Interactive effects of air pollutants and atmospheric moisture stress on aspen growth and photosynthesis along an urban-rural gradient

Journal Pre-proof Interactive effects of air pollutants and atmospheric moisture stress on aspen growth and photosynthesis along an urban-rural gradient Zhenhua Wang, Chengzhang Wang, Bin Wang, Xin Wang, Jing Li, Jin Wu, Lingli Liu PII:

S0269-7491(19)36600-X

DOI:

https://doi.org/10.1016/j.envpol.2020.114076

Reference:

ENPO 114076

To appear in:

Environmental Pollution

Received Date: 8 November 2019 Revised Date:

16 January 2020

Accepted Date: 23 January 2020

Please cite this article as: Wang, Z., Wang, C., Wang, B., Wang, X., Li, J., Wu, J., Liu, L., Interactive effects of air pollutants and atmospheric moisture stress on aspen growth and photosynthesis along an urban-rural gradient, Environmental Pollution (2020), doi: https://doi.org/10.1016/j.envpol.2020.114076. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

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Interactive effects of air pollutants and atmospheric moisture stress on

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aspen growth and photosynthesis along an urban-rural gradient

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Zhenhua Wang1, 2, Chengzhang Wang1, 2, Bin Wang1, 2, Xin Wang1, Jing Li1, 2, Jin Wu3,

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Lingli Liu1, 2*

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1. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

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2. University of Chinese Academy of Sciences, Beijing 100049, China

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3. School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong

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Article type: Full Research Papers

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*

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Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan,

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Beijing, 100093, China. E-mail: [email protected]

Corresponding author: Lingli Liu, State Key Laboratory of Vegetation and

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Conflict of Interest: All authors declare they have no actual or potential competing

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financial interests.

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Manuscript submitted to Environmental Pollution 7th of November 2019 Manuscript information: 37 pages, two table, seven figures, five supplementary tables, and ten supplementary figures.

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Abstract

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Atmospheric pollution could significantly alter tree growth independently and

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synergistically with meteorological conditions. North China offers a natural experiment

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for studying how plant growth responds to air pollution under different meteorological

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conditions, where rapid economic growth has led to severe air pollution and climate

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changes increase drought stress. Using a single aspen clone (Populus euramericana

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Neva.) as a ‘phytometer’, we conducted three experiments to monitor aspen leaf

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photosynthesis and stem growth during in situ exposure to atmospheric pollutants along

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the urban-rural gradient around Beijing. We used stepwise model selection to select the

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best multiple linear model, and we used binned regression to estimate the effects of air

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pollutants, atmospheric moisture stress and their interactions on aspen leaf

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photosynthesis and growth. Our results indicated that ozone (O3) and vapor pressure

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deficit (VPD) inhibited leaf photosynthesis and stem growth. The interactive effect of O3

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and VPD resulted in a synergistic response: as the concentration of O3 increased, the

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negative impact of VPD on leaf photosynthesis and stem growth became more severe.

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We also found that nitrogen (N) deposition had a positive effect on stem growth, which

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may have been caused by an increase in canopy N uptake, although this hypothesis needs

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to be confirmed by further studies. The positive impact of aerosol loading may be due to

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diffuse radiation fertilization effects. Given the decline in aerosols and N deposition

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amidst increases in O3 concentration and drought risk, the negative effects of

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atmospheric pollution on tree growth may be aggravated in North China. In addition, the

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interaction between O3 and VPD may lead to a further reduction in ecosystem

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productivity.

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Key words: stem growth, ozone, nitrogen deposition, aerosol, VPD

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Capsule: The response of leaf photosynthesis and stem growth to multiple atmospheric

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pollutants.

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1. Introduction

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With recent rapid industrialization and urbanization, many regions in China and India

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face serious air pollution problems (Li et al., 2019; Menon et al., 2002; Wang et al.,

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2014). Ozone (O3), aerosols and N deposition are the three major atmospheric pollutants

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that significantly affect ecosystem health (Kanniah et al., 2012; Schulte-Uebbing and de

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Vries, 2017; Lefohn et al., 2018). Additionally, climate change can alter meteorological

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conditions. For example, temperature and vapor pressure deficit (VPD) are expected to

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increase in most regions under climate warming (Will et al., 2013; Bragazza et al., 2016;

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de Carcer et al., 2018). Air pollutants and their interactions with atmospheric moisture

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stress could greatly affect ecosystem productivity (McLaughlin and Downing, 1995;

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McLaughlin et al., 2007; Wang et al., 2018). A better understanding of how plant

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photosynthesis and growth respond to air pollutants in the context of climate change is

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critical to forecasting how the carbon (C) cycle may change, as well as to informing new

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environmental policy to manage or mitigate these changes.

