1,720,992 research outputs found
Assessment and comparison of multi-annual size profiles of particulate matter monitored at an urban-industrial site by an optical particle counter with a chemometric approach
Full text linkThe size of airborne particles is a key air quality parameter that is related to their composition, transport properties and effects on human health and the environment. Optical particle counters (OPCs) are increasingly used to dynamically characterize the size of ambient air particles. Monitoring campaigns lasting several months or even years generate millions of individual data values that must be effectively processed to extract information. Data mining algorithms as Self-Organizing Map (SOM) can support exploratory data analysis and pattern recognition in aerosol science. The use of SOMs, which offer powerful visualization features using 2D maps, allows us to interpret a large amount of data while avoiding any loss of information on variability from pre-treatments, such as compacting data recorded every minute to hourly or daily means. In the present study, we processed the data collected with an OPC during a long-term monitoring campaign (almost 3 years) conducted near residential buildings positioned very close to a steel plant and used them to assess and compare particulate matter (PM) profiles. About 12 million individual recorded values in total were handled. The current approach enabled us to identify four main PM profiles, follow their variation over time, and relate the differences to changes in the plant management and processes. Furthermore, it is potentially broadly applicable in high-frequency, long-term air quality monitoring campaigns employing different types of instruments to characterize the particle size and chemical composition of both PM and gases
Self-organizing map algorithm as a tool for analysis, visualization and interpretation of electronic nose high dimensional raw data
Full text linkElectronic noses used for outdoor ambient air characterization to assess odor impacts on population can produce large datasets since usually the sampling is conducted with high frequency (e.g. data per minute) for periods that can reach several months, with a number of sensors that ranges usually from four-six as a minimum, up to above thirty. The environmental analyst has thus to deal with large datasets (millions of data) that have to be properly elaborated for obtaining meaningful interpretation of the instrumental signals. A recent review questioned the capability of some classic statistical elaboration tools for application to e-noses, highlighting how very few in field application are present in scientific literature. In the present work we describe: (i) the use of Self-Organizing Map (SOM) algorithm as a tool for analysis and visualization of e-nose raw data collected at a receptor site near a bio-waste composting facility; (ii) a second level clusterization using k-means clustering algorithm to identify "air types" that can be detected at the receptor and (iii) the use of e-nose data related to the plant odour sources as well as odour measurements of ambient air collected at the receptor site, to classify the air types. Eventually we evaluate the frequency and duration of the air type/s identified as malodorous
On odour tolerability criteria from odorant instrumental monitoring
Full text linkAt present one of the most accepted criteria for assessing tolerability for environmental odour impacts is based on a modelling approach that requires the characterizations of the odour flow rates from stationary sources, definition of both a digital terrain model of the studied area and of relevant meteorology during the
considered period of time, so to provide an estimate of the areas where the number of hours exceeds specified hourly odour threshold (e.g. 1, 3 or 5 OUE). Often the 98th percentile of hourly peak odour concentration is considered at sensible receptors. In the case of complex odour sources or if the emitting entity is not collaborative, the experimental approach focused on receptor instrumental monitoring can provide a tool for the assessment of odour nuisance tolerability, so to foster or to force mitigation actions on odour emission sources. Since nowadays instruments allowing single (e.g. H2S monitoring instruments) or multiple (e.g. e-noses) odorant concentration monitoring are available, and they are often positioned at sensible receptors, experimental measurement of the exceedance of odour threshold can be provided, if consensus odour threshold values for the odorant are available. Also for the experimental approach, the 98th percentile of hourly odour concentration can be provided. A case study based on H2S monitoring is proposed; extension to e-noses application is possible, providing their active and efficient presence in the field 12 months a year
Testing performances of a newly designed olfactometer
Full text linkDynamic dilution olfactometry as regulated by EN 13725 requires instrumentation of adequate technology and in order to spread the use of Dynamic Olfactometry high usability of the device is a must. A new dynamic dilution olfactometer has been designed and manufactured after the experience gained in previous prototype development and performance studies as well as from experimental applications. Materials have been selected in order to be compliant with the expected next-to-come updates of the EN13725 technical norm and checks on pneumatic steps required by the odour concentration analysis procedure have been implemented.
