Dense Network Research Articles

Multilateral collaborative governance of Arctic shipping safety: Examining the impact of the Arctic Council via collaborative network analysis

Global climate change has dramatically reduced Arctic sea ice, opening new potential shipping routes, which has garnered international attention. This study explores the role of the Arctic Council in the collaborative governance network focused on Arctic shipping safety. For this purpose, a collection of 49 collaborative events initiated by international organisations and the Arctic Council was used as the data sample. These events are divided into two groups, one containing all the events and the other excluding those initiated by the Arctic Council. As a result, two collaborative networks are developed and evaluated based on network density, average path length, network transitivity, centrality indices (degree, closeness, betweenness), and clustering analysis. The results demonstrate that the Arctic Council significantly enhances the network density and transitivity of the international collaboration network, and reshapes core-periphery dynamics and cohesive subgroup structures. For instance, under the influence of the Arctic Council, Canada, France, and Japan surpass the United Kingdom in centrality (degree, closeness, betweenness). Results of the clustering coefficients reveal the Arctic Council's impact on connections among states, leading to shifts in collaboration patterns. Additionally, subgroup cohesion and density coefficients confirm the Arctic Council's role in promoting collaboration among states and restructuring governance networks.

GIAE-DTI: Predicting Drug-Target Interactions Based on Heterogeneous Network and GIN-based Graph Autoencoder.

Accurate prediction of drug-target interactions (DTIs) is essential for advancing drug discovery and repurposing. However, the sparsity of DTI data limits the effectiveness of existing computational methods, which primarily focus on sparse DTI networks and have poor performance in aggregating information from neighboring nodes and representing isolated nodes within the network. In this study, we propose a novel deep learning framework, named GIAE-DTI, which considers cross-modal similarity of drugs and targets and constructs a heterogeneous network for DTI prediction. Firstly, the model calculates the cross-modal similarity of drugs and proteins from the relationships among drugs, proteins, diseases, and side effects, and performs similarity integration by taking the average. Then, a drug-target heterogeneous network is constructed, including drug-drug interactions, protein-protein interactions, and drug-target interactions processed by weighted K nearest known neighbors. In the heterogeneous network, a graph autoencoder based on a graph isomorphism network is employed for feature extraction, while a dual decoder is utilized to achieve better self-supervised learning, resulting in latent feature representations for drugs and targets. Finally, a deep neural network is employed to predict DTIs. The experimental results indicate that on the benchmark dataset, GIAE-DTI achieves AUC and AUPR scores of 0.9533 and 0.9619, respectively, in DTI prediction, outperforming the current state-of-the-art methods. Additionally, case studies on four 5-hydroxytryptamine receptor-related targets and five drugs related to mental diseases show the great potential of the proposed method in practical applications.

Short-term PV energy yield predictions within city neighborhoods for optimum grid management

The global shift towards the exploitation of renewable energy sources is a key aspect in the transition to carbon-free societies. Although harnessing of PV energy disclosed the numerous environmental and economic benefits of PV systems, the ever-increasing share of PV systems in power generation has revealed their adverse effect on the electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with information needed for efficient capacity management and scheduling and ensure grid stability.Most forecasting models provide promising results in terms of error performance; however, they need weather variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatio-temporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model performance. The main objective was to provide the solar PV energy sector with important information for building a smart grid for cities or regions.Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485 km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results that can be compared with existing results from similar studies. Overall, the proposed method can provide important information that can assist in decision-making for the management, optimization, and visualization of grids across cities.

Superhydrophobic Cellulose Nanofiber Aerogels for Efficient Hemostasis with Minimal Blood Loss.

Reducing unnecessary blood loss in hemostasis is a major challenge for traditional hemostatic materials due to uncontrolled blood absorption. Tuning the hydrophilic and hydrophobic properties of hemostatic materials provides a road to reduce blood loss. Here, we developed a superhydrophobic aerogel that enabled remarkably reduced blood loss. The aerogel was fabricated with polydopamine-coated and fluoroalkyl chain-modified bacterial cellulose via a directional freeze-drying method. Primarily, the hydrophobic feature prevented blood from uncontrolled absorption by the material and overflowing laterally. Additionally, the aerogel had a dense network of channels that allowed it to absorb water from blood due to the capillary effect, and fluoroalkyl chains trapped the blood cells entering the channels to form a compact barrier via hydrophobic interaction at the bottom of the aerogel, causing quick fibrin generation and blood coagulation. The animal experiments reveal that the aerogel reduced the hemostatic time by 68% and blood loss by 87 wt % compared with QuikClot combat gauze. The study demonstrates the superiority of superhydrophobic aerogels for hemostasis and provides new insights into the development of hemostatic materials.

