Intense harmful algal blooms (HABs) can occur in the backwaters of tributaries supplying large-scale reservoirs. Due to the characteristics of process-based models and difficulties in modelling complex nonlinear processes, traditional models have difficulties disentangling the driving factors of HABs. In this study, we used data-driven methods (i.e., correlation analysis and machine-learning models) to identify the most important drivers of HABs in the Xiangxi River, a tributary of the Three Gorges Reservoir, China (2017–2018), for the dry season (from October to mid-April) and wet season (from April to September). We utilized the maximal information coefficient (MIC) combined with a time lag strategy and prior knowledge to quantitatively identify the driving variables of HABs. An extra trees regression (ETR) model was developed to assess the relative importance of causal variables driving algal blooms for the different periods. The results showed that water temperature was the most important driver for the duration of the study, followed by total nitrogen. Nitrogen had a stronger effect on algal blooms than phosphorus during both the wet and dry seasons. HABs were mainly affected by ammonia nitrogen in the wet season and by other forms of nitrogen in the dry season. In contrast, rather than the water temperature and nutrients, the operation of the Three Gorges Dam (difference between inflow and outflow discharge rate) was the most significant factor for algal blooms during the dry season, but its influence sharply declined during the wet season. This study showed that the key drivers of HABs can differ between seasons and suggests that HAB management should take seasonality into account.

Microplastics (MPs) have been shown to act as sorbent phases and thus carriers of organic chemicals in the aquatic environment. Therefore, concerns exist that MP ingestion increases the uptake and accumulation of organic chemicals by aquatic organisms. However, it is unclear if this pathway is relevant compared to other exposure pathways. Here we compared the bioconcentration capacity of two hydrophobic organic chemicals (i.e., chlorpyrifos and hexachlorobenzene) in a freshwater fish (Danio rerio) when exposed to chemicals through water only and in combination with contaminated polyethylene MPs. Additionally, a suite of biomarker analyses (acetylcholine esterase, glutathione S-transferase, alkaline phosphatase, catalase) was carried out to test whether MPs can enhance the toxic stress caused by chemicals. Two 14-day semi-static experiments (one for each chemical) were carried out with adult fish. Each experiment consisted of (1) a control treatment (no chemicals, no MPs); (2) a treatment in which fish were exposed to chlorpyrifos or hexachlorobenzene only through water; (3) a treatment in which fish were exposed to the chemicals through water and contaminated polyethylene MPs (100 mg MP/L). Two additional treatments were included for the biomarker analysis. These contained MPs at two different concentrations (5 and 100 mg MP/L) but no chemicals. The presence of contaminated MPs in contaminated water did not enhance but rather decreased the bioconcentration of both chemicals in fish compared to the treatment that contained contaminated water in absence of MPs. This was more pronounced for hexachlorobenzene, which is more hydrophobic than chlorpyrifos. Enzyme activity levels in fish were only significantly altered in the presence of MPs for alkaline phosphatase. This study indicates that MP presence in freshwater ecosystems is not expected to increase the risks associated with chemical bioconcentration in aquatic organisms and that other exposure pathways (i.e., uptake via respiration, skin permeability) may be of higher importance.

