scholarly journals Emerging Investigator Series: COVID-19 lockdown effects on aerosol particle size distributions in northern Italy

2021 ◽  
Author(s):  
Jiali Shen ◽  
Alessandro Bigi ◽  
Angela Marinoni ◽  
Janne Lampilahti ◽  
Jenni Kontkanen ◽  
...  
Keyword(s):  
Particle Size ◽  
Size Distribution ◽  
Aerosol Particle ◽  
Air Pollutants ◽  
Particle Number ◽  
Northern Italy ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Number Size Distribution ◽  
Aerosol Particle Size

Impact of lockdown measures on the air pollutants and particle number size distribution.

Supervised regression learning for predictions of aerosol particle size distributions from PM2.5, total particle number and meteorological parameters at Helsinki SMEAR3 station

2020 ◽  
Author(s):  
Juha Kangasluoma ◽  
Yusheng Wu ◽  
Runlong Cai ◽  
Joel Kuula ◽  
Hilkka Timonen ◽  
...  
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Surface Area ◽  
Aerosol Particle ◽  
Particle Number ◽  
Meteorological Parameters ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Aerosol Particle Size ◽  
Regression Learning

<p>Supervised regression learning for predictions of aerosol particle size distributions from PM2.5, total particle number and meteorological parameters at Helsinki SMEAR3 station</p><p> </p><p>J. Kangasluoma<sup>1</sup>, Y. Wu<sup>1</sup>, R. Cai<sup>1</sup>, J. Kuula<sup>2</sup>, H. Timonen<sup>2</sup>, P. P. Aalto<sup>1</sup>, M. Kulmala<sup>1</sup>, T. Petäjä<sup>1</sup></p><p> </p><p><sup>1</sup> Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, Finland</p><p><sup>2 </sup>Finnish Meteorological Institute, Erik Palménin aukio 1, 00560 Helsinki, Finland</p><p> </p><p>Atmospheric particulate material is a significant pollutant and causes millions premature deaths yearly especially in urban city environments. To conduct epidemiological studies and quantify of the role of sub-micron particles, especially role of the ultrafine particles (<100 nm), in mortality caused by the particulate matter, long-term monitoring of the particle number, surface area, mass and chemical composition are needed. Such monitoring on large scale is currently done only for particulate mass, namely PM2.5 (mass of particulates smaller than 2.5 μm), while large body of evidence suggests that ultrafine particles, which dominate the number of the aerosol distribution, cause significant health effects that do not originate from particle mass.</p><p> </p><p>The chicken-egg-problem here is that monitoring of particle number or surface area is not required from the authorities due to lack of epidemiological evidence showing the harm and suitable instrumentation (although car industry already voluntarily limits the ultrafine particle number emissions), while these epidemiological studies are lacking because of the suitable lack of data. Here we present the first step in solving this “lack of data issue” by predicting aerosol particle size distributions based on PM2.5, particle total number and meteorological measurements, from which particle size distribution, and subsequently number, surface area and mass exposure can be calculated.</p><p> </p><p>We use baggedtree supervised regression learning (from Matlab toolbox) to train an algorithm with one full year data from SMEAR3 station at 10 min time resolution in Helsinki during 2018. The response variable is the particle size distribution (each bin separately) and the training variables are PM2.5, particle number and meteorological parameters. The trained algorithm is then used with the same training variables data, but from 2019 to predict size distributions, which are directly compared to the measured size distributions by a differential mobility particle sizer.</p><p> </p><p>To check the model performance, we divide the predicted distributions to three size bins, 3-25, 25-100 and 100-1000 nm, and calculate the coefficient of determination (r<sup>2</sup>) between the measured and predicted number concentration at 10 min time resolution, which are 0.79, 0.60 and 0.50 respectively. We also calculate r<sup>2</sup> between the measured and predicted number, surface area and mass exposure, which are 0.87, 0.79 and 0.74, respectively. Uncertainties in the prediction are mostly random, thus the r<sup>2</sup> values will increase at longer averaging times.</p><p> </p><p>Our results show that an algorithm that is trained with particle size distribution data, and particle number, PM2.5 and meteorological data can predict particle size distributions and number, surface area and mass exposures. In practice, these predictions can be realized e.g. in air pollution monitoring networks by implementing a condensation particle counter at each site, and circulating a differential mobility size spectrometer around the sites.</p><p> </p><p> </p>

