Wind-sensitive Interpolation of Urban Air Pollution Forecasts

被引:32
作者
Contreras, Lidia [1 ]
Ferri, Cesar [1 ]
机构
[1] Univ Politecn Valencia, DSIC, Cami Vera S-N, Valencia 46022, Spain
来源
INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE 2016 (ICCS 2016) | 2016年 / 80卷
关键词
Machine Learning; Urban Air Pollution; Spatial Interpolation; LAND-USE REGRESSION; MODEL; NETWORK;
D O I
10.1016/j.procs.2016.05.343
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
People living in urban areas are exposed to outdoor air pollution. Air contamination is linked to numerous premature and pre-native deaths each year. Urban air pollution is estimated to cost approximately 2% of GDP in developed countries and 5% in developing countries. Some works reckon that vehicle emissions produce over 90% of air pollution in cities in these countries. This paper presents some results in predicting and interpolating real-time urban air pollution forecasts for the city of Valencia in Spain. Although many cities provide air quality data, in many cases, this information is presented with significant delays (three hours for the city of Valencia) and it is limited to the area where the measurement stations are located. We compare several regression models able to predict the levels of four different pollutants (NO, NO2, SO2, O-3) in six different locations of the city. Wind strength and direction is a key feature in the propagation of pollutants around the city, in this sense we study different techniques to incorporate this factor in the regression models. Finally, we also analyse how to interpolate forecasts all around the city. Here, we propose an interpolation method that takes wind direction into account. We compare this proposal with respect to well-known interpolation methods. By using these contamination estimates, we are able to generate a real-time pollution map of the city of Valencia.
引用
收藏
页码:313 / 323
页数:11
相关论文
共 23 条
[1]   The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies [J].
Arain, M. A. ;
Blair, R. ;
Finkelstein, N. ;
Brook, J. R. ;
Sahsuvaroglu, T. ;
Beckerman, B. ;
Zhang, L. ;
Jerrett, M. .
ATMOSPHERIC ENVIRONMENT, 2007, 41 (16) :3453-3464
[2]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[3]   openair - An R package for air quality data analysis [J].
Carslaw, David C. ;
Ropkins, Karl .
ENVIRONMENTAL MODELLING & SOFTWARE, 2012, 27-28 :52-61
[4]  
European Environment Agency, 2015, SOER 2015 EUR ENV ST
[5]   Weekday/weekend ozone differences: What can we learn from them? [J].
Heuss, JM ;
Kahlbaum, DF ;
Wolff, GT .
JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION, 2003, 53 (07) :772-788
[6]   A review of land-use regression models to assess spatial variation of outdoor air pollution [J].
Hoek, Gerard ;
Beelen, Rob ;
de Hoogh, Kees ;
Vienneau, Danielle ;
Gulliver, John ;
Fischer, Paul ;
Briggs, David .
ATMOSPHERIC ENVIRONMENT, 2008, 42 (33) :7561-7578
[7]   Open-source machine learning: R meets Weka [J].
Hornik, Kurt ;
Buchta, Christian ;
Zeileis, Achim .
COMPUTATIONAL STATISTICS, 2009, 24 (02) :225-232
[8]   Spatial interpolation of air pollution measurements using CORINE land cover data [J].
Janssen, Stijn ;
Dumont, Gerwin ;
Fierens, Frans ;
Mensink, Clemens .
ATMOSPHERIC ENVIRONMENT, 2008, 42 (20) :4884-4903
[9]   A modelling system for predicting urban air pollution:: comparison of model predictions with the data of an urban measurement network in Helsinki [J].
Karppinen, A ;
Kukkonen, J ;
Elolähde, T ;
Konttinen, M ;
Koskentalo, T .
ATMOSPHERIC ENVIRONMENT, 2000, 34 (22) :3735-3743
[10]  
Khare M., 2006, Artificial neural networks in vehicular pollution modelling, V41