Chemometrics for soil pollution monitoring
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Chemometrics, defined as mathematics and statistics in chemistry, has been widely present
in soil pollution monitoring studies for quite some time. The chemometric approach
incorporates a large number of algorithms and models. In most cases, chemometric
methods are used to optimize the measurement procedures to gather soil pollution
monitoring data or to elucidate qualitative and quantitative relationships within these data,
which may be quite complex and be overlooked when using classical methodologies. Soil
pollution monitoring involves the processes of sampling and analyzing specific toxic
pollutants, such as heavy metal(loid)s, polycyclic aromatic hydrocarbons (PAH),
polychlorinated biphenyls (PCB), petroleum hydrocarbons, pesticides, and radionuclides.
With the help of modern analytical techniques, the spatial and temporal changes in
concentrations of these pollutants usually generate a vast amount of data. In order to
achieve meaningful soil monitoring, both sampling/a...nalysis and data assessment
procedures must be carefully planned and performed. This often includes factorial design
strategies for experiment optimization. Two factorial designs, one used for screening the
selected parameters and the other used for their optimization, are usually used together.
Alternatively, a sequential optimization may be made by applying the Simplex algorithm.
On the other hand, among the most used chemometric methods for soil pollution
monitoring data evaluation, two groups can be distinguished: supervised and unsupervised
pattern recognition methods. In supervised methods, input and output data are known, and
the primary goal is to find the relationship between inputs and outputs. These methods are
most often used in classification so that the soil sample joins an already defined group.
There is also an application in regression, where the input values of soil contaminant
predict the output result. This group includes partial least squares regression (PLS),
discriminant analysis (DA), artificial neural network (ANN), support vector machines
(SVM). In unsupervised methods, no prior knowledge of any relationship in the soil
pollution dataset is required. The aim is to identify the underlying structure within the
dataset. These methods are commonly used for dimensionality reduction and exploratory
analysis. The following unsupervised methods are frequently used: principal component
analysis (PCA), cluster analysis (CA), positive matrix factorization (PMF), and Kohonen
self-organizing maps (SOM). In addition to the chemometric tools mentioned above,
geographic information system (GIS) analysis protocols, such as spatial autocorrelation,
inverse distance weighted interpolation, and kriging, are unavoidable in soil pollution
monitoring.
Кључне речи:
multivariate / classification / GIS / trace / PCA / sourceИзвор:
Book of Abstracts / 3rd International and 15th National Congress Soils for Future Under Global Challenges, 21-24 September 2021 Sokobanja, Serbia, 2021, 7-Издавач:
- Belgrade : Serbian Society of Soil Science
Институција/група
Tehnološko-metalurški fakultetTY - CONF AU - Onjia, Antonije PY - 2021 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/7096 AB - Chemometrics, defined as mathematics and statistics in chemistry, has been widely present in soil pollution monitoring studies for quite some time. The chemometric approach incorporates a large number of algorithms and models. In most cases, chemometric methods are used to optimize the measurement procedures to gather soil pollution monitoring data or to elucidate qualitative and quantitative relationships within these data, which may be quite complex and be overlooked when using classical methodologies. Soil pollution monitoring involves the processes of sampling and analyzing specific toxic pollutants, such as heavy metal(loid)s, polycyclic aromatic hydrocarbons (PAH), polychlorinated biphenyls (PCB), petroleum hydrocarbons, pesticides, and radionuclides. With the help of modern analytical techniques, the spatial and temporal changes in concentrations of these pollutants usually generate a vast amount of data. In order to achieve meaningful soil monitoring, both sampling/analysis and data assessment procedures must be carefully planned and performed. This often includes factorial design strategies for experiment optimization. Two factorial designs, one used for screening the selected parameters and the other used for their optimization, are usually used together. Alternatively, a sequential optimization may be made by applying the Simplex algorithm. On the other hand, among the most used chemometric methods for soil pollution monitoring data evaluation, two groups can be distinguished: supervised and unsupervised pattern recognition methods. In supervised methods, input and output data are known, and the primary goal is to find the relationship between inputs and outputs. These methods are most often used in classification so that the soil sample joins an already defined group. There is also an application in regression, where the input values of soil contaminant predict the