High secondary aerosol contribution to particulate pollution during haze events in China.

Rapid industrialization and urbanization in developing countries has led to an increase in air pollution, along a similar trajectory to that previously experienced by the developed nations. In China, particulate pollution is a serious environmental problem that is influencing air quality, regional and global climates, and human health. In response to the extremely severe and persistent haze pollution experienced by about 800 million people during the first quarter of 2013 (refs 4, 5), the Chinese State Council announced its aim to reduce concentrations of PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 micrometres) by up to 25 per cent relative to 2012 levels by 2017 (ref. 6). Such efforts however require elucidation of the factors governing the abundance and composition of PM2.5, which remain poorly constrained in China. Here we combine a comprehensive set of novel and state-of-the-art offline analytical approaches and statistical techniques to investigate the chemical nature and sources of particulate matter at urban locations in Beijing, Shanghai, Guangzhou and Xi'an during January 2013. We find that the severe haze pollution event was driven to a large extent by secondary aerosol formation, which contributed 30-77 per cent and 44-71 per cent (average for all four cities) of PM2.5 and of organic aerosol, respectively. On average, the contribution of secondary organic aerosol (SOA) and secondary inorganic aerosol (SIA) are found to be of similar importance (SOA/SIA ratios range from 0.6 to 1.4). Our results suggest that, in addition to mitigating primary particulate emissions, reducing the emissions of secondary aerosol precursors from, for example, fossil fuel combustion and biomass burning is likely to be important for controlling China's PM2.5 levels and for reducing the environmental, economic and health impacts resulting from particulate pollution.

Trends in Chemical Composition of Global and Regional Population-Weighted Fine Particulate Matter Estimated for 25 Years.

We interpret in situ and satellite observations with a chemical transport model (GEOS-Chem, downscaled to 0.1° × 0.1°) to understand global trends in population-weighted mean chemical composition of fine particulate matter (PM2.5). Trends in observed and simulated population-weighted mean PM2.5 composition over 1989-2013 are highly consistent for PM2.5 (-2.4 vs -2.4%/yr), secondary inorganic aerosols (-4.3 vs -4.1%/yr), organic aerosols (OA, -3.6 vs -3.0%/yr) and black carbon (-4.3 vs -3.9%/yr) over North America, as well as for sulfate (-4.7 vs -5.8%/yr) over Europe. Simulated trends over 1998-2013 also have overlapping 95% confidence intervals with satellite-derived trends in population-weighted mean PM2.5 for 20 of 21 global regions. Over 1989-2013, most (79%) of the simulated increase in global population-weighted mean PM2.5 of 0.28 µg m-3yr-1 is explained by significantly (p < 0.05) increasing OA (0.10 µg m-3yr-1), nitrate (0.05 µg m-3yr-1), sulfate (0.04 µg m-3yr-1), and ammonium (0.03 µg m-3yr-1). These four components predominantly drive trends in population-weighted mean PM2.5 over populous regions of South Asia (0.94 µg m-3yr-1), East Asia (0.66 µg m-3yr-1), Western Europe (-0.47 µg m-3yr-1), and North America (-0.32 µg m-3yr-1). Trends in area-weighted mean and population-weighted mean PM2.5 composition differ significantly.

Spatial Variation of Aerosol Chemical Composition and Organic Components Identified by Positive Matrix Factorization in the Barcelona Region.

The spatial distribution of PM1 components in the Barcelona metropolitan area was investigated using on-road mobile measurements of atmospheric particle- and gas-phase compounds during the DAURE campaign in March 2009. Positive matrix factorization (PMF) applied to organic aerosol (OA) data yielded 5 factors: hydrocarbon-like OA (HOA), cooking OA (COA), biomass burning OA (BBOA), and low volatility and semivolatile oxygenated OA (LV-OOA and SV-OOA). The area under investigation (∼500 km(2)) was divided into six zones (city center, harbor, industrial area, precoastal depression, 2 mountain ranges) for measurements and data analysis. Mean zonal OA concentrations are 4.9-9.5 µg m(-3). The area is heavily impacted by local primary emissions (HOA 14-38%, COA 10-18%, BBOA 10-12% of OA); concentrations of traffic-related components, especially black carbon, are biased high due to the on-road nature of the measurements. The formation of secondary OA adds more than half of the OA burden outside the city center (SV-OOA 14-40%, LV-OOA 17-42% of OA). A case study of one measurement drive from the shore to the precoastal mountain range furthest downwind of the city center indicates the importance of nonfossil over anthropogenic secondary OA based on OA/CO.

Addressing the different paces of climate and air quality combustion emissions across the world.

Greenhouse gases (GHG) and air pollutants (AP) share several anthropic sources but evolve differently in time across the various regions of the globe. Fossil and biological fuel combustion is by far the single process producing the highest amounts of both types of compounds. We have analyzed the paces of change of both GHG and AP emissions across the world and in some selected highly emitting regions using purposely designed indicators. We have observed that, overall, combustion processes are generally producing a lower amount of pollutants per unit of GHG emitted in 2018 than in 1970, with the noticeable exception of ammonia emissions in transport. Nevertheless, comparing countries at different development levels, evidence of possible further improvement clearly emerges, depending on the technological evolution of the most important emitting sectors and on the implementation of appropriate control measures and policies.

Near-real-time global gridded daily CO2 emissions 2021.

We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from the near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies' effectiveness and make adjustments quickly.

Near-real-time global gridded daily CO2 emissions.

Precise and high-resolution carbon dioxide (CO2) emission data is of great importance in achieving carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO2 Emissions Dataset (GRACED) from fossil fuel and cement production with a global spatial resolution of 0.1° by 0.1° and a temporal resolution of 1 day. Gridded fossil emissions are computed for different sectors based on the daily national CO2 emissions from near-real-time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Energy Infrastructure Emissions Database (GID), Emission Database for Global Atmospheric Research (EDGAR), and spatiotemporal patters of satellite nitrogen dioxide (NO2) retrievals. Our study on the global CO2 emissions responds to the growing and urgent need for high-quality, fine-grained, near-real-time CO2 emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors from January 1, 2019, to December 31, 2020. This gives thorough insights into the relative contributions from each sector. Furthermore, it provides the most up-to-date and fine-grained overview of where and when fossil CO2 emissions have decreased and rebounded in response to emergencies (e.g., coronavirus disease 2019 [COVID-19]) and other disturbances of human activities of any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will enable policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt.

High resolution temporal profiles in the Emissions Database for Global Atmospheric Research.

Emissions into the atmosphere from human activities show marked temporal variations, from inter-annual to hourly levels. The consolidated practice of calculating yearly emissions follows the same temporal allocation of the underlying annual statistics. However, yearly emissions might not reflect heavy pollution episodes, seasonal trends, or any time-dependant atmospheric process. This study develops high-time resolution profiles for air pollutants and greenhouse gases co- emitted by anthropogenic sources in support of atmospheric modelling, Earth observation communities and decision makers. The key novelties of the Emissions Database for Global Atmospheric Research (EDGAR) temporal profiles are the development of (i) country/region- and sector- specific yearly profiles for all sources, (ii) time dependent yearly profiles for sources with inter-annual variability of their seasonal pattern, (iii) country- specific weekly and daily profiles to represent hourly emissions, (iv) a flexible system to compute hourly emissions including input from different users. This work creates a harmonized emission temporal distribution to be applied to any emission database as input for atmospheric models, thus promoting homogeneity in inter-comparison exercises.