A probabilistic water quality index for river water quality assessment: a case study

Available water quality indices have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs. This paper presents a hybrid probabilistic water quality index by utilizing fuzzy inference systems (FIS), Bayesian networks (BNs), and probabilistic neural networks (PNNs). The outputs of two traditional water quality indices, namely the indices proposed by the National Sanitation Foundation and the Canadian Council of Ministers of the Environment, are selected as inputs of the FIS. The FIS is trained based on the opinions of several water quality experts. Then the trained FIS is used in a Monte Carlo analysis to provide the required input-output data for training both the BN and PNN. The trained BN and PNN can be used for probabilistic water quality assessment using water quality monitoring data. The efficiency and applicability of the proposed methodology is evaluated using water quality data obtained from water quality monitoring system of the Jajrood River in Iran.     

Evaluation of a real-time monitoring system for river quality — a trade-off between risk attitudes,costs, and uncertainty

Uncertainty is definitely one of the key topics in environmental assessment and management. Typically, attempts to reduce uncertainty are subject to expenses. But how to compare and trade-off expenses and the reduced uncertainty? They only seldom allow the use of a single unit. Instead, the whole analysis and decision procedure is very subjective. This paper presents one approach to handle such problems, namely the combined use of Bayesian influence diagrams, and probabilistic risk attitude analysis. The approach was used in the evaluation of three alternatives for a real time river water quality forecasting system. A trade-off analysis of risk attitudes, costs and uncertainty indicated the levels of socioeconomic utility required for investments in the respective systems, and accordingly illuminated the impact of the uncertainties involved on inference and decision-making with various risk attitudes and discount rates.     

Pesticide vapours in confined atmospheres. Determination of dichlorvos by SPME-GC-MS at the microg m(-3) level

A method based on SPME is described for assessing the gaseous dichlorvos concentration in confined atmospheres like a greenhouse after a pesticide application. Sampling was made by using SPME with PDMS fibres immersed into a 250 mL sampling flask into which air samples were dynamically pumped from the analysed atmosphere. Sampling duration was 40 min and samples were then analysed by GC-MS. Calibration was performed from a vapour saturated air sample and gas phase diluted samples, and this procedure afforded a curve with a regression coefficient (R2) higher than 0.98. The repeatability of these measurements was observed with an RSD of 2.5%. This analysis procedure was then applied for the determination of gaseous dichlorvos concentrations versus time, in the atmosphere of an experimental 8 m2 and 20 m3 greenhouse. The pesticide was sprayed according to real cultivation conditions and measurements were made from 2 up to 74 h after application affording observed concentrations in the range of decades and hundreds of microg m(-3) (corresponding limits of detection and quantification were found at the level of a few microg m(-3)).     

Design and field application of an automated cartridge sampler for VOC concentration and flux measurements

One of the major limitations in advancing the understanding of tropospheric ozone and aerosol generation and developing strategies for their control is the technical ability to accurately measure volatile organic compounds (VOCs). This paper describes the design of a constant flow VOC sampler. The versatile sampler can be used for fully automated concentration and flux measurements of VOCs. The sampler incorporates a microprocessor control unit and provides highly accurate mass flow control and significant ease of operation. Sampling sequences can be programmed directly or by remote control through a PC. All important operational parameters necessary for a complete sampling audit trail are logged. Compact weatherproof housings and low power consumption allow operation at remote sites and locations which are sensitive to disturbances or have restricted access. Inner wetted surfaces of the sampler are constructed from non-contaminating materials that do not sorb or emit VOC, and thus permit the collection of representative samples even in environments with very low VOC concentrations. The cartridge magazine provides a maximum of 20 sequential cartridge samples, which allows for long-term air quality assessments. In the dual channel mode, two samples can be collected simultaneously through two independent sample loops, providing ten sequential sample pairs. This design allows the parallel collection of (a) quality assurance backup samples, (b) samples on two different types of cartridges/sorbents to allow a variety of analyses, or (c) differential samples for flux measurements using enclosure, aerodynamic profile, or relaxed eddy accumulation (REA) methods. Field applications including airplane profile measurements above a tropical rainforest area, as well as gradient and REA measurements over a mid-latitude mixed forest stand are described, and demonstrate the validity and flexibility of the system. In particular, the application of the VOC sampler as an integrated part of a REA system is emphasized.     

