Ozone (O3) pollution is one of the predominant environmental problems, and exposure to high O3 concentrations has a significant negative influence on both human health and ecosystems. Therefore, it is essential to analyze spatio-temporal characteristics of O3 distribution and to evaluate O3 exposure levels. In this study, O3 monitoring and satellite data were used to estimate O3 daily, seasonal and one-year exposure levels based on the Bayesian maximum entropy (BME) model with a spatial resolution of 1 km × 1 km in the Beijing-Tianjin-Hebei (BTH) region, China. Leave-one-out cross-validation (LOOCV) results showed that R2 for daily and one-year exposure levels were 0.81 and 0.69, respectively, and the corresponding values for RMSE were 19.58 μg/m3 and 4.40 μg/m3, respectively. The simulation results showed that the heavily polluted areas included Tianjin, Cangzhou, Hengshui, Xingtai, and Handan, while the clean areas were mainly located in Chengde, Qinhuangdao, Baoding, and Zhangjiakou. O3 pollution in summer was the most severe with an average concentration of 134.5 μg/m3. In summer, O3 concentrations in 87.7% of the grids were more than 100 μg/m3. In contrast, winter was the cleanest season in the BTH region, with an average concentration of 51.1 μg/m3.

Black carbon (BC) exposure in China continues to be relatively high, prompting researchers to assess BC exposure levels using data from monitoring sites, satellite remote sensing, and models. However, data regarding the application of a combined strategy comprising the analysis of monitoring data and various types of data to simulate BC exposure levels are lacking. Hence, the current study seeks to estimate short-and long-term BC exposure levels by combining national monitoring data with data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2). Furthermore, this study attempts to improve the spatio-temporal resolution of BC exposure levels using Bayesian maximum entropy (BME). The BME model per-formed well in terms of estimating short-(R2 symbolscript 0.74 and RMSE symbolscript 1.76 mu g/m3) and long-term (R2 symbolscript 0.76 and RMSE symbolscript 1.3 mu g/m3) exposure. Premature mortalities and economic losses were also assessed by applying localised concentration-response coefficients simulated in China. A total of 74,500 (95% confidence interval (CI): 23,900-124,500) and 538,400 (95% CI: 495,000-581,300) all-cause premature mortality cases were found to be associated with short-and long-term BC exposure, respectively. Meanwhile, short-term BC exposure was associated with economic losses ranging from 7.5 to 13.2 billion US dollars (USD) (1 USD symbolscript 6.36 RMB on January 19, 2022) based on amended human capital (AHC) and willingness to pay (WTP), accounting for 0.06%- 0.1% of China's total gross domestic product (GDP) in 2017 (1.2 symbolscript 104 billion USD), respectively. The economic losses for long-term exposure varied from 53 to 93.2 billion USD based on AHC and WTP, accounting for 0.4%- 0.8% of China's total GDP in 2017, respectively.

In recent years, ozone (O3) concentration has shown a decreasing trend in the Beijing–Tianjin–Hebei (BTH) region in China. However, O3 pollution remains a prominent problem. Accurate estimation of O3 exposure levels can provide support for epidemiological studies. A total of 13 variables were combined to estimate short- and long-term O3 exposure levels using the geographically weighted regression (GWR) model in the BTH region with a spatial resolution of 1 × 1 km from 2017 to 2020. Five variables were left in the GWR model. O3 concentration was positively correlated with temperature, wind speed, and SO2, whereas is was negatively correlated with precipitation and NO2. Results showed that the model performed well. Leave-one-out cross-validation (LOOCV) R2 for short- and long-term simulation results were 0.91 and 0.71, and the values for RMSE were 11.14 and 3.49 μg/m3, respectively. The annual maximum 8 h average O3 concentration was the highest in 2018 and the lowest in 2020. Decreasing concentrations of major precursors of O3 due to the regional joint prevention and control may be the reason. O3 concentration was high in the southeast of the BTH region, including in Hengshui, Handan, Xingtai and Cangzhou.

