Seasonal and diurnal variations of biogenic volatile organic compounds in highland and lowland ecosystems in southern Kenya

The East African lowland and highland areas consist of water-limited and humid ecosystems. The magnitude and seasonality of biogenic volatile organic compounds (BVOCs) emissions from these functionally contrasting ecosystems are limited due to a scarcity of direct observations. We measured mixing ratios of BVOCs from two 15 contrasting ecosystems, humid highlands with agroforestry and dry lowlands with bushland, grassland, and agriculture mosaics, during both the rainy and dry seasons of 2019 in southern Kenya. We present the diurnal and seasonal characteristics of BVOC mixing ratios and their reactivity, and estimated emission factors (EFs) for certain BVOCs from the African lowland ecosystem based on field measurements. The most abundant BVOCs were isoprene and monoterpenoids (MTs), with isoprene contributing > 70 % of the total BVOC mixing ratio during daytime, while MTs 20 accounted for > 50 % of the total BVOC mixing ratio during nighttime at both sites. The contributions of BVOCs to the local atmospheric chemistry were estimated by calculating the reactivity towards the hydroxyl radical (OH), ozone (O3), and the nitrate radical (NO3). Isoprene and MTs contributed the most to the reactivity of OH and NO3, while sesquiterpenes dominated the contribution of organic compounds to the reactivity of O3. The mixing ratio of isoprene measured in this study was lower to that measured in the relevant ecosystems in west 25 and south Africa, while that of monoterpenoids was similar. Isoprene mixing ratios peaked daily between 16:00 and 20:00 with a maximum mixing ratio of 809 parts per trillion by volume (pptv) and 156 pptv in the highlands, and 115 pptv and 25 pptv in the lowlands, during the rainy and dry seasons, respectively. MT mixing ratios reached their daily maximum between midnight and early morning (usually 04:00 to 08:00) with mixing ratios of 254 pptv and 56 pptv in the highlands, and 89 pptv and 7 pptv in the lowlands, in the rainy and dry seasons, respectively. The dominant species within the MT 30 group were limonene, α-pinene, and β-pinene. EFs for isoprene, MTs, and 2-methyl-3-buten-2-ol (MBO) were estimated using an inverse modeling approach. The estimated EFs for isoprene and β-pinene agreed very well with what is currently assumed in the world’s most extensively used biogenic emissions model, the Model of Emissions of Gases and Aerosols from Nature (MEGAN), for warm C4 grass, but the estimated EFs for MBO, α-pinene, and especially limonene, were significantly higher than that assumed in 35 MEGAN for the relevant plant functional type. Additionally, our results indicate that the EF for limonene might be seasonally dependent in savanna ecosystems.

