Editorial Feature

Analysis of the Occurrence of Pharmaceutical Pollution in Global Rivers

Active pharmaceutical ingredients (APIs) are released into the natural environment during their use, disposal, and manufacture. There is proof that environmental exposure to APIs has harmful impacts on the ecosystems and humans’ health. To comprehend the possible impacts of these pharmaceutical exposures, it is crucial to know the concentrations that happen in riverine environments.

rivers, pollution, pharmaceutical

Image Credit: GoncharukMaks/Shutterstock.com

Although a substantial amount of data is available on the many APIs’ concentrations in surface waters, there are large gaps in knowledge of such exposures.

In this study, researchers show a complete universal study of pharmaceutical occurrence in the rivers of less than 50% of the countries in the world (n = 1,052 sites). They present an exclusive, first-class, and comparable dataset based on the concentrations of 61 APIs and chose compounds employed in medicine and daily consumables (such as caffeine and nicotine).

The targeted compounds were chosen depending on earlier prioritization exercises and anticipated to occur in the environment. They are also expected to be of possible environmental concern. Enabling a precise comparison of pharmaceutical exposure data on an international scale, the research team used one sensitive dataset and globally verified analytical and sampling methods employed in a research laboratory.

Results and Discussion

Samples of surface water were gathered in duplicate ones—from 1,052 sampling sites during campaigns of 137 sampling campaigns covering 104 countries all over the world (Figure 1) and examined 61 APIs, which led to 128,344 data points. A sampling campaign included the water samples’ collection from several sampling sites along a river or rivers flowing inside a town, city, or local area.

Based on these and the UBA database of pharmaceuticals present in the environment, 36 countries had never been monitored earlier for APIs (Figure 2).

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

Figure 1. Locations of studied rivers/catchments (n = 137) for our global study. Points indicate groups of sampling sites across respective river catchments and countries are shaded based upon the total number of sampling sites. Image Credit: Wilkinson, et al., 2022

Cumulative API concentrations quantified across 137 studied river catchments 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.

Figure 2. Cumulative API concentrations quantified across 137 studied river catchments 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. Image Credit: Wilkinson, et al., 2022

For the detected APIs, total detection frequencies were from 0.1% for itraconazole (antifungal), fluoxetine (antidepressant), and ketotifen (antihistamine), to 62% for carbamazepine (Figure 3a) within respective river catchments. At more than 50% of all the sampling sites globally, caffeine and metformin were also detected.

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

Figure 3. (a) Detection frequencies and (b) number of APIs detected at sampling sites in the global monitoring study, excluding sites without the detection of any API, and (c) box-and-whisker plots of concentrations (ng/L) of individual APIs, indicating the mean, minimum, maximum, and upper and lower quartile concentrations for each API globally. Image Credit: Wilkinson, et al., 2022

In this study, APIs’ concentrations were detected to be highest in lower-middle-income countries than that of countries with any other income-classification—as reported by the World Bank (Figure 4a).

(a) Cumulative concentration of APIs observed across respective river catchments (signified by a blue dot, n = number of sampling sites) organized by World Bank GNI per capita 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.

Figure 4. (a) Cumulative concentration of APIs observed across respective river catchments (signified by a blue dot, n = number of sampling sites) organized by World Bank GNI per capita 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. Image Credit: Wilkinson, et al., 2022

Statistical links, which were complementing this result, were found between API pollution and certain socioeconomic variables supporting national economies and health through distance-based linear modeling. At this point, pharmaceutical pollution was most positively linked with the population, local unemployment, median age, and poverty rates. On the other hand, it was negatively linked with the death rate of a country (Figure 4b).

Concentrations above the PNEC were found in 140 monitoring sites for sulfamethoxazole (Figure 5). The research data also clearly show that organisms present in riverine systems are exposed to complicated mixtures of APIs (Figure 3b). In the Kai Tak River in Hong Kong, the highest number of 34 APIs were identified at a single site. Therefore, due to the toxicological interactions of these mixtures, ecological risks can be a lot greater than anticipated for the single APIs.

