Traffic-related Emissions Research Articles

The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China.

To develop accurate air pollution control policies, it is necessary to determine the sources of different types of fugitive dust in mining and metallurgy areas. A method integrating principal component analysis and a positive matrix factorization model was used to identify the potential sources of heavy metals (HMs) in five different types of fugitive dust. The results showed accumulation of Mn, Fe, and Cu can be caused by natural geological processes, which contributed 38.55% of HMs. The Ni and Co can be released from multiple transport pathways and accumulated through local deposition, which contributed 29.27%. Mining-related activities contributed 20.11% of the HMs and showed a relatively high accumulation of As, Sn, Zn, and Cr, while traffic-related emissions contributed the rest of the HMs and were responsible for the enrichment in Pb and Cd. The co-applied source-identification models improved the precision of the identification of sources, which revealed that the local geological background and mining-related activities were mainly responsible for the accumulation of HMs in the area. The findings can help the government develop targeted control strategies for HM dispersion efficiency.

Tunnel measurements reveal significant reduction in traffic-related light-absorbing aerosol emissions in China

Light-absorbing aerosols (LAA), including black carbon (BC) and brown carbon (BrC), profoundly impact regional and global climate. Vehicle emission is an important source of LAA in urban areas, but real-world emission features of LAA from the urban vehicle fleet are not fully understood. This study evaluates traffic-related BC and BrC emission factors (EFs) and their vehicular emission inventories via road tunnel measurements in Tianjin, China, in 2017 and 2021. The distance-based and fuel-based EFs of BC for the mixed fleet were 1.05 ± 1.28 mg km−1 veh−1 and 0.057 ± 0.057 g (kg-fuel)−1, respectively, in 2021, with a dramatic decrease of 80.6 % compared to those in 2017. The BC EFs for gasoline vehicles (GVs, including traditional gasoline and hybrid vehicles) and diesel vehicles (DVs) were 0.55 ± 0.065 mg km−1 veh−1 and 10.5 ± 2.52 mg km−1 veh−1, respectively, in 2021. Compared to 2017, the BrC EFs also decreased significantly in 2021, by 10.8–53.6 %, with 0.32 ± 0.45 mg km−1 veh−1 and 0.018 ± 0.020 g (kg-fuel)−1 of distance-based and fuel-based EFs for mixed fleet. The BrC EFs for GVs and DVs were 0.091 ± 0.024 mg km−1 veh−1 and 3.06 ± 0.91 mg km−1 veh−1, respectively, in 2021. Based on the BC and BrC EFs for GVs and DVs and annual mileage for each vehicle category, the annual vehicular LAA emission inventories were estimated. From 2017 to 2021, the annual vehicular LAA emissions in Tianjin have been significantly reduced, by 69 % for BC and 10 % for BrC. DVs account for a small amount of the vehicle population (8.4 %), but contribute to most of the BC (83 %) and BrC (86 %). Our study demonstrates the significant reduction of vehicular light-absorbing aerosols emission due to vehicle pollution prevention and control in China.

Longitudinal Ambient PM2.5 Measurement at Fifteen Locations in Eight Sub-Saharan African Countries Using Low-Cost Sensors

