Quantifying the Impact of Surface Ozone on Human Health and Crop Yields in China

Abstract

In recent years, surface ozone (O₃) pollution has emerged as a significant barrier to the continued improvement of air quality in China, making O₃ risk assessment a critical research priority. Using nationwide O₃ monitoring data, this research investigated the spatial characteristics of O₃ pollution and assessed its potential impacts on human health and crop yields. The results showed that the maximum daily 8 h average O₃ (MDA8 O₃) exhibited higher concentrations in eastern and northern regions, and lower concentrations in the western and southern regions of China. Long-term O₃ exposure was associated with an estimated 175,154 all-cause deaths nationwide, with the highest health risks observed in Shandong, Henan, and Jiangsu provinces. The AOT40 values for the winter wheat and single-rice growing seasons in China were 9.30 × 10³ ppb·h and 1.29 × 10⁴ ppb·h, respectively. Moreover, O₃ exposure led to relative yield losses of 22.1% for winter wheat and 9.3% for single rice, corresponding to crop yield losses (CPLs) of 63 million metric tons and 14 million metric tons, respectively. Higher winter wheat CPL values were primarily concentrated in Henan, Shandong, and Hebei, while higher single rice CPL values were observed in Jiangsu, Hubei, and Anhui. This study presents a novel coupling of O₃ pollution exposure with human health and agricultural risk assessments across China, emphasizing the need for region-specific O₃ management strategies to protect public health and ensure agricultural sustainability. In conclusion, this study highlights the importance of targeted O₃ control in densely populated and major crop-producing areas to mitigate health risks and yield losses, thus safeguarding ecosystem health and food security.

1. Introduction

Surface ozone (O₃) is a secondary pollutant primarily formed through complex photochemical reactions involving nitrogen oxides (NO_x), volatile organic compounds (VOC_s), and carbon monoxide (CO) emitted by both human activities and natural sources [1]. Recently, the increasing consumption of fossil fuels, the expansion of transportation, rapid industrialization, and increasing energy demand have significantly elevated emissions of O₃ precursors, resulting in high O₃ concentrations in many regions worldwide [2,3,4]. Studies indicated that over the past few decades, O₃ concentrations in the Northern Hemisphere had been increasing annually, with an average rise of 0.5% to 2% [5,6]. Based on this trend, projections suggested that by 2100, O₃ levels could exceed 80 ppb [7,8]. Elevated O₃ concentrations threaten human health and exert significant toxic effects on ecosystems [9,10,11,12].

O₃, a strong oxidant, can irritate the respiratory system. Prolonged exposure to elevated O₃ levels may induce or worsen respiratory conditions, such as asthma and chronic obstructive pulmonary disease (COPD) [13]. Numerous public health studies have shown a strong link between O₃ exposure and both respiratory and cardiovascular diseases. In China, a study by Yan et al. [14] found that a 5 ppb increase in MDA8 O₃ concentration was linked to a 0.42% increase in all-cause mortality, a 0.50% increase in respiratory mortality, and a 0.44% increase in cardiovascular mortality. Similarly, a meta-analysis by Shang et al. [15] reported corresponding increases of 0.48%, 0.73%, and 0.45%, respectively. Furthermore, each 5 ppb increase in MDA8 O₃ resulted in a 0.24%, 0.29%, and 0.60% rise in mortality from coronary heart disease, stroke, and hypertension, respectively [1]. A study by Sun et al. [16] indicated that a 5 ppb increase in MDA8 O₃, daily mean O₃ concentration, and maximum daily 1 h average O₃ (MDA1 O₃) led to non-accidental mortality increases of 0.37%, 0.60%, and 0.26%, respectively. These studies developed models to assess O₃’s health impacts, which support regional health risk assessments. For instance, simulated hourly O₃ data from the WRF-CMAQ model were used to estimate COPD-related deaths due to O₃ exposure, ranging from 32,225 to 141,649 in 2014 [17] and from 55,341 to 80,280 in 2015 [18]. Feng et al. [19] and Maji et al. [20] estimated that the total number of health-related deaths in China caused by O₃ in 2015 and 2016 was 74,316 and 74,200, respectively, based on nationwide O₃ monitoring data.

O₃ enters terrestrial ecosystems primarily through dry deposition, causing leaf damage, growth inhibition, and reduced crop yields [21]. Moreover, the stress induced by O₃ diminishes vegetation’s ability to absorb CO₂, disrupting the greenhouse gas balance and exacerbating global warming [22]. Research has demonstrated that O₃ levels in China are elevated enough to threaten the yields of crops like wheat, maize, and soybean [23]. Studies on the impact of O₃ on crop yields typically involve applying various concentration gradients to assess its effects. As a result, relationships between different O₃ risk assessment indicators and crop yield losses have been established [24]. Commonly used O₃ concentration-based indicators include M7 and M12, which represent the average O₃ concentration during specific daytime periods in a given growth stage. These indicators reflect crop responses to O₃ stress based solely on O₃ concentration. However, the negative impacts of O₃ on crops are cumulative, and thus in 1996, researchers proposed using SUM06 and W126 as standards for plant protection. Studies in Europe have demonstrated that when O₃ concentrations exceed 40 ppb, they can significantly affect crops [24]. Additionally, the accumulated exposure index AOT40 has been found to exhibit a strong linear relationship with crop damage and is easy to calculate and interpret [24]. As a result, this indicator remains widely used in global O₃ risk assessments. For example, Sinha et al. [25] used the AOT40 index to estimate relative yield losses for wheat, rice, maize, and cotton in India, ranging from 27 to 41%, 21 to 26%, 3 to 5%, and 47 to 58%, respectively. Zhao et al. [26] utilized the AOT40 indicator to estimate that O₃ pollution could lead to yield losses of 10–36% for wheat and 7–24% for rice in the Yangtze River Delta.

