ALBERT

Beschreibung: The Beijing–Tianjin–Hebei (BTH) region is a metropolitan area with the most severe fine particle (PM2.5) pollution in China. An accurate emission inventory plays an important role in air pollution control policy making. In this study, we develop a unit-based emission inventory for industrial sectors in the BTH region, including power plants, industrial boilers, steel, non-ferrous metal smelting, coking plants, cement, glass, brick, lime, ceramics, refineries, and chemical industries, based on detailed information for each enterprise, such as location, annual production, production technology/processes, and air pollution control facilities. In the BTH region, the emissions of sulfur dioxide (SO2), nitrogen oxide (NOx), particulate matter with diameter less than 10 µm (PM10), PM2.5, black carbon (BC), organic carbon (OC), and non-methane volatile organic compounds (NMVOCs) from industrial sectors were 869, 1164, 910, 622, 71, 63, and 1390 kt in 2014, respectively, accounting for a respective 61 %, 55 %, 62 %, 56 %, 58 %, 22 %, and 36 % of the total emissions. Compared with the traditional proxy-based emission inventory, much less emissions in the high-resolution unit-based inventory are allocated to the urban centers due to the accurate positioning of industrial enterprises. We apply the Community Multi-scale Air Quality (CMAQ; version 5.0.2) model simulation to evaluate the unit-based inventory. The simulation results show that the unit-based emission inventory shows better performance with respect to both PM2.5 and gaseous pollutants than the proxy-based emission inventory. The normalized mean biases (NMBs) are 81 %, 21 %, 1 %, and −7 % for the concentrations of SO2, NO2, ozone (O3), and PM2.5, respectively, with the unit-based inventory, in contrast to 124 %, 39 %, −8 %, and 9 % with the proxy-based inventory; furthermore, the concentration gradients of PM2.5, which are defined as the ratio of the urban concentration to the suburban concentration, are 1.6, 2.1, and 1.5 in January and 1.3, 1.5, and 1.3 in July, for simulations with the unit-based inventory, simulations with the proxy-based inventory, and observations, respectively, in Beijing. For O3, the corresponding gradients are 0.7, 0.5, and 0.9 in January and 0.9, 0.8, and 1.1 in July, implying that the unit-based emission inventory better reproduces the distributions of pollutant emissions between the urban and suburban areas.

Beschreibung: The climatic and health effects of aerosols are strongly dependent on the intra-annual variations in their loading and properties. While the seasonal variations of regional aerosol optical depth (AOD) have been extensively studied, understanding the temporal variations in aerosol vertical distribution and particle types is also important for an accurate estimate of aerosol climatic effects. In this paper, we combine the observations from four satellite-borne sensors and several ground-based networks to investigate the seasonal variations of aerosol column loading, vertical distribution, and particle types over three populous regions: the Eastern United States (EUS), Western Europe (WEU), and Eastern and Central China (ECC). In all three regions, column AOD, as well as AOD at heights above 800 m, peaks in summer/spring, probably due to accelerated formation of secondary aerosols and hygroscopic growth. In contrast, AOD below 800 m peaks in winter over WEU and ECC regions because more aerosols are confined to lower heights due to the weaker vertical mixing. In the EUS region, AOD below 800 m shows two maximums, one in summer and the other in winter. The temporal trends in low-level AOD are consistent with those in surface fine particle (PM2.5) concentrations. AOD due to fine particles (1.4 µm diameter), generally shows small variability, except that a peak occurs in spring in the ECC region due to the prevalence of airborne dust during this season. When aerosols are classified according to sources, the dominant type is associated with anthropogenic air pollution, which has a similar seasonal pattern as total AOD. Dust and sea-spray aerosols in the WEU region peak in summer and winter, respectively, but do not show an obvious seasonal pattern in the EUS region. Smoke aerosols, as well as absorbing aerosols, present an obvious unimodal distribution with a maximum occurring in summer over the EUS and WEU regions, whereas they follow a bimodal distribution with peaks in August and March (due to crop residue burning) over the ECC region.

