As a result of the efforts to introduce the principle of the 3Rs (replacement, reduction, and refinement) into animal testing, alternative in vitro skin corrosion test methods have been developed and standardized globally. However, alternative in vitro skin corrosion test methods have some limitations in terms of the use of humanely killed rats or commercial models and kits. The present study focused on the applicability of invertebrates as alternative in vivo skin models. Even though earthworm skin comprises the same biomolecules as human skin, the possibility of using earthworm skin as an alternative for skin testing remains unexplored. In this study, we developed a half-pipe tool for earthworm skin corrosion testing and optimized the test protocol. Subsequently, the applicability of the earthworm half-pipe assay for corrosion testing with six chemicals, including inorganic acids, organic acids, and alkalis, was investigated using stereomicroscopy and electron microscopy. It was observed that the specific concentrations for earthworm skin corrosion were lower than those for animal or in vitro tests. Therefore, the sensitivity of the earthworm half-pipe assay indicates that it could be useful as a screening tool before conducting in vivo animal tests or in vitro skin tests. This new method can contribute to research on alternative skin corrosion tests by reducing ethical issues, time, and cost while achieving effective results.

The accumulation of arsenic in crop plants has become a worldwide concern that affects millions of people. The major source of arsenic in crop plants is irrigation water and soil. In this study, Serendipita indica, an endophytic fungus, was used to investigate the protection against arsenic and its accumulation in the tomato plant. We found that inoculation of S. indica recovers seed germination, plant growth and improves overall plant health under arsenic stress. A hyper-colonization of fungus in the plant root was observed under arsenic stress, which results in reduced oxidative stress via modulation of antioxidative enzymes, glutathione, and proline levels. Furthermore, fungal colonization restricts arsenic mobilization from root to shoot and fruit by accumulating it exclusively in the root. We observed that fungal colonization enhances the arsenic bioaccumulation factor 1.48 times in root and reduces the arsenic translocation factor by 2.96 times from root to shoot and 13.6 times from root to fruit compared to non colonized plants. Further, investigation suggests that S. indica can tolerate arsenic by immobilizing it on the cell wall and accumulating it in the vacuole. This study shows that S. indica may be helpful for the reduction of arsenic accumulation in crops grown in arsenic-contaminated agriculture fields.

Organochlorine contaminants (OCs) – organochlorine pesticides (OCPs) and industrial products and byproducts – are included in different monitoring programmes and surveys, involving various animal species. Fish-eating birds are suitable indicator species for OCs. Adult birds may be difficult to capture, but chicks can be sampled more easily. Blood of birds is a potentially suitable non-destructive matrix for analysis, as OC levels in blood reflect their concentrations in the body. The study was aimed at investigating how age of fast-growing Grey heron (Ardea cinerea) chicks affects contaminant levels in their blood and thus how important is sampling at exact age for biomonitoring purposes. In 1999 on Lake Engure in Latvia whole blood samples of heron chicks were collected at three different time points, with seven and nine days in between the first and second and second and third sampling points, respectively. Twenty-two chicks were sampled at all three times. In total, 102 samples were analysed for 19 polychlorinated biphenyl (PCB) congeners, DDT metabolites – DDE and DDD, hexachlorobenzene (HCB), α-, β-, γ-hexachlorocyclohexane (HCH), and trans-nonachlor. Total PCB concentrations averaged around 2000 ng/g dry extracted matter (EM). DDE was the dominant individual contaminant (ca. 800 ng/g EM), followed by CB-153, -138, and −118. Most of the other analysed OCs were below 100 ng/g EM. No significant (p > 0.05) differences in OC concentrations were found between the three sampling occasions, except for trans-nonachlor. This means that blood can safely be sampled for biomonitoring purposes during the 17 days’ time window. The analysed legacy contaminants may serve as model substances for other persistent organic pollutants.

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.