Predicting indoor air quality during infectious disease conditions relies on models simulating particle materials (PM)/bioaerosols distribution. Understanding the thermo-fluid properties of exhaled air is crucial for comprehending disease transmission dynamics. This study employs a computational fluid dynamics (CFD) model to simulate cough-induced particle dispersion in a closed space. Furthermore, the number of released particles and the presence of SARS-CoV-2 viral genomes by a cough were assessed (in eight COVID-19 patients). According to the CFD model, in the first 30 s of cough, the vertical height and lateral breadth of the particles' dispersion were up to 138cm and 92cm, respectively. As the distance from the patient's respiratory zone increased, the lateral distribution width of particles expanded, reaching 1.3 m at 2.4 m away. Larger droplets (> 62.5µ) were deposited at shorter distances, while smaller particles remained airborne longer. The comparison of experimental and simulated results focused on particle dispersion at specific distances from the patient, particularly in the 2.5µ range. The distribution pattern of PM and PM at a distance of 1 and 2 m for women, not men, is similar to the distribution pattern of PM in CFD modeling. Viral genome detection was more prevalent in particles near the left side of the body, especially within the first 20 min post-cough, exhibiting a correlation with CFD predictions.

The aim of this study was to investigate the impact of improved deep learning model on the predictive performance of PM concentration.

Environmental pollution is increasingly negatively affecting our lives, requiring advanced methods and materials that are highly effective for pollutant treatment processes. This study proposes the synthesis of zero-valent iron nanoparticles (nZVIs) through a green chemistry approach, which were then encapsulated in calcium alginate (Alg) spheres for application in the treatment of Rhodamine B (RhB) and Methylene Blue (MB). The morphology and structure of the alginate particles encapsulating zero-valent iron nanoparticles (Alg-nZVIs) were characterized by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). The analytical results indicate that the material consists of alginate polymer particles with an average diameter of 2.5 mm, containing nZVIs with an average size of 50 nm. Factors affecting the treatment of RhB and MB, including the proportion of components in the material, pH, solution concentration, and treatment time, were studied and evaluated by UV-Vis method. This material showed high removal efficiency for RhB and MB. 0.08 ml nZVIs in 1 g of Alg-nZVIs beads treated 100 mL of RhB 5 mg/L at pH 7 for 180 min with an efficiency of over 90%. The same amount of material effectively treated 100 mL of MB 5 g/L at pH 3 for 120 min with an efficiency of over 90%. The prepared Alg-nZVIs spheres were easy to collect and reuse for up to 6 cycles with a decrease in removal efficiency of less than 15%. Alginate-nZVIs spheres are derived from readily available and natural materials through a clean, cost-effective, and economically sustainable technique.

The Naseri Artificial Wetland was created by the discharge of agricultural drainage water, including effluent from the sugarcane development project. The continuous inflow of drainage water from the sugarcane development units has altered the natural regime of the wetland. Considering the high probability of herbicides entering agricultural runoff, this study was conducted to identify atrazine and to assess the health risks of it in this wetland.

Biofuels and bioenergy production are increasingly being viewed as viable alternatives to conventional energy sources because of their renewable nature and ability to reduce greenhouse gas emissions. Waste products, lignocellulosic materials, and agricultural residues are some of the feedstocks that can be used to create biofuels, including biodiesel, bioethanol, and biogas. The production of biofuels not only promotes sustainable energy but also addresses environmental problems. This review article explores the challenges posed by dependence on non-sustainable resources and the environmental benefits of renewable energy sources. It provides a detailed examination of the advancements, possibilities, and obstacles linked to biofuels and bioenergy. It outlines the harmful effects of prolonged fossil fuel use on the environment, including soil degradation, air and water contamination, and climate change, highlighting the critical necessity to shift towards renewable energy alternatives. The analysis evaluates the socioeconomic effects of bioenergy and its capacity to enhance food and energy security, generate employment, and boost rural economies. Nevertheless, it also recognizes important obstacles that need to be overcome for wider adoption, competition with food crops, issues related to water consumption, and regulatory constraints. It explores the potential of hydrogen fuel cell vehicles and battery electric vehicles as replacements for conventional vehicles that rely on fossil fuels. It emphasizes the need to explore alternative feedstock sources and implement next-generation conversion processes prioritising environmental sustainability by incorporating recent advancements in machine intelligence (MI), including machine learning and artificial intelligence techniques. The study dedicates considerable effort to exploring the global regulatory and policy landscape, including how various nations promote bioenergy initiatives through financial incentives, blending mandates, and sustainability criteria. To encourage the adoption of bioenergy solutions and facilitate a fair and effective energy transition, the research winds up by highlighting the importance of international collaboration, interdisciplinary investigation, and innovation. With the appropriate laws and technologies in place, biofuels and bioenergy could play an important role in achieving a sustainable, low-carbon future.

