Yangtze River Delta Research Articles

Ecological risk of synthetic phenolic antioxidants: A study based on their spatial distribution in water, sediment, and soil from the Yangtze River Delta, China

Environmental occurrence and risks of novel synthetic phenolic antioxidants (SPAs) remain largely unclear. By using a typical algae (Chlorella pyrenoidosa) as model organism, we evaluated the ecological risks of both traditional and novel SPAs, based on their concentrations in water, sediment, and soil collected from the Yangtze River Delta, China. Detection frequencies (DFs) of 10 novel SPAs were 25–100% in water, 3–100% in sediment, and 0–100% in soil, with geometric means (GMs) of 2700 ng/L, 1270 ng/g, and 2440 ng/g, respectively. For 8 traditional SPAs, DFs were 50–100% (GM: 680 ng/L), 3–100% (534 ng/g), and 47–100% (2240 ng/g) in water, sediment, and soil, respectively. AO3114 was the main pollutant in water, while AO1010 dominated in sediment and soil. Notably, low-molecular-weight SPAs showed migration behavior from sediment to water. Four SPAs (AO626, AO1035, AO1098, and AO1076) showed dose- and time-dependent toxicity on Chlorella pyrenoidosa. As time progressed, sediment-released SPAs became more toxic than those in water. Two SPAs (AO1135 and BHT-Q) posed high risks (RQW > 1) to green algae, daphnia, and fish. The SPA mixture exhibited high risks (RQmix > 1) to these organisms, increasing with the trophic level. This research holds valuable guidance for further SPA risk assessments.

Measurement report: Characteristics of airborne black-carbon-containing particles during the 2021 summer COVID-19 lockdown in a typical Yangtze River Delta city, China

Abstract. Black-carbon-containing (BCc) particles are ubiquitous in ambient air, significantly contributing to particulate matter (PM) pollution. The unexpected outbreak of the COVID-19 pandemic in the summer of 2021 prompted a localized and prolonged lockdown in Yangzhou, situated in the Yangtze River Delta, China. This lockdown led to significant alteration of local anthropogenic emissions, while neighboring cities continued regular operations, providing a unique opportunity for the investigation of BCc particle characteristics influenced by varying emission conditions. Single-particle aerosol mass spectrometer (SPA-MS) analysis revealed a notable decrease in the proportion of freshly emitted BCc particles during the lockdown (LD) period. However, PM2.5 concentrations remained relatively unchanged, with an observed increase in the proportion of aged BCc particles during LD compared to the period before the lockdown (BLD). The study also underscores the significant role of regional transport in PM2.5 pollution during the campaign. Moreover, reactive trace gases (e.g., NOx, SO2, and volatile organic compounds – VOCs) could form thick coatings on pre-existing particles, likely via enhanced heterogeneous hydrolysis under high relative humidity (RH), resulting in significant BCc particle growth (∼ 600 nm), as well as PM2.5 concentration, during LD. Our study highlights that short-term, strict local emission controls may not effectively reduce PM pollution due to the complex production and transmission characteristics of BCc particles and the nonlinear responses of PM2.5 to its precursors. Achieving further effective PM2.5 reduction mandates a focus on nuanced control of BCc particles and necessitates a comprehensive and extensive approach with a regionally coordinated and balanced control strategy through joint regulation.

Assessing the impact of urban road transport development on haze pollution in the Yangtze River Delta region

The aim of this paper is to explore whether and how urban road transport (URT) development affects haze pollution. One of the innovations of this paper is that URT development is measured by road accessibility with novel digital elevation model datasets, which have been used by few scholars. The endogenous problem caused by revere causality issue in the relationship between URT development and haze pollution is also considered. Based on the panel data of prefecture-level cities of Yangtze River Delta (YRD) region in China from 2011 to 2018, this paper uses long-lagged values of URT development as the instrumental variable, employing the two-stage least squares (2SLS) method. The study shows that URT development leads to an increase of haze pollution. Moreover, mechanism tests based on moderating and mediating models support the finding that decreasing haze pollution resulted from better connection effects, while rising agglomeration effects tend to bring about increasing haze pollution, and the latter effect is larger in magnitude than the former. Current URT development may have long-term negative consequences for livability of YRD cities, and urban decision makers should reconsider the effectiveness of the current road transport investment and construction.

