Particulate Matter in Ambient Air

Görgen, Reinhold; Lambrecht, Udo

doi:10.1163/187601007x00479

Cited by 6 publications

(2 citation statements)

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“…Berlin) cannot comply with the daily limit value for PM 10 , which is in force since 2005. As stated by Goergen and Lambrecht (2007) Berlin experienced particle episodes in February, March and October during the year 2005 with PM 10 concentrations up to 130μg/m 3 in two sites, one close to a very busy street downtown and the other at the periphery of the city.…”

Section: Resultsmentioning

confidence: 85%

Air Quality in Mining Areas: The Case of Stratoni, Chalkidiki, Greece

Papastamatiou¹,

Skarpelis²,

Argyraki³

2017

geosociety

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Section: Resultsmentioning

confidence: 85%

Air Quality in Mining Areas: The Case of Stratoni, Chalkidiki, Greece

Papastamatiou¹,

Skarpelis²,

Argyraki³

2017

geosociety

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“…Berlin is situated in the approximately 200 km northwest of the industrialized area at Germany's borders with Poland and the Czech Republic, an area which is called the "Black Triangle" (Lenschow et al, 2001). Sometimes, the concentration of PM10 in Berlin exceeds the EU limit (Gorgen and Lambrecht, 2007). About 64.4% of PM10 in Berlin stems from non-Berlin emission sources (regional background sources) and the emission sources of the remaining 35.6% come from the urban background and traffic PM10 sources (Rauterberg-Wulff et al, 2013).…”

Section: The Study Areamentioning

confidence: 99%

The development of a dense urban air pollution monitoring network

Shahraiyni

Sodoudi

Kerschbaumer

et al. 2015

Atmospheric Pollution Research

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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 shutdown stations by using the Artificial Neural Network (ANN), and, consequently, the results of the shutdown stations were simulated and reconstructed. However, input-variables selection is a prerequisite 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 shutdown stations were simulated and reconstructed. The mean error, bias and absolute error of the simulations were 27.7%,-0.03 (μg/m 3), and 7.4 (μg/m 3), 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 reconstructed (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.

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Re-construction of the shut-down PM10 monitoring stations for the reliable assessment of PM10 in Berlin using fuzzy modelling and data transformation

Shahraiyni

Sodoudi

Kerschbaumer

et al. 2017

Environ Monit Assess

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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.

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Particulate Matter in Ambient Air

Cited by 6 publications

References 0 publications

Air Quality in Mining Areas: The Case of Stratoni, Chalkidiki, Greece

Air Quality in Mining Areas: The Case of Stratoni, Chalkidiki, Greece

The development of a dense urban air pollution monitoring network

Re-construction of the shut-down PM10 monitoring stations for the reliable assessment of PM10 in Berlin using fuzzy modelling and data transformation

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