Search Results (149)

Fine particulate matter with an aerodynamic diameter of 2.5 μm or smaller (PM2.5) is a major environmental health risk that threatens individuals’ health, quality of life, and sustainable well-being. In the digital era, protective behaviors are increasingly shaped by people’s ability to access, evaluate, and use health information from online sources. This cross-sectional descriptive correlational study examined the levels of health literacy, digital health literacy, and self-care behaviors for PM2.5 protection and examined their associations with self-care behaviors among adults in Mueang District, Samut Songkhram Province, Thailand. A proportionate stratified sample of 375 adults from 11 subdistricts completed structured questionnaires. Data were analyzed using descriptive statistics and multiple linear regression. Most participants had moderate health literacy (55.2%), digital health literacy (52.0%), and self-care behaviors for PM2.5 protection (56.3%). The health literacy and digital health literacy dimensions jointly explained 28.1% of the variance in self-care behaviors. Using digital information for health decision-making showed the largest unique association with self-care behaviors (β = 0.31), followed by decision-making for PM2.5 protection (β = 0.26) and evaluation of information credibility (β = 0.24). Understanding PM2.5 information did not contribute independently after the other literacy dimensions were considered. PM2.5 risk communication should therefore move beyond information provision and strengthen credibility assessment, information appraisal, and action-oriented decision-making while addressing socioeconomic and digital-access barriers. Full article

Fuel poverty is shaped by interacting social, environmental and housing conditions, yet these links remain underexplored at city scale. The analysis is framed as an ecological, cross-sectional assessment of spatial associations rather than as a causal proof of a closed feedback mechanism. This study examines the relationship between fuel poverty, deprivation, particulate air pollution and housing typology across 54 wards in Liverpool, UK. Ward-level fuel poverty and Index of Multiple Deprivation (IMD) data were integrated with 2023–2024 annual mean particulate matter (PM_2.5 and PM₁₀) from 58 low-cost air-quality sensors and classified housing types. Regression models were used to compare individual, additive and interaction effects. Fuel poverty ranged from 12.4% to 25.29%, while PM_2.5 and PM₁₀ frequently exceeded World Health Organization guideline values. IMD was the strongest individual predictor of fuel poverty (R² = 0.281, $p<0.001$). The preferred additive model including IMD, PM_2.5, PM₁₀ and housing type explained 43.5% of the variance, with Victorian Terraces emerging as a significant risk factor. Although interaction models suggested pollution-deprivation coupling, model selection and uncertainty diagnostics favoured the simpler additive specification. The findings support targeted retrofit, fuel-poverty and emissions-control policies in deprived urban neighbourhoods where inefficient housing and environmental stressors compound energy insecurity and where local action can contribute to more equitable urban sustainability. Full article

Non-exhaust emissions (NEEs), particularly brake wear particles (BWPs), have become a dominant source of traffic-related particulate matter (PM), accounting for approximately 77% of PM10 and 60% of PM2.5 emissions. Accurate quantification of these emissions is essential under increasingly stringent regulations such as Euro 7. However, measurement reliability is strongly influenced by particle transport and sampling losses. This review provides a state-of-the-art analysis of laboratory-scale methodologies for investigating BWP emissions, focusing on pin-on-disc (PoD) tribometers and inertia dynamometer systems. Particular attention is given to chamber design, airflow management, sampling configurations, and the mechanisms governing particle transport efficiency. The literature indicates that PoD systems are often affected by complex and non-uniform flow fields, leading to incomplete particle capture and reduced representativeness, whereas inertia dynamometers, especially when coupled with constant volume sampling (CVS), provide more controlled and reproducible conditions. Key loss mechanisms, including inertial deposition, diffusion, gravitational settling, and non-isokinetic sampling effects, are major contributors to uncertainty. The reviewed studies highlight that aerodynamic limitations in PoD systems, particularly box-shaped chambers, promote flow recirculation and particle losses. Advanced optimization approaches that combine artificial neural networks (ANNs) with computational fluid dynamics (CFD) simulations show strong potential to improve system design and measurement reliability. Full article