Air pollution is a major global public health issue causing considerable morbidity and mortality. Measuring levels of air pollutants and facilitating access to the data has been identified as a pathway to raise awareness and initiate dialogue between relevant stakeholders. Low-and middle-income countries (LMICs) urgently need simple, low-cost approaches to generate such data, especially in settings with no or unreliable data. We established a network of easy-to-use low-cost air quality sensors (PurpleAir-II-SD) to monitor fine particulate matter (PM2.5) concentrations at 15 sites, in 11 cities across eight sub-Saharan Africa (sSA) countries between February 2020 and January 2021. Annual PM2.5 concentrations, seasonal and temporal variability were determined. Time trends were modelled using harmonic regression. Annual PM2.5 concentrations ranged between 10 and 116 µg/m3 across study sites, exceeding the current WHO annual mean guideline level of 5 µg/m3. The largest degree of seasonal variation was seen in Nigeria, where seven sites showed higher PM2.5 levels during the dry than during the wet season. Other countries with less pronounced dry/wet season variations were Benin (20 µg/m3 versus 5 µg/m3), Uganda (50 µg/m3 versus 45 µg/m3), Sukuta (Gambia) (20 µg/m3 versus 15 µg/m3) and Kenya (30 µg/m3 versus 25 µg/m3). Diurnal variation was observed across all sites, with two daily PM2.5 peaks at about 06:00 and 18:00 local time. We identified high levels of air pollution in the 11 African cities included in this study. This calls for effective control measures to protect the health of African urban populations. The PM2.5 peaks around ‘rush hour’ suggest traffic-related emissions should be a particular area for attention.

Regional heterogeneity and driving factors of road runoff pollution from urban areas in China.

Due to the multiple influences of natural and anthropogenic factors, stormwater runoff from urban roads generally presents heterogeneous pollution among cities. The identification of regional heterogeneity and related driving factors of road runoff pollution is of significance for the optimal management of road runoff pollution according to the local circumstances. In this study, the regional heterogeneity of urban road runoff pollution from fourteen representative cities in China is analyzed for four typical pollutants including total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). The results show wide variations in TSS, COD, TN and TP pollution among cities, with the average event mean concentrations ranging from 77.0 to 1347.9, 31.4 to 488.1, 0.81 to 8.46, 0.139 to 1.930mg/L, respectively. One-way ANOVA analyses demonstrate significant differences in road runoff pollution among cities. The TSS pollution is significantly heavier for northern and northwestern inland cities than that for eastern and southern cities. Pearson correlation analysis and Stepwise linear regression analysis are performed to identify and rank the influence of climate, population, economy, industry structure, traffic and environmental quality. Direct relationships of road runoff pollution are detected with PM2.5, PM10, secondary industry, tertiary industry, annual rainfall, and urban green coverage, among which PM10 and urban green coverage are the most important and common factors exerting positive and negative influences on road runoff pollution, respectively. Based on the findings of this work, improvement of atmospheric particulate pollution and increase in urban greenness are recommended measures to manage the road runoff pollution. Furthermore, the traffic-related emissions accompanying the upgrading of industry structure should be effectively controlled to attenuate the TSS and COD pollution in road runoff.

Increased contribution to PM2.5 from traffic-influenced road dust in Shanghai over recent years and predictable future

Traffic contributes to fine particulate matter (PM2.5) in the atmosphere through engine exhaust emissions and road dust generation. However, the evolution of traffic related PM2.5 emission over recent years remains unclear, especially when various efforts to reduce emission e.g., aftertreatment technologies and high emission standards from China IV to China V, have been implemented. In this study, hourly elemental carbon (EC), a marker of primary engine exhaust emissions, and trace element of calcium (Ca), a marker of road dust, were measured at a nearby highway sampling site in Shanghai from 2016 to 2019. A random forest-based machine learning algorithm was applied to decouple the influences of meteorological variables on the measured EC and Ca, revealing the deweathered trend in exhaust emissions and road dust. After meteorological normalization, we showed that non-exhaust emissions, i.e., road dust from traffic, increased their fractional contribution to PM2.5 over recent years. In particular, road dust was found to be more important, as revealed by the deweathered trend of Ca fraction in PM2.5, increasing at 6.1% year−1, more than twice that of EC (2.9% year−1). This study suggests that while various efforts have been successful in reducing vehicular exhaust emissions, road dust will not abate at a similar rate. The results of this study provide insights into the trend of traffic-related emissions over recent years based on high temporal resolution monitoring data, with important implications for policymaking.

