Pearl River Research Articles

Kolmogorov-Zurbenko filter coupled with machine learning to reveal multiple drivers of surface ozone pollution in China from 2015 to 2022

Near-surface ozone pollution is a significant concern in China. Its meteorological drivers are uncontrolled, stressing an urgent need to quantify the anthropogenic-driven components. This study employs explainable machine learning (ML) algorithms to predict ozone concentrations, emphasizing the anthropogenic-driven trends after accounting for meteorological effects. Results present that radiation is the most important meteorological factor affecting ozone pollution in the Yangtze River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SCB), while temperature is dominant for the North China Plain (NCP). Even at lower temperatures, stronger solar radiation can still lead to the accumulation of higher ozone concentrations. The anthropogenic-driven ozone concentration showed an upward trend in China, with an interannual growth rate of 2.61 μg/m3 a−1 from 2015 to 2022. Nonetheless, its rising trend experienced a post-2019 downturn, due to the COVID-19 lockdown and emission reduction strategies. It started to rise in 2022. Regionally, NCP has the highest ozone concentration, the SCB has the most pronounced increase in 2022, but the PRD has no noticeable variation and no significant seasonal change after 2019. As for precursor emissions, the urban areas in China are mostly located in the VOC-limited (volatile organic component) and transitional regimes, highlighting that VOC control is more cost-effective.

Characteristics and partitions of traditional and emerging organophosphate esters in soil and groundwater based on machine learning

Organophosphate esters (OPEs) pose hazards to both humans and the environment. This study applied target screening to analyze the concentrations and detection frequencies of OPEs in the soil and groundwater of representative contaminated sites in the Pearl River Delta. The clusters and correlation characteristics of OPEs in soil and groundwater were calculated by self-organizing map (SOM). The risk assessment and partitions of OPEs in industrial park soil and groundwater were conducted. The results revealed that 14 out of 23 types of OPEs were detected. The total concentrations (Σ23OPEs) ranged from 1.931 to 743.571 ng/L in the groundwater, and 0.218 to 79.578 ng/g in the soil, the former showed highly soluble OPEs with high detection frequencies and concentrations, whereas the latter exhibited the opposite trend. SOM analysis revealed that the distribution of OPEs in the soil differed significantly from that in the groundwater. In the industrial park, OPEs posed acceptable risks in both the soil and groundwater. The soil could be categorized into Zone I and II, and the groundwater into Zone I, II, and III, with corresponding management recommendations. Applying SOM to analyze the characteristics and partitions of OPEs may provide references for other new pollutants and contaminated sites.

Exploring water accumulation dynamics in the Pearl River estuary from a Lagrangian perspective

Exploring water accumulation dynamics in the Pearl River estuary from a Lagrangian perspective

Land Use Carbon Budget Pattern and Carbon Compensation Mechanism of Counties in the Pearl River Basin: A Perspective Based on Fiscal Imbalance

Land Use Carbon Budget Pattern and Carbon Compensation Mechanism of Counties in the Pearl River Basin: A Perspective Based on Fiscal Imbalance

Residual distribution and risk assessment of neonicotinoids in urban green space soils of the pearl river delta, South China: A socioeconomic analysis

Urban green spaces are the soil component in cities that interacts most closely with humans. This study investigated the residues of seven neonicotinoids (NEOs) in soils from urban green spaces within the Pearl River Delta (PRD) region and analyzed the correlation between the residue characteristics and the region's economic development. Notably, we introduced the Nemerow Index method, a comprehensive approach, to quantify the overall pollution level of NEOs in the soil of urban park green spaces and utilized this to assess the cumulative exposure probability risks for different populations in this scenario. We found that: (1) The soil of urban park green spaces exhibited varying degrees of NEOs contamination (Σ7NEOs: N.D.–137.31; 6.25 μg/kg), with imidacloprid and clothianidin constituting the highest proportions (89.46 % and 83.60 %); (2) The residual levels of NEOs in Children's Park were significantly higher than those in community parks within Guangzhou, with an average value of 13.30 μg/kg compared to 3.30 μg/kg; (3) The residual characteristics of NEOs exhibited a positive correlation with regional economic development; specifically, the per capita GDP well correlated with IMIRPF, a summation of seven NEOs in imidacloprid equivalents via relative potency factors (R2 =0.86). Regions with higher economic development typically exhibited elevated IMIRPF levels; (4) The fitted cumulative probability distributions for average daily exposure doses revealed that children's exposure was an order of magnitude higher than adults'. Despite this, the exposure risks for both groups remained within acceptable limits.