Mitigating the pollution of water by emerging contaminants (ECs) presents a critical environmental challenge that demands innovative, effective, cost-efficient, and sustainable strategies. In this study, the potential of TiO₂-modified activated carbon (AC) for the sequestration of ECs from water was evaluated through a combined experimental and in silico approach, using molecular modeling based on density functional theory (DFT). Unmodified AC removed 67.76-82.09% of ECs such as carbamazepine, flumequine, clarithromycin, azithromycin, and roxithromycin, and 44.54-52.27% of sulfamerazine, sulfamethoxazole, sulfamonomethoxine, trimethoprim, and levofloxacin. Incorporating TiO₂ and utilizing sunlight improved removal efficiencies to 93.09-99.91%. The hydrophobicity of contaminants significantly influenced adsorption. Kinetic and isotherm analyses indicated chemical interaction-driven, monolayer adsorption, with the Langmuir model fitting best (R² = 0.9856-0.9975). Textural analysis of TiO₂-AC (10% TiO₂) revealed a surface area of 557.72 m²·g⁻¹ and a pore volume of 0.317 cm³·g⁻¹, supporting its high adsorption potential. Fourier transform infrared spectroscopy and molecular modeling identified functional groups facilitating adsorption, while DFT provided insights into energetic and non-covalent interactions (NC-interaction) including hydrogen bonding, van der Waals forces (VDW-forces), and charge transfer that occur during the process. TiO₂-modified AC demonstrates high efficiency for pharmaceutical removal from water, highlighting great promise as a sustainable and advanced adsorbent material, offering practical solutions for tackling diverse water pollution challenges.

Antibiotic contamination in aquatic systems demands advanced oxidation solutions. This study develops a nano zero-valent iron (nZVI)-activated peroxide system to address sulfadiazine (SDZ) persistence and associated ecological risks.

Climate change is a global issue that presents significant challenges for countries worldwide, including Iran. Researchers need up-to-date information on climate change within their own country, including statistics on its severity, efforts to address it, and the impacts on the environment, temperatures, extreme weather events, water resources, agriculture, biodiversity, migration, air quality, and human health. This review provides an overview of these topics in the context of Iran, discussing challenges, sustainable practices, renewable energy, government responses, and international collaborations to mitigate climate change effects. It aims to offer a comprehensive perspective on the current and potential future implications of climate change in Iran. Climate change in Iran has resulted in higher temperatures, droughts, and wildfires, impacting agriculture and exacerbating water scarcity. Extreme weather events such as floods and storms are causing damage to infrastructure. Climate change poses a significant threat to global health, with direct consequences including severe storms, heat stress, and deteriorating air quality. Despite this uncertainty, it is imperative to adapt to the adverse effects of climate change. Rising global temperatures are contributing to more frequent and severe extreme weather events, resulting in widespread damage and loss of life. Iran's efforts to address climate change include investing in renewable energy, and implementing sustainable practices. Collaboration between the government and local communities is crucial for mitigating the effects of climate change through effective policies and initiatives. Iran aims to reduce greenhouse gas emissions and promote sustainability through investments in renewable energy and energy efficiency initiatives.

Comprehensive metabolomic profiling in reproductive medicine is sought to clarify the specific mechanisms underlying potential exposome-metabolome interactions in adverse pregnancy outcomes. Taking the advantages of longitudinal data, untargeted metabolomics, and machine learning coupled with traditional analysis, we aimed to study the associations between altered metabolome in the first and third trimesters of pregnancy and subsequent implications to explore causal associations. Totally, 201 pregnant women from a low- and middle-income community (LMIC), known for high levels of environmental pollution, were enrolled during their first trimester, 13 ended in pregnancy failure. Gas chromatography-mass spectrometry (GC-MS) was used to obtain untargeted metabolic profiles and to quantify relative levels of metabolome signatures in serum samples. Data processing and analysis were conducted to select features associated with adverse pregnancy outcomes (including miscarriage, stillbirth, preterm birth, and infant death), adjusting for participants' occupational status, education level, smoking, and the season of conception. Metabolic network and pathway enrichment analyses were then conducted to explore metabolome-associated pregnancy failure. Statistical and machine learning methods were used to visualize the associations between metabolomic features and the risk of adverse pregnancy and neonatal outcomes, accounting for other covariates. The pattern of associations between maternal metabolome during pregnancy and birth outcomes revealed a clear separation of pregnancy failure cases from medically approved healthy-term births ( < 0.05). L-alanine, dioctyl phthalate, L-phenylalanine, L-threonine, cholesterol, L-serine, proline, L-isoleucine, L-valine, arabinofuranose and gluconic acid were upregulated in the pregnancy failure participants, while glycine, L-lactic acid, arachidonic acid, L-tryptophan, creatinine, palmitic acid, L-tyrosine, ornithine, glutamic acid, phosphate, 1,5-anhydrosorbitol, taurine, 3-hydroxybutyric acid, oxoproline, D-glucose, oleic acid and linoleic acid were less abundant. Specific metabolite patterns linked to pregnancy failure were discovered by machine learning methods over the course of pregnancy. Our analysis identified L-alanine, cholesterol, D-glucose, and urea as potential biomarkers for the early detection of pregnancy failure. While promising, further studies are needed to validate these findings and assess their clinical applicability, particularly in populations highly exposed to environmental pollutants.

