Geophysical and hydrological data assimilation to monitor water content dynamics in the rocky unsaturated zone

In recent years, geophysics is increasingly used to study the flow and transport processes in the vadose zone. Particularly, when the vadose zone is made up of rocks, it is difficult to install sensors in the subsurface to measure hydrological state variables directly. In these cases, the electrical resistivity tomography (ERT) represents a useful tool to monitor the hydrodynamics of the infiltration and to estimate hydraulic parameters and state variables, such as hydraulic conductivity and water content. We propose an integrated approach aimed at predicting water content dynamics in calcarenite, a sedimentary carbonatic porous rock. The uncoupled hydrogeophysical approach proposed consists in 4D ERT monitoring conducted during an infiltrometer test under falling head conditions. Capacitance probes were installed to measure water content at different depths to validate the estimations derived from ERT. A numerical procedure, based on a data assimilation technique, was accomplished by combining the model (i.e., Richards’ equation) with the observations in order to provide reliable water content estimations. We have used a new data assimilation method that is easy to implement, based on the ensemble Kalman filter coupled with Brownian bridges. This approach is particularly suitable for strongly non-linear models, such as Richards’ equation, in order to take into account both the model uncertainty and the observation errors. The proposed data assimilation approach was tested for the first time on field data. A reasonable agreement was found between observations and predictions confirming the ability of the integrated approach to predict water content dynamics in the rocky subsoil.     

Statistical Simulation to Estimate Uncertain Behavioral Parameters of Hybrid Energy-Economy Models

In energy-economy modeling, new hybrid models attempt to combine the technological explicitness of bottom-up models with the macroeconomic feedbacks and statistically estimated behavioral parameters of top-down models. However, statistical estimation of behavioral parameters (portraying firm and household technology choices) with such models is challenged by the number of uncertain variables and the lack of historical data on technologies in terms of capital costs, operating costs, and market shares. Multiple combinations of parameter values might equally explain past technology choices. This paper reports on the application of a Bayesian statistical simulation approach for estimating the most likely values for these key behavioral parameters in order to best explain past technology choices and then simulate policies to influence future technology choices. The method included (1) data collection of key technology market shares, capital costs, and operating costs over the past; (2) backcasting a hybrid energy-economy model over a historical time period; and (3) the application of Markov chain Monte Carlo statistical simulation using the Metropolis–Hastings algorithm as a tool for estimating distributions for key parameters in the model. The results provide a means of indicating the uncertainty bounds around key behavioral parameters when generating forecasts of the effect of certain policies. However, the results also indicate that this approach may have limited applicability, given that future available technologies may differ substantially from past technologies and that it is difficult to separate the effects of parameter uncertainty from model structure uncertainty.     

Biological complexity confounds the separation of point- and non-point sources of human impact on the natural world

Identifying process from pattern is one of the most vexing tasks inenvironmental monitoring. Given information on the distribution of speciesin a pre-defined area, together with comprehensive data on how environmentalconditions in that area have altered through time, is it possible toidentify the factors controlling the species‘ layout? Here, the practicalsignificance of this quandary is demonstrated using a series ofenvironmentally-degraded coastal lagoons in New South Wales. The TuggerahLakes (33°17′S,151°30′E) have over the last 50 yearsexperienced significant changes in species‘ distributions. Seagrasses,macroalgae, phytoplankton, molluscs, prawns and the jellyfish Catostylus mosaicus have altered in spatial pattern. Two human activitieshave been blamed for these perturbations: (1) agricultural clearance ofnative vegetation from the catchment, with associated input of top-soil andnutrients; (2) the commissioning of a coal-fired power station in 1967, withmassive uptake and recirculation of lake water for cooling purposes. In thispaper, spatial changes in macrophyte distributions over the last 50 yearsare reviewed in an attempt to identify the true source(s) of perturbation.The model adopted assumes that the power station is a point source of impactwhile nutrient inputs from the catchment are a diffuse source of impact;changes in species distributions can hypothetically be related back to thesesources according to whether they are localised or widespread. However,after a comprehensive analysis of available macrophyte data derived frominterviews, aerial photography and line transect methodologyies theconclusion is reached that changes in biogeographical pattern around theTuggerah Lakes cannot be attributed to specific anthropogenic pressures atanything beyond the coarsest of levels. This is considered to be the normfor most coastal management situations where natural background variation(’noise‘) and the complexity of linkages between physical, chemical andbiological components confounds the identification of causal relationships.The practical implications of this conclusion are discussed in the contextof litigation and remedial management design. Emphasis is placed on theneed to adopt an adaptive approach to estuarine management, incorporatingexplicit recognition of the limitations of available data, and to developnew techniques for identifying cause-effect relationships.     

