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The Eastern Crossroad is one of the national roads that connect the city of Jambi with other cities, districts, and provinces, resulting in relatively heavy traffic and frequent congestion on this road. This has led to the concentration of carbon monoxide (CO), which can result in a decline in ambient air quality. The research used a quantitative descriptive approach aimed at depicting the ambient air quality of CO on Mendalo Darat Road, which is part of Eastern Crossroad. The research revealed the highest vehicle density of 17,954 units in the morning on Tuesday, with the highest emission rate of 114,290 µg/m.s, and the lowest density on Sunday morning with 6,568 units and an emission rate of 44,207 µg/m.s. The highest accumulation of CO emission levels occurred on Tuesday evening, reaching 38,536.44 µg/Nm3. Overall, the ambient air quality of CO on Mendalo Darat Road tends to exceed the quality standards. The accumulation of increased CO emissions correlates closely with the road's national status, increased vehicle density due to high community activity, changes in the day's status (working day), and road conditions with traffic signal devices.

Urban traffic emissions have significant environmental and health implications. Diverging from traditional research that primarily aims to improve traffic flow and efficiency, this study specifically focuses on the environmental impact on traffic emissions, conducting a comprehensive analysis within the Manhattan urban network through four route guidance strategies. The performance of these strategies is examined across various vehicle densities, and their impact on four traffic pollutant emissions (Carbon-dioxide, Nitrogen Oxides, Volatile Organic Compounds and Particulate Matter) is assessed. Moreover, our innovative approach analyzes emissions from the perspectives of both travel distance and trip frequency, placing special emphasis on trip frequency to provide practical insights with high real-world applicability. The results highlight the potential and limitation of the Congestion Coefficient Strategy. Under equal travel demands, the Congestion Coefficient Strategy showed promise in reducing carbon emissions. However, at lower vehicle densities, it led to a significant increase in emissions. This revelation pointed to the need for modifications to the strategy when applied in scenarios with lower traffic density. Recognizing this limitation, we introduced a modified strategy that achieved remarkable reductions in emissions across diverse vehicle densities, effectively overcoming the challenges posed by the original Congestion Coefficient Strategy. These findings offer valuable insights for policymakers and transportation planners in selecting optimal route guidance strategies to reduce pollutant emissions. Future studies will explore the efficacy of these strategies in road networks characterized by different topological configurations. [ABSTRACT FROM AUTHOR]

In recent years, the utilization of intelligent transportation systems (ITS) has been significantly augmented due to advancements in the fields of computer science and information technology. Connected and autonomous vehicle (CAV) technology has gained a significant amount of attention in the research community. The advancements in connected and autonomous vehicle technology are expected to have an influence on the current transportation networks in the near future. The main purpose of this study is to investigate the influence of different driving behaviors of CAVs, namely defensive, normal, and aggressive, on a grid urban network consisting of unsignalized intersections in terms of traffic efficiency, traffic emissions, fuel consumption, and traffic safety by utilizing an open-source simulation tool called SUMO. Three different demand configurations and varying penetration rates of different behaviors for CAVs were taken into account in the scope of this study. According to the findings of the study, while CAVs with defensive behavior lead to an increase in average time loss, CAVs with aggressive behavior reduce the average time loss with increased penetration rates. The reduction in vehicle emissions and fuel consumption is not valid for all driving behaviors of CAVs. It was observed that it depends on the driving behavior of CAVs, penetration rates, and demand configurations. In terms of traffic safety, the worst performance was obtained by CAV-aggressive behavior. Furthermore, it was found that there is a trade-off between traffic efficiency and traffic safety metrics regarding the driving behavior of CAVs. [ABSTRACT FROM AUTHOR]

The exponential growth of urban centers has exacerbated the prevalence of traffic-related issues. This surge has amplified the conflict between the escalating need for travel among individuals and the constricted availability of road infrastructure. Consequently, the escalation of traffic accidents and the exacerbation of environmental pollution have emerged as increasingly pressing concerns. Urban road intersections, serving as pivotal junctures for vehicle convergence and dispersal, have remained a focal point for scholarly inquiry regarding enhanced operational efficacy and safety. Concurrently, vehicles navigating intersections are subject to external influences, such as pedestrian crossings and signal controls, causing frequent fluctuations in their operational dynamics. These fluctuations contribute to heightened exhaust emissions, exacerbating air pollution and posing health risks to pedestrians frequenting these intersections. A reasonable signal timing scheme can enable more vehicles to pass through the intersection safely and smoothly and reduce the pollutants generated by transportation. Therefore, optimizing signal timing schemes at intersections to alleviate traffic problems is a topic that needs to be studied urgently. In this paper, the emission model based on specific power is analyzed. Through an analysis of the correlation between specific power distribution intervals and the emission rates of individual pollutants, it has been observed that vehicle emission rates are at their lowest during idle speed, progressively increasing with rising vehicle speeds. Investigation into specific power distribution based on variables, such as vehicle type, frequency of stops, and varying delays, has led to the deduction that the peak specific power of vehicles at intersections consistently occurs within the (0, 1) interval. Furthermore, it has been established that high-saturation intersections exhibit higher peak specific power compared to low-saturation intersections. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) is an important component in the atmosphere, affecting air quality, health, radiation balance, and global climate. To assess regional air quality in the city of Fez, an intensive field campaign was carried out in the autumn of 2019 in the Middle Atlas region of Morocco. Aerosol sampling was performed simultaneously at two urban sites in the city of Fez: (1) Fez University (FU), a sub-urban site, and (2) Fez Parc (FP), an urban site located in the city center of Fez, using PM10 collectors. Various laboratory analyses were carried out, including PM mass, trace metals, inorganic ions, OC/EC, sugar compounds, and PAHs. The results indicate that the PM10 mass (61 ng m−3) was comparable at both sites, with a 37–107 ng m−3 range. Most of the 19 investigated PAHs at the FU site (10.2 ± 6.2 ng m−3) were low-molecular-weight PAHs, while the most abundant PAHs at the FP site (6.9 ± 3.8 ng m−3) were mainly higher-molecular-weight PAHs. A diagnostic ratio analysis at both sites indicated that PAHs originated from fossil fuel combustion and traffic emissions from diesel engines, with Ant/(Ant + Phe) and Flu/(Flu + Pyr) ratios averaging 0.22 and 0.84, respectively. PMF analysis identified traffic emissions as a major source (30%), with secondary inorganic aerosols (20%) and biomass burning (14%). Polar plots highlight the dominance of local anthropogenic activities in PM pollution, with vehicular emissions, industrial activities, and biomass burning. This study shows that local sources and combustion processes significantly contribute to PM10 sources in Morocco, providing insights into air pollution mitigation in North Africa. [ABSTRACT FROM AUTHOR]

