Predicting bioremediation of hydrocarbons: Laboratory to field scale

There are strong drivers to increasingly adopt bioremediation as an effective technique for risk reduction of hydrocarbon impacted soils. Researchers often rely solely on chemical data to assess bioremediation efficiently, without making use of the numerous biological techniques for assessing microbial performance. Where used, laboratory experiments must be effectively extrapolated to the field scale. The aim of this research was to test laboratory derived data and move to the field scale. In this research, the remediation of over thirty hydrocarbon sites was studied in the laboratory using a range of analytical techniques. At elevated concentrations, the rate of degradation was best described by respiration and the total hydrocarbon concentration in soil. The number of bacterial degraders and heterotrophs as well as quantification of the bioavailable fraction allowed an estimation of how bioremediation would progress. The response of microbial biosensors proved a useful predictor of bioremediation in the absence of other microbial data. Field-scale trials on average took three times as long to reach the same endpoint as the laboratory trial. It is essential that practitioners justify the nature and frequency of sampling when managing remediation projects and estimations can be made using laboratory derived data. The value of bioremediation will be realised when those that practice the technology can offer transparent lines of evidence to explain their decisions.     

Numerical simulation of an array of fences in Saemangeum reclaimed land

This paper discusses about the quantitative effect of windbreak fences on wind velocity in the reclaimed land at Saemangeum in South Korea. Windbreak fences were constructed in the reclaimed land to reduce the wind velocity to prevent the generation and diffusion of dust. However, up to this time, no in-depth studies were conducted to quantitatively measure the effect of the windbreak fences on wind velocity thus an optimum windbreak structure is not yet determined. Using CFD simulations, the effects of fence porosity, fence height, and the distance between the adjacent fences were investigated. A wind tunnel experiment was initially conducted and data gathered were used to develop the CFD models. From the experiments and CFD simulations, the overall percentage difference of the measured velocities was 7.20% which is generally acceptable to establishing the reliability of the CFD models. The reduction effect on wind velocity was measured in between the adjacent fences up to a height of 0.6 m from the ground surface. In terms of porosity ( = 0, 0.2, 0.4, 0.6), 0.2 was found to be the optimum value. Conversely, the effect of fence height (0.6, 0.8 and 1.0 m) showed no significant difference; therefore, 0.6 m height is recommended. In addition, the reduction effect of distance between the adjacent fences (2, 4 and 6 m) on wind velocity having a 0.2 porosity has decreased to about 75% regardless of the distance. In the case of the reclaimed land in Saemangeum, a decrease of 75% can prevent the generation and diffusion of dusts. However, the source of dusts is very large. Therefore, constructing an array of windbreak with 6 m distance between them is deemed necessary.     

Examining intra-urban variation in fine particle mass constituents using GIS and constrained factor analysis

Recent studies have used land use regression (LUR) techniques to explain spatial variability in exposures to PM_2.5 and traffic-related pollutants. Factor analysis has been used to determine source contributions to measured concentrations. Few studies have combined these methods, however, to construct and explain latent source effects. In this study, we derive latent source factors using confirmatory factor analysis constrained to non-negative loadings, and develop LUR models to predict the influence of outdoor sources on latent source factors using GIS-based measures of traffic and other local sources, central site monitoring data, and meteorology. We collected 3–4 day samples of nitrogen dioxide (NO₂) and PM_2.5 outside of 44 homes in summer and winter, from 2003 to 2005 in and around Boston, Massachusetts. Reflectance analysis, X-ray fluorescence spectroscopy (XRF), and high-resolution inductively-coupled plasma mass spectrometry (ICP-MS) were performed on particle filters to estimate elemental carbon (EC), trace element, and water-soluble metals concentrations. Within our constrained factor analysis, a five-factor model was optimal, balancing statistical robustness and physical interpretability. This model produced loadings indicating long-range transport, brake wear/traffic exhaust, diesel exhaust, fuel oil combustion, and resuspended road dust. LUR models largely corroborated factor interpretations through covariate significance. For example, ‘long-range transport’ was predicted by central site PM_2.5 and season; ‘brake wear/traffic exhaust’ and ‘resuspended road dust’ by traffic and residential density; ‘diesel exhaust’ by percent diesel traffic on nearest major road; and ‘fuel oil combustion’ by population density. Results suggest that outdoor residential PM_2.5 source contributions can be partially predicted using GIS-based terms, and that LUR techniques can support factor interpretation for source apportionment. Together, LUR and factor analysis facilitate source identification, assessment of spatial and temporal variability, and more refined source exposure assignment for evaluation of source contributions to health outcomes in epidemiological studies.     

