This work introduces the results of an intensive 15-day surface observation campaign of methane (CH4) and adapts a new analytical method to compute and attribute CH4 emissions. The selected area has a high atmospheric concentration of CH4 (campaign-wide minimum/mean/standard deviation/max observations: 2.0, 2.9, 1.3, and 16 ppm) due to a rapid increase in the mining, production, and use of coal over the past decade. Observations made in concentric circles at 1km, 3km, and 5km around a high production high gas coal mine were used with the mass conserving model free emissions estimation approach adapted to CH4, yielding emissions of 0.73, 0.28, and 0.15 ppm/min respectively. Attribution used a 2-box mass conserving model to identify the known mine’s emissions from 0.042-5.3 ppm/min, and a previously unidentified mine’s emission from 0.22-7.9 ppm/min. These results demonstrate the importance of quantifying the spatial distribution of methane in terms of control of regional-scale CH4 emissions.

The contribution of biomass burning to the total aerosol loading over Monsoon Asia is both significant while also continuing to increase in recent decades. To better match the spatio-temporal distribution of aerosols and trace gasses observed in the free troposphere, this work applied a 3-D constrained emission inventory based on top-down remotely sensed NO2 measurement to investigate the most extreme of the annually occurring biomass burning seasons in Monsoon Asia. In 2016 this constituted an extreme event observed over a 6-day period covering millions of square kilometers, including over regions which are typically in the rainy phase of the Monsoon at this time. The results are shown to be consistent with respect to TRMM precipitation, AERONET measurements, MODIS AOD, MOPITT CO, and reanalysis meteorology, over both the biomass burning source as well as the millions of square kilometers downwind both to the East and to the Southwest. Reproducing the observed long-range transport pattern requires the time of biomass burning to be increased, regions not previously identified as burning to be actual source regions, and the emissions of BC to be 6.6 to 11.9 time larger than current inventories. The underlying mechanism for this long-range transport involves a new 3-D pathway that can occur during the transition from the North to the South Monsoon. The results are also consistent with the new idea that large loadings of BC in the lower free troposphere may significantly affect the meteorological field and the overall vertical distribution of aerosols in the tropical troposphere.

Nitrogen oxides (NOx) are markers of combustion contributing to ozone, secondary aerosol, and acid rain, and are required to run models focusing on atmospheric environmental protection. This work presents a new model free inversion estimation framework using daily TROPOMI NO2 columns and observed fluxes from the continuous emissions monitoring systems (CEMS) to quantify emissions of NOx at 0.05°×0.05°. The average emission is 0.72±0.11Tg/yr from 2019 through 2021 over Shanxi, a major energy producing and consuming province in Northern China. The resulting emissions demonstrates significant spatial and temporal differences with bottom-up emissions databases, with 54% of the emissions concentrated in 25% of the total area. Two major forcing factors are horizontal advective transport (352.0±51.2km) and first order chemical loss (13.1±1.1hours), consistent with a non-insignificant amount of NOxadvected into the free troposphere. The third forcing factor, the computed ratio of NOx/NO2, on a pixel-by-pixel basis has a significant correlation with the combustion temperature and energy efficiency of large energy consuming sources. Specifically, thermal power plants, cement, and iron and steel companies have high NOx/NO2 ratios, while coking, industrial boilers, and aluminum show low ratios. Variance maximization applied to the daily TROPOMI NO2 columns identifies three modes dominate the variance and attributes them to this work’s computed emissions, remotely sensedTROPOMI UVAI, and transport based on TROPOMI CO. Using satellite observations for emission estimates in connection with CEMS allows the rapid update of emissions, while also providing scientific support for the identification and attribution of anthropogenic sources.

Current approaches to estimate NOx emissions fail to account for new and small sources, biomass burning, and sources which change rapidly in time, generally don’t account for measurement error, and are either based on models, or do not consider wind, chemistry, and dynamical effects. This work introduces a new, model-free analytical environment that assimilates daily TROPOMI NO2 measurements in a mass-conserving manner, to invert daily NOx emissions. This is applied over a rapidly developing and energy-consuming region of Northwest China, specifically chosen due to substantial economic and population changes, new environmental policies, large use of coal, and access to independent emissions measurements for validation, making this region representative of many rapidly developing regions found across the Global South. This technique computes a net NOx emissions gain of 70% distributed in a seesaw manner: a more than doubling of emissions in cleaner regions, chemical plants, and regions thought to be emissions-free, combined with a more than halving of emissions in city centers and at well-regulated steel and powerplants. The results allow attribution of sources, with major contributing factors computed to be increased combustion temperature, atmospheric transport, and in-situ chemical processing. It is hoped that these findings will drive a new look at emissions estimation and how it is related to remotely sensed measurements and associated uncertainties, especially applied to rapidly developing regions. This is especially important for understanding the loadings and impacts of short-lived climate forcers, and provides a bridge between remotely sensed data, measurement error, and models, while allowing for further improvement of identification of new, small, and rapidly changing sources.