Pub Date : 2025-09-01Epub Date: 2025-07-23DOI: 10.1016/j.eehl.2025.100172
Wanchao Song, Mengxuan Wang, Hua Zou, Guoshuai Liu
Electrocatalytic denitrification (ECDN) offers a sustainable prospect by enabling efficient NO3− conversion to harmless N2. However, the N2-selective ECDN remains challenging due to the sluggish kinetics of N–N coupling during NO3− reduction. Here, we developed a novel electrocatalyst of dual single-atomic sites on double-shelled mesoporous carbon spheres (FeNC@MgNC-DMCS) using a continuous sequential modular assembly and pyrolysis approach. The outer Mg–N4 shell creates medium basicity sites that function as the proton fence, which optimizes the spatial distribution of H∗ species and suppresses ∗N protonation pathways that would otherwise lead to ammonia formation. Concurrently, the inner Fe–N4 shell promotes N–N coupling for N2 production. 92.8% NO3− removal and 95.2% N2 selectivity was achieved by the optimized FeNC@MgNC-DMCS catalyst. Furthermore, long-term flow cell testing demonstrated remarkable durability, highlighting the practical potential of FeNC@MgNC-DMCS for sustainable wastewater treatment applications. This work introduces a catalyst design paradigm that integrates a proton-repelling interface to decouple H∗ availability from N2 formation pathways, thereby enabling the development of high-performance ECDN catalysts with balanced activity and selectivity for environmental remediation applications.
{"title":"Selective electrocatalytic denitrification to N2 via dual single-atomic sites on double-shelled mesoporous carbon spheres","authors":"Wanchao Song, Mengxuan Wang, Hua Zou, Guoshuai Liu","doi":"10.1016/j.eehl.2025.100172","DOIUrl":"10.1016/j.eehl.2025.100172","url":null,"abstract":"<div><div>Electrocatalytic denitrification (ECDN) offers a sustainable prospect by enabling efficient NO<sub>3</sub><sup>−</sup> conversion to harmless N<sub>2</sub>. However, the N<sub>2</sub>-selective ECDN remains challenging due to the sluggish kinetics of N–N coupling during NO<sub>3</sub><sup>−</sup> reduction. Here, we developed a novel electrocatalyst of dual single-atomic sites on double-shelled mesoporous carbon spheres (FeNC@MgNC-DMCS) using a continuous sequential modular assembly and pyrolysis approach. The outer Mg–N<sub>4</sub> shell creates medium basicity sites that function as the proton fence, which optimizes the spatial distribution of H∗ species and suppresses ∗N protonation pathways that would otherwise lead to ammonia formation. Concurrently, the inner Fe–N<sub>4</sub> shell promotes N–N coupling for N<sub>2</sub> production. 92.8% NO<sub>3</sub><sup>−</sup> removal and 95.2% N<sub>2</sub> selectivity was achieved by the optimized FeNC@MgNC-DMCS catalyst. Furthermore, long-term flow cell testing demonstrated remarkable durability, highlighting the practical potential of FeNC@MgNC-DMCS for sustainable wastewater treatment applications. This work introduces a catalyst design paradigm that integrates a proton-repelling interface to decouple H∗ availability from N<sub>2</sub> formation pathways, thereby enabling the development of high-performance ECDN catalysts with balanced activity and selectivity for environmental remediation applications.</div></div>","PeriodicalId":29813,"journal":{"name":"Eco-Environment & Health","volume":"4 3","pages":"Article 100172"},"PeriodicalIF":17.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-06-19DOI: 10.1016/j.eehl.2025.100167
Shuhan Lei , Wanjing Liu , Baoshan Xing , Jun Wang , Jiake Xu , Chaoqi Wang , Cheng Zhang , Peng Gao , Jun Wang , Lusheng Zhu
The current study investigates the potential of carbon dots (CDs) as an eco-friendly “plant vaccine” for controlling rice blast disease caused by Magnaporthe oryzae. These CDs offer a promising alternative to commercial fungicides that threaten environmental and human health. Foliar application of CDs (at 100 and 200 mg/L) at the tillering stage enhanced rice blast resistance across the entire life cycle. CDs significantly reduced the leaf blast disease index, with infection rates of 30.8%–49.5%, outperforming the commercial fungicide isoprothiolane (57.3%). CDs significantly increased grain yield (186%–198%), starch content in grains (27.0%–27.5%), and protein content in grains (25.4%–36.1%) relative to infected controls. Moreover, CDs demonstrated lower toxicity to soil organisms (Eisenia fetida and Caenorhabditis elegans) than isoprothiolane. Mechanistically, CDs stabilized chloroplast homeostasis, amplified photosynthesis, and enhanced carbohydrate allocation, thereby synchronously activating systemic resistance through indole acetic and jasmonic acid signaling. These dual agricultural and environmental benefits position CDs as a sustainable crop protection strategy, reconciling food security with ecological safety.
