Firefighting foams contain per- and polyfluoroalkyl substances (PFAS) – a class of compounds widely used as surfactants. PFAS are persistent organic pollutants that have been reported in waterways and drinking water systems across the United States. These substances are of interest to both regulatory agencies and the general public because of their persistence in the environment and association with adverse health effects. PFAS can be released in large quantities during industrial incidents because they are present in most firefighting foams used to suppress chemical fires; however, little is known about persistence of PFAS in public waterways after such events. In response to large-scale fires at Intercontinental Terminal Company (ITC) in Houston, Texas in March 2019, almost 5 million liters of class B firefighting foams were used. Much of this material flowed into the Houston Ship Channel and Galveston Bay (HSC/GB) and concerns were raised about the levels of PFAS in these water bodies that have commercial and recreational uses. To evaluate the impact of the ITC incident response on PFAS levels in HSC/GB, we collected 52 surface water samples from 12 locations over a 6-month period after the incident. Samples were analyzed using liquid chromatography–mass spectrometry to evaluate 27 PFAS, including perfluorocarboxylic acids, perfluorosulfonates and fluorotelomers. Among PFAS that were evaluated, 6:2 FTS and PFOS were detected at highest concentrations. Temporal and spatial profiles of PFAS were established; we found a major peak in the level of many PFAS in the days and weeks after the incident and a gradual decline over several months with patterns consistent with the tide- and wave-associated water movements. This work documents the impact of a large-scale industrial fire, on the environmental levels of PFAS, establishes a baseline concentration of PFAS in HSC/GB, and highlights the critical need for development of PFAS water quality standards.

Organisms and ecosystems are generally exposed to mixtures of chemicals rather than to individual chemicals, but there have been relatively few detailed analyses of the mixtures of pesticides that occur in surface waters. This study examined over 2600 water samples, analysed for between 21 and 47 pesticides, from 15 waterways that discharge to the lagoon of the Great Barrier Reef in Queensland, Australia between July 1, 2011 and June 30, 2015. Essentially all the samples (99.8%) contained detectable concentrations (>limit of detection) of pesticides and pesticide mixtures. Approximately, 10% of the samples contained no quantifiable (>limit of reporting) pesticides, 10% contained one quantifiable pesticide and 80% contained quantifiable mixtures of 2–20 pesticides. Approximately 82% of samples that contained quantifiable mixtures had more than two modes of action (MoAs), but only approximately 6% had five or more MoAs. The mode, average and median number of quantifiable pesticides in all the samples were 2, 5.1 and 4, respectively. The most commonly detected compounds both individually and in mixtures were the pesticides atrazine, diuron, imidacloprid, hexazinone, 2,4-D, and the degradation product desethylatrazine. The number of pesticides and modes of action of pesticides in mixtures differed spatially and were affected by land use. Waterways draining catchments where sugar cane was a major land use had mixtures with the most pesticides.

The application of surfactants is an effective way to inhibit the migration of organic contaminants (OCs) from soil to plants, and thus would be a great candidate method for producing safe agricultural products in organic-contaminated farmland. In this study, it was found that cetyltrimethyl ammonium bromide (CTMAB) reduced the OCs in cabbage by 22.0–64.1%, and those in lettuce by 18.8–36.5%. We developed a mathematical model to predict the accumulation of OCs in plants in the presence of surfactants. The successive partitioning of OCs among three phases, namely, soil, soil water and plant roots, was considered. The equilibrium of OC between the soil and soil water was scaled using the sorption coefficient of OCs on soils normalized by the soil organic carbon (Kₒc) and carbon-normalized OCs sorption coefficient with the sorbed surfactants (Kₛₛ). To precisely calculate the Kₒc and Kₛₛ, the bioavailable and bound OCs were measured using a sequential extraction method. Linear positive correlations between the logarithm of Kₒc (or Kₛₛ) and the logarithm of the octanol-water partition coefficient (log Kₒw) of OCs were established for laterite soils, paddy soils and black soils. In the presence of CTMAB, the equilibrium of OCs between the soil water and plant roots was scaled using the carbon-normalized OC sorption coefficient with the sorbed surfactants (Kₛf), whose logarithmic value was linearly correlated with the log Kₒw of the OCs. A three-phase-successive partition-limited model was developed based on these relationships, demonstrating an average prediction accuracy of 76.6 ± 36.8%. Our results indicated that the decrease in bioavailable OCs in soils and the increase in sorption of OCs on roots should be taken into consideration when predicting plant uptake. This research provides a validated mathematical model for predicting the concentration of OCs in plants in the presence of surfactants.

