Emissions of methane (CH₄) in the Permian basin (USA) have been derived for 2019 and 2020 from satellite observations of the Tropospheric Monitoring Instrument (TROPOMI) using the divergence method, in combination with a data driven method to estimate the background column densities. The resulting CH₄ emission data, which have been verified using model data with known emissions, have a spatial resolution of approximately 10 km. The CH₄ emissions show moderate spatial correlation with the locations of oil and gas production and drilling activities in the Permian basin, as well as with emissions of nitrogen oxides (NO_x). Analysis of the emission maps and time series indicates that a significant fraction of methane emissions in the Permian basin is from frequent widespread emissions sources, rather than from a few infrequent very large unplanned releases, which is important considering possible CH₄ emission mitigation strategies. In addition to providing spatially resolved emissions, the divergence method also provides the total emissions of the Permian basin and its main sub-basins. The total CH₄ emission of the Permian is estimated as 3.0 ± 0.7 Tg yr⁻¹ for 2019, which agrees with other independent estimates based on TROPOMI data. For the Delaware sub-basin, it is estimated as 1.4 ± 0.3 Tg yr⁻¹ for 2019, and for the Midland sub-basin 1.2 ± 0.3 Tg yr⁻¹. In 2020 the emissions are 9% lower compared to 2019 in the entire Permian basin, and respectively 19% and 27% for the Delaware and Midland sub-basins.

COVID-19 caused a historic collapse in fossil fuel demand, a general decline in economic activity, and hydrocarbon price volatility. This resulted in an unprecedented scenario to evaluate the contribution of the O&G (Oil and Gas) industry NO₂ (nitrogen dioxide) emissions in the Permian basin (United States), currently the second largest hydrocarbon-bearing area on Earth. TROPOMI (Tropospheric Monitoring Instrument), on board the Sentinel-5P satellite, has captured the impact of the oil and gas industry emissions during the COVID-19 lockdown. A generalized drop (∼30%) of NO₂ emissions derived using the divergence method in comparison with 2019 was observed following the decline in production and drilling (13% and 68% respectively) during the lockdown. NO₂ tropospheric columns were less impacted with a smaller decrease (∼4%) across the basins. This study demonstrates that the impact of the COVID-19 lockdown on NO₂ emissions was not only present in urban areas but also in vast O&G production regions, which shows the potential of TROPOMI to assess future pollution mitigation strategies for this industry.

The production of crude oil and natural gas is associated with emissions of air pollutants, such as nitrogen oxides (NOx = NO + NO2) and volatile organic compounds, which are precursors for the formation of ground-level ozone. Knowledge of these emissions is critical to the understanding and mitigation of local air quality. NOx emissions from oil and gas production activities are not well described in commonly used emission inventories, and discrepancies of several factors have been found in the past. Here we present an easy and computationally efficient method to quantify NOx emissions from satellite NO2 observations that can be applied to evaluate common emission inventories and provide timely input for chemistry transport models. Using NO2 columns from the TROPOspheric Monitoring Instrument (TROPOMI), we calculated annually averaged NOx emissions from the divergence of NO2 column fluxes for six oil and gas production regions in the United States. Derived NOx emissions for the years 2018 to 2020 range between 4.8 and 81.1 t/day, and observed trends over time are consistent with changes in industrial activity. To evaluate the method, we compared our results with the fuel-based oil and gas NOx inventory (FOG) and performed sensitivity studies using model output from the Weather Research Forecasting model with Chemistry (WRF-Chem). We found that annually averaged NOx emissions from oil and gas production activities can in most cases be calculated within an uncertainty of 50%, while simultaneously derived emission maps show the spatial distribution of NOx emissions with a high level of detail. For future use, this method can easily be applied globally.

Remote sensing holds great potential for detecting stress in vegetation caused by hydro-carbons, but we need to better understand the effects of hydrocarbons on plant growth and specific spectral expression. Willow (Salix viminalis var. Tora) cuttings and maize (Zea mays var. Lapriora) seedlings were grown in pots of loam soil containing a hydrocarbon-contaminated layer at the base of the pot (crude or refined oil) at concentrations of 0.5, 5, or 50 g·kg⁻¹ . Chlorophyll concen-tration, biomass, and growth of plants were determined through destructive and nondestructive sampling, whilst reflectance measurements were made using portable hyperspectral spectrometers. All biophysical (chlorophyll concentration and growth) variables decreased in the presence of high concentrations of hydrocarbons, but at lower concentrations an increase in growth and chlorophyll were often observed with respect to nonpolluted plants, suggesting a biphasic response to hydrocarbon presence. Absorption features were identified that related strongly to pigment concentration and biomass. Variations in absorption feature characteristics (band depth, band area, and band width) were dependent upon the hydrocarbon concentration and type, and showed the same biphasic pattern noted in the biophysical measurements. This study demonstrates that the response of plants to hydrocarbon pollution varies according to hydrocarbon concentration and that remote sensing has the potential to both detect and monitor the variable impacts of pollution in the landscape.