Syndromes of Global Change: a qualitative modelling approach to assist global environmental management

A novel transdisciplinary approach to investigate Global Change (GC) is presented. The approach rests on the decomposition of the intrigue dynamics of GC into patterns of civilization–nature interactions (syndromes) by an iterative scientific process of observations, data and system theoretical analyses, and modelling attempts. We illustrate the approach by a detailed analysis of the Sahel Syndrome, which describes the rural poverty driven overuse of natural resources. The investigation is performed by (i) identifying relevant symptoms and interlinkages which are characteristics for this pattern, and (ii) a qualitative model representing the internal dynamics of the essential flywheel. The geographical patchwork of the regions affected by the syndrome which is obtained by global data analysis, proves the high global relevance of this pattern. The qualitative model is employed for an evaluation of basic policy strategies debated in the context of rural poverty driven environmental degradation. It turns out that a mixed policy of combating poverty and introducing soil preserving agricultural techniques and practices is most promising to tackle the syndrome dynamics.     

Fuzzy-based computational intelligence to support screening decision in environmental impact assessment: A complementary tool for a case-by-case project appraisal

Screening is a key stage in environmental impact assessment (EIA), but the most common approach based on policy delineation are inherently arbitrary. On the other hand, a case-by-case approach can be complex, slow, and costly. This paper introduces a computational intelligence based on hybrid fuzzy inference system (h-FIS), combining data-driven and expert knowledge, in order to assess its capability of supporting a case-by-case screening in project appraisal. For empirical research, a dataset with appraisal variables of projects highway was made available by a Brazilian environmental protection agency (EPA). Firstly, using this dataset, multivariate analyses were performed to find criteria (x_i) capable of indicating statistically significant differences among projects, previously screened by EPA experts into three types (simplified, preliminary, and comprehensive) of environmental impact study (EIS). Then, h-FIS was built through machine learning, using the FRBCS·W algorithm, with x_i as input predictors and the type of EIS as the output target. The performances of alternative approaches were compared using cross-validation accuracy tests and the kappa index, with a significance level of 0.05. As a result, the h-FIS achieved accuracy of 92.6% and a kappa index of 0.88, which represented almost perfect agreement between the screening decision provided by the h-FIS and the one performed by the EPA experts. In conclusion, the fuzzy-based computational intelligence was capable of dealing with the complexity involved in screening decision. Therefore h-FIS be considered a promising complementary tool for a case-by-case project appraisal in EIA. For further advances, future research should assess other algorithms, such as genetic fuzzy systems, in order to strengthen the proposed system and make it generally applicable in other projects subject to EIA.     

Using multi-criteria decision analysis to assess the vulnerability of drinking water utilities

Outbreaks of microbiological waterborne disease have increased governmental concern regarding the importance of drinking water safety. Considering the multi-barrier approach to safe drinking water may improve management decisions to reduce contamination risks. However, the application of this approach must consider numerous and diverse kinds of information simultaneously. This makes it difficult for authorities to apply the approach to decision making. For this reason, multi-criteria decision analysis can be helpful in applying the multi-barrier approach to vulnerability assessment. The goal of this study is to propose an approach based on a multi-criteria analysis method in order to rank drinking water systems (DWUs) based on their vulnerability to microbiological contamination. This approach is illustrated with an application carried out on 28 DWUs supplied by groundwater in the Province of Québec, Canada. The multi-criteria analysis method chosen is measuring attractiveness by a categorical based evaluation technique methodology allowing the assessment of a microbiological vulnerability indicator (MVI) for each DWU. Results are presented on a scale ranking DWUs from less vulnerable to most vulnerable to contamination. MVI results are tested using a sensitivity analysis on barrier weights and they are also compared with historical data on contamination at the utilities. The investigation demonstrates that MVI provides a good representation of the vulnerability of DWUs to microbiological contamination.     

