scholarly journals Prediction of Aerosol Particle Size Distribution Based on Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yali Ren ◽  
Jiandong Mao ◽  
Hu Zhao ◽  
Chunyan Zhou ◽  
Xin Gong ◽  
...  
Keyword(s):  
Neural Network ◽  
Integral Equation ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Bp Neural Network ◽  
Scattering Theory ◽  
Mie Scattering ◽  
Aerosol Particle Size ◽  
Mie Scattering Theory

Aerosol plays a very important role in affecting the earth-atmosphere radiation budget, and particle size distribution is an important aerosol property parameter. Therefore, it is necessary to determine the particle size distribution. However, the particle size distribution determined by the particle extinction efficiency factor according to the Mie scattering theory is an ill-conditioned integral equation, namely, the Fredholm integral equation of the first kind, which is very difficult to solve. To avoid solving such an integral equation, the BP neural network prediction model was established. In the model, the aerosol optical depth obtained by sun photometer CE-318 and kernel functions obtained by Mie scattering theory were used as the inputs of the neural network, particle size distributions collected by the aerodynamic particle sizer APS 3321 were used as the output, and the Levenberg–Marquardt algorithm with the fastest descending speed was adopted to train the model. For verifying the feasibility of the prediction model, some experiments were carried out. The results show that BP neural network has a better prediction effect than that of the RBF neural network and is an effective method to obtain the aerosol particle size distribution of the whole atmosphere column using the data of CE-318 and APS 3321.

scholarly journals Neural network modelling to estimate particle size distribution based on other particle sections and meteorological parameters

2021 ◽  
Author(s):  
Pak Lun Fung ◽  
Martha Arbayani Zaidan ◽  
Ola Surakhi ◽  
Sasu Tarkoma ◽  
Tuukka Petäjä ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Meteorological Parameters ◽  
Aerosol Size Distribution ◽  
Sufficient Information ◽  
Electrical Mobility ◽  
Particle Sizer ◽  
Mass Concentrations

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 

scholarly journals Aerosol particle size distribution in the stratosphere retrieved from SCIAMACHY limb measurements

2018 ◽  
Vol 11 (4) ◽  
pp. 2085-2100 ◽  
Author(s):  
Elizaveta Malinina ◽  
Alexei Rozanov ◽  
Vladimir Rozanov ◽  
Patricia Liebing ◽  
Heinrich Bovensmann ◽  
...  
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Aerosol Particle ◽  
Volcanic Eruptions ◽  
Stratospheric Aerosol ◽  
Solar Light ◽  
Data Set ◽  
Aerosol Particle Size ◽  
Distribution Parameters

Abstract. Information about aerosols in the Earth's atmosphere is of a great importance in the scientific community. While tropospheric aerosol influences the radiative balance of the troposphere and affects human health, stratospheric aerosol plays an important role in atmospheric chemistry and climate change. In particular, information about the amount and distribution of stratospheric aerosols is required to initialize climate models, as well as validate aerosol microphysics models and investigate geoengineering. In addition, good knowledge of stratospheric aerosol loading is needed to increase the retrieval accuracy of key trace gases (e.g. ozone or water vapour) when interpreting remote sensing measurements of the scattered solar light. The most commonly used characteristics to describe stratospheric aerosols are the aerosol extinction coefficient and Ångström coefficient. However, the use of particle size distribution parameters along with the aerosol number density is a more optimal approach. In this paper we present a new retrieval algorithm to obtain the particle size distribution of stratospheric aerosol from space-borne observations of the scattered solar light in the limb-viewing geometry. While the mode radius and width of the aerosol particle size distribution are retrieved, the aerosol particle number density profile remains unchanged. The latter is justified by a lower sensitivity of the limb-scattering measurements to changes in this parameter. To our knowledge this is the first data set providing two parameters of the particle size distribution of stratospheric aerosol from space-borne measurements of scattered solar light. Typically, the mode radius and w can be retrieved with an uncertainty of less than 20 %. The algorithm was successfully applied to the tropical region (20° N–20° S) for 10 years (2002–2012) of SCIAMACHY observations in limb-viewing geometry, establishing a unique data set. Analysis of this new climatology for the particle size distribution parameters showed clear increases in the mode radius after the tropical volcanic eruptions, whereas no distinct behaviour of the absolute distribution width could be identified. A tape recorder, which describes the time lag as the perturbation propagates to higher altitudes, was identified for both parameters after the volcanic eruptions. A quasi-biannual oscillation (QBO) pattern at upper altitudes (28–32 km) is prominent in the anomalies of the analysed parameters. A comparison of the aerosol effective radii derived from SCIAMACHY and SAGE II data was performed. The average difference is found to be around 30 % at the lower altitudes, decreasing with increasing height to almost zero around 30 km. The data sample available for the comparison is, however, relatively small.

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