Traffic Emissions Research Articles

Congestion and Pollutant Emission Analysis of Urban Road Networks Based on Floating Vehicle Data

Global warming caused by greenhouse gas (GHG) is receiving increasingly attention from all over the world, and urban transportation is a significant source of greenhouse gas and pollutant emission. However, the research on traffic state of urban road networks (URNs) based on sparse floating vehicle data (FVD) is insufficient. Therefore, we mainly utilize big data techniques to explore the congestion and pollutant emission of URN with FVD. Firstly, the location of vehicles is identified and matched with the URN. We then grid the FVD and city maps to more accurately identify areas of congestion and emission in later section. Following this, we use the congestion index and K-means clustering algorithm to evaluate the traffic state over time, pollutant emission is calculated based on emission calculation standards and carbon emission is estimated by using the fuel consumption-speed model. The results indicate that congestion and emission are very severe during peak hours (e.g., 8:00 a.m.), particularly in some transportation hub areas, such as high-speed rail stations. During off-peak hours (e.g., 11:00 p.m.), congestion and emission are relatively lower. The negative correlation between congestion index and emission is also revealed. This study provides some practical approaches to more accurately estimate the overall urban traffic state by using sparse traffic data, and may offer support to urban traffic managers in managing traffic congestion and pollutant emissions.

Self-Supervised Spatiotemporal Clustering of Vehicle Emissions With Graph Convolutional Network.

Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air pollution from road traffic, is a challenging representation learning task due to the lack of supervision. Some recent work building upon graph convolutional network (GCN) models the intrinsic spatiotemporal correlations among the nodes in road networks as graph representations for clustering. However, these existing methods ignore the interactions between spatial and temporal variations in vehicle emissions, resulting in incomplete descriptions and inaccurate detection of the evolution pattern of air pollution. To address this issue, this article proposes a two-way self-supervised spatiotemporal representation learning scheme, in which the temporal and spatial features are progressively learned in a mutually reinforced manner. Our proposed method is based on the observation that though the variation in vehicle emissions in the road network is consistent in the spatial and temporal domains, its expression is more distinct in temporal sequences. To this end, the input emission data are first projected into an initial temporal representation space spanned by the captured features from a pretrained BiLSTM network. Then the generated distribution of temporal features is used to construct an objective constraint for high-purity clustering through a two-way self-supervised mechanism, which is leveraged as a constraint for the feature clustering of a GCN. Furthermore, to eliminate the initial errors, a joint optimization scheme is presented to generate the decoupled clustering results through the progressive refinement of representation and clustering. Our proposed method is evaluated on the traffic emission dataset of Xian city in 2020, and the experimental results have demonstrated the superiority against the state-of-the-art.

The impact of technology on Urban infrastructure

The rapid advancement of technology has brought transformative changes to urban infrastructure, reshaping the way cities function and interact with their inhabitants. Smart technologies, including IoT devices, data analytics, and artificial intelligence, have revolutionized traditional urban systems. Intelligent transportation systems optimize traffic flow, reducing congestion and emissions. Smart grids enhance energy efficiency and sustainability, while digital sensors monitor and manage critical infrastructure, ensuring resilience and quick response to emergencies. The integration of technology into urban planning enhances resource allocation, promotes sustainability, and improves overall quality of life. However, challenges such as data privacy, digital inequality, and the potential for cyber threats necessitate careful consideration in the adoption of these technologies. Balancing innovation with ethical and equitable implementation is crucial to harness the full potential of technology for sustainable and resilient urban infrastructure.

Influence of obstacles on urban canyon ventilation and air pollutant concentration: An experimental assessment

Air pollution in cities, intensified by vehicular traffic emissions and reduced ventilation, poses a significant health risk. Obstacles, like solid or vegetation barriers, are being considered as strategies to reduce pollution exposure for pedestrians and nearby residents in street canyons. This study utilises wind tunnel experiments, to simulate a typical urban canyon with street intersections on both sides, in a 1:200 scale and an H/W=0.5, positioned perpendicular to the free stream wind flow. A passive scalar representing a vehicular pollutant is released along the length of the street canyon. Concentration measurements inside the canyon are performed to determine the effect of parked cars, boundary walls, hedges and trees on pollutant concentration exposure for pedestrians on the sidewalk. Results show that one circulating vortex is generated within the canyon, driving the pollutant to accumulate along the leeward (upwind) wall. Tightly parked cars, boundary walls and hedges, placed along the sidewalk near the leeward wall, can reduce pedestrian pollutant exposure by 15 %, 23 % and 11 % respectively along this sidewalk. Attributes such as obstacle height, surface roughness and porosity play a key role in their performance. However, trees, when placed in the same area, increase pedestrian pollutant exposure by 51 % and 17 % under dense and sparse tree arrangements, respectively. While a broader analysis that considers the variability of vegetation attributes (e.g., porosity, stand density) is desirable, this study remains crucial for validating numerical simulations and suggesting optimal urban measures to reduce pollution exposure for citizens.