Topographic factor in the groundwater estimations??a case study in typical semi-arid hard rock environments of Andhra Pradesh

Water table fluctuation (??h) can be used to rapidly assess changes in groundwater storage. But ??h gives acceptable results only if the point of observation is ideally located in the catchment of interest and gives average ??h of the area, a condition which is rarely met. However, if large numbers of observation wells are located within a basin (a catchment) the average ??h can be computed and used. But, a better way is to use points obtained by shallowest water level and deepest water levels to construct a wedge of water table fluctuation across the area of interest; the mean height of this wedge can be assumed to be the mean ??h in the area. And when there is only one observation well, the fact that water table is a subdued replica of the topography, is made use to construct the wedge of water table fluctuation. The results from some randomly selected observations in typical semi-arid, hard rock environment in Andhra Pradesh show that through this approach mean ??h can be effectively used to get change in groundwater storage in area. The mean recharge obtained in this study is in the order of 75 and mean draft is 58?mm/a, while mean recharge and draft obtained by conventional technique are 66 and 54?mm/a, respectively. The most likely specific yield around the middle reaches of a catchment ranges between 0.012 and 0.041 which is within the range given by Groundwater Estimation Committee of India for hard rocks.     

Evaluating the urban climate of a typically tropical city of northeastern Brazil

This study attempted to assess a bioclimate index and the occurrence of an urban heat island in the city of Campina Grande, northeastern Brazil, using data taken from mobile measurements and Automatic Weather Stations (AWS). The climate data were obtained during two representative months, one for the dry season (November 2005) and one for the rainy season (June 2006) at seven points in an urban area. Ten-minute air temperatures recorded by an AWS installed in urban areas were compared to those from a similar station located in a suburban area to assess the urban heat island (UHI). The data were collected using a 23X data logger (Campbell Scientific, Inc.) programmed for collecting data every second. The thermal discomfort level was analyzed by Thom’s discomfort index (DI), and an analysis of variance was applied for assessing if there was any statistically significant difference at the 1% and 5% significance level of thermal comfort among points. Mann–Kendall statistical test was used for identifying possibly significant trends in a time series for air temperature, relative humidity, and class A pan evaporation for the city of Campina Grande. The present study found UHI intensities of 1.48°C and ??0.7°C for the months taken as representative of the dry and rainy seasons, respectively. Summer in the city has partially comfortable conditions while the winter is fully comfortable. There are significant changes in DI hourly values between seasons. Only during the rainy season did all points of the city have a comfortable condition until 8:19 h, at which time they become partially comfortable for the rest of the day. Results indicated that there was a 1.5°C increase in air temperature and a 7.2% reduction in relative humidity throughout the analyzed time series. The DI also showed a statistically significant increasing trend (Mann–Kendall test, p?<?0.01) for the dry and rainy seasons and annual period of approximately 1°C in the last 41 years in the city of Campina Grande.     

Screening of δ-aminolevulinic acid dehydratase from Pseudomonas strains as biosensor for lead and some other metals contamination

δ-Aminolevulinic acid dehydratase (ALAD; E.C. 4.2.1.24) is a metalloprotein and plays a crucial role in heme synthesis. Its sensitivity to toxic metals depends on the metallic co-factors. In this study the effects of some heavy metals on ALADs activity of five Pseudomonas isolates from Akar?ay stream (Afyonkarahisar) have been studied in order to determine whether their ALADs could be used as biosensor for lead and other heavy metals contamination. The data obtained from the study were analysed statistically by using SPSS 10.0 software for Windows. According to the results, Ni(II) increased the ALAD activity of Pseudomonas putida, Pseudomonas pseudoalcaligenes and Pseudomonas aureginosa ATTC 27853. Mn(II) also increased the enzymic activity in all strains examined except P. pseudoalcaligenes. These were found not to be statistically meaningful. P. aeruginosa 2’s enzymic activity was inhibited by Mg(II) and Zn(II), significantly (p’lt;’.05). There was a statistically meaningful relation between enzymic activity of both P. pseudoalcaligenes, P. putida and increasing Pb(II) concentration (p’lt;’.05). In addition, a formula was also deviced in order to determine the doses of metals in the environment of the organisms. As a result of the study, we can suggest that Pseudomonas ALADs can be used as a biosensor for lead and some other heavy metal exposure in aquatic environments.     

