Search Results (32)

Liquefied natural gas (LNG) container trucks are regarded as clean energy vehicles with the potential to reduce air pollution. However, their CO₂ emissions remain relatively high and are not yet well understood. In this study, the actual CO₂ emissions of LNG container trucks in Shanghai were measured using a portable emissions measurement system (PEMS). This study quantitatively analyzed the relationship between traffic congestion levels and CO₂ emissions on elevated roadways, providing new insights into the impact of urban traffic conditions. In addition, distinct emission patterns were revealed under different uphill, downhill, and level road conditions, highlighting the substantial effects of roadway geometry on vehicle carbon emissions. Based on these findings, engine-related factors were identified as the dominant contributors, explaining 74% of the emission variance, while road slope analysis showed that uphill driving increased emissions by 13.41% compared with flat roads, whereas downhill driving reduced them by 76.22%. Finally, an efficient carbon emission prediction model for LNG container trucks was developed using machine learning methods. This study enriches the understanding of carbon emissions from LNG container trucks and provides theoretical support for their future applications in sustainable freight transportation. Full article

Agricultural soils near roadways are increasingly contaminated with presumably contaminating elements (PCEs), raising concerns for food safety and health risks in Bangladesh. This study quantified Mn, As, Co, Cr, Zn, Ni, Cu, Cd and Pb in roadside agricultural farm soils at three depths (0–5, 5–10, 10–15 cm) across industrial, peri-urban, and research areas using ICP-MS. The average mass fractions ranked as Mn > Zn > Cr > Ni > Cu > Pb > Co > As > Cd with peri-urban soils exhibiting the elevated levels of Cr (80.48 mg.kg⁻¹ and Ni (65.81 mg.kg⁻¹). Contamination indices indicated Cd (Contamination Factor: 2.01–2.53) and Ni (Contamination Factor: up to 2.27) as the most enriched elements, with all sites showing a Pollution Load Index (PLI) >1 (1.07–1.66), reflecting cumulative soil deterioration. Cd posed moderate ecological risk (Er: 60.3–75.9), whereas other PCEs were low risk. Health risk assessment showed elevated non-carcinogenic hazard indices (HI: 7.87–10.5 for children; 3.72–4.78 for adults), with Mn, Cr, and Co as major contributors. Cumulative carcinogenic risk (CCR) values were dominated by Cr, reaching 7.22 × 10⁻⁴ in industrial areas and 3.98 × 10⁻⁴ in peri-urban areas, exceeding the acceptable range (10⁻⁶–10⁻⁴). Metal mass fractions were consistently higher in surface soils (0–5 cm) than at deeper layers, indicating anthropogenic deposition from traffic and industry. Multivariate analysis distinguished geogenic (Cr-Ni-Cu; Mn-Co-As) from anthropogenic (Cd-Pb-Zn) sources. These findings identify Cd and Cr as priority pollutants, highlighting the need for soil management and pollution control near roadways in Bangladesh. Full article

In recent decades, road transport has become one of the dominant factors shaping environmental conditions, with both beneficial and adverse consequences. While transport infrastructure facilitates access to essential services and supports societal well-being, vehicular emissions remain a major source of air quality degradation. Among the pollutants released, nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) are of particular concern due to their adverse health effects, especially in densely trafficked urban areas. Pollutant levels are determined not only by traffic intensity but also by external influences such as meteorological conditions and roadway design. This study examines how different intersection configurations affect ambient concentrations of PM₁₀, PM_2.5, and NO₂. Field monitoring and dispersion modeling were carried out for a three-arm intersection and a roundabout. NO₂ concentrations were quantified using a reference chemiluminescence method, while PM₁₀ and PM_2.5 were measured with an optical aerosol spectrometer. Traffic flow characteristics associated with each intersection geometry were simulated in PTV Vissim, and pollutant dispersion patterns were subsequently analyzed using the CadnaA modeling environment. Field measurements revealed lower PM concentrations (reduction in PM₁₀, PM_2.5–10 and PM_2.5 concentration—30.1%, 45.1% and 22.8%) and higher NO₂ concentrations (increase in NO₂ concentration—143.3%) at the roundabout. Full article