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Different atmospheric pollutants likely have different effects on plant growth. With

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its strong oxidizing effect, O3 damages mesophyll cells and inhibits photosynthetic

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enzyme activity (Ainsworth et al., 2012; Li et al., 2017; Moura et al., 2018), thereby

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reducing tree growth and ecosystem productivity (Gregg et al., 2003; Talhelm et al.,

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2014). In contrast, aerosols may promote plant growth by increasing the fraction of

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diffusion radiation, and thus enhancing canopy light use efficiency (Gu et al., 2003;

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Matsui et al., 2008; Mercado et al., 2009). In addition, the cooling effect of aerosols

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could also promote photosynthesis by reducing midday depression (Steiner and

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Chameides, 2005) and by alleviating atmospheric moisture stress (Wang et al., 2018).

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However, heavy aerosol loading could also reduce plant growth by inducing light

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limitation or depositing toxins (Cohan et al., 2002; Mahowald, 2011; Yue and Unger,

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2017). Furthermore, nitrate (NO3−) and ammonium (NH4+) were the dominant ionic

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species in the aerosols in Northern China, constituting about 25% and 16% of the total

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mass of water-soluble ions, respectively (Yao et al., 2002). The deposition of these

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N-containing particles could stimulate ecosystem productivity by alleviating N limitation

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(Thomas et al., 2010; Schulte-Uebbing and de Vries, 2017). Nitrogen deposition could

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also negatively impact plant growth by acidifying soils or by inducing nutrient imbalance

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(Penuelas et al., 2013). Although numerous studies have investigated the effects of

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individual pollutants on plant growth, few field studies have explored the combined

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effects of multiple pollutants and evaluated the relative importance of each pollutant on

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plant growth (Bytnerowicz et al., 2007; Matyssek et al., 2012).

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Meteorological conditions may affect plant growth either on their own or through

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their interactions with atmospheric pollutants (McLaughlin and Taylor 1981, McLaughlin

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et al., 2007; Grantz et al., 2018; Wang et al., 2018). The climate has become warmer and

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drier in North China and other regions, which increases atmospheric moisture stress by

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increasing VPD (Wu et al., 2011; Will et al., 2013). Higher VPD could greatly inhibit

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plant photosynthesis, which thereby reduces tree growth and the efficacy of ecosystem C

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sinks (Novick et al., 2016; de Carcer et al., 2018). Increased atmospheric moisture stress

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could exacerbate or alleviate the impacts of air pollutants on plant growth. For example,

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previous studies have found that high VPD-induced stomatal closure reduced O3 uptake

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and mitigated the negative effects of O3 exposure (Hayes et al., 2012; Li et al., 2015),

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whereas low VPD increased foliar injury by increasing O3 stomatal uptake (McLaughlin

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and Taylor 1981). Low VPD did enhance the positive effect of aerosols on tree growth

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though (Wang et al., 2018). Air pollution exposure could also alter plant sensitivity to

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moisture stress. Many studies have found that prolonged chronic O3 exposure results in

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the loss of stomatal sensitivity to environmental stimuli, such as soil moisture and VPD

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(Hayes et al., 2012; Dusatrt et al., 2019). The sluggish response of stomata potentially

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reduces water use efficiency and increases transpiration, even under drought stress

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(Mclaughlin et al., 2007; Uddling et al., 2009). The interaction between air pollutants and

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atmospheric moisture stress adds an additional dimension of uncertainty to projections of

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future ecosystem productivity because these interactions are not well understood

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(Bytnerowicz et al., 2007; Matyssek et al., 2012).

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The North China Plain is one of the most polluted areas in China (Zhang et al., 2012;

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Wang et al., 2014) and is projected to experience increased drought stress under climate

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warming (Piao et al., 2010; Chen and Sun, 2017). Therefore, it is important to explore

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how air pollutants and their interaction with atmospheric moisture stress affect plant

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growth in this region (Yao et al., 2018). The Beijing metropolitan area is located at the

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northern end of the North China Plain, topping the wide range of O3, aerosol and N

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deposition levels along the urban-rural gradient. Here, we used a fast-growing clone of

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aspen (Populus euramericana Neva.) as a ‘phytometer’ and conducted three experiments:

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we first established a tree barrel experiment at six sites along the urban-rural pollution

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gradient. At each site, we used the same soil, and we blocked rainwater to eliminate the

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confounding effects caused by differences in soil condition and precipitation between

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sites (Experiment 1). Then, we chose two sites with aspen stands planted with the same

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aspen clone in the same year, and we monitored stem growth, atmospheric pollutants and

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meteorological conditions daily (Experiment 2). Finally, we intensively measured leaf

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stomatal conductance and photosynthesis under different levels of air pollution in one

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aspen stand (Experiment 3). With the three experiments, we aimed to address the

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following two questions: (i) How does aspen growth respond to multiple air pollutants?

