北京城郊地区二氧化碳通量特征

利用位于北京市顺义气象局45 m气象塔上36 m高度的湍流观测资料,对该区域2008年11月1日至2009年10月31日共365d的二氧化碳通量(CO2)的时间变化和各方位的分布特征进行了分析研究,并计算了CO2年排放量。结果表明,CO2受交通因素和居民日常生活排放的影响较小,冬季耗能取暖会显著增加CO2的排放量;受供暖排放和植物生长季节光合作用的影响,冬季的CO2通量值在全天绝大多数时刻均高于其他季节,其日平均值为15.6μmol m-2s-1,显著高于春、夏、秋季的日平均值5.6、5.7和8.8μmol m-2s-1(t-test,P0.001)。各方向CO2通量值的大小与其源区内土地利用/覆盖方式以及建筑物的使用功能和使用性质密切相关,住宅楼、饭店、工厂、旅馆等人工建筑面积占比例越大,CO2排放量越大;而植被覆盖比例较高的方向CO2值较小。观测点周边区域是CO2的排放源,且年平均排放量达到13.6 kg m-2a-1,但低于同一时期北京市内高密度住宅区域的CO2年排放量。     

Spatiotemporal Characteristics,Determinants and Scenario Analysis of CO2 Emissions in China Using Provincial Panel Data

This paper empirically investigated the spatiotemporal variations, influencing factors and future emission trends of China’s CO₂ emissions based on a provincial panel data set. A series of panel econometric models were used taking the period 1995–2011 into consideration. The results indicated that CO₂ emissions in China increased over time, and were characterized by noticeable regional discrepancies; in addition, CO₂ emissions also exhibited properties of spatial dependence and convergence. Factors such as population scale, economic level and urbanization level exerted a positive influence on CO₂ emissions. Conversely, energy intensity was identified as having a negative influence on CO₂ emissions. In addition, the significance of the relationship between CO₂ emissions and the four variables varied across the provinces based on their scale of economic development. Scenario simulations further showed that the scenario of middle economic growth, middle population increase, low urbanization growth, and high technology improvement (here referred to as Scenario BTU), constitutes the best development model for China to realize the future sustainable development. Based on these empirical findings, we also provide a number of policy recommendations with respect to the future mitigation of CO₂ emissions.     

Satellite‐derived estimations of spatial and seasonal variation in tropospheric carbon dioxide mass over China

China has frequently been questioned about the data transparency and accuracy of its energy and emission statistics. Satellite‐derived remote sensing data potentially provide a useful tool to study the variation in carbon dioxide (CO₂) mass over areas of the earth's surface. In this study, Greenhouse gases Observing SATellite (GOSAT) tropospheric CO₂ concentration data and NCEP/NCAR reanalysis tropopause data were integrated to obtain estimates of tropospheric CO₂ mass variations over the surface of China. These variations were mapped to show seasonal and spatial patterns with reference to China's provincial areas. The estimates of provincial tropospheric CO₂ were related to statistical estimates of CO₂ emissions for the provinces and considered with reference to provincial populations and gross regional products (GRP). Tropospheric CO₂ masses for the Chinese provinces ranged from 53 ± 1 to 14,470 ± 63 million tonnes were greater for western than for eastern provinces and were primarily a function of provincial land area. Adjusted for land area troposphere CO₂ mass was higher for eastern and southern provinces than for western and northern provinces. Tropospheric CO₂ mass over China varied with season being highest in July and August and lowest in January and February. The average annual emission from provincial energy statistics of CO₂ by China was estimated as 10.3% of the average mass of CO₂ in the troposphere over China. The relationship between statistical emissions relative to tropospheric CO₂ mass was higher than 20% for developed coastal provinces of China, with Shanghai, Tianjin, and Beijing having exceptionally high percentages. The percentages were generally lower than 10% for western inland provinces. Provincial estimates of emissions of CO₂ were significantly positively related to provincial populations and gross regional products (GRP) when the values for the provincial municipalities Shanghai, Tianjin, and Beijing were excluded from the linear regressions. An increase in provincial GRP per person was related to a curvilinear increase in CO₂ emissions, this being particularly marked for Beijing, Tianjin, and especially Shanghai. The absence of detection of specific elevation of CO₂ mass in the troposphere above these municipalities may relate to the rapid mixing and dispersal of CO₂ emissions or the proportion of the depth of the troposphere sensed by GOSAT.     

