The production rate of industrial and agricultural waste is increasing due to population growth. Soil is the most important receiver of industrial and agricultural waste. Contaminants such as heavy metals in various waste after reception by the soil, immediately become part of the cycle that has different impacts on the environment. Geopolymer, as a chemical stabilizer has the potential to stabilize heavy metals in the soil. In this research, several geopolymers for the stabilization of heavy metals in soil were synthesized. Silicon dioxide (SiO2) and aluminosilicate (Al2SiO4) must be used to produce the geopolymers. Rice husk ash was used as the SiO2 source. Also, Iranian zeolite and sepiolite, and red clay soil were utilized as the source of Al2SiO4. The synthesized geopolymers were investigated for the adsorption of nickel and cadmium. Also, batch and column studies of using geopolymers for the chemical stabilization of heavy metals in soil were conducted. The results revealed a high adsorption capacity of the geopolymers. The zeolite, sepiolite, and red clay geopolymer-soil samples adsorbed 100% of the heavy metals (i.e., Ni and Cd) at a concentration of 100 ppm. The zeolite geopolymer adsorbent adsorbed 57% and 96% of Ni and Cd at a concentration of 1000 ppm, respectively. In general, it was concluded that the use of geopolymer compounds in soils with high heavy metal adsorption capacity could be an efficient approach to prevent groundwater resource pollution.

This paper is the first to report on the role of a wastewater treatment plant (WWTP) in Sari, as a source of microplastics (MPs) in the Caspian Sea. Composite 270-liter/24-hour samples were taken the treated effluent of the WWTP in winter and spring, two seasons with different levels of human activity. The effluent contained 380±52.5 and 423±44.9 MPs/m3 in winter and spring, respectively, with the total numbers of MPs/m3 not differing between the two seasons. The dominant type of MPs in the effluent was microfibers with 237±68.7 and 328±33.4 per m3 in winter and spring, respectively. In both seasons, fiber sizes of

This work explores the synthesis and characterization of two novel nanocomposites that can be used in various applications, such as aqueous solution adsorption of pollutants. The first nanocomposite consists of tin (Sn)-doped titanium dioxide (TiO2) on activated carbon, while the other one consists of polypyrole (PPy), chitosan (CS), and Sn-doped TiO2. A contrast was made of their effective adsorbent materials for the removal of Cibacron Brilliant Yellow dye from aqueous solutions. Different analytical techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), energy dispersive X-ray analysis (EDX), and Fourier transform - infrared (FT-IR) were used to analysis the nanocomposite samples. SEM images show that the average particle diameter of PPy-CS/Sn-doped TiO2 NC is 75 ± 3 nm, while Sn-doped TiO2/AC particles have an average diameter of 40 ± 2 nm. The greater PPy-CS/Sn-doped TiO2 nanocoposite particle diameter indicates that the polymers cover the Sn-doped TiO2 nanoparticles, which leads to higher in the diameter of the particles. The adsorption efficiency of Sn-doped TiO2/AC was higher than that of PPy-CS/Sn-doped TiO2 sample due to its smaller particle size which resulted in a higher surface area which provides more adsorption sites. However, both samples showed remarkable adsorption capacity, where the adsorption capacity of Sn-doped TiO2/AC and PPy-CS/Sn-doped TiO2 were 104 and 103 mg/g, respectively.

The main objective of this paper is to propose K-Nearest-Neighbor (KNN) algorithm for predicting NOx emissions from natural gas electrical generation turbines. The process of producing electricity is dynamic and rapidly changing due to many factors such as weather and electrical grid requirements. Gas turbine equipment are also a dynamic part of the electricity generation since the equipment characteristics and thermodynamics behavior change as turbines age and equipment degrade gradually. Regular maintenance of turbines are also another dynamic part of the electrical generation process, affecting performance of equipment as parts and components may be upgraded over time. This analysis discovered using KNN, trained on a relatively small dataset produces the most accurate prediction rates in comparison with larger historical datasets. This observation can be explained as KNN finds the historical K nearest neighbor to the current input parameters and approximates a rated average of similar observations as prediction. This paper incorporates ambient weather conditions, electrical output as well as turbine performance factors to build a machine learning model predicting NOx emissions. The model can be used to optimize the operational processes for harmful emissions reduction and increasing overall operational efficiency. Latent algorithms such as Principle Component Algorithms (PCA) have been used for monitoring the equipment performance behavior change which deeply influences process paraments and consequently determines NOx emissions. Typical statistical methods of performance evaluations such as multivariate analysis, clustering and residual analysis have been used throughout the paper. This paper incorporates ambient weather conditions, electrical output as well as turbine performance factors to build a machine learning model predicting NOx emissions. The model can be used to optimize the operational processes for harmful emissions reduction and increasing overall operational efficiency. Latent algorithms such as Principle Component Algorithms (PCA) have been used for monitoring the equipment performance behavior change which deeply influences process paraments and consequently determines NOx emissions. Typical statistical methods of performance evaluations such as multivariate analysis, clustering and residual analysis have been used throughout the paper.

