Assessing thermal power effluent-induced air quality and associated environmental stress on Blumea lacera and Phyla nodiflora using chemometric, remote sensing and machine learning approach
Human interventions leading to effluent generation, which are critical threats to the environment. The Kolaghat Thermal Power Plant emits daily 7,500–8,000 metric tonnes of fly ash. Our investigation revealed fly ash deposition in the surrounding soil, containing high levels of copper (5.75 mg/kg) a...
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Taylor & Francis Group
2024-11-01
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| Series: | Geology, Ecology, and Landscapes |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/24749508.2024.2430042 |
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| author | Mahabub Rabbani Mehebub Sarwar Hossain Sk Saruk Islam Sujit Kumar Roy Aznarul Islam Ismail Mondal Sk Md Abu Imam Saadi |
| author_facet | Mahabub Rabbani Mehebub Sarwar Hossain Sk Saruk Islam Sujit Kumar Roy Aznarul Islam Ismail Mondal Sk Md Abu Imam Saadi |
| author_sort | Mahabub Rabbani |
| collection | DOAJ |
| description | Human interventions leading to effluent generation, which are critical threats to the environment. The Kolaghat Thermal Power Plant emits daily 7,500–8,000 metric tonnes of fly ash. Our investigation revealed fly ash deposition in the surrounding soil, containing high levels of copper (5.75 mg/kg) and iron (53.05 mg/kg). To evaluate the tolerance efficiency of Blumea lacera and Phyla nodiflora, we analysed various anatomical and biochemical parameters in plants from both polluted and non-polluted sites. The study integrated Sentinel-2 and Sentinel-5P satellite data with field investigations to validate six environmental indices, including CO, LST, NDVI, NO2, O3, and SO2. Machine learning (ML)-based models were employed for predictive analysis. In-situ findings indicated that stem pith length, cortical cells, epidermis of B. lacera, and cortex, vascular bundles, vessels of P. nodiflora, were smaller in polluted sites. Chlorophyll content in B. lacera was significantly reduced in polluted areas. ML-based parameters obtained an outstanding average accuracy of R2 (0.7–0.75), as well as root mean square error and mean absolute error. In conclusion, stress-tolerant activity in these plants manifests as inhibited growth. Both species, particularly B. lacera, show potential for phytoremediation, offering a natural solution to reduce pollutants at polluted sites. |
| format | Article |
| id | doaj-art-33c9eace52cc4a77a94f9f7c006aff4a |
| institution | Kabale University |
| issn | 2474-9508 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Taylor & Francis Group |
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| series | Geology, Ecology, and Landscapes |
| spelling | doaj-art-33c9eace52cc4a77a94f9f7c006aff4a2024-11-22T12:52:15ZengTaylor & Francis GroupGeology, Ecology, and Landscapes2474-95082024-11-0111910.1080/24749508.2024.2430042Assessing thermal power effluent-induced air quality and associated environmental stress on Blumea lacera and Phyla nodiflora using chemometric, remote sensing and machine learning approachMahabub Rabbani0Mehebub Sarwar Hossain1Sk Saruk Islam2Sujit Kumar Roy3Aznarul Islam4Ismail Mondal5Sk Md Abu Imam Saadi6Department of Biological Sciences, Aliah University, Newtown, Kolkata, IndiaDepartment of Biological Sciences, Aliah University, Newtown, Kolkata, IndiaDepartment of Zoology, Raja Narendra Lal Khan Women’s College, Midnapore, West Bengal, IndiaInstitute of Water and Flood Management (IWFM), Bangladesh University of Engineering and Technology (BUET), Dhaka, BangladeshDepartment of Geography, Aliah University, Kolkata, IndiaDepartment of Marine Science, University of Calcutta, Kolkata, IndiaDepartment of Biological Sciences, Aliah University, Newtown, Kolkata, IndiaHuman interventions leading to effluent generation, which are critical threats to the environment. The Kolaghat Thermal Power Plant emits daily 7,500–8,000 metric tonnes of fly ash. Our investigation revealed fly ash deposition in the surrounding soil, containing high levels of copper (5.75 mg/kg) and iron (53.05 mg/kg). To evaluate the tolerance efficiency of Blumea lacera and Phyla nodiflora, we analysed various anatomical and biochemical parameters in plants from both polluted and non-polluted sites. The study integrated Sentinel-2 and Sentinel-5P satellite data with field investigations to validate six environmental indices, including CO, LST, NDVI, NO2, O3, and SO2. Machine learning (ML)-based models were employed for predictive analysis. In-situ findings indicated that stem pith length, cortical cells, epidermis of B. lacera, and cortex, vascular bundles, vessels of P. nodiflora, were smaller in polluted sites. Chlorophyll content in B. lacera was significantly reduced in polluted areas. ML-based parameters obtained an outstanding average accuracy of R2 (0.7–0.75), as well as root mean square error and mean absolute error. In conclusion, stress-tolerant activity in these plants manifests as inhibited growth. Both species, particularly B. lacera, show potential for phytoremediation, offering a natural solution to reduce pollutants at polluted sites.https://www.tandfonline.com/doi/10.1080/24749508.2024.2430042Fly ashsoil pollutionchlorophyll contentsAPTImachine learningremote sensing |
| spellingShingle | Mahabub Rabbani Mehebub Sarwar Hossain Sk Saruk Islam Sujit Kumar Roy Aznarul Islam Ismail Mondal Sk Md Abu Imam Saadi Assessing thermal power effluent-induced air quality and associated environmental stress on Blumea lacera and Phyla nodiflora using chemometric, remote sensing and machine learning approach Geology, Ecology, and Landscapes Fly ash soil pollution chlorophyll contents APTI machine learning remote sensing |
| title | Assessing thermal power effluent-induced air quality and associated environmental stress on Blumea lacera and Phyla nodiflora using chemometric, remote sensing and machine learning approach |
| title_full | Assessing thermal power effluent-induced air quality and associated environmental stress on Blumea lacera and Phyla nodiflora using chemometric, remote sensing and machine learning approach |
| title_fullStr | Assessing thermal power effluent-induced air quality and associated environmental stress on Blumea lacera and Phyla nodiflora using chemometric, remote sensing and machine learning approach |
| title_full_unstemmed | Assessing thermal power effluent-induced air quality and associated environmental stress on Blumea lacera and Phyla nodiflora using chemometric, remote sensing and machine learning approach |
| title_short | Assessing thermal power effluent-induced air quality and associated environmental stress on Blumea lacera and Phyla nodiflora using chemometric, remote sensing and machine learning approach |
| title_sort | assessing thermal power effluent induced air quality and associated environmental stress on blumea lacera and phyla nodiflora using chemometric remote sensing and machine learning approach |
| topic | Fly ash soil pollution chlorophyll contents APTI machine learning remote sensing |
| url | https://www.tandfonline.com/doi/10.1080/24749508.2024.2430042 |
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