Application of an integrated loach-plant-substrate-microbes non-aerated saturated vertical flow constructed wetlands: Mechanisms of pollutants removal and greenhouse gases reduction.

This study established an integrated loach-plant-substrate-microbes non-aerated saturated vertical flow constructed wetlands (VFCWs) to enhance pollutants removal efficiencies and reduce greenhouse gas emissions simultaneously. The results of the VFCWs experiment indicated that the removal efficiencies of chemical oxygen demand, total phosphorous, and total nitrogen in loach systems were significantly higher than those of non-loach systems, achieving 59.16%, 35.98%, and 40.96%, respectively. The CH4 and N2O emission fluxes were also significantly reduced in the integrated system, resulting in lower global warming potential (GWP) and GWP per unit of pollutants removal. Loaches promoted the transportation of oxygen, facilitated the re-contact and utilization of sediments, reduced CH4 emission, and enhanced nitrogen conversion and phosphorus accumulation. Increased bioavailable carbon and nitrate-nitrogen in the integrated system improved the abundance of denitrifying bacteria, which supported complete denitrification, reducing N2O emissions with high pollutant removal.

A Systematic Measurement of Street Quality through Multi-Sourced Urban Data: A Human-Oriented Analysis.

Many studies have been made on street quality, physical activity and public health. However, most studies so far have focused on only few features, such as street greenery or accessibility. These features fail to capture people's holistic perceptions. The potential of fine grained, multi-sourced urban data creates new research avenues for addressing multi-feature, intangible, human-oriented issues related to the built environment. This study proposes a systematic, multi-factor quantitative approach for measuring street quality with the support of multi-sourced urban data taking Yangpu District in Shanghai as case study. This holistic approach combines typical and new urban data in order to measure street quality with a human-oriented perspective. This composite measure of street quality is based on the well-established 5Ds dimensions: Density, Diversity, Design, Destination accessibility and Distance to transit. They are combined as a collection of new urban data and research techniques, including location-based service (LBS) positioning data, points of interest (PoIs), elements and visual quality of street-view images extraction with supervised machine learning, and accessibility metrics using network science. According to these quantitative measurements from the five aspects, streets were classified into eight feature clusters and three types reflecting the value of street quality using a hierarchical clustering method. The classification was tested with experts. The analytical framework developed through this study contributes to human-oriented urban planning practices to further encourage physical activity and public health.