Endometriosis is a gynaecological disease characterised by the presence of endometriotic tissue outside of the uterus impacting a significant fraction of women of childbearing age. Evidence from epidemiological studies suggests a relationship between risk of endometriosis and exposure to some organochlorine persistent organic pollutants (POPs). However, these chemicals are numerous and occur in complex and highly correlated mixtures, and to date, most studies have not accounted for this simultaneous exposure. Linear and logistic regression models are constrained to adjusting for multiple exposures when variables are highly intercorrelated, resulting in unstable coefficients and arbitrary findings. Advanced machine learning models, of emerging use in epidemiology, today appear as a promising option to address these limitations. In this study, different machine learning techniques were compared on a dataset from a case-control study conducted in France to explore associations between mixtures of POPs and deep endometriosis. The battery of models encompassed regularised logistic regression, artificial neural network, support vector machine, adaptive boosting, and partial least-squares discriminant analysis with some additional sparsity constraints. These techniques were applied to identify the biomarkers of internal exposure in adipose tissue most associated with endometriosis and to compare model classification performance. The five tested models revealed a consistent selection of most associated POPs with deep endometriosis, including octachlorodibenzofuran, cis-heptachlor epoxide, polychlorinated biphenyl 77 or trans-nonachlor, among others. The high classification performance of all five models confirmed that machine learning may be a promising complementary approach in modelling highly correlated exposure biomarkers and their associations with health outcomes. Regularised logistic regression provided a good compromise between the interpretability of traditional statistical approaches and the classification capacity of machine learning approaches. Applying a battery of complementary algorithms may be a strategic approach to decipher complex exposome-health associations when the underlying structure is unknown.

Microplastics are known to be associated with co-contaminants, but little is understood about the mechanisms by which these chemicals are transferred from ingested plastic to organisms. This study simulates marine avian gastric conditions in vitro to examine the bioaccessibility of authigenic metals (Fe, Mn) and trace metals (Co, Pb) that have been acquired by polyethylene microplastic pellets from their environment. Specifically, different categories of pellet were collected from beaches in Cornwall, southwest England, and exposed to an acidified saline solution of pepsin (pH ∼ 2.5) at 40 °C over a period of 168 h with extracted metal and residual metal (available to dilute aqua regia) analysed by ICP-MS. For Fe, Mn and Co, kinetic profiles consisted of a relatively rapid initial period of mobilisation followed by a more gradual approach to quasi-equilibrium, with data defined by a diffusion model and median rate constants ranging from about 0.0002 (μg L⁻¹)⁻¹ h⁻¹ for Fe to about 7 (μg L⁻¹)⁻¹ h⁻¹ for Co. Mobilisation of Pb was more complex, with evidence of secondary maxima and re-adsorption of the metal to the progressively modified pellet surface. At the end of the time-courses, maximum total concentrations were 38.9, 0.81, 0.014 and 0.10 μg g⁻¹ for Fe, Mn, Co and Pb, respectively, with maximum respective percentage bioaccessibilities of around 60, 80, 50 and 80. When compared with toxicity reference values for seabirds, the significance of metals acquired by microplastics from the environment and exposed to avian digestive conditions is deemed to be low, but studies of a wider range of plastics and metal associations (e.g. as additives) are required for a more comprehensive risk assessment.