In order to assess the environmental impact of soil polluted with trace metals, representative soils were collected surrounding the abandoned Pb–Zn mine mill (SM soils), and the new temporary ore storage site (SS soils), which are located in the vicinity of Medina (Aures), North-east of Algeria. Total digestion has been used to determine the total content of Zinc, Copper, Lead, Cadmium, and Arsenic, then it was analysed by inductively coupled plasma atomic emission spectrometry (ICP-AES). The sequential extraction Tessier scheme was also used to extract the chemical forms of Zn, Cu, and Pb, and their concentrations in each fraction were analyzed by atomic absorption spectrometry. Lead was the most abundant trace metals, its concentration in mg.kg-1 ranged between (67.20 – 46000), followed by Zinc (26 - 1853), Copper (32 – 495), Arsenic (8 – 116), and Cadmium (0.3 - 7.30). Sequential extraction shows that Zinc was mainly associated with reducible and residual fractions. Copper was bound predominantly with the minerals in the residual fraction, followed by the organic matter. Lead was bound mainly with carbonate fraction in SM soils, while Pb in the SM soils was mainly associated with the reducible fraction. The Pb, Zn, Cu mobility factor was significantly higher in SM soils than in SS soils. These results indicate that anthropic activities related to ore concentration and mining process lead to increased concentration of trace metals in surrounding soils, hence their mobility and bioavailability, this consists a potential risk to the environment and biota.

Bed Sediment is a dynamic and complex material that plays an important role in the aquatic ecosystems and provides habitat from a highly various community of organisms. To address the major issue, this study, which its substantive subject done for the first time in Iran, aimed to assess the current status of Darreh-rood river's health and quality using SOD rate and its associated factors including Texture, fine-PSD, TOC and TP of bed sediments along with some basic field parameters of river-water. All required samples were collected from 10 sampling points in due course. SOD data with regard to related factors were calculated and analyzed. The rates of SOD ranged from 0.69 to 1.57 g O2/m2/day. Moreover, this index was classified in varied quality domains. Afterwards, a predictive equation was determined among SOD rate and its associated parameters using MATLAB software. Finally, the Results showed that the river quality and health suitability in research area are in categories slightly clean and slightly degraded, in targeted zones during the study period. Also, the increase in TOC and TP concentrations together with a decrease in sediment particle size was led to an increase in SOD-rate accordingly. In conclusion, the consequences of this study under Survivability-based Adaptive Management (SAM) perspective can be used as a rapid diagnostic tool to support water policy decision-makers and other stakeholders to promote the best practices for protecting the health conditions of riverine systems, focusing on selecting the appropriate points for discharging the wastewaters into the receiving water-bodies.

In this study, the estimated amounts of discarded face masks due to COVID 19 were investigated. In this context, the amount of waste face masks was determined separately according to the mask types used (nonwoven, meltblown, and 3-ply, pleated) and the importance of waste face masks was revealed. According to obtained data, the estimated total daily face mask use in Turkey is 72,351,638. The highest amounts of nonwoven, meltblown, and 3-ply face mask waste were determined as 26.88, 36.29, and 43.68 tonnes/day for İstanbul city, respectively. Total amounts of nonwoven, meltblown and 3-ply face mask waste in Turkey were calculated as 144.7, 195.35, and 235.14 tonnes/day, respectively. The top 5 provinces with the highest amount of waste masks are listed as follows; İstanbul (nonwoven=26.88, meltblown=36.29, 3ply=43.68 tonnes/day), Ankara (nonwoven=9.91, meltblown=13.38, 3ply=16.11 tonnes/day), İzmir (nonwoven=7.76, meltblown=10.47, 3ply=12.61 tonnes/day), Bursa (nonwoven=5.40, meltblown=7.29, 3ply=8.78 tonnes/day), and Antalya (nonwoven=4.45, meltblown=6.01, 3ply=7.23 tonnes/day), respectively. In Turkey, 91.3% of medical waste collected in health institutions in 2019 (90,920 tonnes) was sterilized and disposed of in storage areas (83,010 tonnes). 8.7% of medical waste was sent to incineration facilities and disposed (7,910 tonnes). Considering these values, 132, 178.35, and 214.7 tonnes/day of nonwoven, meltblown, and 3-ply face mask wastes can be disposed by sterilization and the remaining 12.7, 17, and 20.44 tonnes/day by incineration, respectively.

