Spectrophotometric and HPLC method for quantification of Loratadine on swabs for cleaning validation

Cleaning validation is a major challenge in multi-product pharmaceutical industries. UV spectrophotometric and HPLC methods have been developed and validated for determination of residual amount of Loratadine. Both methods were validated for linearity, range, accuracy, precision, and robustness. The limit of quantification was 1 mg L^?1 by UV spectrophotometric method and 0.5 mg L^?1 by HPLC method. A spike recovery study was done on a stainless steel (316 grade) plate and specific residual cleaning level (SRCL) was down to 6 μg 25.8 cm^?2. Recovery was found to be more than 70%. Both methods were simple, highly sensitive, precise, and accurate, and have potential of being useful for routine quality control.     

An enhanced environmental multimedia modeling system based on fuzzy-set approach: II. Model validation and application

Part I of this study develops an enhanced environmental multimedia modeling system (EMMS) based on fuzzy-set approach. Once the model development is complete, the composite module and the entire modeling system need to be tested and validated to ensure that the model can simulate natural phenomena with reasonable and reliable accuracy. The developed EMMS is first tested in a complete case study. And then verification results are conducted to compare with extensively researched literature data. In the third step, the data from an experimental landfill site is used for a pilot-scale validation. The comparisons between EMMS outputs and the literature data indicate that the EMMS can perform accurate modeling simulation. The modules of EMMS could support the entire environmental multimedia modeling system. Further field-scale validation is finished. The results are satisfactory. Most of the modeling yields closely match the monitoring data collected from sites. In addition, with the aid of fuzzy-set approach, EMMS can be a reliable and powerful tool to address the complex environmental multimedia pollution problems and provide an extensive support for decision makers in managing the contaminated environmental systems.     

Calibration and uncertainty analysis of a simple model of silica-limited diatom growth in the Elbe River

A simple Lagrangian water quality model was designed to investigate the hypothesis of sporadic silica limitations of diatom growth in the lower Elbe River in Germany. For each fluid parcel a limited reservoir of silica was specified to be consumed by diatoms. The model's simplicity notwithstanding, a set of six selected model parameters could not be fully identified from existing observations at one station. After the introduction of prior knowledge of the ranges of meaningful parameter values, calibration of the over-parameterised model manifested itself primarily in the generation of posterior parameter covariances. Estimations of the covariance matrix based on (a) second order partial derivatives of a quadratic cost function at its optimum and (b) Monte Carlo simulations exploring the whole space of parameter values gave consistent results. Diagonalisation of the covariance matrix yielded two linear parameter combinations that were most effectively controlled by data from periods with and without lack of silica, respectively. The two parameter combinations were identified as the essential inputs that govern the successful simulation of intermittently decreasing chlorophyll a concentrations in summer. A satisfactory simulation of the pronounced chlorophyll a minimum in spring, by contrast, was found to be beyond the means of the simple model.     

Inverse uncertainty characteristics of pollution source identification for river chemical spill incidents by stochastic analysis

Identifying source information after river chemical spill occurrences is critical for emergency responses. However, the inverse uncertainty characteristics of this kind of pollution source inversion problem have not yet been clearly elucidated. To fill this gap, stochastic analysis approaches, including a regional sensitivity analysis method, identifiability plot and perturbation methods, were employed to conduct an empirical investigation on generic inverse uncertainty characteristics under a well-accepted uncertainty analysis framework. Case studies based on field tracer experiments and synthetic numerical tracer experiments revealed several new rules. For example, the release load can be most easily inverted, and the source location is responsible for the largest uncertainty among the source parameters. The diffusion and convection processes are more sensitive than the dilution and pollutant attenuation processes to the optimization of objective functions in terms of structural uncertainty. The differences among the different objective functions are smaller for instantaneous release than for continuous release cases. Small monitoring errors affect the inversion results only slightly, which can be ignored in practice. Interestingly, the estimated values of the release location and time negatively deviate from the real values, and the extent is positively correlated with the relative size of the mixing zone to the objective river reach. These new findings improve decision making in emergency responses to sudden water pollution and guide the monitoring network design.

