Decoupling wastewater-related greenhouse gas emissions and water stress alleviation across 300 cities in China is challenging yet plausible by 2030

Abstract

Urban wastewater treatment and reuse are crucial for achieving water sustainability. Yet, pathways of realizing water-climate synergies in the planning of wastewater infrastructure remain unclear. In this paper, we examine the nexus of urban water stress and climate impact resulting from the expansion of wastewater infrastructures across over 300 cities in China. We demonstrate how the effect of alleviating urban water stress from wastewater treatment and reclaimed water reuse has been highly uneven across cities, costing the country a 183% increase in life-cycle greenhouse gas emissions during 2006-2015. Decoupling the climate impact of wastewater infrastructure from significant water stress is plausible by 2030, potentially leading to up to 35% reduction of greenhouse gas emissions. However, given the growing demand for water reuse and investment in the diffusion of low-carbon technologies at each life-cycle stage of wastewater systems, this decoupling would be a challenging endeavor.

Toward an intensive understanding of sewer sediment prokaryotic community assembly and function

“…Combined with in-field measurements of hydrogen sulfide concentrations, we should pay attention to the hydrogen sulfide risk in multifunctional area sewers. Recently, amounts of studies have reported methane production from freshwater sewerage systems, implying that sewer sediment is an important greenhouse gas contributor to the environment ( Chen et al, 2020 ; Chen S. et al, 2023 ). Our work revealed that Methanospirillum, and Methanoregulaceae , associated with methane production, were more abundant in the commercial area sediments ( Supplementary Figure 3 ).…”

City level water withdrawal and scarcity accounts of China

“…Total water withdrawal was, on average, 4% lower in Zhou et al 24 than in this study. The reasons for these differences could be twofold: (1) these data are derived from different sources, i.e., city water resources bulletin in the present study, compared to simulations combined with provincial and national water resources bulletin in Zhou et al 24 ; and (2) there have been name changes, merging and separation of data between cities in the historical statistics 73 , as summarized in Table 4 for detail.…”

Modeling Indirect Greenhouse Gas Emissions Sources from Urban Wastewater Treatment Plants: Integrating Machine Learning Models to Compensate for Sparse Parameters with Abundant Observations

“…Studies on wastewater treatment technology policies have focused on their impact on surface water quality control, ,, but discarded the quantification of their impacts on ECI and SY. Similarly, studies on GHG emissions in wastewater treatment often assumed the same ECI and SY in WWTPs with similar technologies, disregarding specific operating conditions. , …”

“…Similarly, studies on GHG emissions in wastewater treatment often assumed the same ECI and SY in WWTPs with similar technologies, disregarding specific operating conditions. 28, 29 A key challenge lies in effectively mining the value of observation data with a wide spatial-temporal span but limited recorded parameters, which is a common barrier in scientific domains, 30 to model ECI and SY on a nationwide scale. This is due to the large quantity of WWTP operation data available (such as month-by-month records from more than 5,000 WWTPs in China 6 ), but a limited variety of recorded operation parameters (biomass yield of heterotrophic and autotrophic microorganisms, N content in organic matters, proportion of biodegradable or inert, and soluble or particle organic matters in COD, etc.)…”