Search Results (181)

Urban Green Spaces (UGSs) are essential for ecological sustainability and public health, offering benefits such as air pollution reduction, urban cooling, and recreational opportunities. However, existing evaluation tools remain inconsistent, often assessing isolated dimensions like accessibility or aesthetics without fully integrating health considerations. A systematic approach is needed to understand how these tools measure UGS quality and their relevance to health outcomes. This study employs a literature review (PRISMA framework) to analyze UGS evaluation tools with a focus on quality and health implications. A search in Scopus and Web of Science identified 14 relevant studies. Data extraction examined tool structure, assessed dimensions, data collection methods, geographic applications, and integration of health indicators. The review identified 13 distinct tools varying in complexity and methodology, from standardized checklists to GIS-based analyses. While key dimensions included accessibility, safety, aesthetics, and biodiversity, health-related factors were inconsistently integrated. Few tools explicitly assessed physical, mental, or social health outcomes. Technological innovations, such as Google Street View and AI-based analysis, emerged as enhancements for UGS evaluation. Despite methodological advances, gaps remain in linking UGS quality assessments to health outcomes. The lack of standardized health metrics limits applicability in urban planning. Future research should focus on interdisciplinary frameworks integrating environmental and health indicators to support the creation of sustainable and health-promoting UGS. Full article

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a key economic region in China facing increasing pressure to balance socioeconomic development with environmental protection and energy conservation. This study examines the interrelationships among energy consumption, air pollutants (PM_2.5, NO₂, and SO₂), and carbon dioxide (CO₂) emissions in the GBA from 2000 to 2020. Using spatial correlation matrices and temporo-spatial decoupling analysis, we assess spatial patterns, temporal dynamics, and interactions among these factors. Results show that the GBA has made significant progress in reducing air pollution and carbon emissions. Notably, since 2013, concentrations of PM_2.5, NO₂, and SO₂ have decoupled markedly from energy consumption, reflecting effective pollution control measures. Although CO₂ emissions have decreased more gradually, the trend remains positive, indicating steady advances in carbon management. These findings underscore the need for continued optimization of the energy structure to achieve coordinated control of energy use, air quality, and carbon emissions—essential for promoting sustainable, high-quality development in the region. Full article

In recent decades, significant efforts have been devoted to constructing energy-efficient buildings, providing comfortable indoor environments. However, measures such as enhanced airtightness, while reducing infiltration through the building envelope, might consequently reduce natural ventilation. This reduction is a critical concern because natural ventilation is an essential factor in controlling indoor air quality (IAQ), and its diminution could therefore worsen IAQ. Sick building syndrome has emerged as a term used to describe health hazards linked to the time spent indoors but with no particular cause. Since people spend most of their time indoors, the demand for continuous and real-time IAQ management to reduce human exposure to pollutants has increased considerably. In this context, low-cost sensors (LCS) for IAQ monitoring have become popular, driven by recent technological advancements and increased awareness regarding indoor air pollution and its negative health impacts. Although LCS do not meet the performance requirements of reference and regulatory equipment, they provide informative measurements, offering high-resolution monitoring, emission source identification, exposure mitigation, real-time IAQ assessment, and energy efficiency management. This perspective article proposes a general model for LCS systems (and subsystems) implementation and presents a prospective analysis of their strengths and limitations for IAQ management, reviews the literature regarding sensor system technologies, and offers design recommendations. It provides valuable insights for researchers and practitioners in the field of IAQ and discusses future trends. Full article

