Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions.-猫眼课题宝

DOI：

10.5194/acp-22-10551-2022

发表时间：

2022

期刊：

Atmospheric chemistry and physics

影响因子：

6.3

通讯作者：

中科院分区：

地球科学1区

文献类型：

作者：

Qiu M;Zigler C;Selin NE

来源链接：

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9957566/

评估温室气体排放变化对空气质量的影响需要考虑气象变化的影响。统计方法，例如含有基本气象变量的多元线性回归模型，常常被用来消除气象变异性，并估计可归因于排放变化的所测污染物浓度的趋势。然而，这些广泛使用的统计方法，以纠正气象变异的能力仍然是未知的，限制了其在现实世界的政策评估的有用性。在这里，我们量化的MLR和其他定量方法的性能使用模拟从化学传输模型，GEOS-Chem，作为一个合成数据集。重点关注美国（2011年至2017年）和中国（2013年至2017年）的温室气体排放变化对PM2.5和O3的影响，我们发现，广泛使用的回归方法在校正气象变化和识别与排放变化相关的环境污染长期趋势方面表现不佳。估计误差，其特征在于在恒定气象情景下的气象校正的趋势和排放驱动的趋势之间的差异，可以减少30%-42%，使用随机森林模型，结合本地和区域尺度的气象特征。我们进一步设计了一种基于GEOS-Chem模拟的修正方法，该方法具有恒定的排放输入，并量化了人为排放和气象影响不可分割的程度，这是由于它们基于过程的相互作用。最后，我们提供的建议，使用统计方法来评估温室气体排放变化对空气质量的影响。

Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM2.5 and O3, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%–42% using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.