Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning.

Airborne trace elements (TEs) present in atmospheric fine particulate matter (PM2.5) exert notable threats to human health and ecosystems. To explore the impact of meteorological conditions on shaping the pollution characteristics of TEs and the associated health risks, we quantified the variations in pollution characteristics and health risks of TEs due to meteorological impacts using weather normalization and health risk assessment models, and analyzed the source-specific contributions and potential sources of primary TEs affecting health risks using source apportionment approaches at four sites in Shandong Province from September to December 2021. Our results indicated that TEs experience dual effects from meteorological conditions, with a tendency towards higher TE concentrations and related health risks during polluted period, while the opposite occurred during clean period. The total non-carcinogenic and carcinogenic risks of TEs during polluted period increased approximately by factors of 0.53-1.74 and 0.44-1.92, respectively. Selenium (Se), manganese (Mn), and lead (Pb) were found to be the most meteorologically influenced TEs, while chromium (Cr) and manganese (Mn) were identified as the dominant TEs posing health risks. Enhanced emissions of multiple sources for Cr and Mn were found during polluted period. Depending on specific wind speeds, industrialized and urbanized centers, as well as nearby road dusts, could be key sources for TEs. This study suggested that attentions should be paid to not only the TEs from primary emissions but also the meteorology impact on TEs especially during pollution episodes to reduce health risks in the future.

Characteristics of secondary inorganic aerosols and contributions to PM2.5 pollution based on machine learning approach in Shandong Province.

Primary emissions of particulate matter and gaseous pollutants, such as SO2 and NOx have decreased in China following the implementation of a series of policies by the Chinese government to address air pollution. However, controlling secondary inorganic aerosol pollution requires attention. This study examined the characteristics of the secondary conversion of nitrate (NO3-) and sulfate (SO42-) in three coastal cities of Shandong Province, namely Binzhou (BZ), Dongying (DY), and Weifang (WF), and an inland city, Jinan (JN), during December 2021. Furthermore, the Shapley Additive Explanation (SHAP), an interpretable attribution technique, was adopted to accurately calculate the contributions of secondary formations to PM2.5. The nitrogen oxidation rate exhibited a significant dependence on the concentration of O3. High humidity facilitates sulfur oxidation. Compared to BZ, DY, and WF, the secondary conversion of NO3- and SO42- was more intense in JN. The light-gradient boosting model outperformed the random forest and extreme-gradient boosting models, achieving a mean R2 value of 0.92. PM2.5 pollution events in BZ, DY, and WF were primarily attributable to biomass burning, whereas pollution in Jinan was contributed by the secondary formation of NO3- and vehicle emissions. Machine learning and the SHAP interpretable attribution technique offer a precise analysis of the causes of air pollution, showing high potential for addressing environmental concerns.

Contributions of various driving factors to air pollution events: Interpretability analysis from Machine learning perspective.

The air quality in China has been improved substantially, however fine particulate matter (PM2.5) still remain at a high level in many areas. PM2.5 pollution is a complex process that is attributed to gaseous precursors, chemical, and meteorological factors. Quantifying the contribution of each variable to air pollution can facilitate the formulation of effective policies to precisely eliminate air pollution. In this study, we first used decision plot to map out the decision process of the Random Forest (RF) model for a single hourly data set and constructed a framework for analyzing the causes of air pollution using multiple interpretable methods. Permutation importance was used to qualitatively analyze the effect of each variable on PM2.5 concentrations. The sensitivity of secondary inorganic aerosols (SIA): SO42-, NO3- and NH4+ to PM2.5 was verified by Partial dependence plot (PDP). Shapley Additive Explanation (Shapley) was used to quantify the contribution of drivers behind the ten air pollution events. The RF model can accurately predict PM2.5 concentrations, with determination coefficient (R2) of 0.94, root mean square error (RMSE) and mean absolute error (MAE) of 9.4 µg/m3 and 5.7 µg/m3, respectively. This study revealed that the order of sensitivity of SIA to PM2.5 was NH4+＞NO3-＞SO42-. Fossil fuel and biomass combustion may be contributing factors to air pollution events in Zibo in 2021 autumn-winter. NH4+ contributed 19.9-65.4 µg/m3 among ten air pollution events (APs). K, NO3-, EC and OC were the other main drivers, contributing 8.7 ± 2.7 µg/m3, 6.8 ± 7.5 µg/m3, 3.6 ± 5.8 µg/m3 and 2.5 ± 2.0 µg/m3, respectively. Lower temperature and higher humidity were vital factors that promoted the formation of NO3-. Our study may provide a methodological framework for precise air pollution management.

Effects of Glycyrrhiza polysaccharide on growth performance, appetite, and hypothalamic inflammation in broilers.

We analyzed the effects of Glycyrrhiza polysaccharide (GCP) on growth performance, appetite, and hypothalamic inflammation-related indexes in broilers. One-day-old male AA broilers were randomly divided into four groups: Control, L-GCP, M-GCP, and H-GCP (0, 300, 600, and 900 mg GCP/kg feed), with six repetition cages for each treatment and 12 broilers in each repeat for a period of 42 days. From day 1 to day 21, the addition of GCP to the diet significantly improved the ADFI and the ADG of broilers, and the mRNA levels of NPY and AgRP were significantly increased while POMC and CART were decreased in the hypothalamus of broilers; GCP also significantly decreased the mRNA levels of IL-1ß, IL-6, TNF-α, TLR-4, MyD88, and NF-κB, and increased the IL-4 and IL-10 in the hypothalamus from day 1 to day 42. The concentrations of appetite-related factors and inflammatory factors in serum were changed in the same fashion. Supplementation with 600 mg/kg GCP had the optimal effect in broilers, and GCP has the potential to be used as a feed additive in the poultry production industry.

In this work, we analyzed the effects of Glycyrrhiza polysaccharide on growth performance, appetite, and hypothalamic inflammation in broilers. In the past, antibiotics were added to poultry feed to prevent disease and improve growth performance; however, the extensive use of antibiotics can negatively affect livestock and poultry, and endanger the health of consumers. Glycyrrhiza polysaccharide has many biological activities. In order to explore the possibility of Glycyrrhiza polysaccharide as a substitute for antibiotics, we evaluated the effects of Glycyrrhiza polysaccharide added to the diet of broilers for 42 days. Our research confirmed that Glycyrrhiza polysaccharide supplementation increased food intake by regulating appetite-related factors in hypothalamus and serum, and also alleviated inflammation.