Development and Application of an Asymptotic Level Transport Pollution Model for Luxembourg Energy Air Quality Project
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Modélisation De L'impact Du Trafic Routier Sur La Pollution De L'air Et Des
Mod´elisationde l'impact du trafic routier sur la pollution de l'air et des eaux de ruissellement Masoud Fallah Shorshani To cite this version: Masoud Fallah Shorshani. Mod´elisationde l'impact du trafic routier sur la pollution de l'air et des eaux de ruissellement. Sciences de l'environnement. Universit´eParis-Est, 2014. Fran¸cais. <NNT : 2014PEST1068>. <tel-01127301> HAL Id: tel-01127301 https://pastel.archives-ouvertes.fr/tel-01127301 Submitted on 7 Mar 2015 HAL is a multi-disciplinary open access L'archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destin´eeau d´ep^otet `ala diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publi´esou non, lished or not. The documents may come from ´emanant des ´etablissements d'enseignement et de teaching and research institutions in France or recherche fran¸caisou ´etrangers,des laboratoires abroad, or from public or private research centers. publics ou priv´es. Thèse de doctorat de l’Université Paris-Est Présentée par Masoud Fallah Shorshani pour l’obtention du diplôme de docteur de l’Université Paris-Est Spécialité : SIE – Sciences, Ingénierie et Environnement Modélisation de l’impact du trafic routier sur la pollution de l’air et des eaux de ruissellement Soutenue le 4 juillet 2014 Jury composé de Ludovic Leclercq, IFSTTAR Président du jury & examinateur Isabelle Braud, IRSTEA Rapporteur Lionel Soulhac, École Centrale de Lyon Rapporteur Frédéric Mahé, AIRPARIF Examinateur Guido Petrucci, Vrije Universiteit Brussel Examinateur Céline Bonhomme, LEESU Co-encadrante Michel André, IFSTTAR Co-directeur de thèse Christian Seigneur, CEREA Directeur de thèse Abstract: Road traffic emissions are a major source of pollution in cities. -
Table of Contents
CALPUFF Modeling System Version 6 User Instructions April 2011 Section 1: Introduction Table of Contents Page 1. OVERVIEW ...................................................................................................................... 1-1 1.1 CALPUFF Version 6 Modeling System............................................................... 1-1 1.2 Historical Background .......................................................................................... 1-2 1.3 Overview of the Modeling System ....................................................................... 1-7 1.4 Major Model Algorithms and Options................................................................. 1-19 1.4.1 CALMET................................................................................................ 1-19 1.4.2 CALPUFF............................................................................................... 1-23 1.5 Summary of Data and Computer Requirements .................................................. 1-28 2. GEOPHYSICAL DATA PROCESSORS.......................................................................... 2-1 2.1 TERREL Terrain Preprocessor............................................................................. 2-3 2.2 Land Use Data Preprocessors (CTGCOMP and CTGPROC) ............................. 2-27 2.2.1 Obtaining the Data.................................................................................. 2-27 2.2.2 CTGCOMP - the CTG land use data compression program .................. 2-29 2.2.3 CTGPROC - the land use preprocessor -
Technical Assistance for Improving Emissions Control the Role Of
This Project is Co-Financed by the European Union and the Republic of Turkey This Project is c This project is co-financed by the European Union and the Republic of Turkey Technical Assistance for Improving Emissions Control Service Contract No: TR0802.03-02/001 Identification No: EuropeAid/128897/D/SER/TR The Role of Emissions Dispersion Modelling in Cost Benefit Analysis Applied to Urban Air Quality Management: Part 1-the Approach (Version 2: 18 May 2012) This publication has been produced with the assistance of the European Union. The content of this publication is the sole responsibility of the Consortium led by PM Group and can in no way be taken to reflect the views of the European Union. Contracting Authority: Central Finance and Contracting Unit, Turkey Implementing Authority / Beneficiary: Ministry of Environment and Urbanisation Project Title: Improving Emissions Control Service Contract Number: TR0802.03-02/001 Identification Number: EuropeAid/128897/D/SER/TR PM Project Number: 300424 This project is co-financed by the European Union and the Republic of Turkey The Role of Emissions Dispersion Modelling in Cost Benefit Analysis Applied to Urban Air Quality Management: Part 1 – the Approach Version 2: 18 May 2012 PM File Number: 300424-06-RP-200 PM Document Number: 300424-06-205(2) CURRENT ISSUE Issue No.: 2 Date: 18/05/2012 Reason for Issue: Final Version for Client Approval Customer Approval Sign-Off Originator Reviewer Approver (if required) Scott Hamilton, Peter Print Name Russell Frost Jim McNelis Faircloth, Chris Dore Signature Date PREVIOUS ISSUES (Type Names) Issue No. Date Originator Reviewer Approver Customer Reason for Issue 1 14/03/2012 Scott Hamilton, Peter Russell Frost Jim McNelis For client review / comment Faircloth, Chris Dore CFCU / MoEU 300424-06-RP-205 (2) TA for Improving Emissions Control 18 May 2012 CONTENTS GLOSSARY OF ACRONYMS ...................................................................................... -
