Terms Of Regression Coefficients Research Articles

Thermodynamic Modeling of Solubility of Some Antibiotics in Supercritical Carbon Dioxide Using Simplified Equation of State Approach

Antibiotics are playing crucial role in the treatment of humans since the last few centuries. Their usage has several benefits along with side effects. The efficacy of antibiotics for the treatment of ailments may be retained by controlling the drug dosage. This may be achieved with supercritical fluid technology (SFT). The antibiotic drug solubility in supercritical carbon dioxide (scCO2) is available only at specific temperature and pressure conditions, for effective utilization of SFT, solubility at various conditions are required. Equation of state (EoS) method is used for solubility data modeling and it requires critical properties of the solute, molar volume of the solute and sublimation pressure of the solute along with fugacity coefficient, pressure and temperature. These properties are estimated using group contribution methods. For antibiotics solute critical properties, molar volume and sublimation pressure are unavailable and existing group contribution methods are also not applicable due to the lack of functional group contributions in their techniques. Thus, there is a need to address EoS methodology without using solute properties. Hence, a new EoS methodology for solubility modeling is, proposed without using critical properties of the solute, molar volume of the solute and vapour pressure of the solute. Thus, this study focuses on the development of new solubility model that correlates antibiotics using equation of state (EoS). Importantly, the proposed solubility model does not use the critical properties of the antibiotics. Correlating ability of the proposed model is indicated in terms of regression coefficient and arithmetic average relative deviation percentage (AARD %).

Crop Insurance Premium Recommendation System Using Artificial Intelligence Techniques

Purpose: The objective of this study is to build a crop insurance premium recommender model which will be fair to both crop insurance policy holders and crop insurance service providers. Theoretical Framework: The Nonparametric Bayesian Model (modified) is the name of the proposed model suggested by Maulidi et al. (2021) and it consists of six variables which are regional risk, cultivation time period, land area, claim frequency, discount eligibility (local variable) and premium. Discount eligibility variable is introduced to encourage right farming practices among farmers. Design/methodology/approach: Descriptive research method is used in this study as it is used to accurately represent the characteristics of a group of items. The population for this study is 943 respondents. The entire dataset is used for in-depth and accurate analysis. Five Artificial Intelligence models (Machine Learning models) are proposed for crop insurance premium prediction and they are Ada Boost Regressor, Gradient Boosting Regressor, Extra Trees Regressor, Support Vector Regressor and K-Neighbors Regressor. Among them Gradient Boosting Regression model has given the highest accuracy. Thus, Gradient Boosting Regression model is the most suitable model to be recommended for crop insurance premium prediction. Findings and Suggestions: Regional risk, land area, claim frequency and cultivation time period is the order of independent variables from highest to least in terms of regression coefficient. This relative importance helps Non-Banking Financial Companies (NBFCs) to suggest farmers that they should concentrate most on the regional risk or chances of crop failure in a particular region in which they are doing agriculture and least on the cultivation time period of a crop or the season in which a crop is cultivated. Two suggestions for future researchers are to extend this research work to other parts of Tamil Nadu and to apply hybrid machine learning techniques to the proposed model. Practical Implication: Unlike the existing formula-based traditional method used for calculating crop insurance premium, artificial intelligence models (machine learning models) can automatically learn the changes that take place with respect to the nature of variables in the proposed model and improve its accuracy based on new data. Hence, the crop insurance premium suggested by the most accurate model among the artificial intelligence models used in this study will be fair to both NBFCs and farmers. Here, fair means moderate. On the other hand, the crop insurance premium suggested by the existing formula-based method may not be fair in the long term as they cannot automatically learn the changes that take place with respect to the nature of variables in the proposed model and improve. Originality/value: In this research article, the relative importance of independent variables in the proposed model is determined and it helps NBFCs to suggest farmers that they should concentrate most on the region they are doing agriculture and least on the cultivation time period of a crop. Additionally, a machine learning model which can automatically learn and improve itself is used and hence the crop insurance premium predicted by it will be fair. Finally, the entire population containing 943 respondents details is analysed.

