DOI•
Evaluating the impact of the COVID-19 lockdown on the pollutant emissions reduction in Nanjing
01 Jan 2021-Vol. 41, Iss: 8, pp 2959-2965
TL;DR: In this paper, a high-resolution data assimilation experiment was carried out to invetigate the pollution emissions changes before and during the COVID-19 lockdown by adjusting the chemical initial conditions and the pollutant emissions simultaneously through the use of the online regional chemical transport model, WRF-Chem and an ensemble square-root system.
Abstract: The COVID-19 led to a stringent lockdown in most cities in China to effectively prevent the spread of the epidemic, resulting in reductions in air pollutant emissions. A high-resolution data assimilation experiment was carried out to invetigate the pollution emissions changes before and during the COVID-19 lockdown by adjusting the chemical initial conditions and the pollutant emissions simultaneously through the use of the online regional chemical transport model, WRF-Chem and an ensemble square-root system. Numerical results showed that PM2.5 emissions and NO emissions in February fell by about 4.4% and 30% respectively, compared to the emissions before the COVID-19 lockdown in Jan 2020. This work show that data assimilation has the potential to inverse emission with high-resolution. © 2021, Science Press. All right reserved.
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TL;DR: In this article , the authors compared the impact of household isolation measures on air pollution in Shenyang during COVID-19 by using meteorological and pollutant data, mathematical statistics and spatial analysis methods.
Abstract: During COVID-19, Shenyang implemented strict household isolation measures, resulting in a sharp reduction in anthropogenic emission sources, providing an opportunity to explore the impact of human activities on air pollution. The period from January to April of 2020 was divided into normal period, blockade period and resumption period. Combined with meteorological and pollutant data, mathematical statistics and spatial analysis methods were used to compare with the same period of 2015-2019. The results showed that PM2.5, PM10, NO2 and O3 increased by 32.6%, 13.2%, 4.65% and 22.7% in the normal period, among which the western area changed significantly. During the blockade period, the concentration of pollutants decreased by 35.79%, 35.87%, 32.45% and -4.84%, of which the central area changed significantly. During the resumption period, the concentration of pollutants increased by 21.8%, 8.7%, 5.7% and -6.3%, and the area with the largest change was located in the western. During the blockade period, a heavy pollution occurred with PM2.5 as the main pollutant. The WRF-Chem model and the HYSPLIT model were used to reproduce the pollution occurrence process. The result showed that winds circulated as zonal winds during the pollution process at high altitudes. These winds were controlled by straight westerly and weak northwesterly airflows in front of the high pressure, and the ground was located behind the warm low pressure. Weather conditions were relatively stable. Thus, high temperatures (average > 10 ℃), high humidity (40%-60%) and slow wind (2 m/s) conditions prevailed for a long time in the Shenyang area. The unfavorable meteorological conditions lead to the occurrence of pollution. The backward trajectory showed that the potential source areas were concentrated in the urban agglomeration around Shenyang, and sporadic contributions came from North Korea.
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TL;DR: In this paper, the authors explored the impact of the COVID-19 lockdown on the concentration of air pollutants emitted by different sectors (industrial sector and transportation sector) in Nanjing for the first time.
Abstract: To avoid the spread of COVID-19, China has implemented strict lockdown policies and control measures, resulting in a dramatic decrease in air pollution and improved air quality. In this study, the air quality model WRF-Chem and the latest MEIC2019 and MEIC2020 anthropogenic emission inventories were used to simulate the air quality during the COVID-19 lockdown in 2020 and the same period in 2019. By designing different emission scenarios, this study explored the impact of the COVID-19 lockdown on the concentration of air pollutants emitted by different sectors (industrial sector and transportation sector) in Nanjing for the first time. The results indicate that influenced by the COVID-19 lockdown policies, compared with the same period in 2019, the concentrations of PM2.5, PM10, and NO2 in Nanjing decreased by 15%, 17.1%, and 20.3%, respectively, while the concentration of O3 increased by 45.1% in comparison; the concentrations of PM2.5, PM10 and NO2 emitted by industrial sector decreased by 30.7%, 30.8% and 14.0% respectively; the concentrations of PM2.5, PM10 and NO2 emitted by transportation sector decreased by 15.6%, 15.7% and 26.2% respectively. The COVID-19 lockdown has a greater impact on the concentrations of PM2.5 and PM10 emitted by the industrial sector, while the impact on air pollutants emitted by the transportation sector is more reflected in the concentration of NO2. This study provides some theoretical basis for the treatment of air pollutants in different departments in Nanjing.
