Pub Date : 2025-02-16DOI: 10.1016/S1674-2370(25)00012-2
{"title":"Thanks to our academic editors and peer reviewers","authors":"","doi":"10.1016/S1674-2370(25)00012-2","DOIUrl":"10.1016/S1674-2370(25)00012-2","url":null,"abstract":"","PeriodicalId":23628,"journal":{"name":"Water science and engineering","volume":"18 1","pages":"Page I"},"PeriodicalIF":3.7,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419759","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 : 2025-02-14DOI: 10.1186/s42825-024-00187-1
Takuya Ehiro, Takeshi Onji
This study presents a method for classifying cow and horse leather using a small number of digital microscope images and topological data analysis. In this method, hair pore coordinates in the images are used as essential information for classification. First, the coordinates were semiautomatically extracted using conventional image processing methods and persistent homology (PH) computation. Binary images with white pixels corresponding to the coordinates were generated, and their PHs were computed using filtration based on the Manhattan distance. In addition to the pairwise distance between the two pores, zeroth- and first-order lifetimes were used as explanatory variables to construct the classifier. Among the three explanatory variables, the zeroth-order lifetime resulted in the highest classification accuracy (86%) for the test data. Furthermore, we constructed logistic regression (LR) and random forest (RF) models using the zeroth-order lifetime computed from all images and conducted model interpretation. In both LR and RF, information on a zeroth-order lifetime of less than 10 was used as an important explanatory variable. Additionally, the inverse analysis of birth–death pairs suggested that the zeroth-order lifetime contains topological information distinct from the conventional pairwise distance. Our proposed method is designed to be robust in data-limited situations because it only uses hair pore coordinates as explanatory variables and does not require other information, such as hair pore density or pore size. This study demonstrates that accurate classifiers can be obtained using topological features related to hair pore arrangement.
Graphical Abstract
{"title":"Topological data analysis with digital microscope leather images for animal species classification","authors":"Takuya Ehiro, Takeshi Onji","doi":"10.1186/s42825-024-00187-1","DOIUrl":"10.1186/s42825-024-00187-1","url":null,"abstract":"<div><p>This study presents a method for classifying cow and horse leather using a small number of digital microscope images and topological data analysis. In this method, hair pore coordinates in the images are used as essential information for classification. First, the coordinates were semiautomatically extracted using conventional image processing methods and persistent homology (PH) computation. Binary images with white pixels corresponding to the coordinates were generated, and their PHs were computed using filtration based on the Manhattan distance. In addition to the pairwise distance between the two pores, zeroth- and first-order lifetimes were used as explanatory variables to construct the classifier. Among the three explanatory variables, the zeroth-order lifetime resulted in the highest classification accuracy (86%) for the test data. Furthermore, we constructed logistic regression (LR) and random forest (RF) models using the zeroth-order lifetime computed from all images and conducted model interpretation. In both LR and RF, information on a zeroth-order lifetime of less than 10 was used as an important explanatory variable. Additionally, the inverse analysis of birth–death pairs suggested that the zeroth-order lifetime contains topological information distinct from the conventional pairwise distance. Our proposed method is designed to be robust in data-limited situations because it only uses hair pore coordinates as explanatory variables and does not require other information, such as hair pore density or pore size. This study demonstrates that accurate classifiers can be obtained using topological features related to hair pore arrangement.</p><h3>Graphical Abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":640,"journal":{"name":"Journal of Leather Science and Engineering","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://JLSE.SpringerOpen.com/counter/pdf/10.1186/s42825-024-00187-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404136","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 : 2025-02-11DOI: 10.1016/j.aiia.2025.02.005
Billy G. Ram , Kirk Howatt , Joseph Mettler , Xin Sun
Addressing the computational bottleneck of training deep learning models on high-resolution, three-dimensional images, this study introduces an optimized approach, combining distributed learning (parallelism), image resolution, and data augmentation. We propose analysis methodologies that help train deep learning (DL) models on proximal hyperspectral images, demonstrating superior performance in eight-class crop (canola, field pea, sugarbeet and flax) and weed (redroot pigweed, resistant kochia, waterhemp and ragweed) classification. Utilizing state-of-the-art model architectures (ResNet-50, VGG-16, DenseNet, EfficientNet) in comparison with ResNet-50 inspired Hyper-Residual Convolutional Neural Network model. Our findings reveal that an image resolution of 100x100x54 maximizes accuracy while maintaining computational efficiency, surpassing the performance of 150x150x54 and 50x50x54 resolution images. By employing data parallelism, we overcome system memory limitations and achieve exceptional classification results, with test accuracies and F1-scores reaching 0.96 and 0.97, respectively. This research highlights the potential of residual-based networks for analyzing hyperspectral images. It offers valuable insights into optimizing deep learning models in resource-constrained environments. The research presents detailed training pipelines for deep learning models that utilize large (> 4k) hyperspectral training samples, including background and without any data preprocessing. This approach enables the training of deep learning models directly on raw hyperspectral data.