The joint impact of PM2.5 constituents on the risk of cerebrovascular diseases hospitalization: A large community-based cohort study

Air pollution poses significant health risks to urban areas, with limited focus on the chronic association of PM2.5 and its constituents on cerebrovascular diseases (CERs), especially regarding the joint associations. This study explores the individual and joint associations between PM2.5 constituents and CER hospitalization risks through a cohort analysis of 36,271 adults in the Pearl River Delta, South China, from 2015 to 2020. Cox proportional hazards regression and quantile-based g-computation models were used to quantify the individual and joint associations of annual mean concentrations of PM2.5 constituents with hospitalization for CERs. 1151 participants were hospitalized due to CERs during the five-year follow-up period. Joint associations analyses identified that one quartile increase in co-exposure may result in hazard ratios of 1.530 (1.441–1.623), 1.840 (1.710–1.980), and 1.609 (1.491–1.737) for CERs, total, and ischemic stroke hospitalization, respectively. The adverse effect was primarily driven by organic matter and chlorine. Men, those with a history of tobacco or alcohol use or with low residential greenness, were more susceptible to CERs hospitalization following PM2.5 constituents co-exposure. Upcoming strategies should focus on monitoring and regulating PM2.5 constituents, encouraging healthy lifestyles, and enhancing urban greenery.

Comparative Study on Early-Maturing High-Quality Sweet Corn Varieties Introduced from Abroad for Autumn Cultivation in the Pearl River Delta Region

Comparative Study on Early-Maturing High-Quality Sweet Corn Varieties Introduced from Abroad for Autumn Cultivation in the Pearl River Delta Region

Quantitative impacts of dominant large-scale circulation systems on surface ozone pollution in China

Tropospheric ozone pollution has been worsened over most regions of China, adversely affecting human health and ecosystems. The long-term ozone concentration depends largely upon atmospheric circulations. Here, we conducted meteorological adjustment to quantitatively assess the influences of meteorological factors on the ozone evolution in China's seven city clusters during the warm season (April to October) from 2013 to 2020. Our analysis indicated that northern and eastern regions experienced ozone increases driven by emission changes. Southern regions, particularly the Pearl River Delta (PRD), exhibited ozone rise primarily due to meteorological conditions despite emission changes. In the Sichuan Basin (SCB) and Central Yangtze River Plain (CYP), where ozone levels decreased, meteorological conditions played a significant role in suppressing the ascent of ozone. Empirical orthogonal functions (EOF) analyses suggested that the spatiotemporal trend of meteorology-associated ozone was strongly correlated with the variation of East Asian Trough (EAT), South Asian High (SAH) and the western Pacific subtropical high (WPSH). The top three EOF patterns explained 33.4 %, 21.8 %, and 16.0 % of the total variance in meteorology-associated ozone. Absolute principal component scores-multiple linear regression (APCS-MLR) analyse quantitatively identified that enhanced EAT and SAH with a northward location of WPSH were favourable to surface ozone formation in central and eastern regions, but unfavourable to ozone formation in edge regions such as SCB.

