Sources and monthly and seasonal concentration variation study of atmospheric particulates and particles-bound PAEs

Dialogue between algorithms and soil: Machine learning unravels the mystery of phthalates pollution in soil

Soil is a major environmental sink for the emerging organic pollutants phthalates (PAEs), and the determination of key factors influencing PAEs accumulation in soil is crucial for agricultural sustainability and food security. Aiming at the time-consuming and inefficient characteristics of traditional batch experiments and statistical prediction models in comprehensively capturing PAEs dynamics in soil, an intelligent analysis framework based on machine learning was proposed and developed. In this study, thirty features were incorporated, including soil PAEs-concentrations, pollutant emissions, agricultural inputs, soil physicochemical properties, and climatic parameters. Six data-driven machine learning models were established: Random Forest Regression (RFR), Gradient Boosting Regression Tree (GBRT), Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN). Results showed that the MLP model exhibited optimal performance in predicting soil PAEs concentrations (R²=0.8637), followed by SVR (R²=0.8132) and XGBoost (R²=0.8096). Through feature importance analysis, it was determined that hydrometeorological factors, soil moisture conditions, and nutritional characteristics were the key factors controlling PAEs spatial distribution. Furthermore, non-linear effect analysis elucidated significant synergistic interactions among these environmental covariates. The spatiotemporal prediction model revealed continuous declining trends in PAEs pollution levels in eastern coastal regions over the next 5-10 years, while accumulation tendencies were observed in inland provinces particularly in Guizhou. This study demonstrates the effectiveness and advantages of machine learning in predicting soil PAEs-pollution, providing a new perspective for pollutant risk assessment and management in the era of environmental big data.

Pollution characteristics and health risk of sixty-five organics in one drinking water system: PAEs should be prioritized for control

Currently, a large number of emerging organic contaminants have been detected in domestic and international drinking water systems. However, there are differences among the research methods, which lead to system errors in directly comparing the hazards of different contaminants, so it is difficult to analyze the priority control pollutants and the risk control target in drinking water from previous studies. Therefore, we selected a drinking water treatment plant (DWTP) in the east of China, and detected trihalomethanes (THMs), antibiotics, phthalate esters (PAEs), organophosphate esters (OPEs), per and polyfluoroalkyl substances (PFASs), a total of sixty-five organic contaminants in one batch water sample of four seasons, and carried out the whole process monitoring of "Source water-DWTP-Network-Users", and calculated the health risks of contaminants in tap water. The results showed that DWTP could effectively remove antibiotics and PAEs; the removal rate of coagulation for antibiotics can be up to 47%; the release of PAEs in the plastic water supply pipe leads to a significant increase of the concentrations in the water transportation system, which can reach 2.92 times of that in finished water; compared with other contaminants, THMs and PAEs in tap water have higher health risks. This study reveals that THMs and PAEs are priority control organic pollutants, and the water supply network is the key risk control target in the drinking water system, providing a theoretical basis for how to ensure the safety of drinking water.

Fate of phthalates in a river receiving wastewater treatment plant effluent based on a multimedia model

Phthalic acid esters (PAEs) can enter environment media by secondary effluent discharge from wastewater treatment plants (WWTP) into receiving rivers, thus posing a threat to ecosystem health. A level III fugacity model was established to simulate the fate and transfer of four PAEs in a study area in Tianjin, China, and to evaluate the influence of WWTP discharge on PAEs levels in the receiving river. The results show that the logarithmic residuals of most simulated and measured values of PAEs are within one order of magnitude with a good agreement. PAEs in the study area were mainly distributed in soil and sediment phases, which accounted for 84.66%, 50.26%, 71.96% and 99.09% for dimethyl phthalate (DMP), diethyl phthalate (DEP), dibutyl phthalate (DBP) and di-(2-ethylhexyl) phthalate (DEHP), respectively. The upstream advection accounted for 77.90%, 93.20%, 90.21% and 90.93% of the total source of DMP, DEP, DBP and DEHP in the river water, respectively, while the contribution of secondary effluent discharge was much lower. Sensitivity analysis shows that emission and inflow parameters have greater influences on the multimedia distributions of PAEs than physicochemical and environmental parameters. Monte Carlo analysis quantifies the uncertainties and verifies the reliability of the simulation results.

The reaction laws and toxicity effects of phthalate acid esters (PAEs) ozonation degradation on the troposphere

Low-molecular-weight (LMW) phthalate acid esters (PAEs) tend to enter the atmosphere, flying for several kilometers, so it is easy to endanger human health. This work is the first to use quantum chemistry calculations (Gaussian 16 program) and computational toxicology (ECOSAR, TEST, and Toxtree software) to comprehensively study the ozonolysis mechanism of six LMW PAEs (dimethyl phthalate (DMP), diethyl phthalate (DEP), dipropyl phthalate (DPP), diisopropyl phthalate (DIP), dibutyl phthalate (DBP), and diisobutyl phthalate (DIBP)) in the atmosphere and the toxicity of DMP (take DMP as an example) in the conversion process. The results show that the electron-donating effect of the ortho position of the LMW PAEs has the most obvious influence on the ozonolysis. We summarized the ozonation reaction law of LMW PAEs at the optimal reaction site. At 298 K, the law of initial ozonolysis total rate constant of the LMW PAEs is k_DIP > k_DPP > k_DIBP > k_DMP > k_DEP > k_DBP, and the range is 9.56 × 10^-25 cm³ molecule^-1 s^-1 - 1.47 × 10^-22 cm³ molecule^-1 s^-1. According to the results of toxicity assessment, the toxicity of products is lower than DMP for aquatic organisms after ozonolysis. But those products have mutagenicity, developmental toxicity, non-genotoxicity, carcinogenicity, and corrosiveness to the skin. The proposed ozonolysis mechanism promotes our understanding of the environmental risks of PAEs and provides new ideas for studying the degradation of PAEs in the tropospheric gas phase.

Role of atmospheric particulate matter exposure in COVID-19 and other health risks in human: A review

Due to intense industrialization and urbanization, air pollution has become a serious global concern as a hazard to human health. Epidemiological studies found that exposure to atmospheric particulate matter (PM) causes severe health problems in human and significant damage to the physiological systems. In recent days, PM exposure could be related as a carrier for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus transmission and Coronavirus disease 2019 (COVID-19) infection. Hence, it is important to understand the adverse effects of PM in human health. This review aims to provide insights on the detrimental effects of PM in various human health problems including respiratory, circulatory, nervous, and immune system along with their possible toxicity mechanisms. Overall, this review highlights the potential relationship of PM with several life-limiting human diseases and their significance for better management strategies.