Seasonal variability in multimedia transport and fate of benzo[a]pyrene (BaP) affected by climatic factors

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.

The past 40 years' assessment of urban-rural differences in Benzo[a]pyrene contamination and human health risk in coastal China

China has implemented many environmental regulations to battle against polycyclic aromatic hydrocarbon (PAH) contamination since the 1990s. It remains unclear how the exposure levels of PAHs changed quantitatively since reform and opening up in 1978 in China, whether the human health risks decreased or not, and how about the discrepancy between urban and rural areas. Here, taking Benzo[a]pyrene (BaP) in the rapidly urbanized Bohai region of China as a case, we used the improved Berkeley-Trent-Urban-Rural model to simulate the multimedia concentrations of BaP from 1980 to 2020 based on BaP emissions at a regional scale. The total emission of BaP in 1990 was the highest, with a value of 240 t, while the urban emission peaked in 2010. The BaP emissions from rural areas were two to seven times higher than urban areas, and the differences became smaller over time. Despite this, the average modeled BaP concentrations in urban air and soil were two to tens fold higher than in rural areas, particularly in highly urbanized or industrialized cities. Mostly, the concentrations of BaP in rural areas peaked in 1990, while those in urban areas peaked in 1990 or 2010. Early urbanized Beijing and Tianjin were the hot-spot cities of BaP contamination before 2000, while after 2010, higher concentrations were found in late industrialized Shandong and Hebei. BaP posed potential cancer risks to local residents, and air inhalation accounted for more than 80 % of the total risk. Under the stronger implementation of environmental regulations since the 1990s, it showed great health benefits, particularly for the urban residents in Beijing and Tianjin. The biggest decline in cancer risk was found in the period 2010-2020, and the average decreasing rates were 61.4 % and 57.4 % for urban and rural areas, respectively.

Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate

Polycyclic aromatic hydrocarbons (PAHs) refer to a group of several hundred compounds, among which 16 are identified as priority pollutants, due to their adverse health effects, frequency of occurrence, and potential for human exposure. This study is focused on benzo(a)pyrene, being considered an indicator of exposure to a PAH carcinogenic mixture. For this purpose, we have applied the XGBoost model to a two-year database of pollutant concentrations and meteorological parameters, with the aim to identify the factors which were mostly associated with the observed benzo(a)pyrene concentrations and to describe types of environments that supported the interactions between benzo(a)pyrene and other polluting species. The pollutant data were collected at the energy industry center in Serbia, in the vicinity of coal mining areas and power stations, where the observed benzo(a)pyrene maximum concentration for a study period reached 43.7 ngm-3. The metaheuristics algorithm has been used to optimize the XGBoost hyperparameters, and the results have been compared to the results of XGBoost models tuned by eight other cutting-edge metaheuristics algorithms. The best-produced model was later on interpreted by applying Shapley Additive exPlanations (SHAP). As indicated by mean absolute SHAP values, the temperature at the surface, arsenic, PM10, and total nitrogen oxide (NOx) concentrations appear to be the major factors affecting benzo(a)pyrene concentrations and its environmental fate.

Atmospheric Deposition of Benzo[a]pyrene: Developing a Spatial Pattern at a National Scale

Benzo[a]pyrene (BaP), an indicator of polycyclic aromatic hydrocarbons (PAHs) in the atmosphere, is an important ambient air pollutant with significant human health and environmental effects. In the Czech Republic (CR), BaP, together with aerosol and ambient ozone, ranks (with respect to limit value exceedances and resulting population exposure) among the most problematic air pollutants. The aim of this study is to develop atmospheric deposition patterns of BaP in three years, namely 2012, 2015 and 2019, reflecting different BaP ambient levels. With respect to the available measurements, we accounted for dry deposition fluxes, neglecting wet contribution. We assumed, nevertheless, that the real atmospheric deposition is dominated by dry pathways in our conditions, which is supported by measurements from the rural site of Košetice. The dry deposition spatial pattern was constructed using an inferential approach, with two input layers, i.e., annual mean ambient air BaP concentrations, and deposition velocity of 0.89 cm·s−1. Though our results show an overall decrease in BaP loads over the years, the BaP deposition fluxes, in particular in the broader Ostrava region, remain very high. The presented maps can be considered an acceptable approximation of total BaP deposition and are useful for further detailed analysis of airborne BaP impacts on the environment.