A Sequencing Batch Reactor (SBR) with activated sludge was operated with synthetic wastewater containing ibuprofen (IBU) to investigate the biomass stress-responses under long-term IBU exposure. There were 3 different phases: phase I as the control without IBU for 56 days, phase II (40 days), and phase III (60 days) containing IBU at 10 and 5 mg L − 1 each. The overall performance of the SBR as well as the extracellular polymeric substances (EPS) in terms of polysaccharides, proteins, and humic acid substances were estimated. Morphological parameters of microbial aggregates in the presence of IBU (phase II and phase III) were assessed by quantitative image analysis (QIA). Removal efficiencies of chemical oxygen demand (COD) and ammonium (NH 4 + ) were significantly reduced by IBU. Loosely bound EPS (LB-EPS) decreased during phase II and phase III, and tightly bound EPS (TB-EPS) was slightly higher in phase II than phase I. TB-EPS proteins were greater in phase II, perhaps to protect microbial cells from IBU exposure. These findings provided insight into both activated sludge stress-responses and EPS composition under long-term IBU exposure. Spearman correlation showed that EPS and morphological parameters significantly affected sludge settleability and flocculation. QIA also proved to be a powerful technique in investigating dysfunctions in activated sludge under IBU exposure.
The monitoring of emerging pollutants in wastewaters is nowadays an issue of special concern, with the classical quantification methods being time and reagent consuming. In this sense, a FTIR transmission spectroscopy based chemometric methodology was developed for the determination of eight of these pollutants. A total of 456 samples were, therefore, obtained, from an activated sludge wastewater treatment process spiked with the studied pollutants, and analysed in the range of 200 cm −1 to 14,000 cm −1. Then, a k-nearest neighbour (kNN) analysis aiming at identifying each sample pollutant was employed. Next, partial least squares (PLS) and ordinary least squares (OLS) modelling approaches were employed in order to obtain suitable prediction models. This procedure resulted in good prediction abilities regarding the estimation of atrazine, desloratadine, paracetamol, β-estradiol, ibuprofen, carbamazepine, sulfamethoxazole and ethynylestradiol concentrations in wastewaters. These promising results suggest this technology as a fast, eco-friendly and reagent free alternative methodology for the quantification of emerging pollutants in wastewaters.
In biological wastewater treatment (WWT), microorganisms live and grow held together by a slime matrix comprised of extracellular polymeric substances (EPS), forming a three-dimensional microbial structure of aggregates (flocs or granules) and by chemical binding forces. Furthermore, microscopic observations showed that microbial cells within the flocs were cross linked with EPS, forming a network of polymers with pores and channels. The EPS are typically composed of organic substances such as polysaccharides (PS), proteins (PNs), humic acid substances (HAS), nucleic acids, and lipids. It has been established that EPS play an essential role in aggregate flocculation, settling, and dewatering. Moreover, in the presence of toxic substances, such as pharmaceutical compounds and pesticides, EPS form a protective layer for the aggregated biomass against environmental disturbances that might play an important role in the transport and transformation of micropollutants. Some researchers indicated that there is an increase in EPS concentration under toxic conditions, which can induce an increase in the size of microbial aggregates. In this contribution, we critically review the available information on the impact of micropollutants on microbial EPS production and the relationship between EPS and microbial aggregate structure. Also, a general definition, composition, and factors that affect EPS production are presented.
Due to the fast deforestation rates in the tropics, multiple international efforts have been launched to reduce deforestation and develop consistent methodologies to assess forest extension and change. Since 2010 Colombia implemented the Mainstream Sustainable Cattle Ranching project with the participation of small farmers in a payment for environmental services (PES) scheme where zero deforestation agreements are signed. To assess the fulfillment of such agreements at farm level, ALOS-1 and ALOS-2 PALSAR fine beam dual imagery for years 2010 and 2016 was processed with ad-hoc routines to estimate stable forest, deforestation, and stable nonforest extension for 2615 participant farms in five heterogeneous regions of Colombia. Landsat VNIR imagery was integrated in the processing chain to reduce classification uncertainties due to radar limitations. Farms associated with Meta Foothills regions showed zero deforestation during the period analyzed (2010-2016), while other regions showed low deforestation rates with the exception of the Cesar River Valley (75 ha). Results, suggests that topography and dry weather conditions have an effect on radar-based mapping accuracy, i.e., deforestation and forest classes showed lower user accuracy values on mountainous and dry regions revealing overestimations in these environments. Nevertheless, overall ALOS Phased Array L-band SAR (PALSAR) data provided overall accurate, relevant, and consistent information for forest change analysis for local zero deforestation agreements assessment. Improvements to preprocessing routines and integration of high dense radar time series should be further investigated to reduce classification errors from complex topography conditions. played an especially important role in landscape change dynamics within the country [2]. Currently, ranching represents one of the key economic subsectors in Colombia, contributing to approximately 3.5% of the overall Gross Domestic Product (GDP) and 27% of the agricultural and livestock GDP [3]. Cattle ranching exploited more than 38 million hectares over the last 50 years, holding approximately 23.5 million heads, supporting 7% and 28% of national and rural employment, respectively.Information related to forest trends are critical to different actors involved in the decision-making of policies and investments promoting the conservation of forests and their ecosystem services. Globally, several efforts have been put in place to develop consistent and robust methodologies to assess forest extension and change [4][5][6][7][8][9][10]. As a response to the rapid advance of global forest loss and degradation, the UN Framework Convention on Climate Change (UNFCCC) launched the Reducing Emissions from Deforestation and Forest Degradation program (REDD+). The general aim of REDD+ is to contribute to the mitigation of climate change by reducing greenhouse gas (GHG) emissions by decreasing and reversing forest loss and degradation, and by increasing the removal of GHGs through conservation and the expansion of forests [11]...
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