Optimizing Helicopter Air Ambulance Dispatch to Improve Sustainability in Interfacility Transfers.

Abstract Introduction Healthcare practices in the US contribute to the climate crisis and plastic pollution through greenhouse gas emissions and reliance on single-use plastic materials. The healthcare sector is responsible for 8.5% of US emissions and produces two million tons of solid waste per year (Eckelman et al, PLOS One 2016; Practice Greenhealth). There is limited current literature examining the contribution of sleep medicine practices to US healthcare emissions and waste production. The purpose of this study was to quantify and categorize the waste generated by laboratory-based sleep studies and to identify targetable areas for waste reduction. Methods A comprehensive waste audit was conducted over a three-day period in an urban academic sleep laboratory. Each piece of waste generated by patient care during this time was collected, weighed and separated by individual study. The weight of waste attributed to all home sleep apnea tests (HSATs) completed during this period was estimated by weighing sample test components available in the lab. In addition to calculation of total waste produced, sub-analyses were conducted to determine average waste produced by study type and expected reductions in waste output based on different proposed practices. Results The 24 in-lab sleep studies (6 diagnostic PSGs, 11 split night PSGs, 6 titration studies, 1 inpatient NOMAD split night study) completed during the three-day period generated 24.18 kg of solid waste. The 75 WatchPAT HSATs and one home NOMAD test generated an estimated 9.97 kg of solid waste. On average, diagnostic PSGs produced 381.33 g of waste, split night PSGs 708.8 g of waste, and titration studies 752.6 g of waste. When applied to the total studies conducted during 2023, we estimate 885.77 kg of waste produced by in-lab studies annually (1596 kg if including HSATs). Conclusion Sleep medicine testing, including laboratory-based sleep studies and HSATs, produce a significant amount of waste. An important factor in reducing waste produced by sleep studies is by minimizing the use of single use items when feasible. Based on our lab’s practices, waste can be reduced by switching to reusable EEG leads, sanitizing and reusing PAP water reservoirs, and replacing individual soap/lotion containers with large volume dispensers. Support (if any)

Abstract Border carbon taxes are considered as an important instrument to promote sustainable practices abroad and to level the playing field for domestic emission‐intensive and trade‐exposed (EITE) industries. We find that US emissions pricing plays a critical role in the effectiveness of border carbon taxes in protecting the international competitiveness of Canadian EITE producers. Border carbon taxes are more effective when the US follows the other OECD countries with stringent CO2 emissions pricing than when the US abstains from emissions pricing. In the latter case, border carbon taxes reduce the competitiveness of Canadian EITE export supply to the US (Canada's most important export destination), weakening the initial protective effect of border carbon taxes on the Canadian domestic market.

This paper examines an approximation method to qualitatively assess the air-cleaning performance (i.e., specifically the elimination of aerial nitrogen oxide, NO, released within vehicular exhaust) by full-scale pavements which contain photo-catalytically reactive titanium dioxide under optimal conditions. Two hypothetical road configurations were considered using this method, including both a two-lane, low traffic density (i.e., 4,000 full-day AADT) and a four-lane, moderate traffic density (i.e., 10,000 full-day AADT) design. These options were then comparatively examined on the basis of expected European Union or United States vehicular emission levels day-time-only percentile elimination approximation results were derived using an extrapolation of lab-based specific contaminant elimination rates (i.e., mass NO removed per surface area per time) relative to contaminant release rates which were projected for EU or US vehicular contaminant emission levels. approximation method, and assuming best-case scenario conditions (i.e., original, un-aged, peak catalytic performance under optimal temperature, relative humidity, etc. conditions), day-timeonly percentile removals in the ~mid-60% to ~90% range were predicted for EU two- and fourlane roadways with low to moderate traffic densities. These EU contaminant elimination approximation percentiles were higher than the actual, observed range (e.g., typically ~mid-10% to ~mid-60% day-time removal percentiles) of published contaminant elimination values which had been measured according to gas-phase contaminant changes during a number of full-scale studies completed at various EU locations and with EU-related vehicle types and emissions. In the case of similar US highway options, th approximated day-time-only elimination percentile results were lower than what was predicted for similar EU road options, with a range of ~30% to ~40%. These latter, lower US road approximations were believed to be related to higher expected US versus EU vehicle emission levels (i.e., by a factor of ~two- to ~three-fold for light and heavy duty vehicles).