This study investigated the hourly inorganic aerosol chemistry and its impact on atmospheric visibility over an urban area in Central Taiwan, by relying on measurements of aerosol light extinction, inorganic gases, and PM₂.₅ water-soluble ions (WSIs), and simulations from a thermodynamic equilibrium model. On average, the sulfate (SO₄²⁻), nitrate (NO₃⁻), and ammonium (NH₄⁺) components (SNA) contributed ∼90% of WSI concentrations, which in turn made up about 50% of the PM₂.₅ mass. During the entire observation period, PM₂.₅ and SNA concentrations, aerosol pH, aerosol liquid water content (ALWC), and sulfur and nitrogen conversion ratios all increased with decreasing visibility. In particular, the NO₃⁻ contribution to PM₂.₅ increased, whereas the SO₄²⁻ contribution decreased, with decreasing visibility. The diurnal variations of the above parameters indicate that the interaction and likely mutual promotion between NO₃⁻ and ALWC enhanced the hygroscopicity and aqueous-phase reactions conducive for NO₃⁻ formation, thus led to severely impaired visibility. The high relative humidity (RH) at the study area (average 70.7%) was a necessary but not sole factor leading to enhanced NO₃⁻ formation, which was more directly associated with elevated ALWC and aerosol pH. Simulations from the thermodynamic model depict that the inorganic aerosol system in the study area was characterized by fully neutralized SO₄²⁻ (i.e. a saturated factor in visibility reduction) and excess NH₄⁺ amidst a NH₃-rich environment. As a result, PM₂.₅ composition was most sensitive to gas-phase HNO₃, and hence NOx, and relatively insensitive to NH₃. Consequently, a reduction of NOx would result in instantaneous cuts of NO₃⁻, PM₂.₅, and ALWC, and hence improved visibility. On the other hand, a substantial amount of NH₃ reduction (>70%) would be required to lower the aerosol pH, driving more than 50% of the particulate phase NO₃⁻ to the gas phase, thereby making NH₃ a limiting factor in shifting PM₂.₅ composition.

A pyrene-degrading consortium OPK containing Mycolicibacterium strains PO1 and PO2, Novosphingobium pentaromativorans PY1 and Bacillus subtilis FW1 effectively biodegraded medium- and long-chain alkanes as well as mixed hydrocarbons in crude oil. The detection of alkB and CYP153 genes in the genome of OPK members supports its phenotypic ability to effectively degrade a broad range of saturated hydrocarbons in crude oil. Zeolite-immobilized OPK was developed as a ready-to-use bioproduct and it exhibited 74% removal of 1000 mg L⁻¹ crude oil within 96 h in sterilized seawater without nutrient supplementation and maintained high crude oil-removal activity under a broad range of pH values (5.0–9.0), temperatures (30–40 °C) and salinities (20–60‰). In addition, the immobilized OPK retained a high crude oil removal efficacy in semicontinuous experiments and showed reusability for at least 5 cycles. Remarkably, bioaugmentation with zeolite-immobilized OPK in sandy soil microcosms significantly increased crude oil (10,000 mg kg⁻¹ soil) removal from 45% to 80.67% within 21 days compared to biostimulation and natural attenuation. Moreover, bioaugmentation with exogenous immobilized OPK stimulated an increase in the relative abundances of Alcanivorax genus, indigenous hydrocarbon-degrading bacteria, which in turn enhanced removal efficiency of crude oil contamination from sandy soil microcosms. The results indicate positive interactions between the bioaugmented immobilized consortium, harboring Mycolicibacterium as a key player, and indigenous Alcanivorax, which exhibited crucial functions for improving crude oil removal efficacy. The knowledge obtained forms an important basis for further synthesis and handling of a promising bio-based product for enhancing the in situ bioremediation of crude oil-polluted marine environments.

Accurate mapping of air pollutants is essential for epidemiological studies and environmental risk assessments. Concentrations measured by air quality monitoring stations (AQMS) have primarily been used to assess the exposure of PM₂.₅. However, the low coverage and amount of monitoring stations affect the errors of spatial interpolation or geostatistical estimates. In contrast to other integrated approaches developed for improved air pollution estimates, this study utilizes data from low-cost microsensors densely deployed in Taiwan to improve the popular spatial interpolation approach called inverse distance weighting (IDW). A large dataset from thousands of low-cost sensors could improve spatial interpolation by describing the distribution of PM₂.₅ in detail. Therefore, this study presents a clustering-based method to assess the distribution of PM₂.₅. Then, a smarter IDW is performed based on correlated observations from the selected air quality stations. The publicly available data chosen for this investigation pertained to Taiwan, which has deployed 74 monitoring stations and more than 11,000 low-cost sensors since December 2020. The results of leave-one-out cross-validation indicate that there are fewer PM₂.₅ estimation errors in the developed approach than in estimations that use kriging across almost all of the months and sampled dates of 2019 and 2020, particularly those with higher PM₂.₅ spatial heterogeneities. Spatial heterogeneities could result in more significant estimation errors in mainstream approaches. The root mean square error of the monthly average estimate for PM₂.₅ ranged from 1.17 to 3.86 μg/m³. We also found that the clustering of one month characterizing the pattern of PM₂.₅ distribution could perform well in spatial interpolations based on historical data from monitoring stations. According to the information on the openaq platform, low-cost sensors are in demand in cities and areas. This trend might pave the way for the application of the proposed approach in other areas for superior exposure assessments.

