Erosion hotspot identified along the sandy coast of Shanwei: characteristics and origin

Based on the measured beach profile data of Sanzhou Bay from 2015 to 2019,an erosion hotspot was identified along the Shanwei coastline of eastern Guangdong,where the maximum retreat distance of the shoreline exceeded 80 m and the erosion rate was more than 20 m/a.To determine the time at which the erosion hotspot started and the potential causes of its formation,this study used 63 Landsat satellite images from 1986 to 2019 to construct a time series of shoreline positions over the past 30 years by extracting their high-tide shorelines.Next,the M-K trend test method was introduced to evaluate the non-linear shoreline behavior based on the single-transect method.The results showed that the time of approximately 2013 marked the start of the erosion hotspot,the erosion hotspot was characterized by erosion rates of more than 2 m/a(a maximum rate of 31.6 m/a),and the affected shoreline more than 4.3 km from 2013 to 2019.Furthermore,this erosion hotspot was proved to be caused by artificial sand mining in the nearshore zone,which destroyed the original beach’s morphodynamic equilibrium.With the aid of storm events,soil cliffs composed of loose sediment on the backshore were sacrificed to achieve a new equilibrium,resulting in an extremely significant retreat parallel to the coast on the west side of the study area,which reflects the combined effect of human and natural processes.This study provides a concrete example of the rapid response of shorelines to artificial sand mining activities,and the associated finding is a stark warning about the cautious development and utilization of coastal zones and the strict regulation of human activities.

Predicting standard penetration test N-value from cone penetration test data using artificial neural networks

Standard Penetration Test（SPT） and Cone Penetration Test（CPT） are the most frequently used field tests to estimate soil parameters for geotechnical analysis and design.Numerous soil parameters are related to the SPT N-value.In contrast,CPT is becoming more popular for site investigation and geotechnical design.Correlation of CPT data with SPT N-value is very beneficial since most of the field parameters are related to SPT N-values.A back-propagation artificial neural network（ANN） model was developed to predict the N6o-value from CPT data.Data used in this study consisted of 109 CPT-SPT pairs for sand,sandy silt,and silty sand soils.The ANN model input variables are：CPT tip resistance（qc）,effective vertical stress（σ’v）,and CPT sleeve friction（fs）.A different set of SPT-CPT data was used to check the reliability of the developed ANN model.It was shown that ANN model either under-predicted the N60-value by 7-16%or over-predicted it by 7-20%.It is concluded that back-propagation neural networks is a good tool to predict N60-value from CPT data with acceptable accuracy.

Irrigation of Romaine Lettuce (Lactuca sativa)Using Wastewater Treated byNon-Conventional Technologies

The aim of this study was to assess the capacity for reuse of wastewater treated in stabilisation ponds and subsequently reclaimed by means of different filtration systems at pilot scale. An analysis of filtered water showed turbidity values of below 5 NTU, a total suspended solids (TSS) content of 7 mg/l, and Escherichia coli values of up to 1.6 log CFU/100 ml. These results fall within the parameters stipulated in RD 1620/2007 Spanish Water Reuse Regulations governing the reuse of reclaimed wastewater for agricultural purposes. The water reclaimed by means of filtration systems was used to irrigate Romainelettuce (Lactuca sativa longifolia), comparing growth with that of the same variety irrigated with water from the supply network. The results showed a mean difference in lettuce growth of up to 300% in favour of the crop irrigated with reclaimed water.