Emission Standards Research Articles

Nonlinear interaction and compounding factors of vehicle parameters influencing exhaust pollution.

One of the main causes of air pollution, particularly in large cities, is vehicles due to it continued use of hydrocarbon fuels. The understanding of nonlinear interactions of vehicle parameters uncovers more realistic relationships for enhancing formulation of strategies to address vehicle-related pollution. Thus, the study aims to evaluate the interaction and quadratic effect of vehicle parameters on Hydrocarbon (HC), Carbon dioxide (CO2), Carbon monoxide (CO), and Nitrogen oxide (NOx) emissions. The SV-5Q Vehicle Exhaust Gas Analyzer was used to collect emission concentrations data from one thousand and two (1002) light-duty petrol vehicles at three (3) government-accredited vehicle inspection sites in Accra, Ghana. Pollution control devices, maintenance frequency, and vehicle age were also collected. The linear regression analysis revealed that vehicle age showed a positive linear relationship with CO emissions. Maintenance frequency, on the other hand, demonstrated a negative linear relationship with both CO and HC emissions. The interaction between vehicle age and maintenance frequency positively impacted CO and HC emissions, while the interaction between vehicle age and emission technology had a negative effect on CO. Additionally, the combined effect of frequency of maintenance and emission technology significantly reduced CO emissions but increased HC emissions. Notably, the quadratic effect of vehicle age positively influenced CO emissions. Similarly, CO, HC, and NOx emissions were positively correlated with the squared effect of emission technology. Stricter emissions standards, encouraging frequent maintenance and testing of vehicular exhaust emissions, and doing away with over-aged vehicles are recommended to control and reduce vehicular exhaust emissions.

Assessment of Air Pollution Tolerance Index (APTI) and Expected Performance Index (EPI) of Plants along Nsukka, Enugu Road, Nigeria

Air pollution is a worldwide ecological problem that threatens human health through harmful emissions as vehicular exhaust emission. There is a need to examine ecological role of plants growing around major roads to identify their susceptibility and resistance towards air pollutants. Air Pollution Tolerance Index (APTI) and Expected Performance Index (EPI) of some plants growing along Nsukka - Enugu road in Enugu State, Nigeria were estimated to explore plants’ ability to absorb air pollutants. Twenty-eight plant species comprising herbs, shrubs and tree species were sampled and determination of APTI used combined biochemical parameters measurements. The APTI results showed that herbs are sensitive with APTI range of < 7 to 11.91, shrubs are mostly moderate tolerant with range of 9.38 to ≤ 17.43 while trees species are tolerant that ranged from ≥ 10.58 to ≥ 23.26 at t ≤ 1.51, p>0.05 in the experimental and control sites respectively. Based on APTI grades, Rauvolfia. vomitoria (11.91) had highest sensitive index and Erigeron canadensis (6.45) had the least, Persea americana (16.11) had highest moderate tolerant index and Ricinus. communis had the least while Terminalia. catappa (23.26) produced the highest tolerant index and Gmelina arborea (17.50) had the least in the experimental site. The EPI revealed that plants can be categorized as fit, very fit and unfit for plantation and those under ‘excellent to best’ performer category can be highly recommended as ‘very fit’ for plantation to control air pollution. Dacryodes edulis (94.74%) was in the ‘Best’ performer category and the best recommended species for plantation planting to monitor and control air pollutants.

A geospatial approach for dynamic on-road emission through open-access floating car data

Abstract This paper presents a geospatial approach for quantifying street-level on-road emissions of carbon dioxide (CO2), nitrogen oxides (NOx), and carbon monoxide (CO). By leveraging an existing open-access database of real-time congestion information derived from floating car data, we tested three methods to map high-resolution dynamic traffic emissions. To demonstrate the robustness and accuracy of the methods, we showcased results for summer workdays and winter weekends in the Helsinki Metropolitan Area (HMA). The three methods employed include (1) a physics-based relation known as the macroscopic fundamental diagram, (2) a data-driven input-adaptive generalized linear model (GLM), and (3) their ensemble (ENS). These methods estimated traffic density with satisfactory accuracy (R 2 = 0.60–0.88, sMAPE = 31%–68%). Utilizing speed-dependent emission factors retrieved from a European database, the results compared favorably against the downscaled national emission inventory, particularly for CO2 (R 2 = 0.70–0.77). Among the three methods, GLM exhibited the best overall performance in the HMA, while ENS provided a robust upscaling solution. The modeled emissions exhibited dynamic diurnal and spatial behavior, influenced by different functional road classes, fleet compositions and congestion patterns. Congestion-induced emissions were calculated to account for up to 10% of the total vehicular emissions. Furthermore, to anticipate the forthcoming transportation transformation, we calculated emission changes under scenarios with various penetration rates of connected and autonomous vehicles (CAVs) using this geospatial approach. The introduction of CAVs could result in emission reductions of 3%–14% owing to congestion improvements.

