Proposed formulation of surface water quality and modelling using gene expression,machine learning,and regression techniques

Environmental Science and Pollution Research - The rising water pollution from anthropogenic factors motivates further research in developing water quality predicting models. The available models...     

Effect of ambient polycyclic aromatic hydrocarbons and nicotine on the structure of Aβ42 protein

● B[a]P, nicotine and phenanthrene molecules altered the secondary structure of Aβ₄₂. ● β-content of the peptide was significantly enhanced in the presence of the PAHs. ● Nicotine made stable cluster with Aβ₄₂ peptide via hydrogen bonds. ● Phenanthrene due to its small size, interfered with the Aβ₄₂ monomer more strongly. Recent studies have correlated the chronic impact of ambient environmental pollutants like polycyclic aromatic hydrocarbons (PAHs) with the progression of neurodegenerative disorders, either by using statistical data from various cities, or via tracking biomarkers during in-vivo experiments. Among different neurodegenerative disorders, PAHs are known to cause increased risk for Alzheimer’s disease, related to the development of amyloid beta (Aβ) peptide oligomers. However, the complex molecular interactions between peptide monomers and organic pollutants remains obscured. In this work, we performed an atomistic molecular dynamics study via GROMACS to investigate the structure of Aβ₄₂ peptide monomer in the presence of benzo[a]pyrene, nicotine, and phenanthrene. Interestingly the results revealed strong hydrophobic, and hydrogen-bond based interactions between Aβ peptides and these environmental pollutants that resulted in the formation of stable intermolecular clusters. The strong interactions affected the secondary structure of the Aβ₄₂ peptide in the presence of the organic pollutants, with almost 50 % decrease in the α-helix and 2 %–10 % increase in the β-sheets of the peptide. Overall, the undergoing changes in the secondary structure of the peptide monomer in the presence of the pollutants under the study indicates an enhanced formation of Aβ peptide oligomers, and consequent progression of Alzheimer’s disease.     

Risk assessment of heavy metal(loid)s via Spinacia oleracea ingestion after sewage water irrigation practices in Vehari District

Environmental Science and Pollution Research - The use of sewage water as an irrigation source can be beneficial in agricultural practices, however, it may result in human health risks due to the...     

Environmental forensic investigation of the air pollution from a cement manufacturing unit

Abstract

Cement manufacturing is a process that results in the emission of significant quantities of suspended particulate matter (SPM) to the ambient air. An environmental forensic investigation was carried out in the surroundings of a major cement manufacturing unit at a place called Coimbatore in the southern Indian state of Tamil Nadu. The investigation was carried out to identify the contribution of the cement manufacturing unit to the SPM concentration of the surrounding air environment. The sampling points’ selection and sample collection were done following the principles outlined in the INTERPOL Manual for Pollution Crime Forensic Investigation. On-site monitoring of the air samples was carried out using Mini Laser Aerosol Spectrometer (GRIMM, Mini-LAS Model 11R). The instrument was capable of measuring particles ranging from 0.25 to 32 µm and classifying them into 31 size channels. The test results at majority of the monitoring locations were well above the limits specified in the National Ambient Air Quality Standards of India. Microscopic studies of the dust samples were carried out for surface texture and particle shape. The spatial distribution of particles was analysed using geographic information system (GIS) for the visual identification of the extent of the pollution by keeping the cement factory as the focal point. The results from the GIS and microscopic analysis established the role of the cement factory in the particulate matter pollution of its surroundings, specifically in the areas North-West of the factory. The successfully adopted procedure can serve as a guideline for the environmental forensic investigation of similar pollution incidences.     

Kinetic,equilibrium and thermodynamic studies for the sorption of metribuzin from aqueous solution using banana peels,an agro-based biomass

Banana peels were employed for the removal of metribuzin from aqueous solution. Sorption in the batch mode was optimized regarding pH, contact time, sorbent dose, initial pesticide concentrations, and temperature. The sorption data were fitted to pseudo-first-order, pseudo-second-order, intraparticle diffusion, Elovich, and liquid film diffusion model, the pseudo-second-order exhibiting best fit (R² = 0.9803). Of the four most common sorption isotherm models (Langmuir, Freundlich, Tempkin, and Dubinin–Radushkevich), the data followed the Langmuir isotherm with highest correlation. The maximum adsorption capacity was found to be 167 mg g^?1. Gibbs free energy, enthalpy, and entropy showed that the sorption was exothermic and spontaneous.     

Composting of municipal solid waste by different methods improved the growth of vegetables and reduced the health risks of cadmium and lead

Environmental Science and Pollution Research - Reutilization of putrescible municipal solid wastes (MSW) in agriculture can provide valuable plant nutrients. However, it may pose serious...     