Adsorption is currently one of the promising technologies widely used in the clean-up of heavy metal ions in the aquatic environment due to its affordability, ease of use, and efficiency. The study investigated the efficacy of activated and functionalised carbon prepared from the stem bark of (C-PA) using ethylenediaminetetraacetic acid (EDTA), to obtain M-PA.

This study aims to evaluate the environmental performance of a hypothetical wastewater treatment plant (WWTP) with activated sludge modeling and life cycle assessment (LCA). In order to simulate the treatment performance of an AO (anaerobic-anoxic-aerobic) process for low-, medium-, and high-strength municipal wastewaters, activated sludge model no.3 (ASM3) was employed. Simulation results were then used for performing LCA of wastewater treatment plant to assess the environmental impacts associated with wastewater treatment system. Additionally, net environmental benefit (NEB) approach that is useful for wastewater systems was also used to determine the eutrophication potential reduction of the hypothetical WWTP. The LCA results show that global warming, photochemical oxidation, and eutrophication potential impact categories were affected by characteristics of wastewater treated. The highest values of these impact categories (7.87E-01 kg CO-eq., 1.73E-04 kg CH-eq., and 1.28E-02 kg PO-eq./m.treated wastewater; respectively) were determined for high-strength wastewater. Considering eutrophication potential reduction, the highest NEB value was found 0.042 kg PO-eq/m.wastewater for high-strength wastewater, followed by medium-strength (0.027 kg PO-eq/m.wastewater) and low-strength (0.013 kg PO-eq/m.wastewater) wastewater. The results of the study is crucial to indicate that combining LCA with other decision support tools ensures achieving predictive and reliable results for proving the performance of WWTPs.

Alzheimer's disease (AD) is a prevalent and severe neurodegenerative disorder influenced by both genetic and environmental factors-such as air pollution, toxic elements, pesticides, and infectious agents. In recent years, machine learning techniques have become essential in biomedical research, advancing fields like drug delivery and medical imaging through predictive modeling and pattern recognition. Functional connectivity derived from functional magnetic resonance imaging (fMRI) serves as a promising noninvasive biomarker for AD by mapping the brain's connectome and revealing neural network disruptions. In this study, we employed the Robust Multitask Feature Extraction Method to evaluate six supervised machine learning algorithms logistic regression, naïve Bayes, support vector machine, random forest, XGBoost, and CatBoostmfor AD diagnosis. A dataset of 140 fMRI images from an equal number of AD patients and healthy individuals (mean age 67.3 ± 6.7 years) was analyzed. The XGBoost algorithm demonstrated exceptional performance, achieving an accuracy of 98.2%, a recall of 96.6%, perfect precision (100%), an F1-Score of 98.2%, and a Matthews correlation coefficient of 0.96 effectively minimizing false positives and negatives. Although CatBoost and Random Forest also yielded robust results, logistic regression and naïve Bayes showed lower reliability. Overall, XGBoost emerges as a robust solution for the early and precise prediction of Alzheimer's disease, carrying significant implications for proactive patient care and treatment strategies. Beyond these findings, emerging research is exploring multimodal imaging techniques-such as PET and EEG and deeper neural network architectures to further enhance early diagnostic accuracy and treatment personalization in AD.