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Estimating the critical phosphorus loading of shallow lakes with the ecosystem model PCLake: Sensitivity, calibration and uncertainty

There is a vast body of knowledge that eutrophication of lakes may cause algal blooms. Among lakes, shallow lakes are peculiar systems in that they typically can be in one of two contrasting (equilibrium) states that are self-stabilizing: a ‘clear’ state with submerged macrophytes or a ‘turbid’ state dominated by phytoplankton. Eutrophication may cause a switch from the clear to the turbid state, if the P loading exceeds a critical value. The ecological processes governing this switch are covered by the ecosystem model PCLake, a dynamic model of nutrient cycling and the biota in shallow lakes. Here we present an extensive analysis of the model, using a three-step procedure. (1) A sensitivity analysis revealed the key parameters for the model output. (2) These parameters were calibrated on the combined data on total phosphorus, chlorophyll-a, macrophytes cover and Secchi depth in over 40 lakes. This was done by a Bayesian procedure, giving a weight to each parameter setting based on its likelihood. (3) These weights were used for an uncertainty analysis, applied to the switchpoints (critical phosphorus loading levels) calculated by the model. The model was most sensitive to changes in water depth, P and N loading, retention time and lake size as external input factors, and to zooplankton growth rate, settling rates and maximum growth rates of phytoplankton and macrophytes as process parameters. The results for the ‘best run’ showed an acceptable agreement between model and data and classified nearly all lakes to which the model was applied correctly as either ‘clear’ (macrophyte-dominated) or ‘turbid’ (phytoplankton-dominated). The critical loading levels for a standard lake showed about a factor two uncertainty due to the variation in the posterior parameter distribution. This study calculates in one coherent analysis uncertainties in critical phosphorus loading, a parameter that is of great importance to water quality managers.     

Estimating the biogenic enhancement factor of weathering using an inverse viability method

To determine the influence of the biosphere on weathering we use a dynamic model of the global carbon cycle. It takes into account the most important processes for the long-term evolution of the Earth. The model is solved under a slowly changing environment of increasing solar luminosity and volcanic activity and continental area. By comparing the model results for the global average temperature with data derived from δ¹⁸O values from cherts it is possible to quantify the biogenic enhancement factor of weathering. For this purpose a newly developed inverse viability method is applied, which allows for calculating the range of possible biogenic enhancement factors consistent with the data. We find that in the Precambrian the weathering was 5.4 times lower than in the Phanerozoic era. This supports the hypothesis that the Cambrian explosion was caused by a positive feedback between the spread of biosphere, increased silicate weathering, and a consequent cooling of the climate.     

Modelling soil carbon development in Swedish coniferous forest soils—An uncertainty analysis of parameters and model estimates using the GLUE method

Boreal forest soils such as those in Sweden contain a large active carbon stock. Hence, a relatively small change in this stock can have a major impact on the Swedish national CO₂ balance. Understanding of the uncertainties in the estimations of soil carbon pools is critical for accurately assessing changes in carbon stocks in the national reports to UNFCCC and the Kyoto Protocol. Our objective was to analyse the parameter uncertainties of simulated estimates of the soil organic carbon (SOC) development between 1994 and 2002 in Swedish coniferous forests with the Q model. Both the sensitivity of model parameters and the uncertainties in simulations were assessed. Data of forests with Norway spruce, Scots pine and Lodgepole pine, from the Swedish Forest Soil Inventory (SFSI) were used. Data of 12 Swedish counties were used to calibrate parameter settings; and data from another 11 counties to validate. The “limits of acceptability” within GLUE were set at the 95% confidence interval for the annual, mean measured SOC at county scale. The calibration procedure reduced the parameter uncertainties and reshaped the distributions of the parameters county-specific. The average measured and simulated SOC amounts varied from 60 t C ha⁻¹ in northern to 140 t C ha⁻¹ in the southern Sweden. The calibrated model simulated the soil carbon pool within the limits of acceptability for all calibration counties except for one county during one year. The efficiency of the calibrated model varied strongly; for five out of 12 counties the model estimates agreed well with measurements, for two counties agreement was moderate and for five counties the agreement was poor. The lack of agreement can be explained with the high inter-annual variability of the down-scaled measured SOC estimates and changes in forest areas over time. We conclude that, although we succeed in reducing the uncertainty in the model estimates, calibrating of a regional scale process-oriented model using a national scale dataset is a sensitive balance between introducing and reducing uncertainties. Parameter distributions showed to be scale sensitive and county specific. Further analysis of uncertainties in the methods used for reporting SOC changes to the UNFCCC and Kyoto protocol is recommended.     

