[Retrieving Dustfall Distribution in Beijing City Based on Ground Spectral Data and Remote Sensing].

Dust-fall distribution of vegetation leaves can indicate the degree of air pollution; therefore the analysis of spatial characteristics of urban vegetation dust-fall has important practical significance for making more effective air pollution control policy. Based on the data of weight of dust, spectral reflectance and leaf area of Euonymus japonicus, Sophora japonica, poplar and davidiana collected in the main area of Beijing city, we compared the curve of spectrum of four plants "dust leaves" to "clean leaves"; the correlation analysis between leaf spectral reflectance ratio (Dust/Clean) of narrow band and satellite band was processed with the weight of dust-fall respectively, with application of four plants leaf data. Then, we built the regression model of the satellite band reflectance and NDVI with dustfall weight respectively, and we used the best model to retrieve the dust-fall distribution of vegetation coverage area in Beijing city, furthermore, we obtained the dust distribution of the whole Beijing city through interpolation. Finally, we carried out the rationality verification of the result by the land cover and land use of the high dust region, as well as the average concentration of PM10. The results showed that, dust leaves had an obviously lower reflectance than clean leaves in 780~1 300 nm which belonged to near-infrared bands; therewas a higher correlation between narrow band reflectance and dust-fall weight in 520~620 and 1 390~1 600 nm, up to -0.626; the coefficients of determination (R2) of inversion models were respectively 0.446 and 0.465,which were constructed by green band and NDVI of Landsat8 with dust-fall weight. Using the model established with NDVI to retrieving the dust-fall distribution of Beijing city, the results demonstrate that the distribution of dust-fall is high in north and low in south, high in east and low in west, high in downtown and low in the suburbs. This study offers a low-cost and effective method for investigating dust-fall distribution in urban area, and provides data support to analysis sources of pollution for the environmental protection department.

Dustfall Heavy Metal Pollution During Winter in North China.

In order to study heavy metal pollution in dustfall during Winter in North China, forty-four dustfall samples were collected in North China Region from November 2013 to March 2014. Then forty trace elements content were measured for each sample by inductively coupled plasma-mass spectrometry. Finally, the contamination characteristics of the main heavy metals were studied through a multi-method analysis, including variability analysis, Pearson correlation analysis and principal component analysis. Results showed that the relative contents of cadmium (Cd), zinc (Zn), copper (Cu), bismuth (Bi), lead (Pb) exceeded the standards stipulated in Chinese soil elements background values by amazing 4.9 times. In this study, conclusions were drawn that dustfall heavy metal pollution in the region was mainly caused by transport pollution, metallurgy industrial pollution, coal pollution and steel industrial pollution.

[Concurrent Measurement of Wet and Bulk Deposition of Trace Metals in Urban Beijing].

To characterize the dry and wet deposition of atmospheric trace elements in urban Beijing, both active and passive samplers were used to collect bulk and wet sedimentation samples between May 2014 and April 2015.The concentrations of 19 trace elements (Na, Mg, Al, K, Ca, V, Cr, Mn, Fe, Cu, Zn, As, Se, Mo, Cd, Sb, Tl, Th, and U) in the samples were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). The results show that the concentrations of metals in bulk deposition samples[7160.68 µg·L-1 (Ca)-0.02 µg·L-1 (Th)] were generally higher than those in wet deposition samples[4237.74 µg·L-1 (Ca)-0.01 µg·L-1 (Th)], but the enrichment factors of each metal in the two kinds of samples were less different. Of note, the enrichment factors of Cu, As, Tl, Zn, Cd, Se, and Sb were all larger than 100, thus indicating that these heavy metals were mainly from anthropogenic sources. The statistical analysis of the air mass trajectory shows that the precipitation chemistry in urban Beijing is mainly affected by southward air flows. The air mass originating from the southwest region always had higher concentrations of Ca, Mg, Fe, Al, Cu, Mo, U, and Th, whereas the air mass from the south had higher concentrations of K, Zn, Mn, Sb, Cd, and Tl. During the observation period, the bulk deposition fluxes of metals varied from 3591.35 mg·(m2·a)-1 (Ca)-0.01 mg·(m2·a)-1 (Th), and wet deposition fluxes varied from 1847.78 mg·(m2·a)-1 (Ca)-0.01 mg·(m2·a)-1 (Th). The dry deposition fluxes of the 19 metals varied from 1743.57 mg·(m2·a)-1 (Ca)-0.01 mg·(m2·a)-1 (Th). The particle size has important implications in the evaluation of the relative importance of dry deposition versus wet deposition during the scavenging of trace elements in air.

[Enrichment Levels and Comprehensive Pollution Assessment of Dust Heavy Metals in Winter in Beijing].

