The theory-guided air quality model solves the mathematical equations of chemical and physical processes in pollution transportation numerically. While the data-driven model, as another scientific research paradigm with powerful extraction of complex high-level abstractions, has shown unique advantages in the PM₂.₅ prediction applications. In this paper, to combine the two advantages of strong interpretability and feature extraction capability, we integrated the partial differential equation of PM₂.₅ dispersion with deep learning methods based on the newly proposed DPGN model. We extended its ability to perform long-term multi-step prediction and used advection and diffusion effects as additional constraints for graph neural network training. We used hourly PM₂.₅ monitoring data to verify the validity of the proposed model, and the experimental results showed that our model achieved higher prediction accuracy than the baseline models. Besides, our model significantly improved the correct prediction rate of pollution exceedance days. Finally, we used the GNNExplainer model to explore the subgraph structure that is most relevant to the prediction to interpret the results. We found that the hybrid model is more biased in selecting stations with Granger causality when predicting.

Natural processes and human activities play a crucial role in changing the nitrogen cycle and increasing nitrous oxide (N₂O) emissions, which are accelerating at an unprecedented rate. N₂O has serious global warming potential (GWP), about 310 times higher than that of carbon dioxide. The food production, transportation, and energy required to sustain a world population of seven billion have required dramatic increases in the consumption of synthetic nitrogen (N) fertilizers and fossil fuels, leading to increased N₂O in air and water. These changes have radically disturbed the nitrogen cycle and reactive nitrogen species, such as nitrous oxide (N₂O), and have impacted the climatic system. Yet, systematic and comprehensive studies on various underlying processes and parameters in the altered nitrogen cycle, and their implications for the climatic system are still lacking. This paper reviews how the nitrogen cycle has been disturbed and altered by anthropogenic activities, with a central focus on potential pathways of N₂O generation. The authors also estimate the N₂O–N emission mainly due to anthropogenic activities will be around 8.316 Tg N₂O–N yr⁻¹ in 2050. In order to minimize and tackle the N₂O emissions and its consequences on the global ecosystem and climate change, holistic mitigation strategies and diverse adaptations, policy reforms, and public awareness are suggested as vital considerations. This study concludes that rapidly increasing anthropogenic perturbations, the identification of new microbial communities, and their role in mediating biogeochemical processes now shape the modern nitrogen cycle.

Responses to COVID-19 altered environmental exposures and health behaviours associated with non-communicable diseases. We aimed to (1) quantify changes in nitrogen dioxide (NO₂), noise, physical activity, and greenspace visits associated with COVID-19 policies in the spring of 2020 in Barcelona (Spain), Vienna (Austria), and Stockholm (Sweden), and (2) estimated the number of additional and prevented diagnoses of myocardial infarction (MI), stroke, depression, and anxiety based on these changes. We calculated differences in NO₂, noise, physical activity, and greenspace visits between pre-pandemic (baseline) and pandemic (counterfactual) levels. With two counterfactual scenarios, we distinguished between Acute Period (March 15th – April 26th, 2020) and Deconfinement Period (May 2nd – June 30th, 2020) assuming counterfactual scenarios were extended for 12 months. Relative risks for each exposure difference were estimated with exposure-risk functions. In the Acute Period, reductions in NO₂ (range of change from −16.9 μg/m³ to −1.1 μg/m³), noise (from −5 dB(A) to −2 dB(A)), physical activity (from −659 MET*min/wk to −183 MET*min/wk) and greenspace visits (from −20.2 h/m to 1.1 h/m) were largest in Barcelona and smallest in Stockholm. In the Deconfinement Period, NO₂ (from −13.9 μg/m³ to −3.1 μg/m³), noise (from −3 dB(A) to −1 dB(A)), and physical activity levels (from −524 MET*min/wk to −83 MET*min/wk) remained below pre-pandemic levels in all cities. Greatest impacts were caused by physical activity reductions. If physical activity levels in Barcelona remained at Acute Period levels, increases in annual diagnoses for MI (mean: 572 (95% CI: 224, 943)), stroke (585 (6, 1156)), depression (7903 (5202, 10,936)), and anxiety (16,677 (926, 27,002)) would be anticipated. To decrease cardiovascular and mental health impacts, reductions in NO₂ and noise from the first COVID-19 surge should be sustained, but without reducing physical activity. Focusing on cities’ connectivity that promotes active transportation and reduces motor vehicle use assists in achieving this goal.

