An overview of the application of electrocoagulation for mine wastewater treatment.

One of the challenges of the twenty-first century is related to the discharge and disposal of mine effluents and wastewater resulting from mine dewatering, precipitation, and surface runoff in mines, especially acidic effluents that contain a variety of toxic and heavy metals and are the main sources of surface and groundwater pollution. Various physical, chemical, and biological methods have been developed and used to treat mine effluents. All proposed methods have their own disadvantages that make their use challenging. One of the new methods used for wastewater treatment is the electrical coagulation process, which has attracted the attention of researchers in recent years due to its advantages such as simplicity, environmental friendliness, and low cost. The present review focused on the applications of electrocoagulation for mine wastewater treatment as well as metals recovery. In addition, the main mechanisms, advantages, and weaknesses of electrocoagulation were reviewed.

Arsenic removal from solution using nano-magnetic compound: optimization modeling by response surface method.

This study was aimed to investigate the effectiveness of compounds containing iron and manganese to reduce the mobility of arsenic and its effective adsorption and optimize the arsenic adsorption process by CCD. In this study, MnFe2O4 nanoparticles (MFO-n) were synthesized using the co-precipitation method to remove arsenic and reduce its toxicity in solution. Several tests including Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), vibrating sample magnetometer (VSM), X-ray fluorescence (XRF), and Brunauer-Emmett-Teller (BET) tests were used to characterize the synthesized MFO-n. To model and optimize the As adsorption process using the response surface methodology, four independent variables affecting the efficiency of arsenic adsorption were investigated. These variables including pH (3 to 11), concentration of arsenic in solution (1000 to 4000 µg/L), concentration of nanoparticles (1 to 5 g/L), and time (15 to 195 min) were investigated. The central composite design (CCD) approach was used to design the experiments and optimize the model parameters. The variance analysis indicated that the prediction of As adsorption from solution by the synthesized nanoadsorbent using the CCD model was well performed (p < 0.0001) with high accuracy (R2 = 0.97). The results further indicated that the optimum quantity of pH, concentration of nanoparticles, time, and initial concentration of As are 5, 2 g/L, 60 min, and 3250 µg/L, respectively. The highest As elimination from the solution was estimated to be 94.77%. Our results further indicated that MFO-n had high efficiency in eliminating both toxic arsenic species from the solution.

Modeling the impact of the COVID-19 lockdowns on urban surface ecological status: A case study of Milan and Wuhan cities.

The COVID-19 pandemic has caused unprecedent negative impacts on our society, however, evidences show a reduction of anthropogenic pressures on the environment. Due to the high importance of environmental conditions on human life quality, it is crucial to model the impact of COVID-19 lockdown on environmental conditions. Consequently, the objective of this study was to model the impact of COVID-19 lockdown on the urban surface ecological status (USES). To this end, the Landsat-8 images of Milan for three pre-lockdown dates (Feb 13, 2018 (MD1), April 18, 2018 (MD2) and Feb 3, 2020 (MD3)) and one date over the lockdown (April 14, 2020 (MD4)), and Wuhan for three pre-lockdown dates (Dec 17, 2017 (WD1), March 23, 2018 (WD2) and Dec 7, 2019 (WD3)) and one lockdown date (Feb 9, 2020 (WD4)) were used. First, pressure-state-response (PSR) framework parameters including index-based built-up index (IBI), vegetation cover (VC), vegetation health index (VHI), land surface temperature (LST) and Wetness were calculated. Second, by combining the PSR framework parameters based on comprehensive ecological evaluation index (CEEI), the USES were modeled on different dates. Thirdly, the USES during the COVID-19 lockdown was compared with the USES for pre-lockdown. The mean (standard deviation) of CEEI for Milan on MD1, MD2, MD3 and MD4 were 0.52 (0.12), 0.60 (0.19), 0.57 (0.13) and 0.45 (0.16), respectively. Also, these values for Wuhan on WD1, WD2, WD3 and WD4 were 0.63 (0.14), 0.67 (0.15), 0.60 (0.13) and 0.57 (0.13), respectively. Due to the lockdowns, the mean CEEI of built-up, bare soil and green spaces for Milan and Wuhan decreased by [0.18, 0.02, 0.08], [0.13, 0.06, 0.05], respectively. During the lockdown period, the USES improved substantially due to the reduction of anthropogenic activities in the urban environment.

