Quantification of Central and Eastern China's atmospheric CH4 enhancement changes and its contributions based on machine learning approach.

Methane is the second largest anthropogenic greenhouse gas, and changes in atmospheric methane concentrations can reflect the dynamic balance between its emissions and sinks. Therefore, the monitoring of CH4 concentration changes and the assessment of underlying driving factors can provide scientific basis for the government's policy making and evaluation. China is the world's largest emitter of anthropogenic methane. However, due to the lack of ground-based observation sites, little work has been done on the spatial-temporal variations for the past decades and influencing factors in China, especially for areas with high anthropogenic emissions as Central and Eastern China. Here to quantify atmospheric CH4 enhancements trends and its driving factors in Central and Eastern China, we combined the most up-to-date TROPOMI satellite-based column CH4 (xCH4) concentration from 2018 to 2022, anthropogenic and natural emissions, and a random forest-based machine learning approach, to simulate atmospheric xCH4 enhancements from 2001 to 2018. The results showed that (1) the random forest model was able to accurately establish the relationship between emission sources and xCH4 enhancement with a correlation coefficient (R²) of 0.89 and a root mean-square error (RMSE) of 11.98 ppb; (2)The xCH4 enhancement only increased from 48.21±2.02 ppb to 49.79±1.87 ppb from the year of 2001 to 2018, with a relative change of 3.27%±0.13%; (3) The simulation results showed that the energy activities and waste treatment were the main contributors to the increase in xCH4 enhancement, contributing 68.00% and 31.21%, respectively, and the decrease of animal ruminants contributed -6.70% of its enhancement trend.

Government environmental control measures on CO2 emission during the 2014 Youth Olympic Games in Nanjing: Perspectives from a top-down approach.

Strict air pollution control measures were conducted during the Youth Olympic Games (YOG) period at Nanjing city and surrounding areas in August 2014. This event provides a unique chance to evaluate the effect of government control measures on regional atmospheric pollution and greenhouse gas emissions. Many previous studies have observed significant reductions of atmospheric pollution species and improvement in air quality, while no study has quantified its synergism on anthropogenic CO2 emissions, which can be co-reduced with air pollutants. To better understand to what extent these pollution control measures have reduced anthropogenic CO2 emissions, we conducted atmospheric CO2 measurements at the suburban site in Nanjing city from 1st July to 30th September 2014 and 1st August to 31st August 2015, obvious decrease in atmospheric CO2 was observed between YOG and the rest period. By coupling the a priori emission inventory with atmospheric transport model, we applied the scale factor Bayesian inversion approach to derive the posteriori CO2 emissions in YOG period and regular period. Results indicate CO2 emissions from power industry decreased by 45%, and other categories also decreased by 16% for manufacturing combusting, and 37% for non-metallic mineral production. Monthly total anthropogenic CO2 emissions were 9.8 (±3.6) × 109 kg/month CO2 for regular period and decreased to 6.2 (±1.9) × 109 kg/month during the YOG period in Nanjing city, with a 36.7% reduction. When scaling up to whole Jiangsu Province, anthropogenic CO2 emissions were 7.1 (±2.4) × 1010 kg/month CO2 for regular period and decreased to 4.4 (±1.2) × 1010 kg/month CO2 during the YOG period, yielding a 38.0% reduction.

Multiple-model based simulation of urban atmospheric methane concentration and the attributions to its seasonal variations: A case study in Hangzhou megacity, China.

Cities are treated as global methane (CH4) emission hotspots and the monitoring of atmospheric CH4 concentration in cities is necessary to evaluate anthropogenic CH4 emissions. However, the continuous and in-situ observation sites within cities are still sparsely distributed in the largest CH4 emitter as of China, and although obvious seasonal variations of atmospheric CH4 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 CH4 concentrations, year-round CH4 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 CH4 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 CH4 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 CH4 background contributed by -8.1 ppb, -44.6 ppb, and -1.0 ppb, respectively, and the CH4 enhancements contributed by 13.9 ppb, 65.7 ppb, and 51.1 ppb. (2) The RF model showed the highest accuracy in simulating CH4 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 CH4 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 CH4 emission and enhancement variations were overwhelming BLH changes and other meteorological parameters.