40-Year (1978-2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing

Distinct latitudinal patterns and drivers of topsoil nitrogen and phosphorus across urban forests in eastern China

Nitrogen (N) and phosphorus (P) are the two most important macronutrients supporting forest growth. Unprecedented urbanization has created growing areas of urban forests that provide key ecosystem services for city dwellers. However, the large-scale patterns of soil N and P content remain poorly understood in urban forests. Based on a systematic soil survey in urban forests from nine large cities across eastern China, we examined the spatial patterns and key drivers of topsoil (0-20 cm) total N content, total P content, and N:P ratio. Topsoil total N content was found to change significantly with latitude in the form of an inverted parabolic curve, while total P content showed an opposite latitudinal pattern. Variance partition analysis indicated that regional-scale patterns of topsoil total N and P contents were dominated by climatic drivers and partially regulated by time and pedogenic drivers. Conditional regression analyses showed a significant increase in topsoil total N content with lower mean annual temperature (MAT) and higher mean annual precipitation (MAP), while topsoil total P content decreased significantly with higher MAP. Topsoil total N content also increased significantly with the age of urban park and varied with pre-urban soil type, while no such effects were found for topsoil total P content. Moreover, topsoil N:P ratio showed a latitudinal pattern similar to that of topsoil total N content and also increased significantly with lower MAT and higher MAP. Our findings demonstrate distinct latitudinal trends of topsoil N and P contents and highlight a dominant role of climatic drivers in shaping the large-scale patterns of topsoil nutrients in urban forests.

An Integrated Multi-Source Dataset for Measuring Settlement Evolution in the United States from 1810 to 2020

Understanding changes in the built environment is vital for sustainable urban development and disaster preparedness. Recent years have seen the emergence of a variety of global, continent-level, and nation-wide datasets related to the current state and the evolution of the built environment, human settlements or building stocks. However, such datasets may face limitations like incomplete coverage, sparse building information, coarse resolution, and limited timeframes. This study addresses these challenges by integrating three spatial datasets to create an extensive, attribute-rich sequence of settlement layers spanning 200 years for the contiguous U.S. This integration process involves complex data processing, merging property-level real estate, parcel, and remote sensing-based building footprint data, and creating gridded multi-temporal settlement layers. This effort unveils the latest edition (Version 2) of the Historical Settlement Data Compilation for the U.S. (HISDAC-US), which includes the latest land use and structural information as of the year 2021. It enables detailed research on urban form and structure, helps assess and map the built environment's risk to natural hazards, assists in population modeling, supports land use analysis, and aids health studies.

Asymmetric response of vegetation GPP to impervious surface expansion: Case studies in the Yellow and Yangtze River Basins

Terrestrial gross primary production (GPP) changes due to impervious surfaces significantly impact ecosystem services in watersheds. Understanding the asymmetric response of vegetation GPP to impervious surface expansion is essential for regional development planning and ecosystem management. However, the asymmetric response of vegetation GPP to the impacts of impervious surface expansion is unknown in different watersheds. This paper selected the Yellow River and Yangtze River basins as case studies. We characterized the overall change in GPP based on changes in impervious surface ratio (ISR), determined impervious surface expansion's direct and indirect impacts on GPP in the two watersheds, and further analyzed the asymmetric response of the compensatory effects of indirect influences on the impervious surface expansion in different watersheds. The results showed that: (1) The vegetation GPP decreased with increasing ISR in the Yangtze River Basin, while that in the Yellow River Basin first increased and then reduced. (2) The direct impacts of increased ISR reduced vegetation GPP, while the indirect impacts both had a growth-compensating effect. Growth compensation stabilized at approximately 0.40 and 0.30 in the Yellow and Yangtze River Basins. (3) When the ISR was 0.34-0.56, the growth compensation could offset the reduction of GPP due to direct impact and ensure that the background vegetation GPP was not damaged in the Yellow River Basin. In contrast, the background vegetation GPP was inevitably impaired with increased ISR in the Yangtze River Basin. Therefore, this study suggests that the ISR should be ensured to be between 0.34 and 0.56 to maximize the impervious surface of the Yellow River Basin without compromising the background vegetation GPP. While pursuing impervious surface expansion in the Yangtze River Basin, other programs should be sought to compensate for the loss to GPP.

Health benefits from the rapid reduction in ambient exposure to air pollutants after China's clean air actions: progress in efficacy and geographic equality

Clean air actions (CAAs) in China have been linked to considerable benefits in public health. However, whether the beneficial effects of CAAs are equally distributed geographically is unknown. Using high-resolution maps of the distributions of major air pollutants (fine particulate matter [PM2.5] and ozone [O3]) and population, we aimed to track spatiotemporal changes in health impacts from, and geographic inequality embedded in, the reduced exposures to PM2.5 and O3 from 2013 to 2020. We used a method established by the Global Burden of Diseases Study. By analyzing the changes in loss of life expectancy (LLE) attributable to PM2.5 and O3, we calculated the gain of life expectancy (GLE) to quantify the health benefits of the air-quality improvement. Finally, we assessed the geographic inequality embedded in the GLE using the Gini index (GI). Based on risk assessments of PM2.5 and O3, during the first stage of CAAs (2013 to 2017), the mean GLE was 1.87 months. Half of the sum of the GLE was disproportionally distributed in about one quarter of the population exposed (GI 0.44). During the second stage of CAAs (2017 to 2020), the mean GLE increased to 3.94 months and geographic inequality decreased (GI 0.18). According to our assessments, CAAs were enhanced, from the first to second stages, in terms of not only preventing premature mortality but also ameliorating health inequalities. The enhancements were related to increased sensitivity to the health effects of air pollution and synergic control of PM2.5 and O3 levels. Our findings will contribute to optimizing future CAAs.

The Association Between Urbanization and Electrocardiogram Abnormalities in China: a Nationwide Longitudinal Study

The health effects of urbanization are controversial. The association between urbanization and reversible subclinical risks of cardiovascular diseases (e.g., electrocardiogram (ECG) abnormalities) has rarely been studied. This study aimed to assess the association between urbanization and ECG abnormalities in China based on the China National Stroke Screening Survey (CNSSS). We used changes in the satellite-measured impervious surfaces rate and nighttime light data to assess the level of urbanization. Every interquartile increment in the impervious surfaces rate or nighttime light was related to a decreased risk of ECG abnormalities, with odds ratios of 0.894 (95% CI, 0.869-0.920) or 0.809 (95% CI, 0.772-0.847), respectively. And we observed a U-shaped nonlinear exposure-response relationship curve between the impervious surfaces rate and ECG abnormalities. In conclusion, the current average level of urbanization among the studied Chinese adults remains a beneficial factor for reducing cardiovascular risks.

Epidemiology of Hemorrhagic Fever with Renal Syndrome and Host Surveillance in Zhejiang Province, China, 1990-2021

Hemorrhagic fever with renal syndrome (HFRS) is caused by hantaviruses (HVs) and is endemic in Zhejiang Province, China. In this study, we aimed to explore the changing epidemiology of HFRS cases and the dynamics of hantavirus hosts in Zhejiang Province. Joinpoint regression was used to analyze long-term trends in the incidence of HFRS. The comparison of animal density at different stages was conducted using the Mann-Whitney Test. A comparison of HV carriage rates between stages and species was performed using the chi-square test. The incidence of HFRS shows a continuous downward trend. Cases are widely distributed in all counties of Zhejiang Province except Shengsi County. There was a high incidence belt from west to east, with low incidence in the south and north. The HFRS epidemic showed two seasonal peaks in Zhejiang Province, which were winter and summer. It showed a marked increase in the age of the incidence population. A total of 23,073 minibeasts from 21 species were captured. Positive results were detected in the lung tissues of 14 rodent species and 1 shrew species. A total of 80% of the positive results were from striped field mice and brown rats. No difference in HV carriage rates between striped field mice and brown rats was observed (χ2 = 0.258, p = 0.611).

Mapping impervious surface area increase and urban pluvial flooding using Sentinel Application Platform (SNAP) and remote sensing data

Expansion of urban impervious surface (UIA) and increased urban pluvial flooding (UPF) have an impact on urban dynamics, socioeconomic activities, and our environment. Therefore, monitoring the increase in UIS and its effect on UPF is essential. The notion of this research is based on the mapping of impervious surface area increase in three major cities of Pakistan. There were two key objectives: (i) Mapping impervious surface area growth using the global impervious surface area index (GISAI) on Google Earth Engine from 1992 to 2022 and (ii) mapping the pluvial flood extent in selected urban areas using Sentinel-1 Ground Range Detected (GRD) data. Thus, we have utilized the GISAI for mapping urban impervious surface area (UISA) using Landsat time-series data on GEE. Our research findings revealed that about 16.8%, 23.5%, and 16.4% of the impervious surface have been increased in Islamabad, Lahore, and Karachi, respectively. Also, Lahore city has the highest overall accuracy, aiming at the GISAI of 93%, followed by Karachi and Islamabad with an overall accuracy of 86% and 85%, respectively. The results indicated that urban flooding has occurred in those areas where the ISA has grown during the last three decades. It shows significant changes in the impervious surface area that cause enhanced urban pluvial flooding in major cities of Pakistan. Also, Sentinel-1 data and the SNAP tool significantly mapped flooded areas in the selected zones. So, providing cities and local governments with increased quick flood detection capabilities is essential. It can also provide feasible policy recommendations for Pakistan decision-makers in city management. Therefore, we suggest a modeling-based solution to identify high-risk locations in major cities for upcoming UPF events.

