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Predicting spatial explicit information of soil nutrients is critical for sustainable soil management and food security under climate change and human disturbance in agricultural land. Digital soil mapping (DSM) techniques can utilize soil-landscape information from remote sensing data to predict the spatial pattern of soil nutrients, and it is important to explore the effects of remote sensing data types on DSM. This research utilized Landsat 8 (LT), Sentinel 2 (ST), and WorldView-2 (WV) remote sensing data and employed partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM) algorithms to characterize the spatial pattern of soil total nitrogen (TN) in smallholder farm settings in Yellow River Basin, China. The overall relationships between TN and spectral indices from LT and ST were stronger than those from WV. Multiple red edge band-based spectral indices from ST and WV were relevant variables for TN, while there were no red band-based spectral indices from ST and WV identified as relevant variables for TN. Soil moisture and vegetation were major driving forces of soil TN spatial distribution in this area. The research also concluded that farmlands of crop rotation had relatively higher TN concentration compared with farmlands of monoculture. The soil prediction models based on WV achieved relatively lower model performance compared with those based on ST and LT. The effects of remote sensing data spectral resolution and spectral range on enhancing soil prediction model performance are higher than the effects of remote sensing data spatial resolution. Soil prediction models based on ST can provide location-specific soil maps, achieve fair model performance, and have low cost. This research suggests DSM research utilizing ST has relatively high prediction accuracy, and can produce soil maps that are fit for the spatial explicit soil management for smallholder farms.

The concentrations and characteristics of 16 polycyclic aromatic hydrocarbons (PAHs) in 48 oil samples randomly collected from 30 catering services that employ six cooking methods were quantified via high-performance liquid chromatography (HPLC). These 16 PAHs were detected in almost all of the samples. The levels of Σ16PAHs, Σ4PAHs, benzo[a]pyrene (BaP), and total BaP equivalents (ΣBaPeq) for the six cooking methods exceeded the legal limit. The concentrations of Σ4PAHs were approximately 9.5 to 16.4 times the legal limit proposed by the European Commission (Off J Eur Union 215:4-8, 2011), and the level of BaP exceeded the national standard in China by 4.7- to 10.6-fold, particularly in oil from fried foods. Low molecular weight PAHs (LMW PAHs) were predominant in fried food oil from different catering services and accounted for 94.8 % of these oils, and the ΣBaPeq of the high molecular weight PAHs (HMW PAHs) was 11.5-fold higher than that of the LMW PAHs. The concentrations of Σ16PAHs (3751.9-7585.8 μg/kg), Σ4PAHs (144.6-195.7 μg/kg), BaP (79.7-135.8 μg/kg), and ΣBaPeq (231.0-265.4 μg/kg) were highest in the samples from fast food restaurants/buffets (FB), followed by those from fried food stalls (FS) and then cooking restaurants/cafeterias (RC). The results of this study suggest that the government should strengthen control and supervision of PAH contamination in food and edible oils.

Cooking fumes are an important indoor source of polycyclic aromatic hydrocarbons (PAHs). Because indoor pollution has a more substantial impact on human health than outdoor pollution, PAHs from cooking fumes have drawn considerable attention. In this study, 16 PAHs emitted through deep-frying and frying methods using rapeseed, soybean, peanut, and olive oil were examined under a laboratory fume hood. Controlled experiments were conducted to collect gas- and particulate-phase PAHs emitted from the cooking oil fumes, and PAH concentrations were quantified via high-performance liquid chromatography (HPLC). The results show that deep-frying methods generate more PAHs and benzo[a]pyrene (B[a]P) (1.3 and 10.9 times, respectively) because they consume greater volumes of edible oil and involve higher oil temperatures relative to those of frying methods. In addition, the total B[a]Peq concentration of deep-frying is 2.2-fold larger than that of frying. Regarding the four types of edible oils studied, rapeseed oil produced more PAH emission than the other three oil varieties. For all of the cooking tests, three- and four-ringed PAHs were the main PAH components regardless of the food and oil used. Concerning the PAH partition between gas and particulate phase, the gaseous compounds accounted for 59-96 % of the total. Meanwhile, the particulate fraction was richer of high molecular weight PAHs (five-six rings). Deep-frying and frying were confirmed as important sources of PAH pollution in internal environments. The results of this study provide additional insights into the polluting features of PAHs produced via cooking activities in indoor environments.