Halogenated flame retardants (HFRs) were measured in 470 waste plastic articles from Ireland between 2019 and 2020. We identified articles containing concentrations of polybrominated diphenyl ethers (PBDEs), hexabromocyclododecane (HBCDD), and tetrabromobisphenol-A (TBBP-A) exceeding European Union limits. Enforcement of existing limits of 1000 mg/kg will render an estimated 3.1% (2800 t) of articles in the waste categories studied unrecyclable, increasing to: 4.0, 4.9, and 5.6% if limits were reduced to 500, 200, and 100 mg/kg respectively. Meanwhile, enforcing limits of 1,000, 500, 200, and 100 mg/kg will respectively remove 78, 82, 84, and 85% of PBDEs, HBCDD, and TBBP-A present in such waste. Other FRs targeted were detected infrequently and predominantly at very low concentrations. However, 2,4,6-tris(2,4,6-tribromophenoxy)-1,3,5-triazine (TTBP-TAZ) was detected in 3 display/IT product samples at 14,000 to 32,000 mg/kg, indicating elevated concentrations of FRs used as alternatives to PBDEs and HBCDD, will likely increase in future. Comparison with data for Ireland in 2015–16, revealed concentrations and exceedances of limits for PBDEs, HBCDD, and TBBP-A were similar or have declined. For end-of-life vehicle fabrics and foams, HBCDD and ΣPBDE concentrations declined significantly (p < 0.05) since 2015–16. Moreover, ΣPBDE concentrations in waste small domestic appliances are significantly lower in 2019–20, with a similarly significant decline for TBBP-A in waste IT and telecommunications articles. In contrast, HBCDD concentrations in waste extruded polystyrene increased significantly between 2015–16 and 2019–20. For other waste categories studied, no statistically significant temporal trends are evident (p > 0.05). Fewer samples exceeded PBDE and HBCDD limits in 2019–20 (7.8%) than 2015–16 (8.7%), while exceedances for TBBP-A fell from 2.4% in 2015–16 to 0.57% in 2019–20. While comparison between the 2015–16 and 2019-20 datasets provide a preliminary indication of changes, further monitoring is required if the impact of legislation designed to eliminate HFRs from the waste stream is to be fully evaluated.

Hourly PM₂.₅ speciation data have been widely used as an input of positive matrix factorization (PMF) model to apportion PM₂.₅ components to specific source-related factors. However, the influence of constant source profile presumption during the observation period is less investigated. In the current work, hourly concentrations of PM₂.₅ water-soluble inorganic ions, bulk organic and elemental carbon, and elements were obtained at an urban site in Nanjing, China from 2017 to 2020. PMF analysis based on observation data during specific pollution (firework combustion, sandstorm, and winter haze) and emission-reduction (COVID-19 pandemic) periods was compared with that using the whole 4-year data set (PMFwₕₒₗₑ). Due to the lack of data variability, event-based PMF solutions did not separate secondary sulfate and nitrate. But they showed better performance in simulating average concentrations and temporal variations of input species, particularly for primary source markers, than the PMFwₕₒₗₑ solution. After removing event data, PMF modeling was conducted for individual months (PMFₘₒₙₜₕ) and the 4-year period (PMF₄₋yₑₐᵣ), respectively. PMFₘₒₙₜₕ solutions reflected varied source profiles and contributions and reproduced monthly variations of input species better than the PMF₄₋yₑₐᵣ solution, but failed to capture seasonal patterns of secondary salts. Additionally, four winter pollution days were selected for hour-by-hour PMF simulations, and three sample sizes (500, 1000, and 2000) were tested using a moving window method. The results showed that using short-term observation data performed better in reflecting immediate changes in primary sources, which will benefit future air quality control when primary PM emissions begin to increase.

The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., KXAD₋A) are indispensable to obtain POPs concentration, and the KXAD₋A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of KXAD₋A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting KXAD₋A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R²ₐdⱼ and Q²ₑₓₜ were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R²ₐdⱼ = 0.991, Q²ₑₓₜ = 0.935, RMSEₜᵣₐ = 0.271 and RMSEₑₓₜ = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting KXAD₋A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.

We present a comprehensive review of the sediment quality triad (SQT) assessment studies in Korea. The bibliographic analysis was applied to evaluate how approaches in sediment assessment have evolved. A meta-analysis was performed, to evaluate potential risks of sedimentary persistent toxic substances (PTSs) reported in Korean coastal waters. Within the framework, we identified and discussed current status and spatiotemporal trends in contamination of both classic and emerging PTSs over the recent decadal period. Out of 26 target regions in Korea, five hotspots (Sihwa, Masan, Ulsan, Taean, and Gwangyang) of concern could be identified. Four of those regions have been designated as Specially-Managed Sea Area under the implementation of Total Pollution Load Management System in Korea, except for Taean coast (oil spill site). Meantime, we could identify three stepwise research phases based on a bibliographic analysis; Phase 1 (1995–2008), Phase 2 (2009–2015), and Phase 3 (2016–2020). It is noteworthy that a technical evolution of the SQT assessment by the phase was featured. It was also evidenced that in-depth studies adopting multiple lines of evidence (LOEs) became prevailed upon approaching Phase 3. In a quantitative manner, the toxicity explanatory power of target PTSs increased by about 10% in Phase 3 compared to the earlier phases. The meta-analysis using ratio-to-mean value method applied for the data set having all three LOEs indicated general improvement of sediment qualities in the hotspots. However, their associations quite varied across regions and years, reflecting a dynamicity in oceanographic settings and/or heterogeneity in toxicological effect or benthic community response. At present, SQT assessment adopting the increased LOEs generally supports better assessment. In conclusion, we suggest that future SQT studies globally should reaffirm the utility of the “multiple LOEs approach”, focusing on the identification and management of causative toxicants that driving negative ecological impacts on marine ecosystems.