Since the implementation of the “Air Pollution Prevention and Control Action Plan” in 2013, fine particulate matter (PM_2.5) concentrations in China have significantly decreased. However, O₃ pollution has become an increasingly prominent issue. Studies showed that, from 2015 to 2019, the annual average PM_2.5 concentration in China decreased by 3.4 μg/m³ per year, while the MDA8 O₃ concentration increased by 1.7 ppb per year [27]. By 2019, the national average MDA8 O₃ concentration had reached approximately 47.5 ppb [27]. The 14th Five-Year Plan for China’s Ecological and Environmental Protection shifted the focus of air pollution control from PM_2.5 to VOC_s and O₃. As a result, O₃ pollution has become a significant barrier to further improvements in air quality. Additionally, China, as the world’s most populous country and a key producer of wheat and rice, faces severe threats to both human health and the yields of these essential crops due to high O₃ concentrations.

This study aims to evaluate the current level of O₃, investigate its impact on human health, and estimate the yield losses of winter wheat and single rice due to O₃. The findings provide policymakers with the scientific basis to formulate strategies to mitigate O₃ pollution, which in turn can reduce its harmful impacts on human health and crops yields.

2. Methods

2.1. O₃ Data and Study Area Division

The hourly O₃ concentration data used in this study covered the period from 1 January 2019 to 31 December 2019, and were sourced from the National Real-time Urban Air Quality Release Platform of China (https://air.cnemc.cn:18007; accessed on 1 January 2020). These historical data were recorded and stored on the platform (https://quotsoft.net/air/, accessed on 12 December 2024) [28,29,30]. As of the end of 2019, 1641 monitoring stations had been set up across 367 cities nationwide (Figure 1).

The reasons for selecting the 2019 data in this study are as follows. Firstly, since the outbreak of the COVID-19 pandemic in 2020, ensuing lockdown measures have led to significant changes in transportation, industrial, and agricultural activities. Particularly between 2020 and 2022, China implemented a three-year lockdown, which notably affected the formation and concentration of O₃ [24]. Consequently, the data from 2020 to 2022 cannot represent typical pollution levels. Secondly, 2019 was a relatively normal year, with stable data that were not influenced by pandemic-related measures. Therefore, it provides a more accurate depiction of the spatial variation of O₃ under normal background conditions. Based on these reasons, the use of 2019 data allows us to generate more accurate and reliable results for this study.

This study selected nine representative regions in China based on the method proposed by Ma et al. [30]. These regions include the Beijing–Tianjin–Hebei (BTH) area, the Yangtze River Delta (YRD), the Pearl River Delta (PRD), the Sichuan Basin (SB), the Yungui Plateau (YP), central China (CC), northeast China (NEC), northwest China (NWC), and Tibet.

2.2. Quality Control of O₃ Data

Due to potential issues such as instrument malfunctions, adverse weather conditions, data transmission errors, and other uncontrollable factors, the O₃ data published by the platform may include outliers and missing values. Although some preprocessing was performed before the data release, these measures were insufficient. Therefore, stricter data screening and quality control procedures are necessary to ensure the scientific validity of subsequent analyses. To enhance data quality, this study applied quality control methods consistent with previous research [25], primarily using Z-Score normalization. Z-Score normalization is a widely used data processing technique that transforms data of varying magnitudes into a unified Z-Score value, facilitating easier comparison. The formula for its calculation is as follows:

(1)$z={\displaystyle \frac{(x-\mu )}{\mathrm{\sigma }}}$

where z is the standard score that represents the degree of deviation of a specific data point from the overall mean, x is the value of a specific data point, μ is the mean of the dataset, and σ is the standard deviation of the dataset. The absolute value of the Z-Score indicates the distance between the original data point and the mean, measured in standard deviations. In data processing, if the absolute value of a data point’s Z-Score is excessively large, it may suggest that the data point is an outlier and should be removed. Specifically, a data point should be removed from the dataset if it meets any of the following conditions [29]: ① if the absolute value of the Z-Score exceeds 4, indicating that the data point deviates from the mean by more than 4 standard deviations; ② if the difference between the current data point and the previous one exceeds 9, suggesting that the data point has changed too drastically and potentially indicating erroneous data; or ③ if the ratio of the current Z-Score to the sliding average of the surrounding three Z-Scores exceeds 2, indicating that the data point significantly differs from its neighbors and may be an outlier.

After screening and removing outliers, approximately 91% of the remaining data were deemed valid, ensuring the overall quality and representativeness of the dataset. For the few missing values, this study employed linear interpolation to fill the gaps. Linear interpolation relies on the linear relationship between known data points to estimate the missing values. The interpolation formula is as follows:

(2)$X_n=X_i+\left(n-i\right)\times {\displaystyle \frac{\left(X_j-X_i\right)}{\left(j-i\right)}}$

where X_n represents the missing value at the n-th time point, while X_i and X_j are the concentration values before and after the missing value (i.e., at time points i and j); this method allows us to fill in a small number of missing data points, thereby maintaining the continuity and completeness of the dataset. However, if there is a continuous period of missing data for several hours within a given day, or if the missing data constitute a large proportion of the total data for that day, the data for that day will be considered of poor quality and unrepresentative. In such cases, they will be deemed invalid and removed from the dataset. The missing day’s data will then be replaced with the average O₃ concentration for the corresponding hours of that month, ensuring data continuity and minimizing bias from missing data. Through these screening and imputation processes, we have taken steps to ensure the reliability and accuracy of the data, providing a solid foundation for subsequent analysis [29,30].

2.3. Health Impact Assessment of O₃

This study systematically evaluated the health risks associated with long-term O₃ exposure, including all-cause mortality, cardiovascular disease mortality, and respiratory disease mortality, based on the exposure–response relationship derived from the MDA8 O₃ indicator. The first step involves calculating the values that exceed the minimum risk exposure threshold [12].