Climate change affects BVOC emissions and oxidation through environmental conditions ( Fig. 1: red arrows).
Isoprene emissions are known to be both temperature and light dependent (Guenther et al., 1991(Guenther et al., , 1993Wildermuth and Fall, 1996;Niinemets et al., 2004) and have been identified as the main contributor to increasing global BVOC levels in response to global warming (Peñuelas and Staudt, 2010). Besides temperature and light, the emission of isoprene depends 50 on soil water availability and thus responds to soil water stress . The emission of monoterpenes is known to mainly be controlled by temperature, but the emission of certain monoterpenes (e.g., ocimene) depends greatly on the availability of light (Jardine et al., 2015;Guenther et al., 2012;Loreto et al., 1998). Mochizuki et al. (2020) estimated that monoterpene emissions will increase by 15 % with a 1 °C increase in air temperature due to climate warming. The emission of certain monoterpenes is promoted by increasing soil moisture (Schade et al., 1999;Greenberg 55 et al., 2012) and a decline in moisture-limited conditions (Bonn et al., 2019). Increasing atmospheric carbon dioxide (CO2) and air pollution (e.g., O3) are also abiotic factors which affect BVOC emissions negatively or positively (Velikova, 2008;Masui et al., 2021). Since climate variability is rising (Seneviratne et al., 2012), the emission of monoterpenes and isoprene is becoming more variable. This effect becomes especially pronounced in ecosystems that are vulnerable to climatic changes.  Dryland ecosystems and human-modified systems, including savannas, bushland, grassland, and agroforestry are more sensitive and vulnerable to ongoing climate change than other ecosystems (IPCC, 2014). It is estimated that around 18 % 70 of global BVOCs are emitted from grass, shrubs, and crops (Guenther, 2013). This estimate is unfortunately connected with a large degree of uncertainty, since BVOC measurements from these ecosystems are rather scarce (e.g., Guenther et al., 2013). These climate-sensitive ecosystems are widely distributed and cover 55.2 % of tropical Africa (MDAUS BaseVue 2013, https://www.africageoportal.com/datasets/b4a808eba17d4294991880d9e120faee, last access: December 15, 2020), which have high potential on native ecosystem changes (Zabel et al., 2019), e.g. human-modified systems 75 expansion at the expense of grassland and savannas, which can decrease the global BVOC levels (Unger, 2014). However, these aforementioned climate-sensitive ecosystems are also estimated to face a higher frequency of heat waves, hot nights, droughts, and flooding in the future climate (Niang et al., 2014;Kharin et al., 2018), which can promote or inhibit the certain BVOC releases and make BVOC emissions more changeable. Models can simulate certain abiotic effects, for https://doi.org/10.5194/acp-2021-445 Preprint. Discussion started: 17 June 2021 c Author(s) 2021. CC BY 4.0 License. example temperature changes, soil water stress, and CO2 inhibition, on BVOC emissions from these climate-sensitive 80 ecosystems in current and future climate scenarios through the setting of suitable parameterizations, i.e., emission factors (EFs) and activity factors Emmerson et al., 2020). However, field measurements focusing on volatile organic compounds (especially on monoterpenoids (MTs), sesquiterpenes (SQTs), and 2-methyl-3-buten-2-ol (MBO)) from African ecosystems are very limited. Previous measurements in tropical savannas have mainly focused on isoprene and/or monoterpenes (Guenther et al., 1996;Klinger et al., 1998;Greenberg et al., 1999Greenberg et al., , 2003Otter et al., 2002;85 Harley et al., 2003;Stone et al., 2010;Jaars et al., 2016;Liu et al., 2021) (Figure 1: green arrows), and were measured during the local rainy season (except Jaars et al., 2016), which increases the challenge of BVOC estimation in these climate-sensitive African ecosystems.
Thus, the overall objective of this study was to quantify BVOC mixing ratios in the humid highland dominated by agroforestry, and the dry lowlands with bushland and agriculture mosaic landscapes in Kenya during the rainy and dry 90 season of 2019. We hypothesized considerable differences in BVOC mixing ratios between land cover type, at diurnal scale and at season scale. We interested in the diurnal as well as the seasonal variation in BVOC mixing ratios, and we estimated EFs for BVOCs to improve the representation of BVOC emissions from African ecosystems in models.

