Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data

Kernel function-based regression models were constructed and applied to a nonlinear hydro-chemical dataset pertaining to surface water for predicting the dissolved oxygen levels. Initial features were selected using nonlinear approach. Nonlinearity in the data was tested using BDS statistics, which revealed the data with nonlinear structure. Kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using the Gaussian kernel function and their generalization and predictive abilities were compared in terms of several statistical parameters. Model parameters were optimized using the cross-validation procedure. The proposed kernel regression methods successfully captured the nonlinear features of the original data by transforming it to a high dimensional feature space using the kernel function. Performance of all the kernel-based modeling methods used here were comparable both in terms of predictive and generalization abilities. Values of the performance criteria parameters suggested for the adequacy of the constructed models to fit the nonlinear data and their good predictive capabilities.     

Seasonal variations and source profile of <Emphasis Type="Italic">n</Emphasis>-alkanes in particulate matter (PM<Subscript>10</Subscript>) at a heavy traffic site,Delhi

Delhi is one of the most polluted cities in the world. The generation of aerosols in the lower atmosphere of the city is mainly due to a large amount of natural dust advection and sizable anthropogenic activities. The compositions of organic compounds in aerosols are highly variable in this region and need to be investigated thoroughly. Twenty-four-hour sampling to assess concentrations of n-alkanes (ng/m³) in PM₁₀ was carried out during January 2015 to June 2015 at Indira Gandhi Delhi Technical University for Women (IGDTUW) Campus, Delhi, India. The total average concentration of n-alkanes, 243.7 ± 5.5 ng/m³, along with the diagnostic tools has been calculated. The values of CPI₁, CPI₂, and CPI₃ for the whole range of n-alkanes series, petrogenic n-alkanes, and biogenic n-alkanes were 1.00, 1.02, and 1.04, respectively, and C _max were at C₂₅ and C₂₇. Diagnostic indices and curves indicated that the dominant inputs of n-alkanes are from petrogenic emissions, with lower contribution from biogenic emissions. Significant seasonal variations were observed in average concentrations of n-alkanes, which is comparatively higher in winter (187.4 ± 4.3 ng/m³) than during the summer season (56.3 ± 1.1 ng/m³).     

Groundwater Quality Assessment of Tehsil Kheragarh, Agra (India) with Special Reference to Fluoride

Fluoride concentration and other parameters in groundwater from 261 villages in Tehsil Kheragarh of District Agra were assessed and attempts were made to observe the relationship between fluoride and other water quality parameters. Of 658 groundwater samples (collected from separate sources) analysed for fluoride, 27% were in the range of 0–1.0 mg/L, 25% in 1.0–1.5 mg/L, 32% in1.5–3.0 mg/L and 16% above 3.0 mg/L. The highest fluoride concentration recorded was 12.80 mg/L. Significant correlation of fluoride with pH, alkalinity, Na, SiO₂ and PO₄ were observed. Factor analysis was also attempted in order to identify the contributing sources.     

Air pollution perception in ten countries during the COVID-19 pandemic

As largely documented in the literature, the stark restrictions enforced worldwide in 2020 to curb the COVID-19 pandemic also curtailed the production of air pollutants to some extent. This study investigates the perception of the air pollution as assessed by individuals located in ten countries: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the USA. The perceptions towards air quality were evaluated by employing an online survey administered in May 2020. Participants (N = 9394) in the ten countries expressed their opinions according to a Likert-scale response. A reduction in pollutant concentration was clearly perceived, albeit to a different extent, by all populations. The survey participants located in India and Italy perceived the largest drop in the air pollution concentration; conversely, the smallest variation was perceived among Chinese and Norwegian respondents. Among all the demographic indicators considered, only gender proved to be statistically significant.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13280-021-01574-2.     

