A focus group study of factors that promote and constrain the use of satellite-derived fire products by resource managers in southern Africa

Semi-structured focus group interviews were employed to examine factors that affect the likelihood that resource managers in southern Africa will use information on vegetation fires provided by two satellite-derived products: an active fire product and a burned area product. The two products are updated regularly and aim to deliver the state-of-the-art in the global monitoring of fires from satellite remote-sensing. Both products are derived from data transmitted by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors carried onboard NASA's Aqua and Terra satellites. The active fire product can be accessed for free via the internet and on media by users working anywhere in the world; the burned area product will be accessible in a similar manner in 2006. The MODIS fire products provide systematic, near-global coverage and are freely available; as such, they give resource managers new opportunities to obtain or supplement information they need to manage vegetation fires effectively. However, the availability of these products does not mean that resource managers will use them, and many other factors are involved. To understand factors that affect whether southern African resource managers will use the two products, two focus groups were held with members of the Southern African Fire Network (SAFNet) in Malawi, Africa, August 2004. Analysis of the group discussions reveals a number of factors that influence whether they will use the products. The qualitative, in depth nature of the group discussions revealed 12 main factors that influence product use; not least the low international internet bandwidths for African countries outside of South Africa. Analysis of the group discussions also suggests how the uptake of MODIS fire products by resource managers in southern Africa might be enhanced by affecting specific changes to how MODIS products are packaged and delivered.     

精确三维光谱数据活性关系方法应用于ToxCast数据库中雌激素受体数据分类、验证和特征提取的建模策略

本文利用三维光谱数据活性关系方法(3D-SDAR)对ToxCast数据库中1 528种化合物的雌激素潜能(表示为18分复合高通量筛选生物测定)进行建模。由于缺少170核磁共振(NMR)仿真软件,信息量最大的碳-碳3D-SDAR指纹随着一些指标变量增强,这些指标变量包括来自羰基和酰胺的氧原子、酯基、磺酰、硝基、脂肪族羟基和酚羟基官能团。为了评价本文作者所开发模型的预测性能的真实性,美国环境保护署为研究人员提供了2 008种化合物组成一套盲测。其中,543种化合物有文献可查数据—他们的结合力有助于评估开发模型的外部分类精度：得到预测精度0.62,敏感度0.71,和特异度0.53的结果。与其他建模技术相比,本文作者开发的模型几乎没有简化建模集和预测集之间的性能。3D-SDAR映射技术可以识别拟雌激素活性所必需的结构特征：1) 存在一个酚的OH官能团或环己烯酮,2)与第一苯酚环上的氧距离6 ?到8 ?的第二芳香或酚环,3) 存在一个与苯酚环的几何中心距离大约6 ?的甲基官能团,4)一个靠近(距离几何中心～4?)某个苯酚环的羰基官能团。
精选自Svetoslav H. Slavov, Richard D. Beger. Rigorous 3-dimensional spectral data activity relationship approach modeling strategy for toxcast estrogen receptor data classification, validation, and feature extraction. Environmental Toxicology and Chemistry: Volume 36, Issue 3, pages 823–830, July 2017. DOI: 10.1002/etc.3578
详情请见http://onlinelibrary.wiley.com/wol1/doi/10.1002/etc.3578/full
     

A pioneer validation of a state-space model of vessel trajectories (VMS) with observers’ data

In the context of the expansion of animal tracking and bio-logging, state-space models have been developed with the objective to characterise animals’ trajectories and to understand the factors controlling their behaviour. In the fisheries community, the electronic tagging of vessels commonly designated by Vessel Monitoring Systems (VMS) is developing and provides a new insight for the understanding, the analysis and the modelling of the trajectories of vessels and their prospecting behaviour. VMS data are thus a clue for the proper definition of fishing effort which remains a fundamental parameter of tuna stock assessments. In this context, we used the VMS (recording of hourly positions) of the French tropical tuna purse-seiners operating in the Indian Ocean to characterise three types of movement (states) on the VMS trajectories (stillness, tracking, and cruising). Based on empirical evidences, and on the regular frequency of VMS acquisition, this was achieved by the development of a Bayesian Hidden Markov model for the speeds and turning angles derived from the hourly steps of the trajectories. In a second phase, states were related to activities disentangling stillness into fishing or stop at sea. Finally the quality of the model performances was rigorously quantified thanks to observers’ data. Confronting model prediction and true activities allowed estimating that 10% of the hourly steps were misclassified. The assumptions and model’ choices are discussed, highlighting the fact that VMS data and observers’ data having different time resolutions, the effective use of validating data was troublesome. However, without validation, these analyses remain speculative. The validation part of this work represents an important step for the operational use of state-space models in ecology in the broad sense (predators’ tracking data, e.g. birds or mammals trajectories).     