Quantitative flood disaster loss-resilience with the multilevel hybrid evaluation model

The severity of global flood disasters is growing, causing loss of human life and property. Building a high-resilience social system, an important means of sustainable flood control, can address these flood-related issues. Numerous studies have carried out disaster resilience evaluations and explored the correlation between flood disaster loss and intensity, but neglected to analyze the role of resilience construction in disaster loss reduction. This study proposed a research route for linking flood loss and disaster loss to quantify the relationship between the two. Take Guangdong Province, China as a study case, the mixed-effects (ME) model and multilevel hybrid evaluation model (MHEM) were established to assess disaster loss and resilience of cities, respectively. Subsequently, disaster resilience curves were built to quantitatively evaluate disaster resilience and corresponding disaster loss. The results show that (1) the ME model can concurrently build the disaster intensity-loss curves of multiple cities with high fitting accuracy. The MHEM combines multiple methods to determine the evaluation result with the highest consistency, showing high reliability. (2) The central and southern regions of Guangdong Province have low disaster loss and high resilience, while the northern regions have high disaster loss and low resilience. (3) With the improvement of disaster resistance, the reduction in disaster loss gradually decreases. Disaster loss in low-resilience cities exhibits greater randomness than that in high-resilience cities, and increasing their resilience can more significantly reduce their level of loss. This study provides a quantitative basis and available methods for comprehensive responses to natural disasters and adaptation to global climate change.

Evolution in disparity of PM2.5 pollution in China

The spatial disparity of air pollutants is one of the key influential factors for environmental inequality. We quantitatively evaluated the evolution of PM2.5 spatial disparity in China during 2013-2020, and investigated the associations between PM2.5 spatial disparity and economic indicators. Differences in PM2.5 between more- and less-polluted cities declined over time, suggesting decreased absolute disparity. However, the more polluted cities in 2013 remained so in 2017 and 2020, and vice versa, indicating persistent relative disparity. PM2.5 pollution levels increased with higher GDP per capita in less-developed areas of China, but such negative effects weakened over time, while economic development tended to promote cleaner air in developed areas of China. Therefore, policies to improve air quality and promote economic development simultaneously are needed in China to reduce the disparity of air pollution and promote all people to enjoy environmental equality.

Local temperature responses to actual land cover changes present significant latitudinal variability and asymmetry

Land cover changes (LCCs) affect surface temperatures at local scale through biophysical processes. However, previous observation-based studies mainly focused on the potential effects of virtual afforestation/deforestation using the space-for-time assumption, while the actual effects of all types of realistic LCCs are underexplored. Here, we adopted the space-and-time scheme and utilized extensive high-resolution (1-km) satellite observations to perform the first such assessment. We showed that, from 2006 to 2015, the average temperature in the areas with LCCs increased by 0.08 K globally, but varied significantly across latitudes, ranging from -0.05 to 0.18 K. Cropland expansions dominated summertime cooling effects in the northern mid-latitudes, whereas forest-related LCCs caused warming effects elsewhere. These effects accounted for up to 44.6% of overall concurrent warming, suggesting that LCC influences cannot be ignored. In addition, we revealed obvious asymmetries in the actual effects, i.e., LCCs with warming effects occurred more frequently, with stronger intensities, than LCCs with cooling effects. Even for the mutual changes between two covers in the same region, warming LCCs generally had larger magnitudes than their cooling counterparts due to asymmetric changes in transition fractions and driving variables. These novel findings, derived from the assessment of actual LCCs, provide more realistic implications for land management and climate adaptation policies.

Long-term satellite observations show continuous increase of vegetation growth enhancement in urban environment

Urbanization shows continuous expansion and development, ushering in the co-evolution of urban environments and vegetation over time. Recent remote sensing-based studies have discovered prevalent vegetation growth enhancement in urban environments. However, whether there is a temporal evolution of the growth enhancement remains unknown and unexplored. Here we expanded the existing framework for assessing the long-term impact of urbanization on vegetation greenness (enhanced vegetation index, EVI) using long time series of remote sensing images and applied it in Changsha, the capital city of Hunan province in China. Results showed that vegetation growth experienced widespread enhancement from 2000 to 2017, and increased 1.8 times from 2000 to 2017, suggesting strong continuous adaptive capability of vegetation to urban conditions. Although the overall impact of urbanization was negative due to the replacement of vegetated surfaces, the growth enhancement nevertheless offset or compensated the direct loss of vegetated cover during urbanization in the magnitude of 28 % in 2000 to 44 % in 2017. Our study also revealed large spatial heterogeneity in vegetation growth response among various districts at different urbanization levels and found an emergent trend under the observed spatial heterogeneity toward an asymptotic maximum with urbanization, showing EVI converges to 0.22 in highly urbanized areas. We further found that the positive effect of urbanization on vegetation growth is a function of urbanization intensity and time, which implies that the effect of the urban environment on vegetation can be simulated and predicted, and can be verified in more cities in the future. Our study is the first to successfully quantify long-term spatial patterns on the co-evolution of urbanization and vegetation, providing a new understanding of the continuous adaptive responses of vegetation growth to urbanization and shedding light on predicting biological responses to future environmental change.

A distinctive latitudinal trend of nitrogen isotope signature across urban forests in eastern China

Rapid urbanization has greatly altered nitrogen (N) cycling from regional to global scales. Compared to natural forests, urban forests receive much more external N inputs with distinctive abundances of stable N isotope (δ15 N). However, the large-scale pattern of soil δ15 N and its imprint on plant δ15 N remain less well understood in urban forests. By collecting topsoil (0-20 cm) and leaf samples from urban forest patches in nine large cities across a north-south transect in eastern China, we analyzed the latitudinal trends of topsoil C:N ratio and δ15 N as well as the correlations between tree leaf δ15 N and topsoil δ15 N. We further explored the spatial variation of topsoil δ15 N explained by corresponding climatic, edaphic, vegetation-associated, and anthropogenic drivers. Our results showed a significant increase of topsoil C:N ratio towards higher latitudes, suggesting lower N availability at higher latitudes. Topsoil δ15 N also increased significantly at higher latitudes, being opposite to the latitudinal trend of soil N availability. The latitudinal trend of topsoil δ15 N was mainly explained by mean annual temperature, mean annual precipitation, and atmospheric deposition of both ammonium and nitrate. Consequently, tree leaf δ15 N showed significant positive correlations with topsoil δ15 N across all sampled plant species and functional types. Our findings reveal a distinctive latitudinal trend of δ15 N in urban forests and highlight an important role of anthropogenic N sources in shaping the large-scale pattern of urban forest 15 N signature.

Human footprints in the Global South accelerate biomass carbon loss in ecologically sensitive regions

Human activities have placed significant pressure on the terrestrial biosphere, leading to ecosystem degradation and carbon losses. However, the full impact of these activities on terrestrial biomass carbon remains unexplored. In this study, we examined changes in global human footprint (HFP) and human-induced aboveground biomass carbon (AGBC) losses from 2000 to 2018. Our findings show an increasing trend in HFP globally, resulting in the conversion of wilderness areas to highly modified regions. These changes have altered global biomes' habitats, particularly in tropical and subtropical regions. We also found accelerated AGBC loss driven by HFP expansion, with a total loss of 19.99 ± 0.196 PgC from 2000 to 2018, especially in tropical regions. Additionally, AGBC is more vulnerable in the Global South than in the Global North. Human activities threaten natural habitats, resulting in increasing AGBC loss even in strictly protected areas. Therefore, scientifically guided planning of future human activities is crucial to protect half of Earth through mitigation and adaptation under future risks of climate change and global urbanization.

