An enhanced environmental multimedia modeling system based on fuzzy-set approach: I. Theoretical framework and model development

Multimedia environmental modeling is extremely complex due to the intricacy of the systems with the consideration of many related factors. Traditional environmental multimedia models (EMMs) are usually based on one-dimensional and first-order assumptions, which may cause numerical errors in the simulation results. In this study, a new user-friendly fuzzy-set enhanced environmental multimedia modeling system (FEEMMS) is developed, and includes four key modules: an air dispersion module, a polluting source module, an unsaturated zone module, and a groundwater module. Many improvements over previous EMMs have been achieved through dynamically quantifying the intermedia mass flux; incorporating fuzzy-set approach into environmental multimedia modeling system (EMMS); and designing a user-friendly graphic user interface (GUI). The developed FEEMMS can be a useful tool in estimating the time-varying and spatial-varying chemical concentrations in air, soil, and groundwater; characterizing the potential risk to human health presented by contaminants released from a contaminated site; and quantifying the uncertainties associated with modeling systems and subsequently providing robustness and flexibility for the remediation-related decision making.     

The interpretation of noisy data from archaeological field survey: Phosphate analysis

High soil phosphate concentrations are commonly related to intense past human activity although full understanding of this relationship requires further research. At present, practical constraints in the field, the need for extensive sampling and for rapid results, leads to the archaeologist frequently using crude but portable techniques of chemical analysis. The problems associated with the collection and interpretation of archaeological soil phosphate data are discussed. The use of Bayesian change-point analysis is proposed as a suitable statistical aid to the interpretation of such data.     

土壤中总石油烃污染（TPH）的微生物降解与修复研究进展

微生物降解和修复是处理土壤中总石油烃(TPH)污染最简单、有效的方法之一.论文阐述了土壤TPH污染的产生、危害以及物理、化学、生物等修复方法的各自特点,其中重点介绍了微生物修复方法,论述了土壤中TPH在微生物表面的吸附、转运,在微生物体内的降解以及相关降解酶及基因;详细介绍了电子受体、温度、pH、营养元素等外界因素对微生物修复TPH污染的影响,在此基础上对土壤TPH污染的微生物修复现状和发展趋势进行了讨论.     

Editorial: Composite sampling

The high costs of laboratory analytical procedures frequently strain environmental and public health budgets. Whether soil, water or biological tissue is being analysed, the cost of testing for chemical and pathogenic contaminants can be quite prohibitive.Composite sampling can substantially reduce analytical costs because the number of required analyses is reduced by compositing several samples into one and analysing the composited sample. By appropriate selection of the composite sample size and retesting of select individual samples, composite sampling may reveal the same information as would otherwise require many more analyses.Many of the limitations of composite sampling have been overcome by recent research, thus bringing out more widespread potential for using composite sampling to reduce costs of environmental and public health assessments while maintaining and often increasing the precision of sample-based inference.     

Error propagation in spatial modeling of public health data: a simulation approach using pediatric blood lead level data for Syracuse,New York

Lead poisoning produces serious health problems, which are worse when a victim is younger. The US government and society have tried to prevent lead poisoning, especially since the 1970s; however, lead exposure remains prevalent. Lead poisoning analyses frequently use georeferenced blood lead level data. Like other types of data, these spatial data may contain uncertainties, such as location and attribute measurement errors, which can propagate to analysis results. For this paper, simulation experiments are employed to investigate how selected uncertainties impact regression analyses of blood lead level data in Syracuse, New York. In these simulations, location error and attribute measurement error, as well as a combination of these two errors, are embedded into the original data, and then these data are aggregated into census block group and census tract polygons. These aggregated data are analyzed with regression techniques, and comparisons are reported between the regression coefficients and their standard errors for the error added simulation results and the original results. To account for spatial autocorrelation, the eigenvector spatial filtering method and spatial autoregressive specifications are utilized with linear and generalized linear models. Our findings confirm that location error has more of an impact on the differences than does attribute measurement error, and show that the combined error leads to the greatest deviations. Location error simulation results show that smaller administrative units experience more of a location error impact, and, interestingly, coefficients and standard errors deviate more from their true values for a variable with a low level of spatial autocorrelation. These results imply that uncertainty, especially location error, has a considerable impact on the reliability of spatial analysis results for public health data, and that the level of spatial autocorrelation in a variable also has an impact on modeling results.     