Beschreibung: Utilizing a recent observational dataset of particulate matter with diameters less than 2.5 µm (PM2.5) in North China, this study reveals a distinct seesaw feature of abnormally high and low PM2.5 concentrations in the adjacent two months of December 2015 and January 2016, accompanied by distinct meteorological modulations. The seesaw pattern is postulated to be linked to a super El Niño and the Arctic Oscillation (AO). During the mature phase of El Niño in December 2015, the weakened East Asian winter monsoon (EAWM) and the associated low-level southerly wind anomaly reduced planetary boundary layer (PBL) height, favoring strong haze formation. This circulation pattern was completely reversed in the following month, in part due to a sudden phase change of the AO from positive to negative and the beginning of a decay of the El Niño, which enhanced the southward shift of the upper tropospheric jet from December to January relative to climatology, leading to an enhanced EAWM and substantially lower haze formation. This sub-seasonal change in circulation is also robustly found in 1982–1983 and 1997–1998, implicative of a general physical mechanism dynamically linked to El Niño and the AO. Numerical experiments using the Weather Research and Forecasting (WRF) Community Multiscale Air Quality (CMAQ) model were used to test the modulation of the meteorological conditions on haze formation. With the same emission, simulations for three super El Niño periods (1983, 1997 and 2015) robustly show higher PM2.5  concentrations under the mature phase of the super El Niño, but substantially lower PM2.5 concentrations during the decay phase of El Niño (and the sudden AO phase change), further verifying the modulation effect of the sub-seasonal circulation anomaly on PM2.5 concentrations in North China.

Beschreibung: High concentration of fine particles (PM2.5), the primary concern about air quality in China, is believed to closely relate to China's large consumption of coal. In order to quantitatively identify the contributions of coal combustion in different sectors to ambient PM2. 5, we developed an emission inventory for the year 2013 using up-to-date information on energy consumption and emission controls, and we conducted standard and sensitivity simulations using the chemical transport model GEOS-Chem. According to the simulation, coal combustion contributes 22 µg m−3 (40 %) to the total PM2. 5 concentration at national level (averaged in 74 major cities) and up to 37 µg m−3 (50 %) in the Sichuan Basin. Among major coal-burning sectors, industrial coal burning is the dominant contributor, with a national average contribution of 10 µg m−3 (17 %), followed by coal combustion in power plants and the domestic sector. The national average contribution due to coal combustion is estimated to be 18 µg m−3 (46 %) in summer and 28 µg m−3 (35 %) in winter. While the contribution of domestic coal burning shows an obvious reduction from winter to summer, contributions of coal combustion in power plants and the industrial sector remain at relatively constant levels throughout the year.

Beschreibung: The regional transport of particulate matter with diameter less than 2.5 µm (PM2.5) plays an important role in the air pollution of the Beijing–Tianjin–Hebei (BTH) region in China. However, previous studies on regional transport of PM2.5 mainly aim at province level, which is insufficient for the development of an optimal joint PM2.5 control strategy. In this study, we calculate PM2.5 inflows and outflows through the administrative boundaries of three major cities in the BTH region, i.e., Beijing, Tianjin and Shijiazhuang, using the WRF (Weather Research and Forecasting model)-CMAQ (Community Multiscale Air Quality) modeling system. The monthly average inflow fluxes indicate the major directions of PM2.5 transport. For Beijing, the PM2.5 inflow fluxes from Zhangjiakou (in the northwest) and Baoding (in the southwest) constitute 57 % of the total in winter, and Langfang (in the southeast) and Baoding constitute 73 % in summer. Based on the net PM2.5 fluxes and their vertical distributions, we find there are three major transport pathways in the BTH region: the northwest–southeast pathway in winter (at all levels below 1000 m), the northwest–southeast pathway in summer (at all levels below 1000 m), and the southwest–northeast pathway in both winter and in summer (mainly at 300–1000 m). In winter, even if surface wind speeds are low, the transport at above 300 m can still be strong. Among the three pathways, the southwest–northeast happens along with PM2.5 concentrations 30 and 55 % higher than the monthly average in winter and summer, respectively. Analysis of two heavy pollution episodes in January and July in Beijing show a much (8–16 times) stronger transport than the monthly average, emphasizing the joint air pollution control of the cities located on the transport pathways, especially during heavy pollution episodes.