The Role of Tide and Wind in Modulating Density Stratification in the Pearl River Estuary during the Dry Season

Density stratification plays a crucial role in estuarine hydrodynamics and material transport. In this study, we utilized a well-calibrated numerical model to investigate the stratification processes and underlying mechanisms in the dynamically wide Pearl River Estuary (PRE). In the upper estuary, longitudinal straining governs stratification, enhancing it during ebb tide and reducing it during flood tide. The Coriolis force becomes significant in the lower estuary due to the increased basin width, causing seaward freshwater to be confined to the West Shoal, where a pronounced transverse salinity gradient forms. Interacting with lateral current shear, density stratification is most pronounced in this region. The prevailing northeasterly wind creates a mixed layer near the surface, shifting stratification to the middle layer of the water column in the upper estuary. Wind stirring reduces stratification throughout the estuary. Under the wind’s influence, the seaward outflow is confined to a narrower region and shifts westward, resulting in the most apparent stratification occurring on the West Shoal of the PRE due to lateral straining. These findings on the evolution of freshwater pathways and their role in modulating density stratification have significant implications for other wide estuaries, such as Delaware Bay and the La Plata-Parana estuary.

The reward and penalty for ozone pollution control caused by natural sources and regional transport: A case study in Guangdong province

Ground-level O3 pollution in the Pearl River Delta region (PRD) is closely related to anthropogenic, natural emissions and regional transport. However, the interactions among different sources and natural intervention in modulating anthropogenic management have not been comprehensively assessed. Here, the WRF-CMAQ-MEGAN modeling system was utilized to simulate an O3 episode over PRD. The integrated source apportionment method (ISAM) and brute-force top-down combined with factor separation approach (BF-TD-FSA) were applied to quantify source contributions, impacts of individual or multiple sources on O3, and decouple interactions among various emissions; additionally, based on ISAM, O3 isopleths visualized MDA8 O3 response of different source types to anthropogenic perturbations. ISAM concluded considerable MDA8 O3 contributions of regional transport in PRD/NPRD (non-PRD areas in Guangdong province) (38.8 %/35.7 %), followed by anthropogenic (32.7 %/24.8 %), BVOC (biogenic volatile organic compounds, 23.8 %/20.3 %) and SNO (soil NO, ∼4 %) emissions. Compared to concentrated anthropogenic contributions, widespread natural contributions were observed across their source areas and along the transport pathways. The BF-TD also revealed that regional transport had the largest impact (>90 μg m−3) on MDA8 O3 while anthropogenic and BVOC emissions greatly affected downwind PRD (64.5 and 7.7 μg m−3). Negative synergy between anthropogenic and natural emissions (especially BVOC emission) suggested potential natural-induced intensification of anthropogenic impact during O3 management. The MDA8 O3 isopleths further demonstrated significant BVOC-induced reward and regional transport-induced penalty for anthropogenic NOx (ANOx) emission control benefits, leading to additional maximum MDA8 O3 decrease and increase by −27.5 and 13.8 μg m−3 in polluted high-emission grids. The natural-induced reward effect could impose loose requirements on anthropogenic reduction (decreased by 13.3 %–17.7 %) if there were no regional transport-induced control penalty. It is advisable to prioritize ANOx control and seek collaboration on air quality management with neighboring provinces to maximize the natural-induced control reward and achieve desired targets with minimal human efforts.

Continuous distribution pattern and the origin of the high‐velocity layer in the Pearl River Mouth Basin in the northern South China Sea: Constraints by gravity modelling