The effects of aggregation on the performance of the inverse method and indicators of network analysis

Food webs are usually aggregated into a manageable size for their interpretation and analysis. The aggregation of food web components in trophic or other guilds is often at the choice of the modeler as there is little guidance in the literature as to what biases might be introduced by aggregation decisions. We examined the impacts of the choice of the a priori model on the subsequent estimation of missing flows using the inverse method and on the indices derived from ecological network analysis of both inverse method-derived flows and on the actual values of flows, using the fully determined Sylt-Rømø Bight food web model. We used the inverse method, with the least squares minimization goal function, to estimate ‘missing’ values in the food web flows on 14 aggregation schemes varying in number of compartments and in methods of aggregation. The resultant flows were compared to known values; the performance of the inverse method improved with increasing number of compartments and with aggregation based on both habitat and feeding habits rather than diet similarity. Comparison of network analysis indices of inverse method-derived flows with that of actual flows and the original value for the unaggregated food web showed that the use of both the inverse method and the aggregation scheme affected indices derived from ecological network analysis. The inverse method tended to underestimate the size and complexity of food webs, while an aggregation scheme explained as much variability in some network indices as the difference between inverse-derived and actual flows. However, topological network indices tended to be most robust to both the method of determining flows and to the inverse method. These results suggest that a goal function other than minimization of flows should be used when applying the inverse method to food web models. Comparison of food web models should be done with extreme care when different methodologies are used to estimate unknown flows and to aggregate system components. However, we propose that indices such as relative ascendency and relative redundancy are most valuable for comparing ecosystem models constructed using different methodologies for determining missing flows or for aggregating system components.     

Determination of the principal factors of river water quality through cluster analysis method and its prediction

In this paper, an artificial neural network model was built to predict the Chemical Oxygen Demand (COD_Mn) measured by permanganate index in Songhua River. To enhance the prediction accuracy, principal factors were determined through the analysis of the weight relation between influencing factors and forecasting object using cluster analysis method, which optimized the topological structure of the prediction model input items of the artificial neural network. It was shown that application of the principal factors in water quality prediction model can improve its forecasting skill significantly through the comparison between results of prediction by artificial neural network and the measurements of the COD_Mn. This methodology is also applicable to various water quality prediction targets of other water bodies and it is valuable for theoretical study and practical application.     

Estimating the complexity of bird song by using capture-recapture approaches from community ecology

Repertoire size, the number of unique song or syllable types in the repertoire, is a widely used measure of song complexity in birds, but it is difficult to calculate this exactly in species with large repertoires. A new method of repertoire size estimation applies species richness estimation procedures from community ecology, but such capture-recapture approaches have not been much tested. Here, we establish standardized sampling schemes and estimation procedures using capture-recapture models for syllable repertoires from 18 bird species, and suggest how these may be used to tackle problems of repertoire estimation. Different models, with different assumptions regarding the heterogeneity of the use of syllable types, performed best for different species with different song organizations. For most species, models assuming heterogeneous probability of occurrence of syllables (so-called detection probability) were selected due to the presence of both rare and frequent syllables. Capture-recapture estimates of syllable repertoire size from our small sample did not differ significantly from previous estimates using larger samples of count data. However, the enumeration of syllables in 15 songs yielded significantly lower estimates than previous reports. Hence, heterogeneity in detection probability of syllables should be addressed when estimating repertoire size. This is neglected using simple enumeration procedures, but is taken into account when repertoire size is estimated by appropriate capture-recapture models adjusted for species-specific song organization characteristics. We suggest that such approaches, in combination with standardized sampling, should be applied in species with potentially large repertoire size. On the other hand, in species with small repertoire size and homogenous syllable usage, enumerations may be satisfactory. Although researchers often use repertoire size as a measure of song complexity, listeners to songs are unlikely to count entire repertoires and they may rely on other cues, such as syllable detection probability.Communicated by A. Cockburn     