Exposure to ultrafine particles, black carbon and particulate matter during commute in a suburb from the Yangtze River Delta, China

This study evaluated the exposure of commuters to particulate matter (PM), black carbon (BC), and ultrafine particulate matter (UFP) concentrations in five different traffic microenvironments (bike, bus, taxi, electric motorbike, and subway) under various conditions, such as regular and dust days, weekdays and weekends, and peak and off-peak commute hours in Nanjing, China. In the morning peak hours, bicycle commuters were exposed to the highest PM concentration, those on buses to the highest BC concentration (3.4 ± 3.0 μg/m3), and those on buses and taxis to the highest UFP number concentration. Throughout the rides, the commuter on the subway had the lowest mean exposure concentration of PM, BC, and UFP number. Bicycle commuters (16.25 μg/km) had the highest total inhaled dose of PM2.5, followed by e-bikers (1.98 μg/km), bus riders (1.28 μg/km), taxi riders (0.69 μg/km), and subway users (0.45 μg/km). Weekday PM2.5 exposure levels (115 μg/m3) and BC exposure levels (1.8 μg/m3) were higher than weekend levels (170% and 158%, respectively). The mean exposure concentration of PM10 (1299 ± 1286 μg/m3) in the evening peak of dust days was 803% and 994% higher than the mean concentrations on weekdays and holidays, respectively. PM2.5, PM10, and BC concentrations on dust days were significantly higher than those on weekdays and holidays. The overall mean ratios of PM1/PM2.5 and PM2.5/PM10 were 0.47 and 0.36, respectively, and the five commuting microenvironments had higher proportions of 2.5–10 μm and 1–2.5 μm particles in PM10 and PM2.5, respectively. Reducing the sources of particulate matter in the corresponding particle size ranges could lower exposure levels.

Research on the coupling coordination of digital economy and urban green development in the Yangtze River Delta of China

Digital economy has become an important engine for promoting green development.The existing literature mainly analyzed the impact of digital economy on green development, but rarely examines the coordinated development relationship between the two. This study contributes to the literature by constructing a research framework for the coupling coordination of digital economy and urban green development, using entropy method, coupling coordination model, and spatial auto-correlation to examine the coordinated development relationship between the two.The results show that:(1) During the research period, the comprehensive level of digital economy and urban green development in the Yangtze River Delta (YRD) both showed an upward and consistent trend. The level of digital economy was significantly lower than that of green development, but the gap between the two continued to narrow. (2) The overall coupling coordination level between digital economy and green development in the 41 cities of YRD was relatively low but showing a continuous upward trend. It has undergone a transition from near imbalance to primary coordination, still with a long way to realize high-quality coordination. (3) The coupling coordination level of digital economy and urban green development in different regions of YRD showed spatial differences, the order from high to low was Shanghai, Zhejiang, Jiangsu and Anhui province respectively. (4) The coupling coordination of digital economy and urban green development in the YRD exhibited significant spatial positive correlation and spatial dependence characteristics. The research results can provide a new perspective to promote the positive interaction between green development and digital economy.

