Factor analysis and linear regression model (LRM) of metal speciation and physico-chemical characters of groundwater samples

Impacts of Climate and Phenology on the Yields of Early Mature Rice in China

Phenological variables are closely correlated with rice (Oryza sativa L.) yields as they play important roles in influencing and controlling the carbon allocations between plant organs, but their impacts on rice yields and their relative importance compared with climatic variables are not yet well investigated. In this study, the impacts and the relative importance of climatic and phenological variables on the yields of early mature rice were assessed using the trial data from 75 agricultural stations across China, spanning from 1981–2010. We found that both daily maximum (Tmax) and daily minimum (Tmin) temperatures during the growing season (from transplanting to maturity) increased significantly, while sunshine duration (SD) and precipitation (Prep) did not change significantly. The average transplanting date was advanced by 3.18 days/decade, and the heading (maturity) dates were delayed by 2.47 (4.55) days/decade, with yields significantly increased by 9.65 g/m3 per decade across all sites. Partial correlation coefficients between most phenological variables and rice yields were negative, whereas most of the climatic variables were positively correlated with rice yields. The average of partial correlation coefficients between transplanting, heading, and maturity dates and rice yields were −0.10, −0.15, and −0.01, respectively, and the average of coefficients between Tmax, Tmin, SD, and Prep and rice yields were 0.08, 0.02, 0.12, and −0.05, respectively. Interestingly enough, phenological variables were the dominating influencing factors on rice yields at 63% of the sites, suggesting that the relative importance of phenology to rice yields may be even higher than that of climate. The climatic variables were closely correlated with rice yields as they are fundamental growth materials for crops, and phenological variables strongly influenced the growth and development of rice. Our results highlight that phenology should be precisely evaluated in crop models to improve the accuracy of simulating their response to climate change. Furthermore, due to limited understanding of phenological processes, manipulative experiments are urgently needed to comprehensively improve our understanding of rice phenology and rice yield response to ongoing climate change.

AI-Blue-Carba: A Rapid and Improved Carbapenemase Producer Detection Assay Using Blue-Carba With Deep Learning

A rapid and accurate detection of carbapenemase-producing Gram-negative bacteria (CPGNB) has an immediate demand in the clinic. Here, we developed and validated a method for rapid detection of CPGNB using Blue-Carba combined with deep learning (designated as AI-Blue-Carba). The optimum bacterial suspension concentration and detection wavelength were determined using a Multimode Plate Reader and integrated with deep learning modeling. We examined 160 carbapenemase-producing and non-carbapenemase-producing bacteria using the Blue-Carba test and a series of time and optical density values were obtained to build and validate the machine models. Subsequently, a simplified model was re-evaluated by descending the dataset from 13 time points to 2 time points. The best suitable bacterial concentration was determined to be 1.5 optical density (OD) and the optimum detection wavelength for AI-Blue-Carba was set as 615 nm. Among the 2 models (LRM and LSTM), the LSTM model generated the higher ROC-AUC value. Moreover, the simplified LSTM model trained by short time points (0–15 min) did not impair the accuracy of LSTM model. Compared with the traditional Blue-Carba, the AI-Blue-Carba method has a sensitivity of 95.3% and a specificity of 95.7% at 15 min, which is a rapid and accurate method to detect CPGNB.

Assessing the potential origins and human health risks of trace elements in groundwater: A case study in the Khoy plain, Iran

