Novel Techniques for Measuring Greenhouse Gases (2nd Edition)

The monitoring of anthropogenic CO₂ emissions, which increase the atmospheric CO₂ concentration, plays the most important role in the management of emission reduction and control. With the massive increase in satellite-based observation data related to carbon emissions, a data-driven machine learning method has great prospects for predicting anthropogenic CO₂ emissions. Training samples, which are used to model predictions of anthropogenic CO₂ emissions through machine learning algorithms, play a key role in obtaining accurate predictions for the spatial heterogeneity of anthropogenic CO₂ emissions. We propose an approach for predicting anthropogenic CO₂ emissions using the training datasets derived from the clustering of the atmospheric CO₂ concentration and the segmentation of emissions to resolve the issue of the spatial heterogeneity of anthropogenic CO₂ emissions in machine learning modeling. We assessed machine learning algorithms based on decision trees and gradient boosting (GBDT), including LightGBM, XGBoost, and CatBoost. We used multiple parameters related to anthropogenic CO₂-emitting activities as predictor variables and emission inventory data from 2019 to 2021, and we compared and verified the accuracy and effectiveness of different prediction models based on the different sampling methods of training datasets combined with machine learning algorithms. As a result, the anthropogenic CO₂ emissions predicted by CatBoost modeling from the training dataset derived from the clustering analysis and segmentation method demonstrated optimal prediction accuracy and performance for revealing anthropogenic CO₂ emissions. Based on a machine learning algorithm using observation data, this approach for predicting anthropogenic CO₂ emissions could help us quickly obtain up-to-date information on anthropogenic CO₂ emissions as one of the emission monitoring tools. Full article

Remote sensing satellite monitoring involving the use of shortwave infrared (SWIR) solar backscatter radiation to measure atmospheric CH₄ column concentrations provides wide−ranging and accurate data for quantitatively determining atmospheric CH₄ emissions and is highly important for human studies of atmospheric composition and environmental protection. The ESA−launched Sentinel−2 satellite equipped with a tropospheric monitoring instrument (TROPOMI) can provide the concentration of CH₄ columns in every piece of the global atmosphere every day. However, these data may be affected by surface albedo, SWIR, aerosols, cirrus cloud scattering, and other factors. The greenhouse gas observing satellite (GOSAT) launched by Japan has fairly accurate data that are minimally affected by the aforementioned factors; however, its data density is much less than that of the TROPOMI. In this study, we propose a CH₄ model that combines the TROPOMI and GOSAT data. We construct the model by analyzing the data from the TROPOMI and GOSAT at the same location at the same time. Then, we apply the proposed model to a certain location at a certain time with TROPOMI data but without GOSAT data to obtain a large range of high−precision CH₄ data. The most developed urban agglomeration in the Yangtze River Delta in China was selected for model construction and the correlations between the TROPOMI and GOSAT data and their spatial and temporal trends were analyzed. First, we analyzed the CH₄ concentrations in the same area measured by both models. The results revealed a high degree of temporal and spatial correlation in the YRD region. The correlation coefficient reached 0.71 in the metropolitan area of the YRD. At the small−city scale, the correlation is much more significant, with the correlation reaching 0.80, 0.79, and 0.71 for Nanjing, Shanghai, and Ningbo, respectively. The most accurate model was screened through comparative construction to calibrate the TROPOMI data and high−precision and high−coverage CH₄ concentration information was obtained for the study area. Five models (linear model, quadratic term model, cubic term model, lognormal model, and logistic model) were used to select the best−fitting model. The magnitudes of the differences in the CH₄ concentrations calculated by each model were compared. The final results showed that the linear model, as the prediction model, had the highest accuracy, with a coefficient of determination (R₂²) of 0.542. To avoid the specificity of the constructed model, we used the same method in several simulations to validate. The coefficient of determination of the model constructed with different stochastic data was greater than 0.5. Subsequently, we used Nanjing as the study area and applied the same method to construct the model. The coefficient of determination of the model (R₂²) was approximately 0.601. The model constructed in this research can be used not only for data conversion between the same products from different sensors to obtain high−precision data products but also for calibrating newly developed satellite data products that utilize mature data products. Full article

By using space-based measurements of the column-averaged dry air mole fraction of carbon dioxide (XCO₂) from the Orbiting Carbon Observatory-2 (OCO-2) and CO and NO₂ from the Tropospheric Monitoring Instrument (TROPOMI), this study investigates the seasonal variation in the characteristics of CO₂, CO, and NO₂ across major states in the United States. Beyond correlating these trends with natural factors, significant emphasis is placed on human activities, including heating demands, energy usage, and the impacts of the COVID-19 pandemic. Concentration enhancements in observations influenced by anthropogenic emissions from urban regions relative to background values are calculated to estimate gas emissions. Our investigation reveals a strong correlation between NO₂ and CO₂ emissions, as evidenced by a correlation coefficient (r) of 0.75. Furthermore, we observe a correlation of 0.48 between CO₂ and CO emissions and a weaker correlation of 0.37 between CO and NO₂ emissions. Notably, we identify the NO₂ concentration as a reliable indicator of CO₂ emission levels, in which a 1% increase in NO₂ concentration corresponds to a 0.8194% (±0.0942%) rise in annual mean CO₂ emissions. Enhancement ratios among NO₂, CO, and XCO₂ are also calculated, uncovering that high ΔNO₂: ΔXCO₂ ratios often signify outdated industrial structures and production technologies, while low ΔCO: ΔXCO₂ ratios are linked to states that utilize clean energy sources. This approach offers a deeper understanding of the effect of human activities on atmospheric gas concentrations, paving the way for more effective environmental monitoring and policy-making. Full article

