Prediction of dust fall concentrations in urban atmospheric environment through support vector regression

Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters （insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ） are 0.001, 0.5, and 2 000, respectively.

Quantitative estimation of dust fall and smoke particles in Quetta Valley

Tightening of air quality standards for populated urban areas has led to increasing attention to assessment of air quality management areas, where violation of air quality standards occurs, and development of control strategies to eliminate such vio-lation of air quality standards. The Quetta urban area is very densely built and has heavy motorized traffic. The increase of emis-sions mainly from traffic and industry are responsible for the increase in atmospheric pollution levels during the last years. The dust examined in the current study was collected by both deposit gauge and Petri dish methods at various sites of Quetta Valley. Smoke particles were obtained by bladder method from the exhausts of various types of motor vehicles. The concentration of lead found in the smoke ranged from 1.5×10?6 to 4.5×10?6.

Effects of Dust on Male and Female Floral Organs and the Pollination of the Walnut

The Tarim Basin,Xinjiang,China,a region important to the walnut industry,is affected by frequent heavy dust storms.Dust,including micro-particles(diameter<10μm),covers the surface of the walnut tree,thereby changing the surface-atmosphere interface microenvironment,which,in turn,influences the exterior and interior structure of the tree.Dust storms occur in spring and summer,during the flowering period of walnut,which is the key developmental stage leading to fruit formation.This study investigated the effects of dust on female flowers,male flowers,and the pollination of walnut.The morphological changes in the stigma during pollination were recorded.Stigma receptivity was studied via the benzidine–H2O2 method.Morphological features of the female floral organs and pollen were investigated using scanning electron microscopy.Pollen germination and pollen tube growth were examined by fluorescence microscopy.The results showed that dust had a significant inhibitory effect on male and female flowers,resulting in decreased catkin growth,reduced pollen viability(pollen viability was 20.13%),blocked pollen apertures,a reduced pollen germination rate on the stigma,and increased time needed for pollen tube appearance.Dust also had an inhibitory effect on stigma length and receptivity of female walnut flowers,with the length of the walnut stigma being reduced by 0.25~0.80 mm during the flowering process.In addition,there was decreased stigma mucus,resulting in stigma atrophy,decreased amount of pollen on the stigma,weakened stigma receptivity,and accelerated drying of female flowers.In the Tarim Basin,walnut flowering occurd at the same time as dust storms do,which had a negative impact on the floral organ,flowering and pollination of walnut.

Influence of Traffic Volume on Fugitive Road Dust Emissions in Beijing, China

Eighty sites were set up to monitor road dust fall and 80 locations were selected to sample silt loading in April, August, and November 2007 and January 2008 in the Beijing metropolitan area. Fugitive road dust emission strengths were calculated using the AP-42 emission factor model. Silt loading on Beijing urban roads was typically less than 1.0 g/m^2, the PM10 emission strength was typically less than 100 kg/km·day, and road dust fall was typically less than 40 t/km^2·30day. The relationships between traffic volume, silt loading, fugitive road dust emission strength and road dust fall were studied in the Beijing metropolitan area. The results indicate that silt loading and emission factors are negatively correlated with traffic volume, but the PM10 emission rate and road dust-fall are positively correlated with traffic volume.