The coupling effect between economic development and the urban ecological environment in Shanghai port

Potentially toxic elements in urban soils of the coastal city of the Sea of Azov: Levels, sources, pollution and risk assessment

Coastal cities are major centers of economic activity, which at the same time has negative consequences for the environment. The present study aimed to determine the concentrations and sources of PTEs in the urban soils of Taganrog, as well as to assess the ecological and human health risks. A total of 47 urban and 5 background topsoils samples were analyzed by ICP-MS and ICP-AES. A significant excess of Cu, Zn, and Sb was noted in urban soils compared to the upper continental crust and average world-soil (1.7-2.9 times). Statistical analysis showed that the elements in soils were of geogenic, mixed and anthropogenic origin. According to the single pollution index (PI), the greatest danger of soil pollution was represented by anthropogenic elements, namely Cu, W, Pb, Zn, Cd, and Sn, the levels of which were increased in residential and industrial areas. The median contents of As, Mn, Cr, Sr, Mo, Sb, Cu, W, Pb, and Zn were 1.1-2.1 times higher, while Cd and Sn were 2.5 folds higher in the urban soils compared to the background ones. The total pollution index (ZC) showed that only 15% of the soils had high level of pollution, which is typical for the industrial areas. Overall ecological risks were negligible or low in 92% of soils, and were mainly due to elevated levels of Cu, Zn, As, and Pb. Non-carcinogenic risks to humans were mainly related to exposure to La and Pb. The hazard index (HI) values for all PTEs were less than ten, indicating that overall non-carcinogenic risk for adults and children was low-to-moderate and, moderate, respectively. The total carcinogenic risk (TCR) exceeded threshold and corresponded to low risk, with Pb, As, and Co being the most important contributors. Thus, the industrial activities of Taganrog is the main source of priority pollutants.

Application of a novel remote sensing ecological index (RSEI) based on geographically weighted principal component analysis for assessing the land surface ecological quality

In evaluating the integrated remote sensing-based ecological index (RSEIPCA), principal component analysis (PCA) has been extensively utilized. However, the conventional PCA-based RSEI (RSEIPCA) cannot accurately evaluate component indicators' spatially shifting relative significance. This study presented a novel RSEI evaluation strategy based on geographically weighted principal component analysis (RSEIGWPCA) to address this deficiency. Second, compared to the classic RSEIPCA, RSEIGWPCA was tested at English Bazar and surrounding areas using two-fold validation. In this regard, the Jaccard test from a different setting and correlation analysis were utilized to examine the geographical distribution of RSEI derived by PCA and GWPCA. The validation output revealed better effectiveness of GWPCA over PCA in assessing the RSEI. The findings revealed that (i) in RSEI assessment, the spatial heterogeneity of the dataset helped to formulate individual weights by GWPCA that was not performed by PCA; and (ii) the areas having higher RSEI were primarily located around the Chatra wetland of this study area, and the areas with lower RSEI were located mainly in the industrial part. It has been concluded that RSEIGWPCA is a helpful approach in the RSEI evaluating for the regional and local scale like English bazaar city and its neighbourhood.

Effects of emission control areas on sulfur-oxides concentrations--Evidence from the coastal ports in China

The setting of Sulfur limitations in Emission Control Areas (ECAs) is a crucial action of marine environmental governance at the international regulatory levels. In this study, the overall and structural impacts of the two rounds of ECA policies on SOx concentrations were quantified using synthetic control method (SCM) based on time-series data from Chinese coastal ports from 2005 to 2020. From the outcomes, the 1st round of ECA policy announced in 2015 intensified the competition between ECA and non-ECA ports and provided strong support for ECA expansion and enhanced regulation in 2019. In addition, the restrictions on the Sulfur content of marine fuels under the 1st round of ECA policy has only effectively reduced the SOx concentration in the Bohai Rim and the Yangtze River Delta region, whereas the impact on the Pearl River Delta region isn't significant. However, the 2nd round of ECA policy has only effectively impacted the Bohai Rim. In general, the effect of the 1st round of ECA policy is better than that of the 2nd round, which is mainly because the favorable effect of the further expansion of ECA policy is offset by a significant increase in vessel activity in Chinese coastal ports.

