Computational toxicology is a rapidly growing field that utilizes artificial intelligence (AI) and machine learning (ML) to predict the toxicity of chemical compounds. Computational toxicology is an important tool for assessing the risks associated with the exposure of finfish and shellfish to environmental contaminants. By providing insights into the behavior and effects of these compounds, computational models can help to inform management decisions and protect the health of aquatic ecosystems and the humans who depend on them for food and recreation. In aqua-toxicology research, Quantitative Structure-Activity Relationship (QSAR) models are commonly used to establish the relationship between chemical structures and their aquatic toxicity. Various ML algorithms have been developed to construct QSAR models, including Random Forest (RF), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Bayesian networks (BNs), k-Nearest Neighbor (kNN), Probabilistic Neural Networks (PNNs), Naïve Bayes, and Decision Trees. Deep learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have also been applied in computational toxicology to improve the accuracy of QSAR predictions. Moreover, data mining graphs, networks and graph kernels have been utilized to extract relevant features from chemical structures and improve predictive capabilities. In conclusion, the application of artificial intelligence and machine learning in the field of computational toxicology has immense potential to revolutionize aquatic toxicology research. Through the utilization of advanced algorithms and data analysis techniques, scientists can now better understand and predict the effects of various toxicants on aquatic organisms.

The presence of hydrogen sulfide (H2S) gas in the muddy ecosystems is consequence of anthropogenic interference. To understand ecosystem health present study was intended to gauge H2S concentrations involved in annihilation of meiofauna and associated aquatic life from four hotspots including Manora channel, Korangi creeks, Sonmiani, and Bhambhore along the Pakistan coastal belt. Using a handheld gas detector device, it was observed that Bhambhore exhibited lower levels of H2S therefore embraces numerous benthic organisms whereas Manora channel (backwater) and Korangi creek area showed elevated level that does not allow macro-organisms to stay around. The diversity varied across locations, with Bhambhore collecting the most species of mudskippers and Manora creeks collecting the rarest. Overall result of this study reveals that H2S 5~274 ppm is alarming. The data of crabs, mudskippers, fishes, mantis shrimps, shells in relation to the environmental variables of temperature, salinity, conductivity dissolved oxygen and H2S were used to develop canonical correspondence analysis. The variability among first two components was 64.47 and 28.44%, eigenvalue (0.154, 0.068 and trace 0.239) respectively. Considering baseline findings of this study, greater efforts are required for ecosystem resilience for the sake of human health concerns.

Thermal power plants are one of the main sources of CO2 emissions in the world. On the other hand, increasing carbon dioxide emissions as a greenhouse gas is led to global warming and climate change. In this study, CO2 mitigation strategies for Iran’s thermal power plants regarding Intended Nationally Determined Contributions submitted by Iran using modified STIRPAT model examines are presented. In the first step of this research, CO2 emissions from Iran’s power sector are predicted with respect to the parameters including, population, GDP, and electricity generation. In the second step of this research, CO2 mitigation strategies including, using the renewable sources and increasing energy saving as well as power generation efficiency during the years of 2020 to 2025 are analyzed using modified STIRPAT model to reduce carbon dioxide emissions in accordance with Iran’s INDCs. The prediction of carbon dioxide emissions by 2025 represents an increase of 26.5% in carbon dioxide emissions compared to 2017 while estimating carbon dioxide emissions in accordance with Iran’s INDCs allows a maximum increase of 21.4% compared to 2017. In order to reduce carbon dioxide emissions, the average efficiency of power plants by 2025 should be 1.542% higher than in 2017, or 3.086% of the energy savings should be implemented compared to total electricity generation output projected in 2025, or more than 36.22% increment of electricity generation output from renewable energy is expected compared to the projected level in 2025, or a combination of these three solutions.

