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- Springer Journals
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- Copyright © The Author(s) 2021
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- 2197-5620
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- 10.1186/s40663-021-00322-y
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Abstract
Background: Biogenic volatile organic compounds (BVOCs) play an essential role in tropospheric atmospheric chemical reactions. There are few studies conducted on BVOCs emission of dominant forest species in the Jing-Jin- Ji area of China. Based on the field survey, forest resources data and the measured standard emission factors, the Guenther model developed in 1993 (G93) was applied in this paper to estimate the emission of BVOCs from several dominant forest species (Platycladus orientalis, Quercus variabilis, Betula platyphylla, Populus tomentosa, Pinus tabuliformis, Robinia pseudoacacia, Ulmus pumila, Salix babylonica and Larix gmelinii) in the Jing-Jin-Ji area in 2017. Then the spatiotemporal emission characteristics and atmospheric chemical reactivity of these species were extensively evaluated. − 1 Results: The results showed that the total annual BVOCs emission was estimated to be 70.8 Gg C·year , consisting − 1 − 1 − 1 40.5 % (28.7 Gg C·year ) of isoprene, 36.0 % (25.5 Gg C·year ) of monoterpenes and 23.4 % (16.6 Gg C·year )of other VOCs. The emissions from Platycladus orientalis, Quercus variabilis, Populus tomentosa and Pinus tabulaeformis contributed 56.1 %, 41.2 %, 36.0 % and 31.1 %, respectively. The total BVOCs emission from the Jing-Jin-Ji area accounted for 61.9 % and 1.8 % in summer and winter, respectively. Up to 28.8 % of emission was detected from Chengde followed by Beijing with 24.9 %, that mainly distributed in the Taihang Mountains and the Yanshan Mountains. Additionally, the Robinia pseudoacacia, Populus tomentosa, Quercus variabilis, and Pinus tabulaeformis contributed mainly to BVOCs reaction activity. Conclusions: The BVOCs emission peaked in summer (June, July, and August) and bottomed out in winter (December, January, and February). Chengde contributed the most, followed by Beijing. Platycladus orientalis, Quercus variabilis, Populus tomentosa, Pinus tabulaeformis and Robinia pseudoacacia represent the primary contributors to BVOCs emission and atmospheric reactivity, hence the planting of these species should be reduced. Keywords: Biogenic volatile organic compounds (BVOCs), Isoprene, Monoterpenes, Jing-Jin-Ji area, Spatiotemporal characteristics, Chemical reactivity * Correspondence: lunxiaoxiu@bjfu.edu.cn; qiangwang@bjfu.edu.cn College of Environmental Science and Engineering, Beijing Forestry University, 100083 Beijing, China Full list of author information is available at the end of the article © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Lin et al. Forest Ecosystems (2021) 8:52 Page 2 of 14 Background showed that the hourly average concentration of PM 2.5 − 3 Biogenic volatile organic compounds (BVOCs) are low in Beijing reached 318 µg·m on hazy days. Several cit- boiling point compounds commonly synthesized by sec- ies in Hebei province suffered more severe air pollution ondary metabolic pathways in plants. Many vascular (Wang et al. 2012; Li et al. 2016a) used the PM moni- 2.5 plants can discharge BVOCs into the atmosphere (Lor- toring data of 161 cities to analyze the PM pollution 2.5 eto and Schnitzler 2010). Forest is one of the primary in mainland China. The results showed that the Jing-Jin- sources that emit BVOCs, which occupies about 70 % of Ji and its surrounding areas were heavily polluted and the total BVOCs amounts from vegetation. It was esti- ranked at one of China’s current four smoggy regions. mated that the annual emission of BVOCs in the world Zhao et al. (2020) demonstrated that the average annual 6 − 1 was about 10 Gg C·year (Guenther et al. 2012), ac- PM concentration in the Jing-Jin-Ji area decreased by 2.5 − 3 counting for more than 90 % of the total non-methane 8.66 µg·m from 2014 to 2018, indicating the popula- volatile organic compounds (NMVOCs) emission on the tion that exposed to high PM concentration was de- 2.5 ground and far exceeding the anthropogenic compounds creasing. However, the average annual PM 2.5 (Guenther et al. 1995). Isoprene (the simplest 5-carbon concentration value was still far from the national stand- − 3 isoprenoid, C H , 2-methyl 1,3-butadiene) represents the ard limit (35 µg·m ). Also, the ozone is another import- 5 8 highest emission component (Atkinson and Arey 2003) ant pollutant that plagues the urban ambient air quality with an approximately 50 % of the total annual global after PM (Meng et al. 2017; Chen et al. 2013) reported 2.5 − 1 emissions of BVOCs in around 412–601 Tg C·year that the ozone mixing ratio in the Jing-Jin-Ji area was (Guenther et al. 2012). Monoterpenes are the 10-carbon very high, thus causing strong photochemical reactions isoprenoids that account for about 15 % (32–157 Tg from May to September. Wang et al. (2017) pointed out − 1 C·year ) of global BVOCs emission (Guenther et al. that the ozone concentration had exceeded the standard 2012). Both isoprene and monoterpenes are synthesized by 100–200 % in the Jing-Jin-Ji area. During 2014–2018, by the MEP pathway (Loreto and Schnitzler 2010). the ozone concentration in the Jing-Jin-Ji area showed BVOCs are usually formed constitutively or after stress an upward trend with an average positive annual level of − 3 induction. Those components can improve plant toler- 4.90 µg·m as reported by Zhao et al. (2020). Some ance towards abiotic stressors such as high temperature, studies indicated that the