Stability of Protein and Oil Content in Soybean across Dry and Normal Environments—A Case Study in Croatia

Soybean is generally grown as a rainfed crop worldwide and very often is exposed to drought and high temperatures. The objectives of this study were to determine the performance and stability of 32 elite soybean genotypes for seed protein and oil contents across six dry and eight normal environments and to determine the influence of environment on the relationship between the two traits. In the combined ANOVA, genotypes (G), environments (E) and G × E were significant for both traits with protein content being more sensitive to environmental changes than oil content. Mean seed protein content decreased by 4.5% under drought conditions compared to normal ones and ranged from 2.3% to 7.1% for individual genotypes. At the same time drought caused a slight increase in seed oil content of +1.2%, with a range of −1.3% to +4.5% for individual genotypes. Genotype stability in terms of regression coefficient (b) and coefficient of variation (CV) was in moderate to weak negative correlation with mean genotype performance for protein content, while no correlation was observed between genotype stability and mean genotype performance for oil content. Protein and oil content were significantly negatively correlated in normal environments (r = −0.33), while no correlation between the traits was observed in dry environments (r = −0.02).

Estimation of Some Geotechnical Indices of Soils using Machine Learning Techniques

Adoption of a good estimation model for the prediction of sub soils properties before the commencement of a construction project, or at the preliminary stage of project planning is highly imperative. This will mitigate the most unexpected costs incurred during construction which are mostly geotechnical in nature. This research aims to use Machine Learning ML tools such as Multiple Linear Regression (MLR) Artificial Neural Network(ANN),Support Vector Machine(SVM), Random Forest(RF) andM5 Tree (M5P) in geotechnical Engineering with a view to correlate Optimum Moisture Content(OMC), Maximum Dry Density(MDD) and Soaked California Bearing Ratio(SCBR) and Unsoaked California Bearing Ratio (USCBR) from the measured index properties. The results from index properties classified the soils of the study area as A-2-4, A-2-6, A-2-7 and A-7-5 for Ekiti Central Senatorial Districts (ECSD) and A-2-4, A-2-5, A-2-6, A-2-7, A-4, A-5, A-6 and A-7-5 for Ekiti South Senatorial Districts ( ESSD) while Ekiti Northern Senatorial Districts (ENSD) were classified as A-2-4, A-2-5, A-2-6, A-2-7, A-6 and A-7-6. Conversely. The strengths of the developed Machine Learning models have been examined in terms of regression coefficient (R 2 ) and Root Mean Square Error (RMSE) values. It is found that all the five ML models predict OMC %, MDD, SCBR and USCBR close to the experimental value. However, the prediction of OMC %, MDD, SCBR and USCBR by RF is found better than other ML models deployed in this research. DOI: 10.7176/CER/14-3-04 Publication date: May 31 st 2022

On logistic Box–Cox regression for flexibly estimating the shape and strength of exposure‐disease relationships

The shape of the relationship between a continuous exposure variable and a binary disease variable is often central to epidemiologic investigations. This article investigates a number of issues surrounding inference and the shape of the relationship. Presuming that the relationship can be expressed in terms of regression coefficients and a shape parameter, we investigate how well the shape can be inferred in settings which might typify epidemiologic investigations and risk assessment. We also consider a suitable definition of the median effect of exposure, and investigate how precisely this can be inferred. This is done both in the case of using a model acknowledging uncertainty about the shape parameter and in the case of ignoring this uncertainty and using a two‐step method, where in step one we transform the predictor and in step two we fit a simple logistic model with transformed predictor. All these investigations require a family of exposure‐disease relationships indexed by a shape parameter. For this purpose, we employ a family based on the Box–Cox transformation.

Erythrocyte membrane polyunsaturated fatty acid profiles are associated with systemic inflammation and fish consumption in systemic lupus erythematosus: a cross-sectional study.

Objective This study aimed to assess polyunsaturated fatty acid (PUFA) status and association with systemic inflammation and fish consumption in systemic lupus erythematosus (SLE). Methods Parameters of PUFA status including the proportion of omega-6 and -3 fatty acids in highly unsaturated fatty acids (omega-6%, omega-3%), essential fatty acids linoleic acid (LA) and alpha-linolenic acid (ALA), the omega-6 PUFA arachidonic acid (ARA) and the sum of key omega-3 PUFA eicosapentaenoic acid+docosahexaenoic acid (omega-3 status) were measured by gas chromatography in 68 SLE patients (88.2% female, aged 45.7±12.5 years). Associations with serum CRP, disease activity, damage and fish consumption were assessed by linear regression modelling adjusted for age, sex and body mass index. Associations are reported in terms of regression coefficients (β). Results Omega-6 PUFA were associated with higher CRP: omega-6% (β = 0.052, p = 0.02), the ratio of LA/ALA (β = 0.007, p = 0.02) and ARA (β = 0.308, p = 0.001). Conversely, omega-3% was associated with lower CRP (β = −0.051, p = 0.02). Increased dietary PUFA consumption from fish (g/day) was linked to a higher omega-3 status (β = 2.21, p = 0.02) and lower self-reported damage (Brief Index of Lupus Damage; β = −3.22, p = 0.02). Conclusions Omega-3 and omega-6 fatty acid status differentially reflect systemic inflammation in SLE and are linked to fish consumption.