Large-scale digital mapping of topsoil total nitrogen using machine learning models and associated uncertainty map.

Understanding the spatial distribution of soil nutrients and factors affecting their concentration and availability is crucial for soil fertility management and sustainable land utilization while quantifying factors affecting soil nitrogen distribution in Qorveh-Dehgolan plain is mostly lacking. This study, thus, aimed at digital modeling and mapping the spatial distribution of topsoil total nitrogen (TN) in Qorveh-Dehgolan plain with an area of 150,000 ha using random forest (RF), decision tree (DT), and cubist (CB) algorithms. A total of 130 observation points were collected from a depth of 0 to 30 cm from topsoil surfaces based on a random sampling pattern. Then, soil physicochemical properties, calcium carbonate equivalent, organic carbon, and topsoil total nitrogen were measured. A number of 51 environmental variables including 31 geomorphometric attributes derived from a digital elevation model with 12.5-m spatial resolution, 13 spectral indices and reflectance from SENTINEL-2 satellite (MSIsensor), and five soil properties and two spatial variables of latitude and longitude were used as covariates for digital mapping of topsoil total nitrogen. The most appropriate covariates were then selected by the Boruta algorithm in the R software environment. A standard deviation map was produced to show model uncertainty. The covariate selection resulted in the separation of 14 effective covariates in the spatial prediction of topsoil total nitrogen by using the data mining algorithms. The validation of digital mapping of topsoil total nitrogen by RF, DT, and CB models using 20% of independent data showed root mean square error (RMSE) of 0.032, 0.035, and 0.043%; mean absolute error (MAE) of 0.0008, 0.001, and 0.002%; and based on the coefficients of determination of 0.42, 0.38, 0.35, respectively. Relative importance (RI) of environmental covariates using the %IncMSE index indicated the importance of two geomorphometric variables of midslope position and normalized height along with SAVI and NDVI remote sensing variables in the spatial modeling and distribution of total nitrogen in the studied lands. The RF prediction and associated uncertainty maps, with show high accuracy and low standard deviation in the most part of study area, reveled low overfitting and overtraining in soil-landscape modeling; so, this model can lead to the development of a digital map of soil surface properties with acceptable accuracy for sustainable land utilization.

Heterologous expression of an acid phosphatase gene and phosphate limitation leads to substantial production of chicoric acid in Echinacea purpurea transgenic hairy roots.

MAIN CONCLUSION: A high level of the secondary metabolite chicoric acid is produced by intracellular Pi supply and extracellular phosphate limiting in Echinacea purpurea hairy roots. Chicoric acid (CA) is a secondary metabolite which is gained from Echinacea purpurea. It has been found to be one of the most potent HIV integrase inhibitors with antioxidant and anti-inflammatory activities. However, the low-biosynthesis level of this valuable compound becomes an inevitable obstacle limiting further commercialization. Environmental stresses, such as phosphorus (Pi) deficiency, stimulate the synthesis of chemical metabolites, but significantly reduce plant growth and biomass production. To overcome the paradox of dual opposite effect of Pi limitation, we examined the hypothesis that the intracellular Pi supply and phosphate-limiting conditions enhance the total CA production in E. purpurea hairy roots. For this purpose, the coding sequence (CDS) of a purple acid phosphatase gene from Arabidopsis thaliana, AtPAP26, under CaMV-35S promoter was overexpressed in E. purpurea using Agrobacterium rhizogenes strain R15834. The transgenic hairy roots were cultured in a Pi-sufficient condition to increase the cellular phosphate metabolism. A short-term Pi starvation treatment of extracellular phosphate was applied to stimulate genes involved in CA biosynthesis pathway. The overexpression of AtPAP26 gene significantly increased the total APase activity in transgenic hairy roots compared to the non-transgenic roots under Pi-sufficient condition. Also, the transgenic hairy roots showed increase in the level of total and free phosphate, and in root fresh and dry weights compared to the controls. In addition, the phosphate limitation led to significant increase in the expression level of the CA biosynthesis genes. Considering the increase of biomass production in transgenic vs. non-transgenic hairy roots, a 16-fold increase was obtained in the final yield of CA for transgenic E. purpurea roots grown under -P condition compared to +P non-transgenic roots. Our results suggested that the expression of phosphatase genes and phosphate limitation were significantly effective in enhancing the final production yield and large-scale production of desired secondary metabolites in medicinal plant hairy roots.

