The dynamics of shifting cultivation captured in an extended Constrained Cellular Automata land use model

This paper presents an extension to the Constrained Cellular Automata (CCA) land use model of White et al. [White, R., Engelen, G., Uljee, I., 1997. The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics. Environment and Planning B: Planning and Design 24(3), 323–343] to make it better suited for modelling the dynamics of shifting cultivation. In the extended model the time passed since the last land use transition of a location is a factor of its land use potential. The model can now account for the gradual decrease in soil fertility after an area of forest has been cleared for cultivation and also capture the process of regeneration once the plot is fallowed. The model is applied for the Ruhunupura area of Sri Lanka where chena, a particular practice of shifting cultivation, is a common land use that dominates the landscape dynamics. The model is calibrated for the period 1985–2001 and the results are assessed in terms of location to location overlap as well as structural similarity at multiple scales. These results give confidence in the representation of land use dynamics for the main land use classes. On the basis of a long term scenario run for the period 2001–2030, it is verified that the model captures stylized facts related to chena dynamics, in particular shortening fallow periods and increasingly long cultivation periods of chena, as a result of increasing land use pressure.     

Estimation of pasture infectivity according to weather conditions through a fuzzy parametrized model for the free-living stage of Ostertagia ostertagi

Gastrointestinal parasitism is one of the diseases that has the highest economic impact on the Argentinian beef production system, rendering it inefficient. In the region of the Humid Pampas, it has been estimated that 22 million dollars are lost annually because of the death of calves and 170 million dollars are lost in sub-clinic costs. A mathematical model with fuzzy parameters was constructed for the analysis of the free-living stages of gastrointestinal parasites, with the purpose of estimating the pool of L3 larvae available for migration to pasture and the levels of infection in pasture at any time of the year under different climatic conditions. The model is formulated in terms of a system of three difference equations. These equations describe the abundance of parasites in each of the successive stages of the population development. The model was calibrated and tested with data gathered through fieldwork carried out in Tandil (37°19′S, 59°08′05″W), province of Buenos Aires (Argentina) and the corresponding weather data. A comparison between model simulations and fieldwork data obtained in other locations achieved satisfactory results.     

A discrete model for estimating the development time from egg to infecting larva of Ostertagia ostertagi parametrized using a fuzzy rule-based system

Ostertagia ostertagi is a nematode, predominantly affecting cattle in the Pampean region of Argentina. A mathematical model parametrized using fuzzy rule-based systems of the Takagi-Sugeno-Kant type (FTSK) for estimating the development time from egg to infecting larval stage L3 of the gastrointestinal parasite O. ostertagi is here proposed. The estimation of development time of O. ostertagi is essential for the generation of appropriate control mechanisms, since this provides information about the time when parasites are ready to migrate to pastures. For the purpose of reflecting the natural environmental conditions, the mean daily temperature is taken as the main and only regulator of the development time. Humidity conditions are considered to be sufficient for the normal development of the larvae. Hence the individual's daily growth is a function of its length and the mean temperature recorded on the previous day. It is expressed in terms of a difference equation with fuzzy parameters, which are defined using laboratory data. Model outputs are tested against results of field experiments. Simulation results are very satisfactory, yielding a mean estimation error (MEE) of 0.64 weeks, with variance 0.34, and a determination coefficient R² = 0.74. The model clearly exhibits an inverse relationship between development time and temperature both in controlled and in field conditions. It also exhibits a very sensitive response both to the order in which the temperature sequence occurs, - reproducing the differences observed between spring and autumn - and to the amplitude of the temperature range.     

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.     

A fuzzy goal programming with interval target model and its application to the decision problem of renewable energy planning

Environmental and Ecological Statistics - Optimizing sustainable renewable energy portfolios is one of the most complicated decision making problems in energy policy planning. This process involves...     

