Spatiotemporal variations & driving mechanisms of tourism transport carbon emissions in coastal China

Although coastal marine sediments harbor diverse bacteria with important ecological and environmental functions, a comprehensive view of their community characteristics is still lacking in typical environments along the China coast. We studied the diversity and composition of bacterial community in the sediment of Jiaozhou Bay and characterized their spatiotemporal patterns, aiming to analyze the effects of geographic heterogeneity, seasonal difference, anthropogenic activity, and eutrophication gradient on the bacterial ecology.

Social vulnerability assessment has been recognized as a reliable and effective measure for informing coastal hazard management. Although significant advances have been made in the study of social vulnerability for over two decades, China’s social vulnerability profiles are mainly based on administrative unit. Consequently, no detailed distribution is provided, and the capability to diagnose human risks is hindered. In this study, we established a social vulnerability index (SoVI) in 2000 and 2010 at a spatial resolution of 250 m for China’s coastal zone by combining the potential exposure index (PEI) and social resilience index (SRI). The PEI with a 250 m resolution was obtained by fitting the census data and multisource remote sensing data in random forest model. The county-level SRI was evaluated through principal component analysis based on 33 socioeconomic variables. For identifying the spatiotemporal change, we used global and local Moran’s I to map clusters of SoVI and its percent change in the decade. The results suggest the following: (1) Counties in the Yangtze River Delta, Pearl River Delta, and eastern Guangzhou, except several small hot spots, exhibited the most vulnerability, especially in urban areas, whereas those in Hainan and southern Liaoning presented the least vulnerability. (2) Notable increases were emphasized in Tianjin, Yangtze River Delta, and Pearl River Delta. The spatiotemporal variation and heterogeneity in social vulnerability obtained through this analysis will provide a scientific basis to decision-makers to focus risk mitigation effort.

Spatiotemporal variability and trends of rainfall and temperature in the tropical moist montane ecosystem: Implications to climate-smart agriculture in Geshy watershed, Southwest Ethiopia

Climate change has far-reaching impacts on ecosystems and the frequency and intensity of extreme global climate events have been increasing over the past century; therefore, assessing ecosystem vulnerability to extreme climate change is critical for sustainable and adaptive ecosystem management. As a climatically sensitive region, coastal China is currently experiencing significant environmental changes. To identify how extreme climate affects ecosystem vulnerability, we calculated and analyzed the spatiotemporal variation in extreme climates, net primary productivity (NPP), and spatial characteristics of ecosystem vulnerability to extreme climate change, and discussed the response characteristics of different ecosystems to extreme climate events based on meteorological data and NPP (1986–2015). The results demonstrated that (1) coastal China has become increasingly warmer over the last thirty decades but the precipitation trend is different in the north and south: precipitation increased in the south and decreased in the north. (2) NPP is rising overall, with the forest ecosystem growing the fastest, particularly since 2010. (3) The ecosystem vulnerability of coastal areas in China is mainly classified as mild or non-vulnerable. However, there were apparent differences in the vulnerability of different ecosystems, with dry land and shrub ecosystems having the highest mean vulnerability. (4) The effects of extreme climates on the vulnerability of different ecosystems and ecosystems in different habitats vary. Overall, rising extreme temperatures can significantly increase the ecosystem vulnerability in the coastal areas of China. The paddy field ecosystem was more influenced by extreme temperatures than other ecosystems, with the southern paddy field ecosystem more influenced than the northern paddy field ecosystem. Our study advances the understanding of vegetation dynamics and their driving mechanisms and provides support for scientifically informed ecological management practices in coastal China.

Spatio-temporal variation in the potential ammonia oxidation rates and microbial communities of mangrove wetlands with different sediment textures in South China.

