South Korea has experienced a rapid increase in ozone concentrations in surface air together with China for decades. Here we use a 3-D global chemical transport model, GEOS-Chem nested over East Asia (110 E - 140 E, 20 N–50 N) at 0.25° × 0.3125° resolution, to examine locally controllable (domestic anthropogenic) versus uncontrollable (background) contributions to ozone air quality at the national scale for 2016. We conducted model simulations for representative months of each season: January, April, July, and October for winter, spring, summer, and fall and performed extensive model evaluation by comparing simulated ozone with observations from satellite and surface networks. The model appears to reproduce observed spatial and temporal ozone variations, showing correlation coefficients (0.40–0.87) against each observation dataset. Seasonal mean ozone concentrations in the model are the highest in spring (39.3 ± 10.3 ppb), followed by summer (38.3 ± 14.4 ppb), fall (31.2 ± 9.8 ppb), and winter (24.5 ± 7.9 ppb), which is consistent with that of surface observations. Background ozone concentrations obtained from a sensitivity model simulation with no domestic anthropogenic emissions show a different seasonal variation in South Korea, showing the highest value in spring (46.9 ± 3.4 ppb) followed by fall (38.2 ± 3.7 ppb), winter (33.0 ± 1.9 ppb), and summer (32.1 ± 6.7 ppb). Except for summer, when the photochemical formation is dominant, the background ozone concentrations are higher than the seasonal ozone concentrations in the model, indicating that the domestic anthropogenic emissions play a role as ozone loss via NOₓ titration throughout the year. Ozone air quality in South Korea is determined mainly by year-round regional background contributions (peak in spring) with summertime domestic ozone formation by increased biogenic VOCs emissions with persistent NOₓ emissions throughout the year. The domestic NOₓ emissions reduce MDA8 ozone around large cities (Seoul and Busan) and hardly increase MDA8 in other regions in spring, but it increases MDA8 across the country in summer. Therefore, NOₓ reduction can be effective in control of MDA8 ozone in summer, but it can have rather countereffect in spring.

The comprehensive profiles of antibiotic resistance genes (ARGs) in the Antarctic water environments and their potential health risks are not well understood. The present study characterized the bacterial community compositions and ARG profiles of freshwater (11 samples) and seawater (28 samples) around the Fildes Region (an ice-free area in Antarctica) using a shotgun metagenomic sequencing approach for the first time. There were significant differences in the compositions of the bacterial community and ARG profiles between freshwater and seawater. In the 39 water samples, 114 ARG subtypes belonging to 15 ARG types were detectable. In freshwater, the dominant ARGs were related to multidrug and rifamycin resistance. In seawater, the dominant ARGs were related to peptide, multidrug, and beta-lactam resistance. Both the bacterial community compositions and ARG profiles were significantly related to certain physicochemical properties (e.g., pH, salinity, NO₃⁻). Procrustes analysis revealed a significant correlation between the bacterial community compositions and ARG profiles of freshwater and seawater samples. A total of 31 metagenome-assembled genomes (MAGs) carrying 35 ARG subtypes were obtained and identified. The results will contribute to a better evaluation of the ARG contamination in relation to human health in the Antarctic aquatic environments.

Ecological risk assessment of contaminated sediment has become a fundamental component of water quality management programs, supporting decision-making for management actions or prompting additional investigations. In this study, we proposed a machine learning (ML)-based approach to assess the ecological risk of contaminated sediment as an alternative to existing index-based methods and costly toxicity testing. The performance of three widely used index-based methods (the pollution load index, potential ecological risk index, and mean probable effect concentration) and three ML algorithms (random forest, support vector machine, and extreme gradient boosting [XGB]) were compared in their prediction of sediment toxicity using 327 nationwide data sets from Korea consisting of 14 sediment quality parameters and sediment toxicity testing data. We also compared the performances of classifiers and regressors in predicting the toxicity for each of RF, SVM, and XGB algorithms. For all algorithms, the classifiers poorly classified toxic and non-toxic samples due to limited information on the sediment composition and the small training dataset. The regressors with a given classification threshold provided better classification, with the XGB regressor outperforming the other models in the classification. A permutation feature importance analysis revealed that Cr, Cu, Pb, and Zn were major contributors to toxicity prediction. The ML-based approach has the potential to be even more useful in the future with the expected increase in available sediment data.