A hydrodynamic and water quality modeling study of spatial and temporal patterns of phytoplankton growth in a stratified lake with buoyant incoming flow

A three-dimensional hydrodynamic and water quality model was applied to Lake Paldang, a lake in South Korea that is stratified by incoming flows. The spatial and temporal patterns of phytoplankton growth in this lake were determined from the model. The model was calibrated and verified using data measured under different hydrological conditions. The model results were in reasonable agreement with the field measurements, in both the calibration and verification phases. The distributions of water quality and residence time in the lake and phytoplankton response to changes in nutrient loads were examined with the model, and the influence of the hydrodynamics on phytoplankton response was analyzed. The simulation results indicated that Lake Paldang is an essentially phosphorus-limited system, but that phytoplankton growth is limited by low water temperature and short residence time during the winter and the summer monsoon period, respectively. The results of sensitivity analyses also suggested that the hydrodynamics within the lake may have an indirect influence on phytoplankton responses to changes in the limiting nutrient loads, and that reducing phosphorus loading from Kyoungan Stream should be a high priority policy for controlling algal blooms during the pre- and post-monsoon periods. From this study, it was concluded that the three-dimensional water quality model incorporating hydrodynamic processes could successfully simulate phytoplankton response to changes in nutrient loads and that it could become a useful tool for identifying the essential factors determining phytoplankton growth and for developing the best management policy for algal blooms in Lake Paldang.     

赤潮多发区深圳湾叶绿素a的时空分布及其影响因素

于2008年2月至2008年11月分四个季度调查了赤潮多发区深圳湾叶绿素a的时空分布,并分析了叶绿素a与主要环境因子之间的关系.结果表明,深圳湾叶绿素a质量浓度范围为3.07～309.94 mg·m~(-3),年平均值为42.29 mg·m~(-3).四季叶绿素a平均质量浓度由高到低排列分别为:春季(108.33 mg·m~(-3))>夏季(35.2 mg·m~(-3))>秋季(16.68 mg·m~(-3))>冬季(8.96 mg·m~(-3)).叶绿素a的质量浓度在冬季和春季呈现由湾内向湾外递减的分布特征,而夏季和秋季呈现由西岸向东岸递减的分布特征,整体则呈现由湾内向湾外递减的分布特征.相关分析显示深圳湾叶绿素a与水温、COD、TOC、PO_4~(3-)-P和浮游植物密度因子显著正相关,与DIN因子极显著正相关,说明叶绿素a与水温、DIN、PO_4~(3-)-P、COD、TOC和浮游植物密度之间有着比其它因子更为密切的关系.以叶绿素a作为富营养化评价标准,发现深圳湾海域富营养化严重,存在随时爆发赤潮的潜在风险.     

A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions

Spatial information in the form of geographical information system coverages and remotely sensed imagery is increasingly used in ecological modeling. Examples include maps of land cover type from which ecologically relevant properties, such as biomass or leaf area index, are derived. Spatial information, however, is not error-free: acquisition and processing errors, as well as the complexity of the physical processes involved, make remotely sensed data imperfect measurements of ecological attributes. It is therefore important to first assess the accuracy of the spatial information being used and then evaluate the impact of such inaccurate information on ecological model predictions. In this paper, the role of geostatistics for mapping thematic classification accuracy through integration of abundant image-derived (soft) and sparse higher accuracy (hard) class labels is presented. Such assessment leads to local indices of map quality, which can be used for guiding additional ground surveys. Stochastic simulation is proposed for generating multiple alternative realizations (maps) of the spatial distribution of the higher accuracy class labels over the study area. All simulated realizations are consistent with the available pieces of information (hard and soft labels) up to their validated level of accuracy. The simulated alternative class label representations can be used for assessing joint spatial accuracy, i.e., classification accuracy regarding entire spatial features read from the thematic map. Such realizations can also serve as input parameters to spatially explicit ecological models; the resulting distribution of ecological responses provides a model of uncertainty regarding the ecological model prediction. A case study illustrates the generation of alternative land cover maps for a Landsat Thematic Mapper (TM) subscene, and the subsequent construction of local map quality indices. Simulated land cover maps are then input into a biogeochemical model for assessing uncertainty regarding net primary production (NPP).     