Comparative assessment of the environmental pollution and marine economic growth of Guangxi and China by using the environmental Kuznets curve fitting model

This study examined the nexus between per capita gross ocean product (GOP) growth and total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) discharged from land-based sources in Guangxi and China. Multiple pollution indicators, such as red tide area (RTA), seawater quality area (SWQA), and eutrophication area (EA), were used as marine environmental quality indicators, and annual time series data during the period 2010-2019 were employed. The data were analyzed using the environment Kuznets curve fitting model. Results showed that the average annual growth rates of the GOP and gross domestic product (GDP) of China were 9.88% and 10.79%, respectively, and those of Guangxi were 13.62% and 10.02%, respectively. The average annual GOP ratio in GDP for Guangxi and China was 6.59 and 9.47, respectively. The marine tertiary industry was the most dominant marine industry; it accounted for 41.12-50.01% (mean: 46.12%) of Guangxi's GDP and 47-60% (mean: 52.47%) of China's GDP. The TP, COD, SWQA, and EA of Guangxi and the TP, TN, COD, SWQA, and EA of China displayed inverted U-shaped GOP growth. These findings indicate that the marine economic growth and marine environmental quality of Guangxi and China are harmonious. However, TN increased synchronously with marine economic growth in Guangxi. Therefore, the industrial structure must be further optimized, pollutant discharge management must be strengthened, and the harmonious development of Guangxi's marine economy and marine environment needs to be promoted.

The allometric relationship between carbon emission and economic development in Yangtze River Delta: fusion of multi-source remote sensing nighttime light data

Exploring the allometric relationship between carbon emission and economic development can provide guidance for policy-makers who hope to accelerate carbon emission reduction and achieve high-quality development. First, based on the established DMSP/OLS and NPP/VIIRS nighttime light datasets, this study simulated the carbon emissions of the Yangtze River Delta from 2000 to 2020. Second, our research analyzed the spatiotemporal evolution characteristics of carbon emissions. Third, adopting allometric growth model, we explored the allometric relationship between economic development and carbon emissions in Yangtze River Delta. The main conclusions are as follows. First, four prediction models, namely, linear fitting, support vector machine, random forest, and CNN-BiLSTM deep learning, were compared to simulate the accuracy of carbon emissions. Consequently, the CNN-BiLSTM deep learning estimation model presented the best accuracy. Second, both the carbon emissions in YRD as a whole showed an increasing trend, with the largest growth rate appearing in Shanghai and the smallest growth rate occurring in Lishui. Moreover, the high-carbon emission areas were mainly distributed in the core city cluster, which are enclosed by Shanghai, Nanjing, and Hangzhou. Finally, the allometric relationship between economic development and carbon emissions was dominated by one-level negative during the sample period, and the relative growth rate of carbon emissions is lower than that of the economic development, which made the YRD at a basic coordinate stage of weak expansion of economy.

Evaluation of coupling coordination development between digital economy and green finance: Evidence from 30 provinces in China

The convergence of China's digital economy and green finance holds great significance for fostering a sustainable and high-quality developmental path. However, existing studies have not explored the coupling coordination development between these two crucial subsystems. To bridge this gap, this paper employs a modified coupling coordination degree (CCD) model to assess and affirm the coupling coordination degree between the digital economy and green finance across 30 provinces in China from 2015-2021. Based on degree results, provinces are classified into three clusters by using K-means and hierarchical clustering algorithm. Our findings unveil that the current level of coupling coordination development in China is at a primary coordination stage. Although regional disparities significantly exist, the overall level of coordination remains steadily increasing, with the eastern region outperforming the western region. Additionally, we determine that the COVID-19 pandemic's disruption on the coupling coordination development of these systems has been limited. This research sheds light on the evolution of coupling systems and offers practical recommendations for strengthening the coordinated development of the digital economy and green finance.

Characteristics of real-world ship energy consumption and emissions based on onboard testing

The Yangtze River ships are generally overpowered and less energy efficient. In this study, a Yangtze ship was selected as the test ship, and its characteristics were investigated through energy consumption and emission testing under multiple operating conditions. The results show that the ship operates at 25-50 % engine load for 72.2 % of the time, and at this partial load, 9.72 % more CO2 and 9.81 % more NOX can be emitted than at the rated power. The concentrations of exhaust vary under different operating conditions. The emission factor of CO was the highest under departure conditions; CO2 and SO2 were the highest under anchoring conditions; and NOx was the highest under cruising conditions. The accuracy of the emission factors obtained by the direct calculation method was improved by 30 % compared to the concentration estimation method. This study can help understand the real level of energy consumption and emissions from in-service ships.

