The continuous rise in atmospheric CO2 levels has led to persistent ocean acidification, which negatively impacts marine environments crucial for marine life and alters the chemical composition of seawater. This phenomenon carries significant implications for human society. Utilizing surface seawater pH data from the North Pacific spanning 1995 to 2019, this study investigates the overall and localized spatiotemporal variations in pH within the region, as well as the factors influencing these variations. Additionally, it conducts a quantitative analysis of the different influencing factors. The findings reveal a consistent downward trend in surface seawater pH in the North Pacific, decreasing from 8.073 to 8.029, with notable seasonal variations. The highest pH values are recorded in winter, followed by spring, with lower values in autumn and summer. Spatially, the pH values are higher in the northwest and lower in the southeast, with the most pronounced acidification occurring in the central and western regions, while other areas exhibit more uniform acidification levels. Spatial correlation analysis indicates that surface seawater pH in the North Pacific generally shows a negative correlation with sea surface temperature (SST), salinity (SSS), and chlorophyll-a concentration (chl a) and a positive correlation with dissolved oxygen (DO). Among these factors, SST exerts the greatest influence on seawater pH, followed by DO and SSS. The degree of acidification varies across different regions, and the dominant influencing factors differ accordingly. In the equatorial central region (A), the primary factors are chl a and SST; in the eastern regions of China and Japan (B) and the western region of Canada (C), DO and SSS are the main controlling factors. An interaction analysis of each pair of dominant factors using the geodetector shows that their respective contributions to regions A, B, and C are 70%, 90%, and 50%, respectively. Understanding the primary factors driving acidification in different regions can aid in comprehending the biological and environmental impacts of acidification in those areas and provide valuable insights for mitigating marine acidification.

China is part of the western Pacific region, which is the source of the most frequent tropical cyclones in the world. These cause severe disasters each year, including huge economic losses and casualties. To better understand their frequency and intensity, remote sensing tropical cyclone data were obtained for the entire Northwest Pacific region for the period 1977–2018. MATLAB and ArcGIS were used to analyse the frequency and intensity of tropical cyclones and their characteristics in various regions of China. At the same time, the influence factors of tropical cyclone characteristics such as El Niño and SST were analyzed by correlation analysis and Geographical detector. The annual frequency of tropical cyclones in the Northwest Pacific showed a fluctuating state, but the overall trend was decreasing. In particular, since 1994, the overall frequency decreased significantly but rebounded in recent years, while the intensity did not change significantly. It was found that cyclone intensity is lower when the frequency is higher, and vice versa. 85% of tropical cyclones occurred in summer and autumn, with the highest intensities in autumn, when the maximum average wind speed peaks at 37 m/s. The area with the most frequent tropical cyclones was 5–20° N, 125–155° E. A total of 314 tropical cyclones arrived in China during the study period, an average of about 7.5 per year. Their frequency and intensity gradually decreased as they moved from coastal to inland areas. Both SST and El Niño are significantly related to the formation and development of tropical cyclones, and the contribution of multiple factors interaction to the variation characteristics of tropical cyclones is significantly higher than that of single factors. Understanding the characteristics of the Pacific tropical cyclones is an important step in planning disaster prevention framework.

In the recent significant rise in atmospheric CO2, seawater’s continuous acidification is altering the marine environment’s chemical structure at an unprecedented rate. Due to its potential socioeconomic impact, this subject attracted significant research interest. This study used traditional linear regression, nonlinear regression random forest, and the BP neural network algorithm to establish a prediction model for surface seawater pH based on data of North Pacific sea surface temperature (SST), salinity (SSS), chlorophyll-a concentration (Chl-a), and pressure of carbon dioxide on the sea surface (pCO2) from 1993 to 2018. According to existing research, three approaches were found to be highly accurate in reconstructing the surface seawater pH of the North Pacific. The highest-performing models were the linear regression model using SSS, Chl-a, and pCO2, the random forest model using SST and pCO2, and the BP neural network model using SST, SSS, Chl-a, and pCO2. The BP neural network model outperformed the linear regression and random forest model when comparing the root mean square error and fitting coefficient of the three best models. In addition, the best BP neural network model had substantially higher seasonal applicability than the best linear regression and the best random forest model, with good fitting effects in all four seasons—spring, summer, autumn, and winter. The process of CO2 exchange at the sea–air interface was the key factor affecting the pH of the surface seawater, which was found to be negatively correlated with pCO2 and SST, and positively correlated with SSS and Chl-a. Using the best BP neural network model to reconstruct the surface seawater pH over the North Pacific, it was found that the pH exhibited significant temporal and spatiotemporal variation characteristics. The surface seawater pH value was greater in the winter than the summer, and the pH decline rate over the past 26 years averaged 0.0013 yr−1, with a general decreasing tendency from the northwest to the southeast. The highest value was observed in the tropical western Pacific, while the lowest value was observed in the eastern equatorial region with upwelling, which is consistent with the findings of previous studies.

The primary objective of this paper is to assess the impact of mining waste trade on China's green development between 1980 and 2021, utilizing the Autoregressive Distributed Lag (ARDL) technique. The key findings reveal that the coefficients associated with mining waste trade amount to 0.15% and 0.07% for the long-term and short-term perspectives, respectively, indicating a positive influence of mining waste trade on green growth in China. Furthermore, enhancements in the circular economy index are demonstrated to effectively promote green economic growth in China. Conversely, the ecological footprint poses a threat to China's green prosperity. The adverse effects of the ecological footprint on green growth underscore that sustainable and environmentally-friendly economic growth potential diminishes as the ecological footprint expands. This paper puts forward practical policy recommendations which include reforming regulations concerning mining waste trade, fostering innovative technologies for efficient and eco-friendly processing and treatment of mining waste, actively creating or connecting to an e-commerce trading platform for mining waste, and implementing eco-friendly logistics and transportation practices in mineral waste trade.