The objectives of this study were to measure some trace element concentrations in the groundwater of the Khoy area in northwestern Iran, understand their potential origins using multivariate statistical approaches (correlation analysis, cluster analysis and factor analysis), and evaluate their non-carcinogenic human health risks to local residents through drinking water intake. The trace element status of the groundwater and the associated health risks in the study area have not previously been reported. Groundwater water samples were collected from 54 water sources in July 2017 in the study area. Samples were measured for EC, pH, major and minor elements and some trace elements (Fe, Mn, Al, Zn, Cr, Pb, Cd, Co, Ni and As). The levels of EC, F, Cd, Pb, Zn, As and all the major ions except K exceeded permissible levels for drinking water. Multivariate analysis showed that the quality of groundwater was mainly controlled by geogenic factors followed by anthropogenic impacts. Health risk assessment results indicated that Cr and As in the groundwater, with hazard quotient values of 0.0001 and 11.55, respectively, had the lowest and highest impacts of non-carcinogenic risk to adults and children in the area. The high-risk samples were mainly situated in the northeast and southwest of the Khoy plain where the groundwater was saline. The health risk associated with water consumption from the unconfined aquifer was higher than that from the confined aquifer in the study area. Special attention should be paid to groundwater management in the high-risk areas to control factors (e.g., EC, pH and redox) that stimulate the release of trace elements into groundwater.

Chemical behavior of Cu, Zn, Cd, and Pb in a eutrophic reservoir: speciation and complexation capacity

This research aimed at evaluating cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn) speciation in water samples as well as determining water quality parameters (alkalinity, chlorophyll a, chloride, conductivity, dissolved organic carbon, dissolved oxygen, inorganic carbon, nitrate, pH, total suspended solids, and water temperature) in a eutrophic reservoir. This was performed through calculation of free metal ions using the chemical equilibrium software MINEQL+ 4.61, determination of labile, dissolved, and total metal concentrations via differential pulse anodic stripping voltammetry, and determination of complexed metal by the difference between the total concentration of dissolved and labile metal. Additionally, ligand complexation capacities (CC), such as the strength of the association of metals-ligands (logK'ML) and ligand concentrations (C L) were calculated via Ruzic's linearization method. Water samples were taken in winter and summer, and the results showed that for total and dissolved metals, Zn > Cu > Pb > Cd concentration. In general, higher concentrations of Cu and Zn remained complexed with the dissolved fraction, while Pb was mostly complexed with particulate materials. Chemical equilibrium modeling (MINEQL+) showed that Zn(2+) and Cd(2+) dominated the labile species, while Cu and Pb were complexed with carbonates. Zinc was a unique metal for which a direct relation between dissolved species with labile and complexed forms was obtained. The CC for ligands indicated a higher C L for Cu, followed by Pb, Zn, and Cd in decreasing amounts. Nevertheless, the strength of the association of all metals and their respective ligands was similar. Factor analysis with principal component analysis as the extraction procedure confirmed seasonal effects on water quality parameters and metal speciation. Total, dissolved, and complexed Cu and total, dissolved, complexed, and labile Pb species were all higher in winter, whereas in summer, Zn was mostly present in the complexed form. A high degree of deterioration of the reservoir was confirmed by the results of this study.

Characteristics and source identification of dissolved trace elements in the Jinshui River of the South Qinling Mts., China

Dissolved trace elements and physiochemical parameters were analyzed to investigate their physicochemical characteristics and identify their sources at 12 sampling sites of the Jinshui River in the South Qinling Mts., China from October 2006 to November 2008. The two-factor ANOVA indicated significant temporal variations of the dissolved Cu, Fe, Sr, Si, and V (p < 0.001 or p < 0.05). With the exception of Sr (p < 0.001), no significant spatial variations were found. Distributions and concentrations of the dissolved trace elements displayed that dissolved Cu, Fe, Sr, Si, V, and Cr were originated from chemical weathering and leaching from the soil and bedrock. Dissolved Cu, Fe, Sr, As, and Si were also from anthropogenic inputs (farming and domestic effluents). Correlation and regression analysis showed that the chemical and physical processes of dissolved Cu was influenced by water temperature and dissolved oxygen (DO) to some degree. Dissolved Fe and Sr were affected by colloid destabilization or sedimentary inputs. Concentrations of dissolved Si were slightly controlled by biological uptake. Principal component analysis confirmed that Fe, Sr, and V resulted from domestic effluents, agricultural runoff, and confluence, whereas As, Cu, and Si were from agricultural activities, and Cr and Zn through natural processes. The research results provide a reference for ecological restoration and protection of the river environment in the Qinling Mts., China.

