Journal of Environmental Sciences-china最新文献

Based on observed meteorological elements, photolysis rates (J-values) and pollutant concentrations, an automated J-values predicting system by machine learning (J-ML) has been developed to reproduce and predict the J-values of O¹D, NO₂, HONO, H₂O₂, HCHO, and NO₃, which are the crucial values for the prediction of the atmospheric oxidation capacity (AOC) and secondary pollutant concentrations such as ozone (O₃), secondary organic aerosols (SOA). The J-ML can self-select the optimal “Model + Hyperparameters” without human interference. The evaluated results showed that the J-ML had a good performance to reproduce the J-values where most of the correlation (R) coefficients exceed 0.93 and the accuracy (P) values are in the range of 0.68-0.83, comparing with the J-values from observations and from the tropospheric ultraviolet and visible (TUV) radiation model in Beijing, Chengdu, Guangzhou and Shanghai. The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days, respectively. Compared with O₃ concentrations by using J-values from the TUV model, an emission-driven observation-based model (e-OBM) by using the J-values from the J-ML showed a 4%-12% increase in R and 4%-30% decrease in ME, indicating that the J-ML could be used as an excellent supplement to traditional numerical models. The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values, and the other dominant factors for all J-values were 2-m mean temperature, O₃, total cloud cover, boundary layer height, relative humidity and surface pressure.

Lake ecosystems are extremely sensitive to nitrogen growth, which leads to water quality degradation and ecosystem health decline. Nitrogen depositions, as one of the main sources of nitrogen in water, are expected to change under future climate change scenarios. However, it remains not clear how nitrogen deposition to lakes respond to future meteorological conditions. In this study, a source-oriented version of Community Multiscale Air Quality (CMAQ) Model was used to estimate nitrogen deposition to 263 lakes in 2013 and under three RCP scenarios (4.5, 6.0 and 8.5) in 2046. Annual total deposition of 58.2 Gg nitrogen was predicted for all lakes, with 23.3 Gg N by wet deposition and 34.9 Gg N by dry deposition. Nitrate and ammonium in aerosol phase are the major forms of wet deposition, while NH₃ and HNO₃ in gas phase are the major forms of dry deposition. Agriculture emissions contribute to 57% of wet deposition and 44% of dry deposition. Under future meteorological conditions, wet deposition is predicted to increase by 5.5% to 16.4%, while dry deposition would decrease by 0.3% to 13.0%. Changes in wind speed, temperature, relative humidity (RH), and precipitation rates are correlated with dry and wet deposition changes. The predicted changes in deposition to lakes driven by meteorological changes can lead to significant changes in aquatic chemistry and ecosystem functions. Apart from future emission scenarios, different climate scenarios should be considered in future ecosystem health evaluation in response to nitrogen deposition.

The removal of ammonia nitrogen (NH₄⁺-N) and bacteria from aquaculture wastewater holds paramount ecological and production significance. In this study, Pt/RuO₂/g-C₃N₄ photocatalysts were prepared by depositing Pt and RuO₂ particles onto g-C₃N₄. The physicochemical properties of photocatalysts were explored by X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), X-ray diffraction (XRD), and UV–vis diffuse reflectance spectrometer (UV–vis DRS). The photocatalysts were then applied to the removal of both NH4+-N and bacteria from simulated mariculture wastewater. The results clarified that the removals of both NH₄⁺-N and bacteria were in the sequence of g-C₃N₄ < RuO₂/g-C₃N₄ < Pt/g-C₃N₄ < Pt/RuO₂/g-C₃N₄. This magnificent photocatalytic ability of Pt/RuO₂/g-C₃N₄ can be interpreted by the transfer of holes from g-C₃N₄ to RuO₂ to facilitate the in situ generation of HClO from Cl⁻ in wastewater, while Pt extracts photogenerated electrons for H₂ formation to enhance the reaction. The removal of NH₄⁺-N and disinfection effect were more pronounced in simulated seawater than in pure water. The removal efficiency of NH₄⁺-N increases with an increase in pH of wastewater, while the bactericidal effect was more significant under a lower pH in a pH range of 6–9. In actual seawater aquaculture wastewater, Pt/RuO₂/g-C₃N₄ still exhibits effective removal efficiency of NH₄⁺-N and bactericidal performance under sunlight. This study provides an alternative avenue for removement of NH₄⁺-N and bacteria from saline waters under sunlight.

