A high-resolution air pollutants emission inventory in 2013 for the Beijing-Tianjin-Hebei region, China

Qi, Ji; Zheng, Bo; Li, Meng; Yu, Fang; Chen, Chuchu; Liu, Fei; Zhou, Xinyu; Yuan, Jing; Zhang, Qiang; He, Kebin

doi:10.1016/j.atmosenv.2017.09.039

Cited by 148 publications

(83 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The chemical transformation of pollutants contained within each dispersing plume is represented using the GRS chemistry scheme (Malkin et al, 2016). Typically, regional CTMs such as WRF-Chem and CMAQ use detailed chemical mechanisms containing hundreds or even thousands of reactions involving NO, NO 2 , O 3 and VOCs, including homogeneous and heterogeneous aerosol production (Sarwar and Luecken, 2008). The GRS, however, simplifies these to the following seven reactions:…”

Section: Chemistrymentioning

confidence: 99%

Street-scale air quality modelling for Beijing during a winter 2016 measurement campaign

et al. 2020

Self Cite

View full text Add to dashboard Cite

Abstract. We examine the street-scale variation of NOx, NO2, O3 and PM2.5 concentrations in Beijing during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) winter measurement campaign in November–December 2016. Simulations are performed using the urban air pollution dispersion and chemistry model ADMS-Urban and an explicit network of road source emissions. Two versions of the gridded Multi-resolution Emission Inventory for China (MEIC v1.3) are used: the standard MEIC v1.3 emissions and an optimised version, both at 3 km resolution. We construct a new traffic emissions inventory by apportioning the transport sector onto a detailed spatial road map. Agreement between mean simulated and measured pollutant concentrations from Beijing's air quality monitoring network and the Institute of Atmospheric Physics (IAP) field site is improved when using the optimised emissions inventory. The inclusion of fast NOx–O3 chemistry and explicit traffic emissions enables the sharp concentration gradients adjacent to major roads to be resolved with the model. However, NO2 concentrations are overestimated close to roads, likely due to the assumption of uniform traffic activity across the study domain. Differences between measured and simulated diurnal NO2 cycles suggest that an additional evening NOx emission source, likely related to heavy-duty diesel trucks, is not fully accounted for in the emissions inventory. Overestimates in simulated early evening NO2 are reduced by delaying the formation of stable boundary layer conditions in the model to replicate Beijing's urban heat island. The simulated campaign period mean PM2.5 concentration range across the monitoring network (∼15 µg m−3) is much lower than the measured range (∼40 µg m−3). This is likely a consequence of insufficient PM2.5 emissions and spatial variability, neglect of explicit point sources, and assumption of a homogeneous background PM2.5 level. Sensitivity studies highlight that the use of explicit road source emissions, modified diurnal emission profiles, and inclusion of urban heat island effects permit closer agreement between simulated and measured NO2 concentrations. This work lays the foundations for future studies of human exposure to ambient air pollution across complex urban areas, with the APHH-China campaign measurements providing a valuable means of evaluating the impact of key processes on street-scale air quality.

show abstract

Section: Chemistrymentioning

confidence: 99%

Street-scale air quality modelling for Beijing during a winter 2016 measurement campaign

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The uncertainties (i.e., 95% confidence intervals around the central estimates) of Chinese emissions of SO 2 , NO x , total PM, PM 10 , PM 2.5 , black carbon (BC), and organic carbon (OC) were estimated to be −14%~13%, −13%~37%, −11%~38%, −14%~45%, −17%~54%, −25%~136%, and −40%~121%, respectively [22]. The average uncertainties of emissions from the JJJ region were estimated through the Monte Carlo method as −18% to 16% for SO 2 , −17% to 15% for NO x , −24% to 23% for PM 2.5 , −19% to 18% for PM 10 , −25% to 23% for CO, −48% to 44% for NMVOC, −54% to 48% for NH 3 , −54% to 49% for BC, and −59% to 55% for OC, which was compiled using a bottom-up approach based on detailed data of major air pollution sources [23]. In total, the model predictions of PM 2.5 generally agree with ambient measured values, but the model performance varies in different regions.Since the enforcement of the Air Pollution Prevention and Control Action Plan posted by the State Council in 2013, winter PM 2.5 pollution in China has decreased over the period 2013-2016 in response to reduced coal consumption and pollution prevention efforts.…”