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What is the relative importance of each air pollutant on aspen growth? (ii) Will

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atmospheric moisture stress alter a plant’s sensitivity to air pollution, and if so, by what

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physiological mechanism?

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2. Materials and methods

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The study was conducted in the vicinity of Beijing, which has a temperate

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continental monsoon climate with mean annual temperature of 13°C and mean annual

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precipitation of 538 mm. Using atmospheric pollution data obtained from the Beijing

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Municipal Environmental Monitoring Center (www.bjmemc.com.cn), we selected six

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sites near the air quality monitoring station that had different combinations of O3,

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aerosols and N deposition levels: Haidian, Changping, Miyun, Shunyi, Tongzhou and

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Fangshan (Table 1, Fig. 1).

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2.1. Experiment 1

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growing season

Biomass growth across the pollution gradient throughout the

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In April 2015, 96 aspen cuttings (Populus euramericana Neva.) were planted in

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128-litre barrels, all filled with soil that had been collected from the top 20 cm at a

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farmland in Tongzhou (N39.76º, E116.78º). The soil texture was silt loam. Soil C

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concentration was 1.27 ± 0.01 mg·g-1, and N concentration was 1.28±0.11 mg·g-1. Two

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aspen seedlings were planted in each barrel. After one month, the one seedling that was

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closest to the average height of all seedlings was retained to maintain as much uniformity

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in the initial state of the seedlings as possible. In May 2015, eight barrels were

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transported to each site. To reduce the confounding effects caused by the precipitation

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difference between sites, we placed a transparent baffle 25 cm above the soil to block

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rainfall from entering the soil in the barrel. All barrels were irrigated with 10 liters of

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water twice a month.

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The tree height (H) and basal stem diameter (D) were measured with an electronic

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vernier caliper (a precision of 0.01 mm) and a tape measure (a precision of 1 mm) at the

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beginning and end of the 2016 growing season. In November 2016, we cut 48

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two-year-old aspen seedlings from the 6 sites of Experiment 1 and 18 one-year-old aspen

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seedlings from our another experiment at the Haidian site, and establish the following

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allometric growth equation (R2=0.984, P <0.0001, Fig. S1):

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Biomass = 0.00256 * (H*D2)0.96

(1)

Tree biomass was estimated with equation 1, and the biomass growth rate for the 2016 growing season was calculated with the following equation:

(2)

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where Biomass1 and Biomass2 are the biomasses at time1 and time2, respectively.

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2.2. Experiment 2: Daily stem growth

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To better address how air pollutants and atmospheric moisture stress affected aspen

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growth, we conducted another experiment at Haidian and Fangshan. Haidian had a higher

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average O3 level than Fangshan (Table 1). In early April 2014, 144 aspen clone cuttings

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were planted at each site in an arrangement of 12 columns by 12 rows with 1 m × 1 m

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spacing. In spring 2015, automatic dendrometers with a precision of 4.4*10-5 mm

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(Ecomatic, Germany) were installed on eight trees at each site. The changes in diameter

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at breast height (DBH) were recorded every 30 minutes, and cross sectional area (A) at

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the breast height was calculated based on the DBH:

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(3)

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Relative growth rate of cross sectional area (RGR) in rain-free days during the

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2015-2016 growing season was estimated using the following equation:

(4)

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where A1 and A2 are the cross sectional areas at time1 and time2, respectively.

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2.3. Experiment 3: Leaf stomatal conductance and net photosynthesis rate

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To assess how changes in tree growth might relate with leaf physiology response,

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the stomatal conductance (gs) and net photosynthesis rate (An) were intensively measured

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in situ in newly mature sunlit leaves at the Haidian site. We selected four trees with

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similar growth conditions to measure leaf gs and An. We chose the fourth or fifth sunlit

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leaves from the top of the branch, and the measurements were conducted at half hour

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intervals from 8:00-18:00 on cloud-free days during the growing season (June-August).

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The gs and An were measured by a portable open gas-exchange system LI-6400 (Li-Cor,

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Inc., Lincoln, NE, USA) under ambient conditions. We used the data with

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photosynthetically active radiation (PAR) > 1500 µmol photo m-2 s-1 in order to eliminate

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effects of light interference. The conditions in the leaf cuvette were monitored

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continuously, with the CO2 concentration ranging from 370 to 410 ppm, the leaf

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temperature (Tleaf) ranging from 32.9 to 42.9 °C, the relative humidity (RH), ranging

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from 10.7% to 44.2.0% and the leaf-air vapor pressure deficit (VPDL) ranging from 2.1

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to 6.3 kPa.

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2.4. Atmospheric pollutants and meteorological conditions

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We used the hourly O3 mean and the daily 12-h AOT40 to estimate the impact of O3

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on plant growth and photosynthesis. The daily 12-h AOT40 values were calculated using

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the hourly mean O3 concentration for the 12-h period from 08:00 to 20:00. Particulate

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matter smaller than 2.5 micrometers (PM2.5) was used to represent aerosol loading.

