An estimated 23 million people live on floodplains affected by potentially dangerous concentrations of toxic waste derived from past and present metal mining activity. We analyzed the global dimensions of this hazard, particularly in regard to lead, zinc, copper, and arsenic, using a georeferenced global database detailing all known metal mining sites and intact and failed tailings storage facilities. We then used process-based and empirically tested modeling to produce a global assessment of metal mining contamination in river systems and the numbers of human populations and livestock exposed. Worldwide, metal mines affect 479,200 kilometers of river channels and 164,000 square kilometers of floodplains. The number of people exposed to contamination sourced from long-term discharge of mining waste into rivers is almost 50 times greater than the number directly affected by tailings dam failures.

Members of the Anopheles gambiae complex are major malaria vectors in Africa. We tested the hypothesis that the range and relative abundance of the two major vectors in the complex, An. gambiae sensu stricto and An. arabiensis, could be defined by climate. Climate was characterized at mosquito survey sites by extracting data for each location from climate surfaces using a Geographical Information System. Annual precipitation, together with annual and wet season temperature, defined the ranges of both vectors and were used to map suitable climate zones. Using data from West Africa, we found that where the species were sympatric, An. gambiae s.s. predominated in saturated environments, and An. arabiensis was more common in sites subject to desiccation (r² =0.875, p <0.001). We used the nonlinear equation that best described this relationship to map habitat suitability across Africa. This simple model predicted accurately the relative abundance of both vectors in Tanzania (r_s =0.745, p =0.002), where species composition is highly variable. The combined maps of species' range and relative abundance showed very good agreement with published maps. This technique represents a new approach to mapping the distribution of malaria vectors over large areas and may facilitate species–specific vector control activities.

We investigated local-scale variation in malaria transmission and infection in children within a continuous landscape by retrospective spatial analysis of entomological and clinical data collected during 1988 and 1989 in The Gambia, West Africa. Parasite prevalence was negatively correlated with vector abundance and exposure to malaria parasites in 10 villages where entomological surveillance had been carried out. Variation in bednet use did not explain this finding. Mosquito-breeding habitat was retrospectively mapped using 20-m spatial resolution multispectral SPOT satellite imagery from 1988. From these data we estimated by linear regression the risk of exposure to malaria parasites in 26 villages where clinical surveys of children had been made. As exposure increased, so did parasite prevalence; but at higher levels of exposure, parasite prevalence declined. Our findings demonstrate marked differences in exposure to malaria in villages over distances of less than 2 km from mosquito breeding sites and suggest that there are also large differences in immunity between neighbouring settlements.

Providing timely and accurate maps of surface water is valuable for mapping malaria risk and targeting disease control interventions. Radar satellite remote sensing has the potential to provide this information but current approaches are not suitable for mapping African malarial mosquito aquatic habitats that tend to be highly dynamic, often with emergent vegetation. We present a novel approach for mapping both open and vegetated water bodies using serial Sentinel-1 imagery for Western Zambia. This region is dominated by the seasonally inundated Upper Zambezi floodplain that suffers from a number of public health challenges. The approach uses open source segmentation and machine learning (extra trees classifier), applied to training data that are automatically derived using freely available ancillary data. Refinement is implemented through a consensus approach and Otsu thresholding to eliminate false positives due to dry flat sandy areas. The results indicate a high degree of accuracy (mean overall accuracy 92% st dev 3.6) providing a tractable solution for operationally mapping water bodies in similar large river floodplain unforested environments. For the period studied, 70% of the total water extent mapped was attributed to vegetated water, highlighting the importance of mapping both open and vegetated water bodies for surface water mapping.