output result. This group includes partial least squares regression (PLS), discriminant analysis (DA), artificial neural network (ANN), support vector machines (SVM). In unsupervised methods, no prior knowledge of any relationship in the soil pollution dataset is required. The aim is to identify the underlying structure within the dataset. These methods are commonly used for dimensionality reduction and exploratory analysis. The following unsupervised methods are frequently used: principal component analysis (PCA), cluster analysis (CA), positive matrix factorization (PMF), and Kohonen self-organizing maps (SOM). In addition to the chemometric tools mentioned above, geographic information system (GIS) analysis protocols, such as spatial autocorrelation, inverse distance weighted interpolation, and kriging, are unavoidable in soil pollution monitoring. PB - Belgrade : Serbian Society of Soil Science C3 - Book of Abstracts / 3rd International and 15th National Congress Soils for Future Under Global Challenges, 21-24 September 2021 Sokobanja, Serbia T1 - Chemometrics for soil pollution monitoring SP - 7 UR - https://hdl.handle.net/21.15107/rcub_technorep_7096 ER -
@conference{ author = "Onjia, Antonije", year = "2021", abstract = "Chemometrics, defined as mathematics and statistics in chemistry, has been widely present in soil pollution monitoring studies for quite some time. The chemometric approach incorporates a large number of algorithms and models. In most cases, chemometric methods are used to optimize the measurement procedures to gather soil pollution monitoring data or to elucidate qualitative and quantitative relationships within these data, which may be quite complex and be overlooked when using classical methodologies. Soil pollution monitoring involves the processes of sampling and analyzing specific toxic pollutants, such as heavy metal(loid)s, polycyclic aromatic hydrocarbons (PAH), polychlorinated biphenyls (PCB), petroleum hydrocarbons, pesticides, and radionuclides. With the help of modern analytical techniques, the spatial and temporal changes in concentrations of these pollutants usually generate a vast amount of data. In order to achieve meaningful soil monitoring, both sampling/analysis and data assessment procedures must be carefully planned and performed. This often includes factorial design strategies for experiment optimization. Two factorial designs, one used for screening the selected parameters and the other used for their optimization, are usually used together. Alternatively, a sequential optimization may be made by applying the Simplex algorithm. On the other hand, among the most used chemometric methods for soil pollution monitoring data evaluation, two groups can be distinguished: supervised and unsupervised pattern recognition methods. In supervised methods, input and output data are known, and the primary goal is to find the relationship between inputs and outputs. These methods are most often used in classification so that the soil sample joins an already defined group. There is also an application in regression, where the input values of soil contaminant predict the output result. This group includes partial least squares regression (PLS), discriminant analysis (DA), artificial neural network (ANN), support vector machines (SVM). In unsupervised methods, no prior knowledge of any relationship in the soil pollution dataset is required. The aim is to identify the underlying structure within the dataset. These methods are commonly used for dimensionality reduction and exploratory analysis. The following unsupervised methods are frequently used: principal component analysis (PCA), cluster analysis (CA), positive matrix factorization (PMF), and Kohonen self-organizing maps (SOM). In addition to the chemometric tools mentioned above, geographic information system (GIS) analysis protocols, such as spatial autocorrelation, inverse distance weighted interpolation, and kriging, are unavoidable in soil pollution monitoring.", publisher = "Belgrade : Serbian Society of Soil Science", journal = "Book of Abstracts / 3rd International and 15th National Congress Soils for Future Under Global Challenges, 21-24 September 2021 Sokobanja, Serbia", title = "Chemometrics for soil pollution monitoring", pages = "7", url = "https://hdl.handle.net/21.15107/rcub_technorep_7096" }
Onjia, A.. (2021). Chemometrics for soil pollution monitoring. in Book of Abstracts / 3rd International and 15th National Congress Soils for Future Under Global Challenges, 21-24 September 2021 Sokobanja, Serbia Belgrade : Serbian Society of Soil Science., 7. https://hdl.handle.net/21.15107/rcub_technorep_7096
Onjia A. Chemometrics for soil pollution monitoring. in Book of Abstracts / 3rd International and 15th National Congress Soils for Future Under Global Challenges, 21-24 September 2021 Sokobanja, Serbia. 2021;:7. https://hdl.handle.net/21.15107/rcub_technorep_7096 .
Onjia, Antonije, "Chemometrics for soil pollution monitoring" in Book of Abstracts / 3rd International and 15th National Congress Soils for Future Under Global Challenges, 21-24 September 2021 Sokobanja, Serbia (2021):7, https://hdl.handle.net/21.15107/rcub_technorep_7096 .