Estimating soil heavy metals concentration at large scale using visible and near-infrared reflectance spectroscopy.

This study was aimed (i) to examine using diffuse reflectance spectra within VNIR region to estimate soil heavy metals concentrations at large scale, (ii) to compare the influence of different pre-processing models on predictive model accuracy, and (iii) to explore the best predictive models. A number of 325 topsoil samples were collected and their spectral data, pH, clay content, organic matter, Ni, and Cu concentrations were determined. To improve spectral data, various pre-processing methods including Savitzky-Golay smoothing filter, Savitzky-Golay smoothing filter with first and second derivatives, and standard normal variant (SNV) were used. Partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) models were employed to build calibration models for estimating soil heavy metals concentration followed by evaluation of provided predictive models. Results indicated that Cu had stronger correlation coefficients with spectral bands compared to Ni. Cu and Ni demonstrated strongest correlations at wavelengths 1925 and 1393 nm, respectively. Based on RMSE, R2, and RPD statistics, the PLSR model with Savitzky-Golay filter pretreatment provided the most accurate predictions for both Cu and Ni (R2 = 0.905, RMSE = 0.00123, RPD = 2.80 for Ni; R2 = 0.825, RMSE = 0.00467, RPD = 2.04 for Cu) where such prediction was much better for Ni than for Cu. Reasonable results with lower accuracy and stability were obtained for PCR (R2 = 0.742, RMSE = 0.00181, RPD = 1.91 for Ni; R2 = 0.731, RMSE = 0.00578, RPD = 1.65 for Cu) and SVMR (R2 = 0.643, RMSE = 0.00091, RPD = 3.80 for Ni; R2 = 0.505, RMSE = 0.00296, RPD = 3.22 for Cu). We concluded that reflectance spectroscopy technique could be applied as a reliable tool for detection and prediction of soil heavy metals.

Climate variability impacts on rainfed cereal yields in west and northwest Iran.

In order to assess the response of wheat and barley to climate variability, the correlation between variations of yields with local and global climate variables was investigated in west and northwest Iran over 1982-2013. The global climate variables were the El Niño-Southern Oscillation (ENSO), Arctic Oscillation (AO), and North Atlantic Oscillation (NAO) signals. Further, minimum (T min), maximum (T max), and mean (T mean) temperature, diurnal temperature range (DTR), precipitation, and reference evapotranspiration (ET0) was used as local weather factors. Pearson's correlation coefficient was applied to analyze the relationships between climatic variables and yields. Unlike T min, T mean, ET0, and T max, the yields were significantly associated with the entire growing season (EGS) DTR in most sites. Therefore, considering weather extreme variables such as DTR sheds light on the crop-temperature interactions. It is also found that the April-May-June (AMJ), October-November-December (OND), and EGS rainfall variations markedly influence the yields. Unlike the AO and NAO indices, the Niño-4 and SOI (the ENSO-related signals) were significantly correlated with the OND and EGS precipitation and DTR. Thus, the ENSO anomalies highly impact rainfed yields through influencing the OND and EGS rainfall and DTR in the studied sites. As the correlation coefficient of the OND and July-August-September (JAS) Niño-4 with yields was significant (p < 0.05) for almost all locations, the JAS and OND Niño-4 may be a good proxy for cereal yield forecasting. Further, an insignificant increment and a significant reduction in yields are expected in La Niña and El Niño years, respectively, relative to neutral years.

Effect of ozonation and UV-LED combination on simultaneous removal of toxic elements during electrocoagulation.

Cyanide and heavy metals pose significant risks as contaminants in certain industrial effluents. This study aims to concurrently eliminate cyanide and specific heavy metals from synthetic wastewater resembling gold processing effluent, employing an improved electrocoagulation method incorporating ozone and UV-LED. The investigation delves into the effects of pH, electrode type, current density, reaction time, and ozonation. The findings revealed notable removal efficiencies: 98% for cyanide, 76% for nickel, 85% for copper, and 84% for zinc when utilizing a stainless steel electrode as the cathode. Optimal removal rates were achieved at 94% for cyanide, 93% for copper, 92% for zinc, and 83% for nickel, employing the UV-LED-ozone technique with an ozonation flow rate of 4 mg/s at pH = 10. Notably, when Al-Gr-SS-Fe electrodes and a current of 15 mA/cm2 were applied, these removal efficiencies were observed. Therefore, the most favorable conditions for the concurrent removal of pollutants from synthetic wastewater involved maintaining a pH of 10, utilizing SS-Fe as anode and Al-Gr as cathode electrodes, and employing a current density of 15 mA/cm2. The addition of ozonation with a flow rate of 4 mg/s, along with UV-LED, further enhanced the removal process. In summary, it can be inferred that the enhanced electrocoagulation method outperformed conventional electrocoagulation, leading to increased elimination of cyanide and selected heavy metals.

