Marine debris has become a major form of pollution and a serious ecosystem health concern. The present study evaluates the accumulation, origin, and fate of debris in intertidal coral habitats of Mumbai-one of the world's highly populated coastal cities on the west coast of India. Predominantly, seven hermatypic coral species belonging to seven genera and five families were identified and mainly represented by Pseudosidastrea, Porites, and Bernardpora. In terms of number, the mean density of marine debris was 1.60 ± 0.13 SE items/m², which is higher than the global average. The mean density of plastic debris was 1.46 ± 0.14 SE items/m². Approximately 9% of total coral colonies were in physical contact with debris, and 22% of these colonies showed visible signs of partial bleaching. Single use plastic bags and wrappers were dominant plastic debris. The study area was characterized as ‘very poor cleanliness’ according to the Beach Quality Indexes, which include the Clean Coast Index, General Index, and Hazardous Items Index. The numerical model indicates the influence of river discharge and probable areas of plastic accumulation with high tidal currents in this region, maneuvering the spatial advection of litter in the nearshore areas. Combined analysis of ground-truthing and model simulation implies that the possible contributing sources of litter were representatives of land-based and sea-originated. The overall results point to increasing anthropogenic stressors threatening coastal coral communities, including marine debris pollution. It is advocated to adopt an integrated coastal zone management approach supported by coordinated policy frameworks could guide the mitigation of the debris footprint in coastal environments.

Contribution of liquid water content (LWC) to the levels of the carcinogenic particulate nitro(so) compounds and the chemistry affecting LWC were investigated based on the observation of seven nitrosamines and two nitramines in rural (Seosan) and urban (Seoul) area in South Korea during October 2019 and a model simulation. The concentrations of both the total nitrosamines and nitramines were higher in Seosan (12.48 ± 16.12 ng/m³ and 0.65 ± 0.71 ng/m³, respectively) than Seoul (7.41 ± 13.59 ng/m³ and 0.24 ± 0.15 ng/m³, respectively). The estimated LWC using a thermodynamic model in Seosan (12.92 ± 9.77 μg/m³) was higher than that in Seoul (6.20 ± 5.35 μg/m³) mainly due to higher relative humidity (75 ± 9% (Seosan); 62 ± 10% (Seoul)) and higher concentrations of free ammonia (0.13 ± 0.09 μmol/m³ (Seosan); 0.08 ± 0.01 μmol/m³ (Seoul)) and total nitric acid (0.09 ± 0.07 μmol/m³ (Seosan); 0.04 ± 0.02 μmol/m³ (Seoul)) in Seosan while neither fog nor rain occurred during the sampling period. The relatively high concentrations of the particulate nitrosamines (>30 ng/m³) only observed probably due to the higher LWC (>10 μg/m³) in Seosan. It implies that aqueous phase reactions involving NO₂ and/or uptake from the gas phase enhanced by LWC could be promoted in Seosan. Strong correlation between the concentrations of nitrosodi-methylamine (NDMA), an example of nitrosamines, simulated by a kinetic box model including the aqueous phase reactions and the measured concentration of NDMA in Seosan (R = 0.77; 0.37 (Seoul)) indicates that the aqueous phase reactions dominantly enhanced the NDMA concentrations in Seosan. On the other hand, it is estimated that the formation of nitrosamines by aqueous phase reaction was not significant due to the relatively lower LWC in Seoul compared to that in Seosan. Furthermore, it is presumed that nitramines are mostly emitted from the primary emission sources. This study implies that the concentration of the particulate nitrosamines can be promoted by aqueous phase reaction enhanced by LWC.

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.

Chronic toxicity of copper (Cu) at sublethal levels is associated with ionoregulatory disturbance and oxidative stress. These factors were considered in a toxicokinetic-toxicodynamic model in the present study. The ionoregulatory disturbance was evaluated by the activity of the Na⁺/K⁺-ATPase enzyme (NKA), while oxidative stress was presented by lipid peroxidation (LPO) and glutathione-S-transferase (GST) activity. NKA activity was related to the binding of Cu²⁺ and Na ⁺ to NKA. LPO and GST activity were linked with the simulated concentration of unbound Cu. The model was calibrated using previously reported data and empirical data generated when zebra mussels were exposed to Cu. The model clearly demonstrated that Cu might inhibit NKA activity by reducing the number of functional pump sites and the limited Cu-bound NKA turnover rate. An ordinary differential equation was used to describe the relationship between the simulated concentration of unbound Cu and LPO/GST activity. Although this method could not explain the fluctuations in these biomarkers during the experiment, the measurements were within the confidence interval of estimations. Model simulation consistently shows non-significant differences in LPO and GST activity at two exposure levels, similar to the empirical observation.

