Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution.

A fine global future land use/land cover (LULC) is critical for demonstrating the geographic heterogeneity of earth system dynamics and human-earth interaction. In this study, we produced a 1 km global future LULC dataset that takes into account future climate and socio-economic changes as well as the impact of simulated results of the former year on temporally adjacent periods. By incorporating the variations in climatic and socio-economic factors, we differentiated LULC suitability probabilities for historical and future periods across representative SSP-RCP scenarios. Then, by using an improved cellular automata model-PLUS to simulate the patch-level changes of various land classes, we iteratively downscaled water-basin-level LULC demands in various future scenarios to a spatial resolution of 1 km. Our dataset achieves a high degree of simulation accuracy (Kappa = 0.94, OA = 0.97, FoM = 0.10) and precisely captures the spatial-temporal heterogeneity of global LULC changes under the combined effects of climate change and socio-economic development. This robust and fine-scale LULC dataset provides valuable spatially-explicit information essential for earth system modeling and intricate dynamics between anthropogenic activities and the environment.

Mapping the heterogeneity of global methane footprint in China at the subnational level.

Achieving the ambitious Global Methane Pledge announced in the Glasgow Climate Pact requires collaborative efforts from both the signatory countries and China which serves as the world's largest emitter. Considering the heterogeneity of economic structures within China and the relocation of emissions between regions via the global economic network, it is vital to investigate how China's methane emissions at the subnational level are linked to global final consumption. In this paper, we mapped global methane footprint in China from 2007 to2015 at the subnational level, by nesting China's interprovincial input-output tables into global multiregional input-output accounts and upscaling grid-level methane emission data of the Edgar database to the provincial level. Our results suggested that global methane footprint in China shifted westward, and the United States, European Union, Japan, and Hong Kong were the main drivers of China's local methane emissions. By illustrating the international and interprovincial trade flows of methane emissions, this study demonstrated that southeast coastal provinces were the hotspots for global methane footprint while middle inland provinces were the emission hotspots for China's domestic demands. We also showed how China's methane emissions were distributed through the nested global economic network to different economic agents. Moreover, emission trends of key exporting sectors for China's eight economic zones were detailed discussed. The outcome of this study may be fully supportive for identifying the heterogeneous effects of global methane footprint in China and implicative for interprovincial and international collaborations towards methane emission mitigation.

Comparison of the association between different ozone indicators and daily respiratory hospitalization in Guangzhou, China.

Background: Epidemiological studies have widely proven the impact of ozone (O3) on respiratory mortality, while only a few studies compared the association between different O3 indicators and health. Methods: This study explores the relationship between daily respiratory hospitalization and multiple ozone indicators in Guangzhou, China, from 2014 to 2018. It uses a time-stratified case-crossover design. Sensitivities of different age and gender groups were analyzed for the whole year, the warm and the cold periods. We compared the results from the single-day lag model and the moving average lag model. Results: The results showed that the maximum daily 8 h average ozone concentration (MDA8 O3) had a significant effect on the daily respiratory hospitalization. This effect was stronger than for the maximum daily 1 h average ozone concentration (MDA1 O3). The results further showed that O3 was positively associated with daily respiratory hospitalization in the warm season, while there was a significantly negative association in the cold season. Specifically, in the warm season, O3 has the most significant effect at lag 4 day, with the odds ratio (OR) equal to 1.0096 [95% confidence intervals (CI): 1.0032, 1.0161]. Moreover, at the lag 5 day, the effect of O3 on the 15-60 age group was less than that on people older than 60 years, with the OR value of 1.0135 (95% CI: 1.0041, 1.0231) for the 60+ age group; women were more sensitive than men to O3 exposure, with an OR value equal to 1.0094 (95% CI: 0.9992, 1.0196) for the female group. Conclusion: These results show that different O3 indicators measure different impacts on respiratory hospitalization admission. Their comparative analysis provided a more comprehensive insight into exploring associations between O3 exposure and respiratory health.