{"title":"Addressing computation resource exhaustion associated with deep learning training of three-dimensional hyperspectral images using multiclass weed classification","authors":"Billy G. Ram , Kirk Howatt , Joseph Mettler , Xin Sun","doi":"10.1016/j.aiia.2025.02.005","DOIUrl":"10.1016/j.aiia.2025.02.005","url":null,"abstract":"<div><div>Addressing the computational bottleneck of training deep learning models on high-resolution, three-dimensional images, this study introduces an optimized approach, combining distributed learning (parallelism), image resolution, and data augmentation. We propose analysis methodologies that help train deep learning (DL) models on proximal hyperspectral images, demonstrating superior performance in eight-class crop (canola, field pea, sugarbeet and flax) and weed (redroot pigweed, resistant kochia, waterhemp and ragweed) classification. Utilizing state-of-the-art model architectures (ResNet-50, VGG-16, DenseNet, EfficientNet) in comparison with ResNet-50 inspired Hyper-Residual Convolutional Neural Network model. Our findings reveal that an image resolution of 100x100x54 maximizes accuracy while maintaining computational efficiency, surpassing the performance of 150x150x54 and 50x50x54 resolution images. By employing data parallelism, we overcome system memory limitations and achieve exceptional classification results, with test accuracies and F1-scores reaching 0.96 and 0.97, respectively. This research highlights the potential of residual-based networks for analyzing hyperspectral images. It offers valuable insights into optimizing deep learning models in resource-constrained environments. The research presents detailed training pipelines for deep learning models that utilize large (> 4k) hyperspectral training samples, including background and without any data preprocessing. This approach enables the training of deep learning models directly on raw hyperspectral data.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 131-146"},"PeriodicalIF":8.2,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487682","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 : 2025-02-11DOI: 10.1016/j.aiia.2025.01.013
Ameer Tamoor Khan , Signe Marie Jensen , Abdul Rehman Khan
Effective weed management plays a critical role in enhancing the productivity and sustainability of cotton cultivation. The rapid emergence of herbicide-resistant weeds has underscored the need for innovative solutions to address the challenges associated with precise weed detection. This paper investigates the potential of YOLOv8, the latest advancement in the YOLO family of object detectors, for multi-class weed detection in U.S. cotton fields. Leveraging the CottonWeedDet12 dataset, which includes diverse weed species captured under varying environmental conditions, this study provides a comprehensive evaluation of YOLOv8's performance. A comparative analysis with earlier YOLO variants reveals substantial improvements in detection accuracy, as evidenced by higher mean Average Precision (mAP) scores. These findings highlight YOLOv8's superior capability to generalize across complex field scenarios, making it a promising candidate for real-time applications in precision agriculture. The enhanced architecture of YOLOv8, featuring anchor-free detection, an advanced Feature Pyramid Network (FPN), and an optimized loss function, enables accurate detection even under challenging conditions. This research emphasizes the importance of machine vision technologies in modern agriculture, particularly for minimizing herbicide reliance and promoting sustainable farming practices. The results not only validate YOLOv8's efficacy in multi-class weed detection but also pave the way for its integration into autonomous agricultural systems, thereby contributing to the broader goals of precision agriculture and ecological sustainability.