Impacts and threshold effects of urban–rural integration on the transition of arable land use functions

This study examines the impact of urban–rural integration (URI) on the transition of arable land use functions (TALUF), providing both theoretical and empirical insights into how URI can promote TALUF. Utilizing panel data from 41 cities in the Yangtze River Delta (YRD) from 2003 to 2022, this research employs the entropy method, Durbin model, and spatial threshold model to assess the levels of URI and TALUF in the region and examine the direct impact, spatial spillover effects, and threshold effects of URI on TALUF. The findings are: (1) During the study period, both TALUF and URI levels in the YRD showed continuous improvement, although significant spatial imbalances persisted, with regional differentiation gradually diminishing. (2) URI exerts a significant inhibitory effect on TALUF and produces a negative spatial spillover effect. (3) URI exerts a single threshold effect on TALUF: below the economic development threshold of 0.733, URI and its spatial term inhibit TALUF, whereas above this threshold, they promote it. Consequently, fostering high-quality regional economic development, enhancing intercity cooperation, driving agricultural technological innovation, and increasing agricultural input intensity are essential strategies for arable land use policies in the context of URI.

Transportation Infrastructure Upgrading, Border Effect, and Pollution Transfer: Evidence from China's Yangtze River Delta

Transportation Infrastructure Upgrading, Border Effect, and Pollution Transfer: Evidence from China's Yangtze River Delta

Decoupling Analysis between Socio-Economic Growth and Air Pollution in Key Regions of China

The coordinated development of atmospheric pollution and socio-economic growth plays a core role in the sustainable development of cities and regions. The relationship between socio-economic growth and air pollution can be described using decoupling analysis. The seven key regions of China (168 cities), including Beijing–Tianjin–Hebei and its surrounding areas (BTHSR), the Yangtze River Delta region (YRDR), the Fen-Wei Plain (FWP), the Chengdu–Chongqing region (CCR), the urban agglomeration of the middle reaches of the Yangtze River (MLRYR), the Pearl River Delta region (PRDR), and other provincial capitals and municipalities with specialized plans (OPCCSP) were taken as targets to investigate the spatiotemporal evolution characteristics of AQI values and PM2.5 concentrations from 2014 to 2022. Then, the decoupling relationship between the AQI/PM2.5 and the socio-economic growth index (SEGI) in these key regions was deeply researched by the Tapio decoupling model. The main results were as follows: (1) Although the continuous improvement in air quality was observed in these seven key regions in China, the PM2.5 concentration in the BTHSR and FWP was still higher than 35 μg·m−3. The AQI showed a spatial pattern of high in the north and low in the south, and the distribution of PM2.5 in China was high in the east and low in the west. (2) The decoupling degree between air pollution and socio-economic growth was relatively high in the PRDR and YRDR. In contrast, the degree of decoupling was poor in the FWP and OPCCSP. The decoupling states were primarily influenced by industrial structure, energy consumption, and urbanization. (3) The decoupling of air pollution from socio-economic growth was in a strong decoupling state throughout the majority of the study period, achieving a comparatively ideal decoupling state in 2018. However, the overall decoupling states of the seven regions were not sustainable, and the decoupling stability was relatively poor. During the research period, the decoupling state between socio-economic growth and air pollution changed and was unstable.

A Novel Approach for Predicting CO2 Emissions in the Building Industry Using a Hybrid Multi-Strategy Improved Particle Swarm Optimization–Long Short-Term Memory Model

The accurate prediction of carbon dioxide (CO2) emissions in the building industry can provide data support and theoretical insights for sustainable development. This study proposes a hybrid model for predicting CO2 emissions that combines a multi-strategy improved particle swarm optimization (MSPSO) algorithm with a long short-term memory (LSTM) model. Firstly, the particle swarm optimization (PSO) algorithm is enhanced by combining tent chaotic mapping, mutation for the least-fit particles, and a random perturbation strategy. Subsequently, the performance of the MSPSO algorithm is evaluated using a set of 23 internationally recognized test functions. Finally, the predictive performance of the MSPSO-LSTM hybrid model is assessed using data from the building industry in the Yangtze River Delta region as a case study. The results indicate that the coefficient of determination (R2) of the model reaches 0.9677, which is more than 10% higher than that of BP, LSTM, and CNN non-hybrid models and demonstrates significant advantages over PSO-LSTM, GWO-LSTM, and WOA-LSTM hybrid models. Additionally, the mean square error (MSE) of the model is 2445.6866 Mt, and the mean absolute error (MAE) is 4.1010 Mt, both significantly lower than those of the BP, LSTM, and CNN non-hybrid models. Overall, the MSPSO-LSTM hybrid model demonstrates high predictive accuracy for CO2 emissions in the building industry, offering robust support for the sustainable development of the industry.

