The objective of this study was to validate the hypothesis that increased serum concentrations of perfluoroalkyl substances (PFASs) cause kidney damage. A causal interpretative study was designed using the US 2003–2018 National Health and Nutrition Examination Survey (NHANES) datasets.Three statistical models, including multivariable linear regression, generalized additive model, and regression discontinuity model (RDM), were applied to the US 2003–2018 NHANES datasets to evaluate the causal relationship between the four PFAS agents and estimated glomerular filtration rate (eGFR). Directed acyclic graphs were plotted for a more valid causal inference.In the RDM, when the natural logarithm of each PFAS agent increases by 1 ng/mL after each cut-off value, eGFR decreased 4.63 mL/min/1.73 m² for perfluorooctanoic acid, 3.42 mL/min/1.73 m² for perfluorooctane sulfonic acid, 2.37 mL/min/1.73 m² for perfluorohexane sulfonic acid, and 2.87 mL/min/1.73 m² for perfluorononanoic acid. The possibility of reverse causation that increased serum PFAS concentration is the consequence of reduced eGFR, not the cause, was low, and an additional adjustment of potential confounders was not needed.This study contributes to the understanding of PFAS-induced kidney damage. Further longitudinal epidemiological and toxicological studies are recommended.

Mangrove sediments are prone to anthropogenic activities that could enrich antibiotics resistance genes (ARGs). The emergence and dissemination of ARGs are of serious concern to public health worldwide. Therefore, a comprehensive resistome analysis of global mangrove sediment is of paramount importance. In this study, we have implemented a deep machine learning approach to analyze the resistome of mangrove sediments from Brazil, China, Saudi Arabia, India, and Malaysia. Geography (RANOSIM = 39.26%; p < 0.005) as well as human intervention (RANOSIM = 16.92%; p < 0.005) influenced the ARG diversity. ARG diversity was also inversely correlated to the human development index (HDI) of the host country (R = −0.53; p < 0.05) rather than antibiotics consumption (p > 0.05). Several genes including multidrug efflux pumps were significantly (p < 0.05) enriched in the sites with human intervention. Resistome was consistently dominated by rpoB2 (19.26 ± 0.01%), multidrug ABC transporter (10.40 ± 0.23%), macB (8.84 ± 0.36n%), tetA (4.13 ± 0.35%), mexF (3.26 ± 0.19%), CpxR (2.93 ± 0.2%), bcrA (2.38 ± 0.24%), acrB (2.37 ± 0.18%), mexW (2.19 ± 0.17%), and vanR (1.99 ± 0.11%). Besides, mobile ARGs such as vanA, tet(48), mcr, and tetX were also detected in the mangrove sediments. Comparative analysis against terrestrial and ocean resistomes showed that the ocean ecosystem harbored the lowest ARG diversity (Chao1 = 71.12) followed by mangroves (Chao1 = 258.07) and terrestrial ecosystem (Chao1 = 294.07). ARG subtypes such as abeS and qacG were detected exclusively in ocean datasets. Likewise, rpoB2, multidrug ABC transporter, and macB, detected in mangrove and terrestrial datasets, were not detected in the ocean datasets. This study shows that the socioeconomic factors strongly determine the antibiotic resistome in the mangrove. Direct anthropogenic intervention in the mangrove environment also enriches antibiotic resistome.

Environmental exposures are one of the greatest threats to human health, yet we lack tools to answer simple questions about our exposures: what are our personal exposure profiles and how do they change overtime (external exposome), how toxic are these chemicals, and what are the sources of these exposures? To capture variation in personal exposures to airborne chemicals in the gas and particulate phases and identify exposures which pose the greatest health risk, wearable exposure monitors can be deployed. In this study, we deployed passive air sampler wristbands with 84 healthy participants (aged 60–69 years) as part of the Biomarkers for Air Pollutants Exposure (China BAPE) study. Participants wore the wristband samplers for 3 days each month for five consecutive months. Passive samplers were analyzed using a novel gas chromatography high resolution mass spectrometry data-processing workflow to overcome the bottleneck of processing large datasets and improve confidence in the resulting identified features. The toxicity of chemicals observed frequently in personal exposures were predicted to identify exposures of potential concern via inhalation route or other routes of airborne contaminant exposure. Three exposures were highlighted based on elevated toxicity: dichlorvos from insecticides (mosquito/malaria control), naphthalene partly from mothballs, and 183 polyaromatic hydrocarbons from multiple sources. Other exposures explored in this study are linked to diet and personal care products, cigarette smoke, sunscreen, and antimicrobial soaps. We highlight the potential for this workflow employing wearable passive samplers for prioritizing chemicals of concern at both the community and individual level, and characterizing sources of exposures for follow up interventions.

