Distribution characteristics of reactive silicon in six water bodies in the Yangtze River Basin in China.

Terrestrial silicon (Si) from biogeochemically weathered rocks and soils into oceans must pass through several water bodies, resulting in some Si immobilized. Hence, the knowledge on Si distribution characteristics in different water bodies at a basin scale is helpful to understand Si immobilization. A total of 65 surface sediments and corresponding overlying water samples were sampled from six water bodies (Dianchi Lake, DL; Dadu River, DR; Tuojiang River, TR; Honghu Lake, HL; Donghu Lake, DhL; Taihu Lake, TL) in the Yangtze River Basin of China, total dissolved Si (TDSi) in overlying water and exchangeable Si (Ex-Si), active non-biogenic Si (NBSi), and total acid dissolved Si (TADSi) in sediments were analyzed. Water chemical parameters (pH, EC, and TDP) and sediment components (LOI, TN, TP, and TADFe) showed that the water environment characteristics of six water bodies differed. TDSi differed among regions and between lakes and rivers, significantly higher in water bodies in the upper reaches and rivers than the middle or lower reaches and lakes (p < 0.05), respectively. Ex-Si in sediments in the upper reaches was significantly higher than in the middle or lower reaches (p < 0.05), except for DhL, whose Ex-Si was the highest. Mean TADSi and active NBSi were significantly higher in lakes than rivers (p < 0.05). Oxidation of sediments significantly increased TDSi in overlying water and active NBSi in sediments (p < 0.01). Si forms in six water bodies significantly depended on components of the sediments (e.g. active Ca2+, Mg2+, Fe, and Al3+) and water chemical parameters (p < 0.05). Our results suggest that immobilization of Si in water bodies in the Yangtze River Basin depends on the types of water bodies and sediments, lakes and Fe-Al dominated sediments have a high potential to immobilize Si, but anthropogenic interference should not be ignored.

Estimating acceptable exposure time for bioaerosols emission in a wastewater treatment plant by reverse quantitative microbial risk assessment based on various risk benchmarks.

Populations exposed to bioaerosols over time in wastewater treatment plants (WWTPs) will be infected. Then, the reverse quantitative microbial risk assessment (QMRA) provides a quantitative framework for the estimation of acceptable exposure time to protect people from excessive exposure and then manage their health risk. In this study, the acceptable exposure time for staffs and visiting researchers exposed to S. aureus or E. coli bioaerosols emitted from aeration ponds in WWTPs was estimated and analyzed by Monte Carlo simulation-based reverse QMRA (using the 1E-4 pppy suggested by the US EPA or 1E-6 DALYs pppy suggested by the WHO as benchmarks). The 1E-3 and 1E-2 pppy were selected as a series of loose annual infection risk benchmarks to calculate a practical acceptable exposure time. The results showed that for the acceptable exposure time in each specific exposure scenario, the exposure of females was consistently 0.3-0.4 times longer than that of males; the exposure of staffs was 3.6-3.9 times shorter than that of visiting researchers; the exposures of populations in the rotating-disc aeration mode were consistently 6.3-6.6 and 2.8-3.1 times longer than those in the microporous aeration mode for S. aureus and E. coli bioaerosols, respectively. The acceptable exposure time with the use of personal protective equipment (PPE) was 33.4-35.0 times as long as that without PPE. The US EPA benchmark is stricter than the WHO benchmark with regard to the estimation of the acceptable exposure time of S. aureus or E. coli bioaerosols. The 1E-3 pppy is more appropriate and practical than the US EPA benchmark, but the 1E-2 pppy is notably too loose for health risk management. This research can assist managers of WWTPs to formulate a justified exposure time and develop applicable administrative and personal intervention strategies. The results can enrich the knowledge bases of reverse QMRA to elect a series of loose health-based target risk benchmarks for health risk management.