Although pollutants pose environmental and human health risks, the majority are not routinely monitored and regulated. Organic pollutants emanate from a variety of sources, and can be classified depending on their chemistry and environmental fate. Classification of pollutants is important because it informs fate processes and apposite removal technologies. The occurrence of emerging contaminants (ECs) in water bodies is a source of environmental and human health concern globally. Despite being widely reported, data on the occurrence of ECs in South Africa are scarce. Specifically, ECS in wastewater in the Northern Cape in South Africa are understudied. In this study, various ECs were screened in water samples collected from three wastewater treatment plants (WWTPs) in the province. The ECs were detected using liquid chromatography coupled to high resolution Orbitrap mass spectrometry following Oasis HLB solid-phase extraction. The main findings were: (1) there is a wide variety of ECs in the WWTPs, (2) physico-chemical properties such as pH, total dissolved solids, conductivity, and dissolved organic content showed reduced values in the outlet compared to the inlet which confirms the presence of less contaminants in the treated wastewater, (3) specific ultraviolet absorbance of less than 2 was observed in the WWTPs samples, suggesting the presence of natural organic matter (NOM) that is predominantly non-humic in nature, (4) most of the ECs were recalcitrant to the treatment processes, (5) pesticides, recreational drugs, and analgesics constitute a significant proportion of pollutants in wastewater, and (6) NOM removal ranged between 35 and 90%. Consequently, a comprehensive database of ECs in wastewater in Sol Plaatje Municipality was created. Since the detected ECs pose ecotoxicological risks, there is a need to monitor and quantify ECs in WWTPs. These data are useful in selecting suitable monitoring and control strategies at WWTPs.

Coral reefs are amongst the most biodiverse ecosystems on earth, but are significantly impacted by agricultural runoff. Despite herbicides being commonly detected in coastal waters, the possibility of herbicide accumulation in coral reef species has largely been overlooked. We investigate the accumulation of several herbicides in five species of coral reef invertebrates collected from ten sites along the Maputaland coast, South Africa. Multiple herbicide residues were detected in 95% of the samples, with total average concentrations across sites ranging between 25.2 ng g⁻¹ to 51.3 ng g⁻¹ dw. Acetochlor, alachlor and hexazinone were the predominant herbicides detected at all sites, with atrazine and simazine detected less frequently. Significant interactive effects were detected between sites nested in reef complex crossed with species, based on multiple and total herbicide concentrations. In general, multivariate herbicide concentrations varied significantly between species within and across most sites. Contrastingly, the concentrations of the different herbicides and that of total herbicide did not differ between conspecifics at most sites nested in their respective reef complexes. On average, highest total herbicide concentrations were measured in soft coral (Sarcophyton glaucum; 90.4 ± 60 ng g⁻¹ and Sinularia gravis; 42.7 ± 25 ng g⁻¹) and sponge (Theonela swinhoei; 39.0 ± 40 ng g⁻¹) species, while significantly lower concentrations were detected in hard corals (Echinopora hirsutissima; 10.5 ± 5.9 ng g⁻¹ and Acropora austera; 5.20 ± 4.5 ng g⁻¹) at most sites. Agricultural runoff entering the ocean via the uMfolozi-St Lucia Estuary and Maputo Bay are likely sources of herbicide contamination to coral reefs in the region. There is an urgent need to assess the long-term effects of herbicide exposure on coral reef communities.

The continuous influx of opioid compounds into aquatic environments has become an increasing and persistent concern, due to their extensive use. This is especially alarming as wastewater treatment plants (WWTPs) are unable to completely remove them. Despite the reported health concerns, the occurrence of opioid compounds in the environment has not received much attention. The present study investigates the occurrence of 19 opioids in four WWTPs and their respective receiving water bodies. All wastewater samples revealed opioids at concentration ranging from ng/L to μg/L with most influents having higher concentrations than effluents. WWTPs appeared to perform poorly (p > 0.05 between influents and effluents), and were unable to remove some opioids including Methadone (−27.3%) from the Leeuwkuil WWTP, Codeine (−21.7%) and Thebaine (−3.77%) from the Sandspruit WWTP, and Hydrocodone (−1.06%) from the Meyerton WWTP, respectively. Samples collected from the Leeuwkuil WWTP were the most contaminated, with eighteen out of nineteen opioid analogues exceeding 1 μg/L. Upstream surface water contained less opioids (most < LOQ) than downstream (p < 0.05), with Hydrocodone, Oxycodone, Hydromorphone, Fentanyl, Ketamine and Dihydrocodeine not detected. The occurrence of high concentrations of opioid analogues in downstream surface water (298 ng/L −10.8 μg/L for Klip River, 4.49 ng/L −13.1 μg/L for Vaal River, 70.5 ng/L −10.0 μg/L for Soutspruit River and 8.0 ng/L – 2.43 μg/L for Sun Spruit River) was directly linked to their mass loads in the respective wastewater effluent samples.