Currently, alleviating antimony (Sb) contamination in aqueous solutions is crucial for restoring and recovering ecological and environmental health. Due to its toxicity, bioaccumulation and mobile characteristics, developing an efficient technique for antimony decontamination is imperative. Herein, we prepared a Fe–Cu-chitosan (FCC) composite by a one-step coprecipitation method, in which nanoscale Fe/Cu acts as the active sites and the whole structure is exhibited as porous microscale particles. A Fe/Cu proportion of 2/1 (FCC-2/1) was determined to be the optimum proportion for antimony adsorption, specifically 34.5 mg g⁻¹ for Sb(III) and 26.8 mg g⁻¹ for Sb(V) (initial concentration: 5.0 mg L⁻¹). Spectral characterization, batch experiments and density functional theory (DFT) simulations were applied to determine the adsorption mechanism, in which surface hydroxyls (-OH) were responsible for antimony complexion and Fe–Cu coupling was a major contributor to adsorption enhancement. According to kinetic analysis, Cu provided an electrostatic attraction during the adsorption process, which facilitated the transportation of antimony molecules to the material interface. In the meantime, the FCC electronic structure was modified due to the optimization of the Fe–Cu interface coupling. Based on the Mullikan net charge, the intrinsic Fe–O–Cu bond might favor interfacial electronic redistribution. When the antimony molecule contacted the adsorption interface, the electrons transferred swiftly as Fe/Cu 3d and O 2p orbital hybridization occurred, thus inducing a stabilizing effect. This work may offer a new perspective for binary oxide construction and its adsorption mechanism analysis.

Riverine carbon (C) composition and export are closely related to changes in the coastal environment and climate. Excessive C inputs from rivers to seas and their subsequent decomposition could result in harmful algal blooms and ecosystem degradation in the coastal sea. In this study, we explored the C transportation and composition in the 24 major rivers of the Bohai Sea (BS) Rim based on the investigation of dissolved organic carbon (DOC), carbon stable isotopes (δ¹³CDOC) and chromophoric dissolved organic matter (CDOM). The results showed that the riverine DOC concentrations were high (10.6 ± 6.04 mg/L) in the BS Rim compared with the DOC levels in the main rivers in Eastern China (4.98 ± 2.45 mg/L). The δ¹³CDOC ranged from −28.29‰ to −25.32‰ in the rivers of the BS Rim, suggesting that the DOC mainly originated from riverine plankton, soil organic matter mainly induced by C3 plants, and sewage. The excitation-emission matrix fluorescence spectroscopy of the CDOM indicated that a soluble, microbial by product-like material accounted for the largest proportion (approximately 40%) of CDOM in these rivers and that CDOM mainly originated from autochthonous riverine sources with high protein-like components. The rivers in the BS Rim transported approximately 0.55 Tg C of DOC to the BS each year, with more than 70% of reactive C based on the CDOM composition. The DOC yields in terms of unit drainage area transported from the small rivers to the BS were higher compared to those of the larger rivers in the world, which indicated that the small rivers in the Bohai Rim could be an important source of the C in the BS. This study would enrich our understanding of environmental evolution in coastal areas with numerous small rivers.