contribution of BVOCs such oxidative stress and biotic stressors (e.g. competing as monoterpenes and sesquiterpenes to SOA formation plants and microorganisms) (Loreto and Schnitzler 2010; was substantial (Steinbrecher et al. 2009; Aksoyoglu Filella et al. 2013). et al. 2011; Ghirardo et al. 2016) found that the contri- BVOCs play an essential role in tropospheric atmos- bution of BVOCs released by vegetation to SOA gener- pheric chemical reactions (Sartelet et al. 2012; Kulmala ation in Beijing was increased by two-fold within 2005– et al. 2013). BVOCs are the main precursors to form 2010. Carlo et al. (2004) reported that the isoprene is tropospheric ozone and atmospheric aerosols, promoting highly reactive with hydroxyl radical (·OH) than most the formation of secondary pollutants such as peroxya- anthropogenic volatile organic compounds (AVOCs). cetyl nitrate (PANs), secondary organic aerosols (SOA), Geng et al. (2011) suggested that BVOCs can contribute particulate matter (PM), aldehydes and ketones (Claeys to the surface ozone concentrations. The above litera- et al. 2004; Laothawornkitkul et al. 2009). In particular, tures show that the role of BVOCs in ozone formation the formation of ozone occurs when the isoprene is dis- cannot be ignored in the Jing-Jin-Ji area especially dur- sociated and react with NO (Fehsenfeld et al. 1992) ing summer (Xie et al. 2008; Ran et al. 2011), which while the formation medium of secondary organic aero- would have led to severe impacts on human health, eco- sols (Claeys et al. 2004) is contributed by monoterpenes nomic development, ecological environment, and cli- and sesquiterpenes via cloud condensation nuclei, hence mate change (Pierre et al. 2017). Considering the affecting the local or global climate. particular geographical location, diverse vegetation com- The Jing-Jin-Ji area locating on the North China Plain position, and the enormous influence of BVOCs on represents the core of north China and the most devel- ozone and secondary aerosols, it is crucial to clarify the oped city cluster in China. With the rapid development BVOCs emission emitted from dominant forest species of the economy and the acceleration of urbanization, the in the Jing-Jin-Ji area. issue of air pollution requires immediate attention. The At present, the estimation of BVOCs emission from Jing-Jin-Ji area has been plagued by severe photochem- plants have been conducted through various studies ical pollution and haze for many years (Tang et al. 2009; using different methods, including models, land cover Han et al. 2013). It is known that the accumulation of and meteorological data. Different results of the Jing-Jin- particulate matter, primarily fine particulate matter Ji area were obtained from different models and input PM is the main factor that causes the haze (Li et al. parameters at home and abroad (Klinger et al. 2002; 2.5 2013; Zhai et al. 2016; Hsu et al. 2017; Zhao et al. 2013) Song et al. 2012; Li et al. 2016b). Most of the previous Lin et al. Forest Ecosystems (2021) 8:52 Page 3 of 14 estimations were conducted by grouping the forest spe- dominant forest species in the Jing-Jin-Ji area. Based on cies into several plant functional types (PFT) and then the field vegetation investigation and measured forest using the global average emission rates and biomass species emission rates, this study used the algorithm in density of each vegetation type. However, such estima- G93 model (Guenther et al. 1993) to estimate the tion was unsatisfactory because the differences between BVOCs emission from dominant forest species in the plant species and regions were ignored (Xia and Xiao Jing-Jin-Ji area to establish a more localized emission in- 2019). Besides, most of the studies employed the recom- ventory and eliminate its uncertainty further, analyzed mended values of emission factors in the literature dir- their spatiotemporal distribution characteristics and ectly or selected the measured values of adjacent areas chemical reactivity to provide a theoretical basis for fu- without the measured local primary data; hence the final ture air pollution prevention and control measures. results may not be representative (Song et al. 2012; Zhang et al. 2018). Different forest species or regions Materials and methods with different environments will cause different emission Site description components and release rates of BVOCs (Owen et al. Jing-Jin-Ji area is located in the North China Plain 2002). The difference between the forest species could (Fig. 1), including Beijing, Tianjin, and Hebei (36°05′– be the primary factor to determine the BVOCs emission, 42°40′ N, 113°27′–119°50′ E), with a total area of about and different geographical locations own disparate envir- 21.72 ha. This area is surrounded by Bohai Sea in the onmental conditions (temperature, PAR) that will also east, Tai-hang Mountains in the West and Yanshan distinguish the emissions eventually. Therefore, the pre- Mountains in the north, 735 km from north to south, cise emission rates and fluxes of BVOCs from specific and 576 km from east to west, with various terrains and forest species in different regions should be obtained be- warm temperate continental monsoon climate. The an- fore the evaluation of the spatiotemporal effects and in- nual average temperature is ranged from 0 to 13 C with teractions of BVOCs on the atmospheric environment. an average yearly precipitation of 300–800 mm. There Until present, there has been limited evidence that sup- are various vegetation types in this area including forest, ports the systematic comparison of the BVOCs mea- shrub, grassland, and so on. The sampling sites were se- sured emission rates of dominant forest