The high toxicity and persistence of polychlorinated biphenyls (PCBs) in the environment demands the development of effective remediation for PCBs-contaminated soils. In this study, electrokinetic (EK) remediation integrated with iron-carbon material (Fe/C) was established and used to remediate PCB28 (1 mg kg⁻¹) contaminated soil under a voltage gradient of 1 V cm⁻¹. Effects of Fe/C dosage, soil type, and remediation time were investigated. The operational condition was optimized as 4 g kg⁻¹ Fe/C, yellow soil, and 14 d-remediation, achieving PCB28 removal efficiency of 58.6 ± 8.8% and energy utilization efficiency of 146.5. Introduction of EK-Fe/C did not significantly affect soil properties except for slight soil moisture content increase and total Fe content loss. Soil electrical conductivity exhibited an increasing trend from anode to cathode attributed to EK-induced electromigration and electroosmosis. EK accelerated the corrosion and consumption of reactive Fe⁰/Fe₃C in Fe/C by generating acid condition. Fe/C in turn effectively prevented EK-induced soil acidification and maintained soil neutral to weak alkaline condition. A synergistic effect between EK and Fe/C was revealed by the order of PCB28 removal efficiency-EK-Fe/C (58.6 ± 8.8%) > EK (37.7 ± 1.6%) > Fe/C (6.8 ± 5.0%). This could be primarily attributed to EK and Fe/C enhanced Fenton reaction, where EK promoted Fe/C dissolution and H₂O₂ generation. In addition to oxidation by Fenton reaction generated ·OH, EK-mediated electrochemical oxidation, Fe/C-induced reduction and migration of Fe/C adsorbed PCBs were all significant contributors to PCB28 removal in the EK-Fe/C system. These findings suggest that the combination of EK and Fe/C is a promising technology for remediation of organics-contaminated soil.

In this study, the biochar (BC) supported Fe–Cu bimetallic stabilized by PVP (Fe–Cu/PVP/BC) were prepared and utilized to enhance the nitrate (NO₃⁻) removal and the selectivity toward nitrogen (N₂). Results showed the optimum Fe:Cu:BC ratio and the dosage of the BC (pyrolysis at 700 °C) supported Fe–Cu bimetallic stabilized by polyvinylpyrrolidone (PVP) (Fe–Cu/PVP/BC₇₀₀) were respectively 1:2:3 and 1 mg L⁻¹ with the selectivity toward N₂ of 31 %. This was mainly due to the synergy among Fe⁰, Cu⁰ and BC in the Fe–Cu/PVP/BC. The addition of Fe⁰ could reduce the NO₃⁻ through providing electron. The Cu⁰ and BC improved the selectivity of NO₃⁻ to N₂ through forming [Cu–NO₂⁻ₐdₛ] and adjusting redox potential. The addition of Fe–Cu/PVP/BC could supply electrons for denitrification and enhance the relative abundances of Azospira and Thauera related to denitrification to improve NO₃⁻ removal. This result was further confirmed by the variations of denitrifying functional genes (narG, nirK, nirS and nosZ). This research provided an effective method to improve NO₃⁻ removal during surface water treatment in constructed wetlands (CWs) by adding Fe–Cu/PVP/BC.

Between 9 March and 18 May 2020, strict lockdown measures were adopted in Italy for containing the COVID-19 pandemic: in Rome, despite vehicular traffic on average was more than halved, it was not observed a evident decrease of the airborne particulate matter (PM) concentrations, as assessed by air quality data. In this study, daily PM₁₀ filters were collected from selected automated stations operated in Rome by the regional network of air quality monitoring: their magnetic properties – including magnetic susceptibility, hysteresis parameters and FORC (first order reversal curves) diagrams - were compared during and after the lockdown, for outlining the impact of the COVID-19 measures on airborne particulate matter. In urban traffic sites, the PM₁₀ concentrations did not significantly change after the end of the lockdown, when vehicular traffic promptly returned to its usual levels; conversely, the average volume and mass magnetic susceptibilities approximately doubled, and the linear correlation between volume magnetic susceptibility and PM₁₀ concentration became significant, pointing out the link between PM₁₀ concentrations and the increasing levels of traffic-related magnetic emissions. Magnetite-like minerals, attributed to non-exhaust brakes emissions, dominated the magnetic fraction of PM₁₀ near urban traffic sites, with natural magnetic components emerging in background sites and during exogenous dusts atmospheric events. Magnetic susceptibility constituted a fast and sensitive proxy of vehicular particulate emissions: the magnetic properties can play a relevant role in the source apportionment of PM₁₀, especially when unsignificant variations in its concentration levels may mask important changes in the traffic-related magnetic fraction. As a further hint, increasing attention should be drawn to the reduction of brake wear emissions, that are overcoming by far fuel exhausts as the main particulate pollutant in traffic contexts.