{"title":"Advancing precision agriculture: A comparative analysis of YOLOv8 for multi-class weed detection in cotton cultivation","authors":"Ameer Tamoor Khan , Signe Marie Jensen , Abdul Rehman Khan","doi":"10.1016/j.aiia.2025.01.013","DOIUrl":"10.1016/j.aiia.2025.01.013","url":null,"abstract":"<div><div>Effective weed management plays a critical role in enhancing the productivity and sustainability of cotton cultivation. The rapid emergence of herbicide-resistant weeds has underscored the need for innovative solutions to address the challenges associated with precise weed detection. This paper investigates the potential of YOLOv8, the latest advancement in the YOLO family of object detectors, for multi-class weed detection in U.S. cotton fields. Leveraging the CottonWeedDet12 dataset, which includes diverse weed species captured under varying environmental conditions, this study provides a comprehensive evaluation of YOLOv8's performance. A comparative analysis with earlier YOLO variants reveals substantial improvements in detection accuracy, as evidenced by higher mean Average Precision (mAP) scores. These findings highlight YOLOv8's superior capability to generalize across complex field scenarios, making it a promising candidate for real-time applications in precision agriculture. The enhanced architecture of YOLOv8, featuring anchor-free detection, an advanced Feature Pyramid Network (FPN), and an optimized loss function, enables accurate detection even under challenging conditions. This research emphasizes the importance of machine vision technologies in modern agriculture, particularly for minimizing herbicide reliance and promoting sustainable farming practices. The results not only validate YOLOv8's efficacy in multi-class weed detection but also pave the way for its integration into autonomous agricultural systems, thereby contributing to the broader goals of precision agriculture and ecological sustainability.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 182-191"},"PeriodicalIF":8.2,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520252","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 : 2025-02-11DOI: 10.1016/j.aiia.2025.02.001
Niharika Vullaganti, Billy G. Ram, Xin Sun
Amidst the growing food demands of an increasing population, agricultural intensification frequently depends on excessive chemical and fertilizer applications. While this approach initially boosts crop yields, it effects long-term sustainability through soil degradation and compromised food quality. Thus, prioritizing soil health while enhancing crop production is essential for sustainable food production. Site-Specific Nutrient Management (SSNM) emerges as a critical strategy to increase crop production, maintain soil health, and reduce environmental pollution. Despite its potential, the application of SSNM technologies remain limited in farmers' fields due to existing research gaps. This review critically analyzes and presents research conducted in SSNM in the past 11 years (2013–2024), identifying gaps and future research directions. A comprehensive study of 97 relevant research publications reveals several key findings: a) Electrochemical sensing and spectroscopy are the two widely explored areas in SSNM research, b) Despite numerous technologies in SSNM, each has its own limitation, preventing any single technology from being ideal, c) The selection of models and preprocessing techniques significantly impacts nutrient prediction accuracy, d) No single sensor or sensor combination can predict all soil properties, as suitability is highly attribute-specific. This review provides researchers, and technical personnel in precision agriculture, and farmers with detailed insights into SSNM research, its implementation, limitations, challenges, and future research directions.
{"title":"Precision agriculture technologies for soil site-specific nutrient management: A comprehensive review","authors":"Niharika Vullaganti, Billy G. Ram, Xin Sun","doi":"10.1016/j.aiia.2025.02.001","DOIUrl":"10.1016/j.aiia.2025.02.001","url":null,"abstract":"<div><div>Amidst the growing food demands of an increasing population, agricultural intensification frequently depends on excessive chemical and fertilizer applications. While this approach initially boosts crop yields, it effects long-term sustainability through soil degradation and compromised food quality. Thus, prioritizing soil health while enhancing crop production is essential for sustainable food production. Site-Specific Nutrient Management (SSNM) emerges as a critical strategy to increase crop production, maintain soil health, and reduce environmental pollution. Despite its potential, the application of SSNM technologies remain limited in farmers' fields due to existing research gaps. This review critically analyzes and presents research conducted in SSNM in the past 11 years (2013–2024), identifying gaps and future research directions. A comprehensive study of 97 relevant research publications reveals several key findings: a) Electrochemical sensing and spectroscopy are the two widely explored areas in SSNM research, b) Despite numerous technologies in SSNM, each has its own limitation, preventing any single technology from being ideal, c) The selection of models and preprocessing techniques significantly impacts nutrient prediction accuracy, d) No single sensor or sensor combination can predict all soil properties, as suitability is highly attribute-specific. This review provides researchers, and technical personnel in precision agriculture, and farmers with detailed insights into SSNM research, its implementation, limitations, challenges, and future research directions.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 147-161"},"PeriodicalIF":8.2,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143507923","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 : 2025-02-08DOI: 10.13227/j.hjkx.202312034
Xiao-Fang Shen, Xin-Yan Gong, Xian Yuan, Qing-Hua Li
Co-pyrolysis with other biomass is a promising method for municipal sludge treatment and has attracted great attention. However, the dominant mechanism by which the heavy metals in municipal sludge are immobilized during the co-pyrolysis remains unknown. In this study, municipal-sludge biochar was prepared by pyrolysis and co-pyrolysis, and the effects of pyrolysis temperature （400-800 ℃） and the addition of shaddock peel on the properties of biochar, the contents of heavy metals （Cu, Zn, Pb, Cd, Ni, and Cr）, and their environmental risks were investigated. Based on the analysis of characterization results and heavy metal contents in the biochar, it was observed that co-pyrolysis promoted the formation of stabilized crystalline minerals （e.g., CdPbO3, Pb5（PO4）3OH, CuCl, and ZnS）, which reduced the potential risk of heavy metals in biochar. Furthermore, aromatic groups were detected and could interact with heavy metals through cation-π interaction. Further analysis revealed that the immobilization was enhanced by the complexation between heavy metals and the functional groups in biochar, such as -OH and -CO-NH-, which played the main role in the stabilization of Cu and Ni at low pyrolysis temperatures. However, surface sorption and pore filling, due to the increase in specific surface area and porosity, dominated the immobilization of Cd, Cr, Pb, and Zn. The leaching concentrations of heavy metals in co-pyrolysis biochar were much lower than the limit values of "Identification Standards for Hazardous Wastes-Identification for Extraction Toxicity" （GB 5085.2-2007） and those by US EPA 1311, 1990. Additionally, the potential ecological risk index （RI value） of heavy metals in biochar was significantly reduced by co-pyrolysis compared to that of sludge or biochar without the co-pyrolysis. This study reveals the dominant immobilization mechanism for specific heavy metals during co-pyrolysis of municipal sludge with shaddock peel and provides an alternative practical strategy for the safe disposal of municipal sludge and biomass wastes.