species in the lected in the forest parks with abundant plant resources Jing-Jin-Ji area. According to the order of volume from and the collection of the air samples were performed on high to low, we have selected Platycladus orientalis, bright days. Quercus variabilis, Betula platyphylla, Populus tomen- The sampling sites (solid circles) identified as follows: tosa, Pinus tabuliformis, Robinia pseudoacacia, Ulmus (1) Saihanba National Forest Park; (2) Heilongshan Na- pumila, Salix babylonica and Larix gmelinii as the tional Forest Park; (3) Labagou Primeval Forest Park; (4) Fig. 1 The site locations of the Jing-Jin-Ji area Lin et al. Forest Ecosystems (2021) 8:52 Page 4 of 14 Baicaowa National Forest Park; (5) Wuling Mountain temperature T (K); M is the standard emission rate of − 1 − 1 Scenic Spot; (6) Yunmengshan National Forest Park; (7) monoterpenes and other VOCs (in C, µg·g ·h ) under Huangyangshan Forest Park; (8) Tianjin Jiulongshan Na- standard condition (Ts = 303 K); C is the correction TM tional Forest Park; (9) Xishan National Forest Park; (10) factor for the temperature of monoterpenes and other − 1 Xiaolongmen National Forest Park; (11) Baihuashan Na- VOCs; β (0.09 K ) is empirical constant. tional Nature Reserve; (12) Shangfangshan National For- Experimental results showed that the emission rate of est Park; (13) Wuyuezhai Scenic Spot. isoprene is mainly controlled by leaf temperature and PAR. However, the main factor affecting monoterpenes Estimation model of BVOCs emission and other VOCs emission by plants is temperature Guenther series models are widely used to estimate (Guenther et al. 1993). Therefore, the emission estima- BVOCs emission. Guenther combined the latest experi- tion method of isoprene is: mental data to deduce the G93 algorithm in 1993 E ¼ I B C C ð6Þ ISOP S TI L (Guenther et al. 1993). The model was used to normalize the emission rate of BVOCs under various environmen- The emission estimation method of monoterpenes and tal conditions, including T = 303 K and PAR = 1000 other VOCs is: − 2 − 1 µmol·m ·s . The BVOCs emitted by dominant forest species were classified as isoprene, monoterpenes, and E ; E ¼ M B C ð7Þ MONO OVOC S TM other VOCs (alcohols, aldehydes, ketones, esters, organic − 1 acids, low carbon alkanes and alkenes) in this paper. Ac- where, E is the emission of isoprene (in C, µg·h ); ISOP cording to the model of light and temperature effect E and E are the emissions of monoterpenes MONO OVOC − 1 proposed by G93, the BVOCs emission classified by for- and other VOCs (in C, µg·h ), respectively; and B is the est species were estimated respectively. Specific formulas leaf biomass (dry weight, g) of each species. The re- are as follows: quired meteorological data (temperature and PAR of Isoprene: every month in the Jing-Jin-Ji area in 2017) can be re- trieved from MODIS satellite product data published on the NASA website (https://modis.ornl.gov/data.html). I ¼ IS CL CTI ð1Þ − 1 Determination of model parameters where, I is the emission rate of isoprene (in C, µg·g · − 1 Emission rates h ) under a specific temperature T (K) and PAR − 2 − 1 Air samples were collected using a dynamic headspace (µmol·m ·s ), and I is the standard emission rate of − 1 − 1 method (Jing et al. 2020). The sampling system consists isoprene (in C, µg·g ·h ) under standard condition − 2 − 1 of a transparent Teflon film sampling bag (10 L), an at- (T = 303 K, PAR = 1000 µmol·m ·s ); C and C are L TI mospheric sampler (LaoDong QC-1 S, Beijing Municipal correction factors for the light and temperature of iso- Institute of Labor Protection, China), two drying towers prene, respectively, that can be obtained by Eqs. 2 and 3: (filled with activated carbon particles that have been pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pre-dried for more than 5 h and allochroic silica gel, re- C ¼ αC L= 1 þ α L ð2Þ L L1 spectively), an ozone removal column (Cleanert KI: where, α (0.0027) and C (1.066) are empirical con- Ll 1.4 g/2.5 mL, Agela Technologies, China) and an adsorp- − 2 − 1 stants; L is PAR (µmol·m ·s ). tion tube (tube type: stainless steel tube, Camsco com- pany, USA, filled with Carbograph 2 (60/80), exp½ð C T T Þ=RT T T1 S S C ¼ ð3Þ Carbograph 1 (40/60) and Carbosieve SIII (60/80) adsor- TI 1 þ exp½ð C T T Þ=RT T T2 M S bents). All parts were connected with polytetrafluoro- − 1 − 1 ethylene (PTFE) tubes (Fig. 2). Before sampling, the where, R is a gas constant (8.314 J·K ·mol ); Ts is the adsorption tubes were purged and activated at 270 °C leaf temperature of the standard state (303 K). C T1 − 1 − 1 for 2h under high-purity N (purity: 99.99 %), then (95,000 J·mol ), C (230,000 J·mol )and T 2 T2 M stored under cold storage at 4 °C. When sampling, the (314 K) are empirical constants. branches with healthy foliage were selected and enclosed The monoterpenes and other VOCs emission rate in in the sampling bag. First, the air in the sampling bag algorithm G93 can be calculated using Eqs. 4 and 5: was extracted, and then the air that has passed through M ¼ M C ð4Þ S TM the two drying towers and the ozone adsorption column was pumped in. Finally, the adsorption tube was con- C ¼ exp½ð β T T Þ ð5Þ TM S nected to form a closed loop. The air samples were col- − 1 where, M is the emission rate of monoterpenes and lected at a flow rate of 150 mL·min for 1 h. The − 1 − 1 other VOCs (in C, µg·g ·h ) under a certain temperature and relative humidity in the sampling bag Lin et al. Forest Ecosystems (2021) 8:52 Page 5 of 14 Fig. 2 Flow chart of dynamic headspace sampling were measured by a hand meteorometer instrument bombardment ionization mode with energy of 70 eV and (NK4500, Kestrel, USA). The PAR in the sampling