Multi-Objective Cooperative Adaptive Cruise Control Platooning of Intelligent Connected Commercial Vehicles in Event-Triggered Conditions

With the rapid increase in vehicle ownership and increasingly stringent emission regulations, addressing the energy consumption of and emissions from commercial vehicles have become critical challenges. This study introduces a multi-objective cooperative adaptive cruise control (CACC) strategy, designed for intelligent connected commercial vehicle platoons, operating in event-triggered conditions. A hierarchical control framework is utilized: the upper layer handles reference speed planning based on vehicle dynamics and constraints, while the lower layer uses distributed model predictive control (DMPC) to manage vehicle following. DMPC is chosen for its ability to manage distributed platoons by enabling vehicles to make local decisions, while maintaining system-wide coordination. Additionally, adaptive particle swarm optimization (APSO) is employed during the optimization process to solve the optimal problem efficiently. APSO is employed for its computational efficiency and adaptability, ensuring quick convergence to optimal solutions with reduced overheads. An event-triggering mechanism is integrated to further reduce the computational demands. The simulation results show that the proposed approach reduces fuel consumption by 8.05% and NOx emissions by 10.15%, while ensuring stable platoon operation during dynamic driving conditions. The effectiveness of the control strategy is validated through extensive simulations, highlighting superior performance compared to conventional methods.

Research on Rapid Cycle Test Method in Low-Temperature of Electric Vehicle Driving Range Based on China Light-Duty Vehicle Test Cycle

Research on Rapid Cycle Test Method in Low-Temperature of Electric Vehicle Driving Range Based on China Light-Duty Vehicle Test Cycle

Ideal comfort ellipses and comfort dynamics model for mitigating motion sickness in battery electric vehicle

Due to the rapid response and high torque output of the electric motor, drivers and passengers in battery electric vehicles are more prone to dizziness. This is particularly challenging for mitigating motion sickness, as acceleration performance conflicts with ride comfort. In this article, we develop ideal comfort ellipses based on various driving cycles, which show a comfort zone for driving. A comfort dynamics model is formulated using acceleration, which is then applied to design a model predictive controller. In addition, the motion sickness dose value is used as an indicator to evaluate the effectiveness of the control strategy. The proposed approach is validated on a battery electric vehicle under the Economic Commission for Europe driving cycle, Worldwide harmonised Light-duty vehicles Test Cycle and linear driving condition. The results indicate that, compared to scenarios without the control strategy, this approach reduces the motion sickness dose value by 20.34%, 34.67%, and 24.11%, respectively, enhancing ride comfort and mitigating motion sickness.

Parking Pricing in the Morning Commute Problem Considering Human Exposure to Vehicular Emissions

Walking is the final phase of the morning commute, during which commuters are exposed to vehicular emissions. This study proposes a novel analytical model to evaluate how emission exposure affects commuters’ departure time choices and parking behavior. Different from traditional bottleneck models, our model includes a nonlinear term in the generalized cost function to account for emission exposure. The findings reveal that, at user equilibrium, rational commuters seeking to minimize their own generalized costs will park outward, resulting in undesired scenarios in which all walking commuters suffer from emission exposure. However, we show that in a system-optimal scenario, emission exposure can be eliminated if commuters park inward; the schedule delay cost is minimized in such a parking order. To achieve this outcome, we propose a new spatiotemporal parking pricing scheme designed to reduce the overall system cost and incentivize inward parking patterns. Case studies using empirical data show that this pricing approach, independent of specific parking orders, effectively encourages inward parking, thereby minimizing emissions and improving commuter welfare. Hopefully, findings from this research can provide insights to the development of effective roadside parking pricing strategies.

