Yuxuan Li1, Xinli Cai2, Mingjun Li1, Zhen Jiang3, Feifei Tang3, Shaojie Zhang4,5, Taotao Shui This email address is being protected from spambots. You need JavaScript enabled to view it.1,4,6, Shuguang Zhu This email address is being protected from spambots. You need JavaScript enabled to view it.7,8 

1 School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230000, China
2 Anhui Institute of Urban-Rural Green development and Urban Renewal, Hefei 230022, China
3 Huangshan Tourism Development Co., Ltd., Huangshan 245000, China
4 BIM Engineering Center of Anhui Province, Hefei 230022, China
5 School of Architecture & Urban Planning, Anhui Jianzhu University, Hefei 230022, China
6 Anhui Advanced Technology Research Institute of Green Building, Anhui Jianzhu University, Hefei 230601, China
7 Engineering Research Center of Building Energy Efficiency Control and Evaluation,Ministry of Education, Anhui Jianzhu University, Hefei 230601, China
8 Anhui Institute of Strategic Study on Carbon Dioxide Emissions Peak and Carbon Neutrality in Urban-Rural Development, Anhui Jianzhu University, Hefei 230601, China


Received: October 12, 2023
Revised: December 9, 2023
Accepted: December 11, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.230245  

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Cite this article:

Li, Y., Cai, X., Li, M., Jiang, Z., Tang, F., Zhang, S., Shui, T., Zhu, S. (2024). Review of the Urban Carbon Flux and Energy Balance Based on the Eddy Covariance Technique. Aerosol Air Qual. Res. 24, 230245. https://doi.org/10.4209/aaqr.230245


HIGHLIGHTS

  • The surface energy balance and CO2 flux in the past 14 years were reviewed.
  • The occurrence frequency of research contents was analyzed by using Python.
  • The various factors of surface energy balance and CO2 flux were summarized.
  • The calculation models for both research directions are importance in the future.
 

ABSTRACT


In recent years, notable climate change has prompted more scholars to give more attention to urban climate development. Recognized as the most accurate flux measurement method, the eddy covariance (EC) technique is widely used in the direct observation of CO2 and energy fluxes. Therefore, to better summarize and describe relevant research, this paper was based on all 127 studies in the Web of Science (WOS) that employed the EC technique to observe urban CO2 and energy fluxes and related auxiliary research from 2009 to 2022, and we further selected other classic studies to provide theoretical support to analyze CO2 fluxes, radiation, sensible heat, latent heat, anthropogenic heat and storage heat, which from the two aspects of the CO2 fluxes and surface energy balance (SEB). The variation characteristics of CO2 and energy fluxes were obtained by comparing observation data from various regions at different time scales, and the mechanism of flux change was explained based on the main influencing factors, thereby providing a general summary of the impact of urban heterogeneity. The results show that carbon emissions in urban areas are highly cyclical and vary greatly at different times and seasons. Due to the many factors affecting urban carbon emissions, urban carbon reduction cannot be limited to vegetation research. In addition, the heterogeneity of the underlying surface in urban areas has a great influence on the energy flux parameters. Therefore, it is necessary to improve the energy calculation models to more accurately assess SEB in urban areas. And the long-term observations at different locations are needed. In addition, Python and other means were used to analyze the occurrence frequency of important research objects and methods in the literature, and the research hotspots and correlations between regions were visualized, which enables readers to better understand the field of urban climate.


Keywords: Urban climate, CO2 fluxes, Energy balance, Eddy covariance


1 INTRODUCTION


With the development and expansion of cities, modern urban built-up areas contain high-density building complexes, and the urban climate has tremendously changed. The urban heat island (UHI) phenomenon is becoming increasingly serious in general, and this phenomenon has become more prominent in many cities worldwide. With the use of China as an example, if the high-temperature weather conditions in Chongqing, Wuhan and Nanjing in summer are affected by large-scale climate and geographical factors such as the Western Pacific Subtropical High, the extreme weather events in Fuzhou, Xi'an and other cities in recent years require us to consider the role of the UHI phenomenon. Although the urban thermal environment involves a comprehensive process, the UHI phenomenon has become one of the factors that cannot be ignored. The urbanization process has disrupted the original carbon cycle, and 2022 could be the fifth or sixth hottest year on record (WMO, 2022), with Antarctic glacier monitoring data showing that the glacier area also reached a 44-year low in July. The highest surface air temperature in the UK, France, Portugal, Spain and other regions exceeded the highest recorded value, and the problem of the greenhouse effect is imminent.

The urban microclimate concept was proposed by Luke Howard (in his book London Climate), and scientists such as Timothy R. Oke established a systematic theoretical research system. Urban microclimate research includes energy, hydrology, pollutants, and greenhouse gases, and in this paper, we mainly review the CO2 fluxes and energy. The principle of energy conservation constitutes the basis of our research on the thermal environment; the surface energy balance in urban areas (Oke, 1987) can be expressed as follows:

 

where Q* is the net all-wave radiation, QF is the anthropogenic heat flux, QH is the sensible heat flux, QE is the latent heat flux, ΔQS is the storage heat flux, and ΔQA is the net energy exchanges via vertical or horizontal advection. Among these parameters, Q* is measured with radiometers, and QH and QE are measured via the eddy covariance (EC) technique. Moreover, EC can refine the research scale to ensure the measurement and estimation of each item, while some of the more complex constituent factors can be analyzed using the energy balance model. For example, QF can be subdivided into the above equations using the Large-scale Urban Consumption of energy (LUCY) model (Allen et al., 2011), where QV is the thermal energy generated by fossil fuel combustion in vehicles, QB is the thermal energy released by buildings, and QM is the thermal energy generated by human metabolism. ΔQS in the equation can be obtained via objective hysteresis model (OHM) calculation or the energy balance remainder method. In most cases, ΔQA can be ignored because of its relatively low value.

The carbon cycle is the ecological basis for the study of CO2, and urban CO2 research is no exception, the calculation method of Feigenwinter et al. (2012) is generally used to analyze the carbon source and carbon sink of urban surface carbon flux:

 

where FC is the CO2 flux; ER, EB, RE and GPP denote CO2 resulting from fossil fuel combustion, CO2 resulting from building heating, CO2 resulting from soil and human respiration, and CO2 consumed for photosynthesis, respectively, and NBE is the net CO2 exchange of biomes. In many urban case studies, the function of cities as carbon sources has been recognized. From an urban carbon inventory perspective, this is due to the excessive CO2 input, namely, the high input of carbon-containing substances such as fossil fuels (natural gas, gasoline, and diesel), building materials, and food increases the pressure on the already fragile ecosystems of cities. Finally, carbon-containing substances are emitted into the atmosphere as vertical fluxes through human actions such as combustion. The CO2 fluxes are the characteristic monitoring parameter for urban CO2 emissions, which can suitably reflect the specific CO2 content in the underlying surface and boundary layer of a given city. Compared with the inventory method, EC measurement has the advantages of being more direct, accurate and convenient.

With the advent of advanced sensors and computer storage devices, the EC technique has been recognized as the most accurate flux measurement method. In this paper, the literature on this topic was retrieved from the WOS from 2009 to 2022, yielding a total of 127 studies (Fig. 1), all of which are based on the EC technique. Moreover, the research contents include the energy balance or CO2, which were considered in 43% and 69% (Fig. 2) of the collected articles (in some articles, both aspects were analyzed). Although 127 studies were collected from WOS for this research, the analysis presented in this article is not limited to these in its analysis127 studies. In addition, based on the Python language, the occurrence frequency of certain fields worthy of attention in the literature was determined, and the factors influencing the energy balance and CO2 fluxes were combined to produce a frequency analysis graph (Fig. 3). With the use of the frequency analysis graph, the research content and ideas in this field can be understood more clearly, and the current research hot spots in this field can be better identified.

Fig. 1. Global site distribution of the literature collected from the Web of Science (the same color is adopted for the same country).Fig. 1. Global site distribution of the literature collected from the Web of Science (the same color is adopted for the same country).

Fig. 2. Number of research articles in the two fields from 2009 to 2022.   Fig. 2. Number of research articles in the two fields from 2009 to 2022.
 

Fig. 3. Python is used to crawl 127 articles, count keywords and frequencies and construct a research hotspot content network.Fig. 3. Python is used to crawl 127 articles, count keywords and frequencies and construct a research hotspot content network.

At present, global urban EC flux monitoring research is mainly concentrated in developed regions such as North America and Europe. Fig. 1 shows that most study sites are located in the mid-latitude region between 20°N and 40°N. Of all the energy- or carbon flux-related articles collected, the number of study areas at mid-latitudes accounted for 95% of the total study areas. Moreover, there were very few studies in high- and low-latitude regions. The climate and underlying surface characteristics of countries at different latitudes vary, so strengthening flux monitoring research considering various urban underlying surface and climate characteristics is conducive to increasing the understanding and interpretation of the effect of the urban development process on the heat island and greenhouse effects (Velasco and Roth, 2010). The notable regularity and heterogeneity of energy balance and CO2 fluxes on temporal and spatial scales reflect the significant impact of underlying urban surfaces and anthropogenic activities on the urban microclimate. However, in current urban research, the duration of flux monitoring is generally short, and the research monitoring time is largely less than one year, but short-term flux observations cannot suitably reflect regional flux change trends on a seasonal or annual scale and only provide limited guidance for long-term urban construction (Grimmond, 2006; Grimmond et al., 2010).