In order to generate specific dilutions in a wide range, a high precision stepper motor is used, instead of the more common calibrated orifices. The instrument comes with option of incrementing dilution with a factor of √2 (instead of more usual 2), so to increase resolution of the odour measurements. The consumption of neutral compressed air has been highly reduced in comparison with previous prototypes. The new features of the instrument as well as the available dilution steps will be presented. A careful check of accuracy and operative speed at high dilutions has been performed. Standard n-butanol, and samples from ambient air collected in
close proximity from odour emitting sources have been collected for testing the olfactometer and the panel response. A market top instrument has been considered for sake of comparison
SOMEnv: An R package for mining environmental monitoring datasets by Self-Organizing Map and k-means algorithms with a graphical user interface
No full textThe Self-Organizing Map (SOM) algorithm belongs to the family of artificial neural networks. It is an unsupervised method that requires no a priori knowledge regarding experimental data classification. Further, it can deal with large datasets and non-linear problems, providing powerful visualization features for outcome exploration on 2D maps. For environmental pollution assessments other unsupervised techniques are widely used, such as principal component and hierarchical cluster analyses, but their application for mining large datasets and properly visualizing the results is limited, making them difficult to use for handling of large datasets obtained by high frequency environmental monitoring. This study presents an R package (SOMEnv) that allows non-expert users to elaborate by SOM algorithm environmental variables (pollutants and/or chemical physical properties) recorded with high frequency for a long monitoring period. Additionally, SOMEnv can also be used for elaborating small datasets derived from uneven sampling. All the calculations and outcome visualizations can be done using a graphical user interface (GUI), meaning that experience in R software coding is not necessary, and only a basic knowledge regarding the employed algorithm is needed to interpret the results. The benefits of the SOMEnv package are that (i) both the software environment and tool are freely available; (ii) it is able to handle large datasets; (iii) it provides heuristic rules for SOM initialization; (iv) it has a built-in GUI for performing calculations and visualizing the results. Moreover, it comes with a wide range of visualizations, several of which are dedicated to high frequency data monitoring. An example of application is presented. The package is freely available on the Comprehensive R Archive Network (CRAN) repository
Self-organizing map algorithm for assessing spatial and temporal patterns of pollutants in environmental compartments: A review
Full text linkThe evaluation of the spatial and temporal distribution of pollutants is a crucial issue to assess the anthropogenic burden on the environment. Numerous chemometric approaches are available for data exploration and they have been
applied for environmental health assessment purposes. Among the unsupervised methods, Self-Organizing Map
(SOM) is an artificial neural network able to handle non-linear problems that can be used for exploratory data analysis,
pattern recognition, and variable relationship assessment. Much more interpretation ability is gained when the SOMbased model is merged with clustering algorithms. This review comprises: (i) a description of the algorithm operation
principle with a focus on the key parameters used for the SOM initialization; (ii) a description of the SOM output features and how they can be used for data mining; (iii) a list of available software tools for performing calculations; (iv)
an overview of the SOM application for obtaining spatial and temporal pollution patterns in the environmental compartments with focus on model training and result visualization; (v) advice on reporting SOM model details in a pape
Pattern recognition and anomaly detection by self-organizing maps in a multi month e-nose survey at an industrial site
Full text linkCurrently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses
Disentangling Multiannual Air Quality Profiles Aided by Self-Organizing Map and Positive Matrix Factorization
