Spatio-temporal coupling analysis and tipping points detection of China's coastal integrated land-human activity-ocean system.

The coastal zone is typically highly developed and its ocean environment is vastly exposed to the onshore activities. Land-based pollution, as the "metabolite" of terrestrial human activities, significantly impacts the ocean environment. Although numerous studies have investigated these effects, few have quantified the interactions among land-human activity-ocean across both spatial and temporal scales. In this study, we have developed a land-human activity-ocean systemic framework integrating the coupling coordination degree model and tipping point to quantify the spatiotemporal dynamic interaction mechanism among the land-based pollution, human activities, and ocean environment in China from 2001 to 2020. Our findings revealed that the overall coupling coordination degree of the China's coastal zone increased by 36.9 % over last two decades. Furthermore, the effect of human activities on China's coastal environment remained within acceptable thresholds, as no universal tipping points for coastal pollution or ocean environment has been found over the 20-year period. Notably, the lag time for algal blooms, the key indicator of ocean environment health, was found to be 0-3 years in response to the land economic development and 0-4 years in response to land-based pollution. Based on the differences in spatiotemporal interactions among land-human activity-ocean system, we employed cluster analysis to categorize China's coastal provinces into four types and to develop appropriate management measures. Quantifying the interaction mechanism within the land-human activity-ocean system could aid decision-makers in creating sustainable coastal development strategies. This enables efficient use of land and ocean resources, supports coastal conservation and restoration efforts, and fosters effective management recommendations to enhance coastal sustainability and resilience.

Sustainably developing global blue carbon for climate change mitigation and economic benefits through international cooperation.

Blue carbon is the carbon storage in vegetated coastal ecosystems such as mangroves, salt marshes, and seagrass. It is gaining global attention as its role in climate change mitigation and local welfare growth. However, a global assessment on the long-term spatiotemporal sustainable development status of blue carbon has not been conducted, and the relations among blue carbon ecosystems, driving forces for climate change mitigation, and socioeconomic interventions for development capacity on a global scale are still unclear. Here, we constructed a blue carbon development index (BCDI), comprising three subsystems: driving force, resource endowment, and development capacity, to assess the sustainable development level of 136 coastal countries' blue carbon over 24 consecutive years and explore the relationship among subsystems. We further propose a cooperation model to explore the feasibility of global blue carbon cooperation and quantify benefit allocation to specific countries. The results showed an upward trend in BCDI scores with variations in regional performance over the past two decades, and we found a positive correlation between development capacity and blue carbon resource endowment. Based on the scenario simulations of global cooperation, we found that coastal countries could improve the global average BCDI score, add 2.96 Mt of annual carbon sequestration, and generate $136.34 million in 2030 under Global Deep Cooperation scenario compared with the Business-As-Usual scenario.

A systematic scoping review of environmental and socio-economic effects of COVID-19 on the global ocean-human system.

The global outbreak of the coronavirus disease 2019 (COVID-19) has strongly affected human lives. The restrictions taken to slow down the spread of the virus impact socio-economic activities and the environment. A comprehensive review of these COVID-19 impacts on the ocean-human system is lacking. The current study fills this gap by synthesizing the environmental and socio-economic effects of the COVID-19 pandemic on the global ocean by conducting a systemic scoping review of 92 published articles. From a geospatial perspective, the studies covered a total of 37 countries, mainly from Asia, Europe, and North America, with a particular focus on the Indian Ocean and the Mediterranean Sea. From an environmental perspective, both positive and negative effects on global oceans were summarized. Notably, improved coastal water quality and reduced underwater noise were reported. On the other hand, the increasing COVID-19-related medical waste such as personal protective equipment leads to severe pollution, which threatens the marine ecosystem and wildlife. From a socioeconomic perspective, the impacts of the pandemic were negative throughout with marine tourism and the fishery industry being severely disrupted. Coastal communities suffered from loss of income, unemployment, inequalities and health problems. The COVID-19 pandemic offers an opportunity for transformation of management and economic practices in order to save our ocean and boost progress towards Sustainable Development Goal 14 (SDG 14). Future research should include other sectors such as marine biodiversity, marine renewable energy, climate change, and blue economy development of Small Island Developing States. Effective policies and strategies across land and ocean around the world need to be developed and implemented to enhance resilience of the human-ocean system and to achieve post-pandemic global sustainable ocean development.

High vulnerability and a big conservation gap: Mapping the vulnerability of coastal scleractinian corals in South China.

