Analysis of Essential Meteorological Elements Surrounding Typhoon Sinlaku by Unmanned Aerial Vehicle

In this paper, a successful flight with an unmanned aerial vehicle (UAV) surrounded Typhoon Sinlaku on 15 Sept., 2008 and the preliminary analysis of all the collected data during the observation period has been presented. It is the first time to adopt surrounding method to observe typhoon in mainland of China. During the 3 h field campaign, the flight altitude is about 500 m to observe the essential meteorological elements in boundary layer of typhoon. The average temperature is 22.57&#176C and ranged from 21.50&#176C to 25.80&#176C, while about the relative humidity, the maximum is 100%, the minimum is 80.60% and the average is 97.98%. As for the wind, the average wind speed is 19.68 m/s and the maximum is 30.03 m/s. The typhoon center is a warm structure, the closer to the center, the higher the temperature is and the lower the wind speed is. In conclusion, the mini-UAV has the capability to observe the boundary layer of typhoon.

Historical trends of heavy metal contamination and their sources in lacustrine sediment from Xijiu Lake,Taihu Lake Catchment,China

Concentrations of Cd, Cr, Cu, Pb, Zn and Hg in Xijiu Lake sediment from the Taihu Lake catchment, China, were analyzed. Their contamination state was investigated based on the geoaccumulation index and enrichment factors. Statistical analysis was used to differentiate the anthropogenic versus natural sources of heavy metals （HMs）, and the anthropogenic accumulation fluxes were calculated to quantify anthropogenic contribution to HMs. The results indicated that the lake sediment had been heavily contaminated by Cd, enrichment of Zn and Hg was at a relatively high level, while that of Cu and Pb was in the lower-to-moderate level and Cr was in the low enrichment level. Sources of Cr in the sediment were mainly from natural inputs, while other metals, especially Cd, were predominantly derived from anthropogenic sources. In the past century, anthropogenic accumulation fluxes of Pb, Zn and Hg increased by 0.147.3 mg/（cm2-yr）, 2.4-398.1 mg/（cm2.yr）, and 3.7-110.3 ng/（m2.yr）, respectively, accounting for most inputs of HMs entering the sediment. The contamination state of HMs varied with industrial development of the catchment, which demonstrated that contamination started in the early 20th century, reached the maximal level between the mid-1970s and mid-1990s, and decreased a little after the implementation of constraints on high contamination industries, although the contamination of some HMs, such as Cd, Zn and Hg, is still at high levels.

Oscillation of Surface PM2.5 Concentration Resulting from an Alternation of Easterly and Southerly Winds in Beijing： Mechanisms and Implications

We used simultaneous measurements of surface PM_（2.5） concentration and vertical profiles of aerosol concentration,temperature, and humidity, together with regional air quality model simulations, to study an episode of aerosol pollution in Beijing from 15 to 19 November 2016. The potential effects of easterly and southerly winds on the surface concentrations and vertical profiles of the PM_（2.5） pollution were investigated. Favorable easterly winds produced strong upward motion and were able to transport the PM_（2.5） pollution at the surface to the upper levels of the atmosphere. The amount of surface PM_（2.5） pollution transported by the easterly winds was determined by the strength and height of the upward motion produced by the easterly winds and the initial height of the upward wind. A greater amount of PM_（2.5） pollution was transported to upper levels of the atmosphere by upward winds with a lower initial height. The pollutants were diluted by easterly winds from clean ocean air masses. The inversion layer was destroyed by the easterly winds and the surface pollutants and warm air masses were then lifted to the upper levels of the atmosphere, where they re-established a multi-layer inversion. This region of inversion was strengthened by the southerly winds, increasing the severity of pollution. A vortex was produced by southerly winds that led to the convergence of air along the Taihang Mountains. Pollutants were transported from southern–central Hebei Province to Beijing in the boundary layer. Warm advection associated with the southerly winds intensified the inversion produced by the easterly winds and a more stable boundary layer was formed. The layer with high PM_（2.5） concentration became dee-per with persistent southerly winds of a certain depth. The polluted air masses then rose over the northern Taihang Mountains to the northern mountainous regions of Hebei Province.

Emission inventory evaluation using observations of regional atmospheric background stations of China

Any accurate simulation of regional air quality by numerical models entails accurate and up-to-date emissions data for that region.The INTEX-B2006 （I06）,one of the newest emission inventories recently popularly used in China and East Asia,has been assessed using the Community Multiscale Air Quality model and observations from regional atmospheric background stations of China.Comparisons of the model results with the observations for the species SO2,NO 2,O 3 and CO from the three regional atmospheric background stations of Shangdianzi,Longfengshan and Linan show that the model can basically capture the temporal characteristics of observations such as the monthly,seasonal and diurnal variance trends.Compared to the other three species,the simulated CO values were grossly underestimated by about two-third or one-half of the observed values,related to the uncertainty in CO emissions.Compared to the other two stations,Shangdianzi had poorer simulations,especially for SO2 and CO,which partly resulted from the site location close to local emission sources from the Beijing area;and the regional inventory used was not capable of capturing the influencing factors of strong regional sources on stations.Generally,the fact that summer gave poor simulation,especially for SO2 and O 3,might partly relate to poor simulations of meteorological fields such as temperature and wind.

Tracking a Severe Pollution Event in Beijing in December 2016 with the GRAPES–CUACE Adjoint Model

We traced the adjoint sensitivity of a severe pollution event in December 2016 in Beijing using the adjoint model of the GRAPES–CUACE（Global/Regional Assimilation and Prediction System coupled with the China Meteorological Administration Unified Atmospheric Chemistry Environmental Forecasting System）. The key emission sources and periods affecting this severe pollution event are analyzed. For comaprison, we define 2000 Beijing Time 3 December 2016 as the objective time when PM2.5 reached the maximum concentration in Beijing. It is found that the local hourly sensitivity coefficient amounts to a peak of 9.31 μg m^–3 just 1 h before the objective time, suggesting that PM2.5 concentration responds rapidly to local emissions. The accumulated sensitivity coefficient in Beijing is large during the 20-h period prior to the objective time, showing that local emissions are the most important in this period.The accumulated contribution rates of emissions from Beijing, Tianjin, Hebei, and Shanxi are 34.2%, 3.0%, 49.4%,and 13.4%, respectively, in the 72-h period before the objective time. The evolution of hourly sensitivity coefficient shows that the main contribution from the Tianjin source occurs 1–26 h before the objective time and its peak hourly contribution is 0.59 μg m^-3 at 4 h before the objective time. The main contributions of the Hebei and Shanxi emission sources occur 1–54 and 14–53 h, respectively, before the objective time and their hourly sensitivity coefficients both show periodic fluctuations. The Hebei source shows three sensitivity coefficient peaks of 3.45, 4.27, and 0.71 μg m^–3 at 4, 16, and 38 h before the objective time, respectively. The sensitivity coefficient of the Shanxi source peaks twice, with values of 1.41 and 0.64 μg m^–3 at 24 and 45 h before the objective time, respectively. Overall, the adjoint model is effective in tracking the crucial sources and key periods of emissions for the severe pollution event.