Peat decomposition is progressing in Southeast Asia due to lowered groundwater levels (GWL) caused by drainage. Additionally, droughts during El Niño events significantly lower the GWL, the main environmental factor that controls greenhouse gas (GHG; carbon dioxide (CO 2 ) and methane) emissions in peatlands. Consequently, tropical peatlands have been recognized as a significant source of carbon emissions, and these emissions have been estimated for the region using constant decomposition rates of peat for each land use (Tier 1 emission factors). However, these factors hardly reflect the spatiotemporal variation of the GWL. Furthermore, these estimates do not account for CO 2 uptake through photosynthesis. To reduce uncertainty, we developed a method to estimate spatiotemporal GWL variation from satellite‐derived antecedent precipitation. Using the estimated GWL, we calculated the monthly net ecosystem‐scale GHG emissions from peat forests and managed peatlands using the observed relationship between eddy covariance GHG fluxes and GWL, though carbon losses from deforestation, fires, and fluvial export were not covered in this study. Spatiotemporal variations in GHG emissions across Sumatra, Borneo, and the Malay Peninsula over a decade revealed the following: (a) Peat forests are a net source of CO 2 ‐equivalent GHGs, even when undrained, (b) Decadal mean annual GHG emission rates increase 2.8‐fold when forests are drained and 6.4‐fold when undrained forests are converted to managed peatlands, (c) Droughts increase total annual GHG emissions by 16% across the study area. Additionally, climate models projected precipitation increase in the mid‐21st century, suggesting an increase in GWL and a consequent reduction in peat decomposition.

Wind is one of the most essential clean and renewable energy sources in today’s world. To achieve the goal of carbon emission peak and carbon neutrality in China, it is necessary to evaluate the wind energy resources on the coast of China. Nevertheless, the traditional power law method (PLM) relies on the constant coefficient to estimate the high-altitude wind speed. The constant assumption may lead to significant uncertainties in wind energy assessment, given the large dependence on a variety of factors. To minimize the uncertainties, we here use three machine learning (ML) algorithms to estimate high-altitude wind from surface wind. The radar wind profiler and surface synoptic observations at eight coastal stations from May 2018 to August 2020 are used as key inputs to investigate the wind energy resource. Afterwards, three ML and the PLM are used to retrieve the wind speed at 120 m above ground level (WS120). The comparison results show the random forest (RF) is the most suitable model for the estimation of WS120. As such, the diurnal variation of WS120 and wind power density (WPD) are then evaluated based on the WS120 from RF model. For land stations, the hourly mean WPD is larger at daytime from 0900 to 1600 local solar time (LST) and reach a peak at 1400 LST. This is mainly due to the influence of the prevailing sea breeze. On the contrary, the hourly mean WPD of island stations is relatively large at nighttime during 1800 to 2300 LST. This indicates that the wind energy peaks differ by the land surface types. In terms of the spatial distribution of the seasonal mean WS120 and WPD along the coastal region of China, the WPDs at Qingdao, Dayang, and Dongtou are higher than 200 W/m2 in most seasons, and the WPDs at Dongying, Penglai, Qingdao, and Lianyungang are much greater than at Fuqing and Zhuhai. The result shows that the coastal regions of Bohai Sea and Yellow Sea have more abundant wind resources than those of East China Sea and the South China Sea. These findings obtained here provide insights into the development and utilization of wind energy industry on the coast of China in the future.