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Values of hourly O3 and PM2.5 were obtained from the Beijing Municipal Environmental

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Monitoring Center for the six study sites during the study period. Hourly air temperature

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and relative humidity were obtained from a National Meteorological Information Center

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dataset (http://data.cma.cn/), and vapor pressure deficit was calculated from temperature

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and RH (Sadler and Evans, 1989). At each site, six ion exchange columns (three for

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rainwater collection, and three others as blank controls) were installed to measure wet N

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deposition (Xu et al., 2015). The columns were replaced monthly. In addition, hourly

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precipitation was measured with a microclimate monitoring system in the aspen

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plantations at Haidian and Fangshan (Decagon Devices Inc., USA).

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2.2. Statistical Analysis

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To minimize the influence of seasonal variation, RGR was detrended according to the following equation: RGRd = RGR – RGRtrend

(5)

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where RGR is the original time series of daily stem growth rate, RGRtrend is the

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seasonal trend calculated by smoothing the time-series using a centered minimized

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moving window average (period of 15 days, R package ‘forecast’), and RGRd is the

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detrended RGR. The autocorrelation test was conducted with the ‘acf’ function in the

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‘stats’ R package, and the result showed that RGRd was independent on its past after

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detrending (Fig. S3). A similar detrending approach was applied to detrend the seasonal

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dynamics of temperature, VPD, AOT40, and PM2.5 for each year at each site. Cross

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correlation by the ‘ccf’ function in the ‘stats’ package showed that there was no

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significant time lag between the detrended RGR and the environmental factors (Fig. S4).

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Pearson correlations and linear regressions were used to explore the relationship

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between plant growth parameters and environmental variables. A multiple linear

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regression (MLR) model was used to estimate the effects of atmospheric pollutants,

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meteorological factors and their interactions on tree growth, leaf gs and leaf An. Variance

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Inflation Factors (VIFs) were used to detect multi-collinearity, and factors with VIFs

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under 4 were retained. A backward stepwise model using the Akaike information

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criterion (AIC) was used to select the optimal composition of atmospheric pollutants,

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meteorological factors and their interactions to explain variability in plant relative growth

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rate using the ‘stepAIC’ function from the ‘MASS’ R package. The relative importance

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of selected variables and their bootstrapped 95% confidence intervals were estimated

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using the ‘relaimpo’ R package.

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Two tests were conducted to decouple the effect of VPD and O3 on RGR, leaf gs and

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An. In test 1, we partitioned the range of the O3 level into four intervals of equal width,

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and regressed RGR/gs,/An against VPD for each O3 interval. In test 2, we partitioned the

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range of the VPD into four intervals of equal width, and regressed RGR/gs,/An against O3

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for each VPD interval. We then conducted an analysis of covariance (ANCOVA) to

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compare the slopes of the regressions between different intervals using the ‘HH’ R

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package. P < 0.10 was considered to be statistically significant. This analysis allowed us

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to distinguish among the effects of VPD and O3 on RGR, leaf gs and leaf An (Wu et al.,

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2017). All statistical analyses were conducted in R (version 3.3.2).

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3. Results

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3.1. Characterization of atmospheric pollution in the study area

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Ozone, PM2.5 and N deposition showed a distinct spatial gradient in the study area,

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with northeast regions having relatively higher O3 concentrations and N deposition, but

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lower PM2.5 levels than the southwest regions (Fig. 1a, Fig. S2). When investigating the

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temporal correlation between the pollutants, O3 concentration was positively correlated

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with PM2.5 concentration in all sites (Fig. S5a). Similarly, atmospheric N deposition was

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positively correlated with PM2.5 concentration across all sites, but it was not correlated

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with O3 (Fig. S5b-c).

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3.2. Effects of air pollutants on biomass growth rate along the pollution gradient

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Across all six sites, the biomass growth rate during the summer growing season was

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negatively correlated with AOT40 and positively correlated with N deposition (Fig. 2a,

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Fig. 2c, Table S1). When controlling for the effects of AOT40 and N deposition, the

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partial regression showed that the biomass growth rate was positively correlated with

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PM2.5 (Fig. S6). Model selection based on AIC scores indicated that the biomass growth

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during the summer growing season was driven by AOT40, N deposition and PM2.5,

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which together accounted for 46.3% of the variation (Fig. 2d, Table 2).

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3.3. Effects of atmospheric pollutants and VPD on daily tree growth

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After detrending, RGRd decreased significantly with AOT40d and VPDd, but it

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increased with PM2.5d over a daily timescale (Fig. 3a-c, Table S3). VPDd, AOT40d, PM2.5d

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and the interaction between VPDd and AOT40d all significantly explained the daily

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variability of RGRd. Altogether they accounted for 20.4% of the variation (Fig. 3d, Table

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2). In comparing the sites, RGRd was mainly affected by AOT40d at Haidian, but it was

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mainly affected by PM2.5d at Fangshan (Fig. S7-S8).