Arsenic (As) pollution seriously threatens human and ecological health. Microalgal cell wall and extracellular polymeric substances (EPS) are known to interact with As, but their roles in the As resistance, accumulation and speciation in microalgae remain unclear. Here, we used two strains of Chlamydomonas reinhardtii, namely CC-125 (wild type) and CC-503 (cell wall-deficient mutant), to examine the algal growth, EPS synthesis, As adsorption, absorption and transformation under 10–1000 µg/L As(III) and As(V) treatments for 96 h. In both strains, the As absorption increased after the EPS removal, but the growth, As adsorption, and transformation of C. reinhardtii declined. The CC-125 strain was more tolerant to As stress and more efficient in EPS production, As accumulation, and redox transformation than CC-503, irrespective of EPS presence or absence. Three-dimension excitation-emission matrix (3D-EEM) and attenuated total reflectance infrared spectroscopy (ATR-IR) analyses showed that As was bound with functional groups in the EPS and cell wall, such as -COOH, NH and -OH in proteins, polysaccharides and amino acids. Together, this study demonstrated that EPS and cell wall acted as barriers to lower the As uptake by C. reinhardtii. However, the cell wall mutant strain was more susceptible to As toxicity due to lower EPS induction and higher As absorption.

Black phosphorus quantum dots (BPQDs) have been used as the nano-carrier in the field of biomedicine due to their excellent electronic conductivity, optical and thermoelectric properties. However, it is still lack of evaluation of the safety of BPQDs, specifically on the effects and mechanisms of BPQDs on heart. In this study, the specific pathogen-free male mice were orally administered with different doses of BPQDs (0.02, 0.1, 0.5 mg/kg) for 28 days. BPQDs exposure decreased the heart-body ratio and caused cardiac hypertrophy and fibrosis. Additionally, ferroptosis was observed in the cardiac tissue. The recent study has shown BPQDs oral exposure alter gut microbiota. Here, after exposure to 0.1 mg/kg BPQDs for 28 days, the germ-free mice showed neither cardiac injury nor ferroptosis. Taken together, the study demonstrates that BPQDs induce cardiac ferroptosis and fibrosis, possibly mediated by gut microbiota. This research may aid in enhancing understanding of the biosafety of BP nanomaterials and promoting the sustainable development of nanotechnology.

The natural estrogens, including estrone (E₁), 17β-estradiol (E₂), and estriol (E₃), are frequently detected in aquatic environment at relatively high levels. The most commonly used biomarkers for estrogens are mainly expressed in the liver of fish. Analyses of the global gene profiling in fish liver cells under estrogens treatment will provide precise toxicogenomic information of the natural estrogens which is still not well known. In this study, we developed methods for isolation and culture of primary hepatocytes from liver tissue of male Chinese medaka (Oryzias sinensis), and analyzed the global gene expression profiling in the primary hepatocytes treated with E₁ (1, 10, and 100 nmol/L), E₂ (0.01, 0.1, 1, 10, and 100 nmol/L), and E₃ (1, 10, and 100 nmol/L) using RNA-seq. It was found that 175, 248, and 218 genes were differentially expressed in the E₁, E₂, and E₃ groups, respectively. These differentially expressed genes (DEGs) were mainly enriched in Gene Ontology (GO) terms of “response to estradiol”, “response to estrogen”, and “lipid transport”. Of the DEGs, vitellogenin genes, including vtg1, vtg2, and vtg3, were the mostly up-regulated and followed by zona pellucida genes which include zp2.3, zp2l1 and zp3a.2. In addition, genes of slc41a1, zp2.1, esr1, pkd1l1, fam20ca, best3, etc. were also obviously up-regulated by the estrogens in concentration-dependent patterns. RT-qPCR was used to validate the results of RNA-seq and found that vtg2 should be the best biomarker gene for estrogen study, which could well response to natural estrogens and weak estrogenic chemical, propyl 4-hydroxybenzoate.

Metal may affect maternal immune function, but few epidemiological studies have reported the associations between multiple-metal exposure and maternal immunoglobulin (Ig) levels. Based on the Hangzhou Birth Cohort Study, 1059 participants were included, and eleven metals in whole blood samples and serum IgA, IgG, IgE and IgM levels were measured. Linear regression, quantile-based g-computation (QGC), and Bayesian kernel machine regression (BKMR) models were used to evaluate the associations. Compared with the first tertile of metal levels, arsenic (As) was negatively associated with IgE (β = -0.25, 95% confidence interval (CI) = -0.48 to -0.02). Moreover, significant associations of manganese (Mn) with IgA, IgG and IgM were demonstrated (β = 0.10, 95% CI = 0.04 to 0.18; β = 0.07, 95% CI = 0.03 to 0.12; β = 0.10, 95% CI = 0.03 to 0.18, respectively). Cadmium (Cd) were associated with higher levels of IgM. QGC models showed the positive association of the metal mixtures with IgA and IgG, with Mn playing a major role. Mn and Cd had positive contributions to IgM, while As had negative contributions to IgE. In the BKMR models, the latent continuous outcomes of IgA and IgG showed a significant increase when all the metals were at their 60th percentile or above compared to those at their 50th percentile. Therefore, exposure to metals was associated with maternal Igs, and mainly showed that Mn was associated with increased levels of IgA, IgG and IgM, and As was associated with low IgE levels.

