Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990-2017.

Estimates of ground-level ozone concentrations are necessary to determine the human health burden of ozone. To support the Global Burden of Disease Study, we produce yearly fine resolution global surface ozone estimates from 1990 to 2017 through a data fusion of observations and models. As ozone observations are sparse in many populated regions, we use a novel combination of the M3Fusion and Bayesian Maximum Entropy (BME) methods. With M3Fusion, we create a multimodel composite by bias-correcting and weighting nine global atmospheric chemistry models based on their ability to predict observations (8834 sites globally) in each region and year. BME is then used to integrate observations, such that estimates match observations at each monitoring site with the observational influence decreasing smoothly across space and time until the output matches the multimodel composite. After estimating at 0.5° resolution using BME, we add fine spatial detail from an additional model, yielding estimates at 0.1° resolution. Observed ozone is predicted more accurately (R2 = 0.81 at the test point, 0.63 at 0.1°, and 0.62 at 0.5°) than the multimodel mean (R2 = 0.28 at 0.5°). Global ozone exposure is estimated to be increasing, driven by highly populated regions of Asia and Africa, despite decreases in the United States and Russia.

Estimating Wildfire Smoke Concentrations during the October 2017 California Fires through BME Space/Time Data Fusion of Observed, Modeled, and Satellite-Derived PM2.5.

Exposure to wildfire smoke causes adverse health outcomes, suggesting the importance of accurately estimating smoke concentrations. Geostatistical methods can combine observed, modeled, and satellite-derived concentrations to produce accurate estimates. Here, we estimate daily average ground-level PM2.5 concentrations at a 1 km resolution during the October 2017 California wildfires, using the Constant Air Quality Model Performance (CAMP) and Bayesian Maximum Entropy (BME) methods to bias-correct and fuse three concentration datasets: permanent and temporary monitoring stations, a chemical transport model (CTM), and satellite-derived estimates. Four BME space/time kriging and data fusion methods were evaluated. All BME methods produce more accurate estimates than the standalone CTM and satellite products. Adding temporary station data increases the R2 by 36%. The data fusion of observations with the CAMP-corrected CTM and satellite-derived concentrations provides the best estimate (R2 = 0.713) in fire-impacted regions, emphasizing the importance of combining multiple datasets. We estimate that approximately 65,000 people were exposed to very unhealthy air (daily average PM2.5 ≥ 150.5 µg/m3).

Estimating environmental co-benefits of U.S. low-carbon pathways using an integrated assessment model with state-level resolution.

There are many technological pathways that can lead to reduced carbon dioxide emissions. However, these pathways can have substantially different impacts on other environmental endpoints, such as air quality and energy-related water demand. This study uses an integrated assessment model with state-level resolution of the energy system to compare environmental impacts of alternative low-carbon pathways for the United States. One set of pathways emphasizes nuclear energy and carbon capture and storage, while another set emphasizes renewable energy, including wind, solar, geothermal power, and bioenergy. These are compared with pathways in which all technologies are available. Air pollutant emissions, mortality costs attributable to particulate matter smaller than 2.5 µm in diameter, and energy-related water demands are evaluated for 50% and 80% carbon dioxide reduction targets in 2050. The renewable low-carbon pathways require less water withdrawal and consumption than the nuclear and carbon capture pathways. However, the renewable low-carbon pathways modeled in this study produce higher particulate matter-related mortality costs due to greater use of biomass in residential heating. Environmental co-benefits differ among states because of factors such as existing technology stock, resource availability, and environmental and energy policies.

"What We Breathe Impacts Our Health: Improving Understanding of the Link between Air Pollution and Health".

