Introduction

Ozone (O3) in the stratosphere protects lives by effectively absorbing ultraviolet radiation from the sun, but ground-level O3 produced by complex photochemical reactions of precursors such as nitrogen oxides (NOx), volatile organic compounds (VOCS), methane (CH4), and carbon monoxide (CO) is harmful [1]. It has adverse effects on crops and natural vegetation in addition to human health. In the past few decades, due to the intensification of human activities, the emission of O3 precursors has increased sharply, resulting in continuous increase of ground-level O3 at a rate of 0.5–2% per year in the Northern Hemisphere [2]. Based on this upward trend, it is predicted that the average background O3 concentration may reach 68 ppb by 2050, and even as high as 84 nL L−1 in some areas by the end of this century [3,4,5]. Thus, it is crucial to evaluate and prevent adverse effects of O3 using proper methods.

The current severe O3 pollution in North America, Europe, and Asia is already sufficient to have negative effects on surface vegetation and food crops, including leaf damage, decline in photosynthesis, reduction in growth rate, changes in carbon distribution, closure of stomata, and premature aging [6,7,8,9]. If effective measures are not taken to curb the threat of O3, it is estimated that the global O3-induced yield losses of wheat, maize, and soybean in 2030 were 4.0–26.0%, 2.5–8.7%, and 9.5–19.0%, respectively [10], with total economic losses of $12–$35 billion each year. Therefore, an accurate assessment of the potential risks of O3 to crops is not only the basis for implementation of air pollution prevention and control measures, but also an urgent need to improve the adaptability of China’s food production to climate change and environmental pollution.

In recent years, with the rapid development of the national economy and the acceleration of industrialization and urbanization, China is facing the problem of regional air pollution characterized by high concentrations of PM2.5 and O3. In order to improve the current air pollution situation, China’s State Council issued the Air Pollution Prevention and Control Action Plan (APPCAP) on September 10, 2013. Recent studies have suggested that the implementation of the APPCAP has significantly reduced the levels of PM2.5 in eastern China [11, 12], but ground-level O3 is becoming more and more serious, and it has replaced PM2.5 as a new problem of air pollution in some large cities of China [12, 13]. China is a large agricultural country, and agriculture has always been the foundation of economic development. Studies have shown that ground-level O3 that far exceeds the damage threshold of crops reduces crop growth and yield in China [5], thus seriously threatening food security. However, the evaluation methods are inconsistent and results are uncertain for policy-making to avoid the adverse effects of O3.

Based on the above concerns, this paper focus on reviewing ground-level O3 pollution and its potential risks to crops in China, and its structure is as follows: the “Overview of Ground-Level O3 Pollution in China” section reviews the state of ground-level O3 pollution in China; the “Indicators for O3 Impacts on Crops” section systematically summarizes the development of O3 risk assessment indices; the “Research Methods of O3 Flux” section briefly introduces the methods of O3 flux observation and stomatal uptake estimation; the “O3 Dry Deposition in Farmland Ecosystem” section covers the dry deposition of O3 in farmland ecosystem; the “Potential Risk Assessment of Ground-Level O3 Pollution” section gives a detailed review of O3 effects on crop yield; the “Limitations and Future Research Directions” section points out the shortcomings of the current research and provides suggestions for future researches. This review would provide a theoretical basis for the evaluation and control of O3 pollution to avoid agricultural losses.

Overview of Ground-Level O3 Pollution in China

China’s air pollution problem caused by rapid economic development and accelerated urbanization has become increasingly severe. China has also been committed to the monitoring and control of air quality over the past few decades. Since the 1980s, the China Meteorological Administration has established seven atmospheric background stations to conduct long-term monitoring of air quality [14], and thus, some early studies on O3 pollution were limited to station observations due to the lack of large-scale O3 data. For instance, ground-level O3 was found to increase at a rate of 1.13 ppb per year during 2003–2015 at Shangdianzi station in Beijing and 1.80 ppb per year during 1991–2006 at Lin’an station in Zhejiang, respectively [15, 16]. A trend analysis of ground-level O3 at the Waliguan station in Qinghai from 1994 to 2013 indicated that the growth rates during the daytime and nighttime were approximately 0.24 ± 0.16 ppb per year and 0.28 ± 0.17 ppb per year, respectively [17]. In the Pearl River Delta (PRD) region, Li et al. [18] reported that ground-level O3 increased significantly from 2006 to 2011, with a mean trend of 0.86 ppb per year.

