Ground-level ozone is an important atmospheric oxidant, which exhibits considerable spatial and temporal variability in its concentration level. Existing modeling approaches for ground-level ozone include chemical transport models, land-use regression, Kriging, and data fusion of chemical transport models with monitoring data. Each of these methods has both strengths and weaknesses. Combining those complementary approaches could improve model performance. Meanwhile, satellite-based total column ozone, combined with ozone vertical profile, is another potential input. We propose a hybrid model that integrates the above variables to achieve spatially and temporally resolved exposure assessments for ground-level ozone. We used a neural network for its capacity to model interactions and nonlinearity. Convolutional layers, which use convolution kernels to aggregate nearby information, were added to the neural network to account for spatial and temporal autocorrelation. We trained the model with AQS 8-hour daily maximum ozone in the continental United States from 2000 to 2012 and tested it with left out monitoring sites. Cross-validated R2 on the left out monitoring sites ranged from 0.74 to 0.80 (mean 0.76) for predictions on 1 km×1 km grid cells, which indicates good model performance. Model performance remains good even at low ozone concentrations. The prediction results facilitate epidemiological studies to assess the health effect of ozone in the long term and the short term.

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The leak of processed natural gas (PNG) from October 2015 to February 2016 from the Aliso Canyon storage facility, near Los Angeles, California, was the largest single accidental release of greenhouse gases in US history. The Interagency Task Force on Natural Gas Storage Safety and California regulators recently recommended operators phase out single-point-of-failure (SPF) well designs. Here, we develop a national dataset of UGS well activity in the continental US to assess regulatory data availability and uncertainty, and to assess the prevalence of certain well design deficiencies including single-point-of-failure designs. We identified 14 138 active UGS wells associated with 317 active UGS facilities in 29 states using regulatory and company data. Statelevel wellbore datasets contained numerous reporting inconsistencies that limited data concatenation. We identified 2715 active UGS wells across 160 facilities that, like the failed well at Aliso Canyon, predated the storage facility, and therefore were not originally designed for gas storage. The majority (88%) of these repurposed wells are located in OH, MI, PA, NY, and WV. Repurposed wells have a median age of 74 years, and the 2694 repurposed wells constructed prior to 1979 are particularly likely to exhibit design-related deficiencies. An estimated 210 active repurposed wells were constructed before 1917-before cement zonal isolation methods were utilized. These wells are located in OH, PA, NY, and WV and represent the highest priority related to potential design deficiencies that could lead to containment loss. This national baseline assessment identifies regulatory data uncertainties, highlights a potentially widespread vulnerability of the natural gas supply chain, and can aid in prioritization and oversight for high-risk wells and facilities.

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Objective To evaluate the association of short‐term exposure to overall fine particulate matter of <2.5 μm (PM2.5) and wildfire‐specific PM2.5 with emergency department (ED) visits for headache. Background Studies have reported associations between PM2.5 exposure and headache risk. As climate change drives longer and more intense wildfire seasons, wildfire PM2.5 may contribute to more frequent headaches. Methods Our study included adult Californian members (aged ≥18 years) of a large de‐identified commercial and Medicare Advantage claims database from 2006 to 2020. We identified ED visits for primary headache disorders (subtypes: tension‐type headache, migraine headache, cluster headache, and “other” primary headache). Claims included member age, sex, and residential zip code. We linked daily overall and wildfire‐specific PM2.5 to residential zip code and conducted a time‐stratified case‐crossover analysis considering 7‐day average PM2.5 concentrations, first for primary headache disorders combined, and then by headache subtype. Results Among 9898 unique individuals we identified 13,623 ED encounters for primary headache disorders. Migraine was the most frequently diagnosed headache (N = 5534/13,623 [47.6%]) followed by “other” primary headache (N = 6489/13,623 [40.6%]). For all primary headache ED diagnoses, we observed an association of 7‐day average wildfire PM2.5 (odds ratio [OR] 1.17, 95% confidence interval [CI] 0.95–1.44 per 10 μg/m3 increase) and by subtype we observed increased odds of ED visits associated with 7‐day average wildfire PM2.5 for tension‐type headache (OR 1.42, 95% CI 0.91–2.22), “other” primary headache (OR 1.40, 95% CI 0.96–2.05), and cluster headache (OR 1.29, 95% CI 0.71–2.35), although these findings were not statistically significant under traditional null hypothesis testing. Overall PM2.5 was associated with tension‐type headache (OR 1.29, 95% CI 1.03–1.62), but not migraine, cluster, or “other” primary headaches. Conclusions Although imprecise, these results suggest short‐term wildfire PM2.5 exposure may be associated with ED visits for headache. Patients, healthcare providers, and systems may need to respond to increased headache‐related healthcare needs in the wake of wildfires and on poor air quality days.

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