Irregular industrial Carbon monoxide (CO) emissions not only deteriorate global atmospheric environment but also add a large uncertainty to CO emission inventory. This work demonstrates the ...potentiality of the Tropospheric Monitoring Instrument (TROPOMI) data product to derive CO emissions from facility‐level industrial sources. We have demonstrated CO emission estimates of 14 randomly selected facility‐level industrial sources over China with uncertainties of 10%–35% from TROPOMI archive between 2017 and 2020. We found that most of the plumes reveal significantly larger emissions than the registered inventory values. The simplicity of the demonstrated plume quantification method makes it easily applicable everywhere around the globe. This study suggests that future CO satellite constellation can routinely observe point source CO emissions to support environment policy.
Plain Language Summary
Irregular Carbon monoxide (CO) emissions from industrial sources add a large uncertainty to CO emission inventory. Some irregular CO emission events from industrial point sources in China may be not recorded properly by in situ monitoring system since enterprises may turn down the reporting values manually before discharging excessive pollutants to escape punishment. It is also expensive to carry out frequent airborne or ground‐based surveys across the entire industrial area to monitor such irregular CO emission events. This work demonstrates the potentiality of the Tropospheric Monitoring Instrument (TROPOMI) data product to derive CO emissions from facility‐level industrial sources. We have demonstrated CO emission estimates of 14 randomly selected facility‐level industrial sources over China with uncertainties of 10%–35% from TROPOMI archive between 2017 and 2020. We found that most of the plumes reveal significantly larger emissions than the registered inventory values. The simplicity of the demonstrated plume quantification method makes it easily applicable everywhere around the globe. This study suggests that future satellite constellation should monitor CO and co‐emitted gases with high spatial‐temporal resolutions and coverage, which can monitor routinely CO emissions to support environment policy.
Key Points
Tropospheric Monitoring Instrument observations reveal a large Carbon monoxide (CO) emission discrepancy from industrial point sources over China
Regional emission inventory probably underestimated CO emissions of certain industrial point sources over China
Future CO satellite constellation together with monitoring local emissions could support environment policy
Really interesting new gene finger protein (RING) finger protein 121 (RNF-121) is an E3 ubiquitin ligase involved in the regulation of several signaling pathways. Among those signaling pathways, ...nuclear factor-κB (NF-κB) signaling pathway is known to play a critical role in tumorigenesis. However, the relevance between RNF121 and cancer development remains poorly understood. In this study, we found that RNF121 was less expressed in tumor tissues than adjacent normal tissues of renal cell carcinoma (RCC) patients. Overexpression of RNF121 inhibited the growth of human RCC cells (768-O cell line) in vivo. Moreover, RNF121 impeded the proliferation, migration and invasion of human RCC cells in vitro. In addition, we found that RNF121 activated NF-κB signaling pathway via promoting IκBα degradation in human RCC cells. Our findings reveal a previously unrecognized role of RNF121 in RCC development, and provide new insights into RCC prognosis and therapy.
•RNF121 expression was downregulated in RCC.•Overexpression of RNF121 inhibited tumor growth.•RNF121 activated NF-κB signaling pathway in RCC.
In this study, the characterization of Hydrogen Chloride (HCl) seasonal variations and inter-annual linear trend are presented for the first time over the polluted region at Hefei (117°10'E, ...31°54'N), China. The time series of HCl were retrieved by the mid-infrared (MIR) solar spectra recorded by the ground-based high-resolution Fourier transform infrared spectroscopy (FTIR) between July, 2015 and April, 2019. The magnitude of HCl reaches a peak in January (2.70 ± 0.16) × 10
molecules*cm
and a minimum in September (2.27 ± 0.09) × 10
molecules*cm
. The four-year time series of HCl total column show a negative linear trend of (-1.83 ± 0.13) %. The FTIR data are compared with GEOS-Chem data in order to evaluate the performance of the GEOS-Chem model to simulate HCl. In general, total column FTIR data and GEOS-Chem model data are in a good agreement with a correlation coefficient of 0.82. GEOS-Chem model data present a good agreement with FTIR data in seasonal variation and inter-annul trend. The maximum differences occur in January and April with mean differences of 4%-6%. We also present HCl time series observed by 6 NDACC stations (Bremen, Toronto, Rikubetsu, Izana, Reunion.maido, Lauder) in low-middle-latitude sites of the northern and southern hemispheres and Hefei stations in order to investigate the seasonal and annual trends of HCl in low-middle-latitude sites. The HCl total column at the northern hemisphere stations reached the maximum in the late winter or early spring and the minimum in the early winter or late autumn. In general, the seasonal variations of HCl over Hefei is similar to that in other northern hemisphere mid-latitude FTIR stations.
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•A facile and economical catalytic synthesis of 3-substituted isocoumarins was achieved.•2-Chloro-benzoic acids/amides were utilized as starting materials with 1,3-diketones.•Copper ...nanoparticle catalyst shows highly catalytic activity for the 2-chloro-substituted substrates.