From January 2013, the Ministry of Ecology and Environmental of the People’s Republic of China (MEE, PRC) had begun to publish hourly data of six air pollutants including O3 in a web platform, which provided the researchers with a rare opportunity to analyze the distribution of O3 on a regional scale based on observational data [19,20,21]. Figure 1 shows the spatial distributions of maximum daily 8-h average O3 concentration (MDA8 O3) during 2015–2020 in China. Overall, the high-level O3 pollution was mainly concentrated in North China, Central China, and East China, which may be related to the high population density in these areas and the large amount of O3 precursor emissions caused by frequent human activities [22]. The O3 level in South China, Southwest China, and Northeast China was relatively low due to lower anthropogenic emissions and the influence of meteorological conditions [11]. Ground-level O3 and its precursors like NOX and VOCS emissions present a complex non-linear relationship [15], and many previous studies have shown that the generation of O3 depends on the ratio of VOCS/NOX [1, 23, 24]. In general, in regions of high NOx, reducing VOCS emissions could inhibit the production of O3; however, higher NOx concentrations could also promote the formation of nitrous acid (HONO). HONO is one of the major contributors to the primary OH production in the troposphere. Its photolysis produces hydroxyl radical (OH) during the daytime, which enhances the atmospheric oxidation capacity, thereby increasing HO2 and RO2 concentrations leading to elevated O3 concentrations [25, 26], as shown in reactions (1–11). Several studies have also confirmed that the increase in anthropogenic VOCS emissions was the main reason for the increase in O3 concentration in eastern China [12, 14, 20]. Therefore, effective measures must be taken in the future to control VOCS emissions, thereby reducing ground-level O3 pollution. In short, the production of O3 is very complicated, affected by not only the emission of precursors and meteorological factors [14, 15], but also regional transport [11, 14].

$${\text{HONO }} + {\text{ hv }}\left( {\lambda < {4}00\,{\text{nm}}} \right) \, \to {\text{ NO }} + {\text{ OH}}$$
(1)
$${\mathrm{H}}_{{2}} {\mathrm{O}}_{{2}} \left( {\lambda < {555}\,{\text{nm}}} \right) \, + {\text{ hv }} \to {\text{ 2OH}}$$
(2)
$${\mathrm{O}}_{{3}} + {\text{ hv }}\left( {\lambda < {33}0\,{\text{nm}}} \right) \, \to {\text{ O}}^{{1}} {\text{D }} + {\text{ O}}_{{2}}$$
(3)
$${\mathrm{O}}^{{1}} {\text{D }} + {\text{ H}}_{{2}} {\text{O }} \to {\text{ 2OH}}$$
(4)
$${\text{OH }} + {\text{ CO }} \to {\text{ H }} + {\text{ CO}}_{{2}}$$
(5)
$${\text{H }} + {\text{ O}}_{{2}} \to {\text{ HO}}_{{2}}$$
(6)
$${\text{OH }} + {\text{ VOC}}_{{\mathrm{S}}} \to {\text{ RO}}_{{2}}$$
(7)
$${\text{HO}}_{{2}} + {\text{ NO }} \to {\text{ NO}}_{{2}} + {\text{ OH}}$$
(8)
$${\text{RO}}_{{2}} + {\text{ NO }} \to {\text{ NO}}_{{2}} + {\text{ RO}}$$
(9)
$${\text{NO}}_{{2}} + {\text{ hv }} \to {\text{ O }} + {\text{ NO}}$$
(10)
$${\text{O }} + {\text{ O}}_{{2}} \to {\text{ O}}_{{3}}$$
(11)

According to the method presented by Zhao et al. [11], five representative regions including Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), Chengdu-Chongqing (CC), and Fen Wei Plains (FWP) were selected to better understand the annual change characteristics of MDA8 O3 in different regions, as shown in Fig. 2. On the whole, the annual mean MDA8 O3 in BTH, YRD, and FWP were significantly higher than that in entire China (All). MDA8 O3 exhibited an upward trend, from 85.7 μg m−3 in 2015 to 95.1 μg m−3 in 2017 for All, which may be associated with the decline in PM2.5 due to the implementation of APPCAP (2013–2017) [12]. However, it experienced a significant decrease during 2018–2020, reflecting the effectiveness of the implementation of the “Three-Year Action Plan to Win the Blue Sky Defence War” in China. It is noteworthy that MDA8 O3 during 2015–2017 increased most rapidly with 31.8% increase from 78.0 to 102.8 μg m−3 for FWP, followed by BTH (15.3% increase from 90.8 to 104.7 μg m−3) and YRD (13.8% increase from 88.9 to 101.2 μg m−3). From 2018 to 2020, CC had the largest MDA8 O3 reduction (5.7% from 90.5 to 85.3 μg m−3), whereas YRD only showed little reductions (1.5% from 99.0 to 97.5 μg m−3).

Fig. 1
figure 1

Spatial distributions of MDA8 O3 during 2015–2020 across China

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Fig. 2
figure 2

Inter-annual variations of MDA8 O3 from 2015 to 2020 in different regions of China

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Indicators for O3 Impacts on Crops

Concentration- and Flux-Based Indices

Previous studies on O3 impacts on crops are usually based on control experiments such as open-top chambers (OTC) and free air controlled exposure (FACE), both were conducted by setting different gradients of O3 concentration to investigate the effects of O3 on crop growth, photosynthesis, and yield. On the basis of these experiments, two indices were proposed to quantify the potential risks of O3 to crops: concentration- and flux-based indices. Table 1 presents a brief summary of advantages and disadvantages of two metrics. In 1980, the U.S. Environmental Protection Agency (US-EPA) established the National Crop Loss Assessment Network (NCLAN), and researchers used OTC facilities to carry out experiments on the effects of O3 on the growth and yield of wheat, rice, soybeans, maize, and other crops across the USA [27, 28]. These studies showed that there is a good linear relationship between the increase of O3 concentration and the decrease of crop yield, and concentration–response relationships of different crops were established [3]. M7 and M12 are the most commonly used concentration-based exposure indices, which represent the average O3 concentration for 7 h (09:00–15:59) and 12 h (08:00–19:59) during the growing seasons, respectively. The O3 concentration–response relationships for crops are obtained by using mathematical statistics methods, and they are regarded as simple empirical relationships based on experiments. The calculation of M7 and M12 is relatively simple. However, these two indicators use O3 concentration as the only influencing factor to reflect the response of crops to O3 stress, and ignore the impacts of exposure time and crop growth condition [6], which cannot well reflect the actual damage effect of O3 on crops.

Table 1 A comparison of different evaluation indices about the impacts of ground-level O3 on crops
Full size table

With the deepening of the research, some researchers have realized that the negative effects of O3 on crops have a cumulative effect [3]. Therefore, the US-EPA proposed SUM06 and W126 as plant protection standards in 1996. SUM06 refers to the hourly cumulative value of atmospheric O3 concentration greater than 60 ppb. W126 represents the weighted accumulation of O3 concentration during the daytime, which is calculated by multiplying the hourly O3 concentration by weighting index, and then accumulating the value within a certain period of time. European researchers have pointed out that O3 will cause injury to crops when it is higher than 40 ppb, and the O3 exposure index AOT40 has a linear relationship with the reduction of crop yield [27]. Therefore, the AOT40 indicator is generally used as the standard for plant protection in Europe. This indicator is obtained by accumulating hourly mean O3 over a threshold of 40 ppb during the daytime. In general, some cumulative exposure-based indices such as SUM06, W126, and AOT40 take into account not only the influence of O3 concentration, but also the cumulative effect, and so they are more scientific than M7 and M12 [28].

$$\mathrm{M}7 (\text{ppb})=\frac{1}{N}\sum_{i=1}^{N}{[{\mathrm{O}}_{3}]}_{i}$$
(12)
$$\mathrm{M}12 (\text{ppb})=\frac{1}{N}\sum_{i=1}^{N}{[{\mathrm{O}}_{3}]}_{i}$$
(13)
$$\text{SUM}06 \left(\text{ppbh}\right)=\sum_{i=1}^{N}{\left[{\mathrm{O}}_{3}\right]}_{i}\mathrm{ for }[{\mathrm{O}}_{3}]\ge 60\mathrm{ ppb}$$
(14)
$$\mathrm{W}126 \left(\text{ppbh}\right)=\sum_{i=1}^{N}{\left[{\mathrm{O}}_{3}\right]}_{i} {\times W}_{i}$$
(15)
$${\text{AOT40}}\left( {{\text{ppbh}}} \right) = \sum_{i = 1}^{N} \left( {\left[ {{\mathrm{O}}_{3} } \right]_{i} - 40} \right)\quad {\text{for}}\left[ {{\mathrm{O}}_{3} } \right] \ge 40{\text{ppb}})$$
(16)

where N is the number of hours during crop growing seasons and [O3]i is the hourly O3 concentration in ppb. Wi is the weighting index, and its calculation is as follows:

$${W}_{i}=\frac{1}{1+ M\cdot \text{exp}(-\mathrm{A}\cdot {[{\mathrm{O}}_{3}]}_{i}/1000)}$$
(17)

where M = 4403 and A = 126.

Some studies have shown that the impact of O3 on crops is determined by the amount of O3 that enters the crop body through the stomata of the leaves to undergo complex biochemical reactions [29,30,31], which is called the accumulated stomatal flux of O3 (PODY), and its calculation is shown below in Eq. 18. The concentration-based exposure indices like SUM06, W126, and AOT40 ignore the amount of O3 absorbed by crop leaves through the stomata, and they default that the amount of atmospheric O3 is equivalent to the amount of O3 absorbed by the crop and does not involve the effect of stomatal conductance. On the one hand, when O3 concentration in the ambient atmosphere is high, if the stomata resistance of the crop leaves is large, the O3 absorption of the crop leaves would be low, and the damage caused by O3 to crops will be greatly reduced [31]. On the other hand, there are also certain differences in the sensitivity of crops to O3 under different crops, different growth periods of the same crop, and different environmental conditions. In addition, these indices do not take into account the crop’s antioxidant capacity, detoxification capacity, and repair capacity at night, which may overestimate the negative effects of O3 on crops [32]. Nevertheless, SUM06 and AOT40 are still widely recognized and applied.

$${\text{POD}}_{Y} = \sum_{i = 1}^{N} {\max}[F_{{{\text{st}}}} - Y,0],\Delta t$$
(18)

where Fst is the stomatal O3 uptake flux in nmol O3 m−2 s−1, Y is the flux threshold, and Δt = 1 h [31].

Ground-level O3 enters the terrestrial ecosystem through dry deposition, and then affects crop growth in the form of stomata absorption and non-stomata deposition. The stomata on the surface of crop leaves are the main channel for gas exchange with the outside world, which controls the photosynthesis and transpiration of crops. When the stomata are open during the daytime, O3 enters the crop body through the stomata pathway. During the nighttime, the stomata are almost completely closed, and the amount of O3 entering the crop body will be greatly reduced, or even close to zero. Studies have shown that the differences in the sensitivity of different types of crops to O3 are determined by differences in stomata [33], so stomata play a very important role in the impact of O3 on crops. Stomatal conductance indicates the degree of opening of stomata, and its size is related to the regulating effect of stomata. When the stomata resistance of the crop leaves is small and the stomata conductance is large, the amount of O3 entering the crop will increase. Not only that, O3 also affects the stomatal conductance, and many studies have found that O3 can inhibits stomatal conductance, leading to an increase in stomatal resistance, thereby reducing stomatal O3 flux [34]. The stomatal conductance model involves environmental factors such as phenological period, temperature, light intensity, water vapor pressure difference, and soil moisture content, and its simulation is often based on the Jarvis model and the Ball-Berry model [35,36,37]. Stomatal O3 absorption flux is determined by the stomatal conductance and O3 concentration, and therefore, the relationship between them has become an important issue in this field.

Determination of Damage Thresholds for O 3

A few studies have shown that crops have certain defense and detoxification capabilities against O3 stress. The antioxidants such as ascorbate in the cell wall can remove a small part of O3 that enters the body of the crop [29]. Therefore, both concentration-based indices and flux-based indices have a damage threshold, which is an empirical value reflecting the negative effects of O3 on crops, meaning that the impact below this empirical value is negligible. For example, 60 ppb, 40 ppb, and Y represent the threshold values of SUM06, AOT40, and PODY, respectively [38]. In fact, the threshold Y in PODY varies with crop types, growth periods, crop varieties, and environmental factors. So far, there is no model that can completely describe the dynamic process of Y; the only way to determine the threshold Y is to establish the relationship between PODY and crop yield loss. The calculated cumulative stomatal O3 absorption flux had the highest correlation with the relative yields of wheat and potato in European when the threshold Y is set to be 6 nmol m−2 s−1 [29]. A flux threshold of 2 nmol m−2 s−1 was judged as a reasonable for an O3 flux-based response relationship for rice in the Pearl River Delta of South China [39]. Feng et al. [40] proposed that the strongest relationship was found by using the O3 uptake threshold of 12 nmol m−2 s−1 for winter wheat in subtropical China. Wu et al. [31] found that POD4.0 (the accumulated stomatal O3 flux over a threshold of 4.0 nmol m−2 s−1) had the best linear correlation with the relative yield of winter wheat in the Yangtze River Delta, which was consistent with the results of Tong et al. [41], but lower than those reported in European studies [29, 35]. Zhang et al. [42] reported that the O3 absorption flux threshold of 9.6 nmol m−2 s−1 was most reasonable for soybean flux-response relationship in Northeast China.

To sum up, the threshold used for crop yield loss assessment using flux-based indices was set as a constant in the previous studies. Therefore, taking the detoxification effect of crops into account in the O3 risk assessment model can provide a strong basis for accurately predicting crop yield losses.

Applicability of Different Evaluation Indices

A large number of experiments on the effects of O3 on crops showed that flux-based exposure indices provided stronger relationships with the damage effect of crops than concentration-based exposure indices such as AOT40 [5]. An experiment conducted in Europe showed the relative yields of wheat and potato had a stronger correlation with the stomatal O3 flux indices than with AOT40 indices [43]. The study of Karlsson et al. [44] also confirmed that the stomatal O3 flux was more closely correlated with visible foliar O3 injury in Southern European forests than AOT40. Goumenaki et al. [45] suggested that the performance of stomatal O3 flux as a descriptor of O3 risk to lettuce was better than the performance of AOT40 in Europe. Mills et al. [30] collected and analyzed studies on the potential ecological risks of O3 in 17 European countries in the past 20 years and concluded that stomatal O3 flux was better than AOT40 as an index of O3 risk assessment for vegetation. In China, Feng et al. [40] developed and parameterized Jarvis-type stomatal conductance model for winter wheat and used it to establish the relationship between stomatal O3 flux and yield loss. A rice experiment conducted in the Pearl River Delta region of China indicated that the accumulated stomatal O3 uptake flux had a strong negative linear relationship with the relative yield, and its correlation coefficient was higher than that of the AOT40 index [39], whereas in wheat experiments of the North China Plain, both AOT40 and stomatal O3 uptake flux were found to have a good relationship with relative yield [41], which was similar to the study by Gonzalez-Fernandez et al. [46].

Research Methods of O3 Flux

As mentioned above, O3 flux-based index is the future research directions as it can better describe the impact mechanism of O3 on crops than O3 concentration-based index, and thus, the observation and simulation of O3 flux in farmland ecosystems are a prerequisite for accurately implementing O3 risk assessment for agricultural crops. The following introduces some research methods of O3 flux.

