Background. Soil pollution by heavy metals in urban areas is of major concern to city planners and policy makers because of the potential threat to human health. Hence, an investigation of soil pollution is crucial to urban environmental assessment and management. Objective. To determine the spatial distribution and health risk assessment of seven heavy metals (cadmium (Cd), chromium (Cr), copper (Cu), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn)) around Ijebu-ode, southwest Nigeria. Methods. Surface soil samples were analyzed for Cd, Cr, Cu, Mn, Ni, Pb, and Zn levels using standard procedures. Geographic information system (GIS) data, pollution indices (enrichment factor, geo-accumulation index), and the health risk assessment model, respectively, were used to analyze the spatial distribution, pollution level, and potential health risk of heavy metals. Results. Low pH was observed in the urban soils. The average concentrations of the seven heavy metals investigated were in order of Zn > Pb > Mn > Cu > Cd > Ni > Cr. There was high spatial variation in the distribution patterns of the heavy metals. The cancer risks for Cu, Mn, Pb, and Zn for children (1.50 × 10 −3 – 2.71 × 10 −2 ) and Mn, Pb, and Zn for adults (7.89 × 10 −4 – 2.97 × 10 −3 ) were higher than the acceptable range of 1 × 10 −6 - 1 × 10 −4 . Conclusions. Anthropogenic activities from different urban land uses contribute to the pollution levels and spatial distribution of heavy metals in soils. Increasing pollution of urban soil may contribute to the occurrence of some health risk for residents in the study area. Competing Interests. The authors declare no competing financial interests.

This study assessed the spatial disposition of air pollutants and their relationship with meteorological parameters in urban slum settlements of Lagos city. The gaseous pollutants were quantified using a gas analyzer, and the PM2.5 concentration and meteorological parameters were determined using an Air Metric Sampler and Wind Mate, respectively. SPSS for Windows and ArcGIS were used for data analysis. The results revealed that the seasonal variations in SO2, NO2, CO2, and PM2.5 showed a higher level of air pollutant concentration during the dry season than during the wet season. During the wet season, a significant correlation was observed between PM2.5 and temperature at the 1% level (0.957 **), and VOC and SO2 (0.907 *) at the 5% level; during the dry season, significant correlations were observed between NO2 and SO2 at the 1% level (0.9477 **), and PM2.5 and relative humidity (0.832 *) at the 5% level. Atmospheric pressure (72%), temperature (60%), and relative humidity (98.4) were the primary meteorological factors affecting air pollutants such as VOC, CO2, and SO2. The spatial dispersal of air pollutants revealed a high Z score and a moderate p-value, indicating hot spot locations throughout the five selected slum settlements. It is recommended that regular monitoring based on quantifiable economic costs that are beneficial to the well-being of the populace be investigated, and policy-based initiatives for air pollution control based on scientific evidence be advocated for.

Rapid population growth and increasing economic activities have resulted in unsustainable exploitation and rapid decline in the spatial extent of forest reserves in Nigeria. Studying land use dynamics of these forest reserves is essential for analysing various ecological and developmental consequences over time. Land use/land cover mapping, change detection and prediction are essential for decision-making and implementing appropriate policy responses relating to land uses. This paper aims at assessing and predicting changes in land use/land cover at Gambari forest reserve, Nigeria using remote sensing and GIS techniques. The study determined the magnitude, rate and dynamics of change in the spatial extent of the forest reserve between 1984 and 2014 using multi-temporal datasets (Landsat TM 1984 and 2000 and OLI/TIRS 2014). The imageries were classified using ArcGIS 10.0 version with support of ground truth data and Land use Change Modeller (LCM) and Markovian processes were employed to analyse the pattern and trend of change. Prediction of 2044 scenario carried out using neural network, which is a built-in module in the Idrisi. The study revealed dramatic decline in the extent of the forest reserve as both the plantation of exotic tree species (Tectona grandis and Gmelina) and the indigenous stands have been logged in several places for timber and to make way for cultivation of crops. In addition, pressures from other land uses like settlements have also led to increased non-forest uses particularly bare grounds. The study concluded that increasing loss of the indigenous forest and plantation would continue thus having implications for biodiversity conservation in the study area. There is the need for participation of different stakeholders and sectors to solve conflicting demands on limited forest resources and ensure ecosystem integrity.

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Urban sprawl and land use / land cover changes in a suburb of Lagos, Nigeria were assessed using Landsat TM 1984 and 2000 and Landsat OLI of 2014. Five broad land use and land cover classes i.e. built-up area, bare ground, water body, thick forest and light forest were identified and mapped. Thick forest had the largest coverage of 8537.72 hectares (67.52%) of the land cover while built-up was just 1075.99 hectares (8.51 %).Between 1984 and 2014 built up areas gained 6423.38 hectares (59.31 % increase) compared to 8612.09 hectares loss by thick forest cover. A post-classification change analysis from 1984 to 2014 reveals that thick and light forest types had the highest net losses because of conversion to other uses, especially built-up. Urbanization and subsequent urban sprawl is a major factor of land degradation leading to rapid losses of non-urban land uses, especially in the urban fringes. Keywords: Land use/land cover change; change detection; remote sensing; GIS; urbanisation