Global warming both reduces global temperature variance and increases the frequency of extreme weather events. In response to these ambient perturbations, animals may be subject to trans- or intra-generational phenotype modifications that help to maintain homeostasis and fitness. Here, we show how temperature-associated transgenerational plasticity in tilapia affects metabolic trade-offs during developmental stages under a global warming scenario. Tropical tilapia reared at a stable temperature of 27 °C for a decade were divided into two temperature-experience groups for four generations of breeding. Each generation of one group was exposed to a single 15 °C cold-shock experience during its lifetime (cold-experienced CE group), and the other group was kept stably at 27 °C throughout their lifetimes (cold-naïve CN group). The offspring at early life stages from the CE and CN tilapia were then assessed by metabolomics-based profiling, and the results implied that parental cold-experience might affect energy provision during reproduction. Furthermore, at early life stages, progeny may be endowed with metabolic traits that help the animals cope with ambient temperature perturbations. This study also applied the feature rescaling and Uniform Manifold Approximation and Projection (UMAP) to visualize metabolic dynamics, and the result could effectively decompose the complex omic-based datasets to represent the energy trade-off variability. For example, the carbohydrate to free amino acid conversion and enhanced compensatory features appeared to be hypothermic-responsive traits. These multigenerational metabolic effects suggest that the tropical ectothermic tilapia may exhibit transgenerational phenotype plasticity, which could optimize energy allocation under ambient temperature challenges. Knowledge about such metabolism-related transgenerational plasticity effects in ectothermic aquatic species may allow us to better predict how adaptive mechanisms will affect fish populations in a climate with narrow temperature variation and frequent extreme weather events.

Previous studies have indicated that maternal exposure to particles with aerodynamic diameter <2.5 μm (PM₂.₅) is associated with adverse birth outcomes. However, the critical exposure windows remain inconsistent. A retrospective cohort study was conducted in Huai River Basin, Henan, China during 2013–2018. Daily PM₂.₅ concentration was collected using Chinese Air Quality Reanalysis datasets. We calculated exposures for each participant based on the residential address during pregnancy. Binary logistic regression was used to examine the trimester-specific association of PM₂.₅ exposure with preterm birth (PTB), low birth weight (LBW) and term LBW (tLBW), and we further estimated monthly and weekly association using distributed lag models. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for each 10 μg/m³ increase in PM₂.₅ exposure. Stratified analyses were performed by maternal age, infant gender, parity, and socioeconomic status (SES). In total, 196,780 eligible births were identified, including 4257 (2.2%) PTBs, 3483 (1.8%) LBWs and 1770 (0.9%) tLBWs. Maternal PM₂.₅ exposure during the second trimester were associated with the risk of PTB and LBW. At the monthly level, the PTB and LBW risks were associated with PM₂.₅ exposure mainly in the 4th -6th month. By estimating the weekly-specific association, we observed that critical exposure windows of PM₂.₅ exposure and PTB were in the 18th- 27th gestational weeks. Stronger associations were found in younger, multiparous mothers and those with a female baby and in low SES. In conclusion, the results indicate that maternal PM₂.₅ exposure during the second trimester was associated with PTB and LBW. Younger, multiparous mothers and those with female babies and in low SES were susceptible.

Most amyotrophic lateral sclerosis (ALS) cases are sporadic (∼90%) and environmental exposures are implicated in their etiology. Large industrial facilities are permitted the airborne release of certain chemicals with hazardous properties and report the amounts to the US Environmental Protection Agency (EPA) as part of its Toxics Release Inventory (TRI) monitoring program. The objective of this project was to identify industrial chemicals released into the air that may be associated with ALS etiology. We geospatially estimated residential exposure to contaminants using a de-identified medical claims database, the SYMPHONY Integrated Dataverse®, with ∼26,000 nationally distributed ALS patients, and non-ALS controls matched for age and gender. We mapped TRI data on industrial releases of 523 airborne contaminants to estimate local residential exposure and used a dynamic categorization algorithm to solve the problem of zero-inflation in the dataset. In an independent validation study, we used residential histories to estimate exposure in each year prior to diagnosis. Air releases with positive associations in both the SYMPHONY analysis and the spatio-temporal validation study included styrene (false discovery rate (FDR) 5.4e-5), chromium (FDR 2.4e-4), nickel (FDR 1.6e-3), and dichloromethane (FDR 4.8e-4). Using a large de-identified healthcare claims dataset, we identified geospatial environmental contaminants associated with ALS. The analytic pipeline used may be applied to other diseases and identify novel targets for exposure mitigation. Our results support the future evaluation of these environmental chemicals as potential etiologic contributors to sporadic ALS risk.