Explosive growth characteristics of 5.6–560 nm particles and deposition in human respiratory during spring in Yangtze River Delta region, China

Studying the contribution of regional transport to ultrafine particles (UFPs) and the deposition effect of nanoscale particles in human respiratory system is conducive to exploring the impact of atmospheric particles on the environment and human health. Based on the data set of number concentration spectrum in the particle size range of 5.6–560 nm in the spring of Hefei, the Yangtze River Delta region obtained by a fast mobility particle sizer, the explosive growth characteristics, potential source identification and deposition flux analysis of UFPs were systematically studied. The results showed that the frequency of new particle formation (NPF) events during spring was 31.5 %. SO2 and O3 contribute to NPF events. Daytime, higher temperature, stronger solar radiation and lower humidity were more conducive to the explosive growth of UFPs. In addition, regional transport of pollutants from the cities around Hefei played an important role in the accumulation mode particles, which were mainly affected by the land-source air mass from northwest Jiangsu (23.64 %) and the sea-source air mass from the Yellow Sea (23.99 %). It was worth noting that approximately 10,406 ng of UFPs enters the human respiratory system every day. The main deposition area of 5.6–560 nm nanoscale particles was alveolar, 5.6–400 nm is more likely to be deposited on alveolar, while nanoscale particles with particle size between 400 and 560 nm is more likely to be deposited on head airways. This study identified the deposition risk of nanoscale particles in the respiratory system under different particle sizes.

Present and future climate of the Yangtze River Delta region: analysis of the CMIP6 HighResMIP simulations

Present and future climate of the Yangtze River Delta region: analysis of the CMIP6 HighResMIP simulations

Processes and mechanisms of the evolution of container port system in the Yangtze River Delta: An analysis from spatial and network perspectives

the Yangtze River Delta (YRD) has established a large-scale container port system with seaports and Yangtze ports as the mainstay and including other riverports and dry ports, occupying a prominent position in the pattern of national and even the global shipping development. Considering the new trend that the theoretical paradigm of port research has shifted from “spatial” to “network”, and that the focus of research goes from seaports to both seaports and inland ports, this study brings the complexity and uniqueness of the development of the port system in the YRD into the picture. By analyzing the influence of globalization, specialization, marketization, transportation, and greenization on port system development and its pattern formation, this paper elaborates on the gradient process of transportation and services allocation from the coast to the riverside and then to the inland. The evolution of port system is considered to have a temporal gradient between spatial-level transportation expansion and network-level services upgrading, and it is also believed that there is a spatial gradient in logistics supply chain terminalization between coastal and inland areas. Combined with the quantitative analysis from 200 5 to 2020, after entering the stage of port regionalization in the YRD, the container volume is distributed from coastal concentration to inland decentralization at the spatial level, while at the network level, port and shipping service enterprises (PSSEs) maintain a relatively centralized distribution along the coastal areas. At the same time, container volume growth rate fluctuates down, number of PSSEs growth rate fluctuates up, which trend is notable in the seaport. Currently, port regionalization in the YRD is still incomplete; the degree of logistics supply chain terminalization of ports in inland areas is relatively low; and the synergy between spatial and network in the entire YRD region has not yet been formed. This study tries to answer the question of the relationship between different types of ports in the complex port system, which can enrich the theoretical and empirical results of port system research and guide the practice of port and shipping in the YRD.