Dust reflects the natural subsidence of particles and is thus generally accepted as an important environmental indicator. Dust heavy metals find their way into soil or water via atmospheric deposition, potentially damaging plants, aquatic organisms, as well as human beings. In order to study the concentration levels, enrichment degrees, and comprehensive pollution characteristics of the dust heavy metals Cd, Cr, Pb, Cu, Zn, Ni, Co, V, Bi, and Mo in winter in Beijing, 49 groups of dust samples were collected from different locations within the city from November 2013 to March 2014. Heavy metal content (mass percentage) was then measured using Elan DRC Ⅱ type inductively-coupled plasma mass spectrometry (ICP-MS). Results showed that the average content of Cd, Bi, and Mo was less than 10 mg·kg-1, that of Co, Ni, and V was between 10 and 100 mg·kg-1, while that of Pb, Cr, Cu, and Zn was more than 100 mg·kg-1. The amount of Cd, Zn, and Cu in dust samples was generally higher than their secondary standard values in the soil environment. The secondary standard excess rates of Cd, Zn, and Cu were 100%, 97%, and 93.9% in urban environments, and 100%, 100%, and 81.2% in suburban environments, respectively. Results also indicated slight enrichment of Bi, Cu, Ni, and Pb, mainly derived from crustal or soil sources, while Cd, Cr, Mo, and Zn, exhibited mild enrichment, caused by a combination of natural and artificial sources. The paper proposes a "dust heavy metal comprehensive pollution index" model, based on the traditional single pollution assessment method, so as to explore comprehensive pollution characteristics of dust heavy metals in Beijing. Results for the dust heavy metal comprehensive pollution index in Beijing City were in the order of Cd > Zn > Cu > Pb > Cr > Ni > V > Co. Cd, Zn, Cu, and Pb were identified as key pollution factors, with Cd and Zn as primary factors. Results for the dust heavy metal comprehensive pollution index in Beijing suburbs was Cd > Zn > Cu > Pb > Cr > Ni > Co > V. Cd, Zn, Cu, Pb, and Cr were key pollution factors, with Cd and Zn as primary factors.

[Distribution Characteristics and Source Analysis of Dustfall Trace Elements During Winter in Beijing].

The dustfall content is one of the evaluation indexes of atmospheric pollution. Trace elements especially heavy metals in dustfall can lead to risks to ecological environment and human health. In order to study the distribution characteristics of trace elements, heavy metals pollution and their sources in winter atmospheric dust, 49 dustfall samples were collected in Beijing City and nearby during November 2013 to March 2014. Then the contents (mass percentages) of 40 trace elements were measured by Elan DRC It type inductively coupled plasma mass (ICP-MS). Test results showed that more than half of the trace elements in the dust were less than 10 mg x kg(-1); about a quarter were between 10-100 mg x kg-1); while 7 elements (Pb, Zr, Cr, Cu, Zn, Sr and Ba) were more than 100 mg x kg(-1). The contents of Pb, Cu, Zn, Bi, Cd and Mo of winter dustfall in Beijing city.were respectively 4.18, 4.66, 5.35, 6.31, 6.62, and 8.62 times as high as those of corresponding elements in the surface soil in the same period, which went beyond the soil background values by more than 300% . The contribution of human activities to dustfall trace heavy metals content in Beijing city was larger than that in the surrounding region. Then sources analysis of dustfall and its 20 main trace elements (Cd, Mo, Nb, Ga, Co, Y, Nd, Li, La, Ni, Rb, V, Ce, Pb, Zr, Cr, Cu, Zn, Sr, Ba) was conducted through a multi-method analysis, including Pearson correlation analysis, Kendall correlation coefficient analysis and principal component analysis. Research results indicated that sources of winter dustfall in Beijing city were mainly composed of the earth's crust sources (including road dust, construction dust and remote transmission of dust) and the burning of fossil fuels (vehicle emissions, coal combustion, biomass combustion and industrial processes).

[Study on influence of traffic and meteorological factors on inhalable particle matters of different size].

To study the effects of meteorological and traffic factors on the PM2.5 and PM10 concentrations, 28 samples were taken in the Third Ring Road of Beijing, and dust fall weight, velocity of vehicle, traffic volume, temperature, humidity, wind speed, PM2.5 and PM10 concentration data were collected for these samples. Analysis of the collected data on dust fall weight indicated that the traffic had a significant impact on the air quality. The average dust fall weights in the road and away from the traffic source were 0. 284g and 0. 016 g, respectively. The results of the partial experiment indicated that concentrations of PM2.5 and PM10 in residential areas were lower than those in road, furthermore, the PM2.5 at night was often higher than that during daytime, and the mean values of the difference in PM2.5 and PM10 were 101074 n.(cf)-1 and 15386 n.(cf)-1, respectively. Through analysis using the best subset prediction model, it was indicated that PM2.5 and PM10 were both most significantly influenced by temperature and humidity, followed by wind speed, velocity of vehicle and traffic volume. Comparing with PM10, the velocity of vehicle, traffic volume and wind speed had a more significant influence on PM2.5.