Suchergebnisse

AbstractBecause of the shortage of water source in the extreme arid area, generally, there are several years of no irrigation on mature shelterbelts. In this study, the shelterbelt soil in different texture were compared based on distribution analysis of the soil moisture and salt levels of shelterbelts under large‐scale drip irrigation in Kalamiji Oasis in the lower reaches of Tarim River, Northwest China. The following conclusions were drawn. (1) In Kalamiji Oasis, the groundwater level declined at a rate of 0.5 m/a as a result of popularization of drip irrigation technology. (2) There was a larger heterogeneity in distribution for soil moisture in the sandy/loamy shelterbelt than in the clay shelterbelt. (3) Under non‐irrigation conditions, sandy/loamy shelterbelt has lower soil moisture and salinity, and clay shelterbelt has higher soil moisture and salinity. (4) The shelterbelt with the shallowest groundwater depth had highest soil moisture level, and the shelterbelt with the deepest groundwater level had the lowest soil moisture content. (5) In sandy and loamy shelterbelts, the shelterbelts with the shallowest groundwater depths had the lower salinity levels and the shelterbelts with the deepest groundwater depths had the highest salinity levels. (6) In clay shelterbelts, the shelterbelts with the shallowest groundwater depths had the highest salinity levels, and the shelterbelts with the deepest groundwater depths had the lowest salinity levels. Additionally, it is essential to implement irrigation at least once a year.

AbstractSoil salinity level in shelterbelts under different emitter distance and different planting years was compared using 3 years of monitoring data from a typical oasis located in an extremely arid area in northwest China. The variation trend of salinity level during the whole growing season and in nonirrigation season was analysed. The results indicate that: (1) under narrow emitter distance (1.5 m), the soil layer with the highest salinity level was located deepest (50–85 cm). Under wide emitter distance (3 m), the soil layer with the highest salinity level was located shallower (45–80 cm); (2) drip irrigation effectively decreased the soil salinity level. With the increased in irrigating years, the salt‐leaching effect was better. Most of the soil salts were brought downward to the soil layer below 30 cm, 50 cm and 70 cm, respectively, in shelterbelts that have been irrigating for 1 year, 2 years and 3 years; (3) soil salinity level presented an increasing trend during the growing season. The largest change of soil salinity level fore‐and‐aft the irrigation was found in 20‐cm depth, followed by the 40‐cm depth and then the 60‐cm depth; (4) in nonirrigation season, salt was accumulating at the surface in shelterbelts. Among all the shelterbelts with different forest age, the salinity level on the surface was the highest in the 2‐year‐old shelterbelt.

Abstract
Targeting surveillance resources toward individuals at high risk of early infection can accelerate the detection of emerging outbreaks. However, it is unclear which individuals are at high risk without detailed data on interpersonal and physical contacts. We propose a data-driven COVID-19 surveillance strategy using Electronic Health Record (EHR) data that identifies the most vulnerable individuals who acquired the earliest infections during historical influenza seasons. Our simulations for all three networks demonstrate that the EHR-based strategy performs as well as the most-connected strategy. Compared to the random acquaintance surveillance, our EHR-based strategy detects the early warning signal and peak timing much earlier. On average, the EHR-based strategy has 9.8 days of early warning and 13.5 days of peak timings, respectively, before the whole population. For the urban network, the expected values of our method are better than the random acquaintance strategy (24% for early warning and 14% in-advance for peak time). For a scale-free network, the average performance of the EHR-based method is 75% of the early warning and 109% in-advance when compared with the random acquaintance strategy. If the contact structure is persistent enough, it will be reflected by their history of infection. Our proposed approach suggests that seasonal influenza infection records could be used to monitor new outbreaks of emerging epidemics, including COVID-19. This is a method that exploits the effect of contact structure without considering it explicitly.