Search Results (7)

Mycotoxins, as prevalent contaminants in the food chain, exhibit diverse toxicological effects on both animals and humans. Chronic dietary exposure to mycotoxin-contaminated foods may result in the bioaccumulation of these toxins, posing substantial public health risks. This review systematically examines the contamination patterns of mycotoxins across major food categories, including cereals and related products, animal-derived foods, fruits, and medical food materials. Furthermore, we critically evaluated two methodological frameworks for assessing mycotoxin exposure risks: (1) dietary exposure models integrating contamination levels and consumption data and (2) human biomonitoring approaches quantifying mycotoxin biomarkers in biological samples. A key contribution lies in the stratified analysis of exposure disparities among population subgroups (adults, teenagers, children, and infants). Additionally, we summarize current research on the relationship between human mycotoxin biomonitoring and associated health impacts, with a particular emphasis on vulnerable groups such as pregnant women and infants. By elucidating the challenges inherent in existing studies, this synthesis provides a roadmap for advancing risk characterization and evidence-based food safety interventions. Full article

Deoxynivalenol (DON) and Alternaria toxins (ATs) are two common types of mycotoxins in food. Although they are physiologically toxic to animals and various cell lines, data related to the exposure risks and health effects in the human population were still limited, especially for ATs. In this study, we combined food consumption data and human biomonitoring data of 200 pregnant volunteers from different districts of Shanghai to assess the exposure to DON and ATs. In addition, correlations between food consumption and urinary DON and ATs levels, urine biomarkers, and blood indexes were analyzed by regression analysis. For DON, the exposure assessment of the probable daily intake (PDI) indicated that a portion (37.5%) of all participants exceeded the Tolerable Daily Intake (TDI) proposed for DON. For ATs, the PDI values estimated based on the urinary concentrations indicated that 2–100% of all participants exceeded the threshold of toxicological concern (TTC) values for ATs. In addition, we innovatively found some associations between exposure to ATs and abnormal uric acid and high-density lipoprotein cholesterol indexes by regression analysis. Despite the inevitable uncertainties, these results make an important contribution to the understanding of DON and ATs exposure risks and potential health hazards in the pregnant women population. Full article

Data from Landsat-8 and Sentinel-2A/2B are often combined for terrestrial monitoring because of their similar spectral bands. The bidirectional reflectance distribution function (BRDF) effect has been observed in both Landsat-8 and Sentinel-2A/2B reflectance data. However, there is currently no definition of solar zenith angle ($\theta {}_{sz}$) that is suitable for the normalization of the BRDF-adjusted reflectance from the three sensors’ combined data. This paper describes the use of four machine learning (ML) models to predict a global $\theta {}_{sz}$ that is suitable for the normalization of bidirectional reflectance from the combined data in 2018. The observed $\theta {}_{sz}$ collected globally, and the three locations in the Democratic Republic of Congo (26.622°E, 0.356°N), Texas in the USA (99.406°W 30.751°N), and Finland (25.194°E, 61.653°N), are chosen to compare the performance of the ML models. At a global scale, the ML models of Support Vector Regression (SVR), Multi-Layer Perception (MLP), and Gaussian Process Regression (GPR) exhibit comparably good performance to that of polynomial regression, considering center latitude as the input to predict the global $\theta {}_{sz}$. GPR achieves the best overall performance considering the center latitude and acquisition time as inputs, with a root mean square error (RMSE) of 1.390°, a mean absolute error (MAE) of 0.689°, and a coefficient of determination ($R^2$) of 0.994. SVR shows an RMSE of 1.396°, an MAE of 0.638°, and an $R^2$ of 0.994, following GPR. For a specific location, the SVR and GPR models have higher accuracy than the polynomial regression, with GPR exhibiting the best performance, when center latitude and acquisition time are considered as inputs. GPR is recommended for predicting the global $\theta {}_{sz}$ using the three sensors’ combined data. Full article