 
2 METHODS


The studies referenced in this paper are based on the EC technique for the measurement of urban thermal environmental and CO2 data. Human monitoring and simulation of urban climate conditions and greenhouse gases have evolved. Wind tunnel tests using scaled physical models, numerical simulations of atmospheric material transport processes, and sensor-based measurement of underlying surfaces and atmospheric features have all been successfully applied in the determination of urban characteristics. With the advent of advanced sensors and computer storage devices, the EC technique has become an indispensable tool in surface-atmosphere exchange research (Rinne and Ammann, 2012). The establishment of fixed stations has facilitated long-term urban flux observation and enabled real-time monitoring and long-term analysis of urban temperature, humidity, wind speed and direction, precipitation, surface temperature, net all-wave radiation (including up- and downward long- and shortwave radiation), turbulent flux and other factors (Oke, 2006). The general principle of the EC technique is the measurement of the flux of a given target parameter by calculating the covariance between the concentration of the target parameter and the vertical wind speed. From a physics perspective, to obtain the increase in the number of target molecules with the vortex at time 1 and the decrease in the number of target molecules with the vortex at time 2, the vertical flux of the target parameter can be measured at this point during the monitoring period (Burba, 2013).

In urban climate research, division and analysis are generally conducted at three scales, namely, the microscale, local scale and mesoscale, which correspond to the single element scale of the city, the city block scale and the overall city scale, respectively. Notably, the urban climate is also affected by large-scale atmospheric movement (Oke et al., 2017). The EC technique-based research articles in this paper are mainly related the city block scale. Due to the complex underlying surface of cities, a unique boundary layer structure is often formed in urban areas from the perspective of the vertical climate structure. From the surface upward, the urban canopy, roughness sublayer, inertial sublayer, and mixed layer can be distinguished (Arya, 2001). The height of the urban surface is mainly determined by the height of buildings. The section below the urban element height ZH, is referred to as the urban canopy, which is the space for frequent human activities and energy, water vapor and momentum exchange processes. In the urban canopy, buildings are the source of a dry heat plume, creating most of the thermal turbulence. The roughness and inertial sublayers occupy the remaining space of the near-ground layer except the urban canopy, intense and complex turbulent exchange and heat transfer occur between the roughness sublayer and urban canopy, and the height of the roughness sublayer determines the height of the inertial sublayer. The inertial sublayer basically exists in a uniform and stable state along both the horizontal and vertical directions because of its inherent layered structure, and under the assumption that the underlying surface is uniform and the area is sufficiently wide, the turbulence and heat flux in the inertial sublayer do not greatly fluctuate horizontally, so the inertial sublayer is also referred to as the constant-flux layer. In most urban thermal environment and CO2 studies, instruments are required for measurement activities in the inertial sublayer to obtain relatively accurate urban atmospheric environmental data.

The flux footprint can be regarded as the field of view of the measuring instrument. The EC instrument should be installed at a suitable observation position (i.e., the inertial sublayer). In the inertial sublayer, CO2, H2O and heat generated by the surface are completely mixed due to turbulence, a process that can be detected by sensors for footprint analysis. The footprint reflects the contribution of the city as a source area to the urban atmosphere as a whole and reflects the influences of various sources or sinks on the measurement point of the underlying surface. Hence, the footprint can be defined as the contribution of surface elements as sources or sinks to the measured vertical flux or concentration. The interpretation of the relationship between the flux obtained at the measurement point and the urban ground surface has become the key to the delineation of the flux footprint. To date, many mathematical models have been successfully applied in flux footprint calculation, which can be roughly divided into four categories: (1) numerical analytical models, such as the Kormann-Meixner (KM) model (Kormann and Meixner, 2000); (2) Lagrangian stochastic dispersion models (LSDMs), such as the Hsieh model (Hsieh et al., 2000); (3) large-eddy simulation (LES) models (Leclerc et al., 1997); and (4) ensemble-averaged closure models. Among them, the KM model and LSDMs are widely used, while the flux source area model (FSAM), which combines numerical analytical models and source area analysis, is also popular in urban research (Schmid, 1993). In the actual research process, due to the influence of the site location, the heterogeneity of the underlying surface within the source area of the station, and the research focus, the choice of flux footprint calculation model differs among various studies (Table 3), and continuous model development provides more possibilities for flux footprint analysis.

The specific steps of the EC technique for urban radiation, wind, moisture, heat, momentum and CO2 fluxes observation can mainly be divided into the following aspects:

(1) The research purpose should be clarified, and the observation site should be investigated according to the research purpose and specific content to determine the research area.

(2) According to the EC observation conditions, the underlying surface of the observation area should meet the upstream homogeneity requirement as much as possible, and a sufficiently long wind and wave area should remain. When considering the observation sites of high-rise buildings or meteorological towers, the observation building height should reach more than 1.5 times the average height of the surrounding buildings, and during instrument installation, it is also necessary to avoid interference between the various instruments and between instruments and observation towers. In the study of city-scale EC observations, urban observations can often hardly meet these conditions because of the distribution characteristics of the underlying surface of the city, high-rise buildings, and high but uneven building density. Due to the observation difficulty, spatial heterogeneity along different directions should be carefully considered in specific flux analysis.

(3) The characteristics of the underlying surface around the observation site should be analyzed, the flux footprint range should be preliminarily assessed to better account for the main wind direction during instrument installation, and a basis should be provided for the heterogeneity of rough elements for final analysis of the data results.

(4) The collected sensor data should be processed and analyzed. Data processing includes several steps, the first of which is basic metadata correction: unit conversion, spike removal (Schmid et al., 2000), time delay correction (Schmid et al., 2000), detrending (Rannik and Vesala, 1999), and coordinate rotation (Lee et al., 2004). The second step is to implement application-related corrections based on specific circumstances: frequency response correction (Massman and Lee, 2002), Webb, Pearman, and Leuning (WPL) correction (Webb et al., 1980; Ham and Heilman, 2003; Kondo and Tsukamoto, 2008), and sonic anemometer correction. And the third step is to evaluate and control the quality of the corrected data: quality control (Aubinet et al., 2000), gap filling (He et al., 2006), and data integration (Fig. 4).

Fig. 4. Data processing flowchart.Fig. 4. Data processing flowchart.

Most processed data exhibit changes at temporal and spatial scales, along different wind directions and under different weather conditions. At the same time, the mechanism and factors influencing these changes should be analyzed. Therefore, to review the current research results in this direction more completely and clearly, in this paper, we mainly analyzed the content from two aspects, namely, energy balance and CO2 fluxes. We examined the meteorological and urban factors that affect the energy balance, as well as other factors such as traffic, heating and vegetation with the most significant impact on CO2 fluxes. Moreover, to better identify the current hotspots and overall research trends, the Python language was used to analyze and determine the keyword frequency by crawling the literature library by category. We found that the two hotspots in urban climate research, "Greenhouse gas (GHG)" and "Urban heat island", occurred 396 and 225 times, respectively (Fig. 3). We investigated these two phenomena in detail in this paper.

 
3 IMPACT OF URBANIZATION ON THE ENERGY BALANCE AND CHARACTERISTICS OF SURFACE-ATMOSPHERE EXCHANGE


Based on the frequency analysis results, we found that 85.17% of energy balance-focused articles contained the keywords "SEB", for a total of 206 times. Notably, a change in urban surface energy highly impacts the urban climate, the UHI effect and other phenomena, and 89.13% of the articles with the keywords "SEB" mentioned "Anthropological heat" 354 times, accounting for 99.57% of all articles with the keywords "Anthropological heat" (Fig. 3). The impacts of human social development and urban construction on the fluxes of energy balance parameters are direct, notable and long-term. Urban construction work must consider the long-term future occurrence of human problems such as the UHI effect, and studying the impact mechanisms of urban modernization, human production and living activities on the UHI phenomenon could help to explain and address its occurrence. Tables 1 and 2 list the average energy balance parameters in different studies and the daily average of each season, respectively, and the important parameters of the urban energy balance equation are comprehensively analyzed and explained in this chapter.

Table 1. Energy balance parameters at different stations.

 Table 2. Daily average maximum of net radiation and water heat flux in each season.

Under the continuous development and expansion of human living places, cities have been created from natural spaces and constantly developed. In the urbanization process, the most direct change is the evolution of the underlying surface of cities, and the change in the underlying surface exerts a series of impacts on the energy balance of cities, which mainly occurs because urbanization alters the city spatial structure and underlying surface materials, and a higher urbanization degree is often associated with an increase in anthropogenic heat. In the study of the urban energy balance, whether from the perspective of urban evolution or the comparative study of existing cities and surrounding suburban or rural areas, urban research is inseparable from the observation and comparative analysis of areas with low urbanization or different urbanization directions. In most multisite case studies, researchers chose suburban sites near urban sites or rural sites as controls. The results showed that the difference in the spatial scale, namely, the heterogeneity of the underlying surface, greatly influenced the flux parameters of the energy balance equation, which also agrees with the UHI phenomenon associated with urbanization. This spatial scale difference is also our long-term research object. In this article, we analyzed suburban and rural areas as low-urbanization areas, but the differences between these two areas were not separately examined.

 
3.1 Radiation

The change in solar activity is an important factor in the study of flux regularity. The regular activity of the sun produces four seasons, and the change in seasons directly affects the Earth's radiation, which in turn results in temperature changes during the different seasons. The change in seasons also influences the thermal environment, and increases in the temperature directly affect the increase in the sensible heat flux and simultaneously enhance vegetation transpiration, thereby causing an increase in latent heat. Solar radiation is the main source of Earth's energy and an important factor in urban energy balance. Based on statistical data, the seasonal impact on radiation is highly notable, summer often exhibits a higher net all-wave radiation flux. In summer in Beijing, the net all-wave radiation flux can reach 650 W m–2, which is the maximum value in the current database. The lowest net radiation flux was observed in winter, Helsinki (Fig. 5). The extremely low value in Helsinki suggests that snowfall could enhance solar radiation reflection at the surface, thereby affecting the net all-wave radiation flux.