Source Apportionment of Elemental Carbon in Different Seasons in Hebei, China

Black carbon (BC), also termed elemental carbon (EC), is a strong light-absorbing substance. It can disturb the radiation balance between the earth and atmosphere resulting in changing regional and global climate conditions. This study conducted a thorough analysis of EC in Hebei during different seasons and provided comprehensive EC emission data in the Beijing–Tianjin–Hebei (BTH) region for future policy making connected with air pollution mitigation and control. The results showed that the concentration of EC during the sampling period varied from 0.01 to 18.4 μg/m3 with a mean value of 2.6 ± 2.8 μg/m3. The EC source apportionment exercise identified four regular emission sources for all seasons, including traffic-related emissions, coal combustion, biomass burning, and mineral dust. Annually, traffic-related emissions were the primary EC contributor with an annual average contribution of 38%, followed by biomass burning (30%) and coal combustion (25%). In addition, the EC mass concentration at Shijiazhuang was also influenced by diverse pollutants from upwind regions. This study shows that traffic emissions are a major contributor to EC mass concentration in Shangjiazhuang and highlights that regional joint control of air pollution is important to local air quality.

Evaluating zone of influence for traffic-related emissions generated near bus stops: a case study in Nanjing, China

ABSTRACT The estimation of zone of influence (ZoI) at bus stops is the basis for accurately modelling vehicle emissions and assessing the exposure of pedestrians to airborne toxins at emission hotspot locations. The purpose of this study was to investigate the changes in ZoIs for traffic-related emissions at different bus stops. An integrated approach coupling a traffic simulation model (VISSIM) with an emission model (MOVES) was implemented to estimate the vehicle emissions in ZoIs at bus stops for different scenarios. The results demonstrated that lane management mode and traffic conditions had a significant impact on vehicle emissions near different bus stops, and the emission factors (EFs) of pollutants were highest in the acceleration sublink. In addition, the length of the acceleration and queue sublinks mainly determined the length of the ZoIs of bus stops. Owing to the stop-and-go condition, the average contribution of CO, PM2.5, and PM10 towards the ZoI at curbside bus stop and bus bay without exclusive bus lane was significantly higher than under cruising condition. The sensitivity analysis also showed that the emission was more sensitive to acceleration than deceleration. Therefore, the emissions of the departure links for bus stops were significantly higher than those of the approach links. Commuters should reduce the long-time waiting at bus stops, wait downstream of bus stop when waiting for the bus stop, and the vehicles at the bus stop should accelerate slowly when leaving, which will all help to minimize the exposure of commuters to pollutants.

Volatile organic compounds in urban Lhasa: variations, sources, and potential risks

Lhasa is a typical high-altitude city with strong solar radiation and high background ozone levels. With the rapid development and urbanization, the emission of volatile organic compounds (VOCs) in Tibet has been increasing annually. However, VOCs activity and the impact on air quality and human health have scarcely been investigated. We conducted online measurement of VOCs in urban Lhasa during May 2019. The mean mixing ratio (with one standard deviation) of the total VOCs was 21.5 ± 18.6 ppb. Of the total VOCs, alkanes, alkenes, and aromatic hydrocarbons accounted for 57.7%, 20.9%, and 21.4%, respectively. On the basis of VOC atmospheric reactivity, the ozone formation potential (OFP) and hydroxyl radical loss rate (LOH) were 91.7 ppb and 3.1 s−1, respectively. Alkenes accounted for the largest proportion of the OFP and LOH, followed by aromatic hydrocarbons. The results of correlation analysis on the benzene series (BTEX), and the similarity of the diurnal changes in CO, NOy, BTEX, and TVOC mixing ratios indicated that Lhasa city strongly affected by motor vehicle emissions. Source apportionments using positive matrix factorization (PMF) model further confirmed that traffic related emissions, including gasoline automobiles, diesel vehicles, and public transportation vehicles fueled with liquid natural gas contributed the most in total VOCs concentration (44.5%–50.2%), LOH (41.6%–46.8%) and OFP (47.4%–52.3%). Biomass combustion, mainly from the traditional biomass fuel in the plateau, was the second contributor to ambient VOCs (41.3%), LOH (26.4%), and OFP (29.7%), and existed a less variation in diurnal changes with a feature of regional background. Plants contributed only about 1.5% to the VOCs concentration but a relatively high (approximately 14.6%) LOH. The noncarcinogenic risk of BTEX did not exceed the hazard quotient value, but the carcinogenic risk of benzene was 4.47 × 10–6, indicating a potential risk.