Associations between Different Ozone Indicators and Cardiovascular Hospital Admission: A Time-Stratified Case-Crossover Analysis in Guangzhou, China.

Epidemiological studies reported that ozone (O3) is associated with cardiovascular diseases. However, only few of these studies examined the impact of multiple O3 indicators on cardiovascular hospital admissions. This study aimed to explore and compare the impacts of different O3 indicators on cardiovascular hospital admissions in Guangzhou, China. Based upon the data on daily cardiovascular hospital admissions, air pollution, and meteorological factors in Guangzhou from 2014 to 2018, a time-stratified case-crossover design model was used to analyze the associations between different O3 indicators and cardiovascular hospital admissions. Moreover, the sensitivities of different age and gender groups were analyzed for the whole year and different seasons (i.e., warm and cold). During the warm season, for the single-pollutant model, the odds ratio (OR) value of cardiovascular hospital admissions was 1.0067 (95% confidence interval (CI): 1.0037, 1.0098) for every IQR increase in MDA8 O3 at a lag of five days. The effect of O3 on people over 60 year was stronger than that on the 15-60 years age group. Females were more sensitive than males to O3 exposure. These results provided valuable references for further scientific research and environmental improvement in Guangzhou. Given that short-term O3 exposure poses a threat to human health, the government should therefore pay attention to prevention and control policies to reduce and eliminate O3 pollution and protect human health.

Assessment of Summer Regional Outdoor Heat Stress and Regional Comfort in the Beijing-Tianjin-Hebei Agglomeration Over the Last 40 Years.

Outdoor thermal comfort (OTC) is critical for public health, labor productivity, and human life. Growing extreme heat events caused by climate change have a serious impact on OTCs, especially in urban areas. Quantitatively characterizing and evaluating the spatiotemporal changes in OTCs are essential, and more applications are needed in urban agglomerations. Therefore, taking the Beijing-Tianjin-Hebei (BTH) urban agglomeration as the study area, this study aimed to quantitatively assess the summer regional OTC from 1981 to 2020. First, the Universal Thermal Climate Index (UTCI) was used as the indicator of daily thermal stress, and then a Composite Thermal Comfort Score was proposed to evaluate the long-term, summertime, regional OTC considering the extent, duration, and intensity of daytime and nighttime thermal stress. The results showed that (a) the increase in UTCI (0.32°C/10a at daytime and 0.21°C/10a at nighttime) and heat stress frequency (0.88 at daytime and 0.39 d/10a at nighttime) were manifested over BTH, indicating a worse OTC. Spatial and temporal heterogeneity was also demonstrated. (b) The general OTC showed a decreasing north-south gradient pattern. At daytime, the northern mountainous zone presented the best OTC, the southern plain zone, especially Hengshui, Langfang, and Cangzhou, showed the worst. At nighttime, the mountain-plain transition zone showed the best OTC, the northern mountainous zone showed the worst since more cold stress occurred. Our findings will be useful in informing climate change adaptation strategies to ensure urban resilience as extreme heat increases in the context of climate change.

Short-term health impacts related to ozone in China before and after implementation of policy measures: A systematic review and meta-analysis.

This paper presents a meta-analysis of the impacts of short-term exposure to ozone (O3) on three health endpoints: all-cause, cardiovascular, and respiratory mortality in China. All relevant studies from January 1990 to December 2021 were searched from four databases. After screening, 30 studies were included for the meta-analysis. The results showed that a significant rise of 0.41 % (95 % confidence interval (CI): 0.35 %-0.48 %) in all-cause, 0.60 % (95 % CI: 0.51 %-0.68 %) in cardiovascular and 0.45 % (95 % CI: 0.28 %-0.62 %) in respiratory mortality for each 10 µg m-3 increase in the maximum daily 8 h average O3 concentration (MDA8 O3). Moreover, results stratified by heterogeneous time periods before and after implementing a policy measure in 2013, showed that the pooled effects for all-cause and respiratory mortality before were greater than those after, while the pooled effects for cardiovascular mortality before 2013 were slightly smaller than those after. The finding that short-term exposure to O3 was positively related to the three health endpoints was validated by means of a sensitivity analysis. Furthermore, we did not observe any publication bias. Our results present an updated and better understanding of the relationship between short-term exposure to O3 and the three health endpoints, while providing a reference for further assessment of the impact of short-term O3 exposure on human health.