Comparison of sensitivity analysis techniques: A case study with the rice model WARM

The considerable complexity often included in biophysical models leads to the need of specifying a large number of parameters and inputs, which are available with various levels of uncertainty. Also, models may behave counter-intuitively, particularly when there are nonlinearities in multiple input-output relationships. Quantitative knowledge of the sensitivity of models to changes in their parameters is hence a prerequisite for operational use of models. This can be achieved using sensitivity analysis (SA) via methods which differ for specific characteristics, including computational resources required to perform the analysis. Running SA on biophysical models across several contexts requires flexible and computationally efficient SA approaches, which must be able to account also for possible interactions among parameters. A number of SA experiments were performed on a crop model for the simulation of rice growth (Water Accounting Rice Model, WARM) in Northern Italy. SAs were carried out using the Morris method, three regression-based methods (Latin hypercube sampling, random and Quasi-Random, LpTau), and two methods based on variance decomposition: Extended Fourier Amplitude Sensitivity Test (E-FAST) and Sobol’, with the latter adopted as benchmark. Aboveground biomass at physiological maturity was selected as reference output to facilitate the comparison of alternative SA methods. Rankings of crop parameters (from the most to the least relevant) were generated according to sensitivity experiments using different SA methods and alternate parameterizations for each method, and calculating the top-down coefficient of concordance (TDCC) as measure of agreement between rankings. With few exceptions, significant TDCC values were obtained both for different parameterizations within each method and for the comparison of each method to the Sobol’ one. The substantial stability observed in the rankings seem to indicate that, for a crop model of average complexity such as WARM, resource intensive SA methods could not be needed to identify most relevant parameters. In fact, the simplest among the SA methods used (i.e., Morris method) produced results comparable to those obtained by methods more computationally expensive.     

Global perspectives and future research directions for the phytoremediation of heavy metal-contaminated soil: A knowledge mapping analysis from 2001 to 2020

• The overall global perspective of the PHMCS field was obtained. • PHMCS research has flourished over the past two decades. • In total, 8 clusters were obtained, and many new hot topics emerged. • “Biochar,” “Drought,” “Nanoparticle,” etc., may be future hot topics. • Five future directions are proposed. In total, 9,552 documents were extracted from the Web of Science Core Collection and subjected to knowledge mapping and visualization analysis for the field of phytoremediation of HM-contaminated soil (PHMCS) with CiteSpace 5.7 R3 software. The results showed that (1) the number of publications increased linearly over the studied period. The top 10 countries/regions, institutions and authors contributing to this field were exhibited. (2) Keyword co-occurrence cluster analysis revealed a total of 8 clusters, including “Bioremediation,” “Arsenic,” “Biochar,” “Oxidative stress,” “Hyperaccumulation,” “EDTA,” “Arbuscular mycorrhizal fungi,” and “Environmental pollution” clusters (3) In total, 334 keyword bursts were obtained, and the 25 strongest, longest duration, and newest keyboard bursts were analyzed in depth. The strongest keyword burst test showed that the hottest keywords could be divided into 7 groups, i.e., “Plant bioremediation materials,” “HM types,” “Chelating amendments,” “Other improved strategies,” “Bioremediation characteristics,” “Risk assessment,” and “Other.” Almost half of the newest topics had emerged in the past 3 years, including “biochar,” “drought,” “health risk assessment,” “electrokinetic remediation,” “nanoparticle,” and “intercropping.” (4) In total, 9 knowledge base clusters were obtained in this study. The studies of Ali et al. (2013), Blaylock et al. (1997), Huang et al. (1997), van der Ent et al. (2013), Salt et al. (1995), and Salt (1998), which had both high frequencies and the strongest burst scores, have had the most profound effects on PHMCS research. Finally, future research directions were proposed.     

