The development of a dense urban air pollution monitoring network

Hamid Taheri Shahraiyni,Sahar Sodoudi,Andreas Kerschbaumer,Ulrich Cubasch

doi:10.5094/apr.2015.100

Abstract

The importance of air pollution monitoring networks in urban areas is well known because of their miscellaneous applications. At the beginning of the 1990s, Berlin had more than 40 particulate matter monitoring stations, whereas, by 2013, there were only 12 stations. In this study, a new and free–of–charge methodology for the densifying of the PM10 monitoring network of Berlin is presented. It endeavors to find the non–linear relationship between the hourly PM10 concentration of the still–operating PM10 monitoring stations and the shut–down stations by using the Artificial Neural Network (ANN), and, consequently, the results of the shut–down stations were simulated and re–constructed. However, input–variables selection is a pre–requisite for any ANN simulation, and hence a new fuzzy–heuristic input selection has been developed and joined to the ANN for the simulation. The hourly PM10 concentrations of the 20 shut–down stations were simulated and re–constructed. The mean error, bias and absolute error of the simulations were 27.7%, –0.03 (μg/m3), and 7.4 (μg/m3), respectively. Then, the simulated hourly PM10 concentration data were converted to a daily scale and the performance of ANN models which were developed for the simulation of the daily PM10 data were evaluated (correlation coefficient >0.94). These appropriate results imply the ability of the developed input selection technique to make the appropriate selection of the input variables, and it can be introduced as a new input variable selection for the ANN. In addition, a dense PM10 monitoring network was developed by the combination of both the re–constructed (20 stations) and the current (12 stations) stations. This dense monitoring network was applied in order to determine a reliable mean annual PM10 concentration in the different areas in Berlin in 2012.

Highlights

There are some major objectives for the development of air pollutionmonitoringnetworksinurbanareas.Theimportanceand application of air pollution monitoring networks have been well– knownfromthe1960s,andthemainreportedobjectivesanduses ofairpollutionmonitoringintheliteratureare: x Planning for the appropriate urbanization and land use development (WHO, 1977; Trujillo–Ventura and Ellis, 1991; Chenetal.,2006); x Evaluation of the exposure of people to air pollution and its effects on human health, and the protection of the public health (Darby et al, 1974; Hougland and Stephens, 1976; Ott, 1977; WHO, 1977; Modak and Lohani, 1985; Trujillo–Ventura and Ellis, 1991; Kanaroglou et al, 2005; Lozano et al, 2009; Ferradas et al, 2010; Zheng et al, 2011; PopeandWu,2014); x Quantifying the effects of the emission sources (e.g., power plants) on air pollution (Leavitt et al, 1957; Seinfeld, 1972; PopeandWu,2014); x Thecontrolandmanagementofurbanairpollution(Hougland and Stephens, 1976; WHO, 1977; Van Egmond and Onderdelinden,1981); x Theevaluationofairpollutioncontrolprogramsandstrategies (Seinfeld,1972;WHO,1977;Zhengetal.,2011); x The initial assessment of air pollution condition, e.g., the determination of the mean concentrations of air pollutants in urban areas in different time scales (i.e., hourly, daily)(Goldstein and Landovitz, 1977; WHO, 1977; Shannon et al, 1978); x Timeseriesanalysisforthedeterminationofthetrends ofair pollutants (WHO, 1977; Trujillo–Ventura and Ellis, 1991; Pope andWu,2014); x Investigation of the compliance of the concentrations of air pollutants with air quality standards (Hougland and Stephens, 1976;Ott,1977;WHO,1977;VanEgmondandOnderdelinden, 1981; Modak and Lohani, 1985; Chen et al, 2006; Ferradas et al.,2010;PopeandWu,2014) x The evaluation and validation of the mechanistic models describing the spatio–temporal emission, transport and transformationofairpollutants(WHO,1977;VanEgmondand Onderdelinden,1981;Trujillo–VenturaandEllis,1991;Zhenget al.,2011); x The spatial and knowledge–based modeling of air pollutants (Shannon et al, 1978; Modak and Lohani, 1985; Briggs et al, 1997;Briggsetal.,2000;Lozanoetal.,2009;TaheriShahraiyni etal.,2015); x The determination of critical air pollution conditions and notificationtothepeopleaffected(atrisk)andtotherelevant organizations(Seinfeld,1972;WHO,1977;Trujillo–Venturaand Ellis,1991;Chenetal.,2006)
Geostatistical techniques has been widely used for the calculation of the local spatial representativity of each monitoring station and the determination of the location of monitoring stations based upon the minimization of the estimationvariance(Trujillo–VenturaandEllis,1991;Kanaroglouet
The locations of the PM10 stations of the developed dense monitoringnetworkhavebeenpresentedinFigure4.Anautomatic module has been developed by the combination of the 20 developedneuralnetworkmodelsandthismodulewhichusesthe hourly PM10 concentration in the current stations as the input variables, and, its output is the concentration of hourlyPM10inthe20shut–down(simulated)stations

