Co-occurrence of surface ozone (O) and fine particulate matter (PM) pollution (CP) was frequently observed in Beijing-Tianjin-Hebei (BTH). More than 50% of CP days occurred during April-May in BTH, and the CP days reached up to 11 in two months of 2018. The PM or O concentration associated with CP was lower than but close to that in O and PM pollution, indicating compound harms during CP days with double-high concentrations of PM and O. CP days were significantly facilitated by joint effects of the Rossby wave train that consisted of two centers associated with the Scandinavia pattern and one center over North China as well as a hot, wet, and stagnant environmental condition in BTH. After 2018, the number of CP days decreased sharply while the meteorological conditions did not change significantly. Therefore, changes in meteorological conditions did not really contribute to the decline of CP days in 2019 and 2020. This implies that the reduction of PM emission has resulted in a reduction of CP days (about 11 days in 2019 and 2020). The differences in atmospheric conditions revealed here were helpful to forecast the types of air pollution on a daily to weekly time scale. The reduction in PM emission was the main driving factor behind the absence of CP days in 2020, but the control of surface O must be stricter and deeper.

Studying spatio-temporal evolution of epidemics can uncover important aspects of interaction among people, infectious diseases, and the environment, providing useful insights and modeling support to facilitate public health response and possibly prevention measures. This paper presents an empirical spatio-temporal analysis of epidemiological data concerning 2321 SARS-infected patients in Beijing in 2003. We mapped the SARS morbidity data with the spatial data resolution at the level of street and township. Two smoothing methods, Bayesian adjustment and spatial smoothing, were applied to identify the spatial risks and spatial transmission trends. Furthermore, we explored various spatial patterns and spatio-temporal evolution of Beijing 2003 SARS epidemic using spatial statistics such as Moran's I and LISA. Part of this study is targeted at evaluating the effectiveness of public health control measures implemented during the SARS epidemic. The main findings are as follows. (1) The diffusion speed of SARS in the northwest-southeast direction is weaker than that in northeast-southwest direction. (2) SARS's spread risk is positively spatially associated and the strength of this spatial association has experienced changes from weak to strong and then back to weak during the lifetime of the Beijing SARS epidemic. (3) Two spatial clusters of disease cases are identified: one in the city center and the other in the eastern suburban area. These two clusters followed different evolutionary paths but interacted with each other as well. (4) Although the government missed the opportunity to contain the early outbreak of SARS in March 2003, the response strategies implemented after the mid of April were effective. These response measures not only controlled the growth of the disease cases, but also mitigated the spatial diffusion.

For better detecting the spatial-temporal change mode of individual susceptible-infected-symptomatic-treated-recovered epidemic progress and the characteristics of information/material flow in the epidemic spread network between regions, the epidemic spread mechanism of virus input and output was explored based on individuals and spatial regions. Three typical spatial information parameters including working unit/address, onset location and reporting unit were selected and SARS epidemic spread in-out flow in Beijing was defined based on the SARS epidemiological investigation data in China from 2002 to 2003 while its epidemiological characteristics were discussed. Furthermore, by the methods of spatial-temporal statistical analysis and network characteristic analysis, spatial-temporal high-risk hotspots and network structure characteristics of Beijing outer in-out flow were explored, and spatial autocorrelation/heterogeneity, spatial-temporal evolutive rules and structure characteristics of the spread network of Beijing inner in-out flow were comprehensively analyzed. The results show that (1) The outer input flow of SARS epidemic in Beijing concentrated on Shanxi and Guangdong provinces, but the outer output flow was disperse and mainly includes several north provinces such as Guangdong and Shandong. And the control measurement should focus on the early and interim progress of SARS breakout. (2) The inner output cases had significant positive autocorrelative characteristics in the whole studied region, and the high-risk population was young and middle-aged people with ages from 20 to 60 and occupations of medicine and civilian labourer. (3) The downtown districts were main high-risk hotspots of SARS epidemic in Beijing, the northwest suburban districts/counties were secondary high-risk hotspots, and northeast suburban areas were relatively safe. (4) The district/county nodes in inner spread network showed small-world characteristics and information/material flow had notable heterogeneity. The suburban Tongzhou and Changping districts were the underlying high-risk regions, and several suburban districts such as Shunyi and Huairou were the relatively low-risk safe regions as they carried out minority information/material flow. The exploration and analysis based on epidemic spread in-out flow help better detect and discover the potential spatial-temporal evolutive rules and characteristics of SARS epidemic, and provide a more effective theoretical basis for emergency/control measurements and decision-making.

