Spatial distribution of greenhouse gases (CO2 and CH4) on expressways in the megacity Shanghai, China

Frequency division multiplexing and wavelength stabilized 2f/1f wavelength modulation spectroscopy for simultaneous trace CH4 and CO2 detection

A near-infrared (NIR) dual-gas sensor has been developed for simultaneous detection of atmospheric trace methane (CH4) and carbon dioxide (CO2). To realize high sensitivity and high precision, wavelength modulation spectroscopy with 2f/1f (WMS-2f/1f) detection method was adopted for eliminating laser light intensity fluctuation, and laser wavelength locking strategy based on a self-developed proportion integration differentiation (PID) algorithm was used for suppressing laser wavelength shifting effect. Two fiber-coupled DFB diode lasers with central wavelengths near 1653.7 nm and 1579.6 nm are applied for simultaneously measuring CH4 and CO2 spectra, respectively, and frequency division multiplexing (FDM) technique is employed to resolve the potential crosstalk effect. Real-time measurement of ambient atmospheric trace CH4 and CO2 was performed to demonstrate the long-term stability of the sensor system. Allan deviation analysis indicates that detection sensitivity of 0.1 ppm for CH4 and 2.27 ppm for CO2 was achieved with a 1 s average time, which can be further improved to 18 ppb and 0.3 ppm with the optimal integration time of 462 s and 392 s, respectively.

Driving factors and clustering analysis of expressway vehicular CO2 emissions in Guizhou Province, China

Expressways are essential for intercounty trips of passenger travel and freight mobility, which are also an important source of vehicular CO2 emissions in transportation sector. This study takes the expressway system of Guizhou Province as the research objective, and establishes the multi-year expressway vehicular CO2 emission inventories at the county level from 2011 to 2019. We employ the extended STIRPAT model incorporating ridge regression to identify driving factors from six different aspects, and then utilize the affinity propagation cluster method to conduct the differentiation research by dividing Guizhou's counties into four clusters. Based upon clustering analysis, localized and targeted policies are formulated for each cluster to reduce expressway vehicular CO2 emissions. The results indicate that generally: (1) Guizhou's expressway vehicular CO2 emissions manifest a continuously upward trend during 2011-2019. Small-duty passenger vehicle (SDV), light-duty truck (LDT), and heavy-duty truck (HDT) contribute to the largest CO2 emissions in eight vehicle types. (2) GDP and population are the foremost two positive driving factors, followed by urbanization rate and expressway length. The proportion of secondary industry is also a positive driver, but that of tertiary industry exhibits an opposite effect. (3) Regional disparity exists in four county clusters of Guizhou Province. Efficient policies are proposed, such as improving the layout and infrastructure of transportation hubs, promoting multimodal integration, and implementing industrial upgrading as per regional advantages. Sustainable expressway vehicular CO2 emission reduction is realized from both the source of industry and low-carbon modes of transport.

Carbon Sequestration Potential of Biomass Production along Highways in China

In response to climate change, China is making great efforts to increase the green area for carbon sequestration. Road verges, as marginal land with favorable conditions for plant growth and ease of transportation, can be used for biomass production, but the biomass production and carbon sequestration potential have not been assessed. Here, we mapped the biomass production potential of road verges in China by combining a biomass model and Geographic Information System and then evaluated the effect of road runoff and CO2 fertilization on the production according to the runoff coefficient and vehicle emission inventory. Nationwide, road verges can produce 15.86 Mt C yr-1 of biomass. Road runoff contributes to a biomass production of 1.26 Mt C yr-1 through increasing soil water availability, which mainly occurs in arid regions. The CO2 fertilization effect by vehicle emission is considerable in Eastern and Southern China, contributing to a production of 0.09 Mt C yr-1. Life cycle assessment shows that major road verges in China have a carbon sequestration potential of 6.87 Mt C yr-1 currently. Our results revealed that road verges can make a significant contribution to carbon neutrality under proper management.

