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Previously, systematic studies of distribution, sources, and health risks of high F− groundwater used as a drinking-water source in the Sulin coal district, northern Anhui Province of China have not been carried out. In this study, 30 groundwater samples were collected in May 2019, and the data were analyzed using a geographic information system, factor analysis, positive matrix factorization, and risk-based corrective action models. The results indicated that the F− concentration of the groundwater samples ranged from 0.16 to 2.06 mg/L, with a mean value of 1.10 mg/L. The F− concentrations of 53.33% of the groundwater samples exceeded China's maximum permissible limit for drinking water (1.00 mg/L). Quantificational source apportionment revealed that the weathering of F−bearing minerals is the main source (66.20%). Cation exchange (16.30%), agricultural activities (13.20%), and natural geological processes (4.30%) were the other sources of F−. The percentages of infants, children, teens, male adults, and female adults that face health risks due to excess F− intake were approximately 20.00%, 70.00%, 6.67%, 20.00%, and 10.00%, respectively. This research provided useful insights for the proper management of groundwater extraction to mitigate health problems associated with excessive F− intake.

A landslide monitoring technology based on BeiDouand, a wireless sensor network, is proposed in order to solve the problem that landslide has brought severe threat to people’s life and safety of property. The landslide monitoring system based on the BeiDouand wireless sensor network is analyzed and designed from the point of view of hardware and software. BeiDou message and GPRS double redundancy transmission mode are adopted to improve the reliability of transmission. It also adopts the factor compression method to improve the effectiveness of BeiDoutransmission and adopts queue management and packet scheduling mechanism to improve the real-time and reliability of wireless sensor network transmission. The result is that, as the number of visible satellites increases, the efficiency of the algorithm decreases. However, the overall efficiency of the algorithm has been significantly improved. When there are 28 visible satellites, the number of algorithm times decreases from 20,475 to 1,140, and the efficiency of the algorithm increases by 16.9 times. The GDOP simulated by the fast star selection algorithm proposed in this paper is less than 3.9, reaching an excellent grade.