Temporal variations of local traffic CO2 emissions and its relationship with CO2 flux in Beijing, China

Urban traffic is an important source of global CO2 emissions. Uncovering the temporal and structural characteristics can provide scientific support to identify the variation regulation and main subjects of urban traffic CO2 emissions. The road class is one of the most important factors influencing the urban traffic CO2 emissions. Based on the annual traffic field monitoring work in 2014 and the localized MOVES model, this study unravels the temporal variation and structural characteristics of the urban traffic CO2 emissions and conducts a comparative analysis of expressway (5R) and arterial road (DB), two typical classes of urban roads in Beijing. Obvious differences exist in the temporal variation characteristics of the traffic CO2 emissions between the expressway and arterial road at the annual, week and daily scales. The annual traffic CO2 emissions at the expressway (5R, with 47271.15 t) are more than ten times than those of the arterial road (DB, with 4139.19 t). Stronger weekly “rest effect” is observed at the expressway than the arterial road. The daily peak time and duration of the traffic CO2 emissions between the two classes of urban roads show significant differences particular in the evening peak. The differences of the structural characteristics between the two classes of urban roads are mainly reflected on the contribution of the public and freight transportation. Passenger vehicles play a predominant role at both the two classes of urban roads. The public transportation contributed more at DB (24.76%) than 5R (5.47%), and the freight transportation contributed more at 5R (23.41%) than DB (3.49%). The results suggest that the influence of traffic CO2 emissions on the CO2 flux is significant at the residential and commercial mixed underlying urban areas with arterial roads (DB) but not significant at the underlying urban park area with expressway (5R) in this study. The vegetation cover in urban areas have effects on the CO2 reduction. Increasing the design and construction of the green space along the urban roads with busy traffic flow will be an effective way to mitigate the urban traffic CO2 emissions and build the low-carbon cities.

CO2 emissions from fossil fuel combustion (FFCO2) are conventionally estimated from fuel used (as activity data (AD)) and CO2 emissions factor. Recent traffic emission changes under the impact of the COVID-19 pandemic have been estimated using emerging non-fuel consumption data, such as human mobility data that tech companies reported as AD, due to the unavailability of timely fuel statistics. The use of such unconventional activity data (UAD) might allow us to provide emission estimates in near-real time; however, the errors and uncertainties associated with such estimates are expected to be larger than those of common FFCO2 inventory estimates, and thus should be provided along with a thorough evaluation/validation of the methodology and the resulting estimates. Here, we show the impact of COVID-19 on traffic CO2 emissions over the first six months of 2020 in Japan. We calculated CO2 monthly emissions using fuel consumption data and assessed the emission changes relative to 2019. Regardless of Japan’s soft approach to COVID-19, traffic emissions significantly declined by 23.8% during the state of emergency in Japan (April–May). We also compared relative emission changes among different estimates available. Our analysis suggests that UAD-based emission estimates during April and May could be biased by −19.6% to 12.6%. We also used traffic count data for examining the performance of UAD as a proxy for traffic and/or CO2 emissions. We found the assumed proportional relationship between traffic changes and CO2 emissions was not enough for estimating emissions with accuracy, and moreover, the traffic-based approach failed to capture emission seasonality. Our study highlighted the challenges and difficulties in repurposing data, especially ones with limited traceability/reproducibility, for modeling human activities and assessing the impact on the environment, and the importance of a thorough error and uncertainty assessment before using these data in policy applications.

