Pharmaceutical pollution of the world's rivers

Abstract

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.

Keywords: antimicrobials; aquatic contamination; global pollution; pharmaceuticals; wastewater.

Fig. 1.

Locations of studied rivers/catchments (n = 137) for our global study (Dataset S2). Points indicate groups of sampling sites across respective river catchments and countries are shaded based upon the total number of sampling sites.

Fig. 2.

Cumulative API concentrations quantified across 137 studied river catchments (Dataset S6) organized by descending cumulative concentration (ng/L). Percentiles are marked by black lines and countries not previously monitored by crosses above the plot. The cumulative concentrations reported here are calculated as the average of the sum concentration of all quantifiable API residues at each sampling site within respective river catchments.

Fig. 3.

(A) Detection frequencies (Dataset S5) and (B) number of APIs detected at sampling sites in the global monitoring study (Dataset S4), excluding sites without the detection of any API, and (C) box-and-whisker plots of concentrations (ng/L) of individual APIs (Dataset S4), indicating the mean, minimum, maximum, and upper and lower quartile concentrations for each API globally.

Fig. 4.

(A) Cumulative concentration of APIs (Dataset S6) observed across respective river catchments (signified by a blue dot, n = number of sampling sites) organized by World Bank GNI per capita (33) and (B) distance-based redundancy analysis (dbRDA) illustrating the best model of socioeconomic indicators to explain the measured concentration of different classes of pharmaceuticals in respective countries according to the distance-based linear model (DISTLM, AICc = 325.26, r² = 0.241). Vector projections with center coordination at (−3, 0) were performed with multiple partial correlation. Length and direction of the vectors represent the strength and direction of the relationship. Data from each country were classified according to their cumulative active pharmaceutical ingredient concentration: that is, Low: first quartile (the lowest 25%); Lower-middle: second quartile (the next 25%); Higher-middle: third quartile (the next 25%); and High: fourth quartile (the top 25%). Raw data can be found in Dataset S9.

Fig. 5.

Percent of sites in the global monitoring study where concentrations exceeded: lowest PNECs (Dataset S12) derived from apical ecotoxicological endpoints for algae, fish, and daphnia (orange bars); CECs estimated based on human plasma therapeutic concentrations and uptake predictions for fish (green bars); and “safe” target concentrations for AMR selection (blue bars).