Publications by authors named "Ata Allah Nadiri"

11 Publications

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An investigation to human health risks from multiple contaminants and multiple origins by introducing 'Total Information Management'.

Environ Sci Pollut Res Int 2021 Apr 21;28(15):18702-18724. Epub 2021 Jan 21.

Department of Geology, V.O. Chidambaram College, Tuticorin-8, Tamil Nadu, India.

A capability for aggregating risks to aquifers is explored in this paper for cases with sparse data exposed to anthropogenic and geogenic contaminants driven by poor/non-existent planning/regulation practices. The capability seeks 'Total Information Management' (TIM) under sparse data by studying hydrogeochemical processes, which is in contrast to Human Health Risk Assessment (HHRA) by the USEPA for using sample data and a procedure with prescribed parameters without deriving their values from site data. The methodology for TIM pools together the following five dimensions: (i) a perceptual model to collect existing knowledge-base; (ii) a conceptual model to analyse a sample of ion-concentrations to determine groundwater type, origin, and dominant processes (e.g. statistical, graphical, multivariate analysis and geological survey); (iii) risk cells to contextualise contaminants, where the paper considers nitrate, arsenic, iron and lead occurring more than three times their permissible values; (iv) 'soft modelling' to firm up information by learning from convergences and/or divergences within the conceptual model; and (v) study the processes within each risk cell through the OSPRC framework (Origins, Sources, Pathways, Receptors and Consequence). The study area comprises a series of patchy aquifers but HHRA ignores such contextual data and provides some evidence on both carcinogenic and non-carcinogenic risks to human health. The TIM capability provides a greater insight for the processes to unacceptable risks from minor ions of anthropogenic nitrate pollutions and from trace ions of arsenic, iron and lead contaminants.
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http://dx.doi.org/10.1007/s11356-020-11853-2DOI Listing
April 2021

Vulnerability Indexing to Saltwater Intrusion from Models at Two Levels using Artificial Intelligence Multiple Model (AIMM).

J Environ Manage 2020 Feb 3;255:109871. Epub 2019 Dec 3.

Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh, East Azerbaijan, Iran. Electronic address:

Unplanned groundwater exploitation in coastal aquifers results in water decline and consequently triggers saltwater intrusion (SWI). This study formulates a novel modeling strategy based on GALDIT method using Artificial Intelligence (AI) models for mapping the vulnerability to SWI. This AI-based modeling strategy is a two-level learning process, where vulnerability to SWI at Level 1 can be predicted by such models as Artificial Neural Network (ANN), Sugeno Fuzzy Logic (SFL), and Neuro-Fuzzy (NF); and their outputs serve as the input to the model at Level 2, such as Support Vector Machine (SVM). This model is applied to Urmia aquifer, west coast of Lake Urmia, where both are currently declining. The construction of the above four models both at Levels 1 and 2 provide tools for mapping the SWI vulnerability of the study area. Model performances in the paper are studied using RMSE and R metrics, where the models at Level 1 are found to be fit-for-purpose and the SVM at Level 2 is improved particularly with respect to the reduced scale of scatters in the results. Evaluating the result and groundwater samples by Piper diagram confirms the correspondence of SWI status with vulnerability index.
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http://dx.doi.org/10.1016/j.jenvman.2019.109871DOI Listing
February 2020

Sequential treatment of paper and pulp industrial wastewater: Prediction of water quality parameters by Mamdani Fuzzy Logic model and phytotoxicity assessment.

Chemosphere 2019 Jul 5;227:256-268. Epub 2019 Apr 5.

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.