(3)$\Delta t=\left\{\begin{array}{c}0; \mathrm{i}\mathrm{f} \left[{\mathrm{O}}_3\right] \le \mathrm{T}\\ \left[{\mathrm{O}}_3\right]-\mathrm{T} ; \mathrm{i}\mathrm{f} \left[{\mathrm{O}}_3\right]>\mathrm{T} \end{array}\right.$

where [O₃] represents the annual average MDA8 O₃ concentration, in units of ppb, T denotes the safety threshold for O₃-related health effects, representing the concentration level above which adverse health effects are considered likely to occur. According to Turner et al. [31], this threshold is set at 26.7 ppb, based on research that identifies this concentration as the point beyond which prolonged exposure to elevated O₃ levels may lead to negative respiratory and cardiovascular impacts. Δt represents the portion of O₃ exposure that exceeds T, specifically referring to the amount of exposure to O₃ concentrations above the established safety threshold of 26.7 ppb.

The disease burden from O₃ (DB) is then calculated using the following formulas [12]:

(4)$\mathrm{HR}=\exp (\beta \Delta \mathrm{Y})$

(5)$\mathrm{DB}=1-\exp (-\beta \Delta t)$

where β is the exposure–response coefficient, representing the percentage increase in health impact per 10 ppb increase in MDA8 O₃. This coefficient is determined using the hazard ratio (HR), with Turner et al. [31] reporting HR values for all-cause and cardiovascular mortality under long-term O₃ exposure of 1.02 (95% CI: 1.01–1.04) and 1.03 (95% CI: 1.01–1.05), respectively, while Lim et al. [32] reported an HR of 1.04 (95% CI: 1.00–1.09) for respiratory disease mortality.

Finally, the number of O₃-related deaths is calculated using the following formula [27]:

M = y₀ × DB × Pop(6)

where y₀ is the baseline mortality rate, sourced from the China Health Statistics Yearbook 2020, which reports data from the year 2019 for the three diseases considered in this study [20], Pop represents the exposed population aged over 30, and M is the estimated number of deaths attributable to O₃.

2.4. Assessment of O₃-Induced Crop Yield Loss

Numerous prior studies in China have utilized different facilities to conduct a series of field experiments investigating the effects of elevated O₃ concentrations on the yields of various food crops [19,26,33]. Based on these studies, linear functions have been established to describe the relationship between various assessment indices and the relative yields of crops, thereby facilitating the evaluation of potential risks posed by O₃ pollution on crops at a regional scale. This study used exposure–response functions based on the AOT40 to evaluate the effects of O₃ on winter wheat and single rice in China, and its calculation is as follows [28]:

AOT40 = ∑[C(O₃) − 40]    C(O₃) > 40 ppb(7)

where C(O₃) represents the concentration of O₃, measured in ppb. AOT40 represents the cumulative sum of hourly O₃ concentrations exceeding 40 ppb [24], accounting for both the concentration of O₃ and the duration of exposure.

Winter wheat is widely cultivated in China, with major production areas concentrated in the central and eastern provinces. The primary production regions for single rice include Heilongjiang, Jiangsu, Hubei, Sichuan, Anhui, and Hunan. Following the method proposed by Zhao et al. [28], the daily period from 08:00 to 19:59 is defined as the daytime for calculating the AOT40, and the period for O₃ accumulation is defined as the 90 days preceding the harvest. Given the climatic differences across regions, the accumulation period varies accordingly. Therefore, the setting of the accumulation period for both crops was determined based on the study by Xu et al. [33].

Using the formula used by Sinha et al. [25], the crop yield loss caused by O₃ was calculated based on the following formulas [28]:

RYL = 1 − RY(8)

CPL = CP × RYL/(1 − RYL)(9)

where RY and RYL represent the relative yield and relative yield loss, respectively, CP and CPL denote crop production and crop production loss induced by O₃, respectively. It is essential to point out that this study calculated RY for each city using different functions from Table 1 and subsequently determined the RYL. Since yield data are available at the provincial level, the average RYL across all cities in each province was calculated to represent the overall RYL for that province. The CPL for each province was then determined by incorporating the CP of the province.

3. Results and Discussion

3.1. Spatial Pattern of MDA8 O₃

The spatial pattern of MDA8 O₃ across cities and nine representative regions in China for the year 2019 is shown in Figure 2. Overall, O₃ pollution in China displayed a spatial pattern characterized by higher concentrations in the eastern and northern regions and lower levels in the western and southern areas. The highest MDA8 O₃ concentrations were observed in the BTH (52.3 ppb), followed by YRD (50.3 ppb) and CC (48.8 ppb). This pattern corresponded with the findings of Shen et al. [38]. Previous research has also confirmed that high population densities and significant anthropogenic emissions of O₃ precursors in these regions are key drivers of elevated O₃ concentrations [39].

The formation of O₃ is governed by a complex, nonlinear relationship between its precursors, including NO_X and VOC_s, which defines the O₃ formation control regime across different regions [40]. In critical areas such as the BTH and YRD, O₃ formation is primarily VOCs-limited, meaning that cutting VOC_S emissions effectively curbs O₃ formation. However, lowering NO_x emissions might inadvertently boost O₃ levels [41]. Several studies have identified the increase in O₃ in eastern China as primarily driven by rising anthropogenic VOC_S emissions [42]. Additionally, the reduction in PM_2.5 levels has led to an increase in surface radiation, further intensifying photochemical reactions in the atmosphere and contributing to the rise in O₃ levels [42]. Consequently, stringent VOC emission control measures are urgently needed in these regions to effectively mitigate O₃ levels.

In contrast, MDA8 O₃ levels were relatively lower in western and northeastern China, including regions such as SB (40.0 ppb), YP (40.9 ppb), and NEC (41.5 ppb). The humid climates of the SB and YP, combined with the colder climate in the NEC, suppressed atmospheric photochemical reactions, thereby reducing O₃ formation [28]. Furthermore, these areas had relatively fewer anthropogenic emission sources, lower levels of industrialization, and lower population densities, which contributed to reduced emissions of O₃ precursors and further limited O₃ concentrations [28]. Additionally, favorable atmospheric dispersion conditions and relatively low PM₂.₅ levels helped to reduce O₃ accumulation [28].

In summary, the formation of O₃ is a complicated process, driven by meteorological conditions, emissions of O₃ precursors, and regional pollutant transport. Additionally, interactions among pollutants across different regions further affect O₃ generation and distribution.