Addressing urban traffic congestion is a pressing issue requiring efficient solutions that need to be analyzed regarding travel time and pollutant emissions. The traffic weighted multi-maps (TWM) method has been proposed as an efficient mechanism for congestion mitigation that enables differential traffic routing and path diversity by strategically distributing different network views (maps) to the drivers. Previous works have focused on TWM generation by creating optimal edge weights, but the complexity exponentially increases with the network size and traffic group diversity. This work describes how congestion and emissions can be addressed using TWM maps based on the k-shortest paths for the traffic flows (instead of individuals) that are optimally assigned and distributed to the components of the traffic flow. The map allocation strategies optimal TWM (OTV), optimal TWM per path flow with linear constraints (LCTV), and its variant unconstrained optimal TWM per path flow (UCTV) are described. They use maps generated from the k-shortest paths of the traffic flows (kSP-TWM). The heuristic solution obtained is compared with the theoretical static traffic assignment estimation baseline with different configurations, regarding congestion reduction, total travel time enhancement, and pollutant emissions. Experiments are developed using a synthetic traffic grid network scenario with a mesoscopic simulation. They show that the solution provided is adequate for its proximity to the theoretical equilibrium solutions and can generate minimum emissions patterns. The presented solution opens new possibilities for further congestion and pollutant management studies and seamless integration with existing traffic management frameworks. [ABSTRACT FROM AUTHOR]

This paper investigates the historical sources of soil-lead contamination in Santa Ana, California. Even though dangerous levels of soil-lead have been found in a wide variety of communities across the United States, public health institutions lack clarity on the historical origins of these crises. This study uses geo-spatial data collected through archival research to estimate the impact of two potential sources of lead contamination in the past -- lead-paint and leaded gasoline. It examines, through a combination of statistical and historical methods, the association between lead concentrations in contemporary soil samples and patterns in the evolution of the city's physical features, such as the growth of urbanized areas and the historical flow of traffic. We emphasize the value of historical data collected through archival research for understanding the sources of environmental lead, particularly leaded gasoline, which our study found to be the most likely and most prominent contributor to soil-lead in Santa Ana's environment. This research contributes to environmental-justice advocacy efforts to reframe lead poisoning as a systemic environmental issue and outlines the path forward to community-level remediation strategies.

Based on the development of the concept of a resource-saving and environmentally friendly society, needing to develop low-carbon and sustainable urban transportation. Most of the pollutants come from the emissions of motor vehicle exhaust. Therefore, this paper analyzes the relationship between driving behavior and traffic emissions, to constrain driver behavior to reduce pollutant emissions. The GPS data are preprocessed by using Navicat for data integration, data screening, data sorting, etc., and then, the speed data are cleaned by using a combination of box-and-line plots and linear interpolation in SPSS. Second, this paper uses principal component analysis (PCA) to downsize 12 indicators such as average speed, average acceleration, and maximum speed and then adopts K-MEANS and K-MEDOIDS methods to cluster the driver’s behavioral indicators, selects the aggregation method based on the clustering indexes optimally, and analyzes the driver’s driving state by using the symbolic approximation aggregation method; finally, according to the above research results and combined with the MOVES traffic emission model to analyze the relationship between the driver’s driving mode, driving state, and traffic emissions, the decision tree can be used to predict the unknown driving mode of the driver to estimate the degree of its emissions. [ABSTRACT FROM AUTHOR]

• A portable open-path instrument based on DOAS was developed for mobile measurement of ambient NH 3. • The on-road NH 3 and NO concentrations were measured for the arterial highway of Shanghai. • The measured NH 3 /NO was associated to the proportions of light gasoline vehicles and heavy-duty diesel vehicles in the fleet. • The mileage-based NH 3 emission factor was estimated to be 17.9 ± 6.3 mg/km per vehicle based on the tunnel measurement. Increased attentions to vehicle emission of NH 3 have been paid since it is generally regarded as an important source in urban areas. Here, we developed a movable instrument based on Differential Optical Absorption Spectroscopy (DOAS) principle for detecting on-road NH 3 , which can avoid the losses in the sampling process attributed to the non-sampling methods. For this mobile DOAS, the temporal resolution, detection limit and relative error for NH 3 were 1 min, 2.29 ppbv and 4.57% ± 2.44%, respectively. By employed to the on-road measurements along the arterial highway in Shanghai, the spatial distributions of NH 3 and NO were obtained, and their dependence of traffic and road conditions were studied. The slopes of linear regression between NH 3 and NO were 0.40, 0.02 and 0.07 on the Middle Ring Road, Outer Ring Road and Chongming Island Ring Road. It indicates that light gasoline vehicles (LGVs) were found to be the main contributor to NH 3 emissions, while heavy-duty diesel vehicles (HDVs) mainly emitted NO. Based on the measured NH 3 in the tunnel, the mileage-based NH 3 emission factor per vehicle was estimated to be 17.9 ± 6.3 mg/km. The reported open-path instrument can be broadly used in on-road pollutant monitoring or vehicle emissions, and the measurements can reveal the real situation of emission characteristics, even find the abnormal operations of vehicle catalyst system. [Display omitted] [ABSTRACT FROM AUTHOR]

Emissions from road traffic, power generation and industry were substantially reduced during pandemic lockdown periods globally. Thus, we analysed reductions in traffic-related air pollution in Australian capital cities during March–April 2020 and then modelled the mortality benefits that could be realised if similar reductions were sustained by structural policy interventions. Satellite, air pollution monitor and land use observations were used to estimate ground-level nitrogen dioxide (NO 2) concentrations in all Australian capital cities during: (a) a typical year with no prolonged air pollution events; (b) a hypothetical sustained reduction in NO 2 equivalent to the COVID-19 lockdowns. We use the WHO recommended NO 2 exposure-response coefficient for mortality (1.023, 95 % CI: 1.008–1.037, per 10 μg/m3 annual average) to assess gains in life expectancy and population-wide years of life from reduced exposure to traffic-related air pollution. We attribute 1.1 % of deaths to anthropogenic NO 2 exposures in Australian cities, corresponding to a total of 13,340 years of life lost annually. Although COVID-19-related reductions in NO 2 varied widely between Australian cities during April 2020, equivalent and sustained reductions in NO 2 emissions could reduce NO 2 -attributable deaths by 27 %, resulting in 3348 years of life gained annually. COVID-19 mobility restrictions reduced NO 2 emissions and population-wide exposures in Australian cities. When sustained to the same extent by policy interventions that reduce fossil fuel consumption by favouring the uptake of electric vehicles, active travel and public transport, the health, mortality and economic benefits will be measurable in Australian cities. [Display omitted] [ABSTRACT FROM AUTHOR]

Health factors concerning the well-being of the urban population urge us to better comprehend the impact of emissions in urban environments on the micro-scale. There is great necessity to depict and monitor pollutant concentrations with high precision in cities, by constructing an accurate and validated digital air quality network. This work concerns the development and application of a CFD model for the dispersion of particulate matter, CO, and NOx from traffic activity in a highly busy area of the city of Augsburg, Germany. Emissions were calculated based on traffic activity during September of 2018 with COPERT Street software version 2.4. The needed meteorological data for the simulations were taken from a sensor's network and the resulting concentrations were compared and validated with high-precision air quality station indications. The model's solver used the steady-state RANS approach to resolve the velocity field and the convection–diffusion equation to simulate the pollutant's dispersion, each one modelled with different molecular diffusion coefficients. A sensitivity analysis was performed to decide the most efficient computational mesh to be used in the modelling. A velocity profile for the atmospheric boundary layer (ABL) was implemented into the inlet boundary of each simulation. The cases concerned applications on the street level in steady-state conditions for one hour. The results were evaluated based on CFD validation metrics for urban applications. This approach provides a comprehensive state-of-the-art 3D digital pollution network for the area, capable of assessing contamination levels at the street scale, providing information for pollution reduction techniques in urban areas, and combining with existing sensor networks for a more thorough portrait of air quality. [ABSTRACT FROM AUTHOR]