Global change, which refers to large-scale changes in the earth system and human society, has been changing the outbreak and transmission mode of many infectious diseases. Climate change affects infectious diseases directly and indirectly. Meteorological factors including temperature, precipitation, humidity and radiation influence infectious disease by modulating pathogen, host and transmission pathways. Meteorological disasters such as droughts and floods directly impact the outbreak and transmission of infectious diseases. Climate change indirectly impacts infectious diseases by altering the ecological system, including its underlying surface and vegetation distribution. In addition, anthropogenic activities are a driving force for climate change and an indirect forcing of infectious disease transmission. International travel and rural-urban migration are a root cause of infectious disease transmission. Rapid urbanization along with poor infrastructure and high disease risk in the rural-urban fringe has been changing the pattern of disease outbreaks and mortality. Land use changes, such as agricultural expansion and deforestation, have already changed the transmission of infectious disease. Accelerated air, road and rail transportation development may not only increase the transmission speed of outbreaks, but also enlarge the scope of transmission area. In addition, more frequent trade and other economic activities will also increase the potential risks of disease outbreaks and facilitate the spread of infectious diseases.

In the Sichuan Basin, seismic activity has been low historically, but in the past few decades, a series of moderate to strong earthquakes have occurred. Especially since 2015, earthquake activity has seen an unprecedented continuous growth trend, and the magnitude of events is increasing. Following the 5.7 Xingwen earthquake on 18 Dec. 2018, which was suggested to be induced by shale gas hydraulic fracturing, a swarm of earthquakes with a maximum magnitude up to M6.0 struck Changning and the surrounding counties. Questions arose about the possible involvement of industrial actions in these destructive events. In fact, underground fluid injection in salt mine fields has been occurring in the Sichuan Basin for more than 70 years. Disposal of wastewater in natural gas fields has also continued for about 40 years. Since 2008, injection for shale gas development in the southern Sichuan Basin has increased rapidly. The possible link between the increasing seismicity and increasing injection activity is an important issue. Although surrounded by seismically active zones to the southwest and northwest, the Sichuan Basin is a rather stable region with a wide range of geological settings. First, we present a brief review of earthquakes of magnitude 5 or higher since 1600 to obtain the long-term event rate and explore the possible link between the rapidly increasing trend of seismic activity and industrial injection activities in recent decades. Second, based on a review of previous research results, combined with the latest data, we describe a comprehensive analysis of the characteristics and occurrence conditions of natural and injection-induced major seismic clusters in the Sichuan Basin since 1700. Finally, we list some conclusions and insights, which provide a better understanding of why damaging events occur so that they can either be avoided or mitigated, point out scientific questions that need urgent research, and propose a general framework based on geomechanics for assessment and management of earthquake-related risks.

Based on the Weather Research and Forecasting model and the Models-3 community multi-scale air quality model (WRF-CMAQ), this study analyzes the impacts of meteorological conditions and changes in air pollutant emissions on the heavy air pollution episode occurred over North China around the 2020 Spring Festival (January to Februray 2020). Regional reductions in air pollutant emissions required to eliminate the PM heavy pollution episode are also quantified. Our results found that meteorological conditions for the Beijing-Tianjin-Hebei and surrounding "2+26" cities are the worst during the heavy pollution episode around the 2020 Spring Festival as compared with two other typical heavy pollution episodes that occurred after 2015. However, because of the substantial reductions in air pollutant emissions in the "2+26" cities in recent years, and the 32% extra reduction in emissions during January to February 2020 compared with the baseline emission levels of the autumn and winter of 2019 to 2020, the maximum PM level during this heavy pollution episode around the 2020 Spring Festival was much lower than that in the other two typical episodes. Yet, these emission reductions are still not enough to eliminate regional heavy pollution episodes. Compared with the actual emission levels during January to February 2020, a 20% extra reduction in air pollutant emissions in the "2+26" cities (or a 45% extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020) could help to generally eliminate regionwide severe pollution episodes, and avoid heavy pollution episodes that last three or more consecutive days in Beijing; a 40% extra reduction in emissions (or a 60% extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020) could help to generally eliminate regionwide and continuous heavy pollution episodes. Our analysis finds that during the clean period after the heavy pollution episode around the 2020 Spring Festival, the regionwide heavy pollution episode would only occur with at least a 10-fold increase in air pollutant emissions.

Regionality, comprehensiveness, and complexity are regarded as the basic characteristics of geography. The exploration of their core connotations is an essential way to achieve breakthroughs in geography in the new era. This paper focuses on the important method in geographic research: Geographic modeling and simulation. First, we clarify the research requirements of the said three characteristics of geography and its potential to address geo-problems in the new era. Then, the supporting capabilities of the existing geographic modeling and simulation systems for geographic research are summarized from three perspectives: Model resources, modeling processes, and operational architecture. Finally, we discern avenues for future research of geographic modeling and simulation systems for the study of regional, comprehensive and complex characteristics of geography. Based on these analyses, we propose implementation architecture of geographic modeling and simulation systems and discuss the module composition and functional realization, which could provide theoretical and technical support for geographic modeling and simulation systems to better serve the development of geography in the new era.

