Long-term exposure to air pollutants, especially particulates, in adulthood is related to cardiovascular diseases and vascular markers of atherosclerosis. However, whether vascular changes in children is related to exposure to air pollutants remains unknown. This study examined whether childhood exposure to air pollutants was related to a marker of cardiovascular risk, carotid intima–media thickness (CIMT) in children aged 11–12 years old. Longitudinal Study of Australian Children (LSAC) recruited parents and their children born in 2003–4. Among the participants, CheckPoint examination was conducted when the children were 11–12 years old. Ultrasound of the right carotid artery was performed using standardized protocols. Average and maximum far-wall CIMT, carotid artery distensibility, and elasticity were quantified using semiautomated software. Annual and life-time exposure to air pollutants was estimated using satellite-based land-use regression by residential postcodes. A total of 1063 children (50.4% girls) with CIMT data, serum cholesterol, and modeled estimates of NO₂ and PM₂.₅ exposure for the period 2003 to 2015 were included. The average and maximum CIMT, carotid distensibility, and elasticity were 497 μm (standard deviation, SD 58), 580 μm (SD 44), 17.4% (SD 3.2), and 0.48%/mmHg (SD 0.09), respectively. The life-time average concentrations of PM₂.₅ and NO₂ were 6.4 μg/m³ (SD 1.4) and 6.4 ppb (SD 2.4), respectively. Both average and maximum CIMT were significantly associated with average ambient PM₂.₅ concentration (average CIMT: +5.5 μm per μg/m³, 95% confidence interval, CI 2.4 to 8.5, and maximum CIMT: +4.9 μm per μg/m³, CI 2.3 to 7.6), estimated using linear regression, adjusting for potential confounders. CIMT was not significantly related to NO₂ exposure. Carotid artery diameter, distensibility, and elasticity were not significantly associated with air pollutants. We conclude that life-time exposure to low levels of PM₂.₅ in children might have measurable adverse impacts on vascular structure by age 11–12 years.

Despite the ubiquitousness of microplastics, knowledge on the exposure of freshwater fish to microplastics is still limited. Moreover, no standard methods are available for analyzing microplastics, and the quality of methods used for the quantification of ingested microplastics in fish should be improved. In this study, we studied microplastic ingestion of common wild freshwater fish species, perch (Perca fluviatilis) and vendace (Coregonus albula). Further, our aim was to develop and validate imaging Fourier-transform infrared spectroscopic method for the quantification of ingested microplastics. For this purpose, enzymatically digested samples were measured with focal plane array (FPA) based infrared microscope. Data was analyzed with siMPle software, which provides counts, mass estimations, sizes, and materials for the measured particles. Method validation was conducted with ten procedural blanks and recovery tests, resulting in 75% and 77% recovery rates for pretreatment and infrared imaging, respectively. Pretreatment caused contamination principally by small <100 μm microplastics. The results showed that 17% of perch and 25% of vendace had ingested plastic. Most of the fish contained little or no plastics, while some individuals contained high numbers of small particles or alternatively few large particles. Perch from one sampling site out of five had ingested microplastics, but vendace from all sampling sites had ingested microplastics. The microplastics found from fish were mostly small: 81% had particle size between 20 and 100 μm, and most of them were polyethylene, polypropylene, and polyethylene terephthalate. In conclusion, the implemented method revealed low numbers of ingested microplastics on average but needs further development for routine monitoring of small microplastics.

This work discusses the identification of the transformation products (TPs) generated during the photolytic degradation of dextromethorphan (DXM) and its metabolite dextrorphan (DXO), under simulated solar radiation in aqueous solutions (Milli-Q water and river water) in order to determinate its behavior into the aquatic environment. Tentative identification of the TPs was performed by liquid chromatography/quadrupole time-of-flight mass spectrometry (LC/QTOF-MS), following a suspect screening approach. The use of high resolution-mass spectrometry (HRMS) allowed the tentative identification of DXM and DXO photoproducts based on the structure proposed by an in silico software, the accurate mass measurement, the MS/MS fragmentation pattern and the molecular formula finding. A total of 19 TPs were found to match some of the accurate masses included in a suspect list, and they were all tentatively identified by their characteristic MS-MS fragments. Most of the TPs identified showed a minor modified molecular structure like the introduction of hydroxyl groups, or demethylation. The time-evolution of precursors and TPs were monitored throughout the experiments, and degradation kinetics were presented for each analyte. Finally, the occurrence of DXM, DXO, and their tentatively proposed photodegradation TPs was evaluated in both surface and wastewater. In all real matrices, the results showed that the highest concentration was detected for DXO, followed by TP-244 (N-desmethyldextrorphan) and DXM.