TranStat: an intelligent system for producing road and maritime transport statistics using big data sources

The development of digital technologies, increasing the availability of big data and advanced processing techniques have enabled Statistics Poland to modernise the system for producing road and maritime transport statistics. As a result of the activities undertaken to adopt modern big data technologies and data from sensors, e.g. the Automatic Identification System (AIS) or the e-TOLL electronic toll collection system, new statistics have been obtained and data dissemination has accelerated. In addition, these activities ensure continuity in data production, especially in situations where collecting data from individuals may be difficult (e.g. the COVID-19 epidemic). The primary purpose of this article is to present the innovative TranStat system that enables the production of road and maritime transport statistics based on large volumes of data in order to shape the country’s transport policy. The system was developed under the GOSPOSTRATEG programme and implemented by Statistics Poland, the Statistical Office in Szczecin, the Maritime University of Szczecin, and the Cracow University of Technology. The study presents the most important aspects of the TranStat system, i.e. the characteristics of data sources, the description of functional subsystems, assumptions of the developed models and result data for traffic statistics, transport performance and exhaust emissions calculations for both types of transport. This study also provides information on smart forms implemented by Polish official statistics, reducing the burden on respondents and the costs of surveys.

Occurrence characteristics, environmental trend, and source analysis of polycyclic aromatic hydrocarbons in the water environment of industrial zones

The middle reaches of the Yellow River are rich in energy resources, with the Kuye River, a first-class river in this region, serving as a vital hub for the coal chemical industry within China. This study investigated the occurrence patterns, environmental trends, and ecological risks associated with polycyclic aromatic hydrocarbons (PAHs) in the Kuye River Basin, offering insights into the environmental dynamics of regions. The findings indicated that the river sediments primarily contained PAHs with medium to high-molecular weights, exhibiting levels ranging from 402.92 ng/g dw to 16,783.72 ng/g dw, while water bodies predominantly featured PAHs with low to medium molecular weights, ranging from 299.34 ng/L to 10,930.9 ng/L. The source analysis of PAHs indicated that industrial and traffic exhaust emissions were the primary contributors to PAHs in the Kuye basin, with sediments serving as a secondary release source based on fugacity fraction. The content of PAHs in sediment correlated closely with the environmental factors, and the PAHs inventory of the basin was 19.97 tons. The increased overall PAH concentration in the basin posed significant ecological and public health concerns, necessitating urgent attention.

Assessment of seasonal variability of PM, BC and UFP levels at a highway toll stations and their associated health risks

As a part of their occupation, workers at toll stations are exposed to traffic emissions during the working shift, which sometimes stretches to 12 h. To assess the exposure and subsequent health risk of these workers, a study was performed on a highway toll station in India. PM1, PM2.5, PM10, BC and UFP concentration were determined inside a toll collectors’ cabin and outside in a free-flowing traffic section (125 m from the toll cabin). The concentrations varied in the following range: PM1 (40.69–226.13 μg m−3), PM2.5 (49.71–247.36 μg m−3), PM10 (83.15–458.14 μg m−3) and BC (2.1–87.5 μg m−3) and UFP: 101–53705 pt cm−3. The mean concentration inside the cabin was 1.34 (PM1), 1.35 (PM2.5), 1.16 (PM10) and 2.91 (BC) times the concentration outside for the summer season. The corresponding levels in the winter season were 1.14 (PM1), 1.11 (PM2.5), 1.11 (PM10), 2.50 (BC) and 1.82 (UFP). In addition to the exhaust emission, the non-exhaust emissions such as resuspension of crustal particles, fly ash and bioaerosols were identified. Using the Multiple Path Particle Dosimetry model for two groups - adults (18–21 years) and adults (21+ years), it was estimated that the pulmonary deposition of in-cabin workers were 50% (PM2.5) −75% (PM1) higher than the workers outside the cabin. Particle mass deposition was found to be higher for adults (21+ years) than adults (18–21 years) for both the seasons. The study quantitatively assessed the health risk faced by the workers in terms of exposure concentration and deposition in respiratory tract. More such studies at different traffic mix and climate can provide better estimates of health risk of toll workers that can be used to devise appropriate strategies for control of it.

