Unveiling the synergistic impact of heavy-metals and alkalinity on microbial diversity using machine learning techniques
Article Type
Research Article
Publication Title
Environmental Sustainability
Abstract
Soil microbes are adaptive to environmental shifts, yet both heavy metal (HMs) contamination and high alkalinity can significantly impair microbial function and soil health. The current study reports the combined effects of alkalinity and bioavailable forms of HMs on calcareous agricultural soils to predict their impact on microbial diversity, utilizing advanced statistical methods. Soil samples were collected from two distinct sites in Muzaffarpur, Bihar, India: Zone 1 (Baruraj block, industrial area) and Zone 2 (Kanti block, roadside area). Zone 1 showed the highest bioavailable HMs (CrWs: 14.73 mg kg− 1; PbWs: 3.39 mg kg− 1) and elevated alkalinity indices such as cation ratio of soil structural stability (CROSS) and monovalent cation ratio (MCAR), compared to Zone 2. These conditions negatively affected microbial and enzymatic activities, as reflected by lower microbial biomass carbon in Zone 1 (247.40 mg kg− 1) compared to Zone 2 (MBC: 354.50 mg kg− 1). Zone 1 also showed the highest levels of calcium (3033.18 mg kg− 1) and carbonate (39.78%), whereas Zone 2 had lower calcium (2055.28 mg kg− 1) and carbonate (37.63%) levels, both of which may hinder plant growth. Moreover, soil bacterial diversity (16 S rRNA gene) analysis revealed distinct microbial compositions between zones, with Firmicutes (Zone 1: 39.46%, Zone 2: 32.18%) and Proteobacteria (Zone 1: 33.15% and Zone 2: 38.55%). In contrast, Sobol sensitivity analysis revealed Synechococcus and Unclassified Clostridiales as the most stress-sensitive taxa. Among models tested, the random forest (RF) model best predicted microbial responses to soil stress. These findings provide key insights into microbial dynamics under combined stressors, supporting sustainable soil restoration strategies.
First Page
869
Last Page
886
DOI
10.1007/s42398-025-00392-6
Publication Date
12-1-2025
Recommended Citation
Mondal, Gourav; Banerjee, Sonali; Ghosh, Saibal; Kumar, Sumit; and Bhattacharyya, Pradip, "Unveiling the synergistic impact of heavy-metals and alkalinity on microbial diversity using machine learning techniques" (2025). Journal Articles. 5653.
https://digitalcommons.isical.ac.in/journal-articles/5653