Volatile organic compounds (VOCs) poses a serious health risk through not only their own toxicity but also their role as precursors of ozone and secondary organic aerosols. The chemical industry, as one of the pillar industries in eastern China, is a key source of VOCs emissions. In this study, speciated VOCs emissions were measured in two chemical plants in eastern China. Oxygenated VOCs and aromatics were found to be the dominant species categories in both plants. The ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) of VOCs from dedicated resin production were both higher than general resin production. Three process-based models were used for the estimation of VOCs emissions from the two tested plants as a case study. The comparison between the emission factor model and the model with best available estimation methods (e.g., the measurement-based method, the mass balance method, the empirical formula method, and the correlation equation method) implied possible overestimation of the widely used emission factor model for the chemical industry. The probabilistic model developed in this study incorporated probability distribution of key parameters and proved to be a promising tool for emission inventory development and uncertainty analysis. The overall uncertainties of VOCs emissions based on the model were (−48%, +147%) and (−48%, +139%) for the two tested plants. In this study, the speciation profiles and estimation methodology for VOCs emissions from the chemical industry in China were both improved, which could benefit the accurate evaluation of the impacts of VOCs emissions.

Mapping soil contamination enables the delineation of areas where protection measures are needed. Traditional soil sampling on a grid pattern followed by chemical analysis and geostatistical interpolation methods (GIMs), such as Kriging interpolation, can be costly, slow and not well-suited to highly heterogeneous soil environments. Here we propose a novel method to map soil contamination by combining high-resolution aerial imaging (HRAI) with machine learning algorithms. To support model establishment and validation, 1068 soil samples were collected from an arsenic (As) contaminated area in Zhongxiang, Hubei province, China. The average arsenic concentration was 39.88 mg/kg (SD = 213.70 mg/kg), with individual sample points determined as low risk (66.9%), medium risk (29.4%), or high risk (3.7%), respectively. Then, identified features were extracted from a HRAI image of the study area. Four machine learning algorithms were developed to predict As risk levels, including (i) support vector machine (SVM), (ii) multi-layer perceptron (MLP), (iii) random forest (RF), and (iii) extreme random forest (ERF). Among these, we found that the ERF algorithm performed best overall and that its prediction performance was generally better than that of traditional Kriging interpolation. The accuracy of ERF in test area 1 reached 0.87, performing better than RF (0.81), MLP (0.78) and SVM (0.77). The F1-score of ERF for discerning high-risk points in test area 1 was as high as 0.8. The complexity of the distribution of points with different risk levels was a decisive factor in model prediction ability. Identified features in the study area associated with fertilizer factories had the most important contribution to the ERF model. This study demonstrates that HRAI combined with machine learning has good potential to predict As soil risk levels.

Nitrous acid (HONO) production from the heterogeneous photochemical reaction of NO₂ on several Chinese soils was performed in a cylindrical reactor at atmospheric pressure. The NO₂ uptake coefficient (γ) and HONO yield (YHONO) on different soils were (0.42–5.16) × 10⁻⁵ and 6.3%–69.6%, respectively. Although the photo-enhanced uptake of NO₂ on different soils was observed, light could either enhance or inhibit the conversion efficiency of NO₂ to HONO, depending on the properties of the soils. Soils with lower pH generally had larger γ and YHONO. Soil organics played a key role in HONO formation through the photochemical uptake of NO₂ on soil surfaces. The γ showed a positive correlation with irradiation and temperature, while it exhibited a negative relationship with relative humidity (RH). YHONO inversely depended on the soil mass (0.32–3.25 mg cm⁻²), and it positively relied on the irradiance and RH (7%–22%). There was a maximum value for YHONO at 298 K. Based on the experimental results, HONO source strengths from heterogeneous photochemical reaction of NO₂ on the soil surfaces were estimated to be 0.2–2.7 ppb h⁻¹ for a mixing layer height of 100 m, which could account for the missing daytime HONO sources in most areas.