Optimizing the flow adjustment of constituent concentrations via LOESS for trend analysis

Trend analysis of stream constituent concentrations requires adjustment for exogenous variables like discharge because concentrations often have variable relations with flow. To remove the influence of flow on stream water quality data, an accurate characterization of the relationship between the constituent and streamflow is needed. One popular method, locally weighted regression (LOESS), provides an effective means for flow-adjusting concentrations. The LOESS fit can be tailored to the data via the smoothing parameter (f), so that the user can avoid overfitting or oversmoothing the data. However, it is a common practice to use a single f value when flow-adjusting water quality data for trend analysis. This study provides a robust, automated method for determining the optimal f value (f_opt) for each dataset via an iterative K-fold cross-validation procedure that minimizes prediction error in LOESS. The method is developed by analyzing datasets of seven different constituents across 17 sites (119 datasets total) from a stream monitoring program in northwest Arkansas (USA). We recommend using 10 iterations of 10-fold cross-validation (10?×?10 CV) in order to select f_opt when flow-adjusting water quality data with LOESS. The use of a default f value did not produce different trend interpretations for the data used here; however, the proposed approach may be helpful in other water quality studies which employ similar statistical fitting methods. Additionally, we provide an implementation of the method in the R statistical computing environment.     

Biosorption kinetics of heavy metals by leaf biomass of Jatropha curcas in single and multi-metal system

Biosorption of Cu²⁺, Zn²⁺, and Cr⁶⁺ from aqueous solutions by leaf biomass of Jatropha curcas was investigated as a function of biomass concentration, initial metal ion concentration, contact time, and pH of the solution systematically. The aim of this study was to optimize biosorption process and find out a suitable kinetic model for the metal removal in single and multi-metal system. The experimental data were analyzed using two sorption kinetic models, viz., pseudo-first- and pseudo-second-order equations, to determine the best fit equation for the biosorption of metal ions Cu²⁺, Zn²⁺, and Cr⁶⁺ onto the leaf biomass of J. curcas in different metal systems. The experimental data fitted well the pseudo-second-order equation and provided the best correlation for the biosorption process. The findings of the present investigation revealed that J. curcas leaf biomass was an eco-friendly and cost-effective biosorbent for the removal of heavy metal ions from wastewater.     

Representing uncertainty in climate change scenarios: a Monte-Carlo approach

Climate change impact assessment is subject to a range of uncertainties due to both incomplete and unknowable knowledge. This paper presents an approach to quantifying some of these uncertainties within a probabilistic framework. A hierarchical impact model is developed that addresses uncertainty about future greenhouse gas emissions, the climate sensitivity, and limitations and unpredictability in general circulation models. The hierarchical model is used in Bayesian Monte-Carlo simulations to define posterior probability distributions for changes in seasonal-mean temperature and precipitation over the United Kingdom that are conditional on prior distributions for the model parameters. The application of this approach to an impact model is demonstrated using a hydrological example. This revised version was published online in July 2006 with corrections to the Cover Date.     

Intervention analysis in environmental engineering

Intervention analysis techniques are described for identifying and statistically modelling trends which may be present in water quality time series. At the exploratory data analysis stage, simple graphical and modelling methods can be employed for visually detecting and examining trends in a time series caused by one or more external interventions. For instance, a plot of a robust locally weighted regression smooth through a graph of the observations over time may reveal trends and other interesting statistical properties contained in the time series. In addition, statistical tests, such as different versions of the nonparametric Mann-Kendall test, can be used to detect the presence of trends caused by unknown or known external interventions. To characterize rigorously and estimate trends which may be known in advance or else detected using exploratory data analysis studies, different parametric methods can be utilized at the confirmatory data analysis stage. Specifically, the time series modelling approach to intervention analysis can be employed to estimate the magnitudes of the changes in the mean level of the series due to the interventions. Particular types of regression models can also be used for estimating trends, especially when there are many missing observations. To demonstrate how intervention analysis methods can be effectively used in environmental impact assessment, representative applications to water quality time series are presented.Invited Paper for Presentation at The Workshop on Statistical Methods for the Assessment of Point Source Pollution, The Canada Centre for Inland Waters, Burlington, Ontario, Canada, L7R 4A6, September 12–14, 1988.     

Conceptualizing strategic environmental assessment: Principles,approaches and research directions

Increasing emphasis has been placed in recent years on transitioning strategic environmental assessment (SEA) away from its environmental impact assessment (EIA) roots. Scholars have argued the need to conceptualize SEA as a process designed to facilitate strategic thinking, thus enabling transitions toward sustainability. The practice of SEA, however, remains deeply rooted in the EIA tradition and scholars and practitioners often appear divided on the nature and purpose of SEA. This paper revisits the strategic principles of SEA and conceptualizes SEA as a multi-faceted and multi-dimensional assessment process. It is suggested that SEA can be conceptualized as series of approaches operating along a spectrum from less to more strategic – from impact assessment-based to strategy-based – with each approach to SEA differentiated by the specific objectives of SEA application and the extent to which strategic principles are reflected in its design and implementation. Advancing the effectiveness of SEA requires a continued research agenda focused on improving the traditional SEA approach, as a tool to assess the impacts of policies, plans and programs (PPPs). Realizing the full potential of SEA, however, requires a new research agenda — one focused on the development and testing of a deliberative governance approach to SEA that can facilitate strategic innovations in PPP formulation and drive transitions in short-term policy and initiatives based on longer-term thinking.     