Salt-induced land and water degradation in the Aral Sea basin: A challenge to sustainable agriculture in Central Asia

Expansion of irrigated agriculture in the Aral Sea Basin in the second half of the twentieth century led to the conversion of vast tracks of virgin land into productive agricultural systems resulting in significant increases in employment opportunities and income generation. The positive effects of the development of irrigated agriculture were replete with serious environmental implications. Excessive use of irrigation water coupled with inadequate drainage systems has caused large‐scale land degradation and water quality deterioration in downstream parts of the basin, which is fed by two main rivers, the Amu‐Darya and Syr‐Darya. Recent estimates suggest that more than 50% of irrigated soils are salt‐affected and/or waterlogged in Central Asia. Considering the availability of natural and human resources in the Aral Sea Basin as well as the recent research addressing soil and water management, there is cause for cautious optimism. Research‐based interventions that have shown significant promise in addressing this impasse include: (1) rehabilitation of abandoned salt‐affected lands through halophytic plant species; (2) introduction of 35‐day‐old early maturing rice varieties to withstand ambient soil and irrigation water salinity; (3) productivity enhancement of high‐magnesium soils and water resources through calcium‐based soil amendments; (4) use of certain tree species as biological pumps to lower elevated groundwater levels in waterlogged areas; (5) optimal use of fertilizers, particularly those supplying nitrogen, to mitigate the adverse effects of soil and irrigation water salinity; (6) mulching of furrows under saline conditions to reduce evaporation and salinity buildup in the root zone; and (7) establishment of multipurpose tree and shrub species for biomass and renewable energy production. Because of water withdrawals for agriculture from two main transboundary rivers in the Aral Sea Basin, there would be a need for policy level interventions conducive for enhancing interstate cooperation to transform salt‐affected soil and saline water resources from an environmental and productivity constraint into an economic asset.     

Oxidative stress and arsenic toxicity: Role of NADPH oxidases

The effect of arsenic (25 and 50 μM As for 1 and 5 d) was analysed in wild type (WT) and Arabidopsis thaliana (L.) Heynh plants deficient in NADPH oxidase C (AtrbohC). The content of H₂O₂ and malondialdehyde (MDA) increased with the As concentration, while the opposite effect was found for NO in WT and AtrbohC plants. The As treatment reduced catalase and increased glutathione reductase activities to the same extent in WT and AtrbohC plants, although the induction of all SOD isoforms (mainly CuZn–SODs) was observed in WT plants, the opposite effects being found in AtrbohC plants. Glycolate oxidase (H₂O₂ producers) considerably increased with the concentration and time of treatment with As in WT and AtrbohC mutants. Arsenic induced the uptake and translocation of P, S, Cu, Zn, and Fe in WT plants, while in AtrbohC plants the opposite trend was noted and the uptake of As became considerably lower than in WT plants. These results suggest that As causes oxidative stress by inducing glycolate oxidase, while NADPH oxidase does not appear to participate in ROS overproduction but could be critical in regulating antioxidant defences as well as the transport and translocation of As and macro/micronutrients.     

Role of some organic inhibitors on the oxidation of dissolved sulfur dioxide by oxygen in rainwater medium

In August 2012, eight rainwater samples were collected and analyzed for pH and metal ions, viz., iron, copper, and manganese. The pH was within the range 6.84–7.65. The rate of oxidation of dissolved sulfur dioxide was determined using these rainwater samples as reaction medium. Kinetics was defined by the rate law: ?d[S(IV)]/dt = R _o = k _o[S(IV)]], where k _o is the first-order rate constant and R _o is the rate of the reaction. The effect of two volatile organic compounds—ethanol and 2-butanol—was examined and found to inhibit the oxidation as defined by the rate law: k _obs = k _o/(1 + B [Inh]), where k _obs is the first-order rate constant in the presence of the inhibitor, [Inh] is the concentration of the inhibitor, and B is the inhibitor parameter—an empirical constant. In the pH range of collected rainwater samples, the values of first-order rate constants ranged from 3.1?×?10^?5 to 1.5?×?10^?4 s^?1 at 25 °C. The values of inhibition parameter were found to be (5.99?±?3.91?×?10⁴) (ethanol) and (3.95?±?2.36)?×?10⁴ (2-butanol) at 25 °C.     

