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Journal articles on the topic 'Environmental quantification'

To see the other types of publications on this topic, follow the link: Environmental quantification.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Bottausci, Sara, Elena-Diana Ungureanu-Comanita, Maria Gavrilescu, and Alessandra Bonoli. "ENVIRONMENTAL IMPACTS QUANTIFICATION OF PVC PRODUCTION." Environmental Engineering and Management Journal 20, no. 10 (2021): 1693–702. http://dx.doi.org/10.30638/eemj.2021.158.

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2

Robu, Brindusa Mihaela, and Gabriel Dan Suditu. "DIGITIZATION OF THE ENVIRONMENTAL IMPACT QUANTIFICATION PROCESS." Environmental Engineering and Management Journal 11, no. 4 (2012): 841–48. http://dx.doi.org/10.30638/eemj.2012.107.

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3

NARA, Matsunori. "Quantification of Concerns for Environmental Risk Evaluation." Proceedings of the Symposium on Global Environment 11 (2003): 183–88. http://dx.doi.org/10.2208/proge.11.183.

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4

Horneck, G. "Quantification of biologically effective environmental UV irradiance." Advances in Space Research 26, no. 12 (January 2000): 1983–94. http://dx.doi.org/10.1016/s0273-1177(00)00172-1.

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5

Allaire, Douglas, George Noel, Karen Willcox, and Rebecca Cointin. "Uncertainty quantification of an Aviation Environmental Toolsuite." Reliability Engineering & System Safety 126 (June 2014): 14–24. http://dx.doi.org/10.1016/j.ress.2014.01.002.

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6

Ye, Ming, Philip D. Meyer, Yu-Feng Lin, and Shlomo P. Neuman. "Quantification of model uncertainty in environmental modeling." Stochastic Environmental Research and Risk Assessment 24, no. 6 (April 28, 2010): 807–8. http://dx.doi.org/10.1007/s00477-010-0377-0.

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7

Fowler, Brian, Dale Hoover, and M. Coreen Hamilton. "The quantification of toxaphene in environmental samples." Chemosphere 27, no. 10 (November 1993): 1891–905. http://dx.doi.org/10.1016/0045-6535(93)90385-i.

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8

ZENITANI, Kenji, and Hidefumi IMURA. "Quantification of the environmental load associated with construction." ENVIRONMENTAL SYSTEMS RESEARCH 22 (1994): 147–53. http://dx.doi.org/10.2208/proer1988.22.147.

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9

Cartailler, Thomas, Anais Guaus, Alexandre Janon, Hervé Monod, Clémentine Prieur, and Nathalie Saint-Geours. "Sensitivity analysis and uncertainty quantification for environmental models." ESAIM: Proceedings 44 (January 2014): 300–321. http://dx.doi.org/10.1051/proc/201444019.

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10

Ehling, U. H. "Quantification of the Genetic Risk of Environmental Mutagens." Risk Analysis 8, no. 1 (March 1988): 45–57. http://dx.doi.org/10.1111/j.1539-6924.1988.tb01153.x.

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11

Kellerer, A. M. "Risk quantification." Radiation and Environmental Biophysics 42, no. 1 (March 2003): 1–5. http://dx.doi.org/10.1007/s00411-003-0181-z.

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12

Heighton, Lynne P., Merle Zimmerman, Clifford P. Rice, Eton E. Codling, John A. Tossell, and Walter F. Schmidt. "Quantification of Inositol Hexa-Kis Phosphate in Environmental Samples." Open Journal of Soil Science 02, no. 01 (2012): 55–63. http://dx.doi.org/10.4236/ojss.2012.21009.

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13

Lunel, Tim, and Jenifer M. Baker. "Quantification of Net Environmental Benefit for Future Oil Spills." International Oil Spill Conference Proceedings 1999, no. 1 (March 1, 1999): 619–27. http://dx.doi.org/10.7901/2169-3358-1999-1-619.

