Soil collembolans live in close proximity to plant roots and may have a role in the phytoextraction of potentially toxic metals from contaminated soils but the underlying mechanisms remain poorly investigated. We hypothesize that soil collembolans may change the root morphology of hyperaccumulators by regulating plant physiological characteristics. Here, a pot experiment was conducted in which a cadmium (Cd) and zinc (Zn) hyperaccumulator (Sedum plumbizincicola) was grown with or without a collembolan (Folsomia candida), and plant transcriptome and hormones as well as the root characteristics of S. plumbizincicola were analyzed. F. candida promoted the growth and Cd/Zn uptake of S. plumbizincicola, the root and shoot biomass increasing by 53.3 and 34.4%, and the uptake of Cd and Zn in roots increased by 83.2 and 65.4%, respectively. Plant root morphology, total root length, root tip number and lateral root number increased significantly by 40.7, 37.2 and 33.8%, respectively, with the addition of F. candida. Transcriptome analysis reveals that the expression levels of defense-related genes in S. plumbizincicola were significantly up-regulated. In addition, the defensive plant hormones, i.e. salicylic acid in the roots, increased significantly by 338%. These results suggest that the plant in defense of the action of F. candida regulated the expression of the corresponding genes and increased the defensive plant hormones, thus modifying root morphology and plant performance. Overall, this study highlights the importance of the regulation by collembolans of plant growth and metal uptake by interaction with hyperaccumulator roots.

In agricultural fields, pesticides are widely used, but their residual presence in the environment poses a threat to humans, animals, insects, and ecosystems. The overuse of pesticides for pest control, enhancement of crop yield, etc. leaves behind a significant residual amount in the environment. Various robust, reliable, and reusable methods using a wide class of composites have been developed for the monitoring and controlling of pesticides. Researchers have discovered that carbon nanomaterials have a wide range of characteristics such as high porosity, conductivity and easy electron transfer that can be successfully used to detect pesticide residues from food. This review emphasizes the role of carbon nanomaterials in the field of pesticide residue analysis in different food matrices. The carbon nanomaterials including carbon nanotubes, carbon dots, carbon nanofibers, graphene/graphene oxides, and activated carbon fibres are discussed in the review. In addition, the review examines future prospects in this research area to help improve detection techniques for pesticides analysis.

Generally, Zn in stormwater runoff is considered as low toxicity, but in the senarios of roads and zinc-based materials roof runoff, the concentration of Zn becomes extremely high and cannot be ignored. Bioretention systems are used to remove heavy metals from stormwater runoff, while Zn adsorption is insufficient by conventional filler and is prone to secondary release when exposed to acid rain or high salinity runoff. This study integrated batch experiments and density functional theory calculation to investigate the mechanisms of how KOH-modified biochar (KBC) influences the removal and release of Zn in bioretention systems. The results revealed that KBC adsorbed 89.0–97.5% Zn in the influent, the main adsorption mechanism were complexation and precipitation, and precipitation is more important. In addition, 67% of Zn was immoblized as the residual form by KBC. In acidic and saline runoff, KBC reduced Zn secondary release by 43.6% and 37.08% compared to the results in the absence of KBC, which was attributed to the convertion of most dissolved Zn in acidic and saline runoff into residual Zn. Therefore, KBC has a considerable application potential not only to decontaminate the runoff of roads and Zn-containing roofs, but also to deal with secondary Zn release in acid rain or under the treatment of snow-melting agents.

The diurnal variation, gas-particle partitioning, health risks, and sources of polycyclic aromatic hydrocarbons (PAHs) were investigated in a northern basin city of China in winter, 2020. The mean concentrations of particulate and gaseous PAHs were 87.90 ng m⁻³ and 69.65 ng m⁻³, respectively, and their concentrations were considerably enhanced during the domestic heating period. The relationship between the gas-particle partitioning coefficient of PAHs (KP) and subcooled liquid vapor pressure of PAHs (PL⁰) indicated organic absorption as the mechanism for this partitioning. However, the dual sorption model confirmed adsorption onto elemental carbon (EC). The health risks indicated by several equivalent parameters showed an important health effect of PAHs, especially of particulate PAHs bound onto PM₂.₅ during the heating period. Environmentally persistent free radicals (EPFRs) were also studied as an auxiliary parameter to evaluate the health impact of PAHs. According to the diagnostic ratios of PAHs and PMF model results, petroleum volatilization and coal combustion were the dominant sources of particulate PAHs during the non-heating and heating periods, respectively. The source apportionment results can help efficiently control PAHs and their health risks.

