Nuisance dust particles have emerged as a significant environmental concern within the Middle Eastern region. The principal aim of this research was to conduct an extensive investigation into the physical and chemical attributes of dust-fall particles located within the city of Sulaymaniyah, northeastern Iraq. Over a period of six months, a total of 72 dust-fall particle samples were systematically gathered from three distinct stations, with intervals of seven days. In addition to quantitative analysis, this study included detailed morphological examinations and mineralogical composition assessments, facilitated through the application of analytical methodologies, including Scanning Electron Microscopy (SEM) and X-Ray Diffraction (XRD). The outcomes of these analytical procedures revealed predominantly irregular shapes of the dust particles, characterized by the presence of quartz and calcite minerals, confirming their natural origin due to wind-driven erosion originating from the arid desert landscapes of Iraq and its neighboring southern and western countries. Moreover, this investigation extended to encompass a comprehensive evaluation of both water-soluble and insoluble fractions, in addition to the overall concentration levels of alkali and alkaline earth metals including sodium (Na), potassium (K), calcium (Ca), and magnesium (Mg). Furthermore, the levels of heavy metals of manganese (Mn), iron (Fe), copper (Cu), and arsenic (As) were investigated. The extent of pollution associated with these elements was assessed through the application of the Geo-accumulation index (Igeo) which revealed that, during the study, calcium, magnesium, and copper demonstrated noticeable levels of contamination within the dust-fall particles of Sulaymaniyah city.

Artificial neural networks (ANNs) simulate an anaerobic co-digestion process of Organic Fraction of Municipal Solid Waste (OFMSW) and bio-flocculated sludge for a mesophilic lab-scale semi-continuous feed reactor. The operational, substrate quality and process control parameters such as Organic Loading Rate, Hydraulic Retention Time, pH, VFA/Alkalinity ratio and Total Solids are input variables and methane yield and Volatile Solids removal are outputs for ANN modelling. The lab-scale experimental results are used to develop a prediction model using fitting application for ANN. The network architecture was optimized to achieve accurate predictions, resulting in a 5-19-2 architecture for methane yield and a 5-17-2 architecture for %VSremoval. The training was performed using the Bayesian Regularization (trainbr) algorithm, leading to high coefficients of determination (R2) of 0.953 and 0.978 for methane yield and %VSremoval, respectively. The results demonstrate the effectiveness of neural network-based modelling in capturing complex relationships within the methane yield process, facilitating accurate prediction of crucial output parameters.

This study was undertaken to evaluate concentration of Uranium (U) in the drinking water of the Tonk district of Rajasthan (India). The main objective of the study is to determine the distribution of Uranium concentration and the geochemical behavior of Uranium in pre-monsoon (PRM) and post-monsoon (POM) drinking water samples. Uranium was measured by LED fluorimeter. Total 318 drinking water samples were collected for both seasons. It is observed that the water quality of all the samples is within the limits prescribed by WHO (30 µg/L) except a few, and can be used for domestic purposes. The Uranium concentration was found to be in the range 0.21 to 173.72 µg/L with a mean value of 8.58 µg/L in pre-monsoon and 0.21 to 162.34 µg/L with a mean value of 11.22 µg/L in post-monsoon samples. The geochemistry of the study area shows rock-water interaction. The order of average anionic concentration is found to be HCO3 – > Cl – > SO4 2– > NO3 –. Although no definite trend of seasonal variation in the concentration of U was observed, large samples have higher Uranium concentrations in post-monsoon than pre-monsoon.

Cedrus deodara is a coniferous tree native to Himalayan region. Its wood is a valuable resource for the timber industry; however, its bark is typically discarded as a waste material. The present study examines the performance of Cedrus deodara bark powder (CD) as an inexpensive adsorbent for elimination of Pb (II) ions. In addition to this multiple linear regression (MLR) and artificial neural network (ANN) models were developed for modelling the adsorption process and prediction of Pb (II) removal efficiency. The structural and chemical properties of CD were explored using Field Emission Scanning Electron Microscope (FE-SEM), Energy Dispersive Spectrometer (EDS), X-Ray Diffractometer (XRD) and Fourier Transform Infrared Spectroscopy (FTIR). Batch experiments were conducted to investigate the influence of factors including pH, contact time, initial Pb (II) concentration and temperature on Pb (II) adsorption. The adsorption followed pseudo-second-order kinetic and Langmuir isotherm models with maximum monolayer uptake capacity 77.52 mg/g. Based on the thermodynamic criteria, the process was endothermic and spontaneous with enthalpy change (ΔH = 8.08 kJ/mol), free energy change (ΔG = -2.44 kJ/mol) and entropy change (ΔS = 0.03 kJ/K/mol). Statistical comparison of MLR model (R2 = 0.817, RMSE = 8.954, MAPE = 17.379 %) and ANN model (R2 = 0.993, RMSE = 1.777, MAPE = 2.054 %) confirmed that ANN model was far more accurate in predicting removal efficiency.

In this work, it was attempted to evaluate and demonstrate disinfection effectiveness of an electrochemical process to entirely remove coliform from wastewater effluent following secondary treatment. For the tests, an experimental bench-scale batch electrochemical cell was constructed, and aluminum electrodes were employed in the electro-disinfection reactor. In the electric disinfection phase, wastewater samples were put in the reactor/disinfector and a direct current (DC) was applied to it. According to findings, a significant decrease occurred in the total number of coliforms in the treated wastewater, and a high improvement occurred in the effluent properties. At a contact time of 15 min and a current density of 5.5 mA/cm2, led to a bacterial killing effectiveness of 97.7% or above. As the current density and contact time increased, a general increase occurred in the bacterial killing efficiency, and the effect of the two above-mentioned factors was much greater than the effect of salinity. Moreover, according to the experimental data, the removal efficiency of chemical oxygen demand (COD) and total suspended solids (TSS) by the aluminum electrodes were 78.50% and 99.93%, respectively. The findings indicate the applicability of the proposed electrochemical treatment to wastewater effluent. Nevertheless, to be able to apply this system at an industrial scale in the future, it is necessary to conduct more research into the optimum operation conditions and make an in-depth comparison of energy consumptions between the electrochemical treatment and the conventional approaches.