The high‐velocity layer in the lower crust is widely distributed in the northern continental margin of the South China Sea. A detailed anatomy of the high‐velocity layer is crucial for understanding the continental rifting and crustal thinning. Based on three seismic reflection profiles across the Pearl River Mouth Basin (PRMB) and the global free‐air gravity anomaly data in this study, by the gravity modelling we construct the crustal structure along three seismic reflection profiles across the depression and uplift zones. The free‐air gravity anomaly data within the uplift and depression zones indicates the distinct zonation, and the high and low values of free‐air gravity anomalies in the basin show the northeastward trend. Based on the gravity modelling along the three seismic profiles, the crustal thickness is of 12–23 km beneath the basin and thickness of the crust is gradually thinning from the continental shelf to the continental slope. The high‐velocity layer extends eastward to the Dongsha Uplift and terminates westward beneath the Baiyun Sag. The average thickness of the high‐velocity layer is 4–6 km and the maximum thickness is about 8 km at the Dongsha Uplift. The thickness variation of the high‐velocity layer suggests the continuous distribution of mantle underplating and intense magma activity. According to the isolated distribution and the continuous distribution of the high‐velocity layer found by previous studies in the northern South China Sea, we use gravity modelling to test which models of the high‐velocity layer within the PRMB are reasonable. According to testing models and preferred models of the crustal structure by gravity modelling, we propose that the continuous distribution pattern of the high‐velocity layer is the best model to interpret the crustal thinning characteristics and the gravity anomaly responses of the high‐velocity layer in the basin. We propose that the high‐velocity layer in the Pearl River Mouth Basin was formed by the long‐term underplating of the high‐temperature melted mantle.

Revealing multi-scale spatial synergy of mega-city region from a human mobility perspective

ABSTRACT Spatial synergy is strengthened integration and connection between cities in a mega-city region, transcending administrative boundaries. The central flow theory suggests that the mega-city regions are formed by the interconnected flows of people across cities, making the spatial synergy can be measured by assessing the aggregation and intensity of flows and interactions between cities and regions. Human mobility data, such as mobile phone data and social media check-ins, enable the tracking of human movements, thus facilitating the transition of central flow theory from theoretical constructs to empirical research. To this end, this study presents an alternative data-driven framework to reveal the multi-scale spatial synergy of mega-city regions from a human mobility perspective. It uncovers homogeneously spatial communities with high inter-city integration using community detection. Strong internal spatial connections of 2.13 billion mobility are filtered using network backbone extraction. An experiment in the Pearl River Delta (PRD), China, demonstrates a multi-scale and multi-core hierarchical spatial synergy in the PRD region. The detailed findings are as follows: (1) Three cities attract the majority of human mobility. Mobility distance is short in urban centers and long in suburban areas. (2) The spatial integration pattern shows the detected communities reveal the hierarchical integration pattern with three main integrated regions: Guangzhou-Foshan-Zhaoqing, Shenzhen-Dongguan-Huizhou, and Zhuhai-Zhongshan-Jiangmen. (3) The spatial connection pattern illustrates the close ties of 9 cities and three core cities, including Guangzhou, Shenzhen, and Foshan. These results provide a human-centric understanding of urban synergy and deeper insights into central flow theory, which supports cooperative development in mega-city regions.

Phytoplankton Diversity, Spatial Patterns, and Photosynthetic Characteristics Under Environmental Gradients and Anthropogenic Influence in the Pearl River Estuary.

The Pearl River Estuary (PRE) is one of the world's most urbanized subtropical coastal systems. It presents a typical environmental gradient suitable for studying estuarine phytoplankton communities' dynamics and photosynthetic physiology. In September 2018, the maximum photochemical quantum yield (Fv/Fm) of phytoplankton in different salinity habitats of PRE (oceanic, estuarine, and freshwater zones) was studied, revealing a complex correlation with the environment. Fv/Fm of phytoplankton ranged from 0.16 to 0.45, with taxa in the upper Lingdingyang found to be more stressed. Community composition and structure were analyzed using 18S rRNA, accompanied by a pigment analysis utilized as a supplementary method. Nonmetric multidimensional scaling analysis indicated differences in the phytoplankton spatial distribution along the estuarine gradients. Specificity-occupancy plots identified different specialist taxa for each salinity habitat. Dinophyta and Haptophyta were the predominant taxa in oceanic areas, while Chlorophyta and Cryptophyta dominated freshwater. Bacillariophyta prevailed across all salinity gradients. Canonical correlation analysis and Mantel tests revealed that temperature, salinity, and elevated nutrient levels (i.e., NO3--N, PO43--P, and SiO32--Si) associated with anthropogenic activities significantly influenced the heterogeneity of community structure. The spatial distribution of phytoplankton, along with in situ photosynthetic characteristics, serves as a foundational basis to access estuarine primary productivity, as well as community function and ecosystem health.

