Indoor air samples were collected from private homes and various occupational indoor environments using passive air sampler and analysed for fluorotelomer alcohols (FTOHs), brominated flame retardants (BFRs), organophosphorus flame retardants (OPFRs) and cyclic volatile methyl siloxanes (cVMSs). The aim was to investigate their occurrence in indoor air, factors that may affect their presence and human daily exposure dose (DED) via inhalation. In general, levels of cVMSs were 3–4 orders of magnitude greater than the other compound classes. OPFRs concentration was found significantly higher than BFRs in indoor air. The most abundant compounds in each chemical class were 8:2 FTOH, 2,4,6-TBP, TNBP and TCEP and decamethylcyclopentasiloxane (D5). Home samples contained higher level of FTOHs, BFRs and cVMSs than occupational environments, whereas concentration of OPFRs in office samples were higher. BFRs concentrations were significantly correlated with building age and with the number of electronic/electrical devices at the sampling sites. Moreover, significantly lower levels of FTOHs and cVMSs were observed in rooms with forced-ventilation system. Estimated DED via inhalation was significantly higher at home than in office and the total DED was on average 3–5 orders of magnitude lower than the reference value.

Propranolol (PRO), a human β-AR (β-adrenergic receptor) antagonist, is considered to result in specific effects in a non-target species, D. magna, based on our previous studies. The present study investigated the effects of β-AR agents, including an antagonist and agonist using pharmacologically relevant endpoints as well as a more holistic gene expression approach to reveal the impacts and potential mode of actions (MOAs) in the model non-target species. Results show that the responses in cardiac endpoints and gene expression in D. magna are partially similar but distinguishable from the observations in different organisms. No effect was observed on heart size growth in PRO and isoprenaline (ISO) exposure. The contraction capacity of the heart was decreased in ISO exposure, and the heart rate was decreased in PRO exposure. Time-series exposures showed different magnitudes of effect on heart rate and gene expression dependent on the type of chemical exposure. Significant enrichment of gene families involved in protein metabolism and biotransformation was observed within the differentially expressed genes, and we also observed differential expression in juvenile hormone-inducible proteins in ISO and PRO exposure, which is suspected of having endocrine disruption potential. Taken together, deviation between the effects of PRO and ISO in D. magna and other organisms suggests dissimilarity in MOAs or attributes of target bio-molecules between species. Additionally, PRO and ISO may act as endocrine disruptors based on the gene expression observation. Results in the present study confirm that it is challenging to predict ecological impact of active pharmaceutical ingredients (APIs) based on the available data acquired through human-focused studies. Furthermore, the present study provided unique data and a case study on the impact of APIs in a non-target organism.

Ibuprofen (IBU), as a commonly used non-steroidal anti-inflammatory drug (NSAID) and pharmaceutical and personal care product (PPCP), is frequently prescribed by doctors to relieve pain. It is widely released into environmental water and soil in the form of chiral enantiomers by the urination and defecation of humans or animals and by sewage discharge from wastewater treatment plants. This study focused on the alteration of metabolism in the adult zebrafish (Danio rerio) brain after exposure to R-(-)-/S-(+)-/rac-IBU at 5 μg L−1 for 28 days. A total of 45 potential biomarkers and related pathways, including amino acids and their derivatives, purine and its derivatives, nucleotides and other metabolites, were observed with untargeted metabolomics. To validate the metabolic disorders induced by IBU, 22 amino acids and 3 antioxidant enzymes were selected to be quantitated and determined using targeted metabolomics and enzyme assay. Stereoselective changes were observed in the 45 identified biomarkers from the untargeted metabolomics analysis. The 22 amino acids quantitated in targeted metabolomics and 3 antioxidant enzymes determined in enzyme assay also showed stereoselective changes after R-(-)-/S-(+)-/rac-IBU exposure. Results showed that even at a low concentration of R-(-)-/S-(+)-/rac-IBU, disorders in metabolism and antioxidant defense systems were still induced with stereoselectivity. Our study may enable a better understanding of the risks of chiral PPCPs in aquatic organisms in the environment.