Black carbon (BC) produced by the incomplete combustion of carbonaceous materials and biomass has become a major factor affecting global climate change and adversely affects health. Based on the ground-measured data and MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2) reanalysis data, BC's pollution characteristics and the health risks caused by BC exposure were investigated in Tianjin, China. The results showed that the highest BC concentration occurred in winter, and the annual average concentration of BC on weekends was higher than that on weekdays in winter. The hourly average monitoring concentration variations of BC in four seasons all had two peaks that appeared 6:00-8:00 and 22:00-2:00. For MERRA-2 reanalysis data, there was one peak appeared during 5:00-7:00 in four seasons. The lowest values appeared during 14:00-16:00 for both ground-measured data and MERRA-2. The relationships between BC concentration and meteorological factors showed that wind speed and temperature inversion played important roles in the atmospheric BC diffusion. The wind directions of southwest, southeast, and north were closely associated with BC in 2019. The Potential Source Contribution Function (PSCF) results showed that the southwestern area of Tianjin, especially the southern Hebei, northern Henan and Beijing, would be the potential source areas of Tianjin BC in 2019. The cancer risks of adults and children caused by BC exposure in Tianjin were higher than the risk levels (1 x 10(-6) to 1 x 10(-4)) recommended by the US Environmental Protection Agency (US EPA) in 2019, resulting in more than 3.78 cases of cancer per 10,000 adults (3.78 x 10(-4)), and more than 1.55 cases of cancer per 10,000 children (1.55 x 10(-4)), respectively. The relative risk (RR) of BC exposure on mortality showed the highest in winter and the lowest in summer. Compared with all-cause and cardiovascular mortality, respiratory mortality caused by the BC showed the highest risk.

Soil calcium carbonate (CaCO3) content is an important soil property. The prediction of soil CaCO3 content is necessary for the sustainable management of soil fertility. In this work, we attempted to incorporate environmental variables directly and through regression models into the framework of Bayesian maximum entropy (BME) to predict CaCO3 content. Firstly, multiple linear regression (MLR) and geographically weighted regression (GWR) were used to establish a relationship between sampling data and environmental variables, including Digital Elevation Model, pH, temperature, rainfall, and fluvo-aquic soils. Prediction results of MLR and GWR served as soft data and were incorporated into the framework of BME to estimate the CaCO3 content. Secondly, soil samples and environmental variables were combined to generate probability distributions of CaCO3 at unsampled points. These probability distributions were used as soft data for the BME to predict the CaCO3 content. The results showed that the GWR method (r = 0.84, RMSE = 24.0 g kg−1) performed better than the MLR method (r = 0.73, RMSE = 30.1 g kg−1). The BME-GWR method outperformed the BME-EV and BME-MLR methods. The r values of BME-GWR, BME-EV, and BME-MLR methods were 0.87, 0.86, and 0.82, respectively, and the RMSEs of the three methods were 22.2, 23.9, and 25.2 g kg−1, respectively. The spatial distribution of CaCO3 content predicted by the above methods was similar and significantly higher in the southwest than in the northeast.

The overall objective of the project was the development of water management system solutions for a sustainable improvement of water quality in the city of Chaohu and in the Chao Lake. The Urban Water Resources Management (UWRM) concept is the innovative approach, which includes both efficient urban water management in urban and suburban areas, as well as interaction with aquatic ecosystems. Data and models for planning purposes and regional water management are made available by using a comprehensive online environmental information system for authorities and water suppliers. The Chao Lake plays a central role as an ecological and economic protection and raw water supplier for the drinking water supply of the population of the city of Chaohu. The research and development project (R&D Project) thus makes an important contribution to the sustainable development of the Chaohu region as part of the Masterplan Ecological Seascape Chaohu of the Anhui Provincial Government. The scientific and technical solutions are implemented in demonstration projects.