envir- a scanning atomic mass range of 30–500 amu. The com- onment was measured using a light quantum meter pounds were retrieved from the database of National Insti- (3415FQF, Spectrum, USA). Table 1 shows the sampling tute of Standards and Technology (NIST) based on their dates and averaged environmental conditions during the retention times and specific charge and quantified using sampling. After collection, the adsorption tube was stored the standard external method. The standard gases used in the refrigerator at 4 °C and analyzed within one week. were as follows: Photochemical Assessment Monitoring The air samples were desorbed on PE TurboMatrix Stations (PAMS) (Spectra/Linde: 57) and n-hexane, iso- (650ATD-Clarus600) and analyzed by thermal desorption prene, α-pinene, β-pinene, α-phellandrene, 3-carene, β- gas chromatography-mass spectrometry (GC-MS, Agilent myrcene, α-terpinene, limonene, γ-terpinene, and ocimene 6890, USA). Most of the organics in the adsorption tube made by the National Institute of Metrology, China. The were released after 5 min in a thermal resolver at 260 °C, TCT-GC-MS instrument has been calibrated using stand- and then the substances were adsorbed into the cold trap ard gases. The recovery rate of standard addition was be- (–25 °C ). The substances condensed in the cold trap were tween 90 and 110 % and coefficients of variance were less quickly heated from 260 to 300 °C at a heating rate of than 5 %. − 1 40 °C·s then transferred to the chromatograph for fur- The emission rates of dominant forest species in the ther separation and analysis. The column used was DB- Jing-Jin-Ji area were respectively calculated according to 5MS (column height of 30 m, inner diameter of 0.25 mm, the following Equation: pore diameter of 0.25 μm) and the carrier gas used was − 1 helium (1.0 mL·min ). The heating process was divided ER ¼ ð8Þ t M into three stages at 40 °C, 160 and 270 °C that maintained at 2, 2 and 3 min, respectively, with a heating rate of where, ER is the chemical substance emission rate of − 1 − 1 − 1 4°C·min . The mass spectrometer adopts an electron each forest species (in C, µg·g ·h ); m is the mass of Table 1 Sampling dates and averaged environmental conditions during the samplings in 2017 a − 2 − 1 b c Sampling Date Binomial name Region PAR (µmol·m ·s ) T (°C) RH (%) 08/23–08/24 Salix babylonica (2)/(7)/(9) 938 32.5 23.7 08/29–08/31 Pinus tabuliformis (1)/(5)/(9)/(11) 1963 33.1 23.9 08/29–08/31 Populus tomentosa (1)/(4)/(5)/(7)/(8) 1369 34.0 27.2 07/29–07/31 Platycladus orientalis (4)/(5)/(7)/(8)/(9) 1606 34.2 27.4 08/29–08/30 Robinia pseudoacacia (5)/(7)/(13) 1206 32.8 23.2 06/20–06/21 Ulmus pumila (2)/(4)/(7) 1537 37.1 23.6 06/05–06/07 Larix gmelinii (1)/(4)/(5)/(6)/(10) 1629 29.1 19.9 07/01–07/02 Quercus variabilis (8)/(9)/(12) 1059 34.5 22.6 07/27–07/28 Betula platyphylla (1)/(3)/(10)/(12)/(13) 875 31.0 24.3 (1)–(13): The specific name of sampling place is shown in the note of Fig. 1 PAR Photosynthetically active radiation T Temperature in the bag RH Relative humidity Lin et al. Forest Ecosystems (2021) 8:52 Page 6 of 14 the chemical substance in adsorption tube (in C, µg); t is (Carter 2008); C is the mass concentration of each com- − 3 the sampling time (h); M is the leaf biomass of forest ponent (µg·m ). species in the sapmling bag (dry weight, g). The chemical reaction of the troposphere during the daytime is mainly OH radicals (·OH). The volatile or- Leaf biomass calculation ganic compounds will first react with ·OH, and then The leaf biomass data in the Jing-Jin-Ji area were ob- react with O and NO under light condition to generate 2 x tained using the method of volume and biomass conver- new free radicals to initiate the chain reaction. The first sion. The statistical method of subdividing species reaction is a key step that determines the rate of atmos- enables them to correspond to more appropriate emis- pheric photochemical reaction chain, so the consump- sion factors and biomass, by considering the difference tion rate of ·OH can be used to evaluate the of the same species in different regions. Based on the photochemical activity of BVOCs. The calculation is volumes obtained from the national forest resource in- shown in Eq. 11: ventory, the leaf biomass of the dominant forest species OH OH was calculated as follows: L ¼ K ½ BVOCs ð11Þ i i i V D OH where L is the consumption rate of each component B ¼ P ð9Þ − 1 OH to atmospheric ·OH (s ); K is the reaction rate con- stant between each component and atmospheric ·OH where B is leaf biomass of forest species (dry weight, g); 3 − 1 − 1 (cm ·molecule ·s )(https://kinetics.nist.gov/kinetics/ V is tree volume (m ); D is the basic density of tree index.jsp); [BVOCs] is the atmospheric molecular con- trunk (the ratio of absolute dry wood mass to raw wood − 3 centration of every component (molecule·cm ). volume); P and P are the proportion of stem and leaf T L in the total biomass of tree layer respectively. The data Results and discussion of P , P and D were retrieved from literature (Wang T L T Emission budgets and compositions of BVOCs et al. 2001). In this study, the proportion of trunk dens- According to Eqs. 6 and 7, based on the measured stan- ity, leaf and stem biomass to the total biomass of domin- dardized emission factors of each forest species, leaf bio- ant forest species in the Jing-Jin-Ji area were presented mass data obtained from the forest resource data and field in Table A1. Tree volumes data were mainly based on survey, and the annual meteorological data through the national forest resources survey data, checked and sup- NASA website, the annual BVOCs emission of every com- plemented by forestry network and field survey. ponent