The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China; Joint Center for Global Change Studies (JCGCS), Beijing 100875, China.
Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195, United States.
Sci Total Environ. 2019 Mar 10;655:423-433. doi: 10.1016/j.scitotenv.2018.11.125. Epub 2018 Nov 12.
Outdoor air pollution is a major killer worldwide and the fourth largest contributor to the burden of disease in China. China is the most populous country in the world and also has the largest number of air pollution deaths per year, yet the spatial resolution of existing national air pollution estimates for China is generally relatively low. We address this knowledge gap by developing and evaluating national empirical models for China incorporating land-use regression (LUR), satellite measurements, and universal kriging (UK). Land use, traffic and meteorological variables were included for model building. We tested the resulting models in several ways, including (1) comparing models developed using forward variable selection vs. partial least squares (PLS) variable reduction, (2) comparing models developed with and without satellite measurements, and with and without UK, and (3) 10-fold cross-validation (CV), Leave-One-Province-Out CV (LOPO-CV), and Leave-One-City-Out CV (LOCO-CV). Satellite data and kriging are complementary in making predictions more accurate: kriging improved the models in well-sampled areas; satellite data substantially improved performance at locations far away from monitors. Variable-selection models performed similarly to PLS models in 10-fold CV, but better in LOPO-CV. Our best models employed forward variable selection and UK, with 10-fold CV R of 0.89 (for both 2014 and 2015) for PM and of 0.73 (year-2014) and 0.78 (year-2015) for NO. Population-weighted concentrations during 2014-2015 decreased for PM (58.7 μg/m to 52.3 μg/m) and NO (29.6 μg/m to 26.8 μg/m). We produced the first high resolution national LUR models for annual-average concentrations in China. Models were applied on 1 km grid to support future research. In 2015, >80% of the Chinese population lived in areas that exceeded the Chinese national PM standard, 35 μg/m. Results here will be publicly available and may be useful for epidemiology, risk assessment, and environmental justice research.
室外空气污染是全球主要的致病因素之一，也是中国疾病负担的第四大主要因素。中国是世界上人口最多的国家，也是每年因空气污染导致死亡人数最多的国家，但现有中国国家空气污染估计的空间分辨率通常相对较低。为了解决这一知识空白，我们开发并评估了包含土地利用回归 (LUR)、卫星测量和通用克立格法 (UK) 的中国国家经验模型。模型构建中包含了土地利用、交通和气象变量。我们通过多种方式测试了这些模型，包括：(1) 比较使用正向变量选择与偏最小二乘法 (PLS) 变量减少的模型；(2) 比较有和没有卫星测量以及有和没有 UK 的模型；(3) 10 折交叉验证 (CV)、留一省 CV (LOPO-CV) 和留一市 CV (LOCO-CV)。卫星数据和克立格在提高预测准确性方面是互补的：克立格使模型在采样良好的地区的性能得到改善；卫星数据大大提高了远离监测站的位置的性能。在 10 折 CV 中，变量选择模型的性能与 PLS 模型相似，但在 LOPO-CV 中的性能更好。我们的最佳模型采用正向变量选择和 UK，2014 年和 2015 年的 PM10 折叠 CV R 分别为 0.89，NO 的 2014 年和 2015 年的折叠 CV R 分别为 0.73 和 0.78。2014-2015 年期间，PM 浓度（58.7μg/m 至 52.3μg/m）和 NO 浓度（29.6μg/m 至 26.8μg/m）下降。我们制作了中国首个高分辨率全国 LUR 模型，用于测量年平均浓度。模型应用于 1km 网格，以支持未来的研究。2015 年，超过 80%的中国人口生活在 PM 浓度超过中国国家标准（35μg/m）的地区。这里的结果将公开提供，可能对流行病学、风险评估和环境正义研究有用。