One of the fundamental sustainable development goals has been recognized as having access to clean water for drinking purposes. In the Anthropocene era, rapid urbanization put further stress on water resources, and associated groundwater contamination expanded into a significant global environmental issue. Natural arsenic and related water pollution have already caused a burden issue on groundwater vulnerability and corresponding health hazard in and around the Ganges delta. A field based hydrogeochemical analysis has been carried out in the elevated arsenic prone areas of moribund Ganges delta, West Bengal, a part of western Ganga- Brahmaputra delta (GBD). New data driven heuristic algorithms are rarely used in groundwater vulnerability studies, specifically not yet used in the elevated arsenic prone areas of Ganges delta, India. Therefore, in the current study, emphasis has been given on integration of heuristic algorithms and random forest (RF) i.e., “RF-particle swarm optimization (PSO)”, “RF-grey wolf optimizer (GWO)” and “RF-grasshopper optimization algorithm (GOA)”, to identify groundwater vulnerable zones on the basis of field based hydrogeochemical parameters. In addition, correspondence health hazard of this area was assessed through human health hazard index. The spatial distribution of groundwater vulnerability revealed that middle-eastern and north-western part of the study area covered by very high and high, whereas central, western and south-western part are covered by very low and low vulnerability zones in outcomes of all the applied models. The evaluation result indicates that RF-GOA (AUC = 0.911) model performed the best considering testing dataset, and thereafter RF-GWO, RF-PSO and RF with AUC value is 0.901, 0.892 and 0.812 respectively. Findings also revealed the groundwater in this study region is quite unfavorable for drinking and irrigation purposes. The suggested models demonstrate their usefulness in foretelling sustainable groundwater resource management in various deltaic regions of the world through taking appropriate measures by policy-makers.

Urban green spaces (UGSs) are often positively associated with the health of urban residents. However, UGSs may also have adverse health effects by releasing biogenic volatile organic compounds (BVOCs) and increasing the ambient concentrations of ozone (O₃) and secondary organic aerosols in urban areas. BVOC emissions from UGSs might be underestimated because of the lack of consideration of the UGS land-use type in urban areas. As such, in this study, we used a newly released satellite dataset, Sentinel-2, with a resolution of 10 m, to derive the classification distribution of UGSs and predict the UGS emissions of BVOCs in Beijing in 2019. The results showed that the annual emissions of BVOCs from UGSs were approximately 2.9 Gg C (95% confidence interval (CI): 2.4–3.3) in the six core districts, accounting for approximately 39% of the total UGS emissions in Beijing. Compared with the results based on Sentinel-2, the BVOC emissions might be underestimated by approximately 37% (95% CI: 11–63) using the commonly used satellite dataset. UGSs produced the highest BVOC emissions in summer (from June to August), accounting for 75.2% of the annual emissions. UGSs contributed the most to the O₃ formation potential in summer, accounting for 41.5% of the total. We could attribute a considerable amount of the O₃ concentration (27.0 μg m⁻³, 95% CI: 21.4–32.6) to the UGS BVOCs produced in the core districts of Beijing in July. The new BVOC emissions dataset based on Sentinel-2 vegetation information facilitates modeling studies on the formation of surface O₃ in urban areas and assessments of the impact of UGSs on public health.

Reliable long-horizon PM₂.₅ forecasts are crucial and beneficial for health protection through early warning against air pollution. However, the dynamic nature of air quality makes PM₂.₅ forecasts at long horizons very challenging. This study proposed a novel machine learning-based model (MCNN-BP) that fused multiple convolutional neural networks (MCNN) with a back-propagation neural network (BPNN) for making spatiotemporal PM₂.₅ forecasts for the next 72 h at 74 stations covering the whole Taiwan simultaneously. Model configuration involved an ensemble of massive hourly air quality and meteorological monitoring datasets and the existing publicly-available PM₂.₅ simulated (forecasted) datasets from an atmospheric chemical transport (ACT) model. The proposed methodology collaboratively constructed two CNNs to mine the observed data (the past) and the forecasted data from ACT (the future) separately. The results showed that the MCNN-BP model could significantly improve the accuracy of spatiotemporal PM₂.₅ forecasts and substantially reduce the forecast biases of the ACT model. We demonstrated that the proposed MCNN-BP model with effective feature extraction and good denoising ability could overcome the curse of dimensionality and offer satisfactory regional long-horizon PM₂.₅ forecasts. Moreover, the MCNN-BP model has considerably shorter computational time (5 min) and lower computational load than the compute-intensive ACT model. The proposed approach hits a milestone in multi-site and multi-horizon forecasting, which significantly contributes to early warning against regional air pollution.