References
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TL;DR: In this article, the authors developed a global model to estimate emissions of volatile organic compounds from natural sources (NVOC), which has a highly resolved spatial grid and generates hourly average emission estimates.
Abstract: Numerical assessments of global air quality and potential changes in atmospheric chemical constituents require estimates of the surface fluxes of a variety of trace gas species. We have developed a global model to estimate emissions of volatile organic compounds from natural sources (NVOC). Methane is not considered here and has been reviewed in detail elsewhere. The model has a highly resolved spatial grid (0.5° × 0.5° latitude/longitude) and generates hourly average emission estimates. Chemical species are grouped into four categories: isoprene, monoterpenes, other reactive VOC (ORVOC), and other VOC (OVOC). NVOC emissions from oceans are estimated as a function of geophysical variables from a general circulation model and ocean color satellite data. Emissions from plant foliage are estimated from ecosystem specific biomass and emission factors and algorithms describing light and temperature dependence of NVOC emissions. Foliar density estimates are based on climatic variables and satellite data. Temporal variations in the model are driven by monthly estimates of biomass and temperature and hourly light estimates. The annual global VOC flux is estimated to be 1150 Tg C, composed of 44% isoprene, 11% monoterpenes, 22.5% other reactive VOC, and 22.5% other VOC. Large uncertainties exist for each of these estimates and particularly for compounds other than isoprene and monoterpenes. Tropical woodlands (rain forest, seasonal, drought-deciduous, and savanna) contribute about half of all global natural VOC emissions. Croplands, shrublands and other woodlands contribute 10–20% apiece. Isoprene emissions calculated for temperate regions are as much as a factor of 5 higher than previous estimates.
3,859 citations
TL;DR: The Georgia Institute of Technology's Goddardard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model is used to simulate the aerosol optical thickness t for major types of tropospheric aerosols including sulfate, dust, organic carbon (OC), black carbon (BC), and sea salt.
Abstract: The Georgia Institute of Technology‐Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model is used to simulate the aerosol optical thickness t for major types of tropospheric aerosols including sulfate, dust, organic carbon (OC), black carbon (BC), and sea salt The GOCART model uses a dust emission algorithm that quantifies the dust source as a function of the degree of topographic depression, and a biomass burning emission source that includes seasonal and interannual variability based on satellite observations Results presented here show that on global average, dust aerosol has the highest t at 500 nm (0051), followed by sulfate (0040), sea salt (0027), OC (0017), and BC (0007) There are large geographical and seasonal variations of t, controlled mainly by emission, transport, and hygroscopic properties of aerosols The model calculated total ts at 500 nm have been compared with the satellite retrieval products from the Total Ozone Mapping Spectrometer (TOMS) over both land and ocean and from the Advanced Very High Resolution Radiometer (AVHRR) over the ocean The model reproduces most of the prominent features in the satellite data, with an overall agreement within a factor of 2 over the aerosol source areas and outflow regions While there are clear differences among the satellite products, a major discrepancy between the model and the satellite data is that the model shows a stronger variation of t from source to remote regions Quantitative comparison of model and satellite data is still difficult, due to the large uncertainties involved in deriving the t values by both the model and satellite retrieval, and by the inconsistency in physical and optical parameters used between the model and the satellite retrieval The comparison of monthly averaged model results with the sun photometer network [Aerosol Robotics Network (AERONET)] measurements shows that the model reproduces the seasonal variations at most of the sites, especially the places where biomass burning or dust aerosol dominates
1,301 citations
TL;DR: In this paper, the authors present the adjoint of the global chemical transport model GEOS-Chem, focusing on the chemical and thermodynamic relationships between sulfate and ammonium-nitrate aerosols and their gas-phase precursors.
