Seasonal Sources and Cycling of Nitrogen Revealed by Stable Isotopes in the Northeastern Beibu Gulf, China

Isotope measurements were performed on dissolved nitrate (NO3−) and ammonium (NH4+) in the coastal waters of the northeastern Beibu Gulf, China, to investigate the seasonal nitrate sources and their biogeochemical processes, which are due to the rapid development of local industrialisation and urbanisation. The high N/P ratio observed in the coastal bay during both fall and spring suggests that P is a limiting nutrient, which in turn indicates that increasing P causes conditions favourable for algal blooms. Higher nutrient concentrations and δ15N-NO3− and δ15N-NH4+ values were found in the nearshore area in the fall, suggesting that nutrients originated mainly from land-based pollution. A Bayesian isotope mixing model was used to calculate the contribution of potential NO3- sources and the results showed that in the nearshore area, NO3− originated mainly from manure and sewage (58%). In the spring, however, in addition to the impact of urban sewage effluents, the exchange of sediment and water was another important factor causing higher nutrient concentrations and positive NO3− isotopes in the nearshore area. There were lower concentrations of nutrients and an increase in δ15N-NO3− and δ15N-NH4+ values in the offshore area in the fall, and the NO3− loss in the surface water was mainly caused by the process of assimilation. However, the exchange of sediment and water was the dominant factor causing higher nutrient concentrations (except for NO3−) and positive dual nitrate isotopes but lower NO3− concentration in the offshore area during the spring. Overall, isotope analysis of NO3− and NH4+ helps to illustrate the major sources of the former and their biological transformation in the northeastern Beibu Gulf.

Prospective species for marine aquaculture in Primorski Krai

Here is given data about current state of marine aquaculture objects in Primorsky Krai, and some ecological features of its nearshore area for cultivation of hydrobionts, belonging to different biogeographical groups. Ecological conditions influencing on efficiency of different species industrial cultivation were analyzed. Main part of marine aquaculture production in the next decade will be produced by traditional cultivated species mostly.

A study of microwave over-sea propagation with high-potential x-band doppler radar

Subject and Purpose. The paper is devoted to the microwave propagation over the sea in the nearshore region. Emphasis is on microwave attenuation measurements in the semi-shade and deep-shade areas with a view to study conditions of the microwave propagation on a beyond-the-horizon path in the nearshore area using a radar method. Methods and Methodology. A radar technique, developed and tested, provides measuring a microwave attenuation coefficient along a 60 km long beyond-the-horizon path in the tropospheric surface layer. High-grade radar and Doppler radar beacons (racons) are employed. Of interest are experimentally obtained temporal dependences of signals from racons situated at different heights and, also, spatial field distribution curves from a steadily moving racon running a height 1 to 27 m for 25 minutes. Results. A prototype of high-grade X-band coherent continuous-wave (Doppler) radar has been designed and fabricated. A radar technique has been developed and tested for the analysis of microwave beyond-the-horizon propagation conditions in the shadow region, which involves continuous-wave (Doppler) radar and Doppler racons with a radar cross section (RCS) of about 60 m2. Conclusions. The technique proposed enables reliable tracking of racon responses up to a double radiohorizon range on over-sea paths. This technique can be used for radar calibrations when dealing with various location problems in the nearshore area, including detection and tracking small-size and low-flying targets and their radar identification.

Wave data prediction and reconstruction by recurrent neural networks at the nearshore area of Norderney

<p><span>Sea level rise, a possible increase in frequency and intensity of storms and other effects of global warming exert pressure on the coastal regions of the North Sea. Also storm surges threaten the basis of existence for many people in the affected areas. As well as for building coastal protection or offshore structures, detailed knowledge of wave data, especially the wave height, is of particular interest. Therefore, the nearshore wave climate at the island Norderney is measured by buoys since the early 1990s. Caused by crossing ships or weather impacts, these buoys can be damaged. This leads to a huge amount of missing data in the wave data time series, which are the basis for numerical modelling, statistical analysis and developing coastal protection.<br>Artificial neural networks are a common method to reconstruct and forecast wave heights nowadays. This study shows a new technique to reconstruct and forecast significant wave height measured by buoys in the nearshore area of the Norderney coastline. Buoy data of the period 2004 to 2017 from the NLWKN – Coastal Research Station at Norderney were used to train three different statistical and machine learning models namely linear regression, feed-forward neural network and long short-term memory (LSTM), respectively. An energy density spectrum was tested against calculated sea state parameter as input. The LSTM – a recurrent neural network – is the proposed algorithm to reconstruct wave height data. It is especially designed for sequential data, but was performed on wave spectral data in this study for the first time. Depending on the input parameter of the respectively model, the LSTM can reconstruct and forecast time series of arbitrary length.<br>Using information about wind speed and direction and water depth, as well as the wave height of two neighboring buoy stations, the LSTM reconstructs the wave height with a correlation coefficient of 0.98 between measured and reconstructed data.<br>Unfortunately, the forecasting and reconstruction error of extreme events is highly underestimated, though these events are of great interest for climate and ocean science. Currently, this error is being specifically attempted to improve. Compared to numerical modeling, the machine learning approach requires less computational effort. Results of this study can be used to complete spatial and temporal wave height datasets, providing a better basis for trend analysis in relation to climate change and for validating numerical models for decision making in coastal protection and management.</span></p>

Contamination status and ecological risk of heavy metals in surface sediment of Kelantan River and its nearshore area, Malaysia

A study on contamination status and ecological risk of heavy metals in surface sediment at selected sites on Kelantan River and its nearshore area was carried out. Ten samples along Kelantan River and 25 samples from the nearshore were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES) to determine heavy metal concentrations. Sediment samples were also analyzed for particle size compositions, total organic matter and pH content. The average concentrations for As, Cd, Cr, Cu, Mn, Ni, Pb, Zn were 7.38, 1.31, 17.71, 11.40, 507.15, 5.97, 22.61, 32.95 mg/kg for riverine and 14.14, 4.59, 29.79, 14.07, 389.96, 9.65, 62.21, 41.04 mg/kg for nearshore samples respectively. The potential ecological risk index showed stations Bekok, Manek Urai, and RH under considerable risk followed by station Pasir Mas under moderate risk. The pollution load index classified four nearshore sites (KW10, KW17, KW18, KW37) as polluted. The geo-accumulation index (Igeo) categorized moderate contamination for Cd and Pb. The enrichment factor (EF) along the river categorized extremely high enrichment for Cd, and significant enrichment for As, Pb and Mn while Pb and As were under very high and significant enrichment in nearshore areas. Pb, Cu, Zn, As, Ni and Cr showed significant correlations with each other.