Distribution of heavy metals and their potential pollution prediction using spatial modelling in the floodplain farmland around municipal waste dumpsite in Yola Adamawa State Nigeria

Floodplains are receptors of sediments from upland municipal areas, waste dumpsites, and adjacent rivers polluting floodplain farmlands used for both rainfed and irrigation continuous cropping. This study was therefore undertaken to study the distribution of heavy metals (HMs) and their potential pollution prediction using spatial modelling in the floodplain farmland around municipal waste dumpsite in Yola Adamawa, State Nigeria. Soil samples were collected systematically at 6 points (0, 20, 40, 60, 80 and 100 m) along three traverses from the dumpsite at interval of 20 m. Mean values of iron (Fe) and chromium (Cr) across the study area were significantly higher than at the dumpsite, while Cu and Zn were significantly higher at dumpsite and decreased as spatial distance increased away from dumpsite. Clay significantly influenced spatial distribution of HMs’ Fe and Cr, while sand and soil pH had negatively effects on HMs. Increase in total organic carbon, available phosphorus (P), exchangeable calcium (Ca) and magnesium (Mg) increased concentration of manganese (Mn), copper (Cu), lead (Pb), chromium (Cr) and zinc (Zn). Among the HMs, copper had the prediction model with highest r - value and was obtained using the square root of spatial distance {Cu = 225.397 - 1.5328(SD) ½} with the highest R-Squared value of 40.98%, though less than 50 %. The overall means of contamination factor (CF) indicated a decreasing order of Pb > Zn > Cd > Cu > Mn > Cr > Fe. The enrichment factor (EF) index had moderate risk for all the metals with only Cr having low risk to the floodplain. Assessment of HMs using CF, EF and potential ecological risks (PER) models had similar trend of correlations with spatial distances from dumpsite, hence were considered to be more appropriate models to use for predicting HMs pollution compared to index of geo-accumulation (Igeo) model in the study area.