Copper nanoparticles were utilized as a highly efficient catalyst for a facile and economical synthesis of 3-substituted isocoumarins with 2-chlorobenzoic acids and 1,3-diketones as starting materials. The copper nanoparticles catalyst showed highly catalytic activity for the 2-chloro-substituted substrates to afford 3-substituted isocoumarins in good to excellent yields. Furthermore, good catalytic activity was also observed when 2-chlorobenzoic amides were utilized as substrates instead of the benzoic acids.
Observations and numerical models are mainly used to investigate the spatiotemporal distribution and vertical structure characteristics of aerosols to understand aerosol pollution and its effects. ...However, the limitations of observations and the uncertainties of numerical models bias aerosol calculations and predictions. Data assimilation combines observations and numerical models to improve the accuracy of the initial, analytical fields of models and promote the development of atmospheric aerosol pollution research. Numerous studies have been conducted to integrate multi-source data, such as aerosol optical depth and aerosol extinction coefficient profile, into various chemical transport models using various data assimilation algorithms and have achieved good assimilation results. The definition of data assimilation and the main algorithms will be briefly presented, and the progress of aerosol assimilation according to two types of aerosol data, namely, aerosol optical depth and extinction coefficient, will be presented. The application of vertical aerosol data assimilation, as well as the future trends and challenges of aerosol data assimilation, will be further analysed.
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Top-down constraints of CO2 emissions from coal-fired power plants are critical to improving the accuracy of CO2 emission inventory and designing carbon reduction strategies. Different top-down ...models based on satellite observation have been proposed in previous studies, but discrepancies between these models and the underlying drivers are rarely explored, limiting the confidence of their application for monitoring point-source CO2 emissions from satellite. Here, we apply three top-down models to estimate CO2 emissions from individual coal-fired power plants in the United States (US) and China in 2014–2021 from Orbiting Carbon Observatory 2 (OCO-2) satellite observations. The first one applies the Gaussian plume model to optimize emissions by fitting modeled CO2 enhancement to the observation. The second and third methods apply the same inversion framework using the maximum likelihood estimation, but with WRF-Chem and WRF-FLEXPART as forward models, respectively. We evaluate consistency between the three methods in estimating emissions of 10 power plants in the US, using daily reported values from the US Environmental Protection Agency (EPA) as a benchmark, and then apply the three methods to quantify emissions of 13 power plants in China. Results show that the WRF-Chem and WRF-FLEXPART based inversion results are consistent with the EPA reported emission rates, with correlation coefficients (r) of 0.76 and 0.85 and mean biases (MB) of 4.06 and 3.97 ktCO2/d relative to EPA reports at all 10 power plants, respectively. The Gaussian plume model driven by wind fields from WRF-Chem model without the wind rotation shows comparable ability in reproducing the EPA reported emission rates at 7 power plants (r = 0.82, MB = 6.17), but is not applicable for the other three cases. We find that application of the high-resolution three-dimensional wind fields can better capture the shape of observed plumes, especially under complex wind conditions, compared to the Gaussian plume model which relies on wind field at a single point, and thus the Gaussian plume model has difficulty to optimize emissions under inhomogeneous wind fields or when observations are far away from the power plant. In general, using the WRF-FLEXPART model as the forward model in the inverse analysis shows advanced capability to simulate narrow-shape plumes in the absence of numerical diffusion which is inherent in Eulerian model such as WRF-Chem. Emissions estimated by the three top-town methods show a moderate consistency at 13 coal-fired power plant cases in China, with 8 of 13 cases showing differences of <30% between at least two methods. However, large differences emerge when wind fields are inhomogeneous and number of available observations is limited. Using different meteorological wind fields and OCO-2 data versions can also bring substantial differences to the posterior emissions for all three approaches. We find that the posterior CO2 emissions, though only reflecting instantaneous emission rates at satellite overpass time, are not proportional to the reported capacities of these power plants, indicating that attributing CO2 emissions simply based on the capacity of power plants in some bottom-up approaches may have significant discrepancies. Our study exposes the capability and limitation of different top-down approaches in quantifying point-source CO2 emissions, advancing their application for better serving increasing constellations of point-source imagers in the future.
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•We use three methods to optimize CO2 emissions at power plants from satellite.•Mean emission estimates for three methods differed from reported values by 3.97 to 6.17 ktCO2/day.•Inverse analysis with a Lagrangian model agrees best with EPA reported emissions.•Derived CO2 emissions at Chinese power plants are not proportional to the capacities.
The UV/Vis/near infrared spectrometer SCIAMACHY on board the European ENVISAT satellite is the only one instrument which enables quantitative total column retrieval of atmospheric carbon monoxide ...(CO) with high sensitivity to the lower troposphere. Although the general selection of SCIAMACHY observations are the measurements with effective cloud fractions below 20%, due to much higher albedo of clouds compared to ground surface, the remaining effects of clouds can still be large (even up to 100%). Since inaccurate corrections will lead to a wrong interpretation of the results, the accurate cloud correction is essential; in this paper we applied a cloud correction scheme which explicitly considers the cloud fraction, cloud top height and surface albedo of individual observations. In this paper, the sensitivity of cloud effect is described and the difference between corrected and uncorrected result is studied.