Accounting for rate instability and spatial patterns in the boundary analysis of cancer mortality maps

Boundary analysis of cancer maps may highlight areas where causative exposures change through geographic space, the presence of local populations with distinct cancer incidences, or the impact of different cancer control methods. Too often, such analysis ignores the spatial pattern of incidence or mortality rates and overlooks the fact that rates computed from sparsely populated geographic entities can be very unreliable. This paper proposes a new methodology that accounts for the uncertainty and spatial correlation of rate data in the detection of significant edges between adjacent entities or polygons. Poisson kriging is first used to estimate the risk value and the associated standard error within each polygon, accounting for the population size and the risk semivariogram computed from raw rates. The boundary statistic is then defined as half the absolute difference between kriged risks. Its reference distribution, under the null hypothesis of no boundary, is derived through the generation of multiple realizations of the spatial distribution of cancer risk values. This paper presents three types of neutral models generated using methods of increasing complexity: the common random shuffle of estimated risk values, a spatial re-ordering of these risks, or p-field simulation that accounts for the population size within each polygon. The approach is illustrated using age-adjusted pancreatic cancer mortality rates for white females in 295 US counties of the Northeast (1970–1994). Simulation studies demonstrate that Poisson kriging yields more accurate estimates of the cancer risk and how its value changes between polygons (i.e., boundary statistic), relatively to the use of raw rates or local empirical Bayes smoother. When used in conjunction with spatial neutral models generated by p-field simulation, the boundary analysis based on Poisson kriging estimates minimizes the proportion of type I errors (i.e., edges wrongly declared significant) while the frequency of these errors is predicted well by the p-value of the statistical test.

A mechanistic model of GPS collar location data: Implications for analysis and bias mitigation

Global positioning system (GPS) collars have revolutionized the collection of animal location data; however, it is well-recognized that considerable bias can be present in these data due to habitat or behavior-induced obstruction of satellite signals resulting in inaccurate or missing locations. To date, no explicit theoretical framework of GPS fix acquisition specific to animal telemetry has been presented, and studies make differing assumptions regarding factors influencing GPS fix acquisition and how these data should be analyzed. Inappropriate statistical models have been used, interaction effects have been misunderstood, and the implementation of bias mitigation techniques has been problematic. Herein we outline current conceptual and analytical problems in the GPS animal telemetry literature, and subsequently present a theoretical model-based framework for GPS fix acquisition that clarifies the single and interactive effects of habitat and behavioral obstruction, fix interval, and collar model on GPS collar performance. By recognizing that GPS fix acquisition is a Bernoulli process, it becomes apparent that all forms of obstruction inherently interact with each other, making generalizations across study areas, study species, and collar models problematic. Stationary collar tests to determine the probability of fix acquisition (PFA), location accuracy, and the response to sources of obstruction are thus of limited applicability to animal-deployed collars. Bias mitigation techniques that extrapolate PFA models across samples, especially those using stationary collar tests to correct animal-deployed collars, are theoretically unsound. It is also demonstrated that nonlinearities in the relationships between sources of obstruction and PFA complicate PFA modeling with limited data and that even slight model misspecification can lead to considerable errors in correction factors, especially when using inverse weighting to mitigate bias. By emphasizing the importance of GPS collar sensitivity and ephemeris retention, the theoretical framework predicts that newer, more sensitive GPS collars will be less severely biased by sources of obstruction than reported for the older, less sensitive collars that have been used in the majority of GPS performance studies to date and we expect this trend to continue. This heuristic modeling exercise should be of value to researchers planning and analyzing studies using GPS collars and it also establishes a starting point for future theoretical investigations into GPS collar performance and bias mitigation.     

