Global warming will largely increase CH4 emissions from waste treatment: insight from the first city scale CH4 concentration observation network in Hangzhou city, China
Abstract. Atmospheric CH4 is the second largest anthropogenic contributor to global warming, however its emissions, components, spatiotemporal variations, and projected changes present large uncertainties from city to national scales. CH4 emissions from waste treatment account for >50 % of total anthropogenic CH4 emissions at the city scale, and considering the high sensitivity of CH4 emission factors (EFs) to temperature for biological process-based sources, such as waste treatment, large bias will occur when estimating future CH4 emissions under different global warming scenarios. Furthermore, the relationships between temperature and waste treatment CH4 emissions have only been determinized in a few site-specific studies, and these findings lack representativeness for the whole city scale, which contains various biophysical conditions and shows heterogeneous distribution. These factors increase the difficulty of evaluating city-scale CH4 emissions (especially from waste treatments), and the projected changes remain unexplored. Here, we conduct the first tower-based CH4 observation network with three sites in Hangzhou city, which is located in the developed Yangtze River Delta (YRD) area and ranks as one of the largest megacities in China. We found that the a priori total annual anthropogenic CH4 emissions and waste treatment emissions were overestimated by 36.0 % and 47.1 % in Hangzhou city, respectively. However, the total emissions in the larger region of Zhejiang Province or the YRD area was only slightly underestimated by 7.0 %. Emissions from waste treatment showed obvious seasonal patterns according to the air temperature. By using the constructed linear relationship between monthly waste treatment CH4 emissions and air temperature, we found that the waste treatment EFs increased by 38 %~50 % as the temperature increased by 10 °C. Together with the projected temperature changes from four climate change scenarios, the global warming-induced EFs in Hangzhou city will increase at rates of 2.2 %, 1.2 %, 0.7 % and 0.5 % per decade for RCP8.5, RCP6.0, RCP4.5 and RCP2.6 scenarios, respectively, and by 17.6 %, 9.6 %, 5.6 %, and 4.0 % at the end of this century, respectively. Additionally, the relative changes derived for the whole of China also showed high heterogeneity and indicated large uncertainty in projecting future national total CH4 emissions. Hence, we strongly suggest the temperature-dependent EFs and positive feedback between global warming and CH4 emissions should be considered in future CH4 emission projections and climate change models.
Cheng Hu et al.
Cheng Hu et al.
Cheng Hu et al.
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This study uses continuously measured methane measurements at three tower locations in and around Hangzhou, China, to investigate temporal variations of emissions, especially from the treatment of waste. The authors use the WRF-STILT (Weather Research and Forecasting – Stochastic Time-Inverted Lagrangian Transport) model combined with a Bayesian inversion framework to compare the data driven results with the prior emissions inventory. They conclude that emissions have been overestimated for the city of Hangzhou and that there is a seasonality to the emissions that can only be explained by the waste treatment sector.
This topic is very timely and important for understanding the influence of climate change on emissions of this high global warming potential pollutant, but several issues in this paper need clarification before publication.
The footprint for the Damingshan site is only slightly influenced by emissions in the urban core.
More detailed comments follow:
Abstract: mention the types of waste included in this study
Line 72: Out of curiosity, what are the top five anthropogenic sources of CH4 in China?
Line 91: USEPA
Line 106: replace “absence” with “omission”
Lines 143-145: City-scale studies have not focused on waste treatment sources because there are many sources, as in Hangzhou. Yadav et al. (2019; JGR Atmospheres) were able to see the effects of the closure of a landfill in the Los Angeles, CA area that was included in the prior inventory and not seen in the modelled results.
Pages 6-7: In the description of the sites, please summarize the regional, seasonal wind patterns and any differences between the sites.
Lines 188-190: How frequently were standards run? What uncertainty, including both precision and accuracy, did you assign for the measurements?
Line 238: What does “fuel exploitation from coal, oil, and natural gas” include? Extraction, transportation, refining, distribution, and combustion, or some subset of these?
Line 239: How and where is the energy for buildings generated? E.g., natural gas power plants in the suburbs, coal burned in the buildings, …?
Lines 241-245: Is 0.5° high enough spatial resolution for your study region?
Line 287: reference for CCGCRV? Thoning et al., 1989, JGR 94, 8549-8565; https://gml.noaa.gov/ccgg/mbl/crvfit/crvfit.html
Line 295: Is it meaningful to give an annual average when 1-2 months are missing data?
Line 295: replace “variations” with “trends”
Line 296: What are the “similar atmospheric transport processes?” Summarize seasonal wind direction and speed patterns.
Line 309: replace “YON” with “TAP”
Line 310: replace “temporal” with “spatial”
Lines 320-321: Figure 3 does not show significant differences in the size of the footprints at the different sites. You might want to expand the scale to show this.
Lines 323-326: Cities shown significant diurnal variation in PBLH.
Line 345: Not sure what you mean by “amplitudes” here – amplitude of the seasonal variations? I don’t see obvious differences. The absolute average abundances are different.
Line 348: The simulated data for Linan actually approximate the observations very well!
Line 364: It is very much to be expected that the Hangzhou site is more influenced by local emissions since it is in the urban core. What are the major emitters within 5-10 km of the site?
Lines 366-368: How did you show that the Linan and Damingshan sites are influenced by emissions from a much larger region? The footprints don’t indicate this.
Lines 375-378: Can you give a reference for the statement that waste treatment emissions are larger during the daytime than at night?
Line 420: Emissions from waste treatment dominated the total CH4 and the seasonal pattern, but you do show significant seasonal variations for the other anthropogenic sources in Figure 7a. Can you split those up at all? Can you say anything about the natural gas distribution infrastructure – more leaks in winter than summer, …?
Line 479: Where are these values of SFs shown? They are not from Table S2.
Figures: In general, please improve the resolution of the figures. It is very difficult to impossible to read the small text, even when expanding the figures on the screen.
Figure 1: What are the divisions within Hangzhou City?
Figure 2: Use the same color schemes on all figures and parts within figures for the same sites.
Figure 3: Replace “lg” with “log.” Are the waste treatment CH4 emissions in panel (e) also from EDGAR v6.0?
Figure 4: Need higher resolution graphics, especially for panel (d) and (e). The note at the end of the caption may be incorrect. In panel (e), is it true that the blue color for the bar charts include all of Zhejiang, including Hangzhou? Do the blue regions in the pie charts represent Zhejiang minus Hangzhou?
Figure 8: What region is this figure describing?