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BVOC mixing ratios and meteorological measurements were set up in Taita Taveta County in southern Kenya. The county consists of dry savannas located in the lowlands between 500 and 1000 m a.s.l., and highlands ranging from approximately 1100 to 2200 m a.s.l. (Pellikka et al., 2018).
Taita Taveta County has two rainy and two dry seasons annually due to the Intertropical Convergence Zone forming a bimodal rainfall pattern. The first rainy season (often referred to as the long rains) occurs between March and June, while 100 the second rainy season (referred to as the short rains) is between October and December. The two rainy seasons are separated by dry seasons, with a short hot and dry season from January to February and a long cool and dry season from June to September (Ayugi et al., 2016;Wachiye et al., 2020). The highlands receive more rainfall than the lowlands. The annual precipitation is on average 1132 mm in Mgange (1768 m a.s.l.), corresponding to about twice the rainfall received in Voi at 560 m a.s.l. (587 mm) (Erdogan et al., 2011). The annual temperature is 18.5 °C in Taita Research Station in 105 the highlands and 22.3 °C in Maktau field site in the lowlands between 2013 and 2021. Both meteorological measurements are managed by the University of Helsinki, Finland. The length of sunlight remains 12 ± 0.5 hour through the entire year, with sunrise around 06:00 ± 0.5 hour and sunset about 18:00 ± 0.5 hour depending on the season (all times are given as East Africa Time, UTC +3).
The experimental sites were set up in the highlands in Wundanyi at Taita Research Station of the University of Helsinki 110 and in the lowlands in the Maktau field site to represent the highland and lowland ecosystems, respectively (Fig. 2). The Taita Research Station (3° 40′ S, 38° 36′ E; 1415 m) is located in the middle of the Taita Hills on a windward slope. The landscape is characterized by small agricultural fields with a variety of crops, such as maize, beans, avocados, and grass, with small native or exotic forest stands. The measurement station, which is fenced off, is surrounded by agroforestry landscape, with the closest native and exotic forests at 200 m distance. The natural ecosystem of the Wundanyi site is 115 humid montane forest (Pellikka et al., 2009). Broadleaf evergreen trees and lush grass covered the ground layer during the rainy season at the Wundanyi site (Fig. 2b), while part of the leaves were shed from trees and grass was dried out https://doi.org/10.5194/acp-2021-445 Preprint. Discussion started: 17 June 2021 c Author(s) 2021. CC BY 4.0 License. around our instrument during the dry season (Fig. 2c). The Maktau field site (3° 25′ S, 32° 74′ E; 1056 m) is located in the lowlands in which the natural ecosystem would be Acacia-commiphora bushland on savanna (Amara et al., 2020).
The measurement site is located inside a fenced farm growing maize, cassava, beans, and papaya trees, surrounded by 120 bushland. The soil on this site was not ploughed yet and field was not sown or re-planted during our rainy season measurements (Fig. 2d). The instrument was positioned near young cassava bush, with a distance of 50 m from the nearest bushland edge. In the dry season, we collected the samples two weeks after the maize was harvested, the dry maize residuals still remaining on the ground (Fig. 2e). The bushland surrounding the field was almost leafless during the dry season sampling, while during the rainy season sampling, the new leaves were starting to sprout. The sites were chosen 125 for two reasons: 1) they represented typical highland agroforestry and lowland dry agriculture ecosystems with typical bushland and forest cover, and 2) they provided safety and electricity for continuous measurements. with a few fire scars. The brownish areas are areas with less land cover, such as dry bushland, dryland agriculture, and areas used for livestock management. Photographs (b, c, d, e) show the phenological conditions and the surrounding environments of the measurement sites during sampling in the rainy season in April and dry season in September.

Sample collection and chemical analysis of BVOC mixing ratios
We conducted four campaigns, each lasting several days, in the highlands and lowlands during the onset of the rainy 135 season from April 10 to 17, 2019 and during the dry season from September 1 to 19, 2019 (Table A1).  Louis, MO, USA). The sampling time was generally 4 hours but was only 2 hours during the second campaign due to frequent power failures (Table A1). The sampling took place 25 cm above the ground so that flowing water during heavy rainfall events did not disturb the measurements.
All samples were analyzed in the laboratory of the Finnish Meteorological Institute. An automatic thermal desorption device (PerkinElmer TurboMatrix 650) was connected to a gas chromatograph (PerkinElmer Clarus 600) with a DB-5MS column (50 m × 0.25 mm, film 0.5 µm) and a mass-selective detector (PerkinElmer Clarus 600T). We desorbed all sample tubes at 300 °C for 5 minutes before cryo-focusing the samples in a Tenax TA cold trap (−30 °C) and injecting them into 150 the column by rapidly heating the cold trap to 300 °C. The method has been described in detail in Helin et al. (2020).
Standards in methanol solutions were used to calibrate the MBO, MTs, and SQTs. We injected the standards into the sampling tubes and flushed away the methanol for 10 minutes before the analysis. The gaseous calibration standard (National Physical Laboratory) was applied for isoprene. Calibration samples were analyzed together with real samples. , and soil moisture (CS650 sensor, Campbell Scientific, UK) were additionally measured at Maktau. PPFD sensor was positioned around 4 m above the ground. Soil moisture was measured depths of 10 and 30 cm. Root-zone soil moisture calculation has been described in Räsänen et al. (2020).