{"title":"[Immobilization of Heavy Metals in Municipal Sludge by Co-pyrolysis of Shaddock Peel and Sludge].","authors":"Xiao-Fang Shen, Xin-Yan Gong, Xian Yuan, Qing-Hua Li","doi":"10.13227/j.hjkx.202312034","DOIUrl":"https://doi.org/10.13227/j.hjkx.202312034","url":null,"abstract":"<p><p>Co-pyrolysis with other biomass is a promising method for municipal sludge treatment and has attracted great attention. However, the dominant mechanism by which the heavy metals in municipal sludge are immobilized during the co-pyrolysis remains unknown. In this study, municipal-sludge biochar was prepared by pyrolysis and co-pyrolysis, and the effects of pyrolysis temperature （400-800 ℃） and the addition of shaddock peel on the properties of biochar, the contents of heavy metals （Cu, Zn, Pb, Cd, Ni, and Cr）, and their environmental risks were investigated. Based on the analysis of characterization results and heavy metal contents in the biochar, it was observed that co-pyrolysis promoted the formation of stabilized crystalline minerals （e.g., CdPbO<sub>3</sub>, Pb<sub>5</sub>（PO<sub>4</sub>）<sub>3</sub>OH, CuCl, and ZnS）, which reduced the potential risk of heavy metals in biochar. Furthermore, aromatic groups were detected and could interact with heavy metals through cation-π interaction. Further analysis revealed that the immobilization was enhanced by the complexation between heavy metals and the functional groups in biochar, such as -OH and -CO-NH-, which played the main role in the stabilization of Cu and Ni at low pyrolysis temperatures. However, surface sorption and pore filling, due to the increase in specific surface area and porosity, dominated the immobilization of Cd, Cr, Pb, and Zn. The leaching concentrations of heavy metals in co-pyrolysis biochar were much lower than the limit values of \"Identification Standards for Hazardous Wastes-Identification for Extraction Toxicity\" （GB 5085.2-2007） and those by US EPA 1311, 1990. Additionally, the potential ecological risk index （RI value） of heavy metals in biochar was significantly reduced by co-pyrolysis compared to that of sludge or biochar without the co-pyrolysis. This study reveals the dominant immobilization mechanism for specific heavy metals during co-pyrolysis of municipal sludge with shaddock peel and provides an alternative practical strategy for the safe disposal of municipal sludge and biomass wastes.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 2","pages":"944-955"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143441430","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 : 2025-02-08DOI: 10.13227/j.hjkx.202401204
Wen-Hui Kang, Yin-Zhu Zhou, Jin-Long Zhou, Feng Jiang, Shuang-Bao Han, Mi Lei, Jin-Wei Liu
Groundwater is the main source of water for daily life, agricultural irrigation, and industrial production in the Manas River Basin （MRB） in Xinjiang. To explore the spatial distribution and sources of inorganic components in groundwater and their negative effects on human health, 37 groundwater samples were collected in the plain area of MRB. The spatial distribution, sources, and potential health risks of inorganic components in groundwater were analyzed using Kriging interpolation, PMF source analysis, Pearson correlation analysis, and a health risk assessment model. The results showed that all of the groundwater in the area was weakly alkaline. Groundwater TDS and Cl- had similar distribution characteristics, being higher in the eastern part of the plain area. The high SO42- area was mainly distributed in the periphery of Manas County and the edge of the Gurbantonggut desert. The distribution of groundwater NO3--N, which was greatly affected by human activities, showed spatial heterogeneity, and the high-value areas were mainly distributed in urban areas. Arsenic （As）, fluoride （F）, and iodine （I） were important inorganic components affecting groundwater quality, with over-limit rates of 51.35%, 45.95%, and 51.35% according to the Standard for Groundwater Quality （GB/T 14848-2017）. High As, F, and I groundwater was distributed in the low plain area north of the West Bank Canal, which had a small topographic slope, hydraulic gradient, and aquifer particles with weak groundwater runoff intensity and was conducive to the enrichment of groundwater As, F, and I. Source apportionment showed that groundwater in different aquifers had similar sources or evolution processes. The concentration of inorganic components was controlled by leaching concentration, point source pollution under the geological environment-domestic sewage-agricultural irrigation, agricultural activities, point source pollution caused by domestic sewage, an alkaline-reducing environment, and ion exchange. Health risk assessment showed that As in groundwater was the main inorganic component threatening human health. The non-carcinogenic risk for children and adults could be ignored, whereas the carcinogenic risk cannot be ignored, and children were more sensitive to the risk of cancer caused by inorganic substances in the groundwater. Therefore, more attention should be paid to As exposure to drinking water safety, especially for children.