{"title":"Carbon dots as a green alternative for preventing Magnaporthe oryzae infection in rice: Mechanisms of disease resistance","authors":"Shuhan Lei , Wanjing Liu , Baoshan Xing , Jun Wang , Jiake Xu , Chaoqi Wang , Cheng Zhang , Peng Gao , Jun Wang , Lusheng Zhu","doi":"10.1016/j.eehl.2025.100167","DOIUrl":"10.1016/j.eehl.2025.100167","url":null,"abstract":"<div><div>The current study investigates the potential of carbon dots (CDs) as an eco-friendly “plant vaccine” for controlling rice blast disease caused by <em>Magnaporthe oryzae.</em> These CDs offer a promising alternative to commercial fungicides that threaten environmental and human health. Foliar application of CDs (at 100 and 200 mg/L) at the tillering stage enhanced rice blast resistance across the entire life cycle. CDs significantly reduced the leaf blast disease index, with infection rates of 30.8%–49.5%, outperforming the commercial fungicide isoprothiolane (57.3%). CDs significantly increased grain yield (186%–198%), starch content in grains (27.0%–27.5%), and protein content in grains (25.4%–36.1%) relative to infected controls. Moreover, CDs demonstrated lower toxicity to soil organisms (<em>Eisenia fetida</em> and <em>Caenorhabditis elegans</em>) than isoprothiolane. Mechanistically, CDs stabilized chloroplast homeostasis, amplified photosynthesis, and enhanced carbohydrate allocation, thereby synchronously activating systemic resistance through indole acetic and jasmonic acid signaling. These dual agricultural and environmental benefits position CDs as a sustainable crop protection strategy, reconciling food security with ecological safety.</div></div>","PeriodicalId":29813,"journal":{"name":"Eco-Environment & Health","volume":"4 3","pages":"Article 100167"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-06-27DOI: 10.1016/j.eehl.2025.100168
Xia Zhu , Huili Yan , Chen Tu , Ruijie Li , Han Zhang , Yuan Li , Shuai Yang , Fangjie Zhao , Willie J.G.M. Peijnenburg , Mi Ma , Zhenyan He , Yongming Luo
Arsenic (As) contamination in paddy soils is a global problem, threatening rice production and food safety. Hyperaccumulator plants have garnered significant attention for their potential to remove pollutants from contaminated soil. However, no natural hyperaccumulators have been found for the phytoremediation of As-contaminated paddy soils under flooding conditions. One promising strategy is to genetically engineer Oryza sativa (rice) to hyperaccumulate As for effective phytoremediation of paddy soil. A key challenge remains in increasing metal accumulation without compromising tolerance. Here, PvACR3 from the As hyperaccumulator Pteris vittata was introduced under the control of a rice root-specific promoter pLsi1 to create high-As-accumulating and tolerant transgenic remediation rice. The remediation rice strains exhibited robust growth, with shoot As concentration reaching up to 451–557 mg/kg in a hydroponic experiment with 20 μM NaAsO2 treatment, and 45.9–80.3 mg/kg in pot experiments with moderately As-contaminated paddy soils. Compared to wild-type rice, the pLsi1::PvACR3 transgenic rice removed 23.5 times more As from the same paddy soils. By harvesting rice shoots before grain filling, the soil pore water As was almost completely depleted, and the acid-soluble and reducible fractions of As were significantly reduced. This study presents the first transgenic remediation rice characterized by high As accumulation, tolerance, and adaptability to paddy soils under flooding conditions for effective phytoremediation.
