Dissolved nitrogen in salt-affected soils reclaimed by planting rice: How is it influenced by soil physicochemical properties?

Planting rice is an effective way to reclaim salt-affected soils, but overapplying nitrogen fertilizer has resulted in a large loss in the amounts of soil dissolved nitrogen (SDN) from paddy fields. While the dynamic of SDN and its response to changes in soil physicochemical properties by planting rice are well-studied in non-salt-affected soils, little is known about the relationship between the SDN and soil physicochemical properties in reclaimed salt-affected soils. To fill this knowledge gap, soil samples were collected from bare salt-affected soils and three paddy fields with different reclaimed years (4, 9, 20) in six soil layers. Compared with bare salt-affected soils, soil salinity and sodicity exhibited trends of firstly increasing and then decreasing, whereas organic matter and total nitrogen tended to increase with the extension of the reclamation year. Soil dissolved organic carbon and total dissolved phosphorous showed decreasing trends. The sand content showed an increasing tendency, whereas the silt and clay contents tended to decrease. Ammonium nitrogen concentrations in reclaimed paddy fields were higher than those of bare salt-affected soils, and nitrate nitrogen concentrations in reclaimed paddy fields were smaller than those of bare salt-affected soils. However, the changing trends of dissolved organic nitrogen concentrations were not consistent among paddy fields with different reclamation years. Meanwhile, statistical analysis results revealed significant correlations between SDN and soil physicochemical properties. Moreover, dominant drivers influencing SDN were grouped using principal component analysis, identifying the following factors including soil sodicity, active nutrients, soil texture and water retention. Redundancy analysis also revealed that the soil physicochemical properties explained 69.65% of the variation in SDN and the influenced relationship between soil physicochemical properties and SDN nutrients. This study enhances our understanding of the mechanisms influencing SDN during planting rice and has implications for the management of the nutrient application of reclaimed salt-affected soils.

High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM2.5 Distribution in Beijing, China.

PM2.5 is one of the primary components of air pollutants, and it has wide impacts on human health. Land use regression models have the typical disadvantage of low temporal resolution. In this study, various point of interests (POIs) variables are added to the usual predictive variables of the general land use regression (LUR) model to improve the temporal resolution. Hourly PM2.5 concentration data from 35 monitoring stations in Beijing, China, were used. Twelve LUR models were developed for working days and non-working days of the heating season and non-heating season, respectively. The results showed that these models achieved good fitness in winter and summer, and the highest R2 of the winter and summer models were 0.951 and 0.628, respectively. Meteorological factors, POIs, and roads factors were the most critical predictive variables in the models. This study also showed that POIs had time characteristics, and different types of POIs showed different explanations ranging from 5.5% to 41.2% of the models on working days or non-working days, respectively. Therefore, this study confirmed that POIs can greatly improve the temporal resolution of LUR models, which is significant for high precision exposure studies.