Interpretation of the coupling mechanism of ecological security and urbanization based on a Computation-Verification-Coupling framework: Quantitative analysis of sustainable development

In recent decades, China's rapid urbanization has produced numerous economic benefits while simultaneously creating substantial risks to ecological security. China's 14th Five-Year Plan and the United Nations Sustainable Development Goals (SDGs) have recently explicitly called for the coordinated development of ecological security and urbanization. Given this context, it is important to explore the mechanism by which ecological security and urbanization are coupled and coordinated to promote sustainable development. In this study, an index of the relationship between ecological security and urbanization was established via high-resolution data, and a "Computation-Verification-Coupling" (CVC) framework was constructed. The accuracy of the ecological security index was verified using a linear regression model, and the coordination level between ecological security and urbanization was analyzed via a coupled coordination model (CCM). The results revealed a steady increase in the ecological security index from 2010 to 2020; the proportion of the area above the medium level increased from 63.1 % to 74.1 %. The urbanization index in core counties exhibited rapid growth, with level V urbanized areas expanding from 5.5 % to 9.9 %. The ecological security verification model produced a coefficient of determination (R²) of 0.75685, indicating a satisfactory degree of predictive capability. From 2010-2020, the coupled coordination improved, with the high coordination area accounting for 48.8 % and the extreme discoordination area decreasing from 1.8 % to 1.0 %. Coordinated development exhibited a stable progression, characterized by a cyclical evolution from initial coupling to antagonistic coupling and finally to coordinated development. This framework can be used not only to investigate the relationship between ecological security and urbanization but also to provide a quantifiable measure of progress toward achieving the SDGs.

A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images

High-resolution and multi-temporal impervious surface area maps are crucial for capturing rapidly developing urbanization patterns. However, the currently available relevant maps for the greater Mekong subregion suffer from coarse resolution and low accuracy. Addressing this issue, our study focuses on the development of accurate impervious surface area maps at 10-m resolution for this region for the period 2016-2022. To accomplish this, we present a new machine-learning framework implemented on the Google Earth Engine platform that merges Sentinel-1 Synthetic Aperture Radar images and Sentinel-2 Multispectral images to extract impervious surfaces. Furthermore, we also introduce a training sample migration strategy that eliminates the collection of additional training samples and automates multi-temporal impervious surface area mapping. Finally, we perform a quantitative assessment with validation samples interpreted from Google Earth. Results show that the overall accuracy and kappa coefficient of the final impervious surface area maps range from 92.75% to 92.93% and 0.854 to 0.857, respectively. This dataset provides comprehensive measurements of impervious surface coverage and configuration that will help to inform urban studies.

Anthropogenic activities dominated tropical forest carbon balance in two contrary ways over the Greater Mekong Subregion in the 21st century

The tropical forest carbon (C) balance threatened by extensive socio-economic development in the Greater Mekong Subregion (GMS) in Asia is a notable data gap and remains contentious. Here we generated a long-term spatially quantified assessment of changes in forests and C stocks from 1999 to 2019 at a spatial resolution of 30 m, based on multiple streams of state-of-the-art high-resolution satellite imagery and in situ observations. Our results show that (i) about 0.54 million square kilometers (21.0% of the region) experienced forest cover transitions with a net increase in forest cover by 4.3% (0.11 million square kilometers, equivalent to 0.31 petagram of C [Pg C] stocks); (ii) forest losses mainly in Cambodia, Thailand, and in the south of Vietnam, were also counteracted by forest gains in China due mainly to afforestation; and (iii) at the national level during the study period an increase in both C stocks and C sequestration (net C gain of 0.087 Pg C) in China from new plantation, offset anthropogenetic emissions (net C loss of 0.074 Pg C) mainly in Cambodia and Thailand from deforestation. Political, social, and economic factors significantly influenced forest cover change and C sequestration in the GMS, positively in China while negatively in other countries, especially in Cambodia and Thailand. These findings have implications on national strategies for climate change mitigation and adaptation in other hotspots of tropical forests.

Mortality burden due to ambient nitrogen dioxide pollution in China: Application of high-resolution models

BACKGROUND

A large gap exists between the latest Global Air Quality Guidelines (AQG 2021) and Chinese air quality standards for NO₂. Assessing whether and to what extent air quality standards for NO₂ should be tightened in China requires a comprehensive understanding of the spatiotemporal characteristics of population exposure to ambient NO₂ and related health risks, which have not been studied to date.

OBJECTIVE

We predicted ground NO₂ concentrations with high resolution in mainland China, explored exposure characteristics to NO₂ pollution, and assessed the mortality burden attributable to NO₂ exposure.

METHODS

Daily NO₂ concentrations in 2019 were predicted at 1-km spatial resolution in mainland China using random forest models incorporating multiple predictors. From these high-resolution predictions, we explored the spatiotemporal distribution of NO₂, population and area percentages with NO₂ exposure exceeding criterion levels, and premature deaths attributable to long- and short-term NO₂ exposure in China.

RESULTS

The cross-validation R²and root mean squared error of the NO₂ predicting model were 0.80 and 7.78 μg/m³, respectively,at the daily level in 2019.The percentage of people (population number) with annual NO₂ exposure over 40 μg/m³ in mainland China in 2019 was 10.40 % (145,605,200), and it reached 99.68 % (1,395,569,840) with the AQG guideline value of 10 μg/m³. NO₂ levels and population exposure risk were elevated in urban areas than in rural. Long- and short-term exposures to NO₂ were associated with 285,036 and 121,263 non-accidental deaths, respectively, in China in 2019. Tightening standards in steps gradually would increase the potential health benefit.

CONCLUSION

In China, NO₂ pollution is associated with significant mortality burden. Spatial disparities exist in NO₂ pollution and exposure risks. China's current air quality standards may no longer objectively reflect the severity of NO₂ pollution and exposure risk. Tightening the national standards for NO₂ is needed and will lead to significant health benefits.

Atmospheric ammonia in China: Long-term spatiotemporal variation, urban-rural gradient, and influencing factors

In recent years, atmospheric ammonia (NH₃) concentrations have increased in China. Ammonia control has become one of the next hot topics in air pollution mitigation with the increasing cost of acid gas emission reduction. In this study, using Infrared Atmospheric Sounding Interferometer (IASI) satellite observations, we analyzed the spatiotemporal distribution, the urban-rural gradient of the vertical column densities (VCDs) of NH₃ and the contribution of influencing factors (meteorology, social, atmospheric acid gases, and NH₃ emissions) in China from 2008 to 2019 using hotspot analysis, circular gradient analysis, geographical and temporal weighted regression, and some other methods. Our results showed that NH₃ VCDs in China have significantly increased (31.88 %) from 2008 to 2019, with the highest occurring in North China Plain. The average NH₃ VCDs in urban areas were significantly higher than those in rural areas, and the urban-rural gap in NH₃ VCDs was widening. The results of circular gradient analysis showed an overall decreasing trend in NH₃ VCDs along the urban-rural gradient. We used a geographically and temporally weighted regression model to analyze the contribution of various influencing factors to NH₃ VCDs: meteorology (30.13 %), social (27.40 %), atmospheric acid gases (23.20 %), and NH₃ emissions (19.28 %) factors. The results showed substantial spatiotemporal differences in the influencing factors. Atmospheric acid gas was the main reason for the increase in NH₃ VCDs from 2008 to 2019. A more thorough understanding of the spatiotemporal distribution, urban-rural variations, and factors influencing NH₃ in China will aid in developing control strategies to reduce PM_2.5.

Health Impacts of Long-Term NO2 Exposure and Inequalities among the Chinese Population from 2013 to 2020

Nitrogen dioxide (NO₂) is associated with mortality and many other adverse health outcomes. In 2021, the World Health Organization established a new NO₂ air quality guideline (AQG) (annual average <10 μg/m³). However, the burden of diseases attributable to long-term NO₂ exposure above the AQG is unknown in China. Nitrogen oxide is a major air pollutant in populous cities, which are disproportionately impacted by NO₂; this represents a form of environmental inequality. We conducted a nationwide risk assessment of premature deaths attributable to long-term NO₂ exposure from 2013 to 2020 based on the exposure-response relationship, high-resolution annual NO₂ concentrations, and gridded population data (considering sex, age, and residence [urban vs rural]). We calculated health metrics including attributable deaths, years of life lost (YLL), and loss of life expectancy (LLE). Inequality in the distribution of attributable deaths and YLLs was evaluated by the Lorenz curve and Gini index. According to the health impact assessments, in 2013, long-term NO₂ exposure contributed to 315,847 (95% confidence interval [CI]: 306,709-319,269) premature deaths, 7.90 (7.68-7.99) million YLLs, and an LLE of 0.51 (0.50-0.52) years. The high-risk subgroup (top 20%) accounted for 85.7% of all NO₂-related deaths and 85.2% of YLLs, resulting in Gini index values of 0.81 and 0.67, respectively. From 2013 to 2020, the estimated health impact from NO₂ exposure was significantly reduced, but inequality displayed a slightly increasing trend. Our study revealed a considerable burden of NO₂-related deaths in China, which were disproportionally frequent in a small high-risk subgroup. Future clean air initiatives should focus not only on reducing the average level of NO₂ exposure but also minimizing inequality.