Methane emission reduction through hydrogen blending in a large bore 2-stroke lean-burn natural gas compressor engine

Impending and increasingly stringent emissions regulations regarding natural gas compressor engines drive the research behind blending hydrogen with natural gas to make these internal combustion engines and their combustion process more efficient. This investigation seeks to answer two fundamental questions: will blending hydrogen with natural gas reduce overall engine fuel consumption, and can greenhouse gas emissions be reduced by blending hydrogen with natural gas? A 4-cylinder Cooper–Bessemer GMV engine, housed at Colorado State University’s Powerhouse facility, was investigated for hydrogen–natural gas blending using multiple engine configurations. A lean-burn engine uses an active pre-combustion chamber as its ignition source, along with electronically activated high pressure fuel injection in the main combustion chamber. One configuration tested utilized high-pressure fuel injection and blending in hydrogen, up to 40% by volume, in both the main chamber and pre-combustion chamber fuel supplies. A second configuration, where the main combustion chamber fuel was solely natural gas and only the pre-combustion chamber received hydrogen-blended natural gas, was also tested. The final configuration to be tested used low pressure fuel injection with mechanically actuated valves in the main chamber with a traditional spark plug ignition source. All engine configurations saw reductions in methane emissions of up to 30% using blended natural gas and hydrogen. Carbon dioxide emissions were also shown to be reduced for the two configurations. A reduction in brake-specific fuel consumption of up to 2% was also seen for two configurations. These results support the hypothesis that blending hydrogen into natural gas can reduce engine total fuel consumption and reduce greenhouse gas emissions.

Analysis of EU’s Coupled Carbon and Electricity Market Development Based on Generative Pre-Trained Transformer Large Model and Implications in China

With increasing global attention on carbon emission control and sustainable development, the carbon market has become an important tool for promoting environmental sustainability and carbon emission regulation. As an important part of energy trading, the electricity market is closely related to the carbon market and their effective coupling is critical for achieving sustainable energy transitions. Power generation can effectively link carbon prices and electricity prices. Through the promulgation of reasonable policies, we can promote the coupling of the carbon market and the electricity market, and then, through macro-control, promote the steady and joint development of the carbon and electricity markets, improve the construction of the carbon rights market, reduce greenhouse gas emissions, and create a sustainable future for humanity. Given the relative maturity of the EU carbon market and electricity market, this paper identifies key points of market volatility through the coupling coordination model analysis and uses the GPT large model analysis to analyze policy factors that have a greater impact on carbon market and electricity market transactions. The research results show that market-regulating policies and expectation management policies have the greatest impact on the degree of coupling between the carbon and electricity markets. The impact of secondary policies such as international cooperation policies and structural adjustment policies is relatively insignificant. Although the development of China’s carbon market faces many challenges, reasonable policy interventions are expected to achieve significant results in the domestic carbon market. Finally, this paper also draws on analogy analysis to draw corresponding implications for China from the EU’s carbon–electricity coupling policy promulgation.

Multi-model effective radiative forcing of the 2020 sulfur cap for shipping

Abstract. New regulations of sulfur emissions from shipping were introduced in 2020, reducing emissions of SO2 from international shipping by ∼ 80 %. As SO2 is an aerosol precursor, this drop in emissions over the ocean will weaken the total aerosol effective radiative forcing (ERF) that has historically masked an uncertain fraction of the warming due to the increased concentration of greenhouse gases in the atmosphere. Here, we use four global climate models and a chemical transport model to calculate the ERF resulting from an 80 % reduction in SO2 emissions from international shipping relative to 2019 emission estimates. The individual model means range from 0.06 to 0.09 W m−2, corresponding to the ERF resulting from the increase in CO2 concentration over the last 2 to 3 years. The full uncertainty in the ERF due to the new regulation is not quantified but will very likely be high considering the contribution of uncertainties in shipping SO2 emissions, the sulfur cycle, the modelling of cloud adjustments and the impact of interannual variability on the method for calculating radiative forcing.

Accelerating Agile Quality Assurance with AI-Powered Testing Strategies

The infusion of Artificial Intelligence (AI) into Agile software development is revolutionizing the domain of software testing, reshaping conventional methodologies to meet the demands of today’s complex and accelerated development cycles. Agile frameworks, renowned for their iterative workflows and adaptability, often encounter limitations in scaling to the velocity and intricacy of modern projects. AI emerges as a game-changer, introducing sophisticated capabilities such as hyper-automation, predictive defect analytics, and context-aware decision-making, thereby addressing these limitations with precision and scalability. This paper investigates the transformative influence of AI on Agile testing methodologies, with a focus on specific use cases, the operational efficiencies gained through AI-augmented workflows, and the seamless collaboration between human testers and intelligent systems. A comprehensive, architecture-driven framework for embedding AI into Agile testing cycles is presented, with empirical validation through case studies that demonstrate tangible improvements in accuracy, productivity, and sprint adaptability. Keywords: Agile Methodology, Artificial Intelligence, Quality Assurance, Predictive Analytics, Test-Driven Development (TDD), Behavior-Driven Development (BDD), Natural Language Processing