Volatile organic compounds (VOCs) are important precursors of ozone (O₃) and secondary organic aerosols (SOAs). Tracing VOC pollution sources is important for controlling VOC emissions and reducing O₃ and SOAs. We built a novel mobile proton transfer reaction mass spectrometry (M-PTR-MS) instrument to image the distribution of VOCs and trace their emission sources in cities and industrial parks. The M-PTR-MS is composed of a vibration-resistant proton transfer reaction mass spectrometry (PTR-MS) with a global positioning system receiver, modified box vehicle, and geographic information system (GIS) software. The PTR-MS, mounted on a vehicle, sends VOC data and vehicle position information to the GIS software. These data are used to image the space distribution of VOCs in real time while the vehicle platform is in motion and the VOC sources are precisely traced using the GIS. The spatial data resolution of the M-PTR-MS is typically 0.8 m. The limits of detection, sensitivity, and repeatability of the M-PTR-MS are 43.5 ppt, 347 counts ppb⁻¹, and 2.4% (RSD, n = 5), respectively. The intensity of reagent ions is stable over 8 h (RSD = 0.45%). Compared with commercial PTR-MS equipment, the M-PTR-MS demonstrated high consistency, with a correlation coefficient of 92.665%. Several field experiments were conducted in China using the M-PTR-MS. In one field experiment, the VOC distribution along three different routes was surveyed; the navigation monitoring lasted 1.8 h over a distance of 26.7 km at an average speed of 15 km h⁻¹. The VOC sources in an industrial park were identified by analyzing the components near different factories. The main species from a VOC source in an underground garage was related to paint. The M-PTR-MS instrument can be used by environmental protection agencies to trace VOC pollution sources in real time, and by researchers to survey VOC emissions in regions of concern.

During the last years, declines in honey bee colonies are being registered worldwide. Cholinergic pesticides and their extensive use have been correlated to the decline of pollinators and there is evidence that pesticides act as neuroendocrine disruptors affecting the metabolism of neuropeptides. However, there is a big absence of studies with quantitative results correlating the effect of pesticide exposure with changes on neuropeptides insects, and most of them are conducted under laboratory conditions, typically with individual active ingredients. In this study, we present an analytical workflow to evaluate pesticide effects on honey bees through the analysis of (neuro)peptides. The workflow consists of a rapid extraction method and liquid chromatography with triple quadrupole for preselected neuropeptides. For non-target analysis, high resolution mass spectrometry, multivariate analysis and automatic identification of discriminated peptides using a specific software and protein sequence databases. The analytical method was applied to the analysis of target and non-target (neuro)peptides in honey bees with low and high content of a wide range of pesticides to which have been exposed in field conditions. Our findings show that the identification frequency of target neuropeptides decreases significantly in honey bees with high concentration of pesticides (pesticide concentrations ≥ 500 μg kg⁻¹) in comparison with the honey bees with low content of pesticides (pesticide concentrations ≤ 20 μg kg⁻¹). Moreover, the principal component analysis in non-target search shows a clear distinction between peptide concentration in honey bees with high level of pesticides and honey bees with low level. The use of high resolution mass spectrometry has allowed the identification of 25 non-redundant peptides responsible for discrimination between the two groups, derived from 18 precursor proteins.

Fungi are well associated with the degradation of hydrocarbons by the production of different enzymes, among which catalases (CBH), laccases (LCC) and peroxidases (LiP and MnP) are of immense importance. In this study, crude oil tolerance and enzyme secretions were demonstrated by rhizospheric fungal strains. Four most abundant strains were isolated from the rhizosphere of grasses growing in aged oil spill sites and identified through morphological characterization and molecular PCR-amplification of 5.8–28S ribosomal rRNA using ITS1 and ITS4 primers. These strains were subjected to crude oil tolerance test at 0–20% concentrations. Presence and transcriptase responses of putative genes lig (1–6), mnp, cbh (1.1, 1.1 and 11), and lcc encoding lignin peroxidase, manganese peroxidase, catalase, and laccase enzymes respectively were also studied in these strains using RT-PCR. In addition, activities of secreted enzymes by each strain were studied in aliquots. The strains were identified as Aspergillus niger asemoA (KY473958), Talaromyces purpurogenus asemoF (KY488463), Trichoderma harzianum asemoJ (KY488466), and Aspergillus flavus asemoM (KY488467) through sequencing and comparing the sequences’ data at NCBI BLAST search software. All the isolated strains showed tolerance to crude oil at 20% concentration, but the growth rate reduced with increasing in oil concentrations. All the isolated strains possess the tested genes and lig 1–6 gene was overexpressed in A. niger and T. harzianum while lcc and mnp genes were moderately expressed in all the four strains. Almost 145 U.mL⁻¹ of lignin and manganese peroxidase, 87 U.mL⁻¹ of catalase, and 180 U.mL⁻¹ of laccase enzymes were produced by these strains and it was also observed that these strain mostly produced studied enzymes in response to increasing crude oil concentrations. Considering the robust nature and diverse production of these catalytic enzymes by these strains, they can be exploited for various bioremediation technologies as well as other biotechnological applications.