Percent of sites in the global monitoring study where concentrations exceeded: lowest PNECs 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).

Figure 5. Percent of sites in the global monitoring study where concentrations exceeded: lowest PNECs 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). Image Credit: Wilkinson, et al., 2022

Out of 13 detected antimicrobials, concentrations of nine (Figure 5) surpassed these safe concentrations for a minimum of one sampling site, with ciprofloxacin surpassing the safe limit at 64 sites.

The highest exceedance of the safe target was detected for metronidazole at a sampling location in Barisal, Bangladesh, where the greatest concentration of this antibiotic was over 300 times higher than the safe target. The presence of wastewater disposal near the river and in the close vicinity of pharmaceutical manufacturing activities was noted by the sampling team via on-the-ground observation.

Methodology

Similar water sampling kits were sent to project collaborators and were asked to develop a sampling campaign that comprised 5–10 sampling sites nearby rivers that were passing via a populated area (city, town, or village). Upon discussion with each project collaborator, a definition of prospective sources of pharmaceutical pollution impacting each river catchment was enabled.

By lowering the sampling bucket into the water with the use of the cord attached, water collection was achieved at each site. After a primary rinsing with the native water, an aliquot of water was then extracted into a syringe. Then, the syringe filter was attached and primed.

Photographs and environmental data were collected at each site. All collaborators were given videos and a step-by-step guide that detailed the needed sample collection protocol to make sure of consistency throughout all sampling campaigns.

After collection, samples were kept frozen until they are sent for analysis to a single analytical center in the United Kingdom employing a single analytical method through express air shipment. After the delivery at the University of York, samples were maintained at −20 °C until an analysis is over.

Analysis was carried out at the Centre of Excellence in Mass Spectrometry situated at the University of York (United Kingdom) using high-pressure liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Statistical analysis was performed using SPSS, Microsoft Excel, and Primer using PERMANOVA+ (v7.0.17, Primer-e).

Socioeconomic and population data were obtained from the open database of the World Bank. Threat quotients for a valuation of possible ecotoxicity risk were created by dividing the observed environmental concentrations by the least predicted no-effect concentration or critical environmental concentration that was derived for each API studied.

Conclusion

In this study, researchers demonstrated how the application of a minimal-design sampling protocol with quick and economical analytical approaches and a well-connected international community enables the investigation of API exposures and consequent risks in rivers on a global scale globally.

Although this study concentrated only on 61 priority APIs, the method could be used for other APIs and other classes of pollutants as well. The integration of non-targeted analytical methods can also enable the detection of unknown global pollutants.

As a grouping of 127 authors from 86 institutions across the globe, the research team demonstrated that pollution of the global rivers through medicinal chemicals is a global problem that poses threat to aquatic ecology and potential AMR selection. It may also pose threat to the accomplishment of the United Nations Sustainable Development Goal 6.3 by 2030.

As the world moves toward 2030, the new model in environmental monitoring needs to involve an inclusive, global, and interconnected effort. Only via global collaboration, it is possible to generate the monitoring data needed to make knowledgeable decisions on mitigation approaches essential to decrease the environmental effects of chemicals.

Journal Reference:

Wilkinson, J. L., et al. (2022) Pharmaceutical pollution of the world’s rivers. Proceedings of the National Academy of Sciences, 119(8), p. e2113947119. Available Online: https://www.pnas.org/content/119/8/e2113947119.

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Laura Thomson

Written by

Laura Thomson

Laura Thomson graduated from Manchester Metropolitan University with an English and Sociology degree. During her studies, Laura worked as a Proofreader and went on to do this full time until moving on to work as a Website Editor for a leading analytics and media company. In her spare time, Laura enjoys reading a range of books and writing historical fiction. She also loves to see new places in the world and spends many weekends looking after dogs.

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