Connection between the Spatial Characteristics of the Road and Railway Networks and the Air Pollution (PM10) in Urban–Rural Fringe Zones

Atmospheric particulate matter (PM10) is one of the most important pollutants for human health, and road transport could be a major anthropogenic source of it. Several research studies have shown the impact of roads on the air quality in urban areas, but the relationship between road and rail networks and ambient PM10 concentrations has not been well studied, especially in suburban and rural landscapes. In this study, we examined the link between the spatial characteristics of each road type (motorway, primary road, secondary road, and railway) and the annual average PM10 concentration. We used the European 2931 air quality (AQ) station dataset, which is classified into urban, suburban, and rural landscapes. Our results show that in urban and rural landscapes, the spatial characteristics (the density of the road network and its distance from the AQ monitoring points) have a significant statistical relationship with PM10 concentrations. According to our findings from AQ monitoring sites within the urban landscape, there is a significant negative relationship between the annual average PM10 concentration and the density of the railway network. This result can be explained by the driving wind generated by railway trains (mainly electric trains). Among the road network types, all road types in the urban landscape, only motorways in the suburban landscape, and only residential roads in the rural landscape have a significant positive statistical relationship with the PM10 values at the AQ monitoring points. Our results show that in the suburban zones, which represent the rural–urban fringe, motorways have a strong influence on PM-related air pollution. In the suburban areas, the speed of vehicles changes frequently near motorways and intersections, so higher traffic-related PM10 emission levels can be expected in these areas. The findings of this study can be used to decrease transportation-related environmental conflicts related to the air quality in urban, urban–rural fringe, and rural (agricultural) landscapes.

Comparison of Methods for Sampling Particulate Emissions from Tires under Different Test Environments

Traffic-related emissions are strongly criticised by the public because they contribute to climate change and are classified as hazardous to health. Combustion engine emissions have been regulated by limit values for almost three decades. There is currently no legal limit for non-exhaust emissions, which include tire wear particle emissions and resuspension. As a result, the percentage of total vehicle emissions has risen continuously. Some of the particles emitted can be assigned to the size classes of particulate matter (≤10 µm) and are therefore of particular relevance to human health. The literature describes a wide range of concepts for sampling and measuring tire wear particle emissions. Because of the limited number of studies, the mechanisms involved in on-road tests and their influence on the particle formation process, particle transport and the measuring ability can only be described incompletely. The aim of this study is to compare test bench and on-road tests and to assess the influence of selected parameters. The first part describes the processes of particle injection and particle distribution. Based on this, novel concepts for sampling and measurement in the laboratory and in the field are presented. The functionality and the mechanisms acting in each test environment are evaluated on the basis of selected test scenarios. For example, emissions from external sources, the condition of the road surface and the influence of the driver are identified as influencing factors. These analyzes are used to illustrate the complexity and limited reproducibility of on-road measurements, which must be taken into account for future regulations.

