Machine Learning Models Accurately Model Ozone Exposure during Wildfire Events

&nbsp; Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient. This study compares machine learning prediction models for ground-level ozone during wildfires, evaluating the predictive accuracy of ten algorithms on the daily 8-hour maximum average ozone during a 2008 wildfire event in northern California. Models were evaluated using a leave-one-location-out cross-validation (LOLO CV) procedure to account for the spatial and temporal dependence of the data and produce more realistic estimates of prediction error. LOLO CV avoids both the well-known overly optimistic bias of k-fold cross-validation on dependent data and the conservative bias of evaluating prediction error over a coarser spatial resolution via leave-k-locations-out CV. Gradient boosting was the most accurate of the ten machine learning algorithms with the lowest LOLO CV estimated root mean square error (0.228) and the highest LOLO CV R&circ;2 (0.677). Random forest was the second best performing algorithm with an LOLO CV R&circ;2 of 0.661. The LOLO CV estimates of predictive accuracy were less optimistic than 10-fold CV estimates for all ten models. The difference in estimated accuracy between the 10-fold CV and LOLO CV was greater for more flexible models like gradient boosting and random forest. The order of estimated model accuracy depended on the choice of evaluation metric, indicating that 10-fold CV and LOLO CV may select different models or sets of covariates as optimal, which calls into question the reliability of 10-fold CV for model (or variable) selection. These prediction models are designed for interpolating ozone exposure, and are not suited to inferring the effect of wildfires on ozone or extrapolating to predict ozone in other spatial or temporal domains. This is demonstrated by the inability of the best performing models to accurately predict ozone during 2007 southern California wildfires.</p

Assessment of source contributions to seasonal vegetative exposure to ozone in the U.S.

W126 is a cumulative ozone exposure index based on sigmoidally weighted daytime ozone concentrations used to evaluate the impacts of ozone on vegetation. We quantify W126 in the U.S. in the absence of North American anthropogenic emissions (North American background or “NAB”) using three regional or global chemical transport models for May–July 2010. All models overestimate W126 in the eastern U.S. due to a persistent bias in daytime ozone, while the models are relatively unbiased in California and the Intermountain West. Substantial difference in the magnitude and spatial and temporal variability of the estimates of W126 NAB between models supports the need for a multimodel approach. While the average NAB contribution to daytime ozone in the Intermountain West is 64–78%, the average W126 NAB is only 9–27% of current levels, owing to the weight given to high O3 concentrations in W126. Based on a three-model mean, NAB explains ~30% of the daily variability in the W126 daily index in the Intermountain West. Adjoint sensitivity analysis shows that nationwide W126 is influenced most by NOx emissions from anthropogenic (58% of the total sensitivity) and natural (25%) sources followed by nonmethane volatile organic compounds (10%) and CO (7%). Most of the influence of anthropogenic NOx comes from the U.S. (80%), followed by Canada (9%), Mexico (4%), and China (3%). Thus, long-range transport of pollution has a relatively small impact on W126 in the U.S., and domestic emissions control should be effective for reducing W126 levels

Immunotherapeutic targeting of membrane Hsp70-expressing tumors using recombinant human granzyme B

Background: We have previously reported that human recombinant granzyme B (grB) mediates apoptosis in membrane heat shock protein 70 (Hsp70)-positive tumor cells in a perforin-independent manner

Source Contributions to Carbon Monoxide Concentrations During KORUS‐AQ Based on CAM‐chem Model Applications

We investigate regional sources contributing to CO during the Korea United States Air Quality (KORUS-AQ) campaign conducted over Korea (1 May to 10 June 2016) using 17 tagged CO simulations from the Community Atmosphere Model with chemistry (CAM-chem). The simulations use three spatial resolutions, three anthropogenic emission inventories, two meteorological fields, and nine emission scenarios. These simulations are evaluated against measurements from the DC-8 aircraft and Measurements Of Pollution In The Troposphere (MOPITT). Results show that simulations using bottom-up emissions are consistently lower (bias: -34 to -39%) and poorer performing (Taylor skill: 0.38-0.61) than simulations using alternative anthropogenic emissions (bias: -6 to -33%; Taylor skill: 0.48-0.86), particularly for enhanced Asian CO and volatile organic compound (VOC) emission scenarios, suggesting underestimation in modeled CO background and emissions in the region. The ranges of source contributions to modeled CO along DC-8 aircraft from Korea and southern (90 degrees E to 123 degrees E, 20 degrees N to 29 degrees N), middle (90 degrees E to 123 degrees E, 29 degrees N to 38.5 degrees N), and northern (90 degrees E to 131.5 degrees E, 38.5 degrees N to 45 degrees N) East Asia (EA) are 6-13%, similar to 5%, 16-28%, and 9-18%, respectively. CO emissions from middle and northern EA can reach Korea via transport within the boundary layer, whereas those from southern EA are transported to Korea mainly through the free troposphere. Emission contributions from middle EA dominate during continental outflow events (29-51%), while Korean emissions play an overall more important role for ground sites (up to 25-49%) and plumes within the boundary layer (up to 25-44%) in Korea. Finally, comparisons with four other source contribution approaches (FLEXPART 9.1 back trajectory calculations driven by Weather Research and Forecasting (WRF) WRF inert tracer, China signature VOCs, and CO to CO2 enhancement ratios) show general consistency with CAM-chem.National Science Foundation (NSF); U.S. Department of Energy (DOE); National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Program; NCAR Advanced Study Program Postdoctoral Fellowship; Environment Research and Technology Development Fund of the Ministry of the Environment, Japan [2-1505, 2-1803]; National Science Foundation; NASA [NNX16AD96G, NNX16AE16G, NNX17AG39G]6 month embargo; published online: 1 February 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Genetic variants of Anaplasma phagocytophilum from 14 equine granulocytic anaplasmosis cases