Air pollution contributes to the premature deaths of millions of people each year around the world, and air quality problems are growing in many developing nations. While past policy efforts have succeeded in reducing particulate matter and trace gases in North America and Europe, adverse health effects are found at even these lower levels of air pollution. Future policy actions will benefit from improved understanding of the interactions and health effects of different chemical species and source categories. Achieving this new understanding requires air pollution scientists and engineers to work increasingly closely with health scientists. In particular, research is needed to better understand the chemical and physical properties of complex air pollutant mixtures, and to use new observations provided by satellites, advanced in situ measurement techniques, and distributed micro monitoring networks, coupled with models, to better characterize air pollution exposure for epidemiological and toxicological research, and to better quantify the effects of specific source sectors and mitigation strategies.

Repeating cardiopulmonary health effects in rural North Carolina population during a second large peat wildfire.

BACKGROUND: Cardiovascular health effects of fine particulate matter (PM2.5) exposure from wildfire smoke are neither definitive nor consistent with PM2.5 from other air pollution sources. Non-comparability among wildfire health studies limits research conclusions. METHODS: We examined cardiovascular and respiratory health outcomes related to peat wildfire smoke exposure in a population where strong associations were previously reported for the 2008 Evans Road peat wildfire. We conducted a population-based epidemiologic investigation of associations between daily county-level modeled wildfire PM2.5 and cardiopulmonary emergency department (ED) visits during the 2011 Pains Bay wildfire in eastern North Carolina. We estimated changes in the relative risk cumulative over 0-2 lagged days of wildfire PM2.5 exposure using a quasi-Poisson regression model adjusted for weather, weekends, and poverty. RESULTS: Relative risk associated with a 10 µg/m(3) increase in 24-h PM2.5 was significantly elevated in adults for respiratory/other chest symptoms 1.06 (1.00-1.13), upper respiratory infections 1.13 (1.05-1.22), hypertension 1.05 (1.00-1.09) and 'all-cause' cardiac outcomes 1.06 (1.00-1.13) and in youth for respiratory/other chest symptoms 1.18 (1.06-1.33), upper respiratory infections 1.14 (1.04-1.24) and 'all-cause' respiratory conditions 1.09 (1.01-1.17). CONCLUSIONS: Our results replicate evidence for increased risk of cardiovascular outcomes from wildfire PM2.5 and suggest that cardiovascular health should be considered when evaluating the public health burden of wildfire smoke.

Modeled response of ozone to electricity generation emissions in the northeastern United States using three sensitivity techniques.

UNLABELLED: Electrical generation units (EGUs) are important sources of nitrogen oxides (NOx) that contribute to ozone air pollution. A dynamic management system can anticipate high ozone and dispatch EGU generation on a daily basis to attempt to avoid violations, temporarily scaling back or shutting down EGUs that most influence the high ozone while compensating for that generation elsewhere. Here we investigate the contributions of NOx from individual EGUs to high daily ozone, with the goal of informing the design of a dynamic management system. In particular, we illustrate the use of three sensitivity techniques in air quality models-brute force, decoupled direct method (DDM), and higher-order DDM-to quantify the sensitivity of high ozone to NOx emissions from 80 individual EGUs. We model two episodes with high ozone in the region around Pittsburgh, PA, on August 4 and 13, 2005, showing that the contribution of 80 EGUs to 8-hr daily maximum ozone ranges from 1 to >5 ppb at particular locations. At these locations and on the two high ozone days, shutting down power plants roughly 1.5 days before the 8-hr ozone violation causes greater ozone reductions than 1 full day before; however, the benefits of shutting down roughly 2 days before the high ozone are modest compared with 1.5 days. Using DDM, we find that six EGUs are responsible for >65% of the total EGU ozone contribution at locations of interest; in some locations, a single EGU is responsible for most of the contribution. Considering ozone sensitivities for all 80 EGUs, DDM performs well compared with a brute-force simulation with a small normalized mean bias (-0.20), while this bias is reduced when using the higher-order DDM (-0.10). IMPLICATIONS: Dynamic management of electrical generation has the potential to meet daily ozone air quality standards at low cost. We show that dynamic management can be effective at reducing ozone, as EGU contributions are important and as the number of EGUs that contribute to high ozone in a given location is small (<6). For two high ozone days and seven geographic regions, EGUs would best be shut down or their production scaled back roughly 1.5 days before the forecasted exceedance. Including online sensitivity techniques in an air quality forecasting model can provide timely and useful information on which EGUs would be most beneficial to shut down or scale back temporarily.

