Chemometrics for environmental monitoring: a review

Hydrological and climate impacts on river characteristics of pahang river basin, Malaysia

The climate, geomorphological changes, and hydrological elements that have occurred have all influenced future flood episodes by increasing the likelihood and intensity of extreme weather occurrences like extreme precipitation events. River bank erosion is a natural geomorphic process that occurs in all channels. As modifications of sizes and channel shapes are made to transport the discharge, sediment abounds from the stream catchment, and floods are triggered dramatically. The aim of this study is to analyze the flood-sensitive regions along the Pahang River Basin and determine how climate and river changes would have an impact on flooding based on hydrometeorological data and information on river characteristics. The study is divided into three stages, namely the upstream, middle stream, and downstream of the Pahang River. The main primary hydrometeorological data and river characteristics, such as Sinuosity Index, Dominant Slope Range and Entrenchment Ratio collected as important inputs in the statistical analysis process. The statistical analyses, namely HACA, PCA, and Linear Regression applied in river classification. The result showed that the middle stream and downstream areas demonstrated the worst flooding affected by anthropogenic and hydrological factors. Rainfall distribution is one of the factors that contributed to the flood disaster. There are strong correlations between the Sinuosity Index (SI) and water level, which indicates that changes occurred at both planform and stream classification. The best management practices towards sustainability are based on the application of the outcomes that have been obtained after the analysis of Pahang River planform changes, Pahang River geometry, and the local rainfall pattern in the Pahang River Basin.

Self-organizing map algorithm for assessing spatial and temporal patterns of pollutants in environmental compartments: A review

The 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 SOM-based 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 paper to attain comparability and reproducibility among published papers as well as advice for extracting valuable information from the model results is presented.

Leveraging visible and near-infrared spectroelectrochemistry to calibrate a robust model for Vanadium(IV/V) in varying nitric acid and temperature levels

Spectroelectrochemistry and optimal design of experiments can be used to rapidly build accurate models for species quantification and enable a greater level of process awareness. Optical spectroscopy can provide vital elemental and molecular information, but several hurdles must be overcome before it can become a widely adopted analytical method for remote analysis in the nuclear field. Analytes with varying oxidation state, acid concentration, and fluctuating temperature must be efficiently accounted for to minimize time and resources in restrictive hot cell environments. The classic one-factor-at-a-time approach is not suitable for frequent calibration/maintenance operations in this setting. Therefore, a novel alternative was developed to characterize a system containing vanadium(IV/V) (0.01-0.1 M), nitric acid (0.1-4 M), and varying temperatures (20-45 °C). Spectroelectrochemistry methods were used to acquire a sample set selected by optimal design of experiments. This new approach allows for the accurate analysis of vanadium and HNO₃ concentration by leveraging UV-Vis-NIR absorption spectroscopy with robust and accurate chemometric models. The top model's root mean squared error of prediction percent values were 3.47%, 4.06%, 3.40%, and 10.9% for V(IV), V(V), HNO₃, and temperature, respectively. These models, efficiently developed using the designed approach, exhibited strong predictive accuracy for vanadium and acid with varying oxidation states and temperature using only spectrophotometry, which advances current technology for real-world hot cell applications. Additionally, Nernstian analysis of the V(IV/V) standard potential was performed using traditional absorbance methods and multivariate curve resolution (MCR). The successful tests demonstrated that MCR Nernst tests may be valuable in highly convoluted spectral systems to better understand the redox processes' behavior.

In silico NIR spectroscopy - A review. Molecular fingerprint, interpretation of calibration models, understanding of matrix effects and instrumental difference

Quantum mechanical calculations are routinely used as a major support in mid-infrared (MIR) and Raman spectroscopy. In contrast, practical limitations for long time formed a barrier to developing a similar synergy between near-infrared (NIR) spectroscopy and computational chemistry. Recent advances in theoretical methods suitable for calculation of NIR spectra opened the pathway to modeling NIR spectra of various molecules. Accurate theoretical reproduction of NIR spectra of molecules reaching the size of long-chain fatty acids was accomplished so far. In silico NIR spectroscopy, where the spectra are calculated ab initio, provides substantial improvement in our understanding of the overtones and combination bands that overlap in staggering numbers and create complex lineshape typical for NIR spectra. This improves the comprehension of the spectral information enabling access to rich and detail molecular footprint, essential for fundamental research and useful in routine analysis by NIR spectroscopy and chemometrics. This review article summarizes the most recent accomplishments in the emerging field with examples of simulated NIR spectra of molecules reaching long-chain fatty acids and polymers. In addition to detailed NIR band assignments and new physical insights, simulated spectra enable innovative support in applications. Understanding of the difference in the performance observed between miniaturized NIR spectrometers and chemical interpretation of the chemometric models are noteworthy here. These new elements integrated into NIR spectroscopy framework enable a knowledge-based design of the analysis with comprehension of the processed chemical information.

