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Multidecadal ozone trends in China and implications for human health and crop yields: a hybrid approach combining a chemical transport model and machine learning
oleh: J. Mao, A. P. K. Tai, A. P. K. Tai, A. P. K. Tai, D. H. Y. Yung, T. Yuan, K. T. Chau, Z. Feng, Z. Feng
| Format: | Article |
|---|---|
| Diterbitkan: | Copernicus Publications 2024-01-01 |
Deskripsi
<p>Surface ozone (O<span class="inline-formula"><sub>3</sub></span>) is well known for posing significant threats to both human health and crop production worldwide. However, a multidecadal assessment of the impacts of O<span class="inline-formula"><sub>3</sub></span> on public health and crop yields in China is lacking due to insufficient long-term continuous O<span class="inline-formula"><sub>3</sub></span> observations. In this study, we used a machine learning (ML) algorithm to correct the biases of O<span class="inline-formula"><sub>3</sub></span> 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 O<span class="inline-formula"><sub>3</sub></span> concentrations and thus further improves our estimates of the impacts of O<span class="inline-formula"><sub>3</sub></span> on human health and crop yields. The warm-season trends of increasing O<span class="inline-formula"><sub>3</sub></span> 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 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> yr<span class="inline-formula"><sup>−1</sup></span> from 1981 to 2019, respectively. In more recent years, O<span class="inline-formula"><sub>3</sub></span> concentrations experienced more fluctuations in the four major regions. Our results show that only BTHs have a perceptible increasing trend of 0.81 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> yr<span class="inline-formula"><sup>−1</sup></span> during 2013–2019. Using accumulated O<span class="inline-formula"><sub>3</sub></span> 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 <span class="inline-formula">+0.03</span> % yr<span class="inline-formula"><sup>−1</sup></span>, <span class="inline-formula">+0.04</span> % yr<span class="inline-formula"><sup>−1</sup></span>, <span class="inline-formula">+0.27</span> % yr<span class="inline-formula"><sup>−1</sup></span>, and <span class="inline-formula">+0.13</span> % yr<span class="inline-formula"><sup>−1</sup></span>, respectively. The estimated annual all-cause premature deaths induced by O<span class="inline-formula"><sub>3</sub></span> increased from <span class="inline-formula">∼55 900</span> in 1981 to <span class="inline-formula">∼162 000</span> in 2019 with an increasing trend of <span class="inline-formula">∼2980</span> deaths per year. The annual premature deaths related to respiratory and cardiovascular disease are <span class="inline-formula">∼34 200</span> and <span class="inline-formula">∼40 300</span> in 1998 and <span class="inline-formula">∼26 500</span> and <span class="inline-formula">∼79 000</span> in 2019, having a rate of change of <span class="inline-formula">−546</span> and <span class="inline-formula">+1770</span> deaths per year during 1998–2019, respectively. Our study, for the first time, used ML to provide a robust dataset of O<span class="inline-formula"><sub>3</sub></span> concentrations over the past 4 decades in China, enabling a long-term evaluation of O<span class="inline-formula"><sub>3</sub></span>-induced crop losses and health impacts. These findings are expected to fill the gap of the long-term O<span class="inline-formula"><sub>3</sub></span> trend and impact assessment in China.</p>