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The inappropriate combustion of furniture-industry waste can be a source of serious environmental problems. We proposed a method which is capable to distinguish the emission resulting from the combustion of wood-based materials, the essential component of such waste. The originality of the approach consists in the classification of gas mixtures instead of focussing on the individual pollutants emitted to the atmosphere. The classification of emission was based on the measurements applying differential ion mobility spectrometry (DMS) and Fourier transform infrared (FTIR) spectrometry, for comparison. There were successfully distinguished emissions associated with combustion of wood-based materials: OSB board, MDF board and plywood (≥95% correct classifications in the class (ccc)) and wood: pellet and kindling wood (≥92% ccc). Results of classification based on DMS and FTIR measurements were similar. Emissions from the combustion of individual materials were best distinguished using DMS (100% ccc), as compared with FTIR, which offered lower performance, mostly >90% ccc. Regarding pairwise classification, the most distinctive were emissions from the combustion of plywood (DMS: 99% ccc; FTIR: 99% ccc), MDF board (DMS: 99% ccc; FTIR: 99% ccc) and OSB board (DMS: 99% ccc; FTIR: 98% ccc). Emissions form kindling wood (DMS: 100% ccc; FTIR: 95% ccc) and pellet (DMS: 97% ccc; FTIR: 98% ccc) caused a bit more confusion. In most cases, results of classification based on DMS and FTIR measurements were comparable. The success of classification based on DMS measurements proved that it is possible to detect the harmful emission without determining the chemical composition of the flue gas. This solution represents a new approach to air quality monitoring, which recently attracts increasing attention.

The first carbon aerogels prepared by autocondensation of condensed (flavonoid) tannin, a cheap renewable resource extracted from Mimosa barks, are presented. Mixing aqueous solutions of tannin and sodium silicate indeed produced the very fast gelation of tannins, thus leading to organic hydrogels which were first converted into organic aerogels by supercritical drying, and then into carbon aerogels after pyrolysis at 900 °C. Subsequent dissolution of the silica nanoparticles thus formed within the carbon structure further increased significantly the porosity of the materials. Silica thus appeared to be both the gelation catalyst and a pore-directing agent. In this work, the influence on the porosity of the silica/tannin ratio and of the nature of the etching agent used for removing the silica was thoroughly investigated by combining several techniques. The conditions for producing either the most porous materials or those with the highest surface areas were identified and discussed. Keywords: Condensed tannins, Autocondensation reactions, Mesoporous carbons, Carbon aerogels

Due to their natural origin and broad availability, industrial tannin extracts are extending their application field from the classical, and still principal, application in tannery, to oenology, pharmacy, adhesives and material science. Despite the growing interest, not much is known about the thermal valorization of these biomolecules. In this article we present the thermal properties of the eight most common commercial tannin extracts and their elemental chemical composition to shed light on the end-life of the bio-materials based on these polyphenols. Results have shown that hydrolysable tannins can be managed similarly to wood, while condensed tannins have higher heating values but also generally higher chlorine content. Sumac and gambier leaf extracts registered contained heating values and significant amounts of ashes rich in calcium and sulfur. The high chlorine content observed for the extracts of mimosa (Acacia Mearnsii) does not hinder the combustion of its derived products which can still be thermally valorized. Mimosa tannin-based materials can also be added up to 2% to wood pellets and briquettes without significantly affecting the overall quality of the biofuels according to the in-force standards.

The discovery of properties and applications of unknown materials is one of the hottest research areas in materials science. In this work, we navigate a route towards these goals by the development of a new type of graphyne nanostructure. It is synthesised by a Sonogashira cross-coupling reaction of 1,3,5-triethynylbenzene with cyanuric chloride resulting in an extended carbon-based material called TCC. Also, we modify the obtained TCC via fluorination using XeF2 at various concentrations to investigate the effect of fluorination on the triple bonds and the conjugated structure of graphyne. In this study, we put special emphasis on the determination of the impact of the fluorine content and the type of CF functionalities on the morphology, chemical and electronic structure, biocompatibility, electrical conductivity and possible applicability as anode materials for Li-ion batteries. The obtained results indicate that the character of C–F bonds influences the final properties of fluorinated materials. The polar C–F bonds are preferable for cell proliferation while CF2 groups are most suitable for battery devices, however, the appearance of PTFE-like units may have a negative impact on battery specific capacitance as well as on cell viability.

