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600 results for “Contamination”
РИС. 5. ЗагрЯЗнение готовых обраЗцов длЯ СЭМ при длительном хранении в негерметичных условиЯх (A–C) либо при хранении проШедШих процедуру мацерированиЯ беЗ последуюЩего обеЗЗараживаниЯ (D, E). A–С. Бактерии на поверхности глохидиев (Nodularia douglasiae, р. ИлистаЯ, бассейн оЗ. Ханка, Приморский кр.). А. ВнеШний вид глохидиЯ, основное ЗагрЯЗнение на створке в верхней части фото. В. Крючок глохидиЯ, основное ЗагрЯЗнение в левой части фото. С. Створка, вид иЗнутри. D. Единичные бактерии на створке глохидиЯ, вид иЗнутри (Kunashiria japonica, оЗ. Утиное, о-в Зелёный, Курильские о-ва). E. Гифы гриба на створке глохидиЯ, вид на наружную пору (Beringiana beringiana, оЗ. АЗабачье, Камчатка). МасШтаб 50 мкм (А, C), 10 мкм (В, D), 1 мкм (Е). Микроскопы Zeiss MERLIN (А, B, C, E), Zeiss EVO 40 (D), напыление хромом (А–С), Золотом (D), углеродом (Е). FIG. 5. Contamination of the SEM ready-made samples during long-term storage under unsealed conditions (A–C) or during storage the samples that have passed the maceration procedure without subsequent disinfection (D, E). A–C. Bacteria on the glochidia surface (Nodularia douglasiae, Ilistaya River, Khanka Lake basin, Primorsky Krai). A. Glochidium with the main pollution on the valve in the upper part of the photo. B. Hook with the main pollution on the left side of the photo. C. Interior valve. D. Bacteria on the interior valve (Kunashiria japonica, Utinoe Lake, Zeliony Island, Kuril Islands). E. Fungal hyphae on the pore of exterior valve (Beringiana beringiana, Azabachye Lake, Kamchatka). Scale bars 50 μm (A, C), 10 μm (B, D), 1 μm (E). Zeiss MERLIN (A, B, C, E) and Zeiss EVO 40 (D) microscopes, sputter coating with chromium (A–C), gold (D), and carbon (E). in Методика подготовки раковин глохидиев (Bivalvia, Unionidae) длЯ работы на сканируюЩем Электронном микроскопе
РИС. 5. ЗагрЯЗнение готовых обраЗцов длЯ СЭМ при длительном хранении в негерметичных условиЯх (A–C) либо при хранении проШедШих процедуру мацерированиЯ беЗ последуюЩего обеЗЗараживаниЯ (D, E). A–С. Бактерии на поверхности глохидиев (Nodularia douglasiae, р. ИлистаЯ, бассейн оЗ. Ханка, Приморский кр.). А. ВнеШний вид глохидиЯ, основное ЗагрЯЗнение на створке в верхней части фото. В. Крючок глохидиЯ, основное ЗагрЯЗнение в левой части фото. С. Створка, вид иЗнутри. D. Единичные бактерии на створке глохидиЯ, вид иЗнутри (Kunashiria japonica, оЗ. Утиное, о-в Зелёный, Курильские о-ва). E. Гифы гриба на створке глохидиЯ, вид на наружную пору (Beringiana beringiana, оЗ. АЗабачье, Камчатка). МасШтаб 50 мкм (А, C), 10 мкм (В, D), 1 мкм (Е). Микроскопы Zeiss MERLIN (А, B, C, E), Zeiss EVO 40 (D), напыление хромом (А–С), Золотом (D), углеродом (Е). FIG. 5. Contamination of the SEM ready-made samples during long-term storage under unsealed conditions (A–C) or during storage the samples that have passed the maceration procedure without subsequent disinfection (D, E). A–C. Bacteria on the glochidia surface (Nodularia douglasiae, Ilistaya River, Khanka Lake basin, Primorsky Krai). A. Glochidium with the main pollution on the valve in the upper part of the photo. B. Hook with the main pollution on the left side of the photo. C. Interior valve. D. Bacteria on the interior valve (Kunashiria japonica, Utinoe Lake, Zeliony Island, Kuril Islands). E. Fungal hyphae on the pore of exterior valve (Beringiana beringiana, Azabachye Lake, Kamchatka). Scale bars 50 μm (A, C), 10 μm (B, D), 1 μm (E). Zeiss MERLIN (A, B, C, E) and Zeiss EVO 40 (D) microscopes, sputter coating with chromium (A–C), gold (D), and carbon (E).