Red mud and phosphogypsum are voluminous industrial by-products worldwide. They have long been disposed of in landfills or open storage, leading to a waste of resource and environmental pollution. This study provides a novel approach to recycle these industrial by-products as sustainable red mud-phosphogypsum-Portland cement (RPPC) binders for stabilization/solidification (S/S) of multimetal-contaminated soil. The physical strength, metal leachability and microstructure of S/S soil were investigated after 7-day and 28-day curing, as well as freezing-thawing (F-T) cycle and wetting-drying (W-D) cycle. The results show that the strength of soil treated by all binders fulfilled the uniaxial compressive strength requirement (350 kPa) of S/S waste in landfills. Microstructural analyses show that the main hydration products of the RPPC S/S soil are ilmenite, ettringite, anhydrite and hydrated calcium silicate. The 10% and 15% RPPC binders have a competitive metal immobilization ability compared with 10% PC, but the immobilization priority is different: Pb > Zn > Cd in RPPC system and Zn > Cd > Pb in PC system, respectively, probably due to the precipiataion of Pb²⁺ with the abundant SO₄²⁻ in phosphogypsum in RPPC system. The strength of RPPC and PC treated soil was still higher than 350 kPa except for RPPC7.5 after 10 freeze-thaw or 10 wetting-drying cycles. The RPPC binder performed worse than PC binder after both freeze-thaw and wetting-drying cycles, especially at a lower dosage. Only the metal leaching concentrations of samples treated by RPPC15 and PC10 could fulfil the Chinese standards for hazardous wastes.

Estimating accurately airborne pollutant emissions source information (source strength and location) is important for achieving effective air pollution management or adequate emergency responses to accidents. Inversion method is one of the useful tools to identify the source parameters. The atmospheric dispersion scheme has been proven to be the key to determining the source inversion performance by influencing the accuracy of the dispersion models. Modifying the atmospheric dispersion scheme is an important potential method to improve the inversion performance, but this has not been studied previously. To fill this gap, a novel approach for parameter sensitivity analysis combined with an optimization method was proposed to improve the source inversion performance by optimizing empirical scheme. The dispersion coefficients σy and σz of the typical BRIGGS scheme under different atmospheric dispersion conditions were optimized and used for air pollutant dispersion and source inversion. The results showed that the prediction performance of the air pollutant concentrations was greatly improved with statistical indices |FB| and NMSE decreased by 0.22 and 2.07, respectively; FAC2 and R increased by 0.10, and 0.08, respectively. For source inversion, the results of the significance analysis suggested that the accuracy in the source strength and location parameter (x0) were both significantly improved by ∼271% (relative deviation reduced from 60.0% to 16.2%) and ∼121% (absolute deviation reduced from 27.6 to 12.5 m). The improvement of source strength inversion accuracy was more significant under unstable atmospheric conditions (stability class A, B, and C); the mean absolute relative deviation was reduced by 97.5%. These results can help to obtain more accurate source information and to provide reliable reference for air pollution managements or emergency response to accidents. This study provides a novel and versatile approach to improve estimation performance of pollutant emission sources and enhances our understanding of source inversion.

Nitrogen is the main nutrient in soil. The long-term addition of N leads to changes in the soil dissolved organic matter (DOM) and other quality indicators, which affects the adsorption and accumulation of organic pollutants. The use of organic fertilizer is important for the development of green agriculture. However, organic fertilizers (especially sludge organic fertilizers (SOFs) contain phthalates (PAEs) that may accumulate in the soil and result in environmental contamination. How this accumulation response varies with the magnitude of long-term N addition, especially in different soil layer profiles, remains unclear. Here, changes in the content of PAEs in the soil–plant system without and after SOFs application were studied through field experiments in soils with different N addition backgrounds (CK, N1, N3 (0, 100, 300 kg N ha⁻¹ yr⁻¹ respectively)). Our results showed that the application of SOFs increase the accumulation of PAEs in soil profiles and plant systems, increasing human health risks. The content of Σ₅PAEs in the topsoil increased from 0.96 ± 0.10 to 1.86 ± 0.09 mg kg⁻¹. Moreover, under a high N addition background and SOFs application, the characteristics of soil DOM change, and the accumulation of PAEs in soil was nearly 30% higher compared with the low N group. Some suggestions such as removing PAEs from SOFs during preparation, conducting soil surveys before applying PAEs, and using soil amendments, which are provided for optimizing the trialability and environmental safety of SOFs application.