Flux Chamber Method

The flux box method is regarded as a traditional method to directly measure the gas exchange of plants. There are two types of flux chambers: static (closed, passive) and dynamic (flow-through, active) [47]. Among them, the static chamber method is the most widely used experimental measurement method. During the measurement process, a chamber is used to cover the measured crop, and the contact port between the chamber and the base is sealed with water to ensure that the air in the chamber does not have any exchange with the outside world. The gas concentrations in the chamber are measured at a certain time interval, and then, the following formula is used to calculate the gas exchange flux:

$${\mathrm{F}}_{{\mathrm{g}}} { = }h_{{\mathrm{c}}} \times \frac{{M_{{\mathrm{g}}} \times P}}{{{\mathrm{R}} \times {\mathrm{T}}}} \times \frac{{\partial_{c} }}{{\partial_{t} }}$$
(19)

where Fg is the gas flux (mg m−2 h−1), hc is the height of the chamber (m), Mg is the molar mass of the gas (g mol−1), P is the air pressure (Pa), R is the universal gas constant (R = 8.3144 Pa m3 mol−1 K−1), T is the temperature (K), c is the gas volume fraction in the chamber (× 10−9), and t is the time for the chamber to cover the measured crop (s). The static chamber method has the advantages of simple principle, easy operation, and low cost, but its disadvantages are that the natural turbulent state of the air on the surface of the measured crop is changed, which affects the measurement of exchange flux to a certain extent [48].

The dynamic chamber is an open system, its working principle is that the opposite sides of the chamber are opened so that a certain flow of air can pass through the chamber, and then, the gas exchange flux can be obtained by measuring the inlet and outlet concentration of the gas. The calculation method is as follows:

$$F_{{\mathrm{g}}} = Q \times \rho_{{\mathrm{g}}} \times \frac{{C_{1} - C_{2} }}{A}$$
(20)

where Q is the flow rate of the gas in the chamber (m3 h−1), ρg is the gas density (g m−3), and C1 and C2 are the outlet and inlet concentrations of the gas, respectively (mL L−1). A is the basal area of the chamber (m2). When this method is used to measure the gas exchange flux, it is necessary to minimize the difference of air pressures between inside and outside of the chamber to avoid the gas flowing into or out of the chamber through the gap, which can affect the accuracy of the measurement results. In general, it is very accurate and reliable to use the dynamic chamber method to measure gas exchange flux. However, the equipment required to perform the measurements is expensive and the operation is more complicated. Although the static box method has certain drawbacks, it can realize continuous monitoring and is very easy to operate, so it has been widely used so far.

Model Simulation Method

Previous studies on O3 flux using flux chamber method are mostly based on controlled and semi-controlled experiments, and are limited to specific study areas, which consumes a lot of manpower and material resources. The model simulation method can overcome the above shortcomings very well and can be used to describe the entire change process, but this method is mainly applied to the stomata O3 absorption flux on the leaf scale. There are many models that can estimate the stomata O3 flux, among which the Jarvis-type multiplicative stomatal conductance model is the most commonly used [38], which is calculated by multiplying the leaf stomatal conductance and the O3 concentration [29]:

$$g_{{{\text{sto}}}} = g_{{{\max}}} \times \min (f_{{{\text{phen}}}} , f_{{{\mathrm{O}}_{3} }} ) \times f_{{{\text{PAR}}}} \times \max \left[ {f_{\min } , (f_{{{\text{temp}}}} f_{{{\text{VPD}}}} )} \right]$$
(21)

where gsto is the calculated value of stomatal conductance of O3 (mmol m−2 s−1), gmax is the maximum value obtained from a large number of observations of stomatal conductance (mmol m−2 s−1), fmin is the minimum relative stomatal conductance, the factors fphen, \({\mathrm{f}}_{{\mathrm{O}}_{3}}\), fPAR, ftemp, and fVPD are expressed as the adjustment coefficients of phenology, O3, radiation, temperature, and VPD (vapor pressure deficit) to gmax, and their values are between 0 and 1. The continuous stomatal conductance can be calculated by using the observed hourly climate and environmental factor data, and finally, the following formulas can be used to estimate the stomatal O3 absorption flux:

$${\mathrm{F}}_{\text{st}}\text{ = }\frac{\left[{\mathrm{O}}_{3}\right]}{{\mathrm{r}}_{\mathrm{b}}\text{+}{\mathrm{r}}_{\mathrm{c}}}\times \frac{{g}_{\text{sto}}}{{g}_{\text{sto}}\text{+}{g}_{\text{ext}}}$$
(22)
$${r}_{\mathrm{b}}\text{ = }\frac{1}{{g}_{\mathrm{b}}}=\frac{1}{0.125\cdot \left(\sqrt{\frac{u}{w}} \right)\times 1000}$$
(23)
$${r}_{\mathrm{c}}\text{ = }\frac{1}{{g}_{\text{sto}}}$$
(24)

where Fst is the leaf stomata O3 flux (nmol m−2 s−1), [O3] is O3 concentration, gb is the leaf boundary layer conductance (mmol m−2 s−1), gext is usually set to be 16.4 mmol m−2 s−1, u is the wind speed at canopy height (m), and w is the maximum width of leaf (m). It is necessary to determine the parameter values in the stomatal conductance model before calculating the stomatal O3 absorption flux. The model simulation method can predict the stomatal O3 uptake flux of crop leaves in different regions, and thus, further research is needed to combine atmospheric models or satellite remote sensing methods to extend it to the regional scale.

