Environmental Geochemistry and Health最新文献

The ammonium concentration in the biogas slurry after anaerobic digestion of municipal residual sludge is very high, it is difficult to be treated effectively by traditional methods. This study proposed a method for removing high concentrations of nitrogen via iron cycling driven by intermittent aeration (20 min every 9 days at 10 vvm (air volume/culture volume/min). Results demonstrated that Fe(II) in slurry decreased rapidly after aeration (3.4 mg Fe(II)/(L·min)), then it rose again after stopping aeration, resulting in the cycle of indigenous iron of slurry. The product of Fe(II) oxidation during aeration was confirmed to be Fe(OH)₃ through X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), which was the least crystalline and the most reactive Fe(III) (hydr-)oxide, enabling Fe(III)-ammonium oxidation (Feammox) effectively. The total nitrogen (TN, 302.6 mg/L) removal efficiency reached 82.1% after 30 days in the intermittent aeration group, significantly higher than that in the anoxic control group (30.1%) (p = 0.032). Microbial analysis revealed that iron-reducing bacteria, including Pseudomonas (5.1%), Thiobacillus (1.7%), and Geobacter (0.4%), were enriched in the aeration group, while nitrifying and Anammox bacteria (e.g., Nitrospina, Nitrosospira) were not detected. Additionally, compared to the control, the electron transfer capacity after experiment in the aeration group increased by more than 50%. Further experiments with higher TN (714.9 ± 12.1 mg/L) validated the methods robustness, achieving 77.8% TN removal. The above results indicated that intermittent aeration can trigger the iron cycle, enrich iron-reducing bacteria and enhance nitrogen removal. This study highlighted intermittent aeration as a strategy for treating low C/N biogas slurry.

Pollution of cultivated soils from heavy metals (HMs) is a global issue that threatens soil quality, food security, and human health. This study assessed HM concentrations from 60 soil samples from various cocoa farms in the Bono Region of Ghana. Additionally, the study determined the impact of these metals using pollution indices, eco-environmental risk assessments, and health risk assessments. The results indicated that the concentrations of the eight selected HMs surpassed their respective reference background values and were ordered as: Fe > Cu > Zn > Cd > As > Pb > Ni > Hg. The concentrations of Hg, Ni, Pb, and Zn were lower than the WHO/FAO soil quality standards. Yet, the levels of As, Cd, Cu, and Fe surpassed the quality standards by 166.40, 14,425.00, 405.36, and 320.97%, respectively. The farmland soils were significantly enriched with As, Cd, Hg, and Pb, indicating a very high level of soil contamination based on their CF values. The study revealed that soil contamination from HMs, especially Cd can adversely affect soil biota and the ecosystem based on the computed ecological risk, toxic risk index, and modified hazard quotient. The higher hazard quotient (HI > 1) and total cancer risk (TCR > 1E-04) from HM in the children compared to the adults denote non-carcinogenic and cancer health risk in the children population. From the results, the HMs in the cocoa soils have compromised eco-environmental and human health, calling for prompt action to restore soil quality and enhanced policies on agrochemicals and fertilizer usage.

Numerous macrolide antibiotics (MLs) have been widely utilized in the fields of disease treatment, aquaculture, and livestock industry. These antibiotics are metabolized in the human body and subsequently enter wastewater treatment plants (WWTPs) through domestic wastewater. However, residues may enter natural water bodies, potentially exerting adverse effects on human health. This highlights necessity of understanding antibiotic usage patterns in proximity to water supply sources. Taihu Lake, a significant freshwater reservoir in China, is currently facing a serious issue of antibiotic pollution predicament. In this study, wastewater samples were collected from a WWTP located in the Taihu Lake Basin over six-month period under varying ambient temperatures (October 2022 to March 2023). The concentrations of four MLs-erythromycin, roxithromycin, azithromycin, and clarithromycin-were determined. To estimate the population served by the WWTP during the sampling period, a multi-parameter model was employed. Furthermore, the per capita consumption levels of MLs were estimated with wastewater-based epidemiology (WBE). Notably, all four antibiotics were detected in the collected wastewater samples. Erythromycin exhibited the highest consumption level at 3849.0 ± 1843.1 mg/1000 inh/d, followed by Azithromycin (2229.1 ± 981.5 mg/1000 inh/d) and Roxithromycin (1511.2 ± 774.3 mg/1000 inh/d), yet clarithromycin had the lowest consumption level at 33.8 ± 39.9 mg/1000 inh/d. A significant increase in antibiotic usage was observed in January 2023 that the other sampling months, accompanying with low ambient temperatures. The data set on MLs consumption provides valuable insights for evaluating antibiotic usage patterns under varying temporal and climatic conditions.

