College of Ecology and the Environment, Joint Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China.
Quzhou Meteorological Bureau, Quzhou, 324000, China.
Environ Pollut. 2024 Nov 15;361:124781. doi: 10.1016/j.envpol.2024.124781. Epub 2024 Aug 22.
Cities are treated as global methane (CH) emission hotspots and the monitoring of atmospheric CH concentration in cities is necessary to evaluate anthropogenic CH emissions. However, the continuous and in-situ observation sites within cities are still sparsely distributed in the largest CH emitter as of China, and although obvious seasonal variations of atmospheric CH concentrations have been observed in cities worldwide, questions regarding the drivers for their temporal variations still have not been well addressed. Therefore, to quantify the contributions to seasonal variations of atmospheric CH concentrations, year-round CH concentration observations from 1st December 2020 to 30th November 2021 were conducted in Hangzhou megacity, China, and three models were chosen to simulate urban atmospheric CH concentration and partition its drivers including machine learning based Random Forest (RF) model, atmospheric transport processes based numerical model (WRF-STILT), and regression analysis based Multiple Linear Regression (MLR) model. The findings are as follows: (1) the atmospheric CH concentration showed obvious seasonal variations and were different with previous observations in other cities, the seasonality were 5.8 ppb, 21.1 ppb, and 50.1 ppb between spring-winter, summer-winter and autumn-winter, respectively, where the CH background contributed by -8.1 ppb, -44.6 ppb, and -1.0 ppb, respectively, and the CH enhancements contributed by 13.9 ppb, 65.7 ppb, and 51.1 ppb. (2) The RF model showed the highest accuracy in simulating CH concentrations, followed by MLR model and WRF-STILT model. (3) We further partition contributions from different factors, results showed the largest contribution was from temperature-induced increase in microbial process based CH emissions including waste treatment and wetland, which ranged from 38.1 to 76.3 ppb when comparing different seasons with winter. The second largest contribution was from seasonal boundary layer height (BLH) variations, which ranged from -13.4 to -6.3 ppb. And the temperature induced seasonal CH emission and enhancement variations were overwhelming BLH changes and other meteorological parameters.
城市被视为全球甲烷（CH）排放热点，监测城市大气 CH 浓度对于评估人为 CH 排放至关重要。然而，作为中国最大的 CH 排放国，城市内部的连续和原位观测站点仍然分布稀疏，尽管全球城市的大气 CH 浓度都存在明显的季节性变化，但关于其时间变化的驱动因素问题仍未得到很好的解决。因此，为了量化大气 CH 浓度季节性变化的贡献，我们于 2020 年 12 月 1 日至 2021 年 11 月 30 日在杭州市开展了全年的 CH 浓度观测，并选择了三种模型来模拟城市大气 CH 浓度，并分解其驱动因素，包括基于机器学习的随机森林（RF）模型、基于大气传输过程的数值模型（WRF-STILT）和基于回归分析的多元线性回归（MLR）模型。结果如下：（1）大气 CH 浓度表现出明显的季节性变化，与其他城市的先前观测结果不同，春季-冬季、夏季-冬季和秋季-冬季之间的季节性分别为 5.8 ppb、21.1 ppb 和 50.1 ppb，其中 CH 背景贡献分别为-8.1 ppb、-44.6 ppb 和-1.0 ppb，CH 增强贡献分别为 13.9 ppb、65.7 ppb 和 51.1 ppb。（2）RF 模型在模拟 CH 浓度方面表现出最高的准确性，其次是 MLR 模型和 WRF-STILT 模型。（3）我们进一步分解了不同因素的贡献，结果表明，最大的贡献来自于温度诱导的微生物过程中 CH 排放的增加，包括废物处理和湿地，在不同季节与冬季相比，范围为 38.1 至 76.3 ppb。第二大贡献来自于季节性边界层高度（BLH）变化，范围为-13.4 至-6.3 ppb。并且，温度诱导的季节性 CH 排放和增强变化超过了 BLH 变化和其他气象参数。
