International Journal of Environment and Pollution (11 papers in press)

Regular Issues

• Open Access

  Predicting the cooling capacity of green buildings using probabilistic neural network models ( Free Full-text Access )

  by Hua Zheng, Pengming Wang 
  Abstract: This paper proposes a probabilistic neural network (PNN) model to predict the cooling capacity of green buildings, addressing nonlinear factors and uncertainties often overlooked by traditional regression models. The PNN model uses climate and building features as inputs, applies radial basis function (RBF) in the hidden layer for nonlinear mapping, and generates cooling capacity predictions with confidence intervals. Historical data is used to optimise parameters via backpropagation, and k-fold cross-validation prevents overfitting. Experimental results show that the PNN model achieves an R2 value above 0.95 and a 96.67% confidence interval coverage rate across different climate conditions. Compared to traditional models, the PNN demonstrates superior performance in handling nonlinearities and uncertainty in cooling capacity prediction.
  Keywords: green building cooling capacity prediction; PNN; probabilistic neural network; nonlinear modelling; uncertainty processing; data preprocessing.
  DOI: 10.1504/IJEP.2025.10072094
   
  

• Open Access

  Evaluation and trend prediction of the relationship between carbon emissions, energy, and sustainable growth based on neural networks ( Free Full-text Access )

  by Tingting Tan 
  Abstract: This study investigates the relationship between carbon emissions (CE), energy, and sustainable growth using neural networks. Data from five regions North America, South America, Europe, Asia Pacific, and Africa were analysed to model CE trends based on energy structure and consumption. A neural network model was trained and optimised to predict correlations among CE, energy use, and economic growth. Focusing on China, the study examines vehicle emissions, fuel-powered versus new energy vehicle sales, and their impact on CE and the economy. Results show a strong correlation between energy consumption and CE (R = 0.99), with energy efficiency and composition also influencing emissions. As new energy vehicle adoption rises, fossil fuel demand declines, helping curb total CE, support carbon neutrality, and promote sustainable development. The model demonstrates that optimising energy structure is key to balancing economic growth and environmental protection.
  Keywords: carbon emissions; neural networks; energy mix; energy consumption; data analysis; sustainable development; climate change.
  DOI: 10.1504/IJEP.2025.10072992
   
  

• Open Access

  Promoting the transformation of digital economy structure based on artificial intelligence in the low carbon economy environment ( Free Full-text Access )

  by Fan Chen, Aijun Liu 
  Abstract: This study investigates the impact of artificial intelligence (AI) on economic structure (ES) transformation within a low-carbon economy. Focusing on ES advancement and rationalisation, an empirical model is established incorporating control variables such as policy, openness, informatisation, and population density. Using dynamic panel analysis, results show that AI significantly promotes both ES advancement and rationalisation at the 1% level in the first lagged period. The findings indicate that AI enhances industrial efficiency and supports green development, playing a crucial role in driving sustainable, high-quality economic growth. This research provides valuable insights for policymakers seeking to integrate AI into low-carbon economic strategies.
  Keywords: economic restructuring; artificial intelligence; low-carbon economic environment; economic model analysis; expert system.
  DOI: 10.1504/IJEP.2025.10073301
   
  

• Open Access

  New algorithm for numerical simulation of beach evolution under extreme weather and neural network optimisation prediction model ( Free Full-text Access )

  by Songzhe Li, Hongqian Zhang 
  Abstract: This study proposes a novel beach evolution prediction algorithm integrating convolutional neural networks and numerical simulation to enhance accuracy under extreme weather. An improved deep-water flow model, based on the Navier-Stokes and sand-water mixing equations, captures hydrodynamic changes influenced by wind waves, tides, and currents. Meteorological and oceanic data are preprocessed using local weighted regression and interpolation methods to ensure quality. A neural network model dynamically predicts beach evolution, with k-fold cross-validation ensuring stability across extreme weather scenarios. Results show high accuracy, with mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) all below 0.4 and prediction errors under 12%.
  Keywords: extreme weather; beach evolution; numerical simulation; neural network; prediction analysis.
  DOI: 10.1504/IJEP.2025.10073562
   
  

