The present study investigated wastewater treatment for the removal of 11 different virus types (pepper mild mottle virus; Aichi virus; genogroup I, II, and IV noroviruses; enterovirus;sapovirus; group-A rotavirus; adenovirus; and JC and BK polyomaviruses) by two wastewater treatment facilities utilizing advanced
Bardenpho technology and compared the results with conventional treatment processes. To our knowledge, this is the first study comparing full-scale treatment processes that all received sewage influent from the same region. The incidence of viruses in wastewater was assessed with respect to absolute abundance, occurrence, and reduction in monthly samples collected throughout a 12 month period in southern Arizona. Samples were concentrated via an
electronegative filter method and quantified using TaqMan-based quantitative polymerase chain reaction (qPCR). Results suggest that Plant D, utilizing an advanced Bardenpho process as secondary treatment, effectively reduced pathogenic viruses better than facilities using conventional processes. However, the absence of cell-culture assays did not allow an accurate assessment of infective viruses. On the basis of these data, the Aichi virus is suggested as a conservative viral marker for adequate wastewater treatment, as it most often showed the best correlation coefficients to viral pathogens, was always detected at higher concentrations, and may overestimate the potential virus risk.
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The energy implication of climate change on urban wastewater systems
Urban wastewater service provision is an important energy consumer as well as a potentially important energy producer. This study aims to advance understandings on the influence of climate change on the intra- and inter-annual patterns of wastewater treatment plants’ net life cycle energy consumption. Historic monthly operational data of a wastewater treatment plant in the northeast United States were obtained and its current net life cycle energy demand was investigated. Comprehensive multivariate and multiple linear regression analyses were then performed. The main climate variables (temperature, rainfall, and snowfall) and the wastewater characteristics (flow rate, water temperature, total suspended solids, 5-day biochemical oxygen demand, and chemical oxygen demand) were used to develop regression models for energy that is directly and indirectly consumed and generated at the treatment plant. Two different approaches, a lumped and a month-based method, for conducting the regression analysis were investigated. Whenever possible, these two approaches were combined to improve the predictive power of the regression models. The obtained result shows the treatment plant’s direct energy use consists of more than 86% of the total energy consumption currently. Various energy recovery strategies allow the treatment plant to offset more than 15% of its total energy consumption. The future annual wastewater influent of the plant was projected to decrease towards the end of the century under climate change, with a significantly larger seasonal variation. The influent wastewater quality is projected to decrease, leading to higher direct and indirect energy consumption for treatment. Projections of future intra-annual responses show that the seasonal variations of wastewater flowrate as well as the monthly cumulative energy demand can potentially experience a two-fold increase, resulting in more frequent system shocks and create operational difficulties.
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- Award ID(s):
- 1706143
- NSF-PAR ID:
- 10189216
- Date Published:
- Journal Name:
- Journal of cleaner production
- Volume:
- 267
- ISSN:
- 0959-6526
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The temporal variability of precipitation and potential evapotranspiration affects streamflow from daily to long‐term scales, but the relative roles of different climate variabilities on streamflow at daily, monthly, annual, and mean annual scales have not been systematically investigated in the literature. This paper developed a new daily water balance model, which provides a unified framework for water balance across timescales. The daily water balance model is driven by four climate forcing scenarios (observed daily climate and observed daily climate with its intra‐monthly, intra‐annual, and inter‐annual variability removed) and applied to 78 catchments. Daily streamflow from the water balance model is aggregated to coarser timescales. The relative roles of intra‐monthly, intra‐annual, and inter‐annual climate variability are evaluated by comparing the modeled streamflow forced with the climate forcings at two consecutive timescales. It is found that daily, monthly, and annual streamflow is primarily controlled by the climate variability at the same timescale. Intra‐monthly climate variability plays a small role in monthly and annual streamflow variability. Intra‐annual climate variability has significant effects on streamflow at all the timescales, and the relative roles of inter‐annual climate variability are also significant to the monthly and mean annual streamflow, which is often disregarded. The quantitative evaluation of the roles of climate variability reveals how climate controls streamflow across timescales.