Suspended particulate matter (SPM) is a key environmental driver in aquatic ecosystems and plays a significant role in shaping microbial communities, particularly in sediment-rich rivers. Dam construction alters hydrological regimes and creates distinct SPM gradients; however, the response mechanisms of microeukaryotic plankton communities remain poorly understood. In this study, we used 18S rRNA gene high-throughput sequencing to characterize microeukaryotic plankton communities across riverine, lacustrine, and transitional zones of the Xiaolangdi Reservoir on the Yellow River (China). Our results revealed distinct community compositions in the lacustrine zone, with SPM identified as the primary factor driving community differentiation. Alpha diversity was highest in the riverine zone, while beta diversity differences among zones were dominated by species turnover. Dominant taxa included Cryptophyta (44.71% ± 30.79%), Metazoa (18.98% ± 17.71%), Perkinsea (7.97% ± 9.78%), Chlorophyta (7.06% ± 5.80%), and Dinophyta (6.06% ± 6.73%). Metazoa, Dinophyta, and Phaeophyta were enriched in high-SPM riverine waters, whereas Alveolata dominated low-SPM lacustrine zones. Community assembly was primarily deterministic, governed mainly by homogeneous selection, with stochastic processes exerting stronger influence in riverine zones. Network analysis indicated that riverine zones exhibited more complex and stable networks, lacustrine zones showed higher local but lower global connectivity, and transitional zones displayed stronger interactions but lower stability. These findings advance our understanding of microeukaryotic plankton responses to dam-induced environmental changes and provide a basis for assessing biodiversity impacts in regulated river systems. Full article

Transboundary fine particulate matter (PM2.5) in Southeast Asia is commonly assessed using static source–receptor frameworks or descriptive associations that may not resolve how directional dependence changes through time under shifting meteorological conditions. This study examines regional PM2.5 as a time-varying, meteorology-adjusted directional coupling system using monthly data for 2003–2024 from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) meteorological covariates, climate controls, and administrative aggregation. Using a rolling-window directed network framework based on Peter and Clark Momentary Conditional Independence (PCMCI) causal discovery, we inferred lagged conditional-dependence networks from covariate-adjusted PM2.5 anomalies and summarized their structure at national and first-order administrative levels. The inferred network structure varies over time but retains measurable continuity across rolling windows. At the country level, cross-border links consistently account for a large share of the directed structure, indicating that PM2.5 variability within the study domain is strongly shaped by transboundary coupling rather than by country-contained dynamics alone. A recurrent backbone of country-level directional coupling corridors emerges, including persistent links among China, Indonesia, Myanmar, and Thailand. At the first administrative level, stable gateways and receptor basins become more evident, especially the bidirectional coupling corridor between Yunnan Province, China, and Shan State, Myanmar, which appears throughout the full window sequence. These results show that subnational structure can reveal transport-relevant coupling patterns that national summaries may conceal. The framework provides an interpretable basis for corridor-oriented monitoring and regime-aware early warning, while the inferred links should be interpreted as directional statistical dependence rather than direct emissions attribution or resolved physical transport pathways. Full article

Building on the regional model evaluation presented in Part I, this study investigates the processes controlling air pollutant formation and transport over West Asia, with a focus on the United Arab Emirates (UAE). Two representative months, January 2022 and June 2021, are selected for detailed analysis using Chemical Process Analysis (CPA) and Integrated Process Rate (IPR) diagnostics. The results indicate predominantly VOC-limited ozone (O₃) formation across urban and coastal regions, with seasonal and spatial transitions toward NO_x-limited regimes, particularly in rural and downwind areas. IPR diagnostics show that local chemistry and vertical transport are the dominant contributors to O₃ variability, whereas fine particulate matter with a diameter of 2.5 µm or less (PM_2.5) variability is primarily driven by vertical transport and emissions. Horizontal transport and land–sea circulation play an important role in shaping the spatial distribution of both pollutants, especially along coastal zones. Comparisons among urban, coastal, and rural sites further highlight the influence of topography, land use, and meteorological conditions on pollutant dynamics. These process-based insights provide a scientific basis for refining emission control strategies, improving regional air quality management, and supporting evidence-based policies to mitigate air pollution impacts on human health and the environment in West Asia. Full article

Heatwave conditions are increasingly being recognized as important drivers of urban air-quality variability in southern European cities, particularly in inland urban environments exposed to persistent summer warming and atmospheric stagnation. This study examines the long-term variability of O₃, NO₂, and PM_2.5 concentrations in Valladolid, Spain, between 2006 and 2024, focusing particular attention on the occurrence and persistence of heatwave conditions. Ground-level ozone (O₃), nitrogen dioxide (NO₂), and fine particulate matter (PM_2.5) were analyzed to assess temporal variability, seasonal behavior, long-term trends, and exceedance characteristics. Results indicate an increasing persistence of heatwave episodes during the study period, particularly after 2015, with recent events exhibiting longer duration and broader regional extent. O₃ concentrations showed stronger accumulation during warm-season conditions, which is consistent with enhanced photochemical activity under elevated temperatures, while NO₂ concentrations generally declined over time. PM_2.5 variability reflected both local emissions and episodic regional influences, including Saharan dust intrusions. These findings highlight the growing relevance of heatwave conditions in shaping urban air-quality variability in medium-sized inland cities of the Iberian Peninsula. Full article