{"title":"Promoter pLsi1 driven PvACR3 expression in rice enhances arsenic phytoextraction in paddy soils","authors":"Xia Zhu , Huili Yan , Chen Tu , Ruijie Li , Han Zhang , Yuan Li , Shuai Yang , Fangjie Zhao , Willie J.G.M. Peijnenburg , Mi Ma , Zhenyan He , Yongming Luo","doi":"10.1016/j.eehl.2025.100168","DOIUrl":"10.1016/j.eehl.2025.100168","url":null,"abstract":"<div><div>Arsenic (As) contamination in paddy soils is a global problem, threatening rice production and food safety. Hyperaccumulator plants have garnered significant attention for their potential to remove pollutants from contaminated soil. However, no natural hyperaccumulators have been found for the phytoremediation of As-contaminated paddy soils under flooding conditions. One promising strategy is to genetically engineer <em>Oryza sativa</em> (rice) to hyperaccumulate As for effective phytoremediation of paddy soil. A key challenge remains in increasing metal accumulation without compromising tolerance. Here, <em>PvACR3</em> from the As hyperaccumulator <em>Pteris vittata</em> was introduced under the control of a rice root-specific promoter <em>pLsi1</em> to create high-As-accumulating and tolerant transgenic remediation rice. The remediation rice strains exhibited robust growth, with shoot As concentration reaching up to 451–557 mg/kg in a hydroponic experiment with 20 μM NaAsO<sub>2</sub> treatment, and 45.9–80.3 mg/kg in pot experiments with moderately As-contaminated paddy soils. Compared to wild-type rice, the <em>pLsi1::PvACR3</em> transgenic rice removed 23.5 times more As from the same paddy soils. By harvesting rice shoots before grain filling, the soil pore water As was almost completely depleted, and the acid-soluble and reducible fractions of As were significantly reduced. This study presents the first transgenic remediation rice characterized by high As accumulation, tolerance, and adaptability to paddy soils under flooding conditions for effective phytoremediation.</div></div>","PeriodicalId":29813,"journal":{"name":"Eco-Environment & Health","volume":"4 3","pages":"Article 100168"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-24eCollection Date: 2025-09-01DOI: 10.1016/j.eehl.2025.100171
Shengchun Qi, Shuyan Wang, Yu Xia, Songcan Chen, Huijie Lu
Soils are important reservoirs of human pathogenic bacteria that can spread to humans through various pathways. Metagenomics enables high-throughput pathogen identification by mapping sequencing reads to known pathogen genomes. However, this approach has several limitations, e.g., sequence assembly is time-consuming, and reliance on reference databases may overlook potential pathogens lacking close genomic matches. Here, we developed a novel, virulence factor (VF) based machine learning method using the K-Nearest Neighbors model (VF-KNN) for identifying human pathogenic bacteria from soil metagenomes. Through learning the VF features of pathogenic and non-pathogenic bacteria, VF-KNN could achieve the desired performance in soil pathogen identification (AUC: 0.95, Accuracy: 0.85). Model prediction accuracy (0.95) was further validated using 61 pathogenic strains isolated from soil. For the top 15 most frequent soil pathogens, the prediction accuracy was >0.90 at 0.4X-1.0X genome coverage. VFs contributing significantly to pathogen identification were associated with regulation, effector delivery, motility, etc. By using VF-KNN, the averaged abundance of total potential pathogens in topsoils across China was 0.44% (n = 336), predominantly concentrated in the eastern coastal provinces. Compared with the conventional method based on a predefined pathogen list, VF-KNN identified 28% more potential pathogenic species, including some newly reported but not in the predefined list (e.g., Mycolicibacterium cosmeticum). Agricultural land exhibited significantly higher pathogen abundance and diversity than the other land types. This newly developed VF-KNN method is applicable for pathogen identification in broader environments.