The Role of Natural Variability in Projections of Climate 10.1002/2016GL071565 Change Impacts on U.S
PUBLICATIONS Geophysical Research Letters RESEARCH LETTER The role of natural variability in projections of climate 10.1002/2016GL071565 change impacts on U.S. ozone pollution Key Points: Fernando Garcia-Menendez1,2 , Erwan Monier2 , and Noelle E. Selin2,3,4 • Natural variability can significantly fl in uence model-based projections of 1Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, North Carolina, climate change impacts on air quality 2 • Multidecadal simulations or initial USA, Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, 3 condition ensembles are needed to Massachusetts, USA, Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, identify an anthropogenic-forced Massachusetts, USA, 4Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, climate signal in O3 concentrations Cambridge, Massachusetts, USA • It is difficult to attribute the impacts of climate change on O3 to human influence before midcentury or under Abstract Climate change can impact air quality by altering the atmospheric conditions that determine stabilization scenarios pollutant concentrations. Over large regions of the U.S., projected changes in climate are expected to favor formation of ground-level ozone and aggravate associated health effects. However, modeling studies Supporting Information: exploring air quality-climate interactions have often overlooked the role of natural variability, a major source • Supporting Information S1 of uncertainty in projections. Here we use the largest ensemble simulation of climate-induced changes in air Correspondence to: quality generated to date to assess its influence on estimates of climate change impacts on U.S. ozone. F. Garcia-Menendez, We find that natural variability can significantly alter the robustness of projections of the future climate’s [email protected] effect on ozone pollution. -
CMIP5-Derived Single-Forcing, Single-Model, and Single-Scenario Wind-Wave Climate Ensemble: Configuration and Performance Evaluation
Journal of Marine Science and Engineering Article CMIP5-Derived Single-Forcing, Single-Model, and Single-Scenario Wind-Wave Climate Ensemble: Configuration and Performance Evaluation Alvaro Semedo 1,2,*, Mikhail Dobrynin 3, Gil Lemos 2, Arno Behrens 4, Joanna Staneva 4, Hylke de Vries 5, Andreas Sterl 5, Jean-Raymond Bidlot 6 ID , Pedro M. A. Miranda 2 ID and Jens Murawski 7 1 Department of Water Science and Engineering, IHE-Delft, P.O. Box 3015, 2601 DA Delft, The Netherlands 2 Instituto Dom Luiz, Faculdade de Ciências, University of Lisbon, 1749-016 Lisbon, Portugal; [email protected] (G.L.); [email protected] (P.M.A.M.) 3 Institute of Oceanography, Center for Earth System Research and Sustainability (CEN), Universität Hamburg, 20095 Hamburg, Germany; [email protected] 4 Helmholtz-Zentrum Geesthacht Centre for Materials and Coastal Research, D-21502 Geesthacht, Germany; [email protected] (A.B.); [email protected] (J.S.) 5 Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731GA De Bilt, The Netherlands; [email protected] (H.d.V.); [email protected] (A.S.) 6 European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, UK; [email protected] 7 Danish Meteorological Institute, 2100 Copenhagen Ø, Denmark; [email protected] * Correspondence: [email protected]; Tel.: +31-152-152-387 Received: 22 June 2018; Accepted: 25 July 2018; Published: 1 August 2018 Abstract: A Coupled Model Intercomparison Project Phase 5 (CMIP5)-derived single-forcing, single-model, and single-scenario dynamic wind-wave climate ensemble is presented, and its historic period (1979–2005) performance in representing the present wave climate is evaluated. -
Predicting Air Quality Near Roadway Intersections Through the Applicat
University of Central Florida STARS Electronic Theses and Dissertations, 2004-2019 2004 Predicting Air Quality Near Roadway Intersections Through The Applicat Brian Kim University of Central Florida Part of the Environmental Engineering Commons Find similar works at: https://stars.library.ucf.edu/etd University of Central Florida Libraries http://library.ucf.edu This Doctoral Dissertation (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations, 2004-2019 by an authorized administrator of STARS. For more information, please contact [email protected]. STARS Citation Kim, Brian, "Predicting Air Quality Near Roadway Intersections Through The Applicat" (2004). Electronic Theses and Dissertations, 2004-2019. 200. https://stars.library.ucf.edu/etd/200 PREDICTING AIR QUALITY NEAR ROADWAY INTERSECTIONS THROUGH THE APPLICATION OF A GAUSSIAN PUFF MODEL TO MOVING SOURCES by BRIAN Y. KIM B.S. University of California at Irvine, 1990 M.S. California Polytechnic State University at San Luis Obispo, 1996 A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Civil and Environmental Engineering in the College of Engineering and Computer Science at the University of Central Florida Orlando, Florida Fall Term 2004 ABSTRACT With substantial health and economic impacts attached to many highway-related projects, it has become imperative that the models used to assess air quality be as accurate as possible. The United States (US) Environmental Protection Agency (EPA) currently promulgates the use of CAL3QHC to model concentrations of carbon monoxide (CO) near roadway intersections. -