A Novel Topology Adaptation Strategy for Dynamic Sparse Training in Deep Reinforcement Learning.

Deep reinforcement learning (DRL) has been widely adopted in various applications, yet it faces practical limitations due to high storage and computational demands. Dynamic sparse training (DST) has recently emerged as a prominent approach to reduce these demands during training and inference phases, but existing DST methods achieve high sparsity levels by sacrificing policy performance as they rely on the absolute magnitude of connections for pruning and randomly generating connections. Addressing this, our study presents a generic method that can be seamlessly integrated into existing DST methods in DRL to enhance their policy performance while preserving their sparsity levels. Specifically, we develop a novel method for calculating the importance of connections within the model. Subsequently, we dynamically adjust the sparse network topology by dropping existing connections and introducing new connections based on their respective importance values. Through validation on eight widely used simulation tasks, our method improves two state-of-the-art (SOTA) DST approaches by up to 70% in episode return and average return across all episodes under various sparsity levels.

Efficient computation of Katz centrality for very dense networks via negative parameter Katz

Abstract Katz centrality (and its limiting case, eigenvector centrality) is a frequently used tool to measure the importance of a node in a network, and to rank the nodes accordingly. One reason for its popularity is that Katz centrality can be computed very efficiently when the network is sparse, ie having only O(n) edges between its n nodes. While sparsity is common in practice, in some applications one faces the opposite situation of a very dense network, where only O(n) potential edges are missing with respect to a complete graph. We explain why and how, even for very dense networks, it is possible to efficiently compute the ranking stemming from Katz centrality for unweighted graphs, possibly directed and possibly with loops, by working on the complement graph. Our approach also provides an interpretation, regardless of sparsity, of ‘Katz centrality with negative parameter’ as usual Katz centrality on the complement graph. For weighted graphs, we provide instead an approximation method that is based on removing sufficiently many edges from the network (or from its complement), and we give sufficient conditions for this approximation to provide the correct ranking. We include numerical experiments to illustrate the advantages of the proposed approach.

Lightweight multi-scale feature dense cascade neural network for scene understanding of intelligent autonomous platform

Understanding the surrounding environment is an essential part of intelligent autonomous platform to complete autonomous driving or other autonomous tasks. Thereinto, scene understanding is a key technology to understand environmental information. For intelligent autonomous platform, it not only requires high accuracy, but also has a strict requirement for the inference speed. Moreover, it should be able to perform normally in complex environments such as target occlusion, the influence of illumination, and objects at different sizes. For tackling these problems, in this study, a lightweight multi-scale feature dense cascade neural network (LMFDCNet) is proposed in real-time scene understanding in complex scenes. Furthermore, trilateral fusion module is adopted to enhance low-level detailed features or high-level semantic features of each branch to describe the object better in different environments. A multi-scale upsampling module that fuses contextual information at different scales allows objects to be represented at different size. The results indicate that LMFDCNet produces 99.1 % classification accuracy on the tobacco dataset. On the Cityscapes dataset, we achieve 74.2 % Mean Intersection over Union (MIoU) with 13.6 M parameters at the speed of 38.2 FPS on a single 1070Ti card. To test the performance on the real-time application in the real world, LMFDCNet is deployed on the autonomous platform for real-time vision task such as classification of tobacco leaf state in curing process and semantic segmentation in the driving process of autonomous platform. The results reveal that LMFDCNet perform well on the AI embedded device and could be applied into intelligent autonomous platform.

