Tropospheric NO2 Vertical Column Densities Research Articles

Quantifying urban, industrial, and background changes in NO<sub>2</sub> during the COVID-19 lockdown period based on TROPOMI satellite observations

Abstract. The COVID-19 lockdown had a large impact on anthropogenic emissions of air pollutants and particularly on nitrogen dioxide (NO2). While the overall NO2 decline over some large cities is well-established, understanding the details remains a challenge since multiple source categories contribute. In this study, a new method of isolation of three components (background NO2, NO2 from urban sources, and NO2 from industrial point sources) is applied to estimate the impact of the COVID-19 lockdown on each of them. The approach is based on fitting satellite data by a statistical model with empirical plume dispersion functions driven by a meteorological reanalysis. Population density and surface elevation data as well as coordinates of industrial sources were used in the analysis. The tropospheric NO2 vertical column density (VCD) values measured by the Tropospheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor over 261 urban areas for the period from 16 March to 15 June 2020 were compared with the average VCD values for the same period in 2018 and 2019. While the background NO2 component remained almost unchanged, the urban NO2 component declined by −18 % to −28 % over most regions. India, South America, and a part of Europe (particularly, Italy, France, and Spain) demonstrated a −40 % to −50 % urban emission decline. In contrast, the decline over urban areas in China, where the lockdown was over during the analysed period, was, on average, only -4.4±8 %. Emissions from large industrial sources in the analysed urban areas varied greatly from region to region from -4.8±6 % for China to -40±10 % for India. Estimated changes in urban emissions are correlated with changes in Google mobility data (the correlation coefficient is 0.62) confirming that changes in traffic were one of the key elements in the decline in urban NO2 emissions. No correlation was found between changes in background NO2 and Google mobility data. On the global scale, the background and urban components were remarkably stable in 2018, 2019, and 2021, with averages of all analysed areas all being within ±2.5 % and suggesting that there were no substantial drifts or shifts in TROPOMI data. The 2020 data are clearly an outlier: in 2020, the mean background component for all analysed areas (without China) was -6.0%±1.2 % and the mean urban component was -26.7±2.6 % or 20σ below the baseline level from the other years.

The effectiveness of emission control policies in regulating air pollution over coastal ports of China: Spatiotemporal variations of NO2 and SO2

Air pollution over coastal port areas has become a major concern of today's world. As one of the largest shipping countries, China has issued a series of emission control policies to regulate air pollution in these areas. This study investigated the effectiveness of these policies by exploring the spatiotemporal variations of NO2 and SO2 in the five coastal port clusters i.e., Bohai Rim Area (BRA), Yangtze River Delta (YRD), Pearl River Delta (PRD), Southeast Coast (SEC) and Southwest Coast (SWC) of China from 2005 to 2020 using tropospheric NO2 and planetary boundary layer SO2 Vertical Column Densities (VCDs) retrieved from Ozone Monitoring Instrument (OMI). The NO2 levels were found to be the highest around 2011, whereas the SO2 levels showed decreasing trends after 2007, with rebounds found around 2011. BRA exhibited the highest average air pollution level compared to the other port clusters. PRD has implemented emission control policies earlier than the other port clusters, whereas YRD and BRA have controlled the NO2 and SO2 levels more effectively during 2010–2020. SWC saw an increase in the NO2 level during 2015–2020. As for individual ports, air pollution in Guangzhou port has been controlled most effectively during the entire study period; Qinhuangdao, Tangshan, Tianjin, Lianyungang, Qingdao and Rizhao have made great strides in reducing air pollution during 2010–2020. However, Beibu Gulf and Haikou showed clear increases in NO2 levels during 2018–2020 which may be attributed to strong growth in cargo throughput. Findings from this study can potentially help different authorities evaluate their effectiveness of air pollution control actions and support their future decision making.