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ABSTRACT Net Environmental Benefit Analysis (NEBA) is increasingly used as a framework to assess the environmental benefits and disadvantages of a chosen response action. This analysis can be used to account for political and economic impacts as well as the effects on the natural environment. Until recently the discussion has focused on qualitative assessments due to the lack of quantitative information collected at spills. This paper uses examples of best practice of NEBA from different spills around the world to outline the information that should be collected at future spills in order to determine the level of Net Environmental Benefit that has been achieved by the response operation. In the first instance immediate feedback may well provide information which aids decision making at the time of the incident. However, a key role for this information is also to enable responders around the world to establish best practice for a wide range of environmental sensitivities. Case histories of Net Environmental Benefits will provide a basis for the overall contingency planning process, recognising that post-spill decisions are best and most rapidly made in the light of pre-spill analyses, consultations and agreements by all the appropriate organisations.

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14

Jechalke, Sven, Simone Dealtry, Kornelia Smalla, and Holger Heuer. "Quantification of IncP-1 Plasmid Prevalence in Environmental Samples." Applied and Environmental Microbiology 79, no. 4 (December 14, 2012): 1410–13. http://dx.doi.org/10.1128/aem.03728-12.

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ABSTRACTTo study the role of broad-host-range IncP-1 plasmids in bacterial adaptability to irregular environmental challenges, a quantitative real-time PCR assay was developed that specifically detects thekorBgene, which is conserved in all IncP-1 plasmids, in environmental samples. IncP-1 plasmid dynamics in a biopurification system for pesticide wastes were analyzed.

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15

Ziegel, Eric R. "Statistical Methods for Detection and Quantification of Environmental Contamination." Technometrics 45, no. 2 (May 2003): 176–77. http://dx.doi.org/10.1198/tech.2003.s139.

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16

C. Mahaney, Andrew Stewart, Volli K, William. "Quantification of SEM microtextures useful in sedimentary environmental discrimination." Boreas 30, no. 2 (June 1, 2001): 165–71. http://dx.doi.org/10.1080/03009480117276.

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17

Mahaney, William C., Andrew Stewart, and Volli Kalm. "Quantification of SEM microtextures useful in sedimentary environmental discrimination." Boreas 30, no. 2 (June 1, 2001): 165–71. http://dx.doi.org/10.1080/030094801750203170.

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18

Hiissa, Tarja, Heli Sirén, Tapio Kotiaho, Margit Snellman, and Aimo Hautojärvi. "Quantification of anions and cations in environmental water samples." Journal of Chromatography A 853, no. 1-2 (August 1999): 403–11. http://dx.doi.org/10.1016/s0021-9673(99)00508-7.

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19

Firth, Anton, Bo Zhang, and Aidong Yang. "Quantification of global waste heat and its environmental effects." Applied Energy 235 (February 2019): 1314–34. http://dx.doi.org/10.1016/j.apenergy.2018.10.102.

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20

MAHANEY, WILLIAM C., ANDREW STEWART, and VOLLI KALM. "Quantification of SEM microtextures useful in sedimentary environmental discrimination." Boreas 30, no. 2 (June 28, 2008): 165–71. http://dx.doi.org/10.1111/j.1502-3885.2001.tb01220.x.

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21

Sharkey, Freddie H., Ibrahim M. Banat, and Roger Marchant. "Detection and Quantification of Gene Expression in Environmental Bacteriology." Applied and Environmental Microbiology 70, no. 7 (July 2004): 3795–806. http://dx.doi.org/10.1128/aem.70.7.3795-3806.2004.

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22

Galanda, Dusan, Pavol Rajec, Lubomir Mátel, Olga Rosskopfová, and Silvia Dulanská. "Separation Techniques for Quantification of Radionuclides in Environmental Samples." Scientific World JOURNAL 9 (2009): 1206–14. http://dx.doi.org/10.1100/tsw.2009.124.