Spatial Distribution Characteristics and Driving Factors of Little Giant Enterprises in China’s Megacity Clusters Based on Random Forest and MGWR

As a representative of potential “hidden champions”, a concept originating in Germany, specialized and innovative Little Giant Enterprises (LGEs) have become exemplary models for small and medium-sized enterprises (SMEs) in China. These enterprises are regarded as crucial support for realizing the strategy of building a strong manufacturing country and addressing the weaknesses in key industrial areas. This paper begins by examining urban agglomerations, which serve as the main spatial carriers for industrial restructuring and high-quality development in manufacturing. Based on data from LGEs in the Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations from 2019 to 2023, the study employs the Random Forest (RF) and Multi-scale Geographically Weighted Regression (MGWR) methods to conduct a comparative analysis of their spatial patterns and influencing factors. The results are as follows: (1) LGEs exhibit spatial clustering in both the YRD and PRD regions. Enterprises in the YRD form a “one-axis-three-core” pattern within a distance of 65 km, while enterprises in the PRD present a “single-axis” pattern within a distance of 30 km, with overall high clustering intensity. (2) The YRD is dominated by traditional manufacturing and supplemented by high-tech services. In contrast, the PRD has a balanced development of high-tech manufacturing and services. Enterprises in different industries are generally characterized by a “multi-point clustering” characteristic, of which the YRD displays a multi-patch distribution and the PRD a point–pole distribution. (3) Factors such as industrial structure, industrial platforms, and logistics levels significantly affect enterprise clustering and exhibit scale effects differences between the two urban clusters. Factors such as industrial platforms, logistics levels, and dependence on foreign trade show positive impacts, while government fiscal expenditure shows a negative impact. Natural geographical location factors exhibit opposite effects in the two regions but are not the primary determinants of enterprise distribution. Each region should leverage its own strengths, improve urban coordination and communication mechanisms within the urban cluster, strengthen the coordination and linkage of the manufacturing industry chain upstream and downstream, and promote high-tech industries, thereby enhancing economic resilience and regional competitiveness.

The changes prediction on terrestrial water storage in typical regions of China based on neural networks and satellite gravity data

Accurate prediction of regional terrestrial water storage change (TWSA) is of great significance for water resources planning and management, and early warning of extreme climate disasters. Aiming at the problem that the conventional methods on prediction of TWSA time series are difficult to be accurate, the six typical regions are selected in China as examples, including the upper reaches of the Yangtze River (UYR), the southwest region (SWR), the Liaohe River Basin (LRB), the North China Plain (NCP), the Qinghai-Tibet Plateau (QTP), and the Pearl River Basin (PRB). The mascon product from GRACE/GRACE-FO provided by CSR is used to extract TWSA time series in six typical areas. The improved Back Propagation (BP) neural network, Long Short-Term Memory (LSTM) neural network and the latest Bidirectional LSTM (BiLSTM-attention) neural network model based on attention mechanism are proposed to predict and analyze the regional TWSA. In the experiment, the selection of the optimal model parameters such as the number of hidden layer nodes and the number of hidden units of the neural network model is tested and analyzed in detail. Meanwhile, the model prediction results are compared with the traditional least squares method and random forest (RF) prediction method. The root mean square error (RMSE), determination coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE) and mean absolute percentage error (MAPE) were used to evaluate the accuracy of the predicted results. The results show that the improved BP, LSTM and Bi-LSTM-attention neural network models all achieve higher prediction accuracy in UYR and SWR areas. RMSE is less than 2.641 cm, R2 is as high as 0.8 or more, NSE is above 0.6, and MAPE is within 0.1. Compared with the least square method, the RMSE of the predicted results from three neural network decreased by 0.998 cm, 0.700 cm and 0.7563 on average, and the R2 increased by 81.75%, 69.89% and 72% on average. Compared with RFML method, the RMSE from three neural network is reduced by 0.601 cm, 0.316 cm and 0.360, and R2 is increased by 38.20%, 24.60% and 27.06% on average. NSE and RMSE are improved to varying degrees in the above regions. It shows that the improved BP, LSTM and BiLSTM-attention model used can effectively predict TWSA. The research methods and results in this paper can provide important reference for the rational utilization of regional water resources and disaster risk assessment.

