Most of the third world countries having rivers passing through them suffer from the water contaminant problem. This problem is considered so difficult to get the water quality within the standard allowable limits for drinking, as well as for industrial and agricultural purposes. This research aims to assess the water quality of the Tigris River using the water quality index method and GIS software. Twelve parameters (Ca, Mg, Na, K, Cl, SO₄, HCO₃, TH, TDS, BOD₅, NO₃, and EC) were taken from 14 stations along the river. The weighted arithmetic method was applied to compute the water quality index (WQI). The interpolation method (IDW) was applied in ArcGIS 10.5 to produce the prediction maps for 12 parameters at 11 stations along the Tigris River during the wet and dry seasons in 2016. The regression prediction was applied on three stations in the Tigris River between observed values and predicted values, from the prediction maps, in both seasons. The results showed that the regression prediction for all parameters was given the acceptable values of the determination coefficient (R²). Furthermore, the state of water quality for the Tigris River was degraded downstream of the Tigris River, especially at the station (8) in Aziziyah in the wet and dry seasons and increase degradation clearly at Qurnah (Basrah province) in the south of Iraq. This paper considers the whole length of the Tigris River for the study. This is important to give comprehensive knowledge about the contamination reality of the river. Such that it becomes easier to understand the problem of contamination, analyze it, and then find the suitable treatments and solutions.

Al-Hillah Qadhaa is located in the central part of Iraq. It covers an area of 908 km² with a total population of 856,804 inhabitants. This Qadhaa is the capital of Babylon Governorate. Presently, no landfill site exists in that area based on scientific site selection criteria. For this reason, an attempt has been carried out to find the best locations for landfills. A total of 15 variables were considered in this process (groundwater depth, rivers, soil types, agricultural land use, land use, elevation, slope, gas pipelines, oil pipelines, power lines, roads, railways, urban centres, villages and archaeological sites) using a geographic information system. In addition, an analytical hierarchy process was used to identify the weight for each variable. Two suitable candidate landfill sites were determined that fulfil the requirements with an area of 9.153 km² and 8.204 km². These sites can accommodate solid waste till 2030.

Selecting a landfill site is a difficult task because the process depends on many factors and restrictions. Landfill is an optimal solution for the disposal of solid waste in Al-Musayiab Qadhaa, which is located in the northern part of Babylon Governorate. At the moment, there is no landfill site in that area that follows the scientific selection site criteria. For this reason, in this research, fifteen variables were considered (groundwater depth, rivers, soil types; agriculture lands use, land use, elevation, slope, gas pipelines, oil pipelines, power lines, roads, railways, urban centers, villages and archeological sites) using geographic information system (GIS) to find out the best suitable landfill site. In addition, two methods of multi-criteria decision-making were used to derive weights for criterion’s maps on GIS to obtain potential landfill sites. The first method is analytical hierarchy process (AHP) which was used to identify the weight for each criterion from the matrix of pairwise comparisons. The second method was the simple additive weighting (SAW) which is a simple method to solve the problem of the selection landfill sites. After comparison of the results obtained based on combining two final maps resulted from methods of AHP and SAW using GIS environment to determine the pixels percentage of matching and non-matching for two maps, two suitable candidate landfill sites were identified that satisfy the requirements with an area of 7.965–5.952 km². Area of these sites can accommodate the solid waste generated from the Qadhaa up to 2030.

Landfill siting is a complex process. It is one of the major problems in waste management, where many factors should be taken into consideration when selecting a suitable site for landfill in any given area. At the present time, there are many random waste disposal sites distributed throughout Al-Hashimiyah Qadhaa in Iraq. In this study, the Geographic Information System (GIS) and the Analytical Hierarchy Process (AHP) were used to select the best sites for landfill. The process of selecting sites for landfill in Al-Hashimiyah Qadhaa comprised two steps. First, fifteen different criteria were mapped and incorporated into overlay analyses within GIS software to produce the final suitability index map for the site. The second step comprises the exclusion of unsuitable areas from the final map to simplify identification of the candidate sites for landfill in the study area. The weightings of criteria were identified using AHP, and the weightings of the sub-criteria of each criterion were determined based on multiple factors. In order to accommodate solid waste from 2020 until 2030, two suitable candidate landfill sites were determined which fulfill the required area of 1.013 km² with areas of 1.374 km² and 1.288 km² respectively.

Comparison of two conventional analytical techniques such as X-ray fluorescence (XRF) and inductively coupled plasma mass spectrometry (ICP-MS) for measuring Pb concentrations in soil samples was achieved using field and laboratory work. Seventy-three samples were collected from urban areas surrounding the large lead smelter at South Australia, as an indicator of the environment impact of smelter activity. Soil Pb concentrations were determined using hand-held XRF analyser under laboratory conditions. ICP-MS analysis on digested soils (using a microwave-assisted nitric acid digestion-extraction) was applied to validate p-XRF data. The analysis showed that Pb concentrations determined by XRF correlated with high linearity with Pb concentrations determined by ICP-MS measurements (R² = 0.89). Statistical test (t test) was applied to the data of both methods applied without any significant difference between the two techniques. These results indicated that ICP-MS corroborated XRF for Pb soil measurements and suggests that XRF was a reliable and quick alternative to traditional analytical methods in studies of environmental health risk assessment, allowing for much larger sampling regimes in relatively shorter times and could be applied in the field.

In recent years, the problem of rising salinity levels in the Shatt al-Arab river in southern Iraq has been repeated, which has directly affected the living and health situation and the agricultural activity of these areas. Six sampling stations were selected along Shatt al-Arab to estimate the concentration of total dissolved solids (TDS) in the river; these stations included the following: Qurna, Labani, City Centre, Kateban, Corniche, and Sihan. In addition, three Landsat-8 satellite images which were taken at the same time as collected samples also used for detecting the salinity in the river. After processing of atmospheric correction and inserted remote sensing indices, the reflectance of water extracted from satellite images was used to express the spectral characteristics of different TDS concentrations. Correlation and regression were used to obtain accurate models for detecting the salinity depending on the spectral reflectance of Landsat 8 operational land image OLI. The results presented Pearson correlation (r) value of 0.70, 0.97, and 0.71, and correlation coefficient (R²) of 0.56, 0.94, and 0.85 between field data with spectral data of salinity index 2 (SI-2) derived from the green and blue bands of Landsat obtained in 2015, 2017, and 2018 respectively. In conclusion, remote sensing and GIS technologies coupled with spectral modeling are useful tools for providing a solution of future water resources planning and management, and also offer great undertaking as a means to improve knowledge of water quality and support water decision making.