Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data

© 2015 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5

Great wall of solar panels to mitigate yellow dust storm

Mitigation of the large scale yellow dust storm is a serious problem facing China. We propose the approach of building windbreak walls equipped with solar panels in the proximity of dust origins. The solar panels generate electricity in the sunny days; the walls break the wind and remove airborne dusts based on the impactor principle during wind storms. Preliminary calculation indicates the walls may be able to remove the major fraction of the airborne dusts and the generated electricity could be significant. More detailed studies are needed to prove the feasibility of the approach.</p

2nd UMN-CAS Bilateral Seminar on PM2.5 science, health effects and control technologies, October 7-8, 2015 at 3M Innovation Center, Maplewood, MN, USA

Department of Civil and Environmental Engineerin

Urban-scale SALSCS, Part II: A Parametric Study of System Performance

Following the experimental and numerical investigations of the Xi&rsquo;an demonstration unit in Part I, Part II presents a parametric study on the proposed urban-scale SALSCS by using the validated numerical model. This study is aimed at understanding the influence of different variables on system performance, namely, the solar irradiation, ambient-air temperature and ground temperature at a 2-m depth as ambient parameters, and the inlet and outlet heights of the solar collector, collector width, tower width and tower height as geometric parameters. The effect of pressure drop across the filters on the system flow rate has been evaluated as well. The parameters that considerably influence the system performance have been identified.</p