Quantifying Contributions of Local Emissions and Regional Transport to NOX in Beijing Using TROPOMI Constrained WRF-Chem Simulation

Estimating global 0.1° scale gridded anthropogenic CO2 emissions using TROPOMI NO2 and a data-driven method

Satellite remote sensing is a promising approach for monitoring global CO2 emissions. However, existing satellite-based CO2 observations are too coarse to meet the requirements of fine-scale global mapping. We propose a novel data-driven method to estimate global anthropogenic CO2 emissions at a 0.1° scale, which integrates emissions inventories and satellite data while bypassing the inadequate accuracy of CO2 observations. Due to the co-emitted anthropogenic emissions of nitrogen oxides (NOx = NO + NO2) and CO2, high-resolution NO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) are employed to map the global anthropogenic emissions at a global 0.1° scale. We construct the driving features from NO2 data and also incorporate gridded CO2/NOx emission ratios and NOx/NO2 conversion ratios as driving data to describe co-emissions. Both ratios are predicted using a long short-term memory (LSTM) neural network (with an R2 of 0.984 for the CO2/NOx emission ratio and an R2 of 0.980 for the NOx/NO2 conversion ratio). The data-driven model for estimating anthropogenic CO2 emissions is implemented by random forest regression (RFR) and trained using the Emissions Database for Global Atmospheric Research (EDGAR). The satellite-based anthropogenic CO2 emission dataset at a global 0.1° scale agrees well with the national CO2 emission inventories (an R2 of 0.998 with Global Carbon Budget (GCB) and an R2 of 0.996 with EDGAR) and consistent with city-level emission estimates from Carbon Monitor Cities (CMC) with the R2 of 0.824. This data-driven method based on satellite-observed NO2 provides a new perspective for fine-resolution anthropogenic CO2 emissions estimation.

Identification of NOx emissions and source characteristics by TROPOMI observations - A case study in north-central Henan, China

With the development of industries, air pollution in north-central Henan is becoming increasingly severe. The TROPOspheric Monitoring Instrument (TROPOMI) provides nitrogen dioxide (NO2) column densities with high spatial resolution. Based on TROPOMI, in this study, the nitrogen oxides (NOx) emissions in north-central Henan are derived and the emission hotspots are identified with the flux divergence method (FDM) from May to September 2021. The results indicate that Zhengzhou has the highest NOx emissions in north-central Henan. The most prominent hotspots are in Guancheng Huizu District (Zhengzhou) and Yindu District (Anyang), with emissions of 448.4 g/s and 300.3 g/s, respectively. The Gaussian Mixture Model (GMM) is applied to quantify the characteristics of emission hotspots, including the diameter, eccentricity, and tilt angle, among which the tilt angle provides a novel metric for identifying the spatial distribution of pollution sources. Furthermore, the results are compared with the CAMS global anthropogenic emissions (CAMS-GLOB-ANT) and Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC), and they are generally in good agreement. However, some point sources, such as power plants, may be missed by both inventories. It is also found that for emission hotspots near transportation hubs, CAMS-GLOB-ANT may not have fully considered the actual traffic flow, leading to an underestimation of transportation emissions. These findings provide key information for the accurate implementation of pollution prevention and control measures, as well as references for future optimization of emission inventories. Consequently, deriving NOx emissions from space, quantifying the characteristics of emission hotspots, and combining them with bottom-up inventories can provide valuable insights for targeted emission control.