{"title":"[Distribution, Source Apportionment, and Health Risk Assessment of Inorganic Components in Groundwater in the Plain Area of Manas River Basin in Xinjiang].","authors":"Wen-Hui Kang, Yin-Zhu Zhou, Jin-Long Zhou, Feng Jiang, Shuang-Bao Han, Mi Lei, Jin-Wei Liu","doi":"10.13227/j.hjkx.202401204","DOIUrl":"https://doi.org/10.13227/j.hjkx.202401204","url":null,"abstract":"<p><p>Groundwater is the main source of water for daily life, agricultural irrigation, and industrial production in the Manas River Basin （MRB） in Xinjiang. To explore the spatial distribution and sources of inorganic components in groundwater and their negative effects on human health, 37 groundwater samples were collected in the plain area of MRB. The spatial distribution, sources, and potential health risks of inorganic components in groundwater were analyzed using Kriging interpolation, PMF source analysis, Pearson correlation analysis, and a health risk assessment model. The results showed that all of the groundwater in the area was weakly alkaline. Groundwater TDS and Cl<sup>-</sup> had similar distribution characteristics, being higher in the eastern part of the plain area. The high SO<sub>4</sub><sup>2-</sup> area was mainly distributed in the periphery of Manas County and the edge of the Gurbantonggut desert. The distribution of groundwater NO<sub>3</sub><sup>-</sup>-N, which was greatly affected by human activities, showed spatial heterogeneity, and the high-value areas were mainly distributed in urban areas. Arsenic （As）, fluoride （F）, and iodine （I） were important inorganic components affecting groundwater quality, with over-limit rates of 51.35%, 45.95%, and 51.35% according to the Standard for Groundwater Quality （GB/T 14848-2017）. High As, F, and I groundwater was distributed in the low plain area north of the West Bank Canal, which had a small topographic slope, hydraulic gradient, and aquifer particles with weak groundwater runoff intensity and was conducive to the enrichment of groundwater As, F, and I. Source apportionment showed that groundwater in different aquifers had similar sources or evolution processes. The concentration of inorganic components was controlled by leaching concentration, point source pollution under the geological environment-domestic sewage-agricultural irrigation, agricultural activities, point source pollution caused by domestic sewage, an alkaline-reducing environment, and ion exchange. Health risk assessment showed that As in groundwater was the main inorganic component threatening human health. The non-carcinogenic risk for children and adults could be ignored, whereas the carcinogenic risk cannot be ignored, and children were more sensitive to the risk of cancer caused by inorganic substances in the groundwater. Therefore, more attention should be paid to As exposure to drinking water safety, especially for children.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 2","pages":"843-853"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442082","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 : 2025-02-08DOI: 10.13227/j.hjkx.202402145
Hui-Qiu Liu, Sheng-Xian Yang, Xin Chao, Bing-Jie Yan, Pei-Pei Wei, Xiang-Jun Wu, Sang Ba
Periphytic algae is an important primary producer in the river ecosystem, and it is one of the important indicators for the monitoring and evaluation of river water ecology. As a typical plateau river, the assembly process of the algae community in the Yarlung Zangbo River is still unclear. In May 2022 and July and September 2023, algae samples were collected, and water environmental factors were determined in the lower reaches of the Yarlung Zangbo River. The algal species were identified, and cell abundance and biomass were calculated using in vivo observation and fixed staining techniques to explore the diversity pattern of algae communities in the lower reaches of the Yarlung Zangbo River, to clarify the relative importance of deterministic and random processes in the assembly of algal communities, and to reveal the influencing factors driving the assembly process. The results showed that there were significant differences in the time of water environmental factors, mainly concentrated between spring, summer, summer, and autumn. A total of 274 species of algae were identified in the three seasons, belonging to 7 phyla, 9 classes, 18 orders, 31 families, and 63 genera, and the community differences between seasons were significant （P=0.001）. They all showed Bacillariophyta-Chlorophyta-Cyanophyta, and the community assembly process of algae was affected by both a deterministic process and random process but was dominated by the deterministic process. The interspecific relationship was mainly cooperative, and the co-occurrence network in autumn showed stronger network complexity and connectivity. The pH and turbidity were important water environmental factors affecting the community structure of algae, which could indirectly affect the community assembly process of algae by driving the metabolic rate and ecological adaptability of algae species.