Analysis of PCDD/Fs and dioxin-like PCBs in atmospheric deposition samples from the Flemish measurement network: Optimization and validation of a new CALUX bioassay method

Since the CALUX (Chemically Activated LUciferase gene eXpression) bioassay is a fast, sensitive and inexpensive tool for the analysis of a high number of samples, validation of new methods is urgently needed. In this study, a new method for the analysis of PCDD/Fs and dioxin-like PCBs in atmospheric deposition samples with the CALUX bioassay was developed, optimized and validated. The method consists of 4 steps: filtration, extraction, clean up and bioassay analysis. To avoid the use of large amounts of toxic solvents, new techniques were used for filtration and extraction: a C18 filter was used instead of a liquid/liquid extraction and an Accelerated Solvent Extractor (ASE) was used instead of the traditional soxhlet extraction. After pre-oxidation of the sample extract, clean up was done using a multi-layer silica gel column coupled to a carbon column. The PCDD/F and PCB fractions were finally analyzed with the H1L7.5c1 and/or the H1L6.1c3 mouse hepatoma cell lines. The limit of quantification was 1.4 pg CALUX-BEQ m⁻² d⁻¹ for the PCBs and 5.6 pg CALUX-BEQ m⁻² d⁻¹ for the PCDD/Fs, when using the new sensitive H1L7.5c1 cell line. The GC-HRMS recovery for all PCDD/F congeners was between 55% and 112%, with a mean recovery of 90%. CALUX recoveries of spiked procedural blanks were between the accepted ranges of 80-120%. Repeatability and reproducibility were satisfactory and no interferences from metals were detected. The first results from the Flemish measurement program showed good correlation between CALUX and GC-HRMS.     

Technical,human, and organizational factors affecting failures of firefighting systems (FSs) of atmospheric storage tanks: Providing a risk assessment approach using Fuzzy Bayesian Network (FBN) and content validity indicators

Most risk assessment methods have problems such as uncertainty, static structure, and lack of validation. Also, in most of these studies, less attention has been paid to human, managerial, and organizational issues. Therefore, this study proposes a risk assessment method based on the Fuzzy Bayesian Network (FBN) to prevent failure of firefighting systems (FFSs) in the atmospheric Storage Tanks of a Petrochemical Industry. The first stage of the study is the development of a fault tree (FT) and investigation of basic events (BEs). In this study, content validity indices and brainstorming technique were used to validate the FT structure and reduce the uncertainties of Completeness, Modeling, and Parameter. After determining the probability of basic events (BEs) by the expert team opinions and fuzzy logic, events were transmitted to the Bayesian Network (BN) and then analyzed with deductive and inductive reasoning, followed by sensitivity analysis in the GeNIe software. Finally, results of a case study in the Atmospheric Storage Tanks of the Methanol Floating Roof of a Petrochemical Industry showed that FBN simulation and FT validation could provide a practical way to determine FFSs probabilities, identify impactful events, and reduce the above uncertainties. Also, taking account of hidden factors of events, such as organizational and managerial factors, can help managers to prevent FFSs in tanks.     

