Kjelleruporr7920

The detection of composition or pollution trends of vast environmental water areas, from a river, lake or sea, requires the determination of the mean concentration of the studied component in the studied area at defined depth in, at least, two occasions. Mean concentration estimates of a large area are robust to system heterogeneity and, if expressed with uncertainty, allow assessing if observed trends are meaningful or can be attributed to the measurement process. Mean concentration values and respective uncertainty are more accurately determined if various samples are collected from the studied area and if samples coordinates are considered. The spatial representation of concentration variation and the subsequent randomization of this model, given coordinates and samples analysis uncertainty, allows an improved characterization of studied area and the optimization of the sampling process. Recently, this evaluation methodology was described and implemented in a user-friendly MS-Excel file. This tool was upgraded to allow determinations close to zero concentration and "bottom-up" uncertainty evaluations of collected samples analysis. Since concentrations cannot be negative, this prior knowledge is merged with the original measurements in a Bayesian uncertainty evaluation that improves studied area description and sampling modelling. The Bayesian assessment avoids the underestimation of concentrations distribution by assuming that negative concentrations are impossible. This tool was successfully applied to the determination of reactive phosphate concentration in a vast ocean area of the Portuguese coast. The new version of the developed tool is made available as Supplementary Material.Lead dioxide (PbO2(s)) is a corrosion product of lead-containing plumbing materials in water distribution pipelines. The presence of reductants in water could cause the release of soluble lead (mainly Pb(II)) from PbO2(s). Lead in drinking water is detrimental to public health. This paper presents the first application of ferrate (FeVIO42-, Fe(VI)) to decreasing the generation of soluble lead in water containing PbO2(s) and common reducing constituents (e.g., natural organic matter (NOM), iodide (I-), and bromide (Br-)) at different pH conditions (i.e., 6.0, 7.0, and 8.0). The released soluble lead from PbO2(s) was found to be dominantly controlled by NOM in water, via the redox dissolution of PbO2(s) and the reduction of PbO2(s) by reducing moieties of NOM. The feasibility of both processes increased when pH decreased. The I- and Br- in water played minor roles in generating soluble lead. Fe(VI) reacted with reducing functional groups of NOM, as determined by 13C nuclear magnetic resonance spectroscopy. Water pretreatment with Fe(VI) inhibited the reaction of NOM with PbO2(s) and therefore, caused lower soluble lead concentrations compared to water samples without Fe(VI) treatment. This study indicates that Fe(VI) pretreatment is a potential approach to controlling soluble lead in drinking water.Cadmium (Cd) contamination, which poses a serious threat to human health, has been recognized as a major threat to the agricultural system and crop production. Salicylic acid (SA) is a signaling molecule that plays an important role in against Cd toxicity. Previously, we found that spraying rice with SA could reduce the Cd accumulation in rice grains grown in Cd-contaminated soil. In this study, we studied the specific mechanism of SA spray on reducing Cd accumulation in rice grain. The results showed that treatment with SA could alleviate Cd toxicity in rice by increasing the activities of antioxidant enzymes that reduce hydrogen peroxide (H2O2) accumulation, but not by changing the pH, or total or available Cd of the soil. The key factor by which SA treatment reduced Cd accumulation in rice grains was by decreasing the Cd content in rice leaves at the flowering stage. This indicated that SA could modulate the Cd accumulation in shoots, reducing the Cd translocation to rice grains. Furthermore, SA could increase the H2O2 content, activating the SA-signaling pathway and modulating the expression levels of Cd transporters (OsLCT1 and OsLCD) in rice leaves to increase Cd tolerance and reduce Cd accumulation in the rice grain. Thus, spraying rice with SA may be effective measure to cope with Cd contamination in paddy soils.Size-segregated airborne fine (PM2.1) and coarse (PM>2.1) particulates were measured in an urban environment over central Indo-Gangetic plain in between 2015 and 2018 to get insights into its nature, chemistry and sources. Mean (±1σ) concentration of PM2.1 was 98 (±76) μgm-3 with a seasonal high during winter (DJF, 162 ± 71 μgm-3) compared to pre-monsoon specific high in PM>2.1 (MAMJ, 177 ± 84 μgm-3) with an annual mean of 170 (±69) μgm-3. PM2.1 was secondary in nature with abundant secondary inorganic aerosols (20% of particulate mass) and water-soluble organic carbon (19%) against metal enriched (25%) PM>2.1, having robust signature of resuspensions from Earth's crust and road dust. Ammonium-based neutralization of particulate acidity was essentially in PM2.1 with an indication of predominant H2SO4 neutralization in bisulfate form compared to Ca2+ and Mg2+-based neutralization in PM>2.1. Molecular distribution of n-alkanes homologues (C17-C35) showed Cmax at C23 (PM2.1) and C18 (PM>2.1) with weak dominance of odd-numbered n-alkanes. Carbon preference index of n-alkanes was close to unity (PM2.1 1.4 ± 0.3; PM>2.1 1.3 ± 0.4). Fatty acids (C12-C26) were characterized with predominance of even carbon with Cmax at n-hexadecanoic acid (C160). Low to high molecular weight fatty acid ratio ranged from 2.0 (PM>2.1) to 5.6 (PM2.1) with vital signature of anthropogenic emissions. Levoglucosan was abundant in PM2.1 (758 ± 481 ngm-3) with a high ratio (11.6) against galactosan, emphasizing robust contribution from burning of hardwood and agricultural residues. selleck products Receptor model resolves secondary aerosols and biomass burning emissions (45%) as the most influential sources of PM2.1 whereas, crustal (29%) and secondary aerosols (29%) were found responsible for PM>2.1; with significant variations among the seasons.