{"title":"[Environmental Screening Drives the Assembly Process of Periphytic Algae Community in the Lower Reaches of Yarlung Zangbo River].","authors":"Hui-Qiu Liu, Sheng-Xian Yang, Xin Chao, Bing-Jie Yan, Pei-Pei Wei, Xiang-Jun Wu, Sang Ba","doi":"10.13227/j.hjkx.202402145","DOIUrl":"https://doi.org/10.13227/j.hjkx.202402145","url":null,"abstract":"<p><p>Periphytic algae is an important primary producer in the river ecosystem, and it is one of the important indicators for the monitoring and evaluation of river water ecology. As a typical plateau river, the assembly process of the algae community in the Yarlung Zangbo River is still unclear. In May 2022 and July and September 2023, algae samples were collected, and water environmental factors were determined in the lower reaches of the Yarlung Zangbo River. The algal species were identified, and cell abundance and biomass were calculated using in vivo observation and fixed staining techniques to explore the diversity pattern of algae communities in the lower reaches of the Yarlung Zangbo River, to clarify the relative importance of deterministic and random processes in the assembly of algal communities, and to reveal the influencing factors driving the assembly process. The results showed that there were significant differences in the time of water environmental factors, mainly concentrated between spring, summer, summer, and autumn. A total of 274 species of algae were identified in the three seasons, belonging to 7 phyla, 9 classes, 18 orders, 31 families, and 63 genera, and the community differences between seasons were significant （<i>P</i>=0.001）. They all showed Bacillariophyta-Chlorophyta-Cyanophyta, and the community assembly process of algae was affected by both a deterministic process and random process but was dominated by the deterministic process. The interspecific relationship was mainly cooperative, and the co-occurrence network in autumn showed stronger network complexity and connectivity. The pH and turbidity were important water environmental factors affecting the community structure of algae, which could indirectly affect the community assembly process of algae by driving the metabolic rate and ecological adaptability of algae species.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 2","pages":"889-899"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442122","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 : 2025-02-08DOI: 10.13227/j.hjkx.202401141
Jing-Xin Zhang, Yu-Xin Gu, Jia-Qi Shen, Ming-Qi Zhang, Ming Cong, Yan Sun, Jiao-Jie He, Li-Wei Yang
The Yellow River Basin plays an important role in China's economic development and ecological protection. Based on remote sensing ecological index （RSEI） data, climate data, digital elevation data, and night-time remote sensing data, the RSEI index was used as the ecological environment quality evaluation index. such as Theil Sen slope estimation, Hurst index, and Mann-Kendall test were used to evaluate the spatiotemporal changes in the ecological environment quality of the Yellow River Basin from 2001 to 2021. Geodetectors were used to quantitatively detect and analyze the key factors and their interactions in the ecological environment of the basin. The results showed that： Firstly, the overall ecological environment quality in the Yellow River Basin was slightly lower than the moderate level and gradually improved from the northwest to the southeast. The areas with moderate and lower grades accounted for 83.12% of the total area in the basin. The ecological environment quality has shown an upward trend in the past 21 years, which was confirmed by a significance test with a confidence level of 95%. Secondly, the ecological quality within the region showed a phased change of rising decreasing rising, with frequent area transfer between adjacent ecological levels and migration of ecological positions at different levels. The proportion of ecological improvement areas in the past 21 years reached 58.09%, but 51.75% of the regional improvement trend did not pass the significance test. Thirdly, the future ecological environment quality in the basin is expected to deteriorate, with the proportion of continuously deteriorating areas accounting for 24.39% and the proportion of areas transitioning from improvement to deterioration accounting for 5.08%. Attention should be paid to the ecological deterioration risks in Qinghai and Gansu, and the trend of ecological degradation in Inner Mongolia should be curbed. Fourthly, the results of the geographical detector indicated that the ecological environment in the Yellow River Basin was mainly influenced by population density, and the influence of climate factors represented by rainfall has been continuously increasing. The interaction between the two factors enhanced the explanatory power of the factors, with the main interaction type being mutual enhancement, accounting for 70.2%. In the future, efforts should be made to strengthen the ecosystem governance in the Yellow River Basin and carry out comprehensive ecological restoration work considering both human activities and climate change. The evaluation of the ecological environment in the Yellow River Basin can provide a theoretical basis and data support for implementing targeted measures and promoting ecological protection and high-quality development in the basin.