Antibiotic resistance is known to impact treatment efficiency of Plesiomonas infections negatively with fatal outcomes. This study investigated antibiogram fingerprint of P. shigelloides (n = 182) isolated from three South Africa rivers using the disc diffusion technique. Environmental pollution and analogous health risk (given infections) that could associate with the freshwaters and empirical treatment of Plesiomonas were assessed using Antibiotic Resistance Index (ARI) and Multiple Antibiotic Resistance Indices (MARI), respectively. Thirteen EUCAST recommended (ERAs) and eleven non-recommended antibiotics (NAs) used as first line agents in the treatment of gastroenteritis and extraintestinal infections were tested. Resistance against ERAs decreased from cefoxitin (37.91%), cefuroxime (35.17%), cefepime (31.87%), ceftriaxone (29.67%), ciprofloxacin (18.13%), trimethoprim-sulfamethoxazole (10.44%), piperacillin/tazobactam (8.79%), ertapenem (4.95%), norfloxacin (4.40%), levofloxacin (2.75%), meropenem (1.10%) to imipenem (0.55%). The isolates had higher resistance (≥36.07%) against NAs but were susceptible to amikacin (67.58%), gentamycin (73.08%), and tetracycline (80.77%). MARI of the isolates were significantly different between ERAs and NAs (P-value < 0.05) and had an average of 0.17 ± 0.18 and 0.45 ± 0.13, respectively. About 33.87% and 95.63% of the isolates had MARI value from 0.23 to 0.62 and 0.27–0.82 to ERAs and NAs, respectively. Also, ERAs-based and NAs-based ARI across sampling units showed significantly different (P-value < 0.05) means of 0.18 ± 0.09 and 0.46 ± 0.05, respectively. MARI attributed low risk of empirical treatment to recommended antibiotics but higher risk to non-recommended antibiotics. Model estimated successful and unsuccessful empirical treatment of infections risks due to resistance in the isolates using recommended antibiotics as 65.93% and 34.07%, respectively; 1.65% and 98.35% in the case of non-recommended antibiotics, respectively. ARI based on recommended antibiotics identified potential environmental pollutions in a number of sites. Resistance in freshwater P. shigelloides especially against cephalosporin, quinolones and fluoroquinolones is distressing and might suggests high pollution of the freshwaters in the Eastern Cape Province.

Evidence is accumulating about the impacts of plastics on marine life. The prevalence of plastics in seabird nests has been used as an indicator of levels of this pollutant in the ocean. However, the lack of a framework for defining sample sizes and errors associated with estimating the prevalence of plastic in nests prevents researchers from optimising time and reducing impacts of fieldwork. We present a method to determine the confidence intervals for the prevalence of debris in seabird nests and provide, for the first time, information on the prevalence of these items in nests of the Hartlaub’s gull Larus hartlaubii, the African penguin Spheniscus demersus, the great white pelican Pelecanus onocrotalus, and the white-breasted cormorant Phalacrocorax lucidus in South Africa. The method, based on observations and resampling simulations and tested here for nests of 12 seabird species from 15 locations worldwide, allows for straightforward hypothesis testing. Appropriate sample sizes can be defined by combining this method with a Bayesian approach. We show that precise estimates of prevalence of debris in nests can be obtained by sampling around 250 nests. Smaller sample sizes can be useful for obtaining rough estimates. For the Hartlaub’s gull, the African penguin, the great white pelican, and the white-breasted cormorant, debris were present in 0.75%, 3.00%, 6.41%, and 25.62% of the respective nests. Our approach will help researchers to determine errors associated with the prevalence of debris recorded in seabird nests and to optimise time and costs spent collecting data. It can also be applied to estimate confidence intervals and define sample sizes for assessing prevalence of plastic ingestion by any organism.

Ingestion of anthropogenic litter has been well documented in marine vertebrates, but comparatively little is known about marine invertebrates. We report macrolitter ingestion by the sandy anemone Bunodactis reynaudi at Muizenberg beach in False Bay, South Africa. Monthly surveys from May 2015 to August 2019 collected 491 ingested litter items (9.4 ± 14.9 items·month⁻¹, 39.8 ± 71.5 g·month⁻¹), of which >99% were plastic. The number of ingested items was correlated with the abundance of stranded items and ingestion peaked in autumn when seasonal rains washed more litter into the bay. Most ingested litter was clear (39%), white (16%) and black/purple (15%). Comparison with environmental litter showed selection for flexible plastics, particularly bags/packets and food packaging. Experimental feeding trials found that B. reynaudi selected for pieces of HDPE bag suspended in seawater for 2–20 days, suggesting that biofilms enhance the palatability of flexible plastics. Studies are needed to assess the possible impacts of plastic ingestion on B. reynaudi. While only a small proportion of the population currently ingest litter, ingestion might become more common if environmental litter loads increase. This might negatively affect the anemone’s ability to respond to other environmental changes such as increasing levels of heavy metal pollution.

