title: Re-construction of the shut-down PM10 monitoring stations for the reliable assessment of PM10 in Berlin using fuzzy modelling and data transformation.
abstract: A dense monitoring network is vital for the reliable assessment of PM10 in different parts of an urban area. In this study, a new idea is employed for the re-construction of the 20 shut-down PM10 monitoring stations of Berlin. It endeavours to find the non-linear relationship between the hourly PM10 concentration of both the still operating and the shut-down PM10 monitoring stations by using a fuzzy modelling technique, called modified active learning method (MALM). In addition, the simulations were performed by using not only raw PM10 databases but also log-transformed PM10 databases for skewness reduction. According to the results of hourly PM10 simulation (root mean square error about 13.0 μg/m3, correlation coefficient 0.88), the shut-down stations have been appropriately simulated and the idea of dense monitoring network development by the re-construction of the shut-down stations was realised. The results of simulations using raw and log-transformed databases showed that data transformation has no significant effect on the performance of MALM in the simulation of shut-down PM10 stations. By the combination of the 11 still operating stations and the 20 re-constructed stations, a dense monitoring network was generated for Berlin and was utilised for the calculation of the reliable monthly and mean annual PM10 concentration for five different PM10 zones in Berlin (the suburban-background, urban-background, urban-traffic, rural-background and suburban-traffic areas). The results showed that the mean annual concentration of PM10 at the five zones increased by about 13.0% in 2014 (26.3 μg/m3) in comparison with 2013 (23.3 μg/m3). Furthermore, the mean annual concentration of PM10 in the traffic lanes of the suburban (2013 25.0 μg/m3, 2014 26.9 μg/m3) and urban (2013 27.7 μg/m3, 2014 31.3 μg/m3) areas is about 14 and 20% higher than the PM10 concentration of suburban-background (2013 21.3 μg/m3, 2014 24.5 μg/m3) and urban-background (2013 23.0 μg/m3, 2014 26.1 μg/m3) areas, respectively.
authors.affiliation: Institut für Meteorologie, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6-10, 12165, Berlin, Germany. hamid.taheri@met.fu-berlin.de.|Remote Sensing Research Center, Sharif University of Technology, Tehran, Iran. hamid.taheri@met.fu-berlin.de.
authors.forename: Hamid
authors.initials: H
authors.lastname: Taheri Shahraiyni
authors.affiliation: Institut für Meteorologie, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6-10, 12165, Berlin, Germany.
authors.forename: Sahar
authors.initials: S
authors.lastname: Sodoudi
authors.affiliation: Senate Department for Urban Development and the Environment, Berlin, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Kerschbaumer
authors.affiliation: Institut für Meteorologie, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6-10, 12165, Berlin, Germany.
authors.forename: Ulrich
authors.initials: U
authors.lastname: Cubasch
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D001604:Berlin; D002947:Cities; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004784:Environmental Monitoring / Q000379:methods*; D017143:Fuzzy Logic; D005843:Geography; D007858:Learning; D052638:Particulate Matter / Q000032:analysis; D011336:Probability
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter
keywords: Berlin; Log transformation; Modified active learning method (MALM); PM10; Re-constructed stations