Environmental Kuznets curve for CO2 emissions in China: A spatial panel data approach

Different from previous studies which mainly focused on conventional estimation techniques, this paper examines the CO₂ EKC hypothesis of China using a spatial panel data model to avoid the coefficient estimation error covering the period of 1997–2012. Furthermore, a comparative analysis of the turning points between the non-spatial panel model and spatial panel model is conducted. The results show that the relationship between economic growth and CO₂ emissions shapes as an inverted-N trajectory. Spatial spillovers effects are confirmed to affect the shape of the CO₂ environmental Kuznets curve. There exists an apparent block distribution in spatial structure of China's provincial CO₂ emissions. Specifically, CO₂ emissions have a relatively sharp increase from the eastern regions to the central and the western regions of China. It has also been found that urbanization and coal combustion are main factors on increasing CO₂ emissions. While the trade openness contributes to slight decrease in CO₂ emissions. The government should make targeted carbon-reduction policies for CO₂ emission reduction.     

Response of isoprene emission to ambient CO2 changes and implications for global budgets

We explore the potential role of atmospheric carbon dioxide (CO₂) on isoprene emissions using a global coupled land–atmosphere model [Community Atmospheric Model–Community Land Model (CAM–CLM)] for recent (year 2000, 365 ppm CO₂) and future (year 2100, 717 ppm CO₂) conditions. We incorporate an empirical model of observed isoprene emissions response to both ambient CO₂ concentrations in the long‐term growth environment and short‐term changes in intercellular CO₂ concentrations into the MEGAN biogenic emission model embedded within the CLM. Accounting for CO₂ inhibition has little impact on predictions of present‐day global isoprene emission (increase from 508 to 523 Tg C yr^?1). However, the large increases in future isoprene emissions typically predicted in models, which are due to a projected warmer climate, are entirely offset by including the CO₂ effects. Projected global isoprene emissions in 2100 drop from 696 to 479 Tg C yr^?1 when this effect is included, maintaining future isoprene sources at levels similar to present day. The isoprene emission response to CO₂ is dominated by the long‐term growth environment effect, with modulations of 10% or less due to the variability in intercellular CO₂ concentration. As a result, perturbations to isoprene emissions associated with changes in ambient CO₂ are largely aseasonal, with little diurnal variability. Future isoprene emissions increase by more than a factor of two in 2100 (to 1242 Tg C yr^?1) when projected changes in vegetation distribution and leaf area density are included. Changing land cover and the role of nutrient limitation on CO₂ fertilization therefore remain the largest source of uncertainty in isoprene emission prediction. Although future projections suggest a compensatory balance between the effects of temperature and CO₂ on isoprene emission, the enhancement of isoprene emission due to lower ambient CO₂ concentrations did not compensate for the effect of cooler temperatures over the last 400 thousand years of the geologic record (including the Last Glacial Maximum).     

Carbon emission accounting and spatial distribution of industrial entities in Beijing—Combining nighttime light data and urban functional areas