Colony Collapse Disorder (CCD) is an environmental threat on a global scale due to the irreplaceable role of bees in crop pollination. Varroa destructor (V.d.), a parasite that attacks honeybee colonies, is one of the primary causes of honey bee population decline and the most serious threat to the beekeeping sector. This work demonstrates the possibility of quantitatively determining bee colony infestation by V.d. using gas sensing. The results are based on analysing the experimental data acquired for eighteen bee colonies in field conditions. Their infestation rate was in the 0 to 24.76% range. The experimental data consisted of measurements of beehive air with a semiconductor gas sensor array and the results of bee colony V.d. infestation assessment using a flotation method. The two kinds of data were collected in parallel. Partial Least Square regression was applied to identify the relationship between the highly multivariate measurement data provided by the gas sensor array and the V.d. infestation rate. The quality of the developed quantitative models was very high, as demonstrated by the coefficient of determination exceeding R2 = 0.99. Moreover, the prediction error was

The study focused on a method of detection for bee colony infestation with the Varroa destructor mite, based on the measurements of the chemical properties of beehive air. The efficient detection of varroosis was demonstrated. This method of detection is based on a semiconductor gas sensor array and classification module. The efficiency of detection was characterized by the true positive rate (TPR) and true negative rate (TNR). Several factors influencing the performance of the method were determined. They were: (1) the number and kind of sensors, (2) the classifier, (3) the group of bee colonies, and (4) the balance of the classification data set. Gas sensor array outperformed single sensors. It should include at least four sensors. Better results of detection were attained with a support vector machine (SVM) as compared with the k-nearest neighbors (k-NN) algorithm. The selection of bee colonies was important. TPR and TNR differed by several percent for the two examined groups of colonies. The balance of the classification data was crucial. The average classification results were, for the balanced data set: TPR = 0.93 and TNR = 0.95, and for the imbalanced data set: TP = 0.95 and FP = 0.53. The selection of bee colonies and the balance of classification data set have to be controlled in order to attain high performance of the proposed detection method.

Particulate matter (PM) is an important component of air. Nowadays, major attention is payed to fine dust. It has considerable environmental impact, including adverse effect on human health. One of important issues regarding PM is the temporal variation of its concentration. The variation contains information about factors influencing this quantity in time. The work focuses on the character of PM concentration dynamics indoors, in the vicinity of emission source. The objective was to recognize between the homogeneous or heterogeneous dynamics. The goal was achieved by detecting normal and anomalous diffusion in fluctuations of PM concentration. For this purpose we used anomalous diffusion exponent, β which was derived from Mean Square Displacement (MSD) analysis. The information about PM concentration dynamics may be used to design sampling strategy, which serves to attain representative information about PM behavior in time. The data analyzed in this work was collected from single-point PM concentration monitoring in the vicinity of seven emission sources in industrial environment. In majority of cases we observed heterogeneous character of PM concentration dynamics. It confirms the complexity of interactions between the emission sources and indoor environment. This result also votes against simplistic approach to PM concentration measurement indoors, namely their occasional character, short measurement periods and long term averaging.

Indoor air quality (IAQ) in residential buildings is an issue of increasing importance. IAQ has a serious impact on human wellbeing, performance and health. In order to minimize negative consequences, considerable efforts are made to assure high quality of indoor air. Yet, attention should be payed to the fact that occupants, in particular their activities, heavily influence this component of environment. Therefore, the aim of this work was to develop a method, which allows to characterise the impact of occupant activity on indoor air. The method is based on a semi-quantitative measure called occupant activity impact (OAI) profile. It is individually created for each activity. The profile is prepared using indoor air parameters, which are significantly affected by the activity of occupants. The profile displays how these measurable parameters change due to the occurrence of the activity. The main elements of the method of occupant activity impact characterization are: 1) measurements of multiple indoor air parameters, 2) recording the occurrence of indoor activities, and 3) evaluation of the relation between indoor air parameters and occupant activities. The first task is realized using sensor technique. The second goal is attained by observation. The third aim is achieved using Fisher’s discriminant ratio. The method presented in this work allows to evaluate the character of impact, its range, direction and magnitude. Using an experimental data collected in an exemplary household, we demonstrated that OAI profile is effective in quantifying and analysing the impact of human activity on indoor microclimate.

