Significant wintertime PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; mitigation in the Yangtze River Delta, China from  2016 to 2019: observational constraints on anthropogenic emission controls

Wang, Liqiang; Yu, Shaocai; Li, Pengfei; Chen, Xue; Li, Zhen; Zhang, Hongjie; Li, Mengying; Mehmood, Khalid; Liu, Weiping; Chai, Tianfeng; Zhu, Yannian; Seinfeld, John H.

doi:10.5194/acp-2020-510

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“…The grid-specific methane emissions were originally taken from the bottom-up emission inventories (EDGARv6.0). Other general configurations could be found in our previous studies [2,3].…”

Section: Methods For Uncertainty Analysissupporting

confidence: 78%

“…The boundary condition was obtained from a regional CTM (the two-way coupled WRF-CMAQ model) simulation with 3 × 3 km 2 horizontal resolution over a 12 × 12 km 2 domain. Other configurations were shown in our previous results [2,3,5]. Note that the inner domain did not feedback with the outer domain (i.e., so-called one-way nested simulations).…”

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confidence: 69%

“…The grid-specific methane emissions were originally taken from the bottom-up emission inventories (EDGARv6.0). Other general configurations could be found in our previous studies [2,3].Methane emissions were estimated via the Bayesian inverse solution which optimized a single state vector 𝐱 as: 𝐱 ̂= 𝐱 𝐀 + 𝐒 𝐀 𝐊 𝑻 (𝐊𝐒 𝐀 𝐊 𝑻 + 𝐒 𝐂 ) −𝟏 (𝐲 − 𝐊𝐱 𝐀 ), (Eq. 6)where 𝐱 ̂ was the optimized state vector containing individual elements for daily emissions as well as daily background concentrations; 𝐱 𝐀 was the prior taken as the mean reported emission rate from the bottom-up emission inventories; 𝐊 was the Jacobian constructed by running perturbation simulations for the state vector element; 𝐒 𝐀 was the prior error covariance matrix using 100% error for the emissions and10% for the boundary conditions; 𝐲 contained the TROPOMI observations; and 𝐒 𝐂 was…”

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confidence: 99%

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Supplementary material to "A versatile spaceborne architecture for immediate monitoring of the global methane pledge"

Wang¹,

Guo²,

Huo³

et al. 2022

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Methods for uncertainty analysis(1) Uncertainties in the PRISMA-based methane retrievals. The PRISMA-based methane retrievals can present systematic and random errors. We thus evaluated their performance using end-to-end simulations, as shown in previous studies. First, the ideal plumes were prescribed via the large-eddy-driven Weather and Research Forecasting Model (the WRF-Chem-LES model) [1]. The key configuration included common wind fields (i.e., 3.5 m/s), high resolution (i.e., 30 ×30 m 2 ), and constant emission rates (e.g., 500, 1000, and 1500 kg/h). On this basis, uncorrelated noises with random increments were then added.They represented the expected instrument precision presenting normal distributions with zero mean biases and standard deviations of 1 ~ 5 %. Second, the enhancements of volume mixing ratios were converted into two-way spectral atmospheric transmittance. The calculation basis included air mass factors based on the real observation zenith angles, vertical profiles of dry air column densities, and methane absorption cross-section data. These three data came from the satellite instrument records, ERA5 reanalysis dataset, and the HIgh-resolution TRANSmission molecular absorption (HITRAN2016) database. Third, the subsequent transmittance spectra were convolved with the PRISMA-based spectral responses and then multiplied by the original PRISMA top-of-atmosphere radiance spectrums. To prevent across-track variations in spectral calibration, we performed such processes on a per-column basis. Finally, the resulting PRISMA-based top-of-atmosphere radiance images were processed with the same matched-filter algorithm over the cases explored in this work. Methane retrieval results via the WRF-Chem-LES simulations and associated biases were presented in Table S3. Therefore, we did not observe systematic errors in the PRISMA-based methane retrievals. Detailed evaluations are shown in Supplementary Information.(2) Uncertainties in the TROPOMI-based methane emission estimates. We provided independent emission estimates for the TROPOMI-based methane hotspots using the WRF-Chem model. On this basis, the differences between the WRF-Chembased and IME-based results reflected the intrinsic uncertainties in the IME method.The WRF simulation was nudged to National Centers for Environmental Prediction final analysis data at 0.25° × 0.25° spatial resolution and six-hour temporal resolution. For each hotspot, the model was performed at 5 × 5 km 2 horizontal resolution over a 50 × 50 km 2 domain. The boundary condition was obtained from the CAMS reanalysis dataset. Note that the inner domain did not feedback with the outer domain (i.e., so-called one-way nested simulations). The grid-specific methane emissions were originally taken from the bottom-up emission inventories (EDGARv6.0). Other general configurations could be found in our previous studies [2,3].Methane emissions were estimated via the Bayesian inverse solution which optimized a single state vector 𝐱 as: 𝐱 ̂= 𝐱 𝐀 + 𝐒 𝐀 𝐊 𝑻 (𝐊𝐒 𝐀 𝐊 𝑻 + 𝐒 𝐂 ) −𝟏 (𝐲 − 𝐊𝐱 𝐀 )...

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“…The grid-specific methane emissions were originally taken from the bottom-up emission inventories (EDGARv6.0). Other general configurations could be found in our previous studies [2,3].…”

Section: Methods For Uncertainty Analysissupporting

confidence: 78%

mentioning

confidence: 69%

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confidence: 99%

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