Non-Methane Volatile Organic Compounds (NMVOCs) generate ozone (O3) when they are oxidized in the presence of oxides of nitrogen, modulate the oxidative capacity of the atmosphere and can lead to the formation of aerosol. Here, we assess the capability of a chemical transport model (GEOS-Chem) to simulate NMVOC concentrations by comparing ethane, propane and higher alkane observations in remote regions from the NOAA Flask Network and the World Meteorological Organization's Global Atmosphere Watch (GAW) network. Using the Community Emissions Data System (CEDS) inventory we find a significant underestimate in the simulated concentration of both ethane (35%) and propane (64%), consistent with previous studies. We run a new simulation where the total mass of anthropogenic NMVOC emitted in a grid box is the same as that used in CEDS, but with the NMVOC speciation derived from regional inventories. For US emissions we use the National Emissions Inventory (NEI), for Europe we use the UK National Atmospheric Emissions Inventory (NAEI), and for China, the Multi-resolution Emission Inventory for China (MEIC). These changes lead to a large increase in the modelled concentrations of ethane, improving the mean model bias from -35% to -4%. Simulated propane also improves (from -64% to -48% mean model bias), but there remains a substantial model underestimate. There were relatively minor changes to other NMVOCs. The low bias in simulated global ethane concentration is essentially removed, resolving one long-term issue in global simulations. Propane concentrations are improved but remain significantly underestimated, suggesting the potential for a missing global propane source. The change in the NMVOC emission speciation results in only minor changes in tropospheric O3 and OH concentrations.

Making MOVES move: Fast emissions estimates for repeated transportation policy scenario analyses

Soy-based biodiesel can reduce well-to-wheels greenhouse gas (GHG) emissions per unit energy (i.e., gCO2e/MJ) by 66%–72% as compared to the petroleum-based diesel fuel with currently adopted agricultural and industrial practices. Biodiesel can reduce particulate matter and carbon monoxide emissions with a manageable degree of increase in NOx emissions. From the perspective of GHG emissions reduction per unit travelling distance (i.e., gCO2e/mile), the application of B20 in compression ignition engines without the adjustment in engine control unit (ECU) settings will not extract the best carbon emissions reduction that B20 could achieve. Optimizing the engine control settings permits re-calibration to achieve the maximum brake fuel conversion efficiency (BFE) based on comprehensive understanding on the impact of both “fuel” and “ECU calibration” on BFE and other criteria pollutant emissions. The maximum GHG emissions reduction with B20 application is experimentally measured with the optimized ECU calibration, thus providing the understanding of the combined impact of biodiesel fuel and calibrations on engine performance and emissions. Six steady operating modes were considered, that can be combined to estimate the US federal test procedure BFE and emissions over the Federal Test Protocol (FTP) 75 cycle. Combined with the weight factors to simulate the EPA FTP 75 cycle from these 6 “mini-map” test points, 0.53% improvement in the energy requirement per unit traveling distance (i.e., MJ/mile) is achieved for B20 with the final ECU calibration, in addition to the degree of GHG emissions reduction on a “gCO2e/MJ” basis from the use of B20 blend of soy biodiesel of ∼12.5% reduction in gCO2e/MJ, for a total GHG emissions reduction of 13%.