Selection of appropriate sampling stations in a lake through mapping

Much valuable information is obtained from water quality measurements and monitoring of lakes around the world. A powerful tool is the use of mapping techniques, as it offers potential use in water quality research. Both remote sensing techniques and traditional water quality monitoring are required to collect data at sampling stations. This study suggests another approach to determine the most appropriate distribution of sampling stations in water reservoirs that will be mapped for water quality parameters. Tests were conducted for the proposed approach for Secchi disc depth (SDD), chlorophyll-a, turbidity and suspended solids parameters in Lake Beysehir, Turkey. Results of analysis are available for a total of 30 sampling stations in August 2006. Ten sampling stations were used to model Lake Beysehir while the others were used for validation of the model. Sampling stations that offered the best representation of the lake for each parameter were determined. Then, the best representative sampling stations for all parameters in the study were determined. Moreover, in order to confirm the accuracy of these re-determined sampling stations, modelling was performed on the results of the analysis of June 2006, and it was found that the values obtained from the re-determined sampling stations were acceptable.     

Environmental Bioindication Studies by Bayesian Network with Use of Grey Heron as Model Species

The article presents a procedure for assessing the quality of the environment, using eggshells of birds as a biomarker implemented into a Bayesian network. An environmental quality index (EQI) was proposed and calculated on the basis of local quality indicators. Experimental data on concentrations of toxic elements in grey heron (Ardea cinerea) eggshells (biomarker of river valleys) were used to determine the empirical variables (nodes) and the probability distributions on the set of these variables. A probabilistic graphical model represents a multitude of relationships between variables in a system that enables the prediction of EQI. The model presented is a useful tool for environmental quality management.     

An Inexact Fuzzy-robust Two-stage Programming Model for Managing Sulfur Dioxide Abatement Under Uncertainty

In this study, an inexact fuzzy-robust two-stage programming (IFRTSP) method is developed for tackling multiple forms of uncertainties that can be expressed as discrete intervals, probabilistic distributions and/or fuzzy membership functions. The model can reflect economic penalties of corrective measures against any infeasibilities arising due to a particular realization of system uncertainties. Moreover, the fuzzy decision space can be delimited into a more robust one with the uncertainties being specified through dimensional enlargement of the original fuzzy constraints. A management problem in terms of regional air pollution control has been studied to illustrate the applicability of the proposed approach. Results indicate that useful solutions for planning the air quality management practices have been generated. They can help decision makers identify desired pollution-abatement strategy with minimized system cost and maximized environmental efficiency.     

A proposed coast-wide reference monitoring system for evaluating wetland restoration trajectories in Louisiana

Wetland restoration efforts conducted in Louisiana under the Coastal Wetlands Planning, Protection and Restoration Act require monitoring the effectiveness of individual projects as well as monitoring the cumulative effects of all projects in restoring, creating, enhancing, and protecting the coastal landscape. The effectiveness of the traditional paired-reference monitoring approach in Louisiana has been limited because of difficulty in finding comparable reference sites. A multiple reference approach is proposed that uses aspects of hydrogeomorphic functional assessments and probabilistic sampling. This approach includes a suite of sites that encompass the range of ecological condition for each stratum, with projects placed on a continuum of conditions found for that stratum. Trajectories in reference sites through time are then compared with project trajectories through time. Plant community zonation complicated selection of indicators, strata, and sample size. The approach proposed could serve as a model for evaluating wetland ecosystems.     