Characteristics, sources, and health risks of trace metals in PM2.5

ObjectivesThe concentrations and characteristics of trace metals in PM2.5 vary in time and location and can result in various health impacts. The current study aimed to investigate the characteristics and distributions of PM2.5-bound trace metals and further identify the possible sources and conduct health risk assessment in Zhejiang, China. MethodsPM2.5 samples were collected during 2015–2019 at seven monitoring sites in Zhejiang, China. Twelve trace metals in ambient PM2.5 were determined. The positive matrix factorization (PMF) model was applied to quantify the potential sources of PM2.5. The chemical daily intake (CDI) via ingestion, exposure concentration (EC) via inhalation and dermally absorbed dose (DAD) via contact were estimated to assess the health risks. ResultsThe seasonal variations of both PM2.5 and trace metals showed the highest in winter and the lowest in summer. Among the studied trace metals, Aluminum (Al), Lead (Pb) and Manganese (Mn) had the highest concentrations. Soil dust, coal combustion and traffic-related emissions were the main sources of the air pollution at most sites in Zhejiang. Health risk assessment showed that ingestion was the primary exposure pathway, and children were more likely to be affected by ambient trace metals. The non-carcinogenic risks of arsenic (As), Pb, Antimony (Sb) and Thallium (Tl) as well as carcinogenic risks of As, Pb and nickel (Ni) should be paid more attention to. ConclusionsPM2.5-bound trace metals are of great public health concern, particularly related to the potential health risks of As, Pb and Tl. Corresponding emission control measures of metal elements in PM2.5 should be considered.

Measurement report: The importance of biomass burning in light extinction and direct radiative effect of urban aerosol during the COVID-19 lockdown in Xi'an, China

Abstract. Due to the complexity of emission sources, a better understanding of aerosol optical properties is required to mitigate climate change in China. Here, an intensive real-time measurement campaign was conducted in an urban area of China before and during the COVID-19 lockdown in order to explore the impacts of anthropogenic activities on aerosol light extinction and the direct radiative effect (DRE). The mean light extinction coefficient (bext) decreased from 774.7 ± 298.1 Mm−1 during the normal period to 544.3 ± 179.4 Mm−1 during the lockdown period. A generalised additive model analysis indicated that the large decline in bext (29.7 %) was due to sharp reductions in anthropogenic emissions. Chemical calculation of bext based on a ridge regression analysis showed that organic aerosol (OA) was the largest contributor to bext in both periods (45.1 %–61.4 %), and the contributions of two oxygenated OAs to bext increased by 3.0 %–14.6 % during the lockdown. A hybrid environmental receptor model combined with chemical and optical variables identified six sources of bext. It was found that bext from traffic-related emissions, coal combustion, fugitive dust, the nitrate and secondary OA (SOA) source, and the sulfate and SOA source decreased by 21.4 %–97.9 % in the lockdown, whereas bext from biomass burning increased by 27.1 %, mainly driven by the undiminished need for residential cooking and heating. An atmospheric radiative transfer model was further used to illustrate that biomass burning, rather than traffic-related emissions, became the largest positive effect (10.0 ± 10.9 W m−2) on aerosol DRE in the atmosphere during the lockdown. Our study provides insights into aerosol bext and DRE from anthropogenic sources, and the results imply the importance of controlling biomass burning for tackling climate change in China in the future.

Impact from the evolution of private vehicle fleet composition on traffic related emissions in the small-medium automotive city

Understanding the emission characteristics in the evolution of private vehicle fleet composition has become a key issue to be addressed to develop appropriate emission mitigation strategies in transportation sector. In this study, the influence of such evolution on on-road emissions was investigated based on a comprehensive dataset encompassing vehicle fleet composition, demographic, economic, and energy features from a representative small-medium automotive city in North America. The decoupling analysis was carried out to assess the dynamic linkage between environmental pressure exerted by the transportation sector and economic growth at both city level and national level in North America. We also developed an approach that supports the long-term traffic-related air pollutant prediction and investigated the potential influence on urban air quality. A sharp upward trajectory was observed in the quantity of SUVs from 2001 to 2018, gradually replacing the dominance of the quantity of four-door cars. There was a significant shift in the GHG emissions emitted from vehicle types used for passenger transport: emissions from SUVs and trucks rose by 374.0% and 69.3%, respectively, whereas emissions from four-door cars, two-door cars, station wagons, and vans all decreased. The changes in vehicle composition, along with the steady trend in GHG emissions from private fleet and decrease in on-road air pollutant concentrations found in Regina, were a response to the establishment of federal fuel economy standards and improved fuel economy. Relative decoupling was observed in aggregate for Regina and Canada in most of the years while both experienced economic downturns and increases in environmental pressure in the form of emissions from 2014 to 2015. The predicted results also demonstrate the high capability of XGboost machine learning algorithm in predicting on-road air pollutant concentrations of CO, PM2.5, and NOX.