Fig. 5. Daily average value of the net radiation flux at each city station during the different seasons.Fig. 5. Daily average value of the net radiation flux at each city station during the different seasons.

Low-urbanization areas not only exhibit a low degree of urban development modernization but also exhibit the characteristics of a high surface vegetation fraction. Although the similarities of regional climate characteristics and geographical location between urban and low-urbanization areas have been studied, the characteristics of the net radiant flux in the energy equation differ in these two areas. Highly urbanized areas exhibit a lower net all-wave radiation flux, although this difference is not pronounced from a yearly perspective (Fig. 6). Considering the four-component radiation equation, under sunny and stable weather conditions, the difference in the four radiation components increases, and urbanization reduces the downward shortwave radiation K, mainly because the atmospheric pollution in urban areas is much worse.

Fig. 6. Comparison of the net all-wave radiation in different regions and in the same city during different seasons.Fig. 6. Comparison of the net all-wave radiation in different regions and in the same city during different seasons.

The upward longwave radiation L at night and the upward shortwave radiation K during the day are high, which is mainly determined by the heat storage performance and the highly reflective materials of urban buildings, respectively. Data from 2011 for Cairo, Egypt, and from 2018 for Phoenix (Frey et al., 2011; Templeton et al., 2018), confirm this phenomenon. Urbanization causes radiation enhancement, so the UHI phenomenon in urban centers increases at night. For example, comparative observation of urban, suburban and rural sites in Montreal (Bergeron and Strachan, 2012) showed that the urban sites exhibited the highest upward longwave radiation L at night, and at night, the Q* values at the urban sites were always 2–10 W m–2 and 4–25 W m–2 (Fig. 6) lower than those at the suburban and rural sites, respectively. Moreover, a study in Nanjing in 2020 noted that the high incoming longwave radiation L in urban areas at night played an important role in nighttime UHIs in urban areas, and the variation in the incoming longwave radiation L was consistent with temperature (Wang et al., 2020). Additionally, the urbanization degree gradually exacerbates this phenomenon. The results of an in-depth study of urban residential districts, arid fields, and wet fields in Phoenix revealed the largest differences in Q* between the urban residential areas and wetlands (high vegetation rates) (Templeton et al., 2018). If factors such as storms, rainfall and snowfall (Fig. 6) are accounted for on the basis of urbanization, the study of the radiant flux becomes increasingly complicated.

 
3.2 Anthropogenic Heat and Energy Storage Heat

The urbanization process has changed the composition and structure of the underlying surface of cities, and the city population density has soared. Considering the impact of urbanization on the closure of the energy balance, in addition to the above factors that can be directly measured through the EC technique, anthropogenic heat (QF) and storage heat (ΔQS) increase under the influence of urbanization and have received much attention. Moreover, the frequency analysis chart (Fig. 3) shows that the keywords "Anthropogenic heat" and "Storage heat" appeared 356 and 138 times, respectively. QF and ΔQS are usually obtained using bottom-up inventory methods, top-down calculation models, or residual methods (Table 3), but these methods exhibit high uncertainty. In addition, although there are few articles comparing QF and ΔQS between urban and low-urbanization areas, the calculation method for QF and ΔQS reflects the impact of urbanization on these variables. The diurnal trend of QF usually indicates a bimodal pattern, with two QF peaks in the morning and afternoon in Sakai, Japan, and this phenomenon is more pronounced in winter. Nevertheless, QFmax can reach 30 and 35 W m–2 in summer and winter, respectively, accounting for a small proportion of the overall energy distribution, but from a yearly perspective, the total annual QF accounts for 30% of the total annual net radiation flux (Ando and Ueyama, 2017). Therefore, QF cannot be ignored when examining the long-term energy distribution in cities. Data for Montreal in winter (Bergeron and Strachan, 2012) showed that the minimum QF value (25–45 W m–2) at urban sites is higher than the maximum QF value (7–13 W m–2) at suburban sites. Due to the high thermal admittance of urban buildings, a 2020 Nanjing study found that ΔQS stored during the day caused UHIs at night (Wang et al., 2020), while in Sakai, Japan, most of the available energy at night was allocated to ΔQS (Ando and Ueyama, 2017). In Phoenix, Arizona, RESREF = 8.7 W m–2 and RESML = 4.5 W m–2 were obtained in the residential areas (REF) and wet fields (ML), respectively, by Templeton et al. (2018), directly demonstrating the impact of urbanization on ΔQS. In high-latitude cities in winter, QF leads to an increase in ΔQS. Both values may even be greater than the net radiation flux, mainly because of factors such as wintertime residential heating. The impacts of the underlying causes of urbanization on QF and ΔQS can be predicted. Field measurement, model calculation and other methods are still the main choices. Table 3 lists all articles in which QF and ΔQS are calculated with a model. QF is largely calculated with LUCY model and the inventory method, while ΔQS calculations mostly rely on the OHM and residual method for estimation. However, accurate calculation of QF and ΔQS has always been a concern among researchers, and the current models and methods experience certain problems, such as the difficulty of obtaining source data for the inventory method, optimization of QF source calculation via the LUCY model, and notable OHM parameter differences among various articles. In addition to the study of the model itself, common models and artificial neural networks have been coupled, enabling the simulation and calculation of wider ranges of QF and ΔQS.

Table 3. Research area and footprint in different literature, usage of human heat and energy storage heat models (SUEWS: Surface Urban Energy and Water Balance Scheme; CLM(U): Community Land Model; MBEM: Modified Building Energy Model; SURFEX: Masson et al. (2013); BEM: Building Energy Model; BEP: Parametric building energy model; SCADIS: numerical atmospheric boundary-layer model; IMAS: (Identification of Micro-scale Anthropogenic Sources).


 Table 3. (continued).

 
3.3 Sensible and Latent Heat Fluxes

Regarding the heat flux in different urbanized areas, there are obvious differences in the diurnal and seasonal variation process trends and flux magnitudes. Urban centers typically exhibit higher sensible heat fluxes and lower latent heat fluxes (Fig. 8). In terms of the difference between Qand QE, Roth et al. (2017) found that QH exhibited the highest seasonal variation (160–300 W m–2), with only a slight change in QE (105–130 W m–2). This difference in heat flux is not necessarily as pronounced across all study areas, e.g., a 2013 Beijing study showed maximum QH values of 105 W m–2 in summer and 102 W m–2 in winter (Song et al., 2013a) (Table 2 and Fig. 7).

Fig. 7. Maximum values of the sensible and latent flux changes at the different urban stations during the different seasons.Fig. 7. Maximum values of the sensible and latent flux changes at the different urban stations during the different seasons.

In the collected data, the maximum sensible heat flux occurred in Shanghai, at 292 W m–2 (Table 2 and Fig. 7). Differences in urbanization emerge at many study sites; for example, throughout the study period in Oberhausen, the hourly average sensible heat flux was 159 W m–2 at urban sites and 127 W m–2 at suburban sites. The maximum latent heat flux, however, reached 123 W m–2 at urban sites and 232 W m–2 at suburban sites (Goldbach and Kuttler, 2013). The main reason for the large difference in latent heat is the proportion of the underlying vegetation and other natural ecosystems, and urbanization has greatly increased the impermeability of urban environments, which cannot lock in moisture. However, not all studies support this latent heat trend, and seasonal and site latitude characteristics should be considered. Such as the observation study in Helsinki, which found that the vegetation activity declined due to freezing in winter, resulting in no significant difference in QE (Karsisto et al., 2016) (Fig. 8). In Montreal, it was even found that QE was much higher in urban areas than in rural areas in winter (Bergeron and Strachan, 2012), although this difference was not significant throughout the year (Fig. 8). At the same time, due to the increase in building moisture drainage under extensive air conditioning use, the latent heat in Sakai was only approximately 19 W m–2 lower than the average daily maximum value of the sensible heat in summer (Ando and Ueyama, 2017). However, from the synthesis of many studies, the sensible heat in areas with high urbanization dominates the heat flux, and the latent heat at the edges of urban areas or in rural areas with high vegetation is higher. Therefore, the Bowen ratio (β, β = QH/QE) in urban areas is higher and more variable.

 Fig. 8. Changes in the sensible and latent heat fluxes between cities and suburbs during the different seasons.Fig. 8. Changes in the sensible and latent heat fluxes between cities and suburbs during the different seasons.

This spatial difference in heat flux is also determined by the time scale, such as the daily periodicity of human activities, seasonal temperature, summer rainy period, winter snowfall, typhoons, and large-scale climate factors. The onset of rainy and snowy seasons leads to a significant increase in the latent heat flux. Snowfall enhances the surface solar radiation reflection, which affects the net radiant flux. Snowfall often suggests extremely cold weather conditions, and the impact of city heating on the energy balance under extremely low-temperature conditions must be considered. In a study of water heat exchange in the Shanghai area, Ao et al. (2016) found that a sharp increase in summer and autumn rainfall levels was accompanied by an increase in the latent heat flux, while the lowest yearly average Bowen ratio (β, β = QH/QE) was observed in summer, followed by the latent heat flux in autumn. Although the sensible and latent heat fluxes increased to maximum values of 292 W m–2 and 65 W m–2, respectively, in summer (Table 2 and Fig. 7), the latent heat proportion increased, and β stabilized after 12 hours of precipitation, when surface water on most urban impervious surfaces had evaporated. The impact of urbanization on water and heat fluxes is partly due to the reduction in the energy input caused by the numerous high-rise buildings and changes in the radiation characteristics of the underlying surface in cities. This aspect is analyzed in Section 3.1. Moreover, the observed impact can be attributed to the replacement of natural vegetation by artificial underlying surfaces, resulting in a significant reduction in vegetation and an increase in the sensible heat flux in urban areas. In addition, the increase in anthropogenic heat in cities could cause a significant increase in the sensible and latent heat fluxes.