Next-Gen Test Automation in Life Insurance: Self-Healing Frameworks

The exponential complexity of contemporary Life Insurance applications - shaped by stringent regulatory compliance requirements, dynamic customer - centric innovation, and expansive product diversification - has magnified the challenges of maintaining traditional test automation frameworks. These frameworks often falter under the weight of frequent application updates, evolving user interfaces (UI), and fluctuating backend integrations, leading to brittle test scripts and heightened maintenance costs. Self-healing test automation frameworks offer a cutting-edge, AI-driven solution to this paradigm. By leveraging advanced machine learning (ML) algorithms and intelligent heuristics, these frameworks autonomously detect, diagnose, and remediate test failures caused by changes in application elements, workflows, or environments. Through real-time adaptation to UI modifications, API updates, and evolving test environments, self-healing frameworks ensure uninterrupted testing cycles with minimal manual intervention, significantly improving efficiency and scalability. This paper provides a comprehensive exploration of the architectural design and operational mechanisms of self- healing test automation frameworks within the context of Life Insurance applications. Core focus areas include their ability to dynamically recalibrate element locators and adapt test scripts during runtime, thereby enhancing test coverage, optimizing maintenance workflows, and reducing the overall defect detection lifecycle. Through an in-depth analysis, the paper highlights the integration of AI-powered locator optimization engines, the deployment of predictive analytics for early anomaly detection, and the orchestration of continuous self-healing pipelines in CI/CD ecosystems. Detailed case studies from the Life Insurance domain illustrate tangible benefits such as accelerated regression cycles, substantial cost reductions, and improved system resilience. Keywords: Artificial Intelligence, Machine Learning, Self – Healing, Life Insurance Applications, Quality Assurance, Test Maintenance Efficiency

Potential Strategies to Improve Air Quality in Tirana for Current and Future Generations

Tirana, the capital of Albania, faces severe air pollution, posing significant health risks and environmental concerns for its residents. Referring to the data for the year 2023, KTA admits that Tirana exceeds the European Union norms for PM10 and nitrogen dioxide. Annual average concentrations of PM10 and NO2 in Tirana are above the limit values of National Air Quality Standard and World Health Organization. This paper looks at the several issues that lead to Tirana's air pollution and considers ways to lower pollution levels and enhance air quality. The problems with Tirana's air pollution are intricate and varied, resulting from a confluence of elements including as topography, industrial processes, burning of biomass, vehicular emissions, and insufficient monitoring programs. Addressing these challenges requires regulatory interventions, technological innovations, public awareness campaigns and stakeholder engagement to improve air quality. Tirana has a population of 528,000 inhabitants, with a surface area of 40 km² and a population density of 13.2 thousand inhabitants. Tirana has the capacity to develop more resilient, sustainable and habitable urban areas. To realize innovative ideas, enhance green areas, and improve air quality for present and future generations, cooperation amongst urban planners, legislators, community organizations, and citizens is imperative. We identify critical actions for policy makers, urban planners, and stakeholders to mitigate air pollution and build a healthier and more sustainable urban environment in Tirana based on pollution sources, regulatory frameworks, and ongoing initiatives. This paper's goal is to offer solutions for enhancing air quality while advancing environmental sustainability and public health. Received: 2 August 2024 / Accepted: 10 November 2024 / Published: 3 December 2024

Advances in the Degradation of Polycyclic Aromatic Hydrocarbons by Yeasts: A Review

Polycyclic aromatic hydrocarbons (PAHs) are toxic organic compounds produced during the incomplete combustion of organic materials and are commonly found in the environment due to anthropogenic activities such as industrial and vehicular emissions as well as natural sources, mainly volcanic eruptions and forest fires. PAHs are well known for their bioaccumulative capacity and environmental persistence, raising concerns due to their adverse effects on human health, including their carcinogenic potential. In recent years, bioremediation has emerged as a promising, effective, and sustainable solution for the degradation of PAHs in contaminated environments. In this context, yeasts have proven to be key microorganisms in the degradation of these compounds, owing to their ability to metabolize them through a series of enzymatic pathways. This review explores the advancements in yeast-mediated degradation of PAHs, with a particular focus on the role of enzymes such as cytochrome P450 (CYPs), epoxide hydrolases (EHs), and glutathione S-transferases (GSTs), which facilitate the breakdown of these compounds. The review also discusses the applications of genetic engineering to enhance the efficiency of yeasts in PAH degradation and the use of omics technologies to predict the catabolic potential of these organisms. Additionally, it examines studies addressing the degradation of benzo[a]pyrene (BaP) by yeasts such as Debaryomyces hansenii, and the potential future implications of omics sciences for developing new bioremediation.

