Abstract：
Short-term exposure to ground-level ozone (O3) poses significant health risks, particularly respiratory and cardiovascular diseases, and mortality. This study addresses the pressing need for accurate O3 forecasting to mitigate these risks, focusing on South Korea. We introduce Deep Bias Correction (Deep-BC), a novel framework leveraging Convolutional Neural Networks (CNNs), to refine hourly O3 forecasts from the Community Multiscale Air Quality (CMAQ) model. Our approach involves training Deep-BC using data from 2016 to 2019, including CMAQ\'s 72-hour O3 forecasts, 31 meteorological variables from the Weather Research and Forecasting (WRF) model, and previous days\' station measurements of 6 air pollutants. Deep-BC significantly outperforms CMAQ in 2021, reducing biases in O3 forecasts. Furthermore, we utilize Deep-BC\'s daily maximum 8-hour average O3 (MDA8 O3) forecasts as input for the AirQ+ model to assess O3\'s potential impact on mortality across seven major provinces of South Korea: Seoul, Busan, Daegu, Incheon, Daejeon, Ulsan, and Sejong. Short-term O3 exposure is associated with 0.40 % to 0.48 % of natural cause and respiratory deaths and 0.67 % to 0.81 % of cardiovascular deaths. Gender-specific analysis reveals higher mortality rates among men, particularly from respiratory causes. Our findings underscore the critical need for region-specific interventions to address air pollution\'s detrimental effects on public health in South Korea. By providing improved O3 predictions and quantifying its impact on mortality, this research offers valuable insights for formulating targeted strategies to mitigate air pollution\'s adverse effects. Moreover, we highlight the urgency of proactive measures in health policies, emphasizing the significance of accurate forecasting and effective interventions to safeguard public health from the deleterious effects of air pollution.
摘要：
短期暴露于地面臭氧（O3）会带来重大的健康风险，尤其是呼吸系统和心血管疾病，和死亡率。这项研究解决了对准确的O3预测以减轻这些风险的迫切需要，专注于韩国。我们介绍深度偏差校正(Deep-BC)，一种利用卷积神经网络(CNN)的新框架，从社区多尺度空气质量(CMAQ)模型中完善每小时O3预测。我们的方法涉及使用2016年至2019年的数据训练Deep-BC，包括CMAQ的72小时O3预测，来自天气研究与预报(WRF)模型的31个气象变量，和前几天站测量的6种空气污染物。Deep-BC在2021年的表现明显优于CMAQ，减少了O3预测的偏差。此外，我们利用Deep-BC的每日最大8小时平均O3（MDA8O3）预测作为AirQ模型的输入，以评估O3对韩国七个主要省份的死亡率的潜在影响：首尔，釜山,大邱,仁川,大田,蔚山,还有世宗.短期O3暴露与自然原因和呼吸道死亡的0.40％至0.48％以及心血管死亡的0.67％至0.81％相关。按性别分类的分析显示，男性死亡率较高，尤其是呼吸原因。我们的发现强调了针对特定地区的干预措施的迫切需要，以解决空气污染对韩国公共卫生的不利影响。通过提供改进的O3预测并量化其对死亡率的影响，这项研究为制定有针对性的策略以减轻空气污染的不利影响提供了有价值的见解。此外，我们强调在卫生政策中采取积极措施的紧迫性，强调准确预测和有效干预措施的重要性，以保护公众健康免受空气污染的有害影响。