 
4 CHARACTERISTICS OF CARBON FLUX SURFACE-ATMOSPHERE EXCHANGE


Cities have always been recognized as carbon sources, and with the increasing attention given to the greenhouse effect caused by CO2, carbon emissions in cities have also become a research hotspot. Based on decades of research on CO2 fluxes in cities, we found that the trend of carbon fluxes exhibits high periodicity on the daily, weekly and seasonal scales (Wu et al., 2022). Many studies have demonstrated that CO2 fluxes exhibit obvious diurnal circulation characteristics, showing bimodal characteristics during the daytime, and the peaks correspond to the morning and evening traffic peak periods. At most study sites, there was a significant difference in CO2 fluxes between weekdays and weekend days, which is also largely related to traffic emissions. On a seasonal scale, the highest CO2 fluxes occur during the winter season, and conversely, the lowest fluxes occur in summer. This apparent seasonal difference is highly correlated with the air temperature, and in general, CO2 fluxes decrease (increase) with increasing (decreasing) temperature throughout the year. This mainly occurs because very low temperatures can cause an increase in residential heating and affect ecosystem functioning.

Carbon sources and sinks are the basic ways to explain the characteristics of surface-atmosphere exchange, such as urban CO2 fluxes. Therefore, the impacts of traffic, space heating and vegetation on CO2 fluxes have been examined in most systematic EC observation studies of urban CO2 fluxes, which is explained in detail in this article. EC observations of urban CO2 fluxes are valid and necessary, but they also suffer the limitations of harsh observation conditions, limited observation range and inability to distinguish sources. Therefore, the relevant research content is inseparable from the top-down carbon emission inventory model (Yin et al., 2022), which mainly includes building emissions, traffic emissions, human respiration, and soil and vegetation respiration. The CO2 fluxes section in the frequency analysis chart (Fig. 3) presents the frequencies of "Carbon budget", "CO2 flux", and other main factors.

 
4.1 Impact of Traffic

Many articles have shown that traffic contributes the most to urban CO2 emissions (Contini et al., 2012; Liu et al., 2012; Lietzke et al., 2015; Björkegren and Grimmond, 2018; Stagakis et al., 2019; Pérez-Ruiz et al., 2020), and the relative contribution of traffic is still increasing (Velasco and Roth, 2010). The frequency analysis graph (Fig. 3) shows a total occurrence of approximately 4200 of the keywords "Car", "Vehicle" and "Traffic" on the left. In cities, CO2 fluxes often exhibit double peaks and valleys throughout the day; in dense urban center areas, CO2 fluxes basically remain high throughout the day, while during the commuting peak periods in the morning and evening, the CO2 fluxes significantly increase to form peaks. Velasco et al. (2005) monitored carbon fluxes in densely populated areas in Mexico and found lower levels in the morning due to weekend traffic, while a Vancouver monitoring study reported that weekend traffic decreased CO2 fluxes by 25% relative to weekdays (Christen et al., 2011). Moreover, the peak CO2 fluxes on weekends maintained the same trend with decreasing traffic. Conte et al. (2018), in their study of the CO2 concentration and fluxes in the Lecce area, found that the CO2 fluxes during holidays was 46% lower than that on weekdays, reflecting the significant impact of traffic on CO2 fluxes in cities, which also occurred on public holidays in Tokyo (Hirano et al., 2015). Therefore, when urban CO2 fluxes are analyzed on daily and weekly scales, traffic is the most important factor determining the change in carbon fluxes. Throughout the whole year, the contribution of traffic to urban CO2 levels was usually approximately 70% (Basel: 70% (Lietzke et al., 2015); London: 70.4% (Björkegren and Grimmond, 2018); Heraklion: 67% (Stagakis et al., 2019)), while the traffic contribution in Beijing reached 75.5% in summer (Song et al., 2013b).

The sharp drop in traffic due to COVID-19 lockdowns led to a decrease in CO2 fluxes, which has attracted the attention of relevant scholars in 2020. Nicolini et al. (2022), based on data from 13 CO2 monitoring sites across Europe, found that the CO2 emissions at all urban sites significantly decreased during the lockdowns after the COVID-19 outbreak relative to the same period in previous years. The CO2 fluxes were reduced by up to 87% at the 13 stations, and the most important reason for this phenomenon was the implementation of traffic control measures restricting motor vehicles. This also corresponds to the results of studies conducted in Mexico (Velasco et al., 2014) and Vienna (Matthews and Schume, 2022) and targeting the Beijing Olympic Games (Song and Wang, 2012).

Traffic is a well-known carbon source for urban CO2, and relying on urban roads, the greenhouse gas emissions of vehicles are line sources, and line sources are more difficult to manage in data processing. Therefore, the accurate characterization of the impact of traffic on CO2 fluxes in urban research areas is crucial. The traditional bottom-up carbon flux calculation model is too rough in calculating the traffic part and cannot accurately calculate the traffic flow situation every day. Gioli et al. (2015) improved the consistency of inventory data by 47% with monthly EC observations using a proxy model based on local traffic data, and in the same year, Menzer et al. (2015) exploited machine learning in Minneapolis-Saint Paul to analyze continuous traffic counts and time variables. These studies have improved the way of capturing transportation's contribution to CO2 flux, making transportation's CO2 emissions calculations more accurate.

 
4.2 Impact of Space Heating

In the current study of urban CO2 fluxes, especially in the analysis of the annual CO2 fluxes in mid- and high-latitude regions, we found that except for subtropical or tropical regions such as Mexico (Velasco et al., 2005) and Singapore (Ng et al., 2015), winter exhibited the highest average CO2 fluxes. For example, the CO2 flux in Tokyo was more than twice that in summer (Hirano et al., 2015). One of the reasons for this difference is the arrival of the heating time of urban spaces under low-temperature conditions. Traffic is the main factor affecting the total annual CO2 fluxes in cities, and heating is another important contributor to the CO2 fluxes. The frequency analysis results are shown in Fig. 3, and the keyword “heating” occurred 756 times. Heating contributed 30% to the total annual flux in Basel (Lietzke et al., 2015) and 11.6% to the annual flux in Heraklion (Stagakis et al., 2019). CO2 emissions from household, building and industrial heating are responsible for the peak flux; winter heating contributed 38.3% in Beijing (Song et al., 2013b), while space heating accounted for 60% of the total flux in Arnhem in winter (Kleingeld et al., 2018). Therefore, when analyzing the seasonality of CO2 fluxes in cities, winter is usually the season when the CO2 fluxes are highest, and heating is a notable carbon source. In EC monitoring experiments evaluating the impact of space heating on urban CO2 fluxes, it is necessary to consider the characteristics of the underlying surface to determine and calculate possible carbon sources, such as tall buildings and factories with high energy consumption, and to analyze the energy output data of utilities in the area, such as power supply and heating infrastructure. Although the inventory method is considered a very accurate and convenient method when studying the contribution of heating to CO2 flux, the bottom-up model has also been adopted in many studies as an auxiliary calculation method for EC measurements (Matese et al., 2009; Gioli et al., 2015; Menzer and McFadden, 2017; Järvi et al., 2019).

Although researchers prioritized the impact of space heating when considering winter CO2 fluxes, the inhibition effect of winter temperature decline on vegetation and ecosystems cannot be ignored, which is another factor that reduces urban CO2 absorption in winter. This aspect is explained in the next section. At the same time, due to the reform of dense urban center area planning, the space heating system in many cities has been changed to central heating, and heating stations may be beyond the scope of site monitoring, so in this case, researchers investigating CO2 fluxes should analyze and consider mesoscale carbon sources. In the current research articles on CO2 fluxes in urban areas, there are too few long-term CO2 observation experiments and data, among which the longest observation site (10 years) was located in Basel (Schmutz et al., 2016). The study of the urban greenhouse effect based on annual CO2 fluxes could be more strongly supported by data (Fig. 9 and Fig. 10).

Fig. 9. Annual average CO2 flux at urban sites in the collected literature. In Beijing (2013), the observation value at 140 m was used; in Beijing (2018), the average value from 2013 to 2016 was considered; in Vienna (2022), the average value from 2018 to 2020 was used.Fig. 9. Annual average CO2 flux at urban sites in the collected literature. In Beijing (2013), the observation value at 140 m was used; in Beijing (2018), the average value from 2013 to 2016 was considered; in Vienna (2022), the average value from 2018 to 2020 was used.
 

Fig. 10. Annual average CO2 flux observed at suburban or rural sites in the collected literature. Helsinki, the left data column shows the 2009 results of a closed-circuit infrared gas analyzer, and the right column shows the results of an open-circuit infrared gas analyzer.Fig. 10. Annual average CO2 flux observed at suburban or rural sites in the collected literature. Helsinki, the left data column shows the 2009 results of a closed-circuit infrared gas analyzer, and the right column shows the results of an open-circuit infrared gas analyzer.