The Air Pollution Human Health Burden in Different Future Scenarios That Involve the Mitigation of Near-Term Climate Forcers, Climate and Land-Use.

Elevated surface concentrations of ozone and fine particulate matter (PM2.5) can lead to poor air quality and detrimental impacts on human health. These pollutants are also termed Near-Term Climate Forcers (NTCFs) as they can also influence the Earth's radiative balance on timescales shorter than long-lived greenhouse gases. Here we use the Earth system model, UKESM1, to simulate the change in surface ozone and PM2.5 concentrations from different NTCF mitigation scenarios, conducted as part of the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP). These are then combined with relative risk estimates and projected changes in population demographics, to estimate the mortality burden attributable to long-term exposure to ambient air pollution. Scenarios that involve the strong mitigation of air pollutant emissions yield large future benefits to human health (25%), particularly across Asia for black carbon (7%), when compared to the future reference pathway. However, if anthropogenic emissions follow the reference pathway, then impacts to human health worsen over South Asia in the short term (11%) and across Africa (20%) in the longer term. Future climate change impacts on air pollutants can offset some of the health benefits achieved by emission mitigation measures over Europe for PM2.5 and East Asia for ozone. In addition, differences in the future chemical environment over regions are important considerations for mitigation measures to achieve the largest benefit to human health. Future policy measures to mitigate climate warming need to also consider the impact on air quality and human health across different regions to achieve the maximum co-benefits.

Short-Term Exposure to Wildfire Smoke and PM2.5 and Cognitive Performance in a Brain-Training Game: A Longitudinal Study of U.S. Adults.

BACKGROUND: There is increasing evidence that long-term exposure to fine particulate matter [PM ≤2.5µm in aerodynamic diameter (PM2.5)] may adversely impact cognitive performance. Wildfire smoke is one of the biggest sources of PM2.5 and concentrations are likely to increase under climate change. However, little is known about how short-term exposure impacts cognitive function. OBJECTIVES: We aimed to evaluate the associations between daily and subdaily (hourly) PM2.5 and wildfire smoke exposure and cognitive performance in adults. METHODS: Scores from 20 plays of an attention-oriented brain-training game were obtained for 10,228 adults in the United States (U.S.). We estimated daily and hourly PM2.5 exposure through a data fusion of observations from multiple monitoring networks. Daily smoke exposure in the western U.S. was obtained from satellite-derived estimates of smoke plume density. We used a longitudinal repeated measures design with linear mixed effects models to test for associations between short-term exposure and attention score. Results were also stratified by age, gender, user behavior, and region. RESULTS: Daily and subdaily PM2.5 were negatively associated with attention score. A 10 µg/m3 increase in PM2.5 in the 3 h prior to gameplay was associated with a 21.0 [95% confidence interval (CI): 3.3, 38.7]-point decrease in score. PM2.5 exposure over 20 plays accounted for an estimated average 3.7% (95% CI: 0.7%, 6.7%) reduction in final score. Associations were more pronounced in the wildfire-impacted western U.S. Medium and heavy smoke density were also negatively associated with score. Heavy smoke density the day prior to gameplay was associated with a 117.0 (95% CI: 1.7, 232.3)-point decrease in score relative to no smoke. Although differences between subgroups were not statistically significant, associations were most pronounced for younger (18-29 y), older (≥70y), habitual, and male users. DISCUSSION: Our results indicate that PM2.5 and wildfire smoke were associated with reduced attention in adults within hours and days of exposure, but further research is needed to elucidate these relationships. https://doi.org/10.1289/EHP10498.

Device-Sizing and Associated Complications With Left Atrial Appendage Occlusion: Findings From the NCDR LAAO Registry.