A context-intensive approach to imputation of missing values in data sets from networks of environmental monitors

Although networks of environmental monitors are constantly improving through advances in technology and management, instances of missing data still occur. Many methods of imputing values for missing data are available, but they are often difficult to use or produce unsatisfactory results. I-Bot (short for “Imputation Robot”) is a context-intensive approach to the imputation of missing data in data sets from networks of environmental monitors. I-Bot is easy to use and routinely produces imputed values that are highly reliable. I-Bot is described and demonstrated using more than 10 years of California data for daily maximum 8-hr ozone, 24-hr PM_2.5 (particulate matter with an aerodynamic diameter <2.5 μm), mid-day average surface temperature, and mid-day average wind speed. I-Bot performance is evaluated by imputing values for observed data as if they were missing, and then comparing the imputed values with the observed values. In many cases, I-Bot is able to impute values for long periods with missing data, such as a week, a month, a year, or even longer. Qualitative visual methods and standard quantitative metrics demonstrate the effectiveness of the I-Bot methodology.Implications: Many resources are expended every year to analyze and interpret data sets from networks of environmental monitors. A large fraction of those resources is used to cope with difficulties due to the presence of missing data. The I-Bot method of imputing values for such missing data may help convert incomplete data sets into virtually complete data sets that facilitate the analysis and reliable interpretation of vital environmental data.     

The effect of nutrients shortage on plant’s efficiency to capture solar radiations under semi-arid environments

Radiation use efficiency (RUE) is considered critical for calculation of crop yield. The crop productivity can be improved by increasing the interception of solar radiation and maintaining higher RUE for plants. Irrigation water and nitrogen (N) supply are the main limiting factors for RUE in maize (Zea mays L.) across the semi-arid environments. Field experiments were conducted during two consecutive growing seasons (2009–2010) to optimize RUE in relation to N application timings and rates with varying irrigation water management practices. In experiment 1, three N application timings were made, while in experiment 2, three possible water management practices were used. In both experiments, five N rates (100, 150, 200, 250, and 300 kg N ha⁻¹) were applied to evaluate the effects of irrigation water and N on cumulative photosynthetic active radiation (PARi), dry matter RUE (RUE_DM), and grain yield RUE (RUE_GY). The results demonstrated that cumulative PARi and RUEs were not constant during the plant growth under varying the nutrients. The water and N significantly influenced cumulative PARi and RUEs during the both growing seasons. In experiment 1, the maximum cumulative PARi was observed by application of 250 kg N ha⁻¹ in three splits (1/3 N at V2, 1/3 N at V16, and 1/3 N at R1 stage), and the highest RUE_DM was achieved by the application of 300 kg N ha⁻¹. However, the highest RUE_GY was observed by application of 250 kg N ha⁻¹. In experiment 2, the maximum cumulative PARi was attained at normal irrigation regime with 250 kg N ha⁻¹, while the highest RUE_DM and RUE_GY were recorded at normal irrigation regime with the application of 300 kg N ha⁻¹. The regression analysis showed significant and positive correlation of RUE_GY with grain yield. Therefore, optimum water and N doses are important for attaining higher RUE, which may enhance maize grain yield semi-arid environment; this may be considered in formulating good agricultural practices for the environmental conditions resembling to those of this study.

     

Mean Vehicle Speed Distributions for the Spatiotemporal Estimation of Exhaust Emissions

Link Emissions Models estimate traffic-related air pollution emissions at the individual road link level and inform governmental policies for air quality management. The current South Australian Link Emissions Model (CLEM) assumes constant spatiotemporal traffic flow at a single fixed mean speed, a potential limitation as the variability of exhaust emissions with vehicle speed has been established in the literature.We extend CLEM to eliminate the assumption of constant traffic flow, through the derivation of mean Australian vehicle speed distributions for different road types. Specifically, we successfully model the vehicle speed profile data from the second National In-Service Emissions study using Nearest Neighbour Kernel Density Estimation. We propose a mean speed Distribution Link Emissions Model (DLEM) for exhaust emission estimation based on the derived mean speed distributions. DLEM is an augmented, enhanced version of CLEM, accommodating a range of vehicle speeds and road types. The performance of the extended model, DLEM, is analysed in comparison to the current model, CLEM, through a case study analysis of vehicle exhaust emissions on a typical arterial road in Adelaide, South Australia. Results indicate use of DLEM and, by extension, mean vehicle speed distributions, has a strong impact on emission estimation. In particular, the fixed speed model, CLEM, may be substantially underestimating exhaust emissions of carbon monoxide, non-methane volatile organic compounds and particulate matter less than 2.5 μm in diameter. These are common exhaust pollutants that have been extensively linked with adverse health effects including respiratory morbidity and premature mortality.