, benzene, toluene, ethylbenzene, xylenes, styrene, n-hexane, n-pentane, ethane, and propane decreased following the stop in production activities. Using a source apportionment approach, we observed that the "natural gas" drilling source contributed 23.7% to the total VOCs measured during the active phase, and only 0.6% to the total measured VOCs in the idle phase. Near urban oil and gas production sites, residents may face poorer air quality due to the oil and gas activities which may pose adverse health and environmental risks among proximate communities.

Respiratory health, pulmonary function and local engagement in urban communities near oil development

BACKGROUND

Modern oil development frequently occurs in close proximity to human populations. Los Angeles, California is home to the largest urban oil field in the country with thousands of active oil and gas wells in very close proximity to homes, schools and parks, yet few studies have investigated potential health impacts. The neighborhoods along the Las Cienagas oil fields are situated in South LA, densely populated by predominantly low-income Black and Latinx families, many of whom are primarily Spanish-speakers.

METHODS

A cross-sectional community-based study was conducted between January 2017 and August 2019 among residents living <1000 m from two oil wells (one active, one idle) in the Las Cienagas oil field. We collected self-reported acute health symptoms and measured FEV1 (forced expiratory volume in the first second of exhalation) and FVC (forced vital capacity). We related lung function measures to distance and direction from an oil and gas development site using generalized linear models adjusted for covariates.

RESULTS

A total of 961 residents from two neighborhoods participated, the majority of whom identify as Latinx. Participants near active oil development reported significantly higher prevalence of wheezing, eye and nose irritation, sore throat and dizziness in the past 2 weeks. Among 747 valid spirometry tests, we observe that living near (less than 200 m) of oil operations was associated with, on average, -112 mL lower FEV1 (95% CI: -213, -10) and -128 mL lower FVC (95% CI: -252, -5) compared to residents living more than 200 m from the sites after adjustments for covariates, including age, sex, height, proximity to freeway, asthma status and smoking status. When accounting for predominant wind direction and proximity, we observe that residents living downwind and less than 200 m from oil operations have, on average, -414 mL lower FEV1 (95% CI: -636, -191) and -400 mL lower FVC (95% CI: -652, -147) compared to residents living upwind and more than 200 m from the wells.

CONCLUSIONS

Living nearby and downwind of urban oil and gas development sites is associated with lower lung function among residents, which may contribute to environmental health disparities.

Improving Air Pollutant Metal Oxide Sensor Quantification Practices through: An Exploration of Sensor Signal Normalization, Multi-Sensor and Universal Calibration Model Generation, and Physical Factors Such as Co-Location Duration and Sensor Age

As low-cost sensors have become ubiquitous in air quality measurements, there is a need for more efficient calibration and quantification practices. Here, we deploy stationary low-cost monitors in Colorado and Southern California near oil and gas facilities, focusing our analysis on methane and ozone concentration measurement using metal oxide sensors. In comparing different sensor signal normalization techniques, we propose a z-scoring standardization approach to normalize all sensor signals, making our calibration results more easily transferable among sensor packages. We also attempt several different physical co-location schemes, and explore several calibration models in which only one sensor system needs to be co-located with a reference instrument, and can be used to calibrate the rest of the fleet of sensor systems. This approach greatly reduces the time and effort involved in field normalization without compromising goodness of fit of the calibration model to a significant extent. We also explore other factors affecting the performance of the sensor system quantification method, including the use of different reference instruments, duration of co-location, time averaging, transferability between different physical environments, and the age of metal oxide sensors. Our focus on methane and stationary monitors, in addition to the z-scoring standardization approach, has broad applications in low-cost sensor calibration and utility.