Artificial Intelligence (AI) tools based on Machine learning (ML) have demonstrated their potential in modeling climate-related phenomena. However, their application to quantifying greenhouse gas emissions in cities remains under-researched. Here, we introduce a ML-based bottom-up framework to predict hourly CO2 emissions from vehicular traffic at fine spatial resolution (30 × 30 m). Using data-driven algorithms, traffic counts, spatio-temporal features, and meteorological data, our model predicted hourly traffic flow, average speed, and CO2 emissions for passenger cars (PC) and heavy-duty trucks (HDT) at the street scale in Berlin. Even with limited traffic information, the model effectively generalized to new road segments. For PC, the Relative Mean Difference (RMD) was +16% on average. For HDT, RMD was 19% for traffic flow and 2.6% for average speed. We modeled seven years of hourly CO2 emissions from 2015 to 2022 and identified major highways as hotspots for PC emissions, with peak values reaching 1.639 kgCO2 m−2 d−1. We also analyzed the impact of COVID-19 lockdown and individual policy stringency on traffic CO2 emissions. During the lockdown period (March 15 to 1 June 2020), weekend emissions dropped substantially by 25% (−18.3 tCO2 day−1), with stay-at-home requirements, workplace closures, and school closures contributing significantly to this reduction. The continuation of these measures resulted in sustained reductions in traffic flow and CO2 emissions throughout 2020 and 2022. These results highlight the effectiveness of ML models in quantifying vehicle traffic CO2 emissions at a high spatial resolution. Our ML-based bottom-up approach offers a useful tool for urban climate research, especially in areas lacking detailed CO2 emissions data.

The transport sector is one of the most important and potential sectors to achieve low-carbon development in China. As economic growth is desirable, but high-level traffic CO2 emissions are not. This paper estimated the on-road traffic CO2 emissions and investigated the decoupling states of traffic CO2 emissions from economic growth for six cities in Hebei province from 1995 to 2015. In 2015, the on-road traffic CO2 emissions were ranked, as follows: Tangshan (4.75 Mt) > Handan (3.38 Mt) > Baoding (1.38 Mt) > Zhangjiakou (1.05 Mt) > Langfang (1.01 Mt) > Chengde (0.46 Mt). Two turning points of traffic CO2 emissions during the study period were found. From 2008 to 2013, the traffic CO2 emissions increased more rapidly than before. After 2013, the traffic CO2 emissions of three cities (Baoding, Handan and Chengde) began to decrease, and the traffic CO2 emissions’ growth rates of the other three cities (Zhangjiakou, Langfang and Tangshan) became lower than before. The decoupling states during 1996–2015 can be divided into four phases: decoupling-coupling concurrence stage (1996–2000), decoupling dominant stage (2001–2008), coupling dominant stage (2009–2013), and improvement stage (2014–2015). Chengde and Baoding were identified due to their good local practice on decoupling CO2 emissions in transport sector from economic growth. These results will enrich the greenhouse gas inventory of China at city level and provide scientific support to achieve the mitigation of CO2 emissions in the transport sector.

Simulation of urban transport carbon dioxide emission reduction environment economic policy in China: An integrated approach using agent-based modelling and system dynamics

Delay-feedback control strategy for reducing [formula omitted] emission of traffic flow system

Quantifying road traffic CO2 emissions is critical for urban climate and sustainability studies. However, detailed modeling often requires high-resolution input data that is unavailable in many regions. To address this gap, we present a simplified regression-based model that quantifies traffic-related CO2 emissions within Local Climate Zones (LCZs) using readily available data such as building surface area, asphalt surface area, population, traffic lights, and road type. This approach minimizes computational requirements and circumvents the need for traffic data, offering a practical alternative for regions with limited resources.Our results show that road type and asphalt surface area are the most influential variables in describing CO2 emissions. Median CO2 emissions from built LCZs are 1.8 times higher than those from land cover LCZs. The generalized model can explain up to 69% of the emissions for some LCZ. Based on this model, we introduce a look-up table for LCZ-specific traffic CO2 emissions, providing a user-friendly tool to estimate emissions in data-scarce regions. This simplified methodology emphasizes accessibility and efficiency while maintaining robust results, making it an invaluable resource for urban emission studies.