Quantifying current carbon emissions their fine scale spatial distribution is necessary to improve carbon emission management, requirements, and emission reduction strategies of key industries. This study established an entity-level model to estimate carbon emissions by combining geographic information of points of interest (POIs) and nighttime light data from Beijing in 2018. The model accounted for the carbon emissions of Beijing's key entities and industries and simulated their spatial distribution. The results showed a good fit between the carbon emissions of the entities and nighttime light brightness values. The 130-m resolution of the urban carbon emission distribution data had a higher spatial simulation accuracy than that of the 1-km Open-Data inventory for anthropogenic carbon dioxide (ODIAC) data. Through the lens of urban functional areas, the average value of carbon emissions was highest in commercial areas and lowest in public management and service areas, at 78,840.11 tC/km² and 6844.79 tC/km², respectively. In terms of the industrial sector, the transportation industry had the highest carbon emissions, with a total of 31.86 Mt., while non-metal mining and oil and gas extraction had almost no energy consumption, with total carbon emissions of 1.38 Mt. The spatial clustering results showed that the distribution of carbon emissions in Beijing had a significant positive spatial correlation; forming high-high aggregation clusters dominated by the city center and major business districts and a low-low aggregation clusters dominated by the city's suburban areas. The simulation model clearly reflected the fine scale characteristics of carbon emissions, in terms of their quantity and spatial distribution. Results obtained in this study can aid relevant departments to formulate appropriate strategies for collectively guiding industrial enterprises towards carbon neutrality.     

Interannual variability in global soil respiration, 1980–94

We used a climate‐driven regression model to develop spatially resolved estimates of soil‐CO₂ emissions from the terrestrial land surface for each month from January 1980 to December 1994, to evaluate the effects of interannual variations in climate on global soil‐to‐atmosphere CO₂ fluxes. The mean annual global soil‐CO₂ flux over this 15‐y period was estimated to be 80.4 (range 79.3–81.8) Pg C. Monthly variations in global soil‐CO₂ emissions followed closely the mean temperature cycle of the Northern Hemisphere. Globally, soil‐CO₂ emissions reached their minima in February and peaked in July and August. Tropical and subtropical evergreen broad‐leaved forests contributed more soil‐derived CO₂ to the atmosphere than did any other vegetation type (～30% of the total) and exhibited a biannual cycle in their emissions. Soil‐CO₂ emissions in other biomes exhibited a single annual cycle that paralleled the seasonal temperature cycle. Interannual variability in estimated global soil‐CO₂ production is substantially less than is variability in net carbon uptake by plants (i.e., net primary productivity). Thus, soils appear to buffer atmospheric CO₂ concentrations against far more dramatic seasonal and interannual differences in plant growth. Within seasonally dry biomes (savannas, bushlands and deserts), interannual variability in soil‐CO₂ emissions correlated significantly with interannual differences in precipitation. At the global scale, however, annual soil‐CO₂ fluxes correlated with mean annual temperature, with a slope of 3.3 Pg C y^?1 per °C. Although the distribution of precipitation influences seasonal and spatial patterns of soil‐CO₂ emissions, global warming is likely to stimulate CO₂ emissions from soils.     

Spatial Prediction of N2O Emissions in Pasture: A Bayesian Model Averaging Analysis

Nitrous oxide (N₂O) is one of the greenhouse gases that can contribute to global warming. Spatial variability of N₂O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N₂O – environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based) on spatial prediction of N₂O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA) based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m² field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR) model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N₂O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N₂O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N₂O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N₂O emissions across this study region.     

The European carbon balance. Part 1: fossil fuel emissions

We analyzed the magnitude, the trends and the uncertainties of fossil‐fuel CO₂ emissions in the European Union 25 member states (hereafter EU‐25), based on emission inventories from energy‐use statistics. The stability of emissions during the past decade at EU‐25 scale masks decreasing trends in some regions, offset by increasing trends elsewhere. In the recent 4 years, the new Eastern EU‐25 member states have experienced an increase in emissions, reversing after a decade‐long decreasing trend. Mediterranean and Nordic countries have also experienced a strong acceleration in emissions. In Germany, France and United Kingdom, the stability of emissions is due to the decrease in the industry sector, offset by an increase in the transportation sector. When four different inventories models are compared, we show that the between‐models uncertainty is as large as 19% of the mean for EU‐25, and even bigger for individual countries. Accurate accounting for fossil CO₂ emissions depends on a clear understanding of system boundaries, i.e. emitting activities included in the accounting. We found that the largest source of errors between inventories is the use of distinct systems boundaries (e.g. counting or not bunker fuels, cement manufacturing, nonenergy products). Once these inconsistencies are corrected, the between‐models uncertainty can be reduced down to 7% at EU‐25 scale. The uncertainty of emissions at smaller spatial scales than the country scale was analyzed by comparing two emission maps based upon distinct economic and demographic activities. A number of spatial and temporal biases have been found among the two maps, indicating a significant increase in uncertainties when increasing the resolution at scales finer than ≈200 km. At 100 km resolution, for example, the uncertainty of regional emissions is estimated to be 60 g C m^?2 yr^?1, up to 50% of the mean. The uncertainty on regional fossil‐fuel CO₂ fluxes to the atmosphere could be reduced by making accurate ¹⁴C measurements in atmospheric CO₂, and by combining them with transport models.     