Evaluation of factors influencing the groundwater chemistry in a small tropical island of Malaysia

Groun in a very complex way. In this work, multivariate statistical analysis was used to evaluate the factors controlling the groundwater chemistry of Kapas Island (Malaysia). Principal component analysis (P dwater chemistry of small tropical islands is influenced by many factors, such as recharge, weathering and seawater intrusion, among others, which interact with each other CA) was applied to 17 hydrochemical parameters from 108 groundwater samples obtained from 18 sampling sites. PCA extracted four PCs, namely seawater intrusion, redox reaction, anthropogenic pollution and weather factors, which collectively were responsible for more than 87% of the total variance of the island’s hydrochemistry. The cluster analysis indicated that three factors (weather, redox reaction and seawater intrusion) controlled the hydrochemistry of the area, and the variables were allocated to three groups based on similarity. A Piper diagram classified the island’s water types into Ca-HCO₃ water type, Na-HCO₃ water type, Na-SO₄-Cl water type and Na-Cl water type, indicating recharge, mixed, weathering and leached from sewage and seawater intrusion, respectively. This work will provide policy makers and land managers with knowledge of the precise water quality problems affecting the island and can also serve as a guide for hydrochemistry assessments of other islands that share similar characteristics with the island in question.

Physical and chemical characteristics of water from the hydrographic basin of the Poxim River, Sergipe State, Brazil

The Poxim River is one of Sergipe State's major waterways. It supplies water to the State capital, Aracaju, but is threatened by urban and agricultural developments that compromise both the quantity and the quality of the water. This has direct impacts on the daily lives of the region's population. In this work, a multivariate analytical approach was used to investigate the physical and chemical characteristics of the water in the river basin. Four sampling campaigns were undertaken, in November 2005, and in February, May, and September 2006, at 15 sites distributed along the Poxim. The parameters analyzed were conductivity, turbidity, color, total dissolved solids, dissolved oxygen, alkalinity, hardness, chlorophyll-a, and nutrients (total phosphorus, dissolved orthophosphate, nitrite, nitrate, ammoniacal nitrogen, and total nitrogen). Dissolved oxygen contents were very low in the Poxim-Açu River (1.0-2.8), the Poxim River (1.6-4.6), and the estuarine region (1.7-5.1), due to the dumping of wastes and discharges of domestic and industrial effluents containing organic matter into fluvial and estuarine regions of the Poxim. Factor analysis identified five components that were indicative of the quality of the water, and that explained 81.73 % of the total variance.

Appraisal of heavy metals in groundwater in Chennai city using a HPI model

Heavy metal contamination in Chennai city was evaluated using a heavy metal pollution index (HPI) model in conjunction with the spatial distribution maps. Metals such as Cd, Cr, Cu, Pb and Zn in the groundwater were determined using standard methods and the resultant data was utilized in the development of a HPI model. The metal concentrations showed a dominance in the order of Zn > Cu > Cr > Pb > Cd respectively. About 14.3 % of the samples (n = 2), exhibited high HPI (>38) and the highest value, HPI = 97.55, has been recorded from Thiruvanmiyur area. Statistical analysis revealed a positive correlation between metals such as Cd and Cr (r = 0.606), Cd and Cu (r = 0.601), Cr and Cu (r = 0.464) and Pb and Zn (r = 0.416), suggested their common origin. The spatial distribution maps of heavy metals and the HPI suggested that the SW region, especially Adyar and Thiruvanmiyur regions are highly contaminated with the metals. Industrialisation and improper waste dumping were identified as the major cause for the accumulation of metals in the groundwater of Chennai city.