Urbanization-land-use interactions predict antibiotic contamination in soil across urban-rural gradients

Antibiotics ubiquitously occur in soils and pose a potential threat to ecosystem health. Concurrently, urbanization and land-use intensification have transformed soil ecosystems, but how they affect antibiotic contamination remain largely unknown. Therefore, we profiled a broad-scale pattern of antibiotics in soil from agricultural lands and green spaces across urbanization gradients, and explored the hypothetical models to verify the effects of urbanization and land-use intensity on antibiotic contamination. The results showed that antibiotic concentrations and seasonality were higher in agricultural soil than in green spaces, which respectively showed linear or hump-shaped declines along with the increasing distance to urban centers. However, the response of antibiotic pollution to land-use intensity depended strongly on the urbanization level. More importantly, interactions between urbanization and land-use explained, on average, 59.6 % of the variation in antibiotic concentrations in soil across urbanization gradients. The proposed interactions can predict the non-linear changes in soil vulnerability to antibiotic contamination. Our study revealed that the urbanization can modulate the effects of land-use intensity on antibiotic concentration and seasonality in the soil environment, and that there is high stress on peri-urban soil ecosystems due to ongoing land-use changes arising from rapid urbanization processes.

Landslide susceptibility prediction considering land use change and human activity: A case study under rapid urban expansion and afforestation in China

China has been subject to rapid urban expansion and afforestation since the economic reform in 1978. However, the influence of land use and cover changes (LUCCs) and human activities on landslide occurrence is often ignored in landslide susceptibility mapping and zonation (LSMZ). In this study, Enshi City, China, was selected as the study area because of dramatic LUCCs during the last two decades. This study divided landslide affecting factors (AFs) into base affecting factors (BAFs) and land-related affecting factors (LAFs), and 15 landslide susceptibility maps were created by three different types of models. The results showed that the combination 6 of heuristic multi-layer perceptron model with LAFs (HMLP-LAFC₆) model obtained the highest model performance. In addition, any factor combinations of HMLP-LAF model outperformed other two types of models, and the use of land use and cover (LULC) in different periods as well as LUCCs may significantly impact the model performance. Given that land policy adjustments are normally core drivers of LUCC in China, a land planning based LSMZ framework was proposed, which is suitable for LSMZ in rapid LUCC regions with radical land policies. Finally, this paper strongly suggests developing more hybrid models that coupling dynamic AFs, clarifying the quantitative boundaries of time-irrelevant and dynamic AFs, increasing the accuracy of LULC prediction, and improving the abilities of bilateral understanding for effective, integrated, and systematic management of land planning and landslide hazards.

Phased and polarized development of ecological quality in the rapidly-urbanized Pearl River Delta, China

Urbanization is one of the most significant human activities in the Anthropocene, with profound impacts on environmental quality. The lack of an understanding about the relationship between urbanization and ecological quality limits the effectiveness of urban planning and ecological policies in alleviating urban ecological problems. Based on the integrated ecological index RSEI (remote sensing ecological index), this study attempts to clarify the spatio-temporal characteristics of ecological quality in an urbanization process through an empirical study in China's Pearl River Delta (PRD) and explores the relationship between urbanization and ecological quality. Our results show that the ecological development of the PRD in the period of 1986 to 2019 was a phased and polarized process. Two periods are distinguished, based on RSEI dispersion: the period of 1986 to 2003, with slight dispersion, and the period of 2004 to 2019, with higher dispersion. Plain areas show evidence of ecological degradation, whereas a considerable improvement was observed in hilly areas. Industrialization and consummation of legal system were the driving factors behind the phased development of ecological quality, while the differences in landform and land management were the fundamental reasons for the spatial differentiation of ecological quality. The findings of this study provide experience and enlightenment for ecological management and sustainable development strategies in regions seeking rapid growth in their prosperity.

Urbanization-Related Environmental Factors and Hemorrhagic Fever with Renal Syndrome: A Review Based on Studies Taken in China

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease that has threatened Chinese residents for nearly a century. Although comprehensive prevent and control measures were taken, the HFRS epidemic in China presents a rebounding trend in some areas. Urbanization is considered as an important influencing factor for the HFRS epidemic in recent years; however, the relevant research has not been systematically summarized. This review aims to summarize urbanization-related environmental factors and the HFRS epidemic in China and provide an overview of research perspectives. The literature review was conducted following the PRISMA protocol. Journal articles on the HFRS epidemic in both English and Chinese published before 30 June 2022 were identified from PubMed, Web of Science, and Chinese National Knowledge Infrastructure (CNKI). Inclusion criteria were defined as studies providing information on urbanization-related environmental factors and the HFRS epidemic. A total of 38 studies were included in the review. Changes brought by urbanization on population, economic development, land use, and vaccination program were found to be significantly correlated with the HFRS epidemic. By changing the ecological niche of humans-affecting the rodent population, its virus-carrying rate, and the contact opportunity and susceptibility of populations-urbanization poses a biphasic effect on the HFRS epidemic. Future studies require systematic research framework, comprehensive data sources, and effective methods and models.

Evaluation of urban expansion and the impacts on vegetation in Chinese Loess Plateau: a multi-scale study

Vegetation degradation caused by rapid urban expansion is a pressing global challenge. Focusing on the Chinese Loess Plateau (CLP), we use satellite observations from 2000 to 2017 to evaluate the spatiotemporal pattern of urban expansion, and its imprint on vegetation across old urban, new urban, urban, non-urban areas as well as the entire urbanization intensity (UI) gradient (from 0 to 100%). We found a massive increase of urban impervious surface area (UISA) in the CLP from 2000 to 2017, and an uneven expansion of UISA at different urban agglomerations and cities. Less green were found in urban and new urban areas, while old urban and non-urban areas generally showed an improved greening pattern. In addition, the annual maximum EVI (EVI_max) differences between urban and non-urban areas were - 0.0995 on average from 2000 to 2017. The Guanzhong Plain urban agglomeration (GPUA) witnessed the most significant EVI_max differences (- 0.120), and the Ningxia Yanhuang urban agglomeration (NYUA) witnessed the lowest EVI_max differences (- 0.012). The EVI_max showed significantly decreased trends along the entire spectrum of urbanization gradient for 97.4% (38 of 39) cities and five urban agglomerations. The most significant decrease was found in the GUPA (slope =  - 0.0197/10a, p < 0.01), while the smallest drop was found in the NYUA (slope =  - 0.011/10a, p < 0.01). This study offered a fundamental support for understanding the vegetation variation along the urban-rural gradient, which may help stakeholders to make better ecological management policies for urban vegetation in ecologically fragile areas.

Stream water quality optimized prediction based on human activity intensity and landscape metrics with regional heterogeneity in Taihu Basin, China

The driving effects of landscape metrics on water quality have been acknowledged widely, however, the guiding significance of human activity intensity and landscape metrics based on reference conditions for water environment management remains to be explored. Thus, we used the self-organized map, long- and short-term memory (LSTM) algorithm, and geographic detectors to simulate the response of human activity intensity and landscape metrics to water quality in Taihu Lake Basin, China. Fitting results of LSTM displayed that the accuracy was acceptable, and scenario 2 (regional heterogeneity) was more efficient than scenario 1 (regional consistent) in the improvement of water quality. In the driving analysis for the reference conditions, clusters I and II (urban agglomeration areas) were mainly affected by the amount of production wastewater per unit of developed land and the amount of livelihood wastewater per unit of developed land, respectively. Their optimal values were 0.09 × 10³ t/km² (reduction of 35.71%) and 0.2 × 10³ t/km² (reduction of 4.76%). Cluster III (agricultural production areas) was mainly affected by interference intensity, and the optimal value was 2.17 (increased 38.22%), and cluster IV (ecological forest areas) was mainly affected by the fragmentation of cropland, and the optimal value was 1.14 (reduction of 1.72%). The research provides a reference for the prediction of water quality response and presents an ecological and economic sustainability way for watershed governance.

A multi-perspective study of atmospheric urban heat island effect in China based on national meteorological observations: Facts and uncertainties

The atmospheric urban heat island (AUHI) effect, traditionally measured by in-situ sensors mounted on fixed meteorological stations, has been extensively studied by different and imperfect methods. However, facts and uncertainties of the AUHI estimates revealed by the different methods are not well understood at a large scale. Here we examined the spatial-temporal variations of the AUHI effects from multiple perspectives in China's 86 large cities as revealed by national-level meteorological observations at 2-m height from 1981 to 2017. We find relatively consistent patterns of larger urban heating effects in daily minimum temperature, winter, and Northeast China than their counterparts in terms of multiyear mean intensity (AUHII), long-term trend (△AUHII), and contribution to local warming (according to the CTRUMR "urban minus rural" and CTROMR "observation minus reanalysis" methods). Concurrently, a cooling impact or a reduction in the heating effect has been observed in some cities randomly, especially in daily maximum temperature. On average across cities, the AUHII, △AUHI, CTRUMR, and CTROMR for the daily mean temperature amount to 0.33 °C, 0.013 °C 10a-1, 53 %, and 23 % at an annual mean time scale, respectively. Nevertheless, the poor representativeness of weather stations, discrepancies among the quantification methods, nonlinearity of the long-term tendencies, and coupling effects with rural crop land use activities lead to large uncertainties of the AUHI estimates. Our results emphasize the limitations of national-level meteorological stations in characterizing AUHI in China and suggest that the urban heat island remains a "well described but rather poorly understood" phenomenon warranting further investigation by a combined uses of multiple techniques like high-density sensor networks, remote sensing techniques, and high-resolution numerical models.

