Taheri Shahraiyni Hamid, Sodoudi Sahar, Kerschbaumer Andreas, Cubasch Ulrich
Institut für Meteorologie, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6-10, 12165, Berlin, Germany.
Remote Sensing Research Center, Sharif University of Technology, Tehran, Iran.
Environ Monit Assess. 2017 Mar;189(3):134. doi: 10.1007/s10661-017-5826-5. Epub 2017 Feb 28.
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/m, 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/m) in comparison with 2013 (23.3 μg/m). Furthermore, the mean annual concentration of PM10 in the traffic lanes of the suburban (2013 25.0 μg/m, 2014 26.9 μg/m) and urban (2013 27.7 μg/m, 2014 31.3 μg/m) areas is about 14 and 20% higher than the PM10 concentration of suburban-background (2013 21.3 μg/m, 2014 24.5 μg/m) and urban-background (2013 23.0 μg/m, 2014 26.1 μg/m) areas, respectively.
一个密集的监测网络对于可靠评估城市不同区域的PM10至关重要。在本研究中，采用了一种新思路来重建柏林20个已关闭的PM10监测站。该研究试图通过使用一种名为改进主动学习方法（MALM）的模糊建模技术，找出仍在运行的PM10监测站与已关闭的PM10监测站每小时PM10浓度之间的非线性关系。此外，不仅使用原始PM10数据库，还使用经对数变换的PM10数据库进行模拟，以减少数据的偏度。根据每小时PM10模拟结果（均方根误差约为13.0μg/m，相关系数为0.88），已关闭的监测站得到了恰当的模拟，通过重建已关闭的监测站来发展密集监测网络的想法得以实现。使用原始数据库和经对数变换的数据库进行模拟的结果表明，数据变换对MALM在模拟已关闭的PM10监测站时的性能没有显著影响。通过将11个仍在运行的监测站和20个重建的监测站相结合，为柏林生成了一个密集监测网络，并用于计算柏林五个不同PM10区域（郊区背景、城市背景、城市交通、农村背景和郊区交通区域）可靠的月度和年均PM10浓度。结果表明，与2013年（23.3μg/m）相比，2014年五个区域的PM10年均浓度增加了约13.0%（26.3μg/m）。此外，郊区（2013年25.0μg/m，2014年26.9μg/m）和城市（2013年27.7μg/m，2014年31.3μg/m）地区车道上的PM10年均浓度分别比郊区背景（2013年21.3μg/m，2014年24.5μg/m）和城市背景（2013年23.0μg/m，2014年26.1μg/m）地区的PM10浓度高约14%和20%。