3.2. Health Impacts of O₃ on Human Health

Figure 3 illustrates the health impacts of long-term O₃ exposure in China, highlighting several provinces with higher mortality rates in 2019, while Figure 4 provides a detailed distribution of impacts at the city level. The total number of deaths due to O₃ nationwide was 175,154 for all-cause mortality, 116,614 for cardiovascular disease mortality, and 37,465 for respiratory disease mortality. The health risks associated with O₃ exposure are closely related to both O₃ concentrations and population sizes in different regions. Consequently, the highest health risks were observed in Shandong, Henan, and Jiangsu, with corresponding all-cause mortality figures of 18,926, 15,984, and 14,062, respectively. The elevated health risks in these provinces were strongly linked to their high population densities and elevated O₃ pollution levels. In contrast, Tibet, with an MDA8 O₃ concentration of 44.8 ppb and a population of fewer than 4 million, experienced much lower O₃-related all-cause mortality, at only 261.

Previous national-scale O₃ health risk assessments have often relied on atmospheric chemical models or satellite remote sensing data. For example, employing the WRF-CMAQ model to simulate hourly O₃ data, it was reported that O₃ exposure in 2014 and 2015 resulted in 89,391 [17] and 71,900 [18] deaths related to COPD, respectively. However, a study based on observational data indicated that the health risk of O₃ in 2014 was lower than that in 2015 [12]. This discrepancy underscores the importance of using observational data for risk assessments. In China, detailed O₃ observational data are scarce. Despite this limitation, several health risk studies related to O₃ have been conducted based on available monitoring data. Feng et al. [19] and Maji et al. [20] quantified the health effects of O₃ using national monitoring data, estimating 74,316 and 74,200 all-cause deaths attributable to O₃ in 2015 and 2016, respectively. However, these figures were considerably lower than those reported in the present study, primarily due to the higher O₃ in the assessment years of this study.

In health risk assessments, selecting the appropriate O₃ indicator is crucial. Typically, long-term O₃ effects estimated using MDA8 O₃ exceed short-term O₃ effects estimated using the fourth-highest MDA8 O₃ (4MDA8 O₃) [43]. Based on MDA1 O₃ and MDA8 O₃, Lu et al. [12] estimated 32,690 and 49,430 O₃-related respiratory deaths in 69 cities across China, respectively. Additionally, Feng et al. [19] used the SOMO35 indicator to estimate that the number of premature deaths in China due to O₃ in 2015 was 74,316, which was significantly lower than estimates based on MDA8 O₃ and 4MDA8 O₃ indicators for the same period [43].

T is a crucial factor influencing health risk assessment results, but there is currently no clear theoretical basis for its determination. The World Health Organization recommends setting T at 35 ppb, whereas this study follows the approach of Maji et al. [43], setting T at 26.7 ppb. Using the same data sources, assessment indicators, T, and β values as those employed in this study, Maji et al. [43] estimated that all-cause deaths nationwide caused by O₃ in 2019 were 181,000. This estimate generally aligned with the conclusions of this study, thereby verifying the reliability of this study’s conclusions.

O₃ health risk assessments are closely linked to β values. Since no cohort studies on long-term O₃ exposure have been conducted in China, the β values used in this study were derived from epidemiological studies conducted by Turner et al. [31] and Lim et al. [32]. Furthermore, air quality monitoring stations in China are predominantly located in urban areas, whereas the population is more widely distributed across suburban regions, introducing a significant source of uncertainty in health assessments. Prior studies indicated that O₃ pollution was more severe in suburban areas than in urban areas [20]. Therefore, the O₃ health impact assessment in this study may underestimate the actual risks.

3.3. The Impact of O₃ on Winter Wheat and Single-Rice Yields

The AOT40 indicator, commonly used as a plant protection standard, is calculated by accumulating the hourly O₃ concentrations that exceed 40 ppb [12]. This indicator is simple to calculate and exhibits a strong linear relationship with crop yield reduction, which is why it has been widely applied around the world [12]. Figure 5 illustrates the spatial distribution of AOT40 in the winter wheat and single-rice-growing provinces in China in 2019. In many regions, the average O₃ concentration during the crop growing season has notably surpassed the 40 ppb limit for crop harm, suggesting that O₃ pollution poses a significant risk to these two crops in China. Moreover, since AOT40 is calculated solely based on O₃ concentrations, its spatial distribution closely follows that of MDA8 O₃.

The average AOT40 value in the main winter wheat-growing areas of China was 9.30 × 10³ ppb·h. In certain provinces in North China, such as Tianjin (1.93 × 10⁴ ppb·h), Shanxi (1.92 × 10⁴ ppb·h), and Hebei (1.91 × 10⁴ ppb·h), the AOT40 values were relatively high. In contrast, due to lower O₃ concentrations, the AOT40 values in southern and southeastern regions of China were comparatively low. These regions included Guangxi (1.10 × 10³ ppb·h), Fujian (2.60 × 10³ ppb·h), Guangdong (3.10 × 10³ ppb·h), and Hunan (3.50 × 10³ ppb·h). For single rice, the national average AOT40 value was 1.29 × 10⁴ ppb·h. However, due to higher O₃ concentrations, AOT40 values in several provinces exceeded 2.00 × 10⁴ ppb·h, with Tianjin having the highest AOT40 value (2.65 × 10⁴ ppb·h), followed by Henan (2.32 × 10⁴ ppb·h) and Hebei (2.21 × 10⁴ ppb·h). Relatively low AOT40 values were observed in regions such as Heilongjiang (1.60 × 10³ ppb·h) and Yunnan (2.70 × 10³ ppb·h).

