We introduce an innovative method to distinguish soil nitrogen oxides (NOx=NO+NO2) emissions from satellite-based total NOx emissions using its seasonal characteristics. To evaluate the approach, we compare the deviation between the tropospheric NO2 concentration observed by satellite and two atmospheric composition model simulations driven by the newly estimated soil NOx emissions and the Copernicus Atmosphere Monitoring Service (CAMS) inventory. The estimated average soil NOx emissions in Europe are 2.5 kg N ha-1 yr-1 in 2019, and the annual soil NOx emissions is approximately 2.5 times larger than that of the CAMS inventory. Our method can easily be extended to other regions at middle or high latitudes with similar seasonal characteristics of soil emissions. The soil emissions are subtracted from the total NOx emissions yielding realistic anthropogenic NOx emissions. We further show this also yields realistic anthropogenic CO2 emissions using known CO2/NOx factors from bottom-up inventories.
We have analyzed TROPOMI data over the Copperbelt mining region (Democratic Republic of Congo and Zambia). Despite high background values, we find that annual 2019-2022 means of TROPOMI NO2 show local enhancements consistent with six point sources (mines and cities) where high-emission industrial activities take place. We have quantified annual NO2 emissions for the six sources, identified temporal trends in these emissions, and found strong correlations with mine/refinery production data. CAMS-GLOB-ANT v5 inventory emissions are lower than TROPOMI-derived emissions by 61-96 % and lack the temporal trends observed in TROPOMI and mine/oil refinery production. Lack of TROPOMI SO2 enhancements over the point sources analyzed indicates SO2 capture and transformation into sulfuric acid, a profitable byproduct. These results demonstrate the potential for satellite monitoring of mining/oil refining activity which impacts the air quality of local communities. This is particularly important for Africa, where mining is increasing aggressively.
A lockdown was implemented in Canada mid-March 2020 to limit the spread of COVID-19. In the wake of this, declines in nitrogen dioxide (NO2) were observed from the Tropospheric Monitoring Instrument (TROPOMI). A method is presented to quantify how much of this decrease is due to the lockdown itself as opposed to variability in meteorology and satellite sampling. The operational air quality forecast model, GEM-MACH, was used with TROPOMI to determine expected NO2 columns that represents what TROPOMI would have observed for a non-COVID scenario. Decreases in NO2 due to the lockdown were seen across southern Ontario, with an average 40% in Toronto and even larger declines in the city center. Natural and satellite sampling variability accounted for as much as 20-30%. A model run using a lockdown emissions scenario were found to be consistent with TROPOMI suggesting the prescribed declines in transportation and industry emissions are reasonable.
During the COVID-19 lockdown in China low air pollution levels were reported as a consequence of the reduced economic and social activities. Quantification of the pollution reduction is not straightforward due to effects of transport, meteorology, and chemistry. Here we have analysed the NO emission reductions calculated with an inverse algorithm applied to daily NO observations from the TROPOMI instrument onboard the Copernicus Sentinel-5P satellite. This method allows quantification of emission reductions per city, and the analysis of emissions of maritime transport and of the energy sector separately. The reductions we found are 20 to 50% for cities, about 40% for power plants and 15 to 40% for maritime transport depending on the region. The reduction in both emissions and concentrations shows a similar timeline consisting of a sharp reduction around the Spring festival and a slow recovery from mid-February to mid-March.
Efforts to slow the transmission of COVID-19 led to rapid, global ancillary reductions in air pollutant emissions. Here, we quantify the resulting decreases in global NOx emissions and their consequent impact on the production of global tropospheric ozone using a multi-constituent data assimilation system. Total anthropogenic NOx emissions were reduced by at least 15% globally and 18-25% for Europe, North America, and the Middle East in April and May 2020. The efficacy of these reductions in altering ozone concentrations varied substantially in both space and time, with differences driven by local meteorology and chemical production efficiency. Globally, the total tropospheric ozone burden dropped by about 6 TgO 3 (∼2%) in May-June 2020, largely due to emission reductions in Asia and the Americas. Our results show a clear and global atmospheric imprint from COVID-19 mitigation, which altered the atmospheric oxidative capacity, climate radiative forcing, and human health.
Key points: • A new divergence method is developed to estimate methane emissions based on satellite observations, requiring no a priori emissions. • The applicability of this method in identifying and quantifying sources is proven by a GEOS-Chem simulation with known a priori emissions. • The estimated emissions over Texas (United States) based on TROPOMI observations are evaluated and are found to be robust. Abstract We present a new divergence method to estimated methane (CH 4) emissions from satellite observed mean mixing ratio of methane (XCH 4) by deriving the regional enhancement of XCH 4 in the Planetary Boundary Layer (PBL). The applicability is proven by comparing the estimated emissions with its a priori emission inventory from a 3-month GEOS-Chem simulation. When applied to TROPOspheric Monitoring Instrument (TROPOMI) observations, sources from well-known oil/gas production areas, livestock farms and wetlands in Texas become clearly visible in the emission maps. The calculated yearly averaged total CH 4 emission over the Permian Basin is 3.06 [2.82, 3.78] Tg a-1 for 2019, which is consistent with previous studies and double that of EDGAR v4.3.2 for 2012. Sensitivity tests on PBL heights, on the derived regional background and on wind speeds suggest our divergence method is quite robust. It is also a fast and simple method to estimate the CH 4 emissions globally. Plain Language Summary Methane (CH 4) is an important greenhouse gas in the atmosphere and plays a crucial role in the global climate change. It kept increasing over the last decades. About 70% of CH 4 comes from human activities like oil/gas productions or livestock farms. The recently launched TROPOspheric Monitoring Instrument (TROPOMI) provides an opportunity to estimate the emissions of CH 4 on a regional scale. This work presents a new method to fastly derive CH 4 emissions at a fairly high spatial resolution without a priori knowledge of sources.
During the COVID-19 lockdown (24 Jan to 20 March) in China low air pollution levels were reported in the media as a consequence of reduced economic and social activities. Quantification of the pollution reduction is not straightforward due to effects of transport, meteorology, and chemistry. We have analysed the NO emission reductions calculated with an inverse algorithm applied to daily NO observations from TROPOMI onboard the Copernicus Sentinel-5P satellite. This method allows the quantification of emission reductions per city, and the analysis of emissions of maritime transport and of the energy sector separately. The reductions we found are 20 to 50% for cities, about 40% for power plants and 15 to 40% for maritime transport depending on the region. The reduction in both emissions and concentrations shows a similar timeline consisting of a sharp reduction (34 to 50%) around the Spring festival and a slow recovery from mid-February to mid-March.