We estimated the particle number distributions (PNDs), particle number concentrations (PNCs), physicochemical characteristics, meteorological effects, and respiratory deposition doses (RDD) in the human respiratory tract for three different particle modes: nucleation (N6–30), accumulation (N30–300), and coarse (N300–10,000) modes. This study was conducted in three different microenvironments (MEs) in London (indoor, IN; traffic intersection, TI; park, PK) measuring particles in the range of 6 nm–10,000 nm using an electrical low-pressure impactor (ELPI+). Mean PNCs were 1.68 ± 1.03 × 104 #cm−3, 7.00 ± 18.96 × 104 #cm−3, and 0.76 ± 0.95 × 104 #cm−3 at IN, TI, and PK, respectively. The PNDs were high for nucleation-mode particles at the TI site, especially during peak traffic hours. Wind speeds ranging from 0 to 6 ms−1 exhibit higher PNCs for nucleation- and accumulation-mode particles at TI and PK sites. Physicochemical characterisation shows trace metals, including Fe, O, and inorganic elements, that were embedded in a matrix of organic material in some samples. Alveolar RDD was higher for the nucleation and accumulation modes than the coarse-mode particles. The chemical signatures from the physicochemical characterisation indicate the varied sources at different MEs. These findings enhance our understanding of the different particle profiles at each ME and should help devise ways of reducing personal exposure at each ME. [ABSTRACT FROM AUTHOR]

Purpose: With increased populations and movement of people worldwide, traffic emissions will lead to an increase in carbon dioxide emissions, which is one of the greenhouse gases. This will increase outdoor air pollution and other environmental risks that will impact people's health and livelihood, crops, as well as the built environment such as architecture, buildings and other structures. Few studies have been devoted to addressing the effect of air pollution on historic buildings. However, there is no specific study on the impact of traffic-related emissions. Design/methodology/approach: A thorough analysis has been conducted in selecting eight historic districts (HDs) among all of 31 located within the city of San Antonio. Motor Vehicle Emission Simulator (MOVES) software has been utilized to process the data collected from the average annual daily traffic (AADT) and vehicle operational speeds and evaluate the amounts of emissions for each HD. Findings: MOVES outcomes showed that HDs are under the threat of deterioration caused by traffic emissions. Therefore, transportation and environmental planners need to implement sustainable solutions to reduce the impact of CO2 emissions on HDs and, ultimately, historic buildings. Originality/value: This study will help policymakers plan a better course of action for the future conditions associated with the increased traffic volumes. It is also beneficial for the developers, preservationists, architects and all other stakeholders willing to preserve the history of a country. [ABSTRACT FROM AUTHOR]

In this study, we test the performance of a compact gas chromatograph with photoionization detector (GC-PID) and optimize the configuration to detect ambient (sub-ppb) levels of benzene, toluene, ethylbenzene, and xylene isomers (BTEX). The GC-PID system was designed to serve as a relatively inexpensive (~10 k USD) and field-deployable air toxic screening tool alternative to conventional benchtop GCs. The instrument uses ambient air as a carrier gas and consists of a Tenax-GR sorbent-based preconcentrator, a gas sample valve, two capillary columns, and a photoionization detector (PID) with a small footprint and low power requirement. The performance of the GC-PID has been evaluated in terms of system linearity and sensitivity in field conditions. The BTEX-GC system demonstrated the capacity to detect BTEX at levels as high as 500 ppb with a linear calibration range of 0-100 ppb. A detection limit lower than 1 ppb was found for all BTEX compounds with a sampling volume of 1 L. No significant drift in the instrument was observed. A time-varying calibration technique was established that requires minimal equipment for field operations and optimizes the sampling procedure for field measurements. With an analysis time of less than 15 min, the compact GC-PID is ideal for field deployment of background and polluted atmospheres for near-real time measurements of BTEX. The results highlight the application of the compact and easily deployable GC-PID for community monitoring and screening of air toxics.

Addressing urban traffic congestion is a pressing issue requiring efficient solutions that need to be analyzed regarding travel time and pollutant emissions. The traffic weighted multi-maps (TWM) method has been proposed as an efficient mechanism for congestion mitigation that enables differential traffic routing and path diversity by strategically distributing different network views (maps) to the drivers. Previous works have focused on TWM generation by creating optimal edge weights, but the complexity exponentially increases with the network size and traffic group diversity. This work describes how congestion and emissions can be addressed using TWM maps based on the k-shortest paths for the traffic flows (instead of individuals) that are optimally assigned and distributed to the components of the traffic flow. The map allocation strategies optimal TWM (OTV), optimal TWM per path flow with linear constraints (LCTV), and its variant unconstrained optimal TWM per path flow (UCTV) are described. They use maps generated from the k-shortest paths of the traffic flows (kSP-TWM). The heuristic solution obtained is compared with the theoretical static traffic assignment estimation baseline with different configurations, regarding congestion reduction, total travel time enhancement, and pollutant emissions. Experiments are developed using a synthetic traffic grid network scenario with a mesoscopic simulation. They show that the solution provided is adequate for its proximity to the theoretical equilibrium solutions and can generate minimum emissions patterns. The presented solution opens new possibilities for further congestion and pollutant management studies and seamless integration with existing traffic management frameworks.

PM2.5 emissions from heavy-duty diesel trucks (HDDTs) have a significant impact on air quality, human health, and climate change, and seriously threaten the UN Sustainable Development Goals. Globally, a series of emission control measures have been implemented to reduce pollution emissions from HDDTs. Current studies assessing the impact of these measures on air quality and human health have mainly used coarse-grained emission data as input to dispersion model, resulting in the inability to capture the spatiotemporal variability of pollutant concentrations and tending to increase the uncertainty of health impact assessment results. In this study, we quantified the impact of pollution control policies for HDDTs in Beijing on PM2.5 concentrations, human health, and economic losses by integrating policy scenario analysis, pollution dispersion simulation, public health impact and economic benefit assessment models, supported by high spatiotemporal resolution emission data from HDDTs. The results show that PM2.5 concentrations from HDDTs exhibit significant spatial aggregation characteristics, with the intensity of aggregation at night being about twice as high as that during the day. The emission hotspots are mainly concentrated in the sixth, fifth and fourth rings and major highways. Compared to the “business as usual” scenario in 2018, the current policy of updating the fuel standard to China VI and the emission standard to China 6 can reduce PM2.5 concentrations by 96.72%, thereby avoiding 612 premature deaths, which is equivalent to obtaining economic benefits of 1.65 billion CNY. This study further emphasizes the importance of high spatiotemporal resolution emission data during traffic dispersion modeling. The results can help improve the understanding of the effectiveness of emission reduction measures for HDDTs from a health benefit perspective.