2023 홍성 대형 산불로 인한 온실가스별 지역 주민 건강 피해 영향 분석

This study assessed the health impacts of greenhouse gases released by the 2023 large-scale Hongseong wildfire on local residents. Key greenhouse gases produced by wildfires: CO₂, CO, CH₄, N₂O, NO, and NO₂, were selected, and their emissions were quantified using the wildfire emission standards of the Intergovernmental Panel on Climate Change. Through ALOHA modeling, the toxicity and flammability risk ranges for each gas were calculated to analyze their potential health effects on residents. The findings suggest that wildfire-related greenhouse gases may be particularly harmful to the elderly and those with pre-existing conditions. Proactive measures are essential to protect local communities from future wildfires.

Spatial Exposure of EMF from Phone Base Stations in the North of Libreville, Gabon

The electromagnetic environment became dense with the spread of its artificial sources in urban and rural areas. More and more publications are describing their unfavourable effects, including human well-being deterioration, disruption of the nervous system functions or cancer occurrence. Also, there is a legal loophole related to electromagnetic fields (EMF) in many countries. The aim of this contribution is to map people's exposure to EMF from phone base stations in the north of Libreville, Gabon. Data on phone base station emissions have been collected with Spectran 6080HF, processed with MCS Spectrum Analyser 2.1.6, transferred in a WPS spreadsheet, and converted to a shape file before mapping them in ArcMap and examining spatial concentration in QGis. Phone base station emissions vary in space and time between 0 to 488,09 V/m from June to December 2021. The threshold of phone base station emissions standards is exceeded at different periods for all mobile phone network operators, by the Salzburg Resolution and Gabonese standards. Furthermore, the concentration of EMF exposure varies in space and concerns more Akanda than Libreville. This fact explains the spatio-temporal variation of people at risk, as all types of districts and social classes are concerned. Based on the precautionary principle, and because this risk is a matter of accumulation of exposure, it is important to revise standards and raise awareness of EMF pollution as cancer cases and related diseases have grown amidst struggling health systems for years

Combustion and Emission Characteristics of Methanol–Diesel Dual Fuel Engine at Different Altitudes

Currently, in the two technological approaches for using diesel pilot injection to ignite methanol and partially substituting diesel fuel with methanol, neither can fully achieve carbon neutrality in the context of internal combustion engines. Compression-ignition direct-injection methanol marine engines exhibit significant application potential because of their superior fuel economy and lower carbon emissions. However, the low cetane number of methanol, coupled with its high ignition temperature and latent heat of vaporization, poses challenges, especially amidst increasingly stringent marine emission regulations. It is imperative to comprehensively explore the impacts of the engine geometry, intake boundary conditions, and injection strategies on the engine performance. This paper first investigates the influence of the compression ratio on the engine performance, subsequently analyzes the effects of intake conditions on methanol ignition characteristics, and finally compares the combustion characteristics of the engine under different fuel injection timings. When the compression ratio is set at 13.5, only an injection timing of −30 °CA can initiate methanol compression ignition, but the combustion is not ideal. For compression ratios of 15.5 and 17.5, all the injection timings studied can ignite methanol. Reasonable increases in the intake pressure and intake temperature are beneficial for methanol compression ignition. However, when the intake temperature rises from 400 K to 500 K, a decrease in the thermal efficiency is observed. Particularly, at an injection timing of −30 °CA, both the peak cylinder pressure and peak cylinder temperature are higher, the ignition occurs earlier, the combustion process shifts forward, and the combustion efficiency and indicated thermal efficiency are at higher levels. Furthermore, the overall emissions of NOX, HC, and CO are relatively low. Therefore, selecting an appropriate injection timing is crucial to facilitate the compression ignition and combustion of methanol under low-load conditions.