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Abstract Wastewater treatment is an energy‐intensive process and a net emitter of greenhouse gas emissions. A large fraction of these emissions is due to intensive aeration of aerobic bacteria to facilitate break‐down of organic compounds. Algae can generate dissolved oxygen at levels in excess of saturation, and therefore hold the potential to partially displace or complement mechanical aeration in wastewater treatment processes. The objective of this study was to develop an internally consistent experimental and modeling approach to test the hypothesis that algal photosynthetic aeration can speed the removal of organic constituents by bacteria. This framework was developed using a simplified wastewater treatment process consisting of a model bacteria (
Escherichia coli ), a model algae (Auxenochlorella protothecoides ), and a single carbon source that was consumable by bacteria only. This system was then tested both with and without the presence of algae. A MATLAB model that considered mass transfer and biological kinetics was used to estimate the production and consumption of O2and CO2by algae and bacteria. The results indicated that the presence of algae led to 18–66% faster removal of COD by bacteria, and that roughly one‐third of biochemical oxygen demand was offset by algal photosynthetic aeration. -
Land surface albedo is a significant regulator of climate. Changes in land use worldwide have greatly reshaped landscapes in the recent decades. Deforestation, agricultural development, and urban expansion alter land surface albedo, each with unique influences on shortwave radiative forcing and global warming impact (GWI). Here, we characterize the changes in landscape albedo-induced GWI (GWIΔα) at multiple temporal scales, with a special focus on the seasonal and monthly GWIΔα over a 19-year period for different land cover types in five ecoregions within a watershed in the upper Midwest USA. The results show that land cover changes from the original forest exhibited a net cooling effect, with contributions of annual GWIΔα varying by cover type and ecoregion. Seasonal and monthly variations of the GWIΔα showed unique trends over the 19-year period and contributed differently to the total GWIΔα. Cropland contributed most to cooling the local climate, with seasonal and monthly offsets of 18% and 83%, respectively, of the annual greenhouse gas emissions of maize fields in the same area. Urban areas exhibited both cooling and warming effects. Cropland and urban areas showed significantly different seasonal GWIΔα at some ecoregions. The landscape composition of the five ecoregions could cause different net landscape GWIΔα.more » « less
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The Gulf Coast watersheds in the United States contain some of the highest levels of biodiversity of all freshwater systems in North America. Developing environmental management policies to protect and preserve these ecosystems makes the study of the impacts of projected climate change on the future hydrologic cycle crucial. We used the Soil and Water Assessment Tool (SWAT) to estimate the potential hydrologic changes for the mid‐21st century (2050s) and the late 21st century (2080s) in the Mobile River, Apalachicola River, and Suwannee River watersheds in the Gulf Coast region of the United States. These estimates are based on downscaled future climate projections from 20 global circulation models (GCMs) under two representative concentration pathways (RCPs 4.5 and 8.5). SWAT models were calibrated and validated using the multi‐algorithm, genetically adaptive multi‐objective (AMALGAM) technique in a high‐performance computing (HPC) cluster. For the Gulf Coast watersheds, the climate is projected to be warmer and wetter. Projected changes in climatic variables are likely to bring large changes in both annual and seasonal hydrologic processes within these watersheds. We found substantial decreases in mean annual streamflow under RCP8.5 during the 2080s, with up to a 13.0% decrease projected for the Suwannee River watershed compared to the present day. Summer streamflow is projected to be substantially lower during the 2080s, with up to a 25.1% decrease projected for the Suwannee River watershed, during a time of high demand of water resources for agricultural, industrial, and ecosystem services. These hydrologic projections are expected to help in making better‐informed decisions for future water resources and ecosystem management in the Gulf Coast region.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/j.jclepro.2020.121905
