Sunling Gong

Application of the CALIOP layer product to evaluate the vertical distribution of aerosols estimated by global models: AeroCom phase i results

[1] The CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) layer product is used for a multimodel evaluation of the vertical distribution of aerosols. Annual and seasonal aerosol extinction profiles are analyzed over 13 sub-continental regions representative of industrial, dust, and biomass burning pollution, from CALIOP 2007–2009 observations and from AeroCom (Aerosol Comparisons between Observations and Models) 2000 simulations. An extinction mean height diagnostic (Za) is defined to quantitatively assess the models’ performance. It is calculated over the 0–6 km and 0–10 km altitude ranges by weighting the altitude of each 100 m altitude layer by its aerosol extinction coefficient. The mean extinction profiles derived from CALIOP layer products provide consistent regional and seasonal specificities and a low inter-annual variability. While the outputs from most models are significantly correlated with the observed Za climatologies, some do better than others, and 2 of the 12 models perform particularly well in all seasons. Over industrial and maritime regions, most models show higher Za than observed by CALIOP, whereas over the African and Chinese dust source regions, Za is underestimated during Northern Hemisphere Spring and Summer. The positive model bias in Za is mainly due to an overestimate of the extinction above 6 km. Potential CALIOP and model limitations, and methodological factors that might contribute to the differences are discussed.

Investigation on emission factors of particulate matter and gaseous pollutants from crop residue burning

Emission factors of particulate matter (PM), element carbon (EC), organic carbon (OC), SO2, NO(x), CO, CO2, and ten ions (Na+, NH4+, K+, Mg2+, Ca2+, F-, Cl-, NO2-, NO3-, SO4(2-)) were estimated from the domestic burning of four types of commonly produced crop residues in rural China: rice straw, wheat straw, corn stover, and cotton stalk, which were collected from the representative regions across China. A combustion tower was designed to simulate the cooking conditions under which the peasants burned their crop residues in rural China, to measure the emission factors. Results showed that wheat straw had the highest emission factor for the total PM (8.75 g/kg) among the four crop residues, whereas, corn stover and wheat straw have the highest emission factor for EC (0.95 g/kg) and OC (3.46 g/kg), respectively. Corn stover also presents as having the highest emission factors of NO, NO(x), and CO2, whereas, wheat straw, rice straw, and cotton stalk had the highest emission factors of NO2, SO2, and CO, respectively. The water-soluble ions, K+ and Cl-, had the highest emission factors from all the crops. Wheat straw had a relatively higher emission factor of cation species and F-, Cl-, NO2- than other residues.

Transition metal-catalyzed oxidation of atmospheric sulfur: Global implications for the sulfur budget

[1] We use observations of the oxygen-17 excess (Δ 17 O) of sulfate in the Arctic to quantify the sulfate source from aqueous SO 2 (S(IV)) oxidation by O 2 catalyzed by transition metals. Due to the lack of photochemically produced OH and H 2 O 2 in high latitudes during winter, combined with high anthropogenic SO 2 emissions in the Northern Hemisphere, oxidation by O 3 is predicted to dominate sulfate formation during winter in this region. However, Δ 17 O measurements of sulfate aerosol collected in Alert, Canada, are not consistent with O 3 as the dominant oxidant and indicate that a S(IV) oxidant with near-zero Δ 17 O values (O 2 ) is important during winter. We use a global chemical transport model to interpret quantitatively the Alert observations and assess the global importance of sulfate production by Fe(III)- and Mn(II)-catalyzed oxidation of S(IV) by O 2 . We scale anthropogenic and natural atmospheric metal concentrations to primary anthropogenic sulfate and dust concentrations, respectively. The solubility and oxidation state of these metals is determined by cloud liquid water content, source, and sunlight. By including metal-catalyzed S(IV) oxidation, the model is consistent with the Δ 17 O magnitudes in the Alert data during winter. Globally, we find that this mechanism contributes 9-17% to sulfate production. The inclusion of metal-catalyzed oxidation does not resolve model discrepancies with surface SO 2 and sulfate observations in Europe. Oxygen isotope measurements of sulfate aerosols collected near anthropogenic and dust sources of metals would help to verify the importance of this sulfur oxidation pathway.

A decade of dust: Asian dust and springtime aerosol load in the U.S. Pacific Northwest.

[1] We integrate SeaWiFS aerosol optical thickness (AOT) over the Taklamakan and Gobi Deserts with U.S. aerosol observations to study surface aerosol variability in the Northwest U.S. in relation to Asian dust emissions. The results indicate that ∼50% of the interannual variability in springtime average PM 2.5 and PM 10 can be explained by changes in Asian dust emissions. On a seasonal timescale, variations in dust emissions appear to be more important in determining the total material crossing the Pacific than the variations in meteorology represented by the PNA or the LRT3 indices. We are able to explain ∼80% of the interannual variability using three variables: AOT, a transport index, and regional precipitation. This suggests that a strong source, favorable transport and sufficient residence time are needed for Asian dust to have a maximum seasonal impact in the Northwest. The results contextualize case studies and demonstrate the utility of the Deep Blue algorithm.

