Constraining Urban CO2 Emissions Using Mobile Observations from a Light Rail Public Transit Platform

Spatial mapping of greenhouse gases using a UAV monitoring platform over a megacity in China

Urban environments are recognized as main anthropogenic contributors to greenhouse gas (GHG) emissions, characterized by unevenly distributed emission sources over the urban environments. However, spatial GHG distributions in urban regions are typically obtained through monitoring at only a limited number of locations, or through model studies, which can lead to incomplete insights into the heterogeneity in the spatial distribution of GHGs. To address such information gap and to evaluate the spatial representation of a planned GHG monitoring network, a custom-developed atmospheric sampler was deployed on a UAV platform in this study to map the CO2 and CH4 mixing ratios in the atmosphere over Zhengzhou in central China, a megacity of nearly 13 million people. The aerial survey was conducted along the main roads at an altitude of 150 m above ground, covering a total distance of 170 km from the city center to the suburbs. The spatial distributions of CO2 and CH4 mixing ratios in Zhengzhou exhibited distinct heterogeneities, with average mixing ratios of CO2 and CH4 at 439.2 ± 10.8 ppm and 2.12 ± 0.04 ppm, respectively. A spatial autocorrelation analysis was performed on the measured GHG mixing ratios across the city, revealing a spatial correlation range of approximately 2 km for both CO2 and CH4 in the urban area. Such a spatial autocorrelation distance suggests that the urban GHG monitoring network designed for emission inversion purposes need to have a spatial resolution of 4 km to characterize the spatial heterogeneities in the GHGs. This UAV-based measurement approach demonstrates its capability to monitor GHG mixing ratios across urban landscapes, providing valuable insights for GHG monitoring network design.

Efficient Pt/KFI zeolite catalysts for the selective catalytic reduction of NOx by hydrogen

Aiming at purification of NOx from hydrogen internal combustion engines (HICEs), the hydrogen selective catalytic reduction (H2-SCR) reaction was investigated over a series of Pt/KFI zeolite catalysts. H2 can readily reduce NOx to N2 and N2O while O2 inhibited the deNOx efficiency by consuming the reductant H2. The Pt/KFI zeolite catalysts with Pt loading below 0.1 wt.% are optimized H2-SCR catalysts due to its suitable operation temperature window since high Pt loading favors the H2-O2 reaction which lead to the insufficient of reactants. Compared to metal Pt0 species, Ptδ+ species showed lower activation energy of H2-SCR reaction and thought to be as reasonable active sites. Further, Eley-Rideal (E-R) reaction mechanism was proposed as evidenced by the reaction orders in kinetic studies. Last, the optimized reactor was designed with hybrid Pt/KFI catalysts with various Pt loading which achieve a high NOx conversion in a wide temperature range.

Ground-Based Mobile Measurements to Track Urban Methane Emissions from Natural Gas in 12 Cities across Eight Countries

To mitigate methane emission from urban natural gas distribution systems, it is crucial to understand local leak rates and occurrence rates. To explore urban methane emissions in cities outside the U.S., where significant emissions were found previously, mobile measurements were performed in 12 cities across eight countries. The surveyed cities range from medium size, like Groningen, NL, to large size, like Toronto, CA, and London, UK. Furthermore, this survey spanned across European regions from Barcelona, ES, to Bucharest, RO. The joint analysis of all data allows us to focus on general emission behavior for cities with different infrastructure and environmental conditions. We find that all cities have a spectrum of small, medium, and large methane sources in their domain. The emission rates found follow a heavy-tailed distribution, and the top 10% of emitters account for 60-80% of total emissions, which implies that strategic repair planning could help reduce emissions quickly. Furthermore, we compare our findings with inventory estimates for urban natural gas-related methane emissions from this sector in Europe. While cities with larger reported emissions were found to generally also have larger observed emissions, we find clear discrepancies between observation-based and inventory-based emission estimates for our 12 cities.

Exploring the discrepancy between top-down and bottom-up approaches of fine spatio-temporal vehicular CO2 emission in an urban road network

Road transport emissions of high spatial and temporal resolution are useful for greenhouse gas emission assessment in local action plans. However, estimating these high-resolution emissions is not straightforward, and different indirect approaches exist. The main aim of this study is to examine the differences in CO2 emissions obtained with different methods within a street canyon network in Helsinki, Finland, where a mobile laboratory campaign to quantify traffic emissions has been conducted. We compared three aerodynamic resistance based top-down methods (MOST1, MOST2 and BHT) and three activity based bottom-up microscopic emission models (NGM, HBEFAv4.2 and PHEMlight). The resulted CO2 fluxes using different methods could vary a few orders of magnitude. The combination of MOST1 and NGM model leads to the smallest discrepancy (sMAPE = 16.90 %) and the highest correlation coefficient (r = 0.78) among the rest. We evaluated the discrepancies in terms of different spatial (microenvrionments, local climate zones LCZs and grid sizes) and temporal features (seasons and periods of day). Measurements taken in LCZs of open high-rise regions and microenvironments of main road tend to have larger discrepancies between the two approaches. Using a coarser grid would lead to a relatively small discrepancy and high correlation in the wintertime, yet a loss in distinctive spatial variation. The discrepancies were also elevated on winter evenings. Among all explanatory variables, relative humidity shows the strongest relative importance for the discrepancy of the two approaches, followed by LCZs. Therefore, we stress the importance of choosing a suitable model for vehicular CO2 emission calculation based on meteorological conditions and LCZs. Such model comparison made on a local scale directly supports environmental organisations and cities' climate action plans where detailed information of CO2 emissions are needed.