A comprehensive study of the removal of selected biologically active compounds (pharmaceuticals and pesticides) from different water types was conducted using bare TiO₂ nanoparticles and TiO₂/polyaniline (TP-50, TP-100, and TP-150) nanocomposite powders. In order to investigate how molecular structure of the substrate influences the rate of its removal, we compared degradation efficiency of the initial substrates and degree of mineralization for the active components of pharmaceuticals (propranolol, and amitriptyline) and pesticides (sulcotrione, and clomazone) in double distilled (DDW) and environmental waters. The results indicate that the efficiency of photocatalytic degradation of propranolol and amitriptyline was higher in environmental waters: rivers (Danube, Tisa, and Begej) and lakes (Moharač, and Sot) in comparison with DDW. On the contrary, degradation efficacy of sulcotrione and clomazone was lower in environmental waters. Further, of the all catalysts applied, bare TiO₂ and TP-100 were found to be most effective in the mineralization of propranolol and amitriptyline, respectively, while TP-150 appeared to be the most efficient in terms of sulcotrione and clomazone mineralization. Also, there was no significant toxicity observed after the irradiation of pharmaceuticals or pesticides solutions using appropriate catalysts on rat hepatoma (H-4-II-E), mouse neuroblastoma (Neuro-2a), human colon adenocarcinoma (HT-29), and human fetal lung (MRC-5) cell lines. Subsequently, detection and identification of the formed intermediates in the case of sulcotrione photocatalytic degradation using bare TiO₂ and TP-150 showed slightly different pathways of degradation. Furthermore, tentative pathways of sulcotrione photocatalytic degradation were proposed and discussed.

Recently it has been shown that there are two types of magnetite particles in the human brain, some, which occur naturally and are jagged in appearance, and others that arise from industrial sources, such as coal fired power plants, and are spherical. In order to confirm the latter, the magnetic component of coal ash is first purified and characterized by XRD, showing that it is magnetite with an average particle size of 211 nm. Studies confirm the coal ash magnetic behavior, and that the magnetite is not bound to the other components of coal ash but exist as an isolatable component. SEM studies confirm that in the process of burning coal at very high temperatures for industrial uses, the magnetite formed is spherically shaped, as recent studies of brain tissues of highly exposed urban residents have found. As such, the use of coal for industrial applications such as coking in the production of steel and in power plants is indicated to be a major source of the spherical magnetic combustion-associated magnetite fine particle and nanoparticle environmental pollution. The capacity of these magnetic particles to penetrate and damage the blood-brain-barrier and the early development of Alzheimer's disease hallmarks in exposed populations calls for detail analysis of magnetic fine and nanoparticle distribution across the world.Summation: Industrial coal usage produces spherical magnetic particles and nanoparticles, identical to those associated with neurological disorders.

Long- and short-term exposure to PM2.5 is of great concern in China due to its adverse population health effects. Characteristic of the severity of the situation in China is that in the Jing-Jin-Ji region considered in this work a total of 2725 excess deaths have been attributed to short-term PM2.5 exposure during the period January 10–31, 2013. Technically, the processing of large space-time PM2.5 datasets and the mapping of the space-time distribution of PM2.5 concentrations often constitute high-cost projects. To address this situation, we propose a synthetic modeling framework based on the integration of (a) the Bayesian maximum entropy method that assimilates auxiliary information from land-use regression and artificial neural network (ANN) model outputs based on PM2.5 monitoring, satellite remote sensing data, land use and geographical records, with (b) a space-time projection technique that transforms the PM2.5 concentration values from the original spatiotemporal domain onto a spatial domain that moves along the direction of the PM2.5 velocity spread. An interesting methodological feature of the synthetic approach is that its components (methods or models) are complementary, i.e., one component can compensate for the occasional limitations of another component. Insight is gained in terms of a PM2.5 case study covering the severe haze Jing-Jin-Ji region during October 1–31, 2015. The proposed synthetic approach explicitly accounted for physical space-time dependencies of the PM2.5 distribution. Moreover, the assimilation of auxiliary information and the dimensionality reduction achieved by the synthetic approach produced rather impressive results: It generated PM2.5 concentration maps with low estimation uncertainty (even at counties and villages far away from the monitoring stations, whereas during the haze periods the uncertainty reduction was over 50% compared to standard PM2.5 mapping techniques); and it also proved to be computationally very efficient (the reduction in computational time was over 20% compared to standard mapping techniques).

During chlorine disinfection process, reactions between the disinfectant and 17β-estradiol (E2) lead to the formation of halogenated disinfection byproducts (DBPs) which can be a risk to both ecosystem and human health. The degradation and transformation products of E2 in sodium hypochlorite (NaClO) disinfection processes of different water samples were investigated. The reaction kinetics research showed that the degradation rates of E2 were considerably dependent on the initial pH value and the types of water samples. In fresh water, synthetic marine aquaculture water and seawater, the reaction rate constant was 0.133 min−1, 2.067 min−1 and 2.592 min−1, respectively. The reasons for the above phenomena may be due to the different concentrations of bromide ions (Br−) in these three water samples which could promote the reaction between NaClO and E2. Furthermore, Br− could also cause the formation of brominated DBPs (Br-DBPs). The main DBPs, reaction centers and conceivable reaction pathways were explored. Seven halogenated DBPs have been observed including three chlorinated DBPs (Cl-DBPs) and four Br-DBPs. The active sites of E2 were found to be the pentabasic cyclic ring and the ortho position of the phenol moiety as well as C9-C10 position. The identified Cl/Br-DBPs were also confirmed in actual marine aquaculture water from a shrimp pond. The comparison of bio-concentration factors (BCF) values based on calculation of EPI-suite showed that the toxicities of the Br-DBPs were stronger than that of their chloride analogues. The absorbable organic halogens (AOX) analysis also suggested that the DBPs produced in the marine aquaculture water were more toxic than that in the fresh water system.

