A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China.

Accurately predicting carbon trading prices using deep learning models can help enterprises understand the operational mechanisms and regulations of the carbon market. This is crucial for expanding the industries covered by the carbon market and ensuring its stable and healthy development. To ensure the accuracy and reliability of the predictions in practical applications, it is important to evaluate the model's robustness.

Prediction of nitrous oxide emission of a municipal wastewater treatment plant using LSTM-based deep learning models.

Accurate assessment of greenhouse gas emissions from wastewater treatment plants is crucial for mitigating climate change. NO is a potent greenhouse gas that is emitted from wastewater treatment plants during the biological denitrification process. In this study, we developed and evaluated deep learning models for predicting NO emissions from a WWTP in Switzerland.

[Characteristic of Nitrate Adsorption in Aqueous Solution by Iron and Manganese Oxide/Biochar Composites].

In this study, we synthesized Fe/Mn bimetallic oxide coated biochar sorbents by pyrolysis of wheat straw impregnated with ferric chloride and potassium permanganate and investigated their potential to adsorb nitrate in water. X-ray photoelectron spectroscopy and scanning electron microscopy analysis suggests that Fe(Ⅲ)/Mn(Ⅳ) bimetallic oxide particles emerge on the sorbents. The optimized sorbent could achieve a specific surface area of 153.

Quantification of nitrate sources and fates in rivers in an irrigated agricultural area using environmental isotopes and a Bayesian isotope mixing model.

Nitrate (NO) pollution in rivers caused by intensive human activities is becoming a serious problem in irrigated agricultural areas. To identify NO sources and reveal the impact of irrigation projects on NO pollution in rivers, the hydrochemistry and isotopes of irrigation water from the Yellow River (IW) and river water (RW), and potential source samples were analyzed. The mean NO concentrations in the IW and RW were 24.