During wet deposition, particulate matter and gaseous species in the atmosphere are ultimately transported to the Earth’s surface via precipitation and subsequently incorporated into terrestrial ecosystems. Therefore, investigating the fluxes, chemical compositions, and source apportionment of regional wet deposition is of great scientific importance. An analysis of the concentrations, deposition fluxes, spatiotemporal variations, and source apportionment of water-soluble ions in wet deposition can further enhance our understanding of the water-soluble ion characteristics, atmospheric pollution profiles, and potential ecosystem impacts of wet deposition in the Yangtze River Delta and Pearl River Delta regions. Coastal cities in China are most developed regions, and also areas suffering from severe air pollution. This study investigates the chemical characteristics, sources and wet deposition fluxes of water-soluble inorganic ions in precipitation in two typical coastal urban agglomerations of China: Ningbo in the Yangtze River Delta and Guangzhou in the Pearl River Delta. Precipitation samples were collected and analyzed to determine the concentrations of major ions. The results revealed distinct ionic compositions between the two regions. In Ningbo, NO₃⁻ and SO₄²⁻ were the predominant ions accounting for 16.98% to 23.22% of the total, reflecting the influence of anthropogenic emissions from fossil fuel combustion and mobile sources with the NO₃⁻/SO₄²⁻ ratio of 0.90 and 0.70. In Guangzhou, precipitation was characterized by high contributions of SO₄²⁻, NO₃⁻, NH₄⁺, and Ca²⁺, accounting for 17.22% to 23.29% of the total, indicating a mixed influence of industrial emissions, agricultural activities, and construction dust with the NO₃⁻/SO₄²⁻ ratio of 0.92 and 0.87. A clear inverse relationship between rainfall amount and ion concentration was observed at all sites (p < 0.05), demonstrating a significant dilution effect. Seasonality played a crucial role in deposition fluxes. In Ningbo, fluxes peaked during summer from 4667 to 5156 mg·m⁻², while in Guangzhou, distinct dry and rainy season patterns influenced the scavenging efficiency of different ion species. Urban sites exhibited enhanced scavenging of crustal and anthropogenic ions (e.g., Ca²⁺, NH₄⁺) during the rainy season, whereas the coastal site showed elevated fluxes of marine-derived ions (Na⁺, Cl⁻, Mg²⁺, SO₄²⁻) during the same period. The observed trends in ion fluxes suggest a gradual improvement in regional air quality over the study period. These findings elucidate the complex interactions between anthropogenic activities, natural sources, and meteorological factors in shaping the wet deposition chemistry in coastal urban environments, providing essential data for developing regional deposition models and assessing the ecological impacts of atmospheric pollution. Full article

Coastal waters of eastern Guangdong are important fishing grounds and ecologically sensitive areas in the northern South China Sea, where nekton communities are increasingly affected by environmental heterogeneity and human activities. However, systematic studies on the spatial differentiation and driving mechanisms of nekton communities in this region remain insufficient. This study aimed to clarify the community structure, diversity distribution characteristics, and key driving environmental factors of nekton in the coastal waters of eastern Guangdong, and thereby provide scientific support for an ecological health assessment and sustainable utilization of fishery resources in this region. Based on bottom-trawl survey data from 19 stations in the coastal waters of eastern Guangdong, northern South China Sea, this study systematically analyzed the species composition, dominant species, and diversity distribution pattern of nekton and their correlations with environmental factors using methods including the Index of Relative Importance, Alpha diversity indices, Beta diversity indices, and redundancy analysis. A total of 119 nekton species belonging to three phyla, four classes, 14 orders, and 56 families were collected. Among them, there were 79 fish species (accounting for 66.39%), 36 crustacean species (30.25%), and four cephalopod species (3.36%). The dominant species were Trachypenaeus curvirostris and Portunus sanguinolentus (IRI ≥ 1000). Wilcoxon’s test showed that there were significant differences in the Shannon–Wiener index, Gini–Simpson index, and Pielou’s evenness between the nearshore and offshore groups, while no significant regional difference was observed in the richness index. Cluster analysis, based on the Bray–Curtis distance, divided the 19 stations into five clusters, with significant differentiation in species composition and functional structure within the nearshore group. RDA results indicated that environmental factors collectively explained 99.66% of the variation in community structure. Particulate Inorganic Carbon (PIC), Phosphate (PO₄³⁻), Distance to Port, Summer Maximum Chlorophyll-a (Chl-a), and Total Suspended Matter (TSM) were identified as the key driving factors. The coastal waters of eastern Guangdong boast rich nekton species, with significant differences in community structure between nearshore and offshore areas. The heterogeneity of the natural environment and human activity disturbances jointly shape the nekton diversity pattern in this region. The research results can provide a theoretical basis for regional marine ecological protection and fishery resource management. Full article

