Human and natural impacts on the U.S. freshwater salinization and alkalinization: A machine learning approach.

Ongoing salinization and alkalinization in U.S. rivers have been attributed to inputs of road salt and effects of human-accelerated weathering in previous studies. Salinization poses a severe threat to human and ecosystem health, while human derived alkalinization implies increasing uncertainty in the dynamics of terrestrial sequestration of atmospheric carbon dioxide. A mechanistic understanding of whether and how human activities accelerate weathering and contribute to the geochemical changes in U.S. rivers is lacking. To address this uncertainty, we compiled dissolved sodium (salinity proxy) and alkalinity values along with 32 watershed properties ranging from hydrology, climate, geomorphology, geology, soil chemistry, land use, and land cover for 226 river monitoring sites across the coterminous U.S. Using these data, we built two machine-learning models to predict monthly-aggregated sodium and alkalinity fluxes at these sites. The sodium-prediction model detected human activities (represented by population density and impervious surface area) as major contributors to the salinity of U.S. rivers. In contrast, the alkalinity-prediction model identified natural processes as predominantly contributing to variation in riverine alkalinity flux, including runoff, carbonate sediment or siliciclastic sediment, soil pH and soil moisture. Unlike prior studies, our analysis suggests that the alkalinization in U.S. rivers is largely governed by local climatic and hydrogeological conditions.

Concentration, viability and size distribution of bacteria in atmospheric bioaerosols under different types of pollution.

Bacteria are important components of bioaerosols with the potential to influence human health and atmospheric dynamics. However, information on the concentrations and influencing factors of viable bacteria is poorly understood. In this study, size-segregated bioaerosol samples were collected from Aug. 2017 to Feb. 2018 in the coastal region of Qingdao, China. The total microbes and viable/non-viable bacteria in the samples were measured using an epifluorescence microscope after staining with the DAPI (4', 6-diamidino-2-phenylindole) and LIVE/DEAD® BacLight™ Bacterial Viability Kit, respectively. The concentrations of non-viable bacteria increased when the air quality index (AQI) increased from <50 to 300, with the proportion of non-viable bacteria to total microbes increasing from (11.1 ±â€¯12.0)% at an AQI of <50 to (18.4 ±â€¯14.7)% at an AQI of >201. However, the concentrations of viable bacteria decreased from (2.12 ±â€¯2.04) × 104 cells·m-3 to (9.00 ±â€¯1.72) × 103 cells·m-3 when the AQI increased from <50 to 150. The ratio of viable bacteria to total bacteria (viability) decreased from (31.0 ±â€¯14.7)% at 0 < AQI<50 to (8.6 ±â€¯1.0)% at 101 < AQI<150 and then increased to (9.6 ±â€¯5.3)% at an AQI of 201-300. The results indicated that the bacterial viability decreased when air pollution occurred and increased again when pollution became severe. The mean size distribution of non-viable bacteria exhibited a bimodal distribution pattern at an AQI<50 with two peaks at 2.1-3.3 µm and >7.0 µm, while the viable bacteria had two peaks at 1.1-2.1 µm and >7 µm. When the AQI increased from 101 to 300, the size distribution of viable/non-viable bacteria varied with an increased proportion of fine particles. The multiple linear regression analysis results verified that the AQI and PM10 had important effects on the concentrations of non-viable bacteria. These results highlight impacts of air pollution on viable/non-viable bacteria and the interactions between complex environmental factors and bacteria interactions, improving our understanding of bioaerosols under air pollution conditions.