Scleractinian corals build the most complex and diverse ecosystems in the ocean with various ecosystem services, yet continue to be degraded by natural and anthropogenic stressors. Despite the rapid decline in scleractinian coral habitats in South China, they are among the least concerning in global coral vulnerability maps. This study developed a rapid assessment approach that combines vulnerability components and species distribution models to map coral vulnerability within a large region based on limited data. The approach contained three aspects including, exposure, habitat suitability, and coral-conservation-based adaptive capacity. The exposure assessment was based on seven indicators, and the habitat suitability was mapped using Maximum Entropy and Random Forest models. Vulnerability of scleractinian corals in South China was spatially evaluated using the approach developed here. The results showed that the average exposure of the study region was 0.62, indicating relatively high pressure. The highest exposure occurred from the east coast of the Leizhou Peninsula to the Pearl River Estuary. Aquaculture and shipping were the most common causes of exposure. Highly suitable habitats for scleractinian corals are concentrated between 18°N-22°N. Only 21.6 % of the potential coral habitats are included in marine protected areas, indicating that there may still be large conservation gaps for scleractinian corals in China. In total, 37.7 % of the potential coral habitats were highly vulnerable, with the highest vulnerability appearing in the Guangdong Province. This study presents the first attempt to map the vulnerability of scleractinian corals along the coast of South China. The proposed approach and findings provide an essential tool and information supporting the sustainable management and conservation of coral reef ecosystems, addressing an important gap on the world's coral reef vulnerability map.

A novel marine spatial management tool for multiple conflicts recognition and optimization of marine functional zoning in the East China sea.

Marine spatial planning (MSP) is to manage incompatible functional use for achieving spatial homogeneity in sea. However most MSP strategies focus on single-target sea use demand ignoring multiple-conflicts of different demands. Thus, this study develops a spatial management model and quantitatively recognizes two types of spatial conflicts among eight sea use functions in the Zhejiang coasts, China. Under the simulation of three different management scenarios including independent, joint and overall-value managements respectively, we further propose a conflict optimization scheme in the scenarios of sea uses with different intensities, different types of sea use combinations, and different site selection. Most importantly, this study demonstrates the spatial management model is a powerful and efficient tool for spatial multiple-conflicts trade-off and matching sea use demands under the practical approach of marine functional zoning (MFC) in China.

The contribution of ocean-based solutions to carbon reduction in China.

Climate change has become a great challenge for humanity. However, the current climate change mitigation measures, primarily concentrate on land, more or less neglecting the vital role of the ocean-based solutions. While the ocean is a crucial regulator of the global climate, ocean-based solutions could also play an essential role in climate change mitigation and policymaking. This paper developed an Ocean-based Solutions Carbon Reduction Assessment Model (OSCRAM) that addresses coastal ecosystems, ocean energy, marine transportation, fishery, and seabed to estimate the oceanic contribution to climate change mitigation. It has been applied to evaluate the capacity of carbon emission reduction through oceans in China. We found that the total contribution for carbon emission reduction was about 6.86 Tg CO2 per year, and it may reach 139.39 Tg in 2030 under the target scenario. The results indicated that the ocean has huge potential to reduce carbon emissions. The development of marine energy and low-carbon marine shipping may have more potential for emission reduction in China, and the government should also protect and restore coastal wetlands for their enormous carbon storage. It can also provide a reference for the globe and other nations in achieving emission reduction goals.

The decade long achievements of China's marine ecological civilization construction (2006-2016).

China needs to balance between current population pressures and a vulnerable marine environment, creating a national, political outline or management strategy dubbed an ecological civilization construction. The nation's effort to protect and maintain a sustainable ocean and address the relevant economic, resource and environmental issues relies on Marine Ecological Civilization (MEC) construction. The quantification of MEC progress is essential to track the management performance and guide the subsequent development and implementation. This study evaluates the performance of China's MEC from 2006 to 2016 based on a comprehensive index system. Our findings are as follows: During 2006-2016, the overall MEC performance score increased from 0.3426 to 0.4850 nationwide. Large space-time variations exist among the eleven coastal regions. The Shandong and Guangdong regions showed relatively good performances, whereas the Jiangsu, Guangxi and Shanghai regions had low scores. A decade long change in MEC scores showed that Hebei achieved the largest increase ratio. Marine management was improved by implementing various conservation strategies by China's government. Marine education and human talent introduction deserve more attention in less developed areas such as Hainan and Guangxi, and poor marine environmental quality was an urgent issue of the Yangtze river estuary economic zone. More accessible marine monitoring dataset are needed to track future space-time progress dynamics towards MEC construction. Our results provide a decade long retrospect of China's MEC achievements, and the quantified evaluation for each coastal region can provide valuable insight to policy-makers.

Estimation of anthropogenic heat emissions in China using Cubist with points-of-interest and multisource remote sensing data.