Insights into the fabrication and antibacterial effect of fibrinogen hydrolysate-carrageenan loading apigenin and quercetin composite hydrogels

Escherichia coli and Staphylococcus aureus are the most prevalent pathogenic bacteria, often resulting in the foodborne disease outbreaks through food spoilage and foodborne infections. To prevent and control food spoilage and foodborne infections induced by Escherichia coli and Staphylococcus aureus, the antibacterial hydrogels were fabricated using fibrinogen hydrolysate-carrageenan (AHs-C) and flavonoids (apigenin and quercetin), and the antibacterial effect of the composite hydrogels against Escherichia coli and Staphylococcus aureus was further investigated. The results of mechanical property exhibited that the composite hydrogels with 0.2 % of apigenin and quercetin (AHs-C-Ap/Que) showed the highest hardness and swelling property compared with the separate addition of apigenin or quercetin. Scanning electron microscopy and atomic force microscopy showed that the dense networks were formed in the hydrogels of AHs-C-Ap/Que., and the average roughness of AHs-C-Ap/Que. significantly increased to 30.70 nm compared with AHs-C. 1H NMR and FTIR spectra demonstrated that apigenin and quercetin were bound to AHs-C by hydrogen bond, hydrophobic interaction and Schiff base, where the interactions between Ap/Que. and AHs-C was stronger compared with the separate addition of apigenin or quercetin. The hydrogels of AHs-C-Ap/Que. showed the highest antibacterial capacity and antibacterial adhesion against Escherichia coli and Staphylococcus aureus. The antibacterial adhesion assay showed that 99 % removal ratios for E. coli and S. aureus were observed in AHs-C-Ap/Que. hydrogels, which showed a great potential to prevent food spoilage and foodborne infections.

Complex network analysis of cryptocurrency market during crashes

This paper identifies the cryptocurrency market crashes and analyses its dynamics using the complex network. We identify three distinct crashes during 2017–20, and the analysis is carried out by dividing the time series into pre-crash, crash, and post-crash periods. Partial correlation based complex network analysis is carried out to study the crashes. Degree density (ρD), average path length (l̄), and average clustering coefficient (cc¯) are estimated from these networks. We find that both ρD and cc¯ are smallest during the pre-crash period, and spike during the crash suggesting the network is dense during a crash. Although ρD and cc¯ decrease in the post-crash period, they remain higher than pre-crash levels for the 2017–18 and 2018–19 crashes suggesting a market attempt to return to normalcy. We get l̄ is minimal during the crash period, suggesting a rapid flow of information. A dense network and rapid information flow suggest that during a crash uninformed synchronized panic sell-off happens. However, during the 2019–20 crash, the values of ρD, cc¯, and l̄ did not vary significantly, indicating minimal change in dynamics compared to other crashes. The findings of this study may guide investors in making decisions during market crashes.

Unraveling cereal physical barriers composed of cell walls and protein matrix: Insights from structural changes and starch digestion

Physical barriers composed of cell walls and protein matrix in cereals, as well as their cooking changes, play important roles in starch digestion. In this study, the physical barriers of native and cooked highland barley (HB), brown rice (BR), and oats (OA) kernels and their contribution to starch digestion were investigated. The resistant starch content was similar in cereal flours, but varied among cooked kernels (HB > BR > OA: 45.05 %, 10.30 %, and 24.71 %). The water adsorption, gelatinization enthalpy, and decrease in hardness of HB kernels were lower than those of OA and BR kernels. Microstructural observations of native kernels showed that HB had the thickest cell walls. After cooking, the lowest cell wall deformation and a dense continuous network developed from the protein matrix were observed in HB kernels. During digestion, undigested starch granules encapsulated by the stable cell walls and strong protein network were observed in HB kernels, but not in BR or OA kernels. Furthermore, the heavily milled HB kernels still had more resistant starch than the intact OA and BR kernels. Therefore, the physical barriers of HB kernels exhibited stronger inhibition of starch gelatinization and digestion. Differences in cereal physical barriers led to various inhibitory effects.

Low-rank sparse fully-connected tensor network for tensor completion

Fully-connected tensor network (FCTN) has recently drawn lots of attention in tensor completion due to its full description of all correlations between any two modes. However, the FCTN model has multiple ranks, and existing methods often ignore the difficulty brought about by rank selection, especially when the model is rank-sensitive. To overcome this drawback, this paper proposed a low-rank sparse FCTN for tensor completion. Specifically, we theoretically show that, for a tensor with FCTN structure, its subtensor can be represented as a coefficient sum of basic tensors, where the coefficients are remained in the FCTN’s factors. This means that factors’ sparsity can improve the robustness of FCTN to larger rank selection. Moreover, according to the relation between the target tensor and its FCTN’s factors, low-rank constrain is used to enhance rank robustness. Lastly, we optimize the proposed model by the alternating direction method of multipliers (ADMM) algorithm. Experimental results show that the low-rank sparse constraint effectively improves the rank robustness of the FCTN model, and achieves excellent results compared with other state-of-the-art completion methods.