Beschreibung: The Beijing–Tianjin–Hebei (BTH) region has been suffering from the most severe fine-particle (PM2. 5) pollution in China, which causes serious health damage and economic loss. Quantifying the source contributions to PM2. 5 concentrations has been a challenging task because of the complicated nonlinear relationships between PM2. 5 concentrations and emissions of multiple pollutants from multiple spatial regions and economic sectors. In this study, we use the extended response surface modeling (ERSM) technique to investigate the nonlinear response of PM2. 5 concentrations to emissions of multiple pollutants from different regions and sectors over the BTH region, based on over 1000 simulations by a chemical transport model (CTM). The ERSM-predicted PM2. 5 concentrations agree well with independent CTM simulations, with correlation coefficients larger than 0.99 and mean normalized errors less than 1 %. Using the ERSM technique, we find that, among all air pollutants, primary inorganic PM2. 5 makes the largest contribution (24–36 %) to PM2. 5 concentrations. The contribution of primary inorganic PM2. 5 emissions is especially high in heavily polluted winter and is dominated by the industry as well as residential and commercial sectors, which should be prioritized in PM2. 5 control strategies. The total contributions of all precursors (nitrogen oxides, NOx; sulfur dioxides, SO2; ammonia, NH3; non-methane volatile organic compounds, NMVOCs; intermediate-volatility organic compounds, IVOCs; primary organic aerosol, POA) to PM2. 5 concentrations range between 31 and 48 %. Among these precursors, PM2. 5 concentrations are primarily sensitive to the emissions of NH3, NMVOC + IVOC, and POA. The sensitivities increase substantially for NH3 and NOx and decrease slightly for POA and NMVOC + IVOC with the increase in the emission reduction ratio, which illustrates the nonlinear relationships between precursor emissions and PM2. 5 concentrations. The contributions of primary inorganic PM2. 5 emissions to PM2. 5 concentrations are dominated by local emission sources, which account for over 75 % of the total primary inorganic PM2. 5 contributions. For precursors, however, emissions from other regions could play similar roles to local emission sources in the summer and over the northern part of BTH. The source contribution features for various types of heavy-pollution episodes are distinctly different from each other and from the monthly mean results, illustrating that control strategies should be differentiated based on the major contributing sources during different types of episodes.

Beschreibung: Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM2.5 in the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25 to −0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1h. The study demonstrates that ensemble predictions from combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories, and the results are publicly available for future health effect studies.

Beschreibung: A version of the WRF-Chem model with fully coupled aerosol–meteorology–snowpack is employed to investigate the impacts of various aerosol sources on precipitation and snowpack in California. In particular, the impacts of locally emitted anthropogenic and dust aerosols, and aerosols transported from outside California are studied. We differentiate three pathways of aerosol effects: aerosol–radiation interaction (ARI), aerosol–snow interaction (ASI), and aerosol–cloud interaction (ACI). The convection-permitting model simulations show that precipitation, snow water equivalent (SWE), and surface air temperature averaged over the whole domain (34–42∘ N, 117–124∘ W, not including ocean points) are reduced when aerosols are included, therefore reducing large biases in these variables due to the absence of aerosol effects in the model. Aerosols affect California water resources through the warming of mountaintops and the reduction of precipitation; however, different aerosol sources play different roles in changing surface temperature, precipitation, and snowpack in California by means of various weights of the three pathways. ARI by all aerosols mainly cools the surface, leading to slightly increased SWE over the mountains. Locally emitted dust aerosols warm the surface of mountaintops through ASI, in which the reduced snow albedo associated with dusty snow leads to more surface absorption of solar radiation and reduced SWE. Transported aerosols and local anthropogenic aerosols play a dominant role in increasing nonprecipitating clouds but reducing precipitation through ACI, leading to reduced SWE and runoff on the Sierra Nevada, as well as the warming of mountaintops associated with decreased SWE and hence lower surface albedo. The average changes in surface temperature from October 2012 to June 2013 are about −0.19 and 0.22 K for the whole domain and over mountaintops, respectively. Overall, the averaged reduction during October to June is about 7 % for precipitation, 3 % for SWE, and 7 % for surface runoff for the whole domain, while the corresponding numbers are 12, 10, and 10 % for the mountaintops. The reduction in SWE is more significant in a dry year, with 9 % for the whole domain and 16 % for the mountaintops. The maximum reduction of ∼ 20 % in precipitation occurs in May and is associated with the maximum aerosol loading, leading to the largest decrease in SWE and surface runoff over that period. It is also found that dust aerosols can cause early snowmelt on the mountaintops and reduced surface runoff after April.