Low-cost urban carbon monitoring network and implications for china: a comprehensive review

The development and renewal of gas sensor technology have enabled more and more low-cost gas sensors to form a carbon monitoring network to meet the requirements of the city. In the context of China's commitment to achieving the "double carbon" target by 2060, this paper reviews the principles of four standard gas sensors and the application of several low-cost sensors in urban carbon monitoring networks, with the aim of providing a practical reference for the future deployment of carbon monitoring networks in Chinese cities. Moreover, the types, prices, and deployment of the sensors used in each project are summarized. Based on this review, non-dispersive infrared sensors have the best performance among the sensors and are commonly used in many cities. Lots of urban climate networks in cities were summarized by many reviews in the literature, but only a few sensors were studied, and they did not consider carbon dioxide (CO2) sensors. This review focuses on the dense CO2 urban monitoring network, and some case studies are also discussed, such as Seoul and San Francisco. To address the issue of how to better ensure the balance between cost and accuracy in the deployment of sensor networks, this paper proposes a method of simultaneously deploying medium-precision and high-precision fixed sensors and mobile sensors to form an urban carbon monitoring network. Finally, the prospects and recommendations, such as different ways to mitigate CO2 and develop an entire carbon monitoring system for future urban carbon monitoring in China, are also presented.

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.

"Urban Respiration" Revealed by Atmospheric O2 Measurements in an Industrial Metropolis

Urban regions, which "inhale" O₂ from the air and "exhale" CO₂ and atmospheric pollutants, including harmful gases and fine particles, are the largest sinks of atmospheric O₂, yet long-term O₂ measurements in urban regions are currently lacking. In this study, we report continuous measurements of atmospheric O₂ in downtown Lanzhou, an industrial metropolis in northwestern China. We found declines in atmospheric O₂ associated with deteriorated air quality and robust anticorrelations between O₂ and gaseous oxides. By combining O₂ and pollutants measurements with a Lagrangian atmospheric transport model, we quantitatively break down "urban respiration" (ΔO_2URB) into human respiration (ΔO_2RES) and fossil fuel combustion (ΔO_2FF). We found increased ΔO_2FF contribution (from 66.92% to 72.50%) and decreased ΔO_2RES contribution (from 33.08 to 27.50%) as O₂ declines and pollutants accumulate. Further attribution of ΔO_2FF reveals intracity transport of atmospheric pollutants from industrial sectors and suggests transportation sectors as the major O₂ sink in downtown Lanzhou. The varying relationships between O₂ and pollutants under different conditions unfold the dynamics of urban respiration and provide insights into the O₂ and energy consumption, pollutant emission, and intracity atmospheric transport processes.

Flame-Made La2O3-Based Nanocomposite CO2 Sensors as Perspective Part of GHG Monitoring System

Continuous monitoring of greenhouse gases with high spatio-temporal resolution has lately become an urgent task because of tightening environmental restrictions. It may be addressed with an economically efficient solution, based on semiconductor metal oxide gas sensors. In the present work, CO₂ detection in the relevant concentration range and ambient conditions was successfully effectuated by fine-particulate La₂O₃-based materials. Flame spray pyrolysis technique was used for the synthesis of sensitive materials, which were studied with X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), diffuse reflectance infrared Fourier transform spectroscopy (DRIFTs) and low temperature nitrogen adsorption coupled with Brunauer–Emmett–Teller (BET) effective surface area calculation methodology. The obtained materials represent a composite of lanthanum oxide, hydroxide and carbonate phases. The positive correlation has been established between the carbonate content in the as prepared materials and their sensor response towards CO₂. Small dimensional planar MEMS micro-hotplates with low energy consumption were used for gas sensor fabrication through inkjet printing. The sensors showed highly selective CO₂ detection in the range of 200–6667 ppm in humid air compared with pollutant gases (H₂ 50 ppm, CH₄ 100 ppm, NO₂ 1 ppm, NO 1 ppm, NH₃ 20 ppm, H₂S 1 ppm, SO₂ 1 ppm), typical for the atmospheric air of urbanized and industrial area.

Wintertime Nitrous Oxide Emissions in the San Joaquin Valley of California Estimated from Aircraft Observations

Nitrous oxide (N₂O) is a long-lived greenhouse gas that also destroys stratospheric ozone. N₂O emissions are uncertain and characterized by high spatiotemporal variability, making individual observations difficult to upscale, especially in mixed land use source regions like the San Joaquin Valley (SJV) of California. Here, we calculate spatially integrated N₂O emission rates using nocturnal and convective boundary-layer budgeting methods. We utilize vertical profile measurements from the NASA DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) campaign, which took place January-February, 2013. For empirical constraints on N₂O source identity, we analyze N₂O enhancement ratios with methane, ammonia, carbon dioxide, and carbon monoxide separately in the nocturnal boundary layer, nocturnal residual layer, and convective boundary layer. We find that an established inventory (EDGAR v4.3.2) underestimates N₂O emissions by at least a factor of 2.5, that wintertime emissions from animal agriculture are important to annual totals, and that there is evidence for higher N₂O emissions during the daytime than at night.