During hydraulic fracturing, wastewaters - termed flowback and produced water (FPW) - are created as a by-product during hydrocarbon extraction. Given the large volumes of FPW that a single well can produce, and the history of FPW release to surface water bodies, it is imperative to understand the hazards that hydraulic fracturing and FPW pose to aquatic biota. Using rainbow trout embryos as model organisms, we investigated impacts to cardio-respiratory system development and function following acute (48 h) and sub-chronic (28-day) FPW exposure by examining occurrences of developmental deformities, rates of embryonic respiration (MO₂), and changes in expression of critical cardiac-specific genes. FPW-exposed embryos had significantly increased rates of pericardial edema, yolk-sac edema, and tail/trunk curvatures at hatch. Furthermore, when exposed at three days post-fertilization (dpf), acute 5% FPW exposures significantly increased embryonic MO₂ through development until 15 dpf, where a switch to significantly reduced MO₂ rates was subsequently recorded. A similar trend was observed during sub-chronic 1% FPW exposures. Interestingly, at certain specific developmental timepoints, previous salinity exposure seemed to affect embryonic MO₂; a result not previously observed. Following acute FPW exposures, embryonic genes for cardiac development and function were significantly altered, although at termination of sub-chronic exposures, significant changes to these same genes were not found. Together, our evidence of induced developmental deformities, modified embryonic MO₂, and altered cardiac transcript expression suggest that cardio-respiratory tissues are toxicologically targeted following FPW exposure in developing rainbow trout. These results may be helpful to regulatory bodies when developing hazard identification and risk management protocols concerning hydraulic fracturing activities.

Little is known about the main sources of ambient particulate matter (PM) in the 22 Eastern Mediterranean Region (EMR) countries. We designed this study to systematically review all published and unpublished source apportionment (SA), identification and characterization studies as well as emission inventories in the EMR. Of 440 articles identified, 82 (11 emission inventory ones) met our inclusion criteria for final analyses. Of 22 EMR countries, Iran with 30 articles had the highest number of studies on source specific PM followed by Pakistan (n = 15 articles) and Saudi Arabia (n = 8 papers). By contrast, there were no studies in Afghanistan, Bahrain, Djibouti, Libya, Somalia, Sudan, Syria, Tunisia, United Arab Emirates and Yemen. Approximately 72% of studies (51) were published within a span of 2015–2021.48 studies identified the sources of PM₂.₅ and its constituents. Positive matrix factorization (PMF), principal component analysis (PCA) and chemical mass balance (CMB) were the most common approaches to identify the source contributions of ambient PM. Both secondary aerosols and dust, with 12–51% and 8–80% (33% and 30% for all EMR countries, on average) had the greatest contributions in ambient PM₂.₅. The remaining sources for ambient PM₂.₅, including mixed sources (traffic, industry and residential (TIR)), traffic, industries, biomass burning, and sea salt were in the range of approximately 4–69%, 4–49%, 1–53%, 7–25% and 3–29%, respectively. For PM₁₀, the most dominant source was dust with 7–95% (49% for all EMR countries, on average). The limited number of SA studies in the EMR countries (one study per approximately 9.6 million people) in comparison to Europe and North America (1 study per 4.3 and 2.1 million people respectively) can be augmented by future studies that will provide a better understanding of emission sources in the urban environment.

Multiwalled carbon nanotubes (MWCNTs) were oxidized using a mixture of H₂SO₄ and HNO₃, and the oxidized MWCNTS were decorated with magnetite (Fe₃O₄). Finally, poly-N-isopropyl acrylamide-co-butyl acrylate (P-NIPAM) was added to obtain P-NIPAM/Fe/MWCNT nanocomposites. The nanosorbents were characterized by various techniques, including X-ray diffraction, transmission electron microscopy, scanning electron microscopy, thermogravimetric analysis, and Brunauer–Emmett–Teller analysis. The P-NIPAM/Fe/MWCNT nanocomposites exhibited increased surface hydrophobicity. Owing to their higher adsorption capacity, their kerosene removal efficiency was 95%; by contrast, the as-prepared, oxidized, and magnetite-decorated MWCNTs had removal efficiencies of 45%, 55%, and 68%, respectively. The P-NIPAM/Fe/MWCNT nanocomposites exhibited a sorbent capacity of 8.1 g/g for kerosene removal from water. The highest kerosene removal efficiency from water was obtained at a process time of 45 min, sorbent dose of 0.005 g, solution temperature of 40 °C, and pH 3.5. The P-NIPAM/Fe/MWCNTs showed excellent stability after four cycles of kerosene removal from water followed by regeneration. The reason may be the increase in the positive charge of the polymer at pH 3.5 and the increased adsorption affinity of the adsorbent toward the kerosene contaminant. The pseudo second-order model was found to be the most suitable model for studying the kinetics of the adsorption reaction.

