Low Water Quality Research Articles

Tree-Based Ensemble Learning Models for Wall Temperature Predictions in Post-Critical Heat Flux Flow Regimes at Subcooled and Low-Quality Conditions

Abstract Accurately predicting post-critical heat flux (CHF) heat transfer is an important but challenging task in water-cooled reactor design and safety analysis. Although numerous heat transfer correlations have been developed to predict post-CHF heat transfer, these correlations are only applicable to relatively narrow ranges of flow conditions due to the complex physical nature of the post-CHF heat transfer regimes. In this paper, a large quantity of experimental data is collected and summarized from the literature for steady-state subcooled and low-quality film boiling regimes with water as the working fluid in vertical tubular test sections. A low-quality water film boiling (LWFB) database is consolidated with a total of 22,813 experimental data points, which cover a wide flow range of the system pressure from 0.1 to 9.0 MPa, mass flux from 25 to 2750 kg/m2 s, and inlet subcooling from 1 to 70 °C. Two machine learning (ML) models, based on random forest (RF) and gradient boosted decision tree (GBDT), are trained and validated to predict wall temperatures in post-CHF flow regimes. The trained ML models demonstrate significantly improved accuracies compared to conventional empirical correlations. To further evaluate the performance of these two ML models from a statistical perspective, three criteria are investigated and three metrics are calculated to quantitatively assess the accuracy of these two ML models. For the full LWFB database, the root-mean-square errors between the measured and predicted wall temperatures by the GBDT and RF models are 5.7% and 6.2%, respectively, confirming the accuracy of the two ML models.

Statistical analyses of pore radii on the performance of PET-nanocomposite membranes in the removal of iron and anions from Ibeshe River

The industrialization in Lagos State has impacted the Ibeshe watershed, as a result, lower water quality is being experienced. Hence, a novel way on the potential use of waste to treat Ibeshe watershed by synthesizing of graphene oxide and incorporating it into PET bottle waste membranes was studied. Polyethylene terephthalate (PET) membranes embedded with 1wt%, 2wt%, and 3wt% graphene oxide (GO) (M1, M2, and M3), were prepared via non-solvent-induced phase separation on polyester nonwoven support with the use of polyethylene glycol (PEG) as an additive. The surface morphologies of the three membranes appeared to be considerably different. The pore volume steadily reduces with an upsurge in the quantity of GO embedded in the membranes. A statistical assessment employing a uniform distribution curve was done using Python and the outcomes depict that the distribution of the radius data was firmly gathered around the mean. From the adsorption study, Fe2+ does not have the power to attract HCO3− in the transitory state at the surface of the membrane; hence HCO3− could not reach equilibrium at 80 min. The membranes’ performance was studied via flux and the rejection of iron and anions. The rejection rate calculated for the three anions and iron was observed to be high in the M3 membrane. The M3 membrane% rejection of the anions and iron is 96%, 85%%, 72%, and 60% for NO3−, Cl−, HCO3− and Fe, respectively. An upsurge in the amount of GO improved the water flux; hence, 3wt% GO gave the maximum water flux.

Proline Accumulation and Growth of Bean Leaf (Phaseolus vulgaris L.) With Biochar Application in The Shallow Water Table Environment

Agronomic constraints for vegetable cultivation in riparian wetlands are low soil quality and shallow water table conditions. This study aims to identify the effect of biochar application and shallow water table on proline accumulation and plant growth of bean leaf (Phaseolus vulgaris L.) at the generative stage. This study was carried out from April to August 2018 in factorial randomized block design. The first factor was doses of biochar (0, 1, and 2 kg.m-2), and the second was shallow water table condition (20 cm and 10 cm below the soil surface) given in the generative stage. The results showed that biochar application and the shallow water table significantly affected leaf parameters; proline accumulation and increases in the sucrose levels occurred on the leaves. Proline content increased started at 3 days after treatment (DAT) and decreased at 6 DAT until recovery day (7 DAT). The biochar application of 2 kg.m-2 provided good aeration in the soil, which enhanced the bean's ability to survive under excess water. Proline accumulation is one of the adaptation mechanisms of beans to abiotic stress in shallow water table conditions.

