Environmental Science: Processes & Impacts最新文献

Microorganisms constitute an essential component of the indoor ecosystem and may pose potential health risks after inhalation. However, evidence regarding the impact of indoor airborne microbiome on general respiratory health is scarce. Additionally, while air purification has been shown to be an effective strategy for controlling culturable bioaerosols, its impact on indoor airborne microbiome remains unclear. To determine the impact of indoor airborne microbial exposure on lung function, and whether and how air purification can modify indoor airborne microbiome, we conducted a randomized, double-blind, crossover study employing air purification intervention among 68 healthy young adults in Beijing, China. Indoor airborne bacteria and fungi were characterized using amplicon sequencing technology and quantified by qPCR. Our results indicated positive associations between indoor airborne microbial α-diversity and lung function indices; however, adverse effects from total microbial load were observed. Males were more susceptible to microbial exposure than females. Beneficial effects from richness in Actinobacteria, Bacteroidia, Oxyphotobacteria, Bacilli, Clostridia, Alphaproteobacteria, Gammaproteobacteria, Dothideomycetes, and Sordariomycetes, and detrimental effects from five Proteobacteria genera, including Dechloromonas, Hydrogenophaga, Klebsiella, Pseudomonas, and Tolumonas, were also identified. Air purification contributed to decreased fungal diversity and total fungal load, but not the overall microbial community structure. Our study demonstrates the significant role of indoor airborne microbiome in regulating human respiratory health and provides inspiration for improving health through manipulation of indoor microbiome. Meanwhile, our study also underscores the importance of balancing the potential benefits from decreased microbial load and the underlying risks from reduced microbial diversity while applying environmental microbial interventions.

Ice nucleation processes in the earth's atmosphere are critical for cloud formation, radiation, precipitation, and climate change. We investigated the physicochemical properties and ice nucleation potential of selected viral aerosols, including their RNA and proteins, using advanced techniques such as scanning-transmission electron microscopy (S/TEM), small angle X-ray scattering (SAXS), particle analyzers, and a peltier chamber. The experiments revealed that RNA particles obtained from MS2 bacteriophage had a mean freezing point of -13.9 ± 0.3 °C, comparable to the average ice nucleation temperature of global dust particles, which is approximatively -15 °C. RNA from MS2, Influenza, SARS-CoV-1 and SARS-CoV-2 demonstrated average ice nucleation temperatures of -13.9 ± 0.3 °C, -13.7 ± 0.3 °C, -13.7 ± 0.3 °C, and -15.9 ± 0.4 °C, respectively. SAXS analysis indicated a high local crystallinity value of 0.5 of MS2 RNA particles, hinting that high crystalline nature may contribute to their effectiveness as ice nuclei. Dilution experiments show that viral RNA consistently catalyzes ice nucleation. The addition of dust-containing particles, such as Fe₂O₃, CuO, and TiO₂, to MS2 bacteriophage droplets enhanced ice nucleation, as did UV radiation. We herein discuss the implications of this work on ice nucleation and freezing processes.

Per- and polyfluoroalkyl substances (PFAS) are chemicals of high concern and are undergoing hazard and risk assessment worldwide. Reliable physicochemical property (PCP) data are fundamental to assessments. However, experimental PCP data for PFAS are limited and property prediction tools such as quantitative structure-property relationships (QSPRs) therefore have poor predictive power for PFAS. New experimental data from Endo 2023 are used to improve QSPRs for predicting poly-parameter linear free energy relationship (PPLFER) descriptors for calculating water solubility (S_W), vapor pressure (VP) and the octanol-water (K_OW), octanol-air (K_OA) and air-water (K_AW) partition ratios. The new experimental data are only for neutral PFAS, and the QSPRs are only applicable to neutral chemicals. A key PPLFER descriptor for PFAS is the molar volume and this work compares different versions and makes recommendations for obtaining the best PCP predictions. The new models are included in the freely available IFSQSAR package (version 1.1.1), and property predictions are compared to those from the previous IFSQSAR (version 1.1.0) and from QSPRs in the US EPA's EPI Suite (version 4.11) and OPERA (version 2.9) models. The results from the new IFSQSAR models show improvements for predicting PFAS PCPs. The root mean squared error (RMSE) for predicting log K_OWversus expected values from quantum chemical calculations was reduced by approximately 1 log unit whereas the RMSE for predicting log K_AW and log K_OA was reduced by 0.2 log units. IFSQSAR v.1.1.1 has an RMSE one or more log units lower than predictions from OPERA and EPI Suite when compared to expected values of log K_OW, log K_AW and log K_OA for PFAS, except for EPI Suite predictions for log K_OW which have a comparable RMSE. Recommendations for future experimental work for PPLFER descriptors for PFAS and future research to improve PCP predictions for PFAS are presented.

