Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM₂.₅ can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression models are usually used to assess air pollution impacts on human health. However, when there are databases missing, linear statistical regression may not process well and alternative data processing should be considered. Nonlinear Artificial Neural Networks (ANN) are not employed to research environmental health pollution even though another advantage in using ANN is that the output data can be expressed as the number of hospital admissions. This research applied ANN to assess the impact of air pollution on human health. Three well-known ANN were tested: Multilayer Perceptron (MLP), Extreme Learning Machines (ELM) and Echo State Networks (ESN), to assess the influence of PM₂.₅, temperature, and relative humidity on hospital admissions due to respiratory diseases. Daily PM₂.₅ levels were monitored, and hospital admissions for respiratory illness were obtained, from the Brazilian hospital information system for all ages during two sampling campaigns (2008–2011 and 2014–2015) in Curitiba, Brazil. During these periods, the daily number of hospital admissions ranged from 2 to 55, PM₂.₅ concentrations varied from 0.98 to 54.2 μg m⁻³, temperature ranged from 8 to 26 °C, and relative humidity ranged from 45 to 100%. Of the ANN used in this study, MLP gave the best results showing a significant influence of PM₂.₅, temperature and humidity on hospital attendance after one day of exposure. The Anova Friedman's test showed statistical difference between the appliance of each ANN model (p < .001) for 1 lag day between PM₂.₅ exposure and hospital admission. ANN could be a more sensitive method than statistical regression models for assessing the effects of air pollution on respiratory health, and especially useful when there is limited data available.

Environmental contaminant monitoring traditionally relies on targeted analysis, and very few tools are currently available to monitor “unexpected” or “unknown” compounds. In the present study, a non-targeted workflow (suspect screening) was developed to investigate plastic-related chemicals and other environmental contaminants in a top predator freshwater fish species, the northern pike, from the St. Lawrence River, Canada. Samples were extracted using sonication-assisted liquid extraction and analyzed by high performance liquid chromatography coupled with quadrupole time of flight mass spectrometry (HPLC-QTOF-MS). Ten bisphenol compounds were used to test the analytical performances of the method, and satisfactory results were obtained in terms of instrumental linearity (r2 > 0.97), recoveries, (86.53–119.32%), inter-day precision and method detection limits. The non-targeted workflow data processing parameters were studied, and the peak height filters (peak filtering step) were found to influence significantly the capacity to detect and identify trace chemicals in pike muscle extracts. None of the ten bisphenol analogues were detected in pike extracts suggesting the absence of accumulation for these chemicals in pike muscle. However, the non-targeted workflow enabled the identification of diethyl phthalate (DEP) and perfluorooctanesulfonic acid (PFOS) in pike extracts. This approach thus can be also applied to various contaminants in other biological matrices and environmental samples.

Iodination of dissolved organic matter (DOM) initiated by manganese oxide may represent an important source of organoiodine compounds (OICs) for iodide-containing waters. Here, Suwannee River natural organic matter was selected as model DOM, the OICs formation in simulated freshwater samples from iodinated DOM induced by manganese oxide (δ-MnO2) was investigated at different pHs and concentrations of iodide and δ-MnO2 by using negative ion electrospray ionization coupled with Fourier transform ion cyclotron resonance mass spectrometry (ESI-FT-ICR MS). While no OIC was observed in DOM control samples without δ-MnO2, hundreds of OICs were detected in the presence of δ-MnO2, suggesting the enhanced role of δ-MnO2 played in DOM iodination. The relative abundance was defined as the value of dividing the peak intensity of OICs by the highest m/z peak intensity constantly occurred in each mass spectrum, and selected as a parameter for partly reflecting the real level of OICs. The relative abundance of most OICs were around or greater than 1%, and several OICs with higher relative abundance were identified as diiodo-5-hydroxy-4-cyclopentene-1,3-dione, diiodomethane and diiodoacetic acid. The numbers of the formed OICs increased with the increase concentrations of iodide/δ-MnO2 and the decrease of pH, and nearly all OICs formed at lower levels of iodide/δ-MnO2 and/or higher pH were overlapped by that at higher levels of iodide/δ-MnO2 and/or lower pH, indicating the reliability of FT-ICR MS analysis techniques and data processing method. The OICs were formed mainly from the iodination of typical lignin-like and tannin-like compounds, as well as the precursor compounds with higher relative abundance through substitution reactions. Our findings demonstrate that the OICs formation by δ-MnO2-initiated DOM iodination should receive more attention and the concentration, exact structure and toxicity of the OICs need to be further investigated.

Marine aquaculture areas are facing stressed environmental challenges, especially the degradation of coastal ecosystems. Here a coordinated time-series study was used to investigate the coastal bacterioplankton biodiversity dynamics of the Yellow Sea, China. Bacterial 16S rRNA gene sequencing revealed a temporal pattern of decreasing of diversity in summer. Functional prediction indicated that metabolic pathways related to the adenosine triphosphate (ATP)-binding cassette transporters and other membrane transporters were significantly enriched in May, while the genetic information processing category was most abundant in March. The May microbiomes showed most significant positive correlation with phosphate concentration, while the August and November microbiomes correlated with temperature and chemical oxygen demand (COD) most, and the March microbiomes showed significant correlation with Cu2+ level, pH and salinity. The correlations between representative bacteria and environmental parameters revealed in this study may provide insights into the potential influences of human aquaculture activities, on the biodiversity of coastal bacterioplankton.