Towards the improvements of simulating the chemical and optical properties of Chinese aerosols using an online coupled model − CUACE/Aero

ABSTRACT CUACE/Aero, the China Meteorological Administration (CMA) Unified Atmospheric Chemistry Environment for aerosols, is a comprehensive numerical aerosol module incorporating emissions, gaseous chemistry and size-segregated multi-component aerosol algorithm. On-line coupled into a meso-scale weather forecast model (MM5), its performance and improvements for aerosol chemical and optical simulations have been evaluated using the observations data of aerosols/gases from the intensive observations and from the CMA Atmosphere Watch network, plus aerosol optical depth (AOD) data from CMA Aerosol Remote Sensing network (CARSNET) and from Moderate Resolution Imaging Spectroradiometer (MODIS). Targeting Beijing and North China region from July 13 to 31, 2008, when a heavy hazy weather system occurred, the model captured the general variations of PM10 with most of the data within a factor of 2 from the observations and a combined correlation coefficient (r) of 0.38 (significance level=0.05). The correlation coefficients are better at rural than at urban sites, and better at daytime than at nighttime. Chemically, the correlation coefficients between the daily-averaged modelled and observed concentrations range from 0.34 for black carbon (BC) to 0.09 for nitrates with sulphate, ammonium and organic carbon (OC) in between. Like the PM10, the values of chemical species are higher for the daytime than those for the nighttime. On average, the sulphate, ammonium, nitrate and OC are underestimated by about 60, 70, 96.0 and 10.8%, respectively. Black carbon is overestimated by about 120%. A new size distribution for the primary particle emissions was constructed for most of the anthropogenic aerosols such as BC, OC, sulphate, nitrate and ammonium from the observed size distribution of atmospheric aerosols in Beijing. This not only improves the correlation between the modelled and observed AOD, but also reduces the overestimation of AOD simulated by the original model size distributions of primary aerosols. The normalised mean error has been reduced to 62% with the CARSNET observations and 76% with MODIS, from the original 111% and 143%, respectively. The factors resulting in the underestimation of aerosol concentrations and other discrepancies in the model are explored, and improvements in enhancing the model performance are proposed from the analysis. It is found that the accuracy in meteorological predictions plays a critical role on the simulation of the occurrence and accumulation of heavy pollution episode, especially the circulation winds and the treatment of Planetary Boundary Layer (PBL).

Aerosol monitoring at multiple locations in China: contributions of EC and dust to aerosol light absorption

This paper reports on the analysis of 24-h aerosol data measured during 2006, at 14 monitoring sites in China. Measurements included seven-wavelength Aethalometers, thermal/optical reflectance analyses of filter samples and determination of dust aerosols. Black (elemental) carbon (BC, EC) is found to be the principal light-absorbing aerosol over many parts of China; however, the fraction of apparent light absorption attributed to dust varied from 14% in winter, to 11% in spring, to 5% in summer to 9% in autumn. Aerosol light absorption in urban areas was larger than in rural areas by factors of 2.4 in winter, 3.1 in spring and 2.5 in both summer and autumn. These differences may lead to contrasts in radiative, thermal and cloud modification effects between urban and rural areas. Absorption ‘hotspots’ were located in the Sichuan Basin, the provinces south of Beijing, the Pearl Delta River regions and the Guanzhong Plain. The mass absorption coefficient for aerosol BC (σBC) based on Aethalometer data is estimated to be 11.7 m2 g-1 at 880 nm wavelength (λ) with inverse (λ-1) wavelength scaling, whereas the mass absorption coefficient for dust (σdust) is 1.3 m2 g-1 on average without significant wavelength dependence.

Influences of Meteorological Conditions on Interannual Variations of Particulate Matter Pollution during Winter in the Beijing-Tianjin-Hebei Area

To investigate the interannual variations of particulate matter (PM) pollution in winter, this paper examines the pollution characteristics of PM with aerodynamic diameters of less than 2.5 and 10 μm (i.e., PM2.5 and PM10), and their relationship to meteorological conditions over the Beijing municipality, Tianjin municipality, and Hebei Province—an area called Jing–Jin–Ji (JJJ, hereinafter)—in December 2013–16. The meteorological conditions during this period are also analyzed. The regional average concentrations of PM2.5 (PM10) over the JJJ area during this period were 148.6 (236.4), 100.1 (166.4), 140.5 (204.5), and 141.7 (203.1) μg m–3, respectively. The high occurrence frequencies of cold air outbreaks, a strong Siberian high, high wind speeds and boundary layer height, and low temperature and relative humidity, were direct meteorological causes of the low PM concentration in December 2014. A combined analysis of PM pollution and meteorological conditions implied that control measures have resulted in an effective improvement in air quality. Using the same emissions inventory in December 2013–16, a modeling analysis showed emissions of PM2.5 to decrease by 12.7%, 8.6%, and 8.3% in December 2014, 2015, and 2016, respectively, each compared with the previous year, over the JJJ area.

The simulation study of the interaction between aerosols and heavy air pollution weather in East China

The aerosol-radiation interaction is coupled into an online mesoscale aerosol forecasting model. The direct radiative feedbacks on heavy air pollution weather due to aerosols are simulated and studied. The preliminary results show aerosols direct radiative effects may cool the near surface atmosphere in East Asia, reduce turbulent diffusion process and further weaken low pressure system related with heavy pollution episode. All results suggests that aerosols direct radiative feedback is benefits to keep the mesoscale stable condition of the local atmosphere in East China and shows positive feedback on the keeping and developing of heavy air pollution episode in East Asia.