Methane emission estimates using chamber and tracer release experiments for a municipal waste water treatment plant
This study presents two methods for estimating methane emissions from a waste water treatment plant (WWTP) along with results from a measurement campaign at a WWTP in Valence, France. These methods, chamber measurements and tracer release, rely on Fourier transform infrared spectroscopy and cavity ring-down spectroscopy instruments. We show that the tracer release method is suitable for quantifying facility- and some process-scale emissions, while the chamber measurements provide insight into individual process emissions. Uncertainties for the two methods are described and discussed. Applying the methods to CH4 emissions of the WWTP, we confirm that the open basins are not a major source of CH4 on the WWTP (about 10% of the total emissions), but that the pretreatment and sludge treatment are the main emitters. Overall, the waste water treatment plant is representative of an average French WWTP.
Human activities cause greenhouse gas (GHG) emissions at a large scale, changing the atmospheric chemical composition by measurable and consequential amounts. Anthropogenic GHG emissions such as methane (CH4) now represent a significant fraction of total greenhouse gas emissions into the atmosphere. To better understand the anthropogenic sources of GHGs, with the goal of ultimately reducing these emissions, it is essential to accurately quantify the emissions at different spatial scales, from the country to the process scale, and to monitor the possible temporal variabilities. We can sort estimation methods into two groups depending on the type of measurement used: the top-down approach based on atmospheric measurements of GHGs at different scales (global, regional, local) and the bottom-up approach that uses activity data, emission factors and flux modeling to calculate emissions. Both approaches can be applied from the global to the process scale depending on the representativity of the measurements.
Methane is a potent anthropogenic greenhouse gas with a global warming potential 28 times as strong as that of CO2 on a 100-year time horizon (Stocker et al., 2013). Primary sources of anthropogenic methane emissions are landfills, waste water treatment plants (WWTPs), rice paddies, ruminants and manure management, oil and gas production and transport activities. Combining the two approaches by using top-down measurements at all scales to validate or adjust benchmark bottom-up calculations and emission factors can help not only improve inventories by a more robust quantification but also provide valuable information for how to prioritize emission reduction activities.
In France, methane emissions from waste management (waste water treatment and landfills) accounted for about 19% of the total methane emissions in 2011 following the national inventory from CITEPA (CITEPA, 2013). Landfills are the largest emitter with 17 %, but waste water treatment plants still represent a non-negligible part (2 %). However, these values are estimated with 100% uncertainty due to the difficulty in accurately estimating the biological demand of oxygen (BOD), quantity of CH4 emitted by kg of BOD, fraction of treated incoming waste water and anoxic/oxic conditions, which are the parameters used by CITEPA to derive CH4 emissions from WWTP (CITEPA, 2013). Several studies have been conducted in different countries to provide more accurate estimates of the emissions for WWTPs. Cakir and Stenstrom (2005) and El-Fadel and Massoud (2001) present estimations based on process modeling, but some studies such as Czepiel et al. (1993), Wang et al. (2011) and Daelman et al. (2012) calculate emissions using CH4 measurements with mass budget. Finally, a recent study by Yoshida et al. (2014) used the tracer release method as described in this paper to estimate CH4 and N2O emissions from a WWTP. In these papers, emissions vary from 0.011 to 1.3 kgyr1 per population equivalent depending on the WWTP design (e.g., depending on the use of aerobic or anaerobic processes, presence of a sludge digester) and the estimation method as the tracer release allows the capturing of leakage emissions that could be omitted by the other methods. For municipal WWTPs using activated sludge (aerobic) treatment, emissions still vary from 0.039 to 0.309 kgyr1 per population equivalent. This range of estimate shows that the WWTP CH4 emissions depend on the design and the size of the WWTP. In France, according to the BDERU for 2008 (database for urban waste water, http://www.statistiques. developpement-durable.gouv.fr/lessentiel/ar/306/1168/ assainissement-traitement-collectif-eaux-usees.html), there are about 18 600 WWTPs, half of which treat water for a fewer-than-500 population equivalent. However, the 6% of WWTP with more than 10 000 population equivalent treat 80% of the waste water. In this study, we focused on one of these medium-sized WWTPs that employs activated sludge treatment. We used two methods – chamber measurements and tracer release method with acetylene – that have been rarely used on WWTPs to calculate GHG emissions at the process and the plant scale. We aimed not only to estimate the total emissions of the site but also to investigate individual processes and evaluate the missing elements between these two measurement scales. Another goal was to estimate the uncertainties for each method to provide a more robust emission estimation and be able to compare our results with other studies or inventories. An intensive measurement campaign was thus conducted at one of the WWTP of Valence, France, from 17 to 21 September 2012. First, we present the details of the site under study, followed by the different emission estimation methods, measurement techniques and instruments employed during the experimental campaign. Finally, we present and discuss the results obtained for CH4 from the process scale up to the site scale. All the emission estimates hereafter refer directly to CH4, i.e., the notation kg of CH4 day1 or kg of CH4 yr1 per population equivalent is replaced by kg day1 or kg yr1 per population equivalent.
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