施硅对增温稻田CH4和N2O排放的影响

夜间增温幅度大于白天是气候变暖的显著特征。夜间增温影响水稻生产及CH4和N2O排放。硅是作物有益元素,施硅可提高产量,减少稻田CH4排放。增温或施硅单因子对稻田CH4和N2O排放影响已有报道,但二者耦合如何影响水稻生产及稻田CH4和N2O排放,尚不清楚。通过田间模拟试验,研究了夜间增温下施硅对水稻生长、产量及温室气体持续增温/冷却潜势和排放强度的影响。采用铝箔反光膜夜间(19:00-6:00)覆盖水稻冠层进行模拟夜间增温试验。增温设2水平,即常温对照(CK)和夜间增温(NW);施硅量设2水平,即Si0(不施硅)和Si1(钢渣硅肥,200 kgSiO2/ha)。结果表明,施硅可缓解夜间增温对水稻根系活力的抑制作用,降低夜间增温对水稻地上部、地下部干重和产量的抑制作用。夜间增温显著提高CH4累计排放量,而施硅显著降低CH4累计排放量。夜间增温下施硅处理稻田CH4累计排放量在分蘖期、拔节期、抽穗-扬花期和灌浆成熟期比未施硅处理分别低48.12%、49.16%、61.59%和39.13%。夜间增温或施硅均促进稻田N2O排放,夜间增温下施硅在上述生育期以及全生育期的累计排放量依次比对照高78.17%、51.45%、52.01%、26.14%和40.70%。研究认为,施硅可缓解夜间增温对稻田综合增温潜势和排放强度的促进作用。     

中国农业系统近40年温室气体排放核算

基于排放因子法构建了包含种植业和牲畜养殖业的农业系统温室气体排放核算体系,系统核算了1980-2020年我国全国尺度上的农业系统温室气体排放总量和变化趋势,并在区县级尺度下对1980、2000、2011年的中国农业系统的温室气体排放量进行核算,对比不同阶段农业系统温室气体排放变化的时空异质性规律。研究发现：1980-2020年我国农业系统温室气体排放量呈波动增长趋势,增长了近46%。CH₄是农业系统排放贡献最大的温室气体,占总排放量的47.33%。我国农业系统温室气体排放与不同地区农业生产方式有关,CH₄排放量高的地区主要位于我国主要水稻产区以及旱地作物产区。CO₂排放量高的地区主要位于东北、西北等地区以及华东地区。N₂O排放量较高地区主要位于西北的主要畜牧养殖地区,以及我国农业经济发展水平高的中南部地区。研究有助于揭示我国农业温室气体排放的动态特征,现状规律,以及空间差异性特征,从农业减排角度为实现双碳目标提供科学参考。     

Soil CO2 emission estimated by different interpolation techniques

Soil CO₂ emissions are highly variable, both spatially and across time, with significant changes even during a one-day period. The objective of this study was to compare predictions of the diurnal soil CO₂ emissions in an agricultural field when estimated by ordinary kriging and sequential Gaussian simulation. The dataset consisted of 64 measurements taken in the morning and in the afternoon on bare soil in southern Brazil. The mean soil CO₂ emissions were significantly different between the morning (4.54 ??mol m^?2 s^?1) and afternoon (6.24 ??mol m^?2 s^?1) measurements. However, the spatial variability structures were similar, as the models were spherical and had close range values of 40.1 and 40.0 m for the morning and afternoon semivariograms. In both periods, the sequential Gaussian simulation maps were more efficient for the estimations of emission than ordinary kriging. We believe that sequential Gaussian simulation can improve estimations of soil CO₂ emissions in the field, as this property is usually highly non-Gaussian distributed.     