Vehicle Activity Dataset: A Multimodal Dataset to Understand Vehicle Emissions with Road Scenes for Eco-Routing

In the field of smart mobility, Artificial Intelligence (AI) approaches are influential and can make a highly beneficial contribution. Our project aims to develop a real-time ecological map of road traffic. This map will allow electric vehicles (EVs) and thermal vehicles (TVs) to display the cost of energy consumption and CO2 emissions on different road sections. In urban environments, road traffic emissions are a significant contributor to environmental pollution, with vehicle emissions being a major component. Addressing these impacts requires a thorough understanding of the operational behavior of vehicles on different road infrastructures within the region. This paper presents a novel, comprehensive dataset, the Vehicle Activity Dataset (VAD), designed to assess the emissions and fuel consumption characteristics of vehicles about their actual operating environment. Constructed from a large number of real-world driving scenarios, VAD incorporates emission data collected by an industrial Portable Emission Measurement System (PEMS), road scenes captured by an RGB camera, and the detection of different object classes within these images. The primary objective of VAD is to provide a comprehensive understanding of the relationship between vehicle emissions and the diverse range of objects present on the road. Experimental results in real road traffic environments through different studies demonstrate the robustness of the developed dataset.

Does mangrove leave falling dominate the bury of polycyclic aromatic hydrocarbons in the mangrove of China?

Mangrove wetlands are vital coastal ecosystems that can absorb and accumulate pollutants. Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants that pose potential risks to ecosystems and human health. However, their source and transport fate in mangrove areas are poorly understood. This study investigates 29 PAHs pollution of water and sediment in Zhangjiangkou Mangrove Wetland, the northernmost large-scale mangrove wetland reserve in China. We examine the distribution, source, transport mechanisms and risk assessment of PAHs. The results show that the concentrations of PAHs in mangrove sediment range from 55.62 to 347.36 ng/g (DW), with 5-ring PAHs being the dominant species. While the concentrations of PAHs in surface water range from 10.61 to 46.39 ng/L, with 2-ring PAHs and alkylated PAHs being the dominant species. The PAHs concentrations in surface water and sediment of river are higher than those in mangrove area, indicating that mangrove water could receive PAHs through tidal exchange. Based on diagnostic ratios (DRs), principal component analysis (PCA), and positive matrix factorization (PMF), we infer that the leaf deposition (48.55%) could be an important pathway of PAHs in mangrove sediment except for river water transport (51.45%), while the PAHs in estuary water originate mainly from point sources such as biomass burning (50.96%) and traffic emission (49.04%). The range of toxic equivalents in surface water and sediment was 2.73–16.09 ng TEQ g−1 and 0.03–3.63 ng/L, respectively. Although the ecological risk assessment suggests that the PAHs pollution in surface water and sediment poses a low risk, we recommend more attention to the protection of the mangrove ecosystem. This study reveals that mangrove leaf falling might be a significant mechanism of PAH sequestration in the mangrove system, which deserves more attention in future research.

Unlocking Mutual Gains-An Experimental Study on Collaborative Autonomous Driving in Urban Environment.

Convoy driving, a specialized form of collaborative autonomous driving, offers a promising solution to the multifaceted challenges that transportation systems face, including traffic congestion, pollutant emissions, and the coexistence of connected autonomous vehicles (CAVs) and human-driven vehicles on the road, resulting in mixed traffic flow. While extensive research has focused on the collective societal benefits of convoy driving, such as safety and comfort, one critical aspect that has been overlooked is the willingness of individual vehicles to participate in convoy formations. While the collective benefits are evident, individual vehicles may not readily embrace this paradigm shift without explicit tangible benefits and incentives to motivate them. Moreover, the objective of convoy driving is not solely to deliver societal benefits but also to provide incentives and reduce costs at the individual level. Therefore, this research bridges this gap by designing and modeling the societal benefits, including traffic flow optimization and pollutant emissions, and individual-level incentives necessary to promote convoy driving. We model a fundamental diagram of mixed traffic flow, considering various factors such as CAV penetration rates, coalition intensity, and coalition sizes to investigate their relationships and their impact on traffic flow. Furthermore, we model the collaborative convoy driving problem using the coalitional game framework and propose a novel utility function encompassing incentives like car insurance discounts, traffic fine reductions, and toll discounts to encourage vehicle participation in convoys. Our experimental findings emphasize the need to strike a balance between CAV penetration rate, coalition intensity, size, and speed to realize the benefits of convoy driving at both collective and individual levels. This research aims to align the interests of road authorities seeking sustainable transportation systems and individual vehicle owners desiring tangible benefits, envisioning a future where convoy driving becomes a mutually beneficial solution.

Degradation of urban soil quality due to air and road traffic: relevant info and research

Aircraft operations at an airport result in the emission of various substances. Simultaneously, in an airport area, road traffic is very intense, also contributing to the emission of the same substances. In this work, potentially toxic elements, essentially trace metals, and others not categorised as such (alloys elements and ceramic particles that constitute particulate matter) are considered, which end up being deposited on soils. The main purpose of this study is to identify soil quality degradation potentially connected to aircraft operations related to Lisbon airport, Portugal. At the same time, an attempt was made to differentiate the contribution of soil quality degradation resulting from road traffic. Several geotechnical and chemical parameters were determined for all of the 102 collected samples. X-ray fluorescence analysis was performed to identify and quantify the main elements present in all samples. Some samples were also subjected to X-ray diffraction to determine the mineralogical composition. The data obtained allow the creation of distribution maps of the detected elements and, after multivariate statistical analysis, conclude which ones are related to very intense road traffic and which ones are related to aircraft traffic emissions, in an urban area, based on a holistic interpretation of all intervenient variables.

Urban geochemistry of heavy metals in road dust from Cairo megacity, Egypt: enrichment, sources, contamination, and health risks

Traffic emissions, overpopulation, and poor urban planning have degraded the environmental quality of the Greater Cairo megacity. As road dust is considered the main sink of pollutants in urban environments, thirty road dust samples from six major roads were collected. ICP-OES was used to analyze the samples in order to determine the contamination grade using the pollution indices, decipher metal sources and geochemical associations using multivariate analysis, and evaluate the ecological and human health risks. The average contents of heavy metals were arranged in decreasing order: Fe (1181.12), Zn (201.36), Pb (66.10), Ni (32.42), Cu (26.07), Cr (26.00), Co (14.78), As (2.76), and Hg (0.93) mg/kg. The spatial pattern showed that new urban communities exhibited lower contamination levels compared to unplanned old districts, especially for traffic elements (Pb, Zn, and Cu). The highest Igeo mean value was observed for Hg (3.34) followed by Zn (1.34), Pb (1.24), Ni (0.10), As (− 0.19), Co (− 0.24), Cu (− 0.73), and Cr (− 1.05). Additionally, CF values indicated very high contamination levels for Pb and Zn, whereas the highest PLI value was observed at Ramses Street. All EF values were higher than 1.5, indicating the anthropogenic enrichment of metals. RI values classified Cairo as a region with considerable ecological risk, observing that Hg was the main contributor. Correlation analysis and PCA showed that Pb and Zn originated from vehicle exhaust, Hg, Cu, and As from domestic and industrial activities, and Fe and Co from natural origin. Cr and Ni exhibited potential carcinogenic risks for children through ingestion; conversely, the non-carcinogenic risks were within safe levels. As a result, the use of clean energy is recommended to mitigate the accumulation of heavy metals in road dust and to improve the quality of life.

