Wetland emission and atmospheric sink changes explain methane growth in 2020.

Atmospheric methane growth reached an exceptionally high rate of 15.1 ± 0.4 parts per billion per year in 2020 despite a probable decrease in anthropogenic methane emissions during COVID-19 lockdowns1. Here we quantify changes in methane sources and in its atmospheric sink in 2020 compared with 2019. We find that, globally, total anthropogenic emissions decreased by 1.2 ± 0.1 teragrams of methane per year (Tg CH4 yr-1), fire emissions decreased by 6.5 ± 0.1 Tg CH4 yr-1 and wetland emissions increased by 6.0 ± 2.3 Tg CH4 yr-1. Tropospheric OH concentration decreased by 1.6 ± 0.2 per cent relative to 2019, mainly as a result of lower anthropogenic nitrogen oxide (NOx) emissions and associated lower free tropospheric ozone during pandemic lockdowns2. From atmospheric inversions, we also infer that global net emissions increased by 6.9 ± 2.1 Tg CH4 yr-1 in 2020 relative to 2019, and global methane removal from reaction with OH decreased by 7.5 ± 0.8 Tg CH4 yr-1. Therefore, we attribute the methane growth rate anomaly in 2020 relative to 2019 to lower OH sink (53 ± 10 per cent) and higher natural emissions (47 ± 16 per cent), mostly from wetlands. In line with previous findings3,4, our results imply that wetland methane emissions are sensitive to a warmer and wetter climate and could act as a positive feedback mechanism in the future. Our study also suggests that nitrogen oxide emission trends need to be taken into account when implementing the global anthropogenic methane emissions reduction pledge5.

National emissions inventory and future trends in greenhouse gases and other air pollutants from civil airports in China.

Civil aviation is an important source of air pollutants, but this field has received insufficient attention in China. Based on the standard emissions model of the International Civil Aviation Organization (ICAO) and actual flight information from 241 airports, this study estimated a comprehensive emissions inventory for 2010-2020 by considering the impacts of mixing layer height. The results showed that annual pollutant emissions rapidly trended upward along with population and economic growth; however, the emissions decreased owing to the impacts of the COVID-19 pandemic. In 2020, the emissions of carbon monoxide (CO), nitrogen oxides (NOX), particulate matter (PM), methane (CH4), nitrous oxide (N2O), carbon dioxide (CO2), and water vapor (H2O) were 34.34, 65.73, 0.10, 0.34, 0.40, 14,706.26, and 5733.11 Gg, respectively. The emissions of total volatile organic compounds (VOCs) from China's civil airports in 2020 were estimated at 17.20 Gg; the major components were formic acid (1.70 Gg), acetic acid (1.62 Gg), 1-butylene (1.03 Gg), acetone (0.96 Gg), and acetaldehyde (0.93 Gg). The distribution of pollutant emissions was consistent with the level of economic development, mainly in Beijing, Guangzhou, and Shanghai. In addition, we estimated future pollution trends for the aviation industry under four scenarios. Under the comprehensive scenario, which considered the impacts of economic growth, passenger turnover, cargo turnover, COVID-19, and technological efficiency, the levels of typical pollutants were expected to increase by nearly 1.51-fold from 2010 to 2035.

Methane sources from waste and natural gas sectors detected in Pune, India, by concentration and isotopic analysis.

Methane (CH4) is a potent greenhouse gas and also plays a significant role in tropospheric chemistry. High-frequency (sub-hourly) measurements of CH4 and carbon isotopic ratio (δ13CH4) have been conducted at Pune (18.53°N, 73.80°E), an urban environment in India, during 2018-2020. High CH4 concentrations were observed, with a mean of 2100 ± 196 ppb (1844-2749 ppb), relative to marine background concentrations. The δ13CH4 varied between -45.11 and -50.03 ‰ for the entire study period with an average of -47.41 ± 0.94 ‰. The diurnal variability of CH4 typically showed maximum values in the morning (08:00-09:00 local time) and minimum in the afternoon (15:00 local time). The deepest diurnal amplitude of ~500 ppb was observed during winter (December-February), which was reduced to less than half, ~200 ppb, during the summer (March-May). CH4 concentration at Pune showed a strong seasonality (470 ppb), much higher than that at Mauna Loa, Hawaii. On the other hand, δ13CH4 records did not show distinct seasonality at Pune. The δ13CH4 values revealed that the significant sources of CH4 in Pune were from the waste sector (enhanced during the monsoon season; signature of depleted δ13CH4), followed by the natural gas sector with a signature of enriched δ13CH4. Our analysis of Covid-19 lockdown (April to May 2020) effect on the CH4 variability showed no signal in the CH4 variability; however, the isotopic analysis indicated a transient shift in the CH4 source to the waste sector (early summer of 2020).

Detection of fossil-fuel CO2 plummet in China due to COVID-19 by observation at Hateruma.

The COVID-19 pandemic caused drastic reductions in carbon dioxide (CO2) emissions, but due to its large atmospheric reservoir and long lifetime, no detectable signal has been observed in the atmospheric CO2 growth rate. Using the variabilities in CO2 (ΔCO2) and methane (ΔCH4) observed at Hateruma Island, Japan during 1997-2020, we show a traceable CO2 emission reduction in China during February-March 2020. The monitoring station at Hateruma Island observes the outflow of Chinese emissions during winter and spring. A systematic increase in the ΔCO2/ΔCH4 ratio, governed by synoptic wind variability, well corroborated the increase in China's fossil-fuel CO2 (FFCO2) emissions during 1997-2019. However, the ΔCO2/ΔCH4 ratios showed significant decreases of 29 ± 11 and 16 ± 11 mol mol-1 in February and March 2020, respectively, relative to the 2011-2019 average of 131 ± 11 mol mol-1. By projecting these observed ΔCO2/ΔCH4 ratios on transport model simulations, we estimated reductions of 32 ± 12% and 19 ± 15% in the FFCO2 emissions in China for February and March 2020, respectively, compared to the expected emissions. Our data are consistent with the abrupt decrease in the economic activity in February, a slight recovery in March, and return to normal in April, which was calculated based on the COVID-19 lockdowns and mobility restriction datasets.