Chokka squid (Loligo reynaudii) from three sites along the South African coast were analyzed for halogenated natural products (HNPs) and anthropogenic persistent organic pollutants (POPs). HNPs were generally more than one order of magnitude more abundant than POPs. The most prevalent pollutant, i.e. the HNP 2,3,3′,4,4′,5,5′-heptachloro-1′-methyl-1,2′-bipyrrole (Q1), was detected in all chokka squid samples with mean concentrations of 105, 98 and 45 ng/g lipid mass, respectively, at the Indian Ocean (site A), between both oceans (site B) and the South Atlantic Ocean (site C). In addition, bromine containing polyhalogenated 1′-methyl-1,2′-bipyrroles (PMBPs), 2,4,6-tribromophenol (2,4,6-TBP, up to 28 ng/g lipid mass), polybrominated methoxy diphenyl ethers, MHC-1, TBMP and other HNPs were also detected. Polychlorinated biphenyls (PCBs) were the predominant class of anthropogenic POPs. PCB 153 was the most abundant PCB congener in chokka squid from the Indian Ocean, and PCB 138 in samples from the South Atlantic Ocean and between both oceans.

The development of antibiotic resistance and dissemination of its determinants is an emerging public health problem as it compromises treatment options of infections that were, until recently, treatable. Investigation of outbreaks of vancomycin resistant enterococci (VRE) suggests that the environment serves as a significant reservoir for antibiotic resistance genes (ARGs). However, there is a paucity of data regarding the presence of ARGs in the water sources in South Africa. In this study, water samples collected from wastewater treatment plants (WWTPs), surface water and hospital sewage were screened for enterococci harbouring genes conferring resistance to four classes of antibiotics. Enterococci isolates harbouring ARGs were detected in raw influent and treated wastewater discharge from WWTPs and hospital sewage water. Plasmid and transposon encoded ermB (macrolide), tetM and tetL (tetracycline) as well as aph(3’)-IIIa (aminoglycosides) genes were frequently detected among the isolates, especially in E. faecalis. The presence of enterococci harbouring ARGs in the treated wastewater suggest that ARGs are discharged into the environment where their proliferation could be perpetuated. Among the enterococci clonal complexes (CCs) recovered from wastewater were E. faecium CC17 (ST18), which is frequently associated with hospital outbreaks and a novel E. faecalis sequence type (ST), ST780.

Polyurethane foam passive samplers were deployed between May 2016 and January 2017 to evaluate concentrations of polybrominated diphenyl ethers (PBDEs), selected alternative flame retardants (AFRs) and total hexabromocyclododecane (HBCDD) (sum of α-, β-, and γ-HBCDD). The PUF air samplers were deployed in semi–urban, urban, industrial and landfill sites in Gauteng Province, South Africa. The acquired results presented a clear semi urban–industrial–urban–landfill concentration gradient for all BFRs measured. Taking into account 2 sampling periods (cold and warm periods) (n = 16), the atmospheric concentrations of ∑₉PBDEs, HBCDDs and ∑AFRs were 100–2820 pg m⁻³, 12–117 pg m⁻³ and 41–4660 pg m⁻³, respectively, for the sparsely populated residential area, densely populated residential area, industrial area and the landfill area. In all cases, BDE 47, 99, and 209 were the most dominant congeners with high detection frequencies. The highest calculated daily exposure dose in Gauteng Province atmosphere was 0.61 and 1.54 ng kg ⁻¹ – bw d ⁻¹ for adults and children respectively. The estimated total intake of PBDEs was 0.47–33.4 ng kg ⁻¹ – bw d ⁻¹, which was generally below the lowest adverse effect limit (LOAEL), suggesting that the residents of Gauteng Province may not be significantly affected as a result of their exposure to these pollutants through inhalation. However, this does not necessarily suggest that the pollutants are harmless to human health, since they have the tendency to bioaccumulate in biological systems. Incidentally, this is the first study from Africa to report on the atmospheric concentrations of PBDEs, HBCDDs and AFRs in urban, landfill and industrial areas. The findings from this study further highlight the contributory role of landfills as potential sources of BFRs into the atmosphere.

There is a paucity of air quality data in sub-Saharan African countries to inform science driven air quality management and epidemiological studies. We investigated the use of available remote-sensing aerosol optical depth (AOD) data to develop spatially and temporally resolved models to predict daily particulate matter (PM₁₀) concentrations across four provinces of South Africa (Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape) for the year 2016 in a two-staged approach. In stage 1, a Random Forest (RF) model was used to impute Multiangle Implementation of Atmospheric Correction AOD data for days where it was missing. In stage 2, the machine learner algorithms RF, Gradient Boosting and Support Vector Regression were used to model the relationship between ground-monitored PM₁₀ data, AOD and other spatial and temporal predictors. These were subsequently combined in an ensemble model to predict daily PM₁₀ concentrations at 1 km × 1 km spatial resolution across the four provinces. An out-of-bag R² of 0.96 was achieved for the first stage model. The stage 2 cross-validated (CV) ensemble model captured 0.84 variability in ground-monitored PM₁₀ with a spatial CV R² of 0.48 and temporal CV R² of 0.80. The stage 2 model indicated an optimal performance of the daily predictions when aggregated to monthly and annual means. Our results suggest that a combination of remote sensing data, chemical transport model estimates and other spatiotemporal predictors has the potential to improve air quality exposure data in South Africa's major industrial provinces. In particular, the use of a combined ensemble approach was found to be useful for this area with limited availability of air pollution ground monitoring data.