Pub Date : 2025-09-24DOI: 10.1007/s00244-025-01159-0
Fortunate Davhana, Marc Humphries, Gareth Hunter, Nimmi Seoraj-Pillai, Xander Combrink
Lead (Pb) poisoning poses a significant threat to wildlife. A primary cause of Pb poisoning is the unintentional ingestion of Pb ammunition and fishing weights, which are still used for hunting and fishing in numerous regions globally. While the effects of Pb poisoning on birds and mammals are well established, impacts on reptiles are less well documented and difficult to assess under field conditions. In this study, we investigated the effects of extreme Pb exposure on captive sub-adult Nile crocodiles (Crocodylus niloticus; n = 18). We administered Pb dosages in the form of fishing weights (54–215 g) and monitored changes in blood lead concentrations, packed cell volumes, urine Pb concentrations, growth, and body condition over a 48-week period. Crocodiles exhibited a remarkable tolerance to exceptionally high Pb exposure over the duration of the study. Despite the lack of obvious clinical signs of Pb toxicity, elevated BPb concentrations were linked to lower PCVs, indicating anaemia across all treatment groups by week eight. However, crocodiles showed a sustained erythropoietic response which may be contributing to their resilience to acute Pb toxicity. While Pb exposure did not significantly affect body condition, it was associated with a discernible reduction in weight gain over the duration of the study. Our estimation of a 5.8–7.3-year timeframe for complete dissolution of the Pb fishing weights in the experimental crocodiles’ stomachs carries significant implications for wild populations, which are likely to be exposed to Pb for far longer than 48-week duration of this study.
铅中毒对野生动物构成重大威胁。铅中毒的一个主要原因是无意中摄入铅弹药和捕鱼砝码,在全球许多地区仍用于狩猎和捕鱼。虽然铅中毒对鸟类和哺乳动物的影响已得到证实,但对爬行动物的影响却没有充分的记录,而且很难在实地条件下进行评估。在这项研究中,我们研究了极端Pb暴露对圈养的亚成年尼罗鳄(Crocodylus niloticus; n = 18)的影响。在48周的时间里,我们以捕鱼重量(54-215 g)的形式给予铅剂量,并监测血铅浓度、堆积细胞体积、尿铅浓度、生长和身体状况的变化。在研究期间,鳄鱼对异常高的铅暴露表现出显著的耐受性。尽管缺乏明显的铅毒性临床症状,但BPb浓度升高与pcv降低有关,表明所有治疗组在第8周时均出现贫血。然而,鳄鱼表现出持续的红细胞生成反应,这可能有助于它们对急性铅中毒的恢复。虽然铅暴露对身体状况没有显著影响,但在研究期间,它与体重增加的明显减少有关。我们估计,在实验鳄鱼胃中完全溶解Pb捕鱼重量需要5.8-7.3年的时间,这对野生种群具有重要意义,因为野生种群暴露于Pb的时间可能远远超过本研究的48周。
{"title":"Exposure of Sub-adult Nile Crocodiles (Crocodylus niloticus) to Extreme Lead Concentrations: A 48-week Experimental Study with Implications for Wild Populations","authors":"Fortunate Davhana, Marc Humphries, Gareth Hunter, Nimmi Seoraj-Pillai, Xander Combrink","doi":"10.1007/s00244-025-01159-0","DOIUrl":"10.1007/s00244-025-01159-0","url":null,"abstract":"<div><p>Lead (Pb) poisoning poses a significant threat to wildlife. A primary cause of Pb poisoning is the unintentional ingestion of Pb ammunition and fishing weights, which are still used for hunting and fishing in numerous regions globally. While the effects of Pb poisoning on birds and mammals are well established, impacts on reptiles are less well documented and difficult to assess under field conditions. In this study, we investigated the effects of extreme Pb exposure on captive sub-adult Nile crocodiles (<i>Crocodylus niloticus</i>; <i>n</i> = 18). We administered Pb dosages in the form of fishing weights (54–215 g) and monitored changes in blood lead concentrations, packed cell volumes, urine Pb concentrations, growth, and body condition over a 48-week period. Crocodiles exhibited a remarkable tolerance to exceptionally high Pb exposure over the duration of the study. Despite the lack of obvious clinical signs of Pb toxicity, elevated BPb concentrations were linked to lower PCVs, indicating anaemia across all treatment groups by week eight. However, crocodiles showed a sustained erythropoietic response which may be contributing to their resilience to acute Pb toxicity. While Pb exposure did not significantly affect body condition, it was associated with a discernible reduction in weight gain over the duration of the study. Our estimation of a 5.8–7.3-year timeframe for complete dissolution of the Pb fishing weights in the experimental crocodiles’ stomachs carries significant implications for wild populations, which are likely to be exposed to Pb for far longer than 48-week duration of this study.</p></div>","PeriodicalId":8377,"journal":{"name":"Archives of Environmental Contamination and Toxicology","volume":"89 3","pages":"372 - 385"},"PeriodicalIF":2.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00244-025-01159-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-17DOI: 10.1007/s00244-025-01141-w
Younes Garosi, Mohsen Sheklabadi, Shamsollah Ayoubi, Iman Kimiaee, Eric C. Brevik, Christian Conoscenti
This study utilized the methodology of digital soil mapping (DSM) to investigate the spatial prediction of toxic metals and their environmental covariates in the Ghorveh Plain, western Iran. The environmental covariates are defined as the factors that control the distribution of toxic metals at the geographical scale under investigation. They could be used for predicting the sources and monitoring of pollution. A total of 150 soil samples (0–30 cm) were analyzed for toxic metal concentrations and some soil properties. A comprehensive set of environmental variables was obtained from remote sensing imagery, DEM, and ancillary data, which were identified as likely to control the spatial distributions of toxic metals. The genetic algorithm was utilized to identify “all-relevant” environmental covariates for each toxic metal. Three machine