A comprehensive exploration of characteristics and source attribution of carbonaceous aerosols in PM2.5 in an East China megacity

A total of 84 PM2.5 (fine particulate matter) aerosol samples were collected between October 2020 and August 2021 within an urban site in Hangzhou, an East China megacity. Chemical species, such as organic carbon (OC), elemental carbon (EC), as well as char, soot, and n-alkanes, were analyzed to determine their pollution characteristics and source contributions. The mean yearly concentrations of OC, EC, char, soot, and total n-alkanes (∑n-alkane) were 8.76 ± 3.61 μg/m3, 1.44 ± 0.76 μg/m3, 1.21 ± 0.69 μg/m3, 0.3 ± 0.1 μg/m3, and 24.2 ± 10.6 ng/m3. The OC, EC, and ∑n-alkanes were found in the highest levels during winter and lowest during summer. There were strong correlations between OC and EC in both winter and spring, suggesting similar potential sources for these carbonaceous components in both seasons. There were poor correlations among the target pollutants due to summertime secondary organic carbon formation. Potential source contribution functions analysis showed that local pollution levels in winter and autumn were likely influenced by long-range transportation from the Plain of North China. Source index and positive matrix factorization models provided insights into the complex sources of n-alkanes in Hangzhou. Their major contributors were identified as terrestrial plant releases (32.7%), traffic emissions (28.8%), coal combustion (27.3%), and microbial activity (11.2%). Thus, controlling vehicular emissions and coal burning could be key measures to alleviate n-alkane concentrations in the atmosphere of Hangzhou, as well as other Chinese urban centers.

Bioaccumulation and Potential Risk Assessment of Heavy Metals in Tropical Bamboo Plantations of Dendrocalamus brandisii under Two Cultivation Patterns in Yunnan, China

Heavy metal (HM) pollution nowadays is a hot issue concerning global ecological and food safety. As one of the most important woody bamboos for edible shoots in Southeast Asia and southwest China, Dendrocalamus brandisii usually occurs in red soil with a high HM geological background. However, the bioaccumulation process and possible risks of HMs in their cultivation area remain unclear. In this study, a comprehensive risk assessment of HM pollution was conducted in the main cultivation area of D. brandisii under two cultivation patterns in Yunnan, China. The results revealed that moderate to heavy HM pollution existed in the soil of the study area, while bamboo shoots displayed either no pollution or weak contamination. The “large-area intensive afforestation” cultivation pattern can better control HM pollution in soil and shoots than the “small-scale farmer management” pattern. Strong and complex correlations among HMs were unveiled in both soil and shoot samples. The Cr content of 39% of the shoot samples exceeded China’s national standards. Cu and Zn were the two most easily accumulated HMs in shoots, with BCF of 0.1235 and 0.1101, respectively. Bioaccumulations of As and Cd were positively correlated with their concentrations in soil. Furthermore, the positive matrix factorization model (PMF) identified three main sources of soil HMs, i.e., Pb and Zn from traffic emissions accounting for 30%; Ni, Mn, and Cr from pedogenic parent material making up 35.4%; and As from mining and metallurgical activities accounting for 34.6%. Monte Carlo simulations suggested the probability of total noncarcinogenic risk for children from bamboo shoots was 24.82% and that As in the soil was the primary element of health risk to children (HQc = 13.94%). These findings emphasize the urgent need to control and mitigate HM pollution from the identified sources and may contribute to the sustainable cultivation of D. brandisii and food safety in Yunnan and similar tropical areas with high HM contents.

The impact of vehicle parameters on road PM10 vehicle resuspended emissions: A case in South African low-income settlement

About 70% of the roads in low-income settlements are unpaved and close (≤15m) to residents, thus a major source of ambientand indoor PM10 concentrations. International studies have suggested that decreasing vehicle speed and managing vehicle type onpaved or unpaved roads can reduce vehicle dust emissions. These mitigation strategies should be examined before being adoptedand gazetted into South African air quality management plans. This study aimed to characterise roads and traffic, emphasisingdetermining the impact of vehicle type on PM10 emissions. GIS was used to determine the proportion of paved and unpaved roads. Atraffic counter was used to monitor vehicles and to determine traffic composition, diurnal cycles, and average speed per road type.Field campaigns (summer; 6 days; 15 hrs per day) were carried out in Bokamoso to monitor PM10 concentration usinga TSI DustTrak DRX® real-time optical aerosol counter. The Box Model method quantifies vehicle PM10 emission factors for heavy-duty, medium-duty, and motor vehicles. About 0.88 km of road within Bokamoso is paved with a daily traffic volume of >2000, and 3.6 km is unpaved with >250 daily traffic volume. Paved road heavy-duty vehicle non-exhaust PM10 emissions reported a positive medium to strong coefficient of determination to vehicle speed increase with a coefficient of determination of 0.59. Even though there is a positive coefficient of determination between heavy-duty vehicle non-exhaust PM10 emission factors and speed on unpaved roads, it is weak (0.2) due to low and less variable vehicle speed. Motor vehicle paved and unpaved road non-exhaust emission factors showed no significant coefficient of determination with increased vehicle speed. Paved road non-exhaust emission factors were not significantly variable with vehicle type. They ranged between an average of 0.15-0.18 g/km/h, with motor vehicles reporting an average of 0.18 g/km/h while heavy-duty vehicles reported an average of 0.15 g/km/h. On the contrary, unpaved road non-exhaust traffic PM10 emissions ranged between 0.16-0.3 g/km/h. The emission factors presented may be used to model vehicle traffic emissions, improve on-road vehicle dust emissions impact assessment and as a guide when deciding on local applicable road dust mitigation strategies