Some of China's major cities have entered the middle and late stages of urbanization, and the development focus of these cities has gradually shifted from outward expansion to inward renewal. The community, as the basic unit of a city, is undoubtedly the main object of urban renewal. In order to efficiently and effectively address the problems in current community construction, it is necessary to conduct a large-scale in-depth assessment of the community livability, which can directly imply the satisfaction of residents with their quality of life. This study achieved the first comprehensive livability assessment at the individual community scale for 42 major cities of China from the perspective of remotely sensed and crowd-sourced geographic information. Specifically, we produced abundant fine-grained datasets for 42 cities, including high-resolution land cover maps interpreted from Ziyuan-3 satellites (ZY-3, 2.1 m), building height, point-of-interest, and boundaries of 101,630 communities. As designed in our proposed framework, the community livability was evaluated by 5 level-1 indicators, 27 level-2 indicators and an integrated community livability index (CLI). A number of interesting findings were obtained from this assessment: (1) According to the expert questionnaires, living comfort was considered as the most important livability factor for residents with the highest weight, while the building environment was rated the least. The negative factors (e.g., the factories around the community) impacted more on livability than the positive ones. (2) Most communities in major Chinese cities were characterized by dense buildings and sparse green spaces. (3) In these cities, community security construction was severely insufficient, particularly in less developed regions. (4) Imbalanced community livability development was prevalent across cities, and simultaneously, the CLI distribution within cities also exhibited significant spatial aggregation and heterogeneity. This research is expected to reveal the status quo of community livability in China, and thus allow for targeted policy formulation.

Coronavirus disease 2019 (COVID-19) is the most serious infectious disease pandemic in the world in a century, and has had a serious impact on the health, safety, and social and economic development of all mankind. Since the earth entered the "Anthropocene", human activities have become the most important driving force of the evolution of the earth system. At the same time, the epidemic frequency of major human infectious diseases worldwide has been increasing, with more than 70% of novel diseases having zoonotic origins. The review of several major epidemics in human history shows that there is a common rule, i.e., changes in the natural environment have an important and profound impact on the occurrence and development of epidemics. Therefore, the impact of the natural environment on the current COVID-19 pandemic and its mechanisms have become scientific issues that need to be resolved urgently. From the perspective of the natural environment, this study systematically investigated several major issues concerning the environmental transmission and risk prevention of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). From a macroscopic temporal and spatial scale, the research focus on understand the impact of the destruction of the natural environment and global changes on the outbreak of infectious diseases; the threat of zoonotic diseases to human health; the regularity for virus diffusion, migration and mutation in environmental media; the mechanisms of virus transmission from animals and environmental media to humans; and environmental safety, secondary risk prevention and control of major epidemics. Suggestions were made for future key research directions and issues that need attention, with a view to providing a reference for the prevention and control of the global coronavirus disease 2019, and to improving the ability of response to major public health emergencies.

With an increasing number of air quality monitoring stations installed around the Chinese mainland, high-resolution aerosol observations become available, allowing improvements in air pollution monitoring and aerosol forecasting. However, the multi scales (especially small-scale) information included in high-resolution aerosol observations could not be effectively utilized by the traditional three-dimensional variational method (3DVAR). This study attempted to extend the traditional 3DVAR to a multi-scale 3DVAR with two iteration steps, two-scale-3DVAR (TS-3DVAR), to improve the effectiveness of assimilating high-resolution observations. In TS-3DVAR, the large-scale and small-scale components of observation information were decomposed from the original high-resolution observations using a Gaussian smoothing method and then assimilated using the corresponding large-scale or small-scale background error covariances which were derived from the partitioned background error samples. The data assimilation (DA) analysis field generated by TS-3DVAR is more accurate than 3DVAR in reproducing the field's multi-scale characteristics, which could thus be used as the initial chemical field of the air quality model to improve aerosol forecasting. Particulate matter with an aerodynamic diameter of less than 2.5 μm (PM) and 10.0 μm (PM) from the surface air quality monitoring stations from November 01 to November 30, 2018 at 00:00 were assimilated daily to verify the effects of TS-3DVAR and 3DVAR on the aerosol analysis and forecast accuracy. The results showed that TS-3DVAR better constrained both large-scale and small-scale, especially the spatial wavelengths in a range of 54-216 km and those above 351 km. The average power spectra of the TS-3DVAR assimilation increment in the two wavelength ranges were 71.70% and 35.33% higher than those of 3DVAR. As a result, the TS-3DVAR was more effective than 3DVAR in improving the accuracy of the initial chemical field, and thereby the forecasting capability for PM. In the initial chemical field, the 30-day average correlation coefficient (Corr) of PM of TS-3DVAR was 0.052 (6.12%) higher than that of 3DVAR, and the root mean square error (RMSE) of TS-3DVAR was 3.446 μg m (16.4%) lower than that of 3DVAR. For the forecasting capability for PM mass concentration, the 30-day average Corr of TS-3DVAR during the 0-24 hour forecast period was 0.025 (5.08%) higher than that of 3DVAR, and the average RMSE was 2.027 μg m (4.85%) lower. The positive effect of TS-3DVAR on the improvement of forecasting capability can last for more than 24 h.