The water quality in Poyang Lake has changed in recent 20 years according to the long-term data analysis. The main driving factors of water quality evolution trends are the increasing pollutant loads and hydrological regime alterations. The water levels in the lake have declined, which led to the shortening of the limnetic faces period of the lake. The seasonal allocations of basin inflow and lake outflow have also been altered mildly by some hydraulic facilities in the basin. To study the impacts of changing hydrological regime on water quality, a two-dimensional depth-averaged model for hydrodynamics and water quality is set up, validated, and applied to simulate two hydrological processes before and after Three Gorges Reservoir as 1956–2002 and 2003–2015 scenarios. The results reveal that water quality in lake are mainly determined by the pollutant loads and are influenced by the hydrological regime particularly in spring and autumn. The hydrological regime changes of Poyang Lake have resulted in 10.6 and 11.7% increases in TN concentrations associate with 12.4 and 13.6% increases in TP concentrations during the periods of April–May and September–October.

The human–environment relationship within the Yellow River basin has a long history, because favorable environmental circumstances allowed the early emergence of societies along the river banks, and hence, the Yellow River basin was the birthplace of ancient Chinese civilization. On the other hand, the Yellow River is “China’s sorrow” due to the constant occurrences of flooding events throughout history. In recent decades, the Yellow River basin is facing a spectacular economic boom, but mainly achieved at the expenses of the environment by over-exploiting the natural resources provided within the basin, which causes various challenges on ecology and society. Water scarcity, pollution, and ecosystem degradation accompanied with biodiversity decline have been further aggravated by anthropogenic-induced climate change. To address the pressing socio-ecological challenges, various conservation and management plans and strategies have been issued, often consulted by international bodies. This article is a comprehensive overview of the current state and recent developments that have occurred in the Yellow River basin and presents and discusses current and pressing socio-ecological challenges. Additionally, we address different policy and management instruments that have been launched to ensure a long-term sustainable development within the basin.

Due to extensive water pollution in Chinese rivers and lakes, large efforts have to be made to improve the quality of drinking water and manage the sewage water treatment process. We propose a general workflow for integrating a large number of heterogeneous data sets relating to various hydrological compartments into a Virtual Geographic Environment (VGE). This allows both researchers and stakeholders to easily access complex data collections in a unified context, find interrelations or inconsistencies between data sets and evaluate simulation results with respect to other observations or simulations in the same region. A prototype of such a VGE has been set up for the region around Chao Lake, containing more than 20 spatial data sets and collections as well as first simulation result. The prototype has been successfully presented to researchers and stakeholders from China and Germany.

In this paper, a combination of a novel interpolation method and a local regression method was employed to improve the estimation accuracy of monthly precipitation over China. After the normalized processing and Box-Cox transformation of the data, we used the geographically weighted regression (GWR) method to describe the spatial precipitation trend, and then interpolated the residual by using a modified high accuracy surface modeling method (HASM-PRE). A high quality database of monthly precipitation with a resolution of 1 km(2) was constructed based on the meteorological stations. Results showed that wet years and dry years appear alternatively, and trend analysis of precipitation data series from 1981 to 2010 showed that the probability of years with extreme precipitation has increased in recent years. Precipitation in winter is rather uncertain and more dynamic from year to year compared to precipitation in summer.

Due to rapid economic development and population growth, China is facing severe water problems that include sea-level rise and increasing salinization, floods, water pollution, water shortage, soil erosion and ecosystem deterioration, as well as biodiversity loss. In recent decades, China is progressively more concerned with its water issues that are now at the center of social and political attention. Having to overcome similar challenges, Germany has taken a leading role in the field of water sciences and technology. In particular, China can benefit from the lessons learnt in Germany concerning the rehabilitation of water resources in areas heavily affected by chemical industry and mining after the reunification in 1989. German-Chinese cooperation in water sciences started over 25 years ago and dealt with increasing challenges in the 21st century. Following the open space workshop during the Water Research Horizon Conference in Berlin 2014, this article provides a view of some of the challenges and potential opportunities of German-Chinese cooperation in water science and technology.