Recycling of industrial wastewater meeting quality standards for agricultural and industrial demands is a viable option. In this study, paper and pulp industrial wastewater were treated with three biological treatments viz. aerobic, anaerobic and sequential (i.e. 20 days of anaerobic followed by 20 days of aerobic cycle), associated with simulation modeling by Mamdani Fuzzy Logic (MFL) model of some selected parameters. Electric air diffuser and minimal salt medium in sealed plastic bottles at control temperature were used for aerobic and anaerobic treatments, respectively. The significant reduction in chemical (COD: 81%) and biological oxygen demand (BOD: 71%), total suspended (TSS: 65%), dissolved solids (TDS: 60%) and turbidity (68%) was recorded during sequential treatment. The treated water was irrigated to determine its phytotoxic effects on seed germination, vigor and seedling growth of mustard (Brassica campestris). Sequential treatment greatly reduced phytotoxicity of wastewater and showed the highest germination percentage (90%) compared to aerobic (60%), anaerobic (70%) treatments and untreated wastewater (30%). Regression analysis also endorsed these findings (R = 0.76-0.95 between seed germination, seedling growth and vigor). MFL technique was adopted to simulate sequential treatment process. The results support higher performance of MFL model to predict TDS, TSS, COD, and BOD based on the physico-chemical water quality parameters of raw wastewater, time of treatment and treatment type variation. Based on these findings, we conclude that the sequential treatment could be a more effective strategy for treatment of pulp and paper industrial wastewater with efficiency to be used for agricultural industry without toxic effects.
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http://dx.doi.org/10.1016/j.chemosphere.2019.04.022DOI Listing
July 2019

Mapping specific vulnerability of multiple confined and unconfined aquifers by using artificial intelligence to learn from multiple DRASTIC frameworks.

J Environ Manage 2018 Dec 13;227:415-428. Epub 2018 Aug 13.

Department of Civil Engineering, Faculty of Engineering, University of Maragheh, P.O. Box 55136-553, Maragheh, East Azerbaijan, Iran. Electronic address:

An investigation is presented to improve on the performances of the Basic DRASTIC Framework (BDF) and its variation by the Fuzzy-Catastrophe Framework (FCF), both of which provide an estimate of intrinsic aquifer vulnerabilities to anthropogenic contamination. BDF prescribes rates and weights for 7 data layers but FCF is an unsupervised learning framework based on a multicriteria decision theory by integrating fuzzy membership function and catastrophe theory. The challenges in the paper include: (i) the study area comprises confined and unconfined aquifers and (ii) Artificial Intelligence (AI) is used to run Multiple Framework (AIMF) in order to map specific vulnerability due to a specific contaminant. Predicted results by AIMF are referred to as Specific Vulnerability Indices, as the learned VIs are referenced to site-specific nitrate-N. The results show that correlation coefficient between BDF or FCF with nitrate-N is lower than 0.7 but the AIMF strategy improves it to greater than 0.95. The results are evidence for the proof-of-concept for transforming frameworks to models capable of further learning.
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http://dx.doi.org/10.1016/j.jenvman.2018.08.019DOI Listing
December 2018

Introducing a new framework for mapping subsidence vulnerability indices (SVIs): ALPRIFT.

Sci Total Environ 2018 Jul 20;628-629:1043-1057. Epub 2018 Feb 20.

Department of GIS, Faculty of Geography, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.

Proof-of-concept (PoC) is presented for a new framework to serve as a proactive capability to mapping subsidence vulnerability of Shabestar plain of approximately 500km overlaying an important aquifer supporting a region renowned for diversity of agricultural products. This aquifer is one of 12 in East and West Azerbaijan provinces, Northwest Iran, which surround the distressed Lake Urmia, with its water table declined approximately 4m in between 2004 and 2014. The decline of water table in aquifers undermines their soil texture and structure by exposure to pressures under their weight and thereby induce or trigger land subsidence. Inspired by the DRASTIC framework to map intrinsic aquifer vulnerability to anthropogenic pollution, the paper introduces the ALPRIFT framework for subsidence, which comprises the seven data layers of Aquifer media (A), Land use (L), Pumping of groundwater, Recharge (R), aquifer thickness Impact (I), Fault distance (F) and decline of water Table (T). The paper prescribes rates to account for local variations and weights for the relative importance of the data layers. The proof-of-concept for ALPRIFT is supported by the correlation of Subsidence Vulnerability Indices (SVIs) with measured subsidence values, which renders a value of 0.5 but improves significantly to 0.86 when using fuzzy logic. Similar improvements are suggested by the ROC/AUC performance metric.
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http://dx.doi.org/10.1016/j.scitotenv.2018.02.031DOI Listing
July 2018

Introducing the risk aggregation problem to aquifers exposed to impacts of anthropogenic and geogenic origins on a modular basis using 'risk cells'.