Based on the exposure–response functions presented in Table 1, the RYLs for winter wheat and single rice in China in 2019 were calculated, as shown in Figure 6. The exposure–response functions by Wang et al. [34] and Tong et al. [36] calculated the RYL for winter wheat at 20.4% and 19.6%, respectively, both of which were significantly lower than the estimate of 26.2% provided by Feng et al. [35]. Meanwhile, the exposure–response functions by Feng et al. [37] and Wang et al. [34] estimated the RYL for rice at 6.6% and 11.9%, respectively. Considering the variations in estimates from different exposure–response functions, the average RYL for winter wheat and single rice nationwide due to O₃ in 2019 was 22.1% and 9.3%, respectively. As shown in Figure 7, the RYL for winter wheat was highest in Tianjin (46.1%), Shanxi (46.0%), and Hebei (45.6%), primarily due to the higher AOT40 levels in these provinces. However, major producing areas such as Shandong (36.7%), Henan (33.3%), and Jiangsu (33.0%) still exhibited RYLs exceeding 25%. For single rice, the highest RYLs were observed in Tianjin (19.6%), Henan (17.1%), and Hebei (16.3%). In contrast, in many key rice-producing regions such as Hubei (13.1%), Jiangsu (12.8%), Anhui (12.7%), Hunan (8.8%), Sichuan (5.3%), and Heilongjiang (1.2%), the RYLs were all below 15%.

Similarly to O₃ health risk assessments, previous regional-scale O₃ agricultural risk assessment studies have primarily relied on atmospheric chemical models. For instance, Avnery et al. (2011) [44] used a CTM model to predict that O₃ pollution could cause wheat RYL in China to reach 20–26% by 2030. Tang et al. [45] employed the WRF/Chem-CHASER model to estimate that O₃ exposure in China in 2020 could result in a wheat RYL of 17–19%. Lin et al. [17] analyzed O₃ pollution in 2014 using the WRF-CMAQ model, estimating yield losses for winter wheat and rice to be 8.5–14.0% and 3.9–15.0%, respectively. The assessment results of this study for wheat were slightly higher than those estimated by Tang et al. [45] and Lin et al. [17], but they fell within the range predicted by Avnery et al. [44]. Additionally, the rice yield loss estimates in this study were also consistent with those of Lin et al. [17]. Notably, observational data can provide more accurate and reliable O₃ agricultural risk assessments, offering deeper insights into the damage caused by O₃ to crops. Using observational O₃ data, Ren et al. [46] estimated that O₃ pollution in the YRD led to wheat and rice yield losses ranging from 10 to 36% and 7 to 24%, respectively, which aligns with the area covered in this study. Furthermore, the results of this study corroborated nationwide wheat and rice yield loss estimates due to O₃ based on the same methodology used by Xu et al. [33], thereby demonstrating the accuracy of the findings.

Figure 8 illustrates the impact of O₃ on the national and major winter wheat and single-rice-producing provinces’ CPLs. In 2019, O₃ pollution resulted in national winter wheat and single rice CPLs of 6.30 × 10⁷ metric tons (mt) and 1.40 × 10⁷ mt, respectively. In comparison, this estimate was significantly higher than the winter wheat CPL estimates of 4.00 × 10⁷ mt for 2014 by Lin et al. [17] and 1.80 × 10⁷ mt for 2020 by Tang et al. [45]. The discrepancy could be attributed to two factors: first, their studies relied on simulated O₃ data; second, they used relatively outdated CP data in their CPL calculations. Additionally, our estimate for the single rice CPL was nearly identical to the 1.70 × 10⁷ mt estimate by Lin et al. [17]. Moreover, our estimates were somewhat higher than those reported by Zhao et al. [28] and lower than those by Xu et al. [33], based on observed data, further supporting the validity of the CPL estimates in this study.

At the provincial level, the three provinces with the highest estimated winter wheat CPL were Henan (1.80 × 10⁷ mt), Shandong (1.40 × 10⁷ mt), and Hebei (1.20 × 10⁷ mt). CPL is influenced by both RYL and CP. Consequently, some provinces with high RYLs—such as Shanxi, Tianjin, Beijing, and Ningxia—still showed relatively low CPL values, owing to their smaller CP values, despite having RYLs exceeding 30%. For single rice, the highest CPL was observed in Jiangsu (2.90 × 10⁶ mt), followed by Hubei (2.60 × 10⁶ mt) and Anhui (2.10 × 10⁶ mt). Major single rice-production provinces, such as Heilongjiang, Sichuan, and Hunan (with CPLs greater than 1.00 × 10⁷ mt), have relatively low CPLs due to their RYLs being below 10%. Therefore, future O₃ control measures should focus on the primary crop-producing regions, with particular emphasis on provinces with high CPL, to mitigate the impact of O₃ concentrations on agricultural crops.

4. Conclusions

This study systematically quantified the effects of O₃ on human health and the yields of winter wheat and single rice in China for the year 2019 using observational data. The results revealed significant regional variations in O₃ concentrations, with higher levels in the eastern and northern regions and lower levels in the western and southern areas. Regions such as BTH, YRD, and CC exhibited elevated O₃ concentrations. Regarding health risks, O₃ exposure was estimated to contribute to approximately 175,154 deaths nationwide, with provinces like Shandong, Henan, and Jiangsu experiencing higher health risks due to elevated O₃ levels and denser populations. In terms of crop yield loss, the AOT40 index assessment indicated that reductions of 6.30×10⁷ mt for winter wheat and 1.40 × 10⁷ mt for single rice, highlighting the significant impact of O₃ on crop production and food security in China.

In summary, the conclusions of this study make a direct contribution to understanding the dual threats posed by O₃ pollution to both public health and food security in China. While previous studies have broadly examined the effects of O₃ on human health and crop yields, they often lack region-specific detail or focus on specific crop types. This study, however, offers a more detailed, region-specific analysis, providing crucial insights into the areas most affected by O₃ pollution and the associated risks to human health and agricultural production. Furthermore, this research presents new data on the health impacts in densely populated provinces and on specific crop yield losses for winter wheat and single rice, areas that have not been fully addressed in prior studies.

In conclusion, this study underscores the urgent need for region-specific O₃ control measures in major agricultural and densely populated areas of China to mitigate both health risks and crop yield losses. Implementing targeted policies to reduce O₃ pollution can help minimize the adverse impacts on human health and food security. Furthermore, expanding the coverage and improving the quality of O₃ monitoring networks will enhance future environmental health risk assessments and policy development, supporting more effective interventions to address O₃ pollution.