Benzene, toluene, and xylene, called BTX compounds, are broadly distributed in the environment. They frequently co-occur in air, water and soil because of common emission sources. However, these compounds differ considerably in terms of impact, in particular on human health. Due to this fact, measurement techniques are needed, which allow for identification of individual BTX compounds in the environment. The aim of the work was to show that differential ion mobility spectrometry (DMS) is capable of recognizing benzene, toluene or xylene in filtered air containing various amount of water vapor. For this purpose, the compounds recognition was represented as the classification problem. The discriminative power of DMS spectra, obtained for negative and positive ions, were explored as the basis for classification. We demonstrated that benzene, toluene, or xylene may be recognized with 100% success rate, in filtered air with varying humidity (from 0 to 77% RH). The best results, achieved with a relatively simple k -nearest neighbor classifier, were based on DMS spectrum for negative ions, recorded in full range of separation and compensation voltages. We believe, that the demonstration of a successful coupling of DMS and classification technique will contribute to further development of this analytical technique as well as its application in the environmental assessment.

Particulate matter (PM) is one of the criteria pollutants which has been determined as harmful to public health and the environment. For this reason the ability to recognize its emission sources is very important. There are a number of measurement methods which allow to characterize PM in terms of concentration, particles size distribution, and chemical composition. All these information are useful to establish a link between the dust found in the air, its emission sources and influence on human as well as the environment. However, the methods are typically quite sophisticated and not applicable outside laboratories. In this work, we considered PM emission source discrimination method which is based on continuous measurements of PM concentration with a relatively cheap instrument and stochastic analysis of the obtained data. The stochastic analysis is focused on the temporal variation of PM concentration and it involves two steps: (1) recognition of the category of distribution for the data i.e. stable or the domain of attraction of stable distribution and (2) finding best matching distribution out of Gaussian, stable and normal-inverse Gaussian (NIG). We examined six PM emission sources. They were associated with material processing in industrial environment, namely machining and welding aluminum, forged carbon steel and plastic with various tools. As shown by the obtained results, PM emission sources may be distinguished based on statistical distribution of PM concentration variations. Major factor responsible for the differences detectable with our method was the type of material processing and the tool applied. In case different materials were processed by the same tool the distinction of emission sources was difficult. For successful discrimination it was crucial to consider size-segregated mass fraction concentrations. In our opinion the presented approach is very promising. It deserves further study and development.

The problem of indoor air quality (IAQ) has attracted a great deal of attention recently. One of interesting aspects of this problem is the characterization of human impact on IAQ. This work presents a method which allows to determine the diversity of building occupants influence on indoor air. The method is based on the supervised clustering of the indoor air measurement data in domains of various indoor air parameters. The introduced approach was applied to examine the exemplary data set. The diversity of impact was studied in the domain of air parameters which characterize thermal conditions indoors and in the domain of chemical parameters of indoor air. The obtained results showed that the diversity of impact was greatly related to the fact that occupants’ activities occurred in combinations, rather than individually. It was also demonstrated that the diversity of impact was domain dependent. Most of examined activities showed greater diversity of influence in the domain of parameters which characterize thermal conditions. This shows that the activity-specific impacts would be rather associated with the domain of chemical parameters of indoor air. The proposed method allows to obtain information which can be useful in the diagnostics of IAQ and for the control of ventilation.

This work was focussed on the recognition of the emission of volatile compounds resulting from the combustion of engineered wood products waste. This kind of waste is broadly used for heating purposes in an unauthorised way, giving rise to unorganised emissions. The recognition of such events is very difficult due to the complexity of the produced gas mixture. We proposed to apply differential ion mobility spectrometry (DMS). This is a promising technique in terms of complex gas mixtures measurements. The recognition was based on the measurements of ambient air in the vicinity of the emission source and classification. The ensemble of classification trees was chosen as the classier. The obtained results showed that volatile compounds resulting from the combustion of wood based boards waste produced the distinctive DMS spectra, which could be used as the basis for the effective recognition. We achieved almost 100 % successful recognition of: 1) ambient air which contained volatile compounds resulting from OSB board waste combustion, 2) ambient air which contained volatile compounds resulting from MDF board waste combustion, and 3) ambient air, which was did not contain volatile compounds of this kind. The presented results have a considerable practical value. The DMS spectrometer was successfully applied to recognize wood-based boards waste combustion in field conditions.