{"title":"Identification of human pathogens in soil by virulence gene-based machine learning method.","authors":"Shengchun Qi, Shuyan Wang, Yu Xia, Songcan Chen, Huijie Lu","doi":"10.1016/j.eehl.2025.100171","DOIUrl":"10.1016/j.eehl.2025.100171","url":null,"abstract":"<p><p>Soils are important reservoirs of human pathogenic bacteria that can spread to humans through various pathways. Metagenomics enables high-throughput pathogen identification by mapping sequencing reads to known pathogen genomes. However, this approach has several limitations, e.g., sequence assembly is time-consuming, and reliance on reference databases may overlook potential pathogens lacking close genomic matches. Here, we developed a novel, virulence factor (VF) based machine learning method using the K-Nearest Neighbors model (VF-KNN) for identifying human pathogenic bacteria from soil metagenomes. Through learning the VF features of pathogenic and non-pathogenic bacteria, VF-KNN could achieve the desired performance in soil pathogen identification (AUC: 0.95, Accuracy: 0.85). Model prediction accuracy (0.95) was further validated using 61 pathogenic strains isolated from soil. For the top 15 most frequent soil pathogens, the prediction accuracy was >0.90 at 0.4X-1.0X genome coverage. VFs contributing significantly to pathogen identification were associated with regulation, effector delivery, motility, etc. By using VF-KNN, the averaged abundance of total potential pathogens in topsoils across China was 0.44% (<i>n</i> = 336), predominantly concentrated in the eastern coastal provinces. Compared with the conventional method based on a predefined pathogen list, VF-KNN identified 28% more potential pathogenic species, including some newly reported but not in the predefined list (e.g., <i>Mycolicibacterium cosmeticum</i>). Agricultural land exhibited significantly higher pathogen abundance and diversity than the other land types. This newly developed VF-KNN method is applicable for pathogen identification in broader environments.</p>","PeriodicalId":29813,"journal":{"name":"Eco-Environment & Health","volume":"4 3","pages":"100171"},"PeriodicalIF":17.6,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12355066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144875564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-09DOI: 10.1016/j.eehl.2025.100146
Kexin Yu , Ying Xiong , Renjie Chen , Jing Cai , Yaoxian Huang , Haidong Kan
Benzene, toluene, ethylbenzene, and xylene (BTEX) have been associated with certain cancers in the occupational population. This study aimed to investigate the associations between low-level ambient BTEX exposure and cancer risks in the general population. We leveraged data from the UK Biobank and included individuals free of cancer at 2006–2010 baseline. Annual concentrations of BTEX were estimated using a chemistry-climate model, and the associations between BTEX and incident overall and 18 site-specific cancers were investigated with Cox proportional hazard models. We also fitted restricted cubic splines to explore the exposure-response relationships. The study sample comprised 409,579 participants [mean age 56.2 (8.11) years; 219,315 (53.5%) females]. Over a mean (SD) follow-up period of 11.2 (2.64) years (4,597,164 person-years), 60,777 overall incident cancer cases occurred. The results showed significant associations between overall cancers and benzene [HR 1.93 (95% CI: 1.89, 1.96)], toluene [1.25 (1.23, 1.26)] and xylene [1.11 (1.10, 1.12)]. Benzene and toluene were associated with a higher risk of 18 site-specific cancers. For xylenes (a summation of ethylbenzene, m/p-xylene, and o-xylene in the model), significant associations with multiple myeloma, hepatobiliary tract, thyroid, or connective soft tissue were not observed. Exposure-response curves suggested a higher risk of overall cancer beyond the benzene threshold. For toluene and xylene, there was no threshold or plateau across the range of exposures. This large-scale prospective cohort study demonstrates that long-term exposure to low-level ambient BTEX could increase the risk of overall and site-specific cancers in the general population.