土壤有机碳储量估算的影响因素和不确定性

土壤作为陆地生态系统最大的碳库和碳循环过程的关键环节,其源、汇的变化直接影响全球碳平衡,因此,土壤有机碳储量估算成为生态领域的重要研究内容之一。通过比较不同研究者在不同研究尺度上报道的有机碳储量的研究结果,发现这些研究结果较不一致。分析认为导致土壤有机碳储量评估结果存在较大差异的原因,主要是来自于采样过程中人类干扰以及气候变化等环境要素的波动,特别是研究者所采取的不同估算方法和背景资料。从环境要素（外因）和估算方法（内因）两个角度出发,提出了土壤有机碳储量的研究意义,阐述了造成土壤有机碳储量估算的不确定性和目前研究中存在的问题,并在此基础上,对其未来研究方向和重点进行了展望。     

Uncertainty analyses for calibrating a soil carbon balance model to agricultural field trial data in Sweden and Kenya

How do additional data of the same and/or different type contribute to reducing model parameter and predictive uncertainties? Most modeling applications of soil organic carbon (SOC) time series in agricultural field trial datasets have been conducted without accounting for model parameter uncertainty. There have been recent advances with Monte Carlo-based uncertainty analyses in the field of hydrological modeling that are applicable, relevant and potentially valuable in modeling the dynamics of SOC. Here we employed a Monte Carlo method with threshold screening known as Generalized Likelihood Uncertainty Estimation (GLUE) to calibrate the Introductory Carbon Balance Model (ICBM) to long-term field trail data from Ultuna, Sweden and Machang’a, Kenya. Calibration results are presented in terms of parameter distributions and credibility bands on time series simulations for a number of case studies. Using these methods, we demonstrate that widely uncertain model parameters, as well as strong covariance between inert pool size and rate constant parameters, exist when root mean square simulation errors were within uncertainties in input estimations and data observations. We show that even rough estimates of the inert pool (perhaps from chemical analysis) can be quite valuable to reduce uncertainties in model parameters. In fact, such estimates were more effective at reducing parameter and predictive uncertainty than an additional 16 years time series data at Ultuna. We also demonstrate an effective method to jointly, simultaneously and in principle more robustly calibrate model parameters to multiple datasets across different climatic regions within an uncertainty framework. These methods and approaches should have benefits for use with other SOC models and datasets as well.     

Generalized site occupancy models allowing for false positive and false negative errors

Site occupancy models have been developed that allow for imperfect species detection or "false negative" observations. Such models have become widely adopted in surveys of many taxa. The most fundamental assumption underlying these models is that "false positive" errors are not possible. That is, one cannot detect a species where it does not occur. However, such errors are possible in many sampling situations for a number of reasons, and even low false positive error rates can induce extreme bias in estimates of site occupancy when they are not accounted for. In this paper, we develop a model for site occupancy that allows for both false negative and false positive error rates. This model can be represented as a two-component finite mixture model and can be easily fitted using freely available software. We provide an analysis of avian survey data using the proposed model and present results of a brief simulation study evaluating the performance of the maximum-likelihood estimator and the naive estimator in the presence of false positive errors.     

