Dataset for evaluating WRF-Chem sensitivity to biogenic emission inventories in a tropical region. Global online model (MEGAN) vs local offline model (BIGA)

Cifuentes, Felipe; González, C.; Aristizábal, Beatriz Helena

doi:10.1016/j.dib.2021.107438

Cited by 1 publication

(1 citation statement)

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“…However, topdown inventories have their limitations too. They rely on a limited number of measurements and assumptions about emissions sources, which can lead to significant uncertainties in the estimates (Cifuentes et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

UrbEm: a method and tool to refine regional emission inventories for urban atmospheric studies

Kakouri,

Ramacher,

Kuenen

et al.

Global NEST International Conference on Environmental Science &Amp; Technology

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As cities are growing in size and complexity, the estimation of air pollution exposure requires detailed spatial representation of air pollution levels. Intra-urban atmospheric studies require up-to-date and credible emission inventories. The Copernicus Atmosphere Monitoring Service (CAMS) provides consistent and quality-controlled information on anthropogenic emissions from Europe in the spatial resolution of 0.1 x 0.05 degree (lon/lat). The UrbEm method and tool applies a prompt and effective approach to spatially refine these emissions for any European city (or area). In particular, a top-down approach is built upon the CAMS-REG-AP database of anthropogenic air pollution emissions, creating added-value products of road transport emissions (line sources), mass emitted from industrial units (point sources) and surface emissions attributed at a grid of 1 km resolution. The disaggregation is based on publicly available, well-established, contemporary gridded datasets, which provide high resolution, suitable sector-specific proxies per source category, such as population density from Global Human Settlement (GHS), land use categories from Copernicus Land Monitoring Service (CLC 2018) and road networks from OpenStreetMap. In this work, the downscaling approach is applied in 9 diverse European Urban Centers and the improvements of the spatial disaggregation of CAMS emissions are comparatively examined.

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Section: Introductionmentioning

confidence: 99%