Abstract: We present the adjoint of the global chemical transport model GEOS-Chem, focusing on the chemical and thermodynamic relationships between sulfate – ammonium – nitrate aerosols and their gas-phase precursors. The adjoint model is constructed from a combination of manually and automatically derived discrete adjoint algorithms and numerical solutions to continuous adjoint equations. Explicit inclusion of the processes that govern secondary formation of inorganic aerosol is shown to afford efficient calculation of model sensitivities such as the dependence of sulfate and nitrate aerosol concentrations on emissions of SOx, NOx, and NH3. The adjoint model is extensively validated by comparing adjoint to finite difference sensitivities, which are shown to agree within acceptable tolerances; most sets of comparisons have a nearly 1:1 correlation and R2>0.9. We explore the robustness of these results, noting how insufficient observations or nonlinearities in the advection routine can degrade the adjoint model performance. The potential for inverse modeling using the adjoint of GEOS-Chem is assessed in a data assimilation framework through a series of tests using simulated observations, demonstrating the feasibility of exploiting gas- and aerosol-phase measurements for optimizing emission inventories of aerosol precursors.
377 citations
TL;DR: In this article, the authors assimilated AOD and surface PM2.5 observations with the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVAR) data assimilation system.
Abstract: [1] Total 550 nm aerosol optical depth (AOD) retrievals from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface fine particulate matter (PM2.5) observations were assimilated with the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVAR) data assimilation (DA) system. Parallel experiments assimilated AOD and surface PM2.5observations both individually and simultaneously. New 3DVAR aerosol analyses were produced every 6 h between 0000 UTC 01 June and 1800 UTC 14 July 2010 over a domain encompassing the continental United States. The analyses initialized Weather Research and Forecasting-Chemistry (WRF-Chem) model forecasts. Assimilating AOD, either alone or in conjunction with PM2.5 observations, produced better AOD forecasts than a control experiment that did not perform DA. Additionally, individual assimilation of both AOD and PM2.5 improved surface PM2.5 forecasts compared to when no DA occurred. However, the best PM2.5 forecasts were produced when both AOD and PM2.5 were assimilated. Considering the goodness of both AOD and PM2.5 forecasts, the results unequivocally show that concurrent DA of PM2.5 and AOD observations produced the best overall forecasts, illustrating how simultaneous DA of different aerosol observations can work synergistically to improve aerosol forecasts.
114 citations
TL;DR: The usefulness of remote sensing observations for improving global aerosol modelling is confirmed by comparing a forecast run against independent MODIS Aqua AOT, which shows significant improvement over both ocean and land.
Abstract: We present a fixed-lag ensemble Kalman smoother for estimating emissions for a global aerosol transport model from remote sensing observations. We assimilate AERONET AOT and AE as well as MODIS Terra AOT over ocean to estimate the emissions for dust, sea salt and carbon aerosol and the precursor gas SO2. For January 2009, globally dust emission decreases by 26% (to 3,244 Tg/yr), sea salt emission increases by 190% (to 9073 Tg/yr), while carbon emission increases by 45% (to 136 Tg/yr), compared with the standard emissions. Remaining errors in global emissions are estimated at 62% (dust), 18% (sea salt) and 78% (carbons), with the large errors over land mostly due to the sparseness of AERONET observations. The new emissions are verified by comparing a forecast run against independent MODIS Aqua AOT, which shows significant improvement over both ocean and land. This paper confirms the usefulness of remote sensing observations for improving global aerosol modelling.
41 citations