Predicting Compaction Characteristics of Fine-Grained Soils in Terms of Atterberg Limits

This study presents a set of regression models for predicting maximum dry unit weight $$({\gamma _{{\text{dmax}}}})$$ and optimum moisture content (OMC) of fine-grained soils in terms of their consistency limits. The empirical models were developed by performing experimental investigation on forty (40) natural fine-grained soils, encompassing a wide range of liquid limit (LL) and plastic limit (PL). The compaction characteristics were determined by conducting IS light compaction test (standard Proctor equivalent in Indian standards). Observation shows that $${\gamma _{{\text{dmax}}}}$$ linearly decreases and in contrast, OMC increases in the same fashion with increase in LL or PL. However, in terms of regression coefficient, LL exhibits a superior correlation with $${\gamma _{{\text{dmax}}}}$$ and OMC than PL does. The observed variation trend of compaction characteristics with LL and PL is affirmed by a few previous studies in the domain. A set of two independent models are finally developed for predicting $${\gamma _{{\text{dmax}}}}$$ and OMC of soils taking both LL and PL into account. Reasonably good regression coefficients are obtained in case of both the models (R 2 = 0.90 in case of $${\gamma _{{\text{dmax}}}}$$ and R 2 = 0.86 in case of OMC model). The models are validated by predicting $${\gamma _{{\text{dmax}}}}$$ and OMC and comparing with actually measured values in a published study as well as present study. The root-mean-square error (RMSE) in case of $${\gamma _{{\text{dmax}}}}$$ prediction is 2.1% against the measured values of present study and 7.4–7.5% against measured values in literature. The RMSE involved in case of OMC prediction model is 7% against present values and 17.5–28.2% against measured values in literature.

Who is coming to the artefactual field experiment? Participation bias among Chinese rural migrants

In this paper, we compare participants in an artefactual field experiment in urban China with the survey population of migrants from which they were recruited. The experimental participants were more educated, more likely to lend money to friends, and worked fewer hours than the general population. They differ significantly from non-participants in terms of regression coefficients, such as the effects of wealth and marital status on the probability of being self-employed and distance migrated. We thus find that there was selection into our experiments on the basis of both observable characteristics and on unobserved differences in behavioral relations.

Who is Coming to the Artefactual Field Experiment? Participation Bias Among Chinese Rural Migrants

In this paper, we compare participants in an artefactual field experiment in urban China with the survey population of migrants from which they were recruited. The experimental participants were more educated, more likely to lend money to friends, and worked fewer hours than the general population. They differ significantly from non-participants in terms of regression coefficients, such as the effects of wealth and marital status on the probability of being self-employed and distance migrated. We thus find that there was selection into our experiments on the basis of both observable characteristics and on unobserved differences in behavioral relations.

QSAR, Docking and ADMET Studies of Artemisinin Derivatives for Antimalarial Activity Targeting Plasmepsin II, a Hemoglobin-Degrading Enzyme from P. falciparum

This work presents the development of quantitative structure activity relationship (QSAR) model to predict the antimalarial activity of artemisinin derivatives. The structures of the molecules are represented by chemical descriptors that encode topological, geometric, and electronic structure features. Screening through QSAR model suggested that compounds A24, A24a, A53, A54, A62 and A64 possess significant antimalarial activity. Linear model is developed by the multiple linear regression method to link structures to their reported antimalarial activity. The correlation in terms of regression coefficient (r(2)) was 0.90 and prediction accuracy of model in terms of cross validation regression coefficient (rCV(2)) was 0.82. This study indicates that chemical properties viz., atom count (all atoms), connectivity index (order 1, standard), ring count (all rings), shape index (basic kappa, order 2), and solvent accessibility surface area are well correlated with antimalarial activity. The docking study showed high binding affinity of predicted active compounds against antimalarial target Plasmepsins (Plm-II). Further studies for oral bioavailability, ADMET and toxicity risk assessment suggest that compound A24, A24a, A53, A54, A62 and A64 exhibits marked antimalarial activity comparable to standard antimalarial drugs. Later one of the predicted active compound A64 was chemically synthesized, structure elucidated by NMR and in vivo tested in multidrug resistant strain of Plasmodium yoelii nigeriensis infected mice. The experimental results obtained agreed well with the predicted values.

