State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global and Earth System Sciences, Beijing Normal University, 19 Xinjiekou Street, Haidian, Beijing, 100875, China.
School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China.
Environ Pollut. 2021 Aug 15;283:117099. doi: 10.1016/j.envpol.2021.117099. Epub 2021 Apr 7.
Ground level ozone exerts a strong impact on crop yields, yet how to properly quantify ozone induced yield losses in China remains challenging. To this end, we employed a series of O-crop models to estimate ozone induced yield losses in China from 2014 to 2018. The outputs from all models suggested that the total Relative Yield Losses (RYL) of wheat in China from 2014 to 2018 was 18.4%-49.3% and the total RYL of rice was 6.2%-52.9%. Consequently, the total Crop Production Losses (CPL) of wheat and rice could reach 63.9-130.4 and 28.3-35.4 million tons, and the corresponding Total Economic Losses (TEL) could reach 20.5-44.7 and 11.0-15.3 billion dollars, stressing the great importance and urgency of national ozone management. Meanwhile, the estimation outputs highlighted the large variations between different regional O-crop models when applying to large scales. Instead of applying one unified O-crop models to all regions across China, we also explored the strategy of employing specific O-crop models in corresponding (and neighboring) regions to estimate ozone induced yield loss in China. The comparison of two strategies suggested that the mean value from multiple models may still present an inconsistent over/underestimation trend for different crops. Therefore, it is a preferable strategy to employ corresponding O-crop models in different regions for estimating the national crop losses caused by ozone pollution. However, the severe lack of regional O-crop models in most regions across China makes a robust estimation of national yield losses highly challenging. Given the large variations between O-crop interactions across regions, a systematic framework with massive regional O-crop models should be properly designed and implemented.
地面臭氧对作物产量有很大影响，但如何正确量化中国臭氧引起的产量损失仍具有挑战性。为此，我们采用了一系列 O 作物模型来估算 2014 年至 2018 年期间中国臭氧引起的产量损失。所有模型的输出结果均表明，中国 2014 年至 2018 年期间小麦的总相对产量损失（RYL）为 18.4%-49.3%，水稻的总 RYL 为 6.2%-52.9%。因此，小麦和水稻的作物总产量损失（CPL）可达 63.9-130.4 万吨和 28.3-35.4 万吨，相应的总经济损失（TEL）可达 20.5-44.7 亿美元和 11.0-15.3 亿美元，这凸显了国家臭氧管理的重要性和紧迫性。同时，估算结果突出了在大尺度上应用不同区域 O 作物模型时存在的巨大差异。我们没有采用一个统一的 O 作物模型应用于中国所有地区，而是还探索了在相应（和相邻）地区采用特定 O 作物模型来估算中国臭氧引起的产量损失的策略。两种策略的比较表明，对于不同的作物，多个模型的平均值仍可能存在不一致的高估或低估趋势。因此，在不同地区采用相应的 O 作物模型来估算臭氧污染造成的全国作物损失是一种更可取的策略。然而，中国大部分地区缺乏区域 O 作物模型，这使得对全国产量损失进行稳健估算极具挑战性。鉴于不同地区 O 作物相互作用的巨大差异，需要适当设计和实施一个具有大量区域 O 作物模型的系统框架。