A New Separation Methodology for the Maritime Sector Emissions over the Mediterranean and Black Sea Regions

The aim of this paper is to apply a new lane separation methodology for the maritime sector emissions attributed to the different vessel types and marine traffic loads in the Mediterranean and the Black Sea defined via the European Marine and Observation Data network (EMODnet), developed in 2016. This methodology is implemented for the first time on the Copernicus Atmospheric Monitoring Service Global Shipping (CAMS-GLOB-SHIP v2.1) nitrogen oxides (NOX) emissions inventory, on the Sentinel-5 Precursor Tropospheric Monitoring Instrument (TROPOMI) nitrogen dioxide (NO2) tropospheric vertical column densities, and on the LOTOS-EUROS (Long Term Ozone Simulation—European Operational Smog) CTM (chemical transport model) simulations. By applying this new EMODnet-based lane separation method to the CAMS-GLOB-SHIP v2.1 emission inventory, we find that cargo and tanker vessels account for approximately 80% of the total emissions in the Mediterranean, followed by fishing, passenger, and other vessel emissions with contributions of 8%, 7%, and 5%, respectively. Tropospheric NO2 vertical column densities sensed by TROPOMI for 2019 and simulated by the LOTOS-EUROS CTM have been successfully attributed to the major vessel activities in the Mediterranean; the mean annual NO2 load of the observations and the simulations reported for the entire maritime EMODnet-reported fleet of the Mediterranean is in satisfactory agreement, 1.26 ± 0.56 × 1015 molecules cm−2 and 0.98 ± 0.41 × 1015 molecules cm−2, respectively. The spatial correlation of the annual maritime NO2 loads of all vessel types between observation and simulation ranges between 0.93 and 0.98. On a seasonal basis, both observations and simulations show a common variability. The wintertime comparisons are in excellent agreement for the highest emitting sector, cargo vessels, with the observations reporting a mean load of 0.98 ± 0.54 and the simulations of 0.81 ± 0.45 × 1015 molecules cm−2 and correlation of 0.88. Similarly, the passenger sector reports 0.45 ± 0.49 and 0.39 ± 0.45 × 1015 molecules cm−2 respectively, with correlation of 0.95. In summertime, the simulations report a higher decrease in modelled tropospheric columns than the observations, however, still resulting in a high correlation between 0.85 and 0.94 for all sectors. These encouraging findings will permit us to proceed with creating a top-down inventory for NOx shipping emissions using S5P/TROPOMI satellite observations and a data assimilation technique based on the LOTOS-EUROS chemical transport model.

Trend reversal from source region to remote tropospheric NO2 columns.

Global tropospheric nitrogen dioxide (NO2) changes have different or even opposite impacts on the photochemical formation of ozone in different regions under different weather and emission condition. However, the changes over regions affected by different levels of human activities are not well known. By using the Ozone Monitoring Instrument (OMI) measurements, we analyzed spatial and temporal variability of tropospheric NO2 vertical column densities (VCDs) from the megacity to the background regions during 2005-2019. Consistent with previous research, our results show a rapid decline of tropospheric NO2 column density over regions strongly affected by human activities, especially for source regions. The decline rates of annual mean NO2 VCDs are up to - 2.44% year-1, - 2.37% year-1, and - 1.43% year-1 over megacities of the USA, Europe, and China, respectively. However, the decreasing rate has slowed, and even reversed to an increasing trend, of tropospheric NO2 from megacities to developing and remote regions, especially over ocean and background areas less affected by anthropogenic activity. From 2005 to 2019, the NO2 VCDs over the ocean and background areas increased for all seasons, with the statistically significant (p < 0.05) trends of 1.15%/0.74% year-1 (MAM), 1.20%/1.06% year-1 (JJA), 1.16%/0.82% year-1 (SON), and 0.68%/0.65% year-1 (DJF), respectively, for ocean/background region. Such decreasing/increasing trends of tropospheric NO2 over sources/remote regions may prevent the ozone air pollution to be effectively resolved to achieve air quality goals worldwide.

Spatiotempo\ufeffral evolution analysis of NO2 column density before and after COVID-19 pandemic in Henan province based on SI-APSTE model

Air pollution is the result of comprehensive evolution of a dynamic and complex system composed of emission sources, topography, meteorology and other environmental factors. The establishment of spatiotemporal evolution model is of great significance for the study of air pollution mechanism, trend prediction, identification of pollution sources and pollution control. In this paper, the air pollution system is described based on cellular automata and restricted agents, and a Swarm Intelligence based Air Pollution SpatioTemporal Evolution (SI-APSTE) model is constructed. Then the spatiotemporal evolution analysis method of air pollution is studied. Taking Henan Province before and after COVID-19 pandemic as an example, the NO2 products of TROPOMI and OMI were analysed based on SI-APSTE model. The tropospheric NO2 Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of Henan province before COVID-19 pandemic were studied. Then the tropospheric NO2 VCDs of TROPOMI was used to study the pandemic period, month-on-month and year-on-year in 18 urban areas of Henan Province. The results show that SI-APSTE model can effectively analyse the spatiotemporal evolution of air pollution by using environmental big data and swarm intelligence, and also can establish a theoretical basis for pollution source identification and trend prediction.