Dataset for "Quality and Contamination control" workflow
<p>This dataset is associated with the workflow "Quality and Contamination control for paired end data".</p>
Spatial analysis of the potential impact of contaminated areas on wells and aquifers in the Upper Tietê Basin, São Paulo, Brazil
<p><span>Upper Tietê stands out as the basin with the highest concentration of contaminated areas in São Paulo, Brazil, thereby exerting a pronounced impact on the quality of groundwater and directly affecting the resident population. Recognizing the importance of developing indicators for effective aquifer management, this study proposed to identify and assess the degree of risk, vulnerability, and contamination of aquifers and wells in the Upper Tietê Basin. Were applied the Aquifer Vulnerability Index method to evaluate the aquifer vulnerability in the region; the delimitation of safety perimeters and identification of wells at risk; and the integrated risk index, unifying information on social and aquifer vulnerabilities, hazards and exposure. The results reveal that the aquifers have a high vulnerability, mainly due to the thickness of their unsaturated layer. Furthermore, there were 7,958 wells at risk around the basin (77.15%), and the most vulnerable municipalities are on the outskirts of the basin. This study provides important insights, mainly due to the holistic approach, serving as a basis for identifying the regions with the highest risks, which can be prevented and mitigated with appropriate actions.</span></p>
DIETxPOSOME: a FAIR database detailing food contaminants occurrence in selected foods
<p>The DIETxPOSOME FAIR database provides detailed quantification of 73 contaminants, including 4 heavy metals, 18 polycyclic aromatic hydrocarbons (PAHs), 10 pesticides, 29 mycotoxins, and 12 heterocyclic aromatic amines (HAAs), in 16 food items across various food groups (cereals, vegetables, fruits, starchy roots, dairy products, nuts, meat, eggs, fish, legumes and vegetable oils). Data was obtained through a combination of literature extraction protocols and machine learning.</p> <p>A list of pertinent research articles on food contaminants in globally significant food items was sourced from PubMed. Machine learning enabled the automatic filtering of these papers (<a href="../records/7826130">https://zenodo.org/records/7826130</a>), which were subjected to a manual evaluation. Subsequently, specific foods (wheat, maize, beef, cheese, pasta, potatoes, carrots, rice, bread, chicken eggs, peanuts, beans, cabbage, apple, olive oil, and salmon) were selected based on their comprehensive contaminant profiles. Information was extracted from 145 relevant articles of the 151 research articles pertaining to the selected food items. Following extraction, the data was confirmed and evaluated by three reviewers, adhering to stringent criteria. </p> <p>The food items in the DIETxPOSOME FAIR database can be integrated into complex dietary patterns in an experimental context, for example, aligned with the EAT-Lancet Commission Reference Diet for Healthy and Sustainable Food Systems. This framework allows the simulation of real-world dietary exposure or worst-case scenarios, facilitating comprehensive risk assessment of unavoidable food contaminants.</p> <p> </p>
Figure 3 in Comparative effectiveness of EDTA and citric acid assisted phytoremediation of Ni contaminated soil by using canola (Brassica napus)
Figure 3. The role of EDTA and citric acid on (a) leaf turgor potential and (b) water use efficiency, (c) potassium and (d) sodium at vegetative stage for phytoremediation of Ni by using canola plant.
Figure 4 in Comparative effectiveness of EDTA and citric acid assisted phytoremediation of Ni contaminated soil by using canola (Brassica napus)
Figure 4. The role of EDTA and citric acid on (a) SOD and (b) CAT, (c) POD, (d) total free amino acid, (e) total soluble proteins, (f) total soluble sugars at vegetative stage for phytoremediation of Ni by using canola plant and the role of EDTA and citric acid on Ni contents (mg/ pot) in above ground biomass (g) at vegetative stage for phytoremediation of Ni by using canola.