Real<Emphasis Type="Bold">-</Emphasis>time water quality monitoring using Internet of Things in SCADA

Water pollution is the root cause for many diseases in the world. It is necessary to measure water quality using sensors for prevention of water pollution. However, the related works remain the problems of communication, mobility, scalability, and accuracy. In this paper, we propose a new Supervisory Control and Data Acquisition (SCADA) system that integrates with the Internet of Things (IoT) technology for real-time water quality monitoring. It aims to determine the contamination of water, leakage in pipeline, and also automatic measure of parameters (such as temperature sensor, flow sensor, color sensor) in real time using Arduino Atmega 368 using Global System for Mobile Communication (GSM) module. The system is applied in the Tirunelveli Corporation (Metro city of Tamilnadu state, India) for automatic capturing of sensor data (pressure, pH, level, and energy sensors). SCADA system is fine-tuned with additional sensors and reduced cost. The results show that the proposed system outperforms the existing ones and produces better results. SCADA captures the real-time accurate sensor values of flow, temperature, and color and turbidity through the GSM communication.     

Comparing the Number of Ozone Exceedances in Different Seasons of the Year in Mexico City

Ozone air pollution is a serious problem in several cities of the world. Hence, to analyse the behaviour of this pollutant is a very important issue. One problem of interest is to study the behaviour of the inter-occurrences times between two ozone exceedances, i.e. between two days in which the pollutant’s measurement surpasses a given threshold. Another interest resides in comparing the behaviour of ozone measurements in different seasons of the year. In this paper we use some Poisson models to analyse this problem. The time interval at which the ozone measurements were taken is split into subintervals corresponding roughly to the seasons of the year. We consider three parametric forms for the mean of the Poisson model, and consequently for the mean of the inter-occurrences times. In each model, the parameters describing its mean are estimated using Bayesian inference via Markov chain Monte Carlo methods. The models are applied to the ozone measurements provided by the Mexico City monitoring network. Theoretical results suggest that an increase has occurred in the mean inter-exceedances times and this is corroborated by the observed data. Differences between the behaviour of the pollutant during different seasons of the year are also detected as well as similarities in the same season in different years. Besides estimating the mean of the Poisson models, inference for the possible presence and location of change-points indicating change of parameters of the model is also performed.     

Determination of carbonyls using liquid chromatography-mass spectrometry with atmospheric pressure chemical ionization

A liquid chromatographic method for the determination of aldehydes and ketones based on mass spectrometric detection is described. Recently developed modular derivatizing agents are employed for analysis. These hydrazine reagents, e.g. 4-dimethylamino-6-(4-methoxy-1-naphthyl)-1,3,5-triazine-2-hydrazine (DMNTH), react with the carbonyl compounds with the formation of the respective hydrazones, which are separated by HPLC-MS with atmospheric pressure chemical ionization in the positive mode. Electrospray ionization may also be used for analysis. Particular focus is directed on various calibration approaches, including external calibration with standard solutions and internal calibration with a hydrazone standard of cyclobutanone, an aldehyde not likely to occur in real samples. A second approach for internal calibration is based on the 13C2-labelled acetaldehyde hydrazone standard. Different calibration approaches may then be used for the analysis of real samples. Limits of detection range from 2 x 10(-8) to 5 x 10(-8) mol L-1 for a series of hydrazones, including hydrazones of saturated aldehydes with alkyl chain lengths from 1 to 7 carbon atoms, and hydrazones of selected unsaturated and aromatic aldehydes as well as ketone hydrazones.     

Determination of 16 polycyclic aromatic hydrocarbons in water using fluorinated polyaniline-based solid-phase microextraction coupled with gas chromatography

The study on the performance of a fluorinated polyaniline (PANI) as a fiber coating for solid-phase microextraction (SPME) had been reported and 16 polycyclic aromatic hydrocarbons (PAHs) were selected to evaluate the performance of this fiber. Various parameters including sample volume, extraction temperature, time of desorption and extraction, pH and ionic strength were investigated intensively. A direct comparison between PANI-SPME fiber and commercial fiber was conducted. The results showed that the PANI-SPME coating had high affinity towards target compounds and the proposed method was successfully applied for the detection of real samples: rainfall and Taihu Lake water collected from Southern China. The whole PANI-SPME-GC method offers acceptable accuracy, precision and sensitivity and low detection limits, which is applicable to monitor trace levels of PAHs in real water bodies.     

