Peter Gibba

Environmental exposure to active pharmaceutical ingredients (APIs) can have negative effects on the health of ecosystems and humans. While numerous studies have monitored APIs in rivers, these employ different analytical methods, measure different APIs, and have ignored many of the countries of the world. This makes it difficult to quantify the scale of the problem from a global perspective. Furthermore, comparison of the existing data, generated for different studies/regions/continents, is challenging due to the vast differences between the analytical methodologies employed. Here, we present a global-scale study of API pollution in 258 of the world's rivers, representing the environmental influence of 471.4 million people across 137 geographic regions. Samples were obtained from 1,052 locations in 104 countries (representing all continents and 36 countries not previously studied for API contamination) and analyzed for 61 APIs. Highest cumulative API concentrations were observed in sub-Saharan Africa, south Asia, and South America. The most contaminated sites were in low- to middle-income countries and were associated with areas with poor wastewater and waste management infrastructure and pharmaceutical manufacturing. The most frequently detected APIs were carbamazepine, metformin, and caffeine (a compound also arising from lifestyle use), which were detected at over half of the sites monitored. Concentrations of at least one API at 25.7% of the sampling sites were greater than concentrations considered safe for aquatic organisms, or which are of concern in terms of selection for antimicrobial resistance. Therefore, pharmaceutical pollution poses a global threat to environmental and human health, as well as to delivery of the United Nations Sustainable Development Goals.

ABSTRACT The mean climatology, inter‐model variability and spatio‐temporal patterns of temperature and precipitation over West Africa from Coupled Model Intercomparison Project 5 ( CMIP5 ), CMIP5_SUBSET [ensemble of global climate models driving COordinated Regional climate Downscaling EXperiment ( CORDEX )] and CORDEX multi‐model ensembles are evaluated and intercompared for the monsoon season (June–September). We find that, while CORDEX fails to outperform the simulated mean climatology of temperature by the CMIP5 ensembles, it substantially improves precipitation and provides more realistic fine‐scale features tied to local topography and landuse. This improved performance over the region is found to depend more on the internal models physics than the driving boundary conditions and results from a more consistent and realistic simulation of monsoon precipitation across the various regional climate models ( RCMs ). Rotated empirical orthogonal function ( REOF ) analysis indicates that the CORDEX ensemble captures better the spatio‐temporal variability of both temperature and precipitation (first REOF mode), in particular depicting the warming and Sahel precipitation recovery in recent decades over West Africa. On the other hand, the spatial patterns and associated time series of the last two REOF modes in CORDEX mostly follow the CMIP5_SUBSET pointing towards a strong role of the boundary forcing in the RCM simulation of precipitation variability.