Search Results (75)

Atmospheric microplastics are increasingly recognized as emerging contaminants in urban air, yet evidence from Philippine cities outside Metro Manila remains limited. This study provides a preliminary roadside baseline assessment of airborne microplastics in Cagayan de Oro City, southern Philippines. Atmospheric particles were collected from 12 roadside stations distributed across four urban roads, with three stations per road, during a standardized dry-season midday sampling period, and were subsequently subjected to alkaline digestion, microscopic screening, and ATR-FTIR confirmation. Of 99 visually suspected particles, 44 were verified as synthetic polymers and retained in the final dataset. Mean atmospheric microplastic concentrations ranged from 0.0079 to 0.0212 items m⁻³, with J.R. Borja Street showing the highest concentration and Nazareth Street the lowest. Abundance did not differ significantly among roads, whereas particle shape, color, and polymer composition showed significant differences within the confirmed dataset, while size-class distribution did not. Fibers were the dominant morphology (56.8%), transparent particles were the most common color class (52.3%), and polypropylene and polyethylene terephthalate were the predominant polymers. Taken together, the findings confirm the presence of airborne microplastics across roadside environments in Cagayan de Oro City and suggest that, under the sampled conditions, spatial variation was more evident in particle characteristics than in overall abundance. This study contributes an initial polymer-confirmed roadside dataset for a secondary Philippine city and highlights the value of composition-based assessment in urban air quality monitoring. Full article

Urban air quality management has been playing a significant role due to its effects on public health and pollution characteristics of countries with constantly changing policies. Traditional approaches capture how much pollution is present but are unable to detect changes in the chemical character of the atmosphere, the relationships between co-emitted species, the balance of photochemical processing, and the combustion fingerprint of emission sources. This study introduces a framework that identifies and diagnoses such evolutions within the pollutants of the atmosphere. A chemistry-aware Variational Autoencoder is trained on 19 multivariate pollution features (7 raw concentrations, 5 chemical ratios, 7 temporal gradients) at London Marylebone Road (urban roadside) and North Kensington (urban background) from 2015 to 2019, and tested on 2022–2025. A four-method ensemble framework (VAE reconstruction error, reconstruction probability, Isolation Forest, and statistical Z-score) requires ≥3 agreement to identify high-confidence departed pollution states. Per-feature decomposition of the reconstruction probability diagnoses the chemical character of each departure. At the roadside site, 14.5% of post-COVID hours fall within departed states, dominated by the CO/NO_x combustion ratio (513.2) and the photostationary state proxy (391.4), chemical relationships rather than individual concentrations. This indicates that at the point of emission, London’s fleet modernisation and Ultra Low Emission Zone (ULEZ) have changed the combustion fingerprint and photochemical equilibrium. The same structural indicators are carried over during the COVID-19 lockdown; however, O₃ rises 3.2× during the pandemic period, reflecting suppressed NO titration. Conversely, at the urban background site, where the departures are driven by concentrations and boundary-layer trapping ($r=-0.659$), the combustion fingerprint of the atmosphere is invisible to detect (CO/NO_x$=-45.0$). These findings indicate that London’s emission landscape has undergone fundamental transformations over the past decade, and the consequences of ULEZ and similar interventions or greater impacts of pandemic-related events are non-homogeneously distributed across the relevant region. Full article

Urban forests (UFs) are increasingly promoted as a nature-based solution for mitigating particulate matter (PM) pollution, yet their removal performance can vary depending on surrounding emission sources and environmental conditions. Here, we quantified the particulate matter reduction efficiency (PMRE) of UFs located near roads, industrial complexes, and urban areas, together with background forests in South Korea, based on field observations during the late autumn–spring period across two consecutive years (November–May in 2021–2022 and 2022–2023). We applied vector autoregression (VAR) to examine the dynamic relationships between PMRE and meteorological and air pollutant variables across eight representative sites. The results revealed that PM mitigation dynamics were strongly particle-size-dependent and context-specific. Across all sites, ΔPM₁₀ RE was predominantly self-driven, explaining over 90% of its own variance, whereas fine-particle dynamics showed stronger interdependence. In particular, ΔPM_2.5 RE consistently acted as a key mediator, accounting for up to 70%–80% of the variation in ΔPM_1.0 RE depending on source context. Industrial-complex-adjacent UFs exhibited the strongest cross-variable interactions, while urban-core UFs were largely governed by intrinsic mitigation processes. Roadside UFs showed site-specific responses associated with CO and temperature variability. Notably, PMRE responses exhibited damped oscillation patterns across all source contexts, converging toward equilibrium over time, indicating stabilization of mitigation performance following disturbance events. These findings demonstrate that urban forest air-quality benefits are highly context dependent and governed by particle-size-specific dynamics. Our results provide evidence-based guidance for designing and managing urban forests, emphasizing the need for source-specific strategies and prioritization of PM_2.5-oriented mitigation, particularly in industrial and roadside environments where fine-particle interactions are strongest. Full article

