Using Kriging Method for Mapping Heavy Metal Pollution in Fallujah: Spatial Statistical Approach to Environmental Risk Assessment
Abstract
Kriging is an important statistical method used in spatial studies within regional planning and predictive studies. It is employed to achieve optimal predictions, such as pollutant quantities or various environmental and health impacts. There are many types of pollutants, including:
- Water Pollution: The leakage of chemicals from mining sites into groundwater and water bodies can affect water quality and vitality.
- Ecosystem Destruction: Mining can destroy local plants and animals, affecting ecological balance and leading to biodiversity loss.
- Soil Pollution: Mining can contaminate soil with harmful chemicals, affecting agriculture and plant growth.
- Geological Changes: Mining can lead to changes in groundwater levels, as well as seismic impacts and land subsidence.
This research focused on one type of pollutant, such as copper dust, zinc dust, and iron dust. Each of these metals can have different environmental and health impacts.
In this study, Kriging was used to estimate the pollution caused by copper dust accumulated in specific areas within the city of Fallujah, based on data from 23 locations within the city. These locations were mapped on the city map.
The Kriging method was used to predict pollution levels in 6 sites based on their spatial distribution and not previously measured within the city. Errors in these predictions were calculated, along with the overall estimate of pollution and the confidence limits for the average pollution in the city. The mathematical methodology in this study relied on calculating the variogram function by selecting a central location in the city (CBD) and studying the distribution around it. After obtaining the variogram function, the covariance function was calculated assuming stationarity, which was discussed in this study. The covariance function was then used in the Kriging equations to obtain prediction values, and the results were very encouraging. All calculations were performed using Matlab. These results highlight the utility of spatial statistical methods in monitoring environmental pollution and provide valuable insights for local environmental policy and public health protection.