Assessing spatial variability of SO2 field as detected by an air quality network using Self-Organizing Maps,cluster, and Principal Component Analysis

In Bilbao (Spain), an air quality network measures sulphur dioxide levels at 4 locations. The objective of this paper is to develop a practical methodology to identify redundant sensors and evaluate a network's capability to correctly follow and represent SO₂ fields in Bilbao, in the frame of a continuous network optimization process.The methodology is developed and tested at this particular location, but it is general enough to be useable at other places as well, since it is not tied neither to the particular geographical characteristics of the place nor to the phenomenology of the air quality over the area.To assess the spatial variability of SO₂ measured at 4 locations in the area, three different techniques have been used: Self-Organizing Maps (SOMs), cluster analysis (CA) and Principal Component Analysis (PCA). The results show that the three techniques yield the same results, but the information obtained via PCA can be helpful not only for that purpose but also to throw light on the major mechanisms involved. This might be used in future network optimization stages. The main advantage of cluster analysis and SOMs is that they provide readily interpretable results. All the calculations have been carried out using the freely available software R.     

A computational study of particulate emissions from an open pit quarry under neutral atmospheric conditions

The extraction of minerals from surface mines and quarries can produce significant fugitive dust emissions as a result of site activities such as blasting, road haulage, loading, crushing and stockpiling. If uncontrolled, these emissions can present serious environmental, health, safety and operational issues impacting both site personnel and the wider community.The dispersion of pollutant emissions within the atmosphere is principally determined by the background wind systems characterized by the atmospheric boundary layer (ABL). This paper presents an overview of the construction and solution of a computational fluid dynamics (CFD) model to replicate the development of the internal ventilation regime within a surface quarry excavation due to the presence of a neutral ABL above this excavation. This model was then used to study the dispersion and deposition of fugitive mineral dust particles generated during rock blasting operations. The paths of the mineral particles were modelled using Lagrangian particle tracking. Particles of four size fractions were released from five blast locations for eight different wind directions.The study concluded that dependent on the location of the bench blast within the quarry and the direction of the wind, a mass fraction of between 0.3 and 0.6 of the emitted mineral particles was retained within the quarry. The retention was largest when the distance from the blast location to the downwind pit boundary was greatest.     

Effects of tree size and spatial distribution on growth of ponderosa pine forests under alternative management scenarios

Forest ecosystems may be actively managed toward heterogeneous stand structures to provide both economic (e.g., wood production and carbon credits) and environmental benefits (e.g., invasive pest resistance). In order to facilitate wider adoption of possibly more sustainable forest stand structures, defining growth expectations among alternative management scenarios is crucial. To estimate the effect of tree size and spatial distributions on growth for forest structures commonly considered in uneven-aged forest stand management, large (0.2 ha+) plots were established in 14 uneven-aged ponderosa pine stands in eastern Montana. All study trees were stem-mapped and measured for diameter and 10-year sapwood and basal area increment. A generalized growth model was developed to predict both total and merchantable 10-year basal area increment for nine hypothetical stand structures [three diameter distributions (reverse-J, irregular, flat) × three spatial distributions (clumpy, partial clumpy, uniform)]. Results indicate that the size and spatial distributions of individual trees have a considerable effect on overall stand growth. The greatest total stand growth was in stands with reverse “J” shaped tree size distributions, while the greatest merchantable stand growth was in stands with “flat” diameter distributions and uniform spatial distributions. Through better comprehension of generalized uneven-aged stand growth dynamics, forest managers may better assess the effects of alternative stand structures on stand growth while providing forest stand structures that may be more resilient in a changing climate.     

