Abstract. A severe reduction of greenhouse gas emissions is necessary to reach the objectives of the Paris Agreement. The implementation and continuous evaluation of mitigation measures requires regular independent information on emissions of the two main anthropogenic greenhouse gases, carbon dioxide (CO2) and methane (CH4). Our aim is to employ an observation-based method to determine regional-scale greenhouse gas emission estimates with high accuracy. We use aircraft- and ground-based in situ observations of CH4, CO2, carbon monoxide (CO), and wind speed from two research flights over the Upper Silesian Coal Basin (USCB), Poland, in summer 2018. The flights were performed as a part of the Carbon Dioxide and Methane (CoMet) mission above this European CH4 emission hot-spot region. A kriging algorithm interpolates the observed concentrations between the downwind transects of the trace gas plume, and then the mass flux through this plane is calculated. Finally, statistic and systematic uncertainties are calculated from measurement uncertainties and through several sensitivity tests, respectively. For the two selected flights, the in-situ-derived annual CH4 emission estimates are 13.8±4.3 and 15.1±4.0 kg s−1, which are well within the range of emission inventories. The regional emission estimates of CO2, which were determined to be 1.21±0.75 and 1.12±0.38 t s−1, are in the lower range of emission inventories. CO mass balance emissions of 10.1±3.6 and 10.7±4.4 kg s−1 for the USCB are slightly higher than the emission inventory values. The CH4 emission estimate has a relative error of 26 %–31 %, the CO2 estimate of 37 %–62 %, and the CO estimate of 36 %–41 %. These errors mainly result from the uncertainty of atmospheric background mole fractions and the changing planetary boundary layer height during the morning flight. In the case of CO2, biospheric fluxes also add to the uncertainty and hamper the assessment of emission inventories. These emission estimates characterize the USCB and help to verify emission inventories and develop climate mitigation strategies.
Abstract. Small-scale nonlinear chemical and physical processes over pollution source regions affect the tropospheric ozone (O 3 ), but these processes are not captured by current global chemical transport models (CTMs) and chemistryclimate models that are limited by coarse horizontal resolutions (100-500 km, typically 200 km). These models tend to contain large (and mostly positive) tropospheric O 3 biases in the Northern Hemisphere. Here we use the recently built two-way coupling system of the GEOS-Chem CTM to simulate the regional and global tropospheric O 3 in 2009. The system couples the global model (at 2.5 • long. × 2 • lat.) and its three nested models (at 0.667 • long. × 0.5 • lat.) covering Asia, North America and Europe, respectively. Specifically, the nested models take lateral boundary conditions (LBCs) from the global model, better capture small-scale processes and feed back to modify the global model simulation within the nested domains, with a subsequent effect on their LBCs.
The inhibition of microbial biofilms is a significant concern in food safety. In the present study, the inhibitory effect of sodium citrate and cinnamic aldehyde on biofilm formation at minimum inhibitory concentrations (MICs) and sub‐MICs was investigated for Escherichia coli O157:H7 and Staphylococcus aureus. The biofilm inhibition rate was measured to evaluate the effect of sodium citrate on S. aureus biofilms at 24, 48, 72, and 96 hr. According to the results, an antibiofilm effect was shown by both food additives, with 10 mg/ml of sodium citrate exhibiting the greatest inhibition of S. aureus biofilms at 24 hr (inhibition rate as high as 77.51%). These findings strongly suggest that sodium citrate exhibits a pronounced inhibitory effect on biofilm formation with great potential in the extension of food preservation and storage.
Abstract. Eastern China (27–41∘ N, 110–123∘ E) is heavily polluted by nitrogen dioxide (NO2), particulate matter with aerodynamic diameter below 2.5 µm (PM2.5), and other air pollutants. These pollutants vary on a variety of temporal and spatial scales, with many temporal scales that are nonperiodic and nonstationary, challenging proper quantitative characterization and visualization. This study uses a newly compiled EOF–EEMD analysis visualization package to evaluate the spatiotemporal variability of ground-level NO2, PM2.5, and their associations with meteorological processes over Eastern China in fall–winter 2013. Applying the package to observed hourly pollutant data reveals a primary spatial pattern representing Eastern China synchronous variation in time, which is dominated by diurnal variability with a much weaker day-to-day signal. A secondary spatial mode, representing north–south opposing changes in time with no constant period, is characterized by wind-related dilution or a buildup of pollutants from one day to another. We further evaluate simulations of nested GEOS-Chem v9-02 and WRF/CMAQ v5.0.1 in capturing the spatiotemporal variability of pollutants. GEOS-Chem underestimates NO2 by about 17 µg m−3 and PM2.5 by 35 µg m−3 on average over fall–winter 2013. It reproduces the diurnal variability for both pollutants. For the day-to-day variation, GEOS-Chem reproduces the observed north–south contrasting mode for both pollutants but not the Eastern China synchronous mode (especially for NO2). The model errors are due to a first model layer too thick (about 130 m) to capture the near-surface vertical gradient, deficiencies in the nighttime nitrogen chemistry in the first layer, and missing secondary organic aerosols and anthropogenic dust. CMAQ overestimates the diurnal cycle of pollutants due to too-weak boundary layer mixing, especially in the nighttime, and overestimates NO2 by about 30 µg m−3 and PM2.5 by 60 µg m−3. For the day-to-day variability, CMAQ reproduces the observed Eastern China synchronous mode but not the north–south opposing mode of NO2. Both models capture the day-to-day variability of PM2.5 better than that of NO2. These results shed light on model improvement. The EOF–EEMD package is freely available for noncommercial uses.
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