Development and validation of a spectrophotometric method for soil adsorption study of malathion on three Indian soils

In view of the widespread use of pesticides in agriculture and its associated toxic effects on environment and human beings, the fate of these chemicals in soil is of major concern. The pesticide adsorption process is one of the major factors affecting its persistence and movement in the soil. With a view of studying the fate of malathion in soil, the adsorption of this insecticide was studied on three Indian soils by using batch equilibrium method. To carry out the adsorption study, a new simple, sensitive, and rapid method was developed, based on microwave-assisted alkaline hydrolysis of insecticide to the dimethyl dithiophosphate and its subsequent reaction with copper(I) perchlorate in acetonitrile. On mixing the reagents, a yellow color developed, which was stable for 120?min and was measured at 419?nm. The adsorption of malathion was studied by using Freundlich's adsorption equation and n _f values were observed less than 1 in all the soils. The leaching behavior of the insecticide was studied in terms of ground ubiquity score, which was below 1.8, classifying malathion as non-leacher pesticide, and hence it is not hazardous.     

Simulating the fate of Florida Snowy Plovers with sea-level rise: Exploring research and management priorities with a global uncertainty and sensitivity analysis perspective

Changes in coastal habitats due to sea-level rise provide an uncertain, yet significant threat to shoreline dependent birds. Rising sea levels can cause habitat fragmentation and loss which can result in considerable reduction in their foraging and nesting areas. Computational models and their algorithmic assumptions play an integral role in exploring potential mitigation responses to uncertain and potentially adverse ecological outcomes. The presence of uncertainty in metapopulation models is widely acknowledged but seldom considered in their development and evaluation, specifically the effects of uncertain model inputs on the model outputs. This paper was aimed to (1) quantify the contribution of each uncertain input factor to the uncertainty in the output of a metapopulation model which evaluated the effects of long-term sea-level rise on the population of Snowy Plovers (Charadrius alexandrinus) found in the Gulf Coast of Florida, and (2) determine the ranges of model inputs that produced a specific output for the purpose of formulating environmental management decisions. This was carried out by employing global sensitivity and uncertainty analysis (GSA) using two generic (model independent) methods, the qualitative screening Morris method and a quantitative variance-based Sobol’ method coupled with Monte Carlo filtering. The analyses were applied to three density dependence scenarios: assuming a ceiling-type density dependence, assuming a contest-type density dependence, and assuming that density dependence is uncertain as to being ceiling- or contest-dependent. The sources of uncertainty in the outputs depended strongly on the type of density dependence considered in the model. In general, uncertainty in the outputs highly depended on the uncertainty in stage matrix elements (fecundity, adult survival, and juvenile survival), dispersal rate from central areas with low current populations (the “Big Bend” area of Florida) to the northern, panhandle populations, the maximum growth rate, and density dependence type. Our results showed that increasing the maximum growth rate to a value of 1.2 or larger will increase the final average population of Snowy Plovers assuming a contest-type density dependence. Results suggest that studies that further quantify which density dependence relationship best describes Snowy Plover population dynamics should be conducted since this is the main driver of uncertainty in model outcomes. Furthermore, investigating the presence of Snowy Plovers in the Big Bend region may be important for providing connection between the panhandle and peninsula populations.     