Hydrogeochemical Characteristics, Water Quality, and Human Health Risks of Groundwater in Wulian, North China

Groundwater shortage and pollution are critical issues of global concern. In Wulian County, a typical hilly area, groundwater is the main source of water supply. This study investigates the current situation of groundwater pollution in Wulian City through the analysis of groundwater water chemistry characteristics, water quality evaluation, and health risk evaluation. After the analysis of the controlling factors of chemical components in groundwater and the analysis of ion sources, the main ion sources in groundwater were determined. The results showed that the major cations in groundwater were Ca2+ and Na+ and the major anions were HCO3− and SO42−. Nevertheless, NO3− exceeded the standard to different degrees in pore water (PW), fissure pore water (FPW), and fissure water (FW). The minimum NO3− concentration exceeded the standard in FW. Under the influence of rock weathering and salt rock dissolution, the main hydrochemical types of groundwater were the HCO3-Ca, HCO3-Ca·Mg, and SO4·Cl-Ca·Mg types. According to the water quality evaluation and health risk assessment, the FW area in the south had the highest water quality, where Class I water appeared and potable water was more widely distributed. The PW and FPW areas in the north had lower water quality, with higher health risks. Category V water gradually appeared in the FPW area, which is not suitable as a water supply source. Factor analysis and ion ratio analysis showed that the study area is strongly affected by anthropogenic factors. These research methods have important reference value to the research of groundwater pollution status.

Physiological, Morphological and Biochemical Responses of Exogenous Hydrogen Sulfide in Salt-Stressed Tomato Seedlings

Salinity causes yield and quality losses in agricultural production and therefore great economic losses around the world. Hydrogen sulfide (H2S) is known to play a crucial role to ease physiological and metabolic processes in plants, and also increases the tolerance of the plant against many abiotic stress conditions. In this study, we investigated the effects of H2S treatments (0, 25, 50, 75 and 100 µM NaHS were applied as H2S donor) to the tomato seedlings to alleviate the harmful effects of salt stress (0, 75 and 150 mM NaCl). There was a significant decrease in plant growth and development in parallel with the increased salt level. Visible changes in plant development were observed after the dose of 75 mM NaCl in the tomato seedling. The effects of different doses of exogenous H2S treatment were found to be significant. H2S treatment increased the stress tolerance in tomato seedlings by arranging the mineral element and hormone content. Furthermore, H2S relieved the effect of stress in plants by increasing photosynthetic activity (photosynthesis rate (Pn), transpiration rate (Tr), stomatal conductivity (gs) and intercellular CO2 concentration (Ci)) of the plant. In addition, the effect of H2S on salt stress tolerance in tomato seedlings may be due to its positive effect on mineral element contents. As a result, based on the beneficial effects of H2S in tomato seedlings under salt stress, this treatment can be considered as an alternative resilience method for cultivation in saline soils or irrigation with low quality waters.

Helminth Infection among Children Living in an Urban Area in Tropical Countries: A Systematic Review

Background
 
 Neglected tropical diseases (NTDs) are a group of preventable and treatable diseases, in which soil-transmitted helminth (STH) infections are among the most common infections worldwide. Most affected are the children and this review aims to identify the prevalence, risk factors and impact of STH in an urban area. 
 
 Methodology
 
 This review was guiding using PRISMA review protocol incorporating the research question of “What is the prevalence, risk factors and impact of helminth infection among urban children in tropical countries?". The databases used in this review include SCOPUS, WEB OF SCIENCE, OVID MEDLINE and PUBMED. The articles used include observational and interventional studies conducted among children aged 18 years and less from 2010 to 2020. The main outcome measure was risk factors of STH infection seen in urban children includes social backgrounds, sanitation and policy.
 
 
 Results
 
 From the initial 973 articles found in the database searching, only 13 articles selected for qualitative synthesis after exclusion and screening for eligibility done. The overall prevalence of helminth infection among urban children ranges from 4.8-48.9%. The associated factor that influences helminth infection among children living in an urban area are age, male more common than female, low socioeconomic status, low parents’ education level, living in an urban slum or crowded area, low water quality and sanitation practice and poor hygiene practice. The deworming tablets consumption acts as protective factors for helminth infections. The medium haemoglobin value was significantly lower in helminth-infected children compared to uninfected children and the stunting, underweight as well as moderate acute malnutrition were attributable to helminth infections.
 
