Industrial Park Research Articles (Page 1)

The concentration of air pollutants could be affected by climate change in industrial park zones in Hidalgo state, Mexico (IPHSs). The goals of this work were: (a) to describe the aerosols’ behavior (PM10 and PM2.5) and air pollutants (SO2, NO2, O3, and CO) in the IPHSs and (b) determine the climate variable behavior regarding the presence in IPHSs. The methodology consisted of structuring the time series of climate variables and air pollutants in six analysis regions. Afterwards, an annual average calculation, a count of days exceeding the allowed limits set by the official Mexican norms, an analysis of annual behavior by season, the Sen slope calculation, and correlation among variables were performed. Results demonstrated that Zone 2 is the most polluted, exceeding the allowed limits in the annual average (PM10 > 36 μg/m3, PM2.5 > 10 μg/m3, and NO2 > 0.021 ppm) and having more than 1000, 96, and 11 days where the daily limit was exceeded in PM10, PM2.5, and SO2, respectively. The minimum concentrations of the pollutants were observed during the summer–autumn seasons, coinciding with the highest precipitation. Regarding the correlations, the pollutants are negatively and statistically significantly correlated with precipitation (ranging from −0.81 to −0.43); meanwhile, the maximum temperature (ranging from +0.41 to +0.51) and evaporation (ranging from +0.39 to +0.54) are positively and statistically significantly correlated. In conclusion, the results could suggest that the presence of pollutants in the atmosphere may be influenced by the behavior of nearby regional climatic conditions in the IPHSs.

Industrial parks as critical carriers of China’s industrial development, they face significant pressure for low-carbon transformation. Current research on carbon emissions primarily focuses on national or sectoral levels, leaving a gap in understanding the driving factors and the carbon emissions–economic development mechanism at the industrial park level, which hinders precise emission reduction strategies. This study examines 44 industrial parks in the Yangtze River Delta, analyzes the temporal evolution of carbon emissions and assesses the economic development levels of the industrial parks based on the TOPSIS model. The Tapio decoupling model is adopted to analyze the decoupling status between carbon emissions and economic development, while the LMDI decomposition method is used to explore the driving factors. Empirical findings reveal that carbon emissions in these parks exhibit fluctuating growth, the decoupling state between carbon emissions and economic development is predominantly weak, transitioning to strong decoupling by 2023, key factors driving carbon emissions include per capita industrial development levels and industrial land scale effects. The combined application of the Tapio decoupling model and LMDI decomposition demonstrates universality in analyzing carbon emission drivers in industrial parks, providing theoretical and practical insights for carbon reduction in industrial parks.

This study enhances regional integrated energy systems by proposing a Stackelberg planning–operation model with seasonal hydrogen storage, addressing source–network separation. An equilibrium algorithm is developed that integrates a competitive search routine with mixed-integer optimization. In the price–energy game framework, the hydrogen storage operator is designated as the leader, while energy producers, load aggregators, and storage providers act as followers, facilitating a distributed collaborative optimization strategy within the Stackelberg game. Using an industrial park in northern China as a case study, the findings reveal that the operator’s initiative results in a revenue increase of 38.60%, while producer profits rise by 6.10%, and storage-provider profits surge by 108.75%. Additionally, renewable accommodation reaches 93.86%, reflecting an absolute improvement of 20.60 percentage points. Total net energy imbalance decreases by 55.70%, and heat-loss load is reduced by 31.74%. Overall, the proposed approach effectively achieves cross-seasonal energy balancing and multi-party gains, providing an engineering-oriented reference for addressing energy imbalances in regional integrated energy systems.

Abstract The concentration of chemical enterprises in chemical industry parks (CIPs) has led to the accumulation of hazardous chemical risks, frequent multi‐hazard coupling accidents, and escalation of domino effects. Existing evaluation methods struggle to characterize the interrelations between hazards, adaptability, and recovery characteristics. This work proposes a resilience‐assessment method for multi‐hazard coupling domino‐effects accidents in CIPs, considering safety barriers to fill these gaps. First, multi‐hazard coupling scenarios are identified by integrating the temporal clustering and spatial aggregation features of hazards. Second, the hazard disruption‐system feedback response mechanism is analyzed to establish a quantitative resilience model for CIPs. Third, the probabilities of multi‐hazard interactions and domino‐effect escalation are quantified to evaluate the influence of safety barriers on accident occurrence probabilities. Finally, case simulations are conducted to compare the impacts of different safety‐barrier configurations on resilience, providing recommendations for optimizing safety barriers in CIPs. Results indicate that the effectiveness of safety barriers significantly influences the strength of system adaptability and recovery capabilities in multi‐hazard coupling domino‐effect scenarios. Their performance directly affects the trough depth and recovery slope of the system‐performance curve.

