基于大数据和机器学习方法的中国地面NO2日浓度估算
Estimates of daily ground-level NO2 concentrations in China based on big data and machine learning approaches
摘要：
二氧化氮（NO2）是最重要的大气污染物之一。然而，由于源数据质量和模型计算能力的限制，目前的地面NO2浓度数据缺乏高分辨率覆盖或全国范围的全覆盖。据我们所知，这项研究是第一个估计中国地面NO2浓度的国家覆盖范围以及相对较高的时空分辨率（0.25度;每日间隔）在最近6年（2013-2018）。提出了一种基于K均值的随机森林模型（RF-K），用于多源参数的估计。除了气象参数、卫星反演参数外，我们还首次引入了社会经济参数来评估人类活动的影响。结果表明：（1）RF-K模型的交叉验证R2 = 0.64（MAPE = 34.78%），优于其他模型。(2)我国NO2年均浓度呈微弱上升趋势。而在京津冀、长三角、珠三角等经济区，NO2浓度甚至有所下降或保持不变，尤其是春季。我们的数据集已经验证了这些地区的污染物控制目标已经实现。通过绘制全国地面NO2日浓度图，为我国空气质量管理提供了及时、高质量的数据。我们提供了一个通用的模型框架，以快速生成一个及时的国家大气污染物浓度地图与高时空分辨率，基于改进的机器学习方法。
英文摘要：
Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants. However, current ground-level NO2 concentration data are lack of either high-resolution coverage or full coverage national wide, due to the poor quality of source data and the computing power of the models. To our knowledge, this study is the first to estimate the ground-level NO2 concentration in China with national coverage as well as relatively high spatiotemporal resolution (0.25 degree; daily intervals) over the newest past 6 years (2013-2018). We advanced a Random Forest model integrated K-means (RF-K) for the estimates with multi-source parameters. Besides meteorological parameters, satellite retrievals parameters, we also, for the first time, introduce socio-economic parameters to assess the impact by human activities. The results show that: (1) the RF-K model we developed shows better prediction performance than other models, with cross-validation R2 = 0.64 (MAPE = 34.78%). (2) The annual average concentration of NO2 in China showed a weak increasing trend . While in the economic zones such as Beijing-Tianjin-Hebei region, Yangtze River Delta, and Pearl River Delta, the NO2 concentration there even decreased or remained unchanged, especially in spring. Our dataset has verified that pollutant controlling targets have been achieved in these areas. With mapping daily nationwide ground-level NO2 concentrations, this study provides timely data with high quality for air quality management for China. We provide a universal model framework to quickly generate a timely national atmospheric pollutants concentration map with a high spatial-temporal resolution, based on improved machine learning methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
