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Relevant studies have demonstrated that urban green spaces composed of various types of plants are able to alleviate the morbidity and mortality of respiratory diseases, by reducing air pollution levels. In order to explore the relationship between the spatial pattern of urban green spaces and air pollutant concentrations, this study takes 37 garden cities with subtropical monsoon climate in China as the research object and selects the urban air quality monitoring data and land use type data in 2019 to analyze the relationship between the spatial pattern and the air pollutant concentration through the landscape metrics model and spatial regression model. Moreover, the threshold effect of the impact of green space on air pollutant concentrations is estimated, as well. The results showed that the spatial pattern of urban green space was significantly correlated with the concentrations of PM2.5 (PM with aerodynamic diameters of 2.5 mmor less), NO2 (Nitrogen Dioxide), and SO2 (Sulfur dioxide) pollutants in the air, while the concentrations of PM10 (PM with aerodynamic diameters of 10 mmor less) pollutants were not significantly affected by the green space pattern. Among them, the patch shape index (LSI), patch density (PD) and patch proportion in landscape area (PLAND) of forest land can affect the concentration of PM2.5, NO2, and SO2, respectively. The PLAND, PD, and LSI of grassland and farmland can also have an additional impact on the concentration of SO2 pollutants. The study also found that there was a significant threshold effect within the impact mechanism of urban green space landscape pattern indicators (LSI, PD, PLAND) on the concentrations of PM2.5, NO2, and SO2 air pollutants. The results of this study not only clarified the impact mechanism of the spatial pattern of urban green space on air pollutant concentrations but also provided quantitative reference and scientific basis for the optimization and updating of urban green space to promote public health.

Morindae Officinalis Radix (MOR) and Alpiniae Oxyphyllae Fructus (AOF) have been widely used as dietary supplements and traditional herbal medicines for centuries. Fungal and mycotoxin contamination in MOR and AOF has been reported recently. In this study, fungi in MOR and AOF are first investigated using DNA metabarcoding, and the differences in fungal microbiome between moldy and non−moldy samples are analyzed. The results show that Ascomycota is the most prevailing fungus at the phylum level in MOR and AOF with relative abundances of 49.53–94.32% and 14.81–81.85%, respectively. Penicillium (1.86–76.14%), Cladosporium (1.82–56.65%), and Trichoderma (0.12–19.71%) are the dominant genera in MOR. Penicillium (0.27–56.06%), Papiliotrema (0.04–51.71%), and Cladosporium (3.08–44.41%) are the dominant genera in AOF. Two potential toxigenic fungi were detected, namely, Trichoderma atroviride and Fusarium equiseti. Moreover, the differences in fungal communities between moldy and non−moldy samples were monitored. In conclusion, DNA metabarcoding can be used to assess the fungal microbiome in edible medicinal herbs, thereby providing a basis for ensuring food safety and drug efficacy.

Ziziphi Spinosae Semen (ZSS) has been widely used in traditional Chinese medicine system for decades. Under proper humidity and temperature, ZSS is easily contaminated by fungi and mycotoxins during harvest, storage, and transport, thereby posing a considerable threat to consumer health. In this study, we first used the Illumina MiSeq PE250 platform and targeted the internal transcribed spacer 2 sequences to investigate the presence of fungi in moldy and normal ZSS samples collected from five producing areas in China. Results showed that all 14 samples tested were contaminated by fungi. Ascomycota was the dominant fungus at the phylum level, accounting for 64.36⁻99.74% of the fungal reads. At the genus level, Aspergillus, Candida, and Wallemia were the most predominant genera, with the relative abundances of 13.52⁻87.87%, 0.42⁻64.56%, and 0.06⁻34.31%, respectively. Meanwhile, 70 fungal taxa were identified at the species level. Among these taxa, three potential mycotoxin-producing fungi, namely, Aspergillus flavus, A. fumigatus, and Penicillium citrinum that account for 0.30⁻36.29%, 0.04⁻7.37%, and 0.01⁻0.80% of the fungal reads, respectively, were detected in all ZSS samples. Moreover, significant differences in fungal communities were observed in the moldy and normal ZSS samples. In conclusion, our results indicated that amplicon sequencing is feasible for the detection and analysis of the fungal community in the ZSS samples. This study used a new approach to survey the fungal contamination in herbal materials. This new approach can provide early warning for mycotoxin contamination in herbal materials, thereby ensuring drug efficacy and safety.