Wildfires are a major source of greenhouse gases (GHGs), particulate matter (PM), and atmospheric pollutants, exerting widespread impacts on air quality, human health, and global climate. To address knowledge gaps, this study conducts a literature review of GHG emissions from wildfires across diverse ecosystems and fire regimes. The analysis quantifies emission magnitudes and compositions, evaluates their influence on regional and global climate processes, and synthesizes trends and methodological advances. Results show that the burned area is the main determinant of total emissions, with CO₂ as a robust predictor for estimated CO and CH₄, reflecting coupled emission behavior under varying combustion conditions. The Modified Combustion Efficiency (MCE) demonstrates a stronger predictive capacity for the CO/CO₂ ratio than for CH₄/CO₂, suggesting that CO/CO₂ can be predicted from MCE. Complete combustion dominates most fire events, while incomplete combustion increases the release of CO, CH₄, N₂O, and PM, contributing to tropospheric ozone formation and enhanced radiative forcing. Exposure to PM_2.5 and ozone remains a major health concern in fire-affected regions. This review provides a quantitative synthesis linking combustion efficiency and GHG co-variability, offering insights to refine emission modeling and guide climate mitigation strategies. Full article

Federated learning (FL) is emerging as a pivotal paradigm for environmental monitoring, enabling decentralized model training across edge devices without exposing raw data. This review provides the first structured synthesis of 361 peer-reviewed studies, offering a comprehensive overview of how FL has been implemented across environmental domains such as air and water quality, climate modeling, smart agriculture, and biodiversity assessment. We further provide comparative insights into model architectures, energy-aware strategies, and edge-device trade-offs, elucidating how system design choices influence model stability, scalability, and sustainability. The analysis traces the technological evolution of FL from communication-efficient prototypes to robust, context-aware deployments that integrate domain knowledge, physical modeling, and ethical considerations. Persistent challenges remain, including data heterogeneity, limited benchmarking, and inequitable access to computational infrastructure. Addressing these requires advances in hybrid physics–AI frameworks, privacy-preserving sensing, and participatory governance. Overall, this review positions FL not merely as a technical mechanism but as a socio-technical shift—one that aligns distributed intelligence with the complexity, uncertainty, and urgency of contemporary environmental science. Full article

This study integrates dynamic energy simulation with lifecycle assessment (LCA) to evaluate the energy and carbon performance of advanced glazing systems suitable for hot–arid climates. Using Design Builder software coupled with OpenLCA, six glazing configurations were analyzed under identical building and climatic conditions. The configurations included a conventional single 3 mm float glass pane (C0) as the reference case, a single 3 mm polycarbonate sheet (C1) representing common local construction practice, and four advanced multi-layer systems (C2–C5) incorporating air, argon, and nanogel insulation layers. The inclusion of C0 enabled direct comparison between typical glass construction and emerging polycarbonate-based systems, thereby enhancing the contextual relevance of the analysis. Results demonstrated that thermal and optical properties of glazing systems strongly influence both operational and embodied carbon outcomes. Relative to the conventional glass reference (C0), the nanogel–argon composite (C5) achieved a 32.4% reduction in annual cooling energy and a 28.9% decrease in total lifecycle carbon emissions, with a carbon payback period of approximately 1.1 years. The operational phase dominated total emissions (>97%), confirming that improvements in glazing thermal performance yield substantial long-term benefits even when embodied impacts are considered. While argon filling provided marginal benefit over air cavities, the nanogel insulation contributed the largest performance enhancement. However, the relatively low visible light transmittance (VLT = 0.27) of the C5 system suggests a potential daylight–comfort trade-off that warrants further investigation. The study demonstrates the importance of integrating energy simulation with lifecycle assessment to identify glazing systems that balance energy efficiency, embodied carbon, and indoor environmental quality in hot–arid regions. Full article

The global drive toward sustainability and energy efficiency has accelerated the development of smart buildings integrating the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies optimise energy use, enhance occupant comfort, and advance building management systems. This study examines the integration of IoT and AI in energy-efficient smart buildings, emphasising applications and challenges. A qualitative methodology, combining systematic literature review, case study analysis, and systems analysis, underpins the research. Findings indicate that IoT enables smart metering, real-time energy monitoring, automated lighting and HVAC, occupancy-based energy optimisation, and renewable energy integration. AI complements these functions through predictive maintenance, energy forecasting, demand-side management, intelligent climate control, indoor air quality automation, and behaviour-driven analytics. Together, they reduce carbon emissions, lower operational costs, and improve occupant well-being. However, challenges remain, including data security and privacy risks, interoperability gaps, scalability and cost constraints, and retrofitting difficulties. To address these, the paper proposes a systems thinking-enabled conceptual framework structured around three pillars: adopting IoT and AI as enabling technologies, overcoming integration barriers, and identifying application areas that advance sustainability in smart buildings. This framework supports strategic decision-making toward net-zero and resilient building design. Full article