mentioning

confidence: 99%

Impact of Meteorological Conditions on PM2.5 Pollution in China during Winter

Xue

Lei

et al. 2018

Atmosphere

View full text Add to dashboard Cite

Fine particulate matter (PM2.5) poses a risk to human health. In January 2017, the PM2.5 pollution in China was severe, and the average PM2.5 concentration had increased by 14.7% compared to that in January 2016. Meteorological conditions greatly influence PM2.5 pollution. The relationship between PM2.5 and meteorological factors was assessed using monitoring data and the Community Multiscale Air Quality modeling system (CMAQ) was used to quantitatively evaluate the impacts of variations of meteorological conditions on PM2.5 pollution. The results indicate that variations of meteorological conditions between January 2017 and January 2016 caused an increase of 13.6% in the national mean concentration of PM2.5. Unlike the Yangtze River Delta (YRD), where meteorological conditions were favorable, unfavorable meteorological conditions (such as low wind speed, high humidity, low boundary layer height and low rainfall) contributed to PM2.5 concentration worsening by 29.7%, 42.6% and 7.9% in the Beijing-Tianjin-Hebei (JJJ) region, the Pearl River Delta (PRD) region and the Chengdu-Chongqing (CYB) region, respectively. Given the significant influence of local meteorology on PM2.5 concentration, more emphasis should be placed on employing meteorological means to improve local air quality.

show abstract

“…Especially, abundant black carbon particles incorporated into cloud droplets could lead to a decrease in cloud albedo by absorbing radiation and an increase of temperature in the troposphere, then an acceleration of the evaporation of the cloud droplets (Ackerman et al, 2000;Adachi et al, 2010;Wang et al, 2013;Zuberi et al, 2005). In the past few decades, precipitation was significantly reduced over east central China due to the large amount of anthropogenic aerosols (Qian et al, 2009;Zhao et al, 2006). Because an excess of aerosols in clouds could reduce precipitation, the nonprecipitating clouds in the NCP tend to evaporate back to aerosol particles by solar radiation.…”

Section: Discussionmentioning

confidence: 99%

“…Owing to rapid industrialization and urbanization in Asia, large quantities of aerosol particles from anthropogenic sources are released into the atmosphere, which can dramatically affect the chemical composition of CCN, and furthermore change the properties of clouds such as radiative forcing, lifetime, and precipitation patterns (Drewnick et al, 2007;Ervens, 2015;Li et al, 2011b;Twohy and Anderson, 2008). High concentrations of aerosol particles increase the number of cloud droplets and reduce their size, which further results in the reduction of precipitation efficiency and in extending the lifetime of clouds (Fan et al, 2016;McFiggans et al, 2006;Qian et al, 2009;Rosenfeld, 2000). Moreover, anthropogenic aerosol particles -especially fly ash, metal, and soot particles -are incorporated into cloud droplets, and are transported long distances to affect ecosystems, human health, and radiative forcing Rosenfeld et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Cloud scavenging of anthropogenic refractory particles at a mountain site in North China

Liu

Zhang

et al. 2018

Atmos. Chem. Phys.

View full text Add to dashboard Cite

Abstract. Aerosol–cloud interactions remain a major source of uncertainty in climate forcing estimates. Few studies have been conducted to characterize the aerosol–cloud interactions in heavily polluted conditions worldwide. In this study, cloud residual and cloud interstitial particles were collected during cloud events under different pollution levels from 22 July to 1 August 2014 at Mt. Tai (1532 m above sea level) located in the North China Plain (NCP). A transmission electron microscope was used to investigate the morphology, size, and chemical composition of individual cloud residual and cloud interstitial particles, and to study mixing properties of different aerosol components in individual particles. Our results show that S-rich particles were predominant (78 %) during clean periods (PM2.5<15 µg m−3), but a large number of anthropogenic refractory particles (e.g., soot, fly ash, and metal) and their mixtures with S-rich particles (defined as “S-refractory”) were observed during polluted periods. Cloud droplets collected during polluted periods were found to become an extremely complicated mixture by scavenging abundant refractory particles. We found that 76 % of cloud residual particles were S-refractory particles and that 26 % of cloud residual particles contained two or more types of refractory particles. Soot-containing particles (i.e., S-soot and S-fly ash/metal-soot) were the most abundant (62 %) among cloud residual particles, followed by fly ash/metal-containing particles (i.e., S-fly ash/metal and S-fly ash/metal-soot, 37 %). These complicated cloud droplets have not been reported in clean continental or marine air before. Our findings provide an insight into the potential impacts on cloud radiative forcing from black carbon and metal catalyzed reactions of SO2 in micro-cloud droplets containing soluble metals released from fly ash and metals over polluted air.

show abstract

A high-resolution air pollutants emission inventory in 2013 for the Beijing-Tianjin-Hebei region, China

Cited by 148 publications

References 32 publications

Street-scale air quality modelling for Beijing during a winter 2016 measurement campaign

Street-scale air quality modelling for Beijing during a winter 2016 measurement campaign

Impact of Meteorological Conditions on PM2.5 Pollution in China during Winter

Cloud scavenging of anthropogenic refractory particles at a mountain site in North China

Contact Info

Product

Resources

About