of each forest species in the Jing-Jin-Ji area was es- timated. BVOCs emitted from dominant forest species Methods of chemical activity evaluation were divided into isoprene, monoterpenes (including α- Different BVOCs components have different chemical pinene, β-pinene, β-myrcene, limonene, 3-carene and so compositions and physical properties, thus the atmos- on), and other VOCs (OVOCs) (Table A2). The normal- pheric chemical reaction capacity is different (Goldan ized emission rates of BVOCs in dominant forest species et al. 2004). The chemical reactivity of different compo- in the Jing-Jin-Ji area are tabulated in Table 2. The total nents and their ability to generate ozone can provide ref- annual emission of BVOCs from dominant forest species erence for the control measures of BVOCs. This study in the Jing-Jin-Ji area was estimated to be 70.8 Gg adopted two methods namely the maximum incremental − 1 − 1 C·year , including 40.5 % (28.7 Gg C·year ) of isoprene, reactivity (MIR) method and the ·OH reaction rate − 1 36.0 % (25.5 Gg C·year ) of monoterpenes and 23.4 % OH (L ) method, to comprehensively analyze the chemical − 1 (16.6 Gg C·year ) of other VOCs, respectively. activity of the BVOCs components of dominant forest Different forest species can produce various compo- species in the Jing-Jin-Ji area. nents of BVOCs. Figure 3 shows each component and Ozone formation potential (OFP) was used to evaluate proportion of BVOCs emitted by nine dominant forest the potential release of BVOCs into the atmosphere species in the Jing-Jin-Ji area. Isoprene was emitted as under optimal reaction conditions for ozone generation the main component from broadleaf trees such as Betula and measure the reactivity of different BVOCs compo- platyphylla, Quercus variabilis,and Salix babylonica. nents (Carter 1991). The calculation of OFP is shown in Aside from isoprene, some of the broadleaf trees (i.e. Eq. 10: Populus tomentosa, Robinia pseudoacacia, Ulmus pumila) and most coniferous trees like Platycladus OFP ¼ MIR C ð10Þ i i i orientalis, Pinus tabuliformis, and Larix gmelinii released where OFP is the ozone generation potential of each monoterpenes such as α-pinene, β-pinene, β-myrcene − 3 component (µg·m ); MIR is the maximum incremental and limonene. As shown in Fig. A1, the isoprene emis- response factor of each component ((g O )/(g VOCs)) sion was mainly detected from Quercus variabilis and 3 Lin et al. Forest Ecosystems (2021) 8:52 Page 7 of 14 Table 2 Normalized emission rates of biogenic volatile organic compounds (BVOCs) in dominant forest species in the Jing-Jin-Ji − 1 − 1 area (µg·g ·h ) a b Forest species Isoprene α-pinene β-pinene β-myrcene limonene 3-carene OVOCs TVOCs Platycladus orientalis 0.046 ± 0.017 2.329 ± 0.096 0.097 ± 0.015 0.364 ± 0.021 0.235 ± 0.046 1.362 ± 0.321 1.498 ± 0.537 5.931 ± 0.635 Robinia pseudoacacia 9.767 ± 2.043 0.329 ± 0.067 0.236 ± 0.047 0.090 ± 0.017 0.142 ± 0.008 0.051 ± 0.004 1.870 ± 0.649 12.485 ± 2.145 Betula platyphylla 2.624 ± 0.432 0.010 ± 0.004 nd nd nd nd 1.497 ± 0.529 4.131 ± 0.683 Quercus variabilis 17.017 ± 2.492 nd nd nd nd nd 0.987 ± 0.475 18.004 ± 2.537 Salix babylonica 22.110 ± 3.347 nd nd nd nd nd 1.205 ± 0.486 23.315 ± 3.382 Populus tomentosa 4.810 ± 1.057 0.173 ± 0.044 0.018 ± 0.003 0.104 ± 0.007 0.095 ± 0.042 0.001 ± 0.001 1.260 ± 0.492 6.461 ± 1.168 Pinus tabuliformis 1.157 ± 0.240 2.065 ± 0.378 0.014 ± 0.002 2.576 ± 0.344 1.180 ± 0.233 0.007 ± 0.002 1.435 ± 0.762 8.434 ± 0.977 Ulmus pumila 0.417 ± 0.090 0.117 ± 0.089 nd 0.132 ± 0.019 nd nd 1.263 ± 0.358 1.929 ± 0.380 Larix gmelinii 0.006 ± 0.002 5.195 ± 1.046 2.152 ± 0.928 nd nd nd 1.506 ± 0.951 8.859 ± 1.691 nd not detected OVOCs other VOCs TVOCs total VOCs Populus tomentosa that recording at 41.2 % and 31.1 %, monthly leaf biomass data obtained from the forest re- respectively. On the other hand, Platycladus orientalis source data and field survey, and the monthly meteoro- and Pinus tabuliformis are the significant emitters of logical data through the NASA website in 2017, the monoterpenes, providing 56.1 % and 36.0 %, respectively. monthly and seasonal BVOCs emission of every compo- Therefore, it was revealed that these four forest species nent of each forest species in the Jing-Jin-Ji area were es- are the main contributors to BVOCs emission in the timated. Table A3 and Fig. 4 indicate that the BVOCs − 1 Jing-Jin-Ji area: 19.3 Gg C·year (27.2 %) from Platycla- emission and composition in the Jing-Jin-Ji area demon- − 1 dus orientalis, 14.0 Gg C·year (19.8 %) from Quercus strate significant variations for both monthly and sea- − 1 variabilis, 13.4 Gg C·year (18.9 %) from Populus sonal. A distinct unimodal change with the month for − 1 tomentosa and 12.2 Gg C·year (17.2 %) from of Pinus the emissions of BVOCs was observed. tabuliformis (Table A2). The total BVOCs emission was peaked in July with a − 1 total amount of 15.8 Gg C·year , while recorded a − 1 Monthly and seasonal variations minimum value in January of 0.4 Gg C·year with two As shown in Eqs. 6 and 7, by multiplying the measured orders of magnitude difference. In January, the emission standardized emission factors of each forest species, of isoprene from vegetation hit the lowest point at 1.9 × 6 − 1 Fig. 3 The BVOCs emission (10 g C·year )(a) and proportion of each component (%) (b) from dominant forest species in the Jing-Jin-Ji area (BP: Betula platyphylla; QV: Quercus variabilis; UP: Ulmus pumila; PoT: Populus tomentosa; RP: Robinia pseudoacacia; PO: Platycladus orientalis; PiT: Pinus tabuliformis; SB: Salix babylonica; LG: Larix gmelinii) Lin et al. Forest Ecosystems (2021) 8:52 Page 8 of 14 Fig. 4 The monthly BVOCs emission from dominant forest species in