Association between environmental exposure to p,p′-DDE and lindane and semen quality

Scientific concern exists about the toxic effect of dichlorodiphenyldichloroethylene (p, p′-DDE) and lindane on male infertility, and the mechanism underlying male reproductive toxicity of this pesticide remains unanswered. We investigated not only the possible association between the chlorinated pesticide levels and semen quality in nonoccupationally exposed men, but also the probable mode of action using mitochondrial membrane potential (MMP), reactive oxygen species (ROS), lipid peroxidation (LPO), and sperm chromatin structure assay (SCSA). A study in 278 men (21–40 years old) who visited Obstetrics and Gynecology Department, KGMU, Lucknow, for semen analysis was conducted. We performed semen analysis according to the WHO guidelines, while p, p′-DDE and lindane analysis was done by the GLC and LPO by the spectrophotometer, and the sperm mitochondrial status, ROS, and SCSA with the flow cytometer. The questionnaire data showed no significant difference in the demographic characteristics between the two groups, i.e., trying to conceive >1 year and proven fertility. However, a significant difference in the concentration of p, p′-DDE and lindane was observed between the groups. When the subjects were divided among four categories by quartile of exposure, the subjects in the highest quartile showed low sperm motility as compared to the subjects in the lowest quartile. Pearson’s correlation showed a significant negative correlation between semen p, p′-DDE, lindane level, and sperm quality and positive association with the number of cells with depolarized mitochondria, elevation in ROS production and LPO, and DNA fragmentation index (DFI). The findings are suggestive that these toxicants might cause a decline in semen quality, and these effects might be ROS, LPO, and mitochondrial dysfunction mediated.     

Predicting adsorptive removal of chlorophenol from aqueous solution using artificial intelligence based modeling approaches

The research aims to develop artificial intelligence (AI)-based model to predict the adsorptive removal of 2-chlorophenol (CP) in aqueous solution by coconut shell carbon (CSC) using four operational variables (pH of solution, adsorbate concentration, temperature, and contact time), and to investigate their effects on the adsorption process. Accordingly, based on a factorial design, 640 batch experiments were conducted. Nonlinearities in experimental data were checked using Brock–Dechert–Scheimkman (BDS) statistics. Five nonlinear models were constructed to predict the adsorptive removal of CP in aqueous solution by CSC using four variables as input. Performances of the constructed models were evaluated and compared using statistical criteria. BDS statistics revealed strong nonlinearity in experimental data. Performance of all the models constructed here was satisfactory. Radial basis function network (RBFN) and multilayer perceptron network (MLPN) models performed better than generalized regression neural network, support vector machines, and gene expression programming models. Sensitivity analysis revealed that the contact time had highest effect on adsorption followed by the solution pH, temperature, and CP concentration. The study concluded that all the models constructed here were capable of capturing the nonlinearity in data. A better generalization and predictive performance of RBFN and MLPN models suggested that these can be used to predict the adsorption of CP in aqueous solution using CSC.     

Groundwater quality in Imphal West district, Manipur, India, with multivariate statistical analysis of data

The aim of this paper was to analyze the groundwater quality of Imphal West district, Manipur, India, and assess its suitability for drinking, domestic, and agricultural use. Eighteen physico-chemical variables were analyzed in groundwater from 30 different hand-operated tube wells in urban, suburban, and rural areas in two seasons. The data were subjected to uni-, bi-, and multivariate statistical analysis, the latter comprising cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA). Arsenic concentrations exceed the Indian standard in 23.3 % and the WHO limit in 73.3 % of the groundwater sources with only 26.7 % in the acceptable range. Several variables like iron, chloride, sodium, sulfate, total dissolved solids, and turbidity are also beyond their desirable limits for drinking water in a number of sites. Sodium concentrations and sodium absorption ratio (SAR) are both high to render the water from the majority of the sources unsuitable for agricultural use. Multivariate statistical techniques, especially varimax rotation of PCA data helped to bring to focus the hidden yet important variables and understand their roles in influencing groundwater quality. Widespread arsenic contamination and high sodium concentration of groundwater pose formidable constraints towards its exploitation for drinking and other domestic and agricultural use in the study area, although urban anthropogenic impacts are not yet pronounced.