This study compares the application of two variable selection methods in partial least squares regression (PLSR), the variable importance in projection (VIP) method and the selectivity ratio (SR) method. For this purpose, three different data sets were analysed: (a) physiochemical water quality parameters related to sensorial data, (b) gas chromatography-mass spectrometry (GC-MS) chemical (organic compound) profiles from fossil sea sediment samples related to sea surface temperature (SST) changes, and (c) exposed genes of Daphnia magna female samples related to their total offspring production. Correlation coefficients (r), levels of significance (p-value) and interpretation of the underlying experimental phenomena allowed the discussion about the best approach for variable selection in each case. The comparison of the two variable selection methods in the first water quality data set showed that the SR method is more accurate for sensorial prediction. For the climate data set, when raw total ion current (TIC) GC-MS chromatograms were considered, variables selected using the VIP method were easier to interpret compared with those selected by the SR method. However, when only some chromatographic peak areas (concentrations) were considered, the SR method was more efficient for prediction, and the VIP method selected the most relevant variables for the interpretation of SST changes. Finally, for the transcriptomic data set, the SR method was found again to be more reliable for prediction purposes.
The selection of suitable solvents is a crucially important subject in a wide range of chemical processes. The study presents a solvent selection guide where 151 solvents were assessed, including a significant number of recently reported bio-based solvents. The assessment procedure involves grouping of solvents according to their physicochemical parameters and ranking within clusters according to their toxicological and hazards parameters. Grouping of solvents resulted in formation of three clusters -nonpolar and volatile (35 solvents), nonpolar and sparingly volatile (35 solvents) and polar ones (81 solvents). The comparison of toxicological and hazard related data indicated that solvents from the third cluster should be preferentially chosen. Within each group, a solvent ranking was performed by means of the TOPSIS procedure based on 15 different criteria. Because of lack of certain data (especially toxicological), different ranking confidence levels were introduced. The highest confidence rankings were performed only for some solvents but with all considered criteria. Low confidence rankings were created for all solvents but were based on certain criteria only. The results of our solvent selection guide (SSG) are generally in agreement with results of others but allow for finer ranking of solvents. The assessment procedure is easy to be adapted to individual chemist's needs and allows to include new solvents to the ranking.
The present investigation, carried out as a case study in a typical major city situated in a European coal combustion region (Krakow, Poland), aims at quantifying the impact on the urban air quality of residential heating by coal combustion in comparison with other potential pollution sources such as power plants, industry, and traffic. Emissions were measured for 20 major sources, including small stoves and boilers, and the particulate matter (PM) was analyzed for 52 individual compounds together with outdoor and indoor PM10 collected during typical winter pollution episodes. The data were analyzed using chemical mass balance modeling (CMB) and constrained positive matrix factorization (CMF) yielding source apportionments for PM10, B(a)P, and other regulated air pollutants namely Cd, Ni, As, and Pb. The results are potentially very useful for planning abatement strategies in all areas of the world, where coal combustion in small appliances is significant. During the studied pollution episodes in Krakow, European air quality limits were exceeded with up to a factor 8 for PM10 and up to a factor 200 for B(a)P. The levels of these air pollutants were accompanied by high concentrations of azaarenes, known markers for inefficient coal combustion. The major culprit for the extreme pollution levels was demonstrated to be residential heating by coal combustion in small stoves and boilers (>50% for PM10 and >90% B(a)P), whereas road transport (<10% for PM10 and <3% for B(a)P), and industry (4-15% for PM10 and <6% for B(a)P) played a lesser role. The indoor PM10 and B(a)P concentrations were at high levels similar to those of outdoor concentrations and were found to have the same sources as outdoors. The inorganic secondary aerosol component of PM10 amounted to around 30%, which for a large part may be attributed to the industrial emission of the precursors SO2 and NOx.
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