Tradeoffs between forestry resource and conservation values under alternate policy regimes: A spatial analysis of the western Canadian boreal plains

An important element of resource management and conservation is an understanding of the tradeoffs between marketed products, such as timber, and measures of environmental quality, such as biodiversity. In this paper, we develop an integrated economic-ecological spatial optimization model that we then apply to evaluate alternate forest policies on a 560,000 km² study region of managed boreal forest in Alberta and British Columbia, Canada. The integrated model incorporates dynamic forest sector harvesting, current levels of oil and gas sector development, coarse-filter or habitat-based old forest indicators, a set of empirical forest bird abundance models, and statistical models of the natural and current fire regimes. Using our integrated model, economic tradeoff curves, or production possibility frontiers, are developed to illustrate the cost of achieving coarse-filter targets by a set time (50 years) within a 100-year time horizon. We found levels of ecological indicators and economic returns from the timber industry could both be increased if spatial constraints imposed by the current policy environment were relaxed; other factors being equal, this implies current policy should be revised. We explore the production possibility frontier's relationship to the range of natural variation of old forest habitat, and show how this range can be used to guide choices of preferred locations along the frontier. We also show that coarse-filter constraints on the abundance of certain habitat elements are sufficient to satisfy some fine-filter objectives, expressed as the predicted abundances of various species of songbirds.     

A method for building spatial model of annual weed seed dispersal from experimental data and its application to simulating Bromus sterilis population dispersal

Spatial model of annual weed seed dispersal, in this article, was theoretically derived. According to the requirements of building the spatial model, we designed and done an indoor experiment of weed seed dispersal by wind. In the experiment, the seeds of Bromus sterilis were released at 100 cm height under different wind velocity conditions. Based on the experimental data, the spatial models of seed dispersal of the weed species were built, which were divided into three types according to the coefficient β < 0, β = 0, β > 0. The results showed that dispersal of annual weed seed in any direction obeyed an approximate Gaussian distribution; under the experimental conditions, spatial distribution type of weed seed dispersal changed with variation of wind velocity. Well-known Howard et al.'s model (Howard et al., 1991) of Bromus sterilis seed dispersal is an especial example of the model built in this article. The result of model analysis indicated that the distribution type described by Howard's model was similar to that of seed dispersal of the weed species at the height of 100 cm under the condition of lower wind velocity (about 2.18 m/s). Using CA simulation analysis we found that mean control agent applying to a cell with weed should have a decrease with an increase of wind velocity to prevent weed with the initial configuration from spreading, which implicated less herbicide needs spraying in every cell with weed on average when wind velocity increases.     

A hierarchical Bayesian non-linear spatio-temporal model for the spread of invasive species with application to the Eurasian Collared-Dove

The spread of invasive species is a long studied subject that garners much interest in the ecological research community. Historically the phenomenon has been approached using a purely deterministic mathematical framework (usually involving differential equations of some form). These methods, while scientifically meaningful, are generally highly simplified and fail to account for uncertainty in the data and process, of which our knowledge could not possibly exist without error. We propose a hierarchical Bayesian model for population spread that accommodates data sources with errors, dependence structures between population dynamics parameters, and takes into account prior scientific understanding via non-linear relationships between model parameters and space-time response variables. We model the process (i.e., the bird population in this case) as a Poisson response with spatially varying diffusion coefficients as well as a logistic population growth term using a common reaction-diffusion equation that realistically mimics the ecological process. We focus the application on the ongoing invasion of the Eurasian Collared-Dove.     

