Mycoremediation of unsterilized PCDD/F-contaminated field soil was successfully demonstrated by solid-state fermentation coupled with Pleurotus pulmonarius utilizing a patented incubation approach. The experiments were carried out in four setups with two as controls. The contaminated soil was homogenously mixed with solid inocula, 1:0.5 dry w/w, resulting in an initial concentration of 4432 ± 623 ng WHO-TEQ kg⁻¹. After a 30-day incubation under controlled conditions, the overall removal (approx. 60%) was non-specific. The removal was attributed to degradation by extracellular ligninolytic enzymes and uptake into the fruiting tissue (~110 ng WHO-TEQ kg⁻¹ of mushroom). Furthermore, less recalcitrant chlorinated metabolites were found, implying ether bond cleavage and dechlorination happened during the mycoremediation. These metabolites resulted from the complex interaction between P. pulmonarius and the indigenous microbes from the unsterilized soil. This study provides a new step toward scaling up this mycoremediation technique to treat unsterilized PCDD/F-contaminated field soil.

Thiacloprid is a neonicotinoid insecticide widely exploited in agriculture and easily mobilized towards aquatic environments by atmospheric agents. However, little information about its toxicological effects on aquatic invertebrate bioindicators is available. In this study, specimens of the mussel Mytilus galloprovincialis were exposed to thiacloprid at environmental (4.5 μg L⁻¹) and 100 times higher than environmental (450 μg L⁻¹) concentrations for 20 days. Thiacloprid affected haemolymph biochemical parameters, cell viability in the digestive gland, antioxidant biomarkers and lipid peroxidation in the digestive gland and gills at environmentally relevant concentrations (4.5 μg L⁻¹). In addition, thiacloprid exposure caused histological damage to the digestive gland and gills. Interestingly, the pesticide was detected at levels equal to 0.14 ng g⁻¹ in the soft tissues of sentinels exposed for 20 days to 450 μg L⁻¹ thiacloprid in seawaterμ. Due to its harmful potential and cumulative effects after long-term exposure of M. galloprovincialis, thiacloprid may pose a potential risk to nontarget aquatic organisms, as well as to human health. This aspect requires further in-depth investigation.

Cigarette smoke (CS) affects immune functions, leading to severe outcomes in smokers. Robust evidence addresses the immunotoxic effects of combustible tobacco products. As heat-not-burn tobacco products (HNBT) vaporize lower levels of combustible products, we here compared the effects of cigarette smoke (CS) and HNBT vapor on Jurkat T cells. Cells were exposed to air, conventional cigarettes or heatsticks of HNBT for 30 min and were stimulated or not with phorbol myristate acetate (PMA). Cell viability, proliferation, reactive oxygen species (ROS) production, 8-OHdG, MAP-kinases and nuclear factor κB (NFκB) activation and metallothionein expression (MTs) were assessed by flow cytometry; nitric oxide (NO) and cytokine levels were measured by Griess reaction and ELISA, respectively. Levels of metals in the exposure chambers were quantified by inductively coupled plasma mass spectrometry. MT expressions were quantified by immunohistochemistry in the lungs and liver of C57Bl/6 mice exposed to CS, HNBT or air (1 h, twice a day for five days: via inhalation). While both CS and HBNT exposures increased cell death, CS led to a higher number of necrotic cells, increased the production of ROS, NO, inflammatory cytokines and MTs when compared to HNBT-exposed cells, and led to a higher expression of MTs in mice. CS released higher amounts of metals. CS and HNBT exposures decreased PMA-induced interleukin-2 (IL-2) secretion and impaired Jurkat proliferation, effects also seen in cells exposed to nicotine. Although HNBT vapor does not activate T cells as CS does, exposure to both HNBT and CS suppressed proliferation and IL-2 release, a pivotal cytokine involved with T cell proliferation and tolerance, and this effect may be related to nicotine content in both products.

Biomass burning, a recurring global phenomenon is also considered an environmental menace, making headlines every year in India with onset of autumn months. Agriculture is demographically the broadest economic sector and plays a significant role in the overall socio-economic fabric of India. Hence, disposal of crop residue is done mainly by burning leading to deterioration of air quality. Residue burning in parts of India is blamed for changing air quality in nearby cities. The spatial distribution of these emissions has always been a challenge due to various data constraints. We hereby present a comprehensive spatially resolved seasonal high resolution gridded (∼10 km × ∼10 km) emission inventory of major pollutants from crop residue burning source in India for the latest year 2018. The winter months contributes almost around ∼50% of total emission followed by summer (∼48%), which is the prime cause of changing air quality in nearby cities. Among all the crops; rice, wheat, maize and sugarcane accounts ∼90% of total PM₁₀ load in the country. The estimated emission for PM₂.₅, PM₁₀, BC and OC, CO, NOx, SO₂, VOC, CH₄ and CO₂ are found to 990.68 Gg/yr, 1231.26 Gg/yr, 123.33 Gg/yr, 410.99 Gg/yr, 11208.18 Gg/yr, 484.55 Gg/yr, 144.66 Gg/yr, 1282.95 Gg/yr, 785.56 Gg/yr and 262051.06 Gg/yr respectively. The cropping pattern and its role in different geographic regions are analysed to identify all potential emission hotspots regions scattered across the country. The developed gridded emissions inventory is envisaged to serve as an important input to regional atmospheric chemistry transport model to better quantify its contribution in deteriorating air quality in various regions of India, paving the way to policy makers to better plan the mitigation and control strategies. The developed fundamental tool is likely to be useful for air quality management.

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.