Stability and energy consumption analysis of arctic fleet: modeling and simulation based on future motion of multi-ship

Ensuring the safety of Arctic shipping and preserving the Arctic ecological environment are emerging as key challenges in the shipping sector. Ship collisions and getting trapped in ice are frequently occurring under dynamic ice conditions due to the Arctic environment, making research on ship navigation in Arctic routes significant. Leveraging ship networking technology, we developed an intelligent microscopic model which considered factors such as the future motion trends of multi-ships in front and the influence of pack ice, and carried out a stability analysis of the model utilizing linear and nonlinear methods. Additionally, the accuracy of the theoretical results was further validated through simulation experiments with diverse scenarios. The conclusions manifest that the model can magnify the anti-disturbance ability of traffic flow. Additionally, the problem of energy consumption due to ship speed is explored, and it is determined that the model has a positive intention in reducing speed fluctuations and ship energy consumption. This paper highlights the potential of intelligent microscopic models in studying the safety and sustainability of Arctic shipping routes, providing targeted initiatives to improve safety, efficiency, and sustainability in Arctic shipping.

Spatiotemporal variations of NO2 and its driving factors in the coastal ports of China

Nitrogen Dioxide (NO₂) is one of the major air pollutants in coastal ports of China. Understanding the spatiotemporal varying effects of driving factors of NO₂ is vital for the implementation of differentiated air pollution control measures for different port areas. Based on the Ozone Monitoring Instrument (OMI) satellite data, we adopted a Geographically and Temporally Weighted Regression (GTWR) model to explore the influences of meteorological and socioeconomic factors on the NO₂ Vertical Column Concentrations (VCDs) in coastal ports of China from 2015 to 2021. The results indicate that NO₂ VCD in most ports has decreased since 2016 and the ports with serious NO₂ pollution are mainly distributed in northern China. The associations between NO₂ VCD levels and their drivers exhibit obvious spatiotemporal heterogeneity. Higher wind speed and relative humidity are more helpful to alleviate NO₂ pollution in ports of the Bohai Rim and the Pearl River Delta. Cargo throughput has more closely associated with NO₂ pollution in Beibu Gulf in recent years, yet there is no significant association found for Shanghai ports. The positive relationship between transportation emissions and NO₂ VCD is more significant in southern ports. This work provides some implications for the formulation of targeted emission reduction policies for different ports along the Chinese coast.

Impacts of the COVID-19 epidemic on carbon emissions from international shipping

The COVID-19 epidemic made the most countries to take strict lockdown measures, what has seriously caused an unprecedented impact in the shipping industries, whereas these measures have also played a significant impact to control carbon emissions from international shipping. Here, we try to use the threshold generalized autoregressive conditional heteroscedasticity and the exponential generalized autoregressive heteroscedasticity to investigate whether the fluctuations of the control variable on carbon emissions from international shipping are asymmetric or not. On this basis, the GARCH-MIDAS model is introduced to discuss whether the newly confirmed cases are independent of control variables and have an impact on the fluctuation of carbon emissions. From the results, we find that the information contained in the newly confirmed cases cannot be covered when adding the other control variables. In addition, the newly confirmed cases have a negative impact on the volatility of carbon emissions, while the other control variables significantly increase carbon emissions. This study provides a quantitative research method for the analysis of the volatility and impact factors on international shipping carbon emissions, which helps to formulate more reasonable emission reduction measures and promote the low-carbon transformations of the global shipping industry.

Modelling driving factors of PM2.5 concentrations in port cities of the Yangtze River Delta

PM_2.5 is one of the major air pollutants in port cities of the Yangtze River Delta (YRD) of China. Understanding the driving factors of PM_2.5 is essential to guide air pollution prevention and control. We selected 17 major port cities in YRD to study the driving factors of PM_2.5 in 2019 and 2020. Generalized Additive Models were built to model the non-linear effects of single, multiple and interactions of driving factors on the variations of PM_2.5. NO₂, SO₂ and the day of year are most strongly associated with the variation of PM_2.5 concentration when used alone. Anthropogenic emissions play complicated roles in regulating PM_2.5 concentration. Although the effect of cargo throughput (CT) on PM_2.5 concentration is non-monotonic, higher PM_2.5 levels are found to be associated with higher levels of SO₂ and CT. This work can potentially provide a scientific basis for formulating PM_2.5 prevention and control policies in the region.