| 1. |
- Bhattacharya, Manojit, et al.
(författare)
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ChatGPT's scorecard after the performance in a series of tests conducted at the multi-country level : A pattern of responses of generative artificial intelligence or large language models
- 2024
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Ingår i: Current Research in Biotechnology. - : Elsevier BV. - 2590-2628. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- Recently, researchers have shown concern about the ChatGPT-derived answers. Here, we conducted a series of tests using ChatGPT by individual researcher at multi-country level to understand the pattern of its answer accuracy, reproducibility, answer length, plagiarism, and in-depth using two questionnaires (the first set with 15 MCQs and the second 15 KBQ). Among 15 MCQ-generated answers, 13 +/- 70 were correct (Median : 82.5; Coefficient variance : 4.85), 3 +/- 0.77 were incorrect (Median: 3, Coefficient variance: 25.81), and 1 to 10 were reproducible, and 11 to 15 were not. Among 15 KBQ, the length of each question (in words) is about 294.5 +/- 97.60 (mean range varies from 138.7 to 438.09), and the mean similarity index (in words) is about 29.53 +/- 11.40 (Coefficient variance: 38.62) for each question. The statistical models were also developed using analyzed parameters of answers. The study shows a pattern of ChatGPT-derive answers with correctness and incorrectness and urges for an error-free, next-generation LLM to avoid users' misguidance.
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| 2. |
- Bundschuh, J., et al.
(författare)
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Arsenic in Latin America : New findings on source, mobilization and mobility in human environments in 20 countries based on decadal research 2010-2020
- 2020
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Ingår i: Critical reviews in environmental science and technology. - : Taylor and Francis Inc.. - 1064-3389 .- 1547-6537. ; , s. 1-119
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Forskningsöversikt (refereegranskat)abstract
- Today (year 2020), the globally recognized problem of arsenic (As) contamination of water resources and other environments at toxic levels has been reported in all of the 20 Latin American countries. The present review indicates that As is prevalent in 200 areas across these countries. Arsenic is naturally released into the environment and mobilized from geogenic sources comprising: (i) volcanic rocks and emissions, the latter being transported over thousands of kilometers from the source, (ii) metallic mineral deposits, which get exposed to human beings and livestock through drinking water or food chain, and (iii) As-rich geothermal fluids ascending from deep geothermal reservoirs contaminate freshwater sources. The challenge for mitigation is increased manifold by mining and related activities, as As from mining sites is transported by rivers over long distances and even reaches and contaminates coastal environments. The recognition of the As problem by the authorities in several countries has led to various actions for remediation, but there is a lack of long-term strategies for such interventions. Often only total As concentration is reported, while data on As sources, mobilization, speciation, mobility and pathways are lacking which is imperative for assessing quality of any water source, i.e. public and private.
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| 6. |
- Kumar, Manish, et al.
(författare)
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Lead time of early warning by wastewater surveillance for COVID-19 : Geographical variations and impacting factors
- 2022
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Ingår i: Chemical Engineering Journal. - : Elsevier BV. - 1385-8947 .- 1873-3212. ; 441
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Forskningsöversikt (refereegranskat)abstract
- The global data on the temporal tracking of the COVID-19 through wastewater surveillance needs to be comparatively evaluated to generate a proper and precise understanding of the robustness, advantages, and sensitivity of the wastewater-based epidemiological (WBE) approach. We reviewed the current state of knowledge based on several scientific articles pertaining to temporal variations in COVID-19 cases captured via viral RNA predictions in wastewater. This paper primarily focuses on analyzing the WBE-based temporal variation reported globally to check if the reported early warning lead-time generated through environmental surveillance is pragmatic or latent. We have compiled the geographical variations reported as lead time in various WBE reports to strike a precise correlation between COVID-19 cases and genome copies detected through wastewater surveillance, with respect to the sampling dates, separately for WASH and non-WASH countries. We highlighted sampling methods, climatic and weather conditions that significantly affected the concentration of viral SARS-CoV-2 RNA detected in wastewater, and thus the lead time reported from the various climatic zones with diverse WASH situations were different. Our major findings are: i) WBE reports around the world are not comparable, especially in terms of gene copies detected, lag-time gained between monitored RNA peak and outbreak/peak of reported case, as well as per capita RNA concentrations; ii) Varying sanitation facility and climatic conditions that impact virus degradation rate are two major interfering features limiting the comparability of WBE results, and iii) WBE is better applicable to WASH countries having well-connected sewerage system.
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| 7. |
- Mukherjee, Abhijit, et al.