Impact evaluation of the run-of-river hydropower projects on the water quality dynamics of the Sindh River in the Northwestern Himalayas

As the run-of-river (RoR) hydropower projects remain understudied, we conducted this study to understand how these projects affect the hydro-chemical dynamics and water quality index (WQI) of the Sindh River in the Kashmir Himalayas. We used multivariate statistical techniques and WQI to identify the spatiotemporal dynamics of 18 physico-chemical parameters from 11 sampling stations distributed along the length of river Sindh from December 2017 to December 2019. The dataset was classified into three groups using hierarchical cluster analysis based on similarities between hydro-chemical characteristics, and the results were confirmed by discriminant analysis. Wilk's quotient distribution further showed that ions, nutrients, free carbon dioxide, water temperature, and pH contributed to the formation of clusters. Principle component analysis revealed that the chloride (Cl^-), total phosphorus (TP), ortho-phosphorus (PO₄-P), nitrate-nitrogen (NO₃-N), nitrite-nitrogen (NO₂-N), and sulfate ion (SO₄^2-) are significant factors that influence the water quality. Furthermore, our results suggest that diverting water for RoR operation did not significantly raise the WQI value to the point where water in the bypassed reaches could be declared unfit for drinking. Our analysis concluded that inclusive assessments are vital for framing policies on expanding RoR hydropower in the region.

Chemometric approach in environmental pollution analysis: A critical review

With the ever-increasing global population and industrialization, it has become a call of the hour to start taking care of the environment to balance the ecosystem. For this, effective monitoring and assessment are required, which involves collecting and measuring environmental details, temporal and spatial readings of environmental data, and parameters. However, assessment of the environment is very tedious as it includes monitoring target analytes, identifying their sources, and reporting, which invariably implies that detailed environmental monitoring would be an intricate and expensive process. The traditional protocols in environmental measures are often manual and time demanding, which makes it further difficult. Moreover, several changes also occur within the environment, which could be chemical, physical, or biological, and since these environmental impacts are often cumulative, it becomes difficult to measure an isolated system. Furthermore, the chances of skipping significant results and trends become high. Also, experimental data obtained from the environmental analysis are usually non-linear and multi-variant due to different associations among various contributing variables. Therefore, it is implied that accurate measurements and environment monitoring are not using traditional analytical protocols. Thus, the need for a chemometric approach in environmental pollution analysis becomes paramount due to the inherent limitations associated with the conventional approach of analyzing environmental datasets. Chemometrics has appeared as a potential technique, which enhances the particulars of the chemical datasets by using statistical and mathematical analysis methods to analyze chemical data beyond univariate analysis. Utilizing chemometrics to study the environmental data is a revolutionary idea as it helps identify the relationship between sources of contaminations, environmental drivers, and their impact on the environment. Hence, this review critically explores the concept of chemometrics and its application in environmental pollution analysis by briefly highlighting the idea of chemometrics, its types, applications, advantages, and limitations in the environmental domain. An attempt is also made to present future trends in applications of chemometrics in environmental pollution analysis.

Multivariate Threshold Regression Models with Cure Rates: Identification and Estimation in the Presence of the Esscher Property

