Neural network modelling to estimate particle size distribution based on other particle sections and meteorological parameters
- 1Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, Finland
- 2Helsinki Institute of Sustainability Science, Faculty of Science, University of Helsinki, Finland
- 3Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- 4Department of Computer Science, The University of Jordan, Amman 11942, Jordan
- 5Department of Computer Science, Faculty of Science, University of Helsinki, Finland
- 6Department Material Analysis and Indoor Chemistry, Fraunhofer WKI, D-38108 Braunschweig, Germany
- 7Department of Physics, The University of Jordan, Amman 11942, Jordan
Abstract. In air quality research, often only particle mass concentrations as indicators of aerosol particles are considered. However, the mass concentrations do not provide sufficient information to convey the full story of fractionated size distribution, which are able to deposit differently on respiratory system and cause various harm. Aerosol size distribution measurements rely on a variety of techniques to classify the aerosol size and measure the size distribution. From the raw data the ambient size distribution is determined utilising a suite of inversion algorithms. However, the inversion problem is quite often ill-posed and challenging to invert. Due to the instrumental insufficiency and inversion limitations, models for fractionated particle size distribution are of great significance to fill the missing gaps or negative values. The study at hand involves a merged particle size distribution, from a scanning mobility particle sizer (NanoSMPS) and an optical particle sizer (OPS) covering the aerosol size distributions from 0.01 to 0.42 μm (electrical mobility equivalent size) and 0.3 μm to 10 μm (optical equivalent size) and meteorological parameters collected at an urban background region in Amman, Jordan in the period of 1st Aug 2016–31st July 2017. We develop and evaluate feed-forward neural network (FFNN) models to estimate number concentrations at particular size bin with (1) meteorological parameters, (2) number concentration at other size bins, and (3) both of the above as input variables. Two layers with 10–15 neurons are found to be the optimal option. Lower model performance is observed at the lower edge (0.01 < Dp < 0.02 μm), the mid-range region (0.15 < Dp < 0.5 μm) and the upper edge (6 < Dp < 10 μm). For the edges at both ends, the number of neighbouring size bins is limited and the detection efficiency by the corresponding instruments is lower compared to the other size bins. A distinct performance drop over the overlapping mid-range region is due to the deficiency of a merging algorithm. Another plausible reason for the poorer performance for finer particles is that they are more effectively removed from the atmosphere compared to the coarser particles so that the relationships between the input variables and the small particles is more dynamic. An observable overestimation is also found in early morning for ultrafine particles followed by a distinct underestimation before midday. In the winter, due to a possible sensor drift and interference artefacts, the model performance is not as good as the other seasons. The model by meteorological parameters using 5-min data (R2 = 0.22–0.58) shows poorer results than data with longer time resolution (R2 = 0.66–0.77). The model by the number concentration at the other size bins can serve as an alternative way to replace negative number in size distribution raw dataset thanks to its high accuracy and reliability (R2 = 0.97–1). This negative numbers filling method can maintain a symmetric distribution of errors.
Pak Lun Fung et al.
Pak Lun Fung et al.
Pak Lun Fung et al.
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