learning algorithms, namely random forests (RF), cubist, and regression trees (RT), were employed to establish the statistical relationships between toxic metals and the environmental covariates. The RF model exhibited the most optimal prediction performance. All three models, particularly the RF, demonstrated robust performance, exhibiting minimal impact on the model’s functionality when confronted with alterations in the training and testing data. Consequently, the optimal model, RF, was integrated with a bootstrapping method to generate prediction and uncertainty maps. The soil properties and hydrologic factors were the primary variables influencing the spatial distribution of each toxic metal. This study indicates that the integration of DSM techniques with machine learning models and supplementary datasets offers a viable approach to the generation of maps for monitoring and prioritizing remediation measures in areas contaminated by toxic metals.
{"title":"Assessment of the Spatial Variability of Metal Contaminants Using Digital Mapping","authors":"Younes Garosi, Mohsen Sheklabadi, Shamsollah Ayoubi, Iman Kimiaee, Eric C. Brevik, Christian Conoscenti","doi":"10.1007/s00244-025-01141-w","DOIUrl":"10.1007/s00244-025-01141-w","url":null,"abstract":"<div><p>This study utilized the methodology of digital soil mapping (DSM) to investigate the spatial prediction of toxic metals and their environmental covariates in the Ghorveh Plain, western Iran. The environmental covariates are defined as the factors that control the distribution of toxic metals at the geographical scale under investigation. They could be used for predicting the sources and monitoring of pollution. A total of 150 soil samples (0–30 cm) were analyzed for toxic metal concentrations and some soil properties. A comprehensive set of environmental variables was obtained from remote sensing imagery, DEM, and ancillary data, which were identified as likely to control the spatial distributions of toxic metals. The genetic algorithm was utilized to identify “all-relevant” environmental covariates for each toxic metal. Three machine learning algorithms, namely random forests (RF), cubist, and regression trees (RT), were employed to establish the statistical relationships between toxic metals and the environmental covariates. The RF model exhibited the most optimal prediction performance. All three models, particularly the RF, demonstrated robust performance, exhibiting minimal impact on the model’s functionality when confronted with alterations in the training and testing data. Consequently, the optimal model, RF, was integrated with a bootstrapping method to generate prediction and uncertainty maps. The soil properties and hydrologic factors were the primary variables influencing the spatial distribution of each toxic metal. This study indicates that the integration of DSM techniques with machine learning models and supplementary datasets offers a viable approach to the generation of maps for monitoring and prioritizing remediation measures in areas contaminated by toxic metals.</p></div>","PeriodicalId":8377,"journal":{"name":"Archives of Environmental Contamination and Toxicology","volume":"89 3","pages":"335 - 354"},"PeriodicalIF":2.2,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145079098","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 : 2025-09-09DOI: 10.1007/s00244-025-01149-2
Sarah Kopczynski, Rayna Nolen, David Hala, Fernanda Lases-Hernández, Wendy Escobedo-Hinojosa, Flor Arcega-Cabrera, Ismael Oceguera-Vargas, Antonietta Quigg
Karst water bodies are vital groundwater resources particularly vulnerable to pollution. Protecting their water quality requires documenting contaminants traditionally associated with anthropogenic activities (metals, nutrients, and fecal indicator bacteria) as well as emerging contaminants, such as antibiotic-resistant organisms (AROs) and perfluoroalkyl substances (PFAS). This study detected contaminants in karst-associated water bodies on the Yucatán Peninsula, including 10 sinkholes (cenotes) and one submarine groundwater discharge (SGD) site. The concentrations of metals (strontium, cadmium, nickel, lead), nutrients (phosphate, silicate, ammonium, nitrate, and nitrite), and fecal indicator bacteria (fecal coliforms, Escherichia coli) were consistent with previous reports, sometimes exceeding recommended standards for groundwater or the protection of aquatic life. This included elevated lead (80.3 µg/L) and nitrate (413 μmol/L) concentrations at two cenotes, and elevated E. coli levels (167 – 1800 CFU/100 mL) in five cenotes. Additionally, 34 antibiotic-resistant E. coli strains were identified in nine cenotes and most strains were multidrug-resistant. Perfluorooctanesulfonic acid (PFOS) and perfluorohexanoic acid (PFHxA) were also detected in eight cenotes and the SGD, with total PFAS concentrations from 0.68 to 10.71 ng/L. The absence of associations between contaminants and urban cover suggests karst hydrology influences contaminant cycling—stable isotope signatures (δ18O, δ2H) confirming that most systems are interconnected to regional groundwater flows, that could allow contaminants to travel long distances. The Yucatán Peninsula’s karst is an important freshwater reservoir used for consumption and recreation; the presence of contaminants and the karst’s vulnerability to their spread raises concerns and highlights the need for continued monitoring and conservation.
