Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Comparative evaluation of Tobit and Free Ion Activity Models for optimizing genotype-specific permissible arsenic limits in rice soil and irrigation water.

The Science of the total environment - 15 Jul 2026

Sinha Agnibha, Bhattacharyya Kallol, Sengupta Sudip, Kumar P Dinesh, Golui Debasis, Thakur Shivani

Abstract excerpt

Arsenic (As) contamination in soil and irrigation water poses a significant threat to food safety, particularly in rice-growing regions of Gangetic West Bengal, where As-enriched groundwater is widely used for irrigation. Rice cultivation under flooded, anaerobic conditions promotes As mobilization through reductive dissolution of Fe/Mn hydroxides and the conversion of arsenate [As(V)] to the more mobile arsenite...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Arsenic
Share to Environmental Monitoring

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Models, Chemical

Share to Soil Pollutants

Share to Water Pollutants, Chemical

Source

Identifiers

PMID: 42208479
DOI: 10.1016/j.scitotenv.2026.181884

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Comparative evaluation of Tobit and Free Ion Activity Models for optimizing genotype-specific permissible arsenic limits in rice soil and irrigation water.PMID 42208479Read ↗

Deficit irrigation and organic amendments can reduce dietary arsenic risk from rice: Introducing machine learning-based prediction models from field dataSemantic proximity Read ↗

Assessment of Groundwater Quality and Exposure Risks in a Rural Mining District of Ghana Using Multivariate and Probabilistic ApproachesSemantic proximity Read ↗

Genomic prediction of arsenic tolerance and grain yield in rice. Contribution of trait-specific markers and multi environment modelsSemantic proximity Read ↗

Impact of organic and inorganic amendments on arsenic accumulation by rice genotypes under paddy soil conditions: A pilot-scale investigation to assess health risk.Semantic proximity Read ↗

Source analysis and human health risk assessment of heavy metal in farmland soil-rice system in Nandu River Basin, Zhanjiang City, Guangdong Province, ChinaSemantic proximity Read ↗

Source apportionment and health risk assessment of heavy metals in farmland soils based on the APCS-MLR receptor model and Monte Carlo simulationSemantic proximity Read ↗

Heavy metal signatures in urban and peri-urban agricultural soils across the Mumbai Metropolitan Region, IndiaSemantic proximity Read ↗

Integrated Assessment of Groundwater Quality and Health Risks in Iran’s Bardsir Plain Using Water Quality Indices, TOPSIS, and CDI–HQ AnalysesSemantic proximity Read ↗

Screen low-accumulation rice varieties, and identify soil safety utilization thresholds in Cd/Hg-contaminated paddy fields.Semantic proximity Read ↗

Screening of Stable Low-Cadmium Rice Cultivars and Derivation of Soil Cd Threshold Based on Bioconcentration Factor and Multi-Environment Analysis.Semantic proximity Read ↗

Hydrogeochemistry of intermittent alluvial aquifers controlling arsenic and fluoride contamination for corresponding influencing health risk assessmentSemantic proximity Read ↗

Assessment Of Arsenic Exposure In The Population Of Sabalpur Village Of Saran District Of Bihar With Mitigation ApproachSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.