Pub Date : 2025-12-17DOI: 10.1007/s11270-025-08965-1
Zhaoxia Song, Yalin Li, Peng Li, Lei Liu, Dan Shi, Gang Li
A self-made equipment was used to study the electrokinetic remediation of copper-contaminated soil. The speciation transformation rules of copper (Cu) at different remediation times and voltages were explored. Experimental parameters such as electrolyte pH value, soil pH value, soil moisture content, and soil conductivity were measured and analyzed. The Community Bureau of Reference (BCR) sequential extraction procedure was applied to measure the speciation of Cu in soil. Results showed that after the electrokinetic remediation, the experimental parameters changed to varying degrees, which had a certain impact on the removal and transformation of Cu in the soil. The acidic region in the soil was more conducive to the speciation transformation of Cu than the alkaline region. Higher moisture content and larger electrical current accelerated the migration of ions in the soil. Results of the BCR sequential extraction method revealed that the ratio of EX-Cu content increased from 37.50% to 50.60%, the ratio of RED-Cu content decreased from 12.25% to 10.50%. The proportion of OXI-Cu content initially increased and then decreased, while the ratio of RES-Cu content decreased from the anode to the cathode. However, due to the stable properties of OXI-Cu and RES-Cu, the proportions of OXI-Cu and RES-Cu changed minimally during the process of remediation.
{"title":"Influence of Citric Acid Enhanced Electrokinetic Remediation on the Conversion and Removal of Copper from Soil","authors":"Zhaoxia Song, Yalin Li, Peng Li, Lei Liu, Dan Shi, Gang Li","doi":"10.1007/s11270-025-08965-1","DOIUrl":"10.1007/s11270-025-08965-1","url":null,"abstract":"<div><p>A self-made equipment was used to study the electrokinetic remediation of copper-contaminated soil. The speciation transformation rules of copper (Cu) at different remediation times and voltages were explored. Experimental parameters such as electrolyte pH value, soil pH value, soil moisture content, and soil conductivity were measured and analyzed. The Community Bureau of Reference (BCR) sequential extraction procedure was applied to measure the speciation of Cu in soil. Results showed that after the electrokinetic remediation, the experimental parameters changed to varying degrees, which had a certain impact on the removal and transformation of Cu in the soil. The acidic region in the soil was more conducive to the speciation transformation of Cu than the alkaline region. Higher moisture content and larger electrical current accelerated the migration of ions in the soil. Results of the BCR sequential extraction method revealed that the ratio of EX-Cu content increased from 37.50% to 50.60%, the ratio of RED-Cu content decreased from 12.25% to 10.50%. The proportion of OXI-Cu content initially increased and then decreased, while the ratio of RES-Cu content decreased from the anode to the cathode. However, due to the stable properties of OXI-Cu and RES-Cu, the proportions of OXI-Cu and RES-Cu changed minimally during the process of remediation.</p></div>","PeriodicalId":808,"journal":{"name":"Water, Air, & Soil Pollution","volume":"237 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145761069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1007/s11270-025-08951-7
C. Kohila, K. Meena Alias Jeyanthi, P. Kasthuri Rengan
Air pollution is a life-threatening public health issue, with millions of premature deaths caused by air pollution every year. To reduce health hazards, environmental damage, and financial losses, effective monitoring and management are crucial. Though the air quality prediction and forecasting have been done since the 1980s, there are significant challenges involved due to their complexity and global impact. Apart from manual AQI measurements by government agencies, novel methods like deep learning techniques can be used to predict AQI in both scalable and cost-effective ways. Deep learning techniques can be accessed using large datasets and detection using nonlinear relationships. A hybrid CNN–LSTM model was developed and trained with AQI datasets (2015–2024) to address challenges in spatial and temporal pattern recognition. The model achieved remarkable accuracy, exceeding existing architectures such as LSTM, GRU, CNN, and WLSTM. The proposed hybrid CNN–LSTM model had outstanding predictive performance, shown by an accuracy of 96.8%, a high R2 of 0.92, and low error rates (RMSE = 8.66–13.5, MAE = 0.0514) over datasets from 2015 to 2024. These results confirm the model's robustness and generalization ability for AQI prediction. The findings demonstrate the model's effectiveness in real-time air quality monitoring, assisting policymakers and environmental agencies in making educated decisions for sustainable pollution control.