{"title":"Investigation of Anthropogenic and Emerging Contaminants in Sinkholes (Cenotes) of the Great Mayan Aquifer, Yucatán Peninsula","authors":"Sarah Kopczynski, Rayna Nolen, David Hala, Fernanda Lases-Hernández, Wendy Escobedo-Hinojosa, Flor Arcega-Cabrera, Ismael Oceguera-Vargas, Antonietta Quigg","doi":"10.1007/s00244-025-01149-2","DOIUrl":"10.1007/s00244-025-01149-2","url":null,"abstract":"<div><p>Karst water bodies are vital groundwater resources particularly vulnerable to pollution. Protecting their water quality requires documenting contaminants traditionally associated with anthropogenic activities (metals, nutrients, and fecal indicator bacteria) as well as emerging contaminants, such as antibiotic-resistant organisms (AROs) and perfluoroalkyl substances (PFAS). This study detected contaminants in karst-associated water bodies on the Yucatán Peninsula, including 10 sinkholes (cenotes) and one submarine groundwater discharge (SGD) site. The concentrations of metals (strontium, cadmium, nickel, lead), nutrients (phosphate, silicate, ammonium, nitrate, and nitrite), and fecal indicator bacteria (fecal coliforms, <i>Escherichia coli</i>) were consistent with previous reports, sometimes exceeding recommended standards for groundwater or the protection of aquatic life. This included elevated lead (80.3 µg/L) and nitrate (413 μmol/L) concentrations at two cenotes, and elevated <i>E. coli</i> levels (167 – 1800 CFU/100 mL) in five cenotes. Additionally, 34 antibiotic-resistant <i>E. coli</i> strains were identified in nine cenotes and most strains were multidrug-resistant. Perfluorooctanesulfonic acid (PFOS) and perfluorohexanoic acid (PFHxA) were also detected in eight cenotes and the SGD, with total PFAS concentrations from 0.68 to 10.71 ng/L. The absence of associations between contaminants and urban cover suggests karst hydrology influences contaminant cycling—stable isotope signatures (δ<sup>18</sup>O, δ<sup>2</sup>H) confirming that most systems are interconnected to regional groundwater flows, that could allow contaminants to travel long distances. The Yucatán Peninsula’s karst is an important freshwater reservoir used for consumption and recreation; the presence of contaminants and the karst’s vulnerability to their spread raises concerns and highlights the need for continued monitoring and conservation.</p></div>","PeriodicalId":8377,"journal":{"name":"Archives of Environmental Contamination and Toxicology","volume":"89 3","pages":"279 - 299"},"PeriodicalIF":2.2,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00244-025-01149-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pollution from past industrial activities can remain unnoticed for years or even decades because the pollutant has only recently gained attention or been identified by measurements. Modeling the emission history of pollution is essential for estimating population exposure and apportioning potential liability among stakeholders. This paper proposes a novel approach for reconstructing the history of polychlorinated dibenzo-p-dioxin (PCDD) and polychlorinated dibenzofuran (PCDF) pollution from municipal solid waste incinerators (MSWIs) with unknown past emissions. The proposed methodology relies on the search for technical and operational data on the pollution source in archives, the extraction of representative data from the scientific literature, and the use of kinetic models of the formation and decomposition of PCDD/Fs within combustion chambers. This new methodological tool allows to estimate any MSWI’s stack emission and relative profile of seventeen PCDD/F congeners over time. The approach is validated through a case study of an MSWI in Switzerland. The modeled congener profile achieved a Pearson correlation coefficient of 0.98 with measurements in fly ash washwater. Additionally, the simulated soil quantity (1,115–1,419 gTEQ WHO-2005 or 1,283–1,698 gTEQWHO-2022) agrees in order of magnitude with the estimated quantity from measurements (371 gTEQ WHO-2005 or 425 gTEQ WHO-2022).