Spatio-Temporal Distribution, Ecological Risk Assessment, and Multivariate Analysis of Heavy Metals in Bathinda District, Punjab, India

Abstract

The pollution of agricultural soil due to heavy metals is a serious environmental problem throughout the world due to their persistence and toxicity. The present study was carried out on agricultural soils of district Bathinda, Punjab where a total of 120 soil samples were collected from 40 different locations during pre-monsoon, monsoon, and post-monsoon season. The total mean concentration of heavy metals (arsenic (As), chromium (Cr), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), cadmium (Cd), mercury (Hg), lead (Pb)) was estimated by ThermoScientific–iCAP Qc (Germany) inductively coupled plasma–mass spectrometry (ICP-MS). The concentration of heavy metals was of the order of Fe > Zn > Cr > Ni > Cu > Co > As > Pb > Hg > Cd, Fe > Zn > Cr > Ni > Cu > Co > As > Pb > Hg > Cd, and Fe > Zn > Cr > Ni > Cu > Co > Pb > As > Hg > Cd in pre-monsoon, monsoon, and post-monsoon seasons, respectively. The metals such as Fe, Zn, Cr, and Ni indicated higher concentrations at most of the sites, whereas Hg and Cd showed lower concentrations throughout the region. The total mean concentrations (mg/kg) of the metals were found to be lower than their natural background concentration values. Based on enrichment factor (EF), the soils were moderately contaminated at most of the sites with a few cases where the soil was minimally enriched with heavy metals. Other pollution indices such pollution load index (PLI) and degree of contamination (Cd) also indicated low to moderate level of soil contamination. Besides, risk assessment of heavy metals was also determined using potential ecological risk factor (Ei) and ecological risk index (Ri) which indicated low Ei and Ri in the region for most of the metals. Spatial distribution using interpolation technique, Inverse Distance Weighted (IDW) in ArcGIS 10.6.1 software, showed a significant spatial and seasonal variability of heavy metals throughout the region. Pearson’s correlation coefficient (r) between heavy metal variables was found to be significant at p < 0.05 significance level (As-Cr (r = 0.769), As-Fe (r = 0.760), As-Co (r = 0.883), As-Ni (r = 0.886), As-Cu (r = 0.859), As-Hg (r = 0.678) in pre-monsoon samples; As-Fe (r = 0.613), As-Co (r = 0.669), As-Ni (r = 0.619), As-Cu (r = 0.639) in monsoon samples and As-Cr (r = 0.631), As-Fe (r = 0.715), As-Co (r = 0.710), As-Cu (r = 0.690) in post-monsoon samples) indicated a strong relationship between different variables. Principal component analysis (PCA) technique also proved to be significant in studying the behavioral pattern of variables, where PCA biplots showed different behavior as revealed from some strong associations. Finally, continuous monitoring of the sites is suggested to avoid further contamination and degradation of soil quality, despite low contamination levels in the region.