J Environ Manage 2018 Jul 10;217:654-667. Epub 2018 Apr 10.

Department of Geology, Faculty of Sciences, University of Urmia, Urmia, West Azerbaijan, Iran. Electronic address:

Proof-of-concept is presented in this paper to a methodology formulated for indexing risks to groundwater aquifers exposed to impacts of diffuse contaminations from anthropogenic and geogenic origins. The methodology is for mapping/indexing, which refers to relative values but not their absolute values. The innovations include: (i) making use of the Origins-Source-Pathways-Receptors-Consequences (OSPRC) framework; and (ii) dividing a study area into modular Risk (OSPRC) Cells to capture their idiosyncrasies with different origins. Field measurements are often sparse and comprise pollutants and water table, which are often costly; whereas supplementary data are general-purpose data, which are widely available. Risk mapping for each OSPRC cell is processed by dividing a study area into pixels and for each pixel, the risk from both anthropogenic and geogenic origins are indexed by using algorithms related to: (i) Vulnerability Indices (VI), which identify the potential for risk exposures at each pixel; and (ii) velocity gradient, which expresses the potency to risk exposures across the risk cell. The paper uses DRASTIC for anthropogenic VI but introduces a new framework for geogenic VI. The methodology has a generic architecture and is flexible to modularise risks involving any idiosyncrasies in a generic way in any site exposed to environmental pollution risks. Its application to a real study area provides evidence for the proof-of-concept for the methodology by a set of results that are fit-for-purpose and provides an insight into the study area together with the identification of its hotspots.
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http://dx.doi.org/10.1016/j.jenvman.2018.04.011DOI Listing
July 2018

The problem of identifying arsenic anomalies in the basin of Sahand dam through risk-based 'soft modelling'.

Sci Total Environ 2018 Feb 26;613-614:693-706. Epub 2017 Sep 26.

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran. Electronic address:

An investigation is undertaken to identify arsenic anomalies at the complex of Sahand dam, East Azerbaijan, northwest Iran. The complex acts as a system, in which the impounding reservoir catalyses system components related to Origin-Source-Pathways-Receptor-Consequence (OSPRC) viewed as a risk system. This 'conceptual framework' overlays a 'perceptual model' of the physical system, in which arsenic with geogenic origins diffused into the formations through extensive fractures swept through the region during the Miocene era. Impacts of arsenic anomalies were local until the provision of the impounding reservoir in the last 10years, which transformed it into active system-wide risk exposures. The paper uses existing technique of: statistical, graphical, multivariate analysis, geological survey and isotopic study, but these often seem ad hoc and without common knowledgebase. Risk analysis approaches are sought to treat existing fragmentation in practices of identifying and mitigating arsenic anomalies. The paper contributes towards next generation best practice through: (i) transferring and extending knowledge on the OSPRC framework; (ii) introducing 'OSPRC cells' to capture unique idiosyncrasies at each cell; and (iii) suggesting a 'soft modelling' procedure based on assembling knowledgebase of existing techniques with partially converging and partially diverging information levels, where knowledgebase invokes model equations with increasing resolutions. The data samples from the study area for the period of 2002-12 supports the study and indicates the following 'risk cells' for the study area: (i) local arsenic risk exposures at south of the reservoir, (ii) system-wide arsenic risks at its north; and (iii) system-wide arsenic risk exposures within the reservoir even after dilution.
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http://dx.doi.org/10.1016/j.scitotenv.2017.08.027DOI Listing
February 2018

Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures.

Sci Total Environ 2017 Sep 22;593-594:75-90. Epub 2017 Mar 22.