 
4.3 Vegetation

The notable difference in CO2 emissions in space also reflects the complex impact of the underlying surface, but in the current analyses of urban CO2 sources, the contribution of vegetation to the CO2 fluxes are usually relatively low. Vegetation is the only carbon sink, and vegetation is therefore also a research hotspot. The frequency analysis map (Fig. 3) also shows that the keyword “Vegetation” appeared 1667 times. Many comparative observations of urban centers and high-vegetation areas showed that ecosystems are important factors affecting CO2 fluxes (Velasco and Roth, 2010). Ward et al. (2015b), in their study of the CO2 differences between dense urban centers, residential suburbs, and primeval forests, reported that the maximum daily average CO2 fluxes in the urban areas reached 35 µmol m–2 s–1 in summer and 80 µmol m–2 s–1 in winter. From a carbon budget perspective, urban central areas are carbon sources at any time of the year, while primeval forest areas function as carbon sinks at all times, except for the limited carbon source at night. In block analysis of the source area, Järvi et al. (2012) found that the average annual emissions stemming from roads reached 3500 g C m–2, while those in a high-vegetation area reached 870 g C m–2, which more intuitively reveals the difference. The role of vegetation in reducing urban CO2 emissions is self-evident. Nordbo et al. (2012a) analyzed COfluxes monitoring data from 14 sites worldwide to explore the quantitative relationship between the urban green area coverage and urban CO2 fluxes, and they found that cities can exist in a carbon neutral state when the green coverage in the studied urban areas reaches 80%, which further highlights that dense urban areas are large carbon sources and that urban carbon neutrality may not be achievable by relying only on vegetation. Whether this phenomenon occurs in high latitudes or in temperate climate areas suitable for vegetation growth, vegetation is not sufficient to offset CO2 emissions (Järvi et al., 2019; Weissert et al., 2016). In addition to comparative studies, urban ecosystem studies have been conducted in recent years, and the changes in the CO2 fluxes of green roofs, as well as their relationship with precipitation and other factors, were studied in Berlin (Heusinger and Weber, 2017a; Konopka et al., 2021). At a Georgia site in the United States, the impact of common lawns on the CO2 fluxes were highlighted (Pahari et al., 2018). In addition to selecting vegetation in cities for targeted research, established dedicated urban high-vegetation areas were considered and explored, such as urban wetlands, parks, and campuses (Kordowski and Kuttler, 2010; Zhang et al., 2019; Rana et al., 2021; Wu et al., 2021), and the keyword "Park" appeared 482 times (Fig. 3). Quantification of the impact of vegetation on CO2 fluxes is an area of continuous improvement and research. Parametric models of vegetation and organisms were established for Helsinki (2019) and Phoenix (2020) to simulate regional CO2 fluxes, and EC observations were used to verify the model accuracy (Järvi et al., 2019; Pérez-Ruiz et al., 2020).

The influence of plants on the urban CO2 fluxes differs among different seasons. Normally, in summer, plants achieve their maximum CO2 uptake. The optimal photosynthetic temperature can be reached (Bergeron and Strachan, 2011), followed by an increase in the carbon sequestration time of plants in summer, resulting in higher CO2 absorption by plants. Park et al. (2022), in a comparative study of downtown Seoul and high-vegetation areas, found that when the temperature increased to 24 degrees, the sensitivity of vegetation CO2 absorption to the temperature increased 7.44 times, and CO2 absorption was more sensitive to the temperature below 18 degrees. This also suggests that the effects of low temperatures on plants could be severe (Ueyama and Ando, 2016). At the same time, the higher the temperature is, the higher the rate of CO2 absorption by plants, but too high a temperature could lead to temperature-related inhibition of plants. Zhang et al. (2019) studied the relationship between underlying plants and CO2 fluxes in urban campus areas in Shanghai, one of the largest cities in China, and found that the CO2 uptake rate of different plants during different seasons varied, which also provided a reference for the construction of urban green areas.

 
5 DISCUSSION


In terms of the energy balance, solar activity significantly impacts the energy balance at different latitudes. Considering the same region, the influence of the solar activity during the four seasons on the energy flux has been investigated in urban energy balance research on different time scales. Summer usually attains the highest net all-wave radiation flux, whereas the lowest values occur in winter. The sensible and latent fluxes also indicate the same change trends. The effect of precipitation on the energy balance is also significant, especially the increase in the latent heat flux, which is often related to the arrival of the rainy season or snowy period. Furthermore, high-latitude studies showed that snowfall is another reason for the low net radiation in winter. When analyzing observational data, we must consider the meteorological background conditions during data collection, and normalized analysis of atmospheric stability and flux data revealed that the atmospheric stability in cities is very complex, usually showing neutral or unstable conditions, and the improvement in the nighttime atmospheric stability is particularly critical for data analysis. Flux footprint analysis is a common tool when analyzing fixed stations, tiled data sets often represent different underlying surface morphological characteristics, and the dominant wind direction must be determined first among meteorological parameters. In addition to the influences of uncontrollable factors (meteorology) on the energy balance, the influences of controllable factors (urbanization) on the energy balance have increased, which is the key to the UHI effect. Urbanization tends to reduce the net radiation flux, mainly due to the multiple effects of the poor atmospheric conditions and highly reflective materials in cities. Urbanization also results in sensible heat dominating the heat flux. Storage heat and anthropogenic heat may be the most affected components of the urbanization process, and a dense population and concentrated high-rise buildings could cause a large increase in storage heat and anthropogenic heat, which is often the key factor leading to the heat island effect.

There are 26% more articles on urban CO2 than on energy balance (Fig. 2), which is also reflected in the frequency analysis graph (Fig. 3). After summarizing all the articles on urban CO2 fluxes, we determined the three factors with the greatest influence (traffic, heating, and vegetation). We found that traffic was the most important factor influencing urban CO2 fluxes in most regions, especially on annual, weekly and daily scales, contributing a considerable amount of CO2, with 60% or more in certain highly developed urban centers. The role of space heating cannot be ignored when studying CO2 fluxes in cities with cold winters, especially at high latitudes, and in some areas, winter heating contributes even more to CO2 fluxes than traffic. In recent years, there have been an increasing number of studies on urban vegetation. The vegetation type, temperature, moisture and CO2 concentration all affect the uptake of CO2 by vegetation. However, many studies have indicated that due to the limitations of vegetation photosynthesis, vegetation is not the most important factor in resolving the problem of excessive urban CO2 emissions, and energy conservation and emission reduction are the key aspects for alleviating the greenhouse effect and achieving carbon neutrality.

 
6 CONCLUSION


In the study of urban energy balance and CO2 fluxes, we accumulated a large amount of urban observation data, formed a more systematic theoretical system, and determined the change trends of energy and CO2 fluxes and their influencing factors. However, from the current literature, the observation and research of urban energy and CO2 fluxes should still be supported by long-term observation data, which will be helpful to better understand the long-term change trends of energy allocation and CO2 fluxes in urban areas. Moreover, the detailed study of weather conditions is helpful to predict urban meteorological disasters, compare the energy balance at different latitudes, and increase the understanding of the energy balance state in different cities. In terms of the research direction, improving QF, ΔQS and carbon flux calculation models and methods remains the focus of current research, the implementation of mesoscale measurements remains very difficult, and models may provide a breakthrough point. At the same time, we found that urban ecosystems are important for improving urban conditions, but it may not be possible to limit the UHI phenomenon and CO2 emissions only by increasing vegetation.

In future urban energy and CO2 fluxes research, data from urban sites at different latitudes are still needed to enrich the database and standardize the installation and instrument usage conditions at each site to obtain reliable results. At the same time, it is necessary to establish long-term observation platforms in developing countries to obtain the long-term impact of urban development on the UHI and greenhouse phenomena. In anthropogenic heat estimation, more precise data sources or calculations are needed to determine traffic, building and human respiration emissions. The use of new energy, development of carbon capture and utilization technology, and continued carbon sequestration research may become important factors of the reduction in urban carbon fluxes.

 
ACKNOWLEDGMENTS


This research was financially supported by the National Natural Science Foundation of China (NO. 52008001), Project of Science and Technology Plan of Department of Housing and Urban-Rural Development of Anhui Province (2022-YF045), Cultivation and Research Project of Higher Education Top Talent in Anhui Province (gxbjZD2022030), Natural Science Foundation of the Anhui Higher Education Institutions of China (2022AH050255).


