Impact of Climate Change on Fine Particulate Matter (PM2.5)(PM_{2.5}) Air Quality

This dissertation investigates the impact of 2000-2050 climate change on fine particulate matter $(PM_{2.5})$ air quality. We first applied a multiple linear regression model to study the correlations of total $PM_{2.5}$ and its components with meteorological variables using the past decadal $PM_{2.5}$ observations over the contiguous US. We find that daily variation in meteorology can explain up to 50% of $PM_{2.5}$ variability. Temperature is positively correlated with sulfate and organic carbon (OC) almost everywhere. The correlation of nitrate with temperature is negative in the Southeast but positive in California and the Great Plains. Relative humidity (RH) is positively correlated with sulfate and nitrate, but negatively with OC. Precipitation is strongly negatively correlated with all $PM_{2.5}$ components. We then compared the observed correlations of $PM_{2.5}$ with meteorological variables with results from the GEOS-Chem chemical transport model. The results indicate that most of the correlations of $PM_{2.5}$ with temperature and RH do not arise from direct dependence but from covariation with synoptic transport. We applied principal component analysis and regression to identify the dominant meteorological modes controlling $PM_{2.5}$ variability, and showed that 20-40% of the observed $PM_{2.5}$ daily variability can be explained by a single dominant meteorological mode: cold frontal passages in the eastern US and maritime inflow in the West. From 1999-2010 observations we further showed that interannual variability of annual mean $PM_{2.5}$ in most of the US is strongly correlated with the synoptic period T of the dominant meteorological mode as diagnosed from a spectral-autoregressive analysis. We then used the observed local $PM_{2.5}$-to-period sensitivity to project $PM_{2.5}$ changes from the 2000-2050 changes in T simulated by fifteen IPCC AR4 GCMs following the SRES A1B scenario. We project a likely increase of $\sim 0.1 \mu g m^{-3}$ in annual mean $PM_{2.5}$ in the eastern US arising from less frequent frontal ventilation, and a likely decrease of $\sim 0.3 \mu g m^{-3}$ in the northwestern US due to more frequent maritime inflows. These circulation-driven changes are relatively small, representing only a minor climate penalty or benefit for $PM_{2.5}$ regulatory purpose.Engineering and Applied Science

Satellite-derived Constraints on the Effect of Drought Stress on Biogenic Isoprene Emissions in the Southeast US

While substantial progress has been made to improve our understanding of biogenic isoprene emissions under unstressed conditions, there remain large uncertainties in 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 Southeast U.S. (SE US), a region of high isoprene emissions and prone to drought. OMI HCHO column density is found to be 5.3 % (mild drought) &ndash; 19.8 % (severe drought) higher than that in no-drought conditions. A global chemical transport model, GEOS-Chem, with the MEGAN2.1 emission algorithm can simulate this direction of change, but the simulated increases at the corresponding drought levels are 1.4&ndash;2.0 times of OMI HCHO, suggesting the need for a drought-stress algorithm in the model. By minimizing the model-to-OMI differences in HCHO to temperature sensitivity under different drought levels, we derived a top-down drought stress factor (&gamma;d_OMI) in GEOS-Chem that parameterizes using water stress and temperature. The algorithm led to an 8.6 % (mild drought) &ndash; 20.7 % (severe drought) reduction in isoprene emissions in the SE US relative to the simulation without it. With &gamma;d_OMI the model predicts a non-uniform trend of increase in isoprene emissions with drought severity that is consistent with OMI HCHO and a single site&rsquo;s isoprene flux measurements. Compared with a previous drought stress algorithm derived from the latter, the satellite-based drought stress factor performs better in capturing the regional scale drought-isoprene responses as indicated by the close-to-zero mean bias between OMI and simulated HCHO columns under different drought conditions. The drought stress algorithm also reduces the model&rsquo;s high bias in organic aerosols (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 &lt;1 ppb increase in O3 in high-isoprene regions and a 1&ndash;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.</p

A single-point modeling approach for the intercomparison and evaluation of ozone dry deposition across chemical transport models (Activity 2 of AQMEII4)