With the implementation of clean air strategies, PM₂.₅ pollution abatement has been observed in the “2 + 26” cities in the Beijing-Tianjin-Hebei (BTH) region (referred to as the BTH2+26) and their surrounding areas. To identify the drivers for PM₂.₅ concentration decreases in the BTH2+26 cites from the 2016/17 heating season (HS1617) to the 2017/18 heating season (HS1718), we investigated the contributions of meteorological conditions and emission-reduction measures by Community Multi-Scale Air Quality (CMAQ) model simulations. The source apportionments of five sector sources (i.e., agriculture, industry, power plants, traffic and residential), and regional sources (i.e., local, within-BTH: other cities within the BTH2+26 cities, outside-BTH, and boundary conditions (BCON)) to the PM₂.₅ decreases in the BTH2+26 cities were estimated with the Integrated Source Apportionment Method (ISAM). Mean PM₂.₅ concentrations in the BTH2+26 cities substantially decreased from 77.4 to 152.5 μg m⁻³ in HS1617 to 52.9–101.9 μg m⁻³ in HS1718, with the numbers of heavy haze (daily PM₂.₅ ≥150 μg m⁻³) days decreasing from 17-77 to 5–30 days. The model simulation results indicated that the PM₂.₅ concentration decreases in most of the BTH2+26 cities were attributed to emission reductions (0.4–55.0 μg m⁻³, 2.3–81.6% of total), but the favorable meteorological conditions also played important roles (1.9–25.4 μg m⁻³, 18.4–97.7%). Residential sources dominated the PM₂.₅ reductions, leading to decreases in average PM₂.₅ concentrations by more than 30 μg m⁻³ in severely polluted cities (i.e., Shijiazhuang, Baoding, Xingtai, and Beijing). Regional source analyses showed that both local and within-BTH sources were significant contributors to PM₂.₅ concentrations for most cities. Emission controls in local and within-BTH sources in HS1718 decreased the average PM₂.₅ concentrations by 0.1–47.2 μg m⁻³ and 0.3–22.1 μg m⁻³, respectively, relative to those in HS1617. Here we demonstrate that a combination of favorable meteorological conditions and anthropogenic emission reductions contributed to the improvement of air quality from HS1617 to HS1718 in the BTH2+26 cities.

Simulation model is very essential for predicting the environmental fate and the potential environmental consequences of chemical pollutants including those from accidental chemical spills. However very few of such simulation model is seen related to Chinese costal water body. As the first step toward our final goal to develop a simulation model for the prediction and the risk assessment of chemical pollutants in Chinese coastal water, this study developed a three-dimensional (3D) hydrodynamic model of Xiamen Bay (XMB). This hydrodynamic model was externally derived by meteorological data, river discharge and boundary conditions of XMB. We used the model to calculate the physical factors, especially water temperature, salinity and flow field, from June to September 2016 in XMB. The results demonstrated a good match between observations and simulations, which underscores the feasibility of this model in predicting the spatial-temporal concentration of chemical pollutants in the coastal water of XMB. Longitudinal salinity distributions and the mixing profile of river-sea interactions are discussed, including the obvious gradation of salinity from the river towards sea sites shown by the model. We further assumed that 1000 kg and 1000 mg/L of a virtual chemical pollutant leaked out from Jiulong River (JR) estuary (point source) and whole XMB (non-point source), respectively. The model illustrates that it takes three months for XMB to become purified when point source pollution occurs in the estuary, while half a year to be required in the case of non-point source pollution across the entire bay. Moreover, the model indicated that pollutants can easily accumulate in the western coastal zone and narrow waters like Maluan Bay, which can guide environmental protection strategies.

2,4,6-Trinitrotoluene (TNT) is a nitroaromatic explosive, highly toxic and mutagenic for organisms. In this study, we report for the first time the screening and isolation of TNT-degrading bacteria from Antarctic environmental samples with potential use as bioremediation agents. Ten TNT-degrading bacterial strains were isolated from Deception Island. Among them, Pseudomonas sp. TNT3 was selected as the best candidate since it showed the highest tolerance, growth, and TNT biotransformation capabilities. Our results showed that TNT biotransformation involves the reduction of the nitro groups. Additionally, Pseudomonas sp. TNT3 was capable of transforming 100 mg/L TNT within 48 h at 28 °C, showing higher biotransformation capability than Pseudomonas putida KT2440, a known TNT-degrading bacterium. Functional annotation of Pseudomonas sp. TNT3 genome revealed a versatile set of molecular functions involved in xenobiotic degradation pathways. Two putative xenobiotic reductases (XenA_TNT3 and XenB_TNT3) were identified by means of homology searches and phylogenetic relationships. These enzymes were also characterized at molecular level using homology modelling and molecular dynamics simulations. Both enzymes share different levels of sequence similarity with other previously described TNT-degrading enzymes and with their closest potential homologues in databases.