REFERENCES


  1. Allen, L., Lindberg, F., Grimmond, C.S.B. (2011). Global to city scale urban anthropogenic heat flux: model and variability. Int. J. Climatol. 31, 1990–2005. https://doi.org/10.1002/joc.2210

  2. Ando, T., Ueyama, M. (2017). Surface energy exchange in a dense urban built-up area based on two-year eddy covariance measurements in Sakai, Japan. Urban Clim. 19, 155–169. https://doi.org/10.1016/j.uclim.2017.01.005

  3. Ao, X., Grimmond, C.S.B., Chang, Y., Liu, D., Tang, Y., Hu, P., Wang, Y., Zou, J., Tan, J. (2016). Heat, water and carbon exchanges in the tall megacity of Shanghai: challenges and results. Int. J. Climatol. 36, 4608–4624. https://doi.org/10.1002/joc.4657

  4. Arya, S.P. (2001). Introduction to micrometeorology. 2nd ed. New York, USA: Academic Press.

  5. Aubinet, M., Grelle, A., Ibrom, A., Rannik, Ü., Moncrieff, J., Foken, T., Kowalski, A.S., Martin, P.H., Berbigier, P., Bernhofer, Ch., Clement, R., Elbers, J., Granier, A., Grünwald, T., Morgenstern, K., Pilegaard, K., Rebmann, C., Snijders, W., Valentini, R., Vesala, T. (2000). Estimates of the Annual Net Carbon and Water Exchange of Forests: The EUROFLUX Methodology, in: Advances in Ecological Research. Elsevier, pp. 113–175. https://doi.org/10.1016/S0065-2504(08)60018-5

  6. Bergeron, O., Strachan, I.B. (2011). CO2 sources and sinks in urban and suburban areas of a northern mid-latitude city. Atmos. Environ. 45, 1564–1573. https://doi.org/10.1016/j.atmosenv.2010.12.043

  7. Bergeron, O., Strachan, I.B. (2012). Wintertime radiation and energy budget along an urbanization gradient in Montreal, Canada. Int. J. Climatol. 32, 137–152. https://doi.org/10.1002/joc.2246

  8. Björkegren, A., Grimmond, C. (2018). Net carbon dioxide emissions from central London. Urban Clim. 23, 131–158. https://doi.org/10.1016/j.uclim.2016.10.002

  9. Burba, G. (2013). Eddy covariance method for scientific, industrial, agricultural, and regulatory applications: a field book on measuring ecosystem gas exchange and areal emission rates. LI-COR Biosciences, Lincoln, Nebraska.

  10. Chow, W.T.L., Salamanca, F., Georgescu, M., Mahalov, A., Milne, J.M., Ruddell, B.L. (2014a). A multi-method and multi-scale approach for estimating city-wide anthropogenic heat fluxes. Atmos. Environ. 99, 64–76. https://doi.org/10.1016/j.atmosenv.2014.09.053

  11. Chow, W.T.L., Volo, T.J., Vivoni, E.R., Jenerette, G.D., Ruddell, B.L. (2014b). Seasonal dynamics of a suburban energy balance in Phoenix, Arizona. Int. J. Climatol. 34, 3863–3880. https://doi.org/​10.1002/joc.3947

  12. Christen, A., Coops, N.C., Crawford, B.R., Kellett, R., Liss, K.N., Olchovski, I., Tooke, T.R., Van Der Laan, M., Voogt, J.A. (2011). Validation of modeled carbon-dioxide emissions from an urban neighborhood with direct eddy-covariance measurements. Atmos. Environ. 45, 6057–6069. https://doi.org/10.1016/j.atmosenv.2011.07.040

  13. Conte, M., Donateo, A., Contini, D. (2018). Characterisation of particle size distributions and corresponding size-segregated turbulent fluxes simultaneously with CO2 exchange in an urban area. Sci. Total Environ. 622–623, 1067–1078. https://doi.org/10.1016/j.scitotenv.2017.12.040

  14. Contini, D., Donateo, A., Elefante, C., Grasso, F.M. (2012). Analysis of particles and carbon dioxide concentrations and fluxes in an urban area: Correlation with traffic rate and local micrometeorology. Atmos. Environ. 46, 25–35. https://doi.org/10.1016/j.atmosenv.2011.10.039

  15. Cui, W., Chui, T.F.M. (2021). Measurements and simulations of energy fluxes over a high-rise and compact urban area in Hong Kong. Sci. Total Environ. 765, 142718. https://doi.org/10.1016/​j.scitotenv.2020.142718

  16. Demuzere, M., Oleson, K., Coutts, A.M., Pigeon, G., Van Lipzig, N.P.M. (2013). Simulating the surface energy balance over two contrasting urban environments using the Community Land Model Urban. Int. J. Climatol. 33, 3182–3205. https://doi.org/10.1002/joc.3656

  17. Feigenwinter, C., Vogt, R., Christen, A. (2012). Eddy Covariance Measurements Over Urban Areas, in: Aubinet, M., Vesala, T., Papale, D. (Eds.), Eddy Covariance. Springer Netherlands, Dordrecht, pp. 377–397. https://doi.org/10.1007/978-94-007-2351-1_16

  18. Feigenwinter, C., Vogt, R., Parlow, E., Lindberg, F., Marconcini, M., Frate, F.D., Chrysoulakis, N. (2018). Spatial distribution of sensible and latent heat flux in the city of Basel (Switzerland). IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11, 2717–2723. https://doi.org/10.1109/JSTARS.​2018.2807815

  19. Frey, C.M., Parlow, E., Vogt, R., Harhash, M., Abdel Wahab, M.M. (2011). Flux measurements in Cairo. Part 1: in situ measurements and their applicability for comparison with satellite data. Int. J. Climatol. 31, 218–231. https://doi.org/10.1002/joc.2140

  20. Gioli, B., Gualtieri, G., Busillo, C., Calastrini, F., Zaldei, A., Toscano, P. (2015). Improving high resolution emission inventories with local proxies and urban eddy covariance flux measurements. Atmos. Environ. 115, 246–256. https://doi.org/10.1016/j.atmosenv.2015.05.068

  21. Goldbach, A., Kuttler, W. (2013). Quantification of turbulent heat fluxes for adaptation strategies within urban planning. Int. J. Climatol. 33, 143–159. https://doi.org/10.1002/joc.3437

  22. Grimmond, C.S.B. (2006). Progress in measuring and observing the urban atmosphere. Theor. Appl. Climatol. 84, 3–22. https://doi.org/10.1007/s00704-005-0140-5

  23. Grimmond, C.S.B., Roth, M., Oke, T.R., Au, Y.C., Best, M., Betts, R., Carmichael, G., Cleugh, H., Dabberdt, W., Emmanuel, R., Freitas, E., Fortuniak, K., Hanna, S., Klein, P., Kalkstein, L.S., Liu, C.H., Nickson, A., Pearlmutter, D., Sailor, D., Voogt, J. (2010). Climate and more sustainable cities: Climate information for improved planning and management of cities (Producers/Capabilities Perspective). Procedia Environ. Sci. 1, 247–274. https://doi.org/10.1016/j.proenv.2010.09.016

  24. Ham, J.M., Heilman, J.L. (2003). Experimental test of density and energy-balance corrections on carbon dioxide flux as measured using open-path eddy covariance. Agron. J. 95, 1393–1403. https://doi.org/10.2134/agronj2003.1393

  25. He, H., Yu, G., Zhang, L., Sun, X., Su, W. (2006). Simulating CO2 flux of three different ecosystems in ChinaFLUX based on artificial neural networks. Sci. China Ser. Earth Sci. 49, 252–261. https://doi.org/10.1007/s11430-006-8252-z 

  26. Heusinger, J., Weber, S. (2017a). Extensive green roof CO2 exchange and its seasonal variation quantified by eddy covariance measurements. Sci. Total Environ. 607–608, 623–632. https://doi.org/10.1016/j.scitotenv.2017.07.052

  27. Heusinger, J., Weber, S. (2017b). Surface energy balance of an extensive green roof as quantified by full year eddy-covariance measurements. Sci. Total Environ. 577, 220–230. https://doi.org/​10.1016/j.scitotenv.2016.10.168

  28. Hirano, T., Sugawara, H., Murayama, S., Kondo, H. (2015). Diurnal variation of CO2 flux in an urban area of Tokyo. SOLA 11, 100–103. https://doi.org/10.2151/sola.2015-024

  29. Hong, J.W., Lee, S., Lee, K., Hong, J. (2020). Seasonal variations in the surface energy and CO2 flux over a high‐rise, high‐population, residential urban area in the East Asian monsoon region. Int. J. Climatol. 40, 4384–4407. https://doi.org/10.1002/joc.6463

  30. Hsieh, C.I., Katul, G., Chi, T. (2000). An approximate analytical model for footprint estimation of scalar fluxes in thermally stratified atmospheric flows. Adv. Water Resour. 23, 765–772. https://doi.org/10.1016/S0309-1708(99)00042-1

  31. Järvi, L., Nordbo, A., Junninen, H., Riikonen, A., Moilanen, J., Nikinmaa, E., Vesala, T. (2012). Seasonal and annual variation of carbon dioxide surface fluxes in Helsinki, Finland, in 2006–2010. Atmos. Chem. Phys. 12, 8475–8489. https://doi.org/10.5194/acp-12-8475-2012

  32. Järvi, L., Havu, M., Ward, H.C., Bellucco, V., McFadden, J.P., Toivonen, T., Heikinheimo, V., Kolari, P., Riikonen, A., Grimmond, C.S.B. (2019). Spatial modeling of local-scale biogenic and anthropogenic carbon dioxide emissions in Helsinki. J. Geophys. Res. 124, 8363–8384. https://doi.org/10.1029/2018JD029576

  33. Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C.S.B., Oleson, K.W., Kouznetsov, R., Masson, V., Järvi, L. (2016). Seasonal surface urban energy balance and wintertime stability simulated using three land-surface models in the high-latitude city Helsinki. Q. J. R. Meteorol. Soc. 142, 401–417. https://doi.org/10.1002/qj.2659

  34. Kleingeld, E., van Hove, B., Elbers, J., Jacobs, C. (2018). Carbon dioxide fluxes in the city centre of Arnhem, A middle-sized Dutch city. Urban Clim. 24, 994–1010. https://doi.org/10.1016/j.​uclim.2017.12.003

  35. Kondo, F., Tsukamoto, O. (2008). Evaluation of Webb correction on CO2 flux by eddy covariance technique using open-path gas analyzer over asphalt surface. J. Agric. Meteorol. 64, 1–8. https://doi.org/10.2480/agrmet.64.1