Perovskite quantum dots (PQDs) have emerged as versatile nanomaterials with exceptional optoelectronic and photocatalytic properties, offering great potential for environmental remediation and antimicrobial defense. This review provides the comprehensive overview of PQDs as multifunctional platforms capable of both pollutant degradation and light-driven bacterial inactivation, achieving > 99% bacterial inactivation under visible light. It systematically summarizes recent advances in synthesis methods, surface modifications, and mechanistic pathways related to reactive oxygen species (ROS) generation, charge transfer dynamics, and microbe-surface interactions. Both lead-based and lead-free PQDs are discussed, with emphasis on enhancing their stability, biocompatibility, and environmental safety through surface engineering and green synthesis approaches, such as silica encapsulation yielding 85% fluorescence retention after 90 days in water. Furthermore, the review highlights practical implementations in water purification, food safety, and sustainable disinfection technologies, while also addressing biomedical extensions such as wound treatment. Finally, the major challenges-including aqueous instability, potential toxicity, and scalability-are outlined, along with future perspectives for designing eco-friendly, durable, and efficient PQD-based systems. Overall, this work establishes a foundation for advancing PQDs as next-generation nanomaterials for sustainable antimicrobial and photocatalytic applications.

Pharmaceutical pollutants are increasingly recognized as emerging contaminants in aquatic environments. Their persistence, bioactivity, and resistance to conventional treatment processes raise ecological and human health concerns, including the spread of antimicrobial resistance. Adsorption has emerged as a promising polishing step for their removal, but adsorption capacity (Qe, mg/g) varies widely depending on molecular structure and operational conditions, making predictive modeling essential. In this work, we developed machine learning models to predict adsorption capacities for Aspirin, Caffeine, Carbamazepine, Ketoprofen, Sulfamethoxazole, Nimesulide, and Paracetamol using chemoinformatics descriptors derived from SMILES strings and experimental inputs, including equilibrium concentration (Ce), initial concentration (C₀), temperature, and contact time. Feature reduction with LassoCV and multicollinearity analysis yielded a compact, chemically interpretable descriptor set. Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and Artificial Neural Networks (ANN) were optimized with Optuna and evaluated using cross-validation. XGB delivered the best predictive performance (R2 = 0.997, RMSE = 2.62 mg/g), outperforming SVR and ANN. SHAP analysis highlighted the influence of charge-partitioned surface areas and nitro functionalities on adsorption outcomes. The best-performing model was deployed in a Streamlit application, enabling predictions of Qe from SMILES and experimental conditions with built-in applicability-domain checks.

