Atmosphere2015, 6(4), 431-450; doi:10.3390/atmos6040431 - published 27 March 2015 Show/Hide Abstract
Abstract: The temporal and spatial characteristics of meteorological drought have been investigated to provide a framework of methodologies for the analysis of drought in the Beijing-Tianjin-Hebei metropolitan area (BTHMA) in China. Using the Reconnaissance Drought Index (RDI) as an indicator of drought severity, the characteristics of droughts have been examined. The Beijing-Tianjin-Hebei metropolitan area was divided into 253 grid-cells of 27 × 27km and monthly precipitation data for the period of 1960–2010 from 33 meteorological stations were used for global interpolation of precipitation using spatial co-ordinate data. Drought severity was assessed from the estimated gridded RDI values at multiple time scales. Firstly, the temporal and spatial characteristics of droughts were analyzed, and then drought severity-areal extent-frequency (SAF) annual curves were developed. The analysis indicated that the frequency of moderate and severe droughts was about 9.10% in the BTHMA. Using the SAF curves, the return period of selected severe drought events was assessed. The identification of the temporal and spatial characteristics of droughts in the BTHMA will be useful for the development of a drought preparedness plan in the region.
Atmosphere2015, 6(4), 410-430; doi:10.3390/atmos6040410 - published 25 March 2015 Show/Hide Abstract
Abstract: Drought forecasting plays a crucial role in drought mitigation actions. Thus, this research deals with linear stochastic models (autoregressive integrated moving average (ARIMA)) as a suitable tool to forecast drought. Several ARIMA models are developed for drought forecasting using the Standardized Precipitation Evapotranspiration Index (SPEI) in a hyper-arid climate. The results reveal that all developed ARIMA models demonstrate the potential ability to forecast drought over different time scales. In these models, the p, d, q, P, D and Q values are quite similar for the same SPEI time scale. This is in correspondence with autoregressive (AR) and moving average (MA) parameter estimate values, which are also similar. Therefore, the ARIMA model (1, 1, 0) (2, 0, 1) could be considered as a general model for the Al Qassim region. Meanwhile, the ARIMA model (1, 0, 3) (0, 0, 0) at 3-SPEI and the ARIMA model (1, 1, 1) (2, 0, 1) at 24-SPEI could be generalized for the Hail region. The ARIMA models at the 24-SPEI time scale is the best forecasting models with high R2 (more than 0.9) and lower values of RMSE and MAE, while they are the least forecasting at the 3-SPEI time scale. Accordingly, this study recommends that ARIMA models can be very useful tools for drought forecasting that can help water resource managers and planners to take precautions considering the severity of drought in advance.
Atmosphere2015, 6(3), 380-409; doi:10.3390/atmos6030380 - published 18 March 2015 Show/Hide Abstract
Abstract: A large number of studies on trace metals and metalloids (TMs) accumulations in peatlands have been reported in Europe and North America. Comparatively little information is available on peat chronological records of atmospheric TMs flux in China. Therefore, the objective of our study was to determine the concentrations and accumulation rates (ARs) of TMs in Motianling peatland from Great Hinggan Mountain, northeast China, and to assess these in relation to establish a historical profile of atmospheric metal emissions from anthropogenic sources. To meet these aims we analyzed 14 TMs (As, Ba, Cd, Co, Cr, Cu, Mo, Ni, Pb, Sr, Sb, Tl, and Zn) and Pb isotopes (206Pb, 207Pb, 208Pb) using ICP-AES and ICP-MS, respectively, in three peat sections dated by 210Pb and 137Cs techniques (approximately spanning the last 200 years). There is a general agreement in the elemental concentration profiles which suggests that all investigated elements were conserved in the Motianling bog. Three principal components were discriminated by principal component analysis (PCA) based on Eigen-values >1 and explaining 85% of the total variance of element concentrations: the first component representing Ba, Co, Cr, Mo, Ni, Sr and Tl reflected the lithogenic source; the second component covering As, Cu and Sb, and Cd is associated with an anthropogenic source from ore mining and processing; the third component (Pb isotope, Pb and Zn) is affected by anthropogenic Pb pollution from industrial manufacturing and fossil-fuel combustion. The pre-industrial background of typical pollution elements was estimated as the average concentrations of TMs in peat samples prior to 1830 AD and with a 207Pb/206Pb ratio close to 1.9. ARs and enrichment factors (EFs) of TMs suggested enhanced metal concentrations near the surface of the peatland (in peat layers dated from the 1980s) linked to an increasing trend since the 2000s. This pollution pattern is also fingerprinted by the Pb isotopic composition, even after the ban of leaded gasoline use in China. Emissions from coal and leaded gasoline combustions in northern China are regarded as one of the major sources of anthropogenic Pb input in this region; meanwhile, the long-distance transportation of Pb-bearing aerosols from Mongolia should be also taken into consideration. The reconstructed history of TMs’ pollution over the past ca. 200 years is in agreement with the industrial development in China and clearly illustrates the influence of human activities on local rural environments. This study shows the utility of taking multi-cores to show the heterogeneity in peat accumulation and applying PCA, EF and Pb isotope methods in multi-proxies analyses for establishing a high resolution geochemical metal record from peatland.