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The Chemistry Land-surface Atmosphere Soil Slab model was used to estimate mixing layer heights (MLHs) at the lowland site (Python version, Vilà-Guerau de . The model initial conditions were derived from the weather station observations. The sensible and latent fluxes from eddy covariance measurements were used as model input. These flux measurements were corrected by conserving the Bowen ratio using the net radiation measurements (Combe et al., 2015). The diurnal MLH data start from 06:00 and continue to 18:00, and the MLHs ranged from 337 ±

Oxidant concentration estimation
Since the concentrations of oxidants were not measured directly during the campaigns, we used data observed by an Ozone Monitoring Instrument to acquire O3 column densities to estimate O3 concentrations (the conversion method is 180 described at WDC, http://wdc.dlr.de/data_products/SERVICES/PROMOTE_O3/vmr.html, last access: December 15, 2020) and ultraviolet B (UVB) radiation intensity to calculate OH radical proxies using Eq. 1 (Rohrer et al., 2006;Petäjä et al., 2009). The calculated average midday (local noon time) concentrations of O3 were 31 parts per billion by volume (ppbv) and 185 29 ppbv in the rainy and dry seasons, respectively, while the corresponding concentration of OH was estimated to be 1.2 × 10 6 and 1.1 × 10 6 molecule cm −3 in the rainy and dry seasons in our study area, respectively.

Reactivity calculation
Calculating the reactivity of BVOCs gives insight into the relative role of BVOCs in local atmospheric chemistry. The reactivity of BVOCs (Ri,x, where i refers to the BVOC species and x the oxidant species) was calculated by multiplying 190 the mixing ratio of a specific BVOC (i) with the corresponding reaction rate coefficient (ki,x) of oxidants (including O3, OH, NO3) using Eq. 2.
The parameter ki,x was calculated by using the average air temperature during each measurement (calculation equations described in Table A2). All of the reaction rate coefficients used in this study are provided in Table A3.  Table A3.

Emission factor estimation
EFs were estimated for isoprene, MBO, and the detected MTs using inverse modeling. In practice, a simple BVOC  For most of the compounds studied, the daily mean mixing ratio was higher during the rainy season than during the dry season. The daily mean isoprene mixing ratio ranged from 134 to 442 pptv in the highlands and from 22 to 69 pptv in the lowlands in the rainy season. The daily mean mixing ratio of MTs was 117 to 233 pptv in the highlands and 29 to 96 pptv in the lowlands, and that of SQTs was 2 to 30 pptv in the highlands and 1 to 2 pptv in the lowlands during the rainy season.
During the dry season, the isoprene mixing ratio ranged from 36 to 150 pptv in the highlands and from 6 to 15 pptv in the 235 lowlands. The mixing ratio of MTs ranged from 8 to 75 pptv and 3 to 9 pptv, while the mixing ratio of SQTs ranged from 1 to 3 pptv and was less than 1 pptv, in the highlands and lowlands, respectively, during the dry season.