Climate change scenario simulations for urban flood resilience with system dynamics approach: A case study of smart city shanghai in Yangtze River Delta region

With the rapid advancement of technology, cities are increasingly integrating smart designs to enhance their urban resilience, particularly against natural disasters such as floods. This study employs an integrated approach to simulate the social, economic, and environmental conditions under various climate change scenarios to strengthen urban flood resilience in smart cities. The CMIP5 BCC-CSM 1-1 model, using the RCP4.5 scenario, is utilized to project future climate changes. Shanghai, a highly developed smart city in the Yangtze River Delta, was selected as the case study. Five scenarios are examined through a system dynamics model spanning from 2015 to 2030, encompassing current continuation, economic priority, social priority, natural priority, and coordinated development scenarios. The model is built upon a conceptual framework known as PSR-SENCE (Pressure-State-Response model and Social-Economic-Natural Complex Ecosystem theory). The findings reveal a consistent improvement in resilience within the response dimension facilitated by modern technology. Notably, the coordinated development scenario emerges as the most effective in bolstering urban flood resilience, surpassing scenarios prioritizing economic, social, or natural development individually. Smart cities like Shanghai must prioritize technological innovation alongside enhancing organizational governance to address these challenges effectively.

Research on the correlation network of carbon emissions and economic between Chinese urban agglomerations

Urban agglomerations are currently at the center of China's efforts to reduce carbon emission and sustainable economic development. It is critical to investigate the carbon emissions and economic correlation between urban agglomerations in order to assist their carbon emission reduction and economic coordinated development. This paper is based on an environmentally extended multi-regional input-output model, and studies the embodied carbon emissions and economic two-dimensional correlation network between major urban agglomerations in China from 2012 to 2017. The research results indicate that the production-based carbon emissions of the production and supply of electricity and heat industry in the Yangtze River Delta urban agglomeration were higher than the consumption-based carbon emissions, with a significant gap. However, the construction and service industries were the opposite. The Hohhot-Baotou-Ordos-Yulin urban agglomeration experienced carbon emission losses and economic benefits. The Yangtze River Delta urban agglomeration achieved a win-win situation for carbon emissions and economy. The Pearl River Delta urban agglomeration benefited from carbon emissions but suffered economic losses. The Central Plains urban agglomeration faced a loss-loss situation for carbon emissions and economy. Major urban agglomerations in China should implement differentiated emission reduction measures based on their own characteristics. For example, the Hohhot-Baotou-Ordos-Yulin urban agglomeration should utilize natural resources to develop green industries and high value added industries.

Impacts of 10–30-day atmospheric oscillation on persistent compound heatwaves in the Yangtze River Delta with implications for local electricity demand and supply

Impacts of 10–30-day atmospheric oscillation on persistent compound heatwaves in the Yangtze River Delta with implications for local electricity demand and supply

The dual effects of digital inclusive finance on the urban-rural income gap: An empirical investigation in China's Yangtze River Delta region

The digital development of finance has positive implications for improving income inequality. As one of the mainstream research findings, the view that digital inclusive finance could alleviate the urban-rural income gap has been reaffirmed. Further empirical evidence reveals that digital inclusive finance bridges the urban-rural income gap by enhancing deposit behavior and promoting innovation activities. Compared with large population scale cities, the development of digital inclusive finance in small ones has a more significant reduction effect on the urban-rural income gap.

The Impact of Highway Transportation Infrastructure on Carbon Emissions in the Yangtze River Delta Region

The Impact of Highway Transportation Infrastructure on Carbon Emissions in the Yangtze River Delta Region

Comparison of Urban Heat Island Differences in the Yangtze River Delta Urban Agglomerations Based on Different Urban–Rural Dichotomies

Comparison of Urban Heat Island Differences in the Yangtze River Delta Urban Agglomerations Based on Different Urban–Rural Dichotomies

Mapping high-resolution XCO2 concentrations in China from 2015 to 2020 based on spatiotemporal ensemble learning model