Author Contributions

Conceptualization, Y.C. and J.W. (Jiayan Wang); methodology, Y.C., M.K. and H.Z.; formal analysis, J.W. (Jiayan Wang) and J.W. (Jinghan Wang); writing—original draft preparation, Y.C., J.W. (Jiayan Wang), J.W. (Jinghan Wang) and H.Z.; writing—review and editing, Y.C. and H.Z. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Table

Figure 1. Geographical locations of 367 cities in China.

Figure 2. Spatial distribution of MDA8 O3 across cities and 9 representative regions in China in 2019.

Figure 3. Health effects from O3 in China and five key regions in 2019.

Figure 4. Spatial distribution of O3-induced health effects among cities in China in 2019.

Figure 5. Spatial distribution of AOT40 in the growing provinces of winter wheat and single rice in China in 2019.

Figure 6. National mean RYL of winter wheat and single rice in China under various exposure–response functions in 2019. A, B, C, D and E, refer to [34,35,36,37] and [34].

Figure 7. Mean RYL of winter wheat and single rice across Chinese provinces in 2019.

Figure 8. CPL for winter wheat and single rice in China and major crop-producing provinces in 2019.

Table 1

Response relationship between the relative yield of two crops and the AOT40 index.

Crop	Exposure–Response Function	Reference
Winter wheat	RY = 1 − 0.0228 × AOT40	[34]
	RY = 0.961 − 0.0250 × AOT40	[35]
	RY = 1 − 0.0220 × AOT40	[36]
Single rice	RY = 1 − 0.0053 × AOT40	[37]
	RY = 1 − 0.0095 × AOT40	[34]

References

1. Yin, P.; Chen, R.; Wang, L.; Meng, X.; Liu, C.; Niu, Y.; Lin, Z.; Liu, Y.; Liu, J.; Qi, J. et al. Ambient Ozone Pollution and Daily Mortality: A Nationwide Study in 272 Chinese Cities. Environ. Health Perspect.; 2017; 125, 117006. [DOI: https://dx.doi.org/10.1289/EHP1849] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29212061]

2. Trieu, T.T.N.; Goto, D.; Yashiro, H.; Murata, R.; Sudo, K.; Tomita, H.; Satoh, M.; Nakajima, T. Evaluation of summertime surface ozone in Kanto area of Japan using a semi-regional model and observation. Atmos. Environ.; 2017; 153, pp. 163-181. [DOI: https://dx.doi.org/10.1016/j.atmosenv.2017.01.030]

3. Verstraeten, W.W.; Neu, J.L.; Williams, J.E.; Bowman, K.W.; Worden, J.R.; Boersma, K.F. Rapid increases in tropospheric ozone production and export from China. Nat. Geosci.; 2015; 8, pp. 690-695. [DOI: https://dx.doi.org/10.1038/ngeo2493]

4. Kim, Y.; Oh, J.; Kim, S.; Kim, A.; Park, J.; Ahn, S.; Kang, C.; Kim, S.; Lee, H.J.; Lee, J.T. et al. Relationship between short-term ozone exposure, cause-specific mortality, and high-risk populations: A nationwide, time-stratified, case-crossover study. Environ. Res.; 2024; 261, 119712. [DOI: https://dx.doi.org/10.1016/j.envres.2024.119712] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/39096989]

5. Vingarzan, R. A review of surface ozone background levels and trends. Atmos. Environ.; 2004; 38, pp. 3431-3442. [DOI: https://dx.doi.org/10.1016/j.atmosenv.2004.03.030]

6. Sitch, S.; Cox, P.M.; Collins, W.J.; Huntingford, C. Indirect radiative forcing of climate change through ozone effects on the land-carbon sink. Nature; 2007; 448, pp. 791-794. [DOI: https://dx.doi.org/10.1038/nature06059] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17653194]

7. IPCC. Intergovernmental panel on climate change: Summary for policymakers. Climate Change 2014: Impacts, Adaptation and Vulnerability; Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge University Press: Cambridge, UK, 2014.

8. Brown, F.; Folberth, G.A.; Sitch, S.; Bauer, S.; Bauters, M.; Boeckx, P.; Cheesman, A.W.; Deushi, M.; Dos Santos Vieira, I.; Galy-Lacaux, C. et al. The ozone-climate penalty over South America and Africa by 2100. Atmos. Chem. Phys.; 2022; 22, pp. 12331-12352. [DOI: https://dx.doi.org/10.5194/acp-22-12331-2022]

9. Derwent, R.G.; Parrish, D.D.; Manning, A.J.; Spain, T.G.; Simmonds, P.G.; O’Doherty, S. Ozone at Mace Head, Ireland from 1987 to 2021: Declining baselines, phase-out of European regional pollution, COVID-19 impacts. Atmos. Environ.; 2024; 320, 120322. [DOI: https://dx.doi.org/10.1016/j.atmosenv.2023.120322]

10. Mishra, A.K.; Sen Gupta, G.; Agrawal, S.B.; Tiwari, S. Divergent responses of ascorbate and glutathione pools in ozone-sensitive and ozone-tolerant wheat cultivars under elevated ozone and carbon dioxide interaction. J. Hazard. Mater.; 2024; 472, 134453. [DOI: https://dx.doi.org/10.1016/j.jhazmat.2024.134453] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38723481]

11. Pande, P.; Hayes, F.; Bland, S.; Booth, N.; Pleijel, H.; Emberson, L. Ozone dose-response relationships for wheat can be derived using photosynthetic-based stomatal conductance models. Agric. For. Meteorol.; 2024; 356, 110150. [DOI: https://dx.doi.org/10.1016/j.agrformet.2024.110150]

12. Lu, X.; Zhang, L.; Wang, X.; Gao, M.; Li, K.; Zhang, Y.; Yue, X.; Zhang, Y. Rapid increases in warm-season surface ozone and resulting health impact in China since 2013. Environ. Sci. Technol. Lett.; 2020; 7, pp. 240-247. [DOI: https://dx.doi.org/10.1021/acs.estlett.0c00171]

