Reconstructing Global Daily CO2 Emissions via Machine Learning

High temporal resolution CO2 emission data are crucial for understanding the drivers of emission changes, however, current emission dataset is only available on a yearly basis. Here, we extended a global daily CO2 emissions dataset backwards in time to 1970 using machine learning algorithm, which was trained to predict historical daily emissions on national scales based on relationships between daily emission variations and predictors established for the period since 2019. Variation in daily CO2 emissions far exceeded the smoothed seasonal variations. For example, the range of daily CO2 emissions equivalent to 31% of the year average daily emissions in China and 46% of that in India in 2022, respectively. We identified the critical emission-climate temperature (Tc) is 16.5 degree celsius for global average (18.7 degree celsius for China, 14.9 degree celsius for U.S., and 18.4 degree celsius for Japan), in which negative correlation observed between daily CO2 emission and ambient temperature below Tc and a positive correlation above it, demonstrating increased emissions associated with higher ambient temperature. The long-term time series spanning over fifty years of global daily CO2 emissions reveals an increasing trend in emissions due to extreme temperature events, driven by the rising frequency of these occurrences. This work suggests that, due to climate change, greater efforts may be needed to reduce CO2 emissions.

Estimates of daily ground-level NO2 concentrations in China based on big data and machine learning approaches

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.