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The reliable and quantitative measurement of radionuclides is important in order to determine environmental quality and radiation safety, and to monitor regulatory compliance. We examined soil samples from Podunajske Biskupice, near the city of Bratislava in the Slovak Republic, for the presence of several natural (238U,232Th,40K) and anthropogenic (137Cs,90Sr,239Pu,240Pu,241Am) radionuclides. The area is adjacent to a refinery and hazardous waste processing center, as well as the municipal incinerator plant, and so might possess an unusually high level of ecotoxic metals. We found that the levels of both naturally occurring and anthropogenic radionuclides fell within the expected ranges, indicating that these facilities pose no radiological threat to the local environment. During the course of our analysis, we modified existing techniques in order to allow us to handle the unusually large and complex samples that were needed to determine the levels of239Pu,240Pu, and241Am activity. We also rated three commercial techniques for the separation of 90Sr from aqueous solutions and found that two of them, AnaLig Sr-01 and Empore Extraction Disks, were suitable for the quantitative and reliable separation of90Sr, while the third, Sr-Spec Resin, was less so. The main criterion in evaluating these methods was the chemical recovery of90Sr, which was less than we had expected. We also considered speed of separation and additional steps needed to prepare the sample for separation.

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23

Horneck, Gerda. "Quantification of the biological effectiveness of environmental UV radiation." Journal of Photochemistry and Photobiology B: Biology 31, no. 1-2 (November 1995): 43–49. http://dx.doi.org/10.1016/1011-1344(95)07167-3.

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24

Sabzian, M., M. N. Nasrabadi, and M. Haji-Hosseini. "Extraction and quantification system for environmental radioxenon sample analysis." Journal of Environmental Radioactivity 182 (February 2018): 95–100. http://dx.doi.org/10.1016/j.jenvrad.2017.11.035.

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25

Tranter, Morgan, Svenja Steding, Christopher Otto, Konstantina Pyrgaki, Mansour Hedayatzadeh, Vasilis Sarhosis, Nikolaos Koukouzas, Georgios Louloudis, Christos Roumpos, and Thomas Kempka. "Environmental hazard quantification toolkit based on modular numerical simulations." Advances in Geosciences 58 (November 22, 2022): 67–76. http://dx.doi.org/10.5194/adgeo-58-67-2022.

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Abstract. Quantifying impacts on the environment and human health is a critical requirement for geological subsurface utilisation projects. In practice, an easily accessible interface for operators and regulators is needed so that risks can be monitored, managed, and mitigated. The primary goal of this work was to create an environmental hazards quantification toolkit as part of a risk assessment for in-situ coal conversion at two European study areas: the Kardia lignite mine in Greece and the Máza-Váralja hard coal deposit in Hungary, with complex geological settings. A substantial rock volume is extracted during this operation, and a contaminant pool is potentially left behind, which may put the freshwater aquifers and existing infrastructure at the surface at risk. The data-driven, predictive tool is outlined exemplary in this paper for the Kardia contaminant transport model. Three input parameters were varied in a previous scenario analysis: the hydraulic conductivity, as well as the solute dispersivity and retardation coefficient. Numerical models are computationally intensive, so the number of simulations that can be performed for scenario analyses is limited. The presented approach overcomes these limitations by instead using surrogate models to determine the probability and severity of each hazard. Different surrogates based on look-up tables or machine learning algorithms were tested for their simplicity, goodness of fit, and efficiency. The best performing surrogate was then used to develop an interactive dashboard for visualising the hazard probability distributions. The machine learning surrogates performed best on the data with coefficients of determination R2>0.98, and were able to make the predictions quasi-instantaneously. The retardation coefficient was identified as the most influential parameter, which was also visualised using the toolkit dashboard. It showed that the median values for the contaminant concentrations in the nearby aquifer varied by five orders of magnitude depending on whether the lower or upper retardation range was chosen. The flexibility of this approach to update parameter uncertainties as needed can significantly increase the quality of predictions and the value of risk assessments. In principle, this newly developed tool can be used as a basis for similar hazard quantification activities.

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26

Halsall, Karen M., Vanessa M. Ellingsen, Johan Asplund, Richard HW Bradshaw, and Mikael Ohlson. "Fossil charcoal quantification using manual and image analysis approaches." Holocene 28, no. 8 (May 14, 2018): 1345–53. http://dx.doi.org/10.1177/0959683618771488.

