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The continuous uptake of anthropogenic CO2 by the ocean leads to ocean acidification, which is an ongoing threat to the marine ecosystem. The ocean acidification rate was globally documented in the surface ocean but limited below the surface. Here, we present a monthly four-dimensional 1°×1° gridded product of global seawater pH, derived from a machine learning algorithm trained on pH observations at total scale and in-situ temperature from the Global Ocean Data Analysis Project (GLODAP). The constructed pH product covers the years 1992–2020 and depths from the surface to 2 km on 41 levels. Three types of machine learning algorithms were used in the pH product construction, including self-organizing map neural networks for region dividing, a stepwise algorithm for predictor selection, and feed-forward neural networks (FFNN) for non-linear relationship regression. The performance of the machine learning algorithm was validated using real observations by a cross validation method, where four repeating iterations were carried out with 25 % varied observations for each evaluation and 75 % for training. The constructed pH product is evaluated through comparisons to time series observations and the GLODAP pH climatology. The overall root mean square error between the FFNN constructed pH and the GLODAP measurements is 0.028, ranging from 0.044 in the surface to 0.013 at 2000 m. The pH product is distributed through the data repository of the Marine Science Data Center of the Chinese Academy of Sciences at http://dx.doi.org/10.12157/IOCAS.20230720.001 (Zhong et al., 2023). [ABSTRACT FROM AUTHOR]

In recent decades, China's coastal ecological environment has undergone significant changes under multiple pressures such as climate change and high intensity of human activities. The occurrence and scale of harmful algal blooms represented by the red tide and the green tide are increasing rapidly. Moreover environmental problems such as hypoxia and acidification in seawater have become increasingly prominent. It is urgent to find out the occurrence mechanism and prevention measures of marine eco-environmental disasters. Therefore; we explained the connotation of the concept of "marine eco-environmental disaster" for the first time, and systematically interpreted the biogenic element driving mechanisms in the occurrence of eco-environmental disasters in China nearshore area in the aspects of exogenous input of nutrients, the mineralization and decomposition of marine organic matters and nutrient regeneration as well as the abnormal nutrient structure in offshore waters. We pointed out that the drastic increases of terrigenous nutrient discharge caused by enhancing human activities, together with the complex biogeochemical cycle process after biogenic elements entering the sea; led to the frequent occurrence of coastal eco- environmental disasters. On this basis; combined with the latest research progress in this field ; we put forward the regulatory schemes for coastal ecological environment health based on the controlling of seawater biogenic element. That is' based on land-sea integration strategy, the reduction of land-source pollutants, especially inorganic nitrogen from land, coastal zone and into the sea, and the normalization of nutrient structure in coastal seawater through artificial regulation technology, are the key points to reduce and control the occurrence of disasters and improve the quality of the coastal ecosystems. [ABSTRACT FROM AUTHOR]