This study aims at assessing the health-related risk of As, Co, Cr, Ni, and Cu in the soil around Angouran Mineral Processing Complex (AMPC), due to environmentally sensitive nature of the area, having agricultural activities, habitats of animal and plant species, and industrial activities integrated with each other. Soil samples have been collected from 74 points (0-20 cm) of the area and concentrations of heavy metals have been measured, using ICP-OES. The Geoaccumulation Index (Igeo), Enrichment Factor (EF), and Integrated Pollution Index (IPI) have been used to examine the pollution level. Moreover, hazard indices (HI), hazard quotient (HQ) and cancer risk (CR) have been utilized to assess the non-carcinogenic and carcinogenic health risks of heavy metals. The average concentration of heavy metals indicates that metals’ concentration in the soil have increased in the following order: Cr = Ni> As> Cu> Co. Results from Igeo, Ef, and IPI show that As and Ni are placed in the very high pollution category. The non-carcinogenic risk of dermal absorption (adults = 1.30 E + 00, children = 1.35 E + 00) of Cr and Co polluted particles turn out to be very high. In addition, the risk of cancer as a result of the ingestion of As- and Cr-contaminated soil particles is high in both of age groups, with children being 68% more likely to be at risk of cancer than adults. Therefore, actions such as soil remediation should be done to reduce the risk of exposure and protect the health of the residents, especially the farmers.

Energy recovery is a sustainable method of municipal solid waste (MSW) management. The co-incineration of refuse derived fuel (RDF) has shown several economic and environmental advantages. The objective of this research is to assess the impact of RDF recovery on leachate quality using leachate tests and calculation of greenhouse gases (GHG) reduction in the kilns of a cement plant. The qualitative results of the eluate show that there is an impact on leachate quality depending on the type of waste. The values of the chemical oxygen demand (COD), biological oxygen demand (BOD5), electrical conductivity and pH of the leachate from the raw waste after 120 hours of leaching are 29.33 gO2/kg DM, 14.00 g O2/kg DM, 4.27 ms/cm and 7.57. On the other hand, the values of the same quality parameters of the eluate generated by the waste without RDF are 19.33 g O2/kg DM, 20.67 g O2/kg DM, 2.77 ms/cm and 7.13; respectively. The calculation of GHG reduction shows that the substitution of 83,000 tonnes per year of petroleum coke by 15% of RDF (25,493 tonnes per year) can reduces 28,970 tCO2 eq.

Transformation of food and beverage industrial sludge into vermicompost into value-added product simultaneously can control gaseous emission. Addition of biochar in the vermicomposting as a bulking agent increases fertilizer value. This research aimed to investigate the effect of biochar amendment on vermicomposting of the food and beverage industry sludge (FBIS) and cow dung (CD) in a different ratio using earthworm Eisenia fetida. We had further investigated the survival rate of E. fetida and the cocoon productions after 35 days of the vermicomposting. Besides, we have also evaluated the seed germination bioassay using Malabar spinach (Basella alba) to determine the toxicity and maturity of produced compost. The survival and cocoon production of E. fetida were higher in vermicompost amended with 10% biochar. Vermicomposting with biochar resulted in a slight pH shift. Reduction in organic carbon (OC) percentage not so significant in biochar added FBIS and CD. An increase in phosphorus and potassium content and a decrease in nitrogen percentage observed; vermicomposting with biochar resulted in higher seed germination, root elongation, and germination index than vermicomposting without biochar.

This research evaluates the carcinogenic and non-carcinogenic risks from cadmium, lead, and zinc in Bandar Abbas groundwater sources. The samples from 25 wells were analyzed for cadmium, lead and zinc. Total lifetime cancer risk and non-cancer risk assessment from exposure to these pollutants in drinking water (ingestion, inhalation and skin routes) were conducted for people living in these villages. In these regions most of the drinking water supplied, are from these wells which shows the importance of analyzing the quality of them in order to prevent diseases and cancer risks. The highest risk from cadmium seems to be in village Dehno Paein and also this amount for lead occurs in Tifakan Tal-e Gerdu. The highest hazard index (HI) based on human health risk assessment (HHRA) model for cadmium, lead, and zinc through oral, inhalation and dermal pathways were computed as 0.005, 1.63 and 0.043 which are in Dehno Paein, Tifakan Tal-e Gerdu and Faryab. Results show that lead can lead to more cancer cases in these villages that cadmium. The total expected cancer cases from exposure to cadmium in different routes are lower than lead.

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.

This paper presents concentrations and sources of Aliphatic and Aromatic Hydrocarbons in the sediments from Babolsar coastal area and the inlet of Babolrood River in the southern side of the Caspian Sea. The concentration of hydrocarbons in 13 sediment samples from the study area were measured by gas chromatography (GC). Total Petroleum Hydrocarbon (TPH) concentrations in sediment samples in the coastal area ranged from 115 to 201 μg/g. In the inlet samples, TPH concentrations were close to each other and ranged from 294 to 367 μg/g. The TPH results showed moderate level of oil pollution in the study area. Total Polycyclic Aromatic Hydrocarbons (ΣPAHs) concentrations in sediment samples inside the inlet ranged from 498 to 702 ng/g, indicating moderate level of pollution. Concentrations of ΣPAHs in sediment samples in the coastal area ranged from 341 to 1703 ng/g, indicating moderate to less than significant level of pollution. Developed indices for pollutant origins showed that hydrocarbons in all sediment samples collected in the study area had petrogenic origin. The results also showed the Babolrood River as the main source of oil pollution in the sediments in the study area.

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.

I is missing this work, a computational simulation has been performed to investigate the positional effect of reburn fuel injection on NO-reburn. Reburn fuel methane is injected across the coal injection plane at different axial positions of the combustor. Various major NO source mechanisms are considered for NO formation and NO reburn mechanism is used for NO depletion. Temperature profile, species concentration are also investigated, as both NO formation and depletion rate depends on these parameters. It has been observed that, a high temperature flame exists near coal inlet, when the reburn fuel injection plane is closer to coal inlet. On the other hand, the temperature of the flame near the coal inlet decreases when the reburn fuel injection position is far away from coal inlet region.  Moreover, NO sources are observed near coal inlet region, when the reburn fuel is injected closer to coal inlet. On the other hand, only Fuel-NO is observed near coal inlet, when the reburn fuel is injected away from the coal inlet. Maximum NO reduction efficiency is observed at outlet plane when reburn fuel is injected closer to inlet, whereas a relatively lower NO reduction efficiency has been observed at outlet plane when reburn fuel is injected far away from coal inlet region.

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.

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.