Dibutyl phthalate (DBP) is widely used as plasticizer and has been detected in the environment, posing a threat to animal health. However, the effects of DBP on agricultural microbiomes are not known. In this study, DBP levels in black soil were evaluated, and the impact of DBP contamination on the uptake and metabolism of sugars in microbes was assessed by glucose absorption tests, metaproteomics, metabolomics, enzyme activity assays and computational simulation analysis. The results indicated that DBP contamination accelerated glucose consumption and upregulated the expression of porins and periplasmic monosaccharide ATP-binding cassette (ABC) transporter solute-binding proteins (SBPs). DBP and its metabolic intermediates (carboxymuconate and butanol) may form a stable complex with sugar transporters and enhance the rigidity and stability of these proteins. Sugar metabolism resulting in the generation of ATP and reducing agent (NADPH), as well as the expression of some key enzymes (dehydrogenases) were also upregulated by DBP treatment. Moreover, a diverse bacterial community appears to utilize sugar, suggesting that there are widespread effects of DBP contamination on soil microbial ecosystems. The results of this study provide a theoretical basis for investigating the toxicological effects of DBP on microbes in black soil.

We used the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) to simulate elemental carbon (EC) concentrations in Thailand in 2017. The goals were to quantify the respective contributions of local emissions and regional transport outside Thailand to EC pollution in Thailand, and to identify the most effective emission control strategy for decreasing EC pollution. The simulated EC concentrations in Chiang Mai, Bangkok, and Phuket were comparable with the observation data. The correlation coefficient between the simulated and observed EC concentrations was 0.84, providing a good basis for evaluating EC sources in Thailand. The simulated mean EC concentration over the whole country was the highest (1.38 μg m⁻³) in spring, and the lowest (0.51 μg m⁻³) in summer. We conducted several sensitivity simulations to evaluate EC sources. Local emissions (including anthropogenic and biomass burning emissions) and regional transport outside Thailand contributed 81.2% and 18.8% to the annual mean EC concentrations, respectively, indicating that local sources played the dominant role for EC pollution in Thailand. Among the local sources, anthropogenic emissions (including the industry, power plant, residential, and transportation sectors) and biomass burning contributed 75.1% and 6.1% to the annual mean EC concentrations, respectively. As the anthropogenic emissions dominated the EC pollution, we performed four sensitivity simulations by reducing 30% of the emissions from each of the industry, power plant, residential, and transportation sectors in Thailand. The results indicated that controlling transportation emissions in Thailand was the most effective way in reducing the EC pollution. The 30% reduction of transportation emissions decreased the annual mean EC concentrations by 12.1%. In contrast, 30% reductions of the residential, industry, and power plant emissions caused 8.4%, 6.4%, and 4.0% decreases in the annual mean EC concentrations, respectively. The model results could potentially provide useful information for air pollution control strategies in Thailand.

Malaysia is a tropical country that is highly dependent on surface water for its raw water supply. Unfortunately, surface water is vulnerable to pollution, especially in developed and dense urban catchments. Therefore, in this study, a methodology was developed for an extensive temporal water quality index (WQI) and classification analysis, simulations of various pollutant discharge scenarios using QUAL2K software, and maps with NH₃–N as the core pollutant using an integrated QUAL2K-GIS. It was found that most of the water quality stations are categorized as Class III (slightly polluted to polluted). These stations are surrounded by residential areas, industries, workshops, restaurants and wet markets that contribute to the poor water quality levels. Additionally, low WQI values were reported in 2010 owing to development and agricultural activities. However, the WQI values improved during the wet season. High concentrations of NH₃–N were found in the basin, especially during dry weather conditions. Three scenarios were simulated, i.e. 10%, 50% and 70% of pollution discharge into Skudai river using a calibrated and validated QUAL2K model. Model performance was evaluated using the relative percentage difference. An inclusive graph showing the current conditions and pollution reduction scenarios with respect to the distance of Skudai river and its tributaries is developed to determine the WQI classification. Comprehensive water quality maps based on NH₃–N as the core pollutant are developed using integrated QUAL2K-GIS to illustrate the overall condition of the Skudai river. High NH₃–N in the Skudai River affects water treatment plant operations. Pollution control of more than 90% is required to improve the water quality classification to Class II. The methodology and analysis developed in this study can assist various stakeholders and authorities in identifying problematic areas and determining the required percentage of pollution reduction to improve the Skudai River water quality.