Agnieszka Petryk

The 2022–2023 energy crisis fundamentally altered the functioning of the Guarantees of Origin (GO) market in Poland, exposing structural inefficiencies and accelerating regulatory and behavioral changes among market participants. This paper examines GO price dynamics in Poland during the energy...

The use of sewage sludge in agriculture is becoming an increasingly important component of sustainable waste management strategies. However, its effective implementation requires a detailed understanding of the processes occurring in soil following application. The aim of this study was to...

This study assessed the long-term energy self-sufficiency and operational dynamics of a full-scale wastewater treatment plant over the period 2015–2023, with particular emphasis on biogas-driven energy recovery and time-dependent process interactions. The relationship between biogas production and electricity and heat generation was evaluated alongside the influence of different sludge streams on system performance using cross-correlation analysis. The results demonstrated a high level of energy recovery, with biogas-derived electricity covering, on average, 60% of the plant’s demand and reaching a maximum of 74% annually. A very strong correlation was observed between annual biogas production and electricity generation (r = 0.94), confirming the direct energetic coupling of both processes. Monthly analyses further indicated strong consistency between biogas yield and both electricity and heat production (r = 0.55–0.91 and r = 0.86, respectively). Cross-correlation analysis identified Thickened Waste Activated Sludge and Primary Sludge as important process drivers, with statistically significant delayed effects at 10–20 days. In contrast, recirculation-related streams exhibited negligible influence on system dynamics. Statistical analysis revealed that most heavy metals, including Cd, Cr, Ni, and Hg, exhibited high variability (Coefficient Variability > 40%), which can directly impact the stability of methane production. These results indicate that wastewater treatment plants’ energy performance is governed by delayed process responses linked to sludge residence time, highlighting the need for predictive models incorporating at least two weeks of historical operational data. In addition, physicochemical analysis of sewage sludge confirmed generally stable nutrient content, despite variability in biological parameters and heavy metal concentrations. Overall, the study demonstrates that integrating long-term operational datasets with time-lag analysis provides valuable insights for optimizing energy recovery and supporting circular economy strategies in wastewater treatment plants.

Wastewater treatment plants increasingly rely on anaerobic digestion and biogas utilization to reduce operational costs, enhance energy self-sufficiency, and support circular-economy objectives. This study provides a comprehensive, year-round assessment of sludge production, sludge characteristics relevant to digestion, biogas generation, and energy performance at a municipal wastewater treatment plant. The plant generated on average 68.0 m³/d of thickened primary sludge and 24.0 m³/d of excessive sludge (total 92 m³/d), with low daily variability throughout the year. Biogas production remained highly stable, with an annual average of approximately 1300 m³/d and limited daily variation. Although monthly averages ranged from 1004 to 1728 m³/d, within-month variability was low to moderate, indicating that digestion processes responded consistently to changes in sludge quantity and composition. The weak correlation between sludge volume and biogas output (r = 0.29) showed that, besides sludge quantity, factors such as organic content and digester operating conditions also influence biogas yield. Energy performance indicators demonstrated strong self-sufficiency potential: the facility produced 1,095,047 kWh of electricity, covering 56.72% of its annual demand. The high coefficient of determination for self-sufficiency (R² = 0.871) confirmed a strong linear relationship between biogas-derived energy production and reduced grid dependence. Operational correlations further highlighted system coherence, with cogenerator and boiler usage strongly inversely related (r = −0.85) and biogas production positively associated with heat output (r = 0.66). Overall, the results demonstrate a stable and efficient sludge-to-energy system and provide a detailed dataset supporting future optimization of anaerobic digestion processes.