Abstract. We quantify 2019 annual mean methane emissions in the contiguous US (CONUS) at 0.25° × 0.3125° resolution by inverse analysis of atmospheric methane columns measured by the Tropospheric Monitoring Instrument (TROPOMI). A gridded version of the US Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) serves as the basis for the prior estimate for the inversion. We optimize emissions and quantify observing system information content for an eight-member inversion ensemble through analytical minimization of a Bayesian cost function. We achieve high resolution with a reduced-rank characterization of the observing system that optimally preserves information content. Our optimal (posterior) estimate of anthropogenic emissions in CONUS is 30.9 (30.0–31.8) Tg a−1, where the values in parentheses give the spread of the ensemble. This is a 13 % increase from the 2023 GHGI estimate for CONUS in 2019. We find emissions for livestock of 10.4 (10.0–10.7) Tg a−1, for oil and gas of 10.4 (10.1–10.7) Tg a−1, for coal of 1.5 (1.2–1.9) Tg a−1, for landfills of 6.9 (6.4–7.5) Tg a−1, for wastewater of 0.6 (0.5–0.7), and for other anthropogenic sources of 1.1 (1.0–1.2) Tg a−1. The largest increase relative to the GHGI occurs for landfills (51 %), with smaller increases for oil and gas (12 %) and livestock (11 %). These three sectors are responsible for 89 % of posterior anthropogenic emissions in CONUS. The largest decrease (28 %) is for coal. We exploit the high resolution of our inversion to quantify emissions from 70 individual landfills, where we find emissions are on median 77 % larger than the values reported to the EPA's Greenhouse Gas Reporting Program (GHGRP), a key data source for the GHGI. We attribute this underestimate to overestimated recovery efficiencies at landfill gas facilities and to under-accounting of site-specific operational changes and leaks. We also quantify emissions for the 48 individual states in CONUS, which we compare to the GHGI's new state-level inventories and to independent state-produced inventories. Our posterior emissions are on average 27 % larger than the GHGI in the largest 10 methane-producing states, with the biggest upward adjustments in states with large oil and gas emissions, including Texas, New Mexico, Louisiana, and Oklahoma. We also calculate emissions for 95 geographically diverse urban areas in CONUS. Emissions for these urban areas total 6.0 (5.4–6.7) Tg a−1 and are on average 39 (27–52) % larger than a gridded version of the 2023 GHGI, which we attribute to underestimated landfill and gas distribution emissions.

Methane emissions from abandoned oil and gas wells in Colorado

The COVID-19 stay-at-home orders issued in the United States caused significant reductions in traffic and economic activities. To understand the pandemic's perturbations on US emissions and impacts on urban air quality, we developed near-real-time bottom-up emission inventories based on publicly available energy and economic datasets, simulated the emission changes in a chemical transport model, and evaluated air quality impacts against various observations. The COVID-19 pandemic affected US emissions across broad-based energy and economic sectors and the impacts persisted to 2021. Compared with 2019 business-as-usual emission scenario, COVID-19 perturbations resulted in annual decreases of 10–15% in emissions of ozone (O3) and fine particle (PM2.5) gas-phase precursors, which are about two to four times larger than long-term annual trends during 2010–2019. While significant COVID-induced reductions in transportation and industrial activities, particularly in April–June 2020, resulted in overall national decreases in air pollutants, meteorological variability across the nation led to local increases or decreases of air pollutants, and mixed air quality changes across the United States between 2019 and 2020. Over a full year (April 2020 to March 2021), COVID-induced emission reductions led to 3–4% decreases in national population-weighted annual fourth maximum of daily maximum 8-h average O3 and annual PM2.5. Assuming these emission reductions could be maintained in the future, the result would be a 4–5% decrease in premature mortality attributable to ambient air pollution, suggesting that continued efforts to mitigate gaseous pollutants from anthropogenic sources can further protect human health from air pollution in the future.

China and the US were the world's top exporters and carbon emitters and the most crucial trading partners for each other at the same time. Trade interdependence between the two countries affect each country's carbon emissions, and linked to the world's total emissions. In order to research the effect of trade interdependence on carbon emissions of China and the US, we built a dynamic econometric model to distinguish long-term and short-term effects with datasets from 1992 to 2018 by means of the autoregressive- distributed- lag method. The results revealed that a 1% increase in trade interdependence was linked to a 0.038% decrease in China's carbon emissions and a 1.939% decrease in US emissions over the long-term. Moreover, trade interdependence produced a positive effect on China and US emissions in the short-term. In the short-term, trade interdependence decreased China's carbon emissions but increased US carbon emissions. By simulating a 1% of the counterfactual positive shock of trade interdependence, back- of- the- envelope estimations suggested a 0.220% reduction of carbon emissions for China and a 0.034% reduction for the US. At last, trade interdependence between China and the US, which reduced carbon emissions for each country in the long-term, so that policies on trade protectionism might not be necessary.

