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The outbreak of Coronavirus Disease 2019 (COVID-19) in China in January 2020 prompted substantial control measures including social distancing measures, suspension of public transport and industry, and widespread cordon sanitaires (‘lockdowns’), that have led to a decrease in industrial activity and air pollution emissions over a prolonged period. We use a 5 year dataset from China’s air quality monitoring network to assess the impact of control measures on air pollution. Pollutant concentration time series are decomposed to account for the inter-annual trend, seasonal cycles and the effect of Lunar New Year, which coincided with the COVID-19 outbreak. Over 2015–2019, there were significant negative trends in particulate matter (PM2.5, −6% yr−1) and sulphur dioxide (SO2, −12% yr−1) and nitrogen dioxide (NO2, −2.2% yr−1) whereas there were positive trends in ozone (O3, + 2.8% yr−1). We quantify the change in air quality during the LNY holiday week, during which pollutant concentrations increase on LNY’s day, followed by reduced concentrations in the rest of the week. After accounting for interannual trends and LNY we find NO2 and PM concentrations were significantly lower during the lockdown period than would be expected, but there were no significant impacts on O3. Largest reductions occurred in NO2, with concentrations 27.0% lower on average across China, during the lockdown. Average concentrations of PM2.5 and PM10 across China were respectively 10.5% and 21.4% lower during the lockdown period. The largest reductions were in Hubei province, where NO2 concentrations were 50.5% lower than expected during the lockdown. Concentrations of affected pollutants returned to expected levels during April, after control measures were relaxed.

Figure S1. Forest carbon stocks (including aboveground and belowground) across Southeast Asia circa 2000. a, Mean forest carbon stocks at different elevations (black line) and slopes (red line). Inset bars show regional mean carbon stocks in lowlands and mountains. Error bars represent the standard deviation of mean carbon stocks. b, Map of mean carbon stocks in 0.25° cells. Black dots indicate mountain regions.

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Previous estimates of tropical forest carbon loss in the twenty-first century using satellite data typically focus on its magnitude, whereas regional loss trajectories and associated drivers are rarely reported. Here we used different high-resolution satellite datasets to show a doubling of gross tropical forest carbon loss worldwide from 0.97 ± 0.16 PgC yr−1 in 2001–2005 to 1.99 ± 0.13 PgC yr−1 in 2015–2019. This increase in carbon loss from forest conversion is higher than in bookkeeping models forced by land-use statistical data, which show no trend or a slight decline in land-use emissions in the early twenty-first century. Most (82%) of the forest carbon loss is at some stages associated with large-scale commodity or small-scale agriculture activities, particularly in Africa and Southeast Asia. We find that ~70% of former forest lands converted to agriculture in 2001–2019 remained so in 2020, confirming a dominant role of agriculture in long-term pan-tropical carbon reductions on formerly forested landscapes. The acceleration and high rate of forest carbon loss in the twenty-first century suggest that existing strategies to reduce forest loss are not successful; and this failure underscores the importance of monitoring deforestation trends following the new pledges made in Glasgow.

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