BACKGROUND: Left atrial appendage occlusion is an important alternative to anticoagulation in select patients with nonvalvular atrial fibrillation. Trends in real-world device sizing and associated short-term complications have not been characterized. METHODS: Using the National Cardiovascular Data Left Atrial Appendage Occlusion (NCDR LAAO) Registry, patients who underwent left atrial appendage occlusion with a Watchman 2.5 device from January 1, 2016, to June 30, 2020, were identified. Patients were stratified by device size based on left atrial appendage orifice size, and categorized as receiving a device that was undersized, oversized, or per manufacturer recommendation. Relationships between device sizing and short-term outcomes, including pericardial effusion, device embolism, and significant leak, were assessed. RESULTS: Of the 68 456 patients, 6539 (10.5%) of patients received undersized devices, 17 791 (26.0%) according to manufacturer recommendations, and 44 126 (64.4%) received an oversized device. The 27-mm device was most commonly deployed [21 736 (31.8%)], whereas the smallest and largest devices (21 and 33 mm) were least commonly deployed [7695 (11.2%) and 9077 (13.3%), respectively]. Compared with manufacturer recommended sizing, there was no difference in the odds of pericardial effusion for either undersized (1.048 [95% CI' 0.801-1.372]; P=0.733) or oversized (1.101 [95% CI' 0.933-1.298]; P=0.254) devices. Similarly, relative to manufacturer recommended sizing, the odds of a composite adverse outcome of device migration or embolization and significant peridevice leak at 45 days were similar among undersized devices (1.030 [95% CI' 0.735-1.444]; P=0.863) and favorable for oversized devices (0.701 [95% CI' 0.561-0.876]; P=0.002) devices, primarily driven by lower odds of leak. Selection of oversized devices increased significantly over the study period (from 60.3% in 2016 to 66.0% in 2020; P<0.001). CONCLUSIONS: Among patients undergoing left atrial appendage occlusion with the first-generation Watchman device, receipt of oversized devices was common and increased over time. The high prevalence of oversizing was associated with lower odds of significant leak or device embolization without increased odds of other adverse events.

Global trends in ozone concentration and attributable mortality for urban, peri-urban, and rural areas between 2000 and 2019: a modelling study.

BACKGROUND: Data on long-term trends of ozone exposure and attributable mortality across urban-rural catchment areas worldwide are scarce, especially for low-income and middle-income countries. This study aims to estimate trends in ozone concentrations and attributable mortality for urban-rural catchment areas worldwide. METHODS: In this modelling study, we used a health impact function to estimate ozone concentrations and ozone-attributable chronic respiratory disease mortality for urban areas worldwide, and their surrounding peri-urban, peri-rural, and rural areas. We estimated ozone-attributable respiratory health outcomes using a modified Global Burden of Diseases, Injuries, and Risk Factors 2019 Study approach. We evaluate long-term trends with linear regressions of annual ozone concentrations and ozone-attributable mortality against time in years, and examined the influence of each health impact function input parameter to temporal changes in ozone-attributable disease burden estimates for 12 946 cities worldwide by region, from 2000 to 2019. FINDINGS: Ozone-attributable mortality worldwide increased by 46% from 2000 (290 400 deaths [95% CI 151 800-457 600]) to 2019 (423 100 deaths [95% CI 223 200-659 400]). The fraction of global ozone-attributable mortality occurring in peri-urban areas remained unchanged from 2000 to 2019 (56%), whereas urban areas gained in their share of global ozone-attributable burden (from 35% to 37%; 54 000 more deaths). Across all cities studied, average population-weighted mean ozone concentration increased by 11% (46 parts per billion [ppb] to 51 ppb). The number of cities with concentrations above the WHO peak season ozone standard (60 µg/m3) increased from 11 568 (89%) of 12 946 cities in 2000 to 12 433 (96%) cities in 2019. Percent change in ozone-attributable mortality averaged across 11 032 cities within each region from 2000 to 2019 ranged from -62% in eastern Europe to 350% in tropical Latin America. The contribution of ozone concentrations, population size, and baseline chronic respiratory disease rates to the change in ozone-attributable mortality differed regionally. INTERPRETATION: Ozone exposure is increasing worldwide, contributing to disproportionate ozone mortality in peri-urban areas and increasing ozone exposure and attributable mortality in urban areas worldwide. Reducing ozone precursor emissions in areas affecting urban and peri-urban exposure can yield substantial public health benefits. FUNDING: NASA Health and Air Quality Applied Sciences Team, the National Institute for Occupational Safety and Health, and the NOAA Co-operative Agreement with the Cooperative Institute for Research in Environmental Sciences.