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Charcoal particles are evidence of past fire events and macro-charcoal particles have been shown to represent local fire events. There are several methods for the preparation and quantification of macro-charcoal particles, none of which have been universally accepted as standard. Very few studies compare methodological differences and no studies to date compare quantification by mass with quantification by volume using image analysis. Using three cores taken from a peatland located in SE Norway, we compare these two established methods using a generalized linear mixed model (GLMM) and a split-plot ANOVA test. We show that charcoal volume (image analysis method) was a better predictor of charcoal mass than charcoal particle number and the same size classes of charcoal as size class distributions were not spatially and temporally correlated. Although there is still a need for a common and unifying method, our results show that quantification of charcoal particles by image analysis including size (e.g. height in mm) and area (mm2)/volume (mm3) measurements provides more significant results in cross-site or multiple-site studies than quantifications based on particle number. This has implications for the interpretation of charcoal data from regional studies that are used to model drivers of wildfire activity and environmental change in boreal–temperate landscapes during the Holocene.

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27

Wang, Jiajia, Hao Chen, Jing Ma, and Tong Zhang. "Research on application method of uncertainty quantification technology in equipment test identification." MATEC Web of Conferences 336 (2021): 02026. http://dx.doi.org/10.1051/matecconf/202133602026.

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This paper introduces the concepts of equipment test qualification and uncertainty quantification, and the analysis framework and process of equipment test uncertainty quantification. It analyzes the data uncertainty, model uncertainty and environmental uncertainty, and studies the corresponding uncertainty quantification theory to provide technical reference for the application of uncertainty quantification technology in the field of test identification.

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28

Ibekwe, Mark A., Shelton E. Murinda, Stanley Park, Amarachukwu Obayiuwana, Marcia A. Murry, Gregory Schwartz, and Trygve Lundquist. "Comparative Use of Quantitative PCR (qPCR), Droplet Digital PCR (ddPCR), and Recombinase Polymerase Amplification (RPA) in the Detection of Shiga Toxin-Producing E. coli (STEC) in Environmental Samples." Water 12, no. 12 (December 13, 2020): 3507. http://dx.doi.org/10.3390/w12123507.

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E. coli O157:H7 is a foodborne pathogen that constitutes a global threat to human health. However, the quantification of this pathogen in food and environmental samples may be problematic at the low cell numbers commonly encountered in environmental samples. In this study, we used recombinase polymerase amplification (RPA) for the detection of E. coli O157:H7, real-time quantitative PCR (qPCR) for quantification, and droplet digital PCR (ddPCR) for absolute and accurate quantification of E. coli O157:H7 from spiked and environmental samples. Primer and probe sets were used for the detection of stx1 and stx2 using RPA. Genes encoding for stx1, stx2, eae, and rfbE were used to quantify E. coli O157:H7 in the water samples. Furthermore, duplex ddPCR assays were used to quantify the pathogens in these samples. Duplex assay set 1 used stx1 and rfbE genes, while assay set 2 used stx2 and eae genes. Droplet digital PCR was used for the absolute quantification of E. coli O15:H7 in comparison with qPCR for the spiked and environmental samples. The RPA results were compared to those from qPCR and ddPCR in order to assess the efficiency of the RPA compared with the PCR methods. The assays were further applied to the dairy lagoon effluent (DLE) and the high rate algae pond (HRAP) effluent, which were fed with diluted DLE. The RPA detected was <10 CFU/mL, while ddPCR showed quantification from 1 to 104 CFU/mL with a high reproducibility. In addition, quantification by qPCR was from 103 to 107 CFU/mL of the wastewater samples. Therefore, the RPA assay has potential as a point of care tool for the detection of E. coli O157:H7 from different environmental sources, followed by quantification of the target concentrations.

