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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 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.

Differential mobility spectrometry (DMS) is a promising measurement technique. It is used in the detection of chemical warfare agents, explosives, drugs, and volatile organic compounds. The measurement principle is based on separation of gas-phase ions according to their differential mobility in alternating low and high electric fields. The DMS measurement result is a two dimensional spectrum of ion current displayed as a function of separation voltage and compensation voltage. The DMS spectral peaks, in terms of their height, location and width, are affected by gas sample composition, separation field and the gas flow rate. In this work, there is presented the calibration procedure which utilises the univariate and multivariate approach to differential ion mobility spectrum. We demonstrated the possibility of a successful retrieval of quantitative information using partial least squares regression as well as univariate linear regression. However, the multivariate approach outperformed the univariate one in terms of the quality of the model and the concentration prediction accuracy.

Formaldehyde is an important indoor air pollutant. It is released mainly from building materials and consumer products. This contaminant has been recognized as having negative effect on humans. Recently, there are developed methods of reducing its emission as well as removal from indoor air. An example of the last approach is the functionalisation of interior wall paints. The influence of such paint on indoor air was preliminarily examined in this work. The obtained results show that, during intermittent exposures, responses of formaldehyde measurement device were lower in the room painted with functionalised paint compared with the room painted using traditional formula paint. This outcome was statistically significant in 67% of the examined cases at the significance level α=0.01. It was found that the serious problem associated with determining the influence of functionalised paint on indoor air, in real conditions, is the low level of formaldehyde concentrations, compared with the detection limit and accuracy of the available measurement devices.

BTEX compounds can cause a threat to environment and human health. For this reason measurement devices are needed for rapid identification of such pollutants. The paper presents the results of recognition of mixtures of benzene, toluene, ethylbenzene and xylenes in humid air by means of two gas sensor arrays and linear discriminant analysis. Measurements were conducted during stabilization/solidification processes of contaminated soils. High classification ratios were obtained for both arrays (I: 88%–94%; II: 94%–96%). Improvement of identification was achieved when two copies of every sensor were included in analysis – classification rates reached 97.1–100%.

This work focussed on thermal comfort as the basis to control indoor conditions. Its objective is a method to determine thermal preferences of office occupants. The method is based on detection of thermal events. They occur when indoor conditions are under control of occupants. Thermal events are associated with the use of local heating/cooling sources which have user-adjustable settings. The detection is based on Fourier analysis of indoor temperature time series. The relevant data is collected by temperature sensor. We achieved thermal events recognition rate of 86 %. Conditions when indoor conditions were beyond control were detected with 95.6 % success rate. Using experimental data it was demonstrated that the method allows to reproduce key elements of temperature statistics associated with conditions when occupants are in control of thermal comfort.

Volatile organic compounds are one of the most important group of air pollutants. Potential health and environmental problems resulting from their emission prompted the requirement for monitoring these species. It motivates development of new measurement techniques which are fast, cost effective, reliable and field deployable. One of novel approaches is ion mobility spectrometry. It dwells on ion separation in electric field, based on differences in ion mobility. Many variants of this method are developed. In this wok, differential ion mobility spectrometry (DMS) was considered in respect of acetate measurements in air. It was demonstrated that DMS offers linear response to methyl, ethyl, propyl and butyl acetate in concentration range from 0.3 ppm to 7 ppm. Positive ions spectrum has to be utilised for this purpose. We showed that fragments of DMS spectrum which secure linearity are compound-specific. The obtained results are promising from the application point of view.

Featured Application: Prediction of IAQ in the living room based on IAQ monitoring in the kitchen, as a support for IAQ control in apartments. This study explores the capability of machine learning techniques (MLTs) in predicting IAQ in apartments. Sensor data from kitchen air monitoring were used to determine the conditions in the living room. The analysis was based on several air parameters—temperature, relative humidity, CO2 concentration, and TVOC—recorded in five apartments. Multiple input–multiple output prediction models were built. Linear (multiple linear regression and multilayer perceptron (MLP)) and nonlinear (decision trees, random forest, k-nearest neighbors, and MLP) methods were investigated. Five-fold cross-validation was applied, where four apartments provided data for model training and the remaining one was the source of the test data. The models were compared using performance metrics (R2, MAPE, and RMSE). The naive approach was used as the benchmark. This study showed that linear MLTs performed best. In this case, the coefficients of determination were highest: R2 = 0.94 (T), R2 = 0.94 (RH), R2 = 0.63 (CO2), R2 = 0.84 (TVOC, based on the SGP30 sensor), and R2 = 0.92 (TVOC, based on the SGP30 sensor). The prediction of distinct indoor air parameters was not equally effective. Based on the lowest percentage error, best predictions were attained for indoor air temperature (MAPE = 1.57%), relative humidity (MAPE = 2.97%RH), and TVOC content (MAPE = 0.41%). Unfortunately, CO2 prediction was loaded with high error (MAPE = 20.83%). The approach was particularly effective in open-kitchen apartments, and they could be the target for its application. This research offers a method that could contribute to attaining effective IAQ control in apartments. [ABSTRACT FROM AUTHOR]

This work presents a multisensor device which is intended as an element of IoT for indoor environment (IE) monitoring. It is a portable, small-size, lightweight, energy-efficient direct-reading instrument. The device has an innovative design and construction. It offers real-time measurements of a wide spectrum of physical and chemical quantities (light intensity, temperature, relative humidity, pressure, CO2 concentration, content of volatile organic compounds including formaldehyde, NO2, and particulate matter), data storage (microSD; server as an option), transmission (WiFi; GSM and Ethernet as options), and visualization (smartphone application; PC as an option). Commercial low-cost sensors were utilized, which have been arranged in the individual sensing modules. In the case of gas sensors, dynamic exposure was chosen to ensure a minimum response time. The MQTT protocol was applied for data transmission and communication with other devices, as well as with the user. The multisensor device can collect huge amounts of data about the indoor environment to provide the respective information to the IoT. The device can be configured to control actuators of various auxiliary devices and equipment including external systems used for ventilation, heating, and air conditioning. The prototype is fully operational. The exemplary results of IE monitoring were shown. [ABSTRACT FROM AUTHOR]

Geosmin and 2-methylisoborneol (MIB) are the most common causes of unpleasant odours in drinking water. A method was proposed to detect and recognise these compounds in water and determine their concentrations. The method utilises commercial solid-state gas sensors and data analysis. Sample preparation plays an important role. The aqueous solution is converted into a gas sample using a specially designed dynamic headspace. The responses of the sensors are recorded during exposure to headspace vapours in a dynamic mode. The best limit of detection for geosmin, LOD = 6.20 µg/L, was attained with a TGS2602 sensor. The best limit of detection for MIB, LOD = 0.52 µg/L, was attained with a TGS826 sensor. Geosmin and MIB recognition was 100% successful based on TGS826 and TGS2602 response classifications. Geosmin and MIB concentrations were effectively determined in solutions containing one or both compounds. The respective mathematical models utilised the responses of TGS826 and TGS2602. The smallest concentration prediction error was RMSE = 2.19 µg/L (for geosmin) and RMSE = 0.33 µg/L (for MIB). The study demonstrated the application potential of non-specific gas sensors for the early warning monitoring of geosmin and MIB presence in water. Further studies are needed to develop a system that can be tested in field conditions. [ABSTRACT FROM AUTHOR]