Intra-urban pollution monitoring requires fine particulate (PM₂.₅) concentration mapping at ultrahigh-resolution (dozens to hundreds of meters). However, current PM₂.₅ concentration estimation, which is mainly based on aerosol optical depth (AOD) and meteorological data, usually had a low spatial resolution (kilometers) and severe spatial missing problem, cannot be applied to intra-urban pollution monitoring. To solve these problems, top-of-atmosphere reflectance (TOAR), which contains both the information about land and atmosphere and has high resolution and large spatial coverage, may be efficiently used for PM₂.₅ estimation. This study aims to systematically evaluate the feasibility of retrieving ultrahigh-resolution PM₂.₅ concentration at a large scale (national level) from TOAR. Firstly, we make a detailed discussion about several important but unsolved theoretic problems on TOAR-based PM₂.₅ retrieval, including the band selection, scale effect, cloud impact, and mapping quality evaluation. Secondly, four types and eight retrieval models are compared in terms of quantitative accuracy, mapping quality, model generalization, and model efficiency, with the pros and cons of each type summarized. Deep neural network (DNN) model shows the highest retrieval accuracy, and linear models were the best in efficiency and generalization. As a compromise, ensemble learning shows the best overall performance. Thirdly, using the highly accurate DNN model (cross-validated R² equals 0.93) and through combining Landsat 8 and Sentinel 2 images, a 90 m and ∼4-day resolution PM₂.₅ product was generated. The retrieved maps were used for analyzing the fine-scale interannual pollution change inner the city and the pollution variations during novel coronavirus disease 2019 (COVID-19). Results of this study proves that ultrahigh resolution can bring new findings of intra-urban pollution change, which cannot be observed at previous coarse resolution. Lastly, some suggestions for future ultrahigh-resolution PM₂.₅ mapping research were given.

Reliable long-horizon PM₂.₅ forecasts are crucial and beneficial for health protection through early warning against air pollution. However, the dynamic nature of air quality makes PM₂.₅ forecasts at long horizons very challenging. This study proposed a novel machine learning-based model (MCNN-BP) that fused multiple convolutional neural networks (MCNN) with a back-propagation neural network (BPNN) for making spatiotemporal PM₂.₅ forecasts for the next 72 h at 74 stations covering the whole Taiwan simultaneously. Model configuration involved an ensemble of massive hourly air quality and meteorological monitoring datasets and the existing publicly-available PM₂.₅ simulated (forecasted) datasets from an atmospheric chemical transport (ACT) model. The proposed methodology collaboratively constructed two CNNs to mine the observed data (the past) and the forecasted data from ACT (the future) separately. The results showed that the MCNN-BP model could significantly improve the accuracy of spatiotemporal PM₂.₅ forecasts and substantially reduce the forecast biases of the ACT model. We demonstrated that the proposed MCNN-BP model with effective feature extraction and good denoising ability could overcome the curse of dimensionality and offer satisfactory regional long-horizon PM₂.₅ forecasts. Moreover, the MCNN-BP model has considerably shorter computational time (5 min) and lower computational load than the compute-intensive ACT model. The proposed approach hits a milestone in multi-site and multi-horizon forecasting, which significantly contributes to early warning against regional air pollution.

The application of exogenous biodegradation strains in pesticide-polluted soils encounters the challenges of migration and persistence of inoculants. In this study, the degradation characteristics, vertical migration capacity, and microbial ecological risk assessment of an enhanced green fluorescent protein (EGFP)-tagged 2-Methyl-4-chlorophenoxyacetic acid (MCPA)-degrading strain Cupriavidus gilardii T1 (EGFP) were investigated in the laboratory and field soils. The optimum remediation conditions for T1 (EGFP) was characterized in soils. Meanwhile, leaching experiments showed that T1 (EGFP) migrated vertically downwards in soil and contribute to the degradation of MCPA at different depths. After inoculation with T1 (EGFP), a high expression levels of EGFP gene was observed at 28 d in the laboratory soil and at 45 d in the field soil. The degradation rates of MCPA were ≥ 60% in the laboratory soil and ≥ 48% in the field soil, indicating that T1 (EGFP) can efficiently and continuously remove MCPA in both laboratory and field conditions. In addition, the inoculation of T1 (EGFP) not only showed no significant impact on the soil microbial community structure but also can alleviate the negative effects induced by MCPA to some extent. Overall, our findings suggested that T1 (EGFP) strain is an ecologically safe resource for the in situ bioremediation of MCPA-contaminated soils.