Tracking aquatic non-native macroinvertebrate species in Germany using long-term data

Biological invasions pose a global challenge, threatening both biodiversity and human well-being. Projections suggest that as invasions increase, the financial costs associated with management and the ecological harm they cause will also escalate. Here, we examined whether long-term biomonitoring strategies were adequate to identify and track benthic aquatic non-native macroinvertebrate species by using the German subset (151 time series; 129 of which reported non-native species) of the currently most comprehensive European long-term dataset of 1816 macroinvertebrate community time series from 22 European countries. The detection of aquatic non-native species was directly linked to the availability of long-term sites and thus, monitoring effort, having identified the spatio-temporal occurrence of 32 non-native species. The available long-term monitoring site data were mostly concentrated in the western part of Germany, predominantly covering the Rhine River and its tributaries. The spatially biased network of long-term monitoring sites, therefore, naturally skews the detection and reporting of aquatic non-native species toward this area and underestimates Eastern and Southern regions, impeding the comprehension of invasion dynamics. However, based on the available data, we found that the absolute number of non-native species increased and the proportion of non-native species relative to native species decreased over time. This indicates complex ecological interactions between native and non-native species and underlines the value of long-term data for investigating invasion dynamics. Considering the value of comprehensive monitoring networks, a spatially biased network delays the application of management and mitigation plans, possibly worsening the ecological and economic effects of biological invasions in Germany. The results provided here indicate the disadvantages of biased datasets, but simultaneously underline the enormous potential of a dense network of long-term monitoring. Our results also highlight the urgent need to increase and diversify long-term biomonitoring efforts throughout Germany to cover the main freshwater resources and their connections where the introduction risk of non-native species is the highest. Centrally collating such data would provide a profound basis for the monitoring of spreading aquatic non-native species and could serve the implementation of national biosecurity efforts.

Quantitative detection of plasticizer migration in starch/polyvinyl alcohol film with UV-Vis and GC-MS/MS

In this work, the migration of urea and/or formamide plasticizers in crosslinked starch/polyvinyl alcohol (PVA) films under UV irradiation has been accurately quantified by using a combination of GC-MS/MS and UV–Vis analysis. The crosslinked starch/PVA films, prepared with different amide plasticizers (urea, formamide, urea-formamide), were characterized via scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry (DSC), and X-ray photoelectron spectroscopy (XPS). After 7 days of UV irradiation, the starch/PVA film plasticized with urea-formamide (A7-SPF-UF) exhibited a flat surface morphology, high crystal stability, and highest thermal stability. The migration ratio of urea and formamide for A7-SPF-UF was also quantified to be 3.2 % and 4.5 %, respectively, using GC-MS/MS and UV–Vis analysis, indicating optimal migration resistance. The synergistic effect of urea and formamide as plasticizers built the dense network structure of An-SPF-UF and improved the UV aging resistance, toughness and durability of the film. On one hand, this enhances the market competitiveness of the products; on the other hand, it promotes the development of environmentally friendly materials, aligning with sustainability requirements.

Spatial Correlation of Land Use Carbon Budget Based on Social Network Analysis： A Case Study of Chongqing Metropolitan Area

