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The primary cause of hazy weather is PM2.5, and forecasting PM2.5 concentrations can aid in managing and preventing hazy weather. This paper proposes a novel spatiotemporal prediction model called SpatioTemporal-Informer (ST-Informer) in response to the shortcomings of spatiotemporal prediction models commonly used in studies for long-input series prediction. The ST-Informer model implements parallel computation of long correlations and adds an independent spatiotemporal embedding layer to the original Informer model. The spatiotemporal embedding layer captures the complex dynamic spatiotemporal correlations among the input information. In addition, the ProbSpare Self-Attention mechanism in this model can focus on extracting important contextual information of spatiotemporal data. The ST-Informer model uses weather and air pollutant concentration data from numerous stations as its input data. The outcomes of the trials indicate that (1) The ST-Informer model can sharply capture the peaks and sudden changes in PM2.5 concentrations. (2) Compared to the current models, the ST-Informer model shows better prediction performance while maintaining high-efficiency prediction (MAE≈7.50μg/m3,RMSE≈4.31μg/m3,R2≈0.88). (3) The ST-Informer model has universal applicability, and the model was applied to the concentration of other pollutants prediction with good results. [ABSTRACT FROM AUTHOR]

Leymus chinensis is a dominant, rhizomatous perennial C3 species in the grasslands of Songnen Plain of Northern China, and its productivity has decreased year by year. To determine how productivity of this species responds to different precipitation regimes, elevated CO2 and their interaction in future, we measured photosynthetic parameters, along with the accumulation and partitioning of biomass. Plants were subjected to combinations of three precipitation gradients (normal precipitation, versus normal ± 40%) and two CO2 levels (380±20 µmol mol-1,760±20 µmol mol-1) in controlled-environment chambers. The net photosynthetic rate, and above-ground and total biomass increased due to both elevated CO2 and increasing precipitation, but not significantly so when precipitation increased from the normal to high level under CO2 enrichment. Water use efficiency and the ratio of root: total biomass increased significantly when precipitation was low, but decreased when it was high under CO2 enrichment. Moreover, high precipitation at the elevated level of CO2 increased the ratio between stem biomass and total biomass. The effect of elevated CO2 on photosynthesis and biomass accumulation was higher at the low level of precipitation than with normal or high precipitation. The results suggest that at ambient CO2 levels, the net photosynthetic rate and biomass of L. chinensis increase with precipitation, but those measures are not further affected by additional precipitation when CO2 is elevated. Furthermore, CO2 may partly compensate for the negative effect of low precipitation on the growth and development of L. chinensis. [ABSTRACT FROM AUTHOR]

Leymus chinensis is a dominant, rhizomatous perennial C3 species in the grasslands of Songnen Plain of Northern China, and its productivity has decreased year by year. To determine how productivity of this species responds to different precipitation regimes, elevated CO2 and their interaction in future, we measured photosynthetic parameters, along with the accumulation and partitioning of biomass. Plants were subjected to combinations of three precipitation gradients (normal precipitation, versus normal ± 40%) and two CO2 levels (380±20 µmol mol-1,760±20 µmol mol-1) in controlled-environment chambers. The net photosynthetic rate, and above-ground and total biomass increased due to both elevated CO2 and increasing precipitation, but not significantly so when precipitation increased from the normal to high level under CO2 enrichment. Water use efficiency and the ratio of root: total biomass increased significantly when precipitation was low, but decreased when it was high under CO2 enrichment. Moreover, high precipitation at the elevated level of CO2 increased the ratio between stem biomass and total biomass. The effect of elevated CO2 on photosynthesis and biomass accumulation was higher at the low level of precipitation than with normal or high precipitation. The results suggest that at ambient CO2 levels, the net photosynthetic rate and biomass of L. chinensis increase with precipitation, but those measures are not further affected by additional precipitation when CO2 is elevated. Furthermore, CO2 may partly compensate for the negative effect of low precipitation on the growth and development of L. chinensis. [ABSTRACT FROM AUTHOR]