A previous study observed high blood lead levels (BLL) in preschool children attending 50 day care centers (DCC) in São Paulo, Brazil.To identify whether lead levels found in both homes and DCC environments are associated with high blood lead levels.Children attending 4 DCCs, quoted here as NR, VA, PS and PF, were divided into two groups according to BLL: high exposure (HE: ≥13.9 μg/dL; 97.5 percentile of the 2013 year sample) and low exposure (LE: <5 μg/dL). For in situ lead measurements (lead paint mode: mg/cm² and ROHS mode: μg/g) in the children's households and in the DCC environments, a field portable X-ray-fluorescence analyzer was used. Multiple logistic regressions were performed to control for confounding factors. Odds ratios were adjusted for age, sex, day care center's measured lead, and tobacco.In an NR DCC building, 33.8% of the measurements had lead levels >600 μg/g, whereas such levels were observed in 77.1% of NR playground measurements. In VA DCC, 22% and 23% of the measurements in the building and in the playgrounds had levels higher than 600 μg/g, respectively. The percentage of high lead levels in the children's houses of the LE group was 5.9% (95% CI: 4.3–7.6%) and 13.2 (95% CI: 8.3–18.0%) in the HE group. Moreover, a significant association was found between high BLLs and lead levels found both in households and DCCs (p < 0.001). Most of the high lead measurements were found in tiles and playground equipment.Lead exposure estimated from the DCCs, where children spend about 10 h/day, can be as relevant as their household exposure. Therefore, public authorities should render efforts to provide a rigorous surveillance for lead-free painting supplies and for all objects offered to children.

Phthalates are widely used as plasticizers that influence sexual and reproductive development. Here, we investigated whether di-(2-ethylhexyl) phthalate (DEHP) affects macrophage polarization that are associated with tumor initiation and progression. No changes were observed in LPS- or ConA-stimulated in vitro spleen B or T cell proliferation for 48 h, respectively. In contrast, macrophage functions were inhibited in response to DEHP for 12 h as judged by LPS-induced H₂O₂ and NO production and zymosan A-mediated phagocytosis. When six weeks old male mice were pre-exposed to 4.0 mg/kg DEHP for 21 days before the injection of B16F10 melanoma cells and post-exposed to 4.0 mg/kg DEHP for 7 days, tumor nodule formation and the changes in tumor volume were higher than those in control group. Furthermore, when male mice were intraperitoneally pretreated with DEHP for 3 or 4 weeks and peritoneal exudate cells (PECs) or bone marrow-derived macrophages (BMDMs) were incubated with lipopolysaccharide (LPS), the expression of COX-2, TNF-α, and IL-6 was reduced in DEHP-pretreated cells as compared with that in LPS-stimulated control cells. While the production of nitric oxide (NO) for 18 h was reduced by LPS-stimulated PECs and M1-type BMDMs, IL-4 expression was enhanced in LPS-stimulated BMDMs. When BMDMs were incubated with IL-4 for 30 h, arginase 1 for M2-type macrophages was increased in transcriptional and translational level. Data implicate that macrophages were differentially polarized by DEHP treatment, which reduced M1-polarzation but enhanced M2-polarization. Taken together, these data demonstrate that DEHP could affect in vivo immune responses of macrophages, leading to the suppression of their tumor-preventing ability. This suggests that individuals at high risk for tumor incidence should avoid long-term exposure to various kind of phthalate including DEHP.

Fine particulate matter (PM₂.₅) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to map the ground-level PM₂.₅, while some recent studies have found that Aerosol Optical Depth (AOD) derived from satellite images and machine learning techniques may be two elements that can improve spatiotemporal prediction. However, there has been a lack of studies evaluating use of different machine learning techniques with AOD datasets for mapping PM₂.₅, especially in areas with high spatiotemporal variability of PM₂.₅.In this study, we compared the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration. Based on the results, Cubist, random forest and eXtreme Gradient Boosting were the algorithms with better performance, while Cubist was the best (CV-RMSE = 2.64 μg/m3, CV-R² = 0.48). Variable importance analysis indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation.In conclusion, appropriate selection of machine learning algorithms can improve ground-level PM2.5 estimation, especially for areas with nonlinear relationships between PM2.5 and predictors caused by complex terrain. Satellite-derived data such as AOD and land surface temperature (LST) can also be substitutes for traditional datasets retrieved from weather stations, especially for areas with sparse and uneven distribution of stations.