WORM - A NEW OPEN ROAD LINE SOURCE MODEL FOR LOW WIND SPEED CONDITIONS

Emission from road traffic constitutes one of the most important sources of air pollution in urban areas. This paper describes a newly developed air pollution dispersion model for open roads and highways called WORM (Weak Wind Open Road Model), and give some results using this model during low wind speed and (strongly) stable atmospheric conditions at Nordbysletta in Norway, during a 3-4 months period in the winter/spring of 2002

Towards uncertainty mapping in air-quality modelling and assessment

The aim of this paper is to promote the use of uncertainty mapping when spatial assessments of air quality are made. A large number of air quality maps are produced for scientific and policy purposes but rarely are corresponding maps of their uncertainty included. The need for such maps and the methods to produce them are described. Several uncertainty parameters are discussed but it is recommended to use the probability density function as the basis of the uncertainty estimates. Several examples are provided discussing indicative uncertainty, ensemble methods, comparisons with observations, spatial representativeness, uncertainty in exceedances and probability of exceedance.publishe

Uncertainties in assessing the environmental impact of amine emissions from a CO 2 capture plant

In this study, a new model framework that couples the atmospheric chemistry transport model system WRF-EMEP and the multimedia fugacity level III model was used to assess the environmental impact of amine emissions to air from post-combustion carbon dioxide capture. The modelling framework was applied to a typical carbon capture plant artificially placed at Mongstad, west coast of Norway. WRF-EMEP enables a detailed treatment of amine chemistry in addition to atmospheric transport and deposition. Deposition fluxes of WRF-EMEP simulations were used as input to the fugacity model in order to derive concentrations of nitramines and nitrosamine in lake water. Predicted concentrations of nitramines and nitrosamines in ground-level air and drinking water were found to be highly sensitive to the description of amine chemistry, especially of the night time chemistry with the nitrate (NO3) radical. Sensitivity analysis of the fugacity model indicates that catchment characteristics and chemical degradation rates in soil and water are among the important factors controlling the fate of these compounds in lake water. The study shows that realistic emission of commonly used amines result in levels of the sum of nitrosamines and nitramines in ground-level air (0.6–10 pgm−3) and drinking water (0.04–0.25 ngL−1) below the current safety guideline for human health enforced by the Norwegian Environmental Directorate. The modelling framework developed in this study can be used to evaluate possible environmental impacts of emissions of amines from post-combustion capture in other regions of the world

EVALUATION AND INTER-COMPARISON OF OPEN ROAD LINE SOURCE MODELS CURRENTLY IN USE IN THE NORDIC COUNTRIES

The aim of this study is to inter-compare and evaluate operational open road Gaussian line source models currently in use in the Nordic countries Norway, Denmark and Finland. By comparing the models and their results on different datasets, a more robust and objective assessment of model performance and applicability can be made. Four models, HIWAY2-AQ, OML-Highway, CAR-FM1 and WORM, are applied to datasets from three measurement campaigns from each of the mentioned countries. A more specific target is to determine the conditions under which the models perform well or poorly in order to focus attention on these aspects in future model development. The various models arc evaluated primarily with regard to wind speed, wind direction and atmospheric stability in order to identify problem areas. Generally, the correlation between model estimates and observations decreases when normalising with emissions, due to the significant positive correlation between observed concentrations and emissions. Furthermore, we found a reduction of bias when normalising the Norwegian and Danish data, caused by overestimation of the dispersion at lower emission values. This occurs because the initial dispersion is too large in all the models. For higher emissions at the Danish site, the relative bias was higher, compared with the relative bias at the Norwegian site, indicating the influence of traffic density and vehicle speed (which arc both largest at the Danish site) on traffic produced turbulence and model performance. OML-Highway, however, performs best in this regard due to its more advanced parameterisation of traffic produced turbulence based on production and decay of turbulent kinetic energy. With regard to horizontal profiles, RB for CAR-FMI increased as function of distance from the road, indicating that the Lagrangian time scales are too short

Brass Happenings

Presenting a student recital for brass students in USU\u27s Music Department.https://digitalcommons.usu.edu/music_programs/1107/thumbnail.jp

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment