Mohammad Rafiee

Graphical abstract illustrating five key environmental adaptation strategies to enhance wastewater management system (WMS) resilience during drought: adaptive governance and integrated climate planning; implementation of drought response and adaptation plans (DRAPs) and operational adjustment; strategic infrastructure design and upgrading; promotion of drought-tolerant cultivation; and enhanced hydrological management and water storage. The prolonged drought impacts the wastewater management system (WMS) resilience by reducing operational efficiency and violating effluent discharge standards for water reuse. This study aimed to identify and analyze the factors affecting the WMS resilience within sustainable water reuse during drought. Using a qualitative approach, semistructured interviews were conducted with 36 purposefully sampled experts experienced in drought and wastewater management and continued until data saturation was achieved. Conducted between March and June 2025, the study adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist and employed the Graneheim and Lundman framework for data synthesis. The analysis revealed 6 categories and 14 subcategories, including technical factors (operation management and infrastructure resilience); management factors (response preparedness and planning); empowering stakeholders (employee, public, and end-user resilience); socioeconomic factors (economic, social, and cultural); policymaking (legal requirements and monitoring and auditing); and environmental factors (environmental impact and adaptation). These findings indicate the multidimensional components for sustaining WMS resilience during water scarcity. Integrating these diverse factors can strengthen drought preparedness and optimize managerial decision-making, eventually protecting urban water security through comprehensive reuse programs.

Pesticides removal from soil was investigated using a DBD (dielectric barrier discharge) plasma with microsecond pseudo-pulsed high-voltage power supply, focusing on the modeling and optimization of Trifluralin removal as the target pollutant. We performed a detailed parametric analysis (discharge voltage, remediation time, as well as soil moisture and organics content) to determine the optimum operational conditions. Plasma treatment experiments were also carried out on pristine sandy and loam soils. All four factors significantly impacted the herbicide removal, with notable cross effects. Under optimum conditions, 96.1 ± 2.0% Trifluralin and almost 68.5% mineralization were achieved within 17.6 min. In the presence of humic acids, the degradation efficiency decreased considerably, partly due to the competition for reactive species and energy consumption as well. The degradation kinetics fitted well to the pseudo-second-order model (R² = 0.998). The energy efficiencies were respectively 0.997 and 9.252 mg/kWh for 10 and 100 mg/kg Trifluralin, where •OH and ¹O₂ were the dominant reactive species in the targeted herbicide mineralization. The mechanistic degradation pathways were proposed based on the identified transformation products, which primarily encompassed successive hydroxylation, defluorination, and deamination. Furthermore, the toxicity of transformation products was evaluated through both seed germination assessment and the ECOSAR (Ecological Structure–Activity Relationships) predictive model, which indeed verified that the environmental risk was substantially reduced. This study gives a new point of view on cold DBD plasma as a promising and robust technique towards the remediation of soils contaminated with chemically stable and recalcitrant organics that are resistant to conventional treatment methods.

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 (p < 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.

Climate change significantly impacts the risk of eutrophication and, consequently, chlorophyll-a (Chl-a) concentrations. Understanding the impact of water flows is a crucial first step in developing insights into future patterns of change and associated risks. In this study, the Statistical DownScaling Model (SDSM)—a widely used daily downscaling method—is implemented to produce downscaled local climate variables, which serve as input for simulating future hydro-climate conditions using a hydrological model. The vulnerability of water quality, particularly Chl-a concentrations in the Latyan Dam and Tehranpars Water Treatment Plant (TWTP) is assessed through six fuzzy regression models under three scenarios: RCP2.6, RCP4.5, and RCP8.5. Projections indicate an increase in minimum temperatures for the Jajrood watershed ranging from 92 to 93%. Seasonal forecasts suggest significant precipitation during the dry season. The HYMOD model predicts increases in streamflow of approximately 97%, 90%, and 92% by 2050 under RCP2.6, RCP4.5, and RCP8.5, respectively, indicating a heightened risk of flooding that poses economic, health, and environmental concerns. Among the six fuzzy regression models, FGR1, FGR3, and FGR4 demonstrated the most favorable results in modeling Chl-a output from the TWTP. In conclusion, while Chl-a concentrations in the effluent of the TWTP are only slightly influenced by climate change, the effects on streamflow patterns are significant. These findings highlight serious future water quality challenges and increased vulnerability of water resources due to climate change.

Objective: This study has been aimed to investigate factors associated with elevated blood lead levels (BLLs) across different percentiles in individuals employed in the petroleum industry and residents of Kirkuk City . In addition, to provide evidence-based approaches to mitigate exposure risks and safeguard vulnerable populations. Methods: The study involved 357 participants and utilized quantile regression to examine BLL in petroleum industry workers and Kirkuk residents. Results: Median BLLs were higher among workers (17.5 μg/dL) compared to residents in the city (9.9 μg/dL) and suburbs (7.4 μg/dL). Factors such as smoking, outdoor work, and lower education were associated with elevated BLLs, as confirmed by quantile regression. Conclusions: This study found higher BLLs in petroleum workers compared to Kirkuk residents, associated with smoking, outdoor work, and lower education. The results emphasize the need for targeted interventions and policies to mitigate lead exposure risks in this industrial region.

Drug resistance has become a matter of great concern, with many bacteria now resist multiple antibiotics. This study depicts the occurrence of antibiotic-resistant bacteria (ARB) and resistance patterns in five full-scale hospital wastewater treatment plants (WWTPs). Samples of raw influent wastewater, as well as pre- and post-disinfected effluents, were monitored for targeted ARB and resistance genes in September 2022 and February 2023. Shifts in resistance profiles of Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii antimicrobial-resistant indicators in the treated effluent compared to that in the raw wastewater were also worked out. Ceftazidime (6.78 × 105 CFU/mL) and cefotaxime (6.14 × 105 CFU/mL) resistant species showed the highest concentrations followed by ciprofloxacin (6.29 × 104 CFU/mL), and gentamicin (4.88 × 104 CFU/mL), in raw influent respectively. WWTP-D employing a combination of biological treatment and coagulation/clarification for wastewater decontamination showed promising results for reducing ARB emissions from wastewater. Relationships between treated effluent quality parameters and ARB loadings showed that high BOD5 and nitrate levels were possibly contributing to the persistence and/or selection of ARBs in WWTPs. Furthermore, antimicrobial susceptibility tests of targeted species revealed dynamic shifts in resistance profiles through treatment processes, highlighting the potential for ARB and ARGs in hospital wastewater to persist or amplify during treatment.

In this study, four identical laboratory-scale sequencing batch reactors (SBRs) were continuously operated with different concentrations of microbeads (MBs) (5,000–15,000 MBs/L) to investigate the stress-responses of activated sludge under the MB exposure. It was found that the overall treatment performance (organic removal) of SBRs was fairly affected by short-term exposure to low levels of MBs; however, it was adversely affected as the concentration of MBs increased. The average concentration of mixed liquor suspended solids and heterotrophic bacteria in the reactor fed with 15,000 MBs/L were 16 and 30% less than in the pristine control reactor, respectively. Batch experiments further demonstrated that fairly low concentrations of MBs favored the development of dense microbial structures. Further increasing the MB concentrations to 15,000 MBs/L, however, distinctly weakened the settling performance of sludge. Morphological observations revealed suppressed uniformity, strength, and integrity of flocs reactors with the addition of MBs. Microbial community analyses revealed that the abundance of protozoan species declined 37.5, 58, and 64%, respectively, when SBRs were exposed to 5,000; 10,000; and 15,000 MBs/L as compared with the control reactor. The present work provided new insight into the possible effects of MBs on the performances and operational parameters of activated sludge.