the Jing-Jin-Ji area (10 gC) 6 − 1 10 g C·year due to low temperature, less sunlight, maximum leaf area; consequently, the highest enzyme and the limited leaves of biomass. In contrast, the emis- activity was achieved. As a result, high emission of sion of isoprene achieved the highest point at 7.4 Gg BVOCs were observed in summer with the emission of − 1 − 1 C·year in July when the air temperature was high with 43.8 Gg C·year that accounting for 61.9 % of the total presence of strong sunlight, and abundant leaves of bio- annual emissions. In autumn, the temperature difference mass. Nevertheless, the emission of isoprene fluctuated was noticeable. The leaves were transformed from the in other months between the maximum and minimum mature leaves to the decaying leaves. Therefore, the values obtained. The same trend was obtained from the emission of BVOCs showed a decreasing trending in emission of monoterpenes and other VOCs in which the September, followed by a sharp decrease in October, and highest emission was recorded in July while the lowest finally constant in November. The total emission of emission was detected in January. BVOCs recorded in autumn at only 18.5 %. Due to low Overall, the total BVOCs emission was peaked in sum- temperatures and limited solar irradiation in winter, the mer (June, July, and August) and bottomed out in winter emission of BVOCs reached the lowest value of the year (December, January and February). In spring, the BVOCs at only 1.8 %. emission showed an upward trend in which the isoprene was increased the most. Despite the emissions of both Spatial distribution monoterpenes and other VOCs also increased, the values The leaf biomass data of each dominant forest species recorded were comparatively lower than the isoprene from 13 cities in the Jing-Jin-Ji area were obtained based that could be due to the more significant influence of on the forest resources data and field survey, while the sunlight and temperature on isoprene. Under these con- annual meteorological data of each city were retrieved ditions in spring, the vegetation was still in the growing from the NASA website. Then, the standardized emis- stage where the leaves were immature with regular en- sion factors, leaf biomass data and meteorological factors zyme activity. Therefore, the emission of BVOCs in were calculated according to Eqs. 6 and 7 to obtain the spring only accounted for 17.8 % in the whole year. annual BVOCs emission data of each forest species in 13 Comparatively, the average daily temperature and sun- cities of the Jing-Jin-Ji area in 2017. The BVOCs emis- shine time were significantly increased in summer than sion of dominant forest species in 13 cities (Beijing, that in spring. Under these conditions, the vegetation Tianjin, Baoding, Cangzhou, Chengde, Handan, Heng- leaves reached the mature stage and grown with shui, Langfang, Qinhuangdao, Shijiazhuang, Tangshan, Lin et al. Forest Ecosystems (2021) 8:52 Page 9 of 14 Xingtai, Zhangjiakou) throughout 2017 were calculated emission of α-pinene and β-myrcene from coniferous to study the spatial distribution of BVOCs emission in such as Platycladus orientalis and Pinus tabuliformis, the Jing-Jin-Ji area. As shown in Table A4 and Fig. 5, the which could also explain the highest emissions detected BVOCs emission fluxes and the compositions in the for Chengde in spring, summer, and autumn, but lower Jing-Jin-Ji area demonstrate an apparent spatial distribu- than Beijing particularly in winter. Chengde possesses tion. Given that Chengde and Beijing have high coverage the most substantial difference in emissions in winter of vegetation, and the presence of dominant species and summer because the dominant forest species here (Betula platyphylla, Quercus variabilis, Populus tomen- are mostly deciduous trees. The low temperature and tosa and Pinus tabulaeformis) in Wuling Mountain Re- less sunshine in winter would have caused the leaf bio- serve and Saihanba Forest Farm in Chengde showed mass of deciduous trees to emit lower BVOCs; thus the higher BVOCs emission rates. Therefore, Chengde con- emission was reduced significantly (Fig. 6). In general, tributed the highest BVOCs emission of dominant forest the distribution of BVOCs emission fluxes was highly − 1 species in the Jing-Jin-Ji area with 20.4 Gg C·year consistent with the distribution of vegetation. (28.8 %), followed by Beijing with a total discharge of Overall, the BVOCs emission estimated in this study − 1 17.6 Gg C·year (24.9 %). The remaining proportion of were much lower than those estimated by Klinger et al. BVOCs emission was detected from Baoding, Tangshan, (2002) (Table 3). It is due to the difference in the scope Hengshui and Zhangjiakou. Furthermore, the detected of the study objects where this study focused on the spe- − 1 BVOCs emission of Cangzhou (0.4 Gg C·year , 0.6 %) cific dominant forest species while Klinger et al. (2002) − 1 and Langfang (0.5 Gg C·year , 0.8 %) were less than the covered all species in this area, including grasslands, others due to their smaller city area and lower vegetation shrublands, forests, and peatlands. coverage. In terms of compositions (Fig. A2), Chengde presents the largest isoprene emission with 26.8 % (7.7 Chemical activity evaluation − 1 Gg C·year ) of the total isoprene emissions released by The contribution of BVOCs to atmospheric chemical re- dominant forest species in the Jing-Jin-Ji area. This action depends on the level of its emissions and closely could probably due to the extensive vegetation coverage related to their chemical activity. Since the olefins with of deciduous trees with high isoprene emissions such as double bonds are more active compounds in addition