The objective of this work was to propose a way of preparing information about in-cabin air quality for the car driver or passenger. It was assumed that information should be objective, relevant, current, complete and useful. The major elements of our concept were: (1) monitoring of selected air parameters inside the cabin, (2) determination of indicators that characterise indoor air, based on measurements, and (3) the method of information extraction. We proposed to realise the first task with a sensor system. The second and third goals were reached by categorisation. The basis for defining categories were ranges of values of measured parameters or their combinations. In a way, the categories were used to quantify indicators of air quality. Shannon entropy and mutual information were applied to find the best categorisation. The concept was investigated using experimental data from car cabin air monitoring in various driving conditions. We conclude that information about air quality in car cabins may be successfully conveyed using the following indicators: thermal conditions – determined based on temperature and relative humidity measurements; air exchange – determined based on CO 2 concentration; and air freshness – determined based on volatile organic compounds (VOCs) content in car cabin air. Each indicator has three categories: bad, intermediate and good.

Benzene, toluene and xylene (BTX compounds) are chemicals of greatest concern due to their impact on humans and the environment. In many cases, quantitative information about each of these compounds is required. Continuous, fast-response analysis, performed on site would be desired for this purpose. Several methods have been developed to detect and quantify these compounds in this way. Methods vary considerably in sensitivity, accuracy, ease of use and cost-effectiveness. The aim of this work is to show that differential ion mobility spectrometry (DMS) may be applied for determining concentration of BTX compounds in humid air. We demonstrate, this goal is achievable by applying multivariate analysis of the measurement data using partial least squares (PLS) regression. The approach was tested at low concentrations of these compounds in the range of 5-20 ppm and for air humidity in a range 0-12 g/kg. These conditions correspond to the foreseeable application of the developed approach in occupational health and safety measurements. The average concentration assessment error was about 1 ppm for each: benzene, toluene and xylene. We also successfully determined water vapor content in air. The error achieved was 0.2 g/kg. The obtained results are very promising regarding further development of DMS technique as well as its application.

In this work, we focus on a transient state of sensor as a source of chemical information. This problem deserves attention for many reasons. As an example, utilization of transient states may allow for faster and more accurate information acquisition. We have considered a transient state gas sensor response to test analyte applied for quantitative gas determination. We examined the inflection point which is a promising feature of a signal resulting from transient sensor responses. This particular feature was found to be linearly dependent on the target gas concentration. However, the applicability of inflection point method is dependent on the precision of its determination. It raises a number of issues related to signal regularization. The regularization problem was studied from a theoretical point of view. A number of a-priori estimates for regularization strategies were obtained yielding some practical algorithms for calculating the inflection point in a stable way. Next, they were validated using nume...

Honey bees are subject to a number of stressors. In recent years, there has been a worldwide decline in the population of these insects. Losses raise a serious concern, because bees have an indispensable role in the food supply of humankind. This work is focused on the method of assessment of honey bee colony infestation by Varroa destructor. The approach allows to detect several categories of infestation: &ldquo, Low&rdquo, &ldquo, Medium&rdquo, and &ldquo, High&rdquo, The method of detection consists of two components: (1) the measurements of beehive air using a gas sensor array and (2) classification, which is based on the measurement data. In this work, we indicate the sensitivity of the bee colony infestation assessment to the timing of measurement data collection. It was observed that the semiconductor gas sensor responses to the atmosphere of a defined beehive, collected during 24 h, displayed temporal variation. We demonstrated that the success rate of the bee colony infestation assessment also altered depending on the time of day when the gas sensor array measurement was done. Moreover, it was found that different times of day were the most favorable to detect the particular infestation category. This result could indicate that the representation of the disease in the beehive air may be confounded during the day, due to some interferences. More studies are needed to explain this fact and determine the best measurement periods. The problem addressed in this work is very important for scheduling the beekeeping practices aimed at Varroa destructor infestation assessment, using the proposed method.