Estimation of hourly PM1 concentration in China and its application in population exposure analysis.

Particulate pollution is closely related to public health. PM1 (particles with an aerodynamic size not larger than 1 µm) is much more harmful than particles with larger sizes because it goes deeper into the body and hence arouses social concern. However, the sparse and unevenly distributed ground-based observations limit the understanding of spatio-temporal distributions of PM1 in China. In this study, hourly PM1 concentrations in central and eastern China were retrieved based on a random forest model using hourly aerosol optical depth (AOD) from Himawari-8, meteorological and geographic information as inputs. Here the spatiotemporal autocorrelation of PM1 was also considered in the model. Experimental results indicate that although the performance of the proposed model shows diurnal, seasonal and spatial variations, it is relatively better than others, with a determination coefficient (R2) of 0.83 calculated based on the 10-fold cross validation method. Geographical map implies that PM1 pollution level in Beijing-Tianjin-Hebei is much higher than in other regions, with the mean value of ∼55 µg/m3. Based on the exposure analysis, we found about 75% of the population lives in an environment with PM1 higher than 35 µg/m3 in the whole study area. The retrieval dataset in this study is of great significance for further exploring the impact of PM1 on public health.

Stacking machine learning model for estimating hourly PM2.5 in China based on Himawari 8 aerosol optical depth data.

Aerosol optical depth (AOD) from polar orbit satellites and meteorological factors have been widely used to estimate concentrations of surface particulate matter with an aerodynamic diameter <2.5 µm (PM2.5). However, estimations with high temporal resolution remain lacking because of the limitations of satellite observations. Here, we used AOD data with a temporal resolution of 1 h provided by a geostationary satellite called Himawari 8 to overcome this problem. We developed a stacking model, which contained three submodels of machine learning, namely, AdaBoost, XGBoost and random forest, stacked through a multiple linear regression model. Then, we estimated the hourly concentrations of PM2.5 in Central and Eastern China. The accuracy evaluation showed that the proposed stacking model performed better than the single models when applied to the test set, with an average coefficient of determination (R2) of 0.85 and a root-mean-square error (RMSE) of 17.3 µg/m3. Model precision reached its peak at 14:00 (local time), with an R2 (RMSE) of 0.92 (12.9 µg/m3). In addition, the spatial and temporal distributions of PM2.5 in Central and Eastern China were plotted in this study. The North China Plain was determined to be the most polluted area in China, with an annual mean PM2.5 concentration of 58 µg/m3 during daytime. Moreover, the pollution level of PM2.5 was the highest in winter, with an average concentration of 73 µg/m3.

Ground-level PM2.5 estimation over urban agglomerations in China with high spatiotemporal resolution based on Himawari-8.

High concentrations of particulate matter with diameter of <2.5 µm (PM2.5) demonstrate severe effects on human health, especially in the metropolitan agglomerations of China. Estimating PM2.5 based on satellite aerosol optical depth (AOD) is a widely used method. AOD data from Himawari-8, a geostationary satellite, enable improvement of the temporal resolution of PM2.5 estimates to the hourly level, thereby reflecting diurnal variations of pollutants compared with AOD products from polar orbit satellites, which only have one value per day. In this study, PM2.5 concentrations are estimated based on Himawari-8 AOD and other ancillary data by constructing spatiotemporal linear mixed effects model in Central China (CCH), Beijing-Tianjin-Henan (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) regions, respectively. The determination coefficient (R2) between the measurements and estimates of PM2.5 calculated with the tenfold cross-validation method are 0.82, 0.84, 0.80 and 0.74 in CCH, BTH, YRD and PRD, respectively. The spatial distributions of PM2.5 present large regional variation, which is highly correlated with land-use type. Heavily polluted zones are mainly located in urban or rural areas, which have dense population and high anthropogenic emissions. Comparisons among different seasons show that particle pollution during the cold seasons (autumn and winter) is relatively severe with an average PM2.5 of >60 µg/m3 in CCH, BTH and YRD, whereas the level does not greatly change throughout the year in the PRD region. During the daytime, particulate pollution levels are generally high in the morning.

AaPP2C1 negatively regulates the expression of genes involved in artemisinin biosynthesis through dephosphorylating AaAPK1.

Artemisinin is biosynthesized in Artemisia annua and widely used for the treatment of malaria. Abscisic acid (ABA)-responsive kinase 1 (AaAPK1), a member of the SnRK2 family, is involved in the regulation of artemisinin biosynthesis through the phosphorylation of AabZIP1, which directly transactivates genes involved in artemisinin biosynthesis. Through diverse assays - including yeast two-hybrid and bimolecular fluorescence complementation assays - we report that the ABA-responsive protein phosphatase AaPP2C1 physically interacts with AaAPK1. In addition, phos-tag mobility shift assays indicate that AaPP2C1 dephosphorylates AaAPK1. Moreover, dual-luciferase assays demonstrate that the presence of AaPP2C1 reduces the transactivation of artemisinin biosynthesis genes by AabZIP1. These results further refine the post-translational regulatory network of artemisinin biosynthesis, showing that AaPP2C1 is negatively involved through dephosphorylation of AaAPK1.

ARTEMISININ BIOSYNTHESIS PROMOTING KINASE 1 positively regulates artemisinin biosynthesis through phosphorylating AabZIP1.

The plant Artemisia annua produces the anti-malarial compound artemisinin. Although the transcriptional regulation of artemisinin biosynthesis has been extensively studied, its post-translational regulatory mechanisms, especially that of protein phosphorylation, remain unknown. Here, we report that an ABA-responsive kinase (AaAPK1), a member of the SnRK2 family, is involved in regulating artemisinin biosynthesis. The physical interaction of AaAPK1 with AabZIP1 was confirmed by multiple assays, including yeast two-hybrid, bimolecular fluorescence complementation, and pull-down. AaAPK1, mainly expressed in flower buds and leaves, could be induced by ABA, drought, and NaCl treatments. Phos-tag mobility shift assays indicated that AaAPK1 phosphorylated both itself and AabZIP1. As a result, the phosphorylated AaAPK1 significantly enhanced the transactivational activity of AabZIP1 on the artemisinin biosynthesis genes. Substituting the Ser37 with Ala37 of AabZIP1 significantly suppressed its phosphorylation, which inhibited the transactivational activity of AabZIP1. Consistent overexpression of AaAPK1 significantly increased the production of artemisinin, as well as the expression levels of the artemisinin biosynthesis genes. Our study opens a window into the regulatory network underlying artemisinin biosynthesis at the post-translational level. Importantly, and for the first time, we provide evidence for why the kinase gene AaAPK1 is a key candidate for the metabolic engineering of artemisinin biosynthesis.