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Polycyclic aromatic hydrocarbons (PAHs) and carbon dioxide primarily originate from the combustion of fossil fuels and biomass. The implementation of the Chinese "double carbon strategy" is expected to impact the distribution of PAH emissions, consequently influencing the spatial distribution trend of PAHs in surface soil. Therefore, it is crucial to quantitatively evaluate the effectiveness of the Chinese "double carbon strategy" on soil PAH pollution for the purpose of "the reduction of pollution and carbon emissions". This study utilized 15,088 individual PAH concentration data from 943 soil samples collected between 2003 and 2020 in China, in conjunction with PAH emissions at a 10 km resolution, for meta-analysis. The calculated PAH emissions in this study are in line with the global PAH emission inventory (PKU-PAH-2007), with a relative standard deviation at the provincial level of less than 25 %. Subsequently, a novel method was developed using emission density and Kow of PAHs to predict PAH concentrations in surface soil based on a least-squares regression model. Compared to other environmental models, the method established in this study significantly reduced the percent sample deviation to less than 70 %. Furthermore, energy consumption data for China were simulated based on the implementation plan of the "double carbon strategy" to project PAH emissions and soil PAH levels for the years 2030 and 2060. The predicted PAH emissions in China were estimated to decrease to 41,300 t in 2030 and 10,406.5 t in 2060 from 78,815 t in 2020. Moreover, the heavily contaminated areas of soil PAHs (i.e., total PAH concentrations in soil exceeding 1000 µg kg-1) were projected to decrease by 45 % and 82 % in 2030 and 2060, respectively, compared to levels in 2020. These findings suggest that the implementation of the "double carbon strategy" can fundamentally reduce the pollution of PAHs in surface soil of China.

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Although numerous studies have reported the influencing factors of polycyclic aromatic hydrocarbons (PAHs) in surface soil from source, process or soil perspectives, the mechanism of PAHs heterogeneity in surface soil are still not well understood. In this study, the effects of 16 PAHs in surface soil of China sampled between 2003 and 2020 with their 17 "source-process-sink" factors at 1 km resolution (N = 660)) were explored using deep learning (eXtreme Gradient Boosting) to mine key information from complex dataset under the optimized parameters (i.e., learning rate = 0.05, maximum depth = 5, sub-sample = 0.8). It was observed that top five factors of 16 PAH had the largest cumulative contribution (i.e., from 84.8% to 98.1%) on their soil concentrations. PAH emission was the predominant driver, and its effect on soil PAH increases with increasing logKow. Soil was the second driver, in which clay can promote the partition of PAHs with low or middle logKow. However, sand can accumulate those congeners with high logKow. Moreover, the deep learning plus geo-statistical models (with low deviation for testing dataset (N = 283)) were capable of predicting soil PAH concentrations using their drivers with high accuracy. This study improved the understanding of the environmental fate and spatial variability of soil PAHs, as well as provided a novel technique (i.e., deep learning coupled with geo-statistics) for accurate prediction of soil pollutants.

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A national scale survey was conducted to determine an array of inorganic and organic contaminants in agricultural soils from two cultivation modes (greenhouse vs. open field) in 20 provinces across China. The investigated contaminants include organochlorine pesticides (OCPs), phthalate esters (PAEs), polycyclic aromatic hydrocarbons (PAHs), lead (Pb), zinc (Zn), copper (Cu) and cadmium (Cd). The large amounts of agrochemicals used and special cultivation mode in greenhouse caused substantial soil pollution and deterioration of soil quality. Mean concentrations of both OCPs and PAEs in greenhouse soil were approximately 100% higher than those in open field. The pH values were 6.85 ±â€¯1.04 and 7.34 ±â€¯0.84 for greenhouse and open field, respectively (p > 0.05). The soil microbial community was predicted to be affected by pollution in greenhouse through the PICRUSt analysis of 16s rRNA sequences. The 12 variables including various chemicals and soil properties together explained 15% of the observed variation in the community composition. In the studied variables, PAEs and lead were the primary factors affecting microbial diversity in greenhouse soils, while pH had the greatest impact on the microbial community in open field soils. These findings enhanced our understanding of the environmental impact and contamination management of greenhouses worldwide.

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This study aims to reveal the microbial mechanism of in situ surfactant-enhanced bioremediation (SEBR). Various concentrations of rhamnolipids, Tween 80, and sodium dodecyl benzenesulfonate (SDBS) were separately sprayed onto soils contaminated with polycyclic aromatic hydrocarbons (PAHs) for years. Within 90 days, the highest level of degradation (95 %) was observed in the soil treated with rhamnolipids (10 mg/kg), followed by 92 % degradation with Tween 80 (50 mg/kg) and 90 % degradation with SDBS (50 mg/kg). The results of the microbial phospholipid fatty acids (PLFAs) suggest that bacteria dominated the enhanced PAH biodegradation (94 % of the maximum contribution). The shift of bacterial community structure during the surfactant treatment was analyzed by using the 16S rRNA gene high-throughput sequencing. In the presence of surfactants, the number of the operational taxonomic units (OTUs) associated with Bacillus, Pseudomonas, and Sphingomonas increased from 2-3 to 15-30 % at the end of the experiment (two to three times of control). Gene prediction with phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) shows that the PAH-degrading genes, such as 1-hydroxy-2-naphthoate dioxygenase and PAH dioxygenase large subunit, significantly increased after the surfactant applications (p < 0.05). The findings of this study provide insights into the surfactant-induced shifts of microbial community, as well as critical factors for efficient bioremediation.

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Surfactants can affect the biodegradation process and the fate of hydrophobic organic compounds (HOCs) in the environment. Previous studies have shown that surfactants can enhance the biodegradation of HOCs by increasing cell surface hydrophobicity (CSH) and membrane fluidity. In this study, we took this work one step further by investigating the expression levels of three genes of Arthrobacter sp. SA02 in the biodegradation of phenanthrene as a typical HOC at different concentrations of sodium dodecyl benzenesulfonate (SDBS), which is a widely used surfactant. The Δ9 fatty acid desaturase gene codes for Δ9 fatty acid desaturase, which can convert saturated fatty acid to its unsaturated form. The ring-hydroxylating dioxygenase (RHDase) and the 1-hydroxyl-2-naphthoate dioxygenase (1H2Nase) genes code for the RHDase and 1H2Nase enzymes, respectively, which play a key role in decomposing doubly hydroxylated aromatic compounds. The results show that these three genes were upregulated in the presence of SDBS. On the basis of the genetic and physiological changes, we proposed a pathway that links the gene expression with the physiological phenomena, including CSH, membrane fluidity, and intracellular degradation. This study advances our understanding of the surfactant-enhanced biodegradation of HOCs at the gene level, and the proposed pathway should be further validated in the future.

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