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29

Koe, Lawrence C. C., and Derek K. Brady. "Olfactory Quantification of Sewage Odors." Journal of Environmental Engineering 112, no. 2 (April 1986): 311–27. http://dx.doi.org/10.1061/(asce)0733-9372(1986)112:2(311).

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30

Hailu, Nardos, Michiel Postema, Ondrej Krejcar, and Dawit Assefa. "Nebulization Criteria and Quantification." Fluids 5, no. 2 (June 6, 2020): 91. http://dx.doi.org/10.3390/fluids5020091.

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The application of atomization technology is common in fields such as agriculture, cosmetics, environmental sciences, and medicine. Aerosolized drugs are administered using nebulizers to treat both pulmonary and nonpulmonary diseases. The characterization and measurement of nebulizers are of great significance in analyzing the performance and accuracy of the nebulizing system and the advancement of the technology. Nevertheless, the characterization of aerosols has been a long-standing challenge in scientific disciplines ranging from atmospheric physics to health sciences. The study of factors that influence nebulization has not been undertaken systematically using experimental techniques. Numerical modeling (NM) and computational fluid dynamics (CFD) can address such issues. This article provides a concise overview of the literature on the application of computational fluid dynamics to medical nebulizers and aerosol measurements.

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31

Hoshino, Tatsuhiko, and Fumio Inagaki. "Molecular quantification of environmental DNA using microfluidics and digital PCR." Systematic and Applied Microbiology 35, no. 6 (September 2012): 390–95. http://dx.doi.org/10.1016/j.syapm.2012.06.006.

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32

Walter, J., M. Hustedt, V. Wesling, and S. Barcikowski. "Standardized Emission Quantification and Control of Costs for Environmental Measures." Physics Procedia 12 (2011): 31–39. http://dx.doi.org/10.1016/j.phpro.2011.03.005.

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33

Taflanidis, Alexandros A., Eva Loukogeorgaki, and Demos C. Angelides. "Offshore wind turbine risk quantification/evaluation under extreme environmental conditions." Reliability Engineering & System Safety 115 (July 2013): 19–32. http://dx.doi.org/10.1016/j.ress.2013.02.003.

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34

Muthu, Subramanian Senthilkannan, Y. Li, J. Y. Hu, and P. Y. Mok. "Quantification of environmental impact and ecological sustainability for textile fibres." Ecological Indicators 13, no. 1 (February 2012): 66–74. http://dx.doi.org/10.1016/j.ecolind.2011.05.008.

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35

Kaye-Blake, William, Chris Schilling, Ross Monaghan, Ronaldo Vibart, Samuel Dennis, and Elizabeth Post. "Quantification of environmental-economic trade-offs in nutrient management policies." Agricultural Systems 173 (July 2019): 458–68. http://dx.doi.org/10.1016/j.agsy.2019.03.013.

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36

EZUMI, Tsutomu, Eiichiro TSUTSUMI, and Masaaki HIGUCHI. "103 Experimental quantification of craze considers influence in environmental medicine." Proceedings of the Symposium on Evaluation and Diagnosis 2008.7 (2008): 18–20. http://dx.doi.org/10.1299/jsmesed.2008.7.18.

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37

Reijnders, Lucas, and Sam Soret. "Quantification of the environmental impact of different dietary protein choices." American Journal of Clinical Nutrition 78, no. 3 (September 1, 2003): 664S—668S. http://dx.doi.org/10.1093/ajcn/78.3.664s.

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38

Stein, Anke, and Holger Kreft. "Terminology and quantification of environmental heterogeneity in species-richness research." Biological Reviews 90, no. 3 (August 7, 2014): 815–36. http://dx.doi.org/10.1111/brv.12135.

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39

Bartholomeu, Daniela Bacchi, and José Vicente Caixeta Filho. "Quantification of the environmental impacts of road conditions in Brazil." Ecological Economics 68, no. 6 (April 2009): 1778–86. http://dx.doi.org/10.1016/j.ecolecon.2008.11.009.