Automated conservation assessment of the orchid family with deep learning

International Union for Conservation of Nature (IUCN) Red List assessments are essential for prioritizing conservation needs but are resource intensive and therefore available only for a fraction of global species richness. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. We conducted automated conservation assessments for 13,910 species (47.3% of the known species in the family) of the diverse and globally distributed orchid family (Orchidaceae), for which most species (13,049) were previously unassessed by IUCN. We used a novel method based on a deep neural network (IUC-NN). We identified 4,342 orchid species (31.2% of the evaluated species) as possibly threatened with extinction (equivalent to IUCN categories critically endangered [CR], endangered [EN], or vulnerable [VU]) and Madagascar, East Africa, Southeast Asia, and several oceanic islands as priority areas for orchid conservation. Orchidaceae provided a model with which to test the sensitivity of automated assessment methods to problems with data availability, data quality, and geographic sampling bias. The IUC-NN identified possibly threatened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias relative to the IUCN Red List and was robust even when data availability was low and there were geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in identifying species at the greatest risk of extinction.     

Stabilization-based soil remediation should consider long-term challenges

Soil remediation is of increasing importance globally, especially in developing countries. Among available remediation options, stabilization, which aims to immobilize contaminants within soil, has considerable advantages, including that it is cost-effective, versatile, sustainable, rapid, and often results in less secondary pollution. However, there are emerging challenges regarding the long-term performance of the technology, which may be affected by a range of environmental factors. These challenges stem from a research gap regarding the development of accurate, quantitative laboratory simulations of long-term conditions, whereby laboratory accelerated aging methods could be normalized to real field conditions. Therefore, field trials coupled with long-term monitoring are critical to further verify conditions under which stabilization is effective. Sustainability is also an important factor affecting the long-term stability of site remediation. It is hence important to consider these challenges to develop an optimized application of stabilization technology in soil remediation.     

生产企业及周边环境中全氟化合物的污染特征

生产企业作为全氟化合物(perfluorinated compounds,PFCs)的直接来源地,现今被认为是PFCs污染的主要来源之一,同时其对周边环境具有更加直接而重大的影响。我国对生产企业周边环境中PFCs污染特性研究的报道较缺乏,补充丰富各地生产企业周边环境的PFCs污染特征,可为PFCs点源分析和污染溯源提供依据。以湖北省孝昌县某化工有限公司为典型生产企业,采集7个采样点的水体和土壤样品,分析典型地区环境介质中PFCs的污染现状与特征。结果显示,11种目标PFCs污染物在水体中有7种、土壤中有6种不同程度地检出,环境水体中PFCs的总浓度介于4.70~40.22μg·L-1,土壤中PFCs的总浓度介于58.22~2 075.60 ng·g-1之间。全氟辛基磺酸(PFOS)为典型行业周边水体和土壤中最主要的PFCs污染物,其次是水体中的全氟己基磺酸钾(PFHx S)、全氟丁烷磺酸钾(PFBS)和土壤中的全氟辛酸(PFOA)、全氟己基磺酸钾(PFHx S)。PFCs检出浓度的大小与采样点距典型企业的距离极其相关,距离与污染物总量之间呈显著负相关性,但周边环境中PFCs的种类和构成比,不受与点源之间距离的影响。     

Mercury source zone identification using soil vapor sampling and analysis

Development and demonstration of reliable measurement techniques that can detect and help quantify the nature and extent of elemental mercury (Hg(0)) in the subsurface are needed to reduce uncertainties in the decision-making process and increase the effectiveness of remedial actions. We conducted field tests at the Y-12 National Security Complex in Oak Ridge, Tennessee, USA, to determine if sampling and analysis of Hg(0) vapors in the shallow subsurface (<0.3 m depth) can be used to as an indicator of the location and extent of Hg(0) releases in the subsurface. We constructed a rigid polyvinyl chloride push probe assembly, which was driven into the ground. Soil gas samples were collected through a sealed inner tube of the assembly and were analyzed immediately in the field with a Lumex and/or Jerome Hg(0) analyzer. Time-series sampling showed that Hg vapor concentrations were fairly stable over time, suggesting that the vapor phase Hg(0) was not being depleted and that sampling results were not sensitive to the soil gas purge volume. Hg(0) vapor data collected at over 200 push probe locations at 3 different release sites correlated very well to areas of known Hg(0) contamination. Vertical profiling of Hg(0) vapor concentrations conducted at two locations provided information on the vertical distribution of Hg(0) contamination in the subsurface. We conclude from our studies that soil gas sampling and analysis can be conducted rapidly and inexpensively at large scales to help identify areas contaminated with Hg(0).     

