Malawi’s Blantyre City faces escalating waste management challenges due to increased urbanization and inadequate waste collection services. This research utilized remote sensing (RS) and geographic information system (GIS) techniques to map potential illegal dump sites (PIDSs). MODIS and Sentinel-5P satellite imagery and GPS locations of dumpsites were used to extract environmental and spatial variables, including land surface temperature (LST), the enhanced vegetation index (EVI), Formaldehyde (HCHO), and distances from highways, rivers, and official dumps. An analytical hierarchical process (AHP) pairwise comparison matrix was used to assign weights for the six-factor variables. Further, fuzzy logic was applied, and weighted overlay analysis was used to generate the PIDS map. The results indicated that 10.27% of the study area has a “very high” probability of illegal dumping, while only 2% exhibited a “very low” probability. Validation with field data showed that the GIS and RS were effective, as about 89% of the illegal dumping sites were identified. Zonal statistics identified rivers as the most significant contributor to PIDS identification. The findings of this study underscore the significance of mapping PIDS in low-resource regions like Blantyre, Malawi, where inadequate waste management and illegal dumping are prevalent. Future studies should consider additional factors and account for seasonal variations.

Malaria remains one of the critical public health threats, particularly in endemic sub-Saharan countries like Malawi. Although malaria prevalence has declined over the years, the disease continues to pose a notable public health burden, contributing to high levels of morbidity and mortality. This study mapped malaria risk in Nsanje District, southern Malawi. Environmental variables (temperature, rainfall, elevation, slope, and proximity to rivers) were used to model malaria hazard, while socio-economic factors (proximity to health facilities and roads, and population density) defined vulnerability. Land use and land cover derived from Sentinel-2 using the random trees classifier in ArcGIS Pro were used to delineate elements at risk. The analytical hierarchy process and weighted overlay analysis were applied to generate hazard, vulnerability, and risk maps. Additionally, sensitivity analysis was conducted to determine the most influential factors. Results show that temperature and rainfall contributed more within the model to malaria hazard, with 20.4% and 35.1% of the area classified as high- and very high-hazard zones, respectively. Vulnerability was mainly affected by proximity to health facilities and population density, while 43.1% of the district was categorized as high risk and 40.5% as moderate risk. Overall, malaria hazard contributed most to the total risk, followed by vulnerability. The findings of this study are essential for understanding the complex dynamics of malaria transmission, which are influenced by a combination of environmental, climatic, and socio-economic factors. The study recommends enhancing healthcare accessibility and developing early warning systems, including malaria risk maps, to support targeted prevention and control efforts. Future studies should integrate long-term climate projections and real-time, high-frequency environmental and epidemiological data to enhance malaria risk modelling.