While temperature has long been known as a catalyst for pollutant to be more airborne, it is unclear how an increase in temperature impacts on air pollution during heatwaves. Through a regression analysis the relationship between ozone (O3), particulate matter (PM10, particles less than 10 μm in diameter), nitrogen dioxide (NO2), and temperatures in urban and rural areas of Birmingham, it was found that during heatwaves, all pollutant levels rose at each site, with the maximum temperature coinciding with the peak of O3 and PM10. These findings established that the influence of temperature on air pollution did not change according to rural and urban locations although air pollutants (O3, NO2, and PM10) increased with increasing temperatures particularly during heatwaves. However the variation in the amount of ozone, altered by more than 50% according to increases in temperature. This matches studies where the incidence of high levels of pollution have conclusively been found to be much more prevalent during heatwaves of a long duration. The implications of these findings are important to long-term prevention in the framework of heatwave plans and when there is a heatwave forecast, additional measures to reduce air pollutant concentrations may be appropriate to trigger emergency response.

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The quality of drinking water source remains as a major concern in areas of developing and underdeveloped countries worldwide. The treatment and supply of drinking water in Rwanda are carried out by Water and Sanitation Corporation, a state-owned public company. However, it is not able to supply water to all households. Consequently, the non-serviced households depend on natural water sources, like springs, to meet their water requirements. Nevertheless, the water quality in these springs is scarcely known. Therefore, this study assessed and compared metal elements in drinking water sources in the dry and rainy seasons in 2017 using the contamination degree, metal index, and geographic information systems to reveal the spatial distribution of water quality within the considered water sources of springs in Rwanda. The samples were collected monthly from nine water sources of springs and the measured elements are aluminium, calcium, copper, iron, manganese, and zinc. The metal index indicated that during the dry season and rainy season, the sites of Kibungo (1.10 and 1.26) and Kinigi (1.01 and 1.54) have assessed a metal index which is higher than 1. Thus, the water quality of those sites was getting the threshold of warning. The analysis indicated that pollutants are easily transported into water bodies during the rainy season in urban and rural areas to a greater extent than during the dry season .

Application of suitable methods to generate landslide susceptibility maps (LSM) can play a key role in risk management. Rwanda, located in centre-eastern Africa experiences frequent and intense landslides which cause substantial impacts. The main aim of the current study was to effectively generate susceptibility maps through exploring and comparing different statistical and probabilistic models. These included weights of evidence (Wo E), logistic regression (LR), f requency ratio (FR) and statistical index (SI). Experiments were conducted in Rwanda as a study area. Past landslide locations have been identified through extensive field surveys and historical records. Totally, 692 landslide points were collected and prepared to produce the inventory map. This was applied to calibrate and validate the models. Fourteen maps of conditioning factors were produced for landslide susceptibility modelling, namely: elevation, slope degree, topographic wetness index (TWI), curvature, aspect, distance from rivers and streams, d istance to main roads, lithology, soil texture, soil depth, topographic factor (LS), land use/land cover (LULC), precip itation and normalized difference vegetation index (NDVI). Thus, the produced susceptibility maps were validated using the receiver operating characteristic curves (ROC/AUC). The findings fro m this study disclosed that prediction rates were 92.7%, 86.9%, 81.2% and 79.5% respectively for WoE, FR, LR and SI models. The WoE achieved the highest AUC value (92.7%) wh ile the SI produced a lowest AUC value (79.5%). Addit ionally, 20.42% of Rwanda (5,048.07km 2) was modelled as high susceptible to landslides with the western part the highly susceptible comparing to other parts of the country. Conclusively, the comparison of produced maps revealed that all applied models are pro mising approaches for landslide susceptibility studying in Rwanda. The results of the present study may be useful for landslide risk mit igation in the study area and in other areas with similar terrain and geomorphological conditions. More studies should be performed to include other important conditioning factors that exacerbate increases in susc eptibility especially anthropogenic factors.