To support the fulfillment of Sweden’s targets in term of climate change and economic growth, we need to do a distinct study to show the Environmental Kuznets Curve (EKC) pattern in different sector of the economy, as the GDP allocation, energy intensities, GHG emission, and technological development are different between sectors. This kind of study helps to figure out how the different sectors contribute to climate change and could appoint more particular and effective environment-energy policies. For this aim, we analyzed the existence of the EKC by implementing the ARDL Bound test approach in the whole and individual sectors of Sweden’s economy throughout 1990-2019. Our results indicated the contribution of a particular sector on total GHG emissions per capita. Results of the whole economy confirmed the EEKC hypothesis with a turning point in 1996, in which the AFF sector, unlike industry and service, had increased GHG emissions. Disaggregated sectoral analysis showed various results. The industry sector had efficient energy improvement. Policymakers should pay attention to AFF’s GHG emissions, as different sources of energy consumption had not impressive impact in both the short and long term. Also, effective fossil-related policies are necessary for the service sector due to the main contribution to transportation.

In Mirzapur (U.P.), a power-starved district, the UASB (Upflow Anaerobic Sludge Blanket) technique was adopted. Almost all of the available technologies do not treat heavy metals, so, is the case with the UASB also. The present study is to assess how much heavy metal can get accumulated in plant tissues in different species. The result of the present study was that the concentration of Pb(1106.31)>Zn(221.45)>Cd(49.26)>Hg(23.37) mg/Kg in the sludge while the concentration of Zn(93.35)>Pb(52.00)>Hg(16.93)>Cd(1.53) mg/Kg in the soil. When the sludge was mixed with the soil the trend got changed and the trend was Pb(596.36)>Zn(219.86)>Cd(24.70)>Hg(22.63) mg/Kg. Three different species that were chosen for the study were Basella Alba (Spinach), Solanum Lycopersicum (Tomato) & Brassica Juncea (Mustard). The trend of accumulation of studied heavy metals in the Brassica Juncea (Mustard) was Zn(85.33)>Pb(25.88)>Hg(11.23)>Cd(0.99) mg/Kg. In Solanum lycopersicum (Tomato) the trend was Pb(231.11)>Zn(108.72)>Hg(12.43)>Cd(9.41) mg/Kg  and in Basella alba (Spinach) was Zn(103.81)>Pb(83.90)>Hg(10.78)>Cd(4.18) mg/Kg. Overall the study reveals that the accumulation of heavy metals takes place in plants grown in soil mixed with sewage sludge. The reduction in the concentration of Pb, Cd, Hg and Zn in sludge mixed with soil after the harvesting of plant in case of Solanum lycopersicum were 39.38%, 47.93%, 6.18% and 49.89% respectively; while in case of Basella alba these were 25.23%, 57.53%, 71.58% and 49.16% respectively; and in case of Brassica Juncea these reduction were 25.86%, 60.80%, 70.96% and 49.04% respectively.

Technology has made the life of Bangladeshi people very flexible with new inventions. In most cases, here the people depend on these technological devices. These devices, along with various facilities, have also invited a series of problems mostly due to the lack of proper management. The Bangladeshi citizens often leave electronic devices that went bad or became unusable, in landfills, rivers, canals, and open spaces. As these devices possess a variety of toxic substances, dumping huge amounts of electronic waste can pollute the environment and threaten human health. Around 2.7 million metric tons of e-waste are generated each year in Bangladesh. Only 20-30% of this waste is recycled while the rest amount is released in landfills, rivers, canals, and open spaces posing a serious threat to the health and environment. Bangladesh has experienced rapid advancement in the technological sectors over the years. Therefore, it is a must to take steps necessary for avoiding the future jeopardized situation because of e-waste. The present e-waste management in Bangladesh experiences a number of drawbacks such as challenges in incentivization, unhygienic conditions of informal recycling, insufficient law and policy, less awareness, and lack of enthusiasm on part of the corporate to address the critical issues. In spite of the alarming levels of e-pollution in the country, the concerned authorities are yet to take any effective step or formulate any legislation to prevent the existing e-pollution. Moreover, the prevailing environmental laws are not adequate to address the issue and its application is still largely absent.