Interactions between cod, herring and sprat in the changing environment of the Baltic Sea: A dynamic model analysis

The interactions between cod (Gadus morhua), herring (Clupea harengus) and sprat (Sprattus sprattus) in the Central Baltic Sea were examined with a simple dynamic model, an alternative to more complicated and data-demanding multispecies and ecosystem models. The main aims of the study were to compare the effect of alternative structures on the model output and examine the control relationships in the fish assemblage under different environmental conditions. The effect of environmental conditions was modelled using a stock-recruitment equation for cod incorporating an environmental index. The model output was especially sensitive to the functional response in predation by cod on herring and sprat. The type II functional response led to a collapse of the clupeid stocks when cod was abundant, while the type III response produced more realistic stock dynamics. According to the simulations, an abundant cod stock was able to keep the sprat stock at a low level, while the herring stock was less affected and benefited from the decreased density of sprat. Simulation of different fishing scenarios indicated that reducing fishing mortality to the level currently advised by ICES would allow the recovery of the cod stock even in unfavourable environmental conditions.     

A linked model of animal ecology and human behavior for the management of wildlife tourism

Wildlife tourism attractions are characterized as having intricately coupled human-wildlife interactions. Accordingly, the ability to mitigate negative impacts of tourism on wildlife necessitates research into the ecology of the system and of the human dimensions, since plans aimed at optimizing wildlife fitness must also be acceptable to tourists. We developed an integrated systems dynamics model for the management of tourist-stingray interactions at ‘Stingray City Sandbar’ (SCS), Cayman Islands. The model predicts the state of the tourism attraction over time in relation to stingray population size, stingray life expectancy, and tourist visitation under various management scenarios. Stingray population data in the model comprised growth rates and survival estimates (from mark-and-recapture data) and mortality estimates. Inputted changes in their respective rates under different management scenarios were informed by previous research. Original research on the demand of heterogeneous tourist segments for management regulations via a stated choice model was used to calculate changes in the tourist population growth rate from data supplied by the Caymanian government. The management attributes to which tourists were responsive also have anticipated effects on stingray ecology (migration and mortality), and vice versa, thus linking the two components. We found that the model's predictions over a 25-year time span were sensitive to the stingray population growth rate and alternate management options. Under certain management scenarios, it was possible to maximize both the tourist segment in favor of no management and stingray numbers while reducing stingray health. However, the most effective relative strategy included a reduction in visitor density, restricted stingray interactions, and an imposition of a small fee. Over time, although fewer stingrays were predicted to remain at SCS, they would live longer and experience fewer stochastic disease events, and the desirable tourist segment was predicted to predominate. By understanding how management will affect tourist activities and their subsequent impacts on both wildlife health and visitor satisfaction, one can explore the management alternatives that would optimize both.     

Description and sensitivity analysis for the LESV model: Water quality variables and the balance of organisms in a fjordic region of restricted exchange

In this paper we describe a new ecological model for Regions of Restricted Exchange (RRE), such as fjords, estuaries, rias and lagoons. The model is intended to simulate the impact of external nutrient input on microplankton (phytoplankton plus pelagic microheterotrophs) in RREs. We have implemented the model with the practical purpose of finding a safe limit to the capacities of RRE to assimilate fish-farm waste. Sea-cage farming of fish is increasing in fjords in northern and southern hemispheres, and its external nutrient input can lead to environmental problems such as eutrophication and deoxygenation. The model includes a physical system of three layers with exchanges driven by tidal movement, freshwater input, wind stirring. The biological part includes two microplankton compartments, each parameterizing a microbial loop and each containing chlorophyll. The first compartment represents diatoms and associated heterotrophs, and the second compartment represents flagellates and associated heterotrophs. As well as the balance of these organisms, the model simulates concentrations of nutrient N, P, and Si, dissolved oxygen, and water transparency. Chlorophyll and nutrient change are linked by yields (q  ). Losses of microplankton to grazing by mesozooplankton or benthos are simulated by a temperature-dependent grazing pressure acting on a mean loss (_L0)$(L_0)$. The model also includes the ability to simulate point source inputs of nutrients or organic matter and a generic tracer with first order decay. Sea-cage fish-farms exemplify such point sources. In order to explore model behaviour, we included inputs from a 1500 tonnes salmon farm multiplied by a factor (γ)$(\gamma )$. We carried out sensitivity analysis to identify the most influential model parameters and forcing variables in the case of the shallow Scottish fjord, Loch Creran, in 1975 before the introduction of salmon farming. We tested the model fit to this pristine state (γ=0)$(\gamma =0)$, using Major Axis Regression of simulated variables on observed variables. The model successfully follows the seasonal cycles of chlorophyll (summer over both microplanktons) and the limiting nutrients (P, N). The sensitivity analysis identified three sets of key parameters: (γ)$(\gamma )$ and other fish-farm coefficients, which control farm waste effects on an RRE; (_L0)$(L_0)$ parameters for each microplankton, which link these to the rest of the ecosystem and which have implications for future inclusion of shellfish farming in the model and, chlorophyll yields from nutrients (q), which are crucial for the predication of eutrophication and the ecological understanding of the model.     