Environmental factors play a crucial role in shaping human development. This study explores an extension of the United Nations Planetary pressure-adjusted Human Development Index ($PHDI$) by incorporating three methodological refinements: (i) a disaggregated analysis of material footprint data; (ii) the inclusion of a local adjustment factor related to fine particulate matter (PM_2.5) exposure; and (iii) a variation to the planetary pressure aggregation method for obtaining the $PHD{I}^{*}$ index. The geographical scope encompasses 137 countries across the five permanently inhabited continents (Africa, America, Asia, Europe, and Oceania). The analysis first evaluates how these additional parameters deviate from the standard UN framework, followed by a continental assessment of national performances and their underlying drivers. A revised global ranking is presented, with countries categorised into four development levels based on Jenks Natural Breaks-derived cut-off values. Comparative cartographic visualisations highlight the shifts among the standard indexes and the proposed $PHD{I}^{*}$, illustrating that while some high-development countries—primarily in Europe—maintain their status, the inclusion of environmental aspects change the categories of important countries. These results suggest that accounting for localised environmental stressors and a more detailed material footprint analysis provides a more granular representation of the constraints on human development. Full article

Background: Air pollution, antimicrobial use, and population ageing are increasingly recognised as co-occurring pressures shaping population health. This study explores their ecological association with mortality patterns in the province of Messina (Southern Italy), within a One Health-informed framework. Methods: An ecological analysis was conducted using district-by-year data (2015–2024), integrating environmental monitoring (PM₁₀, PM_2.5, NO₂, O₃), outpatient antibiotic consumption, and cause-specific mortality rates. Multivariable regression models were used to assess associations between exposures and mortality outcomes. A post-2020 indicator was included to account for COVID-19-related disruption. Results: Marked geographic variability in pollutant concentrations was observed, with higher levels in urban-industrial districts. Infectious disease mortality increased from 13.8 to 44.6 per 100,000 inhabitants between the pre-pandemic and post-pandemic periods. In Poisson regression models, particulate matter showed a small and non-significant association with respiratory mortality (RR = 1.02, 95% CI: 0.89–1.18), while antibiotic consumption was not independently associated with mortality (RR = 0.99, 95% CI: 0.94–1.05). The post-2020 period was associated with higher mortality estimates (RR = 1.15, 95% CI: 0.72–1.83), although with wide confidence intervals. Conclusions: The findings suggest the co-occurrence of environmental, demographic, and pharmaceutical pressures within the same territories, rather than demonstrating formal synergistic interaction. The observed post-pandemic increase in mortality highlights the importance of accounting for COVID-19-related disruption. These results should be interpreted as exploratory, given the ecological design and limited sample size, but support the need for integrated surveillance approaches within a One Health perspective. Full article

Agroforestry, where trees and shrubs are planted in row-alley systems, can utilize the natural ability of plants to interact with pollutants and serve as a passive biotechnological method for improving air quality. A method for programming air phytoremediation processes is presented, using appropriately shaped plant structures, considering species characteristics and the spatial configuration of plants in row-alley plantings. The main objectives of this study were: to determine the relationship between pollution reduction and the characteristics of plant communities, considering the parameters of individual plants and group characteristics, to determine strategic parameters for the interaction between plants and pollutant flows, and to identify optimization paths for each stage. The optimization of the air phytoremediation process is presented using the example of changes in the fine particulate matter (PM_2.5) concentration pattern, analyzed through numerical experiments using micrometeorological computational fluid dynamics models (ENVI-met software). Ex-ante analysis of hypothetical scenarios showed that introducing appropriate configurations of variable vegetation structure could lead to pollution reductions of up to 19%. The effectiveness of the presented plant systems qualifies this method as a type of bioengineering technology, supporting the multifunctionality of agroforestry systems. Full article