Assessing soil quality is essential for sustainable land use and agricultural productivity, particularly in the case of degraded, sandy, or low-organic-matter soils that suffer from poor water retention and reduced biological activity. One promising tool in this context is the Dexter soil quality index (S-index), which focuses on the relationship between volumetric water content and soil structure stability. Unlike general soil quality indices (SQI) that often integrate chemical or biological indicators, the S index offers a more physically grounded and quantifiable parameter for evaluating soil physical quality and water availability. It is particularly valuable for monitoring changes following organic amendments such as sewage sludge. The aim of this study was to evaluate the influence of sewage sludge application on the Dexter S-index, as an indicator of improved soil usability in terms of volumetric water content and retention capacity. The research was based on controlled pot experiments involving three mineral soil types: sandy- loam clay, loose sand, and clayey silt. Each soil type was amended with sewage sludge at rates equivalent to 50, 100, and 200 Mg∙ha−1. The results showed a clear increase in the S-index with increasing sludge doses across all soil types. The greatest relative improvements were observed in light-textured soils (0.042), where the original S values were lowest (0.018). This confirms the positive effect of sewage sludge on enhancing soil structure and water availability, particularly in sandy soils with inherently low retention capacity.

This article presents a narrative, traditional literature review summarizing current research on the integration of digital technologies in waste management. The study examines how intelligent technologies, including Geographic Information Systems, Big Data analytics, and artificial intelligence, can improve energy efficiency, support sustainable resource use, and enhance the development of low emission and circular waste management systems. The reviewed research shows that the combination of spatial analysis, large-scale data processing, and predictive computational methods enables advanced modeling of waste distribution, the optimization of collection routes, intelligent sorting, and the forecasting of waste generation. Geographic Information Systems support spatial planning, site selection for waste facilities, and environmental assessment. Big Data analytics allows the integration of information from Internet of Things sensors, global positioning systems, municipal databases, and environmental registries, which strengthens evidence-based decision making. Artificial intelligence contributes to automatic classification, predictive scheduling, robotic sorting, and the optimization of recycling and energy recovery processes. The study emphasizes that the integration of these technologies forms a foundation for intelligent waste management systems that reduce emissions, improve operational efficiency, and support sustainable urban development.

Decarbonization of transport is a key element of the energy transition and of achieving the Sustainable Development Goals. Integration of renewable energy into transport systems is assessed together with the potential of electric, hybrid, hydrogen, and biofuel-based propulsion to enable low emission mobility. Literature published from 2019 to 2025 is synthesized using structured searches in Scopus, Web of Science, and Elsevier and evidence is integrated through a thematic comparative approach focused on energy efficiency, life cycle greenhouse gas emissions, and technology readiness. Quantitative findings indicate that battery electric vehicles typically require about 18 to 20 kWh per 100 km, compared with about 60 to 70 kWh per 100 km in energy equivalent terms for internal combustion cars. With higher renewable shares in electricity generation, life cycle CO₂ equivalent emissions are reduced by about 60 to 70 percent under average European grid conditions and up to about 80 percent when renewables exceed 50 percent. Energy storage and smart grid management, including vehicle to grid operation, are identified as enabling measures and are associated with peak demand reductions of about 5 to 10 percent. Hydrogen and advanced biofuels remain important for heavy duty, maritime, and aviation segments where full electrification is constrained.

The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMSs), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMSs and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMSs is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions.

The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMS), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMS and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMS is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions.

Biogas has improved due to technological advancements, environmental awareness, policy support, and research innovation, making it a more cost-effective and environmentally friendly renewable energy source. The Generalized Linear Model (GLM) was employed to examine the relationship between purchased and generated energy from 2007 to 2023. Metrics such as deviance, log likelihood, and dispersion phi were examined to assess model fit. The Mann–Kendall test was utilized to detect trends in energy datasets. Biochemical Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD) reduction was significant, exceeding 97% from 2014 to 2023. However, treated sewage displayed limited susceptibility to biological degradation, with COD to BOD5 ratios increasing from 2.28 to 6.59 for raw sewage and from 2.33 to 7.05 for treated sewage by 2023. Additionally, the efficiency of sewage purification processes was calculated, and multivariate regression analysis was conducted on gas composition data. Principal Coordinate Ordination (PCO) and k-means clustering were used for dimensionality reduction and biogas component clustering, respectively. This research showed that biogas from the waste water treatment process can be used, particularly in methane production. Technological advancements have made biogas production more efficient, enhancing energy generation within a circular economy framework.