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 (C_d) also indicated low to moderate level of soil contamination. Besides, risk assessment of heavy metals was also determined using potential ecological risk factor (E_i) and ecological risk index (R_i) which indicated low E_i and R_i 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.

中文翻译：

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印度旁遮普邦Bathinda区的重金属时空分布，生态风险评估和多元分析

由于重金属的持久性和毒性，重金属对农业土壤的污染在全世界是一个严重的环境问题。本研究是在旁遮普邦的Bathinda地区的农业土壤上进行的，该地区在季风前，季风和季风后季节共从40个不同地点收集了120个土壤样品。重金属的总平均浓度（砷（As），铬（Cr），铁（Fe），钴（Co），镍（Ni），铜（Cu），锌（Zn），镉（Cd），汞（ （Hg），铅（Pb））由ThermoScientific–iCAP Qc（德国）电感耦合等离子体质谱法（ICP-MS）估算。重金属的浓度依次为Fe> Zn> Cr> Ni> Cu> Co> As> Pb> Hg> Cd，Fe> Zn> Cr> Ni> Cu> Co> As> Pb> Hg> Cd，分别在季风前，季风和季风后的季节中，铁和锌>铬>铬>镍>铜>钴>铅>砷>汞>镉> 铁，锌，铬和镍等金属在大多数位置处的浓度较高，而汞和镉在整个区域内的浓度较低。发现金属的总平均浓度（mg / kg）低于其自然本底浓度值。根据富集因子（EF），大多数地点的土壤受到中度污染，少数情况下土壤中的重金属含量极少。其他污染指数，例如污染负荷指数（PLI）和污染程度（C Ni表示大多数位置的浓度较高，而Hg和Cd表示整个区域的浓度较低。发现金属的总平均浓度（mg / kg）低于其自然本底浓度值。基于富集因子（EF），大多数地点的土壤受到中度污染，少数情况下土壤中的重金属含量极少。其他污染指数，例如污染负荷指数（PLI）和污染程度（C Ni表示大多数位置的浓度较高，而Hg和Cd表示整个区域的浓度较低。发现金属的总平均浓度（mg / kg）低于其自然本底浓度值。根据富集因子（EF），大多数地点的土壤受到中度污染，少数情况下土壤中的重金属含量极少。其他污染指数，例如污染负荷指数（PLI）和污染程度（C 在大多数地点，土壤受到中等程度的污染，少数情况下，土壤中的重金属含量最少。其他污染指数，例如污染负荷指数（PLI）和污染程度（C 在大多数地点，土壤受到中等程度的污染，少数情况下，土壤中的重金属含量最少。其他污染指数，例如污染负荷指数（PLI）和污染程度（C_d）还表明土壤污染程度低至中等。此外，还使用潜在的生态风险因子（E _i）和生态风险指数（R _i）来确定重金属的风险评估，这表明大多数金属在该地区的E _i和R _i低。使用插值技术（ArcGIS 10.6.1软件中的反距离权重（IDW））进行空间分布，表明整个区域中重金属的空间和季节变化显着。发现重金属变量之间的皮尔逊相关系数（r）在p <0.05显着性水平下具有显着性（As-Cr（r  = 0.769），As-Fe（r = 0.760）， 季风前样品中的As-Co（r  = 0.883），As-Ni（r  = 0.886），As-Cu（r  = 0.859），As-Hg（r = 0.678）;  季风样品中的As-Fe（r  = 0.613），As-Co（r  = 0.669），As-Ni（r  = 0.619），As-Cu（r = 0.639）和As-Cr（r  = 0.631），As- Fe（r  = 0.715），As-Co（r  = 0.710），As-Cu（r 在季风后的样本中= 0.690）表明不同变量之间存在很强的关系。主成分分析（PCA）技术在研究变量的行为模式方面也被证明具有重要意义，其中PCA双图显示了一些强关联所揭示的不同行为。最后，尽管该区域的污染水平较低，但建议对场地进行连续监测以避免进一步污染和土壤质量下降。