Contrasting the Effects of X-Band Phased Array Radar and S-Band Doppler Radar Data Assimilation on Rainstorm Forecasting in the Pearl River Delta

Contrasting the X-band phased array radar (XPAR) with the conventional S-Band dual-polarization mechanical scanning radar (SMSR), the XPAR offers superior temporal and spatial resolution, enabling a more refined depiction of the internal dynamics within convective systems. While both SMSR and XPAR data are extensively used in monitoring and alerting for severe convective weather, their comparative application in numerical weather prediction through data assimilation remains a relatively unexplored area. This study harnesses the Weather Research and Forecasting Model (WRF) and its data assimilation system (WRFDA) to integrate radial velocity and reflectivity from the Guangzhou SMSR and nine XPARs across Guangdong Province. Utilizing a three-dimensional variational approach at a 1 km convective-scale grid, the assimilated data are applied to forecast a rainstorm event in the Pearl River Delta (PRD) on 6 June 2022. Through a comparative analysis of the results from assimilating SMSR and XPAR data, it was observed that the assimilation of SMSR data led to more extensive adjustments in the lower- and middle-level wind fields compared to XPAR data assimilation. This resulted in an enlarged convergence area at lower levels, prompting an overdevelopment of convective systems and an excessive concentration of internal hydrometeor particles, which in turn led to spurious precipitation forecasts. However, the sequential assimilation of both SMSR and XPAR data effectively reduced the excessive adjustments in the wind fields that were evident when only SMSR data were used. This approach diminished the generation of false echoes and enhanced the precision of quantitative precipitation forecasts. Additionally, the lower spectral width of XPAR data indicates its superior detection accuracy. Assimilating XPAR data alone yields more reasonable adjustments to the low- to middle-level wind fields, leading to the formation of small-to-medium-scale horizontal convergence lines in the lower levels of the analysis field. This enhancement significantly improves the model’s forecasts of composite reflectivity and radar echoes, aligning them more closely with actual observations. Consequently, the Threat Score (TS) and Equitable Threat Score (ETS) for heavy-rain forecasts (>10 mm/h) over the next 5 h are markedly enhanced. This study underscores the necessity of incorporating XPAR data assimilation in numerical weather prediction practices and lays the groundwork for the future joint assimilation of SMSR and XPAR data.

New secondary decomposition ensemble support vector regression for monthly drought index forecasting

Drought prediction on a monthly timescale is conducive to the timely identification of drought characteristics and provides a reference for drought mitigation. Current research is generally based on drought indices on timescales of three months or more for drought prediction, whereas the presence of highly nonlinear and drastic changes in the characteristics of drought indices leads to suboptimal drought prediction for drought index sequences on monthly timescales. In this study, a secondary decomposition model based on extreme-point symmetric mode decomposition (ESMD), variational mode decomposition (VMD), and support vector regression (SVR) was investigated to predict a monthly-scale drought index. To evaluate the predictive ability of the model, it was applied to 100 meteorological stations in the Pearl River Basin of China and compared and analyzed using four comparative models. The results showed that the model proposed in this study has better performance index values, and the prediction accuracies of all stations are improved compared with the four comparative models, with the most significant improvement in the Nash–Sutcliffe efficiency (NSE) index value, followed by the root mean square error (RMSE) and mean absolute error (MAE) index values, and the smallest improvement in the Wilmot consistency index (WIA) index value. The monthly-scale drought quadratic decomposition (ESMD-VMD) combined prediction model established in this study provides a novel method for drought prediction.