Airborne systems such as lightweight and highly portable unmanned aerial vehicles (UAVs) are becoming increasingly widespread in both academia and industry - with an ever-increasing range of applications, including (but not limited to), air quality sampling, wildlife monitoring and land-use mapping.In this work, high-resolution airborne photogrammetry obtained using a multi-rotor system operating at low survey altitudes, is combined with ground-based radiation mapping data acquired at an interim storage facility for wastes removed as part of the large-scale Fukushima clean-up program. The investigation aimed to assess the extent to which the remediation program at a specific site has contained the stored contaminants, as well as present a new methodology for rapidly surveying radiological sites globally. From the three-dimensional rendering of the site of interest, it was possible to not only generate a powerful graphic confirming the elevated radiological intensity existing at the location of the waste bags, but also to also illustrate the downslope movement of contamination due to species leakage from the large 1m³ storage bags. The entire survey took less than 1 h to perform, and was subsequently post-processed using graphical information software to obtain the renderings. The conclusions within this study not only highlight the usefulness of incorporating three-dimensional renderings within radiation mapping protocols, but also conclude that current methods of monitoring these storage facilities in the long term could be improved through the integration of UAVs within the standard protocol.

In order to improve the prediction accuracy and real-time of the air pollutant concentration prediction, this paper proposes self-adaptive neuro-fuzzy weighted extreme learning machine (ANFIS-WELM) based on the weighted extreme learning machine (WELM) and the adaptive neuro-fuzzy inference system (ANFIS) combined air pollutant concentration prediction method. Firstly, Gaussian membership function parameters are selected to fuzzify the input values and calculate the membership degree of each input variable. Secondly, corresponding fuzzy rules are activated, and the firing strength is normalized to calculate the output matrix of hidden nodes. Then, the optimal parameters (C, M), weights are assigned to weighted ELM by using locally weighted linear regression, and the regularized WELM target formula with equality constraint is optimized by the Karush–Kuhn–Tucker (KKT) conditions, the output weight matrix is calculated, and finally the prediction output matrix is calculated. Based on the air pollutant concentration data collected in Datong, Taiwan, the data on the pollutants containing carbon monoxide (CO), nitric oxide (NO), PM2.5 (particulate matter) and PM10, are selected by different historical time series lengths, using genetic algorithm-backpropagation neural network (GA-BPNN), support vector regression (SVR), extreme learning machine (ELM), WELM, ANFIS, regularized extreme learning adaptive neuro-fuzzy inference system (R-ELANFIS) and ANFIS-WELM are built for predict the concentration of each pollutant collected by single monitoring point in single-step time series. The experimental results show that the ANFIS-WELM presented in this paper has better prediction accuracy and real-time performance, realizes the prediction of multi-step time series on the basis of the ANFIS-WELM, and realizes the engineering application of the ANFIS-WELM algorithm package on the self-developed mobile source emissions online monitoring data center software system.

A study of 16 United States Environmental Protection Agency (USEPA) priority listed PAHs associated with particulate matter ≤ 10 μm (PM10) was conducted in Singapore during the period 29th May 2015 to 28th May 2016. The sampling period coincided with an extensive, regional smoke haze episode (5th September to 25th October) that occurred as a result of forest and peat fires in neighboring Indonesia. Throughout this study, 54 atmospheric PM10 samples were collected in 24 h periods using a high volume sampler (HVS) and quarts fiber filters (QFF) as the collection medium. Hysplit software for computing 3-D backward air mass trajectories, diagnostic ratio analysis and ring number distribution calculations were used to examine the sources of PAHs in the atmosphere in Singapore. Under normal conditions the total PAH concentrations were in a range from 0.68 ng m−3 to 3.07 ng m−3, while for the high haze period the results showed approximately double the concentrations with a maximum value of 5.97 ng m−3. Diagnostic ratio (DR) and principal component analysis (PCA) were conducted and indicated the contribution of the traffic as a dominant pyrogenic source of PAHs during normal periods, while results from the haze dataset showed relatively strong influence of smoke from peat and forest fires in Indonesia. Environmental and health risk from PAHs were assessed for both regular and hazy days.

Metals and metalloids are natural components of the biosphere, which are not produced per se by human beings, but whose form and distribution can be affected by human activities. Like all substances, they are a contaminant if present in excess compared to background levels and/or in a form that would not normally occur in the environment. Samples of liver, gills, gonads and muscle from European chub, Squalius cephalus, were analyzed for Al, As, B, Ba, Cr, Cu, Fe, Hg, Mn, Mo, Sr and Zn using inductively coupled plasma optical emission spectrometry (ICP-OES) to highlight the importance of tissue selection in monitoring research. The comet assay or single cell gel electrophoresis (SCGE) was selected as an in vivo genotoxicity assay, a rapid and sensitive method for measuring genotoxic effects in blood, liver and gills of the European chub. Microscopic images of comets were scored using Comet IV Computer Software (Perceptive Instruments, UK).The objective of our study was to investigate two reservoirs, Zlatar and Garasi, and one river, Pestan by: (i) determining and comparing metal and metalloid concentrations in sediment, water and tissues of European chub: liver, gills, muscle and gonads (ii) comparing these findings with genotoxicity of water expressed through DNA damage of fish tissues.A clear link between the level of metals in water, sediment and tissues and between metal and genotoxicity levels at examined sites was not found. This suggests that other xenobiotics (possibly the organic compounds), contribute to DNA damage.