• Open Access

  Empowering sustainable growth: the transformative impact of environmental protection inspections on heavy polluters ( Free Full-text Access )

  by Yingbo Gu, Chang Liu 
  Abstract: This study finds that environmental protection inspections (EPI) significantly improve the green total factor productivity (GTFP) of Chinas A-share heavy polluters, primarily through modest gains in green innovation and compliance behaviour. Using panel data spanning from 2011 to 2019, this study employs the super-slacks-based measure (SBM) model and the MalmquistLuenberger (ML) index to quantify GTFP, and applies a differencein-differences (DID) approach to examine the dynamic relationship between EPI and GTFP. The empirical results indicate that EPI exerts a statistically significant and positive effect on GTFP among heavily polluting enterprises, with green technological innovation identified as a key mediating mechanism. Furthermore, regional heterogeneity analysis reveals that the positive impacts of EPI are primarily concentrated in Chinas eastern and central regions, underscoring the influence of regional economic development levels on the effectiveness of environmental governance.
  Keywords: environmental protection inspections; green total factor productivity; heavy polluters; green technological innovation; environmental governance; sustainable economic development.
  DOI: 10.1504/IJEP.2025.10073694
   
  

• Open Access

  Combating illegal sand pumping: enforcement challenges and policy mechanisms in Taiwans EEZ ( Free Full-text Access )

  by Chiao-Ling Chen, Yi-Che Shih 
  Abstract: In Taiwan, the exclusive economic zone (EEZ), which overlaps with those of neighbouring coastal countries, has become a hotspot for illegal sand pumping by cross-border fleets. These activities have drawn significant criticism from the public and media for contributing to weaknesses in marine law enforcement, environmental degradation, and habitat destruction. In response, legislative, administrative, and civil society actors have demanded stronger enforcement and effective countermeasures including legal reforms, enhanced marine resource conservation, and increased enforcement to demonstrate the Taiwanese governments commitment. This study evaluates Taiwans inter-ministerial coordination strategies and proposes enforcement and management recommendations based on strengths, weaknesses, opportunities, threats (SWOT) and fishbone diagram analyses. The findings aim to inform future research and policy efforts, both domestically and in similarly affected coastal states.
  Keywords: Law Enforcement; SWOT; Sand Pumping; EEZ; Taiwan.
  DOI: 10.1504/IJEP.2025.10074105
   
  
• Ecotoxicity and water quality assessment of the Reconquista River (Buenos Aires, Argentina) by standardized bioassays on early developmental stages of a native amphibian (Rhinella arenarum)  
  by Carolina M. Aronzon, Celina Barreiro, Julieta Peluso, Vanesa Salomone, Marcos Tascon, Gabriela Svartz 
  Abstract: This work evaluated the water quality of the middle and lower basin of the Reconquista River, one of the most affected water bodies in Argentina, by physicochemical and ecotoxicological parameters. Physicochemical parameters were analyzed in situ and in the laboratory in water samples from ten sites distributed in the basin. Standardized toxicity bioassays were carried out with embryos and larvae of the native amphibian, Rhinella arenarum, exposed to dilutions of the surface water samples for a chronic (504h) period. Water Quality Indexes revealed a marked deterioration of the water and evidenced a clear spatial pattern with higher contamination at the central section which also caused a higher significant mortality in both embryos and larvae. The results demonstrate the substantial degradation of this water body and highlight the detrimental impact on the biota and aquatic ecosystem. These findings emphasize the need for an integrated approach to environmental concerns, using multiple indicators and information as an integrative approach of environmental quality assessment.
  Keywords: physicochemical parameters; toxicity bioassays; amphibians; Reconquista River.
  DOI: 10.1504/IJEP.2025.10071747
   