The United States is the world's largest oil/gas methane emitter according to current national reports. Reducing these emissions is a top priority in the US government's climate action plan. Here, we use a 2010 to 2019 high-resolution inversion of surface and satellite observations of atmospheric methane to quantify emission trends for individual oil/gas production regions in North America and relate them to production and infrastructure. We estimate a mean US oil/gas methane emission of 14.8 (12.4 to 16.5) Tg a-1 for 2010 to 2019, 70% higher than reported by the US Environmental Protection Agency. While emissions in Canada and Mexico decreased over the period, US emissions increased from 2010 to 2014, decreased until 2017, and rose again afterward. Increases were driven by the largest production regions (Permian, Anadarko, Marcellus), while emissions in the smaller production regions generally decreased. Much of the year-to-year emission variability can be explained by oil/gas production rates, active well counts, and new wells drilled, with the 2014 to 2017 decrease driven by reduction in new wells and the 2017 to 2019 surge driven by upswing of production. We find a steady decrease in the oil/gas methane intensity (emission per unit methane gas production) for almost all major US production regions. The mean US methane intensity decreased from 3.7% in 2010 to 2.5% in 2019. If the methane intensity for the oil/gas supply chain continues to decrease at this pace, we may expect a 32% decrease in US oil/gas emissions by 2030 despite projected increases in production.

Revisiting the Relationship Between Income Inequality and CO2 Emissions in US: New Evidence from CS-ARDL model

Light-duty vehicles produce exhaust air toxic species in addition to regulated pollutants. Although numerous studies have evaluated the impacts of fuel composition on air toxics, there has been no comprehensive modeling study that considered data from all available sources. To examine the effects of real-world fuel composition on air toxics, black carbon (BC), and particle number (PN) emissions, we conducted a thorough evaluation of US emission studies, pooled data from the Federal Test Procedure (FTP) and Unified Cycle (LA92) driving schedules, and developed models based on fuel and vehicle properties. To address nonlinear blend property responses to ethanol concentration, we built separate linear models (split models) for low (up to 10% ethanol by volume) and mid blends. We then estimated differences in emissions from expected market fuel compositions. We observed differences between the operation of port fuel injection (PFI) and gasoline direct injection (GDI) engines and between test cycles and phases. Results showed that aromatics raised 1,3-butadiene, benzene, toluene, ethylbenzene, and xylene emissions, while ethanol raised acetaldehyde and lowered 1,3-butadiene. Ethanol raises octane number and enables reduction of reformate (and hence aromatics) in a blend. For aldehydes, 1,3-butadiene, and BC the effects differed between PFI and GDI vehicles. For ethanol and acetaldehyde production by PFI and for BTEX, BC, and PN from GDI vehicles, there were differences between comprehensive (i.e., single linear fit) and split models, confirming nonlinearity of the actual ethanol relationships and supporting use of separate models for ethanol blends above and below 10%. To the extent that PFI and GDI models differ, emissions inventory calculations should treat them separately. Estimated market fuel blend compositions for summer regular gasoline yielded quantitative projections of ethanol blend levels on air toxics, BC, and PN emissions. Projections were consistent for all projected fuel grades (summer regular, winter regular, and premium).

Abstract. While substantial progress has been made to improve our understanding of biogenic isoprene emissions under unstressed conditions, large uncertainties remain with respect to isoprene emissions under stressed conditions. Here, we use the US Drought Monitor (USDM) as a weekly drought severity index and tropospheric columns of formaldehyde (HCHO), the key product of isoprene oxidation, retrieved from the Ozone Monitoring Instrument (OMI) to derive top-down constraints on the response of summertime isoprene emissions to drought stress in the southeastern United States (SE US), a region of high isoprene emissions that is also prone to drought. OMI HCHO column density is found to be 6.7 % (mild drought) to 23.3 % (severe drought) higher than that under non-drought conditions. A global chemical transport model, GEOS-Chem, with version 2.1 of the Model of Emissions of Gases and Aerosols from Nature (MEGAN2.1) emission algorithm can simulate this direction of change, but the simulated increases at the corresponding drought levels are 1.1–1.5 times that of OMI HCHO, suggesting the need for a drought-stress algorithm in the model. By minimizing the model–OMI differences in HCHO to temperature sensitivity under different drought levels, we derived a top-down drought stress factor (γd_OMI) in GEOS-Chem that parameterizes using water stress and temperature. The algorithm led to an 8.6 % (mild drought) to 20.7 % (severe drought) reduction in isoprene emissions in the SE US relative to the simulation without it. With γd_OMI the model predicts a nonlinear increasing trend in isoprene emissions with drought severity that is consistent with OMI HCHO and a single site's isoprene flux measurements. Compared with a previous drought stress algorithm derived from the latter, the satellite-based drought stress factor performs better with respect to capturing the regional-scale drought–isoprene responses, as indicated by the near-zero mean bias between OMI and simulated HCHO columns under different drought conditions. The drought stress algorithm also reduces the model's high bias in organic aerosol (OA) simulations by 6.60 % (mild drought) to 11.71 % (severe drought) over the SE US compared to the no-stress simulation. The simulated ozone response to the drought stress factor displays a spatial disparity due to the isoprene-suppressing effect on oxidants, with an <1 ppb increase in O3 in high-isoprene regions and a 1–3 ppbv decrease in O3 in low-isoprene regions. This study demonstrates the unique value of exploiting long-term satellite observations to develop empirical stress algorithms on biogenic emissions where in situ flux measurements are limited.