  36. Konopka, J., Heusinger, J., Weber, S. (2021). Extensive urban green roof shows consistent annual net uptake of carbon as documented by 5 years of eddy‐covariance flux measurements. J. Geophys. Res.: Biogeosci. 126, e2020JG005879. https://doi.org/10.1029/2020JG005879

  37. Kordowski, K., Kuttler, W. (2010). Carbon dioxide fluxes over an urban park area. Atmos. Environ. 44, 2722–2730. https://doi.org/10.1016/j.atmosenv.2010.04.039

  38. Kormann, R., Meixner, F.X. (2000). An analytical footprint model for non-neutral stratification. Boundary Layer Meteorol. 99, 207–224. https://doi.org/10.1023/A:1018991015119

  39. Kotthaus, S., Grimmond, C.S.B. (2012). Identification of Micro-scale Anthropogenic CO2, heat and moisture sources – Processing eddy covariance fluxes for a dense urban environment. Atmos. Environ. 57, 301–316. https://doi.org/10.1016/j.atmosenv.2012.04.024

  40. Leclerc, M.Y., Shen, S., Lamb, B. (1997). Observations and large-eddy simulation modeling of footprints in the lower convective boundary layer. J. Geophys. Res. 102, 9323–9334. https://doi.org/10.1029/96JD03984

  41. Lee, K., Hong, J.W., Kim, J., Jo, S., Hong, J. (2021). Traces of urban forest in temperature and CO2 signals in monsoon East Asia. Atmos. Chem. Phys. 21, 17833–17853. https://doi.org/10.5194/​acp-21-17833-2021

  42. Lee, X., Massman, W., Law, B. (2004). Handbook of micrometeorology: a guide for surface flux measurements and analysis. Kluwer Academic Publishers, Dordrecht, The Netherlands.

  43. Lietzke, B., Vogt, R., Feigenwinter, C., Parlow, E. (2015). On the controlling factors for the variability of carbon dioxide flux in a heterogeneous urban environment. Int. J. Climatol. 35, 3921–3941. https://doi.org/10.1002/joc.4255

  44. Liu, H.Z., Feng, J.W., Järvi, L., Vesala, T. (2012). Four-year (2006–2009) eddy covariance measurements of CO2 flux over an urban area in Beijing. Atmos. Chem. Phys. 12, 7881–7892. https://doi.org/10.5194/acp-12-7881-2012

  45. Massman, W.J., Lee, X. (2002). Eddy covariance flux corrections and uncertainties in long-term studies of carbon and energy exchanges. Agric. For. Meteorol. 113, 121–144. https://doi.org/​10.1016/S0168-1923(02)00105-3

  46. Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.L., Giordani, H., et al. (2013). The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes. Geosci. Model Dev. 6, 929–960. https://doi.org/10.5194/gmd-6-929-2013

  47. Matese, A., Gioli, B., Vaccari, F.P., Zaldei, A., Miglietta, F. (2009). Carbon dioxide emissions of the city center of Firenze, Italy: Measurement, evaluation, and source partitioning. J. Appl. Meteorol. Climatol. 48, 1940–1947. https://doi.org/10.1175/2009JAMC1945.1

  48. Matthews, B., Schume, H. (2022). Tall tower eddy covariance measurements of CO2 fluxes in Vienna, Austria. Atmos. Environ. 274, 118941. https://doi.org/10.1016/j.atmosenv.2022.118941

  49. Menzer, O., Meiring, W., Kyriakidis, P.C., McFadden, J.P. (2015). Annual sums of carbon dioxide exchange over a heterogeneous urban landscape through machine learning based gap-filling. Atmos. Environ. 101, 312–327. https://doi.org/10.1016/j.atmosenv.2014.11.006

  50. Menzer, O., McFadden, J.P. (2017). Statistical partitioning of a three-year time series of direct urban net CO2 flux measurements into biogenic and anthropogenic components. Atmos. Environ. 170, 319–333. https://doi.org/10.1016/j.atmosenv.2017.09.049

  51. Mussetti, G., Brunner, D., Henne, S., Allegrini, J., Krayenhoff, E.S., Schubert, S., Feigenwinter, C., Vogt, R., Wicki, A., Carmeliet, J. (2020). COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees. Geosci. Model Dev. 13, 1685–1710. https://doi.org/​10.5194/gmd-13-1685-2020

  52. Ng, B.J.L., Hutyra, L.R., Nguyen, H., Cobb, A.R., Kai, F.M., Harvey, C., Gandois, L. (2015). Carbon fluxes from an urban tropical grassland. Environ. Pollut. 203, 227–234. https://doi.org/10.1016/​j.envpol.2014.06.009

  53. Nicolini, G., Antoniella, G., Carotenuto, F., Christen, A., Ciais, P., Feigenwinter, C., Gioli, B., Stagakis, S., Velasco, E., Vogt, R., Ward, H.C., Barlow, J., Chrysoulakis, N., Duce, P., Graus, M., Helfter, C., Heusinkveld, B., Järvi, L., Karl, T., Marras, S., et al. (2022). Direct observations of CO2 emission reductions due to COVID-19 lockdown across European urban districts. Sci. Total Environ. 830, 154662. https://doi.org/10.1016/j.scitotenv.2022.154662

  54. Nordbo, A., Järvi, L., Haapanala, S., Wood, C.R., Vesala, T. (2012a). Fraction of natural area as main predictor of net CO2 emissions from cities. Geophys. Res. Lett. 39, 2012GL053087. https://doi.org/10.1029/2012GL053087

  55. Nordbo, A., Järvi, L., Vesala, T. (2012b). Revised eddy covariance flux calculation methodologies – effect on urban energy balance. Tellus B 64, 18184. https://doi.org/10.3402/tellusb.v64i0.18184

  56. Oke, T.R. (1987). Boundary Layer Climates, 2nd ed. London and John Wiley & Sons, New York, Co-published by Routledge, p. 435.

  57. Oke, T.R. (2006). Initial guidance to obtain representative meteorological observations at urban sites, World MeteorologicalOrganization. Instrument and Observing Methods Report No. 81.

  58. Oke, T.R., Mills G., Christen, A., Voogt, J.A. (2017). Urban Climate. Cambridge, UK.

  59. Pahari, R., Leclerc, M.Y., Zhang, G., Nahrawi, H., Raymer, P. (2018). Carbon dynamics of a warm season turfgrass using the eddy-covariance technique. Agric. Ecosyst. Environ. 251, 11–25. https://doi.org/10.1016/j.agee.2017.09.015

  60. Park, C., Schade, G.W., Werner, N.D., Sailor, D.J., Kim, C.H. (2016). Comparative estimates of anthropogenic heat emission in relation to surface energy balance of a subtropical urban neighborhood. Atmos. Environ. 126, 182–191. https://doi.org/10.1016/j.atmosenv.2015.11.038

  61. Park, C., Jeong, S., Park, M.S., Park, H., Yun, J., Lee, S.S., Park, S.H. (2022). Spatiotemporal variations in urban CO2 flux with land-use types in Seoul. Carbon Balance Manage. 17, 3. https://doi.org/10.1186/s13021-022-00206-w

  62. Pérez-Ruiz, E.R., Vivoni, E.R., Templeton, N.P. (2020). Urban land cover type determines the sensitivity of carbon dioxide fluxes to precipitation in Phoenix, Arizona. PLoS One 15, e0228537. https://doi.org/10.1371/journal.pone.0228537

  63. Ramamurthy, P., Pardyjak, E.R. (2011). Toward understanding the behavior of carbon dioxide and surface energy fluxes in the urbanized semi-arid Salt Lake Valley, Utah, USA. Atmos. Environ. 45, 73–84. https://doi.org/10.1016/j.atmosenv.2010.09.049

  64. Rana, G., Martinelli, N., Famulari, D., Pezzati, F., Muschitiello, C., Ferrara, R.M. (2021). Representativeness of carbon dioxide fluxes measured by eddy covariance over a mediterranean urban district with equipment setup restrictions. Atmosphere 12, 197. https://doi.org/​10.3390/atmos12020197

  65. Rannik, Ü., Vesala, T. (1999). Autoregressive filtering versus linear detrending in estimation of fluxes by the eddy covariance method. Boundary Layer Meteorol. 91, 259–280. https://doi.org/​10.1023/A:1001840416858

  66. Rapsomanikis, S., Trepekli, A., Loupa, G., Polyzou, C. (2015). Vertical energy and momentum fluxes in the centre of Athens, Greece during a heatwave period (Thermopolis 2009 Campaign). Boundary Layer Meteorol. 154, 497–512. https://doi.org/10.1007/s10546-014-9979-2

  67. Rinne, J., Ammann, C. (2012). Disjunct Eddy Covariance Method, in: Aubinet, M., Vesala, T., Papale, D. (Eds.), Eddy Covariance, Springer Netherlands, Dordrecht, pp. 291–307. https://doi.org/​10.1007/978-94-007-2351-1_10

  68. Rocha, A.D., Vulova, S., Meier, F., Förster, M., Kleinschmit, B. (2022). Mapping evapotranspirative and radiative cooling services in an urban environment. Sustainable Cities Soc. 85, 104051. https://doi.org/10.1016/j.scs.2022.104051

  69. Roth, M., Jansson, C., Velasco, E. (2017). Multi-year energy balance and carbon dioxide fluxes over a residential neighbourhood in a tropical city. Int. J. Climatol. 37, 2679–2698. https://doi.org/​10.1002/joc.4873