Evaluation and analysis of the projected population of China.

The population has a significant influence on economic growth, energy consumption, and climate change. Many scholars and organizations have published projections for China's future population due to its substantial population amounts. However, these projections have not been evaluated or analyzed, which may lead confusion to extensional studies based on these datasets. This manuscript compares several China's projection datasets at multiscale and analyzes the impacting factors affecting projection accuracy. The results indicate that the slow of actual population growth rates from 2017 is earlier than most datasets projected. Therefore, the turning point of population decline probably comes rapidly before these datasets expected during 2024 and 2034. Furthermore, the projections do not reveal the population decline from 2010 in the Northeast provinces such as Jilin and Heilongjiang, and underrate the population increase in the southern provinces such as Guangdong and Chongqing. According to the results of regression models, the rate of population changes and the number of migrations people play a significant role in projection accuracy. These findings provide meaningful guidance for scholars to understand the uncertainty of those projection datasets. Moreover, for researchers performing population projections, our discoveries provide insights to increase the projection accuracy.

A Health Impact and Economic Loss Assessment of O3 and PM2.5 Exposure in China From 2015 to 2020.

China is in a critical air quality management stage. Rapid industrial development and urbanization has resulted in non-ignorable air pollution, which seriously endangers human health. Assessment of the health impacts and economic losses of air pollution is essential for the prevention and control policy formulation. Based on ozone (O3) and fine particulate matter concentration (PM2.5) monitoring data in 331 Chinese cities from 2015 to 2020, this study evaluated the health effects and the corresponding economic losses of O3 and PM2.5 pollution on three health endpoints. The ratio of population exposed to O3 levels that exceeded the Chinese Ambient Air Quality Standards (CAAQS) increased from 13.35% in 2015 to 14.15% in 2020, which resulted in 133,415 (2015) - 156,173 (2020) all-cause deaths, 88,941 (2015) - 104,051 (2020) cardiovascular deaths, and 28,614 (2015) - 33,456 (2020) respiratory deaths. The ratio of population exposed to PM2.5 levels that exceeded the CAAQS decreased, but in many regions, especially in North China and the Yangtze River Delta, the PM2.5 concentration remained high. By 2020, nearly half of the population in China was still exposed to PM2.5 levels that exceeded the CAAQS, and the corresponding economic losses reached CNY 3.46 and 3.05 billion, respectively. These results improved the understanding of the spatial-temporal variation trends of major air pollutants at city scale in China, and emphasize the continued coordination urgently needed for controlling O3 and PM2.5 following the implementation of the 2013 policy to mitigate air pollution to protect human health.

Temporal and Spatial Heterogeneity of PM2.5 Related to Meteorological and Socioeconomic Factors across China during 2000-2018.

In recent years, air pollution caused by PM2.5 in China has become increasingly severe. This study applied a Bayesian space-time hierarchy model to reveal the spatiotemporal heterogeneity of the PM2.5 concentrations in China. In addition, the relationship between meteorological and socioeconomic factors and their interaction with PM2.5 during 2000-2018 was investigated based on the GeoDetector model. Results suggested that the concentration of PM2.5 across China first increased and then decreased between 2000 and 2018. Geographically, the North China Plain and the Yangtze River Delta were high PM2.5 pollution areas, while Northeast and Southwest China are regarded as low-risk areas for PM2.5 pollution. Meanwhile, in Northern and Southern China, the population density was the most important socioeconomic factor affecting PM2.5 with q values of 0.62 and 0.66, respectively; the main meteorological factors affecting PM2.5 were air temperature and vapor pressure, with q values of 0.64 and 0.68, respectively. These results are conducive to our in-depth understanding of the status of PM2.5 pollution in China and provide an important reference for the future direction of PM2.5 pollution control.