VOCs hyperspectral imaging: A new insight into evaluate emissions and the corresponding health risk from industries

The harm of VOCs emitted from industries to surrounding atmospheric environment and human health was well known and had received continuous attention. In order to improve the quality of urban atmospheric environment and the living environment of urban residents, a large number of original urban industries had been relocated to economically underdeveloped suburbs, which has significantly deteriorated the atmospheric environment in these areas and brought potential health risks to local vulnerable residents, which is actually an unfair manifestation under the background of economic development and ecological civilization construction. There were many residents near industrial parks, but there was a significant lack of VOCs monitoring equipment and data. At present, the time resolution of the most commonly used in situ method was seriously insufficient, and it was unable to quantify the diffusion/transport process of VOCs. It was urgent to have effective detection methods for industrial VOCs plume concentration and diffusion/transport process. In this study, we proposed a hyperspectral imaging technology, which can realize long-term continuous imaging monitoring on plume concentrations of formaldehyde (HCHO), glyoxal (CHOCHO) and benzaldehyde (C6H5CHO) and their corresponding diffusion processes. The deviation between the imaging and in situ sampling concentrations in the outlet was 4-19 %. The spatial resolution of this technique reached meter level, and the temporal resolution of one pixel was better than 20 s. In this study, we carried out hyperspectral imaging of aldehyde VOCs for a chemical facility, a petrochemical facility and an industrial park containing various types of enterprises in the Yangtze River Delta. The maximum observed concentration of HCHO was 120.44 ± 12.14 ug/m3 with the emission flux of 39.27 ± 3.97 g/h, which was emitted from a petrochemical facility in Shanghai. A diffusion/transport model was established, and we found that the spatial distribution of HCHO, CHOCHO and C6H5CHO for the chemical facility case in Shanghai were all mainly along the southeast-northwest direction during one year. The health risk assessment emphasized that residents within 10 km north of the outlet of the chemical facility in Shanghai should pay more attention to the health risks caused by industrial HCHO emissions. More systematically and comprehensively hyperspectral imaging of VOCs emissions for different types of enterprises and different processes were expected to performed to greatly promote the establishment of a dynamic emission inventory and an effective health risk evaluation system in the future.

Assessing the impact of urban form and urbanization process on tropospheric nitrogen dioxide pollution in the Yangtze River Delta, China

Optimizing urban form through urban planning and management policies can improve air quality and transition to demand-side control. Nitrogen dioxide (NO2) in the urban atmosphere, mainly emitted by anthropogenic sources such as industry and vehicles, is a key precursor of fine particles and ozone pollution. Both NO2 and its secondary pollutants pose health risks for humans. Here we assess the interactions between urban forms and airborne NO2 pollution in different cities with various stages of urbanization in the Yangtze River Delta (YRD) in China, by using the machine learning and geographical regression model. The results reveal a strong correlation between urban fragmentation and tropospheric NO2 vertical column density (TVCD) in YRD cities in 2020, particularly those with lower or higher levels of urbanization. The correlation coefficients (R2) between NO2 TVCD and the largest patch index (a metric of urban fragmentation) in different cities are greater than 0.8. For cities at other urbanization stages, population and road density are strongly correlated with NO2 TVCD, with an R2 larger than 0.61. This study highlights the interdependence among urbanization, urban forms, and air pollution, emphasizing the importance of customized urban landscape management strategies for mitigating urban air pollution.

Impacts of TROPOMI-Derived NO X Emissions on NO2 and O3 Simulations in the NCP during COVID-19