13. Chen, K.; Fiore, A.M.; Chen, R.; Jiang, L.; Jones, B.; Schneider, A.; Peters, A.; Bi, J. Future ozone-related acute excess mortality under climate and population change scenarios in China: A modeling study. PLoS Med.; 2018; 15, e1002598. [DOI: https://dx.doi.org/10.1371/journal.pmed.1002598] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29969446]

14. Yan, M.; Liu, Z.; Liu, X.; Duan, H.; Li, T. Meta-analysis of the Chinese studies of the association between ambient ozone and mortality. Chemosphere; 2013; 93, pp. 899-905. [DOI: https://dx.doi.org/10.1016/j.chemosphere.2013.05.040]

15. Shang, Y.; Sun, Z.; Cao, J.; Wang, X.; Zhong, L.; Bi, X.; Li, H.; Zhu, T.; Huang, W. Systematic review of Chinese studies of short-term exposure to air pollution and daily mortality. Environ. Int.; 2013; 54, pp. 100-111. [DOI: https://dx.doi.org/10.1016/j.envint.2013.01.010] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23434817]

16. Sun, Q.; Wang, W.; Chen, C.; Ban, J.; Xu, D.; Zhu, P.; He, M.Z.; Li, T. Acute effect of multiple ozone metrics on mortality by season in 34 Chinese counties in 2013–2015. J. Intern. Med.; 2018; 283, pp. 481-488. [DOI: https://dx.doi.org/10.1111/joim.12724] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29247470]

17. Lin, Y.; Jiang, F.; Zhao, J.; Zhu, G.; He, X.; Ma, X.; Li, S.; Sabel, C.E.; Wang, H. Impacts of O₃ on premature mortality and crop yield loss across China. Atmos. Environ.; 2018; 194, pp. 41-47. [DOI: https://dx.doi.org/10.1016/j.atmosenv.2018.09.024]

18. Liu, H.; Liu, S.; Xue, B.; Lv, Z.; Meng, Z.; Yang, X.; Xue, T.; Yu, Q.; He, K.B. Ground-level ozone pollution and its health impacts in China. Atmos. Environ.; 2018; 173, pp. 223-230. [DOI: https://dx.doi.org/10.1016/j.atmosenv.2017.11.014]

19. Feng, Z.; De Marco, A.; Anav, A.; Gualtieri, M.; Sicard, P.; Tian, H.; Fornasier, F.; Tao, F.; Guo, A.; Paoletti, E. Economic losses due to ozone impacts on human health, forest productivity and crop yield across China. Environ. Int.; 2019; 131, 104966. [DOI: https://dx.doi.org/10.1016/j.envint.2019.104966]

20. Maji, K.J.; Ye, W.F.; Arora, M.; Nagendra, S.M.S. Ozone pollution in Chinese cities: Assessment of seasonal variation, health effects and economic burden. Environ. Pollut.; 2019; 247, pp. 792-801. [DOI: https://dx.doi.org/10.1016/j.envpol.2019.01.049]

21. Ramya, A.; Dhevagi, P.; Poornima, R.; Avudainayagam, S.; Watanabe, M.; Agathokleous, E. Effect of ozone stress on crop productivity: A threat to food security. Environ. Res.; 2023; 236, 116816. [DOI: https://dx.doi.org/10.1016/j.envres.2023.116816]

22. Xie, X.; Wang, T.; Yue, X.; Li, S.; Zhuang, B.; Wang, M.; Yang, X. Numerical modeling of ozone damage to plants and its effects on atmospheric CO₂ in China. Atmos. Environ.; 2019; 217, 116970. [DOI: https://dx.doi.org/10.1016/j.atmosenv.2019.116970]

23. Feng, Z.; Xu, Y.; Kobayashi, K.; Dai, L.; Zhang, T.; Agathokleous, E.; Calatayud, V.; Paoletti, E.; Mukherjee, A.; Agrawal, M. et al. Ozone pollution threatens the production of major staple crops in East Asia. Nat. Food; 2022; 3, pp. 47-56. [DOI: https://dx.doi.org/10.1038/s43016-021-00422-6] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37118490]

24. Zhao, H.; Zhang, Y.X.; Qi, Q.; Zhang, H. Evaluating the impacts of ground-level O₃ on crops in China. Curr. Pollut. Rep.; 2021; 7, pp. 565-578. [DOI: https://dx.doi.org/10.1007/s40726-021-00201-8]

25. Sinha, B.; Singh-Sangwan, K.; Maurya, Y.; Kumar, V.; Sarkar, C.; Chandra, B.P.; Sinha, V. Assessment of crop yield losses in Punjab and Haryana using 2 years of continuous in situ ozone measurements. Atmos. Chem. Phys.; 2015; 15, pp. 9555-9576. [DOI: https://dx.doi.org/10.5194/acp-15-9555-2015]

26. Zhao, H.; Zheng, Y.; Wu, X. Assessment of yield and economic losses for wheat and rice due to ground-level O₃ exposure in the Yangtze River Delta, China. Atmos. Environ.; 2018; 191, pp. 241-248. [DOI: https://dx.doi.org/10.1016/j.atmosenv.2018.08.019]

27. Zhao, H.; Chen, K.; Liu, Z.; Zhang, Y.; Shao, T.; Zhang, H. Coordinated control of PM_2.5 and O₃ is urgently needed in China after implementation of the “air pollution prevention and control action plan”. Chemosphere; 2021; 270, 129441. [DOI: https://dx.doi.org/10.1016/j.chemosphere.2020.129441] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33388503]

28. Zhao, H.; Zheng, Y.; Zhang, Y.; Li, T. Evaluating the effects of surface O₃ on three main food crops across China during 2015–2018. Environ. Pollut.; 2020; 258, 113794. [DOI: https://dx.doi.org/10.1016/j.envpol.2019.113794]