Impacts of organic amendments on carbon stocks of an agricultural soil — Comparison of model-simulations to measurements

Organic amendments such as straw, green manure or farmyard manure are used to mitigate the soil carbon (C) losses from cultivated soils. We investigated the role of various organic amendments with different C quality for development of soil C stocks, by simulating the Ultuna long-term soil organic matter experiment in Sweden with the Yasso07 model. The aim was to evaluate the performance of the Yasso07 soil carbon model in predicting changes in soil C stocks by comparing modeled C stocks to measurements between years 1956–1991. Uncertainty bounds were calculated from the estimated uncertainty in the C inputs and model parameters. The model performance was assessed in terms of regression coefficient (R2), root mean square error (RMSE) and model efficiency (ME). The model could very accurately predict the decrease in soil C stock in bare fallow, and in treatments receiving crop litter inputs and N fertilization. Yasso07 could also predict the increase in C stocks due to different organic matter applications, based on the varying quantity and quality of these C inputs. These results support the use of the model for testing the long-term effects of different agricultural measures aiming to mitigate soil C losses.

Semi-Aerobic Landfill Leachate Treatment Using Carbon–Minerals Composite Adsorbent

Inorganic and organic pollutants continue to pose major problems in receiving water bodies. To overcome such problems, a new composite adsorbent material combining excellent properties of activated carbon, zeolite, and low cost adsorbents, that is, limestone and rice husk ash, was fabricated. The study also determined the capability of the new composite media to remove contaminants from semiaerobic stabilized landfill leachate. The process of identifying the optimum composition of the new adsorbent was carried out using batch technique. Further, isotherm study and regeneration of the media were investigated. Results indicated that there was favorable adsorption by both Langmuir and Freundlich isotherms. However, Langmuir isotherms were slightly better fitted for ammonia, chemical oxygen demand (COD), and color removal in terms of regression coefficients (R2). According to the Langmuir model, adsorption capacity of ammonia, COD, color, and iron reached 37.59 mg/g, 22.99 mg/g, 43.67 Pt-Co/g, and 0.12 mg/g, respectively. In addition, adsorption efficiency relative to fresh media, adsorption capacity for regenerated media to ammonia, COD, color, and iron reached 149.45%, 60.94%, 47.52%, and 75.0%, respectively.

Influence of pH, Ionic Strength and Temperatureon Adsorption of Lead, Copper and Zinc in Natural Soil

Sorption of Pb, Zn, Cu by natural soils was investigated under conditions of variable pH, ionic strength and temperature. The results obtained from adsorption isotherm indicated that these data can be better fitted with the Freundlich equation than with the Langmuir equation in terms of regression coefficients. The parameters in the adsorption process were influenced greatly by solution pH and ionic strength. The Freundlich parameter KF increased with increasing pH and decreasing ionic strength, but the Freundlich parameter n changed adversely. Thermodynamic parameters of the process were calculated from sorption studies performed at different temperatures, and enthalpy changes (△H°) and entropy changes (△S°) of adsorption were found as -20.70 kJ/mol and 34.76 J/mol.K for Pb(II), -7.762 kJ/mol and 1.139 J/mol.K for Cu(II), -20.60 kJ/mol and 51.93 J/mol.K for Zn(II) respectively, showing that adsorption of Pb2+, Cu2+, and Zn2+ on natural soil were exothermic and spontaneous at 5-45°C.