Guided by the “Healthy China” initiative, understanding the impact of the built environment on running behavior is essential for encouraging regular physical activity and advancing public health. This study addresses a critical gap in healthy city research by examining the spatial heterogeneity in how urban environmental contexts affect residents’ running preferences. Focusing on two contrasting areas of Suzhou, namely the historic Gusu District and the modern Industrial Park District, we developed a 5Ds-based analytical framework (density, accessibility, diversity, design, and visual) that incorporates Suzhou’s unique water networks and street features. Methodologically, we used Strava heatmap data and multi-source environmental indicators to quantify built-environment attributes and examined their relationships with running-space selection. We applied linear regression and interpretable machine learning to reveal overall associations, while geographically weighted regression (GWR) was used to capture spatial variations. Results reveal significant spatial heterogeneity in how the built environment influences running-space selection. While the two districts differ in their urban form, runners in Gusu District prefer dense and compact street networks, whereas those in Industrial Park District favor open, natural spaces with higher levels of human vibrancy. Despite these differences, both districts show consistent preferences for spaces with a more intense land use mix, stronger transportation accessibility, and larger parks and green spaces. The multi-dimensional planning strategies derived from this study can improve the urban running environment and promote the health and well-being of residents.

Short-chain PFAS predominate in large-scale lithium battery industrial parks, Eastern China: Source apportionment and downstream impact implications.

Optimization design of nuclear-renewable integrated energy system in industrial parks considering carbon-emissions trading and green-certificate trading

Optimization scheduling strategy of high energy-consumption industrial park participation in green certificate trading and carbon emission trading

Partitioning dynamics of polycyclic aromatic hydrocarbons and their derivatives in water-sediment systems: Implications for microbial community perturbations and enhanced anthropogenic pathogenicity in riverine ecosystems.

Occurrence, spatial distribution, and risk assessment of per- and polyfluoroalkyl substances in soil and groundwater of a petrochemical industrial park in China.

Factors influencing the training effectiveness of firefighters in hydrogen industrial parks: a study based on the stacking method

The object of research is the management of industrialization and reindustrialization processes of the economy in a strategic dimension. The analysis of existing approaches to the formation of industrial policy in Ukraine revealed the main shortcomings, in particular, the fragmentation of state initiatives, the lack of a coherent long-term strategy, the low level of coordination between state and regional institutions, insufficient support for innovation and human capital. One of the most problematic areas is the lack of a comprehensive model of reindustrialization management that would take into account modern technological, economic and environmental challenges. The research used methods of historicism, theoretical generalization, logical-structural analysis, as well as economic and mathematical modeling to construct an integral index of industrialization, cluster grouping and forecasting the dynamics of industrial development of Ukraine until 2033. A quantitative assessment of the level of industrialization of Ukraine over the past 30 years has been obtained and a forecast of reindustrialization has been constructed, which indicates the potential for moderate growth under the condition of implementing an effective industrial policy. This is due to the fact that the proposed model of reindustrialization management is multi-level, provides for the definition of a strategic goal, time horizons and specific instruments (tax incentives, industrial parks, public-private partnership, R&D programs). This ensures the possibility of achieving an increase in the share of industry in GDP, an increase in the industrial production index and technological complexity of exports. Compared to known approaches, the model has advantages in complexity, phased implementation and integration of digital and green technologies, which allows adapting industrial policy to global challenges.

Existing methods plan the distribution network and sub-transmission network separately. This paper proposes a collaborative renewable energy resource siting and sizing planning method for distribution and sub-transmission networks to increase the renewable energy ratio in high-load density industrial parks and promote the hosting capacity of the power grid. First, to accurately measure planning effectiveness, a renewable energy ratio calculation method is proposed, which comprehensively considers the contributions of green electricity from the power grid and renewable energy generation inside and outside the industrial park. Second, a collaborative planning model is proposed, which optimizes access points and access capacity in the distribution and sub-transmission networks for renewable energy around the park. The net load is better matched with the output of renewable energy outside the park through demand response, thereby maximizing the utilization of the park load to host more renewable energy. Finally, the proposed method is verified in a real industrial park. The method outperforms traditional planning methods in terms of renewable energy ratio in the park and renewable energy hosting capacity outside the park.