In recent decades, one of the environmental concerns is contamination with emerging pollutants of microplastics. Microplastics enter the environment through wastewater treatment plants and can absorb harmful pollutants. This study investigated microplastic pollution in the sludge of Zahedan wastewater treatment plants. To investigate microplastic pollution in the sludge of Zahedan wastewater treatment plants, 5 kg samples of sludge were passed through stainless-steel sieves and digested using H2O2 solution. NaCl was used based on density to separate microplastics. FESEM and FTIR analyses were used to investigate the surface morphology and polymer type of microplastics. The abundance of microplastic particles in the sludge ranged from 71-95 N/Kgdry.sludge, and their size varied from 25-500 micrometers. The most common color observed was transparent, and the fiber shape was the most prevalent. The study highlights the importance of addressing microplastic pollution in wastewater treatment plants to prevent harmful effects on the environment. Also, the data obtained from this study can be used to improve the treatment process and understand the removal of microplastics in urban wastewater treatment plants.

Three separate mangrove ecosystems in Kerala's Kannur district were examined for trace metal build-up in sediment and two polychaete species, Marphysa gravelyi and Dendronereis aestuarina. By classifying the areas according to the intensity of anthropogenic activity, metal deposition in polychaete tissue was investigated. ICP-MS was used to assess the heavy metal load and the accumulation of metals in sediment in the range of, Zn 24.37-59 mg/kg, Ni 23.67- 59.25 mg/kg, Cu 11.27- 38.6 mg/kg, Pb 4.5- 16.4 mg/kg, Cd 0.1-1.8 mg/kg, Fe 1.25- 3.67 %, and Al 0.65-2.43 %. The soil sample's Zn concentration was at its highest and heavy metals accumulated in the pattern Zn˃Ni˃Cu˃Pb˃Cd. By just switching the concentrations of Ni and Cu, polychaetes' trace metal concentrations follow the same pattern as those found in soil, however, M. gravelyi was discovered to have larger amounts of accumulation when compared to D. aestuarina, mostly for metals like Zn and Pb. Based on data compiled from all stations, the average concentration of accumulation for Zn was 62.34 mg/kg & 43.45 mg/kg, and for Pb, it was 6.59 mg/kg & 1.86 mg/kg in M. gravelyi and D. aestuarina, respectively. Most metal buildup is found in mangrove soil, which has higher levels of organic carbon and clay particles. The findings imply that D. aestuarina is an organism that is sensitive to pollution and that M. gravelyi is a species that is extremely tolerant of pollution, suggesting that the species can be used to anticipate the state of its surrounding environment.

Multidisciplinary studies that integrate socioenvironmental aspects into the assessment of water resources contamination significantly enhance the identification of its sources. In the present study, an assessment of heavy metal contamination in sediments of Umayo Lagoon and the behavior of local actors was conducted. The concentrations of As, Cd, Cu, Hg, Pb, and Zn were determined and evaluated using international regulations. These data enabled the creation of distribution maps to pinpoint accumulation zones of different metals and suggest their possible sources of origin. The results were compared with the behavior of local actors, addressing three analytical characteristics: feelings, thoughts, and attitudes. Evidence showed contamination by As (18.11 mg/kg), Hg (0.19 mg/kg), and Cd (0.96 mg/kg), likely originating from mining activities, cheese plants, and agriculture. In terms of social aspects, intense emotions were expressed due to the presence of diseases and livestock mortality associated with metal contamination, causing anxiety and fear in the population. The incorporation of socioenvironmental aspects in the contamination assessment aligned with the physicochemical results, achieving identification of the sources of Umayo Lagoon.

In this research, successive electro-oxidation (EO) process was utilized to eliminate some of the primary organic contaminants in effluent wastewater, specifically phenol and chemical oxygen demand (COD). The performance of the electro-oxidation (EO) process was studied by using two graphite electrodes as anodes and three stainless steel electrodes as cathodes, which is a new strategy in this field. Taguchi method has been used to design experiments to approach the best experimental conditions for phenol and COD removal as significant responses. The best operating conditions that resulted in the maximum reduction of phenol and COD were current density (CD = 25 mA/cm2), pH = 4, support electrolyte (NaCl=2g/l), the distance between electrodes (Dist.=5mm), and time of 60 minutes. At these operating conditions, phenol and COD removal were 99.27% and 99.96%, respectively. This work provides important insights into a novel water and wastewater treatment method with a detailed analysis of the results.

In the face of emerging and re-emerging diseases, novel and innovative approaches to population scale surveillance are necessary for the early detection and quantification of pathogens. The last decade has seen the rapid development of wastewater and environmental surveillance (WES) to address public health challenges, which has led to establishment of wastewater-based epidemiology (WBE) approaches being deployed to monitor a range of health hazards. WBE exploits the fact that excretions and secretions from urine, and from the gut are discharged in wastewater, particularly sewage, such that sampling sewage systems provides an early warning system for disease outbreaks by providing an early indication of pathogen circulation. While WBE has been mainly used in locations with networked wastewater systems, here we consider its value for less connected populations typical of lower-income settings, and assess the opportunity afforded by pit latrines to sample communities and localities. We propose that where populations struggle to access health and diagnostic facilities, and despite several additional challenges, sampling unconnected wastewater systems remains an important means to monitor the health of large populations in a relatively cost-effective manner.