Diesel particulate matter—primarily ultrafine particles (UFPs), defined as particles smaller than 0.1 µm—are released by diesel-powered vehicles, especially those used in heavy-duty hauling. While much of the existing research on traffic-related air pollution focuses on urban environments, limited attention has been paid to how complex topography influences the concentration of UFPs, particularly in areas with significant truck traffic. With a focus on Morgantown, West Virginia, an area distinguished by a steep topography, this study investigates how travel over two different terrain conditions affects UFP concentrations close to roadways. Specifically, we sought to determine if the truck count taken from simultaneous video evidence could be used as a surrogate for varying topography in determining the concentration of UFPs. This study shows that “TRUCK COUNT” and “TRUCK SPEED” have a linear relationship and yield a possible surrogate measure of the lung dose of UFP number concentration. Our results demonstrate a statistically significant (p < 0.1) linear relationship between truck count and UFP number concentration (R = 0.77 and 0.40), validating truck count along with truck speed as a medium effect surrogate for estimating near-road UFP exposure. Dose estimation using the Multiple-Path Particle Dosimetry (MPPD) model further revealed that approximately 30% of inhaled UFPs are deposited in the alveolar region, underscoring the public health relevance of this exposure pathway in topographically complex areas. This method ultimately awaits comparison with health effects to determine its true potential as a useful exposure metric. Full article

This study selected green spaces from three residential areas in Hefei as the research subjects, combining behavioral observation methods and a natural experiment to collect behavioral data from 2010 and 2024. The data were then compared using Poisson regression models. Additionally, home visits were conducted to gather residents’ perceptions of the factors contributing to the decline in vitality. Based on the survey data, multilevel regression analysis was performed to explore the decline in RQGS usage vitality and its influencing factors in the context of rapid urbanization. This study found a significant decline in green space visits, particularly during the afternoon (16:00–18:00) and in areas adjacent to roadways. The main influencing factors include emerging leisure choices (such as taking the subway to large parks or preferring indoor activities) and residents’ satisfaction with RQGS characteristics (such as functional zoning, noise pollution, and neighborhood familiarity). Notably, there was no significant correlation between “disposable leisure time” and visit frequency. These findings suggest that, despite the inherent advantages of proximity, the vitality of RQGS faces increasing challenges due to emerging diverse leisure demands and growing environmental disturbances. In contrast to the traditional emphasis on accessibility, this study recommends that future RQGS planning prioritize functional zoning (e.g., dog-walking areas, sports zones), address the needs of vulnerable groups, and focus on mitigating vehicle noise and air pollution rather than merely expanding parking facilities. Interventions should be scheduled for the afternoon and emphasize strengthening community interaction and cohesion to enhance user experience. This research provides valuable scientific evidence and practical guidance for urban planners and policymakers to optimize residential green spaces in the context of rapid urbanization, offering new perspectives for the empirical evaluation of RQGS upgrades. 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

This study examines the differences in particulate matter (PM) properties and microbial compositions between natural and urban environments, providing foundational data for environmental monitoring and biothreat detection. Air samples were collected during the spring and early summer from two distinct locations: a forest/lake area, and an urban parking lot adjacent to a high-traffic roadway. Quantitative phase imaging microscopy and genomic sequencing were employed to characterize particle size distributions, statistical properties, and microbial community structures in these environments. The results revealed significant differences in PM properties between the two locations. Urban air exhibited higher particle concentrations that reflect pollution sources, whereas the natural environment displayed greater variability in particle size and distribution, correlating with diverse biological content. Genomic sequencing showed a lower diversity of microbial communities compared to the forest/lake area but with greater uniformity. To sum up, by integrating optical microscopy and genomic sequencing, this research demonstrates the feasibility of establishing environmental baselines for PM characteristics and bio-component diversity. The findings underscore the potential of combining real-time optical sensing with genomic tools for early biothreat detection and improved environmental monitoring in diverse settings. Full article

Mobile source air toxics (MSATs) are major contributors to urban air pollution, especially near high-traffic roadways, where populations face elevated pollutant exposures. Traditional human health risk assessments, based on deterministic methods, often overlook variability in exposure and the vulnerabilities of sensitive subpopulations. This study introduces and applies a new stochastic modeling approach, utilizing Monte Carlo simulations to evaluate cumulative cancer risks from MSATs exposure through inhalation and ingestion pathways. This method captures variability in exposure scenarios, providing detailed health risk assessments, particularly for vulnerable groups such as children and the elderly. This approach was demonstrated in a case study conducted in Saint Paul, Minnesota, using 2019 traffic data. Deterministic models estimated cumulative cancer risks for adults at 6.24E-02 (unitless lifetime cancer risk), while stochastic modeling revealed a broader range, with the 95th percentile reaching 4.98E-02. The 95th percentile, used in regulatory evaluations, identifies high-risk scenarios overlooked by deterministic methods. This research advances the understanding of MSATs exposure risks by integrating spatiotemporal dynamics, identifying high-risk zones and vulnerable subpopulations, and supporting resource allocation for targeted pollution control measures. Future applications of this methodology include expanding stochastic modeling to evaluate ecological risks from mobile emissions. Full article

