An improved method to predict polycyclic aromatic hydrocarbons in surface freshwater by reducing the input parameters

Comparison of PAH mass emissions and their toxicity-weighted trends in China, 2000-2022

Polycyclic aromatic hydrocarbons (PAHs) emissions are often used as direct evidence for subsequent environmental monitoring, governance, and policy-making. However, the toxicity among individual PAH varies significantly, causing their toxicity-based risk to be overlooked. The analysis revealed that total PAH emissions (EΣPAHs) has peaked in 2007 (i.e., 1.1 × 105 t), then declined to 2022 (i.e., 7.4 × 104 t) in China. However, the toxicity-weighted PAH emissions (EΣTEQ) has not decreased, because the emissions of high molecular weight PAH (EΣHPAHs) with high toxicity has not decreased, although the emissions of low molecular weight PAH (EΣLPAHs) with low toxicity has decreased significantly. It was verified that the trend of variation in PAH emissions is comprehensively determined by the decrease in biomass and increase in fossil fuel consumption. The decline in EΣLPAHs from biomass burning exceeded emission increases attributable to fossil fuel combustion, driving an overall reduction in total LPAHs post-2007 of China. In contrast, EΣHPAHs exhibited temporal variability after 2007, as emission reductions from biomass burning were offset by comparable increases from fossil fuel. Despite efforts to reduce emissions of conventional atmospheric pollutants, EΣTEQ have not decreased due to the rising fossil fuel consumption and limited pollution control efficiency on EΣHPAHs. The findings addressed the gap between PAH mass emissions and their toxicity-weighted trends, which enabled us to pay attention to the variation of EΣTEQ for accurate risk management to any region with shifting PAH source profiles. Moreover, emphasis should be placed on transitioning fossil fuel consumption to clean energy and implementing effective measures to reduce EΣHPAHs in flue gas, such as adsorption and degradation.

Effects of Chinese "double carbon strategy" on soil polycyclic aromatic hydrocarbons pollution

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.

Drivers distinguishing of PAHs heterogeneity in surface soil of China using deep learning coupled with geo-statistical approach

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.

Optimized Derivation of Predicted No-Effect Concentrations (PNECs) for Eight Polycyclic Aromatic Hydrocarbons (PAHs) Using HC10 Based on Acute Toxicity Data

For persistent organic pollutants, a concern of environmental supervision, predicted no-effect concentrations (PNECs) are often used in ecological risk assessment, which is commonly derived from the hazardous concentration of 5% (HC₅) of the species sensitivity distribution (SSD). To address the problem of a lack of toxicity data, the objectives of this study are to propose and apply two improvement ideas for SSD application, taking polycyclic aromatic hydrocarbons (PAHs) as an example: whether the chronic PNEC can be derived from the acute SSD curve; whether the PNEC may be calculated by HC₁₀ to avoid solely statistical extrapolation. In this study, the acute SSD curves for eight PAHs and the chronic SSD curves for three PAHs were constructed. The quantity relationship of HC₅s between the acute and chronic SSD curves was explored, and the value of the assessment factor when using HC₁₀ to calculate PNEC was derived. The results showed that, for PAHs, the chronic PNEC can be estimated by multiplying the acute PNEC by 0.1, and the value of the assessment factor corresponding to HC₁₀ is 10. For acenaphthene, anthracene, benzo[a]pyrene, fluoranthene, fluorene, naphthalene, phenanthrene, and pyrene, the chronic PNECs based on the acute HC₁₀s were 0.8120, 0.008925, 0.005202, 0.07602, 2.328, 12.75, 0.5731, and 0.05360 μg/L, respectively.