The combination of Landsat-8, Landsat-9, Sentinel-2A and Sentinel-2B data provides a new perspective in remote sensing application for terrestrial monitoring. Jointly, these four sensors together offer global 10–30-m multi-spectral data coverage at a higher temporal revisit frequency. In this study, combinations of four sensors were used to examine the revisit interval by modelled orbit swath information. To investigate different factors that could influence data availability, an analysis was carried out for one year based on daytime surface observations of Landsat-8 and Sentinel-2A -2B. We found that (i) the global median average of revisit intervals for the combination of four sensors was 2.3 days; (ii) the global mean average number of surface observations was 141.4 for the combination of Landsat-8 and Sentinel-2A -2B; (iii) the global mean average cloud-weighted number of observations for the three sensors combined was 81.9. Three different locations were selected to compare with the cloud-weighted number of observations, and the results show an appropriate accuracy. The utility of combining four sensors together and the implication for terrestrial monitoring are discussed. Full article

Exposure to fine particulate matter (PM_2.5) is associated with adverse health impacts on the population. Satellite observations and machine learning algorithms have been applied to improve the accuracy of the prediction of PM_2.5 concentrations. In this study, we developed a PM_2.5 retrieval approach using machine-learning methods, based on aerosol products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observation System (EOS) Terra and Aqua polar-orbiting satellites, near-ground meteorological variables from the NASA Goddard Earth Observing System (GEOS), and ground-based PM_2.5 observation data. Four models, which are orthogonal regression (OR), regression tree (Rpart), random forests (RF), and support vector machine (SVM), were tested and compared in the Beijing–Tianjin–Hebei (BTH) region of China in 2015. Aerosol products derived from the Terra and Aqua satellite sensors were also compared. The 10-repeat 5-fold cross-validation (10 × 5 CV) method was subsequently used to evaluate the performance of the different aerosol products and the four models. The results show that the performance of the Aqua dataset was better than that of the Terra dataset, and that the RF algorithm has the best predictive performance (Terra: R = 0.77, RMSE = 43.51 μg/m³; Aqua: R = 0.85, RMSE = 33.90 μg/m³). This study shows promise for predicting the spatiotemporal distribution of PM_2.5 using the RF model and Aqua aerosol product with the assistance of PM_2.5 site data. Full article

In this study, we evaluated estimates and predictions of the PM_2.5 (fine particulate matter) concentrations and emissions in Xuzhou, China, using a coupled Lagrangian particle dispersion modeling system (FLEXPART-WRF). A Bayesian inversion method was used in FLEXPART-WRF to improve the emission calculation and mixing ratio estimation for PM_2.5. We first examined the inversion modeling performance by comparing the model predictions with PM_2.5 concentration observations from four stations in Xuzhou. The linear correlation analysis between the predicted PM_2.5 concentrations and the observations shows that our inversion forecast system is much better than the system before calibration (with correlation coefficients of R = 0.639 vs. 0.459, respectively, and root mean square errors of RMSE = 7.407 vs. 9.805 µg/m³, respectively). We also estimated the monthly average emission flux in Xuzhou to be 4188.26 Mg/month, which is much higher (by ~10.12%) than the emission flux predicted by the multiscale emission inventory data (MEIC) (3803.5 Mg/month). In addition, the monthly average emission flux shows obvious seasonal variation, with the lowest PM_2.5 flux in summer and the highest flux in winter. This pattern is mainly due to the additional heating fuels used in the cold season, resulting in many fine particulates in the atmosphere. Although the inversion and forecast results were improved to some extent, the inversion system can be improved further, e.g., by increasing the number of observation values and improving the accuracy of the a priori emission values. Further research and analysis are recommended to help improve the forecast precision of real-time PM_2.5 concentrations and the corresponding monthly emission fluxes. Full article

We have developed a highly sensitive flexible pressure sensor based on a piezopolymer and silver nanowires (AgNWs) composite. The composite nanofiber webs are made by electrospinning mixed solutions of poly(inylidene fluoride) (PVDF) and Ag NWs in a cosolvent mixture of dimethyl formamide and acetone. The diameter of the fibers ranges from 200 nm to 500 nm, as demonstrated by SEM images. FTIR and XRD results reveal that doping Ag NWs into PVDF greatly enhances the content of β phase in PVDF. This β phase increase can be attributed to interactions between the Ag NWs and the PVDF matrix, which forces the polymer chains to be embedded into the β phase crystalline. The sensitivity of the pressure sensors agrees well with the FTIR and XRD characteristics. In our experiments, the measured sensitivity reached up to 30 pC/N for the nanofiber webs containing 1.5 wt% Ag NWs, which is close to that of poly(vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE), (77/23)]. This study may provide a new method of fabricating high performance flexible sensors at relatively low cost compared with sensors based on [P(VDF-TrFE), (77/23)]. Full article