我国典型城市化石能源消费CO2排放及其影响因素比较研究

城市是化石能源消费和CO_2排放的主要区域。分析典型城市化石能源消费CO_2排放特征,明确不同城市CO_2排放动态及主要影响因素的差异,是开展城市减排行动的重要科学依据。采用IPCC推荐方法及中国的排放参数核算11个典型城市2006—2015年间化石能源消费产生的CO_2排放量。根据各城市经济发展和CO_2排放特征将之分为四类:经济高度发达城市(北京、上海、广州)、高碳排放城市(重庆、乌鲁木齐、唐山)、低排放低增长城市(哈尔滨、呼和浩特和大庆)和低排放高增长城市(贵阳、合肥),并运用对数平均迪氏指数法(Logarithmic Mean Divisia Index,即LMDI分解法)对比分析了四类城市CO_2排放量的影响因素。结果表明:(1)研究期内大部分城市CO_2排放总量有所增加,仅北京和广州呈下降趋势,工业部门CO_2排放在城市排放总量及其变化中占据主导地位;四类城市的人均CO_2排放量表现出与排放总量相似的变化趋势;CO_2排放强度整体上表现为经济高度发达城市(均值为0.88 t CO_2/万元)低排放低增长城市(均值为2.82 t CO_2/万元)低排放高增长城市(均值为3.05 t CO_2/万元)高碳排放城市(均值为6.62 t CO_2/万元)。(2)在城市CO_2排放的影响因素中,经济发展和人口规模均是4类城市CO_2排放增长的促进因素,但经济发展效应的累积贡献值大于人口规模效应;能源强度降低是4类城市CO_2排放最主要的抑制因素,且经济高度发达和高碳排放城市的抑制作用强于其他两类城市;对第三产业GDP年平均增速高于第二产业的6个城市来说,产业结构是CO_2排放的抑制因素;能源结构的变化仅对煤炭消费比重较低且降幅较大的北京和广州的CO_2排放是抑制作用,累积贡献值分别为-21.73Mt和-0.03Mt,而对其他城市,特别是高碳排放城市的CO_2排放具有明显的促进作用。     

旅游交通碳排放的空间结构与情景分析

旅游业作为全球第一大产业,是影响气候变化的重要因素之一,旅游碳排放的相关研究近年来已经引起学者们的关注.选择了九寨沟风景区、西安碑林博物馆、南京珍珠泉风景区3个旅游交通模式差异明显的案例地为例,根据实地问卷调查数据估算了九寨沟风景区、西安碑林博物馆、珍珠泉旅游风景区2010年的旅游交通碳排放总量分别为654.18,108.44和15.92 Gg.通过比较九寨沟、西安碑林和珍珠泉的碳排放累积曲线,得出不同旅游平均距离的景区的碳排放结构均衡度有所不同,旅游平均距离偏低景区的碳排放结构最不均衡.同时,旅游景区的交通碳排放在距离上具有分段性,不同旅游平均距离的景区交通碳排放的空间结构具有明显的差异性.通过4种不同的交通情景分析发现,旅游平均距离高和距离中等的景区对飞机的碳减排敏感度较高,旅游平均距离偏低的景区自驾车的碳减排效果最为明显.研究结果为旅游管理部门根据碳排放结构有针对性的制定差异化的旅游交通碳减排政策提供了参考和借鉴.     