to Quercus variabilis. Asides from Chengde, Beijing also the main BVOCs components released by dominant for- shows a high emissions of monoterpenes (9.4 Gg est species in the Jing-Jin-Ji area are isoprene and mono- − 1 C·year ) that accounting for 36.9 % of the total mono- terpenes, the chemical activity of isoprene and terpenes emissions. This could be attributed to the monoterpenes by the maximum incremental reactivity 6 − 1 Fig. 5 The BVOCs emission (10 g C·year )(a) and proportion of each component (%) (b) in each city in the Jing-Jin-Ji area (BJ: Beijing; TJ: Tianjin; BD: Baoding; CZ: Cangzhou; CD: Chengde; HD: Handan; HS: Hengshui; LF: Langfang; QHD: Qinhuangdao; SJZ: Shijiazhuang; TS: Tangshan; XT: Xingtai; ZJK: Zhangjiakou) Lin et al. Forest Ecosystems (2021) 8:52 Page 10 of 14 Fig. 6 The seasonal BVOCs emission distribution of dominant forest species in the Jing-Jin-Ji area (10 gC) OH (MIR) method and the ·OH reaction rate (L ) method value was obtained for Ulmus pumila. The forest species OH OH were analyzed comprehensively. Figure 7 shows the OFP with higher L value obtained by the L method OH and L values and activity contribution rates of every were Robinia pseudoacacia, Populus tomentosa, Platy- dominant forest species. Overall, the activity contribu- cladus orientalis, Pinus tabuliformis and Quercus varia- OH tion rate of isoprene and monoterpenes of each species bilis while Larix gmelinii had the lowest L value. The calculated using two methods are basically consistent. difference between the two methods was due to their OH Among them, the OFP values of Robinia pseudoacacia, different principles in which the L method reflects the Populus tomentosa and Quercus variabilis obtained by reactivity by calculating the ability of BVOCs and OH the MIR method were higher, followed by Betula platy- radical to produce RO without considering the influ- phylla and Pinus tabuliformis while the lowest OFP ence of other subsequent reactions. Although the MIR Lin et al. Forest Ecosystems (2021) 8:52 Page 11 of 14 Table 3 Comparison of estimated BVOCs emission in different taken into consideration for the estimation. The primary regions sources of uncertainty in BVOCs emission estimation in- 9 − 1 Regions Emissions (10 g C·year ) clude emission factors, leaf biomass, vegetation distribu- tion, model algorithm and meteorological parameters. Isoprene Monoterpenes OVOCs References Estimation of total BVOCs emission released by vegeta- Beijing 13.9 7.8 26.3 Klinger et al. (2002) tion on the ground in the Jing-Jin-Ji area was studied Beijing 4.3 9.4 4.0 This study using different algorithms and data sources. Different Tianjin 2.6 1.4 22.2 Klinger et al. (2002) models, emission factors or vegetation type data would Tianjin 3.2 0.8 0.4 This study produce considerably different emissions (approximately Hebei 99.2 50.9 321.0 Klinger et al. (2002) two-fold) (Carlton and Baker 2011; Hogrefe et al. 2011; Hebei 21.3 15.3 12.3 This study Chen et al. 2018). Given the above, the BVOCs emission estimated in this study are based on the field vegetation OVOCs other VOCs investigation and measured forest species emission rates method considers a series of responses of BVOCs, the lack as an effort to eliminate the uncertainties further. How- of some MIR coefficients also affect the results. Based on the ever, the emission rates obtained through experiments two approaches, Robinia pseudoacacia, Populus tomentosa, are mostly related to plant physiological conditions. Quercus variabilis and Pinus tabulaeformis are the dominant Hence, the differences in the values could be due to un- forest species that contribute higher BVOCs reaction activity certainty or uncontrollable stressors, environmental con- in the Jing-Jin-Ji area. Among them, Robinia pseudoacacia, ditions, or genetically related metabolic processes Populus tomentosa and Quercus variabilis have higher re- (Loreto et al. 2009; Monson et al. 2013). For the sam- activity due to their higher isoprene activity contribution rate. pling method, the dynamic headspace method used in OH The OFP and L value of Robinia pseudoacacia, Populus this study has more air circulation than the static sys- − 3 tomentosa, Quercus variabilis were 156.76 µg·m and tem, thus the real existing environment of plants can be 5 − 3 5 − 3 138.36 × 10 , 87.19 µg·m and 78.86 × 10 , 87.19 µg·m maintained in good condition. However, the disturbance and 78.86 × 10 , respectively. In addition, Pinus tabulaeformis to plants during the process and the unrealistic high was detected with a higher activity contribution rate of concentration of H O caused by transpiration may inter- monoterpenes, especially β-myrcene, limonene, and α- fere with the leaf stomata; consequently the emissions OH pinene, hence the OFP and L were detected at could be impacted (Ortega et al. 2008). Moreover, the − 3 5 43.63 µg·m and 54.67 × 10 , respectively. rapid reactivity of BVOCs would also affect the deviation of flux and the analysis performed by the GC-MS instru- Uncertainty in BVOCs emission estimates ment may also possess potential measurement errors. As Since the emissions of BVOCs by vegetation are influ- for the emission factors in this study, the influence of enced by multiple factors, the uncertainty needs to be solar radiation on monoterpene emissions is not Fig. 7 The reaction activity and activity contribution rate of BVOCs of dominant tree species in the Jing-Jin-Ji area calculated by MIR method (a) OH and L method (b) Lin et al. Forest Ecosystems (2021) 8:52 Page 12 of 14 considered in the calculation. The emission mechanism pseudoacacia were the primary contributors of BVOCs of different monoterpenes varies greatly in which some emission