Heavy metal toxicity has become an impediment to agricultural productivity, which presents major human health concerns in terms of food safety. Among them, arsenic (As) a non-essential heavy metal has gained worldwide attention because of its noxious effects on agriculture and public health. The increasing rate of global warming and anthropogenic activities have promptly exacerbated As levels in the agricultural soil, thereby causing adverse effects to crop genetic and phenotypic traits and rendering them vulnerable to other stresses. Conventional breeding and transgenic approaches have been widely adapted for producing heavy metal resilient crops; however, they are time-consuming and labor-intensive. Hence, finding new mitigation strategies for As toxicity would be a game-changer for sustainable agriculture. One such promising approach is harnessing plant microbiome in the era of ‘omics’ which is gaining prominence in recent years. The use of plant microbiome and their cocktails to combat As metal toxicity has gained widespread attention, because of their ability to metabolize toxic elements and offer an array of perquisites to host plants such as increased nutrient availability, stress resilience, soil fertility, and yield. A comprehensive understanding of below-ground plant-microbiome interactions and their underlying molecular mechanisms in exhibiting resilience towards As toxicity will help in identifying elite microbial communities for As mitigation. In this review, we have discussed the effect of As, their accumulation, transportation, signaling, and detoxification in plants. We have also discussed the role of the plant microbiome in mitigating As toxicity which has become an intriguing research frontier in phytoremediation. This review also provides insights on the advancements in constructing the beneficial synthetic microbial communities (SynComs) using microbiome engineering that will facilitate the development of the most advanced As remedial tool kit in sustainable agriculture.

This study aims to investigate the effect of transportation infrastructure on the decrease of NO₂ air pollution during three COVID-19-induced lockdowns in a vast region of France. For this purpose, using Sentinel-5P satellite data, the relative change in tropospheric NO₂ air pollution during the three lockdowns was calculated. The estimation of regional infrastructure intensity was performed using Kernel Density Estimation, being the predictor variable. By performing hotspot–coldspot analysis on the relative change in NO₂ air pollution, significant spatial clusters of decreased air pollution during the three lockdowns were identified. Based on the clusters, a novel spatial index, the Clustering Index (CI) was developed using its Coldspot Clustering Index (CCI) variant as a predicted variable in the regression model between infrastructure intensity and NO₂ air pollution decline. The analysis revealed that during the three lockdowns there was a strong and statistically significant relationship between the transportation infrastructure and the decline index, CCI (r = 0.899, R² = 0.808). The results showed that the largest decrease in NO₂ air pollution was recorded during the first lockdown, and in this case, there was the strongest inverse correlation with transportation infrastructure (r = −0.904, R² = 0.818). Economic and population predictors also explained with good fit the decrease in NO₂ air pollution during the first lockdown: GDP (R² = 0.511), employees (R² = 0.513), population density (R² = 0.837). It is concluded that not only economic-population variables determined the reduction of near-surface air pollution but also the transportation infrastructure. Further studies are recommended to investigate other pollutant gases as predicted variables.

This study focused on the syngenetic control of polychlorinated-ρ-dibenzodioxins and dibenzofurans (PCDD/Fs) and heavy metals by field stabilization/solidification (S/S) treatment for municipal solid waste incineration fly ash (MSWIFA) and multi-step leachate treatment. Modified European Community Bureau of Reference (BCR) speciation analysis and risk assessment code (RAC) revealed the medium environment risk of Cd and Mn, indicating the necessity of S/S treatment for MSWIFA. S/S treatment significantly declined the mass/toxic concentrations of PCDD/Fs (i.e., from 7.21 to 4.25 μg/kg; from 0.32 to 0.20 μg I-TEQ/kg) and heavy metals in MSWIFA due to chemical fixation and dilution effect. The S/S mechanism of sodium dimethyldithiocarbamate (SDD) and cement was decreasing heavy metals in the mild acid-soluble fraction to reduce their mobility and bioavailability. Oxidation treatment of leachate reduced the PCDD/F concentration from 49.10 to 28.71 pg/L (i.e., from 1.60 to 0.98 pg I-TEQ/L) by suspension absorption or NaClO oxidation decomposition, whereas a so-called “memory effect” phenomena in the subsequent procedures (adsorption, press filtration, flocculating settling, slurry separation, and carbon filtration) increased it back to 38.60 pg/L (1.66 pg I-TEQ/L). Moreover, the multi-step leachate treatment also effectively reduced the concentrations of heavy metals to 1–4 orders of magnitude lower than the national emission standards. Furthermore, the PCDD/Fs and heavy metals in other multiple media (soil, landfill leachate, groundwater, and river water) and their spatial distribution characteristics site were also investigated. No evidence showed any influence of the landfill on the surrounding liquid media. The slightly higher concentration of PCDD/Fs in the soil samples was ascribed to other waste management processes (transportation and unloading) or other local source (hazardous incineration plant). Therefore, proper management of landfills and leachate has a negligible effect on the surrounding environment.