Management of water-quality in a river ecosystem needs to be focused on susceptible regions to eutrophication based on proper measurements. The stress–response relationships between nutrients and primary productivity of phytoplankton allow the derivation of ecologically acceptable thresholds of stressors under field conditions. However, spatio-temporal variations in heterogeneous environmental conditions have hindered the development of locally applicable criteria. To address these issues, we utilized a combination of a geographically specialized artificial neural network (Geo-SOM, geo-self-organizing map) and linear mixed-effect models (LMMs). The model was applied to a 24-month dataset of 54 stations that spanned a wide spatial gradient in the Nakdong River basin. The Geo-SOM classified 1286 observations in the basin into 13 clusters that were regionally and seasonally distinct. Inclusion of the random effects of Geo-SOM clustering improved the performance of each LMM, which suggests that there were significant spatio-temporal variations in the Chla–stressor relationships. These variations arise owing to differences in background seasonality and the effects of local pollutant variables and land-use patterns. Among the 16 environmental variables, the major stressors for Chla were total phosphate (TP) as a nutrient and biological oxygen demand (BOD) as a non-nutrient according to the results of both Geo-SOM and LMM analyses. Based on LMMs with the random effect of the Geo-SOM clusters on the intercept and the slope, we can propose recommended thresholds for TP (18.5 μg L⁻¹) and BOD (1.6 mg L⁻¹) in the Nakdong River. The combined method of LMM and Geo-SOM will be useful in guiding appropriate local water-quality-management strategies and in the global development of large-scale nutrient criteria.

People with chronic obstructive pulmonary disease, cardiovascular disease, or hypertension have a high risk of developing severe coronavirus disease 2019 (COVID-19) and of COVID-19 mortality. However, the association between long-term exposure to air pollutants, which increases cardiopulmonary damage, and vulnerability to COVID-19 has not yet been fully established. We collected data of confirmed COVID-19 cases during the first wave of the epidemic in mainland China. We fitted a generalized linear model using city-level COVID-19 cases and severe cases as the outcome, and long-term average air pollutant levels as the exposure. Our analysis was adjusted using several variables, including a mobile phone dataset, covering human movement from Wuhan before the travel ban and movements within each city during the period of the emergency response. Other variables included smoking prevalence, climate data, socioeconomic data, education level, and number of hospital beds for 324 cities in China. After adjusting for human mobility and socioeconomic factors, we found an increase of 37.8% (95% confidence interval [CI]: 23.8%–52.0%), 32.3% (95% CI: 22.5%–42.4%), and 14.2% (7.9%–20.5%) in the number of COVID-19 cases for every 10-μg/m³ increase in long-term exposure to NO₂, PM₂.₅, and PM₁₀, respectively. However, when stratifying the data according to population size, the association became non-significant. The present results are derived from a large, newly compiled and geocoded repository of population and epidemiological data relevant to COVID-19. The findings suggested that air pollution may be related to population vulnerability to COVID-19 infection, although the extent to which this relationship is confounded by city population density needs further exploration.

Fluorescence spectroscopy is a commonly used technique to analyze dissolved organic matter in aquatic environments. Given the high sensitivity and non-destructive analysis, fluorescence has recently been used to study water-soluble organic carbon (WSOC) in atmospheric aerosols, which have substantial abundance, various sources and play an important role in climate change. Yet, current research on WSOC characterization is rather sparse and limited to a few isolated sites, making it challenging to draw fundamental and mechanistic conclusions. Here we presented a review of the fluorescence properties of atmospheric WSOC reported in various field and laboratory studies, to discuss the current advances and limitations of fluorescence applications. We highlighted that photochemical reactions and relevant aging processes have profound impacts on fluorescence properties of atmospheric WSOC, which were previously unnoticed for organic matter in aquatic environments. Furthermore, we discussed the differences in sources and chemical compositions of fluorescent components between the atmosphere and hydrosphere. We concluded that the commonly used fluorescence characteristics derived from aquatic environments may not be applicable as references for atmospheric WSOC. We emphasized that there is a need for more systematic studies on the fluorescence properties of atmospheric WSOC and to establish a more robust reference and dataset for fluorescence studies in atmosphere based on extensive source-specific experiments.

Nanobiomaterials (NBMs) are a special category of nanomaterials used in medicine. As applications of NBMs are very similar to pharmaceuticals, their environmental release patterns are likely similar as well. Different pharmaceuticals were detected in surface waters all over the world. Consequently, there exists a need to identify possible NBM exposure routes into the environment. As the application of many NBMs is only carried out at specific locations (hospitals), average predicted environmental concentrations (PECs) may not accurately represent their release to the environment. We estimated the local release of poly(lactic-co-glycolic acid) (PLGA), which is investigated for their use in drug delivery, to Swiss surface waters by using population data as well as type, size and location of hospitals as proxies. The total mean consumption of PGLA in Switzerland using an explorative full-market penetration scenario was calculated to be 770 kg/year. 105 hospitals were considered, which were connected to wastewater treatment plants and the receiving water body using graphic information system (GIS) modelling. The water body dataset contained 20,167 river segments and 210 lake polygons. Using the discharge of the river, we were able to calculate the PECs in different river segments. While we calculated high PLGA releases of 2.24 and 2.03 kg/year in large cities such as Geneva or Zurich, the resulting local PECs of 220 and 660 pg/l, respectively, were low due to the high river discharge (330 and 97 m³/s). High PLGA concentrations (up to 7,900 pg/l) on the other hand were calculated around smaller cities with local hospitals but also smaller receiving rivers (between 0.7 and 1.9 m³/s). Therefore, we conclude that population density does not accurately predict local concentration hotspots of NBMs, such as PLGA, that are administered in a hospital context. In addition, even at the locations with the highest predicted PLGA concentrations, the expected risk is low.

