Groundwater quality in the vicinity of a dumpsite in Lagos metropolis,Nigeria

Inappropriate management of municipal solid waste dumpsites is a major cause of groundwater contamination in developing countries,but the extent of the problem is not known.This study investigated groundwater quality in the vicinity of Olusosun dumpsite in Lagos,Nigeria,the most populous city in sub-Saharan Africa.During 2020,monthly groundwater samples were collected in 17 wells and boreholes used as drinking water sources,and analysed for 20 physico-chemical parameters.Differences between sites and seasons were statistically assessed,together with changes in water quality index(WQI).The results indicated that heavy metals(Pb^(2+),Ni^(+),Mn^(2+),Fe^(2+),Cr^(6+)),cations(Ca^(2+),Mg^(2+),K^(+)),total hardness and pH were the main parameters impairing water quality.Drinking water quality standards from both the World Health Organization and Nigeria government were exceeded more often in the wet season than in the dry season.Some groundwater properties were negatively correlated with distance to dumpsite(e.g.,Fe^(2+),Pb^(2+),NO_(3)^(−)).Significant differences between sites were identified,but with no clear spatial trend.WQI varied from excellent(6%-24%of the sites over the study period)to unsuitable for drinking water purposes(12%-18%),with good quality prevailing at most sites(35%-47%).Although groundwater quality declined at 24%of the sites over 2020,the results indicated improvements compared with previous decades.Remediation strategies must be implemented to safeguard public health and the sustainability of water resources.

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.