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A holistic approach combining factor analysis, positive matrix factorization, and chemical mass balance applied to receptor modeling

机译：将因子分析，正矩阵分解和化学物质平衡相结合的整体方法应用于受体建模

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摘要

Rapid urbanization and population growth resulted in severe deterioration of air quality in most of the major cities in India. Therefore, it is essential to ascertain the contribution of various sources of air pollution to enable us to determine effective control policies. The present work focuses on the holistic approach of combining factor analysis (FA), positive matrix factorization (PMF), and chemical mass balance (CMB) for receptor modeling in order to identify the sources and their contributions in air quality studies. Insight from the emission inventory was used to remove subjectivity in source identification. Each approach has its own limitations. Factor analysis can identify qualitatively a minimal set of important factors which can account for the variations in the measured data. This step uses information from emission inventory to qualitatively match source profiles with factor loadings. This signifies the identification of dominant sources through factors. PMF gives source profiles and source contributions from the entire receptor data matrix. The data from FA is applied for rank reduction in PMF. Whenever multiple solutions exist, emission inventory identifies source profiles uniquely, so that they have a physical relevance. CMB identifies the source contributions obtained from FA and PMF. The novel approach proposed here overcomes the limitations of the individual methods in a synergistic way. The adopted methodology is found valid for a synthetic data and also the data of field study.
机译：快速的城市化和人口增长导致印度大多数主要城市的空气质量严重恶化。因此，至关重要的是要确定各种空气污染源的影响，以使我们能够确定有效的控制政策。目前的工作集中在将因子分析（FA），正矩阵分解（PMF）和化学物质平衡（CMB）结合起来进行受体建模的整体方法，以识别空气源及其在空气质量研究中的作用。排放清单中的洞察力被用来消除来源识别中的主观性。每种方法都有其自身的局限性。因子分析可以定性地确定一组最小的重要因子，这些因子可以解释所测数据的变化。此步骤使用来自排放清单的信息来定性地将源剖面与因子负荷进行匹配。这表示通过因素识别了主要来源。 PMF给出了来自整个受体数据矩阵的源剖面和源贡献。来自FA的数据用于降低PMF的等级。只要存在多个解决方案，排放清单便会唯一标识源配置文件，从而使它们具有物理相关性。 CMB标识从FA和PMF获得的源贡献。这里提出的新颖方法以协同的方式克服了各个方法的局限性。发现所采用的方法学对于合成数据以及现场研究数据都是有效的。

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