Analyzing the Spatio-Temporal Dynamics of Urban Land Use Expansion and Its Influencing Factors in Zhejiang Province, China

The 21st century expansion of built-up areas due to rapid urbanization has recently been at the forefront of global land use/land cover research. Knowledge of the changing dynamics of urban land use is crucial for the monitoring of urbanization and the promotion of sustainable urban development. In this paper, Zhejiang Province was selected as the study area. It is a region with rapid urban growth located along the southeastern coast of China, with a highly developed economy but with a shortage of land resources. We employed remotely sensed and socio-economic panel data for the period between 1990 and 2020 to monitor urban land use changes and utilized the spatial Durbin model (SDM) to examine the urbanization process and the various driving factors of rapid urban expansion in Zhejiang Province, China, from 1990 to 2020. The study's results revealed substantial urban growth of about 6899.59 km², i.e., 6.6%, whereas agricultural land decreased by 4320.68 km², i.e., 4.19%. The rapid urban development was primarily attributed to the transformation of farmlands, forestlands, and water bodies into built-up areas by nearly 86.9%, 6.94%, and 6.06%, respectively. The built-up areas revealed features of spatial clustering. The study showed that the expansion hotspots were mainly distributed within the urban fabric of cities such as Hangzhou, Ningbo, Jinhua-Yiwu, and Wenzhou-Taizhou. The results further revealed the substantial influence of urban growth on the local areas of the province. As the core explanatory variables, population and economic development significantly promoted local urban expansion. The study's findings indicated a positive spatial spillover effect as regards the influence of economic development on the study area's urban growth, whereas the spatial spillover effect of the population was negative. Therefore, economic development was a major driving factor contributing immensely to the expansion of urban areas in Zhejiang Province, especially in the 26 mountainous counties of the province. The study enriches our understanding of the transformation of LULC and the changing dynamics of urban areas in China and provides the necessary research data that are vital for urban land-use planners and decision-makers to overcome the negative consequences of the expansion of urban areas due to the continuous economic growth of China.

An Improved Spatiotemporal Data Fusion Method for Snow-Covered Mountain Areas Using Snow Index and Elevation Information

Remote sensing images with high spatial and temporal resolution in snow-covered areas are important for forecasting avalanches and studying the local weather. However, it is difficult to obtain images with high spatial and temporal resolution by a single sensor due to the limitations of technology and atmospheric conditions. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) can fill in the time-series gap of remote sensing images, and it is widely used in spatiotemporal fusion. However, this method cannot accurately predict the change when there is a change in surface types. For example, a snow-covered surface will be revealed as the snow melts, or the surface will be covered with snow as snow falls. These sudden changes in surface type may not be predicted by this method. Thus, this study develops an improved spatiotemporal method ESTARFM (iESTARFM) for the snow-covered mountain areas in Nepal by introducing NDSI and DEM information to simulate the snow-covered change to improve the accuracy of selecting similar pixels. Firstly, the change in snow cover is simulated according to NDSI and DEM. Then, similar pixels are selected according to the change in snow cover. Finally, NDSI is added to calculate the weights to predict the pixels at the target time. Experimental results show that iESTARFM can reduce the bright abnormal patches in the land area compared to ESTARFM. For spectral accuracy, iESTARFM performs better than ESTARFM with the root mean square error (RMSE) being reduced by 0.017, the correlation coefficient (r) being increased by 0.013, and the Structural Similarity Index Measure (SSIM) being increased by 0.013. For spatial accuracy, iESTARFM can generate clearer textures, with Robert's edge (Edge) being reduced by 0.026. These results indicate that iESTARFM can obtain higher prediction results and maintain more spatial details, which can be used to generate dense time series images for snow-covered mountain areas.

Suitability evaluation of carrying capacity and utilization patterns on tidal flats of Bohai Rim in China

Tidal flats in the Bohai Rim are facing threats from human activities. Quantifying the carrying capacity and suitability of tidal flats is of great significance to the regional environment and resource management. In this study, the existing social and natural data were collected and the natural conditions of tidal flats, e.g., the distributions and utilization patterns, were investigated through remote sensing image interpretation and field investigation in the Bohai Rim. Then, a multi-index evaluation system was developed with indexes organized under the framework of the analytic hierarchy process (AHP) and the Drivers-State-Impact (DSI) framework, processed by fuzzy evaluation, and weighted by the entropy method. The studies show that the rapid expansion of industry-port-town, salt pans or aquafarms in the Bohai Rim during 1990-2020 squeezed the space of tidal flats. Despite the limitation of the declining resource condition, the carrying capacity of tidal flats in the Bohai Rim increased slightly during 2000-2018 because of the great improvement in economic and ecological conditions. We estimate 59.93% of the land resources are suitable for economic development while others are temporarily unsuitable for reclamation due to their high ecological importance. The land use data and macro-evaluation system of tidal flat utilization patterns herein can provide references for coastal resource management and ecological restoration.

Forest expansion dominates China’s land carbon sink since 1980

Carbon budget accounting relies heavily on Food and Agriculture Organization land-use data reported by governments. Here we develop a new land-use and cover-change database for China, finding that differing historical survey methods biased China’s reported data causing large errors in Food and Agriculture Organization databases. Land ecosystem model simulations driven with the new data reveal a strong carbon sink of 8.9 ± 0.8 Pg carbon from 1980 to 2019 in China, which was not captured in Food and Agriculture Organization data-based estimations due to biased land-use and cover-change signals. The land-use and cover-change in China, characterized by a rapid forest expansion from 1980 to 2019, contributed to nearly 44% of the national terrestrial carbon sink. In contrast, climate changes (22.3%), increasing nitrogen deposition (12.9%), and rising carbon dioxide (8.1%) are less important contributors. This indicates that previous studies have greatly underestimated the impact of land-use and cover-change on the terrestrial carbon balance of China. This study underlines the importance of reliable land-use and cover-change databases in global carbon budget accounting.

The impact of land-use and cover-change (LUCC) on ecosystem carbon stock in China is poorly known due to large biases in existing databases. Here the authors develop a new LUCC database with corrected false signals and reveal that forest expansion is the dominant driver of China’s recent carbon sink.

Self-Trained Deep Forest with Limited Samples for Urban Impervious Surface Area Extraction in Arid Area Using Multispectral and PolSAR Imageries

Impervious surface area (ISA) has been recognized as a significant indicator for evaluating levels of urbanization and the quality of urban ecological environments. ISA extraction methods based on supervised classification usually rely on a large number of manually labeled samples, the production of which is a time-consuming and labor-intensive task. Furthermore, in arid areas, man-made objects are easily confused with bare land due to similar spectral responses. To tackle these issues, a self-trained deep-forest (STDF)-based ISA extraction method is proposed which exploits the complementary information contained in multispectral and polarimetric synthetic aperture radar (PolSAR) images using limited numbers of samples. In detail, this method consists of three major steps. First, multi-features, including spectral, spatial and polarimetric features, are extracted from Sentinel-2 multispectral and Chinese GaoFen-3 (GF-3) PolSAR images; secondly, a deep forest (DF) model is trained in a self-training manner using a limited number of samples for ISA extraction; finally, ISAs (in this case, in three major cities located in Central Asia) are extracted and comparatively evaluated. The experimental results from the study areas of Bishkek, Tashkent and Nursultan demonstrate the effectiveness of the proposed method, with an overall accuracy (OA) above 95% and a Kappa coefficient above 0.90.

Human expansion into Asian highlands in the 21st Century and its effects

Most intensive human activities occur in lowlands. However, sporadic reports indicate that human activities are expanding in some Asian highlands. Here we investigate the expansions of human activities in highlands and their effects over Asia from 2000 to 2020 by combining earth observation data and socioeconomic data. We find that ∼23% of human activity expansions occur in Asian highlands and ∼76% of these expansions in highlands comes from ecological lands, reaching 95% in Southeast Asia. The expansions of human activities in highlands intensify habitat fragmentation and result in large ecological costs in low and lower-middle income countries, and they also support Asian developments. We estimate that cultivated land net growth in the Asian highlands contributed approximately 54% in preventing the net loss of the total cultivated land. Moreover, the growth of highland artificial surfaces may provide living and working spaces for ∼40 million people. Our findings suggest that highland developments hold dual effects and provide new insight for regional sustainable developments.

