Source Apportionment of PM10 by Positive Matrix Factorization in Urban Area of Mumbai, India

Particulate Matter (PM10) has been one of the main air pollutants exceeding the ambient standards in most of the major cities in India. During last few years, receptor models such as Chemical Mass Balance, Positive Matrix Factorization (PMF), PCA–APCS and UNMIX have been used to provide solutions to the source identification and contributions which are accepted for developing effective and efficient air quality management plans. Each site poses different complexities while resolving PM10 contributions. This paper reports the variability of four sites within Mumbai city using PMF. Industrial area of Mahul showed sources such as residual oil combustion and paved road dust (27%), traffic (20%), coal fired boiler (17%), nitrate (15%). Residential area of Khar showed sources such as residual oil combustion and construction (25%), motor vehicles (23%), marine aerosol and nitrate (19%), paved road dust (18%) compared to construction and natural dust (27%), motor vehicles and smelting work (25%), nitrate (16%) and biomass burning and paved road dust (15%) in Dharavi, a low income slum residential area. The major contributors of PM10 at Colaba were marine aerosol, wood burning and ammonium sulphate (24%), motor vehicles and smelting work (22%), Natural soil (19%), nitrate and oil burning (18%)

<span style="font-size:11.0pt;font-family: "Times New Roman";mso-fareast-font-family:"Times New Roman";mso-bidi-font-family: Mangal;mso-ansi-language:EN-GB;mso-fareast-language:EN-US;mso-bidi-language: HI" lang="EN-GB">Chemometrics data analysis of marine water quality in Maharashtra, west coast of India</span>

97-105<span style="font-size:11.0pt;font-family: " times="" new="" roman";mso-fareast-font-family:"times="" roman";mso-bidi-font-family:="" mangal;mso-ansi-language:en-gb;mso-fareast-language:en-us;mso-bidi-language:="" hi"="" lang="EN-GB">Various chemometrics methods were used to analyze data sets of marine water quality for 9 parameters measured at 34 different sites of Maharashtra from 2007 to 2009 to determine spatial variations in marine water quality and identify pollution sources. Hierarchical cluster analysis (CA) grouped the 34 monitoring sites into three groups based on similarities in marine water-quality characteristics. Discriminant analysis (DA) was important in data reduction because it used five parameters (DO, Ammonia, pH, FC and temperature) to correctly assign 96.4% of the cases. In addition, principal component analysis (PCA) identified three latent pollution sources for organic pollution, industrial pollution and fecal pollution. Furthermore, water quality index was calculated based on four parameters viz. pH, DO, BOD and FC.</span