Summary

Introduction

There are some major objectives for the development of air pollutionmonitoringnetworksinurbanareas.Theimportanceand application of air pollution monitoring networks have been well– knownfromthe1960s,andthemainreportedobjectivesanduses ofairpollutionmonitoringintheliteratureare: x Planning for the appropriate urbanization and land use development (WHO, 1977; Trujillo–Ventura and Ellis, 1991; Chenetal.,2006); x Evaluation of the exposure of people to air pollution and its effects on human health, and the protection of the public health (Darby et al, 1974; Hougland and Stephens, 1976; Ott, 1977; WHO, 1977; Modak and Lohani, 1985; Trujillo–Ventura and Ellis, 1991; Kanaroglou et al, 2005; Lozano et al, 2009; Ferradas et al, 2010; Zheng et al, 2011; PopeandWu,2014); x Quantifying the effects of the emission sources (e.g., power plants) on air pollution (Leavitt et al, 1957; Seinfeld, 1972; PopeandWu,2014); x Thecontrolandmanagementofurbanairpollution(Hougland and Stephens, 1976; WHO, 1977; Van Egmond and Onderdelinden,1981); x Theevaluationofairpollutioncontrolprogramsandstrategies (Seinfeld,1972;WHO,1977;Zhengetal.,2011); x The initial assessment of air pollution condition, e.g., the determination of the mean concentrations of air pollutants in urban areas in different time scales (i.e., hourly, daily)(Goldstein and Landovitz, 1977; WHO, 1977; Shannon et al, 1978); x Timeseriesanalysisforthedeterminationofthetrends ofair pollutants (WHO, 1977; Trujillo–Ventura and Ellis, 1991; Pope andWu,2014); x Investigation of the compliance of the concentrations of air pollutants with air quality standards (Hougland and Stephens, 1976;Ott,1977;WHO,1977;VanEgmondandOnderdelinden, 1981; Modak and Lohani, 1985; Chen et al, 2006; Ferradas et al.,2010;PopeandWu,2014) x The evaluation and validation of the mechanistic models describing the spatio–temporal emission, transport and transformationofairpollutants(WHO,1977;VanEgmondand Onderdelinden,1981;Trujillo–VenturaandEllis,1991;Zhenget al.,2011); x The spatial and knowledge–based modeling of air pollutants (Shannon et al, 1978; Modak and Lohani, 1985; Briggs et al, 1997;Briggsetal.,2000;Lozanoetal.,2009;TaheriShahraiyni etal.,2015); x The determination of critical air pollution conditions and notificationtothepeopleaffected(atrisk)andtotherelevant organizations(Seinfeld,1972;WHO,1977;Trujillo–Venturaand Ellis,1991;Chenetal.,2006). The idea of the developed new input variable selection scheme in this study is that a heuristic partitioning method is utilized for the partitioning of the main MISO (Multi Inputs–Single Output) database for some MISO sub–databases in a successive manner.

Results

Conclusion