Lifetime cancer risk characterization of ambient PM-bound carcinogenic metals and polycyclic aromatic hydrocarbons (PAHs) were examined in the cities of Los Angeles (USA), Thessaloniki (Greece) and Milan (Italy), which share similar Mediterranean climates but are different in their urban emission sources and governing air quality regulations. The samples in Milan and Thessaloniki were mostly dominated by biomass burning activities whereas the particles collected in Los Angeles were primary impacted by traffic emissions. We analyzed the ambient PM2.5 mass concentration of Cadmium (Cd), Hexavalent Chromium (Cr(VI)), Nickel (Ni), Lead (Pb), as well as 13 PAH compounds in the PM samples, collected during both cold and warm periods at each location. Pb exhibited the highest annual average concentration in all three cities, followed by Ni, As, Cr(VI), Cd and PAHs, respectively. The cancer risk assessment based on outdoor pollutants was performed based on three different scenarios, with each scenario corresponding to a different level of infiltration of outdoor pollutants into the indoor environment. Thessaloniki exhibited a high risk associated with lifetime inhalation of As, Cr(VI), and PAHs, with values in the range of (0.97–1.57) × 10−6, (1.80–2.91) × 10−6, and (0.77–1.25) × 10−6, respectively. The highest cancer risk values were calculated in Milan, exceeding the US EPA standard by a considerable margin, where the lifetime risk values of exposure to As, Cr(VI), and PAHs were in the range of (1.29–2.08) × 10−6, (6.08–9.82) × 10−6, and (1.10–1.77) × 10−6, respectively. In contrast, the estimated risks associated with PAHs and metals, except Cr(VI), in Los Angeles were extremely lower than the guideline value, even when the infiltration factor was assumed to be at peak. The lifetime cancer risk values associated with As, Cd, Ni, Pb, and PAHs in Los Angeles were in the range of (0.04–0.33) × 10−6. This observation highlights the impact of local air quality measures in improving the air quality and lowering the cancer risks in Los Angeles compared to the other two cities. [ABSTRACT FROM AUTHOR]

Excessive exposure to heavy metals induces potential adverse health impacts in humans. More specifically, heavy metals in particulate matter (PM) have a significant impact since PM can penetrate human organs and systems, causing several morbidities. In this work, dust samples were collected from 20 different types of roads in a busy zone in Doha during the winter of 2016–2017, where a higher human exposure rate occurs due to extensive outdoor activities during this time of the year. The elemental composition in terms of the mass concentration of 30 elements was determined in each sample via an energy-dispersive X-ray fluorescence (XRF) spectrometer. Then, the toxicity of six heavy metals in these airborne traffic dust samples was investigated. The heavy metals reported to have a hazardous impact on human health are As, Pb, Hg, Cd, Cr, Co, Ni, Cu, and Zn. The extent of carcinogenic and non-carcinogenic risk impact was assessed using pollution indices and then determining the health risks associated with exposure to heavy metals through inhalation, ingestion, and dermal contact. The non-carcinogenic hazard index analysis results indicate no toxicity for all metals. However, the carcinogenic risk factor results show that only chromium might induce a slight risk for children and adults. In light of this, further research is recommended to investigate more areas in urban Doha where more samples can be collected and analyzed. [ABSTRACT FROM AUTHOR]

The article analyzes data from a weekly campaign devoted to the study of the elemental composition of PM10 in a selected receptor, in the suburban area (Mazowieckie Voivodeship). The sampling point was located at the intersection of main roads and in the vicinity of a typical single-family housing, not far from the electrified Warsaw-Białystok railway line and a small heating plant. The research was carried out in the summer season, in order to minimize the impact of municipal emissions on concentrations and elemental composition of PM10. The Horiba PX-375 X-ray fluorescence spectrometer was used to measure the one-hour concentrations of elements related to PM10. On the basis of the obtained results, the enrichment factors for PM10 in the analyzed elements (EF) were calculated and the principal components analysis (PCA) was performed. It was found that despite the fact that in all tested time intervals, the elemental composition of PM10 was noticeably influenced by emissions from transport, the municipal emissions had a significant impact on the elemental composition, especially those related to coal combustion, and thus the concentration of PM10 during the study period. It seems that the possibility of observing the influence of all relevant sources on the composition and concentration of PM10 was possible thanks to the use of hourly-averaged measurements of the elemental composition of PM10. In the case of daily averaged measurements, in a receptor with such PM10 elemental profile, it would be impossible to determine the periods, in which specific - qualitatively completely different - emission sources dominate.

Identifying and quantifying the sources and clarifying the impacts of ultrafine particles (UFP) in the complicated urban environments are important for particle pollution control and UFP-climate interaction understanding. The previous studies have made notable contributions to these aspects and it is necessary to review the achievements. Here, the characteristics of traffic emissions and new particle formation (NPF) events/processes and their effects on urban UFP are summarized mainly based on the latest progresses. The constantly improved techniques of measuring UFP have played a vital role for knowing the sources and impacts of UFP. Meanwhile, the emissions inventories, dispersion models, and receptor models generally perform better when working together and using high resolution input and corrected algorithms. Besides, the interaction between UFP and climate is discussed mainly by linking radiation, cloud condensation nuclei, particle deposition, and the environmental conditions required for nucleation processes. Although for urban UFP, there are consensuses that traffic emissions and nucleation processes are two main sources and UFP and climate interact mainly via radiation and cloud condensation nuclei (CCN), there are many other crucial tasks for future and this work lists seven of them. They involve, scientifically, how much other sources such as industrial and regional sources mix with traffic emissions and nucleation processes in source contributions and how primary pollutants collaborate with UFP (aerosols) in aerosol-climate interactions; and engineeringly, how to improve the integration of the instruments and the instrument customization services according to actual situations. These progresses and future perspectives would help in more accurately quantifying the contributions of emissions and nucleation processes to UFP and better evaluating the impacts of UFP. Despite our efforts, knowledge on the main sources and impacts of urban UFP is limited and detailed solutions for the future tasks are missing here, which need joint efforts from UFP and related fields.

Health factors concerning the well-being of the urban population urge us to better comprehend the impact of emissions in urban environments on the micro-scale. There is great necessity to depict and monitor pollutant concentrations with high precision in cities, by constructing an accurate and validated digital air quality network. This work concerns the development and application of a CFD model for the dispersion of particulate matter, CO, and NOx from traffic activity in a highly busy area of the city of Augsburg, Germany. Emissions were calculated based on traffic activity during September of 2018 with COPERT Street software version 2.4. The needed meteorological data for the simulations were taken from a sensor’s network and the resulting concentrations were compared and validated with high-precision air quality station indications. The model’s solver used the steady-state RANS approach to resolve the velocity field and the convection–diffusion equation to simulate the pollutant’s dispersion, each one modelled with different molecular diffusion coefficients. A sensitivity analysis was performed to decide the most efficient computational mesh to be used in the modelling. A velocity profile for the atmospheric boundary layer (ABL) was implemented into the inlet boundary of each simulation. The cases concerned applications on the street level in steady-state conditions for one hour. The results were evaluated based on CFD validation metrics for urban applications. This approach provides a comprehensive state-of-the-art 3D digital pollution network for the area, capable of assessing contamination levels at the street scale, providing information for pollution reduction techniques in urban areas, and combining with existing sensor networks for a more thorough portrait of air quality.