A new procedure combining GC-MS with accelerated solvent extraction for the analysis of phthalic acid esters in contaminated soils

An optimized procedure based on gas chromatography-mass spectrometry (GC-MS) combined with accelerated solvent extraction (ASE) is developed for the analysis of six phthalic acid esters (PAEs), which are priority soil pollutants nominated by United States Environmental Protection Agency (USEPA). Quantification of PAEs in soil employs ultrasonic extraction (UE) (USEPA 3550) and ASE (USEPA 3545), followed by clean up procedures involving three different chromatography columns and two combined elution methods. GC-MS conditions under selected ion monitoring (SIM) mode are described and quality assurance and quality control (QA/QC) criteria with high accuracy and sensitivity for target analytes were achieved. Method reliability is assured with the use of an isotopically labeled PAE, di-n-butyl phthalate-d4 (DnBP-D4), as a surrogate, and benzyl benzoate (BB) as an internal standard, and with the analysis of certified reference materials (CRM). QA/QC for the developed procedure was tested in four PAE-spiked soils and one PAE-contaminated soil. The four spiked soils were originated from typical Chinese agricultural fields and the contaminated soil was obtained from an electronic waste dismantling area. Instrument detection limits (IDLs) for the six PAEs ranged 0.10–0.31 μg·L^-1 and method detection limits (MDLs) of the four spiked soils varied from a range of 20–70 μg·kg^-1 to a range of 90– 290 μg·kg^-1. Linearity of response between 20 μg·L^-1 and 2 mg·L^-1 was also established and the correlation coefficients (R) were all>0.998. Spiked soil matrix showed relative recovery rates between 75 and 120% for the six target compounds and about 93% for the surrogate substance. The developed procedure is anticipated to be highly applicable for field surveys of soil PAE pollution in China.     

A simulation model for projecting changes in salinity concentrations and species dominance in the coastal margin habitats of the Everglades

Sharp boundaries typically separate the salinity tolerant mangroves from the salinity intolerant hardwood hammock species, which occupy the similar geographical areas of southern Florida. Evidence of strong feedback between tree community-type and the salinity of the unsaturated (vadose) zone of the soil suggests that a severe disturbance that significantly tilts the salinity in the vadose zone might cause a shift from one vegetation type to the other. In this study, a model based upon the feedback dynamics between vegetation and salinity of the vadose zone of the soil was used to take account of storm surge events to investigate the mechanisms that by which this large-scale disturbance could affect the spatial pattern of hardwood hammocks and mangroves. Model simulation results indicated that a heavy storm surge that completely saturated the vadose zone at 30 ppt for 1 day could lead to a regime shift in which there is domination by mangroves of areas previously dominated by hardwood hammocks. Lighter storm surges that saturated the vadose zone at less than 7 ppt did not cause vegetation shifts. Investigations of model sensitivity analysis indicated that the thickness of the vadose zone, coupled with precipitation, influenced the residence time of high salinity in the vadose zone and therefore determined the rate of mangrove domination. The model was developed for a southern Florida coastal ecosystem, but its applicability may be much broader.