Accretion of heavy metals in the catfish <Emphasis Type="Italic">Bagrus bayad</Emphasis> from Taylor Creek,southern Nigeria

Heavy metals in the aquatic environment have, to date, come essentially from naturally occurring geochemical resources. However, this has been enhanced by anthropogenic activities such as crude oil exploration and exploitation activities, resulting in pollution in the Taylor Creek aquatic ecosystem. The catfish species Bagrus bayad and other environmental segments were collected from five selected sites along Taylor Creek, southern Nigeria, and total metal concentration determined. The concentration levels of the metals in B. bayad were higher than the values reported in the literature for fresh fish and may lead to a higher risk of harmful effects. The bivariate regression models relating metals in B. bayad and metals in the surface waters were significant (R ² ≥ 0.9002). The log (bio-concentration factor; BCF) values of Cr and Zn in B. bayad were the highest, whereas the lowest was found for Ni. The ecological distribution of the log (BCF) values was, for all the heavy metals, moderately stable over the creek. All log-transformed bio-magnification factors (BMF) in the creek were positive, which indicates that the metal concentration was greater in B. bayad than in suspended particulate matter (SPM). The absolute log (BMF) values of heavy metals can, therefore, be ranked in order of decreasing magnitude: Cr (3.26) > Zn (2.99) > Cd (2.93) > Fe (2.76) > Pb (2.66) > Mn (2.36) > Ni (2.24). This sequence indicates that toxic metals such as Cd, Cr and Pb are undergoing significant bio-reduction from SPM to B. bayad. The degree of correlation between the metals was different in B. bayad, which suggests that the sources of the metals polluting Taylor Creek were diverse.     

Urbanization and its impact on groundwater: a remote sensing and GIS-based assessment approach

The perils of unplanned urbanization and increasing pressure of human activities on hydro-geomorphologic system often result in modification of the existing recharge mechanism, which leads to many environmental consequences. In the present research, an attempt has been made to investigate the applicability of remote sensing and geographical information system (GIS) in dealing with spatial and temporal variability of dynamic phenomena, like urbanization and its impact on groundwater. This paper covers primarily, quantitative and qualitative impacts of urban growth on the behavior of aquifer in Ajmer city (India). Urban growth of the Ajmer city in last 17 years has been estimated from the satellite images. Database related to urbanization and groundwater has been created in GIS. Groundwater recharge has been computed using a water balance approach known as Water Level Fluctuation Methodology. Recharge estimation methodology has been implemented in GIS to introduce the spatial variability of hydro-geological characteristics. Further, temporal and spatial variations in groundwater quality and quantity have been correlated with urban growth using overlay analysis in GIS. The study reveals a general decline in water table and quality with urbanization. Further, remote sensing and GIS technologies have been found useful in assessment of spatial and temporal phenomena of urbanization and its impact on groundwater system.     

Predicting land cover using GIS, Bayesian and evolutionary algorithm methods

Modelling land cover change from existing land cover maps is a vital requirement for anyone wishing to understand how the landscape may change in the future. In order to test any land cover change model, existing data must be used. However, often it is not known which data should be applied to the problem, or whether relationships exist within and between complex datasets. Here we have developed and tested a model that applied evolutionary processes to Bayesian networks. The model was developed and tested on a dataset containing land cover information and environmental data, in order to show that decisions about which datasets should be used could be made automatically. Bayesian networks are amenable to evolutionary methods as they can be easily described using a binary string to which crossover and mutation operations can be applied. The method, developed to allow comparison with standard Bayesian network development software, was proved capable of carrying out a rapid and effective search of the space of possible networks in order to find an optimal or near-optimal solution for the selection of datasets that have causal links with one another. Comparison of land cover mapping in the North-East of Scotland was made with a commercial Bayesian software package, with the evolutionary method being shown to provide greater flexibility in its ability to adapt to incorporate/utilise available evidence/knowledge and develop effective and accurate network structures, at the cost of requiring additional computer programming skills. The dataset used to develop the models included GIS-based data taken from the Land Cover for Scotland 1988 (LCS88), Land Capability for Forestry (LCF), Land Capability for Agriculture (LCA), the soil map of Scotland and additional climatic variables.