Using the expert model PERPEST to translate measured and predicted pesticide exposure data into ecological risks

An important topic in the registration of pesticides and the interpretation of monitoring data is the estimation of the consequences of a certain concentration of a pesticide for the ecology of aquatic ecosystems. Solving these problems requires predictions of the expected response of the ecosystem to chemical stress. Up until now, a dominant approach to come up with such a prediction is the use of simulation models or safety factors. The disadvantage of the use of safety factors is a crude method that does not provide any insight into the concentration–response relationships at the ecosystem level. On the other hand, simulation models also have serious drawbacks like that they are often very complex, lack transparency, their implementation is expensive and there may be a compilation of errors, due to uncertainties in parameters and processes. In this paper, we present the expert model prediction of the ecological risks of pesticides (PERPEST) that overcomes these problems. It predicts the effects of a given concentration of a pesticide based on the outcome of already performed experiments using experimental ecosystems. This has the great advantage that the outcome is more realistic. The paper especially discusses how this model can be used to translate measured and predicted concentrations of pesticides into ecological risks, by taking data on measured and predicted concentrations of atrazine as an example. It is argued that this model can be of great use to evaluate the outcome of chemical monitoring programmes (e.g. performed in the light of the Water Framework Directive) and can even be used to evaluate the effects of mixtures.     

Soil pollution in day-care centers and playgrounds in Norway: national action plan for mapping and remediation

Systematic geochemical mapping based on sampling and analysis of surface soils (0–2 cm) has been carried out in several Norwegian cities. The soils in the oldest parts of the cities are contaminated with metals (especially Pb) and polycyclic aromatic hydrocarbons (PAH). Due to the fact that children are often in contact with surface soil, it was realized that special focus had to be directed towards soils in day-care centers and playgrounds. The first mapping and remedy program was initiated in Trondheim in 1996. Here, the importance of copper–chromium–arsenic (CCA)-pressure-impregnated wood in playing equipment as a pollution source for soils was documented, and a process was started with the aim to ban this product. Soils from day-care centers in the inner city of Bergen were polluted to a degree that required remediation in 45% out of 87 centers, mainly due to high concentrations of Pb and benzo(a)pyrene. In Oslo, 38% of 700 day-care centers needed remediation due to soil pollution by Pb, BaP, Cd, Hg, Ni and PCB. Removal of CCA-impregnated wood was necessary in more than half of the day-care centers The Norwegian parliament has decided to investigate all outdoor playing areas in day-care centers, playgrounds and schoolyards in Norway, starting in 2007 with day-care centers in the ten largest cities and five most important industrial areas. The Norwegian Institute of Public Health has developed quality criteria for soils in day-care centers and playgrounds for As, Cd, Cr⁶⁺, Hg, Ni, Pb, zinc, PAH_sum16, benzo(a)pyrene, and PCB_sum7. The Geological Survey of Norway has developed guidance for mapping of soil pollution (sampling, chemical analysis and reporting) in day-care centers. Especially the sampling strategy has been developed in the period 1996–2007, and the preferred sampling strategy is to collect at least 10 samples of surface soil (0–2 cm) from (1) “original soil” on the site, (2) artificial man-made “hills”, and (3) soils used for growing vegetables. A total number of 2,000 day-care-centres are to be investigated, and necessary remediation should be completed before the end of 2010.     