The research conducted a pilot-scale comparative study of these four emerging aerobic sewage treatment technologies: R1 - Conventional MBBR (Moving Bed Biofilm Reactor), R2 - Conventional IFAS (Integrated Fixed Film Activated Sludge), R3 - Conventional AGS (Aerobic Granular Sludge), and R4 - hybrid configuration of AGS partly filled with high-performance biocarrier (AGS-BF). The reactors were compared in terms of biomass formation time, system performance and stability, area demand, energy consumption, and other engineering aspects. COD removal and nitrification were high for all reactors. However, total nitrogen removal was moderate for reactors R1 (48%) and R2 (54%), but values ​​above 70% were observed for R3 and R4. P removal was also possible, reaching values ​​below 52% for the MBBR (R1) and IFAS (R2) systems but with values ​​above 80% in the aerobic granulation systems R3 and R4. The inclusion of biocarrier in R4 significantly improved the effluent quality in terms of suspended solids, as well as the denitrification process. AGS reactors offer a 6% area reduction compared to MBBR and IFAS. However, the type of biocarrier used interferes with this direct comparison. In general terms, a reduction of 19% in the electrical energy demand of the aerobic granulation systems was achieved compared to MBBR and 30.5% to the IFAS. The results obtained in this research enable a more comprehensive comparative analysis of the various evaluated emerging aerobic technologies, providing valuable data for the design and operation of sewage treatment plants.

Bioenergy plays a crucial role in addressing the global energy crisis. The utilization of agricultural byproducts for biofuel production through fermentation is well-established. Among various pretreatment methods, breaking lignin and cellulose bonds under heat and pressure to release sugar moieties is the most predominant approach. This study focuses on enhancing sugar yield through the most economical, energy-efficient, and time-saving pretreatment of the highly underrated agricultural residue, cocoa pod shell (CPS), using green-synthesized FeO nanoparticles derived from CPS extract. The synthesized nanoparticles, ranging from 25 nm to 31 nm in size, exhibited an EDS spectrum confirming the atomic composition of C (30.01%), Fe (6.09%), O (59.76%), N (2.36%), P (0.79%), Cl (0.53%), and K (0.46%). FTIR analysis revealed the presence of O-H, C-H, C-Cl, and O = C = O stretching, indicating effective nanoparticle capping. The novel ex-situ hydrolysis process, coupled with induction heating, yielded 356.04 g/L of total sugars and 60.28 g/L of reducing sugars using 10% w/v biomass and 4% acid within just 30 min. RSM and ANN modeling were employed for process validation, yielding R² values of 0.91 and 0.92 for total and reducing sugars, respectively, while ANN modeling achieved R² values of 0.96 and 0.97. This energy-efficient hydrolysis process achieved a significant sugar yield in less time while requiring minimal raw material. It presents a scalable and reliable approach to the industries, providing a promising direction for biofuel production.

A magnetic double-layer metal-organic framework composite (FeO@ZIF-8@ZIF-67) was successfully synthesized through a facile layer-by-layer self-assembly method at room temperature and thoroughly characterized using various techniques. The composite FeO@ZIF-8@ZIF-67 was explored as an adsorbent for the removal of two harmful organic pollutants, Congo red (CR) and tetracycline hydrochloride (TC). Some essential parameters, including initial concentration, adsorbent dose, contact time, pH, and temperature, were systematically optimized. Under optimal conditions, FeO@ZIF-8@ZIF-67 demonstrated the maximum adsorption capacities of 276.77 mg/g for CR and and 356.12 mg/g for TC, respectively. The double-layer structure endowed FeO@ZIF-8@ZIF-67 high adsorption efficiency for CR (99.44%) than the pristine FeO@ZIF-8 (73.26%). Adsorption kinetics and isotherms studies revealed that the adsorption process followed pseudo-second-order kinetics and Langmuir model, indicating a monolayer chemisorption-dominated mechanism. Furthermore, the spent FeO@ZIF-8@ZIF-67 was regenerated through a Fenton-like oxidative degradation reaction, maintaining a removal efficiency above 70% after three consecutive cycles. With its facile synthesis, cost-effectiveness, mild operating conditions, and high selectivity for anionic dyes, FeO@ZIF-8@ZIF-67 emerges as a highly promising material for advanced wastewater treatment applications.

Phthalates are one of the most common environmental contaminants and endocrine disruptors. Environmental exposure to phthalates may increase the risk for allergic diseases. However, the existing literature presents conflicting findings regarding the long-term impact of early-life exposure to these substances.