(författare)
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Occurrence, predictors and hazards of elevated groundwater arsenic across India through field observations and regional-scale AI-based modeling
- 2021
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 0048-9697 .- 1879-1026. ; 759
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Tidskriftsartikel (refereegranskat)abstract
- Existence of wide spread elevated concentrations of groundwater arsenic (As) across South Asia, including India, has endangered a huge groundwater-based drinking water dependent population. Here, using high-spatial resolution As field-observations (similar to 3 million groundwater sources) across India, we have delineated the regional-scale occurrence of elevated groundwater As (>= 10 mu g/L), along with the possible geologic-geomorphologic-hydrologic and human-sourced predictors that influence the spatial distribution of the contaminant. Using statistical and machine learning method, we also modeled the groundwater As concentrations probability at 1 Km resolution, along with probabilistic delineation of high As-hazard zones across India. The observed occurrence of groundwater As was found to be most strongly influenced by geology-tectonics, groundwater-fed irrigated area (%) and elevation. Pervasive As contamination is observed in major parts of the Himalayan mega-river Indus-Ganges-Brahmaputra basins, however it also occurs in several more-localized pockets, mostly related to ancient tectonic zones, igneous provinces, aquifers in modern delta and chalcophile mineralized regions. The model results suggest As-hazard potential in yet-undetected areas. Our model performed well in predicting groundwater arsenic, with accuracy: 82% and 84%; area under the curve (AUC): 0.89 and 0.88 for test data and validation datasets. An estimated similar to 90 million people across India are found to be exposed to high groundwater As from field-observed data, with the five states with highest hazard are West Bengal (28 million), Bihar (21 million), Uttar Pradesh (15 million), Assam(8.6 million) and Punjab (6 million). However it can be much more if the modeled hazard is considered (>250 million). Thus, our study provides a detailed, quantitative assessment of high groundwater As across India, with delineation of possible intrinsic influences and exogenous forcings. The predictive model is helpful in predicting As-hazard zones in the areas with limited measurements.
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| 9. |
- Ahmad, Arslan, et al.
(författare)
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Arsenic reduction to < 1 mu g/L in Dutch drinking water
- 2020
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Ingår i: Environment International. - : Elsevier. - 0160-4120 .- 1873-6750. ; 134
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Forskningsöversikt (refereegranskat)abstract
- Arsenic (As) is a highly toxic element which naturally occurs in drinking water. In spite of substantial evidence on the association between many illnesses and chronic consumption of As, there is still a considerable uncertainty about the health risks due to low As concentrations in drinking water. In the Netherlands, drinking water companies aim to supply water with As concentration of < 1 mu g/L - a water quality goal which is tenfold more stringent than the current WHO guideline. This paper provides (i) an account on the assessed lung cancer risk for the Dutch population due to pertinent low-level As in drinking water and cost-comparison between health care provision and As removal from water, (ii) an overview of As occurrence and mobility in drinking water sources and water treatment systems in the Netherlands and (iii) insights into As removal methods that have been employed or under investigation to achieve As reduction to < 1 mu g/L at Dutch water treatment plants. Lowering of the average As concentration to < 1 mu g/L in the Netherlands is shown to result in an annual benefit of 7.2-14 M(sic). This study has a global significance for setting drinking water As limits and provision of safe drinking water.
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| 10. |
- Ahmad, Arslan, et al.
(författare)
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Impact of phosphate, silicate and natural organic matter on the size of Fe(III) precipitates and arsenate co-precipitation efficiency in calcium containing water
- 2020
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Ingår i: Separation and Purification Technology. - : ELSEVIER. - 1383-5866 .- 1873-3794. ; 235
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Tidskriftsartikel (refereegranskat)abstract
- Removal of arsenic (As) from water by co-precipitation with Fe(III) (oxyhydr)oxides is a widely used technique in water treatment. Nevertheless, As removal efficiency appears to be sensitive to the composition of the water matrix. The aim of this study was to gain a deeper understanding of the independent and combined effects of silicate (Si), phosphate (P), natural organic matter (NOM) and calcium (Ca) on arsenate [As(V)] co-precipitation efficiency and the size of Fe(III) precipitates. We found that, in complex solutions, containing multiple solutes and high levels of Ca, (variations in) Si and P concentrations reduce As(V) removal to some extent, mainly due to a decreased adsorption of As(V) onto Fe(III) precipitates. On the other hand, NOM concentrations reduced As(V) removal to a much greater extent, due to possible formation of mobile Fe(III)-NOM complexes that were difficult to remove by filtration. These findings have a great significance for predicting As(V) removal as a function of seasonal and process-related water quality changes at water treatment plants.
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