This study applies positive matrix factorization (PMF) to 140 groundwater samples collected from four different Public Health Centers in North Brahmaputra basin, Assam, India. The aim of this technique is to identify and quantify the pollution sources (natural and anthropogenic) that affect the water quality. Multivariate statistical analysis, especially factor analysis, is successful in interpreting the water quality data, but it has some limitations: It does not consider analytical uncertainty and factor loadings may be negative which do not give a clear representation of the data. Therefore, we applied PMF to groundwater data and compared the results with those obtained from factor analysis. The major findings from the study are as follows: The first and the second factors show that the natural means are the main source of pollution where Cl, SO₄, Ca, Mg, TA and TH were the main contributors from erosion and weathering of rocks. The Pb and NO₃ from the third and the fourth factor, respectively, are the major sources of contamination from anthropogenic activities such as the use of fertilizers. The fifth factor results in Fe, As, Mn and Cr, suggesting that both natural and anthropogenic processes are the main pollution contributors. PMF exhibits a more realistic representation of data and helps us to better understand the major sources of contamination and the variation in groundwater quality data. Hence, it can be successfully used for the characterization of groundwater chemistry.

这项研究对从印度阿萨姆邦北部布拉马普特拉盆地四个不同的公共卫生中心收集的140个地下水样本应用了正矩阵分解（PMF）。该技术的目的是识别和量化影响水质的污染源（自然的和人为的）。多元统计分析，尤其是因子分析，可以成功地解释水质数据，但是它有一些局限性：它没有考虑分析不确定性，并且因子负荷可能为负，因此无法清晰地表示数据。因此，我们将PMF应用于地下水数据，并将结果与​​因子分析获得的结果进行了比较。该研究的主要发现如下：第一个和第二个因素表明，自然手段是主要的污染源，其中Cl，SO_{由图4可以}看出，Ca，Mg，TA和TH是造成岩石侵蚀和风化的主要因素。第三和第四因素中的Pb和NO ₃分别是人为活动（例如使用化肥）造成污染的主要来源。第五个因素产生了Fe，As，Mn和Cr，这表明自然和人为过程都是主要的污染源。PMF展示了更真实的数据表示，并帮助我们更好地了解了主要的污染源和地下水质量数据的变化。因此，它可以成功地用于地下水化学的表征。