Micrometeorological Method

The gradient method and the eddy correlation method are micrometeorological methods for observing the O3 flux in the terrestrial ecosystem. When using the gradient method to measure O3 flux, two (or multiple) layers’ observations of O3 concentration, wind speed, and temperature should be implemented, and then, the near-surface turbulent flux is calculated by flux-profile relationship [9]:

$$F_{{\mathrm{o}}} = K_{{\mathrm{o}}} \times \frac{{\partial \rho_{{\mathrm{o}}} }}{\partial z}$$
(25)

where Ko is the turbulent exchange coefficient for O3 (m s−1), and its calculations refer to the study of Wu et al. [48]. ∂ρ0/∂z is the vertical gradient of O3 concentration with height z (μg m−3). Since the absolute concentration of O3 is very low and the concentration gradient is relatively small, the calculation of O3 flux by this method requires high-precision instruments and suitable meteorological conditions.

It should be noted that the eddy correlation method is regarded as an advanced method for measuring the atmospheric O3 flux over terrestrial ecosystems in recent years, and it has been applied in the observation of CO2 and water vapor flux [49]. However, it is almost still in a blank state in the application of O3 flux. The calculation formula of O3 flux is as follows [38]:

$${\mathrm{F}}_{\text{o }}\text{=}-\overline{{\rho }_{\mathrm{o}}}\times {V}_{\mathrm{d}}={\rho }_{\mathrm{o}}\times \frac{\overline{{\mathrm{w} }^{^{\prime}}{{S}^{^{\prime}}}_{\mathrm{o}}}}{\overline{{S }_{\mathrm{o}}}}$$
(26)

where Vd is deposition velocity, ρo is the absolute concentration of O3 observed by the slow-response O3 analyzer, w is the vertical wind speed (m s−1), So is the the signal output of the fast response O3 analyzer, the upper horizontal line represents the time average, and the apostrophe represents the pulsation of the variable. Similar to the calculation of eddy covariance fluxes of CO2 and water vapor, the observed O3 flux also undergoes a series of corrections (coordinate rotation correction, spectrum correction, the Webb-Pearman-Leuning correction, etc.) and quality control [38].

O3 Dry Deposition in Farmland Ecosystem

Processes and Its Influencing Factors

The terrestrial ecosystem is one of the important sinks of ground-level O3. Because O3 is difficult to dissolve in water, it enters the terrestrial ecosystem through dry deposition [50]. In recent years, a large number of studies have been conducted on the dry deposition characteristics of ground-level O3 in forests, crops, and grasslands [51,52,53,54,55,56,57]. In general, Vd is used to study the deposition law of O3 in different ecosystems. The change of Vd is largely controlled by the physiological activity of vegetation and meteorological factors, and the Vd of different underlying surfaces is very different. The dry deposition of O3 is affected by environmental factors such as wind speed, temperature, humidity, solar radiation, and soil moisture content. Under moderate or strong solar radiation, when the soil moisture is higher and the water vapor pressure difference is lower, the Vd is greater [57]. Studies have pointed out that the threshold value of soil moisture is 12–13%, and Vd will be reduced when it is lower than this threshold [58,59,60]. Thus, the value of Vd is greater in the rainy season and humid conditions, which is because the soil water use efficiency is greater during this period [61,62,63]. Kurpius et al. [64] showed that VPD is the most important factor affecting Vd, and has a negative correlation with Vd. Zhu et al. [65] reported that the dry deposition process of O3 is most significantly affected by solar radiation and relative humidity, and is positively correlated with relative humidity [66]. In addition, wind speed, atmospheric stability, and friction velocity also have a strong correlation with Vd, indicating that the dry deposition process of O3 is largely controlled by dynamics [67].

Due to the influence of meteorological conditions, Vd presents the obvious diurnal variation characteristics. For farmland ecosystems, Vd during the day is higher than that at night [54]. For example, Zhu et al. [65] used the eddy correlation method to observe the Vd over a summer maize field in Northwestern Shandong Plain of China and found that the average Vd during daytime and nighttime were 0.29 cm s−1 and 0.09 cm s−1, respectively, and the maximum value of Vd was 0.81 cm s−1 at 12:30. In the Vd observations over winter wheat fields based on the gradient method in Nanjing, Li et al. [68] reported that the average values of Vd during the entire observation period were 0.71 cm s−1 during daytime and 0.40 cm s−1 during nighttime. Vd is related to the activity of photosynthesis, because the stomatal conductance is higher when the activity of photosynthesis is stronger. An observation experiment based on the eddy correlation method over a bare soil in Nanjing area in autumn showed that during the observation period, Vd began to rise at about 03:00, reached the maximum at about 08:00, and then gradually decreased [69]. It should be noted that Vd also changes with the advancement of crop growing period. Generally, Vd reaches the maximum during the period from the flowering stage to the filling stage of wheat [70]. Because photosynthesis is the strongest during this period, when the stomata are opened for photosynthesis, O3 is also easy to enter the stomata, and then, Vd gradually decreases due to the senescence of leaves [64, 71, 72].