Mixing ratios of isoprene and monoterpenoids
Isoprene and MTs explained over 88 % of the total BVOC mixing ratios of all collected samples, and their mixing ratios in the rainy season were higher than in the dry season in both the highlands and lowlands. The seasonal mean ± standard 240 deviation of the isoprene mixing ratio was 252.2 ± 285 pptv and 55.3 ± 56 pptv in the rainy season in the highlands and the lowlands, respectively, while the corresponding values were 145.5 ± 73 pptv and 57.8 ± 46 pptv for MTs (Fig. 4).
During the dry season, the mixing ratio of isoprene was 66.6 ± 75 pptv and 11.2 ± 9 pptv in the highlands and the lowlands,    The isoprene mixing ratio ranged from 730 to 1820 pptv in the rainy season of 1996 in a tropical forest in northern Congo, which was covered by evergreen or semi-evergreen trees (Serça et al., 2001). A similar level of isoprene mixing ratio was observed in a forest ecosystem near Enyela, northern Congo, with values ranging from 700 to 1000 pptv at the end of the rainy season of 1996 (Greenberg et al., 1999). In western Africa, isoprene mixing ratios of over 1000 pptv during daylight hours were measured in a forest surrounded by a woodland savanna ecosystem in Benin (Saxton et al., 275 2007). The western Africa and the two central Africa measurements aforementioned all showed at least an order of magnitude higher isoprene mixing ratios compared with the measurements in the highlands (Wundanyi) of this study ( southern Kenya (Liu et al., 2021). The mixing ratios of isoprene and MTs at the lowland site in Maktau were about four times lower than the corresponding levels measured from savanna ecosystems in Central Africa Republic (Boali) and South Africa (Greenberg et al., 1999;Harley et al., 2003), and grass, shrubland in western Senegal (Grant et al., 2008), and considerably lower than the corresponding compound levels from woodland in Botswana .
The mixing ratios of isoprene and limonene in the rainy season in Maktau are higher than the levels of the corresponding 285 compounds in grassland in Welgegund, South Africa, while the mixing ratios of α-pinene and β-pinene, both in the rainy and the dry seasons, as well as isoprene and limonene in the dry season in Maktau, were lower than the values reported by Jaars et al. (2016). The mixing ratios of α-pinene, limonene, and β-pinene in the rainy season in Maktau were all in the range of the mixing ratios of the corresponding compounds in our previous measurements, while that of isoprene was at lower levels than previously reported (Liu et al., 2021). 290

Mixing ratios of sesquiterpenes, MBO, and bornyl acetate
The mixing ratios of SQTs were low and contributed to around 3 % of the total BVOC mixing ratios in all samples. SQTs showed seasonal and diurnal variations similar to those of MTs, but their mixing ratio was much lower than that of MTs, 295 with seasonal mean SQT mixing ratios of 15.0 ± 19 pptv and 1.5 ± 0.9 pptv in the rainy season, and 1.1 ± 2 pptv and 0.5 ± 0.3 pptv in the dry season, in the highlands and lowlands, respectively. SQTs are very reactive and therefore their contribution to the local atmospheric chemistry can still be significant. The highest daily means were measured during the nighttime, which was the same as in the case of the MTs. β-caryophyllene showed the highest mixing ratios among were 12 pptv and 8 pptv in their first and second campaign, respectively, which are higher than the mean MBO mixing ratios measured in the highlands and lowlands in this study. Guenther (2013) stated that MBO is emitted from most isoprene-emitting vegetation at an emission rate of ∼ 1 % of that of isoprene. The Welgegund data (Jaars et al., 2016) showed that MBO is approximately 30 % of the isoprene mixing ratio, and thus their study indicated that MBO at Welgegund is most likely from other MBO emitting species than from isoprene emitters. MBO are higher than 1 % of 315 isoprene mixing ratios in our study, which was 3.7 % and 6.3 % of the isoprene mixing ratio in the highlands in the rainy and dry seasons, respectively, and 7.6 % and 9.8 % in the lowlands. Unfortunately, we could not partition the source of MBO emitter(s) in this study area during our measurements.