The spatiotemporal variation in heavy metals in China's farmland soil over the past 20 years: A meta-analysis.

Accurate information on farmland soil heavy metal elements is needed for pollution management and strategic decision making at the national level. In this paper, we review the Chinese literature on soil heavy metal elements (i.e., arsenic, cadmium, chromium, copper, lead, mercury, and zinc) over the past 20 years using meta-analysis. The overall pollution status, spatiotemporal distribution patterns and driving factors of heavy metals in China's farmland soil are explored by using the geoaccumulation index, the standard deviation ellipse method and the PCA/APCS model, respectively. The results show that most heavy metals in farmland soil from the study cases are similar to the world average. Seven types of elements have increased compared with background values. Cd and Hg have become the top polluting elements in China and industrial and agricultural activities are the main sources of current heavy metal element enrichment. Regional natural-social-economic differences have led to significant spatial heterogeneity of heavy metal pollution, showing an intensity pattern unfavourable to national food security. In the time period, the overall distribution range gradually increased with the accelerated growth of regional industrial output, and there was a tendency for the gravity centre of the pollution studies to migrate inland to the northwest and southwest. Regionally differentiated environmental regulation and pollution remediation measures should be developed for pollution prevention and control in the future.

Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China.

Severe air pollution has significantly impacted climate and human health worldwide. In this study, global and local Moran's I was used to examine the spatial autocorrelation of PM2.5 pollution in North China from 2000-2017, using data obtained from Atmospheric Composition Analysis Group of Dalhousie University. The determinant powers and their interactive effects of socioeconomic factors on this pollutant are then quantified using a non-linear model, GeoDetector. Our experiments show that between 2000 and 2017, PM2.5 pollution globally increased and exhibited a significant positive global and local autocorrelation. The greatest factor affecting PM2.5 pollution was population density. Population density, road density, and urbanization showed a tendency to first increase and then decrease, while the number of industries and industrial output revealed a tendency to increase continuously. From a long-term perspective, the interactive effects of road density and industrial output, road density, and the number of industries were amongst the highest. These findings can be used to develop the effective policy to reduce PM2.5 pollution, such as, due to the significant spatial autocorrelation between regions, the government should pay attention to the importance of regional joint management of PM2.5 pollution.

Spatial pattern of the population casualty rate caused by super typhoon Lekima and quantification of the interactive effects of potential impact factors.

BACKGROUND: Typhoons greatly threaten human life and property, especially in China. Therefore, it is important to make effective policy decisions to minimize losses associated with typhoons. METHODS: In this study, the GeoDetector method was used to quantify the determinant powers of natural and socioeconomic factors, and their interactions, on the population casualty rate of super typhoon Lekima. The local indicator of spatial association (LISA) method was followed to explore the spatial pattern of the population casualty rate under the influence of the identified dominant factors. RESULTS: Both natural and socioeconomic factors were found to have significantly impacted the population casualty rate due to super typhoon Lekima. Among the selected factors, maximum precipitation was dominant factor (q = 0.56), followed by maximum wind speed (q = 0.45). In addition, number of health technicians (q = 0.35) and number of health beds (q = 0.27) have a strong influence on the population casualty rate. Among the interactive effects of 12 influencing factors, the combined effects of maximum precipitation and ratio of brick-wood houses, the maximum precipitation and ratio of steel-concrete houses, maximum precipitation and number of health technicians were highest (q = 0.72). Furthermore, high-risk areas with very high casualty rates were concentrated in the southeastern part of Zhejiang and northern Shandong Provinces, while lower-risk areas were mainly distributed in northern Liaoning and eastern Jiangsu provinces. CONCLUSIONS: These results contribute to the development of more specific policies aimed at safety and successful property protection according to the regional differences during typhoons.

