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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]

Abstract: Placenta and umbilical cord blood are important media for investigating maternal–fetal exposure to environmental pollutants. Historically hexachlorocyclohexanes (HCHs) were once widely-used in China. In this study, residues of HCHs were measured in placenta and umbilical cord blood samples for 40 women from Beijing. The measured median values of HCHs were 62.0 and 68.8 ng/g fat in placenta and umbilical cord blood, respectively. Concentrations of HCHs in placenta and umbilical cord blood of urban cohort were higher than those of rural group due to enhanced consumption of fish, meat, and milk. Residues of HCHs in placenta were significantly correlated with total food consumption, dietary intake, and maternal age, and could be predicted using the parameters dependent upon ingestion of meat and milk. The transplacental exposure of fetuses to HCHs was revealed by a close association between the residual levels in the paired placenta and the paired umbilical cord blood samples. [Copyright &y& Elsevier]

Bulk deposition samples were collected in remote, rural village and urban areas of Beijing–Tianjin region, North China in spring, summer, fall and winter from 2007 to 2008. The annually averaged PAHs concentration and deposition flux were 11.81 ± 4.61 μg/g and 5.2 ± 3.89 μg/m2/day respectively. PHE and FLA had the highest deposition flux, accounting for 35.3% and 20.7% of total deposition flux, respectively. More exposure risk from deposition existed in the fall for the local inhabitants. In addition, the PAHs deposition flux in rural villages (3.91 μg/m2/day) and urban areas (8.28 μg/m2/day) was 3.8 and 9.1 times higher than in background area (0.82 μg/m2/day), respectively. This spatial variation of deposition fluxes of PAHs was related to the PAHs emission sources, local population density and air concentration of PAHs, and the PAHs emission sources alone can explain 36%, 49%, 21% and 30% of the spatial variation in spring, summer, fall and winter, respectively. [Copyright &y& Elsevier]

The concentrations, profiles, sources and spatial distribution of polycyclic aromatic hydrocarbons (PAHs) were determined in 40 surface soil samples collected from Beijing, Tianjin and surrounding areas, North China in 2007, and all sampling sites were far from industrial areas, roadsides and other pollution sources, and across a range of soil types in remote, rural villages and urban areas. The total concentrations of 16 PAHs ranged from 31.6 to 1475.0 ng/g, with an arithmetic average of 336.4 ng/g. The highest PAH concentrations were measured in urban soils, followed by rural village soils and soils from remote locations. The remote–rural village–urban PAH concentration gradient was related to population density, gross domestic product (GDP), long-range atmospheric transport and different types of land use. In addition, the PAH concentration was well correlated with the total organic carbon (TOC) concentration of the soil. The PAH profile suggested that coal combustion and biomass burning were primary PAH sources. [Copyright &y& Elsevier]

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]