Estuaries are one of the most valuable biomes on earth. Although humans are highly dependent on these ecosystems, anthropogenic activities have impacted estuaries worldwide, altering their ecological functions and ability to provide a variety of important ecosystem services. Many anthropogenic stressors combine to affect the soft sedimentary habitats that dominate estuarine ecosystems. Now, due to climate change, estuaries and other marine areas might be increasingly exposed to the emerging threat of megafires. Here, by sampling estuaries before and after a megafire, we describe impacts of wildfires on estuarine benthic habitats and justify why megafires are a new and concerning threat to coastal ecosystems. We (1) show that wildfires change the fundamental characteristics of estuarine benthic habitat, (2) identify the factors (burnt intensity and proximity to water's edge) that influence the consequences of fires on estuaries, and (3) identify relevant indicators of wildfire impact: metals, nutrients, and pyrogenic carbon. We then discuss how fires can impact estuaries globally, regardless of local variability and differences in catchment. In the first empirical assessment of the impact of wildfires on estuarine condition, our results highlight indicators that may assist waterway managers to empirically detect wildfire impacts in estuaries and identify catchment factors that should be included in fire risk assessments for estuaries. Overall, this study highlights the importance of considering fire threats in current and future estuarine and coastal management.

Freshwater environments are more sensitive to anthropogenic influences and usually contain higher concentrations of pollutants than marine environments. Microplastic pollution causes additional stress on freshwater animals; yet, studies evaluating the microplastic occurrence in freshwater biota are still limited. In this study, microplastic occurrence in the gastrointestinal tracts (GIT) and gill of commercial fish species (Prussian carp Carassius gibelio (Bloch, 1782); Abu mullet Planiliza abu (Heckel, 1843); Common carp Cyprinus carpio Linnaeus, 1758; European ell Anguilla Anguilla (Linnaeus, 1758); North African catfish Clarias gariepinus (Burchell, 1822); Goldfish Carassius auratus (Linnaeus, 1758) were reported from Orontes River. MPs abundance in the GIT and gill of six species were found as 5.1 ± 2 MPs fish⁻¹ and 4.4 ± 2 MPs fish⁻¹ with an occurrence of 95% and 74%, respectively. The majority of extracted microplastics were fiber, black and less than 1000 μm in size. FTIR analysis determined the main polymer types as polyester (50%), high-density polyethylene (HDPE) (10%), polypropylene (PP) (8%) and polyethylene terephthalate (PET) (5%). High MPs abundance and frequency of occurence indicate the exposure of microplastic pollution in freshwater biota which could threat the health of both individuals and consumers. Results obtained in this study will increase the acknowledgement of MPs pollution in the Orontes River. Also, this study will provide data to the administrators to set up necessary legislations in freshwater ecosystems.