Experimental investigation of the effect of landfill leachate on the mechanical and cracking behavior of polypropylene fiber-reinforced compacted clay liner.

This paper aims to investigate the effect of leachate on the geotechnical parameters and the cracking behavior of compacted clay liners (CCLs) containing different percentages of polypropylene fibers. Accordingly, 200 compacted clay samples were reinforced with different percentages of fiber contents (FC) (i.e., 0, 0.5, 0.75, and 1%) and prepared with water or leachate to conduct different laboratory tests. First, the physical properties of the clay were determined. Then, the shear strength parameters (i.e., cohesion and friction angle), unconfined compressive strength, and the hydraulic permeability were determined subjected to water or leachate. Notably, the cracking behavior was modeled using visual images of the samples. The leachate increased desiccation cracks in the natural soil from 0.425 to nearly 1.111%. However, the addition of 0.5% (in the case of water) and 1% (in the case of leachate) fiber to the soil reduced the surface desiccation cracks in clay liners to about 0.185 and 0.352%, respectively. In both water- or leachate-prepared samples, the addition of fibers significantly increased the cohesion and friction angle. The shear strengths of the unreinforced leachate-prepared samples were lower than those of the water-prepared samples. The shear strength and unconfined compressive strength of all specimens increased with increasing fiber percentage. The presence of fibers in all samples caused more ductile behavior. The required amount of energy to achieve the maximum strength in the samples increased with increasing FC. By increasing the percentage of fibers, the permeability of the natural soil and the leachate-prepared samples increased. However, the highest permeability was observed in the leachate-prepared samples containing 1% fibers of 8.3 × 10-10 m/s, which is less than 10-9 m/s (maximum allowable permeability for clay liners). Finally, the obtained results were satisfactorily confirmed by scanning electron microscopy (SEM) analysis.

A novel method to quantify urban surface ecological poorness zone: A case study of several European cities.

A set of factors cause the Surface Ecological Status (SES) of urban areas to become largely different from the surrounding rural areas. Hence, the degree of poorness of SES in urban areas versus surrounding rural areas forms a zone, which is named Urban Surface Ecological Poorness Zone (USEPZ). The main objective of this study was to propose a new method to quantify USEPZ Intensity (USEPZI). To this end, Landsat-8 satellite images, water vapor products, and High Resolution Imperviousness Layer (HRIL) datasets of Budapest, Bucharest, Ciechanow, Hamburg, Lyon, Madrid, Porto, and Rome cities were used. Firstly, Single Channel (SC) algorithm, Tasseled cap transformation, and spectral indices were used to model the surface biophysical characteristics including Land Surface Temperature (LST), Wetness, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Soil Index (NDSI). Then, SES was modeled based on the combination of surface biophysical characteristics using Remote Sensing-based Ecological Index (RSEI). Finally, the USEPZI was modeled based on the linear regression function obtained from RSEI-Impervious Surface Percentage (ISP) feature space. The spatial variability of the ISP, LST, NDVI, NDSI and Wetness of the selected cities was found to be heterogeneous. The coefficient of determination (R2) between RSEI and ISP values for Budapest, Bucharest, Ciechanow, Hamburg, Lyon, Madrid, Porto, and Rome cities were obtained to be 0.99, 0.97, 0.98, 0.99, 0.98, 0.99, 0.99, and 0.94, respectively. Also, the USEPZI values of these cities were 0.14, 0.31, 0.41, 0.26, 0.40, 0.81, 0.44 and 0.46, respectively. Our findings show that the significant differences in their SES and USEPZI are due to the surface biophysical characteristics. The USEPZI in the selected cities with humid climate conditions was higher than the selected cities in dry climate conditions. Also, the use of the RSEI-ISP feature space is quite useful in modeling USEPZI of cities in different conditions.

Effect of fertilizer application on soil heavy metal concentration.