{"title":"Long-term exposure to low-level ambient BTEX and site-specific cancer risk: A national cohort study in the UK Biobank","authors":"Kexin Yu , Ying Xiong , Renjie Chen , Jing Cai , Yaoxian Huang , Haidong Kan","doi":"10.1016/j.eehl.2025.100146","DOIUrl":"10.1016/j.eehl.2025.100146","url":null,"abstract":"<div><div>Benzene, toluene, ethylbenzene, and xylene (BTEX) have been associated with certain cancers in the occupational population. This study aimed to investigate the associations between low-level ambient BTEX exposure and cancer risks in the general population. We leveraged data from the UK Biobank and included individuals free of cancer at 2006–2010 baseline. Annual concentrations of BTEX were estimated using a chemistry-climate model, and the associations between BTEX and incident overall and 18 site-specific cancers were investigated with Cox proportional hazard models. We also fitted restricted cubic splines to explore the exposure-response relationships. The study sample comprised 409,579 participants [mean age 56.2 (8.11) years; 219,315 (53.5%) females]. Over a mean (SD) follow-up period of 11.2 (2.64) years (4,597,164 person-years), 60,777 overall incident cancer cases occurred. The results showed significant associations between overall cancers and benzene [HR 1.93 (95% CI: 1.89, 1.96)], toluene [1.25 (1.23, 1.26)] and xylene [1.11 (1.10, 1.12)]. Benzene and toluene were associated with a higher risk of 18 site-specific cancers. For xylenes (a summation of ethylbenzene, m/p-xylene, and o-xylene in the model), significant associations with multiple myeloma, hepatobiliary tract, thyroid, or connective soft tissue were not observed. Exposure-response curves suggested a higher risk of overall cancer beyond the benzene threshold. For toluene and xylene, there was no threshold or plateau across the range of exposures. This large-scale prospective cohort study demonstrates that long-term exposure to low-level ambient BTEX could increase the risk of overall and site-specific cancers in the general population.</div></div>","PeriodicalId":29813,"journal":{"name":"Eco-Environment & Health","volume":"4 2","pages":"Article 100146"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-26DOI: 10.1016/j.eehl.2025.100150
Chaoqing Huang , Qian Wu , Yujie Chen , MinhThu Nguyen , Bin Chen , Song Hong , Chao He
Understanding regional carbon emissions from human activities, particularly their spatio-temporal patterns, is essential for implementing decarbonization strategies and cultivating a low-carbon economy. This study develops a spatial visualization model to estimate carbon emissions in Southeast Asia using calibrated nighttime light data, with DMSP-OLS (Defense Meteorological Satellite Program Operational Linescan System) and NPP-VIIRS (National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite) standardized through polynomial regression and machine learning to ensure consistency. Emissions in Southeast Asia increased by 2.51 times from 1992 to 2022, shifting from gradual to rapid growth. Validation against Open-source Data Inventory for Anthropogenic CO2 (ODIAC) and Emissions Database for Global Atmospheric Research (EDGAR) shows strong agreement in high-emission urban areas but discrepancies in low-emission rural regions due to data sparsity and satellite sensor limits. Spatial analysis reveals that major Southeast Asian cities and their peripheries exhibit robust, sustained growth, while rural, less-developed areas show slower trends, highlighting persistent urban-rural disparities. These urban regions demonstrate a “circular economy advantage”, where per-unit-area carbon emissions steadily rise in economically advantageous zones. Despite high model accuracy, uncertainties persist due to variations in regional economic activities and the limitations of satellite-based emission proxies. Forecasts suggest elevated emission levels in major cities will continue, while changes in other areas remain relatively minimal. Consequently, achieving a low-carbon economy in Southeast Asia requires a top-down approach, emphasizing infrastructure enhancement, resource and energy optimization, and fostering a sustainable, circular socio-economic system.
{"title":"Spatio-temporal dynamics of human-induced carbon emissions in Southeast Asia (1992–2022) based on nighttime light","authors":"Chaoqing Huang , Qian Wu , Yujie Chen , MinhThu Nguyen , Bin Chen , Song Hong , Chao He","doi":"10.1016/j.eehl.2025.100150","DOIUrl":"10.1016/j.eehl.2025.100150","url":null,"abstract":"<div><div>Understanding regional carbon emissions from human activities, particularly their spatio-temporal patterns, is essential for implementing decarbonization strategies and cultivating a low-carbon economy. This study develops a spatial visualization model to estimate carbon emissions in Southeast Asia using calibrated nighttime light data, with DMSP-OLS (Defense Meteorological Satellite Program Operational Linescan System) and NPP-VIIRS (National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite) standardized through polynomial regression and machine learning to ensure consistency. Emissions in Southeast Asia increased by 2.51 times from 1992 to 2022, shifting from gradual to rapid growth. Validation against Open-source Data Inventory for Anthropogenic CO<sub>2</sub> (ODIAC) and Emissions Database for Global Atmospheric Research (EDGAR) shows strong agreement in high-emission urban areas but discrepancies in low-emission rural regions due to data sparsity and satellite sensor limits. Spatial analysis reveals that major Southeast Asian cities and their peripheries exhibit robust, sustained growth, while rural, less-developed areas show slower trends, highlighting persistent urban-rural disparities. These urban regions demonstrate a “circular economy advantage”, where per-unit-area carbon emissions steadily rise in economically advantageous zones. Despite high model accuracy, uncertainties persist due to variations in regional economic activities and the limitations of satellite-based emission proxies. Forecasts suggest elevated emission levels in major cities will continue, while changes in other areas remain relatively minimal. Consequently, achieving a low-carbon economy in Southeast Asia requires a top-down approach, emphasizing infrastructure enhancement, resource and energy optimization, and fostering a sustainable, circular socio-economic system.</div></div>","PeriodicalId":29813,"journal":{"name":"Eco-Environment & Health","volume":"4 2","pages":"Article 100150"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-05-20DOI: 10.1016/j.eehl.2025.100155
Xiaoshu Hou , Yuchen Deng , Lu Qin , Xin Xie , Yilan Sun , Gang Yan , Miao Li
China's dual-carbon goals challenges wastewater treatment plants (WWTPs), requiring integrated pollution control and carbon emission reduction. Critical gaps hinder China's strategies for WWTPs, particularly in identifying upgrade needs, optimizing performance, and assessing the local benchmark plants as references. This study addresses these gaps through greenhouse gas (GHG) emission accounting and impact factor analysis, using monthly data from 2232 WWTPs across China. A tiered assessment methodology was developed to evaluate the synergy between pollution reduction and carbon mitigation, including indicators, methodologies, and assessment criteria. Results indicate that indirect emissions from electricity and chemical consumption accounted for 59.9% of total GHG emissions. Key factors influencing these indirect emissions included plant scale, treatment processes, geographic area, operational load, electricity consumption, and influent quality. Through the tiered assessment, WWTPs were classified into three categories: priority control (861 plants), general control (730 plants), and maintenance (641 plants). Furthermore, 222 benchmark plants were identified as exhibiting optimal synergy between pollution control and carbon reduction. For 80% of the benchmark plants, the ranges for carbon emission intensity, influent COD, influent C/N ratio, electricity consumption intensity, and operating load were 0.258–0.482 kg CO2e/t, 175–338 mg/L, 6.13–10.9, 0.149–0.260 kWh per tonne of influent, and 88.0%–110%, respectively. Achieving these benchmark standards across all WWTPs could lead to a 30% reduction in total GHG emissions. Finally, the study proposes targeted policies to enhance the synergy between pollution control and carbon reduction strategies in China's urban wastewater treatment systems.
{"title":"Synergistic strategies for pollution and carbon emission reduction in China's wastewater treatment: A comprehensive tiered assessment and benchmarking framework","authors":"Xiaoshu Hou , Yuchen Deng , Lu Qin , Xin Xie , Yilan Sun , Gang Yan , Miao Li","doi":"10.1016/j.eehl.2025.100155","DOIUrl":"10.1016/j.eehl.2025.100155","url":null,"abstract":"<div><div>China's dual-carbon goals challenges wastewater treatment plants (WWTPs), requiring integrated pollution control and carbon emission reduction. Critical gaps hinder China's strategies for WWTPs, particularly in identifying upgrade needs, optimizing performance, and assessing the local benchmark plants as references. This study addresses these gaps through greenhouse gas (GHG) emission accounting and impact factor analysis, using monthly data from 2232 WWTPs across China. A tiered assessment methodology was developed to evaluate the synergy between pollution reduction and carbon mitigation, including indicators, methodologies, and assessment criteria. Results indicate that indirect emissions from electricity and chemical consumption accounted for 59.9% of total GHG emissions. Key factors influencing these indirect emissions included plant scale, treatment processes, geographic area, operational load, electricity consumption, and influent quality. Through the tiered assessment, WWTPs were classified into three categories: priority control (861 plants), general control (730 plants), and maintenance (641 plants). Furthermore, 222 benchmark plants were identified as exhibiting optimal synergy between pollution control and carbon reduction. For 80% of the benchmark plants, the ranges for carbon emission intensity, influent COD, influent C/N ratio, electricity consumption intensity, and operating load were 0.258–0.482 kg CO<sub>2</sub>e/t, 175–338 mg/L, 6.13–10.9, 0.149–0.260 kWh per tonne of influent, and 88.0%–110%, respectively. Achieving these benchmark standards across all WWTPs could lead to a 30% reduction in total GHG emissions. Finally, the study proposes targeted policies to enhance the synergy between pollution control and carbon reduction strategies in China's urban wastewater treatment systems.