The focus of this work is to enhance state-of-the-art Machine Learning (ML) models that can predict the aerobic biodegradability of organic chemicals through a data-centric approach. To do that, an already existing dataset that was previously used to train ML models was analyzed for mismatching chemical identifiers and data leakage between test and training set and the detected errors were corrected. Chemicals with high variance between study results were removed and an XGBoost was trained on the dataset. Despite extensive data curation, only marginal improvement was achieved in the classification model's performance. This was attributed to three potential reasons: (1) a significant number of data labels were noisy, (2) the features could not sufficiently represent the chemicals, and/or (3) the model struggled to learn and generalize effectively. All three potential reasons were examined and point (1) seemed to be the most decisive one that prevented the model from generating more accurate results. Removing data points with possibly noisy labels by performing label noise filtering using two other predictive models increased the classification model's balanced accuracy from 80.9% to 94.2%. The new classifier is therefore better than any previously developed classification model for ready biodegradation. The examination of the key characteristics (molecular weight of the substances, proportion of halogens present and distribution of degradation labels) and the applicability domain indicate that no/not a large share of difficult-to-learn substances has been removed in the label noise filtering, meaning that the final model is still very robust.

Pharmaceuticals and personal care products (PPCPs) have received global attention owing to their potential risks to human health and the ecological environment. However, limited research has explored the occurrence and ecological risks of PPCPs in the Qiantang River (QTR). QTR, the largest water system in Zhejiang Province, China, is significantly influenced by human activities. This study investigated the occurrence, distribution, and ecological risks of 10 types of PPCPs in both surface water and sediment within QTR. The findings revealed that the concentrations of PPCPs detected in surface water ranged from 81.26 to 149.45 ng L⁻¹ during the wet season (April) and from 98.66 to 198.55 ng L⁻¹ during the dry season (September). Moreover, in the sediments, PPCP concentration ranged from 63.24 to 80.66 and 72.54 to 75.06 ng per g dw during both wet and dry seasons, respectively. Among the selected PPCPs, triclosan (TCS) exhibited the highest concentration across, different phases and seasons, followed by benzotriazole in surface water. The analysis of sediment–water equilibrium distribution indicated that the diffusion tendency of PPCPs was closely correlated with their molecular weights. Particularly, TCS exhibited dynamic equilibrium between water and sediment. Principal component analysis and positive matrix factorization model results indicated similar pollution sources for the detected PPCPs. The dominant sources of the detected PPCPs were identified as wastewater of electroplating enterprises, discharge from wastewater treatment plants, and domestic sewage. The ecological risk assessment based on the risk quotient method revealed that TCS with the highest detected concentration posed a high risk in surface water and a low risk in sediment across all sampling sites. However, other detected PPCPs showed either no or low risks. Additionally, PPCPs showed a higher ecological risk during the dry season than during the wet season.

The distribution, composition, and risk assessment of 8 EDCs in the surface water of 14 national aquatic germplasm resource reserves (freshwater) were investigated during dry and wet seasons. Bisphenol A (BPA), nonylphenol (NP), and octylphenol (OP) were the main contributors of the 8 EDCs. The concentrations of phenolic pollutants in surface water during the dry season were higher than those in the wet season. However, no significant seasonal differences were found among the steroid hormones. According to the evaluation of estrogenic activity (EEQ > 1.0), E2 and EE2 were the main contributors to estrogenic activity. EDC mixtures posed a higher risk to crustaceans and fish (RQ > 1.0) and a moderate to high risk to algae (RQ > 0.1). Fish were the most sensitive aquatic organisms. In the study areas, EE2, E1, BPA, NP, and E2 had a higher risk than the other three compounds and should be controlled as a priority.