Distinction of Total O 3 Flux

Several studies have shown that O3 deposition can be divided into stomata absorption and non-stomatal deposition pathways, and the non-stomatal deposition includes soil and cuticular depositions. It must be pointed out that the chemical reaction of O3 with NO and VOCS derived from the soil and leaves is another channel for O3 deposition [73]. Currently, it is difficult to continuously observe stomatal O3 absorption flux due to the difficulty of monitoring, and thus, it is usually estimated by stomatal resistance model. In addition, non-stomatal deposition cannot be directly observed, and research on it is mainly based on model methods. There are differences in the proportion of stomatal absorption in the total O3 flux in different growth periods of crops. In China, this proportion during the peak growth period of winter wheat is less than 50–60%, and it gradually decreases during the aging process of wheat leaves [74], which is consistent with the observations of wheat, barley, and onion in Italy [75, 76], whereas in the vigorous growth period of potatoes, it is about 85%, and it decreases to about 20% during the maturity stage. Zhao et al. [77] observed O3 flux in rice fields using the eddy correlation method and used the revised Jarvis stomatal conductance model to distinguish its contribution to different deposition channels and found that the proportions of stomatal O3 absorption and non-stomatal flux to total O3 flux were 34.0% and 66.0%, respectively, and the proportions during the daytime were 49.0% and 51.0%, respectively. A comparison of Vd during the daytime and the nighttime by Zhu et al. [65] indicated that stomatal O3 absorption during the daytime was the main sink of atmospheric O3 over maize field.

Potential Risk Assessment of Ground-Level O3 Pollution

Ground-level O3 pollution and its potential risks to crops have received extensive attention in recent years. Although many studies have evaluated the impact of O3 pollution on crop growth and yield in China, exposure–response relationships used in these studies are mainly based on experiments conducted in Europe and the USA due to the lack of field experiments. In view of differences in climatic conditions and crop varieties, the response of crops to O3 stress in different regions may also be significantly different [78], and thus, there may be some possible limitations when applying external exposure–response relationships to analyze the impact of O3 on crops in China. In order to improve the accuracy of the evaluation results, some researchers have carried out experiments on the effects of elevated atmospheric O3 concentration on crops using OTC and FACE facilities in China [31, 42, 79, 80], and established the relationship between the relative yields of different crops and O3 risk assessment indicators (Table 2), which provides an excellent opportunity for researchers to quantify the local and regional effects of ground-level O3 on crop yields under past, current, and future.

Table 2 A summary on the response relationship between crop yield and O3 evaluation index in China
Full size table

In China, previous studies on crop yield reduction caused by ground-level O3 pollution are usually based on O3 data from a limited number of monitoring sites, satellite data, and model simulation results [85,86,87,88]. At the site scale, the O3-induced yield losses of wheat and rice in Chongqing during 1990–1995 were 0.2–9.8% and 1.1–5.8%, respectively, using the AOT40 and M12 metrics, and those in 2020 were 12.0% and 10.8%, respectively [89]. In the Northwest Shandong Plain of China, the high level of ground-level O3 was estimated to induce the yield loss of 12.9% for winter wheat based on AOT40 index [90]. Due to the lack of observational data, previous studies on regional-scale O3 risk assessment mainly relied on simulated O3 concentrations from atmospheric chemistry models or satellite data [91]. On the basis of the WRF/Chem-CHASER model, the O3-induced yield loss for wheat ranged from 6 to 15% for the whole China [88], whereas in 2014, ground-level O3 was projected to cause the yield losses of 8.5–14.0%, 2.2–5.5%, and 3.9–15.0% for winter wheat, maize, and rice, respectively [92]. In the future projection, Tang et al. [93] used the CTM model to simulate the spatial distribution of AOT40 and stomatal O3 flux for rice across China, and concluded that the O3 risk for single rice, double-early rice, and double-late rice increased by 162–412% from 2000 to 2020. The crop yield losses due to O3 exposure for the year 2030 were also estimated to be 19.7–25.7% for wheat and 5.8–7.9% for maize according to CTM model simulation [10].

