اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدMicroalgae-based bioremediation of water contaminated by pesticides in peri-urban agricultural areas
124
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
The present study evaluated the capacity of a semi-closed, tubular horizontal photobioreactor (PBR) to remove pesticides from agricultural run-off. The study was carried out in July to study its efficiency under the best conditions (highest solar irradiation). A total of 51 pesticides, including 10 transformation products, were selected and investigated based on their consumption rate and environmental relevance. Sixteen of them were detected in the agricultural run-off, and the estimated removal efficiencies ranged from negative values, obtained for 3 compounds, namely terbutryn, diuron, and imidacloprid, to 100%, achieved for 10 compounds. The acidic herbicide MCPA was removed by 88% on average, and the insecticides 2,4-D and diazinon showed variable removals, between 100% and negative values. The environmental risk associated with the compounds still present in the effluent of the PBR was evaluated using hazard quotients (HQs), calculated using the average and highest measured concentrations of the compounds. HQ values > 10 (meaning high risk) were obtained for imidacloprid (21), between 1 and 10 (meaning moderate risk) for 2,4-D (2.8), diazinon (4.6) and terbutryn (1.5), and < 1 (meaning low risk) for the remaining compounds diuron, linuron, and MCPA. The PBR treatment yielded variable removals depending on the compound, similar to conventional wastewater treatment plants. This study provides new data on the capacity of icroalgae-based treatment systems to eliminate a wide range of priority pesticides under real/environmental conditions.
قيم البحث
اقرأ أيضاً
Localizing the sources of electrical activity in the brain from Electroencephalographic (EEG) data is an important tool for non-invasive study of brain dynamics. Generally, the source localization process involves a high-dimensional inverse problem t
hat has an infinite number of solutions and thus requires additional constraints to be considered to have a unique solution. In the context of EEG source localization, we propose a novel approach that is based on dividing the cerebral cortex of the brain into a finite number of Functional Zones which correspond to unitary functional areas in the brain. In this paper we investigate the use of Brodmanns areas as the Functional Zones. This approach allows us to apply a sparsity constraint to find a unique solution for the inverse EEG problem. Compared to previously published algorithms which use different sparsity constraints to solve this problem, the proposed method is potentially more consistent with the known sparsity profile of the human brain activity and thus may be able to ensure better localization. Numerical experiments are conducted on a realistic head model obtained from segmentation of MRI images of the head and includes four major compartments namely scalp, skull, cerebrospinal fluid (CSF) and brain with relative conductivity values. Three different electrode setups are tested in the numerical experiments.
We introduce an image cytometer (I-CYT) for the analysis of phytoplankton in fresh and marine water environments. A linear quantification of cell numbers was observed covering several orders of magnitude using cultures of Tetraselmis and Nannochlorop
sis measured by autofluorescence in a laboratory environment. We assessed the functionality of the system outside the laboratory by phytoplankton quantification of samples taken from a marine water environment (Dutch Wadden Sea, The Netherlands) and a fresh water environment (Lake Ijssel, The Netherlands). The I-CYT was also employed to study the effects of two ballast water treatment systems (BWTS), based on chlorine electrolysis and UV sterilization, with the analysis including the vitality of the phytoplankton. For comparative study and benchmarking of the I-CYT, a standard flow cytometer was used. Our results prove a limit of detection (LOD) of 10 cells/ml with an accuracy between 0.7 and 0.5 log, and a correlation of 88.29% in quantification and 96.21% in vitality, with respect to the flow cytometry results.
Water is an essential resource for all living organisms. The continuous and increasing use of pesticides in agricultural and urban activities results in the pollution of water resources and represents an environmental risk. To control and reduce pest
icide pollution, reliable multi-residue methods for the detection of these compounds in water are needed. In this context, the present work aimed at providing an analytical method for the simultaneous determination of trace levels of 51 target pesticides in water and applying it to the investigation of target pesticides in two agriculture-impacted areas of interest. The method developed, based on an isotopic dilution approach and on-line solid-phase extraction-liquid chromatography-tandem mass spectrometry, is fast, simple, and to a large extent automated, and allows the analysis of most of the target compounds in compliance with European regulations. Further application of the method to the analysis of selected water samples collected at the lowest stretches of the two largest river basins of Catalonia (NE Spain), Llobregat and Ter, revealed the presence of a wide suite of pesticides, and some of them at concentrations above the water quality standards (irgarol and dichlorvos) or the acceptable method detection limits (methiocarb, imidacloprid, and thiacloprid), in the Llobregat, and much cleaner waters in the Ter River basin. Risk assessment of the pesticide concentrations measured in the Llobregat indicated high risk due to the presence of irgarol, dichlorvos, methiocarb, azinphos ethyl, imidacloprid, and diflufenican (hazard quotient (HQ) values>10), and an only moderate potential risk in the Ter River associated to the occurrence of bentazone and irgarol (HQ>1).
Mitigating traffic congestion on urban roads, with paramount importance in urban development and reduction of energy consumption and air pollution, depends on our ability to foresee road usage and traffic conditions pertaining to the collective behav
ior of drivers, raising a significant question: to what degree is road traffic predictable in urban areas? Here we rely on the precise records of daily vehicle mobility based on GPS positioning device installed in taxis to uncover the potential daily predictability of urban traffic patterns. Using the mapping from the degree of congestion on roads into a time series of symbols and measuring its entropy, we find a relatively high daily predictability of traffic conditions despite the absence of any a priori knowledge of drivers origins and destinations and quite different travel patterns between weekdays and weekends. Moreover, we find a counterintuitive dependence of the predictability on travel speed: the road segment associated with intermediate average travel speed is most difficult to be predicted. We also explore the possibility of recovering the traffic condition of an inaccessible segment from its adjacent segments with respect to limited observability. The highly predictable traffic patterns in spite of the heterogeneity of drivers behaviors and the variability of their origins and destinations enables development of accurate predictive models for eventually devising practical strategies to mitigate urban road congestion.
In order to estimate the seismic vulnerability of a densely populated urban area, it would in principle be necessary to evaluate the dynamic behaviour of individual and aggregate buildings. These detailed seismic analyses, however, are extremely cost
-intensive and require great processing time and expertise judgment. The aim of the present study is to propose a new methodology able to combine information and tools coming from different scientific fields in order to reproduce the effects of a seismic input in urban areas with known geological features and to estimate the entity of the damages caused on existing buildings. In particular, we present new software called ABES (Agent-Based Earthquake Simulator), based on a Self-Organized Criticality framework, which allows to evaluate the effects of a sequence of seismic events on a certain large urban area during a given interval of time. The integration of Geographic Information System (GIS) data sets, concerning both geological and urban information about the territory of Avola (Italy), allows performing a parametric study of these effects on a real context as a case study. The proposed new approach could be very useful in estimating the seismic vulnerability and defining planning strategies for seismic risk reduction in large urban areas
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
