The air quality in China, particularly the PM_2.5 (particles less than 2.5 μm in aerodynamic diameter) level, has become an increasing public concern because of its relation to health risks. The distribution of PM_2.5 concentrations has a close relationship with multiple geographic and socioeconomic factors, but the lack of reliable data has been the main obstacle to studying this topic. Based on the newly published Annual Average PM_2.5 gridded data, together with land use data, gridded population data and Gross Domestic Product (GDP) data, this paper explored the spatial-temporal characteristics of PM_2.5 concentrations and the factors impacting those concentrations in China for the years of 2001–2010. The contributions of urban areas, high population and economic development to PM_2.5 concentrations were analyzed using the Geographically Weighted Regression (GWR) model. The results indicated that the spatial pattern of PM_2.5 concentrations in China remained stable during the period 2001–2010; high concentrations of PM_2.5 are mostly found in regions with high populations and rapid urban expansion, including the Beijing-Tianjin-Hebei region in North China, East China (including the Shandong, Anhui and Jiangsu provinces) and Henan province. Increasing populations, local economic growth and urban expansion are the three main driving forces impacting PM_2.5 concentrations.

Ensuring mining safety and efficiency relies heavily on identifying the source of mine water inrush. This review article aims to provide a comprehensive overview of standard methods used to pinpoint the origin of mine water inrush, highlighting the development and progress in the research of discrimination methods. These methods are systematically classified into various categories, encompassing hydrochemistry examination, water level and temperature analysis, geostatistical approaches, machine learning and deep learning methods, as well as the utilization of other analytical techniques. The review not only presents a quantitative and visual analysis of the theoretical methods proposed by scholars but also emphasizes their strengths, weaknesses, and applicability to various mining operations. Furthermore, it explores the increasing utilization of artificial neural networks and machine learning algorithms in source discrimination models, indicating the advancement in this area of research. To further advance the field, specific examples of these methods and their effectiveness in identifying the source of mine water inrush are provided, aiming to stimulate further research. The article also offers detailed recommendations for future research directions and emerging trends, underlining the importance of comprehensive multidisciplinary and multi-method analysis. It suggests exploring emerging technologies such as the Internet of Things (IoT) and cloud computing, while emphasizing the need to develop more accurate and reliable models for source identification. The fusion of artificial intelligence (AI), heightened computational capabilities, online programming, and intelligent data collection systems presents the prospect of transforming the way industries respond to these critical events. By providing a comprehensive overview, analyzing the effectiveness of existing methods, and proposing future research directions, this review aims to contribute to the continuous development and progress of discrimination methods for mine water inrush incidents. Ultimately, it seeks to enhance mining safety and efficiency by facilitating the prompt and accurate identification of the sources of mine water inrush.