Long-memory characteristics of urban roadside air quality |
| |
| Authors: | Jason C Lau WT Hung David D Yuen CS Cheung |
| |
| Institution: | 1. Department of Civil and Structural Engineering, Hong Kong Polytechnic University, Hong Kong;2. Department of Mechanical Engineering, Hong Kong Polytechnic University, Hong Kong;1. Robert Bosch GmbH, 70042 Stuttgart, Germany;2. Institute of Chemical Engineering, University of Bayreuth, 95440 Bayreuth, Germany;1. Atmospheric Composition Research, Finnish Meteorological Institute, P.O. Box 503, FI-00101, Helsinki, Finland;2. Helsinki Region Environmental Services Authority, P.O. Box 100, FI-00066 HSY, Helsinki, Finland;3. Department of Technology, Metropolia University of Applied Sciences, P.O. Box 4021, FI-00180, Helsinki, Finland;4. Aerosol Physics, Faculty of Natural Sciences, Tampere University of Technology, P.O. Box 692, FI-33720, Tampere, Finland;1. Univerisité Grenoble-Alpes, CNRS, Institut des Geosciences pour l’Environnement (IGE), Grenoble, France;2. Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen, CH-5232, Switzerland;3. Leibniz Institute for Tropospheric Research (TROPOS), Permoserstrasse 15, 04318, Leipzig, Germany;4. Atmospheric Science Unit, Department of Environmental Science and Analytical Chemistry (ACES), Stockholm University, Svante Arrhenius väg 8, SE-11418, Stockholm, Sweden;5. NILU-Norwegian Institute for Air Research, Kjeller, Norway;6. Environmental Chemistry Processes Laboratory, Dept. of Chemistry, University of Crete, 71003, Heraklion Crete, Greece;7. National Centre for Atmospheric Science, School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT, UK;8. Department of Environmental Sciences, Center of Excellence in Environmental Studies, King Abdulaziz University, PO Box 80203, Jeddah, 21589, Saudi Arabia;9. European Commission, Joint Research Centre (JRC), Directorate for Energy, Transport and Climate, Air and Climate Unit, Via E. Fermi 2749, I-21027 Ispra (VA), Italy;10. Institute of Environmental Assessment and Water Research (IDAEA-CSIC), c/ Jordi-Girona 18-26, ES08034, Barcelona, Spain;11. Laboratoire de Météorologie Physique, UMR 6016, CNRS/University of Clermont-Ferrand, Clermont-Ferrand, France;12. Division of Nuclear Physics, Department of Physics, Lund University, Lund, Sweden;13. Division of Atmospheric Science, Department of Physics, University of Helsinki, P.O. Box 64, FIN-00014, Helsinki, Finland;1. School of Chemical and Petroleum Engineering, Curtin University, GPO Box U1987, Perth, WA 6845, Australia;2. State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China;1. Graduate Program in Environmental Engineering, Federal University of Technology, Av. Pioneiros 3131, 86036-370 Londrina, PR, Brazil;2. Department of Environmental Science and Analytical Chemistry, Stockholm University, Svante Arrhenius väg 8, SE-11418 Stockholm, Sweden;3. Stockholm Environment and Health Administration, Fleminggatan 4, SE-10420 Stockholm, Sweden;1. School of Traffic and Transportation, Beijing Jiaotong University, Haidian District, Beijing 100044, China;2. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Haidian District, Beijing 100044, China |
| |
| Abstract: | Carbon monoxide is a major contributor to air pollution in urban cities, particularly at the roadside. Hourly, monthly and seasonal mean carbon monoxide concentration data are collected from a roadside air monitoring station in Hong Kong over 7-years. The station is a few metres from a major intersection surrounded by tall buildings. In particular, hourly patterns of concentrations on different days of the week are investigated. The data show that hourly carbon monoxide concentrations resemble the traffic pattern of the area and tend to be lower in the summer. Using a seasonal autoregressive integrated moving average models shows that the daily traffic cycle strongly influences concentrations. Further, it is found that urban roadside carbon monoxide monitoring data exhibits a long-memory process, suggesting that a model incorporating long memory and seasonality effects is needed simulate urban roadside air quality. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