Spatial evaluation of the risk of groundwater quality degradation. A comparison between disjunctive kriging and geostatistical simulation

In some previous papers a probabilistic methodology was introduced to estimate a spatial index of risk of groundwater quality degradation, defined as the conditional probability of exceeding assigned thresholds of concentration of a generic chemical sampled in the studied water system. A crucial stage of this methodology was the use of geostatistical techniques to provide an estimation of the above-mentioned probability in a number of selected points by crossing spatial and temporal information. In this work, spatial risk values were obtained using alternatively stochastic conditional simulation and disjunctive kriging. A comparison between the resulting two sets of spatial risks, based on global and local statistical tests, showed that they do not come from the same statistical population and, consequently, they cannot be viewed as equivalent in a statistical sense. At a first glance, geostatistical conditional simulation may appear to represent the spatial variability of the phenomenon more effectively, as the latter tends to be smoothed by DK. However, a close examination of real case study results suggests that disjunctive kriging is more effective than simulation in estimating the spatial risk of groundwater quality degradation. In the study case, the potentially ‘harmful event’ considered, threatening a natural ‘vulnerable groundwater system,’ is fertilizer and manure application.     

Development of a model to assess environmental performance, concerning HSE-MS principles

The main objective of the present study was to develop a valid and appropriate model to evaluate companies’ efficiency and environmental performance, concerning health, safety, and environmental management system principles. The proposed model overcomes the shortcomings of the previous models developed in this area. This model has been designed on the basis of a mathematical method known as Data Envelopment Analysis (DEA). In order to differentiate high-performing companies from weak ones, one of DEA nonradial models named as enhanced Russell graph efficiency measure has been applied. Since some of the environmental performance indicators cannot be controlled by companies’ managers, it was necessary to develop the model in a way that it could be applied when discretionary and/or nondiscretionary factors were involved. The model, then, has been modified on a real case that comprised 12 oil and gas general contractors. The results showed the relative efficiency, inefficiency sources, and the rank of contractors.     

Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study

This article presents a comparison of two adaptive neuro-fuzzy inference systems (ANFIS)-based neuro-fuzzy models applied for modeling dissolved oxygen (DO) concentration. The two models are developed using experimental data collected from the bottom (USGS station no: 420615121533601) and top (USGS station no: 420615121533600) stations at Klamath River at site KRS12a nr Rock Quarry, Oregon, USA. The input variables used for the ANFIS models are water pH, temperature, specific conductance, and sensor depth. Two ANFIS-based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system, named ANFIS_GRID, and (2) subtractive-clustering-based fuzzy inference system, named ANFIS_SUB. In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The ANFIS results are compared with multiple linear regression models. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models for DO concentration modeling.     

Uncertain spatial reasoning of environmental risks in GIS using genetic learning algorithms

Modeling the impact of air pollution is one of the most important approaches for managing damages to the ecosystem. This problem can be solved by sensing and modeling uncertain spatial behaviors, defining topological rules, and using inference and learning capabilities in a spatial reasoning system. Reasoning, which is the main component of such complex systems, requires that proper rules be defined through expert judgments in the knowledge-based part. Use of genetic fuzzy capabilities enables the algorithm to learn and be tuned to proper rules in a flexible manner and increases the preciseness and robustness of operations. The main objective of this paper was to design and evaluate a spatial genetic fuzzy system, with the goal of assessing environmental risks of air pollution due to oil well fires during the Persian Gulf War. Dynamic areas were extracted and monitored through images from NOAA, and the data were stored in an efficient spatial database. Initial spatial knowledge was determined by expert consideration of the application characteristics, and the inference engine was performed with genetic learning (GL) algorithms. Finally, GL (0.7 and 0.03), GL (0.7 and 0.08), GL (0.98 and 0.03), GL (0.98 and 0.08), and Cordon learning methods were evaluated with test and training data related to samples extracted from Landsat thematic mapper satellite images. Results of the implementation showed that GL (0.98, 0.03) was more precise than the other methods for learning and tuning rules in the concerned application.     

