Global climate forcing of aerosols embodied in international trade

International trade separates regions consuming goods and services from regions where goods and related aerosol pollution are produced. Yet the role of trade in aerosol climate forcing attributed to different regions has never been quantified. Here, we contrast the direct radiative forcing of aerosols related to regions’ consumption of goods and services against the forcing due to emissions produced in each region. Aerosols assessed include black carbon, primary organic aerosol, and secondary inorganic aerosols, including sulfate, nitrate and ammonium. We find that global aerosol radiative forcing due to emissions produced in East Asia is much stronger than the forcing related to goods and services ultimately consumed in that region because of its large net export of emissions-intensive goods. The opposite is true for net importers such as Western Europe and North America: global radiative forcing related to consumption is much greater than the forcing due to emissions produced in these regions. Overall, trade is associated with a shift of radiative forcing from net importing to net exporting regions. Compared to greenhouse gases such as carbon dioxide, the short atmospheric lifetimes of aerosols cause large localized differences between consumption- and production-related radiative forcing. International efforts to reduce emissions in the exporting countries will help alleviate trade-related climate and health impacts of aerosols while lowering global emissions

Reduced carbon emission estimates from fossil fuel combustion and cement production in China.

Nearly three-quarters of the growth in global carbon emissions from the burning of fossil fuels and cement production between 2010 and 2012 occurred in China. Yet estimates of Chinese emissions remain subject to large uncertainty; inventories of China's total fossil fuel carbon emissions in 2008 differ by 0.3 gigatonnes of carbon, or 15 per cent. The primary sources of this uncertainty are conflicting estimates of energy consumption and emission factors, the latter being uncertain because of very few actual measurements representative of the mix of Chinese fuels. Here we re-evaluate China's carbon emissions using updated and harmonized energy consumption and clinker production data and two new and comprehensive sets of measured emission factors for Chinese coal. We find that total energy consumption in China was 10 per cent higher in 2000-2012 than the value reported by China's national statistics, that emission factors for Chinese coal are on average 40 per cent lower than the default values recommended by the Intergovernmental Panel on Climate Change, and that emissions from China's cement production are 45 per cent less than recent estimates. Altogether, our revised estimate of China's CO2 emissions from fossil fuel combustion and cement production is 2.49 gigatonnes of carbon (2 standard deviations = ±7.3 per cent) in 2013, which is 14 per cent lower than the emissions reported by other prominent inventories. Over the full period 2000 to 2013, our revised estimates are 2.9 gigatonnes of carbon less than previous estimates of China's cumulative carbon emissions. Our findings suggest that overestimation of China's emissions in 2000-2013 may be larger than China's estimated total forest sink in 1990-2007 (2.66 gigatonnes of carbon) or China's land carbon sink in 2000-2009 (2.6 gigatonnes of carbon).This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/nature1467

Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence

Nitrogen dioxide (NO2) at the ground level poses a serious threat to environmental quality and public health. This study developed a novel, artificial intelligence approach by integrating spatiotemporally weighted information into the missing extra-trees and deep forest models to first fill the satellite data gaps and increase data availability by 49% and then derive daily 1 km surface NO2 concentrations over mainland China with full spatial coverage (100%) for the period 2019-2020 by combining surface NO2 measurements, satellite tropospheric NO2 columns derived from TROPOMI and OMI, atmospheric reanalysis, and model simulations. Our daily surface NO2 estimates have an average out-of-sample (out-of-city) cross-validation coefficient of determination of 0.93 (0.71) and root-mean-square error of 4.89 (9.95) μg/m3. The daily seamless high-resolution and high-quality dataset "ChinaHighNO2"allows us to examine spatial patterns at fine scales such as the urban-rural contrast. We observed systematic large differences between urban and rural areas (28% on average) in surface NO2, especially in provincial capitals. Strong holiday effects were found, with average declines of 22 and 14% during the Spring Festival and the National Day in China, respectively. Unlike North America and Europe, there is little difference between weekdays and weekends (within ±1 μg/m3). During the COVID-19 pandemic, surface NO2 concentrations decreased considerably and then gradually returned to normal levels around the 72nd day after the Lunar New Year in China, which is about 3 weeks longer than the tropospheric NO2 column, implying that the former can better represent the changes in NOx emissions

ChinaHighNO2: Big Data Seamless 1 km Ground-level NO2 Dataset for China

ChinaHighNO2 is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level NO2 dataset in China from 2019 to 2020. This dataset yields a high quality with cross-validation coefficient of determination (CV-R2) values of 0.93, 0.95, and 0.96, and root-mean-square error (RMSE) values of 4.89, 3.11, and 2.35 µg m-3 on the daily, monthly, and yearly basises, respectively