Combining and solving environment and health is a major issue in China—a country with tremendous economic development perspectives. Recent developments (e.g., the Tianjin Harbor accident in August 2015) show that China is investing more and more resources and efforts to cope with the industrial development (GDP) into alliancing with environmental safeguarding. The Thematic Issue on Environment and Health in China is a starting point for discussion of a variety of environmental issues, such as distribution and fractionation of rare earth elements in soil–water system and human blood (Li et al. 2014a, b, c, d); spatial evaluation of phosphorus retention in riparian zones using remote sensing data concerning the big and scarce data issues (Dong et al. 2014); toxicity contamination and distribution in soils and plants (Li et al. 2014a, b, c, d); the status and challenges of water pollution problems in China: learning from the European experience (Zhou et al. 2014); the Heihe River basin (Xiao et al. 2014), identifying interactions between river water and groundwater in the North China Plain using multiple tracers (Dun et al. 2014); potential hazardous elements (PHEs) in atmospheric particulate matter (APM) in the south of Xi’an during the dust episodes of (2001–2012) chemical fractionation; ecological and health risk assessment (Li et al. 2014a, b, c, d), occurrence and hydrogeochemistry of fluoride in alluvial aquifer of Weihe River (Li et al. 2014a, b, c, d); contamination assessment and health risk of heavy metals in dust from Changqing industrial park of Baoji (Wang et al. 2014a, b); contamination assessment and health risk of heavy metals in dust from Changqing industrial park of Baoji (Su et al. 2014); sources and transports of polycyclic aromatic hydrocarbons in the Nanshan Underground River, China (Alam et al. 2014); introducing a land-use-based spatial analysis method for human health risk evaluation of soil heavy metals (Wang et al. 2013a, b, c);

This paper presents the design and integration of a GIS-based data model for the regional hydrologic simulation in the Meijiang watershed, China. Hydrologic systems (HS) require integration of data and models simulating different processes. Here, an object-oriented approach using Unified Modeling Language (UML) is introduced, which supports the development of GIS-based Geodatabase model-GeoHydro/DataBase (GH/DB). Spatial data, such as basins, stream network, and observation stations are stored in the feature classes. The time series and their attributes are included in the tables. Relationship classes are used to link associated objects. The new development within the scientific program OpenGeoSys (OGS) is the integration of GH/DB into the numerical simulations. The graphical user interface is implemented for the pre- and post-processing of the simulation. As for the case study, a regional hydrologic model is developed in the Meijiang watershed area for the understanding of water infiltration from surface into groundwater via soil layer with various time scales. The integration of databases and modeling tool represents the comprehensive hydrosystems and thus it is a useful tool to understand the different processes and interactions between the related hydrological compartments.

It has been over 15 years since the concept of Virtual Geographic Environments (VGEs) was formally proposed (Lin and Gong 2001). Although the thinking about VGEs never stops since it was born, and the continued development of VGEs has brought about significant achievements resulting from this concept (e.g., Goodchild 2009; Gong et al. 2010; Konecny 2011; Lu 2011; Priestnall et al. 2012; Lin et al. 2013a, b, 2015) as well as related technologies and implementations (e.g., Xu et al. 2011, 2013; Chen et al. 2012, 2013a, b; Zhang et al. 2015a, b; Zhu et al. 2015), there are still some misunderstandings about this ‘new’ branch of Geoscience. Questions generally are related to two concepts: the first concerns the differences between VGEs and game-like virtual worlds, similar virtual communities and cities, and digital earth; the second asks how VGEs can contribute to geographic research beyond traditional Geographic Information Systems (GIS) and Maps (Aydi et al. 2013; Tung et al. 2013). The answer to the first question may be found in the term ‘geographic environment’ which refers not only to the natural surface of the earth, space, or a place, but also involves the social behaviors that interact with natural factors; it is the sphere of direct interaction between nature and society (Kalesnik 1979). An important feature of geographic environments is that they change continuously with time. Although some objects in geographic environments have relatively stable shapes (such as a rock or soils), many exist in constantly changing forms (such as vegetation, air and water), and human beings (both in groups or individual) are active throughout their ‘lives’. Given this definition, to build a virtual mirror that can reflect the real geographic environment, only considering the physical part would result only in a one-sided perspective. Moreover, dynamic geographic phenomena and processes require careful attention, a fact often overlooked by traditional systems. Thus, an ideal virtual environment

