Search

PM 2.5 is the main component of most haze, and the presence of high concentrations of PM 2.5 in the air for an extended time can cause serious effects on human health, so there is an urgent need for research work related to PM 2.5. Traditional PM 2.5 monitoring uses ground-based monitoring stations with low spatial resolution. Other studies have retrieved the Moderate Resolution Imaging Spectroradiometer aerosol optical depth product by the dark-target algorithm. However, the estimated PM 2.5 concentration on the ground will produce missing values, which will lead to the reduction of spatial and temporal resolution. Based on this, this study proposes a machine learning algorithm to estimate PM 2.5 by using the fifth generation reanalysis (ERA5) data set published by the European Center for Medium-range Weather Forecasts (ECMWF). In this study, two different methods of back propagation neural network (BPNN) and random forest (RF) were used to develop the models. Firstly, the meteorological parameters (precipitable water vapor, water vapor pressure and relative humidity, etc.) and pollution parameters (O 3 , CO, NO 2 , SO 2 , PM 10 , and PM 2.5) were used to establish PM 2.5 model in 2021. The results showed that the R2 and RMSE for BPNN and RF were 0.94/0.96 and 10.37/8.77 µg/m3, respectively. Then, due to the lack and the low spatial resolution of the pollution parameters, using only the ERA5 meteorological data with the high spatiotemporal resolution to develop the PM 2.5 model in winter, the R2 of the RF model (0.93) was 0.05 higher and the RMSE (12.50 µg/m3) was 4.19 µg/m3 lower than that of the BPNN model, which indicates that it is feasible to develop the PM 2.5 model using only meteorological parameters. Finally, using the RF model of the second stage and ERA5 meteorological data with a spatial resolution of 0.05° (obtained by cubic spline interpolation) to generate the hourly PM 2.5 map of Beijing and compare it with China High Air Pollutants dataset, the R2 and RMSE of Beijing were 0.78 µg/m3 and 14.85 µg/m3, respectively. On this basis, it is found that the areas with high PM 2.5 concentration are close to the areas with serious pollution in Hebei Province by analyzing the PM 2.5 map of Beijing, and area transport and human activities are important sources of air pollution. [ABSTRACT FROM AUTHOR]

Beijing is an international metropolis, where over-exploration of water resource makes land subsidence becoming more and more serious. The related problems cannot be avoided in the coming years because of the giant increase of population. The aims of this study are to quantify land subsidence over the period 2003 to 2010, grasp the evolution of the process, and investigate the relation with the triggering factors in the northern area of the Beijing plain. Various data, including deep compaction from vertical multiple borehole extensometers, land subsidence from Persistent Scatterer Interferometry and leveling surveys, groundwater levels, hydrogeological setting from wellbores, and Landsat TM image were collected and effectively used to detect the spatial and temporal features of land subsidence and its possible relation with groundwater level changes, compressible layer thickness, and urban development. Results show that land subsidence is unevenly distributed and continuously increased from 2003 to 2010. The average loss of elevation over the monitoring period amounted to 92.5 mm, with rates up to 52 mm/y. The distribution of the subsidence bowl is only partially consistent with that of the groundwater depression cone because of the variable thickness of the most compressible fine deposits. In fact, extensometers reveal that silty-clay layers account for the larger contribution to land subsidence, with the 15 m thick silty-clay layer between 102 and 117 m depth accounting for about 25% of the total subsidence. Finally, no clear correlation has been observed between the subsidence rates and the increase of the load on the land surface connected to the impressive urban development. This study represents a first step toward the development of a physically-based model of the subsidence occurrence to be used for planning remediation strategies in the northern Beijing plain. [ABSTRACT FROM AUTHOR]

Abstract: Forty passive air samplers were deployed to study the occurrence of gas and particulate phase PAHs in remote, rural village and urban areas of Beijing–Tianjin region, North China for four seasons (spring, summer, fall and winter) from 2007 to 2008. The influence of emissions on the spatial distribution pattern of air PAH concentrations was addressed. In addition, the air–soil gas exchange of PAHs was studied using fugacity calculations. The median gaseous and particulate phase PAH concentrations were 222ng/m3 and 114ng/m3, respectively, with a median total PAH concentration of 349ng/m3. Higher PAH concentrations were measured in winter than in other seasons. Air PAH concentrations measured at the rural villages and urban sites in the northern mountain region were significantly lower than those measured at sites in the southern plain during all seasons. However, there was no significant difference in PAH concentrations between the rural villages and urban sites in the northern and southern areas. This urban–rural PAH distribution pattern was related to the location of PAH emission sources and the population distribution. The location of PAH emission sources explained 56%–77% of the spatial variation in ambient air PAH concentrations. The annual median air–soil gas exchange flux of PAHs was 42.2ng/m2/day from soil to air. Among the 15 PAHs measured, acenaphthylene (ACY) and acenaphthene (ACE) contributed to more than half of the total exchange flux. Furthermore, the air–soil gas exchange fluxes of PAHs at the urban sites were higher than those at the remote and rural sites. In summer, more gaseous PAHs volatilized from soil to air because of higher temperatures and increased rainfall. However, in winter, more gaseous PAHs deposited from air to soil due to higher PAH emissions and lower temperatures. The soil TOC concentration had no significant influence on the air–soil gas exchange of PAHs. [Copyright &y& Elsevier]