Since 2009, China has implemented two important place-based policies to promote e-commerce development in selected cities: “Building National E-commerce Demonstration Cities” and “Comprehensive Pilot Zones for Cross-Border E-commerce”. Previous studies reported that these two e-commerce development policies generated local environmental benefits by reducing air pollution and carbon emissions in the policy implementation areas. However, whether these policies have spatial spillover effects on environmental quality in other regions and the extent of such effects have not been sufficiently analyzed. This study aims to empirically assess the environmental spatial spillover effects of these two policies. Based on panel data from 221 prefecture-level cities in China from 2000 to 2021, this study utilizes a spatial econometric regression method to evaluate the policy effects. The study yields three main findings. (1) The policies significantly reduced air pollution concentrations and carbon emissions while increasing vegetation greenness in non-policy implementation areas. Specifically, the policies led to reductions in carbon monoxide (CO), nitrogen dioxide (NO₂), fine particulate matter (PM_2.5), sulfur dioxide (SO₂), and the emissions of carbon dioxide (CO₂), as well as increases in the fractional vegetation cover (FVC), normalized difference vegetation index (NDVI), and net primary productivity (NPP). Our findings indicate that the environmental effects of e-commerce development policies extend beyond the policy-implementing areas. (2) Further heterogeneity tests reveal that the beneficial spatial spillover impacts of e-commerce development policies were observed in cities with different geographical locations, servicification levels, economic scale, and population densities. (3) Mechanism analysis shows that although the policies did not alter the environmental regulation stringency in non-policy regions, they promoted industrial structure upgrading, technological advancement, and green innovation in these areas, thereby explaining the detected spatial spillover effects. Full article

Climate change and environmental degradation pose growing threats to health. Despite increasing recognition of these risks, climate-related education and counseling are rarely incorporated into adult inpatient care. A survey-based study was conducted with 250 adult inpatients on the medicine services at Cooper University Health Care (New Jersey) and Maine Medical Center (Maine). Patients received a standardized 30-s educational statement from their physician on the health impacts of air pollution and extreme heat, with introduction to two smartphone applications on air quality and heat conditions. Survey items evaluated patients’ prior awareness of environmental health risks, willingness to use digital monitoring tools, and perceived barriers to use. Descriptive statistics and content analysis were used for data interpretation. Overall, 84% of participants reported awareness of environmental threats to health, indicating high baseline recognition. However, only 50% expressed willingness to adopt smartphone apps as protective tools with barriers including lack of smartphone access, unfamiliarity with technology, and concerns about utility during hospitalization. Twenty-three percent of participants in Maine did not own a smartphone, as compared with 7% in NJ. Despite less smartphone ownership in Maine compared to NJ, participants showed similar willingness to use the suggested apps for monitoring environmental conditions (53% vs. 49.3%). Responses suggested that while patients generally acknowledge climate-related health risks, enthusiasm for technological solutions varies considerably, especially among older and underserved populations. This study highlights a critical gap between awareness of climate health risks and the adoption of digital health tools for self-protection. While brief inpatient education may increase recognition, technology-based interventions alone may not reach all patient groups. Future strategies should focus on accessible, low-barrier methods of environmental health education in clinical care, including integration into inpatient counseling and discharge planning. Addressing technology access gaps and tailoring resources to diverse populations will be essential for advancing climate-related patient education in healthcare settings. Full article

