Support tools for land use policies based on high resolution regional air quality modelling |
| |
| Institution: | 1. Babeş-Bolyai University, Faculty of Environmental Science and Engineering, 30 Fantanele, Cluj-Napoca, Romania;2. National Meteorological Administration, 97, Sos. București-Ploiești, Sector 1, Bucharest Romania;3. Zentralanstal für Meteorologie und Geodynamik, Hohe Warte 38, 1190 Vienna, Austria;4. University of the Free State, Faculty of Natural and Agricultural Sciences, Disaster Management Training and Education Centre for Africa (DiMTEC), Bloemfontein, South Africa;1. Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, China Meteorological Administration, Guangzhou 510080, China;2. Key Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China;1. French Institute for Public Health Surveillance (InVS), 12 rue du Val d''Osne, 94415 Saint-Maurice Cedex, France;2. INSERM U1018, University of Versailles St Quentin. Centre for Research in Epidemiology and Population Health, Villejuif, France;3. Airparif, 7 rue Crillon, 75004 Paris, France;4. Air Rhône-Alpes, 3 allée des Sorbiers, 69500 Bron, France;5. Air PACA, 146 Rue Paradis, 13006 Marseille, France;6. GeographR, 1 rue de Taulignan, 84000 Avignon, France;7. ASPA, 5 rue de Madrid, 67300 Schiltigheim, France;1. School of Economics and Management, Shanghai Maritime University, Shanghai, China;2. Collaborative Innovation Center on Climate and Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China;3. Department of Economics, College of Business, University of Nevada, Reno, NV, 89557, USA;4. College of Arts and Sciences, University of North Carolina at Pembroke, Pembroke, 28372, USA;1. Department of Geography, Ghent University, Ghent, Belgium;2. Research Group Climate Change and Security, Institute of Geography, University of Hamburg, Hamburg, Germany;3. Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic;4. Faculty of Business, Babes-Bolyai University, Cluj-Napoca, Romania;5. Faculty of Environmental Science and Engineering, Babeş-Bolyai University, Cluj-Napoca, Romania |
| |
| Abstract: | Air pollution is becoming a stringent issue, especially in large urban agglomerations like Bucharest, Romania. National and European air quality regulations are focusing on enforcing emission and immission limit values, but there is a poor correlation between air quality policies and land use policies, both critical to urban development.The objective of this study is to develop a methodological framework for including air quality data in policies concerning urban development and land use change. The novelty is given by the use of high-resolution regional air quality modelling data for key pollutants like PM10, SO2 and NO2 with land use datasets. The study proposes a methodological framework based on the Urban Atlas Project’s land use classes and the WRF-Chem air quality model. Through overlay analysis of the air quality model output and land use datasets, it is possible to derive two major types of information: (1) the land use area and classes exposed to certain pollutant concentration intervals and (2) the variability of the exposed land use classes according to the air pollutants source – receptor dynamics.Results are presented in the form of applications of the methodological framework in two different case studies. The methodological framework could be used as a tool for practitioners to include and use the evidence from air pollution information in the spatial planning policies and practice. In the first case study, three PM10 classes were set up in order to demonstrate the possibility to identify land use classes exposed to certain intervals of PM10 concentrations and their extent. The second case study focuses on the land use classes and their extent exposed to SO2 and NO2 concentrations in two different meteorological scenarios during two days in September 2014.This approach could provide support for decision makers in the policy development process, resulting in more informed decisions regarding future destinations of different land categories taking into account air pollution data and indicators. This study also opens the way for future air quality satellite data (ESA Sentinel 5-P and 5), with resolutions similar to the air quality model outputs to be used in land use planning and policy development purposes. |
| |
| Keywords: | Land use classes Atmospheric pollution Air quality modelling |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