Arsenic and heavy metal concentrations in surface soils and vegetables of Feni district in Bangladesh

An investigation of various heavy metals including the arsenic (As) poisoning in soils and vegetables in five upazillas under Feni district of Bangladesh was performed by neutron activation technique using the neutron irradiation facilities of TRIGA MARK II research reactor at Bangladesh Atomic Energy Research Establishment (BAERE), Savar, Dhaka. A total of 30 samples (15 surface soils and 15 foodstuffs) were studied in five Upazillas namely as, Sonagazi, Dagan Bhuiya, Feni Sadar, Fulgazi and Parsuram of Feni district taking three samples of each kind from each upazilla. Samples of each kind together with the standard reference material (SRM) were irradiated in the same neutron flux and the gamma-rays of nuclides from the irradiated samples were assessed and screened for As, Br, U, Th, Cr, Sc, Fe, Zn and Co in soils and As, Br, Na, K, Cr, Sc, Fe, Zn and Co in vegetables (i.e; eddoe, taro, green papaya, plantain, potato, callaloo, bottle ground and carambola). The measurement of gamma-rays was carried out by means of a calibrated high resolution HPGe detector. The concentration of product nuclides containing in the irradiated samples was determined from the peak count-rates of prominent gamma-lines for the corresponding nuclides. Among all contaminants, only As, Zn and Cr for both samples were focused because of their higher values compared with the local as well as the world typical values. The present results revealed that the mean levels of As in Parsuram, Feni Sadar and Pulgazi upazillas are higher than the world typical value of 2 mg/kg. The mean values of Zn and Cr for all upazillas are higher than the world typical values 32 and 27.9 mg/kg, respectively. For the case of vegetables, the mean concentration of As is found only in Eddoe (5.33 ppm) and Taro (1.46 ppm) collected from Sonagazi and Feni Sadar upazilla; which are higher than the values in Samta (0.1 ppm for eddoe and 0.44 ppm for taro) under Jessore district of Bangladesh. The mean concentrations of Zn and Cr in all kinds of vegetables are higher compared with the existing local values as well as the world typical values. The mean estimated daily dietary intake of As, Zn and Cr from vegetables are found to be 0.105, 12.47 and 3.53 mg respectively, which are higher than the recommended values of some countries. The consumption of toxic metals in vegetables is a risk for public health in the studied area.     

Dissipation and residue of MCPA (4-chloro-2-ethylphenoxyacetate) in wheat and soil

A rapid and simple HPLC method has been developed for the quantitation of 4-chloro-2-methylphenoxyacetic acid (MCPA) in both wheat and soil samples. Samples were extracted in acidic media and cleaned up by solid-phase extraction with C(18) cartridges before HPLC-DAD detection. The limits of detection and quantification of MCPA were 0.02 ng and 0.01 mg/kg for both wheat and soil. The mean recoveries ranged from 87.1% to 98.2%, and the RSDs ranged from 0.604% to 3.44% for the three spiked levels (0.01, 0.1, 0.5 mg/kg). The proposed method was successfully applied to the analysis of MCPA residues in wheat and soil samples from an experimental field. The dissipation half-lives in soil were calculated to be 3.22 days (Beijing) and 3.10 days (Tianjin), respectively. Direct confirmation of the analytes in real samples was achieved by gas chromatography-mass spectrometry. The results indicated that at harvest time, the residues of MCPA in wheat were well below the maximum residue levels and were safe to apply in wheat.