Total cobalt determination in human blood and synovial fluid using inductively coupled plasma-mass spectrometry: method validation and evaluation of performance variables affecting metal hip implant patient samples

Inductively coupled plasma with mass spectrometric detection (ICP-MS) has been used for clinical analysis of cobalt (Co) due to its sensitivity and specificity; however, media-specific validation studies are lacking. This study provides data on performance variables affecting differences between selected analytical platforms (Perkin Elmer and Agilent), tissue sample preparation, storage, and interferences affecting measurements in whole blood, serum, and synovial fluid. The limits of detection (LOD) range from 0.2–0.5 µg/L in serum and synovial fluid, and 0.6–1.7 µg Co/L in whole blood. The Agilent platform with collision reaction cell is more sensitive, while the Perkin Elmer platform with dynamic reaction cell demonstrates more polyatomic interferences near the LOD for serum and whole blood. Split sample analysis showed good accuracy, precision, and reproducibility between serum Co measurements using acid digestion or detergent dilution preparations for persons with metal hip implants or following supplement intake. The results demonstrated reliability of the ICP-MS methodology across the two analytical platforms and between two commercial laboratories for Co concentrations above 5 µg Co/L, but digestion procedures and polyatomic interferences may affect measurements in some media at lower concentrations. These studies validate the described ICP-MS methodology for clinical purposes with precautions at low cobalt concentrations (<5 µg Co/L).     

A comparative decision-making model for sustainable end-of-life vehicle management alternative selection using AHP and extent analysis method on fuzzy AHP

Environmental protection and sustainable development are getting increasing attention in the automobile industries. Environmental consciousness has increased, and sustainability has become an important requirement for the management of end-of-life vehicles (ELVs). The proper management of ELVs can bring improved sustainability performance for any society or organisation. There are several alternative options for ELVs management such as reuse, repair, reconditioning, remanufacturing and recycling. Environmental legislation is forcing original equipment manufacturers (OEMs) to manage their products at the end of their life cycle to reduce their potential environmental impact. An appropriate selection model for sustainable ELV management alternatives in the dynamic, competitive and regulatory environment can enable a firm to satisfy economic, environmental, social and technological requirements. This study proposes an integrated model to select the dimensions and criteria for evaluating sustainable alternatives for the proper management of ELVs. First, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to select the most important dimensions and criteria for sustainable alternative selection. Next, a hierarchy has been constructed to develop a systematic technique to solve the alternatives selection problem. An analytic hierarchy process (AHP) and extent analysis method on the fuzzy AHP (FEAHP) model have been used based on the hierarchy to evaluate the most suitable alternatives from the sustainability point of view. The strength of the proposed model is that it compares the results of both methods, the imprecision of experts’ opinions is considered in the evaluation process and the model is easy to use.     

有机物对蒽酮-硫酸法测定葡萄糖含量的影响

本文主要对污水生物处理中常见小分子有机物如乙醇、乙醛、甲酸、丙酮、乙酸异戊酯进行研究,甄别出干扰物,对不同浓度干扰物的影响及干扰物存在对不同浓度葡萄糖测定的影响进行定量分析.结果表明,乙醇对葡萄糖测定无影响,甲酸、丙酮和乙酸异戊酯使测定结果存在负偏差,乙醛使测定结果存在正偏差,且偏差值随着有机物浓度和葡萄糖浓度的增加而增大.建立了蒽酮-硫酸法葡萄糖含量测定的校正方程,研究结果可为相关领域采用该法测定葡萄糖含量提供参考.     

Citizen science networks in natural history and the collective validation of biodiversity data

Biodiversity data are in increasing demand to inform policy and management. A substantial portion of these data is generated in citizen science networks. To ensure the quality of biodiversity data, standards and criteria for validation have been put in place. We used interviews and document analysis from the United Kingdom and The Netherlands to examine how data validation serves as a point of connection between the diverse people and practices in natural history citizen science networks. We found that rather than a unidirectional imposition of standards, validation was performed collectively. Specifically, it was enacted in ongoing circulations of biodiversity records between recorders and validators as they jointly negotiated the biodiversity that was observed and the validity of the records. These collective validation practices contributed to the citizen science character or natural history networks and tied these networks together. However, when biodiversity records were included in biodiversity‐information initiatives on different policy levels and scales, the circulation of records diminished. These initiatives took on a more extractive mode of data use. Validation ceased to be collective with important consequences for the natural history networks involved and citizen science more generally.     