NO₂ and O₃ simulations have great uncertainties during the COVID-19 epidemic, but their biases and spatial distributions can be improved with NO₂ assimilations. This study adopted two top-down NO _X inversions and estimated their impacts on NO₂ and O₃ simulation for three periods: the normal operation period (P1), the epidemic lockdown period following the Spring Festival (P2), and back to work period (P3) in the North China Plain (NCP). Two TROPOspheric Monitoring Instrument (TROPOMI) NO₂ retrievals came from the Royal Netherlands Meteorological Institute (KNMI) and the University of Science and Technology of China (USTC), respectively. Compared to the prior NO _X emissions, the two TROPOMI posteriors greatly reduced the biases between simulations with in situ measurements (NO₂ MREs: prior 85%, KNMI -27%, USTC -15%; O₃ MREs: Prior -39%, KNMI 18%, USTC 11%). The NO _X budgets from the USTC posterior were 17-31% higher than those from the KNMI one. Consequently, surface NO₂ levels constrained by USTC-TROPOMI were 9-20% higher than those by the KNMI one, and O₃ is 6-12% lower. Moreover, USTC posterior simulations showed more significant changes in adjacent periods (surface NO₂: P2 vs P1, -46%, P3 vs P2, +25%; surface O₃: P2 vs P1, +75%, P3 vs P2, +18%) than the KNMI one. For the transport flux in Beijing (BJ), the O₃ flux differed by 5-6% between the two posteriori simulations, but the difference of NO₂ flux between P2 and P3 was significant, where the USTC posterior NO₂ flux was 1.5-2 times higher than the KNMI one. Overall, our results highlight the discrepancies in NO₂ and O₃ simulations constrained by two TROPOMI products and demonstrate that the USTC posterior has lower bias in the NCP during COVD-19.

Variations of Urban NO2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements

Since the COVID-19 outbreak in 2020, China’s air pollution has been significantly affected by control measures on industrial production and human activities. In this study, we analyzed the temporal variations of NO2 concentrations during the COVID-19 lockdown and post-epidemic era in 11 Chinese megacities by using satellite and ground-based remote sensing as well as in situ measurements. The average satellite tropospheric vertical column density (TVCD) of NO2 by TROPOMI decreased by 39.2–71.93% during the 15 days after Chinese New Year when the lockdown was at its most rigorous compared to that of 2019, while the in situ NO2 concentration measured by China National Environmental Monitoring Centre (CNEMC) decreased by 42.53–69.81% for these cities. Such differences between both measurements were further investigated by using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) remote sensing of NO2 vertical profiles. For instance, in Beijing, MAX-DOAS NO2 showed a decrease of 14.19% (versus 18.63% by in situ) at the ground surface, and 36.24% (versus 36.25% by satellite) for the total tropospheric column. Thus, vertical discrepancies of atmospheric NO2 can largely explain the differences between satellite and in situ NO2 variations. In the post-epidemic era of 2021, satellite NO2 TVCD and in situ NO2 concentrations decreased by 10.42–64.96% and 1.05–34.99% compared to 2019, respectively, possibly related to the reduction of the transportation industry. This study reveals the changes of China’s urban NO2 pollution in the post-epidemic era and indicates that COVID-19 had a profound impact on human social activities and industrial production.

Non-stop industries were the main source of air pollution during the 2020 coronavirus lockdown in the North China Plain

Despite large decreases of emissions of air pollution during the coronavirus disease 2019 (COVID-19) lockdown in 2020, an unexpected regional severe haze has still occurred over the North China Plain. To clarify the origin of this pollution, we studied air concentrations of fine particulate matter (PM_2.5), NO₂, O₃, PM₁₀, SO₂, and CO in Beijing, Hengshui and Baoding during the lockdown period from January 24 to 29, 2020. Variations of PM_2.5 composition in inorganic ions, elemental carbon and organic matter were also investigated. The HYSPLIT model was used to calculate backward trajectories and concentration weighted trajectories. Results of the cluster trajectory analysis and model simulations show that the severe haze was caused mainly by the emissions of northeastern non-stopping industries located in Inner Mongolia, Liaoning, Hebei, and Tianjin. In Beijing, Hengshui and Baoding, the mixing layer heights were about 30% lower and the maximum relative humidity was 83% higher than the annual averages, and the average wind speeds were lower than 1.5 m s^-1. The concentrations of NO₃ ^-, SO₄ ^2-, NH₄ ⁺, organics and K⁺ were the main components of PM_2.5 in Beijing and Hengshui, while organics, K⁺, NO₃ ^-, SO₄ ^2-, and NH₄ ⁺ were the main components of PM_2.5 in Baoding. Contrary to previous reports suggesting a southerly transport of air pollution, we found that northeast transport caused the haze formation.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10311-021-01314-8.