In the context of large-scale renewable integration and increasing demand for power-system flexibility, energy-storage systems are indispensable components of modern grids, and their safe, reliable operation is a decisive factor in investment decisions. To mitigate lifecycle degradation and cost increases caused by frequent charge–discharge cycles, this study puts forward a two-layer energy storage capacity configuration optimization approach with explicit integration of cycle life restrictions. The upper-level model uses time-of-use pricing to economically dispatch storage, balancing power shortfalls while maximizing daily operational revenue. Based on the upper-level dispatch schedule, the lower-level model computes storage degradation and optimizes storage capacity as the decision variable to minimize degradation costs. Joint optimization of the two levels thus enhances overall storage operating efficiency. To overcome limitations of the conventional Whale Optimization Algorithm (WOA)—notably slow convergence, limited accuracy, and susceptibility to local optima—an Improved WOA (IWOA) is developed. IWOA integrates circular chaotic mapping for population initialization, a golden-sine search mechanism to improve the exploration–exploitation trade-off, and a Cauchy-mutation strategy to increase population diversity. Comparative tests against WOA, Gray Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) show IWOA’s superior convergence speed and solution quality. A case study using measured load data from an industrial park in Zhuzhou City validates that the proposed approach significantly improves economic returns and alleviates capacity degradation.

Introduction: Accurate short-term photovoltaic (PV) power prediction is crucial for ensuring the safety of the power grid and promoting the consumption of PV. However, due to factors such as Internet access conditions and information security control, some industrial parks are unable to obtain real-time weather forecast information, and there is an urgent need to develop a PV power prediction method that does not rely on weather forecast information. Method: In this paper, we propose an innovative architecture for the Morphological Filtering Empirical Wavelet Transform (MFEWT) and the Frequency Decoupled Multi-Cycle Fusion Network (FDMFNet). Firstly, MFEWT is used to decompose the historical photovoltaic (PV) power generation into a number of frequency sub-sequences, which are then classified into major and minor components based on their frequency energies. For the primary component, amplitude features are extracted using the Hilbert transform and supplemented with feature columns using the Signal Cycle- Based Adaptive Historical Feature Set Construction Method (CAF), which are then predicted by a convolutional neural network. For the secondary component, the FDMFNet is designed to accurately capture the trend of the high-frequency secondary components through frequency-domain decoupling. Finally, the major and minor components are linearly superimposed to obtain the final PV power prediction. Result: Experimental validation based on photovoltaic (PV) datasets from two industrial parks in China, Wuhan and Qingdao, confirms the effectiveness of the proposed method. In terms of overall prediction accuracy, the MAE of the proposed method is 0.2108, the RMSE is 0.3297, and the R2 is 0.8708. Discussion: The MFEWT proposed in this study effectively suppresses mode mixing. Combined with Hilbert Transform and FDMFNet, it achieves multi-scale feature mining of PV power. This fusion mechanism significantly enhances the model's capability to characterize the intrinsic timefrequency properties of PV sequences. Conclusion: The PV prediction method introduced in this paper, which operates without weather forecast information, demonstrates favorable accuracy and stability in actual ultra-short-term PV power prediction scenarios. It provides a novel solution for PV power forecasting under conditions where meteorological information is lacking.

With the rapid growth of electricity demand in industrial parks and the increasing penetration of renewable energy, vehicle-to-grid (V2G) technology has become an important enabler for mitigating grid stress while improving charging economy. This paper proposes a multi-objective rolling linear-programming-model-based predictive control (LP-MPC) method for coordinated electric vehicle (EV) scheduling in industrial park microgrids. The model explicitly considers transformer capacity limits, EV state-of-charge (SOC) dynamics, bidirectional charging/discharging constraints, and photovoltaic (PV) generation uncertainty. By solving a linear programming problem in a receding horizon framework, the approach simultaneously achieves load peak shaving, valley filling, and EV revenue maximization with real-time feasibility. A simulation study involving 300 EVs, 100 kW PV, and a 1000 kW transformer over 24 h with 5-min intervals demonstrates that the proposed LP-MPC outperforms greedy and heuristic load-leveling strategies in peak load reduction, load variance minimization, and charging cost savings while meeting all SOC terminal requirements. These results validate the effectiveness, robustness, and economic benefits of the proposed method for V2G-enabled industrial park microgrids.