The San Joaquin Valley (SJV) of California has suffered persistent particulate matter (PM) pollution despite many years of control efforts. To further understand the chemical drivers of this problem and to support the development of State Implementation Plan for PM, a time-of-flight aerosol chemical speciation monitor (ToF-ACSM) outfitted with a PM₂.₅ lens and a capture vaporizer has been deployed at the Fresno-Garland air monitoring site of the California Air Resource Board (CARB) since Oct. 2018. The instrument measured non-refractory species in PM₂.₅ continuously at 10-min resolution. In this study, the data acquired from Oct. 2018 to May 2019 were analyzed to investigate the chemical characteristics, sources and atmospheric processes of PM₂.₅ in the SJV. Comparisons of the ToF-ACSM measurement with various co-located aerosol instruments show good agreements. The inter-comparisons indicated that PM₂.₅ in Fresno was dominated by submicron particles during the winter whereas refractory species accounted for a major fraction of PM₂.₅ mass during the autumn associated with elevated PM₁₀ loadings. A rolling window positive matrix factorization analysis was applied to the organic aerosol (OA) mass spectra using the Multilinear Engine (ME-2) algorithm. Three distinct OA sources were identified, including vehicle emissions, local and regional biomass burning, and formation of oxygenated species. There were significant seasonal variations in PM₂.₅ composition and sources. During the winter, residential wood burning and oxidation of nitrogen oxides were major contributors to the occurrence of haze episodes with PM₂.₅ dominated by biomass burning OA and nitrate. In autumn, agricultural activities and wildfires were found to be the main cause of PM pollution. PM₂.₅ concentrations decreased significantly after spring and were dominated by oxygenated OA during March to May. Our results highlight the importance of using seasonally dependent control strategies to mitigate PM pollution in the SJV.

A better understanding of the spatiotemporal dynamics and influencing factors of sulfonamide antibiotic resistance genes (ARGs) distribution in subsurface flow constructed wetlands is essential to improve the ARGs removal efficiency. The spatiotemporal dynamics of sulfonamide ARGs were explored in the vertical upflow subsurface flow constructed wetland (VUSFCW). The results showed that the absolute abundance of ARGs presented a trend of bottom layer > middle layer > top layer. The relative abundance of ARGs decreased significantly from the bottom layer to the middle layer, but increased in the top layer. The bottom layer was the main stage to remove ARGs. The absolute abundance of ARGs at each point in summer was significantly higher than that in winter. Based on the spatiotemporal distribution of ARGs, the internal mechanism of ARGs dynamic change was explored by the partial least square path analysis model. The results showed that physical-chemical factors, microorganisms and antibiotics indirectly affected the spatiotemporal distribution of ARGs mainly through mobile genetic elements. The indirect influence coefficients of physical-chemical factors, microorganisms and antibiotics on the spatiotemporal distribution of ARGs were 0.505, 0.221 and 0.98 respectively. The direct influence coefficient of MGEs on the spatiotemporal distribution of ARGs was 0.895. The results of network analysis showed that the potential host species of ARGs in summer were more abundant than those in winter. The selection mode of sulfonamide ARGs to potential hosts was nonspecific. There is a risk of sulfonamide ARGs infecting pathogens in VUSFCW. Fortunately, VUSFCW has proven effective in reducing the absolute abundance of ARGs and the potential risk of pathogens carrying ARGs. These findings provide a model simulation and theoretical basis for effectively reducing the threat of ARGs.