 
 Conclusion
 
 The prevalence and intensity of helminthiasis were heterogeneous among urban areas of developing countries, however, the endemicity is still high. Further efforts including healthy policies, continuous national deworming programmes and multi-sectoral partnership are required to reduce the prevalence of helminthiasis in the urban area.

Impact of hog farming on water quality of aquatic environments in North Carolina

North Carolina is the second largest hog producer in the nation and the large amounts of waste produced by hog farming can contaminate water bodies through runoff, leaching, and improper waste management. In this study, we collected samples from two water bodies near three hog farms and one aquatic environment that is not near a hog farm. We hypothesized that water bodies near the hog farms would have lower water quality with higher turbidity, total dissolved solids (TDS), and pH than the water body not in proximity to a hog farm because of water contamination with hog waste. To test this hypothesis, we built an inexpensive and portable water quality sensor using an Arduino UNO Rev3 microcontroller to measure the three aforementioned parameters. We collected water from four sublocations at each of the three experimental locations and two timepoints. The results showed that the turbidity was 4–6 times higher, TDS was 1.5–2 times higher, and pH was 3 units higher in the 2 experimental locations compared to the control location. This indicates that the water bodies near hog farms have lower water quality, which is not suitable for aquatic life and is likely to be negatively impacted by hog farming. This study and its findings are important for understanding the impact of hog farming on the proximal water bodies.

Гидроэкологическая оценка коллекторно-дренажного стока с оросительных систем Ростовской области

The purpose of the work is to conduct a comprehensive hydroecological assessment of collector-drainage waters of irrigation systems of the Rostov region in dynamics to establish spatial and temporal patterns of changes in their quality. The objects of the study were 20 main collector channels of fi ve branches of the Federal State Budgetary Institution «Management «Rostovmeliovodkhoz». Structurally, all collectors are open channels in the earth bed. For the hydrochemical assessment, an integral indicator, the coeffi cient of maximum pollution (Cmp), was used, taking into account the priority indicators of the quality of collector-drainage waters (CDW). The dynamics of the coeffi cient of water pollution limit in the reservoirs of the Semikarakorsky branch for the period 2009-2021 showed the absence of sharp fl uctuations in the quality of drainage runoff, the value of the coeffi cient of maximum pollution ranges from 0 to 1, drainage water is characterized as moderately polluted. In the collectors of the Bagaevsky branch, the value of the Cmp fl uctuates in the range of 0…2.7, drainage water is characterized as moderately polluted, polluted. The predominant ions in the drainage runoff are sulfates and sodium. The high content of sulfates in the drainage runoff is due to the introduction of sulphate groundwater, characteristic of this region. A study of collector-drainage water in the reservoirs of the Semikarakorsky and Bagaevsky branches for the content of biogenic elements in 2021 showed a regular excess of phosphates during the entire irrigation period. A comparative analysis of the quality of natural water of the CDW water intakes in the background and collector-drainage waters showed that the hydrochemical composition of the studied waters is approximately the same, which indicates the merger of water bodies and open reservoirs in the earth channel into a single hydrographic network. In 25% of the studied collectors, lower water quality was observed than in the water intake.

Open Sensing System for Long Term, Low Cost Water Quality Monitoring

Water is a major preoccupation for our generation since it is crucial in keeping a healthy ecosystem and supporting biodiversity. The state of aquatic systems and water bodies needs to be continuously monitored to make informed decisions and trigger sanitation when necessary. However, observing and tracking the evolution of many water bodies without disturbing and polluting the biotopes is expensive, not scalable, and thus, infeasible. This article presents a way to make sustainable measurements using a new low-cost, open-source, and autonomous monitoring system deployable in a broad network. The smart buoy is deployed and controlled by a central unit that uses lab-graded sensors to measure ambient factors. The custom electronic board offers sustainable electronics integration emphasizing power path and network connectivity. The smart buoy showed an average power consumption of 1.8 mA and a cost of 932 euros per device. Currently, five spots have been monitored, which allowed the understanding of why biological events, such as a massive fish death, occurred. The system is easily expandable and can be used in various applications to increase the knowledge of the underwater ecosystem.