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40

Jiang, Sunny, Hojabr Dezfulian, and Weiping Chu. "Real-time quantitative PCR for enteric adenovirus serotype 40 in environmental waters." Canadian Journal of Microbiology 51, no. 5 (May 1, 2005): 393–98. http://dx.doi.org/10.1139/w05-016.

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Adenoviruses 40 and 41 have been recognized as important etiological agents of gastroenteritis in children. A real-time PCR method (TaqMan® assay) was developed for rapid quantification of adenovirus 40 (Ad40) by amplifying an 88 bp sequence from the hexon gene. To establish a quantification standard curve, a 1090 bp hexon region of Ad40 was amplified and cloned into the pGEM®-T Vector. A direct correlation was observed between the fluorescence threshold cycle number (Ct) and the starting quantity of Ad40 hexon gene. The quantification was linear over 6-log units and the amplification efficiency averaged greater than 95%. Seeding studies using various environmental matrices (including sterile water, creek water, brackish estuarine water, ocean water, and secondary sewage effluent) suggest that this method is applicable to environmental samples. However, real-time PCR was sensitive to inhibitors present in the environmental samples. Lower efficiency of PCR amplification was found in secondary sewage effluent and creek waters. Application of the method to fecal contaminated waters successfully quantified the presence of Ad40. The sensitivity of the real-time PCR is comparable to the traditional nested PCR assay for environmental samples. In addition, the real-time PCR assay offers the advantage of speed and insensitivity to contamination during PCR set up. The real-time PCR assay developed in this study is suitable for quantitative determination of Ad40 in environmental samples and represents a considerable advancement in pathogen quantification in aquatic environments.Key words: adenovirus, real-time PCR, environmental waters, serotype 40.

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41

Reichert, Peter. "Towards a comprehensive uncertainty assessment in environmental research and decision support." Water Science and Technology 81, no. 8 (January 29, 2020): 1588–96. http://dx.doi.org/10.2166/wst.2020.032.

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Abstract Uncertainty quantification is very important in environmental management to allow decision makers to consider the reliability of predictions of the consequences of decision alternatives and relate them to their risk attitudes and the uncertainty about their preferences. Nevertheless, uncertainty quantification in environmental decision support is often incomplete and the robustness of the results regarding assumptions made for uncertainty quantification is often not investigated. In this article, an attempt is made to demonstrate how uncertainty can be considered more comprehensively in environmental research and decision support by combining well-established with rarely applied statistical techniques. In particular, the following elements of uncertainty quantification are discussed: (i) using stochastic, mechanistic models that consider and propagate uncertainties from their origin to the output; (ii) profiting from the support of modern techniques of data science to increase the diversity of the exploration process, to benchmark mechanistic models, and to find new relationships; (iii) analysing structural alternatives by multi-model and non-parametric approaches; (iv) quantitatively formulating and using societal preferences in decision support; (v) explicitly considering the uncertainty of elicited preferences in addition to the uncertainty of predictions in decision support; and (vi) explicitly considering the ambiguity about prior distributions for predictions and preferences by using imprecise probabilities. In particular, (v) and (vi) have mostly been ignored in the past and a guideline is provided on how these uncertainties can be considered without significantly increasing the computational burden. The methodological approach to (v) and (vi) is based on expected expected utility theory, which extends expected utility theory to the consideration of uncertain preferences, and on imprecise, intersubjective Bayesian probabilities.

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42

Reed, Charles E., and Mark C. Swanson. "Indoor allergens: Identification and quantification." Environment International 12, no. 1-4 (January 1986): 115–20. http://dx.doi.org/10.1016/0160-4120(86)90021-8.

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43

Selahle, Shirley Kholofelo, Anele Mpupa, and Philiswa Nosizo Nomngongo. "A review of extraction, analytical, and advanced methods for the determination of neonicotinoid insecticides in environmental water matrices." Reviews in Analytical Chemistry 40, no. 1 (January 1, 2021): 187–203. http://dx.doi.org/10.1515/revac-2021-0134.