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Experiment 2 found that a significant interaction between AOT40d and VPDd on

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daily RGRd (Fig. 3d). The binned regression showed that the slopes between RGRd and

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VPDd at different AOT40 intervals were marginally significant different (P = 0.09). The

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slopes between RGRd and VPDd became more negative with increasing AOT40d, with

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the lowest slope observed at the highest AOT40 interval (Fig. 4a). At low VPD levels, the

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RGRd did not change with AOT40d, but it decreased significantly at the highest VPD

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interval (P = 0.01; Fig. 4b).

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3.4. Effects of O3 and VPDL on leaf gs and An

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Experiment 3 revealed that, among all the predictors, VPDL and O3 had the greatest

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effects on gs and An (Table 2). The partial correlation and the multiple linear regression

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indicated that both VPDL and O3 significantly reduced leaf gs and An under the saturated

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light conditions, but that VPDL played a more dominant role (Fig. 5-6, Table 2). Leaf gs

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and An decreased significantly with VPDL, and the slopes became steeper at higher levels

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of O3. At low VPDL levels, leaf gs and An did not change with O3, but they significantly

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decreased with increasing O3 at high VPDL levels (Fig. 7c-d). The ANCOVA analysis

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confirmed that the interaction between VPDL and O3 on leaf gs and An were significant

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(Fig. 7a-d).

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4. Discussion

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To evaluate the effects of air pollutants and their interactions with atmospheric

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moisture stress on plant growth, we conducted three field experiments along the

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urban-rural pollution gradient in the Beijing metropolitan area. Our tree barrel study

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(Experiment 1) showed that N deposition stimulated aspen growth (Fig. 2c). Numerous

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studies have demonstrated that N deposition enhances plant productivity (Thomas et al.,

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2010). The basic assumption is that the deposited N is mainly taken up by roots

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(Schulte-Uebbing and de Vries, 2017), although many experiments have also found that

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leaves can directly assimilate dry or wet-deposited N (Gooding and Davies, 1992; Peuke

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et al., 1998; Nair et al., 2016). In our tree barrel experiment, we installed a plastic shelter

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over the soil surface to block precipitation, so the positive effect of N deposition on tree

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growth might be attributed to the direct uptake of deposited N by canopy leaves. Shifts in

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socioeconomic structure and climate change have altered the dry-to-wet and

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ammonia-to-nitrate ratios of N deposition in China (Yu et al., 2019). More studies are

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needed to evaluate how such changes could affect direct canopy N uptake and, thus, the

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annual N supply.

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Field observations and model studies suggest that aerosols promote canopy

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photosynthesis by increasing the diffuse radiation fraction (Knohl and Baldocchi, 2008;

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Mercado et al., 2009). However, field evidence of this interaction is limited (Wang et al.,

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2018). The exceptionally wide ranges of aerosol loading in our study area (Fig. S5) and

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the intensive stem growth measurements in Experiment 2 allowed us to evaluate the role

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of aerosol pollution on tree growth. Here, we used PM2.5 as an index for aerosol loading.

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Our data demonstrated that aerosol loading stimulates aspen stem growth (Fig. 3b, Fig.

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S9c). The positive effect aerosols have on stem growth may be due to the enhanced

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canopy light use efficiency under skies with high aerosol loading (Mercado et al., 2009;

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Wang et al., 2018) because diffuse radiation increased linearly with PM2.5 (Fig. S10). In

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addition, this positive effect could also be partially attributed to high N deposition that

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was induced by aerosol loading (Grantz et al., 2003; Mahowald, 2011). Indeed, several

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national-scale studies conducted in China found that about 20 to 30% of the aerosol

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components were nitrates and ammonias, which are the main sources of atmospheric N

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deposition (Zhang et al., 2012; Huang et al., 2014). Consistent with this, we found that N

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deposition increased with PM2.5 concentration (Fig. S3c).

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Both Experiments 1 and 2 showed that O3 had significant negative effects on tree

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growth. Because N deposition, aerosols and O3 all had significant impacts on stem

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growth, we further conducted a multiple linear regression to evaluate the relative impact

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of each air pollutant. The standardized coefficients indicated that O3 and N deposition

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had greater impacts on biomass growth than PM2.5 did across the pollution gradient and

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throughout the growing season (Fig. 2d, Table 2). On a daily timescale, the negative

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effect of ozone on stem growth was similar to the positive effect caused by PM2.5 (Fig. 3d,

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Table 2). We could not assess the impact of N deposition on stem growth at a daily time

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scale due to the lack of daily N deposition data. At an hourly time scale, leaf gs and An

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were significantly and negatively impacted by O3, though VPDL impacted them even

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more (Fig. 5-6). The timescale dependence of the impact of O3 on tree growth may

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indicate that the effect is cumulative ( McLaughlin and Downing, 1995; Cailleret et al.,

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2018). Although O3 has a much smaller impact on leaf photosynthesis and tree growth

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than VPD over short timescales, the damage to chloroplasts and stomata are cumulative

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and irreversible (Ainsworth et al., 2012; Moura et al., 2018). This O3 damage compounds

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over a long period of time, thus leading to a much greater reduction in tree growth.