We estimated the particle number distributions (PNDs), particle number concentrations (PNCs), physicochemical characteristics, meteorological effects, and respiratory deposition doses (RDD) in the human respiratory tract for three different particle modes: nucleation (N6–30), accumulation (N30–300), and coarse (N300–10,000) modes. This study was conducted in three different microenvironments (MEs) in London (indoor, IN; traffic intersection, TI; park, PK) measuring particles in the range of 6 nm–10,000 nm using an electrical low-pressure impactor (ELPI+). Mean PNCs were 1.68 ± 1.03 × 104 #cm−3, 7.00 ± 18.96 × 104 #cm−3, and 0.76 ± 0.95 × 104 #cm−3 at IN, TI, and PK, respectively. The PNDs were high for nucleation-mode particles at the TI site, especially during peak traffic hours. Wind speeds ranging from 0 to 6 ms−1 exhibit higher PNCs for nucleation- and accumulation-mode particles at TI and PK sites. Physicochemical characterisation shows trace metals, including Fe, O, and inorganic elements, that were embedded in a matrix of organic material in some samples. Alveolar RDD was higher for the nucleation and accumulation modes than the coarse-mode particles. The chemical signatures from the physicochemical characterisation indicate the varied sources at different MEs. These findings enhance our understanding of the different particle profiles at each ME and should help devise ways of reducing personal exposure at each ME.

Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep learning model, iDeepAir, to predict surface-level PM 2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality. Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355 µg/m3 to 12.283 µg/m3 compared with other models. And identifies the ranking of major factors, local meteorological conditions have become a nonnegligible factor. Layer-wise relevance propagation (LRP) is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM 2.5 concentration in various regions of Shanghai. Meanwhile, As the strict and effective industrial emission reduction measurements implementing in China, the contribution of urban traffic to PM 2.5 formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03% in 2011 to 24.37% in 2017 in Shanghai, and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction. We also infer that the promotion of vehicular electrification would achieve further alleviation of PM 2.5 about 8.45% by 2030 gradually. These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control, and eventually benefit people's lives and high-quality sustainable developments of cities. [Display omitted] [ABSTRACT FROM AUTHOR]

The accent of the present study is determination of Urban Aerosol Pollution Island (UAPI) intensity and spatial variability in particulate matter concentration (PM10 and PM2.5) over Delhi. For analysis, the hourly concentration dataset of PM2.5 and PM10 from January 2019 to December 2020 was obtained from ten air quality monitoring stations of Delhi. Additionally, UAPI Index has been calculated to assess the intensity of particulate pollution. The daily, monthly, and annual variations in the trends of PM10, PM2.5, and UAPI index along with related meteorological parameters have been analyzed. Particulate pollution peaked majorly during two seasons, i.e., summer and winter. The highest concentration of PM10 was observed to be 426.77 µg/m3 while that of PM2.5 was observed to be 301.91 µg/m3 in January 2019 for traffic-affected regions. During winters, higher PM2.5 concentration was observed which can be ascribed to increased local emissions and enhanced secondary particle formations. While the increase in PM10 concentrations led to an increment in pollution episodes during summers over most of the sites in Delhi. The UAPI index was found to be declining in 2020 over traffic affected regions (77.92 and 27.22 for 2019 and 2020, respectively) as well as in the background regions (64.91 and 19.80 for 2019 and 2020, respectively) of Delhi. Low traffic intensity and reduced pollutant emission could have been responsible for the reduction of UAPI intensity in the year 2020. The result indicates that lockdown implemented to control the COVID-19 outbreak led to an unexpected decrease in the PM10 pollution over Delhi. [ABSTRACT FROM AUTHOR]

Black carbon (BC) is a harmful pollutant to human health and the environment. The aim of this paper is to evaluate the local contribution of road traffic and residential biomass burning to BC pollution levels at urban scale by implementing an integrated modelling approach with high spatial and temporal resolution. The traffic emission was extended to include BC emission factors considering road network as line emission sources. A new module to calculate emissions from residential biomass burning was developed based on the heating degree days methodology and local surveys. The modelling approach implemented in Coimbra, Portugal, was validated with observations collected during a six-month monitoring campaign at urban traffic and urban background stations using thermo-optical and optical methods. At the traffic station, modelling results showed that 86% of BC concentrations are related to traffic activity, which are mostly due to hot exhaust emissions (76%). Nevertheless, biomass burning has a significant contribution in the whole urban area during winter. The results of monthly average BC concentrations obtained from dispersion modelling revealed a contribution from local road traffic and residential biomass burning emissions of around 4 μg m−3 at both monitoring sites during the coldest period, but this contribution was two times higher for the hotspots identified within the urban area. Moreover, hourly values at hotspots obtained from the modelling may achieve 85 μg m−3, thus stressing the importance of spatial and temporal analysis with high resolution. The validation approach applied in this work highlight the need for more transparency in defining "black carbon" reported by modelling to ensure comparability with measurements obtained by different techniques. • BC modelling was implemented at urban scale and validated using 6-month measurements. • Road transport contributes more than 80% to BC concentrations at a traffic site. • Biomass burning has a significant contribution to urban BC during winter. • Modelling agrees with measurements of EC, but underestimates optical eBC. [ABSTRACT FROM AUTHOR]

With the rising ownership of new energy vehicles (NEVs), accurate road traffic emission estimations are crucial. This study combined mixed traffic flow and license plate recognition (LPR) using improved 3D convolutional neural network (3D-CNN) and Transformer models. The 3D-CNN model is designed for vehicle localization and traffic flow data acquisition, while the Transformer-based LPR model accurately recognizes license plates, distinguishing NEVs from conventional vehicles. The overall model is validated by the training and test datasets, and then with field application along a primary arterial segment in the South 2nd Ring Road, Xi'an, China. The results demonstrate capability for the traffic emission approximation of the mixed traffic flow including new energy vehicles, revealing that mixed traffic flow identification plays an important role in road emission approximation. Results and procedures of the study may provide benchmark for the subsequent research and the verification of related transportation policies. [ABSTRACT FROM AUTHOR]

The incomplete combustion of fossil fuels from petrol, natural gas, and fuel oil in the engine of vehicles contributes to air quality degradation through traffic-related air pollutant emissions. The Real-time affordable multi-pollutant (RAMPs) monitors were installed in Kigali, the capital of Rwanda, to fill the gap in air quality datasets. Using RAMPs, this is the first air quality modelling research in Rwanda aiming to report the concentration of NO2 by comparing In-situ monitored data and modelled results. We targeted NO2 emissions from 27 road networks of Kigali to address the impacts of traffic emissions on air quality over 2021. The American Meteorological Society and Environmental Protection Agency regulatory models (AERMOD and ISCST3) were used for simulation. Statistical indexes include fractional bias (FB), the fraction of the prediction within the factor of two of the observations (FAC2), normalized mean square error (NMSE), geometric mean bias (MG), and geometric variance (VG) used to assess models' reliability. Monitoring shows the annual mean of 16.07 μg/m3, 20.35 μg/m3, and 15.46 μg/m3 at Mont-Kigali, Gacuriro, and Gikondo-Mburabuturo stations, respectively. Modelling shows the daily mean of 111.77 μg/m3 and annually mean of 50.42 μg/m3 with AERMOD and daily mean of 200.26 μg/m3 and annually mean of 72.26 μg/m3 with ISCST3. The FB, NMSE, and FAC2 showed good agreement, while MG and VG showed moderate agreement with AERMOD. The FB, NMSE, and MG showed moderate agreement, while FAC2 and VG disagreed with ISCST3. Traffic and urban residential emissions were identified as potential sources of NO2. Results indicated that Kigali residents are exposed to a significant level of NO2 exceeding World Health Organisation limits. Findings will help track the effectiveness of Rwanda’s recently executed pollution-control policy, suggest evidence based on the recommendations to reduce NO2, and use further dispersion models to support ground-based observations to improve public health.