29. Barrero, M.A.; Orza, J.A.G.; Cabello, M.; Canton, L. Categorisation of air quality monitoring stations by evaluation of PM₁₀ variability. Sci. Total Environ.; 2015; 524–525, pp. 225-236. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2015.03.138] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25897730]

30. Ma, X.; Jia, H.; Sha, T.; An, J.; Tian, R. Spatial and seasonal characteristics of particulate matter and gaseous pollution in China: Implications for control policy. Environ. Pollut.; 2019; 248, pp. 421-428. [DOI: https://dx.doi.org/10.1016/j.envpol.2019.02.038] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30825767]

31. Turner, M.C.; Jerrett, M.; Pope, C.A.; Krewski, D.; Gapstur, S.M.; Diver, W.R.; Beckerman, B.S.; Marshall, J.D.; Su, J.; Crouse, D.L. et al. Long-term ozone exposure and mortality in a large prospective study. Am. J. Respir. Crit. Care Med.; 2016; 193, pp. 1134-1142. [DOI: https://dx.doi.org/10.1164/rccm.201508-1633OC] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26680605]

32. Lim, C.C.; Hayes, R.B.; Ahn, J.; Shao, Y.; Silverman, D.T.; Jones, R.R.; Garcia, C.; Bell, M.L.; Thurston, G.D. Long-term exposure to ozone and cause-specific mortality risk in the United States. Am. J. Respir. Crit. Care Med.; 2019; 200, pp. 1022-1031.

33. Xu, M.; Yao, Q.; Chen, D.; Li, M.; Li, R.; Gao, B.; Zhao, B.; Chen, Z. Estimating the impact of ground ozone concentrations on crop yields across China from 2014 to 2018: A multi-model comparison. Environ. Pollut.; 2021; 283, 117099. [DOI: https://dx.doi.org/10.1016/j.envpol.2021.117099] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33857877]

34. Wang, X.; Zhang, Q.; Zheng, F.; Zheng, Q.; Yao, F.; Chen, Z.; Zhang, W.; Hou, P.; Feng, Z.; Song, W. et al. Effects of elevated O₃ concentration on winter wheat and rice yields in the Yangtze River Delta, China. Environ. Pollut.; 2012; 171, pp. 118-125. [DOI: https://dx.doi.org/10.1016/j.envpol.2012.07.028]

35. Feng, Z.; Tang, H.; Uddling, J.; Pleijel, H.; Kobayashi, K.; Zhu, J.; Oue, H.; Guo, W. A stomatal ozone flux-response relationship to assess ozone-induced yield loss of winter wheat in subtropical China. Environ. Pollut.; 2012; 164, pp. 16-23. [DOI: https://dx.doi.org/10.1016/j.envpol.2012.01.014] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22310057]

36. Tong, L. O₃ and CO₂ Fluxes Monitoring and Modeling of Early Rice in Southern China and Winter Wheat in Northern China. Ph.D. Dissertation; Graduate University of Chinese Academy of Sciences: Beijing, China, 2011; (In Chinese)

37. Feng, Z.; Jin, M.; Zhang, F.; Huang, Y. Effects of ground-level ozone (O₃) pollution on the yields of rice and winter wheat in the Yangtze River Delta. J. Environ. Sci.; 2003; 15, pp. 360-362.

38. Shen, F.; Zhang, L.; Jiang, L.; Tang, M.; Gai, X.; Chen, M.; Ge, X. Temporal variations of six ambient criteria air pollutants from 2015 to 2018, their spatial distributions, health risks and relationships with socioeconomic factors during 2018 in China. Environ. Int.; 2020; 137, 105556. [DOI: https://dx.doi.org/10.1016/j.envint.2020.105556] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32059148]

39. Cai, S.; Wang, Y.; Zhao, B.; Wang, S.; Chang, X.; Hao, J. The impact of the “air pollution prevention and control action plan” on PM_2.5 concentrations in Jing-Jin-Ji region during 2012–2020. Sci. Total Environ.; 2017; 580, pp. 197-209. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2016.11.188] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28011024]

40. Ma, Z.; Xu, J.; Quan, W.; Zhang, Z.; Lin, W.; Xu, X. Significant increase of surface ozone at a rural site, north of eastern China. Atmos. Chem. Phys.; 2016; 16, pp. 3969-3977. [DOI: https://dx.doi.org/10.5194/acp-16-3969-2016]

41. Sicard, P.; Serra, R.; Rossello, P. Spatiotemporal trends in ground-level ozone concentrations and metrics in France over the time period 1999–2012. Environ. Res.; 2016; 149, pp. 122-144. [DOI: https://dx.doi.org/10.1016/j.envres.2016.05.014] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27200478]

42. Li, K.; Jacob, D.J.; Liao, H.; Shen, L.; Zhang, Q.; Bates, K.H. Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China. Proc. Natl. Acad. Sci. USA; 2019; 116, pp. 422-427. [DOI: https://dx.doi.org/10.1073/pnas.1812168116]

43. Maji, K.J.; Namdeo, A. Continuous increases of surface ozone and associated premature mortality growth in China during 2015–2019. Environ. Pollut.; 2021; 269, 116183. [DOI: https://dx.doi.org/10.1016/j.envpol.2020.116183]

44. Avnery, S.; Mauzerall, D.L.; Liu, J.; Horowitz, L.W. Global crop yield reductions due to surface ozone exposure: 2. Year 2030 potential crop production losses, economic damage, and implications for world hunger under two scenarios of O₃ pollution. Atmos. Environ.; 2011; 45, pp. 2297-2309.

45. Tang, H.; Takigawa, M.; Liu, G.; Zhu, J.; Kobayashi, K. A projection of ozone-induced wheat production loss in China and India for the years 2000 and 2020 with exposure-based and flux-based approaches. Glob. Chang. Biol.; 2013; 19, pp. 2739-2752.

46. Ren, X.; Shang, B.; Feng, Z.; Calatayud, V. Yield and economic losses of winter wheat and rice due to ozone in the Yangtze River Delta during 2014–2019. Sci. Total Environ.; 2020; 745, 140847. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.140847] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32758759]