An interval joint-probabilistic programming method for solid waste management: a case study for the city of Tianjin, China

Currently, environmental protection and resources conservation continue to be challenges faced by solid-waste managers in China. These challenges are being further compounded by rapid socioeconomic devel- opment and population growth associated with increased waste generation rates and decreased waste disposal capacities. In response to these challenges, an interval joint-probabilistic mixed-integer programming （IJMP） method is developed for supporting long-term planning of waste management activities in the city of Tianjin, which is one of the largest municipalities in the northern part of China. In the IJMP, joint probabilistic constraints are introduced into an interval-parameter mixed-integer programming framework, such that uncertainties presented in terms of interval values and random variables can be reflected. Moreover, a number of violation levels for the waste-management-capacity constraints are examined, which can facilitate in-depth analyses of tradeoffs among economic objective and system-failure risk. The results indicate that reasonable solutions have been generated. They are valuable for supporting the adjustment of the city＇s existing waste-management practices and the long- term planning of the city＇s waste-management facilities.     

Estimating the number of active and successful bald eagle nests:an application of the dual frame method

Monitoring the number of animals in a wildlife population is an important task. In this paper, we estimate the number of active and successful bald eagle nests in a specific region using dual frame sampling techniques. The dual frame method combines independent samples from an incomplete list frame and an area frame that is assumed to be complete. Hartley's screening estimator is used to combine sample information from both frames. This methodology will be useful for monitoring any wildlife population where the breeding individuals have highly visible nesting territories which tend to be stable over many years.     

不同生长基质中叶状地衣共生藻的分离与鉴定

采用形态鉴定及基于ITS及rbcL序列构建ML系统发育树对采自云南省姚安县及南华县的10种叶状地衣中分离纯化培养的共生藻进行分析研究。鉴定结果表明5个编号分别为Z1、Z4、Z5、Z6、Z9的树生地衣共生藻样品鉴定为念珠藻目（Nostocales）念珠藻科（Nostocaceae）念珠藻属（Nostoc）;2个土生地衣共生藻样品Z2、Z10鉴定为四孢藻目（Tetrasporales）胶球藻科（Coccomyxaceae）胶球藻属（Coccomyxa）;3个石生地衣分离藻样品Z3、Z7、Z8鉴定为绿球藻目（Chlorococcales）栅藻科（Scenedesmaceae）链带藻属（Desmodesmus）。另外从地衣采集地及分子系统分析结果可以看出,尽管Z1、Z5、Z9分别是从采自不同区域（姚安县及南华县）的地衣中分离纯化的,但亲缘关系最近;同样Z4、Z6亲缘关系最近;Z2、Z10亲缘关系最近,Z7、Z8亲缘关系最近。所以地衣共生菌与共生藻的共生关系与其生存的地理区域关系不大,但却和其生长基质有明显的相关性。     

吉林西部土地利用/覆被时空变化驱动力分析

针对吉林两部土地利用/覆被情况,基于TM影像,借助人机交互解译方法,结合1:10万地形图获得1986年、1996年、2000年和2004年的4期土地利用/覆被变化(LUCC)矢量数据.利用土地利用动态度模型、土地利用转移矩阵.全面揭示研究区15年的LUCC时空变化特征,结果表明.盐碱地扩张,湿地、水域萎缩和草地退化已经成为吉林西部生态环境恶化的突出表现.结合研究区的实际情况选取总人口数、农业人口数、粮食总产置等12个指标作为LUCC:变化的丰十会经济驱动因子,借助多元线性回归模型,建立主要土地利用类型的驱动力模型.结果表明吉林西部土地利用变化的最主要社会经济因子是农业人口数、农民人均纯收入、农业生产技术(农业机械总动力、化肥施用量)和农村用电量,同时提出相应的生态环境保护和社会经济措施.