Evaluation of low-cost optical particle counters for monitoring individual indoor aerosol sources

Abstract

Low-cost optical particle counters (OPC) have gained increasing attention in recent years in exposure studies. Previous studies reported that the OPCs’ performance varies considerably with type of particles being measured; however, little information on their performance in monitoring common indoor aerosols is available. Given the significance of exposure to indoor aerosols and their associated adverse health effects, this experimental study investigates the performance of low-cost OPCs in monitoring individual aerosols that are commonly found indoors in a controlled chamber environment. Performances of four low-cost OPCs were examined under exposure to varying concentrations of biological (dust mite, pollen, cat, and dog fur) and non-biological (monodisperse silica and melamine resin) aerosols. Each particle sample was dispersed into the chamber using a computer-controlled syringe injection system, while size-resolved particle number concentrations were simultaneously measured by four low-cost OPCs (OPC N2, IC Sentinel, Speck, and Dylos) as well as a lab-grade reference sensor (AeroTrak). The study results showed measurable effects of particle size, particle type, and concentration on the low-cost OPC responses. Particle concentration had the most dominant effect on the linearity of low-cost sensors. Results also revealed that the sensor responses to four biological particles follow a similar pattern and converge to a linear line as the number concentration increases above 5?cm^?3. As for non-biological particles, the OPC responses were more varied depending on the particle type and size, especially in the concentration range <10?cm^?3. Calibration equations developed in this study provide baseline information for correcting low-cost OPC readings when utilized to measure concentrations of individual indoor aerosol sources.

Copyright © 2019 American Association for Aerosol Research     

Blasting pattern optimization using gene expression programming and grasshopper optimization algorithm to minimise blast-induced ground vibrations

Engineering with Computers - Blast-induced ground vibration is considered as one of the most hazardous phenomena of mine blasting, which can even cause casualties and severe damages to the adjacent...     

Physically crosslinked polyvinyl alcohol/chitosan/gum tragacanth hydrogels loaded with vitamin E for wound healing applications

For the healing process, in this study, an innovative polymeric hydrogel network including polyvinyl alcohol (PVA)/chitosan (CS)/gum tragacanth (GT) loaded with vitamin E (VE) was produced by the freeze–thaw approach. In order to investigate the characteristics of the prepared samples, Fourier transform infrared spectroscopy (FTIR) and scanning electron microscope (SEM) analyzes were performed. Also, water vapor transmission rate (WVTR), swelling ratio, gel fraction and mechanical properties were measured. Then, to observe their cytocompatibility, MTT assay and cell adhesion studies were assessed. The results of FTIR confirmed the presence of PVA, CS, GT, and VE in hydrogel films. As well as, the SEM images showed the effect of the freezing and thawing method in creating a smooth surface with small and regular pores. It was found with adding the CS and GT to PVA improves swelling ratio, gel fraction, WVTR and elongation of hydrogel films. Further, in examining the adhesion and cytotoxicity of the samples, the non-toxic quiddity of the PVA/CS/GT hydrogel films was corroborated. In the end, the antibacterial properties revealed that the film containing GT and CS had the greatest antibacterial activity. According to the observed results, PVA/CS/GT hydrogel films loaded with VE can be good for wound healing applications.     

A novel solution for simulating air overpressure resulting from blasting using an efficient cascaded forward neural network

Air overpressure (AOp) is a hazardous effect induced by the blasting method in surface mines. Therefore, it needs to be predicted to reduce the potential risk of damage. The aim of this study is to offer an efficient method to predict AOp using a cascaded forward neural network (CFNN) trained by Levenberg–Marquardt (LM) algorithm, called the CFNN-LM model. Additionally, a generalized regression neural network (GRNN) and extreme learning machine (ELM) were employed to demonstrate the accuracy level of the proposed CFNN-LM model. To conduct the CFNN-LM, GRNN, and ELM models, an extensive database, related to four quarry sites in Malaysia, was used including 62 sets of dependent and independent parameters. Next, the performances of the aforementioned models were checked and discussed through statistical criteria and efficient graphical tools. Finally, the results showed the superiority of CFNN-LM (R² = 0.9263 and RMSE = 3.0444) over GRNN (R² = 0.7787 and RMSE = 5.1211) and ELM (R² = 0.6984 and RMSE = 6.2537) models in terms of prediction accuracy. Furthermore, three different regression analysis metrics were used to perform the sensitivity analysis, and according to the obtained results, the maximum charge per delay (\(\beta\) = 0.475, SE = 0.115, t-test = 4.125) was considered as the most influential feature in modeling the AOp.