Roadside green spaces function as critical ecological barriers in urban environments, and their plant communities play a key role in improving regional air quality. This study investigates typical roadside plant communities in southern Xinjiang, a region characterized by extreme aridity and frequent dust storms. By quantifying indicators such as dust retention capacity at both individual and community levels, together with leaf surface microstructural characteristics, we evaluate the comprehensive dust retention performance of different community configuration patterns. The results show that: (1) Among the studied species, Juniperus chinensis ‘Kaizuca’ exhibited the highest dust retention capacity per unit leaf area, followed by Juniperus chinensis L. and Rosa rugosa Thunb. Among trees, Platanus acerifolia (Aiton) Willd showed the greatest dust retention capacity per individual plant; among shrubs, Rosa rugosa Thunb. performed strongly, and among herbaceous species, Lolium perenne L. exhibited relatively high dust retention capacity. (2) Leaf dust retention is governed by the synergistic effects of multiple traits, including leaf aspect ratio, stomatal aspect ratio, stomatal protrusion, stomatal density, wax layer characteristics, and surface roughness. Leaf aspect ratio exerts a significant positive direct effect on dust retention, whereas stomatal aspect ratio shows a significant negative direct effect. (3) At the community level, the multi-layered tree–shrub–herbaceous configuration dominated by Platanus acerifolia (Aiton) Willd exhibited the strongest dust retention capacity, making it the most effective configuration for roadside green spaces. Overall, this study provides a robust theoretical framework and empirical evidence for the scientific selection and optimized configuration of roadside vegetation in arid regions, thereby supporting the sustainable improvement of urban roadside air quality in southern Xinjiang. Full article

Roadside public transport stops represent localized air pollution hotspots where short-term exposure may differ substantially from levels reported by urban background monitoring. This study investigates the application of low-cost air quality sensors for long-term characterization of particulate matter and gaseous pollutants in a traffic-dominated urban microenvironment. The novelty of this work lies in the combined use of collocated low-cost sensors, energy-independent solar-powered deployment, height-resolved placement representative of different breathing zones, and integrated statistical and predictive analysis to resolve exposure-relevant pollutant dynamics at a single transport stop. Hourly concentrations of particulate matter (PM) PM₁, PM₂.₅, PM₁₀, nitrogen dioxide (NO₂), and ozone (O₃) were measured over one year at a roadside transport stop adjacent to a four-lane urban road carrying approximately 30,000 vehicles per day. Measurements were obtained using two collocated low-cost sensor units based on optical particle sensing for particulate matter and electrochemical sensing for gases, together with concurrent meteorological observations. Strong agreement between the two particulate matter sensors supported the use of averaged concentrations. Mean PM₂.₅ concentrations were substantially higher in winter (32.4 µg/m³) than in summer (10.4 µg/m³), indicating pronounced seasonal variability. PM₁ and PM₂.₅ exhibited closely aligned temporal patterns, while PM₁₀ showed greater variability. NO₂ displayed sharp diurnal peaks associated with traffic activity, whereas O₃ exhibited opposing seasonal and diurnal behavior and was negatively correlated with both PM₂.₅ (r = −0.32) and NO₂ (r = −0.29). One-hour-ahead predictive models incorporating meteorological and temporal variables achieved coefficients of determination up to 0.84. The results demonstrate that energy-independent low-cost sensor systems can robustly capture temporal patterns, pollutant interactions, and short-term predictability in localized roadside environments relevant to exposure assessment. Full article