Rate of occurrence of failures based on a nonhomogeneous Poisson process: an ozone analyzer case study

Atmospheric pollutant monitoring constitutes a primordial activity in public policies concerning air quality. In São Paulo State, Brazil, the São Paulo State Environment Company (CETESB) maintains an automatic network which continuously monitors CO, SO₂, NO _x , O₃, and particulate matter concentrations in the air. The monitoring process accuracy is a fundamental condition for the actions to be taken by CETESB. As one of the support systems, a preventive maintenance program for the different analyzers used is part of the data quality strategy. Knowledge of the behavior of analyzer failure times could help optimize the program. To achieve this goal, the failure times of an ozone analyzer—considered a repairable system—were modeled by means of the nonhomogeneous Poisson process. The rate of occurrence of failures (ROCOF) was estimated for the intervals 0–70,800 h and 0–88,320 h, in which six and seven failures were observed, respectively. The results showed that the ROCOF estimate is influenced by the choice of the observation period, t ₀?=?70,800 h and t ₇?=?88,320 h in the cases analyzed. Identification of preventive maintenance actions, mainly when parts replacement occurs in the last interval of observation, is highlighted, justifying the alteration in the behavior of the inter-arrival times. The performance of a follow-up on each analyzer is recommended in order to record the impact of the performed preventive maintenance program on the enhancement of its useful life.     

A Numerical Study of Airflow and Pollutant Dispersion Inside an Urban Street Canyon Containing an Elevated Expressway

The goal of this study is to investigate numerically the wind flow and pollutant dispersion within an urban street canyon containing an elevated expressway and reveal the impacts of elevated expressway on the atmospheric environment in the canyon. A two-dimensional numerical model for simulating airflow and pollutant dispersion inside urban street canyons is first developed based on the Reynolds-averaged Navier–Stokes equations coupled with the standard k???ε turbulence model and the convection–diffusion equation for passive species transport, and then it is validated against a wind tunnel experiment. It was found that the model-predicted results agree well with the experimental data. Having established this, the wind fields and pollutant distributions in the canyon containing an elevated expressway are evaluated. The numerical results show that the expressway height above the street floor and the gap distance between the expressway and the building wall have considerable influence on airflow and pollutant level inside a canyon: (1) the vortical flow structure in the canyon varies with the expressway height for a constant gap distance, under certain expressway heights, only one main clockwise vortex is formed, while under others one main vortex as well as one or two secondary vortices above and below the expressway are created; (2) the pollutant level within the canyon increases when an expressway is placed in the canyon, especially when the expressway height equals the building height the flow velocities in the canyon are drastically reduced and air exchange in and above the canyon is seriously impeded by the expressway, which leads to a much higher pollution level in the canyon; and (3) the wider gap distance is favorable to pollutant removal from the canyon.     

Terrestrial vertebrate fauna surveys for the preparation of environmental impact assessments; how can we do it better? A Western Australian example

The Western Australian Environmental Protection Authority (EPA) in 2002 released Position Statement, No. 3, Terrestrial Biological Surveys as an Element of Biodiversity Protection outlining how terrestrial fauna survey data are to be used and interpreted in the preparation of environmental impact assessments (EIA). In 2004, the EPA released its Guidance for the Assessment of Environmental Factors, Terrestrial Fauna Surveys for Environmental Impact Assessment in Western Australia, No. 56. This paper briefly assesses the adequacy of recent terrestrial fauna surveys undertaken to support publicly released EIAs and indicates that the EPA is not always adhering to its own position and guidance statements. This paper argues that the current fauna survey guidelines are in need of improvement. The approach and requirements of some other Australian states are briefly assessed to identify similarities and where improvements can be made to the Western Australian (WA) guidelines. This paper concludes with suggestions on how the process and the guidelines in WA can be revised to more adequately assess the impact of developments on terrestrial vertebrate biodiversity and ecosystem function. These suggestions may have relevance for other areas where fauna surveys are undertaken to support EIAs.