Use of Radioisotope Ratios of Lead for the Identification of Historical Sources of Soil Lead Contamination in Santa Ana, California.

Lead (Pb) is an environmental neurotoxicant that has been associated with a wide range of adverse health conditions, and which originates from both anthropogenic and natural sources. In California, the city of Santa Ana represents an urban environment where elevated soil lead levels have been recently reported across many disadvantaged communities. In this study, we pursued a community-engaged research approach through which trained “citizen scientists” from the surrounding Santa Ana community volunteered to collect soil samples for heavy metal testing, a subset of which (n = 129) were subjected to Pb isotopic analysis in order to help determine whether contamination could be traced to specific and/or anthropogenic sources. Results showed the average 206Pb/204Pb ratio in shallow soil samples to be lower on average than deep samples, consistent with shallow samples being more likely to have experienced historical anthropogenic contamination. An analysis of soil Pb enrichment factors (EFs) demonstrated a strong positive correlation with lead concentrations, reinforcing the likelihood of elevated lead levels being due to anthropogenic activity, while EF values plotted against 206Pb/204Pb pointed to traffic-related emissions as a likely source. 206Pb/204Pb ratios for samples collected near historical urban areas were lower than the averages for samples collected elsewhere, and plots of 206Pb/204Pb against 206Pb/207 showed historical areas to exhibit very similar patterns to those of shallow samples, again suggesting lead contamination to be anthropogenic in origin, and likely from vehicle emissions. This study lends added weight to the need for health officials and elected representatives to respond to community concerns and the need for soil remediation to equitably protect the public.

Comparative Evaluation of Road Vehicle Emissions at Urban Intersections with Detailed Traffic Dynamics

The insufficient development of intelligent dynamic monitoring systems, which operate with big data, obstructs the control of traffic-related air pollution in regulated urban road networks. The study introduces mathematical models and presents a practical comparative assessment of pollutant emissions at urban intersections, with two typical modes of vehicle traffic combined, i.e., freely passing an intersection when the green signal appears and uniformly accelerated passing after a full stop at the stop line. Input data on vehicle traffic at regulated intersections were collected using real-time processing of video streams by Faster R-CNN neural network. Calculation models for different traffic flow patterns at a regulated intersection for dynamic monitoring of pollutant emissions were obtained. Statistical analysis showed a good grouping of intersections into single-type clusters and factor reduction of initial variables. Analysis will further allow us to control and minimize traffic-related emissions in urban road networks. A comparative analysis of pollutant emissions in relation to the basic speed of passing at the intersection of 30 km/h was performed according to the calculations of the mathematical models. When reducing the speed to 10 km/h (similar to a traffic jam), the amount of emissions increases 3.6 times over, and when increasing the speed to 50 km/h, the amount of emissions decreases by 2.3 times. Fuzzy logic methods allow us to make a comparative prediction of the amount of emissions when changing both the speed of traffic and the capacity of the intersection lanes. The study reveals the benefits of using a real-life measurement approach and provides the foundation for continuous monitoring and emission forecasting to control urban air quality and reduce congestion in the road network.

Impact of COVID-19 Lockdown on Oxidative Potential of Particulate Matter: Case of Athens (Greece).