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Under natural conditions, plants are affected by air pollution and climate change

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simultaneously (Matyssek et al., 2012; Tai et al., 2014). The interaction between O3 and

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drought has received particular attention in the past decades because ecosystems are

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increasingly experiencing stresses from both factors (Hayes et al., 2012; Emberson et al.,

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2013; Dumont et al., 2013). Previous studies have found that O3 and soil moisture

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interact to affect plant growth, with trees being more vulnerable to episodic O3 exposure

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when growing in wet sites (Gao et al., 2017; McLaughlin et al., 2007), but drought has

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even been shown to mitigate O3 damage (McLaughlin and Taylor 1981; Li et al., 2015;

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Dusart et al., 2019). Consistently, we found that leaf gs was significant reduced under

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high VPDL (Fig. 5b), which could in turn reduce stomatal uptake of O3. We also found

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that leaf gs was not sensitive to O3 pollution under low VPDL, but under high VPDL it

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decreased with increasing O3 exposure (Fig. 7c). This phenomenon further confirms that

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drought may protect plants from elevated O3 by reducing stomatal conductance. Indeed,

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Li et al. (2015) found that elevated O3 led to earlier and more severe foliar injury under

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sufficient water conditions than under drought conditions.

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Furthermore, we found that the interaction between O3 and VPDL was synergistic:

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high O3 increased the sensitivity of leaf gs to VPDL (Fig. 7a) and resulted in a more rapid

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decrease in leaf An (Fig. 7b). Experiment 2 also found that VPD induced a greater

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negative impact on stem growth as the severity of O3 pollution increased (Fig. 4a). Tree

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stem growth mainly occurs due to the formation of new structural tissue with

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photosynthetics and to the cell expansion driven by turgor changes (De Swaef et al., 2015;

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Steppe et al., 2015). Therefore, the effects of the interaction between VPD and O3 on tree

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growth can be largely traced back to the responses of leaf gs and An (Fig. 7a-b). Our

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finding contradicted the widely reported observation that O3 exposure induced stomatal

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sluggishness (Hayes et al., 2012; Dumont et al., 2013). This may be because the aspen

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clone we studied was O3 tolerant, and its stomata were not damaged by O3 exposure.

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The synergistic interaction between O3 and VPDL resulted in rapid stomatal closure,

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which prevented water loss and O3 damage, but harmed C assimilation. Consistent with

358

our findings, a manipulative experiment in a Beijing suburb also found that Acer

359

truncatum Bunge seedlings growing under a treatment with elevated O3 and drought

360

conditions showed little injury from O3, but their biomass were reduced. The biomass

361

reduction observed under the combined treatment of elevated O3 and drought conditions

362

was more severe than the sum of the separate effects by O3 and drought exposure (Li et

363

al. 2015). In addition, the impacts of air pollutants and drought stress on plant growth

364

and leaf photosynthesis could occur consecutively (McLaughlin and Downing 1995;

365

Hayes et al., 2012). Although our study did not observe a significant lag time between

366

the exposure of air pollutants and the response of stem growth, manipulative experiments

367

are still needed to rigorously assess how the carry-over effects of each pollutant may alter

368

the responses of plant physiological processes to other pollutants and drought stress.

369 370

5. Conclusions

371

Our results indicate that different air pollutants have different effects on plant growth,

372

and that meteorological conditions could greatly alter the impacts of air pollutants on

373

plant growth. Under the dual influence of economic development and pollution control,

374

the compositions and concentrations of air pollutants have undergone significant changes

375

in China in the past few decades (Mills et al., 2018; Li et al., 2019). For example, the

376

PM2.5 concentration in the North China Plain has decreased about 40% from 2013 to

377

2017. N deposition levels have stabilized, and the O3 concentration has increased 3.1 ppb

378

per year (Li et al., 2019; Yu et al., 2019). According to our findings, we should expect

379

that the positive effects of aerosols on tree growth will gradually diminish, but the

380

negative effects of O3 will further be exacerbated in the future.