Rapid urbanization and industrialization in China stimulated the great increase of energy consumption, which leads to drastic rise in the emission of anthropogenic waste heat. Anthropogenic heat emission (AHE) is a crucial component of urban energy budget and has direct implications for investigating urban climate and environment. However, reliable and accurate representation of AHE across China is still lacking. This study presented a new machine learning-based top-down approach to generate a gridded anthropogenic heat flux (AHF) benchmark dataset at 1 km spatial resolution for China in 2010. Cubist models were constructed by fusing points-of-interest (POI) data of varying categories and multisource remote sensing data to explore the nonlinear relationships between various geographic predictors and AHE from different heat sources. The strategy of developing specific models for different components and exploiting the complementary features of POIs and remote sensing data generated a more reasonable distribution of AHF. Results showed that the AHF values in urban centers of metropolises over China range from 60 to 190 W m-2. The highest AHF values were observed in some heavy industrial zones with value up to 415 W m-2. Compared with previous studies, the spatial distribution of AHF from different heating components was effectively distinguished, which highlights the potential of POI data in improving the precision of AHF mapping. The gridded AHF dataset can serve as input of urban numerical models and can help decision makers in targeting extreme heat sources and polluters in cities and making differentiated and tailored strategies for emission mitigation.

Teleconnection between phytoplankton dynamics in north temperate lakes and global climatic oscillation by time-frequency analysis.

We are still facing the knowledge gap of how the water-quality extremes (i.e. phytoplankton blooms), their causes, severity or occurrence could be directly related to the climatic oscillation. Considering that the climatic and phytoplankton concentration time series are highly non-stationary, we applied the advanced time-frequency analysis - Ensemble Empirical Mode Decomposition (EEMD), Hilbert-Huang Spectrum (HHS) and Wavelet Analysis (WA) - to examine the variability of long term phytoplankton dynamics from 1986 to 2014 in five North Temperate Lakes (NTLs). These analysis techniques isolated five separate time series for the surface Chlorophyll a concentrations (CHL) of the five NTLs and a time series for the global climate oscillation (denoted by multivariate ENSO index, MEI), and showed that these time series generally operated at similar time scales. The long-term residual trends of decreasing were found in three lakes (i.e., BM, SP and TR lakes), which are the same to global climate dynamics (MEI). The wavelet analysis reveals strong coherency between MEI and CHL data sets for all lakes, with a periodicity of 64-months. Intuitive associations between the CHL and MEI data set showed that two types of ENSO (El Nino and La Nina) differ in their influences to CHL. Potential mechanisms relating the phytoplankton dynamics in NTLs to climatic oscillation (ENSO) were also discussed.

Heavy metal contamination assessment of surface sediments of the East Zhejiang coastal area during 2012-2015.

This work is the first systematic quantitative analysis of the heavy metal situation along the Zhejiang coastal region focusing on the integrative assessment of the concentrations of seven heavy metals (Cu, Cd, Hg, Zn, Pb, Cr, and As) in surface sediments during the 2012-2015 period. Different heavy metal contamination indices were used for surface sediment quality assessment purposes. The numerical results revealed a noticeable spatial fluctuation of the degree of contamination throughout the region during the four years considered. Higher contamination levels and ecological risks were detected in the southern part of the Zhejiang coastal region. It was found that the Cu, Cd and Hg were the predominant contaminants along the Zhejiang coast with mean regional concentrations varying between 29.1 and 34.2, 0.12-0.17, and 0.044-0.052 mg/kg, respectively. The Cr and Pb exhibited lower contamination levels than the other metals during each one of the years 2012-2015. Stochastic site indicators of heavy metal contamination were used to assess regional uncertainties and obtain useful physical interpretations of the state of contamination of the Zhejiang coast. These indicators can be expressed explicitly in terms of probabilities of heavy metal contamination (either at a global scale or spatially distributed over the coastal region), and therefore they can be considered as risk indicators. It was found that the fraction of the coastal region where excess contamination occurred could never exceed the ratio of the mean heavy metal contamination over the selected threshold. In half of the coast study region, the degree of heavy metal contamination was higher than the median spatial contamination values during the month of August of the years 2012-2015. The spatial means of excess contamination and excess differential contamination increased as the relative area of over-contamination increased. Within the substantially contaminated sub-region of the Zhejiang coast, stronger contamination correlations were observed between locations separated by shorter distances. These correlations were higher when smaller thresholds were considered. As regards the spatial connectivity of the corresponding contamination risks, it was found that 44%, 31%, 39% and 63% of the location pairs in the Zhejiang coast simultaneously experienced moderate risks during the years 2012, 2013, 2014 and 2015, respectively. The ratio of the probability of excess contamination at both locations separated by distances < 20 km over the probability of excess contamination at either one of these two locations was high even for large thresholds, indicating that locations with high contamination are concentrated rather than being dispersed along the Zhejiang coast. Lastly, another interesting finding is that the characterization of the Zhejiang coastal region as over-contaminated is very sensitive to the DC threshold considered, that is, a small increase in the threshold selected can reduce significantly the probability that region is characterized as over-contaminated.