In urban to peri-urban watersheds such as those surrounding San Francisco Bay, stormwater runoff is a major pathway by which contaminants enter aquatic ecosystems. We evaluated the occurrence of 154 organic contaminants via liquid chromatography coupled to tandem mass spectrometry, including organophosphate esters (OPEs), bisphenols, per- and polyfluoroalkyl substances (PFASs), and a suite of novel urban stormwater tracers (SWCECs; i.e., vehicle-derived chemicals, pesticides, pharmaceuticals/personal care products, benzothiazoles/benzotriazoles). Time-averaged composite sampling focused on storms in highly developed watersheds over four wet seasons, with complementary sampling in less-urban reference watersheds, near-shore estuarine sites, and the open Bay. Of the targeted contaminants, 68 (21 SWCECs, 29 OPEs, 3 bisphenols, 15 PFASs) were detected in ≥10 of 26 urban stormwater samples. Median concentrations exceeded 500 ng L⁻¹ for 1,3-diphenylguanidine, hexa(methoxymethyl)melamine, and caffeine, and exceeded 300 ng L⁻¹ for 2-hydroxy-benzothiazole, 5-methyl-1H-benzotriazole, pentachlorophenol, and tris(2-butoxyethyl) phosphate. Median individual PFAS concentrations were <10 ng L⁻¹, with highest concentrations for PFHxA (180 ng L⁻¹), PFOA (110 ng L⁻¹), and PFOS (81 ng L⁻¹). In six of eight urban stormwater samples analyzed for 6PPD-quinone (a tire rubber-derived transformation product), concentrations exceeded coho salmon acute toxicity thresholds, suggesting (sub)lethal impacts for sensitive species. Observed concentrations were generally significantly higher in highly developed watersheds relative to reference watersheds, but not statistically different in near-shore estuarine sites, suggesting substantial transient exposure potential at stormwater outfalls or creek outflows. Results emphasized the role of stormwater in contaminant transport, the importance of vehicles/roadways as contaminant sources, and the value of monitoring broad multi-analyte contaminant suites to enable comprehensive source and toxicity evaluations.

Per- and polyfluoroalkyl substances (PFAS) are recognized for their persistence and ubiquitous occurrence in different environmental compartments. Conventional wastewater treatment plants (WWTPs) cannot effectively remove PFAS from wastewater, and a better understanding of the occurrence and sources of PFAS in this medium would enable effective source abatement. We compared sewage from urban areas exhibiting differentiating characteristics with respect to activities in their catchments. These included a sewer that serves primarily a municipal area, with no commercial activities involving PFAS emissions being identified, another sewer with a strong influence of commercial activities potentially related to PFAS emissions, and the influent of the whole city sewage network. The year-long monitoring campaign consisted of flow-proportional, monthly composite samples and targeted analysis of 29 PFAS compounds. Principal component analysis was used to investigate the relationships between selected PFAS and standard water quality parameters such as ammonium, a known tracer of urine and thus of typical municipal wastewater. Notable findings were seen for PFOS and 6:2 FTS, whose concentrations were most negatively correlated with ammonium. Ammonium concentration data allowed for a normalized per-person median load calculation, which resulted in loads of the observed PFAS ranging from below 0.4 up to 4.7 μg per person per day. Both the commercial area sewer and the city influent exhibited significantly higher (p < 0.05) median loads (>0.9 μg per person per day) in the case of 6:2 FTS and PFOS, compared to the municipal sewer (<0.6 μg per person per day). No statistically significant difference was found for other compounds, such as PFBA, PFHxA, PFOA, and PFHxS. We argue that this approach demonstrates that PFAS can differ in speciation and quantity within an urban wastewater setting, and consideration of both municipal and commercial activities is needed for a proper understanding of sources and emission pathways within the urban environment.