Spatial evolution analysis of groundwater chemistry, quality, and fluoride health risk in southern Hebei Plain, China.

The present study investigated ion and fluoride concentrations in groundwater and their associated health risks to local populations in the southern Hebei Plain during 2018-2020. A total of 336 groundwater samples were collected from monitoring wells at 112 different locations. Statistical analysis, Gibbs diagram, principal ion ratio, and saturation index were carried out to clarify the chemical characteristics and control mechanism of groundwater. The results indicated that the groundwater types in the study area were mainly HCO3-Ca, Cl-Na, and SO4-Ca. The concentrations of cations and anions were Na+ > Ca2+ > Mg2+ > K+ and HCO3- > SO42- > Cl- > NO3- > F-, respectively. Based on the water chemical parameters, the pollution index of groundwater (PIG) was used to comprehensively evaluate the water quality. The results showed that during the study period, 60.41% of groundwater samples were suitable for drinking purposes, and 39.59% needed purification treatment to meet the requirements of drinking water standards. The groundwater quality in the western pre-hill plain areas was good, while the water quality in the northeastern and southeastern areas was poor and contaminated to varying degrees. Groundwater quality was mainly affected by the combined effect of total dissolved solids (TDS), Na+, Mg2+, Cl-, SO42- and HCO3- concentrations. Fluoride concentrations in the groundwater samples ranged from 0.07 to 8.51 mg/L, with 44% of the samples containing fluoride below the recommended limit of 0.5 mg/L, which would put the population at risk of dental caries. Also, 8% of the samples exceeded the permissible limit for fluoride in drinking water (1.5 mg/L), which would expose the local population to the risk of fluorosis. The human health risk evaluation of fluoride showed significant differences in non-carcinogenic effects between two different groups of children and adults. HIin values ranged from 0.08 to 10.19 for children and 0.03 to 4.65 for adults, with hazard indices greater than 1 at 29.16% and 10.11%, respectively. This indicates that children have a greater exposure risk than adults, and the entropy of higher risk is mainly distributed in the northeast of the study area. Based on the above analysis of the spatial evolution of groundwater chemistry, water quality, and fluoride health risks in the southern Hebei Plain region, corresponding protection and management measures were proposed, which also provided reference significance for the effective use of drinking water and health risk prevention in the region.

Adaptive filtering of microseismic data for monitoring a water-conducting fractured zone in a mine.

Water-conducting fractured zones in a rock mass can cause problems in mining. Attempts have been made to monitor their development using microseismic signals. However, due to the lack of prior information, it is difficult to filter out mixed low-frequency interference with traditional denoising methods. In this work, the proposed adaptive filtering algorithm is applied after the wavelet packets are decomposed. It is based on a cross-correlation analysis. The algorithm takes a high-quality signal in the common source waveform as prior information and applies the corresponding correlation coefficients between subbands as a threshold. The algorithm was verified with simulations. The results show that low-frequency interference can be effectively suppressed by filtering. For single-frequency interference, the signal-to-noise ratio increased from - 10.18 to 13.97, and the root-mean-square error was 43.88. For multi-frequency interference, it increased from - 10.01 and - 2.63 to 13.50 and 7.99. The root-mean-square errors were 46.31 and 138.07. The narrower the main frequency band of the interference signal and the less the overlap of the main frequency band of the interference signal and the effective signal, the better the filtering effect. When the algorithm was applied to microseismic data collected in the field, the number of effective channels increased and the accuracy improved. The development of a water-conducting fractured zone in the field was consistent with the microseismic location obtained after interference was removed by the algorithm, which indicates that it is feasible to monitor a water-conducting fractured zone by analyzing microseismic waveforms with the adaptive filtering algorithm.

Spatiotemporal Distribution of U5MR and Their Relationship with Geographic and Socioeconomic Factors in China.

Epidemiological studies conducted around the world have reported that the under-five mortality rate (U5MR) is closely associated with income and educational attainment. However, geographic elements should also remain a major concern in further improving child health issues, since they often play an important role in the survival environment. This study was undertaken to investigate the relationship between the U5MR, geographic, and socioeconomic factors, and to explore the associated spatial variance of the relationship in China using the geographically weighted regression (GWR) model. The results indicate that the space pattern of a high U5MR had been narrowed notably during the period from 2001 to 2010. Nighttime lights (NL) and the digital elevation model (DEM) both have obvious influences on the U5MR, with the NL having a negative impact and DEM having a positive impact. Additionally, the relationship between the NL and DEM varied over space in China. Moreover, the relevance between U5MR and DEM was narrowed in 2010 compared to 2001, which indicates that the development of economic and medical standards can overcome geographical limits.

Spatial Variation of the Relationship between PM 2.5 Concentrations and Meteorological Parameters in China.

Epidemiological studies around the world have reported that fine particulate matter (PM2.5) is closely associated with human health. The distribution of PM2.5 concentrations is influenced by multiple geographic and socioeconomic factors. Using a remote-sensing-derived PM2.5 dataset, this paper explores the relationship between PM2.5 concentrations and meteorological parameters and their spatial variance in China for the period 2001-2010. The spatial variations of the relationships between the annual average PM2.5, the annual average precipitation (AAP), and the annual average temperature (AAT) were evaluated using the Geographically Weighted Regression (GWR) model. The results indicated that PM2.5 had a strong and stable correlation with meteorological parameters. In particular, PM2.5 had a negative correlation with precipitation and a positive correlation with temperature. In addition, the relationship between the variables changed over space, and the strong negative correlation between PM2.5 and the AAP mainly appeared in the warm temperate semihumid region and northern subtropical humid region in 2001 and 2010, with some localized differences. The strong positive correlation between the PM2.5 and the AAT mainly occurred in the mid-temperate semiarid region, the humid, semihumid, and semiarid warm temperate regions, and the northern subtropical humid region in 2001 and 2010.

Spatio-temporal variation of PM2.5 concentrations and their relationship with geographic and socioeconomic factors in China.

The air quality in China, particularly the PM2.5 (particles less than 2.5 µm in aerodynamic diameter) level, has become an increasing public concern because of its relation to health risks. The distribution of PM2.5 concentrations has a close relationship with multiple geographic and socioeconomic factors, but the lack of reliable data has been the main obstacle to studying this topic. Based on the newly published Annual Average PM2.5 gridded data, together with land use data, gridded population data and Gross Domestic Product (GDP) data, this paper explored the spatial-temporal characteristics of PM2.5 concentrations and the factors impacting those concentrations in China for the years of 2001-2010. The contributions of urban areas, high population and economic development to PM2.5 concentrations were analyzed using the Geographically Weighted Regression (GWR) model. The results indicated that the spatial pattern of PM2.5 concentrations in China remained stable during the period 2001-2010; high concentrations of PM2.5 are mostly found in regions with high populations and rapid urban expansion, including the Beijing-Tianjin-Hebei region in North China, East China (including the Shandong, Anhui and Jiangsu provinces) and Henan province. Increasing populations, local economic growth and urban expansion are the three main driving forces impacting PM2.5 concentrations.