Triclinic birnessite (TB), a typical layered Mn oxide which is abundant naturally occurring minerals with a vital impact on the transformation of arsenite (As(III)) by adsorption and oxidation. As one of the most common critical metalloids, ammonium ion (NH₄⁺) universally coexists with birnessite in marine, sediments or groundwater where are contaminated with As(III). In this study, we investigated the impacts of NH₄⁺ on TB towards the transformation of As(III). Compared with the original TB (40.1%), the As(III) removal efficiencies of three different concentration (0.5 M, 1 M and 2 M) NH₄⁺ impressed triclinic birnessite (TB-0.5 N, TB-1N and TB-2N) are increased rapidly in the order of: TB-2N (80.4%) > TB-1N (75.8%) > TB-0.5 N (71.5%). In addition, TB-2N exhibited the highest initial oxidation rate of 0.0031 min⁻¹ which exceeds twice as much as this of TB (0.0014 min⁻¹). And TB-2N could reach the max oxidation efficiency when the As concentration is 0.08 mM. Due to two different mechanisms of As(III) oxidation on birnessites under acidic and alkaline conditions, TB-2N showed a higher removal efficiency than TB at pH 3.0, 5.0, 7.0 and 9.0. Hence, there are two main reasons for the advanced As(III) oxidation capacity of TB-2N. One is the improvement of the average oxidation state of Mn, the other is the increase of oxygen vacancy with the coexistence of NH₄⁺. Moreover, the larger specific surface area of TB-2N also contribute to enhancing As(III) oxidation capacity. This study holds a fundamental understanding of the behavior of triclinic birnessite which is coexisted with ammonium ion towards the transformation of As(III) in the environment.

Exposure to tobacco smoke during pregnancy has been associated with a series of adverse reproductive outcomes; however, the underlying molecular mechanisms are not well-established. We conducted an untargeted metabolome-wide association study to identify the metabolic perturbations and molecular mechanisms underlying the association between cotinine, a widely used biomarker of tobacco exposure, and adverse birth outcomes. We collected early and late pregnancy urine samples for cotinine measurement and serum samples for high-resolution metabolomics (HRM) profiling from 105 pregnant women from the Atlanta African American Maternal-Child cohort (2014–2016). Maternal metabolome perturbations mediating prenatal tobacco smoke exposure and adverse birth outcomes were assessed by an untargeted HRM workflow using generalized linear models, followed by pathway enrichment analysis and chemical annotation, with a meet-in-the-middle approach. The median maternal urinary cotinine concentrations were 5.93 μg/g creatinine and 3.69 μg/g creatinine in early and late pregnancy, respectively. In total, 16,481 and 13,043 metabolic features were identified in serum samples at each visit from positive and negative electrospray ionization modes, respectively. Twelve metabolic pathways were found to be associated with both cotinine concentrations and adverse birth outcomes during early and late pregnancy, including tryptophan, histidine, urea cycle, arginine, and proline metabolism. We confirmed 47 metabolites associated with cotinine levels, preterm birth, and shorter gestational age, including glutamate, serine, choline, and taurine, which are closely involved in endogenous inflammation, vascular reactivity, and lipid peroxidation processes. The metabolic perturbations associated with cotinine levels were related to inflammation, oxidative stress, placental vascularization, and insulin action, which could contribute to shorter gestations. The findings will support the further understanding of potential internal responses in association with tobacco smoke exposures, especially among African American women who are disproportionately exposed to high tobacco smoke and experience higher rates of adverse birth outcomes.

With the rapid development of nanotechnology in agriculture, there is increasing urgency to assess the impacts of nanoparticles (NPs) on the soil environment. This study merged raw high-throughput sequencing (HTS) data sets generated from 365 soil samples to reveal the potential ecological effects of NPs on soil microbial community by means of metadata analysis and machine learning methods. Metadata analysis showed that treatment with nanoparticles did not have a significant impact on the alpha diversity of the microbial community, but significantly altered the beta diversity. Unfortunately, the abundance of several beneficial bacteria, such as Dyella, Methylophilus, Streptomyces, which promote the growth of plants, and improve pathogenic resistance, was reduced under the addition of synthetic nanoparticles. Furthermore, metadata demonstrated that nanoparticles treatment weakened the biosynthesis ability of cofactors, carriers, and vitamins, and enhanced the degradation ability of aromatic compounds, amino acids, etc. This is unfavorable for the performance of soil functions. Besides the soil heterogeneity, machine learning uncovered that a) the exposure time of nanoparticles was the most important factor to reshape the soil microbial community, and b) long-term exposure decreased the diversity of microbial community and the abundance of beneficial bacteria. This study is the first to use a machine learning model and metadata analysis to investigate the relationship between the properties of nanoparticles and the hazards to the soil microbial community from a macro perspective. This guides the rational use of nanoparticles for which the impacts on soil microbiota are minimized.