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The present article discusses and compares five different water quality indices, viz arithmetic water quality index, multiplicative water quality index, unweighted arithmetic water quality index, unweighted multiplicative water quality index, and Harkin's water quality index, which were considered for characterizing the coastal water quality at the Jawaharlal Nehru Port Trust, Bombay, India. Dissolved oxygen, pH, biochemical oxygen demand (BOD), temperature, suspended solid, and turbidity were used as the parameters of water quality indices. The value function graphs used for above-mentioned variables were developed using harbour water quality standards and aquatic life. The product moment correlation coefficients for various water quality indices were determined using the SPSS software package to evaluate correlation among various indices. It was found that the unweighted arithmetic water quality index was higher than weighted arithmetic water quality index while the multiplicative water quality index was lower than unweighted multiplicative water quality index. All the indices were well correlated with each other except Harkin's water quality index. The Harkin's water quality index was different from other water quality indices. The comparison of different form of indices showed that the multiplicative water quality index was the most suitable water quality index for coastal waters.

A long-term study of temperature, pH, turbidity, suspended solid, salinity, dissolved oxygen, biochemical oxygen demand and ammonia nitrogen has been performed in a port and harbour region in India for four years from December 1996 to November 2000. Marine water quality results showed no regular trend. The mean monthly values of temperature, pH, turbidity, suspended solid, salinity, dissolved oxygen, biochemical oxygen demand and ammonia nitrogen were in the range of 22.64 +/- 0.4 to 29.05 +/- 1.37 degrees C; 7.65 +/- 0.04 to 7.81 +/- 0.13; 28.8 +/- 14.7 to 64.2 +/- 32.0 NTU; 283.5 +/- 81.8 to 356.0 +/- 159.7 mg/L; 29.78 +/- 7.18 to 29.78 +/- 1.04 ppt; 4.67 +/- 0.50 to 6.01 +/- 1.02 mg/L; 5.41 +/- 1.92 to 7.56 +/- 2.1 mg/L; and 0.25 +/- 0.07 to 0.63 +/- 0.49 mg/L, respectively. The results of correlation analysis showed that biochemical oxygen demand (BOD) was inversely correlated dissolved oxygen (DO) and poorly correlated with all other parameters. Turbidity and suspended solid were moderately correlated with each other while salinity was moderately correlated with other water quality parameters. In factor analysis, four factors were drawn out of the eight variables, which represented 74% of the variance of the original data. Factor I was related to suspended solid and turbidity. Factor II represented mainly temperature and DO showing inverse relation between these two. Factor III implied the degree of pollution at any monitoring station. Factor IV included pH and salinity. It could be concluded that the factor model represented almost all the variables.

The concentrations of total suspended particulate matter (TSP) and particulate matter less than 10 microns (PM10) were measured at various locations in a Jawaharlal Nehru port and surrounding harbour region. Meteorological data was also collected to establish the correlation with air pollutant concentration. The results are analysed from the standpoint of monthly and seasonal variations, annual trends as well as meteorological effects. The monthly mean concentration of TSP was in the range of 88.2 to 199.3 microg m(-3). The maximum and minimum-recorded value of PM10 was 135.8 and 20.3 microg m(-3), respectively. The annual average concentration of PM10 was 66.1 microg m(-3). There are clear associations between TSP and PM10 data set at all the measured three sites with a correlation coefficient of 0.89, 0.69 and 0.81, respectively. PM10 data appears to be a constant fraction of the TSP data throughout the year, indicating common influences of meteorology and sources. Particle size analysis showed PM10 to be 47% of the total TSP concentration, which is lower than reported for industrial area and traffic junctions in Mumbai. Anthropogenic sources contribute significantly to the PM10 fraction in an industrial region, while contributions from natural sources are more in a port and harbour area. Statistical analysis of air quality data shows that TSP is strongly correlated with wind speed but weakly correlated with temperature. There appears to be a simple inverse relationship between TSP and wind speed data, indicating the dilution and transport by winds.

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