Development of a Training Program for Law Enforcement K9 Handlers to Administer Naloxone.

As the opioid epidemic continues across the United States, law enforcement K9s (LEK9s) are at increased risk of accidental exposure and overdose. This study evaluated a novel training program teaching handlers to administer naloxone to their LEK9 in the event of an overdose. Seventy-five LEK9 handlers from a governmental agency attended a naloxone training session. A presurvey given to the handlers evaluated their knowledge of opioid overdose in LEK9s and their confidence administering naloxone. Officers were educated via a PowerPoint presentation about naloxone and how to administer it to their LEK9. A postsurvey evaluated changes in their knowledge and confidence as a result of the presentation. Sixty-two presurveys and 47 postsurveys were completed. Nearly all handlers had never given their LEK9 an intramuscular or intranasal injection. Most handlers were not comfortable monitoring their LEK9's vital signs for an opioid overdose. After the training, handlers demonstrated a mild increase in comfort level administering intramuscular and intranasal naloxone (15 and 14% increase, respectively). Comfort level monitoring vital signs and symptoms of an opioid overdose increased 38 and 32%, respectively. Handlers may not be fully prepared to assess and treat their LEK9 and may benefit from a targeted training program teaching them to administer naloxone.

Quantifying the Impact of Atmospheric Transport Uncertainty on CO2 Surface Flux Estimates.

We show that transport differences between two commonly used global chemical transport models, GEOS-Chem and TM5, lead to systematic space-time differences in modeled distributions of carbon dioxide and sulfur hexafluoride. The distribution of differences suggests inconsistencies between the transport simulated by the models, most likely due to the representation of vertical motion. We further demonstrate that these transport differences result in systematic differences in surface CO2 flux estimated by a collection of global atmospheric inverse models using TM5 and GEOS-Chem and constrained by in situ and satellite observations. While the impact on inferred surface fluxes is most easily illustrated in the magnitude of the seasonal cycle of surface CO2 exchange, it is the annual carbon budgets that are particularly relevant for carbon cycle science and policy. We show that inverse model flux estimates for large zonal bands can have systematic biases of up to 1.7 PgC/year due to large-scale transport uncertainty. These uncertainties will propagate directly into analysis of the annual meridional CO2 flux gradient between the tropics and northern midlatitudes, a key metric for understanding the location, and more importantly the processes, responsible for the annual global carbon sink. The research suggests that variability among transport models remains the largest source of uncertainty across global flux inversion systems and highlights the importance both of using model ensembles and of using independent constraints to evaluate simulated transport.

Implicit estimation of ecological model parameters.

We introduce an implicit method for state and parameter estimation and apply it to a stochastic ecological model. The method uses an ensemble of particles to approximate the distribution of model solutions and parameters conditioned on noisy observations of the state. For each particle, it first determines likely values based on the observations, then samples around those values. This approach has a strong theoretical foundation, applies to nonlinear models and non-Gaussian distributions, and can estimate any number of model parameters, initial conditions, and model error covariances. The method is called implicit because it updates the particles without forming a predictive distribution of forward model integrations. As a point of comparison for different assimilation techniques, we consider examples in which one or more bifurcations separate the true parameter from its initial approximation. The implicit estimator is asymptotically unbiased, has a root-mean-squared error comparable to or less than the other methods, and is accurate even with small ensemble sizes.

Airborne Lidar Measurements of XCO2 in Synoptically Active Environment and Associated Comparisons With Numerical Simulations.

Frontal boundaries have been shown to cause large changes in CO2 mole-fractions, but clouds and the complex vertical structure of fronts make these gradients difficult to observe. It remains unclear how the column average CO2 dry air mole-fraction (XCO2) changes spatially across fronts, and how well airborne lidar observations, data assimilation systems, and numerical models without assimilation capture XCO2 frontal contrasts (ΔXCO2, i.e., warm minus cold sector average of XCO2). We demonstrated the potential of airborne Multifunctional Fiber Laser Lidar (MFLL) measurements in heterogeneous weather conditions (i.e., frontal environment) to investigate the ΔXCO2 during four seasonal field campaigns of the Atmospheric Carbon and Transport-America (ACT-America) mission. Most frontal cases in summer (winter) reveal higher (lower) XCO2 in the warm (cold) sector than in the cold (warm) sector. During the transitional seasons (spring and fall), no clear signal in ΔXCO2 was observed. Intercomparison among the MFLL, assimilated fields from NASA's Global Modeling and Assimilation Office (GMAO), and simulations from the Weather Research and Forecasting--Chemistry (WRF-Chem) showed that (a) all products had a similar sign of ΔXCO2 though with different levels of agreement in ΔXCO2 magnitudes among seasons; (b) ΔXCO2 in summer decreases with altitude; and (c) significant challenges remain in observing and simulating XCO2 frontal contrasts. A linear regression analyses between ΔXCO2 for MFLL versus GMAO, and MFLL versus WRF-Chem for summer-2016 cases yielded a correlation coefficient of 0.95 and 0.88, respectively. The reported ΔXCO2 variability among four seasons provide guidance to the spatial structures of XCO2 transport errors in models and satellite measurements of XCO2 in synoptically-active weather systems.

Regional impacts of COVID-19 on carbon dioxide detected worldwide from space.

Activity reductions in early 2020 due to the coronavirus disease 2019 pandemic led to unprecedented decreases in carbon dioxide (CO2) emissions. Despite their record size, the resulting atmospheric signals are smaller than and obscured by climate variability in atmospheric transport and biospheric fluxes, notably that related to the 2019­2020 Indian Ocean Dipole. Monitoring CO2 anomalies and distinguishing human and climatic causes thus remain a new frontier in Earth system science. We show that the impact of short-term regional changes in fossil fuel emissions on CO2 concentrations was observable from space. Starting in February and continuing through May, column CO2 over many of the world's largest emitting regions was 0.14 to 0.62 parts per million less than expected in a pandemic-free scenario, consistent with reductions of 3 to 13% in annual global emissions. Current spaceborne technologies are therefore approaching levels of accuracy and precision needed to support climate mitigation strategies with future missions expected to meet those needs.