Numerous coagulants, including natural and chemical coagulants, have been examined in the context of water purification. The use of natural coagulants constitutes an affordable and eco-friendly method of purifying water. The main aim of the current study was represented by investigated the feasibility of coagulant extracted from Castanea Sativa Tree Leaves using three different salts and distilled water. The active coagulant component was extracted using 0.25, 0.5, and 1 M of NaCl and KCl, 0.025, 0.05, and 0.1 M of NaOH, and distilled water. Powdered Castanea Sativa Tree Leaves was also used as a coagulant. Jar tests were performed using synthetic turbid water, a turbidity level of 35 NTU to investigate the coagulants’ activity. The pH was measured to study the influence of a range of different pHs, coagulant doses and initial turbidity were also investigated to optimize the coagulation process. The highest level of activity was achieved using 0.5 ml/l of coagulant extracted with 0.5 M NaCl at pH level 8. Coagulant extracted using 0.05 M NaOH demonstrated the second highest level of activity. Poor coagulant activity was observed for the powdered Castanea Sativa Tree Leaves and distilled water extract. The protein content of the extracted coagulant was 0.322, 0.283, and 0.274 mg/ml using 0.05 M NaCl, 0.5 M NaOH, and 0.5 M KCl, respectively. The use of this natural coagulant was also found to moderately increase organic matter content in the treated water, which was proportional to protein contents of the extracts. Coagulation results were statistically examined using SigmaPlot 12.5 software.

Cong-red dye is a precursor of various products of cotton industry and its toxicity in the aquatic environment is a great concern. Present study was highlighted on the efficacy of the fish scale char (FSC) towards removal of congo red from aqueous solution. The prepared FSC was characterized by zero point charge (pHZPC), scanning electron micrograph with elemental analysis (SEM-EDX) and fourier transform infrared (FTIR). Based in the equilibrium and kinetic study, the Langmuir (R2 = 0.967) and Pseudo-second-order (R2 = 1.00) models are appropriate to describe the dye adsorption process. The randomness and exothermic nature of the system were confirmed by the negative values of both entropy and enthalpy, respectively. Finally, optimization by Response Surface Methodology (RSM) study revealed that the experimental data were nicely fitted with central composite design with very high F value (F = 1596.24, p < 0.0001). Perturbation plot suggested that congo-red dye removal is more sensitive with respect to biosorbent dose, pH and initial concentration. The exhausted adsorbent was regenerated with 0.5(M) NaOH solution. Therefore, it can be concluded that fish scale char could be a valuable materials towards purification of industrial effluent.

Thermochemical conversion of biomass and petrochemical wastes blend is an excellent method to produce valuable fuels and reduce environmental pollution. Bio-oil production via blending of paulownia wood and polypropylene plastic was investigated in a fixed bed horizontal reactor at different reaction temperatures and different Polymer/ Biomass weight ratios. Biomass showed the highest amount of bio-oil production (52.8 wt.%) at 500°C. The results show that with increase in temperature, the production of lighter products (with lower carbon number) has increased. Co-pyrolysis on a horizontal reactor showed positive synergy for the production of liquid and gaseous products. Bio-oil production increased to 61.03 wt.% and the relative oxygen content of the liquid products decreased. In co-pyrolysis with the ratio of 60:40 of PAW: PP, aromatic compounds with 35% by weight constitute the highest amount of liquid product and production of furans and aldehyde/ketones reduced. While this number is equal to 8% for blend of 5% PP and 95% PAW.

Environmental threats from the accumulation of plastic trash are getting worse.  It is robust, lightweight, corrosion-resistant, affordable, and durable. Microorganisms play a significant role in protecting our environment by degrading plastic wastes that are harmful either naturally or by chemical modification.  The current study aims to investigate the biodegradation of synthetic polyethylene through the utilization of a laboratory bioreactor. Various types of additives were introduced to the soil samples before subjecting them to a 30-day UV treatment. The degradation of polyethylene was shown through a reduction in weight following a 24-week incubation period with certain bacterial strains. Experimental findings have revealed that models subjected to UV radiation exhibit the highest degree of vulnerability and degradation. Approximately 52% of polyethylene (PE) films underwent degradation when exposed to soil enhanced with peat moss. In contrast, only 40% and 45% of PE films were destroyed when subjected to garden soil that was untreated and treated with UV radiation, respectively. In contrast, the addition of husk resulted in a 48% to 53% reduction in weight for PE films that were buried for the same duration of the experiment.  The highest level of effectiveness was achieved by the disintegration of the plastic material that was introduced into the soil along with organic fertilizers, resulting in a value of 56.60%. The weight loss outcomes have been substantiated by the utilization of the Atomic Force Electron Microscope (AFM) images, which exhibited the highest magnitude in the experimental model using soil supplemented with fertilizers.