Antibiotics usage in animal production is considered a primary driver of the occurrence, supply and spread of antibiotic resistance genes (ARGs) in the environment. Pig farms and fish ponds are important breeding systems in food animal production. In this study, we compared and analyzed broad ARGs profiles, mobile genetic elements (MGEs) and bacterial communities in a representative pig farm and neighboring fish ponds around Poyang Lake, the largest freshwater lake in China. The factors influencing the distribution of ARGs were also explored. The results showed widespread detection of ARGs (from 57 to 110) among 283 targeted ARGs in the collected water samples. The differences in the number and relative abundance of ARGs observed from the pig farm and neighboring fish ponds revealed that ARG contamination was more serious on the pig farm than in the fish ponds and that the water treatment plant on the pig farm was not very effective. Based on the variance partition analysis (VPA), MGEs, bacterial communities and water quality indicators (WIs) codrive the relative abundance of ARGs. Based on network analysis, we found that total phosphorus and Tp614 were the most important WIs and MGEs affecting ARG abundance, respectively. Our findings provide fundamental data on farms in lakeside districts and provide insights into establishing standards for the discharge of aquaculture wastewater.

Brominated flame retardants (BFRs) are an important group of additives in plastics that increase resistance to ignition and slow down the rate of burning. Because of concerns about their environmental and human health impacts, however, some of the most widely employed BFRs, including hexabromocyclododecane (HBCD) and commercial mixtures of penta-, octa- and deca- (poly)bromodiphenyl ethers (PBDEs), have been restricted or phased out. In this review, the oceanic sources and pathways of PBDEs, the most widely used BFRs, are evaluated and quantified, with particular focus on emissions due to migration from plastics into the atmosphere versus emissions associated with the input of retarded or contaminated plastics themselves. Calculations based on available measurements of PBDEs in the environment suggest that 3.5 and 135 tonnes of PBDEs are annually deposited in the ocean when scavenged by aerosols and through air-water gas exchange, respectively, with rivers contributing a further ∼40 tonnes. Calculations based on PBDE migration from plastic products in use or awaiting or undergoing disposal yield similar net inputs to the ocean but indicate a relatively rapid decline over the next two decades in association with the reduction in the production and recycling of these chemicals. Estimates associated with the input of PBDEs to the ocean when “bound” to marine plastics and microplastics range from about 360 to 950 tonnes per year based on the annual production of plastics and PBDEs over the past decade, and from about 20 to 50 tonnes per annum based on the abundance and distribution of PBDEs in marine plastic litter. Because of the persistence and pervasiveness of plastics in the ocean and diffusion coefficients for PBDEs on the order of 10⁻²⁰ to 10⁻²⁷ m² s⁻¹, microplastics are likely to act as a long-term source of these chemicals though gradual migration. Locally, however, and more important from an ecotoxicological perspective, PBDE migration may be significantly enhanced when physically and chemically weathered microplastics are exposed to the oily digestive fluids conditions of fish and seabirds.

RNA N⁶-methyladenosine (m⁶A) modification regulates the cell stress response and homeostasis, but whether titanium dioxide nanoparticle (nTiO₂)-induced acute pulmonary injury is associated with the m⁶A epitranscriptome and the underlying mechanisms remain unclear. Here, the potential association between m⁶A modification and the bioeffects of several engineered nanoparticles (nTiO₂, nAg, nZnO, nFe₂O₃, and nCuO) were verified thorough in vitro experiments. nFe₂O₃, nZnO, and nTiO₂ exposure significantly increased the global m⁶A level in A549 cells. Our study further revealed that nTiO₂ can induce m⁶A-mediated acute pulmonary injury. Mechanistically, nTiO₂ exposure promoted methyltransferase-like 3 (METTL3)-mediated m⁶A signal activation and thus mediated the inflammatory response and IL-8 release through the degeneration of anti-Mullerian hormone (AMH) and Mucin5B (MUC5B) mRNAs in a YTH m⁶A RNA-binding protein 2 (YTHDF2)-dependent manner. Moreover, nTiO₂ exposure stabilized METTL3 protein by the lipid reactive oxygen species (ROS)-activated ERK1/2 pathway. The scavenging of ROS with ferrostatin-1 (Fer-1) alleviates the ERK1/2 activation, m⁶A upregulation, and the inflammatory response caused by nTiO₂ both in vitro and in vivo. In conclusion, our study demonstrates that m⁶A is a potential intervention target for alleviating the adverse effects of nTiO₂-induced acute pulmonary injury in vitro and in vivo, which has far-reaching implications for protecting human health and improving the sustainability of nanotechnology.