Immunofluorescence detection and localization of B[a]P and TCDD in earthworm tissues

An immunohistochemical method using antibodies against polycyclic aromatic hydrocarbons (PAHs) and dioxins was developed on frozen tissue sections of the earthworm Eisenia andrei exposed to environmentally relevant concentrations of benzo[a]pyrene (B[a]P) (0.1, 10, 50 ppm) and 2,3,7,8-tetrachlorodibenzo-para-dioxin (TCDD) (0.01, 0.1, 2 ppb) in spiked standard soils. The concentrations of B[a]P and TCDD in E. andrei exposed to the same conditions were also measured using analytical chemical procedures. The results demonstrated that tissues of worms exposed to even minimal amount of B[a]P and TCDD reacted positively and specifically to anti-PAHs and -dioxins antibody. Immunofluorescence revealed a much more intense staining for the gut compared to the body wall; moreover, positively immunoreactive amoeboid coelomocytes were also observed, i.e. cells in which we have previously demonstrated the occurrence of genotoxic damage. The double immunolabelling with antibodies against B[a]P/TCDD and the lysosomal enzyme cathepsin D demonstrated the lysosomal accumulation of the organic xenobiotic compounds, in particular in the cells of the chloragogenous tissue as well as in coelomocytes, involved into detoxification and protection of animals against toxic chemicals. The method described is timesaving, not expensive and easily applicable.

Detection and impacts of leakage from sub-seafloor deep geological carbon dioxide storage

Fossil fuel power generation and other industrial emissions of carbon dioxide are a threat to global climate1, yet many economies will remain reliant on these technologies for several decades2. Carbon dioxide capture and storage (CCS) in deep geological formations provides an effective option to remove these emissions from the climate system3. In many regions storage reservoirs are located offshore4, 5, over a kilometre or more below societally important shelf seas6. Therefore, concerns about the possibility of leakage7, 8 and potential environmental impacts, along with economics, have contributed to delaying development of operational CCS. Here we investigate the detectability and environmental impact of leakage from a controlled sub-seabed release of CO2. We show that the biological impact and footprint of this small leak analogue (<1 tonne CO2 d−1) is confined to a few tens of metres. Migration of CO2 through the shallow seabed is influenced by near-surface sediment structure, and by dissolution and re-precipitation of calcium carbonate naturally present in sediments. Results reported here advance the understanding of environmental sensitivity to leakage and identify appropriate monitoring strategies for full-scale carbon storage operations.

Detection and impacts of leakage from sub-seafloor deep geological carbon dioxide storage

Fossil fuel power generation and other industrial emissions of carbon dioxide are a threat to global climate1, yet many economies will remain reliant on these technologies for several decades2. Carbon dioxide capture and storage (CCS) in deep geological formations provides an effective option to remove these emissions from the climate system3. In many regions storage reservoirs are located offshore4, 5, over a kilometre or more below societally important shelf seas6. Therefore, concerns about the possibility of leakage7, 8 and potential environmental impacts, along with economics, have contributed to delaying development of operational CCS. Here we investigate the detectability and environmental impact of leakage from a controlled sub-seabed release of CO2. We show that the biological impact and footprint of this small leak analogue (<1 tonne CO2 d−1) is confined to a few tens of metres. Migration of CO2 through the shallow seabed is influenced by near-surface sediment structure, and by dissolution and re-precipitation of calcium carbonate naturally present in sediments. Results reported here advance the understanding of environmental sensitivity to leakage and identify appropriate monitoring strategies for full-scale carbon storage operations.

Nonlinear PCA with Local Approach for Diesel Engine Fault Detection and Diagnosis

This brief examines the application of nonlinear statistical process control to the detection and diagnosis of faults in automotive engines. In this statistical framework, the computed score variables may have a complicated nonparametric distri- bution function, which hampers statistical inference, notably for fault detection and diagnosis. This brief shows that introducing the statistical local approach into nonlinear statistical process control produces statistics that follow a normal distribution, thereby enabling a simple statistical inference for fault detection. Further, for fault diagnosis, this brief introduces a compensation scheme that approximates the fault condition signature. Experimental results from a Volkswagen 1.9-L turbo-charged diesel engine are included.

Proposed wavelet-neurofuzzy combined system for power qualityviolations detection and diagnosis

A system for the identification of power quality violations is proposed. It is a two-stage system that employs the potentials of the wavelet transform and the adaptive neurofuzzy networks. For the first stage, the wavelet multiresolution signal analysis is exploited to denoise and then decompose the monitored signals of the power quality events to extract its detailed information. A new optimal feature-vector is suggested and adopted in learning the neurofuzzy classifier. Thus, the amount of needed training data is extensively reduced. A modified organisation map of the neurofuzzy classifier has significantly improved the diagnosis efficiency. Simulation results confirm the aptness and the capability of the proposed system in power quality violations detection and automatic diagnosis