The study assessed the concentration and health hazards of Cd, Mn, Pb, Ni and Zn in 54 road surface dust samples. Heavy metal concentrations descended as follows: Mn > Zn > Pb > Ni > Cd and the concentrations were 316.22, 161.24, 88.76, 22.69 and 0.21. Health hazard assessment showed that the modes of exposure descended in the sequence of ingestion > dermal contact > inhalation for children and dermal contact > ingestion > inhalation for adults. The hazard index for both populations was >1 suggesting susceptibility to non-cancer risks. The carcinogenic risk for Ni and Cd was below 1 × 10−4 suggesting a negligible risk of cancer for both populations. The study suggests that more research studies on HM contamination in road dusts are to be undertaken in the country to develop HM pollution threshold values in road dusts and to account for pollution in city centers. [ABSTRACT FROM AUTHOR]

The article studies the data from a weekly campaign devoted to the study of the elemental composition of PM10 in a selected receptor, in a suburban area (Mazowieckie Voivodeship). The sampling point was located at the intersection of main roads and in the vicinity of a typical single-family housing, not far from the electrified Warsaw-Białystok railway line and a small heating plant. The research was carried out in the summer season, in order to minimize the impact of municipal emissions on the concentrations and elemental composition of PM10. A Horiba PX-375 X-ray fluorescence spectrometer was used to measure the one-hour concentrations of elements related to PM10. On the basis of the obtained results, the enrichment factors for PM10 in the analyzed elements (EF) were calculated and the principal components analysis (PCA) was performed. It was found that although the elemental composition of PM10 in all tested time intervals was noticeably influenced by the emissions from transport, the municipal emissions had a significant impact on the elemental composition, especially those related to coal combustion, and thus the concentration of PM10 during the study period. It seems that the possibility of observing the influence of all relevant sources on the composition and concentration of PM10 was possible owing to the use of hourly-averaged measurements of the elemental composition of PM10. In the case of daily averaged measurements, in a receptor with such PM10 elemental profile, it would be impossible to determine the periods, in which specific - qualitatively completely different - emission sources dominate. [ABSTRACT FROM AUTHOR]

Southern European countries such as Portugal still have a low bicycle share (up to 1%). Therefore, it is fundamental to research the potential benefits and drawbacks of modal shifts. This work analyses the implementation of a bike-sharing system (BSS) and its expected implications in emission reductions from road traffic for the case study of Coimbra city, Portugal. Although many different pollutants result from road traffic, we focused on small Particulate Matter (PM2.5) due to its harmful effects on human health. The methodology considers an integrated set of models for a) estimation of potential demand for the use of bike-sharing; b) mobility studies for the analysis of travel behavior; c) optimization modeling for the location of the bike-sharing stations and d) traffic emission modeling, to estimate the reduction of emissions due to bike-sharing modal shift. We considered two different potential scenarios for the location of bike stations. A modal share shift from car use toward bicycle in each is assumed, and the consequent traffic reduction and the associated emissions reduction are analyzed. The results show a non-homogeneous relation between traffic reduction and emission reduction across the urban space, achieving 12.5% of daily PM2.5 emissions in some urban roads. [ABSTRACT FROM AUTHOR]

Combining traffic micro-simulation models and emission models is a primary method to estimate traffic emissions. However, there has been limited research on the impact of traffic micro-simulation model parameters on simulator outputs, which are critical to emissions calculations. Based on combining VISSIM and MOVES, this study uses the Morris and Sobol methods to explore the impact of parameters in VISSIM on operating mode distribution and travel time distribution. Taking an urban signal control intersection with three traffic scenarios in Chengdu, China as an example, this study verifies the methods' feasibility. Apart from these parameters, which have been proved to be necessary calibration parameters, including the desired speed distribution, the desired acceleration function, and the desired deceleration function, an additional 24 parameters related to simulation setting and driving behaviour models are selected as the initial parameters. The number of interaction objects, maximum look-ahead distance, average standstill distance, additive part of safety distance, and safety distance reduction factor close to a stop line, are considered to be the important parameters for this case study. The impact of these five parameters on the bins of operating mode distribution and travel time distribution are further analyzed with One-at-a-time, and these parameters are compared with those reported in previous studies. It is concluded that the important parameters selected in this study are reasonable and can support the calibration of VISSIM parameters for this case's traffic emissions. [ABSTRACT FROM AUTHOR]

Different types of automated vehicles (AVs) have emerged promptly in recent years, each of which might have different potential impacts on traffic flow and emissions. In this paper, the impacts of autonomous automated vehicles (AAVs) and cooperative automated vehicles (CAVs) on capacity, average traffic speed, average travel time per vehicle, and average delay per vehicle, as well as traffic emissions such as carbon dioxide (CO2), nitrogen oxides (NOx), and particulate matter (PM10) have been investigated through a microsimulation study in VISSIM. Moreover, the moderating effects of different AV market penetration, and different freeway segments on AV's impacts have been studied. The simulation results show that CAVs have a higher impact on capacity improvement regardless of the type of freeway segment. Compared to other scenarios, CAVs at 100% market penetration in basic freeway segments have a greater capacity improvement than AAVs. Furthermore, merging, diverging, and weaving segments showed a moderating effect on capacity improvements, particularly on CAVs' impact, with merging and weaving having the highest moderating effect on CAVs' capacity improvement potential. Taking average delay per vehicle, average traffic speed, and average travel time per vehicle into account, simulation results were diverse across the investigated scenarios. The emission estimation results show that 100% AAV scenarios had the best performance in emission reductions in basic freeway and merging sections, while other scenarios increased emissions in diverging and weaving sections. [ABSTRACT FROM AUTHOR]

Implementation of Daylight Savings Time (DST) started in various jurisdictions during the first half of the 20th century. Much debate on the merits of the twice-yearly change in the official local time has ensued since then regarding energy usage, sleep patterns, health outcomes, traffic safety, etc. The DST switch in the official time abruptly shifts anthropogenic emissions that are related to human activities relative to the Coordinated Universal Time (UTC) but does not affect meteorological processes that disperse them, which day-to-day variability is affected by longer time scales. Here, we utilized the DST clock changes as a repeating biannual experiment to study the impact of meteorology on air pollution. We analyzed traffic volume data and up to 20 years of nitrogen oxides (NOx), fine particulate matter (PM2.5), ozone (O3) and carbon monoxide (CO) concentrations, observed at different air quality monitoring (AQM) stations in Israel, demonstrating a clear and significant impact of the daily meteorological cycle on traffic related air pollution (TRAP). In particular, traffic emissions are prerequisite for TRAP, but meteorology was found to dominate the daily patterns of the NOx, O3 and CO concentration fields in the study area. On the other hand, the impact of vehicle emissions on PM2.5 concentrations seems to be very small. Our results highlight the multiscale interplay between pollutant emissions and dispersion processes, especially for pollutants that are emitted near the surface. We demonstrate that while DST clock changes do not affect the emissions intensity, nor the meteorological processes vigor, they do shift human activity-related emissions with respect to the DST-blind dispersion processes. This results in short-term effects on primary traffic-related pollutant concentrations that cancel out over the day, and a consistent yet small effect on secondary traffic-related pollutant concentrations (O3). [ABSTRACT FROM AUTHOR]