Potential for biochar carbon sequestration from crop residues: A global spatially explicit assessment

Global warming necessitates urgent action to reduce carbon dioxide (CO₂) emissions and remove CO₂ from the atmosphere. Biochar, a type of carbonized biomass which can be produced from crop residues (CRs), offers a promising solution for carbon dioxide removal (CDR) when it is used to sequester photosynthetically fixed carbon that would otherwise have been returned to atmospheric CO₂ through respiration or combustion. However, high-resolution spatially explicit maps of CR resources and their capacity for climate change mitigation through biochar production are currently lacking, with previous global studies relying on coarse (mostly country scale) aggregated statistics. By developing a comprehensive high spatial resolution global dataset of CR production, we show that, globally, CRs generate around 2.4 Pg C annually. If 100% of these residues were utilized, the maximum theoretical technical potential for biochar production from CRs amounts to 1.0 Pg C year⁻¹ (3.7 Pg CO₂e year⁻¹). The permanence of biochar differs across regions, with the fraction of initial carbon that remains after 100 years ranging from 60% in warm climates to nearly 100% in cryosols. Assuming that biochar is sequestered in soils close to point of production, approximately 0.72 Pg C year⁻¹ (2.6 Pg CO₂e year⁻¹) of the technical potential would remain sequestered after 100 years. However, when considering limitations on sustainable residue harvesting and competing livestock usage, the global biochar production potential decreases to 0.51 Pg C year⁻¹ (1.9 Pg CO₂e year⁻¹), with 0.36 Pg C year⁻¹ (1.3 Pg CO₂e year⁻¹) remaining sequestered after a century. Twelve countries have the technical potential to sequester over one fifth of their current emissions as biochar from CRs, with Bhutan (68%) and India (53%) having the largest ratios. The high-resolution maps of CR production and biochar sequestration potential provided here will provide valuable insights and support decision-making related to biochar production and investment in biochar production capacity.     

Punching above their weight: Large release of greenhouse gases from small agricultural dams

Freshwater ecosystems play a major role in global carbon cycling through the breakdown of organic material and release of greenhouse gases (GHGs). Carbon dioxide (CO₂) and methane (CH₄) emissions from lakes, wetlands, reservoirs and small natural ponds have been well studied, however, the GHG emissions of highly abundant, small‐scale (<0.01 km²) agricultural dams (small stream and run‐off impoundments) are still unknown. Here, we measured the diffusive CO₂ and CH₄ flux of 77 small agricultural dams within south‐east Australia. The GHG emissions from these waterbodies, which are currently unaccounted for in GHG inventories, amounted to 11.12 ± 2.59 g CO₂‐equivalent m²/day, a value 3.43 times higher than temperate reservoir emissions. Upscaling these results to the entire state of Victoria, Australia, resulted in a farm dam CO₂‐equivalent/day emission rate of 4,853 tons, 3.1 times higher than state‐wide reservoir emissions in spite of farm dams covering only 0.94 times the comparative area. We also show that CO₂ and CH₄ emission rates were both significantly positively correlated with dissolved nitrate concentrations, and significantly higher in livestock rearing farm dams when compared to cropping farm dams. The results from this study demonstrate that small agricultural farm dams can be a major source of greenhouse gas emissions, thereby justifying their inclusion in global carbon budgets.     

Unconsidered sporadic sources of carbon dioxide emission from soils in taiga forests

Long-term monitoring in the Russian taiga zone has shown that all known extreme destructive effects resulting in the weakening and death of tree stands (windfalls, pest attacks, drought events, etc.) can be sporadic, but significant sources of CO₂ soil emission. Among them are (i) a recently found effect of the multiyear CO₂ emission from soil at the bottom of deadwood of spruce trees that died due to climate warming and subsequent pest outbreaks, (ii) increased soil CO₂ emissions due to to the fall of tree trunks during massive windfalls, and (iii) pulse CO₂ emission as a result of the so-called Birch effect after drought events in the taiga zone. According to the modeling, while depending on the spatial and temporal scales of their manifestation, the impact of these sporadic effects on the regional and global soil respiration fluxes could be significant and should be taken into consideration. This is due to continuing Climate Change, and further increase of local, regional and Global human impacts on the atmospheric greenhouse gases balance, and land use, as well.     