Bioremediation of highly contaminated oilfield soil: Bioaugmentation for enhancing aromatic compounds removal

This study evaluated the effectiveness of different amendments--including a commercial NPK fertilizer, a humic substance （HS）, an organic industrial waste （NovoGro）, and a yeast-bacteria consortium--in the remediation of highly contaminated （up to 6% of total petroleum hydrocarbons） oilfield soils. The concentrations of hydrocarbon, soil toxicity, physicochemical properties of the soil, microbial population numbers, enzyme activities and microbial community structures were examined during the 90-d incubation. The results showed that the greatest degradation of total petroleum hydro- carbons （TPH） was observed with the biostimulation using mixture of NPK, HS and NovoGro, a treatment scheme that enhanced both dehydrogenase and lipase activities in soil. Introduction of exogenous hydrocarbon-degrading bacteria （in addition to biostimulation with NPK, HS and NovoGro） had negligible effect on the removal of TPH, which was likely due to the competition between exogenous and autochthonous microorganisms. None- theless, the addition of exogenous yeast-bacteria consor- tium significantly enhanced the removal of the aromatic fraction of the petroleum hydrocarbons, thus detoxifying the soil. The effect of bioaugmentation on the removal of more recalcitrant petroleum hydrocarbon fraction was likely due to the synergistic effect of bacteria and fungi.     

会泽某铅锌矿周边农田土壤重金属生态风险评价

为了了解云南会泽某铅锌矿废周边农田土壤中重金属含量及潜在的生态危害程度,利用野外采样与实验室分析相结合的方法,以会泽某铅锌矿周边农田土壤（0-20 cm）为研究对象,分析其中7种的重金属（Cd、As、Pb、Cr、Cu、Zn和Hg）含量,并采用风险评价代码法和Hankanson潜在生态风险指数法评价对重金属污染程度与潜在生态风险进行评价。结果表明：7种重金属都存在超标或污染,其中Pb、As、Cd等的污染较为严重。统计学分析结果表明,Pb、As、Hg、Zn、Cd来源相同,铅锌矿冶炼污染物的排放可能是导致研究区域农田土壤重金属含量升高的主要原因。7种重金属化学形态也不尽相同：在重金属有效态中,Cd的水溶态和可提取态较高（平均值达到31.2%）;Pb、Cu和Zn可还原态、可氧化态这两部分含量较高,两部分之和的平均值分别可达到27.9%、30%和27.2%;Hg、As和Cr的残渣态含量较高,平均值分别为90.4%、72.9%和76.8%。风险评价代码评价结果表明,54.4%的样点Cd为高生态风险,45.6%的样点Cd为中度生态风险;100%的样点Zn为中度生态风险;Cu有41.2%的点位属于低生态风险,58.8%的点位属于中度生态风险;As和Pb主要以低生态风险为主（所占比例分别为92.6%和91.8%）;Hg主要以无生态风险为主（所占97.1%）。Hakanson潜在生态风险指数法结果表明,7种重金属潜在生态危害大小顺序为：Cd（331）〉Hg（127.5）〉Pb（43.6）〉As（14.9）〉Cu（9.3）〉Zn（2.3）〉Cr（2.1）。7种重金属的综合潜在生态风险指数（RI）的范围为58.2-1839.3。11%的采样点处于轻微生态风险程度,27.1%的采样点处于中等生态风险程度,46.3%的的采样点处于强生态风险程度,15.6%的采样点处于很强的生态风险程度。综上所述,该矿区周边农田土壤受到了严重的重金属污染,由此引起的重金属生态风险不容忽视?     

Transport and dynamics of toxic pollutants in the natural environment and their effect on human health: research gaps and challenge

The source–pathway–receptor (SPR) approach to human exposure and risk assessment contains considerable uncertainty when using the refined modelling approaches to pollutant transport and dispersal, not least in how compounds of concern might be prioritised, proxy or indicator substances identified and the basic environmental and toxicological data collected. The impact of external environmental variables, urban systems and lifestyle is still poorly understood. This determines exposure of individuals and there are a number of methods being developed to provide more reliable spatial assessments. Within the human body, the dynamics of pollutants and effects on target organs from diffuse, transient sources of exposure sets ambitious challenges for traditional risk assessment approaches. Considerable potential exists in the application of, e.g. physiologically based pharmacokinetic (PBPK) models. The reduction in uncertainties associated with the effects of contaminants on humans, transport and dynamics influencing exposure, implications of adult versus child exposure and lifestyle and the development of realistic toxicological and exposure data are all highlighted as urgent research needs. The potential to integrate environmental with toxicological models provides the next phase of research opportunity and should be used to drive empirical and model assessments.     

