The critical parameters namely initial pH, time and current density largely impact the process efficiency of electrocoagulation (EC). Few works have been done on observing the interaction of these critical parameters and the possible combined effect on the overall pollutant removal efficiency. Therefore, the knowledge of the combined effect of critical parameter interaction would enhance the optimization of EC parameters to attain maximum efficiency with limited resources. Using aluminium electrodes with interelectrode distance of 10 mm on synthetic wastewater, representing biotreated palm oil mill effluent (BPOME), with a set range of initial pH, current density, and time of 3-8, 40-160 mA/cm2 and 15 to 60 minutes, respectively, the effect of the three critical variables was investigated. The optimum Chemical Oxygen Demand (COD) removal of 71.5% was determined at pH 6, current density of 160 mA/cm2 (with current 1.75 A) at EC time of 15 minutes. The experiment was validated with real BPOME, resulting in the removal efficiency of 60.7 % COD, 99.91 % turbidity, 100 % total suspended solids (TSS) and 95.7 % colour. Removal of a large quantity of pollutants in a time span of 15 minutes with optimized parameters in EC is notable for a wastewater treatment alternative that requires no extensive use of chemicals. The interaction of parameters observed in this study indicated a synergistic contribution of initial pH and current density in removing maximum wastewater COD in 15 minutes of EC.

Today, indoor air pollution is a major concern. So far, many quantitative and qualitative studies have been conducted on particulate matter pollution in closed environments, but not much research has been done to measure air pollution in subway station. In this study, we have investigated the concentrations of PM10, PM2.5 and TSP particles in 12 underground stations on the oldest and main Tehran metro line, in two seasons, autumn and spring. For sampling suspended particles, we have used a portable direct reading device for monitoring suspended-particles (HAZDUST EPMA5000). We also used Pair T- test to compare the particle concentrations in different modes of the ventilation system (on, off, and inlet air) and Three-way variance analyze. According to the results, the mean concentrations of PM2.5-PM10 - TSP values in line-1 on the station platforms are significantly higher in spring than in autumn, off state of the ventilation system than on state of the ventilation system (P <0.001). Also, the concentration of particles measured in the air of subway stations is higher in the off state of ventilation systems, compared to Inlet air to stations (P<0.001). There is a correlation between concentration of particles measured in different sampling season, condition of the ventilation mode (on, off, inlet air) (P<0.001). Improving the efficiency of ventilation systems (equipped with a suitable filter) and fan in stations is suggested as one of the factors to reduce the concentration of particles, especially in spring.

In this study, the removal of nitrogen from effluent of ammonia plant by Chlorella vulgaris and Spirulina platensis was investigated. For this purpose, microalgae were cultivated in three diluting percentage of the wastewater (1, 3, and 5%) at 29±1 ◦C and light intensity at surface of culture were adjusted to 150 µmol photon / (m2. s). The results showed that Spirulina platensis is more capable than Chlorella vulgaris to grow in high levels of total nitrogen concentration. Also, maximum biomass production rate happened in 1% diluted samples for Chlorella vulgaris and 3% for Spirulina platensis. Furthermore, Chlorella vulgaris reduce total nitrogen concentration up to 55%. This value for Spirulina platensis was about 96%. However, for both species the removal of nitrogen in 1% diluted wastewater was maximum. According to the results of diluted wastewater of ammonia plant, it is a suitable culture medium for microalgae and it can be used to remove the nitrogen before entering the wastewater in nature.