A primary sink of air pollutants and their precursors is dry deposition. Dry deposition estimates differ across chemical transport models, yet an understanding of the model spread is incomplete. Here, we introduce Activity 2 of the Air Quality Model Evaluation International Initiative Phase 4 (AQMEII4). We examine 18 dry deposition schemes from regional and global chemical transport models as well as standalone models used for impact assessments or process understanding. We configure the schemes as single-point models at eight Northern Hemisphere locations with observed ozone fluxes. Single-point models are driven by a common set of site-specific meteorological and environmental conditions. Five of eight sites have at least 3 years and up to 12 years of ozone fluxes. The interquartile range across models in multiyear mean ozone deposition velocities ranges from a factor of 1.2 to 1.9 annually across sites and tends to be highest during winter compared with summer. No model is within 50 % of observed multiyear averages across all sites and seasons, but some models perform well for some sites and seasons. For the first time, we demonstrate how contributions from depositional pathways vary across models. Models can disagree with respect to relative contributions from the pathways, even when they predict similar deposition velocities, or agree with respect to the relative contributions but predict different deposition velocities. Both stomatal and nonstomatal uptake contribute to the large model spread across sites. Our findings are the beginning of results from AQMEII4 Activity 2, which brings scientists who model air quality and dry deposition together with scientists who measure ozone fluxes to evaluate and improve dry deposition schemes in the chemical transport models used for research, planning, and regulatory purposes

An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. Keywords: Protein function prediction, Disease gene prioritizationpublishedVersio

MUSiC : a model-unspecific search for new physics in proton-proton collisions at root s=13TeV

Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1), are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.Peer reviewe

Measurement of prompt open-charm production cross sections in proton-proton collisions at root s=13 TeV

The production cross sections for prompt open-charm mesons in proton-proton collisions at a center-of-mass energy of 13TeV are reported. The measurement is performed using a data sample collected by the CMS experiment corresponding to an integrated luminosity of 29 nb(-1). The differential production cross sections of the D*(+/-), D-+/-, and D-0 ((D) over bar (0)) mesons are presented in ranges of transverse momentum and pseudorapidity 4 < p(T) < 100 GeV and vertical bar eta vertical bar < 2.1, respectively. The results are compared to several theoretical calculations and to previous measurements.Peer reviewe

Search for top squark production in fully hadronic final states in proton-proton collisions at root s=13 TeV

A search for production of the supersymmetric partners of the top quark, top squarks, is presented. The search is based on proton-proton collision events containing multiple jets, no leptons, and large transverse momentum imbalance. The data were collected with the CMS detector at the CERN LHC at a center-of-mass energy of 13 TeV, and correspond to an integrated luminosity of 137 fb(-1). The targeted signal production scenarios are direct and gluino-mediated top squark production, including scenarios in which the top squark and neutralino masses are nearly degenerate. The search utilizes novel algorithms based on deep neural networks that identify hadronically decaying top quarks and W bosons, which are expected in many of the targeted signal models. No statistically significant excess of events is observed relative to the expectation from the standard model, and limits on the top squark production cross section are obtained in the context of simplified supersymmetric models for various production and decay modes. Exclusion limits as high as 1310 GeVare established at the 95% confidence level on the mass of the top squark for direct top squark production models, and as high as 2260 GeV on the mass of the gluino for gluino-mediated top squark production models. These results represent a significant improvement over the results of previous searches for supersymmetry by CMS in the same final state.Peer reviewe

Measurement of the W gamma Production Cross Section in Proton-Proton Collisions at root s=13 TeV and Constraints on Effective Field Theory Coefficients

A fiducial cross section for W gamma production in proton-proton collisions is measured at a center-of-mass energy of 13 TeV in 137 fb(-1) of data collected using the CMS detector at the LHC. The W -> e nu and mu nu decay modes are used in a maximum-likelihood fit to the lepton-photon invariant mass distribution to extract the combined cross section. The measured cross section is compared with theoretical expectations at next-to-leading order in quantum chromodynamics. In addition, 95% confidence level intervals are reported for anomalous triple-gauge couplings within the framework of effective field theory.Peer reviewe