Climate change as a result of the increased greenhouse gas emissions may influence the availability of water resources in many regions on the globe. In the past decades, China has been facing severe shortage of water resources. This study focuses on the assessment of the impact of climate change on both blue and green water resources in ten large river basins in China. The blue and green water resources for these river basins were derived from the terrestrial hydrological fluxes in period 1960–2100, which were simulated with the Max Planck Institute Hydrological Model—MPI-HM. The forcing data for the hydrological model, the precipitation and temperature were obtained from three coupled Atmosphere–Ocean General Circulation Models (GCMs)—ECHAM5, IPSL and CNRM, under A2 and B1 greenhouse gas emission scenarios. The statistical bias correction method was applied on the output from the three GCMs. By using this climate model–hydrology model modeling chain, the impact of climate change on the blue and green water resources was analyzed over the ten Chinese river basins. Here, the projected changes in 2071–2100 are considered relative to 1971–2000. The projected change of monthly mean and annual mean of green water resources show the general increase for all ten river basins; among them, Inland river, Zhemin river and Zhujiang river have larger change signal than other basins. For blue water resources, increases of the annual mean are projected from November to March for Heilongjiang river, Liaohe river and Yellow river, Inland river in Northern China; and decreases are projected for Huaihe river, Zhemin river, Haihe river, Yangzi river, Southwest river, and Zhujiang river basins in Southern China. It is found that climate change has impact on both blue and green water resources over large river basins in China. The sustainable blue water resources management should take into account the different changes in both Northern and Southern China. The results show that a better management of green water resources is of importance for food and ecological securities in the context of global change.

Future climate model scenarios depend crucially on the models' adequate representation of the hydrological cycle. Within the EU integrated project Water and Global Change (WATCH), special care is taken to use state-of-the-art climate model output for impacts assessments with a suite of hydrological models. This coupling is expected to lead to a better assessment of changes in the hydrological cycle. However, given the systematic errors of climate models, their output is often not directly applicable as input for hydrological models. Thus, the methodology of a statistical bias correction has been developed for correcting climate model output to produce long-term time series with a statistical intensity distribution close to that of the observations. As observations, global reanalyzed daily data of precipitation and temperature were used that were obtained in the WATCH project. Daily time series from three GCMs (GCMs) ECHAM5/Max Planck Institute Ocean Model (MPI-OM), Centre National de Recherches Meteorologiques Coupled GCM, version 3 (CNRM-CM3), and the atmospheric component of the L'Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4) coupled model (called LMDZ-4)-were bias corrected. After the validation of the bias-corrected data, the original and the bias-corrected GCM data were used to force two global hydrology models (GHMs): 1) the hydrological model of the Max Planck Institute for Meteorology (MPI-HM) consisting of the simplified land surface (SL) scheme and the hydrological discharge (HD) model, and 2) the dynamic global vegetation model called LPJmL. The impact of the bias correction on the projected simulated hydrological changes is analyzed, and the simulation results of the two GHMs are compared. Here, the projected changes in 2071-2100 are considered relative to 1961-90. It is shown for both GHMs that the usage of bias-corrected GCM data leads to an improved simulation of river runoff for most catchments. But it is also found that the bias correction has an impact on the climate change signal for specific locations and months, thereby identifying another level of uncertainty in the modeling chain from the GCM to the simulated changes calculated by the GHMs. This uncertainty may be of the same order of magnitude as uncertainty related to the choice of the GCM or GHM. Note that this uncertainty is primarily attached to the GCM and only becomes obvious by applying the statistical bias correction methodology.