Understanding land subsidence in the Pearl River Delta region of China based on InSAR observations

Land subsidence occurs in river delta areas. A comprehensive understanding of the factors contributing to land subsidence is crucial for effective geohazard mitigation and land planning. This study investigates the distribution characteristics and spatiotemporal evolution pattern of the land subsidence in the Pearl River Delta based on the InSAR-derived ground deformation data spanning 2016 to 2021; and, the effects of the influencing factors, including the Quaternary sediment thickness, land use classification, land reclamation, and subway construction and operation, on the subsidence are analyzed. The results unveil the presence of four major subsidence zones and several minor coastal subsidence bowls in the research area, with a total area of about 1100 km, all of which exhibit prolonged and intense subsidence. In the remaining regions, land subsidence predominantly exhibits weak and short-term occurrences. The subsidence in the study area follows a distinct linear evolutionary pattern with a slight rebound, potentially linked to the rise of groundwater levels. Regions with Quaternary sediment thickness surpassing 20 m, engaging in aquaculture or agricultural activities, and featuring reclaimed land are predisposed to prolonged and intense ground subsidence. In addition, the construction and operation of subway lines may induce ground subsidence, especially in geologically vulnerable areas. This study provides valuable insights into land subsidence research in delta regions.

A graph-factor-based random forest model for assessing and predicting carbon emission patterns - Pearl River Delta urban agglomeration

As China actively fulfills its emissions reduction obligations to meet the Paris climate goals, it is crucial to explore carbon reduction policies at the city level, given the important role and potential of cities in China's emissions reduction efforts. In this study, a comprehensive assessment and prediction of carbon emission patterns in the Pearl River Delta urban agglomeration was conducted by developing an integrated model that incorporates graph representation learning, factorial analysis, and random forest methods. The main findings are (1) there is significant heterogeneity in carbon emissions across industries and across time and space; (2) carbon clusters can more accurately characterize the flow of carbon emissions than carbon supply chains; (3) the nonmetallic manufacture, electricity supply and transportation industries play a decisive role in carbon emission reduction; and (4) the prediction results show that the carbon emissions of the Pearl River Delta urban agglomeration will reach 349.2 million tons in 2021, and will then show a declining trend year by year, dropping to a projected 179.8 million tons in 2035. Based on the above findings, it is recommended that the monitoring of key carbon emission clusters should be strengthened, and the monitoring data should be utilized to establish a cross-city and cross-industry cooperation mechanism for emission reduction. In addition, a clear set of action plans and strategies should be formulated and implemented to ensure that the carbon emission targets for 2035 are realized.

Impact of Marine Shipping Emissions on Ozone Pollution During the Warm Seasons in China

AbstractAs China's land‐based anthropogenic emissions are decreasing, the impact of marine shipping emissions (MSEs) on the atmosphere, especially in coastal areas, deserves further attention. This study investigates the impact of MSEs on MDA8 ozone (O3) levels during the warm seasons of 2017 in China, considering different seasons and synoptic patterns. The results indicate that the average impact of MSEs on O3 decreases from offshore to inland, peaking at over 29.0 ppb at sea and 13.8 ppb along the coast of mainland China. Influenced by precursor emissions, meteorology and other factors, MSEs contribute differently to O3 in Beijing‐Tianjin‐Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD), with contributions of 3.0, 5.2, and 4.9 ppb, respectively, and ranging from 2.7 to 7.3 ppb in 13 coastal port cities. The O3 impacts of MSEs are higher on polluted days than on clean days, especially during onshore winds. In the BTH, MSEs increase O3 by 5.5 ppb on polluted days and 3.0 ppb on clean days with northeast winds from the Bohai Sea. In the YRD, MSEs increase O3 by 9.4 ppb on polluted days and 7.3 ppb on clean days with southeast winds. MSEs significantly increase O3 levels in the PRD by 11.0 ppb on polluted days and 5.0 ppb on clean days with southeast winds. Although the emission inventories, initial and boundary conditions, etc. may introduce uncertainties, our results still provide useful information for O3 pollution management in coastal cities as a reasonable way to track mass contributions.