Air pollution is considered a global concern due to its impacts on human life and the urban environment. Therefore, precise modeling techniques are necessary to predict air quality in congested areas such as megacities. Recently, machine learning algorithms such as Neural Networks show significant possibilities in air quality studies. This paper proposes a model to estimate air quality in a congested urban area in Baghdad city using Artificial Neural Network (ANN) algorithm and Geospatial Information System (GIS) techniques. Carbon Monoxide (CO) gas is selected as the main air pollutant. The model parameters involve; CO samples, traffic flow, weather data, and land use information collected in the field. The proposed model is implemented in Matlab environment and the results are processed after entering ArcGIS software. Using its spatial analysis tools, the outputs are presented as a map. The final findings indicate the highest value of CO emissions that reached 34 ppm during the daytime. The most polluted areas are located near congested roads and industrial locations in comparison with residential areas. The proposed model is validated by using actual values that are collected from the field, where the model's accuracy is 79%. The proposed model showed feasibility and applicability in a congested urban area due to the integration between the machine learning algorithm and GIS modeling. Therefore, the proposed model in this research can be used as a supportive model for decision making of city managers.

Mineralogy studies can help understand the interactions of geographical, environmental, and geological factors. Considering frequent occurrence of urinary tract stones in the south and west of Iran, the present paper examines trace elements, like heavy metals, in 53 urine stone samples collected from patients in Lorestan Province. It investigates the mineralogy of the stones, using X-ray diffraction. The samples are then classified into five mineral groups (calcium oxalate, urate, cysteine, calcium oxalate-urate, and calcium oxalate/phosphate). Results from this analysis are confirmed by SEM images, showing the crystalline form of the mineral phases. The microscopic studies show that only the mineral group of calcium oxalate (whewellite) could be detected in thin sections, prepared from urinary tract stone samples. The main and trace elements in each group are determined through ICP-MS method with the results showing that calcium is the most abundant substance in urinary tract stones, compared to other elements. This is caused by the role of calcium in most basic functions of cell metabolism. The correlation between magnesium and strontium is 0.64, originated from the placement of high amounts of strontium in calcium oxalate minerals. The positive correlation between sodium and calcium also indicates that sodium is replaced by calcium due to the similarity of the ionic radius in the crystal structure. Results from this study can help us find the causes behind the frequent occurrence of urinary tract stones in Lorestan Province.

The Air Pollution Tolerance index (APTI) of six plants located within Ikpoba Okha gas flaring site in Oredo Local Government Area of Edo State, Nigeria during wet and dry seasons were assessed. Plant samples for this research work were randomly collected from the vicinity of the flaring site. Six (6) sample of each plant was used for laboratory analysis. The plant parameters assessed include relative water content (RWC), the ascorbic acid content (AAC), total leaf chlorophyll (TLC) and pH extract of the leaves and were used to compute the Air pollution tolerance indices (APTI). Based on the analyzed result, the RWC in Drypetes leonensis, Ficus exasperata Vahl, Chromolaena odorata (Linn) and Gmelina arborea Roxb. ex Smith   species in dry season were higher than those in wet season. Icacina tricantha showed a relatively high level of acidity when compared to others. A. boonei De Wild has the highest ascorbic acid content in the leaves in both seasons. The highest level of chlorophyll contents was recorded in the dry season with Drypetes leonensis having the highest, followed by Icacina trichantha. There was no statistically significant difference in pH and total chlorophyll contents between samples collected in wet and dry season; however, there were significant difference observed in ascorbic acid and RWC in both seasons. APTI in wet and dry season showed a statistically significant difference. This study recommends planting of tolerant species that can acts as bio-indicators especially in gas flaring stations in Nigeria.

The present study aims at investigating land use changes (as one of the effective human factors on water systems) as well as its relation with water quality at spatial scales of the entire basin, sub-basin and defined buffers (10 and 15 km) in Zayandeh-Rud Basin, Isfahan, Iran. By means of supervised classification method along with maximum likelihood algorithm, it classifies the land use map into five categories, including agriculture, bare lands, urban areas, vegetation, and water. The research collects data for 11 water quality parameters in seven sampling stations of Zayandeh-Rud Basin in 2002, 2009, and 2015 from Isfahan Water and Sewerage Organization. Correlation analysis is then conducted to investigate the effect of land use changes on water quality at different spatial scales. Land use analysis in the entire basin shows that despite an increase in urban and agricultural lands from 2002 to 2015, bare lands, vegetation, and water covers have had a decreasing trend. Moreover, various land uses at different scales show some correlation with water quality parameters. The strongest correlations in this study belong to sub-basin scale. Therefore, it is recommended to use this spatial scale to investigate the relation between land use and water quality parameters