Clarifying the temporal and spatial changes in the carbon budget in the Chongqing metropolitan area and exploring the spatial correlation of land use carbon budget are of great significance for realizing the regional "double carbon" goal. Using 21 districts and counties in Chongqing metropolitan area as the research scale, the IPCC inventory method, carbon emission coefficient method, Gini coefficient, gravity model, and social network analysis were used to estimate the net carbon emissions from land use in Chongqing metropolitan area from 2000 to 2020, and the spatial correlation of the carbon budget was obtained. The results revealed that： ① In the past 20 years, the carbon budget of the Chongqing metropolitan area showed an overall upward trend, with an average annual growth rate of 2.83%, and the spatial distribution was "higher in the north and south, higher in the east and west, and lower in the middle." ② During the past 20 years, the spatial difference of net carbon emissions in the Chongqing metropolitan area became highly average, and the overall Gini coefficient decreased by 11.42%, whereas the intra-group difference was the largest in key development zones. ③ In the past 20 years, the overall structure of the spatial correlation network of land use carbon budget in the Chongqing metropolitan area has become stable and complex, and the network density and network correlation number have increased by 0.43 and 180, respectively, the network correlation degree has increased to 1, and the network health degree has improved. ④ In the individual network structure of land use carbon budget in the Chongqing metropolitan area, the degree centrality of each district and county has increased, and the increase in the central urban area was the most significant, with an increase of 81, whereas the decline in intermediate centrality and proximity centrality has promoted the regional coordinated development and integration process in the metropolitan area. ⑤ In the past 20 years, the density of net carbon emissions in the Chongqing metropolitan area has increased as a whole, with the density of the core area increasing by 0.35 and the density of the core-edge area increasing by 0.34. By exploring the change and spatial correlation of land use carbon budget in the Chongqing metropolitan area, this study clarified the spatial distribution difference of carbon budget and provided support for regional green development.

Routing algorithm for sparse unstructured P2P networks using honey bee behavior

SummaryUnstructured peer‐to‐peer (P2P) networks pose unique challenges for efficient and scalable routing. In this study, we introduce a novel routing algorithm inspired by the foraging behavior of honey bees named Honey Bee Optimization in P2P Networks (HBO_P2P) to address the inherent limitations of routing in unstructured P2P networks, focusing on improving packet delivery, minimizing hop count, reducing message overhead, and optimizing overall throughput. To evaluate the performance of our proposed algorithm, we conducted comprehensive experiments comparing it with existing algorithms commonly used in P2P networks, namely, particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization (ACO). After the simulation, we got the results as follows: Our algorithm outperforms ACO, GA, and PSO by exhibiting the highest number of data hops indicating potential efficiency in route optimization. Routing overhead is also minimal as compared to ACO, GA, and PSO. The average data packet delay is also low in our algorithm as compared to ACO, GA, and PSO. HBO_P2P achieves the highest throughput, nearly reaching 100 Mbps. While ACO and GA exhibit similar throughput of around 80 Mbps, and PSO has the lowest throughput, approximately 60 Mbps.

Dynamic recovery and recrystallization of an as-cast SX superalloy during hot deformation

The plastic deformation introduced during the cooling stage (above 1000 °C) of directional solidification is one of the primary reasons for the recrystallization of Ni-based single-crystal (SX) turbine blades in aero-engines during subsequent heat treatment. An as-cast SX superalloy DD33 was compressed at 1200 °C with a Gleeble thermo-mechanical simulator to mimic such deformation. The microstructural evolution, dynamic recovery, and dynamic recrystallization nucleation of the as-cast SX superalloy during hot deformation are investigated. The results show that the highest stored energy occurs in the vicinity of the eutectics, and its energy in the interdendritic regions is higher than that in the dendrite cores/arms. The formation of deformation bands and related transition bands near the eutectics are the primary characteristics of microstructural evolution during hot deformation. The dynamic recovery in the eutectic regions includes the entanglement and annihilation of dislocations at eutectic/matrix interface, within nearby γ matrix or within the eutectic γ′ phase, as well as the formation of dense dislocation networks in these sites. Subsequently, the low-angle grain boundaries in the transition bands migrate, merge, and finally transform into high-angle grain boundaries. In other words, the recrystallized grains nucleate near the eutectics via subgrain growth. In contrast, the dislocations only tangle and annihilate at the γ/γ′ interfaces in other interdendritic regions and the dendrite cores/arms without initiating recrystallization under moderate plastic deformation (εplastic = 11.9 %). This study will be helpful for understanding the local microstructural evolution of SX superalloys during directional solidification, as well as the recovery and recrystallization nucleation during the subsequent annealing.

Information Support or Emotional Support? Social Support in Online Health Information Seeking among Chinese Older Adults.

