English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. In air quality research, often only size-integrated particle massconcentrations as indicators of aerosol particles are considered. However,the mass concentrations do not provide sufficient information to convey thefull story of fractionated size distribution, in which the particles ofdifferent diameters (Dp) 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 solve. Due to the instrumental insufficiency and inversion limitations, imputation methods 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 to 10 µm (optical equivalent size) and meteorological parameters collected at an urban background region in Amman, Jordan, in the period of 1 August 2016–31 July 2017. We develop and evaluate feed-forward neural network (FFNN) approaches to estimate number concentrations at particular size bin with (1) meteorological parameters, (2) number concentration at other sizebins and (3) both of the above as input variables. Two layers with 10–15neurons are found to be the optimal option. Worse performance is observed atthe lower edge (0.01&lt;Dp&lt;0.02 µm), the mid-rangeregion (0.15&lt;Dp&lt;0.5 µm) and the upper edge(6&lt;Dp&lt;10 µm). For the edges at both ends, thenumber of neighbouring size bins is limited, and the detection efficiency bythe corresponding instruments is lower compared to the other size bins. Adistinct performance drop over the overlapping mid-range region is due tothe deficiency of a merging algorithm. Another plausible reason for thepoorer performance for finer particles is that they are more effectivelyremoved from the atmosphere compared to the coarser particles so that therelationships between the input variables and the small particles are moredynamic. An observable overestimation is also found in the early morning forultrafine particles followed by a distinct underestimation before midday. Inthe winter, due to a possible sensor drift and interference artefacts, theestimation performance is not as good as the other seasons. The FFNNapproach 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 FFNN approach using the number concentration atthe other size bins can serve as an alternative way to replace negativenumbers in the size distribution raw dataset thanks to its high accuracy andreliability (R2= 0.97–1). This negative-number filling approachcan maintain a symmetric distribution of errors and complement the existingill-posed built-in algorithm in particle sizer instruments.

Data imputation in in situ-measured particle size distributions by means of neural networks