Ground-level ozone is a critical secondary air pollutant and greenhouse gas, especially in urban oil and gas regions, where it poses severe public health and environmental risks. Urban areas in North Texas have experienced persistently elevated ozone levels over the past two decades despite emission control efforts, highlighting the need for advanced forecasting tools. This study presents a hybrid recurrent neural network (RNN) model that combines Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures to predict 8 h average ground-level ozone concentrations over a full annual cycle. The model leverages one-hour lagged ozone precursor pollutants (VOC and NO_x) and seven meteorological variables, using a novel framework designed to capture complex temporal dependencies and spatiotemporal variability in environmental data. Trained and validated on multi-year datasets from two distinctly different urban air quality monitoring sites, the model achieved high predictive accuracy (R² ≈ 0.97, IoA > 0.96), outperforming standalone LSTM and Random Forest models by 6–12%. Beyond statistical performance, the model incorporates Shapley Additive exPlanation (SHAP) analysis to provide mechanistic interpretability, revealing the dominant roles of relative humidity, temperature, solar radiation, and precursor concentrations in modulating ozone levels. These findings demonstrate the model’s effectiveness in learning the nonlinear dynamics of ozone formation, outperforming traditional statistical models, and offering a reliable tool for long-term ozone forecasting and regional air quality management. Full article

Poor air quality can harm human health and the environment. Air quality data are needed to understand and reduce exposure to air pollution. Air sensor data can supplement national air monitoring data, allowing for a better understanding of localized air quality and trends. However, these sensors can have limitations, biases, and inaccuracies that must first be controlled to generate data of adequate quality, and analyzing sensor data often requires extensive data analysis. To address these issues, an R-Shiny application has been developed to assist air quality professionals in (1) understanding air sensor data quality through comparison with nearby ambient air reference monitors, (2) applying basic quality assurance and quality control, and (3) understanding local air quality conditions. This tool provides agencies with the ability to more quickly analyze and utilize air sensor data for a variety of purposes while increasing the reproducibility of analyses. While more in-depth custom analysis may still be needed for some sensor types (e.g., advanced correction methods), this tool provides an easy starting place for analysis. This paper highlights two case studies using the tool to explore PM_2.5 sensor performance under the conditions of wildfire smoke impacts in the Midwestern United States and the performance of O₃ sensors for a year. Full article

Outdoor thermal comfort is a critical determinant of urban livability and public health, particularly in the face of the increasing frequency and intensity of extreme weather events. While meteorological variables are well-established drivers of thermal stress, the influence of ambient air pollution on human thermal perception remains poorly understood and largely overlooked in urban climate research. To address this gap, this study investigates the multidimensional effects of six major air pollutants PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO on the Universal Thermal Climate Index (UTCI) across 370 Chinese cities from 2020 to 2024. Using integrated spatiotemporal analysis, we found significant seasonal, diurnal, and climatic heterogeneity in pollutant–UTCI interactions. Our findings reveal that O₃ and PM₁₀ amplify thermal stress during summer daytime through photochemical heating and radiative forcing, whereas PM_2.5 and CO reduce nocturnal heat loss in winter by trapping long-wave radiation, effectively acting as thermal insulators. These effects are further modulated by local climate: arid regions (e.g., Lanzhou) experience exacerbated O₃-driven heat stress, while cold zones (e.g., Harbin) benefit from particulate-induced warming in winter. Meteorological factors serve as dual regulators; temperature and solar radiation directly elevate the UTCI, while wind and humidity govern pollutant dispersion and thus indirectly shape thermal comfort. This study not only advances the scientific understanding of air pollution’s role in urban thermal environments but also provides actionable, data-driven insights for climate-resilient urban planning, public health interventions, and integrated environmental policies that jointly address air quality and thermal comfort in rapidly urbanizing regions. Full article