Investigation of the correlation between trihalomethane concentrations and socioeconomic factors in NY State

Recent environmental justice studies have found associations between low drinking water quality and socioeconomic indicators such as income and race. Trihalomethanes, probable human carcinogens, are commonly found disinfection by-products (DBPs) in public water systems (PWS). Although trihalomethanes are correlated with socioeconomic indicators, there is a knowledge gap regarding how the level of contaminants correlates with socioeconomic indicators and other physicochemical factors in specific regions. Therefore, we investigated the correlation between trihalomethane concentrations and socioeconomic factors in New York State with more practical and insightful data analysis and interpretation compared to those in previous studies by providing more detailed city-level data and water system characteristics affecting the correlation. We found a negative correlation between median household income and trihalomethane concentrations in the state. The communities served by PWS using groundwater sources had lower trihalomethane concentrations and higher household income, suggesting that water quality parameters contribute to the negative correlation between trihalomethanes and income levels. The inverse association between trihalomethanes and household income may indicate socioeconomic disparity regarding drinking water quality and the need for improved efforts to assist small- and medium-sized community water systems to lower DBP levels in New York State because small- or medium-sized systems may have fewer resources to apply DBP control and removal technologies compared to large systems.

Surface Water Quality Assessment, Prediction, and Modeling of the River Daya in Odisha

A decision tree-based approach is projected to predict surface water quality and is a good tool to assess the quality and guarantee the safe use of water for drinking. Modeling surface water quality using artificial intelligence-based models is essential in projecting suitable mitigation measures; however, it remains a challenge and requires further research to enhance the modeling accuracy. Because of the serious effects of low water quality, a faster and less expensive solution is required. With this motivation, this research explores a series of supervised machine learning algorithms to estimate the water quality. The objective of this study is to assess the surface water quality of the Daya watercourse to determine the optimal procedure to measure quality of drinking water. Samples were collected from designated locations throughout different seasons (winter, summer, rainy) over a period of five years (2016, 2017, 2018, 2019, and 2020). Total dissolved solids, pH, alkalinity, chloride, nitrate, total hardness, calcium, magnesium, iron, fluoride, were all tested, as well as total coliform, fecal coliform, and E. coli. Through this decision tree regression model, accuracy of prediction is 93.77%. This is a significant result, indicating that the decision tree-based approach has the potential to be a useful tool for surface water quality prediction. However, it is important to note that there may be limitations and uncertainties in the model, and further research and validation may be required to improve the accuracy and dependability of forecasts. The catastrophic consequences of poor water quality, as well as the need for faster and less expensive technologies for testing water quality, are the driving factors in this study. The study's findings can help to improve knowledge of water quality in the Daya watercourse and enhance the decision-making processes to ensure safe drinking water.

Structural And Functional Relationship Study In Plant Salinity Stress

In today’s world the increased use of low-quality water for irrigation and soil salinization, saltness is a huge biotic problem that effecting factory growth and product in numerous corridors in the world. In repercussion to salt stress, plant cells accumulate compatible solutes and redistribute ions, all of which contribute them to acclimate to a low soil water potential. In addition, the endogenous abscisic acid (ABA) content increases daily, followed by changes in principle genetic expression in salt stress conditions. Here we are going to discuss about the proteins involved in plant salinity stress and their interactions with the salt tolerant factor which has a negative impact with the plant growth system. Some highly evaluated proteins that were observed under this stress are Plant ferredoxin-like protein (PFLP), Arabidopsis thaliana Salt Tolerance Related Protein (STRP), OsEXPA7, ribulose-1, 5-bisphosphate carboxylase (RuBP), etc. This report is also going to focus on various transcription factors (tf), responsible for the enhanced tolerance under biological activity of plant development. TFs bZIP, MYBs, bHLH, C2H2-Dof, G2-like, AP2-EREBP, NAC, GATA, ARF, ERF, bZIP and MYB which were highly up- regulated in salt stress response.

Sustainable and Integrated Water Supply Management at North Shivaji Nagar, Sangli

Abstract: Water being important part of life or we can say a most needed element for survival. Over the decades water has been treated wisely, people are getting careless for using water. Now due to changing climate the rainfall has been reducing and due to decreasing water table many regions is facing scarcity of water. Even in our region i.e. Sangli region many areas are under scarcity of water, Low quality of water, in time water supply etc. To overcome such situations there is need to take initial regarding sustainable & integrated water supply. This study and research emphasis on integrating water management and supply system and will help to overcome the scarcity of water during months of year.

