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River deltas and estuaries are disproportionally-significant coastal landforms that are inhabited by nearly 600 M people globally. In recent history, rapid socio-economic development has dramatically changed many of the World's mega deltas, which have typically undergone agricultural intensification and expansion, land-use change, urbanization, water resources engineering and exploitation of natural resources. As a result, mega deltas have evolved into complex and potentially vulnerable socio-ecological systems with unique threats and coping capabilities. The goal of this research was to establish a holistic understanding of threats, resilience, and adaptation for four mega deltas of variable geography and levels of socio-economic development, namely the Mekong, Yellow River, Yangtze, and Rhine deltas. Compiling this kind of information is critical for managing and developing these complex coastal areas sustainably but is typically hindered by a lack of consistent quantitative data across the ecological, social and economic sectors. To overcome this limitation, we adopted a qualitative approach, where delta characteristics across all sectors were assessed through systematic expert surveys. This approach enabled us to generate a comparative assessment of threats, resilience, and resilience-strengthening adaptation across the four deltas. Our assessment provides novel insights into the various components that dominate the overall risk situation in each delta and, for the first time, illustrates how each of these components differ across the four mega deltas. As such, our findings can guide a more detailed, sector specific, risk assessment or assist in better targeting the implementation of risk mitigation and adaptation strategies.

River deltas and estuaries are disproportionally-significant coastal landforms that are inhabited by nearly 600 M people globally. In recent history, rapid socio-economic development has dramatically changed many of the World's mega deltas, which have typically undergone agricultural intensification and expansion, land-use change, urbanization, water resources engineering and exploitation of natural resources. As a result, mega deltas have evolved into complex and potentially vulnerable socio-ecological systems with unique threats and coping capabilities. The goal of this research was to establish a holistic understanding of threats, resilience, and adaptation for four mega deltas of variable geography and levels of socio-economic development, namely the Mekong, Yellow River, Yangtze, and Rhine deltas. Compiling this kind of information is critical for managing and developing these complex coastal areas sustainably but is typically hindered by a lack of consistent quantitative data across the ecological, social and economic sectors. To overcome this limitation, we adopted a qualitative approach, where delta characteristics across all sectors were assessed through systematic expert surveys. This approach enabled us to generate a comparative assessment of threats, resilience, and resilience-strengthening adaptation across the four deltas. Our assessment provides novel insights into the various components that dominate the overall risk situation in each delta and, for the first time, illustrates how each of these components differ across the four mega deltas. As such, our findings can guide a more detailed, sector specific, risk assessment or assist in better targeting the implementation of risk mitigation and adaptation strategies.

In order to increase the reliability of fuel cells, an online diagnostic method for detection of operation malfunctions, as well as the early detection of failures in the fuel cells, is necessary. For this purpose, locally resolved current density measurements are an important tool, but the interpretation of the data related to the detection of malfunctions or failures is not straightforward. Here, segmented cell technology is applied to investigate the current density distributions in the anode and cathode electrodes to ascertain their equivalence due to the strong perpendicular coupling of currents. Current density distributions are further used to determine the signature of pinhole formation in the membrane. Different behavior is observed for membrane leakage under open circuit and under applied load conditions. Whereas the cell at open circuit is characterized by a positive current in the vicinity of the pinhole and small negative currents in the remaining area, an applied load leads to large negative currents at the pinhole. The characteristic behavior can be explained by high crossover rates of hydrogen from the anode to the cathode. The nongeneric signature is used to detect the deterioration of a membrane electrode assembly after a test stand malfunction. A sudden pressure drop associated with vaporation of water and the fast cooling of the cell is assumed to trigger the failure of the membrane. After 48 h, fissures in several positions of the membrane near the edges of the cell holder are observed. Through the evolution of leakages in the fuel cell, a malfunction can be detected at an early stage and thereby catastrophic failure of the whole stack may be avoided or anticipated.

In order to increase the reliability of fuel cells, an online diagnostic method for detection of operation malfunctions, as well as the early detection of failures in the fuel cells, is necessary. For this purpose, locally resolved current density measurements are an important tool, but the interpretation of the data related to the detection of malfunctions or failures is not straightforward. Here, segmented cell technology is applied to investigate the current density distributions in the anode and cathode electrodes to ascertain their equivalence due to the strong perpendicular coupling of currents. Current density distributions are further used to determine the signature of pinhole formation in the membrane. Different behavior is observed for membrane leakage under open circuit and under applied load conditions. Whereas the cell at open circuit is characterized by a positive current in the vicinity of the pinhole and small negative currents in the remaining area, an applied load leads to large negative currents at the pinhole. The characteristic behavior can be explained by high crossover rates of hydrogen from the anode to the cathode. The nongeneric signature is used to detect the deterioration of a membrane electrode assembly after a test stand malfunction. A sudden pressure drop associated with vaporation of water and the fast cooling of the cell is assumed to trigger the failure of the membrane. After 48 h, fissures in several positions of the membrane near the edges of the cell holder are observed. Through the evolution of leakages in the fuel cell, a malfunction can be detected at an early stage and thereby catastrophic failure of the whole stack may be avoided or anticipated.

Nitrous oxide (N2O) is the third most abundant anthropogenous greenhouse gas (after carbon dioxide and methane), with a long atmospheric lifetime and a continuously increasing concentration due to human activities, making it an important gas to monitor. In this work, we present a new method to retrieve N2O concentration profiles (with up to two degrees of freedom) from each cloud-free satellite observation by the Infrared Atmospheric Sounding Interferometer (IASI), using spectral micro-windows in the N2O ν3 band, the Radiative Transfer for TOVS (RTTOV) tools and the Tikhonov regularization scheme. A time series of ten years (2011–2020) of IASI N2O profiles and integrated partial columns has been produced and validated with collocated ground-based Network for the Detection of Atmospheric Composition Change (NDACC) and Total Carbon Column Observing Network (TCCON) data. The importance of consistency in the ancillary data used for the retrieval for generating consistent time series has been demonstrated. The Nitrous Oxide Profiling from Infrared Radiances (NOPIR) N2O partial columns are of very good quality, with a positive bias of 1.8 to 4% with respect to the ground-based data, which is less than the sum of uncertainties of the compared values. At high latitudes, the comparisons are a bit worse, due to either a known bias in the ground-based data, or to a higher uncertainty in both ground-based and satellite retrievals.

Nitrous oxide (N2O) is the third most abundant anthropogenous greenhouse gas (after carbon dioxide and methane), with a long atmospheric lifetime and a continuously increasing concentration due to human activities, making it an important gas to monitor. In this work, we present a new method to retrieve N2O concentration profiles (with up to two degrees of freedom) from each cloud-free satellite observation by the Infrared Atmospheric Sounding Interferometer (IASI), using spectral micro-windows in the N2O ν3 band, the Radiative Transfer for TOVS (RTTOV) tools and the Tikhonov regularization scheme. A time series of ten years (2011–2020) of IASI N2O profiles and integrated partial columns has been produced and validated with collocated ground-based Network for the Detection of Atmospheric Composition Change (NDACC) and Total Carbon Column Observing Network (TCCON) data. The importance of consistency in the ancillary data used for the retrieval for generating consistent time series has been demonstrated. The Nitrous Oxide Profiling from Infrared Radiances (NOPIR) N2O partial columns are of very good quality, with a positive bias of 1.8 to 4% with respect to the ground-based data, which is less than the sum of uncertainties of the compared values. At high latitudes, the comparisons are a bit worse, due to either a known bias in the ground-based data, or to a higher uncertainty in both ground-based and satellite retrievals.