381

Moreover, we found that the interactive effect of VPD and O3 resulted in a

382

synergistic response, which suggests that the negative effect of O3 on plant growth could

383

increase with rising atmospheric moisture stress. It should be noted that many other

384

studies have also found that O3 exposure induces stomatal sluggishness (Mclaughlin et

385

al., 2007; Uddling et al., 2009; Hayes et al., 2012; Dumont et al., 2013), which means

386

that the combined effects of O3 and VPD could be additive or even antagonistic,

387

depending on the plant species. The lack of consensus on the effects of O3 on stomatal

388

aperture brings uncertainty to future C and water cycle projections amidst global change,

389

but it also highlights the great importance of performing detailed experiments to help

390

reveal the underlying mechanisms. In particular, thermal imaging has become an

391

increasingly important approach for enabling the remote sensing of stomatal conductance

392

in the field of plant physiology (Berni et al., 2009). Detailed experiments in conjunction

393

with new technologies will reveal critical insights to better characterize species-specific

394

responses to stomatal aperture under different O3 and climate change regimes.

395 396

6. Acknowledgements

397

This study was financially supported by the National Natural Science Foundation of

398

China (31670478, 31600389, and 31522011) and the Chinese National Key Development

399

Program for Basic Research (2017YFC0503902).

400 401

7. References

402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438

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Yue, X., Unger, N., 2017. Aerosol optical depth thresholds as a tool to assess diffuse radiation fertilization of the land carbon uptake in China. Atmos. Chem. Phys. 17, 1329-1342. Zhang, X.Y., Wang, Y.Q., Niu, T., Zhang, X.C., Gong, S.L., Zhang, Y.M., Sun, J.Y., 2012. Atmospheric aerosol compositions in China: spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols. Atmos. Chem. Phys. 12, 779-799.

568

Table 1. Summary of meteorological variables and atmospheric pollutant concentrations at the six sites during the 2016 summer

569

growing season (June-August). Temperature, VPD and PM2.5 are the means of all hourly values with standard error. O3 is the mean of

570

daytime hourly values with standard error. AOT40 is the 12-h cumulative value from 8:00 to 20:00. N deposition is the mean of

571

monthly values with standard error.

Site Miyun Tongzhou Haidian Shunyi Changping Fangshan

Longitude (º) 116.83 116.78 116.22 116.71 116.36 116.12

Latitude (º) 40.40 39.76 40.00 40.21 40.23 39.61

Elevation (m) 77 21 74 38 63 32

Temperature ( ) 24.7 (0.10) 24.9 (0.09) 25.5 (0.10) 25.7 (0.09) 25.8 (0.09) 25.2 (0.09)

VPD (kPa) 1.16 (0.02) 1.12 (0.02) 1.32 (0.02) 1.38 (0.02) 1.44 (0.02) 1.27 (0.02)

O3 (ppb) 67.1 (0.98) 65.8 (0.95) 65.7 (0.90) 61.8 (0.96) 59.0 (0.94) 51.2 (0.79)

AOT40 (ppb·h) 37100 36800 34400 32300 29700 21900

PM2.5 N deposition (µg*m-3) (kg*ha-1month-1) 46.4 (0.75) 3.44 (0.29) 66.7 (0.92) 3.32 (0.28) 53.1 (0.83) 2.72 (0.65) 53.1 (0.80) 3.43 (0.38) 46.6 (0.72) 3.48 (0.45) 52.6 (0.73) 3.73 (0.84)

572

Table 2. Summary of the selected multiple linear regressions for predicting biomass

573

growth rate in Exp.1; RGRd in Exp.2; leaf gs and An in Exp.3. Experiment

R2

Exp.1 Biomass growth rate

0.46

Exp.2 RGRd

0.20

Intercept AOT40 N PM2.5 Intercept VPDd

Standard coefficients 0 -0.43 0.46 0.24 0.04 -0.33

AOT40d

-0.17

0.07

0.02

1.45

PM2.5d

0.15

0.07

0.03

1.33

-0.13 0.49 -0.048

0.06 0.02 0.00

133

0.02 1.12 <0.0001 <0.0001 1.04

-0.00096 27.2 -2.43

1E-04 1.15 0.20

133

<0.0001 1.04 <0.0001 <0.0001 1.04

-0.037

0.008

Adj.R2 Predictors 0.42

0.19

Exp.3 leaf gs

0.62

0.62

VPDd:AOT40d Intercept VPDL

Exp.3 leaf An

0.54

0.54

O3 Intercept VPDL O3

s.e.

d.f.

P value

0.11 0.12 0.12 0.12 0.06 0.07

40

1 < 0.001 < 0.001 0.046 0.52 <0.0001

1.42

212

VIF 1.08 1.10 1.05

<0.0001 1.04

574

The full model of Exp.1: Biomass growth rate ~ Tair + VPD+ AOT40+ N + PM2.5 + VPD:

575

AOT40+ VPD:PM2.5, and the best model: Biomass growth rate ~ N + AOT40+PM2.5.