  70. Salgueiro, V., Cerqueira, M., Monteiro, A., Alves, C., Rafael, S., Borrego, C., Pio, C. (2020). Annual and seasonal variability of greenhouse gases fluxes over coastal urban and suburban areas in Portugal: Measurements and source partitioning. Atmos. Environ. 223, 117204. https://doi.org/​10.1016/j.atmosenv.2019.117204

  71. Schmid, H.P. (1993). Source areas for scalars and scalar fluxes. Boundary Layer Meteorol. 67, 293–318. https://doi.org/10.1007/BF00713146

  72. Schmid, H., Grimmond, C.S.B., Cropley, F., Offerle, B., Su, H.B. (2000). Measurements of CO2 and energy fluxes over a mixed hardwood forest in the mid-western United States. Agric. For. Meteorol. 103, 357–374. https://doi.org/10.1016/S0168-1923(00)00140-4

  73. Schmutz, M., Vogt, R., Feigenwinter, C., Parlow, E. (2016). Ten years of eddy covariance measurements in Basel, Switzerland: Seasonal and interannual variabilities of urban CO2 mole fraction and flux. J. Geophys. Res. 121, 8649–8667. https://doi.org/10.1002/2016JD025063

  74. Shi, Y., Zhang, Y., Li, R. (2019). Local-scale urban energy balance observation under various sky conditions in a humid subtropical region. J. Appl. Meteorol. Climatol. 58, 1573–1591. https://doi.org/10.1175/JAMC-D-18-0273.1

  75. Shui, T., Liu, J., Xiao, Y., Shi, L. (2019). Effects of snow cover on urban surface energy exchange: Observations in Harbin, China during the winter season. Int. J. Climatol. 39, 1230–1242. https://doi.org/10.1002/joc.5873

  76. Simón-Moral, A., Dipankar, A., Roth, M., Sánchez, C., Velasco, E., Huang, X. (2020). Application of MORUSES single-layer urban canopy model in a tropical city: Results from Singapore. Q. J. R. Meteorol. Soc. 146, 576–597. https://doi.org/10.1002/qj.3694

  77. Song, T., Wang, Y. (2012). Carbon dioxide fluxes from an urban area in Beijing. Atmos. Res. 106, 139–149. https://doi.org/10.1016/j.atmosres.2011.12.001

  78. Song, T., Sun, Y., Wang, Y. (2013a). Multilevel measurements of fluxes and turbulence over an urban landscape in Beijing. Tellus B 65, 20421. https://doi.org/10.3402/tellusb.v65i0.20421

  79. Song, T., Wang, Y., Sun, Y. (2013b). Estimation of carbon dioxide flux and source partitioning over Beijing, China. J. Environ. Sci. 25, 2429–2434. https://doi.org/10.1016/S1001-0742(12)60336-2

  80. Stagakis, S., Chrysoulakis, N., Spyridakis, N., Feigenwinter, C., Vogt, R. (2019). Eddy Covariance measurements and source partitioning of CO2 emissions in an urban environment: Application for Heraklion, Greece. Atmos. Environ. 201, 278–292. https://doi.org/10.1016/j.atmosenv.​2019.01.009

  81. Templeton, N.P., Vivoni, E.R., Wang, Z.H., Schreiner-McGraw, A.P. (2018). Quantifying water and energy fluxes over different urban land covers in Phoenix, Arizona. J. Geophys. Res. 123, 2111–2128.https://doi.org/10.1002/2017JD027845

  82. Tonti, N.E., Gassmann, M.I., Pérez, C.F. (2018). First results of energy and mass exchange in a salt marsh on southeastern South America. Agric. For. Meteorol. 263, 59–68. https://doi.org/​10.1016/j.agrformet.2018.08.001

  83. Ueyama, M., Ando, T. (2016). Diurnal, weekly, seasonal, and spatial variabilities in carbon dioxide flux in different urban landscapes in Sakai, Japan. Atmos. Chem. Phys. 16, 14727–14740. https://doi.org/10.5194/acp-16-14727-2016

  84. Ueyama, M., Ando, T. (2020). Cooling effect of an urban park by enhanced heat transport efficiency. J. Agric. Meteorol. 76, 148–153. https://doi.org/10.2480/agrmet.D-20-00022

  85. Velasco, E., Pressley, S., Allwine, E., Westberg, H., Lamb, B. (2005). Measurements of CO2 fluxes from the Mexico City urban landscape. Atmos. Environ. 39, 7433–7446. https://doi.org/​10.1016/j.atmosenv.2005.08.038

  86. Velasco, E., Roth, M. (2010). Cities as net sources of CO2: Review of atmospheric CO2 exchange in urban environments measured by eddy covariance technique. Geogr. Compass 4, 1238–1259. https://doi.org/10.1111/j.1749-8198.2010.00384.x

  87. Velasco, E., Perrusquia, R., Jiménez, E., Hernández, F., Camacho, P., Rodríguez, S., Retama, A., Molina, L.T. (2014). Sources and sinks of carbon dioxide in a neighborhood of Mexico City. Atmos. Environ. 97, 226–238. https://doi.org/10.1016/j.atmosenv.2014.08.018

  88. Wang, L., Li, D., Zhang, N., Sun, J., Guo, W. (2020). Surface urban heat and cool islands and their drivers: An observational study in Nanjing, China. J. Appl. Meteorol. Climatol. 59, 1987–2000. https://doi.org/10.1175/JAMC-D-20-0089.1

  89. Wang, Y.C., Bian, Z.F., Qin, K., Zhang, Y., Lei, S.G. (2019). A modified building energy model coupled with urban parameterization for estimating anthropogenic heat in urban areas. Energy Build. 202, 109377. https://doi.org/10.1016/j.enbuild.2019.109377

  90. Ward, H.C., Evans, J.G., Grimmond, C.S.B. (2013). Multi-season eddy covariance observations of energy, water and carbon fluxes over a suburban area in Swindon, UK. Atmos. Chem. Phys. 13, 4645–4666. https://doi.org/10.5194/acp-13-4645-2013

  91. Ward, H.C., Evans, J.G., Grimmond, C.S.B. (2015a). Infrared and millimetre-wave scintillometry in the suburban environment – Part 2: Large-area sensible and latent heat fluxes. Atmos. Meas. Tech. 8, 1407–1424. https://doi.org/10.5194/amt-8-1407-2015

  92. Ward, H.C., Kotthaus, S., Grimmond, C.S.B., Bjorkegren, A., Wilkinson, M., Morrison, W.T.J., Evans, J.G., Morison, J.I.L., Iamarino, M. (2015b). Effects of urban density on carbon dioxide exchanges: Observations of dense urban, suburban and woodland areas of southern England. Environ. Pollut. 198, 186–200. https://doi.org/10.1016/j.envpol.2014.12.031

  93. Ward, H.C., Rotach, M.W., Gohm, A., Graus, M., Karl, T., Haid, M., Umek, L., Muschinski, T. (2022). Energy and mass exchange at an urban site in mountainous terrain – the Alpine city of Innsbruck. Atmos. Chem. Phys. 22, 6559–6593. https://doi.org/10.5194/acp-22-6559-2022

  94. Webb, E.K., Pearman, G.I., Leuning, R. (1980). Correction of flux measurements for density effects due to heat and water vapour transfer. Q. J. R. Meteorol. Soc. 106, 85–100. https://doi.org/​10.1002/qj.49710644707

  95. Weissert, L.F., Salmond, J.A., Turnbull, J.C., Schwendenmann, L. (2016). Temporal variability in the sources and fluxes of CO2 in a residential area in an evergreen subtropical city. Atmos. Environ. 143, 164–176. https://doi.org/10.1016/j.atmosenv.2016.08.044

  96. Wetherley, E.B., Roberts, D.A., Tague, C.L., Jones, C., Quattrochi, D.A., McFadden, J.P. (2021). Remote sensing and energy balance modeling of urban climate variability across a semi-arid megacity. Urban Clim. 35, 100757. https://doi.org/10.1016/j.uclim.2020.100757

  97. World Meteorological Organization (WMO) (2022). State of the Global Climate 2022 (WMO-No. 1316). 

  98. Wu, K., Davis, K.J., Miles, N.L., Richardson, S.J., Lauvaux, T., Sarmiento, D.P., Balashov, N.V., Keller, K., Turnbull, J., Gurney, K.R., Liang, J., Roest, G. (2022). Source decomposition of eddy-covariance CO2 flux measurements for evaluating a high-resolution urban CO2 emissions inventory. Environ. Res. Lett. 17, 074035. https://doi.org/10.1088/1748-9326/ac7c29

  99. Wu, Y., Mao, X., Zhang, Z., Tang, W., Cao, G., Zhou, H., Ma, J., Yin, X. (2021). Temporal and spatial characteristics of CO2 flux in plateau urban wetlands and their influencing factors based on eddy covariance technique. Water 13, 1176. https://doi.org/10.3390/w13091176

  100. Yin, L., Sharifi, A., Liqiao, H., Jinyu, C. (2022). Urban carbon accounting: An overview. Urban Clim. 44, 101195. https://doi.org/10.1016/j.uclim.2022.101195

  101. Zhang, K., Gong, Y., Escobedo, F.J., Bracho, R., Zhang, X., Zhao, M. (2019). Measuring multi-scale urban forest carbon flux dynamics using an integrated eddy covariance technique. Sustainability 11, 4335. https://doi.org/10.3390/su11164335

  102. Zou, J., Zhou, B., Sun, J. (2017). Impact of eddy characteristics on turbulent heat and momentum fluxes in the urban roughness sublayer. Boundary Layer Meteorol. 164, 39–62. https://doi.org/​10.1007/s10546-017-0244-3  


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