The applicability of spectral analysis in detection of freshwater metal contamination was assessed by developing and testing a novel hyperspectral imaging (HSI) application for aquatic insect larvae (Trichoptera: Hydropsychidae). Larvae were first exposed to four different cadmium (Cd) concentrations: 0, 1, 10, and 100 μg L⁻¹ for 96 h. Individual larvae were then preserved in ethanol, inspected with microscopy for the number of anomalies in larval gills, and imaged by hyperspectral camera operating with wavebands between 500 and 850 nm. Three additional larvae from each exposure were analyzed for tissue Cd concentration. Although the larval tissue Cd concentrations correlated positively with actual water concentrations, the toxicity response of larvae, i.e., frequency of gill abnormalities, did not differ among the Cd concentrations. In contrast, hyperspectral imaging data indicated some concentration-response relationship of larval spectral properties to the Cd exposure, but it was too weak for reliable automatic distinction between exposed and unexposed larvae. In this pilot study a workflow for data processing for a novel application of hyperspectral imaging was developed. Based on the results of this preliminary study, the workflow in the imaging process will be optimized and its potential for detecting metal contamination of aquatic environments reassessed.

Aquatic system contamination is a subject of concern due to the high number of contaminants with the potential to be hazardous for plant and animal species, including humans. A large number of analytical methods have been developed to evaluate the extension of water resource pollution, most of them focused on target residue analysis. Screening methods for potential target and non-target compounds are a potential alternative and could be used for a comprehensive evaluation of samples composition. Liquid chromatography coupled to time of flight mass spectrometry (LC-qTOF-MS/MS) was developed to provide full-spectrum data and high mass resolution with appropriate selectivity and sensitivity for monitoring of 300 pharmaceuticals and pesticides in environmental water samples. The validation was based on anti-doping analysis validation protocols, using three different concentration levels (0.01, 0.1, and 1.0 μg L⁻¹). Confirmation analysis was achieved using two fragment ions monitoring for each analyte. Samples were extracted by solid-phase extraction and analyzed by LC-qTOF-MS/MS. The final method was able to detect 170 chemicals in wastewater treatment plant effluent and 198 chemicals in surface water. The advantage of using post-acquisition data processing provided by a qTOF-MS/MS system allowed for non-target analysis for an illicit drug metabolite determination. The method was successfully applied to real samples of wastewater and surface water.

Di(isononyl)cyclohexane-1,2-dicarboxylate (DINCH) is a plasticizer used in polyvinyl chloride (PVC) products, such as toys and food packaging. Because the use of DINCH is on the rise, the risk of human exposure to this chemical may likewise increase. Discovering markers for assessing human chemical exposure is difficult because the metabolism of chemicals within humans is complex. In this study, two mass spectrometry (MS)-based metabolite profiling data processing methods, the mass defect filter (MDF) method and the signal mining algorithm with isotope tracing (SMAIT) method, were used for DINCH metabolite discovery, and 110 and 18 potential DINCH metabolite signal candidates were discovered, respectively, from in vitro DINCH incubation samples. Of these, the 21 signals were validated as tentative exposure marker signals in a rat model. Interestingly, the two methods generated rather different sets of DINCH exposure markers. Five of the 21 tentative exposure marker signals were verified as the probable DINCH structure-related metabolite signals based on their MS/MS product ion profiles. These five signals were detected in at least one human urine sample. Of the five probable DINCH structure-related metabolite signals, two novel signals might be suitable exposure markers that should be further investigated for their application in human DINCH exposure assessments. These observations indicate that the MDF and SMAIT methods may be used to discover a relatively different set of potential DINCH exposure markers.

Heavy metal pollution is a global ecological safety issue, especially in crops, where it directly threatens regional ecological security and human health. In this study, the back-propagation (BP) neural network optimized by the genetic algorithm (GA) was used to predict the concentration of cadmium (Cd) in rice grain based on influencing factors. As an intelligent information processing system, the GA-BP neural network could learn the laws of Cd movement in the soil-crop system through its own training and use the soil properties to predict the concentration of Cd in grain with high accuracy. The total soil Cd concentration, clay content, Ni concentration, cation exchange capacity (CEC), organic matter (OM), and pH have important impacts and interactions on Cd concentration in rice grain were selected as input factors of the prediction model based on Pearson’s correlation analysis and GeoDetector. By using GA to optimize the initial weight, the prediction accuracy of the GA-BP neural network model was optimal compared with the BP neural network model and multiple regression analysis. Based on the Cd concentration predicted in grain by the model, human exposure and health risk can be assessed quickly, enabling measures to be taken in time to reduce the transfer of Cd from soil to the food chain.

Monitoring on urban water environment and analysis of engineering improvement measures are intricate and time-consuming tasks. In previous studies, the integration of hydrodynamic and water quality models and geographical information system (GIS) usually takes three approaches: loose coupling, tight coupling, and full coupling. However, this paper adopted a special loose coupling approach—case-based reasoning (CBR) to develop an integrated decision support system. This was characterized by invoking the case base stored in the GIS platform as the output of the model. The fused capability of model’s water quality predication and strong spatial data processing analysis of GIS can be realized at the same time by integration. The functionality of the integrated system was illustrated through a case study of Chaohu, a medium-sized city in China, which includes case retrieval, result interpretation, and the visual display in the GIS platform. Results verified the feasibility and operability of the developed method. As a useful tool, the integrated decision support system makes it simpler and more convenient for decision makers to make decisions efficiently and quickly.