  
• Studies for the optimisation of bioleaching of heavy metals from contaminated sediments from Reconquista River  
  by Natalia Porzionato, Ana Elisabeth Tufo, Mariano L. Medina, Celeste M. Grimolizzi, Gustavo A. Curuchet 
  Abstract: Bioleaching processes are effective for removing heavy metals from contaminated sediments, but optimising performance requires understanding interactions between microbial activity, sediment properties, and metal behavior. Most studies emphasise metal solubilisation, often overlooking effects on the solid phase, particle movement, and metal reprecipitation. This study evaluates bioleaching in fixed-bed reactors, testing sulphur addition and bioaugmentation with native microbial strains. It examines relationships among microbial activity, metal mobilisation and reprecipitation, and changes in solid-phase characteristics. Results showed metals were mobilised from upper to lower reactor sections, where they accumulated. Although acidification occurred, it did not reduce pH throughout the column but significantly altered particle size, surface properties, and pore networks. Additionally, compaction from drainage and movement further influenced metal mobility and speciation. Modifying reactor geometry favouring shallower designs with larger surface areas could improve performance by enhancing drainage, microbial distribution, and metal recovery. These findings highlight the importance of physical changes during bioleaching.
  Keywords: bioleaching processes; optimisation; contaminated sediments; Reconquista River; remediation technologies; solid phase.
  DOI: 10.1504/IJEP.2025.10072436
   
  
• Carbon emission forecasting and peak carbon pathway analysis based on combined BP neural network and Grey forecasting models a perspective of Chinas data, 19972021  
  by Ming-Xun Zhu, Qiu-feng Yin, Lei Wu, Huan-ying Li 
  Abstract: This study aims to forecast China's carbon emissions and identify the peak emission year using data from the China Statistical Yearbook from 1997 to 2021. Employing both the Grey forecasting model and the BP neural network, this study predicts that China's carbon emissions will peak in 2030 and then decline year by year, aligning with the nation's carbon peak commitment. The analysis suggests that with the implementation of energy-saving and emission reduction policies, along with technological advancements, China is on track to achieve its green and low-carbon development goals post-peak. This study provides valuable insights for policy formulation towards carbon neutrality by 2060.
  Keywords: Grey model; GM; BP neural network model; carbon emissions; carbon peak.
  DOI: 10.1504/IJEP.2025.10073635
   
  
• Synthesis of activated carbon from coffee husks and its effect on CO2 capture and CH4 and H2 storage  
  by Cristian Toncón, Kiara M. Montiel-Centeno, Cristian A. Diaz, Deicy Barrera, Jhonny Villarroel-Rocha, Liliana Trevani, Laura Conde, Karim Sapag 
  Abstract: This work presents the synthesis of activated carbons from coffee husk pre- treated with steam explosion. The influence of the impregnation ratio (H3PO4/precursor) and impregnation time was evaluated. The synthesised materials were characterised by N2 adsorptiondesorption isotherms at 77 K and CO2 adsorption at 273 K, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy. These techniques confirmed the success of activated carbons from the coffee industry waste. Two selected activated carbons were further evaluated for their CO2, CH4, and H2 adsorption capacities at 308 K, 298 K, and 77 K, respectively, under pressures of up to 10 bar. CA-1 and CA-5 exhibited promising H2 adsorption capacities, comparable to values reported. These findings open up new possibilities for developing porous carbon- based activated materials for advanced gas separation applications.
  Keywords: activated carbon; carbon dioxide capture; methane and hydrogen storage; biomass valorisation.
  DOI: 10.1504/IJEP.2025.10073971
   
  
• Public perception of air quality: the role of temporal distribution characteristics of pollution indicator levels  
  by Xunzhou Ma, Dan Wu 
  Abstract: This study uniquely applies modern portfolio studies to analyze perceptions of air quality, thereby revealing how these perceptions are influenced by air pollution indicators. We found that individuals were less satisfied with local air quality when exposed to distributions with higher mean levels of air quality and were more satisfied with smaller distribution volatility. Moreover, we discovered that evaluations of air quality are influenced by the frequency of extremely polluted or pollution-free episodes rather than by current conditions. Notably, responses to air pollution indicators and recall windows did not significantly affect the respondents’ judgments. Our results suggest that understanding responses to air pollution requires comprehensive analyses beyond standard distribution means. These findings have significant implications for the design of effective policies to improve life satisfaction.
  Keywords: air quality perception; distribution moments; perceived mean; perceived volatility; perceived frequency of extremely high levels; perceived frequency of extremely low levels.
  DOI: 10.1504/IJEP.2025.10074126