Beschreibung: As one common precursor for both PM2.5 and O3 pollution, NOx gains great attention because its controls can be beneficial for reducing both PM2.5 and O3. However, the effectiveness of NOx controls for reducing PM2.5 and O3 are largely influenced by the ambient levels of NH3 and VOC, exhibiting strong nonlinearities characterized as NH3-limited/NH3-poor and NOx-/VOC-limited conditions, respectively. Quantification of such nonlinearities is a prerequisite for making suitable policy decisions but limitations of existing methods were recognized. In this study, a new method was developed by fitting multiple simulations of a chemical transport model (i.e., Community Multiscale Air Quality Modeling System, CMAQ) with a set of polynomial functions (denoted as “pf-RSM”) to quantify responses of ambient PM2.5 and O3 concentrations to changes in precursor emissions. The accuracy of the pf-RSM is carefully examined to meet the criteria of a mean normalized error within 2 % and a maximal normalized error within 10 % by using 40 training samples with marginal processing. An advantage of the pf-RSM method is that the nonlinearity in PM2.5 and O3 responses to precursor emission changes can be characterized by quantitative indicators, including (1) a peak ratio (denoted as PR) representing VOC-limited or NOx-limited conditions, (2) a suggested ratio of VOC reduction to NOx reduction to avoid increasing O3 under VOC-limited conditions, (3) a flex ratio (denoted as FR) representing NH3-poor or NH3-rich conditions, and (4) enhanced benefits in PM2.5 reductions from simultaneous reduction of NH3 with the same reduction rate of NOx. A case study in the Beijing–Tianjin–Hebei region suggested that most urban areas present strong VOC-limited conditions with a PR from 0.4 to 0.8 in July, implying that the NOx emission reduction rate needs to be greater than 20–60 % to pass the transition from VOC-limited to NOx-limited conditions. A simultaneous VOC control (the ratio of VOC reduction to NOx reduction is about 0.5–1.2) can avoid increasing O3 during the transition. For PM2.5, most urban areas present strong NH3-rich conditions with a PR from 0.75 to 0.95, implying that NH3 is sufficiently abundant to neutralize extra nitric acid produced by an additional 5–35 % of NOx emissions. Enhanced benefits in PM2.5 reductions from simultaneous reduction of NH3 were estimated to be 0.04–0.15 µg m−3 PM2.5 per 1 % reduction of NH3 along with NOx, with greater benefits in July when the NH3-rich conditions are not as strong as in January. Thus, the newly developed pf-RSM model has successfully quantified the enhanced effectiveness of NOx control, and simultaneous reduction of VOC and NH3 with NOx can assure the control effectiveness of PM2.5 and O3.

Beschreibung: The interactions between aerosols and ice clouds represent one of the largest uncertainties in global radiative forcing from pre-industrial time to the present. In particular, the impact of aerosols on ice crystal effective radius (Rei), which is a key parameter determining ice clouds' net radiative effect, is highly uncertain due to limited and conflicting observational evidence. Here we investigate the effects of aerosols on Rei under different meteorological conditions using 9-year satellite observations. We find that the responses of Rei to aerosol loadings are modulated by water vapor amount in conjunction with several other meteorological parameters. While there is a significant negative correlation between Rei and aerosol loading in moist conditions, consistent with the “Twomey effect” for liquid clouds, a strong positive correlation between the two occurs in dry conditions. Simulations based on a cloud parcel model suggest that water vapor modulates the relative importance of different ice nucleation modes, leading to the opposite aerosol impacts between moist and dry conditions. When ice clouds are decomposed into those generated from deep convection and formed in situ, the water vapor modulation remains in effect for both ice cloud types, although the sensitivities of Rei to aerosols differ noticeably between them due to distinct formation mechanisms. The water vapor modulation can largely explain the difference in the responses of Rei to aerosol loadings in various seasons. A proper representation of the water vapor modulation is essential for an accurate estimate of aerosol–cloud radiative forcing produced by ice clouds.