This study provides the integrated seasonal assessment of heavy metal contamination in groundwater from the Srivaikundam region, Southern India. Sixty groundwater samples were collected before and after the monsoon, the HPI, HEI, HQ, HI, and CR indices, and GIS-based mapping and multivariate statistics to distinguish geogenic from anthropogenic influences. The pre-monsoon average values (mg/L) were 0.002 (Cd), 0.006 (Pb), 0.006 (As), 0.008 (Cr), 0.012 (Ni), 0.031 (Cu), 0.017 (Mn), 0.141 (Zn), and 0.010 (Fe). Post-monsoon levels dropped to 0.001 (Cd), 0.005 (Pb), 0.005 (As), 0.008 (Cr), 0.010 (Ni), 0.022 (Cu), 0.014 (Mn), 0.116 (Zn), and 0.008 (Fe). All samples met the WHO (WHO (2017) Guidelines for drinking water quality: Fourth edition incorporating the first addendum. World health organization, Geneva.) drinking water guidelines, except for one pre-monsoon sample, when Cd levels surpassed the 0.003 mg/L limit. The Heavy Metal Pollution Index (HPI) varied from 27.74 to 122.33 in pre-monsoon samples and 27.73 to 92.32 in post-monsoon samples. The Heavy Metal Evaluation Index (HEI) values varied from 0.52 to 2.33 and 0.48 to 1.76, respectively. During the pre-monsoon period, health risk analysis revealed that 16% of samples were low risk (HI < 0.5), 52% intermediate (HI = 0.5-1.0), and 32% high risk (HI > 1.0). The post-monsoon values improved, with 25% low risk, 50% moderate, and 25% high risk. Non-carcinogenic hazards (HQ > 1) were found in 26.67% of pre-monsoon and 15.0% of post-monsoon samples. Carcinogenic risk levels were within tolerable ranges (CR < 1 × 10^-4). The novelty of this work lies in its combined use of pollution indices, health risk models, and multivariate statistics to demonstrate how monsoonal recharge and anthropogenic inputs jointly control groundwater quality. The findings highlight a scientific basis for targeted groundwater monitoring, safe drinking water management, and long-term policy strategies in the vulnerable semi-arid river region of South India.

The growing environmental concern due to anthropogenic activities at Central Magazine, Wa (WM), makes it a potential hotspot for soil heavy metal (HM) contamination and associated health risks to nearby workers and residents. Given the lack of documentation in the area, this study aimed to assess soil HM contamination levels in soils resulting from vehicle servicing activities at Central Magazine, Wa, integrating contamination indices, ecological risk evaluation, and physicochemical characterisation to provide a more holistic environmental risk profile. Thirty-one soil samples were collected and grouped into three composites based on workshop operational characteristics: auto BAG (12), auto SET (14), and auto LEG (5). A control sample (WM-BSS) was also included. EDXRF was used to determine HM concentrations. The mean cumulative HM loads (mg/kg) were highest in WM-BAG (49,489.00), followed by WM-LEG (44,907.50), WM-SET (41,801.66), and WM-BSS (25,646.69). Contamination was assessed using enrichment factor (EF), contamination factor (CF), contamination load index (CLI), geo-accumulation index (I_-geo), and ecological risk indices. Concentrations across the operational sites exceeded those at the control site, indicating significant anthropogenic influence. Positive I_-geo values and EF > 1.5 for Pb, Zn, Cu, and Mn confirmed substantial anthropogenic contamination, while consistently elevated CLI values (1.35-1.57) across all sites indicated a progressive deterioration in soil quality. Only WM-BAG soil showed high ecological risk due to elevated Pb levels, while WM-SET and WM-LEG soils posed low risk. This study provides essential data for ecological risk assessment and supports policy formulation for sustainable waste and soil contamination management in auto-industrial zones.