Associations of exposure to ambient fine particulate matter constituents from different pollution sources with semen quality: Evidence from a prospective cohort

The association between ambient fine particulate matter (PM2.5) exposure and semen quality remains inconclusive, possibly due to variations in pollution sources and PM2.5 compositions. Studies investigating the constituents of PM2.5 have been hindered by small sample sizes, and research exploring the relationships between PM2.5 pollution sources and semen quality is lacking. To address this gap, we conducted a comprehensive study based on the Anhui prospective assisted reproduction cohort to evaluate the associations between semen quality and the constituents and pollution sources of PM2.5. This study included 9013 semen samples from 4417 males in the urban districts of Hefei. The median concentrations of PM2.5 constituents, including eight metals and four water-soluble ions (WSIs), were measured for seven days per month at two monitoring stations during the 0–90-day exposure window. A linear mixed-effects model, weighted quantile sum regression, and positive matrix factorisation were used to evaluate the associations of the constituents and pollution sources of PM2.5 with semen quality. The results showed that exposure to PM2.5-bound metals (antimony, arsenic, cadmium, lead, and thallium) and WSIs (sulphate and chloride) were negatively associated with semen quality parameters. Moreover, mixtures of PM2.5-bound metals and WSIs were negatively associated with semen quality. Additionally, PM2.5 derived from traffic emissions was negatively associated with semen quality. In summary, our study revealed that ambient PM2.5 and its constituents, especially metals, were negatively associated with semen quality. Antimony, lead, and thallium emerged as the primary contributors to toxicity, and PM2.5 from traffic emissions was associated with decreased semen quality. These findings have important public health implications for the management of PM2.5 pollution in the context of male reproductive health.

PM2.5 in Cape Town, South Africa: Chemical characterization and source apportionment using dispersion-normalised positive matrix factorization

Understanding fine particulate matter (PM2.5) composition and sources is beneficial to improving visibility, addressing climate change, and mitigating poor air quality and related public health effects. Source apportionment techniques have been instrumental in evaluating the impact of sources and secondary processes on the ambient PM2.5 concentrations in receptor areas. Positive Matrix Factorization (PMF) is now the most commonly used tool due to its ability to provide mixture resolution based on available PM2.5 compositional data. Sampling and analysis of PM2.5 was conducted in Cape Town, South Africa from April 2017 to April 2018. The resulting data were dispersion normalized to address the modifications of the source concentrations resulting from the varying dispersion conditions and thereby permit dispersion normalized PMF (DN-PMF) to be employed. DN-PMF quantified the 6 sources as 2-stroke vehicles/galvanizing industries (16.8%); soil/road dust (12.3%); sulphate/marine diesel (3.6%), traffic (15.7%), sea salt (21.8%), and heating/biomass burning/cooking (15.7%). In addition, air mass back trajectory analysis using the Hybrid Single Particle Lagrangian Trajectory (HYSPLIT) model identified long-range transport pathways to Cape Town. The HYSPLIT results showed air masses from the Atlantic SSW (6%), Atlantic SW (24%), Indian Ocean (31%), and Atlantic WSW (39%) influence air quality. The primary sources affected by the transport clusters were heating, 2-stroke vehicles/galvanizing, road and soil dust, and traffic emissions. These results show that reducing emissions from the local sources will improve air quality.