The first hitting time of a boundary or threshold by the sample path of a stochastic process is the central concept of threshold regression models for survival data analysis. Regression functions for the process and threshold parameters in these models are multivariate combinations of explanatory variates. The stochastic process under investigation may be a univariate stochastic process or a multivariate stochastic process. The stochastic processes of interest to us in this report are those that possess stationary independent increments (i.e., Lévy processes) as well as the Esscher property. The Esscher transform is a transformation of probability density functions that has applications in actuarial science, financial engineering, and other fields. Lévy processes with this property are often encountered in practical applications. Frequently, these applications also involve a ‘cure rate’ fraction because some individuals are susceptible to failure and others not. Cure rates may arise endogenously from the model alone or exogenously from mixing of distinct statistical populations in the data set. We show, using both theoretical analysis and case demonstrations, that model estimates derived from typical survival data may not be able to distinguish between individuals in the cure rate fraction who are not susceptible to failure and those who may be susceptible to failure but escape the fate by chance. The ambiguity is aggravated by right censoring of survival times and by minor misspecifications of the model. Slightly incorrect specifications for regression functions or for the stochastic process can lead to problems with model identification and estimation. In this situation, additional guidance for estimating the fraction of non-susceptibles must come from subject matter expertise or from data types other than survival times, censored or otherwise. The identifiability issue is confronted directly in threshold regression but is also present when applying other kinds of models commonly used for survival data analysis. Other methods, however, usually do not provide a framework for recognizing or dealing with the issue and so the issue is often unintentionally ignored. The theoretical foundations of this work are set out, which presents new and somewhat surprising results for the first hitting time distributions of Lévy processes that have the Esscher property.

Spectra-structure correlations in NIR region of polymers from quantum chemical calculations. The cases of aromatic ring, C=O, C≡N and C-Cl functionalities

Near-infrared (NIR) spectroscopy is a valued analytical tool in various applications involving polymers. However, complex nature of NIR spectra imposes difficulties in their direct interpretation. Here, anharmonic quantum chemical calculations are used to simulate NIR spectra of nine polymers; acrylonitrile butadiene styrene (ABS), ethylene-vinyl acetate (EVAC), polycarbonate (PC), polyethylene terephthalate (PET), polylactide or polylactic acid (PLA), polymethylmethacrylate (PMMA), polyoxymethylene (POM), polystyrene (PS) and polyvinylchloride (PVC). The generalized spectra-structure correlations are derived for these systems with focus given to the manifestation in NIR spectra of aromatic ring, C=O, C≡N and C-Cl functionalities. It is concluded that the nature of NIR polymer bands is only moderately sensitive to the remote chemical neighborhood. The majority of NIR absorption of polymers originates from binary combination bands, while the first overtones are meaningful only in ca. 6200-5500 cm^-1 region. The contribution of the overtone bands is relatively higher for the polymers bearing aromatic rings because of higher intensity of C-H stretching overtones. Highly characteristic combination bands of the modes localized in aromatic ring (ring deformation and CH stretching) are relatively independent on the remaining structure of the polymer. The combination bands originating from C=O group are more sensitive to the chemical neighborhood in near proximity, forming a useful fingerprint for a specific polymer. In contrast, the vibrational bands of C≡N functionality are far less useful in NIR region than in infrared (IR) region. With aid of the calculated absorption bands, structural specificity of NIR spectroscopy of polymers can be markedly improved.

Process design and validation of a new mixed eluent for leaching Cd, Cr, Pb, Cu, Ni, and Zn from heavy metal-polluted soil

Chemical leaching, an emerging technology for treating heavy metal-polluted soils, requires a design for reasonable and new eluent and an evaluation of its efficiency on the simultaneous removal of different elements. In this study, the leaching effect and biodegradability of chelating agents were compared, and ethylenediamine disuccinic acid (EDDS) was selected to combine with ferric chloride (FeCl₃) for the design of a mixed eluent (EDDS + FeCl₃). Through batch experiments, the influences of the eluent concentration and solution pH on leaching were revealed, and leaching efficiencies of EDDS, FeCl₃, and EDDS + FeCl₃ on six heavy metals Cd, Cr, Pb, Cu, Ni, and Zn in the soil were separately analyzed. Results indicated that EDDS + FeCl₃ showed advantages over both EDDS and FeCl₃ alone, and it presented an excellent effect, especially for simultaneously leaching multiple heavy metals from the soil. The highest leaching efficiencies for Cd, Cr, Pb, Cu, Ni, and Zn reached up to 71.36%, 21.29%, 31.14%, 30.25%, 34.05%, and 4.96%, respectively. According to different soil types and target elements, the concentration, pH condition, and mass ratio of EDDS + FeCl₃ could be adjusted for soil remediation. Fourier transform infrared spectroscopy proved that the better leaching effect of EDDS + FeCl₃ was attributed to changes in the number and strength of functional groups in the solution, which enhanced the chelating ability of the mixed eluent and heavy metal ions. Therefore, chemical leaching by EDDS + FeCl₃ for the remediation of multiple heavy metal-contaminated soil is a potential feasible strategy.

Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring

In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.