<p>Abstract</p> <p>Background</p> <p>Equine Granulocytic Anaplasmosis (EGA) is caused by <it>Anaplasma phagocytophilum</it>, a tick-transmitted, obligate intracellular bacterium. In Europe, it is transmitted by <it>Ixodes ricinus</it>. A large number of genetic variants of <it>A. phagocytophilum </it>circulate in nature and have been found in ticks and different animals. Attempts have been made to assign certain genetic variants to certain host species or pathologies, but have not been successful so far. The purpose of this study was to investigate the causing agent <it>A. phagocytophilum </it>of 14 cases of EGA in naturally infected horses with molecular methods on the basis of 4 partial genes (<it>16S rRNA</it>, <it>groEL</it>, <it>msp2</it>, and <it>msp4</it>).</p> <p>Results</p> <p>All DNA extracts of EDTA-blood samples of the horses gave bands of the correct nucleotide size in all four genotyping PCRs. Sequence analysis revealed 4 different variants in the partial <it>16S rRNA</it>, <it>groEL </it>gene and <it>msp2 </it>genes, and 3 in the <it>msp4 </it>gene. One <it>16S rRNA </it>gene variant involved in 11 of the 14 cases was identical to the "prototype" variant causing disease in humans in the amplified part [GenBank: <ext-link ext-link-id="U02521" ext-link-type="gen">U02521</ext-link>]. Phylogenetic analysis revealed as expected for the <it>groEL </it>gene that sequences from horses clustered separately from roe deer. Sequences of the partial <it>msp2 </it>gene from this study formed a separate cluster from ruminant variants in Europe and from all US variants.</p> <p>Conclusions</p> <p>The results show that more than one variant of <it>A. phagocytophilum </it>seems to be involved in EGA in Germany. The comparative genetic analysis of the variants involved points towards different natural cycles in the epidemiology of <it>A. phagocytophilum</it>, possibly involving different reservoir hosts or host adaptation, rather than a strict species separation.</p

Influence of lateral and top boundary conditions on regional air quality prediction: A multiscale study coupling regional and global chemical transport models

The sensitivity of regional air quality model to various lateral and top boundary conditions is studied at 2 scales: a 60 km domain covering the whole USA and a 12 km domain over northeastern USA. Three global models (MOZART-NCAR, MOZART-GFDL and RAQMS) are used to drive the STEM-2K3 regional model with time-varied lateral and top boundary conditions (BCs). The regional simulations with different global BCs are examined using ICARTT aircraft measurements performed in the summer of 2004, and the simulations are shown to be sensitive to the boundary conditions from the global models, especially for relatively long-lived species, like CO and O3. Differences in the mean CO concentrations from three different global-model boundary conditions are as large as 40 ppbv, and the effects of the BCs on CO are shown to be important throughout the troposphere, even near surface. Top boundary conditions show strong effect on O3 predictions above 4 km. Over certain model grids, the model’s sensitivity to BCs is found to depend not only on the distance from the domain’s top and lateral boundaries, downwind/upwind situation, but also on regional emissions and species properties. The near-surface prediction over polluted area is usually not as sensitive to the variation of BCs, but to the magnitude of their background concentrations. We also test the sensitivity of model to temporal and spatial variations of the BCs by comparing the simulations with time-varied BCs to the corresponding simulations with time-mean and profile BCs. Removing the time variation of BCs leads to a significant bias on the variation prediction and sometime causes the bias in predicted mean values. The effect of model resolution on the BC sensitivity is also studied

The Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA)

To explore the various couplings across space and time and between ecosystems in a consistent manner, atmospheric modeling is moving away from the fractured limited-scale modeling strategy of the past toward a unification of the range of scales inherent in the Earth system. This paper describes the forward-looking Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA), which is intended to become the next-generation community infrastructure for research involving atmospheric chemistry and aerosols. MUSICA will be developed collaboratively by the National Center for Atmospheric Research (NCAR) and university and government researchers, with the goal of serving the international research and applications communities. The capability of unifying various spatiotemporal scales, coupling to other Earth system components, and process-level modularization will allow advances in both fundamental and applied research in atmospheric composition, air quality, and climate and is also envisioned to become a platform that addresses the needs of policy makers and stakeholders