The rising infiltration of microplastics (MPs) into aquatic environments is a complex and alarming threat jeopardizing marine biodiversity, destabilizing entire ecosystems, and endangering human health. Traditional methods for identifying and characterizing microplastics are often manual, requiring significant time and effort due to the small size, diverse shapes, and varying sources of microplastics. By integrating artificial intelligence (AI) with traditional environmental approaches, we can make significant progress in mitigating the influence of microplastics on aquatic ecosystems and health of humans. This review emphasizes the goals, benefits, results, and key insights of emerging robotics and various AI models across three critical areas: collection and sorting of microplastic waste, characterization of microplastic waste to determine its abundance, size and chemical composition and predicting and monitoring microplastic degradation. Several countries and organizations are using AI technologies to address microplastic pollution through innovative projects and supportive policies. The review aims to highlight these successful initiatives focused on monitoring, prevention, and cleanup of microplastics in aquatic environments. Further, challenges and future research opportunities on integrating robotics and AI technologies in mitigating microplastic pollution have also been discussed.

Human pesticide exposure results in the development of several chronic diseases, including cardiometabolic, carcinogenic, neurological, and autoimmune processes. The induction of oxidative stress, subsequent tissue injury, and inflammatory response are widely accepted mechanisms related to environmental pollutants-induced diseases. In this line, several studies have been reported on the induction of systematic inflammatory state related to pesticide exposure. Nevertheless, there needs to be a consensus on the best inflammatory biomarker for measuring in response to pesticide exposure, and sources of risk of bias need to be assessed for future studies. This meta-analysis assessed whether pesticide exposure can start an inflammatory response in humans. A systematic review was performed focused on original reports that analyzed the relationship between human pesticide exposure and pro-inflammatory biomarkers. Fifteen studies were analyzed. The present meta-analysis included 3172 participants. The pooled analysis suggested that pesticide exposure can induce an inflammatory response and indicates that standardized clinical inflammatory biomarkers such as C-reactive protein are more recommended than hematological index or pro-inflammatory cytokines. Moreover, the need to consider multivariate statistical analysis is noted. The results suggested that pesticide-induced inflammatory response could be considered a mechanism through pesticide-induced diseases. These findings contribute to our understanding of the health effects of pesticide exposure and show the need for performing future studies to explore this area further, potentially leading to improved public health strategies.

The carcinogenicity of air pollution has been well established and is considered a threat to humans worldwide. Researchers have concluded although the properties of particulate matter (PM) such as size, shape, and mass are of primary importance for the study of air quality, another parameter such as oxidation potential (OP) can be used to determine particle toxicity or the health consequences related to PM samples. Here, the present study examines the characteristics of PM components and their associated oxidation potential in the ambient air of Tehran, Iran using the dithiothreitol (DTT) assay. This study also compares the values of OP, and chemical composition (e.g.; anions and cations, metalloids, and polycyclic aromatic hydrocarbons (PAHs)) in the ambient air of Tehran with other urban areas globally. Sampling was conducted for nine months during three seasons: spring, summer, and autumn, in the ambient air of Tehran city, the capital of Iran from 2021/4/17 to 2021/12/6. According to the US EPA's Sampling Schedule, a high-volume air sampler (operating at a flow rate of 1.415 m/min) was employed for PM on fiberglass filters once every six days. The average value of DTTv was equal to 0.8 ± 0.3 (nmol.minm). The average values of DTTm were equal to 0.017 ± 0.01 (nmol.min µg). Although the values of DTTv and DTTm in Tehran were relatively tolerable compared to other parts of Asia, they were at a high level compared to European and American countries. Nonetheless, DTTv in autumn was significantly higher than in summer and spring, while DTTm was slightly higher in spring than summer.

This study aimed to determine the roles and responsibilities of stakeholders in decision-making, research, policy-making, and the implementation of an adaptation plan, with a comprehensive view of their positions, influence, and power.

Although microplastics (MPs) have been widely detected in the atmosphere, their presence and deposition patterns within the human respiratory system remain poorly understood. This study aimed to investigate the occurrence and characteristics of MPs in bronchial lavage fluid (BLF) and bronchoalveolar lavage fluid (BALF) of the patients undergoing flexible bronchoscopy and to examine the relation between the amount of MPs and patient's demographic characters. Results of this study revealed that MPs concentrations in BLF and BALF were 1.21 MPs/ml and 1.38 MPs/ml, respectively. Fiber constituting was the predominant morphology in the samples (BLF = 76%, BALF = 72%) and the longest fiber dimension was observed in BLF (2425 μm). Individuals employed in industrial and agriculture exhibited significantly higher MPs concentrations. Based on the µ-Raman results, seven distinct polymer types were identified, as polypropylene (PP), polycarbonate (PC), and polyamide (PA) were the most prevalent. Furthermore, SEM-EDS analysis detected the presence of some heavy metals associated with MPs, including Fe, Zn, and Cr. These results provide evidence for the presence of MPs within the human airway, although further research is warranted to elucidate the entry pathways, potential health impacts, and associated respiratory diseases.