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.

Marshes represent a unique ecosystem covering a large area of southern Iraq. In a major environmental disaster, the marshes of Iraq were drained, especially during the 1990s. Since then, droughts and the decrease in water imports from the Tigris and Euphrates rivers from Turkey and Iran have prevented them from regaining their former extent. The aim of this research is to extract the values of the normalized difference vegetation index (NDVI) for the period 1977–2017 from Landsat 2 MSS (multispectral scanner), Landsat 8 OLI (operational land imager) and Sentinel 2 MSI (multi-spectral imaging mission) satellite images and use supervised classification to quantify land and water cover change. The results from the two satellites (Landsat 2 and Landsat 8) are compared with Sentinel 2 to determine the best tool for detecting changes in land and water cover. We also assess the potential impacts of climate change through the study of the annual average maximum temperature and precipitation in different areas in the marshes for the period 1981–2016. The NDVI analysis and image classification showed the degradation of vegetation and water bodies in the marshes, as vast areas of natural vegetation and agricultural lands disappeared and were replaced with barren areas. The marshes were influenced by climatic change, including rising temperature and the diminishing amount of precipitation during 1981–2016.