Most of the intensive human activities usually occur in lowlands. Here the authors report that human activity expansions also were widely distributed in Asian highlands in the 21st century and held dual effects, which provides new insights for regional human activity expansions.

Urban warming increases the temperature sensitivity of spring vegetation phenology at 292 cities across China

Urban spring phenology changes governed by multiple biological and environmental factors significantly impact urban ecosystem functions and services. However, the temporal changes in spring phenology (i.e., the start of the vegetation growing season, SOS) and the magnitude of SOS sensitivity to temperature in urban settings are not well understood compared with natural ecosystems. Therefore, we explored warming impacts on SOS across 292 rural and urban areas from 2001 to 2016. We found that warming occurred in 79.9% of urban areas and 61.3% of rural areas. This warming advanced SOS in 78.3% of the urban settings and 72.8% of the rural areas. The accelerated rate of SOS in urban settings was significantly higher (-0.52 ± 0.86 days/year) than in rural areas (-0.09 ± 0.69 days/year). Moreover, SOS was significantly more sensitive to warming in urban areas (-2.86 ± 3.57 days/°C) than in rural areas (-1.57 ± 3.09 days/°C), driven by urban-rural differences in climatic (precipitation, temperature, and warming speed) and vegetation factors. Precipitation contributed the most had the highest relative importance for controlling SOS, at 45% and 63% for urban and rural areas, respectively. These findings provide a new understanding of the impacts of urbanization and climate change on vegetation phenology. Moreover, our results have implications for urban environment impacts on ecosystems and human health.

Evolution Characteristics and Formation Mechanism of Production-Living-Ecological Space in China: Perspective of Main Function Zones

The main function zone (MFZ) is the major strategy of China's economic development and ecological environment protection. Clarifying the logical relationship between "MFZ strategy" and "territorial spatial layout" is vital to construct regional economic layout and territorial spatial supporting system of high-quality development. However, few studies have revealed the evolution process and formation mechanism of the production-living-ecological space (PLES) structure of China's MFZ over a long period of time. To bridge the gap, based on the land use dataset in China from 1980 to 2020, this study analyzed the evolution patterns of PLES in China's MFZs using multiple methods and measured the formation mechanism of PLES in different types of MFZs with the GeoDetector model. Results showed that the spatial structure of China's national territory has evolved drastically in the past 40 years, showing significant horizontal regional differentiation and vertical gradient differentiation. Ecological space has been continuously decreasing, while production space and living space have been continuously increasing, and the evolution of PLES varied significantly in different MFZs. During the study period, the gravity center of PLES in China all moved westward. The spatial distribution pattern of production space and living space was from northeast to southwest, and the ecological space was from east to west. The evolution of China's territorial spatial structure was subject to the combined effects of natural and socio-economic factors, exhibiting significant differences in different MFZs. Land use intensity had the most prominent influence on the formation of PLES, followed by elevation. The influences of different factors on PLES structure were strengthened mainly through two types of nonlinear enhancement and dual-factor enhancement. This study can provide scientific support for the optimal management and high-quality development of territorial space in China.

Urban Sustainability: Integrating Socioeconomic and Environmental Data for Multi-Objective Assessment

The large concentration of the world’s population in cities, along with rapid urbanization, have brought numerous environmental and socioeconomic challenges to sustainable urban systems (SUS). However, current SUS studies focus heavily on ecological aspects, rely on SUS indicators that are not supported by available data, lack comprehensive analytical frameworks, and neglect SUS regional differences. This paper develops a novel approach to assessing urban sustainability from regional perspectives using commonly enumerated socioeconomic statistics. It integrates land use and land cover change data and ecosystem service values, applies data mining analytics to derive SUS indicators, and evaluates SUS states as trade-offs among relevant SUS indicators. This synthetic approach is called the integrated socioeconomic and land-use data mining–based multi-objective assessment (ISL-DM-MOA). The paper presents a case study of urban sustainability development in cities and counties in Inner Mongolia, China, which face many environmental and sustainable development problems. The case study identifies two SUS types: (1) several large cities that boast well-developed economies, diversified industrial sectors, vital transportation locations, good living conditions, and cleaner environments; and (2) a few small counties that have a small population, small urban construction areas, extensive natural grasslands, and primary grazing economies. The ISL-DM-MOA framework innovatively synthesizes currently available socioeconomic statistics and environmental data as a unified dataset to assess urban sustainability as a total socio-environmental system. ISL-DM-MOA deviates from the current indicator approach and advocates the notion of a data-mining-driven approach to derive urban sustainability dimensions. Furthermore, ISL-DM-MOA diverges from the concept of a composite score for determining urban sustainability. Instead, it promotes the concept of Pareto Front as a choice set of sustainability candidates, because sustainability varies among nations, regions, and locations and differs between political, economic, environmental, and cultural systems.

Annual Maps of Built-Up Land in Guangdong from 1991 to 2020 Based on Landsat Images, Phenology, Deep Learning Algorithms, and Google Earth Engine

Accurate mapping of built-up land is essential for urbanization monitoring and ecosystem research. At present, remote sensing is one of the primary means used for real-time and accurate surveying and mapping of built-up land, due to the long time series and multi-information advantages of existing remote sensing images and the ability to obtain highly precise year-by-year built-up land maps. In this study, we obtained feature-enhanced data regarding built-up land from Landsat images and phenology-based algorithms and proposed a method that combines the use of the Google Earth Engine (GEE) and deep learning approaches. The Res-UNet++ structural model was improved for built-up land mapping in Guangdong from 1991 to 2020. Experiments show that overall accuracy of built-up land map in the study area in 2020 was 0.99, the kappa coefficient was 0.96, user accuracy of built-up land was 0.98, and producer accuracy was 0.901. The trained model can be applied to other years with good results. The overall accuracy (OA) of the assessment results every five years was above 0.97, and the kappa coefficient was above 0.90. From 1991 to 2020, built-up land in Guangdong has expanded significantly, the area of built-up land has increased by 71%, and the proportion of built-up land has increased by 3.91%. Our findings indicate that the combined approach of GEE and deep learning algorithms can be developed into a large-scale, long time-series of remote sensing classification techniques framework that can be useful for future land-use mapping research.

Analysis of the Spatio-Temporal Characteristics of Nanjing’s Urban Expansion and Its Driving Mechanisms

The expansion and evolution of urban areas are the most perceptible manifestations of the transformation of the urban spatial form. This study uses remote sensing images of Nanjing from 2001, 2006, 2011, 2016, and 2021, along with socio-economic data to analyse the spatio-temporal characteristics of the city’s urban expansion. Furthermore, we utilize a binary logistic regression to quantitatively analyse the driving forces in each stage. We find that from 2001 to 2021, Nanjing’s urban area expanded approximately 3.97 times. Notably, the city started moving from a stage of medium-speed development to rapid development in 2006, and then slowed down and returned to medium-speed development in 2011. The urban land mainly expanded in the north, northeast, southeast, and southwest directions in a lopsided cross-shape roughly along the northwest-southeast direction; meanwhile, the city’s centre of gravity continuously moved towards the southeast. Among the driving factors, neighbourhood (distance from planned commercial centres, railways, and highways), topography, and geolocation (distance from the Yangtze River, and elevation) had a greater, albeit inhibitory effect on urban expansion. However, the effects of different socio-economic factors (GDP per capita, resident population, secondary and tertiary industry, etc.) varied across different time periods.

Modeling the impacts of land use/land cover change on meteorology and air quality during 2000-2018 in the Yangtze River Delta region, China

The land use/land cover (LULC) change in the fast-developing city clusters of China exhibits impacts on both the meteorology and air quality. However, this effect, especially in the Yangtze River Delta (YRD), has not been well quantified. In this study, the LULC data are extracted from Landsat satellite imageries for year 2000 and 2018 for the YRD region. The Weather Research and Forecasting with Chemistry (WRF/Chem) model is applied to investigate the impact of historical LULC change on regional meteorology and air pollution over the YRD region during the past two decades. Two simulation scenarios are performed with two sets of LULC data to represent the pre-urbanization (LULC of year 2000) and the most recent urban pattern (LULC of year 2018). Results indicate that rapid urbanization leads to an increase of monthly mean 2-m temperature by 0.4-2.1 °C but decrease of the 10-m wind speed by 0.5-1.3 m/s in urban areas; the maximum increase of daytime planetary boundary layer height (PBLH) in July and November is 289 and 132 m, respectively. Affected by favorable changes in the meteorological conditions due to LULC change, the PM_2.5 concentrations in most urban areas show a decreasing trend, especially during the nighttime in summer. On the contrary, surface ozone (O₃) concentration in urban areas has increased by 7.2-9.8 ppb in summer and 1.9-2.1 ppb in winter. Changes in O₃ concentration are inversely proportional to changes in NO_x and the spatial distribution of PM_2.5. Areas with higher O₃ concentration are consistent with areas of higher temperature and lower wind speed. Our findings reveal that LULC changes during the past years bring observable changes in air pollutant concentrations, which should not be neglected in the YRD region regarding air quality trends as well as policy evaluations under the warming threat.

