Water environment

About: Water environment is a research topic. Over the lifetime, 13384 publications have been published within this topic receiving 125138 citations.

Chapter 3 – Bacterial Growth

Abstract: Publisher Summary Bacterial growth is a complex process that involves numerous anabolic and catabolic reactions, which result in cell division. This chapter describes the various stages of bacterial growth under pure culture conditions and its relevance with growth in the environment. The increase in numbers or bacterial mass can be measured as a function of time under pure culture conditions, where the nutrients and environmental conditions are controlled. Several distinct growth phases can be observed within a growth curve such as the lag phase, the exponential or log phase, the stationary phase, and the death phase. Under batch conditions, the lag phase is the first phase that is characterized by zero growth. The second phase, exponential phase, is characterized by a period of the exponential growth, followed by the stationary phase in which there is no net growth because of several reasons such as nutrient depletion or accumulation of toxic waste products. The final phase of the growth curve is the death phase, characterized by a net loss of culturable cells. At high substrate concentrations, growth usually occurs at the maximum growth rate; however, this type of growth is unlikely to be found under natural conditions in a soil or water environment. The lag phase observed in a natural environment can be much longer than the lag phase normally observed in a batch culture. While the stationary phase is most likely of short duration, if it exists at all, the death phase can certainly be observed.

A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism.

Abstract: The water environment plays an essential role in the mangrove wetland ecosystem. Predicting water quality will help us better protect water resources from pollution, allowing the mangrove ecosystem to perform its normal ecological role. New approaches to solve such nonlinear problems need further research since the complexity of water quality data and they are easily affected by the noise. In this paper, we propose a water quality prediction model named CNN-LSTM with Attention (CLA) to predict the water quality variables. We conduct a case study on the water quality dataset of Beilun Estuary to predict pH and NH3-N. Linear interpolation and wavelet techniques are used for missing data filling and data denoising, respectively. The hybrid model CNN-LSTM is highly capable of resolving nonlinear time series prediction problems, and the attention mechanism captures longer time dependence. The experimental results show that our model outperforms other ones, and can predict with different time lags in a stable manner.