This work evaluates the aerosol oxidative potential (OP) and its changes from modified air pollution emissions during the COVID-19 lockdown period in 2020, with the intent of elucidating the contribution of aerosol sources and related components to aerosol OP. For this, daily particulate matter (PM) samples at an urban background site were collected and analyzed with a chemical (acellular) assay based on Dithiothreitol (DTT) during the COVID-19 restriction period in Athens (Greece). The obtained time-series of OP, PM2.5, organic matter (OM) and SO42− of the pre-, post- and lockdown periods were also compared to the data of the same time periods during the years 2017–2019. Even though all traffic-related emissions have been significantly reduced during the lockdown period (by 30%), there is no reduction in water-soluble OP, organics and sulfate concentrations of aerosol during 2020. The results reveal that the decrease in traffic was not sufficient to drive any measurable change on OP, suggesting that other sources—such as biomass burning and secondary aerosol from long-range transport, which remained unchanged during the COVID lockdown—are the main contributors to OP in Athens, Greece.

Seasonal variability and source apportionment of non-methane VOCs using PTR-TOF-MS measurements in Delhi, India

Volatile organic compounds (VOCs) are ubiquitous atmospheric constituents and play important roles in tropospheric photochemistry. The real-time chemical characterization of VOCs was carried out in this study using the proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS) at an urban site of Delhi for the complete year of 2019. The average concentrations of total VOCs ranging from m/z 31.02 to 181.12 were 131.3 ± 96, 78.7 ± 47.3, 151.2 ± 83.2, 199.6 ± 101.2 ppbv in the winter, summer, monsoon, and post-monsoon seasons, respectively. The positive matrix factorization (PMF) receptor model on an advanced ME-2 engine was used to perform source apportionment analysis. This analysis reveals two traffic-related factors, two solid-fuel combustion factors, two secondary VOC factors, one biogenic, and one solvent-use factor depending upon the season. The traffic-related emissions mainly comprised of aromatics and simple non-aromatics, contributed about 31% (∼one-third) of the total VOCs measured over the study period. Solid fuel combustion factors consisting of phenols, furans, and nitrogen-containing compounds contributed about 28%, while Secondary VOCs contributed about 31% cumulatively over the study period. The contributions of biogenic sources were significant only during the summer and monsoon seasons. A local source near the sampling site was identified as a solvent-use factor, mainly comprised of ethyl acetate due to the renovation work near the sampling site during monsoon and post-monsoon season. The difference in the profile of primary sources was influenced by the local emissions and regional transport of air masses. Meteorological conditions and planetary boundary layer highly influence the formation of secondary VOCs.

Seasonal variation of optical properties and source apportionment of black and brown carbon in Xi'an, China

To acquire the seasonal variation of black carbon (BC) and brown carbon (BrC) light absorption (Abs), PM2.5 samples were obtained over an entire year of 2018 and analyzed for chemical composition and wavelength-dependent light absorption. Contributions of AbsBC and AbsBrC, the absorption Ångström exponent (AAE) and mass absorption cross section (MAC) are examined. The results show that while BC was the dominant light absorbing carbonaceous component at 780 nm–980 nm, BrC made a significant contribution to light absorption at shorter wavelengths (405 nm and 445 nm), especially during spring. At 405 nm, BrC experienced the dominant light absorption contribution in spring, autumn and winter, whereas BC had a greater contribution in summer. The annual average of BrC contributions to total absorption at 405 nm (56.3%) is higher than that of BC (43.7%), with lower BrC contributions at longer wavelengths. Average MACs for BC and BrC at 405 nm were 5.67 ± 10.4 and 1.74 ± 3.78 m2g-1, respectively. The MACBC versus MACBrC were well correlated in autumn and winter implying that BC and BrC had similar primary sources (biomass burning and fossil fuel combustion). Source factors and source-specific seasonal contribution to light absorption were identified using positive matrix factorization (PMF) modeling. Traffic-related emissions had the largest contribution to light absorption in all seasons at all wavelengths. Biomass burning had a greater impact in spring and winter, and coal combustion contributed the highest levels in winter. Mineral dust had minor contributions to light absorption in all seasons.