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Abstract Neonicotinoid insecticides are widely used to kill and prevent unwanted insects from attacking growing crops. Extensive use of insecticides in various compartments of the environment has led to adverse effect on the health of living organisms. Several analytical methodologies have been reported for extraction and quantification of neonicotinoid insecticides in various matrices. The analytical detection techniques range from traditional to modern or state of the art quantification methods. The traditional analytical techniques include gas chromatography and high-performance liquid chromatography. These methods require extensive sample pretreatment before identification, separation, and quantification of target analytes. Advanced detection techniques refer to the sensor technologies based on optical, biorecognition, molecular imprinted polymers chemical, and piezoelectric. In this review, a summary and explanation of the various traditional analytical and advanced methodologies for extraction, separation, detection, and quantification of neonicotinoid insecticides residue in water samples have been discussed.

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44

Michotey, Valérie, Vincent Méjean, and Patricia Bonin. "Comparison of Methods for Quantification of Cytochrome cd1-Denitrifying Bacteria in Environmental Marine Samples." Applied and Environmental Microbiology 66, no. 4 (April 1, 2000): 1564–71. http://dx.doi.org/10.1128/aem.66.4.1564-1571.2000.

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ABSTRACT Two PCR primer sets were developed for the detection and quantification of cytochrome cd 1-denitrifying bacteria in environmental marine samples. The specificity and sensitivity of these primers were tested. Both primer sets were suitable for detection, but only one set, cd3F–cd4R, was suitable for the quantification and enumeration of the functional community using most-probable-number PCR and competitive PCR techniques. Quantification of cytochrome cd 1 denitrifiers taken from marine sediment and water samples was achieved using two different molecular techniques which target the nirS gene, and the results were compared to those obtained by using the classical cultivation method. Enumerations using both molecular techniques yielded similar results in seawater and sediment samples. However, both molecular techniques showed 1,000 or 10 times more cytochromecd 1 denitrifiers in the sediment or water samples, respectively, than were found by use of the conventional cultivation method for counting.

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45

Bind, Marie-Abèle. "Causal Modeling in Environmental Health." Annual Review of Public Health 40, no. 1 (April 2019): 23–43. http://dx.doi.org/10.1146/annurev-publhealth-040218-044048.

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The field of environmental health has been dominated by modeling associations, especially by regressing an observed outcome on a linear or nonlinear function of observed covariates. Readers interested in advances in policies for improving environmental health are, however, expecting to be informed about health effects resulting from, or more explicitly caused by, environmental exposures. The quantification of health impacts resulting from the removal of environmental exposures involves causal statements. Therefore, when possible, causal inference frameworks should be considered for analyzing the effects of environmental exposures on health outcomes.

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46

Kelly, D. G., L. G. I. Bennett, M. M. Fill, K. M. Mattson, K. S. Nielsen, S. D. White, and J. F. Allen. "Analytical methods for the environmental quantification of uranium isotopes: Method of validation and environmental application." Journal of Radioanalytical and Nuclear Chemistry 278, no. 3 (November 28, 2008): 807–11. http://dx.doi.org/10.1007/s10967-008-1801-5.

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47

Olander, Lydia, Eva Wollenberg, Francesco Tubiello, and Martin Herold. "Advancing agricultural greenhouse gas quantification *." Environmental Research Letters 8, no. 1 (February 12, 2013): 011002. http://dx.doi.org/10.1088/1748-9326/8/1/011002.

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48

Audemard, Corinne, Kimberly S. Reece, and Eugene M. Burreson. "Real-Time PCR for Detection and Quantification of the Protistan Parasite Perkinsus marinus in Environmental Waters." Applied and Environmental Microbiology 70, no. 11 (November 2004): 6611–18. http://dx.doi.org/10.1128/aem.70.11.6611-6618.2004.