{"title":"[Analysis of Spatiotemporal Changes and Driving Factors of Ecological Environment Quality in the Yellow River Basin].","authors":"Jing-Xin Zhang, Yu-Xin Gu, Jia-Qi Shen, Ming-Qi Zhang, Ming Cong, Yan Sun, Jiao-Jie He, Li-Wei Yang","doi":"10.13227/j.hjkx.202401141","DOIUrl":"https://doi.org/10.13227/j.hjkx.202401141","url":null,"abstract":"<p><p>The Yellow River Basin plays an important role in China's economic development and ecological protection. Based on remote sensing ecological index （RSEI） data, climate data, digital elevation data, and night-time remote sensing data, the RSEI index was used as the ecological environment quality evaluation index. such as Theil Sen slope estimation, Hurst index, and Mann-Kendall test were used to evaluate the spatiotemporal changes in the ecological environment quality of the Yellow River Basin from 2001 to 2021. Geodetectors were used to quantitatively detect and analyze the key factors and their interactions in the ecological environment of the basin. The results showed that： Firstly, the overall ecological environment quality in the Yellow River Basin was slightly lower than the moderate level and gradually improved from the northwest to the southeast. The areas with moderate and lower grades accounted for 83.12% of the total area in the basin. The ecological environment quality has shown an upward trend in the past 21 years, which was confirmed by a significance test with a confidence level of 95%. Secondly, the ecological quality within the region showed a phased change of rising decreasing rising, with frequent area transfer between adjacent ecological levels and migration of ecological positions at different levels. The proportion of ecological improvement areas in the past 21 years reached 58.09%, but 51.75% of the regional improvement trend did not pass the significance test. Thirdly, the future ecological environment quality in the basin is expected to deteriorate, with the proportion of continuously deteriorating areas accounting for 24.39% and the proportion of areas transitioning from improvement to deterioration accounting for 5.08%. Attention should be paid to the ecological deterioration risks in Qinghai and Gansu, and the trend of ecological degradation in Inner Mongolia should be curbed. Fourthly, the results of the geographical detector indicated that the ecological environment in the Yellow River Basin was mainly influenced by population density, and the influence of climate factors represented by rainfall has been continuously increasing. The interaction between the two factors enhanced the explanatory power of the factors, with the main interaction type being mutual enhancement, accounting for 70.2%. In the future, efforts should be made to strengthen the ecosystem governance in the Yellow River Basin and carry out comprehensive ecological restoration work considering both human activities and climate change. The evaluation of the ecological environment in the Yellow River Basin can provide a theoretical basis and data support for implementing targeted measures and promoting ecological protection and high-quality development in the basin.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 2","pages":"956-971"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143441939","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 : 2025-02-08DOI: 10.13227/j.hjkx.202402046
Chen-Hao Xue, Bin Zou, Yong Xu, Shen-Xin Li, Sha Li
<p><p>This research investigates the spatial and temporal relationship between urbanization levels and air pollution in cities at different stages of urbanization in China, highlighting its significance for guiding cities towards green development with reduced pollution and carbon emissions. The study uses a range of datasets from 2005 to 2020, including per capita GDP raster data, land use type data, remotely sensed PM<sub>2.5</sub> and O<sub>3</sub> concentration data, and meteorological raster data. The urbanization stages for the years 2005, 2010, 2015, and 2020 were classified using the Chenery standard, facilitating a nuanced analysis of urban growth patterns. A one-way analysis of variance（ANOVA）was employed to examine the significance of differences in PM<sub>2.5</sub> and O<sub>3</sub> concentrations across urbanization stages, revealing distinct pollution profiles. Furthermore, multi-scale geographically weighted regression（MGWR）was applied to quantitatively analyze the spatial and temporal correlations between urbanization levels and the concentrations of PM<sub>2.5</sub> and O<sub>3</sub>, offering insights into the complex dynamics at play. The findings indicate a progression through six urbanization stages from 2005 to 2020. In 2005, 110 cities were in the primary product stage （PPS）, and 118 were in the primary industrialization stage （PIS）. By 2010, the urbanization phase had shifted predominantly towards industrialization, with 139 cities in the medium-term industrialization stage （MIS） and 88 in the late