Estimating the Acute Health Impacts of Fire-Originated PM2.5 Exposure During the 2017 California Wildfires: Sensitivity to Choices of Inputs.

Exposure to wildfire smoke increases the risk of respiratory and cardiovascular hospital admissions. Health impact assessments, used to inform decision-making processes, characterize the health impacts of environmental exposures by combining preexisting epidemiological concentration-response functions (CRFs) with estimates of exposure. These two key inputs influence the magnitude and uncertainty of the health impacts estimated, but for wildfire-related impact assessments the extent of their impact is largely unknown. We first estimated the number of respiratory, cardiovascular, and asthma hospital admissions attributable to fire-originated PM2.5 exposure in central California during the October 2017 wildfires, using Monte Carlo simulations to quantify uncertainty with respect to the exposure and epidemiological inputs. We next conducted sensitivity analyses, comparing four estimates of fire-originated PM2.5 and two CRFs, wildfire and nonwildfire specific, to understand their impact on the estimation of excess admissions and sources of uncertainty. We estimate the fires accounted for an excess 240 (95% CI: 114, 404) respiratory, 68 (95% CI: -10, 159) cardiovascular, and 45 (95% CI: 18, 81) asthma hospital admissions, with 56% of admissions occurring in the Bay Area. Although differences between impact assessment methods are not statistically significant, the admissions estimates' magnitude is particularly sensitive to the CRF specified while the uncertainty is most sensitive to estimates of fire-originated PM2.5. Not accounting for the exposure surface's uncertainty leads to an underestimation of the uncertainty of the health impacts estimated. Employing context-specific CRFs and using accurate exposure estimates that combine multiple data sets generates more certain estimates of the acute health impacts of wildfires.

Machine Learning Methodologies for Prediction of Rhythm-Control Strategy in Patients Diagnosed With Atrial Fibrillation: Observational, Retrospective, Case-Control Study.

BACKGROUND: The identification of an appropriate rhythm management strategy for patients diagnosed with atrial fibrillation (AF) remains a major challenge for providers. Although clinical trials have identified subgroups of patients in whom a rate- or rhythm-control strategy might be indicated to improve outcomes, the wide range of presentations and risk factors among patients presenting with AF makes such approaches challenging. The strength of electronic health records is the ability to build in logic to guide management decisions, such that the system can automatically identify patients in whom a rhythm-control strategy is more likely and can promote efficient referrals to specialists. However, like any clinical decision support tool, there is a balance between interpretability and accurate prediction. OBJECTIVE: This study aims to create an electronic health record-based prediction tool to guide patient referral to specialists for rhythm-control management by comparing different machine learning algorithms. METHODS: We compared machine learning models of increasing complexity and used up to 50,845 variables to predict the rhythm-control strategy in 42,022 patients within the University of Colorado Health system at the time of AF diagnosis. Models were evaluated on the basis of their classification accuracy, defined by the F1 score and other metrics, and interpretability, captured by inspection of the relative importance of each predictor. RESULTS: We found that age was by far the strongest single predictor of a rhythm-control strategy but that greater accuracy could be achieved with more complex models incorporating neural networks and more predictors for each participant. We determined that the impact of better prediction models was notable primarily in the rate of inappropriate referrals for rhythm-control, in which more complex models provided an average of 20% fewer inappropriate referrals than simpler, more interpretable models. CONCLUSIONS: We conclude that any health care system seeking to incorporate algorithms to guide rhythm management for patients with AF will need to address this trade-off between prediction accuracy and model interpretability.