As the globe has witnessed the pandemic, epidemic diseases exert a strong impact on human beings and ecosystems. Since the Sun is the primary energy source of the Earth, some scientific pioneers attempted to search for the discernible relation between solar activity and the incidence of epidemics. In this study, the periodic changes and trends of ancient Chinese epidemic data were analyzed in comparison with those of sunspot numbers, a solar activity proxy. The results show that the epidemic and solar activity changes are in good agreement to a certain extent, especially during the Gleissberg and the de Vries cycles. The wavelet coherence shows that the frequency of the epidemic data and sunspot numbers are highly associated. In addition, results from the ensemble empirical mode decomposition illustrate consistent variations in low-frequency decompositions. This study has important implications for further understanding of the potential impact of solar activity on Earth's biosphere, the underlying mechanism of which needs further exploration.

Compared with the surface, the deep environment has the advantages of allowing "super-quiet and ultra-clean"-geophysical field observation with low vibration noise and little electromagnetic interference, which are conducive to therealization of long-term and high-precision observation of multi-physical fields, thus enabling the solution of a series of geoscience problems. In the Panyidong Coal Mine, where there are extensive underground tunnels at the depth of 848 m belowsea level, we carried out the first deep-underground geophysical observations, including radioactivity, gravity, magnetic, magne-totelluric, background vibration and six-component seismic observations. We concluded from these measurements that (1) the background of deep subsurface gravity noise in the long-period frequency band less than 2 Hz is nearly two orders ofmagnitude weaker than that in the surface observation environment; (2) the underground electric field is obviously weaker thanthe surface electric field, and the relatively high frequency of the underground field, greater than 1 Hz, is more than two orders of magnitude weaker than that of the surface electric field; the east-west magnetic field underground is approximately the same asthat at the surface; the relatively high-frequency north-south magnetic field underground, below 10 Hz, is at least one order ofmagnitude lower than that at the surface, showing that the underground has a clean electromagnetic environment; (3) in additionto the high-frequency and single-frequency noises introduced by underground human activities, the deep underground spacehas a sig-nificantly lower background vibration noise than the surface, which is very beneficial to the detection of weakearthquake and gravity signals; and (4) the underground roadway support system built with ferromagnetic material interferesthe geomagnetic field. We also found that for deep observation in the "ultra-quiet and ultra-clean" environment, the existinggeophysical equipment and observation technology have problems of poor adaptability and insufficient precision as well asdata cleaning problems, such as the effective separation of the signal and noise of deep observation data. It is also urgent tointerpret and comprehensively utilize these high-precision multi-physics observation data.

Inter-city mobility is one of the most important issues in the UN Sustainable Development Goals, as it is essential to access the regional labour market, goods and services, and to constrain the spread of infectious diseases. Although the gravity model has been proved to be an effective model to describe mobility among settlements, knowledge is still insufficient in regions where dozens of megacities interact closely and over 100 million people reside. In addition, the existing knowledge is limited to overall population mobility, while the difference in inter-city travel with different purposes is unexplored on such a large geographic scale. We revisited the gravity laws of inter-city mobility using the 2.12 billion trip chains recorded by 40.48 million mobile phone users' trajectories in the Jing-Jin-Ji Region, which contains China's capital Beijing. Firstly, unlike previous studies, we found that non-commuting rather than commuting is the dominant type of inter-city mobility (89.3%). Non-commuting travellers have a travel distance 42.3% longer than commuting travellers. Secondly, we developed more accurate gravity models for the spatial distribution of inter-city commuting and non-commuting travel. We also found that inter-city mobility has a hierarchical structure, as the distribution of inter-city travel volume follows Zipf's law. In particular, the hierarchy of non-commuting travel volume among the cities is more in line with an ideal Zipf distribution than commuting travel. Our findings contribute to new knowledge on basic inter-city mobility laws, and they have significant applications for regional policies on human mobility.