Environmental quality data sets are typically imbalanced, because environmental pollution events are rarely observed in daily life. Prediction of imbalanced data sets is a major challenge in machine learning. Our recent work has shown deep cascade forest (DCF), as a base learning model, is promising to be recommended for environmental quality prediction. Although some traditional models were improved by introducing the cost matrix, little is known about whether cost matrix could enhance the prediction performance of DCF. Additionally, feature extraction is also an important way to potentially improve the model's ability to predict the imbalanced data. Here, we developed two novelty learning models based on DCF: cost-sensitive DCF (CS-DCF) and DCF that combines unsupervised learning models and greedy methods (USM-DCF-G). Subsequently, CS-DCF and USM-DCF-G were successfully verified by an imbalanced drinking water quality data set. Our data presented both CS-DCF and USM-DCF-G show better prediction performance than that of DCF alone did. In particular, USM-DCF-G shows the best performance with the highest F1-score (95.12 ± 2.56%), after feature extraction and selection by using unsupervised learning models and greedy methods. Thus, the two learning models, especially USM-DCF-G, were promising learning models to address environmental imbalanced issues and accurately predict environmental quality.

In this study, we evaluated the effects of geo-climatic parameters and other potential risk factors on the prevalence of chronic toxoplasmosis (CT) in pregnant women. We searched PubMed/MEDLINE, Web of Science, EMBASE, Scopus, and SciELO databases for seroepidemiological studies published between January 1988, and February 2021. We performed meta-analysis and meta-regression by using a random effect model to synthesize data. A total of 360 eligible datasets, including 1,289,605 pregnant women from 94 countries, were included in this study. The highest and lowest prevalence rates were estimated for latitudes of 0–10° (49.4%) and ≥50° (26.8%); and for the longitude of 80–90° (44.2%) and 110–120° (7.8%), respectively. Concerning climatic parameters, the highest and lowest prevalence rates were estimated in regions with the mean relative humidities of >80% (46.6%) and <40% (27.0); annual precipitation between 1000 and 1500 mm (39.2%) and 250–500 mm (26.8%); and mean annual temperature of 20–30 °C (36.5%), and <7 °C (24.9%), respectively. Meta-regression analyses indicated significant increasing trends in prevalence of CT in pregnant women with decrease in geographical latitude (coefficient, = −0.0035), and geographical longitudes (C = −0.0017). While it was positively associated (P < 0.01) with the mean environmental temperature (C = 0.0047), annual precipitation (C = 0.000064), and mean relative humidity (C = 0.002). Our results highlighted various effects of environmental parameters on the prevalence of CT. Therefore, different regions in the world may benefit from different types of interventions, and thus, novel preventive measures in a region should be developed according to local climate, agricultural activities and people culture.

Perfluorooctane sulfonate (PFOS) is one of the most widely used and distributed perfluorinated compounds proven to cause adverse health outcomes. Datasets of ecotoxico-genomics and proteomics have given greater insights for PFOS toxicological effect. However, the molecular mechanisms of hepatotoxicity of PFOS on post-translational modifications (PTMs) regulation, which is most relevant for regulating the activity of proteins, are not well elucidated. Protein glycosylation is one of the most ubiquitous PTMs associated with diverse cellular functions, which are critical towards the understanding of the multiple biological processes and toxic mechanisms exposed to PFOS. Here, we exploit the multilayered glycoproteomics to quantify the global protein expression levels, glycosylation sites, and glycoproteins in PFOS exposure and wild-type mouse livers. The identified 2439 proteins, 1292 glycosites, and 799 glycoproteins were displayed complex heterogeneity in PFOS exposure mouse livers. Quantification results reveal that 241 dysregulated proteins (fold change ≥ 2, p < 0.05) in PFOS exposure mouse livers were involved in the lipid and xenobiotic metabolism. While, 16 overexpressed glycoproteins were exclusively related to neutrophil degranulation, cellular responses to stress, protein processing in endoplasmic reticulum (ER). Moreover, the interactome and functional network analysis identified HP and HSP90AA1 as the potential glycoprotein biomarkers. These results provide unique insights into a deep understanding of the mechanisms of PFOS induced hepatotoxicity and liver disease. Our platform of multilayered glycoproteomics can be adapted to diverse ecotoxicological research.