{"title":"释放Eudrilus eugenae在减轻农药和重金属污染风险方面的潜力:促进机器学习策略以优化环境健康","authors":"Rd Sabina , Riya Dey , Saibal Ghosh , Pradip Bhattacharya , Satya Sundar Bhattacharya , Nazneen Hussain","doi":"10.1016/j.scitotenv.2025.179039","DOIUrl":null,"url":null,"abstract":"<div><div>Agro-industrial waste management remains a critical challenge in sustainable development, particularly due to contamination with heterogeneous micropollutants such as heavy metals (HMs), pesticides, and polyphenols. This study explores an innovative vermistabilization approach using pineapple pomace (PP) to enhance the bioremediation of paper mill sludge (PMS) facilitated by <em>Eudrilus eugeniae</em>. The research demonstrates that the contrasting pH profiles of PMS (a highly alkaline substrate) and PP (a highly acidic substrate) have significantly contributed to nutrient enhancement and stabilization of end products for the mixed feedstock treatments (PP and PMS-based feedstocks) compared to the feedstock treatments in isolations. Results demonstrated a 2.1 fold increase in earthworm population density, and 4–5 fold reduction in organic carbon content confirming its effectiveness of biostabilization in a heterogeneous feed mixture. Vermicomposting enhanced nutrient availability (N, P, K) and microbial metabolic activity by 3–5 folds. Amongst tested ratios, PP + PMS + cowdung (CD) (1:2:1) achieved optimal remediation, reducing HMs (Cd, Pb, Zn, Hg, Ni, Cu, Cr), pesticides (chlorpyrifos, cypermethrin, carbofuran), and polyphenols by 8–9 folds. Integration of Artificial Neural Networks coupled with Sobol sensitivity analysis also identified PP + PMS + CD(1:2:1) as the most effective combination in minimizing potential health risks. Furthermore, Taylor plot analysis determined the best-fit model for predicting health risks associated with various PP and PMS-based complex systems. The findings underscored the potential of utilizing PP along with PMS based feedstock for mitigating pollutants whilst simultaneously enhancing nutrient recovery during vermicomposting. Thus, the machine learning techniques could facilitate the optimization of feedstock compositions, advancing large-scale vermistabilization as a sustainable strategy for agro-industrial waste management.</div></div>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":"971 ","pages":"Article 179039"},"PeriodicalIF":8.0000,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unlocking the potential of Eudrilus eugeniae in mitigating the pollution risk of pesticides and heavy metals: Fostering machine learning tactics to optimize environmental health\",\"authors\":\"Rd Sabina , Riya Dey , Saibal Ghosh , Pradip Bhattacharya , Satya Sundar Bhattacharya , Nazneen Hussain\",\"doi\":\"10.1016/j.scitotenv.2025.179039\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Agro-industrial waste management remains a critical challenge in sustainable development, particularly due to contamination with heterogeneous micropollutants such as heavy metals (HMs), pesticides, and polyphenols. This study explores an innovative vermistabilization approach using pineapple pomace (PP) to enhance the bioremediation of paper mill sludge (PMS) facilitated by <em>Eudrilus eugeniae</em>. The research demonstrates that the contrasting pH profiles of PMS (a highly alkaline substrate) and PP (a highly acidic substrate) have significantly contributed to nutrient enhancement and stabilization of end products for the mixed feedstock treatments (PP and PMS-based feedstocks) compared to the feedstock treatments in isolations. Results demonstrated a 2.1 fold increase in earthworm population density, and 4–5 fold reduction in organic carbon content confirming its effectiveness of biostabilization in a heterogeneous feed mixture. Vermicomposting enhanced nutrient availability (N, P, K) and microbial metabolic activity by 3–5 folds. Amongst tested ratios, PP + PMS + cowdung (CD) (1:2:1) achieved optimal remediation, reducing HMs (Cd, Pb, Zn, Hg, Ni, Cu, Cr), pesticides (chlorpyrifos, cypermethrin, carbofuran), and polyphenols by 8–9 folds. Integration of Artificial Neural Networks coupled with Sobol sensitivity analysis also identified PP + PMS + CD(1:2:1) as the most effective combination in minimizing potential health risks. Furthermore, Taylor plot analysis determined the best-fit model for predicting health risks associated with various PP and PMS-based complex systems. The findings underscored the potential of utilizing PP along with PMS based feedstock for mitigating pollutants whilst simultaneously enhancing nutrient recovery during vermicomposting. Thus, the machine learning techniques could facilitate the optimization of feedstock compositions, advancing large-scale vermistabilization as a sustainable strategy for agro-industrial waste management.</div></div>\",\"PeriodicalId\":422,\"journal\":{\"name\":\"Science of the Total Environment\",\"volume\":\"971 \",\"pages\":\"Article 179039\"},\"PeriodicalIF\":8.0000,\"publicationDate\":\"2025-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science of the Total Environment\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0048969725006746\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science of the Total Environment","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0048969725006746","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Unlocking the potential of Eudrilus eugeniae in mitigating the pollution risk of pesticides and heavy metals: Fostering machine learning tactics to optimize environmental health
Agro-industrial waste management remains a critical challenge in sustainable development, particularly due to contamination with heterogeneous micropollutants such as heavy metals (HMs), pesticides, and polyphenols. This study explores an innovative vermistabilization approach using pineapple pomace (PP) to enhance the bioremediation of paper mill sludge (PMS) facilitated by Eudrilus eugeniae. The research demonstrates that the contrasting pH profiles of PMS (a highly alkaline substrate) and PP (a highly acidic substrate) have significantly contributed to nutrient enhancement and stabilization of end products for the mixed feedstock treatments (PP and PMS-based feedstocks) compared to the feedstock treatments in isolations. Results demonstrated a 2.1 fold increase in earthworm population density, and 4–5 fold reduction in organic carbon content confirming its effectiveness of biostabilization in a heterogeneous feed mixture. Vermicomposting enhanced nutrient availability (N, P, K) and microbial metabolic activity by 3–5 folds. Amongst tested ratios, PP + PMS + cowdung (CD) (1:2:1) achieved optimal remediation, reducing HMs (Cd, Pb, Zn, Hg, Ni, Cu, Cr), pesticides (chlorpyrifos, cypermethrin, carbofuran), and polyphenols by 8–9 folds. Integration of Artificial Neural Networks coupled with Sobol sensitivity analysis also identified PP + PMS + CD(1:2:1) as the most effective combination in minimizing potential health risks. Furthermore, Taylor plot analysis determined the best-fit model for predicting health risks associated with various PP and PMS-based complex systems. The findings underscored the potential of utilizing PP along with PMS based feedstock for mitigating pollutants whilst simultaneously enhancing nutrient recovery during vermicomposting. Thus, the machine learning techniques could facilitate the optimization of feedstock compositions, advancing large-scale vermistabilization as a sustainable strategy for agro-industrial waste management.
期刊介绍:
The Science of the Total Environment is an international journal dedicated to scientific research on the environment and its interaction with humanity. It covers a wide range of disciplines and seeks to publish innovative, hypothesis-driven, and impactful research that explores the entire environment, including the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere.
The journal's updated Aims & Scope emphasizes the importance of interdisciplinary environmental research with broad impact. Priority is given to studies that advance fundamental understanding and explore the interconnectedness of multiple environmental spheres. Field studies are preferred, while laboratory experiments must demonstrate significant methodological advancements or mechanistic insights with direct relevance to the environment.