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This study aims to develop PM2.5 prediction models using air pollutant data (PM10, NO2, SO2, O3, CO, and PM2.5) and meteorological data (temperature, humidity, wind speed, atmospheric pressure, precipitation, and snowfall) measured in South Korea from 2015 to 2019. Two prediction models were developed using an artificial neural network (ANN): a nationwide (NW) model and administrative districts (AD) model. To develop the prediction models, the independent variables daily averages and variances of air pollutant data and meteorological data (independent variables) were used as independent variables, and daily average PM2.5 concentration set as a dependent variable. First, the correlations between independent and dependent variables were analyzed. Second, prediction models were developed using an ANN to predict next-day PM2.5 daily average concentration, both NW and in 16 AD. The ANN models were optimized using a factorial design to determine the hidden layer layout and threshold, and a seasonal (monthly) factor was also considered. In the optimal prediction model, the absolute error in 1 σ was 91% (in-sample 91%, out-of-sample 91%) for the NW model, and the absolute error in 1 σ was 86% (in-sample 88%, out-of-sample 84%) for AD model. The accuracy of these prediction models increases further when they are developed using the next-day weather data, assuming that the weather prediction is accurate. [ABSTRACT FROM AUTHOR]

Supported liquid membranes (including those containing ionic liquids) offer the opportunity for high selectivity for a target gas imparted by the properties of the functional liquid or liquid mixture, along with high flux resulting from gas permeation through a liquid rather than a solid. One disadvantage of supported liquid membranes is limitations on the transmembrane pressure (TMP) they can sustain. Liquids held in the pores of the support by capillary forces are prone to "blow-out" at relatively modest TMP values. Here, we modify anodic aluminum oxide (AAO) membranes with uniformly sized ∼6–8 nm diameter cylindrical silica mesopores to improve the blow-out TMP. This modification increases the feasible TMP for various ionic liquids from ∼4 bar to more than ∼20 bar. Additionally, the liquid in the membrane at depths where the AAO membrane has not been modified (i.e., greater than a few microns) is expelled from the large pores, reducing the effective thickness of the liquid in the membrane and increasing permeance. This approach is analogous to asymmetric polymeric membranes, which are the standard of the commercial membrane industry. These findings open up the opportunity for investigation and use of supported liquid membranes at much higher TMPs than has been previously possible. [Display omitted] • Supported ionic liquid membranes (SILMs) were prepared by structurally modifying a porous support with mesoporous silica. • SILM did not exhibit blow-out of the ionic liquid (IL) even for ΔP >10 bar but the thickness of the IL was reduced. • Gas permeance increases with increasing ΔP due to IL being expelled from the unmodified part of the base pores. • Effective thickness reduction behavior follows Young-Laplace prediction for various ILs. • This approach demonstrates the possibility of stable high flux high pressure gas separation using SILMs. [ABSTRACT FROM AUTHOR]

Recently, carsharing has emerged as an alternative to vehicle ownership in many countries worldwide. This study examines the system parameters of one-way carsharing systems to determine a trade-off between the lost sales rate and system parameters. Four system parameters are considered in this study, namely average number of vehicles per station, rate of one-way trip, average number of staffs per station, and relocation policy. The performance of round-trip carsharing systems is also compared to that of one-way carsharing systems. A regression model is developed to predict the lost sales rate based on the various operational conditions for a one-way carsharing system. The findings of the study provide tools to understand the impact of carsharing system parameters on the efficiency of carsharing operations and predict the performance of such systems when any modifications occur. [ABSTRACT FROM AUTHOR]

We developed equations for nano-sized titanium dioxide (TiO2) particles self preserving time (SPT) lag that combines with agglomerate key parameters such as primary particle size (PPS), geometric standard deviation (GSD) and mass fractal dimension (MFD). A statistical formula has been developed that relies on SPT lag as the key parameter of agglomerates. Finally, this research presents the first analytical solution by integrating these key parameters into one formula, which can be utilized as a handy tool to calculate the time for reaching the asymptotic state. [ABSTRACT FROM AUTHOR]

Separation of olefins from their paraffin analogs relies on energy-intensive cryogenic distillation. Facilitated transport-based membranes that reversibly and selectively bind olefins, but not paraffins, could save considerable amounts of energy. However, the chemical instability of the silver ion olefin-binding carriers in such membranes has been a longstanding roadblock for this approach. We discovered long-term carrier stability against extended exposure to hydrogen, a common contaminant in such streams. Based on UV/Vis absorption and Raman spectroscopy, along with XRD analysis results, certain ionic liquids solubilize silver ions, and anion aggregates surrounding the silver ion carriers greatly attenuate their reduction by hydrogen. Here, we report the stability of olefin/paraffin separation properties under continuous exposure to high pressure hydrogen, which addresses a critical technical roadblock in membrane-based olefin/paraffin separation., (© 2022 Wiley-VCH GmbH.)