Han Tian, Hu Xiaomin, Zhang Jing, Xue Wenhao, Che Yunfei, Deng Xiaoqing, Zhou Lihua
Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
Environ Pollut. 2023 Nov 1;336:122334. doi: 10.1016/j.envpol.2023.122334. Epub 2023 Aug 9.
In recent years, the problem of surface ozone pollution in China has been of great concern. According to observation data from monitoring stations, the concentration of near-surface ozone (O) in China has gradually increased in recent years, and ozone concentration often exceeds the contaminant limit standard, especially in the Beijing-Tianjin-Hebei (BTH) region. High O concentration pollution will adversely affect crop growth, which can cause crop yield losses. Therefore, it is urgent to recognize the situation of ozone pollution in the BTH region and quantitatively evaluate the crop yield losses caused by ozone pollution to develop more effective pollution prevention and control policies. However, the monitoring of ozone concentration in China started relatively late compared with some developed countries, and currently, long-time series data covering the BTH region cannot be obtained, which makes it difficult to evaluate the impact of ozone on crop yield. Therefore, a new method (WRFC-XGB) was proposed in this study to establish a high-precision near-surface O concentration dataset covering the whole BTH region from 2014 to 2019 by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) machine learning algorithm. Through verification with ground observation station data, the results of WRFC-XGB are satisfactory, and R can reach 0.78-0.91. Compared with other algorithms, the accuracy of the near-surface ozone concentration dataset is greatly improved, which can be used to estimate the impact of surface ozone on crop yield. Based on this dataset, the yield loss of winter wheat, rice, and maize caused by O pollution was estimated by using the response equation of the relative yield and ozone dose index. The results showed that the total yield losses of winter wheat, rice and maize from 2014 to 2019 were 2659.21 million tons, 49.23 million tons and 1721.56 million tons due to ozone pollution in the BTH region, respectively, and the highest relative yield loss of crops caused by O pollution could be 29.37% during 2014-2019, which indicated that the impact of ozone pollution on crop yield cannot be ignored, and effective measures need to be developed to control ozone pollution, prevent crop production loss, and ensure people's food security.
近年来，中国地表臭氧污染问题备受关注。根据监测站点的观测数据，近年来中国近地表臭氧（O）浓度呈逐渐上升趋势，臭氧浓度时常超过污染物限值标准，尤其是在京津冀（BTH）地区。高浓度的臭氧污染会对作物生长产生不利影响，导致作物产量损失。因此，迫切需要认清京津冀地区的臭氧污染状况，并定量评估臭氧污染造成的作物产量损失，以制定更有效的污染防治政策。然而，与一些发达国家相比，中国臭氧浓度监测起步较晚，目前无法获取覆盖京津冀地区的长时间序列数据，这使得评估臭氧对作物产量的影响变得困难。因此，本研究提出了一种新方法（WRFC-XGB），通过将气象研究与预报化学（WRF-Chem）模型与极端梯度提升（XGBoost）机器学习算法相结合，建立了一个覆盖2014年至2019年整个京津冀地区的高精度近地表臭氧浓度数据集。通过与地面观测站数据进行验证，WRFC-XGB的结果令人满意，R值可达0.78 - 0.91。与其他算法相比，近地表臭氧浓度数据集的精度有了大幅提高，可用于估算地表臭氧对作物产量的影响。基于该数据集，利用相对产量与臭氧剂量指数的响应方程，估算了臭氧污染导致的冬小麦、水稻和玉米的产量损失。结果表明，2014年至2019年，京津冀地区臭氧污染分别导致冬小麦、水稻和玉米的总产量损失为2659.21万吨、4,923万吨和1721.56万吨，2014 - 2019年期间臭氧污染造成的作物最高相对产量损失可达29.37%，这表明臭氧污染对作物产量产生的影响不容忽视，需要制定有效措施控制臭氧污染，防止作物生产损失，保障人民的粮食安全。