Black carbon (BC) is the most potent light-absorbing component of particulate matter and can have a significant warming impact. On-road vehicles are a major source of BC and a significant contributor to global warming. This paper establishes an updated inventory to quantify the mitigation potential of efforts to control BC emissions from on-road transportation in China. The total emissions of BC from on-road vehicles in China were 152.1 thousand tons in 2017. Heavy-duty diesel fleets accounted for a large percentage of emissions, whereas light-duty gasoline fleets presented a gradually increasing trend of emissions. Historically, comprehensive control policies for on-road vehicle emissions have achieved substantial BC reductions, with a 45% decrease in 2017 compared to 2000. With the implementation of stringent control policies and the development of advanced control technologies, BC emissions from the on-road sector may have a greater reduction potential in the future. By 2035, three various future scenarios representing different stringency levels of emission controls will reduce BC emissions by 58%, 90%, and 93% relative to 2017. The major benefits in reducing BC emissions result from more stringent emission standards and the accelerated retirement of older heavy-duty diesel vehicles. The shorter lifetime of BC than that of CO₂ implies that the mitigation of BC emissions would offer an important opportunity to contribute to alleviating global warming in the short term. Our assessment reveals that in 2035, the most stringent scenario, Scenario PC3, could deliver a CO₂-equivalent emission reduction on a 20-year scale of 234.2 (GWP₂₀₋yᵣ) million tons compared with the NAP Scenario, which is equivalent to reducing the oil consumption in China’s transportation sector by nearly 20% from a climate impact perspective.

Selenium (Se) radioactive wastes can be disposed through stabilization/solidification (S/S) based on the cementitious matrix on hydration products, where hydrocalumite (Ca₂Al-LDH) is expected to play an important role in the retention of SeO₄²⁻. Natural organic matters (NOMs) are known to be a risk to affect the transportation and mobility of undesirable chemical species in the pedosphere which receives the low level radioactive wastes (LLW). In the present work, five amino acids were selected as the simplified models of NOMs in the pedosphere to explore their effects on the stability of Ca₂Al-LDH after immobilized SeO₄²⁻ under alkaline conditions. As the loading amount of amino acids on Ca₂Al-LDH increasing, release of SeO₄²⁻ was enhanced in HGly, H₂Asp, and H₂Cys series, while no enhancement was observed in HPhe and HTrp series. Density functional theory (DFT) calculation predicted ion-exchange of amino acids and CO₃²⁻ with SeO₄²⁻ in a unit cell of LDH model. The intercalation of Asp²⁻ and CO₃²⁻ caused 003 peaks in XRD sharper and d₀₀₃ decreased from 8.15 Å to 7.70 Å which is assigned to Ca₂Al-LDH(Asp, CO₃). In H₂Cys series, the 003 peaks were kept broad and SeO₄²⁻ was still relatively maintained in LDH which was caused by the lower amounts of intercalated CO₃²⁻ in the presence of H₂Cys. Amino acids in the interlayer of Ca₂Al-LDH have several possible configurations, where the most stable one is prone to be in a horizontal direction through hydrogen bonds and Ca–O chemical bonds. This provides an insight on the stability of selenate immobilized in hydrocalumite, which can be produced in cement disposing in the pedosphere for a long term of burying. Not only carbonate but also small molecular organic matters like amino acids possibly give environmental impact on the mobility of low level anionic radionuclides in LDH.