Development of vehicle emission rates based on vehicle-specific power and velocity

Abstract. We use satellite methane observations from the Tropospheric Monitoring Instrument (TROPOMI), for May 2018 to February 2020, to quantify methane emissions from individual oil and natural gas (O/G) basins in the US and Canada using a high-resolution (∼25 km) atmospheric inverse analysis. Our satellite-derived emission estimates show good consistency with in situ field measurements (R=0.96) in 14 O/G basins distributed across the US and Canada. Aggregating our results to the national scale, we obtain O/G-related methane emission estimates of 12.6±2.1 Tg a−1 for the US and 2.2±0.6 Tg a−1 for Canada, 80 % and 40 %, respectively, higher than the national inventories reported to the United Nations. About 70 % of the discrepancy in the US Environmental Protection Agency (EPA) inventory can be attributed to five O/G basins, the Permian, Haynesville, Anadarko, Eagle Ford, and Barnett basins, which in total account for 40 % of US emissions. We show more generally that our TROPOMI inversion framework can quantify methane emissions exceeding 0.2–0.5 Tg a−1 from individual O/G basins, thus providing an effective tool for monitoring methane emissions from large O/G basins globally.

Policies must help decarbonize power and transport sectors.

Abstract. Annual burned areas in the United States have increased 2-fold during the past decades. With more large fires resulting in more emissions of fine particulate matter, an accurate prediction of fire emissions is critical for quantifying the impacts of fires on air quality, human health, and climate. This study aims to construct a machine learning (ML) model with game-theory interpretation to predict monthly fire emissions over the contiguous US (CONUS) and to understand the controlling factors of fire emissions. The optimized ML model is used to diagnose the process-based models in the Fire Modeling Intercomparison Project (FireMIP) to inform future development. Results show promising performance for the ML model, Community Land Model (CLM), and Joint UK Land Environment Simulator-Interactive Fire And Emission Algorithm For Natural Environments (JULES-INFERNO) in reproducing the spatial distributions, seasonality, and interannual variability of fire emissions over the CONUS. Regional analysis shows that only the ML model and CLM simulate the realistic interannual variability of fire emissions for most of the subregions (r>0.95 for ML and r=0.14∼0.70 for CLM), except for Mediterranean California, where all the models perform poorly (r=0.74 for ML and r<0.30 for the FireMIP models). Regarding seasonality, most models capture the peak emission in July over the western US. However, all models except for the ML model fail to reproduce the bimodal peaks in July and October over Mediterranean California, which may be explained by the smaller wind speeds of the atmospheric forcing data during Santa Ana wind events and limitations in model parameterizations for capturing the effects of Santa Ana winds on fire activity. Furthermore, most models struggle to capture the spring peak in emissions in the southeastern US, probably due to underrepresentation of human effects and the influences of winter dryness on fires in the models. As for extreme events, both the ML model and CLM successfully reproduce the frequency map of extreme emission occurrence but overestimate the number of months with extremely large fire emissions. Comparing the fire PM2.5 emissions from the ML model with process-based fire models highlights their strengths and uncertainties for regional analysis and prediction and provides useful insights into future directions for model improvements.

Between 2005 and 2019, a quarter of US fossil fuel production came from federal lands and waters. We estimate that the extraction, transportation and combustion of these fuels resulted in emissions equivalent to roughly 1.4 billion metric tons of carbon dioxide equivalent per year. To better understand their future role in the US emissions profile, we use publicly available data and machine learning to model coal, oil and natural gas production on federal lands and waters to 2030, and calculate associated life cycle climate emissions. We estimate that total emissions from fossil fuels produced on federal lands and waters decline 6% below 2019 levels by 2030; and note that absent additional policy, further reductions may be challenging as some of the cheapest fossil fuels occur on federally owned lands and many are effectively subsidized.