Model calibration and validation for OFMSW and sewage sludge co-digestion reactors

A mathematical model has recently been proposed by the authors to simulate the biochemical processes that prevail in a co-digestion reactor fed with sewage sludge and the organic fraction of municipal solid waste. This model is based on the Anaerobic Digestion Model no. 1 of the International Water Association, which has been extended to include the co-digestion processes, using surface-based kinetics to model the organic waste disintegration and conversion to carbohydrates, proteins and lipids. When organic waste solids are present in the reactor influent, the disintegration process is the rate-limiting step of the overall co-digestion process. The main advantage of the proposed modeling approach is that the kinetic constant of such a process does not depend on the waste particle size distribution (PSD) and rather depends only on the nature and composition of the waste particles. The model calibration aimed to assess the kinetic constant of the disintegration process can therefore be conducted using organic waste samples of any PSD, and the resulting value will be suitable for all the organic wastes of the same nature as the investigated samples, independently of their PSD. This assumption was proven in this study by biomethane potential experiments that were conducted on organic waste samples with different particle sizes. The results of these experiments were used to calibrate and validate the mathematical model, resulting in a good agreement between the simulated and observed data for any investigated particle size of the solid waste. This study confirms the strength of the proposed model and calibration procedure, which can thus be used to assess the treatment efficiency and predict the methane production of full-scale digesters.     

Connecting Diverse Knowledge Systems for Enhanced Ecosystem Governance: The Multiple Evidence Base Approach

Indigenous and local knowledge systems as well as practitioners’ knowledge can provide valid and useful knowledge to enhance our understanding of governance of biodiversity and ecosystems for human well-being. There is, therefore, a great need within emerging global assessment programs, such as the IPBES and other international efforts, to develop functioning mechanisms for legitimate, transparent, and constructive ways of creating synergies across knowledge systems. We present the multiple evidence base (MEB) as an approach that proposes parallels whereby indigenous, local and scientific knowledge systems are viewed to generate different manifestations of knowledge, which can generate new insights and innovations through complementarities. MEB emphasizes that evaluation of knowledge occurs primarily within rather than across knowledge systems. MEB on a particular issue creates an enriched picture of understanding, for triangulation and joint assessment of knowledge, and a starting point for further knowledge generation.     

Validation of QuEChERS method for the determination of 36 pesticide residues in fruits and vegetables from Ghana,using gas chromatography with electron capture and pulsed flame photometric detectors

In this study, “Quick, Easy, Cheap, Effective, Rugged and Safe” ‘QuEChERS’ method was modified for the determination of 36 pesticides fortified at (0.01–1.0) mg kg^?1 in three vegetables and a fruit (lettuce, carrot, tomatoes and pineapples respectively) from Ghana. The method involved extraction with acetonitrile, phase separation with primary secondary amine and magnesium sulfate; the final injection solution was reconstituted in ethyl acetate. Organochlorine and synthetic pyrethroids residues were detected with electron capture detector whereas organophosphorus, pulsed flame photometric detector was used. The recoveries at different concentration levels (0.01, 0.1 and 1.0 mg kg^?1) were in the range of 83% and 93% with relative standard deviation ranging from 2% to 10% (n = 5) and the coefficient of determination (R²) was greater than 0.99 for all the 36 pesticides. The method was successfully tested on 120 real samples from Accra markets and this proved to be useful for monitoring purposes particularly in laboratories that have no gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry.     

Linear and non-linear relationships between soil sorption and hydrophobicity: model, validation and influencing factors

The hydrophobic parameter represented by the octanol/water partition coefficient (log P) is commonly used to predict the soil sorption coefficient (K_oc). However, a simple non-linear relationship between log K_oc and log P has not been reported in the literature. In the present paper, soil sorption data for 701 compounds was investigated. The results show that log K_oc is linearly related to log P for compounds with log P in the range of 0.5-7.5 and non-linearly related to log P for the compounds in a wide range of log P. A non-linear model has been developed between log K_oc and log P for a wide range of compounds in the training set. This model was validated in terms of average error (AE), average absolute error (AAE) and root-mean squared error (RMSE) by using an external test set with 107 compounds. Nearly the same predictive capacity was observed in comparison with existing models. However, this non-linear model is simple, and uses only one parameter. The best model developed in this paper is a non-linear model with six correction factors for six specific classes of compounds. This model can well predict log K_oc for 701 diverse compounds with AAE = 0.37. The reasons for systemic deviations in these groups may be attributed to the difference of sorption mechanism for hydrophilic/polar compounds, low solubility for highly hydrophobic compounds, hydrolysis of esters in solution, volatilization for volatile compounds and highly experimental errors for compounds with extremely high or low sorption coefficients.