空气污染是一个危及生命的公共卫生问题,每年有数百万人因空气污染而过早死亡。为了减少健康危害、环境破坏和经济损失,有效的监测和管理至关重要。虽然自20世纪80年代以来就进行了空气质量预测和预报,但由于其复杂性和全球影响,其中涉及重大挑战。除了政府机构手动测量空气质量外,还可以使用深度学习技术等新方法以可扩展且经济有效的方式预测空气质量。深度学习技术可以使用大型数据集和使用非线性关系的检测来访问。利用AQI数据集(2015-2024)开发并训练了CNN-LSTM混合模型,以解决时空模式识别的挑战。该模型取得了显著的精度,超过了现有的LSTM、GRU、CNN和WLSTM等体系结构。本文提出的CNN-LSTM混合模型对2015 - 2024年数据集的预测准确率为96.8%,R2为0.92,错误率较低(RMSE = 8.66-13.5, MAE = 0.0514)。这些结果证实了该模型对空气质量预测的鲁棒性和泛化能力。研究结果表明,该模型在实时空气质量监测方面的有效性,有助于决策者和环境机构做出明智的决策,实现可持续的污染控制。
{"title":"Predicting Air Quality using a Hybrid Deep Learning Model to achieve Environmental Sustainability","authors":"C. Kohila, K. Meena Alias Jeyanthi, P. Kasthuri Rengan","doi":"10.1007/s11270-025-08951-7","DOIUrl":"10.1007/s11270-025-08951-7","url":null,"abstract":"<div><p>Air pollution is a life-threatening public health issue, with millions of premature deaths caused by air pollution every year. To reduce health hazards, environmental damage, and financial losses, effective monitoring and management are crucial. Though the air quality prediction and forecasting have been done since the 1980s, there are significant challenges involved due to their complexity and global impact. Apart from manual AQI measurements by government agencies, novel methods like deep learning techniques can be used to predict AQI in both scalable and cost-effective ways. Deep learning techniques can be accessed using large datasets and detection using nonlinear relationships. A hybrid CNN–LSTM model was developed and trained with AQI datasets (2015–2024) to address challenges in spatial and temporal pattern recognition. The model achieved remarkable accuracy, exceeding existing architectures such as LSTM, GRU, CNN, and WLSTM. The proposed hybrid CNN–LSTM model had outstanding predictive performance, shown by an accuracy of 96.8%, a high R<sup>2</sup> of 0.92, and low error rates (RMSE = 8.66–13.5, MAE = 0.0514) over datasets from 2015 to 2024. These results confirm the model's robustness and generalization ability for AQI prediction. The findings demonstrate the model's effectiveness in real-time air quality monitoring, assisting policymakers and environmental agencies in making educated decisions for sustainable pollution control.</p></div>","PeriodicalId":808,"journal":{"name":"Water, Air, & Soil Pollution","volume":"237 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145761073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1007/s11270-025-08946-4
Nqobile Motsomane, Anathi Magadlela
Metal contamination from mining, heavy traffic, and illegal mining pose a serious environmental threat, especially in urban and peri-urban areas where soils near roadsides and mine-adjacent zones are exposed to pollutants. Unregulated mining worsens contamination, affecting ecosystems and agricultural lands. Although grasses are often considered ineffective in multi-metal contaminated soils, Eragrostis curvula shows promise as a hardy native species for phytoremediation in such environments. This study examined whether the E. curvula cultivars, Ermelo and Agpal, showed potential to reduce metal concentrations in contaminated soils. Metal concentrations in pre-planting soils, post-harvest soils, and plant tissues were determined, and bioconcentration factors were calculated for samples from all soil collection sites. Pre-planting soils collected from Jameson Park, Kaydale, and Rensburg had high concentrations of iron (36 000–50 000), manganese (800–900), nickel (48–75), strontium (10–20), zinc (71–79), chromium (117–224), and barium (114–117) mg/kg, with a pH of 4.6–5.0 and total cation concentration of 4.7–10.9 cmol/L. The Ermelo cultivar accumulated iron (3 289–6 149), manganese (185–522), nickel (13–26), strontium (9–10), zinc (46–64), chromium (55–85), and barium (36–71) mg/kg, while the Agpal cultivar accumulated corresponding concentrations of 3 092–5 216, 136–488, 4–32, 8–26, 48–57, 21–122, and 37–77 mg/kg. The iron (9 000–32 000), manganese (200–809), nickel (31–63), strontium (8–16), zinc (33–73), chromium (83–137), and barium (73–113) mg/kg of post-harvest soils decreased significantly, while the pH increased to 5.2–5.6 and the total cation concentration was 4.3–7.2 cmol/L. The cultivars had varied bioconcentration factors for iron (0.06–0.15), manganese (0.12–0.57), nickel (0.08–0.53), strontium (0.37–1.10), zinc (0.51–0.93), chromium (0.10–0.89), and barium (0.33–0.68) indicating more efficient accumulation of zinc and strontium, while iron and chromium were taken up to a lesser extent. The presence of E. curvula cultivars in the soils promoted the proliferation of plant growth-promoting bacterial genera, including Bacillus, Pedobacter, Pseudomonas, and Flavobacterium, in the post-harvest soils. The activity of these bacteria and their associated soil enzymes may have contributed to the ability of E. curvula to maintain growth and persist under metal-contaminated conditions. These results highlight E. curvula’s potential as a phytoremediation agent in multi-source metal-polluted soils.