Machine learning based estimation of urban on-road CO2 concentration in Seoul

The urban on-road CO2 emissions will continue to increase, it is therefore essential to manage urban on-road CO2 concentrations for effective urban CO2 mitigation. However, limited observations of on-road CO2 concentrations prevents a full understanding of its variation. Therefore, in this study, a machine learning-based model that predicts on-road CO2 concentration (CO2traffic) was developed for Seoul, South Korea. This model predicts hourly CO2traffic with high precision (R2 = 0.8 and RMSE = 22.9 ppm) by utilizing CO2 observations, traffic volume, traffic speed, and wind speed as the main factors. High spatiotemporal inhomogeneity of hourly CO2traffic over Seoul, with 14.3 ppm by time-of-day and 345.1 ppm by road, was apparent in the CO2traffic data predicted by the model. The large spatiotemporal variability of CO2traffic was related to different road types (major arterial roads, minor arterial roads, and urban highways) and land-use types (residential, commercial, bare ground, and urban vegetation). The cause of the increase in CO2traffic differed by road type, and the diurnal variation of CO2traffic differed according to land-use type. Our results demonstrate that high spatiotemporal on-road CO2 monitoring is required to manage urban on-road CO2 concentrations with high variability. In addition, this study demonstrated that a model using machine learning techniques can be an alternative for monitoring CO2 concentrations on all roads without conducting observations. Applying the machine learning techniques developed in this study to cities around the world with limited observation infrastructure will enable effective urban on-road CO2 emissions management.

Vehicle cabin air quality: influence of air recirculation on energy use, particles, and CO2

In this study, simulations were performed to investigate the influence of different vehicle climate ventilation strategies, mainly the air recirculation (REC) degree, on the cabin air quality and climate system power. The focus of air quality is on the cabin particle concentrations including PM2.5 (particles of aerodynamic diameter less than 2.5 μm), UFP (ultrafine particles of aerodynamic diameter less than 100 nm), and cabin CO2 concentration. Three outside climates (cold, intermediate, and warm) and three outside particle concentrations are studied. The studied vehicle originally shows possibilities to meet WHO PM2.5 guideline of 15 μg/m3 with a new filter. The aged filter have reduced performance, especially when outside concentration is high. Increased REC shows advantages in all the three climates in reducing particles and climate power for the studied vehicle. Application of 70% REC (70% of ventilation air is recirculated air) on average lowers PM2.5 by 55% and 39% for a new and aged filter, respectively. 70% REC with a new filter reduces cabin PM2.5 below guideline of 15 μg/m3 in all conditions. The reduction of UFP counts results are generally similar to that of PM2.5. Increased REC also lessens the average climate system power by up to 27% on average. When REC is increased, the cabin CO2 concentration arises accordingly, and the magnitude is relevant to the passengers. In all studied conditions with 1 passenger, 70% REC does not increase CO2 above the common guideline of 1000 ppm. 70% REC is not recommended with more than 1 passengers in cold and intermediate climate and 2 passengers in warm climate. Besides, to avoid the potential windscreen fog risk in cold climate, REC should be avoided when passengers are more than 3. Except for constant REC values, a sample study investigates a dynamic control of the REC. It shows the possibility of continuously optimizing REC to reduce the climate power and particles, while maintaining the CO2 concentration below 1000 ppm. In warm climate with 1 passenger boarded, the average optimized REC is 90%, which in comparison with base case lead to 44% PM2.5 reduction and 12% climate power reduction.

Which type of electric vehicle is worth promoting mostly in the context of carbon peaking and carbon neutrality? A case study for a metropolis in China

Electric vehicles (EVs) have been promoted acceleratively to reduce greenhouse gas (GHG) emissions, however, the GHG emission reduction potential of different powertrain EVs has not been investigated thoroughly. In this study, we firstly quantified and compared the GHG emissions of different powertrain vehicles in a life cycle perspective with particular focus on energy and climate consequences, for current and future integrated scenarios, to facilitate carbon reduction assessment for Shanghai. Four major types of EVs were considered. The results show that life cycle total energy consumption and GHG emissions of all EVs are lower than that of gasoline internal combustion engine vehicles (GICEVs), among which battery-powered electric vehicles (BEVs) is the lowest. Compared with GICEVs, the total energy use and GHG emissions of BEVs decrease by 34.2% and 41.7% respectively. As the electrification of vehicle powertrain system innovates, the life cycle emissions of GHG are gradually concentrated to the upstream stage. The sensitivity analysis demonstrates that life cycle GHG emissions of vehicles are most sensitive to the proportion of thermal power than other three parameters (utilization rate of recycled steel, vehicle lifetime and curb weight). The scenario analysis indicates that BEVs present the more favorable carbon emission decline performance over other EVs from a long-term perspective. It is estimated that up to 12.5 million tons of GHG emissions could be reduced under the optimistic scenario in 2050 in Shanghai. In the process of energy conversion from oil to electricity in transport in Shanghai, BEVs should be constantly promoted.