</div></div>","PeriodicalId":29813,"journal":{"name":"Eco-Environment & Health","volume":"4 2","pages":"Article 100155"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144189477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-05-14DOI: 10.1016/j.eehl.2025.100154
Lu Lin , Xiaopeng Zhao , Yumeng Li , Jingbo Ling , Jinghua Ren , Qilin Liao , Dongmei Zhou , Xueyuan Gu
The high cadmium (Cd) accumulation ability of wheat has garnered significant attention in China. It is crucial to identify the key factors affecting Cd accumulation in wheat and to develop predictive models to derive the threshold concentration of Cd in soil for safe wheat production. A total of 311 soil–wheat paired datasets were collected from both literature and field surveys in China, in which the ranges of Cd in soil and wheat grain were 0.068–13.500 mg/kg and 0.006–2.190 mg/kg, respectively. Correlation analyses and Partial Least Squares Path Model indicated that soil Cd, soil pH, and CEC together controlled the transfer of Cd from soil to wheat. Multiple linear regression models were successfully established using soil Cd contents or bioavailable Cd (extracted by CaCl2 or calculated using a multi-surface speciation model), pH, and CEC as input variables to predict wheat Cd (RMSE = 0.242–0.327, MAE = 0.188–0.249). Furthermore, the Extreme Random Tree model (RMSE = 0.221, MAE = 0.165) outperformed the other seven machine learning algorithms. The thresholds for both soil total Cd and bioavailable Cd for safe wheat production were further back-calculated according to the permissible value of Cd in wheat grain, which demonstrated enhanced protection accuracy compared to the current soil quality standard. Our findings facilitate a quantitative assessment of Cd accumulation risk in wheat, offering a valuable reference for the safe production of wheat.
{"title":"Cadmium accumulation in wheat grain: Accumulation models and soil thresholds for safe production","authors":"Lu Lin , Xiaopeng Zhao , Yumeng Li , Jingbo Ling , Jinghua Ren , Qilin Liao , Dongmei Zhou , Xueyuan Gu","doi":"10.1016/j.eehl.2025.100154","DOIUrl":"10.1016/j.eehl.2025.100154","url":null,"abstract":"<div><div>The high cadmium (Cd) accumulation ability of wheat has garnered significant attention in China. It is crucial to identify the key factors affecting Cd accumulation in wheat and to develop predictive models to derive the threshold concentration of Cd in soil for safe wheat production. A total of 311 soil–wheat paired datasets were collected from both literature and field surveys in China, in which the ranges of Cd in soil and wheat grain were 0.068–13.500 mg/kg and 0.006–2.190 mg/kg, respectively. Correlation analyses and Partial Least Squares Path Model indicated that soil Cd, soil pH, and CEC together controlled the transfer of Cd from soil to wheat. Multiple linear regression models were successfully established using soil Cd contents or bioavailable Cd (extracted by CaCl<sub>2</sub> or calculated using a multi-surface speciation model), pH, and CEC as input variables to predict wheat Cd (RMSE = 0.242–0.327, MAE = 0.188–0.249). Furthermore, the Extreme Random Tree model (RMSE = 0.221, MAE = 0.165) outperformed the other seven machine learning algorithms. The thresholds for both soil total Cd and bioavailable Cd for safe wheat production were further back-calculated according to the permissible value of Cd in wheat grain, which demonstrated enhanced protection accuracy compared to the current soil quality standard. Our findings facilitate a quantitative assessment of Cd accumulation risk in wheat, offering a valuable reference for the safe production of wheat.</div></div>","PeriodicalId":29813,"journal":{"name":"Eco-Environment & Health","volume":"4 2","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-05-21DOI: 10.1016/j.eehl.2025.100156
Ilker Sengul , Demet Sengul
{"title":"Reinterpretation of a new ChatGPT-empowered, easy-to-use machine learning paradigm: An aide-memoire","authors":"Ilker Sengul , Demet Sengul","doi":"10.1016/j.eehl.2025.100156","DOIUrl":"10.1016/j.eehl.2025.100156","url":null,"abstract":"","PeriodicalId":29813,"journal":{"name":"Eco-Environment & Health","volume":"4 2","pages":"Article 100156"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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