and atmospheric reactivity. In conclusion, it of the monoterpenes are reported to be positively corre- was recommended that the use of these forest species lated with temperature (Guenther et al. 2012). The har- for greening configuration should be avoided since they vesting of leaf biomass was calculated via biological negatively impact the atmosphere due to high emission parameters such as forest vegetation accumulation and or reactivity of BVOCs. trunk density. It should be noted that the manual meas- Abbreviations urement errors would also affect the accuracy of the cal- BVOCs: Biogenic volatile organic compounds; AVOCs: Anthropogenic volatile culation. Furthermore, the meteorological data used in organic compounds; G93: Guenther model developed in 1993; NMVOCs: Non-methane volatile organic compounds; PANs: Peroxyacetyl this study were retrieved from the MODIS satellite pub- nitrate; SOA: Secondary organic aerosols; PM: Particulate matter; lished on the NASA website. The acquisition, process- OH: Hydroxyl radical; PFT: Plant functional types; PAR: Photosynthetically OH ing, analysis and conversion of satellite data would active radiation; MIR: Maximum incremental reactivity; L : OH reaction rate; OFP: Ozone formation potential; OVOCs: Other VOCs; TVOCs: Total VOCs; further introduce various types and degrees of uncer- NIST: National Institute of Standards and Technology; PAMS: Photochemical tainty. For example, the positioning error of the ground Assessment Monitoring Stations station, the positioning error of the satellite and the tropospheric time delay will cause the accuracy and sta- Supplementary Information bility of the satellite calibration and the delay error in The online version contains supplementary material available at https://doi. org/10.1186/s40663-021-00322-y. time transmission. In addition, in the inversion process of meteorological satellite remote sensing, atmospheric Additional file 1: Table A1. The parameters of dominant forest species state deviation, model error, model parameter error, etc., in the Jing-Jin-Ji area. Table A2. The annual emissions of BVOCs from 6 –1 will also affect the inversion accuracy. In detail, there dominant forest species in the Jing-Jin-Ji area (10 g C·year ). Table A3. The monthly emissions of BVOCs from dominant forest species in will be a systematic deviation between the reference the Jing-Jin-Ji area (10 g C). Table A4. The BVOCs emission of 13 cities temperature profile and the actual atmospheric profile 6 –1 in the Jing-Jin-Ji area (10 g C·year ). Figure A1. Contributions of dom- during the seasonal transition, and solar radiation will inant forest species to the components of BVOCs in the Jing-Jin-Ji area. Figure A2. The contribution of BVOCs emitted from the dominant forest cause large diurnal changes in the temperature of the species in each city in the Jing-Jin-Ji area. troposphere bottom, which will cause inversion errors, and the errors caused are more complicated. Therefore, Acknowledgements the estimated BVOCs emission using the satellite detec- Not applicable. tion data may be higher or lower than the actual ob- Authors’ contributions served BVOCs emission. By comparing multiple sets of Xiaoxiu Lun, Qiang Wang and Ying Lin conceived the study; Ying Lin monthly measured data and satellite data, the difference participated in study design, field measurements, data processing and between them is less than 5 %. Overall, it is suggested writing the manuscript; Xiaoxi Jing and Chong Fan took part in the field measurements; Wei Tang and Zhongzhi Zhang took part in the procedure of that future work should be emphasized on the reduction data processing. All authors contributed critically to successive drafts and and prevention of those uncertainties to obtain more ac- gave final approval for publication. curate data. Funding This work was supported by the grants from National Natural Science Conclusions Foundation of China (No.42077454), National Research Program for Key The total annual emission of BVOCs released from Issues in Air Pollution Control (DQGG202126), National Natural Science Foundation of China (No. 41605077). dominant forest species in the Jing-Jin-Ji area was esti- − 1 mated to be 70.8 Gg C·year . Isoprene, monoterpenes, Availability of data and materials and other VOCs contributed 40.5 %, 36.0 %, and 23.4 %, The datasets used and/or analysed during the current study are available from the corresponding author on reasonable requests. respectively. As for monthly and seasonal variations, the emission of BVOCs was peaked in summer (June, July, Declarations and August) and bottomed out in winter (December, Ethics approval and consent to participate January, and February). This explains the summer repre- Not applicable. sents the season with the most severe ozone pollution. In terms of spatial distribution, high BVOCs emission Consent for publication was mainly distributed in the Taihang Mountains and Not applicable. the Yanshan Mountains. Chengde contributed the most Competing interests followed by Beijing, inferring that these two cities should The authors declare that they have no competing interests. have more attention to mitigate BVOCs emission to at- Author details mospheric ozone and particulate pollution. In terms of College of Environmental Science and Engineering, Beijing Forestry forest species, Platycladus orientalis, Quercus variabilis, 2 University, 100083 Beijing, China. 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Journal
"Forest Ecosystems" – Springer Journals
Published: Aug 1, 2021
Keywords: Biogenic volatile organic compounds (BVOCs); Isoprene; Monoterpenes; Jing-Jin-Ji area; Spatiotemporal characteristics; Chemical reactivity