Reactivity of the measured BVOCs with oxidants
The reactivity toward O3, OH, and NO3 was calculated using the measured BVOC mixing ratios (Fig. 6). The O3 reactivity 320 of SQTs was 5 to 30 times higher than for other BVOCs, with β-caryophyllene having the highest contribution to the total O3 reactivity. The strong relative importance of the SQTs compared with other BVOCs for the local O3 reactivity has also been seen in the ambient air of a Scots pine forest in Finland (Hellén et al., 2018). Out of the total BVOCs, MTs contributed most to the NO3 reactivity, an average of 13 and 15 times more than isoprene and SQTs, respectively. MTs also contributed to the OH reactivity, with a 0.7 to 1.9 times higher contribution than isoprene during nighttime, while 325 isoprene is the dominant BVOC contributor to the OH reactivity during the day, with 3.1 to 3.5 times higher contributions than MTs.
Isoprene shows the highest mixing ratio of BVOCs in this study. The atmospheric lifetime of isoprene is 34 hours and 2.3 hours with O3 and OH, respectively. Follow that of isoprene, limonene (~ 2 hours) and α-pinene (~ 4 hours) have higher mixing ratios, and are detected to have a relatively short lifetime with OH and O3 compared with other MTs (except 330 terpinolene and linalool). Higher importance of limonene and α-pinene for OH reactivity than other MTs was also observed in a savanna ecosystem in South Africa (Jaars et al., 2016), which reported that both compounds also had higher mixing ratios than other MTs during their campaigns. Compared with other MTs, limonene has a significantly higher yield for highly oxygenated organic molecules (Ehn et al., 2014;Bianchi et al., 2019), which has been found to be a major component of secondary organic aerosols (e.g., Ehn et al., 2014;Mutzel et al., 2015), for which higher limonene is 335 expected to have a strong impact on local aerosol production in southern Kenya as well. The low mixing ratios of βcaryophyllene and α-humulene have shorter lifetimes with OH and O3 than other SQTs and BVOCs. The lifetimes of βcaryophyllene and α-humulene are a few minutes with O3 and about 1 hour with OH (Table A3).

Estimation of BVOC emission factors
The EFs for isoprene, MBO, and detected MTs, for the agriculture savanna ecosystem surrounding the Maktau site, were estimated for the rainy and dry seasons separately (Fig. 7). The median values of the EF for α-pinene, β-pinene, 3∆carene, camphene, and limonene ( Fig. 7c to g) are higher during the rainy season in April than during the dry season in 345 September, while the median values of the EF for MBO (Fig. 7b) and all other MTs (Fig. 7g) are higher during the dry season than during the rainy season. If the dependency of soil moisture availability on the emission of isoprene is considered, then the EF for isoprene during both the rainy and dry seasons is effectively the same (Fig. 7a). Considering the variability in the estimated EFs for the two different seasons, only the EFs for limonene show no overlap in the indicated error bars (Fig. 7f), which are defined by the minimum and maximum daily estimated EF. Thus, our results 350 suggest that the EF for limonene might be seasonally dependent.
In order to put the estimated EFs into context and to contribute to an improved representation of BVOC emissions from African ecosystems in models, the estimated EFs are compared with the EFs used in MEGAN v2.1 for warm C4 grass and Crop1 . compare very well with the EFs used in MEGAN for warm C4 grass (Fig. 7a, d), and in the case of β-pinene, also for Crop1, since MEGAN assumes the same EF for β-pinene for the two different plant functional types. The estimated median EFs for MBO, α-pinene, 3∆-carene, and limonene are higher than the EFs used in MEGANv2.1 by about 8 (4), 17 (53), 1 (2), and 89 (314)  We emphasize that the estimated EFs are connected with a large degree of uncertainty, since they are not based on flux measurements from the site but are instead determined using observed BVOC mixing ratios and an inverse modeling 380 approach, which is limited by model assumptions and inputs.

Conclusion
In this study we measured mixing ratios of isoprene, MTs, SQTs, bornyl acetate, and MBO in the humid highland and dry lowland ecosystems in Taita Taveta County, southern Kenya, during both a rainy and a dry season.
Isoprene and MTs showed the highest mixing ratios in both the highlands and lowlands, while α-pinene, limonene, 385 and β-pinene accounted for the largest contribution to the total mixing ratio of MTs. Isoprene dominated the total BVOC mixing ratio during daytime and reached diurnal peak mixing ratios in the afternoon in the highlands and in the early evening in the lowlands.   Calculation methods of k values were shown in Table A2.
Data availability. BVOC mixing ratios and meteorological data used in this work are available from the authors upon request (yang.z.liu@helsinki.fi and petri.pellikka@helsinki.fi).