{"title":"Soil Beneath the Grass: Eragrostis Curvula Cultivars Reduce Metal Contamination and Improve Soil Health","authors":"Nqobile Motsomane, Anathi Magadlela","doi":"10.1007/s11270-025-08946-4","DOIUrl":"10.1007/s11270-025-08946-4","url":null,"abstract":"<div><p>Metal contamination from mining, heavy traffic, and illegal mining pose a serious environmental threat, especially in urban and peri-urban areas where soils near roadsides and mine-adjacent zones are exposed to pollutants. Unregulated mining worsens contamination, affecting ecosystems and agricultural lands. Although grasses are often considered ineffective in multi-metal contaminated soils, <i>Eragrostis curvula</i> shows promise as a hardy native species for phytoremediation in such environments. This study examined whether the <i>E. curvula</i> cultivars, Ermelo and Agpal, showed potential to reduce metal concentrations in contaminated soils. Metal concentrations in pre-planting soils, post-harvest soils, and plant tissues were determined, and bioconcentration factors were calculated for samples from all soil collection sites. Pre-planting soils collected from Jameson Park, Kaydale, and Rensburg had high concentrations of iron (36 000–50 000), manganese (800–900), nickel (48–75), strontium (10–20), zinc (71–79), chromium (117–224), and barium (114–117) mg/kg, with a pH of 4.6–5.0 and total cation concentration of 4.7–10.9 cmol/L. The Ermelo cultivar accumulated iron (3 289–6 149), manganese (185–522), nickel (13–26), strontium (9–10), zinc (46–64), chromium (55–85), and barium (36–71) mg/kg, while the Agpal cultivar accumulated corresponding concentrations of 3 092–5 216, 136–488, 4–32, 8–26, 48–57, 21–122, and 37–77 mg/kg. The iron (9 000–32 000), manganese (200–809), nickel (31–63), strontium (8–16), zinc (33–73), chromium (83–137), and barium (73–113) mg/kg of post-harvest soils decreased significantly, while the pH increased to 5.2–5.6 and the total cation concentration was 4.3–7.2 cmol/L. The cultivars had varied bioconcentration factors for iron (0.06–0.15), manganese (0.12–0.57), nickel (0.08–0.53), strontium (0.37–1.10), zinc (0.51–0.93), chromium (0.10–0.89), and barium (0.33–0.68) indicating more efficient accumulation of zinc and strontium, while iron and chromium were taken up to a lesser extent. The presence of <i>E. curvula</i> cultivars in the soils promoted the proliferation of plant growth-promoting bacterial genera, including <i>Bacillus, Pedobacter, Pseudomonas</i>, and <i>Flavobacterium</i>, in the post-harvest soils. The activity of these bacteria and their associated soil enzymes may have contributed to the ability of <i>E. curvula</i> to maintain growth and persist under metal-contaminated conditions. These results highlight <i>E. curvula’</i>s potential as a phytoremediation agent in multi-source metal-polluted soils.</p></div>","PeriodicalId":808,"journal":{"name":"Water, Air, & Soil Pollution","volume":"237 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145761089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1007/s11270-025-08858-3
Collin A. Klaubauf, Anita M. Thompson, William R. Selbig, Laxmi R. Prasad
Urban runoff containing high amounts of nutrients like phosphorus (P) is a well-established driver of surface water eutrophication. In residential areas, a primary source of nutrients is derived from leaf litter. P contained in leaves is leached and transported by stormwater from source to stream. The majority of P leached from leaf litter is in the dissolved phase, which can be difficult to remove using conventional treatment practices, leaving source control as the most viable option. Additional tools are needed to help forecast how different tree species may improve or hinder contributions of nutrients to runoff. For this reason, ten street tree species that are common throughout the contiguous U.S. were chosen to evaluate the effect of species on leachable P from tree leaves using laboratory experiments. After 48 h of exposure to water, the amount of P released ranged from 2.16 mg P g−1 leaf for Silver Maple to 0.03 mg P g−1 leaf for Hackberry. More than half of the P was lost in the first 12 h for eight of the ten tree species, making guided source control important to reduce inputs to surface water from key locations. Results were used to identify ‘hotspots’ of P leaching in Madison, WI and can be used to assess current street tree inventories that can then guide management to areas with the highest nutrient reduction potential and inform urban foresters who may wish to tailor future planting scenarios that minimize nutrients in runoff.