Noise pollution presents significant challenges for urban infrastructure management, highlighting the need for practical assessment tools such as noise maps. These maps enable the visualization and geo-referencing of noise levels, identifying areas requiring immediate intervention and long-term strategic responses. Road sections with traffic exceeding 3 million vehicles per year were selected for measurement. The findings are presented in detail, revealing that the Long-term Day-Night Average Noise Level (L_den) exceeds acceptable limits, affecting approximately 1.899 km² and impacting around 1200 residents within the exceedance zone. Similarly, the equivalent noise level (L_Aeq) surpasses acceptable thresholds over an area of 1.220 km², affecting an additional 700 residents. Notably, there were no exceedances of the key noise impact indicators, including high annoyance (HA), high sleep disturbance (HSD), and ischemic heart disease (IHD). Changes in traffic organisation were implemented to address areas that exceed the applicable noise standards, including a ban on trucks and the introduction of local speed limits. The measures have successfully mitigated the noise problem in Grodzisk County (Poland). Further anti-noise initiatives are planned, including planting vegetation along the roadways. Full article

The well-known Gaussian plume model has proven to be very useful in simulating the atmospheric dispersion of air pollutants (both gaseous and particulates). Nevertheless, the nature of the model presents problems in the actual computation of concentrations when the plume is confined between two parallel boundaries due to the occurrence of multiple reflections. The ground and temperature inversion lid (especially, when the inversion layer is at low levels in the atmosphere) with a chimney stack releasing the effluent below the latter, is one contextual example of horizontal parallel reflecting boundaries. A second example is buildings confining a roadway on either side, with motor vehicles emitting pollution within the street canyon (or urban notch). In such cases, multiple reflections should be accounted for, otherwise the model underpredicts the resulting concentration. This paper presents a mathematical rewriting of the Gaussian plume model equation corrected for multiple reflections when the pollution source is confined between parallel boundaries. The obtained result is most appropriate when the parallel boundaries are rigid, and near-complete reflection is achieved, e.g., street canyon environment (second quoted example). It is worth noting that the relevant mathematical derivations and definitions are all included in the paper to facilitate reading and to ensure comprehensiveness in the presentation. Additionally, the outcome of some preliminary numerical testing is presented. The latter indicates that the new formulation is mathematically stable and yields interesting results. Further numerical investigation and experimental evaluation are merited. Full article

Rapid urbanization has introduced new pollution challenges, with visual pollution becoming particularly prominent. This type of pollution affects both the visual environment and public psychology, impairing aesthetic appreciation. Visual pollution extends beyond outdoor advertising, manifesting in various forms across urban, roadway, and natural areas. Although many studies have identified and analyzed visual pollution, there is still a lack of comprehensive knowledge and awareness of this problem. Until now, visual pollution has never been a unified and complete concept, definition, and research methodology. To address this gap, our systematic literature review examined existing literature to further explore and understand visual pollution. We systematically reviewed research articles published between 2008 and 2023, utilizing three journal databases: Web of Science, Scopus, and Google Scholar. Ultimately, 52 articles met the review criteria. The results of the study showed the types and characteristics of visual pollutants, the locations where visual pollution occurs, the various factors contributing to visual pollution, and the methodologies employed to study visual pollution. This study enhances professionals’ comprehension of visual pollution and its effects on the visual environment, equipping them to implement effective measures to reduce its impact and preserve visual quality in both urban and natural areas. Full article

A roadway path is most commonly perceived as a 3-D element structure placed within its surrounding environment either within or outside urban areas. Design guidelines are usually strictly followed to ensure safe and comfort transportation of people and goods, but in full alignment with the terrain configuration and the available space, especially in urban and suburban areas. In the meantime, vehicles travelling along a roadway consume fuel and emit pollutants in a way that depends on both the driving attitude as well as the peculiar characteristics of road design and/or pavement surface condition. This study focuses on the environmental behavior of roadways in terms of fuel consumption, especially of heavy vehicles that mainly serve the purpose of freight transportation within urban areas. The impact of horizontal and vertical profiles of a roadway structure is theoretically considered through the parameters of speed and longitudinal slope, respectively. Based on theoretical calculations with an already developed model, it was found that the slope plays the most critical role, controlling the rate of fuel consumption increase, as an increase ratio of 2.5 was observed for a slope increase from 2% to 7%. The variation was less intense for a speed ranging from 25 to 45 km/h. The investigation additionally revealed useful discussion points for the need to consider the environmental impact of roadways during the operation phase for a more sustainable management of freight transportation procedures, thereby stimulating an ad hoc development of fuel consumption models based on actual measurements so that local conditions can be properly accounted for and used by road engineers and/or urban planners. Full article