THE EFFECT OF EXCESSIVE SODIUM-CONTAINING IRRIGATION WATERS ON SOIL INFILTRATION RATE

Due to increasing demand for limited water resources, the pressure on irrigation water used in agricultural production is also increasing. One of the alternative solutions to meet the irrigation water needed in agricultural production is to use low-quality water resources. However, use of these waters can cause serious problems for the environment, especially for soil and plants, in the short, medium and long term. The sodium concentration of the irrigation water affects the structure of the soil, infiltration rate and hydraulic conductivity properties, as well as negatively affects the nutrient uptake of the plants. In this study, the effect of irrigation water with excess sodium content on the infiltration rate, which is one of the important parameters for the design and operation of irrigation systems and sustainable agricultural production, was researched. The infiltration rate was measured by the double-ring infiltrometer method. In this study, irrigation water with four different Electrical Conductivity (ECi = 0.6 [control], 5, 10 and 15 dS m-1) and Sodium Adsorption Ratio (SAR) (1, 20, 40 and 50) were used. Cumulative infiltration values varied between 360-446 mm, and a 9-19% reduction was determined as compared to the control treatments, depending on the increasing ECi and SAR ratios. The steady-state infiltration rate of the treatments varied between 24.5-36.5 mm h-1 and decreased by 24-34%. In response to increased EC and SAR in irrigation water, the infiltration rates and cumulative infiltration values of soils decreased.

Magnetized Saline Water Irrigation Enhances Soil Chemical and Physical Properties

Due to rapid population growth and pressure on water resources, it is necessary to use economic and non-traditional techniques for irrigation. One of these techniques is the use of salt water after treatment with a magnetic force. A simulation experiment was conducted with soil columns using three quantities of saline water: 0, 3, and 6 g L−1 (S0, S3, and S6). Magnetic forces of 0, 1000, 2000, 3000, 4000, 5000, 6000, and 7000 gauss were used to study the effects of magnetic forces on leachate and soil physicochemical properties at different depths. The results at all soil depths showed that the pH decreased with increasing salinity from S0 to S3 and S6 by an average of 8.44, 8.28, and 8.27%, respectively. Soil EC decreased significantly with depth by 10–35%. The maximum SAR, SSP, and CROSS values (16.3, 51.1, and 17.6, respectively) were reported when no magnetic force was used, while the lowest values (13.9, 49.9, and 15.3) were recorded when using 3000 gauss under S6 within the soil profile. Magnetizing the water halved the EC of the leachate under S0, while it decreased the EC by 12.4% under S3. Increasing the magnetic force enhanced the leachate SAR, SSP, and CROSS values by 4.9–20.4% on average under S3 and S6. Magnetic forces augmented the hydraulic conductivity at the same salinity level and with increasing salinity, resulting in an increment of 50% at S3 and S6 compared with S0. After nine hours, the maximum cumulative infiltration rate was under 1000 and 4000 gauss. Our results demonstrated the important effects of magnetically treated irrigation water and could therefore support its application in agriculture under conditions of low water resources and quality.

Surface water quality assessment by Random Forest

Abstract The energetic nature of these important water resources makes them the most vulnerable to contamination from additional waste from multiple sources. Water quality monitoring is critical to water environmental management, and successful monitoring provides direction and confirms the effectiveness of water management. Models based on artificial intelligence are fundamental for anticipating appropriate moderation measures for surface water quality. In any case, it remains a challenge and requires a requirement to improve display accuracy. Faster and cheaper control is required due to the real-world impact of low water quality. With this inspiration, this research examines an array of machine-learning calculations to estimate water quality. The proposed approach uses Random Forest for modeling and is also useful for predicting surface water quality in the Kulik geographic region of West Bengal, India. It is a good tool for assessing the quality and ensuring the safe use of drinking water. Various water quality parameters (iron, fluoride, total coliform, fecal coliform, pH, total dissolved solids, magnesium, alkalinity, chloride, total hardness, nitrate, calcium, and Escherichia coli) were measured seasonally (winter, summer, rain) over 10 years (2010–2019). The estimated water quality parameters in this study were total dissolved solids (TDS), pH, and iron.