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires.

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8-20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke.Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health.

A Point-of-Care Algorithm to Guide Proper Device Selection for Ambulatory Electrocardiography.

In the outpatient setting, ambulatory electrocardiography is the most frequently used diagnostic modality for the evaluation of patients in whom cardiac arrhythmias or conduction abnormalities are suspected. Proper selection of the device type and monitoring duration is critical for optimizing diagnostic yield and cost-effective resource utilization. However, despite guidance from major professional societies, the lack of systematic guidance for proper test selection in many institutions results in the need for repeat testing, which leads to not only increased resource utilization and cost of care, but also suboptimal patient care. To address this unmet need at our own institution, we formed a multidisciplinary panel to develop a concise, yet comprehensive algorithm, incorporating the most common indications for ambulatory electrocardiography, to efficiently guide clinicians to the most appropriate test option for a given clinical scenario, with the goal of maximizing diagnostic yield and optimizing resource utilization. The algorithm was designed as a single-page, color-coded flowchart to be utilized both as a rapid reference guide in printed form, and a decision support tool embedded within the electronic medical records system at the point of order entry. We believe that systematic adoption of this algorithm will optimize diagnostic efficiency, resource utilization, and importantly, patient care and satisfaction.

Satellite Monitoring for Air Quality and Health.

Data from satellite instruments provide estimates of gas and particle levels relevant to human health, even pollutants invisible to the human eye. However, the successful interpretation of satellite data requires an understanding of how satellites relate to other data sources, as well as factors affecting their application to health challenges. Drawing from the expertise and experience of the 2016-2020 NASA HAQAST (Health and Air Quality Applied Sciences Team), we present a review of satellite data for air quality and health applications. We include a discussion of satellite data for epidemiological studies and health impact assessments, as well as the use of satellite data to evaluate air quality trends, support air quality regulation, characterize smoke from wildfires, and quantify emission sources. The primary advantage of satellite data compared to in situ measurements, e.g., from air quality monitoring stations, is their spatial coverage. Satellite data can reveal where pollution levels are highest around the world, how levels have changed over daily to decadal periods, and where pollutants are transported from urban to global scales. To date, air quality and health applications have primarily utilized satellite observations and satellite-derived products relevant to near-surface particulate matter <2.5 µm in diameter (PM2.5) and nitrogen dioxide (NO2). Health and air quality communities have grown increasingly engaged in the use of satellite data, and this trend is expected to continue. From health researchers to air quality managers, and from global applications to community impacts, satellite data are transforming the way air pollution exposure is evaluated.

Dynamic registration of preablation imaging with a catheter geometry to guide ablation in a Swine model: validation of image integration and assessment of catheter navigation accuracy.

BACKGROUND: Catheter ablation of atrial and ventricular tachyarrhythmia involves anatomically based cardiac ablation strategies. CT and MRI images provide the most detailed cardiac anatomy available. Integration of these images into a mapping system should produce detailed and accurate models suitable to guide ablation. OBJECTIVE: The purpose of this study was to validate and assess the accuracy of a novel CT and MRI image integration algorithm designed to facilitate catheter navigation and ablation. METHODS: Using a lateral thoracotomy, markers were sutured to the epicardial surface of each cardiac chamber in 12 swine. Detailed CT/MRI anatomy was imported into the mapping system. The CT/MRI image was then integrated with a detailed catheter geometry of the relevant chamber using a new image integration algorithm. The epicardial markers, identified from the CT/MRI images, were then displayed on the surface of the integrated image. Guided only by the integrated CT/MRI, a single RF lesion was directed at the corresponding endocardial site for each epicardial marker. At autopsy, the distance from the endocardial RF lesion to the target site was assessed. RESULTS: The mean position error (CT/MRI) for the left atrium was 2.5 +/- 2.4 mm/5.1 +/- 3.9 mm, for the right atrium 6.2 +/- 6.5 mm/4.3 +/- 2.2 mm, for the right ventricle 6.2 +/- 4.3 mm/6.6 +/- 5.3 mm, and for the left ventricle 4.7 +/- 3.4 mm/3.1 +/- 2.7 mm. There was no cardiac perforation or tamponade. CONCLUSION: CT and MRI images can be effectively utilized for catheter navigation when integrated into a mapping system. This novel registration module with dynamic registration provides effective guidance for ablation.