Atmospheric NO2 Distribution Characteristics and Influencing Factors in Yangtze River Economic Belt: Analysis of the NO2 Product of TROPOMI/Sentinel-5P

Nitrogen dioxide (NO2) has a great influence on atmospheric chemistry. Scientifically identifying the temporal-spatial characteristics of NO2 distribution and their driving factors will be of realistic significance to atmospheric governance in the Yangtze River Economic Belt (YREB). Based on the NO2 data derived from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 satellite (2017~present), spatial autocorrelation analysis, standard deviation ellipse (SDE), and geodetectors were used to systematically analyze the spatial-temporal evolution and driving factors of tropospheric NO2 vertical column density (NO2 VCD) in the YREB from 2019 to 2020. The results showed that the NO2 VCD in the YREB was high in winter and autumn and low in spring and summer (temporal distribution), and high in the northeast and low in the southwest (spatial distribution), with significant spatial agglomeration. High-value agglomeration zones were collectively and stably distributed in the east region, while low-value zones were relatively dispersed. The explanatory power of each potential factor for the NO2 VCD showed regional and seasonal variations. Surface pressure was found to be a core influencing factor. Synergistic effects of factors presented bivariate enhancement or nonlinear enhancement, and interaction between any two factors strengthened the explanatory power of a single factor for the NO2 VCD.

Continental and Ecoregion‐Specific Drivers of Atmospheric NO2 and NH3 Seasonality Over Africa Revealed by Satellite Observations

AbstractAmmonia (NH3) and nitrogen oxides (NOx: nitrogen dioxide [NO2] + nitric oxide [NO]) play important roles in atmospheric chemistry. Throughout most of Africa, emissions of these gases are predominantly from soils and biomass burning. Here we use observations of tropospheric NO2 vertical column densities (VCDs) from the Ozone Monitoring Instrument from 2005 through 2017 and atmospheric NH3 VCDs from the Infrared Atmospheric Sounding Interferometer from 2008 through 2017 to evaluate seasonal variation of NO2 and NH3 VCDs across Africa and in seven African ecoregions. In regions where mean annual precipitation (MAP) is under 500 mm yr−1, we find that NO2 and NH3 VCDs are positively related to monthly precipitation, and where MAP is between 500 and 1,750 mm yr−1 or higher, NO2 VCDs are negatively related to monthly precipitation. In dry ecoregions, temperature and precipitation were important predictors of NH3 and NO2 VCDs, likely related to variation in soil emissions. In mesic ecoregions, monthly NO2 VCDs were strongly related to burned area, suggesting that biomass burning drives seasonality. NH3 VCDs in mesic ecoregions were positively related to both monthly temperature and monthly carbon monoxide (CO) VCDs, suggesting that a mixture of soil and biomass burning emissions influenced NH3 seasonality. In northern mesic ecoregions, monthly temperature explained most of the variance in monthly NH3 VCDs, suggesting that soil sources, including animal excreta, determined NH3 seasonality. In southern mesic ecoregions, monthly CO VCDs explained more variation in NH3 VCDs than temperature, suggesting that biomass burning may have greater influence over NH3 seasonality.

Switching to electric vehicles can lead to significant reductions of PM2.5 and NO2 across China

Switching to electric vehicles can lead to significant reductions of PM2.5 and NO2 across China

Evaluation and Evolution of MAX-DOAS-observed Vertical NO2 Profiles in Urban Beijing

Multiaxis differential absorption spectroscopy (MAX-DOAS) is a newly developed advanced vertical profile detection method, but the vertical nitrogen dioxide (NO2) profiles measured by MAX-DOAS have not yet been fully verified. In this study, we perform MAX-DOAS and tower gradient observations to simultaneously acquire tropospheric NO2 observations in the Beijing urban area from 1 April to 31 May 2019. The average values of the tropospheric NO2 vertical column densities measured by MAX-DOAS and the tropospheric monitoring instrument are 15.8 × 1015 and 12.4 × 1015 molecules cm−2, respectively, and the correlation coefficient R reaches 0.87. The MAX-DOAS measurements are highly consistent with the tower-based in situ measurements, and the correlation coefficients R from the ground to the upper air are 0.89 (60 m), 0.87 (160 m), and 0.76 (280 m). MAX-DOAS accurately measures the trend of NO2 vertical profile changes, although a large underestimation occurs by a factor of two. By analyzing the NO2 vertical profile, the NO2 concentration reveals an exponential decrease with height. The NO2 vertical profile also coincides with the evolution of the boundary layer height. The study shows that the NO2 over Beijing mainly originates from local sources and occurs in the boundary layer, and its vertical evolution pattern has an important guiding significance to better understand nitrate production and ozone pollution.