576

The full model for Exp.2: RGRd~Taird+VPDd+AOT40d+PM2.5d+VPDd:AOT40d+ PM2.5d:

577

VPDd +Taird:PM2.5d+ Taird:AOT40d, and the best model: RGRd ~ VPDd +AOT40d +PM2.5d

578

+ VPDd: AOT40d.

579

The full model of Exp.3: gs/An~ Tleaf+VPDL+PAR+PM2.5 +O3+ VPDL:O3, and the best

580

model: gs/An~ VPDL +O3. The table shows standardized parameter estimates.

581

Abbreviations: s.e., standard error; d.f., degree of freedom; RGRd, detrended relative

582

growth rate of cross sectional area at breast height; Tair, air temperature; VPDd, detrended

583

VPD; AOT40d, detrended AOT40; PM2.5d, detrended PM2.5; Tleaf, leaf temperature; VPDL,

26

584

leaf to air VPD; PAR, photosynthetically active radiation; VIF, variance inflation factors.

585

VIF < 4 indicates no collinearity between the predictors.

586

27

587

Figure legends

588

Fig. 1. Schematic illustration of the experimental design. (a) Spatial distribution of

589

ground level O3 concentrations across the study area, and the locations of the six study

590

sites; (b) Experiment 1: measuring aspen growth in tree barrels at each of the six sites; (c,

591

d) Experiment 2: the growth of aspen stems was measured daily at the plantations in

592

Haidian and Fangshan using a dendrometer; (e) Experiment 3: leaf stomatal conductance

593

and photosynthesis were measured on cloud-free days at Haidian.

594 595

Fig. 2. The effects of atmospheric pollutants on biomass growth rate during the 2016

596

summer growing season. The correlation between biomass growth rate and (a) AOT40, (b)

597

PM2.5 and (c) N deposition, and (d) the relative importance of each pollutant in explaining

598

variation in the biomass growth rate. The AOT40 and N deposition are cumulative values

599

from throughout the summer growing season. The PM2.5 is the hourly mean during the

600

summer growing season. In (a-c), different colors indicate different sites. The black line

601

represents the linear regression across all sites, and the light grey area indicates one

602

standard error interval. The error bar in (d) represents 95% confidence intervals.

603 604

28

605

Fig. 3. The effects of atmospheric pollutants and meteorological conditions on daily

606

aspen stem growth. Response of the detrended RGR (RGRd) to the (a) detrended AOT40

607

(AOT40d) , (b) detrended PM2.5 (PM2.5d), (c) detrended VPD (VPDd), and (d) the relative

608

importance of the factors in explaining the variation in the detrended RGR. In (a-c),

609

different colors indicate different sites. The black line represents the linear regression

610

across all sites, and the light grey area indicates one standard error interval. The error bar

611

in (d) represents 95% confidence intervals, and the : symbol indicates an interaction.

612 613

Fig. 4. Responses of the detrended daily RGR to (a) the detrended VPD at different

614

AOT40 levels and the response of the detrended daily RGR to (b) the detrended AOT40

615

at different VPD levels. Different colors indicate different binned AOT40 intervals.

616 617

Fig. 5. Effects of O3 and VPDL on leaf stomatal conductance (gs). (a) The partial

618

regression between leaf gs and O3, controlling for the effect of VPDL, (b) the partial

619

regression between leaf gs with VPDL, controlling for the effect of O3, and (c) the relative

620

importance of each factor in explaining the variation in leaf gs. The error bar in (c)

621

represents 95% confidence intervals.

622 623

Fig. 6. Effects of O3 and VPDL on leaf net photosynthesis (An). (a) The partial regression

624

between leaf An and O3, controlling for the effect of VPDL, (b) the partial regression

625

between leaf An and VPDL, controlling for the effect of O3, and (c) the relative

626

importance of each factor in explaining variation in leaf An. The error bar in (c) represents

627

95% confidence intervals. 29

628 629

Fig. 7. (a-b) The responses of leaf stomatal conductance (gs) and the leaf net

630

photosynthesis rate (An) to the leaf-air VPD (VPDL) at different O3 intervals, (c-d) and

631

the responses of leaf gs and An to O3 at different VPDL intervals. This data was filtered by

632

PAR > 1500 µmol photo m-2 s-1. Different colors indicate different binned O3 intervals.

633

The solid line represents a significant linear relationship.

634

30

635

Fig. 1

636 637

31

638

Fig. 2

639 640

32

641

Fig. 3

642 643

33

644

Fig. 4

645 646

34

647

Fig. 5

648 649

35

650

Fig. 6

651 652 653

36

654

Fig. 7

655

656

37

Highlights: •

Nitrogen deposition can stimulate aspen growth by increasing canopy nitrogen uptake.

•

Aerosols can promote aspen growth through diffuse radiation fertilization effects.

•

Among all pollutants, ozone is the most important factor to inhibit aspen growth.

•

The effect of VPD on aspen growth is negative and aggravated by ozone pollution.