Air pollution control strategies directly limiting national health damages in the US.

Exposure to fine particulate matter (PM2.5) from fuel combustion significantly contributes to global and US mortality. Traditional control strategies typically reduce emissions for specific air pollutants and sectors to maintain pollutant concentrations below standards. Here we directly set national PM2.5 mortality cost reduction targets within a global human-earth system model with US state-level energy systems, in scenarios to 2050, to identify endogenously the control actions, sectors, and locations that most cost-effectively reduce PM2.5 mortality. We show that substantial health benefits can be cost-effectively achieved by electrifying sources with high primary PM2.5 emission intensities, including industrial coal, building biomass, and industrial liquids. More stringent PM2.5 reduction targets expedite the phaseout of high emission intensity sources, leading to larger declines in major pollutant emissions, but very limited co-benefits in reducing CO2 emissions. Control strategies limiting health damages achieve the greatest emission reductions in the East North Central and Middle Atlantic states.

Using Satellites to Track Indicators of Global Air Pollution and Climate Change Impacts: Lessons Learned From a NASA-Supported Science-Stakeholder Collaborative.

The 2018 NASA Health and Air Quality Applied Science Team (HAQAST) "Indicators" Tiger Team collaboration between NASA-supported scientists and civil society stakeholders aimed to develop satellite-derived global air pollution and climate indicators. This Commentary shares our experience and lessons learned. Together, the team developed methods to track wildfires, dust storms, pollen counts, urban green space, nitrogen dioxide concentrations and asthma burdens, tropospheric ozone concentrations, and urban particulate matter mortality. Participatory knowledge production can lead to more actionable information but requires time, flexibility, and continuous engagement. Ground measurements are still needed for ground truthing, and sustained collaboration over time remains a challenge.

Real-time rotational ICE imaging of the relationship of the ablation catheter tip and the esophagus during atrial fibrillation ablation.

INTRODUCTION: Atrioesophageal fistula is a rare complication of atrial fibrillation (AF) ablation that should be avoided. We investigated whether rotational intracardiac echocardiography (ICE) can help to minimize ablation close to the esophagus. METHODS AND RESULTS: We studied 41 patients referred for catheter ablation of refractory AF. A rotational ICE catheter was inserted into the (LA) to determine the location of the esophagus. The esophagus was identified to be either adjacent to the pulmonary vein (PV) ostium or to a cuff 2 cm outside the ostium. Circumferential ablation was performed at the PV ostium, with the exact ablation location determined by ICE. The relationship of the catheter tip to the esophagus was imaged during energy delivery, allowing interruption when respiration moved the tip closer to the esophagus. Out of 41 patients, the esophagus was seen near left-sided PVs in 32 and near right-sided PVs in three patients. The median distance from LA endocardium to esophagus was 2.2 mm (range, 1.4-6 mm). In 21 of 35 patients with a closely related esophagus, ablation over the esophagus was avoided by ablating either lateral or medial to the esophagus. In 14 patients, the esophagus could not be avoided, and risk was minimized by limiting lesion size. Significant movement (>10 mm) of the esophagus during the procedure occurred in 3/41 cases. CONCLUSION: Rotational ICE can accurately determine the distance of ablation sites from the esophagus. Real-time imaging of the relationship of the ablation catheter tip to the esophagus may reduce the incidence of esophageal injury.