Multidecadal ozone trends in China and implications for human health and crop yields: a hybrid approach combining a chemical transport model and machine learning

Abstract

Surface ozone (O3) is well known for posing significant threats to both human health and crop production worldwide. However, a multidecadal assessment of the impacts of O3 on public health and crop yields in China is lacking due to insufficient long-term continuous O3 observations. In this study, we used a machine learning (ML) algorithm to correct the biases of O3 concentrations simulated by a chemical transport model from 1981-2019 by integrating multi-source datasets. The ML-enabled bias correction offers improved performance in reproducing observed O3 concentrations and thus further improves our estimates of the impacts of O3 on human health and crop yields. The warm-season trends of increasing O3 in Beijing-Tianjin-Hebei and its surroundings (BTHs) as well as in the Yangtze River Delta (YRD), Sichuan Basin (SCB), and Pearl River Delta (PRD) regions are 0.32, 0.63, 0.84, and 0.81 μg m-3 yr-1 from 1981 to 2019, respectively. In more recent years, O3 concentrations experienced more fluctuations in the four major regions. Our results show that only BTHs have a perceptible increasing trend of 0.81 μg m-3 yr-1 during 2013-2019. Using accumulated O3 over a threshold of 40 ppb (AOT40-China) exposure-yield response relationships, the estimated relative yield losses (RYLs) for wheat, rice, soybean, and maize are 17.6 %, 13.8 %, 11.3 %, and 7.3 % in 1981, increasing to 24.2 %, 17.5 %, 16.3 %, and 9.8 % in 2019, with an increasing rate of +0.03 % yr-1, +0.04 % yr-1, +0.27 % yr-1, and +0.13 % yr-1, respectively. The estimated annual all-cause premature deaths induced by O3 increased from ∼55900 in 1981 to ∼162000 in 2019 with an increasing trend of ∼2980 deaths per year. The annual premature deaths related to respiratory and cardiovascular disease are ∼34200 and ∼40300 in 1998 and ∼26500 and ∼79000 in 2019, having a rate of change of -546 and +1770 deaths per year during 1998-2019, respectively. Our study, for the first time, used ML to provide a robust dataset of O3 concentrations over the past 4 decades in China, enabling a long-term evaluation of O3-induced crop losses and health impacts. These findings are expected to fill the gap of the long-term O3 trend and impact assessment in China.