Characteristics of Changes in Urban Land Use and Efficiency Evaluation in the Qinghai–Tibet Plateau from 1990 to 2020

The Qinghai–Tibet Plateau has seen decades of changes in land use/cover as a result of urbanization and regional planning policy. Research on the efficiency of social development aids in the pursuit of social and environmental sustainability. Based on CLUD and socioeconomic statistical data, this study systematically analyses the spatiotemporal evolution characteristics of urban land use in the Qinghai–Tibet Plateau and evaluates its social development efficiency from three perspectives—the holistic, the municipal, and urban hierarchy—by using indicators such as the Moran index, land use efficiency, and urban expansion speed and proportion. Results show that the urbanization rate climbed from 21.26% to 54.95%, and the area of urban lands increased from 201.93 km2 to 796.59 km2 from 1990 to 2020, with urban lands expanding from the Lanzhou–Xining City Area to the central and south of the Qinghai–Tibet Plateau. The holistic urban land use efficiency grew from 1.14 to 1.53, while the UPD decreased slightly from 1.44 to 1.31, and the UED increased steadily from 1.40 to 12.97 per decade. Moreover, we should pay attention to the rational allocation of land in human, social and ecosystem terms to comprehensively improve the quality of urbanization across the plateau.

Impact of Urbanization on Seismic Risk: A Study Based on Remote Sensing Data

The management of seismic risk is an important aspect of social development. However, urbanization has led to an increase in disaster-bearing bodies, making it more difficult to reduce seismic risk. To understand the changes in seismic risk associated with urbanization and then adjust the risk management strategy, remote-sensing technology is necessary. By identifying the types of earthquake-bearing bodies, it is possible to estimate the seismic risk and then determine the changes. For this purpose, this study proposes a set of algorithms that combine deep-learning models with object-oriented image classification and extract building information using multisource remote sensing data. Following this, the area of the building is estimated, the vulnerability is determined, and, lastly, the economic and social impacts of an earthquake are determined based on the corresponding ground motion level and fragility function. Our study contributes to the understanding of changes in seismic risk caused by urbanization processes and offers a practical reference for updating seismic risk management, as well as a methodological framework to evaluate the effectiveness of seismic policies. Experimental results indicate that the proposed model is capable of effectively capturing buildings’ information. Through verification, the overall accuracy of the classification of vulnerability types reaches 86.77%. Furthermore, this study calculates social and economic losses of the core area of Tianjin Baodi District in 2011, 2012, 2014, 2016, 2018, 2020, and 2021, obtaining changes in seismic risk in the study area. The result shows that for rare earthquakes at night, although the death rate decreased from 2.29% to 0.66%, the possible death toll seems unchanged, due to the increase in population.

Characterizing the Patterns and Trends of Urban Growth in Saudi Arabia’s 13 Capital Cities Using a Landsat Time Series

Development and a growing population in Saudi Arabia have led to a substantial increase in the size of its urban areas. This sustained development has increased policymakers’ need for reliable data and analysis regarding the patterns and trends of urban expansion throughout the country. Although previous studies on urban growth in Saudi cities exist, there has been no comprehensive research that focused on all 13 regional capitals within the country. Our study addressed this gap by producing a new annual long-term dataset of 30 m spatial resolution that covered 35 years (1985–2019) and maintained a high overall accuracy of annual classifications across the study period, ranging between 93 and 98%. Utilizing the continuous change detection and classification (CCDC) algorithm and all available Landsat data, we classified Landsat pixels into urban and non-urban classes with an annual frequency and quantified urban land cover change over these 35 years. We implemented a stratified random sampling design to assess the accuracy of the annual classifications and the multi-temporal urban change. The results revealed that Saudi capitals experienced massive urban growth, from 1305.28 ± 348.71 km2 in 1985 to 2704.94 ± 554.04 km2 in 2019 (± values represent the 95% confidence intervals). In addition to the high accuracy of the annual classifications, the overall accuracy of the multi-temporal urban change map was also high and reached 91%. The urban expansion patterns varied from city to city and from year to year. Most capital cities showed clear growth patterns of edge development, that is, a continuous expansion of built-up lands radiating from existing urban areas. This study provides distinct insights into the urban expansion characteristics of each city in Saudi Arabia and a synoptic view of the country as a whole over the past four decades. Our results provided a dataset that can be used as the foundation for future socioeconomic and environmental studies.

Spatiotemporal Dynamics of Ecosystem Services and the Driving Factors in Urban Agglomerations: Evidence From 12 National Urban Agglomerations in China

The natural environment provides multiple ecosystem services for urban development and human quality of life. Given that current cities interact with each other and form urban agglomerations, understanding the spatiotemporal changes in ecosystem services and the driving forces is crucial for sustainable urban development. Using 12 national-level urban agglomerations as a case study, this paper quantifies the spatial patterns of multiple ecosystem service values from 2000 to 2015 and assesses how natural and socioeconomic factors contribute to such changes by using ordinary least squares (OLS) and geographically weighted regression (GWR). The results show the following: (1) spatial discrepancies of ecosystem services exist both in and between urban agglomerations, and ecosystem service values are reduced in more than 70% of urban agglomerations at a rate ranging from 0.02 to 4.27%; (2) elevation, precipitation, and fraction of woodland have positive impacts on ecosystem service values in urban agglomerations; while gross domestic product (GDP), population, and proportion of built-up area have negative effects; (3) both natural and social driving factors impact the ecosystem services of different urban agglomeration in different ways, according to the differences in their driving degrees. We categorized 12 urban agglomerations in China into six typical types: natural-factor dominated, socioeconomic-factor dominated, policy dominated, balanced, natural-factor inclined, and socioeconomic-factor inclined. Our results can be used to inform decision makers and urban planners to propose explicit location strategies to balance natural protection and socioeconomic development and ultimately promote sustainable urbanization across the nation.

Contrasting influences of biogeophysical and biogeochemical impacts of historical land use on global economic inequality

Climate change has significant implications for macro-economic growth. The impacts of greenhouse gases and anthropogenic aerosols on economies via altered annual mean temperature (AMT) have been studied. However, the economic impact of land-use and land-cover change (LULCC) is still unknown because it has both biogeochemical and biogeophysical impacts on temperature and the latter differs in latitudes and disturbed land surface types. In this work, based on multi-model simulations from the Coupled Model Intercomparison Project Phase 6, contrasting influences of biogeochemical and biogeophysical impacts of historical (1850–2014) LULCC on economies are found. Their combined effects on AMT result in warming in most countries, which harms developing economies in warm climates but benefits developed economies in cold climates. Thus, global economic inequality is increased. Besides the increased AMT by the combined effects, day-to-day temperature variability is enhanced in developing economies but reduced in developed economies, which further deteriorates global economic inequality.

Historical land use impacts climate by biogeophysical and biogeochemical effects. Their combined effects on mean and extreme temperature may harm economically disadvantaged countries but benefit those in rich countries, raising questions of equality.

Impacts of Human Activities and Climate Change on Water Storage Changes in Shandong Province, China

The over-exploitation of water resources causes water resource depletion, which threatens water security, human life, and social and economic development. Only by clarifying the spatial pattern, changing trends, and influencing factors of water storage can we promote the rational development of water resources and relieve the pressure on water resources. However, there is still a lack of research on these aspects. In this study, the water-scarce area in Shandong Province, China, was selected to quantify the spatial and temporal changes in the terrestrial water storage (TWS) and groundwater storage (GWS) over the past 30 years. Nighttime light data were used to characterize the urbanization level (UL) and explore the effects of human activities (i.e., UL) and climate change (temperature and precipitation) on the TWS and GWS. The results show that 1) from 1990 to 2018, the overall TWS exhibited a significant decreasing trend (- 0.084 cm yr^-1). The change trend of the GWS was consistent with that of the TWS (- 0.516 m³ yr^-1). Spatially, there was significant spatial heterogeneity in the trend of the TWS and GWS. At the grid and prefectural scales, the TWS mainly exhibited a downward trend in the central and western regions, and an upward trend in the eastern region of Shandong Province. For the GWS, all cities exhibited a decreasing trend at the prefectural scale, whereas 92% of the regions exhibited a decreasing trend with less spatial heterogeneity at the grid scale. 2) Precipitation was the mean factor controlling the total amount of TWS and GWS in Shandong Province. Precipitation and temperature positively affected water storage, and the UL negatively affected it. At the prefectural scale, except for a few cities which were greatly influenced by the UL, the dominant factor of the TWS and GWS was precipitation in the other cities. At the grid scale, for the TWS, precipitation was the predominant factor in 51.82% of the entire region, followed by the UL (44.14%) and temperature (4.04%). For the GWS, precipitation was the predominant factor in 55.73% of the area, and the other 44.27% of the area was mainly influenced by the UL. Overall, precipitation and the UL were the key factors affecting the TWS and GWS. The results of this study provide a theoretical and decision-making basis for the optimal allocation and sustainable use of regional water resources.