Figure 1 in Comparative effectiveness of EDTA and citric acid assisted phytoremediation of Ni contaminated soil by using canola (Brassica napus)
Figure 1. The role of EDTA and CA on (a) plant height and (b) shoot fresh weight at the vegetative stage of two canola cultivars (Con-II and Oscar, respectively) in control and Ni treatment and the role of EDTA and citric acid on (c) dry weight and (d) photosynthetic rate at vegetative stage for phytoremediation of Ni by using canola plant.
Figure 7 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
Figure 7. Association between the survival probability estimate and mass (kg) at the time of release from rehabilitation from the best supported TMMC model: {Φ(mass)}.
Figure 3 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
Figure 3. Map of the tracks taken by seals that traveled the greatest distance from their capture or release location for each of the three groups of seals. SF = San Francisco Bay, TB = Tomales Bay, and TMMC = The Marine Mammal Center. The TMMC seal that traveled the furthest was released from Cypress Point, Monterey.
Figure 6. Association between survival probability estimates with circulating T4 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
Figure 6. Association between survival probability estimates with circulating T4 (nmol/L) at the time of capture/release from the best supported model: {Φ(g + T4)}.
Figure 5 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
Figure 5. The last day of satellite tag transmission for each seal (circles) modeled by two exponential curves (solid lines) with 95% confidence intervals (dotted lines). Data and estimates are in black for the wild (SF and TB) seals and blue for the rehabilitated (TMMC) seals. The gray line represents tag survival based on a normal distribution. The dashed lines display the effect of tag loss on the two survival curves.
Figure 1 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
Figure 1. Relationship between standard length and summed organohalogen contaminants (OH) for the three groups: San Francisco Bay [logOH = (length) × (−0.05) + 13.76, F = 6.117, df = 17, R2 = 0.2646, P = 0.024], Tomales Bay [logOH = (length) × (−0.05) + 24.45, F = 10.3, df = 5, R2 = 0.6733, P = 0.023], and The Marine Mammal Center (not significant).
Identifying contaminants of coal inertinite in charcoal briquettes: Preliminary findings of microscopic analysis
<p>Submitted data was used to write an article: Jelonek, Z., Jelonek, I., Identifying coal-derived inertinite in charcoal briquettes: Preliminary findings of microscopic analysis – article sent for review to the International Journal of Coal Geology. </p> <p> </p> <p><strong>Funding acknowledgments:</strong> The project is co-financed by the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), the National Science Center, Poland (2022/01/1/ST10/00024), and the research activities co-financed by the funds granted under the Research Excellence Initiative of the University of Silesia in Katowice, Poland. </p> <p> </p> <p><strong>Article Abstract</strong>: Despite the widespread popularity of charcoal-based grilling fuels, extensive studies have highlighted various pollutants linked to their production and combustion, posing potential risks to human health and the environment. Since the presence of impurities has been identified as a factor contributing to elevated emissions of harmful gases and particulate matter, a comprehensive quality assessment of grilling fuels is imperative to effectively manage and minimize potential risks to customer health and safety.</p> <p>While identifying many impurities in solid biomass fuels is possible through microscopic analysis, identifying fossil coal contaminants in charcoal briquettes can be challenging. The biggest difficulty arises when coal-derived inertinite and man-made charcoal need to be distinguished as both exhibit numerous visual similarities in microscopic images. Therefore, the goal of this study was to examine the optical morphology of inertinite and charcoal with the aim of differentiating them when they co-occur in charcoal briquettes.</p> <p>The results show that employing high differential interference (DIC) and fluorescence filters, coupled with reflected white light in microscopic analysis, can enhance the observations allowing for easier detection of impurities of inertinite in charcoal-based grilling fuels. Among the most notable distinctions are the high degree of cellular structure preservation and the presence of small pores and protrusions in man-made charcoal; these characteristics are typically absent in the inertinite fragments.</p>
Fig. 1 in Mussels (Perna perna) as bioindicator of environmental contamination by Cryptosporidium species with zoonotic potential
Fig. 1. Map of the studied area in the municipality of Mangaratiba, Rio de Janeiro State, Brazil. Red marker A — Collection site A; Red marker B — Collection site B; Green marker — The river known as "Rio do Saco" which leads to the ocean at collection site B. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).
