Time filter

Source Type

Zou B.,Central South University | Zou B.,Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention LAP3 | Wang M.,Central South University | Wan N.,University of Utah | And 3 more authors.
Environmental Science and Pollution Research

Accurate measurements of PM2.5 concentration over time and space are especially critical for reducing adverse health outcomes. However, sparsely stationary monitoring sites considerably hinder the ability to effectively characterize observed concentrations. Utilizing data on meteorological and land-related factors, this study introduces a radial basis function (RBF) neural network method for estimating PM2.5 concentrations based on sparse observed inputs. The state of Texas in the USA was selected as the study area. Performance of the RBF models was evaluated by statistic indices including mean square error, mean absolute error, mean relative deviation, and the correlation coefficient. Results show that the annual PM2.5 concentrations estimated by the RBF models with meteorological factors and/or land-related factors were markedly closer to the observed concentrations. RBF models with combined meteorological and land-related factors achieved best performance relative to ones with either type of these factors only. It can be concluded that meteorological factors and land-related factors are useful for articulating the variation of PM2.5 concentration in a given study area. With these covariate factors, the RBF neural network can effectively estimate PM2.5 concentrations with acceptable accuracy under the condition of sparse monitoring stations. The improved accuracy of air concentration estimation would greatly benefit epidemiological and environmental studies in characterizing local air pollution and in helping reduce population exposures for areas with limited availability of air quality data. © 2015 Springer-Verlag Berlin Heidelberg Source

Yang P.,Nanjing University of Science and Technology | Liu Y.,Nanjing University of Science and Technology | Chen S.,Nanjing University of Science and Technology | Ma J.,Shanghai University | And 4 more authors.
Materials Research Bulletin

Anodic TiO2 nanotubes (ATNTs) have been investigated extensively. However, the relationship between anodizing curves and the morphologies cannot be explained or quantified by the filed-assisted dissolution theory or plastic flow models. Here, influences of H2O2 and H2O content on anodizing current and morphology of ATNTs were explored and compared in detail. With H2O2 addition, the ginseng-like nanotubes were formed and the anodizing current increased a lot. Based on the oxygen bubble mould, the formation mechanism of the ginseng-like nanotubes has been proposed. Moreover, H2O addition causes an opposite current variation trend to H2O2 addition. The relationships between the morphologies and the anodizing curves were clarified quantitatively by the simulation of the ionic current and electronic current. H2O2 addition accelerates oxygen evolution and therefore electronic current increases with H2O2 content. Moreover, nanotube diameter increases with H2O content mainly due to the dilution of the F− anions and the thicker barrier oxide. © 2016 Elsevier Ltd Source

Ma X.-J.,Donghua University | Qin Y.,Donghua University | Qin Y.,Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention LAP3 | Chen Y.-H.,Donghua University | And 4 more authors.
Zhongguo Huanjing Kexue/China Environmental Science

Using the classification data of different layers aerosols from CALIPSO Satellite Lidar Level 2aerosol retrieval data during the haze periods from January 2007 to November 2010, the seasonally vertical distribution of different types of aerosols including clean marine, dust, polluted continental, clean continental, polluted dust, smoke and other types around Shanghai during haze periods were analyzed. The results showed as follows. In 0~2 km altitudes the frequency of smoke aerosols' occurrence during haze was significantly higher than during haze-free periods, but in 2~8 km dust, polluted dust and polluted continental aerosols' frequency in haze were higher than in haze-free periods. In 0~2 km, polluted continental aerosols' frequency in spring was higher than in other seasons during haze. In 0-2 km, polluted dust and marine aerosol's frequency in summer were higher than in other seasons during haze, especially polluted dust. In autumn the frequency of smoke aerosols in 0~2 km was higher than in 2~6 km. the frequency of polluted dust, smoke and polluted continental aerosols in winter were higher than in other seasons during haze. ©, 2015, Zhongguo Huanjing Kexue/China Environmental Science. All right reserved. Source

Zou B.,Central South University | Zou B.,Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention LAP3 | Zheng Z.,Central South University | Wan N.,University of Utah | And 2 more authors.
International Journal of Geographical Information Science

GIS-based proximity models are one of the key tools for the assessment of exposure to air pollution when the density of spatial monitoring stations is sparse. Central to exposure assessment that utilizes proximity models is the ‘exposure intensity–distance’ hypothesis. A major weakness in the application of this hypothesis is that it does not account for the Gaussian processes that are at the core of the physical mechanisms inherent in the dispersion of air pollutants. Building upon the utility of spatial proximity models and the theoretical reliability of Gaussian dispersion processes of air pollutants, this study puts forward a novel Gaussian weighting function-aided proximity model (GWFPM). The study area and data set for this work consisted of transport-related emission sources of PM2.5 in the Houston-Baytown-Sugar Land metropolitan area. Performance of the GWFPM was validated by comparing on-site observed PM2.5 concentrations with results from classical ordinary kriging (OK) interpolation and a robust emission-weighted proximity model (EWPM). Results show that the fitting R2 between possible exposure intensity calculated by GWFPM and observed PM2.5 concentrations was 0.67. A variety of statistical evidence (i.e., bias, root mean square error [RMSE], mean absolute error [MAE], and correlation coefficient) confirmed that GWFPM outperformed OK and EWPM in estimating annual PM2.5 concentrations for all monitoring sites. These results indicate that a GWFPM utilizing the physical dispersing mechanisms integrated may more effectively characterize annual-scale exposure than traditional models. Using GWFPM, receptors’ exposure to air pollution can be assessed with sufficient accuracy, even in those areas with a low density of monitoring sites. These results may be of use to public health and planning officials in a more accurate assessment of the annual exposure risk to a population, especially in areas where monitoring sites are sparse. © 2015 Taylor & Francis. Source

Xu B.,Tongji University | Xu B.,Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention LAP3 | Gong Y.,Tongji University | Wu Y.,Tongji University

Understanding the effectiveness of cabin air filters is important for assessing human exposure to ultrafine particles (UFPs) of vehicular origin. The filtration efficiency of vehicular UFPs with electric charges was investigated for different electric charge characteristics (charge state, charge polarity). The average filtration efficiency increased ∼10% as the electric charge state on the particles changed in distribution from lightly charged to highly charged. The enhancement of filtration efficiency due to electric charge was different at various filter-face air velocities. As electric charges increased, the filtration efficiency increased 12% and 9% at low air velocity (0.1. m/s) and high air velocity (0.5. m/s), respectively. The filter fiber material poses somewhat effect on the filtration efficiency change due to the electric charge. The effects of filter usage and charge polarity on filtration efficiency due to the electric charge were negligible. A coefficient was empirically derived and successfully accounts for the electric charge effect on UFP filtration efficiency. © 2015 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Source

Discover hidden collaborations