Spatiotemporal variations in the incidence of bacillary dysentery and long-term effects associated with meteorological and socioeconomic factors in China from 2013 to 2017.

Bacillary dysentery is a global public health problem that exhibits manifest spatiotemporal heterogeneity. However, long-term variations and regional determinant factors remain unclear. In this study, the Bayesian space-time hierarchy model was used to identify the long-term spatiotemporal heterogeneity of the incidence of bacillary dysentery and quantify the associations of meteorological factors with the incidence of bacillary dysentery in northern and southern China from 2013 to 2017. GeoDetector was used to quantify the determinant powers of socioeconomic factors in the two regions. The results showed that the incidence of bacillary dysentery peaked in summer (June to August), indicating temporal seasonality. Geographically, the hot spots (high-risk areas) were distributed in northwestern China (Xinjiang, Gansu, and Ningxia) and northern China (including Beijing, Tianjin, and Hebei), whereas the cold spots (low-risk areas) were concentrated in southeastern China (Jiangsu, Zhejiang, Fujian, and Guangdong). Moreover, significant regional differences were found among the meteorological and socioeconomic factors. Average temperature was the dominant meteorological factor in both northern and southern China. In northern and southern China, a 1 °C increase in the average temperature led to an increase of 1.01% and 4.26% in bacillary dysentery risk, respectively. The dominant socioeconomic factors in northern and southern China were per capita gross domestic product and the number of health technicians, with q statistic values of 0.81 and 0.49, respectively. These findings suggest that hot, moist, and overcrowded environments or poor health conditions increase the risk of bacillary dysentery. This study provides suggestions and serves as a basis for surveillance efforts. Further, the suggestions may aid in the control of bacillary dysentery and in the implementation of disease prevention policies.

The Coupled Impact of Emergency Responses and Population Flows on the COVID-19 Pandemic in China.

Coronavirus disease 2019 (COVID-19) has spread around the world and requires effective control measures. Like the human-to-human transmission of the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), the distribution of COVID-19 was driven by population flow and required emergency response measures to slow down its spread and degrade the epidemic risk. The local epidemic risk of COVID-19 is a combination of emergency response measures and population flow. Because of the spatial heterogeneity, the different impacts of coupled emergency responses and population flow on the COVID-19 epidemic during the outbreak period and a control period are unclear. We examined and compared the impact of emergency response measures and population flow on China's epidemic risk after the Wuhan lockdown during the outbreak period and a control period. We found that the population flow out of Wuhan had a long-term impact on the epidemic's spread. In the outbreak period, a large population flow out of Wuhan led to nationwide migration mobility, which directly increased the epidemic in each province. Meanwhile, quick emergency responses mitigated the spread. Although low population flow to provinces far from Hubei delayed the outbreak in those provinces, relatively delayed emergency response increased the epidemic in the control period. Consequently, due to the strong transmission ability of the SARS-CoV-2 virus, no region correctly estimated the epidemic, and the relaxed emergency response raised the epidemic risks in the context of the outbreak.

Use of Entropy in Developing SDG-based Indices for Assessing Regional Sustainable Development: A Provincial Case Study of China.

