Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Improving spatial surrogates for area source emissions inventories in California
Abstract Ten spatial surrogates describing the detailed locations of air pollution emissions in regional air quality assessments for California were updated/created for the base year 2010 and future years from 2015 to 2040: (i) total population, (ii) total housing, (iii) single-family housing, (iv) total employment, (v) service & commercial employment, (vi) industrial employment, (vii) agricultural employment, (viii) industrial-related surrogate, (ix) off-road construction, and (x) on-road construction surrogates. The first seven surrogates were updated using the latest version of census-based datasets at finer resolution. New industrial-related, off/on-road construction surrogates were developed using realistic datasets to more accurately describe the location of construction projects and industrial facilities. Adoption of the new spatial surrogates caused changes to the spatial distribution of air pollution emissions in air quality calculations. The changes to the off-road construction surrogate resulted in the largest shift in PM emissions distribution for year 2015, followed by changes to the on-road construction surrogate. Industrial-related, service & commercial employment, and off-road construction surrogates all contributed to changes in NOx emissions. The changes to SED-derived surrogates were subtle and did not significantly influence emissions. Air quality simulations were carried out over the entire year 2016 to examine the impact of the new surrogate methodologies on simulated concentration fields. Changes to predicted pollutant concentrations followed the same pattern as changes in emissions, which indicates that proximity to sources is a dominant factor to determine the impact of spatial surrogates on model performance. The updated spatial surrogates generally improved predicted PM mass and EC concentrations in the Sacramento area (~10% for PM, ~3% for EC), the Bay Area (~3% for PM, ~1.5% for EC), and the region surrounding Los Angeles (~5% for PM, ~4% for EC). The updated spatial surrogates also improved predicted NOx concentrations in the core region of Los Angeles (~6%). These improvements demonstrate that development and adoption of new methodologies for emissions spatial surrogates can improve the accuracy of regional chemical transport models for criteria air pollutants.
Highlights We updated spatial surrogates that describe location of area-source emissions across California. New spatial surrogates use public datasets and can be projected to future years. Changes to construction surrogates had the largest effect on emissions, followed by employment surrogates. New surrogates improved predicted PM concentrations by 1.5–10% in Sacramento, San Francisco, and Los Angeles in the year 2016.
Improving spatial surrogates for area source emissions inventories in California
Abstract Ten spatial surrogates describing the detailed locations of air pollution emissions in regional air quality assessments for California were updated/created for the base year 2010 and future years from 2015 to 2040: (i) total population, (ii) total housing, (iii) single-family housing, (iv) total employment, (v) service & commercial employment, (vi) industrial employment, (vii) agricultural employment, (viii) industrial-related surrogate, (ix) off-road construction, and (x) on-road construction surrogates. The first seven surrogates were updated using the latest version of census-based datasets at finer resolution. New industrial-related, off/on-road construction surrogates were developed using realistic datasets to more accurately describe the location of construction projects and industrial facilities. Adoption of the new spatial surrogates caused changes to the spatial distribution of air pollution emissions in air quality calculations. The changes to the off-road construction surrogate resulted in the largest shift in PM emissions distribution for year 2015, followed by changes to the on-road construction surrogate. Industrial-related, service & commercial employment, and off-road construction surrogates all contributed to changes in NOx emissions. The changes to SED-derived surrogates were subtle and did not significantly influence emissions. Air quality simulations were carried out over the entire year 2016 to examine the impact of the new surrogate methodologies on simulated concentration fields. Changes to predicted pollutant concentrations followed the same pattern as changes in emissions, which indicates that proximity to sources is a dominant factor to determine the impact of spatial surrogates on model performance. The updated spatial surrogates generally improved predicted PM mass and EC concentrations in the Sacramento area (~10% for PM, ~3% for EC), the Bay Area (~3% for PM, ~1.5% for EC), and the region surrounding Los Angeles (~5% for PM, ~4% for EC). The updated spatial surrogates also improved predicted NOx concentrations in the core region of Los Angeles (~6%). These improvements demonstrate that development and adoption of new methodologies for emissions spatial surrogates can improve the accuracy of regional chemical transport models for criteria air pollutants.
Highlights We updated spatial surrogates that describe location of area-source emissions across California. New spatial surrogates use public datasets and can be projected to future years. Changes to construction surrogates had the largest effect on emissions, followed by employment surrogates. New surrogates improved predicted PM concentrations by 1.5–10% in Sacramento, San Francisco, and Los Angeles in the year 2016.
Improving spatial surrogates for area source emissions inventories in California
Li, Yiting (Autor:in) / Rodier, Caroline (Autor:in) / Lea, Jeremy D. (Autor:in) / Harvey, John (Autor:in) / Kleeman, Michael J. (Autor:in)
Atmospheric Environment ; 247
31.05.2020
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch