Optical Properties of Aerosols and Implications for Radiative Effects in Beijing During the Asia‐Pacific Economic Cooperation Summit 2014

Abstract

An intensive measurement campaign was conducted in Beijing during the Asia‐Pacific Economic Cooperation (APEC) Summit 2014 to investigate the effectiveness of stringent emission controls on aerosol optical properties and direct radiative forcing (DRF). Average values of PM2.5, light scattering (bscat), and light absorption (babs) coefficients decreased by 40, 64, and 56%, respectively, during the APEC control period compared with noncontrol periods. For the APEC control period, the PM2.5 mass scattering and absorption efficiencies were both smaller than the noncontrol period by a factor of ~2. Calculations based on a revised IMPROVE method and linear regression showed that sulfate, nitrate, organic matter, elemental carbon, and fine soil contributed comparably to the light extinction coefficient (bext) in both periods, but the bext values were 27–64% lower during the APEC period. A positive matrix factorization receptor model showed that bext from two secondary aerosol sources, biomass burning, traffic‐related emissions, and coal burning decreased by 26–87% during the APEC control period. The average DRF calculated from the Tropospheric Ultraviolet and Visible radiation model was −11.9 and −4.6 W m−2 at the surface during the noncontrol and APEC control periods, respectively, suggesting an overall cooling effect. The reduction of DRF from each emission source ranged from ~30–80% during the APEC control period. The results suggest that the pollution control measures implemented for APEC substantially reduced air pollution and could help mitigate the cooling effects of aerosols at the surface in Beijing.

Impacts of short-term mitigation measures on PM<sub>2.5</sub> and radiative effects: a case study from a regional background site near Beijing, China

“…This should be attributed to the lower PM2.5 loadings during the NCCPC control period. The DRF reduction ratio (26.3%) during the NCCPC control period is smaller than the value during the APEC period (61.3%, Zhou et al, 2017). Furthermore, the DRF values can be as high as -24.7 and -28.2 W m -2 during the PE1 and PE2, respectively.…”

“…components to bext are found. For example, Li et al (2013) have reported that (NH4)2SO4 (41%) has the largest contribution to bext during the Olympics, followed by NH4NO3 (23%), OM (17%), and EC (9%); Zhou et al (2017) have found that OM 49%is the largest contributor to bext, followed by NH4NO3 (19%), (NH4)2SO4 (13%), and EC (12%). These differences may be attributed to the different reductions in PM2.5 chemical species and the variable RH among studies which can influence the hygroscopic properties of sulfate and nitrate.…”

Measurement report: The importance of biomass burning in light extinction and direct radiative effect of urban aerosol during the COVID-19 lockdown in Xi'an, China

“…In contrast, receptor models can be utilised to conduct multiple optical source apportionment of aerosol. Several studies have used a combination of the receptor model and MLR to indirectly identify sources of aerosol b scat , b abs , and b ext (Cao et al, 2012;Tian et al, 2020;Zhou et al, 2017). For example, Zhou et al (2017) first used positive matrix factorisation analysis to quantify the mass contributions of aerosol from secondary aerosol, biomass burning, trafficrelated emissions, and coal combustion based on the sole chemical species, and MLR was then used to apportion the contribution of each source to b scat and b abs .…”

Measurement report: The importance of biomass burning in light extinction and direct radiative effect of urban aerosol during the COVID-19 lockdown in China

“…In contrast, receptor models can be utilized to resolve multiple optical source apportionment of aerosol. Several studies used a combination of the receptor model and MLR to indirectly identify sources of aerosol b scat , b abs , and b ext (Cao et al, 2012;Tian et al, 2020;Zhou et al, 2017). For example, Zhou et al (2017) firstly used positive matrix factorization analysis to quantify the mass contributions of aerosol from secondary aerosol, biomass burning, traffic-related emissions, and coal burning based on the sole chemical species, and then the MLR was used to apportion the contribution of each source to b scat and b abs .…”

“…Several studies used a combination of the receptor model and MLR to indirectly identify sources of aerosol b scat , b abs , and b ext (Cao et al, 2012;Tian et al, 2020;Zhou et al, 2017). For example, Zhou et al (2017) firstly used positive matrix factorization analysis to quantify the mass contributions of aerosol from secondary aerosol, biomass burning, traffic-related emissions, and coal burning based on the sole chemical species, and then the MLR was used to apportion the contribution of each source to b scat and b abs . In addition, recent studies have attempted to conduct direct optical source apportionment by combining aerosol chemical species with optical coefficients in one receptor model (Forello et al, 2019;Q.…”