Size-resolved simulation of particulate matters and CO2 concentration in passenger vehicle cabins

The main aim of this study is to develop a mathematical size-dependent vehicle cabin model for particulate matter concentration including PM_2.5 (particles of aerodynamic diameter less than 2.5 μm) and UFPs (ultrafine particles of aerodynamic diameter less than 100 nm), as well as CO₂ concentration. The ventilation airflow rate and cabin volume parameters are defined from a previously developed vehicle model for climate system design. The model simulates different filter statuses, application of pre-ionization, different airflow rates and recirculation degrees. Both particle mass and count concentration within 10-2530 nm are simulated. Parameters in the model are defined from either available component test data (for example filter efficiencies) or assumptions from corresponding studies (for example particle infiltration and deposition rates). To validate the model, road measurements of particle and CO₂ concentrations outside two vehicles were used as model inputs. The simulated inside PM_2.5, UFP and CO₂ concentration were compared with the inside measurements. Generally, the simulation agrees well with measured data (Person's r 0.89-0.92), and the simulation of aged filter with ionization is showing higher deviation than others. The simulation using medium airflows agrees better than the simulation using other airflows, both lower and higher. The reason for this may be that the filter efficiency data used in the model were obtained at airflows close to the medium airflow. When all size bins are compared, the sizes of 100-300 nm were slightly overestimated. The results indicated that among others, expanded filter efficiency data as a function of filter ageing and airflow rate would possibly enhance the simulation accuracy. An initial application sample study on recirculation degrees presents the model's possible application in developing advanced climate control strategies.

Analysis of vehicular CO2 emission in the Central Plains of China and its driving forces

The Central Plains of China, represented by Henan province, faces a dramatic rise in vehicular stock and CO₂ emissions. The refined-resolution(1 km × 1 km) vehicular CO₂ emission inventory for Henan province was developed to identify emission patterns. Results show that CO₂ emissions in Henan province reached 77.04 Mt in 2019, and LDGV and HDDT were the major sources that emitted 42.34% and 35.96% of CO₂ emissions, respectively. Based on gridded emission, Moran's Index was used to identify spatial distribution patterns of vehicular CO₂. The higher CO₂ emission intensity areas were concentrated in the central and northern of the province and urban areas in each city, especially in Zhengzhou and its surrounding cities. Moreover, the analysis of the driving forces behind the differences in emissions among cities using the multi-regional (M-R) spatial decomposition model revealed that income and population-scale are significant impacts. In cities such as Zhengzhou, emissions may be dramatically increase owing to high economic growth expectations. 'Polarization phenomenon' of CO₂ emission distribution should be vigilant. Findings provided insights for refined policy-making in Henan province to limit CO₂ emission: (1) Take cities as transportation hubs, e.g., Zhengzhou and Shangqiu, and that in the traffic radiation circle, e.g., Jiaozuo and Zhoukou, as the critical areas for CO₂ emission reduction; (2) Promote electric vehicles as replacement for traditional fuel vehicles; especially for cities with large passenger car emissions, such as Zhengzhou, and cities with large truck emissions, such as Shangqiu and Zhoukou; actively guide new consumer groups to choose EVs, especially in cities with high growth expectations such as Zhengzhou; (3) Rely on the advantages of transportation network to promote the 'road to railway' of bulk cargo transportation and mainly focus on highways with higher CO₂ density, such as Beijing-Hong Kong&Macao Expressway, Shanghai-Xi'an Expressway, Da Guang Expressway, and Lian Huo Expressway.

Plant-based remediation of air pollution: A review

Humans face threats from air pollutants present in both indoor and outdoor environments. The emerging role of plants in remediating the atmospheric environment is now being actively investigated as a possible solution for this problem. Foliar surfaces of plants (e.g., the leaves of cotton) can absorb a variety of airborne pollutants (e.g., formaldehyde, benzene, trimethylamine, and xylene), thereby reducing their concentrations in indoor environments. Recently, theoretical and experimental studies have been conducted to offer better insights into the interactions between plants and the surrounding air. In our research, an overview on the role of plants in reducing air pollution (often referred to as phytoremediation) is provided based on a comprehensive literature survey. The major issues for plant-based research for the reduction of air pollution in both outdoor and indoor environments are discussed in depth along with future challenges. Analysis of the existing data confirms the effectiveness of phytoremediation in terms of the absorption and purification of pollutants (e.g., by the leaves and roots of plants and trees), while being controlled by different variables (e.g., pore characteristics and planting patterns). Although most lab-scale studies have shown that plants can effectively absorb pollutants, it is important for such studies to reflect the real-world conditions, especially with the influence of human activities. Under such conditions, pollutants are to be replenished continually while the plant surface area to ambient atmosphere volume ratio vastly decreases (e.g., relative to lab-based experiments). The replication of such experimental conditions is the key challenge in this field of research. This review is expected to offer valuable insights into the innate ability of various plants in removing diverse pollutants (such as formaldehyde, benzene, and particulate matter) under different environmental settings.