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https://www.selleckchem.com/products/otub2-in-1.html In this study, diatomite was refined by a simple purification method consisting of calcination combined with acid washing. Optimal purification conditions were the focus, including the influence of conditions on diatomite morphology, structure, and specific surface area. The results showed that the optimal conditions were a 550 °C carbonization temperature and 25 wt% HCl. This purified diatomite was then employed to adsorb gallic acid (GA) from molasses wastewater in a series of adsorption experiments, which illustrated that (ḭ) GA adsorption fitted a pseudo-second-order model and the Freundlich equation better with GA adsorption by purified diatomite; (ḭḭ) the adsorption process was physical, nonspontaneous, and endothermic; (ḭḭḭ) the maximum GA adsorption capacity by purified diatomite was 19.852 mg g-1. This study reported the examination of a promising material for sugar mill wastewater pretreatment.Factor analysis models use the covariance of measured variables to identify and apportion sources. These models, particularly positive matrix factorization (PMF), have been extensively used for analyzing particle number concentrations (PNCs) datasets. However, the variation of observed PNCs and particle size distribution are driven by both the source emission rates and atmospheric dispersion as well as chemical and physical transformation processes. This variation in the observation data caused by meteorologically induced dilution reduces the ability to obtain accurate source apportionment results. To reduce the influence of dilution on quantitative source estimates, a methodology for improving the accuracy of source apportionment results by incorporating a measure of dispersion, the ventilation coefficient, into the PMF analysis (called dispersion normalized PMF, DN-PMF) was applied to a PNC dataset measured from a field campaign that includes the Spring Festival event and the start of the COVID-19 lockdown ally r

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