An open-path observatory for greenhouse gases based on near-infrared Fourier transform spectroscopy

Abstract. Monitoring the atmospheric concentrations of the greenhouse gases (GHG) carbon dioxide (CO2) and methane (CH4) is a key ingredient for fostering our understanding of the mechanisms behind the sources and sinks of these gases and for verifying and quantitatively attributing their anthropogenic emissions. Here, we present the instrumental setup and performance evaluation of an open-path GHG observatory in the city of Heidelberg, Germany. The observatory measures path-averaged concentrations of CO2 and CH4 along a 1.55 km path in the urban boundary layer above the city. We combine these open-path data with local in situ measurements to evaluate the representativeness of these observation types on the kilometer scale. This representativeness is necessary to accurately quantify emissions, since atmospheric models tasked with this job typically operate on kilometer-scale horizontal grids. For the operational period between 8 February and 11 July 2023, we find a precision of 2.7 ppm (0.58 %) and 18 ppb (0.89 %) for the dry-air mole fractions of CO2 (xCO2) and CH4 (xCH4) in 5 min measurements, respectively. After bias correction, the open-path measurements show excellent agreement with the local in situ data under atmospheric background conditions. Both datasets show clear signals of traffic CO2 emissions in the diurnal xCO2 cycle. However, there are particular situations, such as under southeasterly wind conditions, in which the in situ and open-path data reveal distinct differences up to 20 ppm in xCO2, most likely related to their different sensitivity to local emission and transport patterns. Our setup is based on a Bruker IFS 125HR Fourier transform spectrometer, which offers a spacious and modular design providing ample opportunities for future refinements of the technique with respect to finer spectral resolution and wider spectral coverage to provide information on gases such as carbon monoxide and nitrogen dioxide.

Source identification and risk assessment of trace metals in surface sediment of China Sea by combining APCA-MLR receptor model and lead isotope analysis

This study aimed to investigate the distribution, pollution, risk and sources of trace metals in sediments along China Sea. Clear spatial variations were found for Cr, Mn, Co, Ni, Cu, Zn, Se, Mo, Ag, Cd, and Pb, whereas As did not show spatial variation. East China Sea (ECS) contained the highest concentrations of Mn, Co, Ni, Cu, Zn, South China Sea (SCS) shallow sea contained the highest concentrations of Zn, Se, Mo, Ag, Cd, and Pb, whereas coral reefs contained the lowest concentrations of trace metals. Spatial variations could be explained by economic development characteristics along China Sea. As, Se and Cd exhibited low to moderate pollution in China Sea sediment, yet pollution for Cu, Zn, Ni, and Ag appeared in some regions. Sediment in ECS had moderate ecological risks and other regions at low ecological risks. The absolute principle component score-multiple linear regression (APCS-MLR) and Pb stable isotope indicated that 43–74% of trace metals (Ni, Cu, Zn, As, Se, Cd, and Pb) were derived from anthropogenic sources like traffic emission, agricultural activities, industrial source. No pollution and ecological risk were observed in coral reefs, yet 39–71% (Pb) was derived from anthropogenic activities such as motor vessels.

Another one bites the dust – Two street canyons studied with magnetic biomonitoring and OSPM modelling

Urban areas form a mosaic of microenvironments and structures, such as street canyons that are susceptible to elevated levels of traffic related pollutants. Street canyons are a relevant topic in air quality research and modelling since they are prone to reduced natural ventilation and when exposed to increased traffic emissions, can pose a serious risk to public health. We applied magnetic biomonitoring using moss bags to evaluate the quality of Operational Street Pollution Model (OSPM) and to study vertical distribution of modelled particulate matter (PM), magnetic PM and heavy metal pollution in a street canyon in Turku and Helsinki, Finland. Moss bags were attached on opposite sides of the streets at 3-, 6-, 9- and 12-m heights for 44 days in late autumn. Samples were analysed for mass-specific magnetic susceptibility (χ), hysteresis parameters and elemental components. High χ values and elemental concentrations of Fe, Al, Ti, Zn, Mn, Cu and Ba were found in both street canyons. Compared to measurements with airborne magnetic PM, the OSPM model underestimated the difference in PM concentrations between the opposite street canyon faces and overestimated the dilution of PM concentrations with altitude. In Turku the results between the OSPM model and magnetic measurements were incompatible. We suspect this is due to meteorological model input data from a distant weather station, street canyon features and a high slope angle contributing to non-uniform emissions in the street canyon. We demonstrate that the moss bag technique is a versatile tool to understand the small-scale variations of concentrations in a variety of complex urban environments.