Managing upstream oil and gas emissions: A public health oriented approach

Oil and natural gas are the largest primary global energy sources, and upstream gas emissions from these fuels can impact global climate change and local public health. This paper employs a public health-oriented perspective that reviews grey and academic literature, industry data, technical reports, and policy trends to highlight issues of emissions monitoring. We identify gaps in the existing landscape of emissions reduction strategies and highlight options for addressing them. Policy recommendations include the use of new digital monitoring technologies to better understand causes of emission events, to create data-driven oil and gas regulations, and to begin accurately measuring the volumes of gases released during oil and gas production. Areas for future research relating to emissions and public health impacts are outlined to further enable oil and gas policy discussions.

Associations Between Dust Storms and Intensive Care Unit Admissions in the United States, 2000-2015

Anthropogenic climate change is influencing the incidence of dust storms and associated human exposure to coarse particulate matter (PM2.5-10) in the United States. Studies have found adverse health consequences related to dust exposure. These consequences include respiratory disease exacerbations and premature mortality, resulting in increased health care utilization. However, the impact of dust storms on critical care demand has not been studied in the United States. We seek to quantify the relationship between dust storms and surges in critical care demand by investigating the association between dust storms and intensive care unit (ICU) admissions at nearby hospitals from 2000 to 2015. ICU data were acquired from Premier, Inc. and encompass 15-20% of all ICU admissions in the United States. Dust storm, meteorology, and air pollutant data were downloaded from the U.S. National Weather Service, the U.S. National Climatic Data Center, and the U.S. Environmental Protection Agency websites, respectively. Associations between ICU admission and dust storms, controlling for temperature, dew point temperature, ambient PM2.5 and ozone, as well as seasonally varying confounders, were estimated using a distributed lag conditional Poisson model with overdispersion. We found a 4.8% (95% CI: 0.4, 9.4; p = 0.033) increase in total ICU admissions on the day of the dust storm (Lag 0) and a 9.2% (95% CI: 1.8, 17.0; p = 0.013) and 7.5% (95% CI: 0.3, 15.2; p = 0.040) increase in respiratory admissions at Lags 0 and 5. North American dust storms are associated with increases in same day and lagged demand for critical care services at nearby hospitals.

Updated neuroimaging using intraoperative brain modeling and sparse data

A strategy to update preoperative imaging for image-guided surgery using readily available intraoperative information has been developed and implemented. A patient-specific three-dimensional finite element model of the brain is generated from preoperative MRI and used to simulate deformation resulting from multiple surgical processes. Intraoperatively obtained sparse imaging data, such as from digital cameras or ultrasonography, is then used to prescribe the displacement of selected points within the model. Interpolation to the resolution of preoperative imaging may then be performed based upon the model. The algorithms for generation of the finite element model and for its subsequent deformation have been successfully validated using a pig brain model, and preliminary clinical application in the operating room has demonstrated feasibility.