<strong class="journal-contentHeaderColor">Abstract.</strong> Accurate estimation of wind speed at wind turbine hub height is of significance for wind energy assessment and exploitation. Nevertheless, the traditional power law method (PLM) generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speed. This inevitably leads to significant uncertainties in estimating the wind speed profile especially under unstable conditions. To minimize the uncertainties, we here use a machine learning algorithm known as random forest (RF) to estimate the wind speed at hub heights such as at 120 m (WS<span class="inline-formula">₁₂₀</span>), 160 m (WS<span class="inline-formula">₁₆₀</span>), and 200 m (WS<span class="inline-formula">₂₀₀</span>). These heights go beyond the traditional wind mast limit of 100–120 m. The radar wind profiler and surface synoptic observations at the Qingdao station from May 2018 to August 2020 are used as key inputs to develop the RF model. A deep analysis of the RF model construction has been performed to ensure its applicability. Afterwards, the RF model and the PLM model are used to retrieve WS<span class="inline-formula">₁₂₀</span>, WS<span class="inline-formula">₁₆₀</span>, and WS<span class="inline-formula">₂₀₀</span>. The comparison analyses from both RF and PLM models are performed against radiosonde wind measurements. At 120 m, the RF model shows a relatively higher correlation coefficient <span class="inline-formula"><i>R</i></span> of 0.93 and a smaller RMSE of 1.09 m s<span class="inline-formula">^−1</span>, compared with the <span class="inline-formula"><i>R</i></span> of 0.89 and RMSE of 1.50 m s<span class="inline-formula">^−1</span> for the PLM. Notably, the metrics used to determine the performance of the model decline sharply with height for the PLM model, as opposed to the stable variation for the RF model. This suggests the RF model exhibits advantages over the traditional PLM model. This is because the RF model considers well the factors such as surface friction and heat transfer. The diurnal and seasonal variations in WS<span class="inline-formula">₁₂₀</span>, WS<span class="inline-formula">₁₆₀</span>, and WS<span class="inline-formula">₂₀₀</span> from RF are then analyzed. The hourly WS<span class="inline-formula">₁₂₀</span> is large during daytime from 09:00 to 16:00 local solar time (LST) and reach a peak at 14:00 LST. The seasonal WS<span class="inline-formula">₁₂₀</span> is large in spring and winter and is low in summer and autumn. The diurnal and seasonal variations in WS<span class="inline-formula">₁₆₀</span> and WS<span class="inline-formula">₂₀₀</span> are similar to those of WS<span class="inline-formula">₁₂₀</span>. Finally, we investigated the absolute percentage error (APE) of wind power density between the RF and PLM models at different heights. In the vertical direction, the APE is gradually increased as the height increases. Overall, the PLM algorithm has some limitations in estimating wind speed at hub height. The RF model, which combines more observations or auxiliary data, is more suitable for the hub-height wind speed estimation. These findings obtained here have great implications for development and utilization in the wind energy industry in the future.

Wind is one of the most essential clean and renewable energy sources in today’s world. To achieve the goal of carbon emission peak and carbon neutrality in China, it is necessary to evaluate the wind energy resources on the coast of China. Nevertheless, the traditional power law method (PLM) relies on the constant coefficient to estimate the high-altitude wind speed. The constant assumption may lead to significant uncertainties in wind energy assessment, given the large dependence on a variety of factors. To minimize the uncertainties, we here use three machine learning (ML) algorithms to estimate high-altitude wind from surface wind. The radar wind profiler and surface synoptic observations at eight coastal stations from May 2018 to August 2020 are used as key inputs to investigate the wind energy resource. Afterwards, three ML and the PLM are used to retrieve the wind speed at 120 m above ground level (WS120). The comparison results show the random forest (RF) is the most suitable model for the estimation of WS120. As such, the diurnal variation of WS120 and wind power density (WPD) are then evaluated based on the WS120 from RF model. For land stations, the hourly mean WPD is larger at daytime from 0900 to 1600 local solar time (LST) and reach a peak at 1400 LST. This is mainly due to the influence of the prevailing sea breeze. On the contrary, the hourly mean WPD of island stations is relatively large at nighttime during 1800 to 2300 LST. This indicates that the wind energy peaks differ by the land surface types. In terms of the spatial distribution of the seasonal mean WS120 and WPD along the coastal region of China, the WPDs at Qingdao, Dayang, and Dongtou are higher than 200 W/m2 in most seasons, and the WPDs at Dongying, Penglai, Qingdao, and Lianyungang are much greater than at Fuqing and Zhuhai. The result shows that the coastal regions of Bohai Sea and Yellow Sea have more abundant wind resources than those of East China Sea and the South China Sea. These findings obtained here provide insights into the development and utilization of wind energy industry on the coast of China in the future.