Atmosphere2015, 6(3), 361-379; doi:10.3390/atmos6030361 - published 17 March 2015 Show/Hide Abstract
Abstract: Secondary organic carbon (SOC) formation and its effects on human health require better understanding in Chinese megacities characterized by a severe particulate pollution and robust economic reform. This study investigated organic carbon (OC) and elemental carbon (EC) in PM2.5 and PM0.25 collected 8–20 March 2012. Samples were collected inside and outside a classroom in a middle school at Xi’an. On average, OC and EC accounted for 20%–30% of the particulate matter (PM) mass concentration. By applying the EC-tracer method, SOC’s contribution to OC in both PM size fractions was demonstrated. The observed changes in SOC:OC ratios can be attributed to variations in the primary production processes, the photochemical reactions, the intensity of free radicals, and the meteorological conditions. Total carbon (TC) source apportionment by formula derivation showed that coal combustion, motor vehicle exhaust, and secondary formation were the major sources of carbonaceous aerosol. Coal combustion appeared to be the largest contributor to TC (50%), followed by motor vehicle exhaust (25%) and SOC (18%) in both size fractions.
Atmosphere2015, 6(3), 341-360; doi:10.3390/atmos6030341 - published 13 March 2015 Show/Hide Abstract
Abstract: Based on the operational regional ensemble prediction system (REPS) in China Meteorological Administration (CMA), this paper carried out comparison of two initial condition perturbation methods: an ensemble transform Kalman filter (ETKF) and a dynamical downscaling of global ensemble perturbations. One month consecutive tests are implemented to evaluate the performance of both methods in the operational REPS environment. The perturbation characteristics are analyzed and ensemble forecast verifications are conducted; furthermore, a TC case is investigated. The main conclusions are as follows: the ETKF perturbations contain more power at small scales while the ones derived from downscaling contain more power at large scales, and the relative difference of the two types of perturbations on scales become smaller with forecast lead time. The growth of downscaling perturbations is more remarkable, and the downscaling perturbations have larger magnitude than ETKF perturbations at all forecast lead times. However, the ETKF perturbation variance can represent the forecast error variance better than downscaling. Ensemble forecast verification shows slightly higher skill of downscaling ensemble over ETKF ensemble. A TC case study indicates that the overall performance of the two systems are quite similar despite the slightly smaller error of DOWN ensemble than ETKF ensemble at long range forecast lead times.
Atmosphere2015, 6(3), 318-340; doi:10.3390/atmos6030318 - published 12 March 2015 Show/Hide Abstract
Abstract: Market strategies have greatly incentivized the use of diesel engines for land transportation. These engines are responsible for a large fraction of black carbon (BC) emissions in the extra-tropical Northern Hemisphere, with significant effects on both air quality and global climate. In addition to direct radiative forcing, planetary-scale transport of BC to the Arctic region may significantly impact the surface albedo of this region through wet and dry deposition on ice and snow. A sensitivity study is made with the University of L’Aquila climate-chemistry-aerosol model by eliminating on-road diesel emissions of BC (which represent approximately 50% of BC emissions from land transportation). According to the model and using emission scenarios for the year 2000, this would imply an average change in tropopause direct radiative forcing (RF) of −0.054 W∙m−2 (globally) and −0.074 W∙m−2 over the Arctic region, with a peak of −0.22 W∙m−2 during Arctic springtime months. These RF values increase to −0.064, −0.16 and −0.50 W∙m−2, respectively, when also taking into account the BC snow-albedo forcing. The calculated BC optical thickness decrease (at λ = 0.55 µm) is 0.48 × 10−3 (globally) and 0.74 × 10−3 over the Arctic (i.e., 10.5% and 16.5%, respectively), with a peak of 1.3 × 10−3 during the Arctic springtime.