scholarly journals Aerosol Microphysical Particle Parameter Inversion and Error Analysis Based on Remote Sensing Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1753 ◽  
Author(s):  
Huige Di ◽  
Qiyu Wang ◽  
Hangbo Hua ◽  
Siwen Li ◽  
Qing Yan ◽  
...  
Keyword(s):  
Particle Size ◽  
Aerosol Particle ◽  
Inversion Method ◽  
Lidar Data ◽  
Normal Distribution Function ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Base Function ◽  
Aerosol Particle Size ◽  
Particle Parameters

The use of Raman and high-spectral lidars enables measurements of a stratospheric aerosol extinction profile independent of backscatter, and a multi-wavelength (MW) lidar can obtain additional information that can aid in retrieving the microphysical characteristics of the sampled aerosol. The inversion method for retrieving aerosol particle size distributions and microphysical particle parameters from MW lidar data was studied. An inversion algorithm for retrieving aerosol particle size distributions based on the regularization method was established. Based on the inversion of regularization, the inversion method was optimized by choosing the base function closest to the aerosol distribution. The logarithmic normal distribution function was selected over the triangle function as the base function for the inversion. The averaging procedure was carried out for three main types of aerosol. The 1% averaging result near the minimum of the discrepancy gave the best estimate of the particle parameters. The accuracy and stabilization of the optimized algorithm for microphysical parameters were tested by scores of aerosol size distributions. The systematic effects and random errors impacting the inversion were also considered, and the algorithm was tested by the data, showing 10% systematic error and 15% random error. At the same time, the reliability of the proposed algorithm was also verified by using the aerosol particle size distribution data of the aircraft. The inversion results showed that the algorithm was reliable in retrieving the aerosol particle size distributions at vertical heights using lidar data.

scholarly journals Remarkable dynamics of nanoparticles in the urban atmosphere

2011 ◽  
Vol 11 (13) ◽  
pp. 6623-6637 ◽  
Author(s):  
M. Dall'Osto ◽  
A. Thorpe ◽  
D. C. S. Beddows ◽  
R. M. Harrison ◽  
J. F. Barlow ◽  
...  
Keyword(s):  
Particle Size ◽  
Size Distribution ◽  
Urban Areas ◽  
Particle Number ◽  
Road Traffic ◽  
Urban Atmosphere ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Number Count ◽  
Adverse Health Effects

Abstract. Nanoparticles emitted from road traffic are the largest source of respiratory exposure for the general public living in urban areas. It has been suggested that adverse health effects of airborne particles may scale with airborne particle number, which if correct, focuses attention on the nanoparticle (less than 100 nm) size range which dominates the number count in urban areas. Urban measurements of particle size distributions have tended to show a broadly similar pattern dominated by a mode centred on 20–30 nm diameter emitted by diesel engine exhaust. In this paper we report the results of measurements of particle number concentration and size distribution made in a major London park as well as on the BT Tower, 160 m aloft. These measurements taken during the REPARTEE project (Regents Park and BT Tower experiment) show a remarkable shift in particle size distributions with major losses of the smallest particle class as particles are advected away from the traffic source. In the Park, the traffic related mode at 20–30 nm diameter is much reduced with a new mode at <10 nm. Size distribution measurements also revealed higher number concentrations of sub-50 nm particles at the BT Tower during days affected by higher turbulence as determined by Doppler Lidar measurements and are indicative of loss of nanoparticles from air aged during less turbulent conditions. These results are suggestive of nanoparticle loss by evaporation, rather than coagulation processes. The results have major implications for understanding the impacts of traffic-generated particulate matter on human health.

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