<strong class="journal-contentHeaderColor">Abstract.</strong> Wind is one of the most essential clean and renewable energy sources in today&rsquo;s world. To achieve the goal of carbon emission peak and carbon neutrality in China, it is necessary to evaluate the wind energy resources on the coast of China. Nevertheless, the traditional power law method (PLM) relies on the constant coefficient to estimate the high-altitude wind speed. The constant assumption may lead to significant uncertainties in wind energy assessment, given the large dependence on a variety of factors. To minimize the uncertainties, we here use three machine learning (ML) algorithms to estimate high-altitude wind from surface wind. The radar wind profiler and surface synoptic observations at eight coastal stations from May 2018 to August 2020 are used as key inputs to investigate the wind energy resource. Afterwards, three ML and the PLM are used to retrieve the wind speed at 120 m above ground level (WS₁₂₀). The comparison results show the random forest (RF) is the most suitable model for the estimation of WS₁₂₀. As such, the diurnal variation of WS₁₂₀ and wind power density (WPD) are then evaluated based on the WS₁₂₀ from RF model. For land stations, the hourly mean WPD is larger at daytime from 0900 to 1600 local solar time (LST) and reach a peak at 1400 LST. This is mainly due to the influence of the prevailing sea breeze. On the contrary, the hourly mean WPD of island stations is relatively large at nighttime during 1800 to 2300 LST. This indicates that the wind energy peaks differ by the land surface types. In terms of the spatial distribution of the seasonal mean WS₁₂₀ and WPD along the coastal region of China, the WPDs at Qingdao, Dayang, and Dongtou are higher than 200 W/m² in most seasons, and the WPDs at Dongying, Penglai, Qingdao, and Lianyungang are much greater than at Fuqing and Zhuhai. The result shows that the coastal regions of Bohai Sea and Yellow Sea have more abundant wind resources than those of East China Sea and the South China Sea. These findings obtained here provide insights into the development and utilization of wind energy industry on the coast of China in the future.

In China, the county is not only an important component of industrial areas and a large contributor of carbon emissions, but also a key administrative unit for the implementation of carbon peak and carbon neutrality goals and policies. The spatiotemporal variations and structural characteristics of carbon emissions at the county scale are of great significance to China's dual goals of regional carbon policy implementation and low carbon spatial planning. Thus, it is important and insightful to conduct an in-depth and detailed examination of these characteristics while focusing on a typical iron and steel industry county-level city in North China. This study systematically calculated the carbon emissions of the county-level city of Wu'an from 2008 to 2017, and explored their structural characteristics and spatiotemporal variations. The results showed that (1) under the influence of macroeconomic and national policies, the carbon emissions of county-level cities dominated by the iron and steel industry show obvious phased characteristics; (2) there is a significant negative correlation between industry carbon emission concentrations and industrial carbon emissions; (3) within the steel industry system, sintering, iron smelting, steelmaking, and metal product processing are the main sources of carbon emissions, and the coal-based production process of the iron and steel industry needs a fundamental reformation; and (4) the carbon emission of Wu'an City shows obvious spatial differentiation characteristics. The geographic distribution of carbon emissions in Wu'an City is very unbalanced and tended to cluster together in urban areas, industrial and mining areas, and major towns. Taking 2014 as the turning point, the spatial pattern of carbon emissions in Wu'an City presents different variation characteristics.

The role of spatiotemporal plant trait variability in model predictions of ecohydrological responses to climate change in a desert shrubland

Mapping contiguous XCO2 by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019

Climate change and China's coastal zones and seas: Impacts, risks, and adaptation

A practical wind farm siting framework integrating ecosystem services — A case study of coastal China