Quantifying the spillover elasticities of urban built environment configurations on the adjacent traffic CO2 emissions in mainland China

Achieving climate neutrality by 2050 requires ground-breaking technological and methodological advancements in climate change mitigation planning and actions from local to regional scales. Monitoring the cities' CO2 emissions with sufficient detail and accuracy is crucial for guiding sustainable urban transformation. Current methodologies for CO2 emission inventories rely on bottom-up (BU) approaches which do not usually offer information on the spatial or temporal variability of the emissions and present substantial uncertainties. This study develops a novel approach which assimilates direct CO2 flux observations from urban eddy covariance (EC) towers with very high spatiotemporal resolution information from an advanced urban BU surface flux model (Part 1 of this study, Stagakis et al., 2023) within a Bayesian inversion framework. The methodology is applied to the city centre of Basel, Switzerland (3 × 3 km domain), taking advantage of two long-term urban EC sites located 1.6 km apart. The data assimilation provides optimised gridded CO2 flux information individually for each urban surface flux component (i.e. building heating emissions, commercial/industrial emissions, traffic emissions, human respiration emissions, biogenic net exchange) at 20 m resolution and weekly time-step. The results demonstrate that urban EC observations can be consistently used to improve high-resolution BU surface CO2 flux model estimations, providing realistic seasonal variabilities of each flux component. Traffic emissions are determined with the greatest confidence among the five flux components during the inversions. The optimised annual anthropogenic emissions are 14.7 % lower than the prior estimate, the human respiration emissions have decreased by 12.1 %, while the biogenic components transformed from a weak sink to a weak source. The root-mean-square errors (RMSEs) of the weekly comparisons between EC observations and model outputs are consistently reduced. However, a slight underestimation of the total flux, especially in locations with complex CO2 source/sink mixture, is still evident in the optimised fluxes.

There are big differences between the driving behaviors of intelligent connected vehicles (ICVs) and traditional human-driven vehicles (HVs). ICVs will be mixed with HVs on roads for a long time in the future. Different intelligent functions and different driving styles will affect the condition of traffic flow, thereby changing traffic efficiency and emissions. In this paper, we focus on China’s expressways and secondary motorways, and the impacts of the ‘single-lane automatic driving system’ (SLADS) on traffic delay, road capacity and carbon dioxide (CO2) emissions were studied under different ICV penetration rates. Driving styles were regarded as important factors for scenario analysis. We found that with higher volume input, SLADS has an optimizing effect on traffic efficiency and CO2 emissions generally, which will be more significant as the ICV penetration rate increases. Additionally, enhancing the aggressiveness of driving behavior appropriately is an effective way to amplify the benefits of SLADS.

Curbing carbon dioxide (CO2) emissions from cities is critical for climate change mitigation given the substantial urban contribution to global anthropogenic greenhouse gas (GHG) emissions. Despite relatively robust inventory estimates of total urban CO2 emissions at regional and global scales, emission inventories at the level of individual cities can be very uncertain due to unavailability of input data and/or uncertainties in downscaling aggregated statistics or emissions. A growing field of research is thus investigating the application of atmospheric measurement and modelling to support CO2 emissions monitoring in cities. Here we present ongoing research comparing local emission inventories of the city of Vienna, Austria with tall tower eddy covariance measurements of CO2 fluxes. In contrast to inverse modelling methods, eddy covariance allows net surface emissions to be directly inferred from the  measured vertical turbulent fluxes in the surface layer above cities. For this analysis local emission inventories were processed with external data (e.g., measurements of local traffic counts and air temperature, proxies from literature) to produce temporally-resolved emissions maps (hour-hectare resolution) from the annual, aggregate inventory estimates for the years 2018 to 2020. For the comparison with the flux measurements, these emission maps were cropped after overlapping these layers with an average flux footprint calculated from flux measurements made during northwesterly flows, when the most densely inhabited districts of the city were sampled. On an annual scale, the flux measurements and inventory estimates of total CO2 emissions agree well with one another. Furthermore, encouraging results were obtained when comparing annual space-heating and traffic emissions from the inventories with respective estimates derived from regression analyses of the eddy fluxes against local air temperature and traffic counts. At sub-annual scales, seasonal and hourly divergences between the inventories and the eddy covariance measurements were indicative of boundary layer dynamics (decoupling between turbulent exchange and fluxes at the surface) as well as a seasonal influence of urban vegetation on net CO2 fluxes.