From a sustainable natural rubber sponge to an activation-free sulfur-rich biocarbon sponge as a potential electrode material for a supercapacitor

A natural rubber sponge (NRP) was prepared by varying the carbon black, blowing agent, and sulfur contents and subsequently used as a precursor for biocarbon sponge (BC) production. The experiments and the machine learning approach used an adaptive multi-input, fuzzy-rules, emulated network (MiRREN) to optimize the NRP formulation to achieve the best BC properties. Based on the results, adding carbon black stabilized the shape of the BC samples after carbonization. The BC samples had an interconnected pore structure and oxygen and sulfur functionalities. The standard isotherm of the sample without carbon black was type IV, which was transformed to type II after adding carbon black, with a wide range in the pore size distribution. The addition of carbon black improved the electrical conductivity from 1.13 S cm−1 to a maximum value of 12.78 S cm−1. At 0.25 A/g, the samples’ specific capacitance was enhanced to 152.7 and 137.9F/g using certain conditions with carbon black. The sample with 50, 6, and 20 phr of carbon black, blowing agent, and sulfur contents, respectively, had the best cyclic performance (113.7 %) at 10,000 cycles of testing, verifying the excellent cyclic durability due to the surface functionalities and the addition of the carbon black, as well as the developed interconnected pore structure. Based on the results of the machine learning study, the pore volume and specific capacitance were likely correlated with the blowing agent content rather than the sulfur content. The optimum formulation of NRP for achieving the best specific capacitance levels at all the applied current densities was 7.02 phr of blowing agent and 20.2 phr of sulfur content.

Enhanced NiO-CaO-based multifunctional material pellets with Ce and La oxides on aluminosilicate support for sorption-enhanced chemical looping steam reforming of glycerol

Multifunctional materials in pellet form composed of NiO-CaO-based aluminosilicate support have been developed for hydrogen (H2) production via sorption-enhanced chemical looping steam glycerol reforming (SE-CL-SGR). The effect of CaO content (50–70 wt%) and an addition of promoter (CeO2 and La2O3) on material properties and H2 production performances were investigated. A good H2 production of 90 %v/v purity for 75 mins has been achieved from 15 wt% NiO, 70 wt% CaO, and 30 wt% aluminosilicate support (15Ni(70Ca.30S)) pellets. However, it totally showed a loss of CO2 adsorption ability by the 5th cycle as a formation of carbon caused the pellet cracking. Rare-earth oxides such as CeO2 and La2O3 have been incorporated into the structure to improve the mechanical properties. An addition of 5 wt% La2O3 (5La-15Ni(70Ca.30S)) provides superior H2 production performance to that with CeO2 and without rare-earth as 95 %v/v H2 purity can be constantly produced for 60 mins throughout five testing cycles. These enhancements indicate that the CeO2 and La2O3 modifications effectively improve the multifunctional material pellet performance and stability, making it suitable for large-scale hydrogen production applications.

Study on the physical mechanical properties and freeze-thaw resistance of energy storage concrete with artificial phase change aggregate

As the main material for major infrastructure in cold regions, the mechanical properties and freeze-thaw resistance of concrete have become key scientific issues affecting the safety and normal operation and maintenance of infrastructure in cold regions. Energy storage concrete with phase change materials (PCM) has high thermal storage performance, which is beneficial to improving the frost resistance of concrete. In our preliminary research work, the artificial phase change aggregate (APCA) containing PCM with high latent heat, good mechanical properties and frost resistance were made and tested. The APCA was added to the concrete and replaced with natural coarse aggregate in different proportions, resulting in several energy storage concrete schemes with different APCA contents. The water absorption characteristics, microstructure and pore characteristics of energy storage concrete with different APCA contents were tested and analyzed through negative pressure saturation test, SEM and MIP. The effects of APCA replacement on the composition, mechanical properties, failure morphology characteristics and frost resistance durability of energy storage concrete at various ages were analyzed through XRD test, strength test and freeze-thaw cycle test. The results showed that replacing natural coarse aggregates with APCA changed the pore characteristics of concrete, reduced its saturation water absorption rate, but did not change the composition of hydration products at various ages of concrete. Replacing with an appropriate amount of APCA in concrete is beneficial for reducing the total porosity, the number of more-harmful pores and harmful pores. APCA reduce the compressive strength and splitting tensile strength of energy storage concrete at various ages. The early splitting strength of energy storage concrete increases rapidly, while the later growth is relatively slow. APCA are beneficial for suppressing the expansion of pores and cracks under freeze-thaw action of concrete. APCA is helpful to improve the freeze–thaw resistance of the energy storage concrete. After 100 freeze-thaw cycles, their strength is still 96% of the 28 day strength, and their compressive strength is about 35 MPa. This study provides a reliable experimental and theoretical basis for improving the freezing resistance of concrete in cold area.