With the technological advances and economic development, the multiplicity and wide variety of applications of electrical and electronic equipment have increased, as well as the amount of end-of-life products (waste of electrical and electronic equipment, WEEE). Accompanying their increasing application, there is an increasing risk to aquatic ecosystems and inhabiting organisms. Among the most common elements present in WEEE are rare earth elements (REE) such as Dysprosium (Dy). The present study evaluated the metabolic and oxidative stress responses of mussels Mytilus galloprovincialis exposed to an increasing range of Dy concentrations, after a 28 days experimental period. The results obtained highlighted that Dy was responsible for mussel’s metabolic increase associated with glycogen expenditure, activation of antioxidant and biotransformation defences and cellular damage, with a clear loss of redox balance. Such effects may greatly impact mussel’s physiological functions, including reproduction capacity and growth, with implications for population conservation. Overall the present study pointed out the need for more research on the toxic impacts resulting from these emerging pollutants, especially towards marine and estuarine invertebrate species.

Environmental distribution and concentration of tetrabromobisphenol A bis- (2-hydroxyethyl) ether (TBBPA-DHEE) and tetrabromobisphenol A mono- (hydroxyethyl) ether (TBBPA-MHEE), are obscure due to the lack of available analytical methods. Here two novel immunoassays were established to systematically investigate their distributions in Taizhou, Eastern China. Five monoclonal antibodies against pollutants were generated with two designed haptens through animal immunization. After matched with different coating antigens/antibodies, ELISAs were established (LOD for TBBPA-DHEE, 0.12 ng/mL, based on OVA-M3/mAb-D4G6; LOD for TBBPA-MHEE, 0.79 ng/mL, based on OVA-M3/mAb-D2G6) and applied for investigation of their occurrences at a typical e-waste recycling area after 2-year samples collection, where the total 33 water, 32 soil and 16 biological samples were collected with the highest concentrations of 3.46 ng/mL, 2.76 ng/g (dry weight, dw) and 5.01 ng/g (dw), respectively. Meanwhile, our study also indicated that at the centralizing e-waste recycling sites the serious pollution for both chemicals still existed despite of various efforts. Besides, obvious improvements were observed at an abandoned e-waste recycling region treated and remedied for many years by the local Chinese government. These findings highlight the importance of policy decisions in treatment of pollutants to reduce organic pollutant-related health risks.

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.

Due to continuous spread of coronavirus disease 2019 (COVID-19) worldwide, long-term effective prevention and control measures should be adopted for public transport facilities, as they are increasing in popularity and serve as the principal modes for travel of many people. The human infection risk could be extremely high due to length of exposure time window, transmission routes and structural characteristics during travel or work. This can result in the rapid spread of the infection. Based on the transmission characteristics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and the nature of public transport sites, we identified comprehensive countermeasures toward the prevention and control of COVID-19, including the strengthening of personnel management, personal protection, environmental cleaning and disinfection, and health education. Multi-pronged strategies can enhance safety of public transportation. The prevention and control of the disease during the use of public transportation will be particularly important when all countries in the world resume production. The aim of this study is to introduce experience of the prevention and control measures for public transportation in China to promote the global response to COVID-19.

Heavy metals (HMs) in an aquatic environment mainly affects fish, and thus, fish are convenient pollution bio-indicators. In this study, the toxic effects of HM mixture (chromium (Cr), cadmium (Cd), copper (Cu)) in 0 mg/L to 3.2 mg/L concentration range was investigated in Cyprinus carpio (28 days). HM accumulation, histopathology, oxidative stress, and gut microbial changes were evaluated. HMs accumulated in the order of Cr > Cu > Cd, primarily in the kidneys and finally scales. Reactive oxygen species generation increased in all exposure groups up to day 14, with maximum generation at 3.2 mg/L mixture, which later decreased on day 28 in all. Malondialdehydeand and superoxide dismutase levels increased from day 7 to 28 with increased HM concentrations, while total protein showed an inverse trend. Gill histopathology showed major changes such as uplifted and disintegrated primary lamella, and secondary lamella shortening. The kidneys were characterized by glomerular necrosis, Bowman’s capsule expansion, and tubular space dilatation. Proteobacteria and Firmicutes abundance increased up to 59.4% and 99.16% in 0.8 mg/L and 3.2 mg/L treatment groups, respectively. This study provided a better understanding on the physiology and gut microbiota alteration in C. carpio under multiple HM stress.