industrialization stage （LIS）. The trend continued towards advanced stages, with the majority of cities in 2015 and 2020 being in the middle to late industrialization and developed stages. The number of cities in the primary developed stage （PDS）and the developed stage （DS）reached 80 and 91, respectively. The spatial distribution of PM<sub>2.5</sub> and O<sub>3</sub> concentration trends and their average values at different urbanization stages showed significant variance. From PPS to DS, the average PM<sub>2.5</sub> concentration initially rose and then declined, with concentrations during the industrialization stage higher than in the primary and developed stages. In contrast, the average O<sub>3</sub> concentration trended upward across all stages, reaching its peak in the developed stage. The MGWR results identified significant regional variations in the impact of urban built-up area proportions on PM<sub>2.5</sub> and O<sub>3</sub> concentrations. High-value areas for PM<sub>2.5</sub> regression coefficients in 2005 and 2010 were predominantly found in the Yunnan-Guizhou-Sichuan urban cluster, extending northeast by 2015 and 2020 to cover most of China. Conversely, high-value areas for O<sub>3</sub> regression coefficients from 2005 to 2020 were mainly in western and central China, with eastern regions, particularly in the south, showing significantly lower coefficients, indicating a negative correlation overall. Synergisti
{"title":"[Spatial-temporal Relationships Between Urbanization Levels and Air Pollution Across Various Stages of Urbanization Nationwide].","authors":"Chen-Hao Xue, Bin Zou, Yong Xu, Shen-Xin Li, Sha Li","doi":"10.13227/j.hjkx.202402046","DOIUrl":"https://doi.org/10.13227/j.hjkx.202402046","url":null,"abstract":"<p><p>This research investigates the spatial and temporal relationship between urbanization levels and air pollution in cities at different stages of urbanization in China, highlighting its significance for guiding cities towards green development with reduced pollution and carbon emissions. The study uses a range of datasets from 2005 to 2020, including per capita GDP raster data, land use type data, remotely sensed PM<sub>2.5</sub> and O<sub>3</sub> concentration data, and meteorological raster data. The urbanization stages for the years 2005, 2010, 2015, and 2020 were classified using the Chenery standard, facilitating a nuanced analysis of urban growth patterns. A one-way analysis of variance（ANOVA）was employed to examine the significance of differences in PM<sub>2.5</sub> and O<sub>3</sub> concentrations across urbanization stages, revealing distinct pollution profiles. Furthermore, multi-scale geographically weighted regression（MGWR）was applied to quantitatively analyze the spatial and temporal correlations between urbanization levels and the concentrations of PM<sub>2.5</sub> and O<sub>3</sub>, offering insights into the complex dynamics at play. The findings indicate a progression through six urbanization stages from 2005 to 2020. In 2005, 110 cities were in the primary product stage （PPS）, and 118 were in the primary industrialization stage （PIS）. By 2010, the urbanization phase had shifted predominantly towards industrialization, with 139 cities in the medium-term industrialization stage （MIS） and 88 in the late industrialization stage （LIS）. The trend continued towards advanced stages, with the majority of cities in 2015 and 2020 being in the middle to late industrialization and developed stages. The number of cities in the primary developed stage （PDS）and the developed stage （DS）reached 80 and 91, respectively. The spatial distribution of PM<sub>2.5</sub> and O<sub>3</sub> concentration trends and their average values at different urbanization stages showed significant variance. From PPS to DS, the average PM<sub>2.5</sub> concentration initially rose and then declined, with concentrations during the industrialization stage higher than in the primary and developed stages. In contrast, the average O<sub>3</sub> concentration trended upward across all stages, reaching its peak in the developed stage. The MGWR results identified significant regional variations in the impact of urban built-up area proportions on PM<sub>2.5</sub> and O<sub>3</sub> concentrations. High-value areas for PM<sub>2.5</sub> regression coefficients in 2005 and 2010 were predominantly found in the Yunnan-Guizhou-Sichuan urban cluster, extending northeast by 2015 and 2020 to cover most of China. Conversely, high-value areas for O<sub>3</sub> regression coefficients from 2005 to 2020 were mainly in western and central China, with eastern regions, particularly in the south, showing significantly lower coefficients, indicating a negative correlation overall. Synergisti","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 2","pages":"705-714"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442301","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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