This paper aims at evaluating the groundwater quality parameters at part of Majmaah City, Saudi Arabia. The study uses multivariate statistics methods (Principal Component Analysis, PCA and/or Factor Analysis, FA) to determine the most important water quality parameters at the study area, aiming at introducing an environmental assessment of its current situation. Water quality parameters that showed levels above the standards would be spotted and reasons behind this contamination will be investigated. Real data are collected from 15 groundwater wells near farms part in Gewy area. The results of the water analysis data were subjected to intensive statistical tests and interpretations ware made in the highlight of physical factors. The paper also introduces a cost effective methodology to minimize the number of water quality parameters to the most important variables based on the multivariate statistical results. The interrelationship between different water quality parameters at the study area was also investigated. Moreover, Geostatistics techniques and GPS data, for the same wells, were utilized for characterization of some water quality parameters in 2D and results are introduced in the form of contour maps and 3D representation of these variables. The obtained results are presented as environmental maps for the water quality parameters at the study area and give, for the first time, a characterization of the water quality at the selected portion of Majmaah city.

One of the most important steps in environmental studies is to get an accurate distribution of the pollutants in terms of its quantity and qualities. Some environmental studies are acquiring a good classification of the particle size including the average mean of the particle. Many researchers use the median size (x50) or until the size passing 80% cumulative undersize (x80) as a measure for evaluation of the particle size distribution resulted from different mineral processing operations such as crushing, grinding, classification, sedimentation, and/or solid-liquid separation or even during the study of the pollutant settlement. These measures are not so accurate to differentiate between different particle size distributions (PSDs) because many PSDs data sets may have the same values of x50 or x80 if these data sets are represented between the particle size, x and the cumulative undersize distribution, F (x). This paper is a trial to introduce a new methodology to determine the mean of a particle size distribution (MPSD) accurately using GatesGaudin-Schuhmann and Rosin-Rammler models. The value of this measure takes into consideration all particle sizes and their corresponding distributions. The results showed that the different PSD, which have the same values of median (x50) have different values of this measure, especially with Rosin-Rammler model. In this paper, the expressions of different types of means of a particle size distribution (arithmetic, quadratic, cubic, geometric, and harmonic) were derived mathematically using the two mentioned models. It is recommended to select RR model to be applied in estimation of the different means of a particle size distribution because it fits the available data better than the GGS model, as well as, it determines the correct values and exerts the actual differences between the different means of different particle size distribution data sets. This work will be continuing by demonstrating a case study using real environmental data in Part (2).