中国能源消费碳排放的时空特征

选择联合国政府间气候变化专门委员会(IPCC)的部门方法和8大类能源,采用1990年至2009年的中国能源统计数据,按照自下而上的思路,对我国各省区的碳排放量进行估算,并从碳排放量、碳排放强度、人均碳排放量和碳排放密指标出发,深入分析了各省区碳排放的时空特征差异。以期对国内碳排放的时空特征分析,有助于决策者和能源分析家提高节能减排政策制定的有效性。     

Nitrous oxide and methane emissions from cryptogamic covers

Cryptogamic covers, which comprise some of the oldest forms of terrestrial life on Earth (Lenton & Huntingford, 2003 ), have recently been found to fix large amounts of nitrogen and carbon dioxide from the atmosphere (Elbert et al., 2012 ). Here we show that they are also greenhouse gas sources with large nitrous oxide (N₂O) and small methane (CH₄) emissions. Whilst N₂O emission rates varied with temperature, humidity, and N deposition, an almost constant ratio with respect to respiratory CO₂ emissions was observed for numerous lichens and bryophytes. We employed this ratio together with respiration data to calculate global and regional N₂O emissions. If our laboratory measurements are typical for lichens and bryophytes living on ground and plant surfaces and scaled on a global basis, we estimate a N₂O source strength of 0.32–0.59 Tg year^?1 for the global N₂O emissions from cryptogamic covers. Thus, our emission estimate might account for 4–9% of the global N₂O budget from natural terrestrial sources. In a wide range of arid and forested regions, cryptogamic covers appear to be the dominant source of N₂O. We suggest that greenhouse gas emissions associated with this source might increase in the course of global change due to higher temperatures and enhanced nitrogen deposition.     

Isoprenoid emission in trees of Quercus pubescens and Quercus ilex with lifetime exposure to naturally high CO2 environment†

The long‐term effect of elevated atmospheric CO₂ on isoprenoid emissions from adult trees of two Mediterranean oak species (the monoterpene‐emitting Quercus ilex L. and the isoprene‐emitting Quercus pubescens Willd.) native to a high‐CO₂ environment was investigated. During two consecutive years, isoprenoid emission was monitored both at branch level, measuring the actual emissions under natural conditions, and at leaf level, measuring the basal emissions under the standard conditions of 30 °C and at light intensity of 1000 µmol m^?2 s^?1. Long‐term exposure to high atmospheric levels of CO₂ did not significantly affect the actual isoprenoid emissions. However, when leaves of plants grown in the control site were exposed for a short period to an elevated CO₂ level by rapidly switching the CO₂ concentration in the gas‐exchange cuvette, both isoprene and monoterpene basal emissions were clearly inhibited. These results generally confirm the inhibitory effect of elevated CO₂ on isoprenoid emission. The absence of a CO₂ effect on actual emissions might indicate higher leaf temperature at elevated CO₂, or an interaction with multiple stresses some of which (e.g. recurrent droughts) may compensate for the CO₂ effect in Mediterranean ecosystems. Under elevated CO₂, isoprene emission by Q. pubescens was also uncoupled from the previous day's air temperature. In addition, pronounced daily and seasonal variations of basal emission were observed under elevated CO₂ underlining that correction factors may be necessary to improve the realistic estimation of isoprene emissions with empirical algorithms in the future. A positive linear correlation of isoprenoid emission with the photosynthetic electron transport and in particular with its calculated fraction used for isoprenoid synthesis was found. The slope of this relationship was different for isoprene and monoterpenes, but did not change when plants were grown in either ambient or elevated CO₂. This suggests that physiological algorithms may usefully predict isoprenoid emission also under rising CO₂ levels.