Chlorinated paraffins (CPs), emerging persistent organic toxic pollutants, are widely used in industrial production as sizing agents and flame retardants. To assess their pollution distribution and risks in soils of Jiangmen Electronic Industrial Park, surface samples from the park and surroundings were collected and detected using gas chromatography-low resolution mass spectrometry-negative chemical ionization source. Results showed the concentrations of short chain chlorinated paraffins (SCCPs) and medium chain chlorinated paraffins (MCCPs) ranged from 144.4 to 1160ng/gdw and 90.5 to 1020ng/gdw, with chlorine contents of 60.8% ~ 62.8% and 56.7% ~ 58.2% respectively. The CPs contents varied significantly among different sampling points, with the highest concentration in canal-side soils. Hierarchical clustering grouped 20 samples into 4 clusters, with the homologue composition mainly C10Cl6-7, C13Cl7-8 and C14Cl7-9. Principal component analysis indicated that the toxic sources might be related to the production and use of commercial products such as CP-42 and CP-52. Risk assessment showed SCCPs RQ 0.0273 ~ 0.2199, covering low and medium risks; MCCPs RQ 0.0032 ~ 0.0364, covering low and extremely low risks. Human exposure assessment showed the CPs exposure of adults and children through soil ingestion and skin contact was lower than 10μg(kg·d)-1, indicating the toxic and health risks in Jiangmen Electronic Industrial Park were low.

The Puebla Metropolitan Area, one of the most industrialized regions in Mexico, shows severe contamination of both surface and groundwater. In this study a multi-tracer approach combining hydrochemistry with environmental isotopes (δ2H, δ18O, 3H) was applied to evaluate groundwater–surface water (GW–SW) interactions and their role in water quality degradation. Elevated concentrations of aluminum, iron, zinc, and lead were detected in the Alseseca and Atoyac Rivers, exceeding national standards, while arsenic, manganese, and lead in groundwater surpassed Mexican and WHO drinking water limits. The main sources of contamination include volcanic inputs from Popocatepetl activity (e.g., arsenic) and untreated discharges from industrial parks (e.g., lead), which together introduce significant loads of Potentially Toxic Elements (PTEs) into surface and groundwater. Isotopic analysis identified three sources for aquifer recharge: (1) recharge from high-altitude meteoric water, (2) mixed GW–SW water recharged at intermediate elevations with heavy metal presence, and (3) recharge from lower altitudes (evaporate water). Tritium confirmed both modern and old recharge, while isotope-based mixing models indicated surface water contributions to groundwater ranging from 18% to 72%. These interpretations were derived from the integrated analysis of hydrochemical and isotopic data, allowing the quantification of recharge sources, residence times, and mixing processes. The results demonstrate that hydraulic connectivity, enhanced by fractures and faults, facilitates contaminant transfer from polluted rivers into the aquifer.

In the midst of a global discourse on the future of work, the voices of African workers, particularly those in low-wage positions and within global value chains, remain largely unheard. This study delves into the perspectives of workers in Ethiopian industrial parks on the future of work and its implications for their ability to reproduce their social lives. While some workers embrace the opportunities presented by industrial transformation and technological advancements, many are wary of possible job disruptions and workplace alterations. Still there are others who are open to adapting to these changes. Drawing on feminist labour geography, the study challenges the simplistic dystopian/utopian dichotomies that structure imaginaries of technological change and the future of work, arguing that workers’ perspectives offer a nuanced pathway to geographically-grounded interpretations. As anthropologists seeking to envision a changing Africa, we must heed local labour-centred knowledge and the ongoing struggles over the reproduction of labour, which serve as the bedrock for large-scale economic transformations.

Freshwater resources face increasing global threats from overuse, pollution and industrial expansion, with industrial wastewater introducing toxic pollutants that jeopardise human health and sustainable development. In Rwanda, rising industrialisation, particularly around industrial parks, has heightened risks to rivers such as Rwamurinzi and Kiruhura. This study assessed the impact of wastewater discharged from the Rwamagana Industrial Park on the quality of these rivers in Eastern Rwanda. Between January and March 2025, both wastewater effluents and river water samples were systematically collected from upstream and downstream sections. Samples were analysed for physico-chemical parameters, including pH, dissolved oxygen and electrical conductivity, as well as heavy metals—lead (Pb), cadmium (Cd), and chromium (Cr). Findings revealed that concentrations of Pb, Cd, and Cr in both wastewater and river water exceeded Rwanda Standards Board limits, while pH and electrical conductivity largely remained within acceptable ranges. The Kiruhura River showed notably higher heavy metal concentrations, closely resembling those in the industrial wastewater and significantly higher than levels in the Rwamurinzi River. Statistical analysis demonstrated a strong correlation between wastewater composition and river water quality, confirming industrial discharges as a primary contamination source. The results highlight the urgent need for improved wastewater treatment systems and stricter regulatory enforcement in industrial parks. While this study focused on water samples and three metals, future research should expand to include sediment analysis and a wider range of pollutants to fully capture the environmental impacts of industrial activities.