Description: Organic Letters DOI: 10.1021/acs.orglett.5b02829

Description: Organic Letters DOI: 10.1021/acs.orglett.6b03125

Description: Water, Vol. 10, Pages 1244: Effect of Aeration Rates and Filter Media Heights on the Performance of Pollutant Removal in an Up-Flow Biological Aerated Filter Water doi: 10.3390/w10091244 Authors: Jiehui Ren Wen Cheng Tian Wan Min Wang Chengcheng Zhang The biological aerated filter (BAF) is an effective biological treatment technology which removes the pollutants in municipal wastewater secondary treatment. However, we still know little about the interaction between the pollutants removal and microbes within the BAF. In this study, we used an up-flow BAF (UBAF) reactor to investigate the relationships between the pollutants removal and microbial community structure at different aeration rates and filter media heights. The microbial community of biofilm was analyzed by Illumina pyrosequencing. Our results showed that the UBAF achieved a better removal efficiency of chemical oxygen demand (COD), NH4+-N, NO3−-N, and total phosphorus (TP) at an aeration rate of 65 L/h. In addition, the COD and NH4+-N removal mainly occurred at 0–25 cm height of filter media. The microbial community structure in the UBAF demonstrated that the relative abundance of the Planctomycetes and Comamonadaceae at 10 cm height of filter media were 11% and 48.1%, respectively, proportions significantly higher than those under others treatments. Finally, the changes in relative abundance of Proteobacteria, Planctomycetes, and Nitrospirae likely explained the mechanism of nitrogen and phosphorus removal. Our results showed that suitable conditions could enhance the microbial community structure to achieve a high pollutants removal in the UBAF.

Description: Remote Sensing, Vol. 10, Pages 1196: Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China Remote Sensing doi: 10.3390/rs10081196 Authors: Davide Fornacca Guopeng Ren Wen Xiao Remote mountainous regions are among the Earth’s last remaining wild spots, hosting rare ecosystems and rich biodiversity. Because of access difficulties and low population density, baseline information about natural and human-induced disturbances in these regions is often limited or nonexistent. Landsat time series offer invaluable opportunities to reconstruct past land cover changes. However, the applicability of this approach strongly depends on the availability of good quality, cloud-free images, acquired at a regular time interval, which in mountainous regions are often difficult to find. The present study analyzed burn scar detection capabilities of 11 widely used spectral indices (SI) at 1 to 5 years after fire events in four dominant vegetation groups in a mountainous region of northwest Yunnan, China. To evaluate their performances, we used M-statistic as a burned-unburned class separability index, and we adapted an existing metric to quantify the SI residual burn signal at post-fire dates compared to the maximum severity recorded soon after the fire. Our results show that Normalized Burn Ratio (NBR) and Normalized Difference Moisture Index (NDMI) are always among the three best performers for the detection of burn scars starting 1 year after fire but not for the immediate post-fire assessment, where the Mid Infrared Burn Index, Burn Area Index, and Tasseled Cap Greenness were superior. Brightness and Wetness peculiar patterns revealed long-term effects of fire in vegetated land, suggesting their potential integration to assist other SI in burned area detection several years after the fire event. However, in general, class separability of most of the SI was poor after one growing season, due to the seasonal rains and the relatively fast regrowth rate of shrubs and grasses, confirming the difficulty of assessment in mountainous ecosystems. Our findings are meaningful for the selection of a suitable SI to integrate in burned area detection workflows, according to vegetation type and time lag between image acquisitions.