Nitrogen oxides (NO_x) emitted mainly from vehicle exhaust significantly contribute to urban air pollution, leading to photochemical smog and secondary particulate matter. Photocatalytic technology has emerged as a promising solution for continuous NO_x decomposition under ultraviolet (UV) irradiation. This study developed an eco-friendly permeable concrete incorporating activated loess and zeolite to improve roadside air quality. The high porosity and adsorption capability of the concrete provided a suitable substrate for a TiO₂-based photocatalytic coating. A single-component coating system was optimized by introducing colloidal silica to enhance TiO₂ particle dispersibility and adding a binder to secure durable adhesion on the concrete surface. The produced permeable concrete met sidewalk quality standards specified in SPS-F-KSPIC-001-2006. Photocatalytic NO_x removal performance evaluated by ISO 22197-1 showed a maximum removal efficiency of 77.5%. Even after 300 h of accelerated weathering, the activity loss remained within 13.8%, retaining approximately 80% of the initial performance. Additionally, outdoor mock-up testing under natural light confirmed NO_x concentration removal and formation of nitrate by-products, demonstrating practical applicability in real environments. Overall, the integration of permeable concrete and a durable, single-component TiO₂ photocatalytic coating provides a promising approach to simultaneously enhance pavement sustainability and reduce urban NO_x pollution. Full article

Urban roadside forests are vital components of green infrastructure that provide multiple ecosystem services, contributing to climate regulation, environmental quality, and urban resilience. This study assessed the multifunctional ecosystem services of roadside tree communities along four representative road types—Coastal Scenic, Commercial Arterial, Residential Secondary, and Industrial Park Roads—in Weihai, a coastal city in eastern China. Based on a complete tree inventory (6742 individuals from 38 species) integrated with the i-Tree Eco model, we quantified three key ecosystem services, carbon storage and annual sequestration, air-pollutant removal, and stormwater interception, and monetized their benefits. Results indicate that roadside forests stored approximately 1120 tons of carbon and sequestered 78 tons annually (≈USD 0.53 million; CNY 3.85 million), removed 1.28 tons of air pollutants per year (≈USD 9370; CNY 68,400), and intercepted 1560 m³ of stormwater (≈USD 5560; CNY 40,600). Commercial Arterial and Coastal Scenic Roads yielded the highest total ecosystem-service values, while Residential Secondary Roads achieved the greatest per-area efficiency. These findings highlight the significant contribution of urban roadside forests to sustainable and climate-resilient city development and underscore their potential role in urban forest planning and management. Full article

The air–ground integrated Internet of Vehicles (IoV), which incorporates unmanned aerial vehicles (UAVs), is a key component of a three-dimensional intelligent transportation system. Task offloading is crucial to improving the overall efficiency of the IoV. However, blackhole attacks and false-feedback attacks pose significant challenges to achieving secure and efficient offloading for heavily loaded roadside units (RSUs). To address this issue, this paper proposes a reputation-aware, multi-objective task offloading method. First, we define a set of multi-dimensional Quality of Service (QoS) metrics and combine K-means clustering with a lightweight Proximal Policy Optimization variant (Light-PPO) to realize fine-grained classification of offloading data packets. Second, we develop reputation assessment models for heterogeneous entities—RSUs, vehicles, and UAVs—to quantify node trustworthiness; at the same time, we formulate the RSU task offloading problem as a multi-objective optimization problem and employ the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to find optimal offloading strategies. Simulation results show that, under blackhole and false-feedback attack scenarios, the proposed method effectively improves task completion rate and substantially reduces task latency and energy consumption, achieving secure and efficient task offloading. Full article

Particulate matter (PM) poses serious health risks, including respiratory and cardiovascular diseases, and is classified as a carcinogen by the World Health Organization and International Agency for Research on Cancer. Roadside air pollution, which is strongly affected by traffic emissions, is a major contributor to urban air quality deterioration. This study investigated the feasibility of establishing a low-cost, Internet of Things (IoT)-based, high-density monitoring network for roadside PM₁₀ and PM_2.5 to support safer and more sustainable road environments. We developed low-cost IoT sensing devices, deployed them at three urban roadside sites with different environmental conditions, and compared their performances with those of nearby public monitoring stations. One-minute resolution data were analyzed using Pearson correlation, cross-correlation, dynamic time warping, Z-score, and the roulette index. The IoT sensor data were strongly correlated with public station data, confirming its reliability as a complementary observation method. Notable site-specific patterns were sharp concentration increases with traffic at an intersection and distinct diurnal and weekly cycles at residential and rooftop sites. These findings demonstrate that low-cost IoT sensing can complement sparse public networks by providing microscale air quality information. This approach offers a practical foundation for smart city development and intelligent roadside environmental management. Full article

