Secondary effluent purification by a large-scale multi-stage surface-flow constructed wetland: A case study in northern China

Abstract During 8–14 June 2000, a 500-hPa blocking event occurred over Mongolia and northern China (near 45°N, 108°E), which was the only case over this region in June since 1981. As the block developed, the initially weak low-level mei-yu front over southern China evolved into a system with strong baroclinity and subsequently moved south. The frontal passage over Taiwan caused temperatures to drop by 10°C, the largest in June over two decades. Using gridded analyses, manually analyzed weather maps, and satellite and surface data, the present study investigates the evolution of this mei-yu front under the influence of the block. The 925-hPa frontogenetical function is computed and effects of different processes are discussed. As the blocking event developed, concurrent ridge–trough amplification in the lower–midtroposphere produced a reversed thermal pattern. The lower-tropospheric high moved southward, and large-scale confluence and deformation were enhanced between the northerly flow and the prefrontal southwesterly flow. The location of the block, to the west-southwest of the Okhotsk Sea area, allowed it to affect the front over southern China and caused it to penetrate inside 20°N, unusual for the month of June. The distribution of the frontogenetical function indicated that the mei-yu frontogenesis and the maintenance of the front were attributed to both deformation and convergence. These two processes together counteracted the strong frontolysis along the frontal zone from diabatic effects, caused by evaporative cooling of frontal precipitation on the warm side and stronger sensible heat transfer (and daytime heating over less cloudy areas) on the cold side of the front. When deformation, convergence, and diabatic effects were all combined, the net total frontogenesis peaked slightly ahead of the frontal zone, thus contributing to the southward propagation of the front in addition to the advection by postfrontal cold air in the present case. When the front moved into the South China Sea, the cross-frontal thermal gradient diminished rapidly, mainly due to the frontolytic effect from sensible heat flux over warm waters.

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Large-scale multi-stage constructed wetlands for secondary effluents treatment in northern China: Carbon dynamics

Environmental Pollution ◽

10.1016/j.envpol.2017.09.048 ◽

2018 ◽

Vol 233 ◽

pp. 933-942 ◽

Cited By ~ 15

Author(s):

Haiming Wu ◽

Jinlin Fan ◽

Jian Zhang ◽

Huu Hao Ngo ◽

Wenshan Guo

Keyword(s):

Constructed Wetlands ◽

Large Scale ◽

Northern China ◽

Carbon Dynamics ◽

Multi Stage ◽

Secondary Effluents

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Spatial distribution of tracers for optical sensing of stream surface flow

10.5194/hess-2020-188 ◽

2020 ◽

Cited By ~ 2

Author(s):

Alonso Pizarro ◽

Silvano F. Dal Sasso ◽

Matthew Perks ◽

Salvatore Manfreda

Keyword(s):

Large Scale ◽

Optical Sensing ◽

Surface Flow ◽

Seeding Density ◽

Stream Surface ◽

Image Velocimetry ◽

Level Of Aggregation ◽

Scale Particle ◽

The One

Abstract. River monitoring is of particular interest for our society that is facing increasing complexity in water management. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities, but also generating new challenges for the harmonised use of devices and algorithms. In this context, optical sensing techniques for stream surface flow velocities are strongly influenced by tracer characteristics such as seeding density and level of aggregation. Therefore, a requirement is the identification of how these properties affect the accuracy of such methods. To this aim, numerical simulations were performed to consider different levels of particle aggregation, particle colour (in terms of greyscale intensity), seeding density, and background noise. Two widely used image-velocimetry algorithms were adopted: i) Particle Tracking Velocimetry (PTV), and ii) Large-Scale Particle Image Velocimetry (LSPIV). A descriptor of the seeding characteristics (based on density and aggregation) was introduced based on a newly developed metric π. This value can be approximated and used in practice as π = ν0.1 / (ρ / ρcν1) where ν, ρ, and ρcν1 are the aggregation level, the seeding density, and the converging seeding density at ν = 1, respectively. A reduction of image-velocimetry errors was systematically observed by decreasing the values of π; and therefore, the optimal frame window was defined as the one that minimises π. In addition to numerical analyses, the Basento field case study (located in southern Italy) was considered as a proof-of-concept of the proposed framework. Field results corroborated numerical findings, and an error reduction of about 15.9 and 16.1 % was calculated – using PTV and PIV, respectively – by employing the optimal frame window.

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