Road transport and traffic congestion significantly contribute to dust pollution, which negatively impacts the growth of roadside plants in urban areas. This study aims to quantify the air pollution tolerance index (APTI) and analyze the impacts of dust deposition on different plant species and trees planted along a busy urban roadside in Lahore, Pakistan by considering seasonal variations. The APTI of each species is determined based on inputs of various biochemical parameters (leaf extract pH, ascorbic acid content, relative water content, and total chlorophyll levels), including dust deposition. In this study, laboratory analysis techniques are employed to assess these factors in selected plant species such as Mangifera indica, Saraca asoca, Cassia fistula, and Syzygium cumini. A statistical analysis is conducted to understand the pairwise correlation between various parameters and the APTI at significant and non-significant levels. Additionally, uncertainties in the inputs and APTI are addressed through a probabilistic analysis using the Monte Carlo simulation method. This study unveils seasonal variations in key parameters among selected plant species. Almost all biochemical parameters exhibit higher averages during the rainy season, followed by the summer and winter. Conversely, dust deposition on plants follows an inverse trend, with values ranging from 0.19 to 4.8 g/cm², peaking during winter, notably in Mangifera indica. APTI values, ranging from 9.39 to 14.75, indicate varying sensitivity levels across species, from sensitive (Syzygium cumini) to intermediate tolerance (Mangifera indica). Interestingly, plants display increased tolerance during regular traffic hours, reflecting a 0.9 to 5% difference between the APTI at peak and regular traffic hours. Moreover, a significant negative correlation (−0.86 at p < 0.05 level) between APTI values and dust deposition suggests a heightened sensitivity to pollutants during the winter. These insights into the relationship between dust pollution and plant susceptibility will help decision makers in the selection of resilient plants for urban areas and improve air quality. Full article

Today’s roadways are subject to traffic congestion, the deterioration of surface-assets (often due to the overreliance on private vehicle traffic), increasing vehicle-operation and fuel costs, and pollutant emissions. In Abu Dhabi, private car traffic forms the major share on urban highways, as the infrastructure was built to a high quality and the public transport network needs expansion, resulting in traffic congestion on major highways. These issues are arguably addressable by appropriate decisions at the planning stage. Microsimulation modeling of driving behavior in Abu Dhabi is presented for empirical assessment of traffic management scenarios. This paper presents a technique for developing, calibrating, validating, and the scenario analysis of a detailed VISSIM-based microsimulation model of a 3.5 km section of a 5-lane divided highway in Abu Dhabi. Traffic-count data collected from two sources, i.e., the local transport department (year 2007) and municipality (2007 and 2015–2016) were used. Gaps in traffic-counts between ramps and the highway mainline were noted, which is a common occurrence in real-world data situations. A composite dataset for a representative week in 2015 was constructed, and the model was calibrated and validated with a 15% (<100 vehicles per hour) margin of error. Scenario analysis of a potential public bus transport service operating at 15 min headway and 40% capacity was assessed against the base case, for a 2015–2020 projected period. The results showed a significant capacity enhancement and improvement in the traffic flow. A reduction in the variation between vehicle travel times was observed for the bus-based scenario, as less bottlenecking and congestion were noted for automobiles in the mainline segments. The developed model could be used for further scenario analyses, to find optimized traffic management strategies over the highway’s lifecycle, whereas it could also be used for similar evaluations of other major roads in Abu Dhabi post-calibration. Full article

Advanced air mobility (AAM) is a sustainable aviation initiative to deliver cargo and passengers in urban and regional locations by electrified drones. The widespread expectation is that AAM adoption worldwide will help to reduce pollution, reduce transport costs, increase accessibility, and enable a more reliable and resilient supply chain. However, most countries lack regulations that legalize AAM. A fragmented regulatory approach hampers the progress of business prospectors and international organizations concerned with human welfare. Therefore, amidst high uncertainty, knowledge of indicators that can predict the propensity for AAM adoption will help nations and organizations plan for drone use. This research finds predictive indicators by assembling a unique dataset of 36 economic, social, environmental, governance, land use, technology, and transportation indicators for 204 nations. Subsequently, the best of 12 different machine learning models ranks the predictive importance of the indicators. The gross domestic product (GDP) and the regulatory quality index (RQI) developed by the Worldwide Governance Indicators (WGI) project were the two top predictors. Just as importantly, the poor predictors were as follows: the social progress index developed by the Social Progress Imperative, the WGI rule-of-law index, land use characteristics such as rural and urban proportions, borders on open waterways, population density, technology accessibility such as electricity and cell phones, carbon dioxide emission level, aviation traffic, port traffic, tourist arrivals, and roadway fatalities. Full article