Autonomous vehicles are a promising solution to traffic congestion, air pollution, accidents, wasted time, and resources. However, remote driver intervention may be necessary in extreme situations to ensure safe roadside parking or complete remote takeover. In these cases, high-quality real-time video streaming is crucial for remote driving. In a preliminary study, we presented a region of interest (ROI) High-Efficiency Video Coding (HEVC) method where the image was segmented into two categories: ROI and background. This involved allocating more bandwidth to the ROI, which yielded an improvement in the visibility of classes essential for driving while transmitting the background at a lower quality. However, migrating the bandwidth to the large ROI portion of the image did not substantially improve the quality of traffic signs and lights. This study proposes a method that categorizes ROIs into three tiers: background, weak ROI, and strong ROI. To evaluate this approach, we utilized a photo-realistic driving scenario database created with the Cognata self-driving car simulation platform. We used semantic segmentation to categorize the compression quality of a Coding Tree Unit (CTU) according to its pixel classes. A background CTU contains only sky, trees, vegetation, or building classes. Essentials for remote driving include classes such as pedestrians, road marks, and cars. Difficult-to-recognize classes, such as traffic signs (especially textual ones) and traffic lights, are categorized as a strong ROI. We applied thresholds to determine whether the number of pixels in a CTU of a particular category was sufficient to classify it as a strong or weak ROI and then allocated bandwidth accordingly. Our results demonstrate that this multi-category ROI compression method significantly enhances the perceptual quality of traffic signs (especially textual ones) and traffic lights by up to 5.5 dB compared to a simpler two-category (background/foreground) partition. This improvement in critical areas is achieved by reducing the fidelity of less critical background elements, while the visual quality of other essential driving-related classes (weak ROI) is at least maintained. Full article

One of the emerging services provided by roadside green infrastructure is its contribution to the quality of landscape phenology, which is measured through the succession of colours and forms throughout the seasons. In the seasonal dynamics of space, flowering phenological patterns play a key role, particularly in early blooming species such as Cornus mas L. Therefore, this paper aims to highlight the significance of the Cornelian cherry as a component of roadside green infrastructure in the southwestern suburban zone of Belgrade. Through an integrative approach to phenological and climatic elements, and by means of a specific case study covering the period from 2007 to 2025, under climate change conditions, the influence of air temperature and precipitation on local flowering patterns of the Cornelian cherry has been assessed. Based on 1140 phenological observations conducted over 19 consecutive years, from January to April, key flowering elements were identified—those that influence pollination, fruiting, and the species’ practical potential. The Mann–Kendall, Sen’s slope, Rayleigh, and Watson–Williams tests were used to examine spatio-temporal changes in flowering patterns, while the Spearman Rank test and circular statistics were applied to quantify correlations among the analysed parameters. The results confirm that Cornelian cherry is an adaptive and sustainable species that continuously provides visual identity during its flowering period, while simultaneously reflecting climate change through phenological responses. These phenological responses are closely linked to local climatic conditions. In addition to enriching landscape phenology with vibrant visual features during the colder months, Cornelian cherry also enhances biodiversity by providing ecosystem services as a nectar-producing species, with its pollen serving as an early and valuable food source for bees. The study also confirms that the seasonal dynamics of landscape phenology can be used as a scientifically valid criterion for assessing the ecological quality of roadside green infrastructure. Full article

While urban greenery is known to regulate microclimates and reduce air pollution, its integrated effects remain insufficiently quantified. Through field monitoring and ENVI-met 5.1 modeling of high-rise residential areas in Jinan, the results demonstrate that: (1) vegetation exhibits distinct spatial impacts in air-quality impacts, reducing roadside PM_2.5 by 26.63 μg/m³ while increasing building-adjacent levels by 17.5 μg/m³; (2) shrubs outperformed trees in PM_2.5 reduction (up to 65.34%), particularly when planted in inner rows, whereas tree crown morphology and spacing showed negligible effects; (3) densely spaced columnar trees optimize cooling, reducing T_a by 3–4.8 °C and the physiological equivalent temperature (PET*) by 8–12.8 °C, while planting trees on the outer row and shrubs on the inner row best balanced thermal and air-quality improvements; (4) each 1 m²/m³ leaf area density (LAD) increase yields thermal benefits (ΔT_a = −1.07 °C, ΔPET* = −1.93 °C) but elevates PM_2.5 by 4.32 μg/m³. These findings provide evidence-based vegetation design strategies for sustainable urban planning. Full article