Evidence for Links between Feeding Behavior of Daphnia magna and Water Framework Directive Elements: Case Study of Crestuma-Lever Reservoir

The Water Framework Directive (WFD) is the European legislation on water policy that assesses water quality according to time-consuming metrics and specific taxonomic needs. In this sense, the objective of this study was to evaluate the sensitivity of Daphnia magna feeding rate assays to assess/discriminate the water quality of heavily modified and artificial water bodies. Monthly, for one year, the quality of the Crestuma-Lever reservoir (in two sampling sites: Crestuma and Marina) was assessed using physical, chemical, and biological (concentration of chlorophyll-a) elements proposed by the WFD. Additionally, D. magna was exposed to the collected water samples and the feeding rates were evaluated to include an ecosystem function evaluation in water quality assessment. The WFD metrics showed that, overall, the Crestuma-Lever reservoir has a rating of Good to Moderate Ecological Potential, regardless of site. Feeding rates varied with the sampling site and months, demonstrating that feeding behavior evaluation is a sensitive tool that allows discriminate potential effects indicative of a lower water quality. This finding was recorded by the decrease in the feeding rate (Crestuma: May, Sept; Marina: Nov, Jan, May), despite the WFD classification, and once the organisms are affected by the components present in the water samples.

Investigating the relationship between agricultural water usage and water quality in California

California has a highly developed agriculture system, but it faces many challenges associated with water supply and water quality. This research examines the relationship between agricultural water use and water quality at multiple scales, including a comparison of southern and northern California, analysis of seven regions, and four counties within the Central Valley, which accounts for more than 70% of agricultural water use in the state. Statistical analyses of georeferenced data from USGS and CIMIS were used to test the hypothesis that higher agricultural water use is associated with lower water quality. Results indicate that while there is no significant difference in water quality between southern and northern California, there are significant differences among regions. Furthermore, within the Central Valley, there is a significant inverse relationship between agricultural water use and two of three water quality indicators, namely dissolved oxygen and dissolved nitrate. Although further research is necessary to establish causality, these findings suggest that policymakers need to consider the effects of water use on water quality in planning California’s future agricultural development.

Hydrogen production from low-quality water: challenges and perspectives

The Next Generation EU plan fosters the development of a large capacity for hydrogen generation. However, water and energy resources are strictly connected to an indissoluble nexus. For that, water electrolysis may counteract the coexistence of two primary UNO Sustainable Development Goals humankind must face to achieve a prosperous and equal society, namely SDG 7 (Affordable access to renewable energy sources) and SDG 6 (clean water). To design innovative energy systems implementing hydrogen as an efficient and sustainable vector, water resources need careful management and energy use ought not to compete with freshwater delivery. Therefore, the present study reviews the technologies available for hydrogen production and their fitness to water quality standards. Among the feeding possibilities to be scrutinized, wastewaters and saline waters are worth attention. Each source of water asks for a specific design and management of the water treatment pre-process. Since these steps are energy-demanding, in some applications, the direct use of low-quality water to produce hydrogen may be envisaged. An example is the direct feeding of seawater to Solid Oxide Electrolysers (SOE). SOEs appear more promising than commercial low-temperature electrolysis systems since water steam production integrates the function of preliminary water treatment.

A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq

Salinity is a classic problem in water quality management since it is directly associated with low water quality indices. Debate continues about selecting the best model for water quality forecasting, it remains a major challenge and causes much uncertainty. Accordingly, identifying the optimal modelling that can capture the salinity behaviour is becoming a common trend in recent water quality research. This study applies novel combined techniques, including data pre-processing and artificial neural network (ANN) optimised with constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA) to forecast monthly salinity data. Historical monthly total dissolved solids (TDS) and electrical conductivity (EC) data of the Euphrates River at Al-Musayyab, Babylon, and climatic factors from 2010 to 2019 were used to build and validate the methodology. Additionally, for more validation, the CPSOCGSA-ANN was compared with the slime mould algorithm (SMA-ANN), particle swarm optimisation (PSO-ANN) and multi-verse optimiser (MVO-ANN). The results reveal that the pre-processing data approaches improved data quality and selected the best predictors’ scenario. The CPSOCGSA-ANN algorithm is the best based on several statistical criteria. The proposed methodology accurately simulated the TDS and EC time series based on R2 = 0.99 and 0.97, respectively, and SI = 0.003 for both parameters.