Sustainable development appears to be the theme of our time. To assess the progress of sustainable development, a simple but comprehensive index is of great use. To this end, a multivariate index of sustainable development was developed in this study based on indicators of the United Nations Sustainable Development Goals (SDGs). To demonstrate the usability of this developed index, we applied it to Fujian Province, China. According to the China SDGs indicators and the Fujian situation, we divided the SDGs into three dimensions and selected indicators based on these dimensions. We calculated the weights and two indices with the entropy weight coefficient method based on collecting and processing of data from 2007 to 2017. We assessed and analyzed the sustainable development of Fujian with two indices and we drew three main conclusions. From 2007 to 2017, the development index of Fujian showed an increasing trend and the coordination index of Fujian showed a fluctuating trend. It is difficult to smoothly improve the coordination index of Fujian because the development speeds of Goal 3 (Good Health and Well-being) and Goal 16 (Peace, Justice, and Strong Institutions) were low. The coordination index of Fujian changed from strong coordination to medium coordination from 2011 to 2012 because the development speed of the environmental dimension suddenly improved. It changed from strong coordination to medium coordination from 2015 to 2016 because the values of the development index of the social dimension were decreasing. To the best of our knowledge, these are the first SDGs-based multivariate indices of sustainable development for a region of China. These indices are applicable to different regions.

Spatiotemporal heterogeneity of PM2.5 and its relationship with urbanization in North China from 2000 to 2017.

Fine particulate matter (PM2.5) pollution is becoming an increasing global concern due to rapid urbanization and socioeconomic development, especially in North China. Although North China experiences poor air quality and high PM2.5 concentrations, their spatial heterogeneity and relationship with the relative spatial risks of air pollution have not been explored. Therefore, in this study, the temporal variation trends (slope values) of the PM2.5 concentrations in North China from 2000 to 2017 were first quantified using the unitary linear regression model, and the Bayesian space-time hierarchy model was introduced to characterize their spatiotemporal heterogeneity. The spatial lag model was then used to examine the determinant power of urbanization and other socioeconomic factors. Additionally, the correlation between the spatial relative risks (probability of a region becoming more/less polluted relative to the average PM2.5 concentrations of the study area), and the temporal variation trends of the PM2.5 concentrations were quantified using the bivariate local indicators of spatial association model. The results showed that the PM2.5 concentrations increased during 2000-2017, and peaked in 2007 and 2013. Spatially, the cities at high risk of PM2.5 pollution were mainly clustered in southeastern Hebei, northern Henan, and western Shandong where the slope values were low, as demonstrated by the value of Moran's I (-0.56). Moreover, urbanization and road density were both positively correlated with PM2.5 pollution, while the proportion of tertiary industry was negatively correlated. Furthermore, a notable increasing trend was observed in some cities, such as Tianjin, Zaozhuang, Qingdao, and Xinyang. These findings can contribute to the development of effective policies from the perspective of rapid urbanization to relieve and reduce PM2.5 pollution.

Visualized analysis of developing trends and hot topics in natural disaster research.

This study visualized and analyzed the developing trends and hot topics in natural disaster research. 19694 natural disaster-related articles (January 1900 to June 2015) are indexed in the Web of Science database. The first step in this study is using complex networks to visualize and analyze these articles. CiteSpace and Gephi were employed to generate a countries collaboration network and a disciplines collaboration network, and then attached hot topics to countries and disciplines, respectively. The results show that USA, China, and Italy are the three major contributors to natural disaster research. "Prediction model", "social vulnerability", and "landslide inventory map" are three hot topics in recent years. They have attracted attention not only from large countries like China but also from small countries like Panama and Turkey. Comparing two hybrid networks provides details of natural disaster research. Scientists from USA and China use image data to research earthquakes. Indonesia and Germany collaboratively study tsunamis in the Indian Ocean. However, Indonesian studies focus on modeling and simulations, while German research focuses on early warning technology. This study also introduces an activity index (AI) and an attractive index (AAI) to generate time evolution trajectories of some major countries from 2000 to 2013 and evaluate their trends and performance. Four patterns of evolution are visible during this 14-year period. China and India show steadily rising contributions and impacts, USA and England show relatively decreasing research efforts and impacts, Japan and Australia show fluctuating activities and stable attraction, and Spain and Germany show fluctuating activities and increasing impacts.