In this paper, the comparison of transportation CO2 characteristics and vehicle emission structure of two typical urban roads in Beijing are studied from the urban road scale. We found that traffic CO2 emissions from the North Fifth Ring Expressway is much higher than the Datun and Beichen West urban main road. In both of the two typical urban roads, private vehicles such as ordinary passenger cars and SUV constitute the main part of transport CO2 emissions. The percentage of CO2 emitted by trucks in the North Fifth Ring is higher than that in the main urban area while the percentage of CO2 emitted by public transport is lower than that in the main urban area. Based on this, some recommendations on transportation carbon emission reduction is proposed in this paper.

Exploring carbon dioxide (CO2) emissions from human activities is essential for urban energy conservation and resource management. Remotely sensed nighttime lights from the Suomi NPP-VIIRS provide spatial consistency in and a low-cost way of revealing CO2 emissions. Although many researches have documented the feasibility of the Suomi NPP-VIIRS data for assessing CO2 emissions, few have systematically revealed the ability of nighttime lights for evaluating CO2 emissions from different industries, such as service industry CO2 emissions (SC), traffic CO2 emissions (TC), and secondary industry CO2 emissions (IC). Here, China was selected as the experimental subject, and we comprehensively explored the relationships between the nighttime lights and SC, TC, and IC, and investigated the factors mediating these relationships. We found that without considering other factors, the nighttime lights only revealed up to 51.2% of TC, followed by 41.7% of IC and 22.7% of SC. When controlling for city characteristic variables, the models showed that there were positive correlations between the Suomi NPP-VIIRS data and SC, IC, and TC, and that nighttime lights have an Inverted-U relationship with SC. The Suomi NPP-VIIRS data are more suitable for revealing SC, TC, and IC in medium-sized and large-sized cities than in small-sized cities and megacities.

Temporal variability in the sources and fluxes of CO2 in a residential area in an evergreen subtropical city

CO is an indirect greenhouse gas because it reacts with OH, therefore increasing the lifetime of methane: its possible indirect radiative forcing has been estimated as larger than that of N2O. Previous studies have indicated that temperate and boreal forests act as a net sink for CO, but the role of tropical rain forest ecosystems has not been investigated. We present the first CO flux measurements from tropical forest and forest soils, and can show that tropical rain forests are a net source of CO to the atmosphere.During two intensive field campaigns at tropical rain forest fieldsite ZF2 (Manaus, Brazil), soil CO fluxes were determined by use of flux chambers. In addition, nighttime vertical CO concentration profiles were measured and different micro-meteorological techniques were applied to estimate ecosystem CO fluxes. Furthermore, we performed nocturnal CO concentration measurements in a seasonally inundated valley, which was hypothesized as a potential hotspot for ecosystem CO emissions.Soil CO fluxes ranged from -0.19 (net soil uptake) to 3.36 (net soil emission) nmol m-2 s-1, averaging ∼1 nmol CO m-2 s-1. Fluxes varied with season and topographic location, with highest fluxes measured in the dry season in a seasonally inundated valley. Nocturnal canopy air profiles show consistent decreases in CO mixing ratios with height, which requires positive surface fluxes between 0.3 and 2.0 nmol CO m-2 s-1. Similar fluxes are derived using a canopy layer budget method, which considered the nocturnal increase in CO over time (1.1 to 2.3 nmol CO m-2 s-1). Using wet season concentration profiles of CO, the estimated valley ecosystem CO production exceeded the measured soil valley CO fluxes, indicating a potential contribution of the valley stream to overall CO emissions.Based on our field observations, we expect that tropical rain forest ecosystems are a net source of CO. Extrapolating our first observation-based tropical rain forest soil emission estimate of ∼1 nmol m-2 s-1, a global tropical rain forest soil emission of ∼16.0 Tg CO yr-1 is suggested. Total ecosystem CO emissions might surpass this estimate, considering that valley streams and inundated areas could serve as local CO emission hotspots. To further improve tropical forest ecosystem CO emission estimates, more in-situ tropical forest soil and ecosystem CO flux measurements are essential.