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ABSTRACT The protistan parasite Perkinsus marinus is a severe pathogen of the oyster Crassostrea virginica along the east coast of the United States. Very few data have been collected, however, on the abundance of the parasite in environmental waters, limiting our understanding of P. marinus transmission dynamics. Real-time PCR assays with SybrGreen I as a label for detection were developed in this study for quantification of P. marinus in environmental waters with P. marinus species-specific primers and of Perkinsus spp. with Perkinsus genus-specific primers. Detection of DNA concentrations as low as the equivalent of 3.3 Ã¯Â¿Â½ 10−2 cell per 10-μl reaction mixture was obtained by targeting the multicopy internal transcribed spacer region of the genome. To obtain reliable target quantification from environmental water samples, removal of PCR inhibitors and efficient DNA recovery were two major concerns. A DNA extraction kit designed for tissues and another designed for stool samples were tested on environmental and artificial seawater (ASW) samples spiked with P. marinus cultured cells. The stool kit was significantly more efficient than the tissue kit at removing inhibitors from environmental water samples. With the stool kit, no significant difference in the quantified target concentrations was observed between the environmental and ASW samples. However, with the spiked ASW samples, the tissue kit demonstrated more efficient DNA recovery. Finally, by performing three elutions of DNA from the spin columns, which were combined prior to target quantification, variability of DNA recovery from different samples was minimized and more reliable real-time PCR quantification was accomplished.

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Stocker, B. D., and F. Joos. "Quantifying differences in land use emission estimates implied by definition discrepancies." Earth System Dynamics 6, no. 2 (November 27, 2015): 731–44. http://dx.doi.org/10.5194/esd-6-731-2015.

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Abstract. The quantification of CO2 emissions from anthropogenic land use and land use change (eLUC) is essential to understand the drivers of the atmospheric CO2 increase and to inform climate change mitigation policy. Reported values in synthesis reports are commonly derived from different approaches (observation-driven bookkeeping and process-modelling) but recent work has emphasized that inconsistencies between methods may imply substantial differences in eLUC estimates. However, a consistent quantification is lacking and no concise modelling protocol for the separation of primary and secondary components of eLUC has been established. Here, we review differences of eLUC quantification methods and apply an Earth System Model (ESM) of Intermediate Complexity to quantify them. We find that the magnitude of effects due to merely conceptual differences between ESM and offline vegetation model-based quantifications is ~ 20 % for today. Under a future business-as-usual scenario, differences tend to increase further due to slowing land conversion rates and an increasing impact of altered environmental conditions on land-atmosphere fluxes. We establish how coupled Earth System Models may be applied to separate secondary component fluxes of eLUC arising from the replacement of potential C sinks/sources and the land use feedback and show that secondary fluxes derived from offline vegetation models are conceptually and quantitatively not identical to either, nor their sum. Therefore, we argue that synthesis studies should resort to the "least common denominator" of different methods, following the bookkeeping approach where only primary land use emissions are quantified under the assumption of constant environmental boundary conditions.

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Rosa, Anderson da Silva, Bruna Piaia Ramborger, Maria Luisa Candido Zago, Dandara Fidélis Escoto, Elton Luis Gasparotto Denardin, and Rafael Roehrs. "Determination of pesticides in hydroponic water for environmental phytoremediation." Ciência e Natura 44 (June 13, 2022): e27. http://dx.doi.org/10.5902/2179460x68460.

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This study validated a simple, and fast method by High Performance Liquid Chromatography with Diode Array Detector (HPLC -DAD) for pesticide phytoremediation analysis. The method was developed in water and in a hydroponic medium. Sample extraction and concentration were performed by Solid Phase Extraction (SPE) with Strata C18-E type cartridges. The SPE-HPLC-DAD method was successfully applied in the detection and quantification of quinclorac, 2,4-D, propanil, bentazon, clomazone and tebuconazole in water and hydroponic medium for 14 days. The method presented excellent results with the linearity of 0.9969 - 0.9994 and the lowest limit of detection (LOD) and quantification (LOQ) of pesticides was 1.7 μg/L and 5.0 μg/L, respectively with RSD <11.92%. The average recovery obtained ranged from 77.62% to 109.73% and RSD <12.70%. A Lactuca sativa species promoted phytoremediation on the 7th day for 2,4-D and tebuconazole and on the 14th day for clomazone.

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