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran. Electronic address:

Driven by contamination risks, mapping Vulnerability Indices (VI) of multiple aquifers (both unconfined and confined) is investigated by integrating the basic DRASTIC framework with multiple models overarched by Artificial Neural Networks (ANN). The DRASTIC framework is a proactive tool to assess VI values using the data from the hydrosphere, lithosphere and anthroposphere. However, a research case arises for the application of multiple models on the ground of poor determination coefficients between the VI values and non-point anthropogenic contaminants. The paper formulates SCFL models, which are derived from the multiple model philosophy of Supervised Committee (SC) machines and Fuzzy Logic (FL) and hence SCFL as their integration. The Fuzzy Logic-based (FL) models include: Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Larsen Fuzzy Logic (LFL) models. The basic DRASTIC framework uses prescribed rating and weighting values based on expert judgment but the four FL-based models (SFL, MFL, LFL and SCFL) derive their values as per internal strategy within these models. The paper reports that FL and multiple models improve considerably on the correlation between the modeled vulnerability indices and observed nitrate-N values and as such it provides evidence that the SCFL multiple models can be an alternative to the basic framework even for multiple aquifers. The study area with multiple aquifers is in Varzeqan plain, East Azerbaijan, northwest Iran.
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http://dx.doi.org/10.1016/j.scitotenv.2017.03.109DOI Listing
September 2017

Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models.

Environ Sci Pollut Res Int 2017 Mar 13;24(9):8562-8577. Epub 2017 Feb 13.

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.

Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling results and baselines, if any. Therefore, this research presents a supervised committee fuzzy logic (SCFL) method, which uses artificial neural networks to overarch and combine a selection of FL models. The indices are expressed by the widely used DRASTIC framework, which include geological, hydrological, and hydrogeological parameters often subject to uncertainty. DRASTIC indices represent collectively intrinsic (or natural) vulnerability and give a sense of contaminants, such as nitrate-N, percolating to aquifers from the surface. The study area is an aquifer in Ardabil plain, the province of Ardabil, northwest Iran. Improvements on vulnerability indices are achieved by FL techniques, which comprise Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL). As the correlation between estimated DRASTIC vulnerability index values and nitrate-N values is as low as 0.4, it is improved significantly by FL models (SFL, MFL, and LFL), which perform in similar ways but have differences. Their synergy is exploited by SCFL and uses the FL modeling results "conditioned" by nitrate-N values to raise their correlation to higher than 0.9.
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http://dx.doi.org/10.1007/s11356-017-8489-4DOI Listing
March 2017

Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM).

Sci Total Environ 2017 Jan 14;574:691-706. Epub 2016 Oct 14.

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azarbaijan, Iran. Electronic address:

This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters.
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http://dx.doi.org/10.1016/j.scitotenv.2016.09.093DOI Listing
January 2017

Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran.

J Environ Health Sci Eng 2016 9;14:13. Epub 2016 Aug 9.

Research Engineer at EnTech Engineering, PC11 broadway 21st floor, New York, NY 10004 USA.

Background: Extensive human activities and unplanned land uses have put groundwater resources of Shiraz plain at a high risk of nitrate pollution, causing several environmental and human health issues. To address these issues, water resources managers utilize groundwater vulnerability assessment and determination of protection. This study aimed to prepare the vulnerability maps of Shiraz aquifer by using Composite DRASTIC index, Nitrate Vulnerability index, and artificial neural network and also to compare their efficiency.

Methods: The parameters of the indexes that were employed in this study are: depth to water table, net recharge, aquifer media, soil media, topography, impact of the vadose zone, hydraulic conductivity, and land use. These parameters were rated, weighted, and integrated using GIS, and then, used to develop the risk maps of Shiraz aquifer.

Results: The results indicated that the southeastern part of the aquifer was at the highest potential risk. Given the distribution of groundwater nitrate concentrations from the wells in the underlying aquifer, the artificial neural network model offered greater accuracy compared to the other two indexes. The study concluded that the artificial neural network model is an effective model to improve the DRASTIC index and provides a confident estimate of the pollution risk.

Conclusions: As intensive agricultural activities are the dominant land use and water table is shallow in the vulnerable zones, optimized irrigation techniques and a lower rate of fertilizers are suggested. The findings of our study could be used as a scientific basis in future for sustainable groundwater management in Shiraz plain.
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http://dx.doi.org/10.1186/s40201-016-0254-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4977699PMC
August 2016