Machine Learning Approaches for Outdoor Air Quality Modelling: a Systematic Review
applied sciences Review Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review Yves Rybarczyk 1,2 and Rasa Zalakeviciute 1,* 1 Intelligent & Interactive Systems Lab (SI2 Lab), Universidad de Las Américas, 170125 Quito, Ecuador; [email protected] 2 Department of Electrical Engineering, CTS/UNINOVA, Nova University of Lisbon, 2829-516 Monte de Caparica, Portugal * Correspondence: [email protected]; Tel.: +351-593-23-981-000 Received: 15 November 2018; Accepted: 8 December 2018; Published: 11 December 2018 Abstract: Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is puzzling why a certain algorithm is chosen over another for a given task. This systematic review intends to clarify this question by providing the reader with a comprehensive description of the principles underlying these algorithms and how they are applied to enhance prediction accuracy. A rigorous search that conforms to the PRISMA guideline is performed and results in the selection of the 46 most relevant journal papers in the area. Through a factorial analysis method these studies are synthetized and linked to each other. The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning and Regressions, whereas forecasting make use of Neural Networks and Support Vector Machines. -
Coupling Multi-Component Models with MPH on Distributed Memory Computer Architectures
Coupling Multi-Component Models with MPH on Distributed Memory Computer Architectures Yun He and Chris Ding Computational Research Division, Lawrence Berkeley National Laboratory University of California, Berkeley, CA 94720, USA [email protected], [email protected] Abstract A growing trend in developing large and complex applications on today’s Teraflop scale computers is to integrate stand-alone and/or semi-independent program components into a comprehensive simulation package. One example is the Community Climate System Model which consists of atmosphere, ocean, land-surface and sea-ice components. Each component is semi-independent and has been developed at a different institution. We study how this multi-component, multi-executable application can run effectively on distributed memory ar- chitectures. For the first time, we clearly identify five effective execution modes and develop the MPH library to support application development utilizing these modes. MPH performs component-name registration, resource allocation and initial component handshaking in a flex- ible way. Keywords: multi-component, multi-executable, component integration, distributed memory architecture, climate modeling, CCSM, PCM. 1 Introduction With rapid increase in computing power of distributed-memory computers, and clusters of Sym- metric Multi-Processors (SMP) application problems grow rapidly both in scale and complexity. Effectively organizing a large and complex simulation program so that it is maintainable, re-usable, sharable and efficient becomes an important task for high performance computing. Building a comprehensive application system utilizing (and modifying) existing codes developed by different groups is a standard development approach. Component-based software engineering (CBSE) is an emerging trend for software develop- ment in both research and applications. -
Feasibility Study: Modelling Environmental Concentrations of Chemicals from Emission Data
EEA Technical report No 8/2007 Feasibility study: modelling environmental concentrations of chemicals from emission data ISSN 1725-2237 EEA Technical report No 8/2007 Feasibility study: modelling environmental concentrations of chemicals from emission data Cover design: EEA Layout: Diadeis and EEA Legal notice The contents of this publication do not necessarily reflect the official opinions of the European Commission or other institutions of the European Communities. Neither the European Environment Agency nor any person or company acting on behalf of the Agency is responsible for the use that may be made of the information contained in this report. All rights reserved No part of this publication may be reproduced in any form or by any means electronic or mechanical, including photocopying, recording or by any information storage retrieval system, without the permission in writing from the copyright holder. For translation or reproduction rights please contact EEA (address information below). Information about the European Union is available on the Internet. It can be accessed through the Europa server (www.europa.eu). Luxembourg: Office for Official Publications of the European Communities, 2007 ISBN 978-92-9167-925-6 ISSN 1725-2237 © EEA, Copenhagen, 2007 European Environment Agency Kongens Nytorv 6 1050 Copenhagen K Denmark Tel.: +45 33 36 71 00 Fax: +45 33 36 71 99 Web: eea.europa.eu Enquiries: eea.europa.eu/enquiries Contents Contents Acknowledgements ................................................................................................... -