High-resolution column-averaged dry air mole fraction of CO2 (XCO2) data is crucial for understanding the spatiotemporal patterns of XCO2 and for mitigating carbon emissions. Due to the limited scanning range of sensors and strict inversion conditions, satellite-retrieved XCO2 data are often significantly incomplete. Machine learning models are widely used to fill these gaps in satellite XCO2 data. However, the limitations of individual machine learning models and the complexity of the spatial distribution of XCO₂ mean that the accuracy of XCO2 predictions still needs improvement. In this study, a new spatiotemporal stacked ensemble learning model (STEL) was developed by combining random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGBoost), optical gradient boosting (LightGBM), and categorical boosting (CatBoost) using the stacking ensemble learning methodology. Considering the spatiotemporal heterogeneity of XCO2, a novel spatiotemporal weighting feature was constructed as part of the model's input parameters. Finally, the XCO2 observed by Orbiting Carbon Observatory 2 (OCO-2) was reconstructed using STEL, and a monthly mean XCO2 dataset covering China from 2015 to 2020 was generated at a spatial resolution of 0.1°. The results show that STEL exhibits superior performance and generalization capabilities compared to individual machine-learning models. R2 RMSE and MAPE were 0.9624, 1.0023 ppm, and 0.1583 % on the test set, and 0.8970, 1.4213 ppm, and 0.2475 % for R2, RMSE, and MAPE in ground validation, respectively. In 10-fold cross-validation, STEL's RMSE was reduced by 9.52 % compared to the best-performing single model (RF). The spatiotemporal trend of CO2 in China from 2015 to 2020 was analyzed using STEL XCO2 data. The results indicate that this dataset accurately reflects the spatiotemporal heterogeneity of XCO2 distribution at a fine scale. Overall, XCO2 exhibited a spatiotemporal pattern of “high in the east and low in the west” and “high in spring and low in summer.” Except in summer, high XCO₂ values were mainly distributed in the North China Plain. XCO2 trends and hotspots showed considerable spatial variation. The Pearl River Delta and Yangtze River Delta urban agglomerations have the fastest XCO2 growth rates, and the distribution of XCO2 hotspots is consistent with the distribution of population and economic centers. In the sparsely populated northwest of China, XCO2 is growing rapidly due to increased thermal power generation and coal mining. XCO2 hotspots in Northwest China are mainly located in Xinjiang, Ningxia, and Inner Mongolia. The methodology and data presented are useful for further research on carbon emissions, carbon sinks, and climate change.

Impacts of forest cover change on local temperature in Yangtze River Delta and Pearl River Delta urban agglomerations of China

The continuous economic and ecological construction in the Yangtze River Delta (YRD) and Pearl River Delta (PRD) has caused frequent temporal and spatial changes in local forests, thus affecting the regional climate. Yet few studies have addressed the temperature feedback through biophysical mechanisms due to forest change in two urban agglomerations of China. We compared MODIS and Landsat-based land cover data to detect a more accurate forest cover change. We then used the moving window strategy and spatiotemporal pattern change analysis method to quantify and compare the actual impact of forest cover change on temperature and the differences in driving factors (e.g., evapotranspiration (ET), albedo, and precipitation) from 2010 to 2020. The results showed that Landsat-based land cover data performed well. The conversion from forest to cropland was dominated in YRD and PRD, followed by the conversion of cropland to forest, with a small proportion of forest converting to impervious surface. The afforested areas in the two regions showed a diurnal cooling effect (-0.18 ± 0.07 °C and -0.10 ± 0.13 °C, respectively), which was greater than the air temperature. Forest converting to impervious surfaces led to stronger warming (0.39 ± 0.37 °C in YRD) than that of cropland (0.05 ± 0.03 °C in YRD and 0.07 ± 0.06 °C in PRD). The daytime LST variations can be explained by ET and inconsistent albedo effects. Seasonally, the cooling effects induced by afforestation predominated during the growing season (spring and summer), accompanied by the relatively high ET. This study shows that rational afforestation and control of deforestation are helpful to achieve sustainable forest management in urban agglomerations and to regulate climate warming.