The uncontrolled release of antibiotics into the environment would be extremely harmful to human health and ecosystems. Therefore, it is in urgent need to monitor the environment and promote the detection and degradation of antibiotics to the relatively harmless by-products to a feasible extent. Graphitic carbon nitride (g-C₃N₄) is a non-metallic n-type semiconductor that can be used for the antibiotic detection and degradation due to its easy synthesis process, excellent chemical stability and unique optical properties. Unfortunately, the utilization of visible light, electron-hole recombination and electron conductivity have hindered its potential applications in the fields of photocatalytic degradation and electrochemical detection. Although previous publications have highlighted the diverse modification methods for the g-C₃N₄-based materials, the underlying structure-performance relationships of g-C₃N₄, especially for the detection and degradation of antibiotics, remains to be further explored. In view of this, the current review centered on the recent progress in the modification techniques of g-C₃N₄, the detection and degradation of antibiotics using the g-C₃N₄-based materials, as well as the potential antibiotic degradation mechanisms of the g-C₃N₄-based materials. Additionally, the underlying applications of the g-C₃N₄-based materials for antibiotic detection and degradation were also prospected. This review would provide a valuable research foundation and the up-to-date information for the g-C₃N₄-based materials to combat antibiotic pollution in the environment.

Aerosol acidity (pH) plays an important role in the multiphase chemical processes of atmospheric particles. In this study, we demonstrated the seasonal trends of aerosol pH calculated with the ISORROPIA-II model in a coastal city of southeast China. We performed quantitative analysis on the various influencing factors on aerosol pH, and explored the responses of aerosol pH to different PM_2.5 and O₃ pollution levels. The results showed that the average aerosol pH was 2.92 ± 0.61, following the order of winter > spring > summer > autumn. Sensitivity tests revealed that SO₄²⁻, NH_x, T and RH triggered the variations of aerosol pH. Quantitative analysis results showed that T (37.9%-51.2%) was the main factors affecting pH variations in four seasons, followed by SO₄²⁻ (6.1%-23.7%), NH_x (7.2%-22.2%) and RH (0–14.2%). Totally, annual mean meteorological factors (52.9%) and chemical compositions (41.3%) commonly contributed the aerosol ΔpH in the coastal city. The concentrations of PM_2.5 was positively correlated with aerosol liquid water content (R² = 0.53) and aerosol pH (R² = 0.26), indicating that the increase in pH was related with the elevated NH₄NO₃ and decreased SO₄²⁻, and also the changes of T and RH. The O_x (O₃ + NO₂) was moderately correlated with aerosol pH (R² = -0.48), attributable to the fact that the proportion of SO₄²⁻ increased under high T and low RH conditions. The study strengthened our understanding of the contributions of influencing factors to aerosol pH, and also provided scientific evidences for chemical processes of atmospheric particles in coastal areas.

Methane (CH₄) is the second greenhouse gas and has a profound impact on global climate change due to its high global warming potential and concentration. By 2022, the CH₄ concentration was approximately 1.9 ppm, which was 264% of the pre-industrial level. The spatiotemporal distribution of CH₄ was investigated by a portable CH₄ detector on an unmanned aerial vehicle and electric bicycles in Shaoxing, a city situated in the Yangtze River Delta, China. The vertical distribution revealed CH₄ concentration generally decreased slowly with height. However, the inversion condition and low atmospheric boundary layer height (ABLH) leaded to the enhancement of CH₄ with height. The highest CH₄ concentration (2.2 ± 0.1 ppm, n = 1428) was observed in winter and the lowest (2.0 ± 0.2 ppm, n = 1530) in spring. Regarding the daily variation, CH₄ concentration peaked at 5:00 local time (LT) and reached its lowest level at 14:00 LT, which was attributed to the daily variation of ABLH, lowest in the early morning and highest in the noon. In urban areas, CH₄ concentrations showed higher levels near restaurants, natural gas stations and sewerage well, with a maximum value of 13.1 ppm, which was caused by CH₄ emission and natural gas leakage from these places. The annual CH₄ emission in Shaoxing were estimated to be approximately 69 ton/(km²·year) by the mass balance approach. Compared with other cities in the world, the CH₄ emission is in higher level which imply some control measures should be conducted to reduce CH₄ emission in Shaoxing.