In recent years, the MEE, PRC has established ambient air quality monitoring sites in almost all cities in China. The measured O3 data can not only truly reflect the status of O3 pollution, but also improve the accuracy of O3 risk assessment [87]. Therefore, evaluating regional-scale O3 agricultural risks based on observational O3 data has already been used in some recent studies. In the Yangtze River Delta of China, ground-level O3 in 2015 was estimated to induce the yield losses of 9.9–36.1% for wheat and 7.3–23.9% for rice [78], whereas in the study by Ren et al. [94], the yield losses of wheat and rice during 2014–2019 were 4.9–11.4% and 9.4–19.3%, respectively. In the southern region of China, the O3 concentration in 2015 caused an estimated yield loss in rice of 1.2–10.4% [95]. In the North China Plain, the O3-induced yield losses during 2014–2017 ranged from 6.4 to 38.2% for wheat and from 8.2 to 13.4% for maize [96, 97]. At a national scale, the yield losses of wheat and rice due to O3 in 2015 were projected to be 6.0% and 8.0%, respectively [91]. Zhao et al. [22] recently projected that ground-level O3 during 2015–2018 caused the yield losses of 20.1–33.3% for winter wheat, 5.0–6.3% for maize, and 3.9–8.8% for rice. It should be pointed out here that different yield losses for the same crop may be calculated among different studies, which mainly depends on the definition of the accumulation period and the selection of O3 exposure indicators and corresponding response functions [78]. In recent years, the stomatal O3 flux is regarded as the best indicator for quantifying the potential risks of O3 to crops, mainly because it involves not only O3 concentration but also environmental factors such as temperature, radiation, water vapor pressure difference, and soil moisture content [5]. However, due to the lack of large-scale meteorological observation data, there are relatively few studies on regional-scale O3 risk assessment based on the stomatal O3 flux, and only a few studies are carried out by atmospheric chemistry models and satellite remote sensing methods [37, 86, 88]. To date, only two studies have evaluated crop yield losses caused by O3 exposure in China using concentration- and flux-based indices. A comparison between these two indices indicated that ground-level O3 reduced wheat yields losses of 6.4–7.2% and 10.3–14.9% in 2000 across China based on AOT40 and stomatal O3 flux, respectively [88], whereas in the study by Feng et al. [98], estimates of O3-induced wheat yield reductions for the whole China based on AOT40 were significantly higher than those based on the stomatal O3 flux. This discrepancy could be explained by the source of data and the choice of dose–response functions [22].

Limitations and Future Research Directions

Since the 1990s, although a lot of studies on the effects of O3 on crops have been carried out in China, there are still many shortcomings needed to be improved. Therefore, in order to scientifically assess the potential risks of ground-level O3 pollution to crops and provide more valuable information for decision-makers, more in-depth research is needed in the following aspects in the future.

Firstly, previous studies on regional-scale O3 agricultural risk assessment using observational data are usually based on a few monitoring sites, which may affect the accuracy and reliability of the assessment results. Starting from 2013, China has successively established a large number of air quality monitoring stations in almost all cities, and thus, it is possible for researchers to evaluate nationwide O3-induced agricultural losses. It should be noted that although China currently has more than 2000 O3 monitoring stations, their spatial distribution is uneven, and most of the stations are located in urban areas. Studies have shown that the O3 concentration in suburban areas was generally higher than that in urban areas [78]. If O3 in a certain city is used to describe the O3 situation in the planting area of this city, the stress effect of O3 on crops will be underestimated. We should consider building more stations in suburban areas to reduce this uncertainty.

Secondly, the response relationship between crop yield and O3 evaluation index in China established by most of the previous studies is based on OTC experiments. However, climatic factors such as temperature, relative humidity, light, soil moisture, and wind in the OTC chamber are significantly different from the external environmental conditions, which leads to different responses of crops to O3 stress. However, the FACE experiment is regarded as the most appropriate method to investigate the yield responses of crops to elevated O3 [5], because the energy and gas exchange of crop canopy will not be disturbed by the FACE systems. A recent study showed that the sensitivity of wheat and rice to O3 under the OTC experiment was lower than that under the FACE experiment, while it was the opposite for soybeans [99]. Therefore, the results from the OTC experiment cannot be fully applied to the regional O3 risk assessment of natural ecosystems, and experimental research based on the O3-FACE platform should be increased in the future. Meanwhile, different varieties of the same crop have different responses to O3 stress [37, 40, 78]. Future research should consider the impact of varieties and establish the relationship between different varieties of crops and evaluation indicators, which is beneficial to cultivate crop varieties resistant to O3 and prevent the adverse effects of O3 on crops.

Thirdly, the damage effect of O3 on crops was determined by O3 flux. As a traditional O3 flux observation, the flux chamber method has been widely used due to its simple operation and low cost, while this method changes the growth environment of the crop body and may bring certain errors to the experimental results. The micrometeorological method is an advanced method for measuring the O3 flux of the ecosystem in recent years. This method has already been applied in Europe and America, but it is still relatively few in China, and further research is still needed. In addition, the regional-scale O3 agricultural risk assessments in previous studies were mainly based on the concentration-based indices, because their calculations are quite simple and only use the hourly O3 concentrations. However, the impacts of climate factors have played an important role in the whole crop growth in fact, and the reduced relative yield was the combination of O3 and climate factors. Therefore, the flux-based index, especially the stomatal O3 flux, is regarded as the most scientific indicator. It must be pointed out that the flux-based index depends on too much parameters, and it is difficult to calculate it by using actual observational data. The current air quality models (e.g., WRF-CMAQ) can solve this issue and provide the hourly simulated climate factors and O3 concentration data. In future research, we should consider combining atmospheric chemistry models and satellite remote sensing methods to estimate the distribution of the flux-based indices, which provides an important basis for scientifically quantifying the risk of O3 to crops on a regional scale.

Finally, crop has a certain defensive ability against O3 damage, which will be affected by factors such as crop species, growth period, and environmental conditions. Previous studies only set it as a threshold when calculating the absorption flux of stomata O3 and did not consider its dynamic changes. Therefore, combining the O3 absorption flux model to simulate the detoxification effect of crops is an inevitable trend in future research.