A large amount of chemicals is annually applied at the agricultural soils as fertilizers and pesticides. Such applications may result in the increase of heavy metals particularly Cd, Pb, and As. The objective of this study was to investigate the variability of chemical applications on Cd, Pb, and As concentrations of wheat-cultivated soils. Consequently, a study area was designed and was divided into four subareas (A, B, C, and D). The soil sampling was carried out in 40 points of cultivated durum wheat during the 2006-2007 periods. The samples were taken to the laboratory to measure their heavy metal concentration, soil texture, pH, electrical conductivity, cationic exchange capacity, organic matter, and carbonate contents. The result indicated that Cd, Pb, and As concentrations were increased in the cultivated soils due to fertilizer application. Although the statistical analysis indicates that these heavy metals increased significantly (P value<0.05), the lead and arsenic concentrations were increased dramatically compared to Cd concentration. This can be related to overapplication of fertilizers as well as the pesticides that are used to replant plant pests, herbs, and rats.

Chive (Allium schoenoprasum L.) response as a phytoextraction plant in cadmium-contaminated soils.

Cadmium (Cd) soil contamination poses a major hazard to safe food production throughout the world, calling upon actions for decontamination using environmentally friendly methods, such as phytoextraction. In this study, the capability of chive (Allium schoenoprasum L.) for phytoextracting Cd from contaminated soils was tested. Growth of chive was studied in a soil spiked with 0, 15, 30, 60, and 120 mg Cd/kg soil, and then, concentrations of Cd in soil, plant shoots, and roots were measured after harvest. Chive dry matter production was not affected significantly by the different Cd levels in soil, except from the maximum Cd concentration (120 mg Cd/kg soil), where dry matter was reduced by 77%. Cadmium accumulation occurred mostly in roots rather than in shoots, with maximum Cd concentrations 482.48 and 26.65 mg/kg of dry matter, respectively. Translocation factor (the proportion of Cd concentration in the aerial plant parts to that in the roots) was below 1 in all contaminated levels and decreased with increasing Cd concentrations in soil, indicating low Cd reallocation from roots to shoots. Maximum amount of Cd absorption (Cd concentration in shoots), maximum contaminant uptake rate, and minimum clean-up time were all observed in Cd concentration 60 mg/kg soil. Based on chive potential to acquire Cd in its roots and shoots, it can be designated as a convenient species for reducing Cd from contaminated soils up to concentrations of 60 mg Cd/kg soil.

Landfill site selection for municipal solid wastes in mountainous areas with landslide susceptibility.

Several cities across the world are located in mountainous and landslide prone areas. Any landfill siting without considering landslide susceptibility in such regions may impose additional environmental adversity. This study was aimed to propose a practical method for selecting waste disposal site that accounts for landslide exposure. The proposed method was applied to a city which is highly proneness to landslide due to its geology, morphology, and climatic conditions. First, information on the previously occurred landslides of the region was collected. Based on this information, proper landslide causative factors were selected and their thematic maps were prepared. Factors' classes were then standardized in 0-1 domain, and thematic layers were weighted by using analytical hierarchy process (AHP). The landslide susceptibility map was prepared afterwards. Unsuitable areas for landfill location were masked in GIS environment by Boolean method, retaining sufficient areas for further evaluation. Nine remaining alternatives were selected through comprehensive field visits and were ranked by using AHP. Consequently, 17 factors in three environmental, economical, and social perspectives were employed. Sensitivity analyses were performed to assess the stability of the alternatives ranking with respect to variations in criterion weights. Based on the obtained landslide susceptible map, nearly 36 % of the entire region is proneness to landslide. The prepared Boolean map indicates that potential areas for landfill construction cover 11 % of the whole region. The results further indicated that if landslide susceptible areas are not considered in landfill site selection, the potential landfill sites would become more than twice. It can be concluded that if any of these landslide prone sites are selected for landfilling, further environmental disaster would be terminated in the future. It can be further concluded that the proposed method could reasonably well be adjusted to consider landslide exposure when siting a solid waste landfill.

An analytical deterministic model for simultaneous phytoremediation of Ni and Cd from contaminated soils.