This study analyzes changes in NO_x concentrations due to the aerodynamic and dry deposition effects of roadside trees in the Jongno area, a central business district of Seoul, Republic of Korea, using a computational fluid dynamics (CFD) model. The simulation results indicate that the on-road NO_x concentration was slightly increased (2.09%) due to the aerodynamic effect of roadside trees. However, the dry deposition effect of roadside trees had a greater impact on reducing NO_x concentrations (−2.77%) along sidewalks. It was observed that the reduction in NO_x concentration due to the dry deposition effect of roadside trees was likely to offset the increase in NO_x concentrations due to the aerodynamic effect of roadside trees, resulting in an overall decrease in NO_x concentrations. Furthermore, sensitivity tests showed that the increase in NO_x concentrations due to the aerodynamic effects of roadside trees was intensified along sidewalks when ambient wind speeds were high, while the decrease in NO_x concentration was proportional to the deposition velocity of roadside trees. Therefore, roadside trees should be planted where aerodynamic effects do not significantly increase NO_x concentrations in order to improve near-road air quality. Full article

Lead isotope ratios vary depending on the origin of the lead, meaning that characteristic isotopic signatures can be used for source identification in environmental samples. Lead in ambient particulate matter was collected and analysed at 23 monitoring stations as part of the UK heavy metals air quality monitoring network to assess compliance with legislative limit values for allowable concentrations of lead in air. For the first time on a nationwide UK basis, isotopic analysis of lead was carried out on these samples to gain further information about the origin of the lead and the sources influencing measured concentrations at each of the monitoring stations. These measurements were undertaken with the novel application of ICP–MS/MS for high throughput analysis of over 200 samples from 23 sites across the UK. Values for 207Pb/206Pb ranged from 0.864 to 0.910 with an average standard error of 0.68%, while 208Pb/206Pb values ranged from 2.08 to 2.187 with an average standard error of 0.84%. The dataset was used to draw conclusions as to the main sources of pollution contributing to each site and has demonstrated the utility of ICP–MS/MS as a fit-for-purpose analytical method for the high throughput of a large number of samples in complex matrices. It was possible to identify different source types at the monitoring stations based on the lead isotope signature observed. Comparison with literature values showed clear links with traffic emissions at roadside sites and leaded petrol at a site near an airfield where small aircraft still use this type of fuel. Full article

Ambient air pollution is the most important environmental factor impacting human health. Urban landscapes present unique air quality challenges, which are compounded by climate change adaptation challenges, as air pollutants can also be affected by the urban heat island effect, amplifying the deleterious effects on health. Nature-based solutions have shown potential for alleviating environmental stressors, including air pollution and heat wave abatement. However, such solutions must be designed in order to maximize mitigation and not inadvertently increase pollutant exposure. This study aims to demonstrate potential applications of nature-based solutions in urban environments for climate stressors and air pollution mitigation by analyzing two distinct scenarios with and without green infrastructure. Utilizing low-cost sensors, we examine the relationship between green infrastructure and a series of environmental parameters. While previous studies have investigated green infrastructure and air quality mitigation, our study employs low-cost sensors in tropical urban environments. Through this novel approach, we are able to obtain highly localized data that demonstrates this mitigating relationship. In this study, as a part of the NERC-FAPESP-funded GreenCities project, four low-cost sensors were validated through laboratory testing and then deployed in two locations in São Paulo, Brazil: one large, heavily forested park (CIENTEC) and one small park surrounded by densely built areas (FSP). At each site, one sensor was located in a vegetated area (Park sensor) and one near the roadside (Road sensor). The locations selected allow for a comparison of built versus green and blue areas. Lidar data were used to characterize the profile of each site based on surrounding vegetation and building area. Distance and class of the closest roadways were also measured for each sensor location. These profiles are analyzed against the data obtained through the low-cost sensors, considering both meteorological (temperature, humidity and pressure) and particulate matter (PM₁, PM_2.5 and PM₁₀) parameters. Particulate matter concentrations were lower for the sensors located within the forest site. At both sites, the road sensors showed higher concentrations during the daytime period. These results further reinforce the capabilities of green–blue–gray infrastructure (GBGI) tools to reduce exposure to air pollution and climate stressors, while also showing the importance of their design to ensure maximum benefits. The findings can inform decision-makers in designing more resilient cities, especially in low-and middle-income settings. Full article