A wastewater-based risk index for SARS-CoV-2 infections among three cities on the Canadian Prairie.

Wastewater surveillance (WWS) is useful to better understand the spreading of coronavirus disease 2019 (COVID-19) in communities, which can help design and implement suitable mitigation measures. The main objective of this study was to develop the Wastewater Viral Load Risk Index (WWVLRI) for three Saskatchewan cities to offer a simple metric to interpret WWS. The index was developed by considering relationships between reproduction number, clinical data, daily per capita concentrations of virus particles in wastewater, and weekly viral load change rate. Trends of daily per capita concentrations of SARS-CoV-2 in wastewater for Saskatoon, Prince Albert, and North Battleford were similar during the pandemic, suggesting that per capita viral load can be useful to quantitatively compare wastewater signals among cities and develop an effective and comprehensible WWVLRI. The effective reproduction number (Rt) and the daily per capita efficiency adjusted viral load thresholds of 85 × 106 and 200 × 106 N2 gene counts (gc)/population day (pd) were determined. These values with rates of change were used to categorize the potential for COVID-19 outbreaks and subsequent declines. The weekly average was considered 'low risk' when the per capita viral load was 85 × 106 N2 gc/pd. A 'medium risk' occurs when the per capita copies were between 85 × 106 and 200 × 106 N2 gc/pd. with a rate of change <100 %. The start of an outbreak is indicated by a 'medium-high' risk classification when the week-over-week rate of change was >100 %, and the absolute magnitude of concentrations of viral particles was >85 × 106 N2 gc/pd. Lastly, a 'high risk' occurs when the viral load exceeds 200 × 106 N2 gc/pd. This methodology provides a valuable resource for decision-makers and health authorities, specifically given the limitation of COVID-19 surveillance based on clinical data.

Different characteristics of microbial diversity and special functional microbes in rainwater and topsoil before and after 2019 new coronavirus epidemic in Inner Mongolia Grassland.

Grassland ecosystems are vital terrestrial ecosystems. As areas sensitive to climate change, they are critical for assessing the effects of global climate change. In China, grasslands account for over 40% of the land area. There is currently limited information on microbial diversity evolution in different grassland areas, particularly microorganisms with ice nucleation activity (INA) and their potential resources with potential influence to regulate regional precipitation and climate. We used Illumina MiSeq to sequence the 16S rRNA V3-V4 hypervariable region and performed a simple droplet freezing experiment to determine the variation in the grassland microbial community species composition and community structure. Rainwater and topsoil samples from the Hulunbuir Grassland in Inner Mongolia collected over three years were characterized. The dominant bacterial genus in the rainwater was Massilia, and the dominant fungus was Cladosporium. Additionally, the dominant bacteria in the soil were Sphingomonas, and the dominant fungus was Gibberella. There were differences in the microbial communities before and after the coronavirus disease epidemic. Pathogenic microorganisms exhibited inconsistent responses to environmental changes. The low relative abundance of known high-INA microorganisms and the higher freezing temperature indicated that unknown high-efficiency biological ice nucleating particles may be present. We found significant differences in species diversity and richness between the rainwater and soil populations in grassland areas by analyzing the sample community structures. Our research results revealed the species composition and structure of the microbiota in grassland ecosystems in China, indicating that environmental media and human activities may affect the microbiota in the grassland area and indicating underlying microorganisms with high INA.

Numerical assessment of a soil moisture controlled wastewater SDI disposal system in Alabama Black Belt Prairie.

To promote the environmental sustainability of rural sanitation, a soil moisture controlled wastewater subsurface drip irrigation (SDI) dispersal system was field tested in the Black Belt Prairie of Alabama, USA. The soil moisture control strategy was designed to regulate wastewater disposal timing according to drain field conditions to prevent hydraulic overloading and corresponding environmental hazard. CW2D/HYDRUS simulation modeling was utilized to explore difficult-to-measure aspects of system performance. While the control system successfully adapted hydraulic loading rate to changing drain field conditions, saturated field conditions during the dormant season presented practical application challenges. The paired field experiment and simulation model demonstrate that soil biofilm growth was stimulated in the vicinity of drip emitters. Although biofilm growth is critical in maintaining adequate COD and NH4+-N removal efficiencies, the efficient removal of biodegradable COD itself by soil biofilm limits denitrification of formed NO3--N . Furthermore, stimulated soil biofilm growth can create soil clogging around drip emitters, which was discerned in the field experiment along with salt accumulation, both of which were verified by simulation. Comparable modeling of system performance in sand and clay media demonstrate that the placement of soil moisture sensors within the drain field can have pronounced impacts on system hydraulic performance, depending on the soil permeability. Overall, the soil moisture control strategy tested is shown as a viable supplemental technology to promote the environmental sustainability of rural sanitation systems.