The high altitude in mountainous regions results in lower atmospheric pressure, oxygen concentration and temperature, leading to lower combustion efficiency in motor vehicles. Therefore, there may be differences in carbon dioxide (CO₂), carbon monoxide (CO), and nitrogen oxides (NO_x) emissions characteristics at different altitudes. In this study, a portable emission measurement system was used to investigate the effects of varying elevations on the emission factors of CO₂, CO, and NO_x on diesel and gasoline-powered vehicles at altitudes ranging from 2270 to 4540 m in the Qinghai–Tibet Plateau of China. Additionally, the influencing factors of CO₂, CO, and NO_x emissions were studied. Results showed that the CO₂, CO, and NO_x emission factors for diesel vehicles varied in the range of 161.83–195.54, 0.59–0.77, and 4.61–6.58 g/km; the population means with 90% confidence intervals were 178.54, 0.68, and 5.60 g/km, respectively. For gasoline vehicles, the CO₂, CO, and NO_x emission factors varied in the range of 161.66–181.98, 0.95–1.06, and 0.12–0.25 g/km; the population means with 90% confidence intervals were 171.82, 1.01, and 0.19 g/km, respectively. Overall, the emission factors of diesel vehicles were higher than those of gasoline vehicles, and the emissions increased with increasing altitude. Atmospheric pressure was identified as the primary environmental factor affecting CO₂, CO, and NO_x emissions. As the speed of motor vehicles increased, the emission of CO₂ also increased, while there was a quadratic relationship with acceleration. This study provides a reference and guidance for vehicle pollution control in high-altitude regions. Full article

In view of the urgent need for high coverage and high-resolution atmospheric CO₂ data in the study of carbon neutralization and global CO₂ change research, this study combines the Kriging interpolation and the Triple Collision (TC) algorithm to fuse three XCO₂ datasets, OCO-2, GOSAT, and CarbonTracker, to obtain a 1° × 1° half-monthly average XCO₂ dataset. Through a sub division of the Kriging interpolation, the average coverages of the OCO-2 and GOSAT XCO₂ interpolating datasets are increased by 53.65% and 48.5%, respectively. In order to evaluate the accuracy of the TC fusion dataset, this study used a reliable reference dataset, TCCON data, as the verification data. Through comparative analysis, the MAE of the fusion dataset is 0.6273 ppm, RMSE is 0.7683 ppm, and R² is 0.8279. It can be seen that the combination of Kriging interpolation and TC algorithm can effectively improve the coverage and accuracy of the XCO₂ dataset. Full article

This study investigates atmospheric ${\delta }^{13}$CH${}_4$ trends, as produced by a global atmospheric transport model using CH${}_4$ inversions from CarbonTracker-Europe CH${}_4$ for 2000–2020, and compares them to observations. The CH${}_4$ inversions include the grouping of the emissions both by ${\delta }^{13}$CH${}_4$ isotopic signatures and process type to investigate the effect, and to estimate the CH${}_4$ magnitudes and model CH${}_4$ and ${\delta }^{13}$CH${}_4$ trends. In addition to inversion results, simulations of the global atmospheric transport model were performed with modified emissions. The estimated global CH${}_4$ trends for oil and gas were found to increase more than coal compared to the priors from 2000–2006 to 2007–2020. Estimated trends for coal emissions at 30${}^{\circ }$ N–60${}^{\circ }$ N are less than 50% of those from priors. Estimated global CH${}_4$ rice emissions trends are opposite to priors, with the largest contribution from the EQ to 60${}^{\circ }$ N. The results of this study indicate that optimizing wetland emissions separately produces better agreement with the observed ${\delta }^{13}$CH${}_4$ trend than optimizing all biogenic emissions simultaneously. This study recommends optimizing separately biogenic emissions with similar isotopic signature to wetland emissions. In addition, this study suggests that fossil-based emissions were overestimated by 9% after 2012 and biogenic emissions are underestimated by 8% in the inversion using EDGAR v6.0 as priors. Full article

The purpose of this study is to investigate the air quality levels of Nanyang city according to Chinese air quality standards. Therefore, in this study, fine particulate matter (PM_2.5), coarse particulate matter (PM₁₀), and total suspended particulate (TSP) were analyzed from 19 November 2021 to 19 March 2022 in Nanyang city. The results show that the average concentrations of PM_2.5, PM_10, and TSP were 106.47 µg/m³, 137.32 µg/m³, and 283.40 µg/m³, respectively. The numbers of days that meet the national secondary air quality standard of 24-h average concentrations were 29.75% for PM_2.5, 63.64% for PM₁₀, and 63.64% for TSP, indicating that most of the time, the air quality of Nanyang city remains polluted in winter, especially with more contributions of PM_2.5 compared to PM₁₀ and TSP. The higher concentrations were observed between 07:00 and 08:00, suggesting that vehicular emissions can be a major cause of air pollution in Nanyang city. The results also show a significant positive correlation between particulate matter and relative humidity, and a weak correlation with temperature and wind speed, which suggests that higher relative humidity increases the formation of particulate matter. This study can provide theoretical support for the local government to formulate air pollution prevention and control policies for Nanyang city. Full article