A global record of annual terrestrial Human Footprint dataset from 2000 to 2018

Human Footprint, the pressure imposed on the eco-environment by changing ecological processes and natural landscapes, is raising worldwide concerns on biodiversity and ecological conservation. Due to the lack of spatiotemporally consistent datasets of Human Footprint over a long temporal span, many relevant studies on this topic have been limited. Here, we mapped the annual dynamics of the global Human Footprint from 2000 to 2018 using eight variables that reflect different aspects of human pressures. The accuracy assessment revealed a good agreement between our mapped results and the previously developed datasets in different years. We found more than two million km² of wilderness (i.e., regions with Human Footprint values below one) were lost over the past two decades. The biome dominated by mangroves experienced the most significant loss (i.e., above 5%) of wilderness, likely attributed to intensified human activities in coastal areas. The derived annual and spatiotemporally consistent global Human Footprint can be a fundamental dataset for many relevant studies about human activities and natural resources.

Measurement(s)	human footprint
Technology Type(s)	remote sensing
Factor Type(s)	Built Environment • Population Density • Nighttime lights • Croplands • Pastures • Roads • Railways • Navigable waterways

Changes in Long-Term PM2.5 Pollution in the Urban and Suburban Areas of China’s Three Largest Urban Agglomerations from 2000 to 2020

Particulate matter (PM2.5) is a significant public health concern in China, and the Chinese government has implemented a series of laws, policies, regulations, and standards to improve air quality. This study documents the changes in PM2.5 and evaluates the effects of industrial transformation and clean air policies on PM2.5 levels in urban and suburban areas of China’s three largest urban agglomerations, Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) based on a new degree of urbanization classification method. We used high-resolution PM2.5 concentration and population datasets to quantify the differences in PM2.5 concentrations in urban and suburban areas of these three urban agglomerations. From 2000 to 2020, the urban areas have expanded while the suburban areas have shrunk. PM2.5 concentrations in urban areas were approximately 32, 10, and 7 μg/m3 higher than those in suburban areas from 2000 to 2020 in BTH, YRD, and PRD, respectively. Since 2013, the PM2.5 concentrations in the urban regions of BTH, YRD, and PRD have declined at average annual rates of 7.30, 5.50, and 5.03 μg/m3/year, respectively, while PM2.5 concentrations in suburban areas have declined at average annual rates of 3.11, 4.23 and 4.69 μg/m3/year, respectively. By 2018, all of the urban and suburban areas of BTH, YRD, and PRD satisfied their specific targets in the Air Pollution and Control Action Plan. By 2020, the PM2.5 declines of BTH, YRD, and PRD exceeded the targets by two, three, and four times, respectively. However, the PM2.5 exposure risks in urban areas are 10–20 times higher than those in suburban areas. China will need to implement more robust air pollution mitigation policies to achieve the World Health Organization’s Air Quality Guideline (WHO-AQG) and reduce long-term PM2.5 exposure health risks.

Urban Expansion in China: Spatiotemporal Dynamics and Determinants

China’s urbanization has attracted many scholars’ attention due to its significant impact on socioeconomic sustainability. Many studies have explored the spatial pattern and effects of the factors influencing urban expansion. However, the spatiotemporal dynamics integrating spatial and temporal dimensions and the spatial scales of the influencing factors are always ignored. This study applied the framework of exploratory space–time data analysis (ESTDA) to investigate the spatiotemporal dynamics of urban expansion across 342 cities in China from 1990 to 2017 and, further, used multiscale geographically weighted regression (MGWR) to estimate the effects of influencing factors on urban expansion. We found that urban expansion had an obvious south–north division, and yet the effects of influencing factors usually showed an east–west division. We also found that the dynamic local spatial dependency of urban expansion was accompanied by a volatile coevolution process and inclined to transfer from heterogeneity to homogeneity, and homogeneity tended to be stable. The coevolution of urban expansion between cities and other neighboring ones became stronger with increases in time and regional integration. These findings support the use of customized urban planning for specific regions in different spatial dependence to improve land-use efficiency and coordinate regional development.

Historical records of trace metals in two sediment cores of Jiaozhou Bay, north China

To investigate the long-term effects of urbanization and industrialization on coastal trace metal contamination, two sediment cores, Q21 (representing 1965-2018) and Q23 (representing 1986-2018), collected from the adjacent coasts of the east old town and west new area of Qingdao were analyzed. Although the concentrations of As, Cd, Co, Cr, Cu, Ni, Pb, Sc, and Zn were higher in Core Q21, the increasing trends in their concentrations and contamination levels were more obvious in Q23, especially since the 2000s. Moreover, the urbanization rates of the new area (1978-2017) were significantly positively correlated with the historical metal concentrations in Q23. Affected by the rapid socio-economic development in the new area, the combined excessive concentrations of the eight metals (excluding Sc) increased faster in Q23 (0.14-78.4 mg/kg) than Q21 (0.58-45.3 mg/kg). Overall, the sediment Core Q23 experienced higher trace metal contamination and ecological risks than Core Q21.

Hierarchical Urban Land Mappings and Their Distribution with Physical Medium Environments Using Time Series of Land Resource Images in Beijing, China (1981–2021)

Rapid urban expansion and structural changes are taking place in China’s capital city, Beijing, but without an update of urban land features in a timely manner our understanding of the new urban heterogeneity is restricted, as land-background data is indispensable for bio-geophysical and bio-geochemical processes. In this plain region, the investigations of multi-scale urban land mappings and physical medium environmental elements such as slope, aspect, and water resource services are still lacking, although Beijing can provide an exemplary case for urban development and natural environments in plains considering the strategic function of China’s capital city. To elucidate these issues, a remote-sensing methodology of hierarchical urban land mapping was established to obtain the urban land, covering structure and its sub-pixel component with an overall accuracy of over 90.60%. During 1981–2021, intense and sustained urban land expansion increased from 467.13 km2 to 2581.05 km2 in Beijing, along with a total growth rate of 452.53%. For intra-urban land structures, a sharp growth rate of over 650.00% (i.e., +1649.54 km2) occurred in terms of impervious surface area (ISA), but a greening city was still evidently observed, with a vegetation-coverage rate of 8.43% and 28.42% in old and newly expanded urban regions, respectively, with a more integrative urban ecological landscape (Shannon’s Diversity Index (SHDI) = −0.164, Patch Density (PD) = −8.305). We also observed a lower rate of ISA (0.637 vs. 0.659) and a higher rate of vegetation cover (0.284 vs. 0.211) in new compared to old urban regions, displaying a higher quality of life during urban expansion. Furthermore, the dominant aspect of low, medium, and high density ISA was captured with the north–south orientation, considering the sunlight conditions and traditional house construction customs in North China, Over 92.00% of the ISA was distributed in flat environment regions with a slope of less than 15°. When the water-resource service radius shifted from 0.5 km to 0.5–1 km and 1–2 km, high density vegetation displayed a dependence on water resources. Our results provide a new survey of the evolution of hierarchical urban land mapping during 1981–2021 and reveals the relationship with physical medium environments, providing an important reference for relevant research.

Mapping Residential Vacancies with Multisource Spatiotemporal Data: A Case Study in Beijing

China has undergone rapid urbanization in the past few decades, and it has been accompanied by overdevelopment. Residential vacancies caused by overdevelopment result in a waste of resources and generate greenhouse gases associated with land surface changes. Due to the poor spatial resolution and limited availability of data, previous studies performed analyses at low resolutions at the county scale, thus lacking spatial detail. In addition, they used complicated subjective indicators difficult to apply to cities of various sizes across China. To understand the detailed spatial pattern of residential vacancies in megacities, we designed a more generally applicable approach with multisource high-resolution spatiotemporal data and tested it in Beijing, the capital of China. At first, a statistical regression with features derived from multisource data was used. Then, the predicted values of the regression function were used as standard heat values, and the observed heat value in each unit was divided by the corresponding standard heat value. Next, residential vacancies were estimated by calculating the quantiles of these division results in all analysis units. This approach requires no prior knowledge or complicated indicators and can be easily applied across cities in China, which is beneficial for development planning at the provincial and national levels.