Soil contamination by heavy metals, due to human activities, is not often limited to a single contaminant. The objective of this study was to develop a simple model for phytoextracting separate and combined Ni and Cd from contaminated soils. The study was further aimed to study phytoextraction potential of ornamental kale and land cress grown in soils contaminated with separate and combined Ni and Cd metals. The results indicated that elevated Ni and Cd concentrations in soil inhibit growth of both ornamental kale and land cress plants. In Ni + Cd treatments, growth and development of both plants were more affected than in either Ni or Cd treatments. Further, in Ni + Cd treatments, Ni concentration in tissues of both plants was increased by increasing soil Ni concentration under various Cd concentrations. At constant Ni concentration, addition of Cd did not appreciably changed Ni content of plant tissues. Land cress demonstrated higher tolerance to soil contamination by Ni and Cd compared to ornamental kale. It also demonstrated higher phytoextraction potential for soil Cd than ornamental kale. Enhanced bioavailability of Ni and Cd ions, due to competitive adsorption and desorption reactions, had no reasonable effect on metal ion accumulation in plant tissues. This indicates that at relatively high soil contamination, metal ion adsorption is no longer a limiting factor for phytoremediation. The newly proposed model, which assumes that metal uptake rate inversely depends on total soil metal ion concentration, reasonably well predicted the cleanup time of Ni, Cd, and Ni at the presence of Cd from the contaminated soils. The model also predicts that phytoremediation process takes much longer time when soil is contaminated by multi-metal ions.

Competitive adsorption-desorption reactions of two hazardous heavy metals in contaminated soils.

Investigating the interactions of heavy metals is imperative for sustaining environment and human health. Among those, Cd is toxic for organisms at any concentration. While Ni acts as a micronutrient at very low concentration but is hazardous toxic above certain threshold value. In this study, the chemical adsorption and desorption reactions of Ni and Cd in contaminated soils were investigated in both single and binary ion systems. Both Ni and Cd experimental data demonstrated Langmuir type adsorption. In the competitive systems, an antagonistic effect was observed, implying that both ions compete for same type of adsorption sites. Adverse effect of Cd on Ni adsorption was slightly stronger than that of opposite system, consistent with adsorption isotherms in single ion systems. Variation in ionic strength indicated that Ca, a much weaker adsorbate, could also compete with Cd and Ni for adsorption on soil particles. Desorption data indicated that Cd and Ni are adsorbed very tightly such that after four successive desorption steps, less than 0.5 % of initially adsorbed ions released into the soil solution. This implies that Ca, at concentration in equilibrium with calcite mineral, cannot adequately compete with and replace adsorbed Ni and Cd ions. This adsorption behavior was led to considerable hysteresis between adsorption and desorption in both single and binary ion systems. In the binary ion systems, desorption of Cd and Ni was increased by increase in both equilibrium concentration of adsorbed ion and concentration of competitor ion. The overall results obtained in this research indicate that Cd and Ni are strongly adsorbed in calcareous soil and Ca, the major dissolved ion, insignificantly influences metal ions adsorption. Consequently, the contaminated soils by Ni and Cd can simultaneously be remediated by environmentally oriented technologies such as phytoremediation.

An integrated multi criteria approach for landfill siting in a conflicting environmental, economical and socio-cultural area.

Landfill site selection is a complicated multi criteria land use planning that should convince all related stakeholders with different insights. This paper addresses an integrating approach for landfill siting based on conflicting opinions among environmental, economical and socio-cultural expertise. In order to gain optimized siting decision, the issue was investigated in different viewpoints. At first step based on opinion sampling and questionnaire results of 35 experts familiar with local situations, the national environmental legislations and international practices, 13 constraints and 15 factors were built in hierarchical structure. Factors divided into three environmental, economical and socio-cultural groups. In the next step, the GIS-database was developed based on the designated criteria. In the third stage, the criteria standardization and criteria weighting were accomplished. The relative importance weights of criteria and subcriteria were estimated, respectively, using analytical hierarchy process and rank ordering methods based on different experts opinions. Thereafter, by using simple additive weighting method, the suitability maps for landfill siting in Marvdasht, Iran, was evaluated in environmental, economical and socio-cultural visions. The importance of each group of criteria in its own vision was assigned to be higher than two other groups. In the fourth stage, the final suitability map was obtained after crossing three resulted maps in different visions and reported in five suitability classes for landfill construction. This map indicated that almost 1224 ha of the study area can be considered as best suitable class for landfill siting considering all visions. In the last stage, a comprehensive field visit was performed to verify the selected site obtained from the proposed model. This field inspection has confirmed the proposed integrating approach for the landfill siting.