Full text linkThe evaluation of air pollution is a critical concern due to its potential severe impacts on human health. Currently, vast quantities of data are collected at high frequencies, and researchers must navigate multiannual, multisite datasets trying to identify possible pollutant sources while addressing the presence of noise and sparse missing data. To address this challenge, multivariate data analysis is widely used with an increasing interest in neural networks and deep learning networks along with well-established chemometrics methods and receptor models. Here, we report a combined approach involving the Self-Organizing Map (SOM) algorithm, Hierarchical Clustering Analysis (HCA), and Positive Matrix Factorization (PMF) to disentangle multiannual, multisite data in a single elaboration without previously separating the sites and years. The approach proved to be valid, allowing us to detect the site peculiarities in terms of pollutant sources, the variation in pollutant profiles during years and the outliers, affording a reliable interpretation
Surface-Enhanced Raman Scattering of Bioaerosol: Where Are We Now? A Systematic Review
Full text linkSurface-enhanced Raman scattering (SERS) spectroscopy has grown in popularity as a bioaerosol monitoring method due to its high sensitivity and specificity, as well as its ability to be performed in complex biological mixtures using portable and relatively inexpensive devices. However, due to a lack of standardised methodologies, SERS sensing of bioaerosols remains difficult. Full-length peer-reviewed journal articles related to the application of SERS spectroscopy to examine bioaerosols were systematically searched in PubMed, Scopus, and Web of Science databases using the PRISMA guidelines. A total of 13 studies met the inclusion criteria for our systematic literature search. A critical evaluation of the experimental aspects involved in the collection of bioaerosols for SERS analysis is presented, as well as the elective applicability and weaknesses of various experimental setups, helping to provide a solid foundation for real-time bioaerosol characterisation using SERS spectroscopy
Derivatized volatile organic compound characterization of Friulano wine from Collio (Italy–Slovenia) by HS-SPME-GC-MS and discrimination from other varieties by chemometrics
Full text linkPurpose: Methods to assess the authenticity and traceability of wines have been extensively studied as enhancers of food quality, allowing producers to obtain market recognition and premium prices. Among analytical techniques, the volatilome profile attained by gas chromatography coupled with mass spectrometry is acquiring more and more attention by the scientific community, together with the use of chemometrics Design/methodology/approach: The volatilome profile of three varieties of blanc wines from the Collio area (namely Ribolla Gialla, Malvasia and Friulano) between Italy and Slovenia, was determined by head space-solid phase micro extraction-gas chromatography-mass spectrometry, enhancing the carbonyl compounds identification with O-(2, 3, 4, 5, 6-pentafluorobenzyl)-hydroxylamine with the aim of identifying the autochthonous Friulano variety. Findings: A two-step chemometric approach based on an unsupervised technique (PCA) followed by a supervised one (PLS-DA) allowed to identify possible markers for discriminating the Friulano Collio variety from the others, in particular two chemical classes were identified by PCA (ketones and long chain esters). PLS-DA showed 87% accuracy in classification. A correct classification (i.e. non-Friulano Collio) of a group of wines obtained from the same grape variety but produced in an extra-Collio area was obtained as well. The results confirmed the benefits of using a derivatization step prior to volatile organic compounds analysis. Research limitations/implications: Among methods to assess the authenticity and traceability of wines, volatilome profile of wines determined by head space-solid phase micro extraction-gas chromatography-mass spectrometry, enhanced by the carbonyl compound identifications with O-(2, 3, 4, 5, 6-pentafluorobenzyl)-hydroxylamine, may have a key role in conjunction with chemometrics and, in particular with principal component analysis and partial least square discriminant analysis. Practical implications: Among methods to assess the authenticity and traceability of Friulano wine, volatilome profile of wines determined by head space-solid phase micro extraction-gas chromatography-mass spectrometry, enhanced by the carbonyl compound identifications with O-(2, 3, 4, 5, 6-Pentafluorobenzyl)Hydroxylamine hydrochloride, may have a key role in conjunction with chemometrics. Originality/value: Few works investigated both wine traceability with a volatilome enhancer and chemometrics of the Friulano wine variety obtaining such an improvement in this wine variety discrimination
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