Removal of Ruthenium from Aqueous Solution by Clinoptilolite

AbstractRuthenium compounds are highly toxic and carcinogenic. In the present study, clinoptilolite was used in the removal of Ru species from aqueous solutions. Clinoptilolite is a good choice of sorbents because it is naturally abundant and therefore cheap. After the process where Ru was removed from the aqueous solution, the clinoptilolite was characterized by X-ray diffraction, X-ray fluorescence, thermogravimetric analysis, and Fourier-transform infrared spectroscopy techniques. The influence of pH, contact time, and temperature on the adsorption of Ru was investigated and the optimum conditions were found to be 2 h of contact time and pH = 2. Pseudo first-order, pseudo second-order, Elovich, and intra-particle diffusion models were used to analyze the adsorption-rate data. The pseudo second-order model was found to be the best kinetics model in terms of matching the experimental results obtained. Adsorption isotherms were constructed to assess the maximum adsorption capacity of clinoptilolite. The Langmuir model fitted the data reasonably well in terms of regression coefficients. Adsorption studies were also performed at different temperatures to calculate the thermodynamic parameters. The numerical value of ΔG0 decreased with increasing temperature, indicating that adsorption is favored at higher temperatures. The positive values of ΔH0 corresponded to the endothermic nature of the adsorption processes. The proposed method of removal is applicable at an industrial scale.

Sorption Potential of Epoxy/Pac Composite for the Removal of Cs+ ion from Aqueous Solutions

Simultaneous Gamma irradiation was used to prepare Epoxy/Pac porous resin. Characterizations of the polymer using Fourier Transform Infrared (FTIR), Thermogravimetric analysis (TGA), scanning electron microscope were performed. The sorption potential of polymer for removal of Cs + from liquid solutions has been investigated. The influences of pH, contact time between liquid and solid phases, initial metal ion concentration, and temperature have been evaluated. Equilibrium isotherms were determined to assess the maximum sorption capacity of polymer for the studied ion. Desorption and durability measurements of the polymer confirmed their use from cost benefit point of view. The equilibrium sorption data were analyzed using Freundlich, Langmuir and Dubinin-Radushkevich (D-R) isotherm models. All tested models fit the data reasonably well in terms of regression coefficients. Sorption studies were also performed at different temperatures to obtain the thermodynamic parameters of the process. The numerical value of ΔG 0 decreases with an increase in temperature, indicating that the sorption reaction of each ion is more favorable at higher temperature. The positive values of ΔH 0 correspond to the endothermic nature of sorption processes.

Adsorption of 60Co radionuclides from aqueous solution by raw and modified bentonite

Bentonite and formaldehyde modified-bentonite (MF-bentonite) were tested for removal of the radioactive isotope 60Co. The two adsorbents were characterized (FTIR spectroscopy and scanning electron microscope) before and after modification. Some enhancement was obtained in the mesopore region due to impregnating the bentonite with 35% of formaldehyde solution and the modified sample had better adsorption performance. Furthermore, the adsorption capacity of MF-bentonite increased by increasing the quantity of formaldehyde deposited on the surface. The adsorption potential of bentonite and MF-bentonite for the removal of 60Co radionuclide from radioactive waste solutions has been investigated. The influence of pH, contact time, adsorbent dosages and temperature is reported. Pseudo first-order and pseudo second-order kinetic models were used to analyze the adsorption rate data. The pseudo second-order model was found to best correlate the kinetic data. Adsorption isotherms were determined to assess the maximum adsorption capacity of bentonite and MF-bentonite. The Langmuir model fitted the data reasonably well in terms of regression coefficients. Adsorption studies were also performed at different temperatures to obtain the thermodynamic parameters. The numerical value of ΔG 0 decreased with an increase in temperature, indicating that the adsorption is more favorable at higher temperature. The positive values of ΔH 0 corresponded to the endothermic nature of adsorption processes.

Single, binary, and multicomponent sorption of iron and manganese on lignite

Acid mine drainage (AMD) has long been a significant environmental problem resulting from the microbial oxidation of iron pyrite in the presence of water and air, affording an acidic solution that contains toxic metal ions. The main objective of this study was to remove metal ions [Fe(II), Fe(III), Mn(II), Zn(II)] from AMD using lignite, a low-cost adsorbent. The lignite sorbent was utilized for the sorption of ferrous, ferric, manganese, zinc, and calcium ions in aqueous solutions. Studies were performed at different pH to find optimum pH. Equilibrium isotherms were determined to assess the maximum adsorption capacity of lignite for different metal ions. Sorption capacities were compared in single, binary, ternary, and multicomponent systems. The sorption data are correlated with Freundlich and Langmuir isotherms in each system. Both Freundlich and Langmuir isotherms fit the data reasonably well in terms of regression coefficients. Sorption studies were also performed at different temperatures to obtain the thermodynamic parameters of the process. The maximum lignite adsorption capacities at 25 °C were 34.22, 25.84, and 11.90 mg/g for Fe(II), Mn(II), and Fe(III), respectively. Adsorption of Fe 2+ (24.70 mg/g at 10 °C and 46.46 mg/g at 40 °C) increased with increased temperature, while Mn 2+ adsorption (28.11 mg/g at 10 °C and 7.70 mg/g at 40 °C) decreased with increased temperature.