Spatiotemporal variations of carbon dioxide (CO2) at Urban neighborhood scale: Characterization of distribution patterns and contributions of emission sources

• Spatiotemporal distributions were analyzed for urban CO 2 concentration via field study. • CO 2 in urban neighborhoods exhibited notable daily and seasonal variations. • The CO 2 showed a gradual decrease in roadside, residential, and green space areas. • Random Forest and eXtreme Gradient Boosting were used for source contribution analysis. • Traffic source had the largest contribution to the CO 2 followed by green space. The gradual increase in atmospheric carbon dioxide (CO 2 ) concentrations has attracted worldwide attention for its strong relationships with global climate change. Considerable efforts are being undertaken to characterize spatiotemporal variations of CO 2 at a city, regional and national level, aiming at providing pipelines for carbon emission reduction. However, there is scarce knowledge of how CO 2 at the urban neighborhood scale is produced and distributed in the context of time and space, which is useful to accurately target source contributions from the ground up and reduce carbon emissions at a fine-grained scale. In this study, mobile measurements of CO 2 concentrations were made in a 2 km × 2 km urban area covering different land use types to separately characterize the spatiotemporal distribution patterns of CO 2 in roadside, residential and green space areas. The results show that CO 2 concentrations in the late afternoon (Local time, UTC+8, LT 17–18) were higher than those at noon (LT 11–12), and that CO 2 concentrations in winter were higher than those in summer. The roadside areas exhibited the highest CO 2 concentration level of 452.66 ± 20.59 ppm, followed by residential areas (436.34 ± 27.02 ppm) and green space areas (428.98 ± 20.49 ppm). The result indicates that traffic sources brought more carbon emissions and contributed to a significant increase in CO 2 concentrations, while urban greenery caused more carbon absorptions and reduced CO 2 concentrations. This can be further confirmed by the observations that CO 2 concentrations in the roadside neighborhood showed a strong positive correlation (R 2 = 0.86) with ambient traffic flow. Then two machine learning models, i.e., Random Forest and eXtreme Gradient Boost, were developed to quantify the individual contribution from different carbon emission sources to the CO 2 distributions, including traffic flow, greening rate, and domestic energy consumption. The results show that traffic-related carbon emissions were the most important influencing factor and accounted for approximately 60% of ambient CO 2 concentrations, followed by greening rate (20%) and domestic energy consumption (10%). These findings can provide insights into spatiotemporal distributions and source contributions of CO 2 in urban neighborhoods and show huge potentials for reducing urban carbon emissions at a fine-grained scale.

Tailpipe and Nontailpipe Emission Factors and Source Contributions of PM10 on Major Freeways in the Los Angeles Basin.

In this study, the emission factors of PM10 and its chemical constituents from various contributing sources including nontailpipe and tailpipe emissions were estimated on two interstate freeways in the Los Angeles basin. PM10 samples were collected on the I-110 and I-710 freeways as well as at the University of Southern California (USC) campus as the urban background site, while freeway and urban background CO2 levels were measured simultaneously. PM10 samples were analyzed for their content of chemical species which were used to estimate the emission factors of PM10 and its constituents on both I-110 and I-710 freeways. The estimated values were employed to determine the emission factors for light (LDV) and heavy-duty vehicles (HDV). The quantified species were also processed by the positive matrix factorization (PMF) model to produce PM10 freeway source profiles and their contribution to PM10 mass concentrations. Using the PMF factor profiles and emission factors on the two freeways, we characterized the emission factors for light-duty and heavy-duty vehicles by each nontailpipe source. Our findings indicated higher nontailpipe emission factors of PM10 and metal elements on the I-710 freeway compared to the I-110 freeway, due to the higher fraction of heavy-duty vehicles (HDVs) on that freeway. Furthermore, the generation of nontailpipe PM10 from resuspension of road dust was twice of tire and brake wear. The results of this study provide significant insights into PM10 freeway emissions and particularly the overall contribution of nontailpipe and tailpipe sources in Los Angeles, which can be helpful to modelers and air quality officials in assessing the importance of individual traffic-related emissions on the overall population exposure.