This study presents a comprehensive evaluation of potentially toxic elements (PTEs) contamination in surface soils of La Paz, Baja California Sur, Mexico, integrating geochemical, mineralogical, and magnetic analyses to identify contamination levels, spatial patterns, and potential health risks. Concentrations of Zn, V, Cr, Pb, Cu, Ni, As, Co, Sb, and Cd frequently exceeded local geochemical background values, with V and Ni particularly elevated near the heavy fuel oil fired (CTPP) and heavy fuel oil/diesel (CCI) power plants, consistent with emissions from fuel combustion. Spatial distribution analysis revealed that point sources dominated the signatures of V and Ni, while diffuse sources, such as vehicular traffic, contributed to elevated Sb, Zn, and Cu, especially along highways and in residential parks. Mixed-source elements (Cr, Pb, Co, As) showed overlapping industrial and urban signatures, with prevailing north-northwest winds likely enhancing contaminant dispersion. Soil color attributes and low frequency-dependent susceptibility indicated the predominance of coarse anthropogenic magnetic particles. Human health risk assessment showed no non-carcinogenic risk for adults, but hazard indices for children exceeded the safe threshold across all land uses, with the highest value at CTPP (HI = 2.22). Total cancer risk values remained within acceptable limits, although As contributions near CTPP approached the upper boundary. These findings highlight the persistent influence of industrial and urban activities on soil quality and underscore the importance of targeted monitoring and mitigation strategies to protect at-risk populations.

Characterizing regional groundwater chemistry and quality is essential for sustainable water resource management, yet remains challenging due to spatial complexity arising from both natural and anthropogenic factors. In this study, a hybrid Self-Organizing Map (SOM) and Principal Component Analysis (PCA) approach was applied, followed by Hierarchical Cluster Analysis (HCA), to interpret the hydrochemical characteristics of groundwater in the Qorveh-Dehgolan basin, Iran. A total of 112 groundwater samples collected during dry and wet seasons were analyzed. To ensure optimal performance, multiple SOM map sizes and normalization techniques (Z-score, Min-Max, and log(1 + x)) were tested and evaluated using Quantization Error (QE), Topographic Error (TE), and Explained Variance (EV). The 8 × 7 SOM grid (56 neurons) was selected as the final configuration, as it produced the lowest QE and TE and the highest EV. The optimized SOM results were subsequently grouped into four clusters based on the combined evaluation of SOM and HCA outcomes. Hydrogeochemical processes were interpreted using Piper and Gibbs diagrams, as well as cation exchange indices. Results indicated a dominant Ca²⁺-HCO₃⁻ water type across all clusters (1-4). Cation concentrations followed the order Ca²⁺ > Mg²⁺ > Na⁺ + K⁺, while the dominant anion sequence was HCO₃⁻ > Cl⁻ > SO₄²⁻. Ionic ratio analyses revealed that elevated NO₃⁻ concentrations are largely attributable to agricultural fertilizer use and domestic wastewater infiltration, highlighting anthropogenic impacts on groundwater quality. In contrast, natural geochemical processes, including silicate weathering and carbonate dissolution, were identified as the predominant mechanisms controlling groundwater evolution. Overall, the integrated SOM-PCA-HCA framework effectively captured both natural and human-induced variability in groundwater chemistry, and distinguished seasonal variations in water quality, underscoring its applicability for sustainable groundwater management in complex aquifer systems.

The integration of green-synthesized nanomaterials with environmental processes offers promising strategies for mitigating anthropogenic contaminants in ecosystems. In this study, TiO₂ nanoparticles were synthesized using aqueous extracts from four herbal plants: Curcuma longa, Ocimum basilicum, Plencranthus amboinicus, and Calotropis procera, representing an eco-friendly approach to nanomaterial fabrication. Comprehensive characterization using SEM, TEM, XRD, UV-Vis spectroscopy, FTIR spectroscopy, UV-DRS, and PL was employed to elucidate the morphology, crystallinity, and electronic properties of the nanoparticles. Microscopy-based analyses revealed distinct plant-mediated morphologies that strongly influenced photocatalytic performance in the degradation of Brilliant Green (BG) dye under sun-light irradiation, with Curcuma longa-derived TiO₂ exhibiting the highest degradation efficiency. This work demonstrates how green synthesis, combined with advanced characterization, provides mechanistic insights into nano-geochemical interactions between nanoparticles and contaminants. The findings highlight the potential of plant-mediated TiO₂ nanoparticles as sustainable photocatalysts for dye remediation and offer a model for designing eco-friendly nanomaterials to address broader environmental challenges.