There is a belief that ambient air pollution  is accountable for degrading the air quality indoors. Although in principle the indoor air quality should be better than that of outdoor air quality given the shielding effect of a house structure. However, ambient air quality can infiltrate and influence indoor air pollution concentrations in low-income urban informal settlements due to rudimentary designed household structures. Given this phenomenon, the current study endeavoured to explore the influence of outdoor exposure concentration on indoor air quality within the informal settlements of urban neighbourhoods. The exposure concentrations of indoor and outdoor particulate matter and nitrogen dioxide pollutants were simultaneously measured during summer and winter seasons. The GilAir Plus air sample pump was used to acquire measurements of particulate matter collected over 48 hours. While nitrogen dioxide gases were measured using passive diffusive samplers. All statistical analyses were performed using Python (version 3.8) Spyder. The current study has discovered that in many instances the results were comparable indoors and outdoors. For instance, this has been corroborated by the nitrogen dioxide discoveries where the current results were slightly comparable as indoor exposure concentrations values were recorded to be between (4 µg/m3 and 13 µg/m3), whilst the outdoor concentration ranged between (6 µg/m3 and 11 µg/m3). Likewise, a similar trend was observed for particulate matter exposure concentrations indoors (14 µg/m3 ) and (12 µg/m3) outdoors. The statistical inferences futher confirmed that the exposure values of indoor and outdoor were not significant (p>0.05) within the study areas of concern.

This investigation aims to study synoptic analysis in the dynamic structure accompanied by air pollution of extreme heat during July 2019 in the Yazd province. The time-series data analysis for the yearly surface air temperatures during the past two decades shows a significant peak surface air temperature in July 2019 in Yazd province. The long-term mean and anomalies of the daily basis (2001 to 2019) for the daily mean sea level pressure show a decrease in pressure with a maximum of about 6 hPa and an increase in geopotential height at 500 hPa with a maximum of about 20-30 gpm (geopotential meter), which has led to an increase in the average daily temperature of about 2 to 4 degrees Celsius. Also, showed high values for Ozone mass mixing ratio over the study area mostly over the west with a maximum of ~92 ppb in Yazd province on 1 July 2019. The AIRS (Atmospheric Infrared Sounder on NASA's Aqua satellite) data shows a positive trend (2003- 2019) for the total daytime Ozone column-averaged over the study area during July. Furthermore, the results of this work obtained from OMI satellite observation show a significant increase in the ultraviolet aerosol index (UVAI) during the study period time. This study shows the recent extreme weather changes in the study area which may be necessary for a better future forecast for heat warnings along with poor air quality and health risk when such events may happen in the future.

Today, the problem of air pollution has been highlighted by rapid population growth and urbanisation, along with the development of industry. Over the last fifty years, much attention has been paid to the relationship between lichens and airborne particulate matter (especially heavy metals). The use of living organisms in air pollution studies is now widely accepted in many countries and the results of these biomonitoring studies are very important for future action. The goal of this study was to determine heavy metals in Kırşehir province using the bag technique, a biomonitoring approach, with Pseudevernia furfuracea (L.) Zopf lichen and to develop a pollution map of the city. In November 2002, lichen specimens were obtained from an unpolluted region in the Yapraklı Mountains, Çankırı, and transplanted to 4 distinct places in Kırşehir. After 3 and 6 months of exposure, they were collected in order to analyse heavy metals (Cu, Cd, Mn, Ni, Pb and Zn) with Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). In addition, chlorophyll-a and chlorophyll-b contents were determined by Dimethyl sulfoxide (DMSO) method. The findings revealed that the heavy metal contents in various stations are the result of industrial, traffic, and heating activities. As a result, P. furfuracea showed excellent bioindicator ability for detecting air pollution.