Biosorption of Basic Dyes Using Sewage Treatment Plant Biosolids

Studies on the removal of three basic dyes (Basic Blue 3, Basic Red 22, Basic Black 9) from aqueous solutions by adsorption on Sewage Treatment Plant (STP) biosolids (sludge) as an adsorbent were carried out with an aim to obtain information on treating effluents from textile and/or dye industries. A series of experiments were undertaken in a batch adsorption technique to access the effect of the process variables i.e. initial dye concentration, contact time, initial pH, adsorbent dose, temperature and agitation rate. The adsorption capacity of basic dyes was higher (22-24 mg gG1) with the lower values of the temperature (25-30°C), adsorbent dosage (0.5-0.75% w/v), higher values of the initial pH (8-9) and agitation rate (150-200 rpm). The equilibrium in the solution was observed within 2 h of operation. The equilibrium isotherm for each dye was determined to describe the biosorption processes. The results showed that the equilibrium data were fitted by both of the Langmuir and Freundlich isotherms while Freundlich isotherms was slightly better fitted for Basic Blue 3 and Langmuir was for the Basic Black 9 in terms of regression coefficients (R2).

Color Removal from Wastewater Using Low-Cost Activated Carbon Derived from Agricultural Waste Material

An activated carbon was developed from coconut shell fibers, characterized and used for the removal of methylene blue (basic) and methyl orange (acidic) dyes from wastewater successfully. Adsorption studies were carried out at different temperatures, particle size, pH, and adsorbent doses. The adsorption data are correlated with both Langmuir and Freundlich models. The results indicate that the Freundlich model fits the data better as compared to the Langmuir model in terms of regression coefficients. Isotherms have been used to obtain the thermodynamic parameters such as free energy, enthalpy, and entropy of adsorption. The kinetic studies were also conducted, and the adsorption of methylene blue and methyl orange follow the first-order rate equation. Various kinetic parameters such as the mass-transfer coefficient, effective diffusion coefficient, activation energy, and entropy of activation were evaluated to establish the mechanisms. It was concluded that methylene blue adsorption occurs through a film diffusion mechanism at low as well as at higher concentrations, while methyl orange adsorption occurs through film diffusion at low concentration and particle diffusion at high concentrations. The sorption capacity of the developed carbon is comparable to the other available adsorbents, and costwise it is quite cheaper.

Chrysophycean cyst assemblages and their relationship to water chemistry in 71 Adirondack Park (New York, USA) lakes mssub: 1994-09-12 msacc: 1995-02-28

Chrysophycean cysts were identified and enumerated from the surface sediments of 71 lakes in Adirondack Park, New York, and their relationships with measured limnological variables were examined with canonical ordination (redundancy analysis). Gradients in the cyst distributions were most strongly related to conductivity, dissolved organic carbon, Secchi depth, and aluminum. Lesser amounts of variance in the cyst data were explained by pH, acid neutralizing capacity (ANC), maximum depth, and altitude. We derived predictive models for 13 environmental variables based on the cyst assemblages using partial least squares (PLS) regression. The best models, in terms of regression coefficients (r 2 ) and root mean square errors of prediction (RMSEP), were obtained for pH and ANC (r 2 = 0.64 ; RMSEP = 0.59 and 0.46 ln (μeq/L+41)), respectively, followed by [Mg] (r 2 = 0.53, RMSEP = 0.36 In (μeq/L+1)), total aluminum (r 2 = 0.48, RMSEP = 0.86 In (μg/L+1)), conductivity (r 2 = 0.46, RMSEP = 0.19 ln (μS/cm+1)), and monomeric aluminum (r 2 =0.35, RMSEP = 0.66 ln (μM+1)). These models can be used to infer historical levels of these variables in paleolimnological studies.