Data providers package for reporting Chemical Contaminants (official data reporting phase) SSD2
<p>In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (<a href="http://registerofquestions.efsa.europa.eu/roqFrontend/mandateLoader?mandate=M-2010-0374">M-2010-0374</a>) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed.</p> <p>This data providers package provides the data collection configuration and supporting materials for reporting <strong>Chemical Contaminants in SSD2</strong>. These are to be used for the official data reporting phase.</p> <p>The package includes:</p> <p>CHECK advice on the values to be reported for the mandatory fields specified for this data collection.</p> <p>The Standard Sample Description Version 2 XSD schema definition for CONTAMINANTS reporting.</p> <p>The STX transformation file which automatically assigns sampEventId and sampAnId when this information is not provided.</p> <p>The general and CONTAMINANTS SSD2 specific business rules applied for the automatic validation of the submitted datasets.</p> <p>Excel Mapping tool to convert excel files after mapping into XML document.</p> <p>Guidance on how to use the Excel Mapping tool.</p> <p>Guidance on how to run the validation report after submitting data to the DCF.</p>
Data providers package for reporting Chemical Contaminants (official data reporting phase) SSD1
<p>In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (<a href="http://registerofquestions.efsa.europa.eu/roqFrontend/mandateLoader?mandate=M-2010-0374">M-2010-0374</a>) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed.</p> <p>This data providers package provides the data collection configuration and supporting materials for reporting <strong>Chemical Contaminants in SSD1</strong>. These are to be used for the official data reporting phase.</p> <p>The package includes:</p> <p>The Standard Sample Description Version 2 XSD schema definition for CONTAMINANTS reporting.</p> <p>The general and CONTAMINANTS SSD1 specific business rules applied for the automatic validation of the submitted datasets.</p> <p>Excel Mapping tool to convert excel files after mapping into XML document.</p> <p>Please follow the instructions below for the correct use of the mapping tool to avoid compromising its functionalities:</p> <ol> <li>Download and save the MS Excel® Standard Sample Description file to your computer (do not open the file before saving and do not change the file name)</li> <li>Download and save the file MS Excel® Simplified Reporting Format (do not open the file before saving)</li> <li>Keep both Excel files in the same folder</li> <li>Open both Excel files and enable the macros</li> <li>Keep both files open in the same Excel instance when filling in the data</li> </ol> <p>Guidance on how to run the validation report after submitting data to the DCF.</p>
SSD2 tools for Chemical Contaminants Occurrence Data
<p>Member States and other stakeholders can submit their chemical contaminants occurrence data using the EFSA tools created for this purpose.</p> <p>Tools are structured in accordance with the SSD2 guidance and provide features that support manual data entry.<br> Please read the instructions before using the tools.</p> <p><strong>This version replaces version 7 to include updates to the list of reportable progLegalRef</strong></p>
Supporting data for: Persistent contamination of raw milk by Campylobacter jejuni ST-883
<p>Supporting data for the article: "Persistent contamination of raw milk by <em>Campylobacter jejuni</em> ST-883"</p>
Figure 4 in Diversity of zooplankton in municipal wastewater-contaminated urban pond ecosystems of the lower Gangetic plains
Figure 4. Hierarchical cluster analyses of the study sites depending on the physicochemical conditions (a) and zooplankton community structure (b).
Figure 4 in Limitations of allometry, morphometry, and fluctuating asymmetry in detecting environmental stress caused by lead soil contamination in aphids under field conditions
Figure 4 Fluctuating asymmetry (mean and error deviation) observed in the antenna and tibia of Brevicoryne brassicae in the presence (Lead (Pb)) and absence (Control) of lead.
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