MARK R THEOBALD.Pdf
TESIS DOCTORAL / Ph.D THESIS An Intercomparison of Modelling Approaches for Simulating the Atmospheric Dispersion of Ammonia Emitted by Agricultural Sources Mark R. Theobald Madrid 2012 E.T.S.I. Agrónomos Universidad Politécnica de Madrid Departamento de Química y Análisis Agrícola Escuela Técnica Superior de Ingenieros Agrónomos An Intercomparison of Modelling Approaches for Simulating the Atmospheric Dispersion of Ammonia Emitted by Agricultural Sources Autor: Mark R. Theobald Licenciado en Ciencias Físicas (MPhys hons) Directores: Dr. Antonio Vallejo Garcia Doctor en Ciencias Químicas Dr. Mark A. Sutton Doctor en Ciencias Físicas Madrid 2012 Acknowledgements ACKNOWLEDGEMENTS This work was funded by the European Commission through the NitroEurope Integrated Project (Contract No. 017841 of the EU Sixth Framework Programme for Research and Technological Development). The European Science Foundation also provided additional funding through COST Action 729 for the attendance of conferences and workshops and for the collaboration with the University of Lisbon (COST-STSM-729-5799). Firstly I would like to thank my two supervisors Dr. Mark A. Sutton and Prof. Antonio Vallejo for their support and guidance throughout this work. I am grateful to Mark not only for his willingness to discuss and direct this work no matter where he was in the world or whatever time of day it was, but also for the support and encouragement I received when I was at CEH Edinburgh. I am also grateful to Antonio for guiding me through the labyrinths of University bureaucracy. I would also like to thank the other research groups with whom I have collaborated throughout this work. Thanks to all my colleagues at CEH Edinburgh with a special mention to Bill Bealey for his help developing the SCAIL model and to Sim Tang for providing the ALPHA samplers and technical support. -
The State of Climate Modeling in the Great Lakes Basin - a Synthesis in Support of a Workshop Held on June 27, 2019 in Ann Arbor, MI
The State of Climate Modeling in the Great Lakes Basin A Synthesis in Support of a Workshop held on June 27, 2019 in Ann Arbor, MI Report By: Ontario Climate Consortium Date: September 13, 2019 Acknowledgements: This synthesis report has been prepared in partnership by the Ontario Climate Consortium (OCC), and Environment and Climate Change Canada. The authors wish to acknowledge the following individuals for their contributions to this project: Kristina Dokoska, Ontario Climate Consortium Nishal Shah, Ontario Climate Consortium Greg Mayne, Environment and Climate Change Canada Wendy Leger, Environment and Climate Change Canada Frank Seglenieks, Environment and Climate Change Canada Armin Dehghan, Environment and Climate Change Canada Shaffina Kassam, Environment and Climate Change Canada Heather Arnold, Environment and Climate Change Canada Shivani Vigneswaran, Environment and Climate Change Canada Recommended Citation: Delaney, F. and Milner, G. 2019. The State of Climate Modeling in the Great Lakes Basin - A Synthesis in Support of a Workshop held on June 27, 2019 in Ann Arbor, MI. Toronto, Canada. www.climateconnections.com | 2 Table of Contents Executive Summary .......................................................................................................................................................................... 4 1. Introduction ................................................................................................................................................................................... 7 1.1 -
Dispersion V3.23
Volume 2 Airviro User’s Reference Working with the Dispersion Module How to simulate the dispersion of pollutants Working with the Dispersion Module How to simulate the dispersion of pollutants Amendments Version Date changed Cause of change Signature 3.11 Ago2007 Upgrade GS 3.12 January2009 Upgrade GS 3.13 January2009 Upgrade GS 3.20 May 2010 Upgrade GS 3.21 Dec 2010 Upgrade GS 3.21 June 2012 Review GS 3.22 April 2013 Release GS 3.23 Jan 2014 Upgrade GS 3.23 January 2014 Review GS 3.23 June 2015 Review GS Contents 2.1 Introduction..............................................................................................................7 2.1.1 Why You Need to Use Dispersion Models.........................................................7 2.1.1.1 What’s the Use of Dispersion Simulations.....................................................7 2.1.1.2 How Can Airviro Help?......................................................................................7 2.1.2 Model Assumptions..............................................................................................8 2.1.3 Brief description of the available models..........................................................9 2.1.4 How does Dispersion Module client work?.....................................................18 2.1.5 Guidance for the beginner:................................................................................18 2.1.6 Overview of the Dispersion Module Main Window.........................................19 2.1.6.1 Changing Weather Conditions – Model settings.........................................19