Mango (Mangifera indica L.) is a popular tropical fruit enjoyed worldwide, with Europe being a significant importer of this fruit. Its climacteric nature and short shelf-life pose challenges for maintaining quality, while emissions from transportation threaten the sustainability of the supply chain. This highlights the importance of low-impact logistics in maintaining fruit quality. This study aimed to evaluate the quality of fresh mangoes in Italy by comparing the different shipping systems (air, sea, and road) for seven cultivars sourced from seven countries. Quality assessment included pomological analysis, PTR-ToF-MS for volatile profiling (n = 11 cultivars × 2 years × 3 replicates), and consumer sensory analysis (n = 65 for untrained panellists in 1 year, n = 8 for trained panellists over 2 years). Results indicated that air and truck transport better preserved fruit quality compared to sea freight, primarily due to shorter transit times, which allowed for harvesting at more advanced ripeness stages. The combination of PTR-ToF-MS and PLS-DA effectively differentiated samples based on the method of transport, showcasing its potential as a quick quality monitoring tool. Mangoes transported by air showed significantly higher levels of volatile organic compounds (VOCs), a 29% greater total soluble solids (TSSs) content, and a 44% lower acidity (TA). Sensorial tests indicated that consumers preferred these mangoes. However, air transport resulted in 30 times higher CO₂ emissions per kg of fruit compared to sea freight (~642,117 CO₂e (kg) vs. ~19,132 CO₂e (kg)), highlighting a critical dilemma between sustainability and quality. These findings provide a framework for developing hybrid logistics strategies that strike a balance between preserving quality and environmental responsibility. Additionally, they support the development of European mango cultivation, which can optimise harvest timing, reduce emissions, and enhance fruit quality. Full article

Radon exposure is the second leading cause of lung cancer worldwide, yet monitoring strategies remain limited, expensive, and unevenly applied. Recent advances in the Internet of Things (IoT) offer the potential to change radon surveillance through low-cost, real-time, distributed sensing networks. This review consolidates emerging research on IoT-based radon monitoring, drawing from both primary radon studies and analogous applications in environmental IoT. A search across six major databases and relevant grey literature yielded only five radon-specific IoT studies, underscoring how new this research field is rather than reflecting a shortcoming of the review. To enhance the analysis, we delve into sensor physics, embedded system design, wireless protocols, and calibration techniques, incorporating lessons from established IoT sectors like indoor air quality, industrial safety, and volcanic gas monitoring. This interdisciplinary approach reveals that many technical and logistical challenges, such as calibration drift, power autonomy, connectivity, and scalability, have been addressed in related fields and can be adapted for radon monitoring. By uniting pioneering efforts within the broader context of IoT-enabled environmental sensing, this review provides a reference point and a future roadmap. It outlines key research priorities, including large-scale validation, standardized calibration methods, AI-driven analytics integration, and equitable deployment strategies. Although radon-focused IoT research is still at an early stage, current progress suggests it could make continuous exposure assessment more reliable, affordable, and widely accessible with clear public health benefits. Full article

Urbanization and mixed-land-use development significantly impact water quality dynamics in watersheds, necessitating continuous monitoring and advanced analytical techniques for sustainable water management. This study employs continuous wavelet analysis to investigate the temporal variability and correlations of real-time water quality parameters in the Credit River watershed, Ontario, Canada. The Integrated Watershed Monitoring Program (IWMP), initiated by the Credit Valley Conservation (CVC) Authority, has facilitated long-term real-time water quality monitoring since 2010. Fundamental and exploratory statistical analyses were conducted to identify patterns, trends, and anomalies in key water quality parameters, including pH, specific conductivity, turbidity, dissolved oxygen (DO), chloride, water temperature ($T_{H_{2}O}^{°}$), air temperature ($T_{air}^{°}$), streamflow, and water level. Continuous wavelet transform and wavelet coherence techniques revealed significant temporal variations, with “1-day” periodicities for DO, pH, ($T_{H_{2}O}^{°}$), and ($T_{air}^{°}$) showing high power at a 95% confidence level against red noise, particularly from late spring to early fall, rather than throughout the entire year. These findings underscore the seasonal influence on water quality and highlight the need for adaptive watershed management strategies. The study demonstrates the potential of wavelet analysis in detecting temporal patterns and informing decision-making for sustainable water resource management in rapidly urbanizing mixed-land-use watersheds. Full article