The study investigates the sources of metals in urban road dusts using elemental concentration and Pb isotopic ratios. The elemental concentrations are also utilized to determine the present heavy metal emissions as well as projected emissions till 2045. Bayesian mixing model for source apportionment highlights the significant contributions of both exhaust and non-exhaust sources to the metal-enriched urban road dusts, with each contributing approximately 40 %. Emission analysis reveals that India's projected electric vehicle (EV) penetration may not be sufficient to suppress the metal emissions from vehicular exhausts. Further challenge is posed by high metal concentrations in the non-exhaust sources, that dominates the emission of some metals compared to exhaust sources. If the metal concentrations remain unchanged, the emission analysis predicts alarming increases in total emissions from all the exhaust and non-exhaust sources by 174 %, 176 %, 163 % and 184 % for Ni, Cu, Zn and Pb, respectively, from 2022 to 2045. Thus, it is crucial to reduce the metal concentrations in traffic emission sources and also impose better regulatory measures to improve the urban metal pollution scenario., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Lifetime cancer risk characterization of ambient PM-bound carcinogenic metals and polycyclic aromatic hydrocarbons (PAHs) were examined in the cities of Los Angeles (USA), Thessaloniki (Greece) and Milan (Italy), which share similar Mediterranean climates but are different in their urban emission sources and governing air quality regulations. The samples in Milan and Thessaloniki were mostly dominated by biomass burning activities whereas the particles collected in Los Angeles were primary impacted by traffic emissions. We analyzed the ambient PM2.5 mass concentration of Cadmium (Cd), Hexavalent Chromium (Cr(VI)), Nickel (Ni), Lead (Pb), as well as 13 PAH compounds in the PM samples, collected during both cold and warm periods at each location. Pb exhibited the highest annual average concentration in all three cities, followed by Ni, As, Cr(VI), Cd and PAHs, respectively. The cancer risk assessment based on outdoor pollutants was performed based on three different scenarios, with each scenario corresponding to a different level of infiltration of outdoor pollutants into the indoor environment. Thessaloniki exhibited a high risk associated with lifetime inhalation of As, Cr(VI), and PAHs, with values in the range of (0.97–1.57) × 10−6, (1.80–2.91) × 10−6, and (0.77–1.25) × 10−6, respectively. The highest cancer risk values were calculated in Milan, exceeding the US EPA standard by a considerable margin, where the lifetime risk values of exposure to As, Cr(VI), and PAHs were in the range of (1.29–2.08) × 10−6, (6.08–9.82) × 10−6, and (1.10–1.77) × 10−6, respectively. In contrast, the estimated risks associated with PAHs and metals, except Cr(VI), in Los Angeles were extremely lower than the guideline value, even when the infiltration factor was assumed to be at peak. The lifetime cancer risk values associated with As, Cd, Ni, Pb, and PAHs in Los Angeles were in the range of (0.04–0.33) × 10−6. This observation highlights the impact of local air quality measures in improving the air quality and lowering the cancer risks in Los Angeles compared to the other two cities.

Excessive exposure to heavy metals induces potential adverse health impacts in humans. More specifically, heavy metals in particulate matter (PM) have a significant impact since PM can penetrate human organs and systems, causing several morbidities. In this work, dust samples were collected from 20 different types of roads in a busy zone in Doha during the winter of 2016–2017, where a higher human exposure rate occurs due to extensive outdoor activities during this time of the year. The elemental composition in terms of the mass concentration of 30 elements was determined in each sample via an energy-dispersive X-ray fluorescence (XRF) spectrometer. Then, the toxicity of six heavy metals in these airborne traffic dust samples was investigated. The heavy metals reported to have a hazardous impact on human health are As, Pb, Hg, Cd, Cr, Co, Ni, Cu, and Zn. The extent of carcinogenic and non-carcinogenic risk impact was assessed using pollution indices and then determining the health risks associated with exposure to heavy metals through inhalation, ingestion, and dermal contact. The non-carcinogenic hazard index analysis results indicate no toxicity for all metals. However, the carcinogenic risk factor results show that only chromium might induce a slight risk for children and adults. In light of this, further research is recommended to investigate more areas in urban Doha where more samples can be collected and analyzed.

The effects of air pollution on the general public received much attention recently. Personal exposure and deposition fraction of aerosol particles were studied in Vilnius, Lithuania, focusing on individuals working in an office and driving to work. Aerosol monitoring in the urban background was found to give an indication of the minimum concentrations of particulate matter (PM) expected at urban roads, as these correspond to the lowest PM concentrations measured there. In March 2021, PM2.5 concentrations at the urban background monitoring station reached values above the annual limit of 5 μg/m3 the World Health Organization in 50% of cases. Our study shows significant differences in exposure to air pollution in a car cabin and in a modern office. According to the multiple-path particle dosimetry model, the exposure of the person in the office is about 14 times lower than driving a car, where the minute deposition dose for PM1 is 0.072 µg/min for the period when the PM2.5 concentration in the urban background reaches 10 µg/m³. Compared to the PM2.5 mass concentration at the urban background station, the mean PM2.5 concentration in the vehicle reaches values that are 2–3 times higher. During the working day, when driving takes less than 10% of the time considered (commuting plus working), PM exposure during driving accounts for about 80% of the PM exposure caused by PM concentration in the office. [ABSTRACT FROM AUTHOR]

Air pollution impacts human health and the environment, especially in urban cities with substantial industrial activities and vehicular traffic emissions. Despite increasingly strict regulations put in place by regulatory agencies, air pollution is still a significant environmental problem in cities across the world. The objective of this study was to evaluate the environmental pollution from stationary and mobile sources using real-time monitoring and sampling techniques to characterize size-segregated particulate matter (PM), black carbon (BC), and ozone (O3) at the Port of Albany, NY. Air pollution monitoring was carried out for 3 consecutive weeks under a 24-hour cycle in 2018 at the New York State Department of Environmental Conservation (NYSDEC) site within the Port. Sampling was done with an AEROCET 531, optical particle sizer (OPS), ozone monitor, and MicroAeth AE51. Higher mass and number concentrations of size-segregated particles were observed during the daytime. PM2.5 and PM10 concentrations ranged from 1 to 271 micrograms per cubic meter (µg/m3) and 1 to 344 µg/m3, respectively. While these values do not exceed the level of the USEPA 24-hour standards, frequent sharp peaks were observed at higher concentrations. Size-segregated PM at sizes 0.3 µm and 0.374 µm recorded maximum concentrations of 101,631 particle number per cubic centimeter (#/cm3) and 43,432 #/cm3, respectively. Wide variations were observed in the particle number concentrations for 0.3 µm, 0.374 µm, and 0.465 µm sizes, which ranged from 1521 to 101,631 #/cm3; 656 to 43,432 #/cm3; and 311 to 29,271 #/cm3, respectively. BC concentration increased during morning and evening rush hours with the maximum concentration of 11,971 ng/m3 recorded at 8:00 AM. This suggests that mobile sources are the primary contributor to anthropogenic sources of BC within the Port. Episodic elevations in the concentrations of size-segregated PM and BC confirmed the contribution of industrial and vehicular activities around the Port of Albany. This study underscores the importance of measuring particles on a size-segregated basis in order to more fully understand the contributions of the multiple sources present within and surrounding a port environment. [ABSTRACT FROM AUTHOR]