Urban business-to-business distribution in Casablanca relies heavily on light commercial vehicles (LCVs) operating in a constrained street environment where loading/unloading access, intersection capacity, and recurring bottlenecks jointly shape performance and environmental impacts. However, high-resolution freight origin–destination (OD) observations and junction calibration data are limited, which complicates direct estimations of congestion and externalities attributable to commercial activity. This study develops a reproducible, large-scale modeling workflow that couples tour-based freight demand generation in order units with simulation-based traffic assignment (SBA) on a metropolitan network and translates network performance into emissions and monetary losses. Warehouses are modeled as primary producers and commercial activity zones as attractors via sector-tagged production and attraction functions; the resulting order distribution is converted to OD vehicle trips using the tour-based trip generation procedure with the mean targets-per-tour fixed to one to ensure numerical stability, yielding a direct-shipment approximation appropriate for stress–response analysis. Junction impedance is represented through turn-type volume–delay relationships and node-level impedance procedures, and congestion is evaluated using vehicle kilometers traveled/vehicle hours traveled (VKT/VHT)-based indicators, delay-intensity measures, and link/node bottleneck rankings. Across demand-scaling scenarios, VKT increases from 302,159 to 1,017,686 veh·km/day, while network delay rises nonlinearly from 392.5 to 2738.4 veh·h/day, indicating saturation-driven amplification of time losses. The Handbook of Emission Factors for Road Transport (HBEFA)-compatible emission estimates scale with activity: total carbon dioxide (CO₂) increases from 154.1 to 519.5 t/day, and nitrogen oxides (NOx) and particulate matter (PM_2.5) totals rise proportionally under fixed fleet assumptions. Monetizing delay with a purchasing-power-adjusted value-of-time range yields a congestion cost per trip that increases from approximately 0.20 to 0.41 Moroccan dirham, MAD/trip (at 60 MAD/veh·h), consistent with rising delay intensity. Bottleneck extraction shows welfare losses to be structurally concentrated on a small persistent corridor set, led by ‘Boulevard de la Résistance’, with recurrent hotspots including ‘Rue d’Arcachon’ and ‘Rue d’Ifni’. The framework supports policy-relevant reporting of congestion, emissions, and welfare impacts under data scarcity, with explicit sensitivity bounds. Full article

This study investigates the characterisation of particulate matter (PM) across different size fractions (TSP, PM₁₀, PM₄, PM_2.5, and PM₁) in Porto, Portugal, over a 2-year period. Sampling was conducted at two heights (ground level and rooftop), integrating real-time measurements and filter-based analyses to evaluate seasonal and spatial variations. Elemental composition was determined using Inductively Coupled Plasma–Mass Spectrometry (ICP-MS), enabling detailed assessments of 30 chemical elements. Meteorological parameters, including temperature, precipitation, wind speed, and direction, were analysed to understand their influence on PM concentrations. Results indicate that significant seasonal trends, with higher PM concentrations observed during autumn and winter, were associated with low boundary layer height, promoting greater mixing of particles, enhanced deposition, and higher anthropogenic emissions, with average seasonal TSP values ranging from 0.001 to 0.059 µg m⁻³. Elemental analysis revealed distinct profiles at ground and rooftop levels, with Ba, Cu, Pb, Mg, and Na among the most frequently detected elements; ground-level samples showed stronger contributions from local sources, such as traffic, while rooftop samples reflected regional and long-range transport. Meteorological factors, such as precipitation and wind speed, exhibited negative correlations with PM concentrations, underscoring their role in atmospheric washing. These findings highlight the complex interplay of local and regional factors in shaping PM dynamics and emphasise the importance of multi-level monitoring for effective air-quality management. Full article

The intensification of large-scale wildfires, driven by climate change, presents a critical threat to agricultural ecosystems, specifically during the vulnerable sowing season in March. Departing from the prevailing focus on urban air quality, this study elucidates the spatiotemporal dynamics of particulate matter (PM) in eight major Korean agricultural regions during the March 2025 wildfires. By employing a Generalized Additive Model (GAM), we characterized the complex non-linear interactions between PM concentrations and meteorological variables. The analysis reveals a substantial elevation in PM levels during the wildfire event relative to the pre-fire baseline. Most notably, the Sangju region experienced the most acute accumulation, with PM-10 and PM-2.5 concentrations surging by 74% and 46%, respectively; this intensification was significantly compounded by topographic trapping and surface inversion phenomena. Furthermore, GAM results identified temperature and relative humidity as the primary determinants of PM retention, whereas wind speed demonstrated a distinct non-linear, U-shaped effect, facilitating particulate resuspension at higher velocities. These findings quantitatively underscore the susceptibility of agricultural environments to wildfire-induced aerosols and highlight the imperative for establishing agriculture-specific monitoring networks and early warning protocols to safeguard crop productivity. Full article