Forest productivity decline caused by successional paludification of boreal soils.

Long-term forest productivity decline in boreal forests has been extensively studied in the last decades, yet its causes are still unclear. Soil conditions associated with soil organic matter accumulation are thought to be responsible for site productivity decline. The objectives of this study were to determine if paludification of boreal soils resulted in reduced forest productivity, and to identify changes in the physical and chemical properties of soils associated with reduction in productivity. We used a chronosequence of 23 black spruce stands ranging in postfire age from 50 to 2350 years and calculated three different stand productivity indices, including site index. We assessed changes in forest productivity with time using two complementary approaches: (1) by comparing productivity among the chronosequence stands and (2) by comparing the productivity of successive cohorts of trees within the same stands to determine the influence of time independently of other site factors. Charcoal stratigraphy indicates that the forest stands differ in their fire history and originated either from high- or low-severity soil burns. Both chronosequence and cohort approaches demonstrate declines in black spruce productivity of 50-80% with increased paludification, particularly during the first centuries after fire. Paludification alters bryophyte abundance and succession, increases soil moisture, reduces soil temperature and nutrient availability, and alters the vertical distribution of roots. Low-severity soil burns significantly accelerate rates of paludification and productivity decline compared with high-severity fires and ultimately reduce nutrient content in black spruce needles. The two combined approaches indicate that paludification can be driven by forest succession only, independently of site factors such as position on slope. This successional paludification contrasts with edaphic paludification, where topography and drainage primarily control the extent and rate of paludification. At the landscape scale, the fire regime (frequency and severity) controls paludification and forest productivity through its effect on soil organic layers. Implications for global carbon budgets and sustainable forestry are discussed.     

Uncertainty analysis of a pharmacokinetic modeling for inorganic arsenic

This paper presents an uncertainty and sensitivity analysis of a pharmacokinetic modeling of inorganic arsenic deposition in rodents for a short‐term exposure. Efforts to develop the pharmacokinetic model are directed towards predicting the kinetic behavior of inorganic arsenic in the body, including tissue and blood concentrations, and especially, the urinary excretion of arsenic and its methylated metabolites. However, the use of the model raises an important question when fixed values of model parameters are used: how is the uncertainty in the model prediction based on the collective uncertainties in the model inputs? This study focuses on an “epistemic”; uncertainty in order to handle this problem. In this case, the uncertainty refers to an input that has a single value which cannot be known with precision due to a lack of knowledge about items or its measurement. The combination of the pharmacokinetic model and the uncertainty analysis would help understand the uncertainties in risk assessment associated with inorganic arsenic.     

Bioremediation of oily sludge-contaminated soil by stimulating indigenous microbes

In situ bioremediation of oily sludge-contaminated soil by biostimulation of indigenous microbes through adding manure was conducted at the Shengli oilfield in northern China. After bioremediation for 360 days, total petroleum hydrocarbon (TPH) content was reduced by 58.2% in the treated plots compared with only 15.6% in the control plot. Moreover, bioremediation significantly improved the physicochemical properties of the soil in the treated plot. Soil microbial counts and community-level physiological profiling were also examined. Manure addition increased TPH degraders and polycyclic aromatic hydrocarbon (PAH) degraders in the contaminated soil by one to two orders of magnitude. The activity and biodiversity of soil microbial communities also increased markedly in the treated plot compared with that of the control. Finally, biotoxicity was used to evaluate the soils and a sharp increase in the EC50 of the soil after bioremediation was observed, indicating that bioremediation had reduced the toxicity of the soil.