Abstract The traffic congestion in the Hsuehshan tunnel and at the Toucheng interchange has led to traffic-related air pollution with increasing concern. To ensure the authenticity of our simulation, the concentration of the last 150 m in Hsuehshan tunnel was simulated using the computational fluid dynamics fluid model. The air quality at the Toucheng interchange along a 2 km length highway was simulated using the California Line Source Dispersion Model. The differences in air quality between rush hours and normal traffic conditions were also investigated. An unmanned aerial vehicle (UAV) with installed PM2.5 sensors was developed to obtain the three-dimensional distribution of pollutants. On different roads, during the weekend, the concentrations of pollutants such as SOx, CO, NO, and PM2.5 were observed to be in the range of 0.003–0.008, 7.5–15, 1.5–2.5 ppm, and 40–80 μg m− 3, respectively. On weekdays, the vehicle speed and the natural wind were 60 km h− 1 and 2.0 m s− 1, respectively. On weekdays, the SOx, CO, NO, and PM2.5 concentrations were found to be in the range of 0.002–0.003, 3–9, 0.7–1.8 ppm, and 35–50 μg m− 3, respectively. The UAV was used to verify that the PM2.5 concentrations of vertical changes at heights of 9.0, 7.0, 5.0, and 3.0 m were 45–48, 30–35, 25–30, and 50–52 μg m− 3, respectively. In addition, the predicted PM2.5 concentrations were 40–45, 25–30, 45–48, and 45–50 μg m− 3 on weekdays. These results provide a reference model for environmental impact assessments of long tunnels and traffic jam-prone areas. These models and data are useful for transportation planners in the context of creating traffic management plans.

Ammonia (NH 3) plays an important role in the formation of atmospheric particulate matter, but the contribution of traffic-related emissions remains unclear, particularly in megacities with a large number of vehicles. Taking the opportunity of the stringent COVID-19 lockdowns implemented in Beijing and Shanghai in 2022, this study aims to estimate the traffic-related NH 3 emissions in these two megacities based on satellite observations. Differences between urban and suburban areas during the lockdown and non-lockdown periods are compared. It was found that despite different dominating sources, the overall NH 3 concentrations in urban and suburban areas were at a similar level, and the lockdown resulted in a more prominent decrease in urban areas, where traffic activities were most heavily affected. The traffic-related contribution to the total emission was estimated to be ∼30% in megacities, and ∼40% in urban areas, which are about 2–10 times higher than that in previous studies. The findings indicate that the traffic-related NH 3 emissions have been significantly underestimated in previous studies and may play a more critical role in the formation of air pollution in megacities, especially in winter, when agricultural emissions are relatively low. This study highlights the importance of traffic-related NH 3 emissions in Chinese megacities and the need to reassess the emissions and their impacts on air quality. [Display omitted] • A new way to estimate traffic-related NH 3 emissions using satellite observations was provided. • The COVID-19 lockdowns resulted in greater NH 3 reduction in urbans than in suburbs. • Traffic-related NH 3 emissions in megacities may have been remarkably underestimated. • Traffic-related NH 3 emissions in winter may play a more important role in PM 2.5 formation. [ABSTRACT FROM AUTHOR]

Examining the impact of various factors associated with plume dispersion is essential for quantifying ship pollution in port areas, particularly adjacent to urban environments. This paper concerns the application of Computational Fluid Dynamics (CFD) modelling to study the effect of such factors on air pollution at the passenger port of Marseille, France. Pedestrian-level pollution maps are created for several emission scenarios involving variables such as wind conditions, docking location, emission reduction technology, different emission sources, as well as parameters related to the vessel. Depending on wind direction, docking location and vessel's engine power, different parts of the studied area are exposed to ship pollution. Within a typical examined range, funnel exhaust temperature, funnel height and wind speed deviate up to 78 %, 45 % and 59 % from the baseline scenario, respectively, indicating significant sensitivity of ship pollution to changes in these factors. The lower buoyancy effects of using a scrubber lead to a 17 % larger area exposed to increased ship pollution instead of using Ultra Low Sulphur Fuel Oil (ULSFO). Additionally, it is estimated that an individual vessel may contribute up to six times more to the NO x pollution of a roundabout located 800 m downwind, compared to the local road traffic. The current work can provide valuable information to various stakeholders such as port authorities, decision-makers, or even air quality modelers regarding the impact of shipping emissions on air pollution in port areas, constituting a useful assessment and advisory tool. [Display omitted] • High-resolution CFD modelling for pedestrian-level pollution mapping in port areas. • Scrubber use increases ship pollution area by 17 % compared to the use of ULSFO. • Ship NOx pollution up to six times more than local traffic 800 m downwind of the ship. • Pedestrian pollution key factors: wind speed, funnel temperature and height. • Ship pollution evaluation tool for authorities, decision-makers and modellers. [ABSTRACT FROM AUTHOR]

Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR) model for more accurate spatial predictions for small data samples. The LUQR is an integration of the LUR and quantile regression, which both have advantages in predictions with a small data set of samples. In this study, the LUQR model is applied in predicting spatial distributions of annual mean PM 2.5 concentrations across the Greater Sydney Region, New South Wales, Australia, with observations at 19 valid monitoring stations in 2020. Cross validation shows that the goodness-of-fit can be improved by 25.6–32.1% by LUQR models when compared with LUR, and prediction root mean squared error (RMSE) and mean absolute error (MAE) can be reduced by 10.6–13.4% and 19.4–24.7% by LUQR models, respectively. This study also indicates that LUQR is a more robust model for the spatial prediction with small data samples than LUR. Thus, LUQR has great potentials to be widely applied in spatial issues with a limited number of observations. [ABSTRACT FROM AUTHOR]

The Latin American and Caribbean (LAC) region suffers from persistent inequality, climate change vulnerabilityn and persistent air quality problems. The Aburrá Valley, Medellín, located in Colombia, is a narrow tropical valley with strong thermal inversion and unstable atmospheric conditions. In addition, local air pollutant concentration is strongly associated with Atmospheric Boundary Layer (ABL) variability. It is common to have in Medellín air quality episodes mainly exacerbated by the prolonged absence of precipitation. Although air pollution control strategies aimed at improving air quality have been recently implemented by the municipality of Medellín, values of NO2 concentration remain unaltered during the last 8 years with values close to critical values. The present study analyzed a sensitive street in Medellín that has been subjected to recent public urban interventions with the construction of new public spaces and buildings without considering the effect of NO2 concentration in planning such interventions. Therefore, it is expected to currently have areas in the urban area of Medellín with unhealthy NO2 concentrations. This work addresses this problem by selecting an area of study in order to consider recent urban interventions conducted by the University of Antioquia, including new pedestrian areas in the context of NOx dispersion using CFD analysis. The numerical results allow the identification of low wind speed zones where NOx dispersion is also modeled. [ABSTRACT FROM AUTHOR]