This study analyzed the potential use of Mahagoni wood charcoal (MWC) and Mahagoni bark charcoal (MBC) as biosorbent for reactive red 120 (RR 120) dye removal from aqueous solutions. The effect of different operating parameters such as contact time (1–210 min), pH (3–11), adsorbent dose (1–20 g/L), and initial RR 120 concentration (5–70 mg/L) on adsorption processes was studied under batch adsorption experiments. The maximum removal of RR 120 by MWC (78%) and MBC (88%) was achieved when the optimum conditions were initial RR 120 concentration (5 mg/L), pH (3), adsorbents dose (10 g/L) and equilibrium contact time (150 min). The RR 120 adsorption data of MWC and MBC were better described by the Langmuir and Freundlich isotherm models, respectively. The MWC and MBC showed maximum adsorption capacities of 3.806 and 5.402 mg/g, respectively. Kinetic adsorption data of all adsorbents (MWC and MBC) followed the pseudo-second-order model and this adsorption process was controlled by chemisorption with multi-step diffusion. A lower desorption rate advocated that both strong and weak binding forces could exist between RR 120 molecules and adsorbents. The study results revealed that the utilization of either MWC and or MBC as an adsorbent for treating RR 120 is effective and environmentally friendly.

The study investigates the effect of aluminium oxide nanoparticles as an additive to Madhuca Indica (mahua) methyl ester blends on performance, emission analysis of a single-cylinder direct injection diesel engine operated at a constant speed at different operating conditions. The test fuels are indicated as B10A0.2, B10A0.4, B20A0.2, B20A0.4 and diesel respectively. The results indicate that the brake thermal efficiency for aluminium oxide nanoparticles blended biodiesel increases slightly when compared to the mineral diesel. The carbon monoxide (CO), unburnt hydrocarbon (HC) and smoke emission marginally decrease as compared to mineral diesel. Oxides of nitrogen (NOx) emissions are minimum for the aluminium oxide nanoparticles blended mahua methyl esters. Higher cylinder gas pressure and heat release rate were observed for aluminium oxide nanoparticles blended mahua methyl ester. From the study, the blending of aluminium oxide nanoparticles in biodiesel blends produces a most promising results in engine performance and also reduces the harmful emission from the engines.

Air quality prediction is highly important in view of the health impacts caused by exposure to air pollutants in urban air. This work has presented a model based on support vector machine (SVM) technique to predict daily average carbon monoxide (CO) concentrations in the atmosphere of Tehran. Two types of SVM regression models, i.e. -SVM and -SVM techniques, were used to predict average daily CO concentration as a function of 12 input variables. Then, forward selection (FS) technique was applied to reduce the number of input variables. After converting 12 input variables to 7 using the FS, they were fed to SVM models (FS-(-SVM) and FS-(-SVM)). Finally, a comparison among SVM models operation and previously developed techniques, i.e. classical regression model and artificial intelligent methods such as ANN and adaptive neuro-fuzzy inference system (ANFIS) was carried out. Determination of coefficient (R2) and mean absolute error (MAE) for -SVM (-SVM) were 0.87 (0.40) and 0.87 (0.41), respectively, while they were 0.90 (0.39) and 0.91 (0.35) for ANN and ANFIS, respectively. Results of developed SVM models indicated that both FS-(-SVM) and FS-(-SVM) regression techniques were superior. Furthermore, it was founded that the performance of FS-(-SVM) and FS-(-SVM) models were generally a bit better than the best FS-ANFIS and FS-ANN solutions for short term forecasting of CO concentrations.