Improved knowledge of the chemistry and drivers of surface ozone over the
Qinghai-Tibet Plateau (QTP) is significant for regulatory and control
purposes in this high-altitude region in the Himalayas. ...In this study, we
investigate the processes and drivers of surface ozone anomalies (defined as deviations of ozone levels relative to their seasonal means) between 2015 and 2020 in urban areas over the QTP. We separate quantitatively the contributions of anthropogenic emissions and meteorology to surface ozone anomalies by using the random forest (RF) machine-learning model-based meteorological normalization method. Diurnal and seasonal surface ozone anomalies over the QTP were mainly driven by meteorological conditions, such as temperature, planetary boundary layer height, surface incoming shortwave flux, downward transport velocity and inter-annual anomalies were mainly driven by anthropogenic emission. Depending on region and measurement hour, diurnal surface ozone anomalies varied over −27.82 to 37.11 µg m−3, whereas meteorological and anthropogenic contributions varied over −33.88 to 35.86 µg m−3 and −4.32 to 4.05 µg m−3 respectively. Exceptional meteorology drove 97 % of surface ozone non-attainment events from 2015 to 2020 in the urban areas over the QTP. Monthly averaged surface ozone anomalies from 2015 to 2020 varied with much smaller amplitudes than their diurnal anomalies, whereas meteorological and anthropogenic contributions varied over 7.63 to 55.61 µg m−3 and 3.67 to 35.28 µg m−3 respectively. The inter-annual trends of surface ozone in Ngari, Lhasa, Naqu, Qamdo, Diqing, Haixi and Guoluo can be attributed to anthropogenic emissions in 95.77 %, 96.30 %, 97.83 %, 82.30 %, 99.26 % and 87.85 %, and meteorology in 4.23 %, 3.70 %, 2.17 %, 3.19 %, 0.74 % and 12.15 % respectively. The inter-annual trends of surface ozone in other cities were fully driven by anthropogenic emission, whereas the increasing inter-annual trends would have larger values if not for the favorable meteorological conditions. This study can not only improve our knowledge with respect to spatiotemporal variability of surface ozone but also provide valuable implications for ozone mitigation over the QTP.
Atmospheric ammonia (NH3) plays an important role in the formation of fine particulate matter, leading to severe environmental degradation and human health issues. In this work, ground-based Fourier ...transform infrared spectrometry (FTIR) observations are used to obtain the total columns of atmospheric NH3 at Hefei, China, from December 2016 to December 2020. After the presentation of the retrieval algorithm and uncertainty budget, we perform a spatio-temporal analysis of the dataset. Over the four years, NH3 columns have been increasing by 15.82% (2017–2018), 3.83% (2018–2019) and 3.68% (2019–2020). A clear seasonal cycle is observed, with the largest surface concentrations (12.93 ± 6.40 ppb) observed in June to August, and the lowest (4.08± 2.66 ppb) in November to January. The diurnal cycles of NH3 exhibit increased morning and afternoon concentrations. Interpretation of the diurnal cycles is difficult, however, the absence of a peak during rush hours, and the absence of correlation with CO and NO2 suggest that agriculture and not traffic is the main source of NH3 at Hefei. The polar plots of NH3 columns with wind and back trajectories of air masses calculated by the HYSPLIT model confirmed that agriculture was the dominant source of ammonia in four seasons, while urban anthropogenic emissions contributed to the high level of NH3 in summer over the Hefei site. We end this paper with a short validation exercise of NH3 columns retrieved from measurements of the IASI satellite data with the FTIR measurements over Hefei. Correlation coefficients (R) between the two datasets are 0.79 and 0.75 for IASI-A and IASI-B, with the slope of 0.96 and 1.10, respectively. The mean difference is −3.44 × 1015 and −3.96 × 1015 molec cm−2, with standard deviation of 7.16 × 1015 and 8.10 × 1015 molec cm−2, respectively. These results demonstrate the IASI and FTIR data, over Hefei, are in broad agreement.
In order to know about the temporal and spatial distribution of atmospheric ammonia, and what influence ammonia columns over Hefei, China, we observed the total columns of atmospheric ammonia by ground-based FTIR remote sensing from December 2016 to December 2020. The time series of the ammonia column observed at the Hefei site are plotted. The green dots are the individual measurements of NH3; the red diamonds represent the daily averaged NH3; the blue line is the fitting curve of the individual NH3 columns. We discussed the seasonal trend, annual variability, diurnal variation of NH3, and the main factors that influence gaseous NH3 concentrations in Hefei. Display omitted
•FTIR measurements of NH3 column and comparison with satellite data are reported.•FTIR observations captured the spatio-temporal variation of NH3 columns.•Agriculture was the dominant source of ammonia in four seasons over Hefei.•IASI satellite data are in broad agreement with FTIR data over Hefei.
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