The present study aims to select the most appropriate aggregation function for estimation of the Ganga River pollution index ͑GRPI͒. Following the Delphi technique based on expert opinion, 16 water pollutant variables are selected; the weights of each pollutant variable based on their relative significance are determined, and the average subindex curves for each variable are drawn. Using the weights, average parameter's value and the corresponding subindex value, 18 different aggregation functions are tested and analyzed. Literature reveals that most aggregation methods suffer from ambiguity and eclipsing problems due to faulty selection of aggregation function. From the results of the present analysis, 12 aggregation functions are screened out on the basis of ambiguity and eclipsing, constant functional behavior, and nonaccountability of weights in functions criteria. Finally, the remaining 6 aggregation functions are subjected to sensitivity analysis. From the results of sensitivity analysis, it is concluded that the weighted arithmetic mean function, being a true linear, least ambiguous and eclipsing free function, is the most representative aggregation function for estimation of GRPI for River Ganges.

The paper addresses an iterative method by which a test can be dichotomized in parallel halves and ensures maximum split-half reliability. The method assumes availability of data on scores of binary items. Since, it was aiming at splitting a test in parallel halves, no assumption was made regarding form or availability of reference test. Empirical verification is also provided. Other properties of the iterative methods discussed. New measures of degree of parallelism given. Simultaneous testing of single multidimensional hypothesis of equality of mean, variance and correlation of parallel tests can also be carried out by testing equality of regression lines of test scores on scores of each of the parallel halves, ANOVA or by Mahalanobis 2. The iterative method can be extended to find split-half reliability of a battery of tests. The method thus provides answer to much needed question of splitting a test uniquely in parallel halves ensuring maximum value of the split-half reliability. The method may be adopted while reporting a test.

Particulate matter with 10 μm or less in diameter (PM 10 ) have adverse effects on environment and human health. To reduce PM 10 emissions in India, it is essential to have models that accurately estimate and predict PM 10 concentrations for reporting and monitoring purposes. In this paper Exponential Smoothing Technique and Autoregressive (AR) models are developed to forecast 1-month ahead value of PM 10 for Allahabad city which is novelty of this study. AR (1) and AR (5) models are developed using Burge and Yule Walker methods. The mean absolute percentage error (MAPE) for Burge method in AR (1) and AR (5) are 14.23% and 10.20%. The MAPE for Yule Walker method in AR (1) and AR (5) are 32.72% and 31.13%. The MAPE in Exponential Smoothing Technique is 5.81% which shows it forecasts better than AR model based on Burge and Yule Walker methods. It is found that Burge Method in AR (5) has less MAPE than Yule Walker Method. Therefore Exponential Smoothing Technique can be used to forecast PM 10 for cities in India, showing it is beneficial for giving prior information for human health.

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