Online Health Information Seeking (OHIS) serves as an alternative form of social capital that can help older adults alleviate offline medical-related stress. This study collected and analyzed user interaction data from Patient-to-Doctor and Patient-to-Peer platforms and compared the roles of social support between them. Significant differences were identified in the dimensions of social support (information, emotional, and companion) on the Patient-to-Peer platforms compared with Patient-to-Doctor platforms (p < 0.05). The overall and core-core network density values for social support on Patient-to-Peer platforms were higher than those on Patient-to-Doctor platforms. Patient-to-Doctor interactions focused on information support, displaying a more centralized and efficient network with structural holes pertaining to treatment effects. By contrast, Patient-to-Peer interactions provided more emotional support, with a dispersed and redundant network containing structural holes related to individual information. Companion support was found to be weaker on both platforms. Additionally, digital literacy, surrogate seeking, and altruistic information significantly explained the variances between the two platforms (p < 0.01), with surrogate seeking playing a crucial role. These findings enhance our understanding of OHIS disparities among older adults and their surrogates, offering valuable insights for developing effective support systems and regulatory frameworks for health information platforms.

Spectrum Sensing Method Based on STFT-RADN in Cognitive Radio Networks.

To address the common issues in traditional convolutional neural network (CNN)-based spectrum sensing algorithms in cognitive radio networks (CRNs), including inadequate signal feature representation, inefficient utilization of feature map information, and limited feature extraction capabilities due to shallow network structures, this paper proposes a spectrum sensing algorithm based on a short-time Fourier transform (STFT) and residual attention dense network (RADN). Specifically, the RADN model improves the basic residual block and introduces the convolutional block attention module (CBAM), combining residual connections and dense connections to form a powerful deep feature extraction structure known as residual in dense (RID). This significantly enhances the network's feature extraction capabilities. By performing STFT on the received signals and normalizing them, the signals are converted into time-frequency spectrograms as network inputs, better capturing signal features. The RADN is trained to extract abstract features from the time-frequency images, and the trained RADN serves as the final classifier for spectrum sensing. Experimental results demonstrate that the STFT-RADN spectrum sensing method significantly improves performance under low signal-to-noise ratio (SNR) conditions compared to traditional deep-learning-based methods. This method not only adapts to various modulation schemes but also exhibits high detection probability and strong robustness.

Network compression with configuration models and the minimum description length.

Random network models, constrained to reproduce specific statistical features, are often used to represent and analyze network data and their mathematical descriptions. Chief among them, the configuration model constrains random networks by their degree distribution and is foundational to many areas of network science. However, configuration models and their variants are often selected based on intuition or mathematical and computational simplicity rather than on statistical evidence. To evaluate the quality of a network representation, we need to consider both the amount of information required to specify a random network model and the probability of recovering the original data when using the model as a generative process. To this end, we calculate the approximate size of network ensembles generated by the popular configuration model and its generalizations, including versions accounting for degree correlations and centrality layers. We then apply the minimum description length principle as a model selection criterion over the resulting nested family of configuration models. Using a dataset of over 100 networks from various domains, we find that the classic configuration model is generally preferred on networks with an average degree above 10, while a layered configuration model constrained by a centrality metric offers the most compact representation of the majority of sparse networks.

How Local Pollution and Transboundary Air Pollution Impact Air Quality in Jakarta?

Jakarta’s rapid population growth and industrialization have severely impacted air quality, with limited monitoring stations and increasingly complex pollutant sources influenced by meteorological factors. Source apportionment studies are critical for addressing urban air quality issues because knowing the source of pollution is an important step in overcoming urban air quality problems. This study uses the Geographically Weighted Regression (GWR) and Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) models to analyze the distribution and sources of NO2, SO2, and CO in Jakarta and surrounding areas. Data includes spatial information from road networks, industries, and the energy sector during the wet (December) and dry (June) months of 2023. GWR results show that network density significantly contributes to pollutant concentrations, particularly during the dry month, while industrial and energy sectors display seasonal variations. The HYSPLIT model indicates that in December, air masses carrying pollutants mainly originate from the northwest and north, and in June, from the southeast and southwest. These patterns underscore the regional influence of transboundary air pollution from industrial and energy plants. The combined GWR and HYSPLIT findings provide a comprehensive understanding of air pollution dynamics and the critical role of meteorological factors in pollutant distribution.