Long-Term Variations of Global Solar Radiation and Atmospheric Constituents at Sodankylä in the Arctic

An empirical model of global solar irradiance (EMGSI) under all sky conditions was developed by using solar radiation and meteorological parameters at Sodankylä. The calculated hourly global solar irradiance is in agreement with that observed at the ground during 2008–2011 and at the top of the atmosphere (TOA). This model is used to calculate the global solar irradiance at the ground and its attenuation in the atmosphere due to absorbing and scattering substances in 2000–2018. The sensitivity test indicates that the responses of global solar irradiance to changes in water vapor and scattering factors are nonlinear and negative, and global solar irradiance is more sensitive to changes in scattering (expressed by the scattering factor S/G, S and G are diffuse and global solar radiation, respectively) than to changes in water vapor. Using this empirical model, we calculated the albedos at the TOA and the surface, which are in agreement with the satellite-retrieved values. A good relationship between S/G and aerosol optical depth (AOD) was determined and used to estimate AOD in 2000–2018. An empirical model for estimation of tropospheric NO2 vertical column density (VCD) was also developed and used to calculate tropospheric NO2 VCD in 2000–2018. During 2000–2018, the estimated global solar irradiance decreased by 0.92%, and diffuse irradiance increased by 1.28% per year, which is ascribed to the increases of S/G (1.73%) and water vapor (0.43%). Annual surface air temperature increases by 0.07 °C per year. Annual mean loss of global solar irradiance caused by absorbing and scattering substances and total loss are 1.94, 1.17 and 3.11 MJ m−2, respectively. Annual mean losses of absorbing and scattering global solar irradiance show negative and positive trends, respectively, and the annual total loss increases by 0.24% per year. Annual mean losses due to absorption were much larger than those due to scattering. The calculated albedos at the TOA are smaller than at the surface. The calculated and satellite-retrieved annual albedos decrease at the TOA and increase at the surface. During 2000–2018, annual means of the AOD and the tropospheric NO2 VCD increased by 8.23% and 0.03% per year, respectively.

Variability of NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; concentrations over China and effect on air quality derived from satellite and ground-based observations

Abstract. The variation of NO2 concentrations in mainland China is analyzed on different timescales, from decadal to weekly, using both satellite data and data from ground-based monitoring networks. TROPOMI (TROPOspheric Monitoring Instrument) data were used to study the spatial variations of tropospheric NO2 vertical column densities (TVCDs) over the study area during 16–20 weeks after the Chinese Spring Festival (25 January 2020). These data were used to select 11 regions for more detailed analysis of the variation of NO2 TVCDs on a decadal timescale. In this analysis, monthly and annual averaged NO2 TVCDs derived from OMI (Ozone Monitoring Instrument) observations were used for the years 2011 to 2019. The results show the NO2 TVCD trends for different regions, all decreasing in response to emission reduction policies but with a different onset and a possible halt of the decrease in recent years; trends and period in the south of the study area are different from those in the north. Variations of NO2 TVCDs on shorter timescales, monthly and weekly, were analyzed using TROPOMI data. In addition, the variations of weekly-averaged ground-based NO2 concentrations in 11 major cities were analyzed together with those for O3 and PM2.5. In particular these data were used to determine their effect on the air quality as expressed by the air quality index (AQI). For quantitative estimates, the use of weekly concentrations is more accurate than the use of monthly values, and the effects of long-term trends and their reversal needs to be taken into account for the separation of effects of the lockdown and the Spring Festival. Neglecting the possible reversal of the trends leads to overestimation of the lockdown effect in the south and underestimation in the north. The ground-based data confirm earlier reports, based on satellite observations, that the expected improvement of air quality due to the reduction of NO2 concentrations was offset by the increase of the concentrations of O3 and the different effects of the lockdown measures on PM2.5, as well as effects of meteorological influences and heterogeneous chemistry. The AQI seems to be mostly influenced by PM2.5 rather than NO2. A qualitative comparison between time series of satellite and ground-based NO2 observations shows both similarities and differences. The study further shows the different behaviors in city clusters in the north and south of China, as well as inland in the Sichuan and Guanzhong basins. Effects of other holidays and events are small, except in Beijing where the air quality in 2020 was notably better than in previous years. This study was undertaken for China, but the methodology and results have consequences for air quality studies in other areas, and part of the conclusions are generally applicable.

Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

In this paper, we present the estimation of surface NO2 concentrations over Germany using a machine learning approach. TROPOMI satellite observations of tropospheric NO2 vertical column densities (VCDs) and several meteorological parameters are used to train the neural network model for the prediction of surface NO2 concentrations. The neural network model is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations. Neural network estimation of surface NO2 concentrations show good agreement with in situ monitor data with Pearson correlation coefficient (R) of 0.80. The results also show that the machine learning approach is performing better than regional CTM simulations in predicting surface NO2 concentrations. We also performed a sensitivity analysis for each input parameter of the neural network model. The validated neural network model is then used to estimate surface NO2 concentrations over Germany from 2018 to 2020. Estimated surface NO2 concentrations are used to investigate the spatio-temporal characteristics, such as seasonal and weekly variations of NO2 in Germany. The estimated surface NO2 concentrations provide comprehensive information of NO2 spatial distribution which is very useful for exposure estimation. We estimated the annual average NO2 exposure for 2018, 2019 and 2020 is 15.53, 15.24 and 13.27 µµg/m3, respectively. While the annual average NO2 concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 µµg/m3. In addition, we used the surface NO2 data set to investigate the impacts of the coronavirus disease 2019 (COVID-19) pandemic on ambient NO2 levels in Germany. In general, 10–30% lower surface NO2 concentrations are observed in 2020 compared to 2018 and 2019, indicating the significant impacts of a series of restriction measures to reduce the spread of the virus.

Reductions in NO2 burden over north equatorial Africa from decline in biomass burning in spite of growing fossil fuel use, 2005 to 2017

Socioeconomic development in low- and middle-income countries has been accompanied by increased emissions of air pollutants, such as nitrogen oxides [NOx: nitrogen dioxide (NO2) + nitric oxide (NO)], which affect human health. In sub-Saharan Africa, fossil fuel combustion has nearly doubled since 2000. At the same time, landscape biomass burning-another important NOx source-has declined in north equatorial Africa, attributed to changes in climate and anthropogenic fire management. Here, we use satellite observations of tropospheric NO2 vertical column densities (VCDs) and burned area to identify NO2 trends and drivers over Africa. Across the northern ecosystems where biomass burning occurs-home to hundreds of millions of people-mean annual tropospheric NO2 VCDs decreased by 4.5% from 2005 through 2017 during the dry season of November through February. Reductions in burned area explained the majority of variation in NO2 VCDs, though changes in fossil fuel emissions also explained some variation. Over Africa's biomass burning regions, raising mean GDP density (USD⋅km-2) above its lowest levels is associated with lower NO2 VCDs during the dry season, suggesting that economic development mitigates net NO2 emissions during these highly polluted months. In contrast to the traditional notion that socioeconomic development increases air pollutant concentrations in low- and middle-income nations, our results suggest that countries in Africa's northern biomass-burning region are following a different pathway during the fire season, resulting in potential air quality benefits. However, these benefits may be lost with increasing fossil fuel use and are absent during the rainy season.

Nitrogen dioxide reductions from satellite and surface observations during COVID-19 mitigation in Rome (Italy)

Lockdown restrictions were implemented in Italy from 10 March 2020 to contain the COVID-19 pandemic. Our study aims to evaluate air pollution changes, with focus on nitrogen dioxide (NO2), before and during the lockdown in Rome and in the surroundings. Significant NO2 declines were observed during the COVID-19 pandemic with reductions of − 50%, − 34%, and − 20% at urban traffic, urban background, and rural background stations, respectively. Tropospheric NO2 vertical column density (VCD) from the TROPOspheric Monitoring Instrument (TROPOMI) was used to evaluate the spatial-temporal variations of the NO2 before and during the lockdown for the entire area where the surface stations are located. The evaluation is concerned with the pixels including one or more air quality stations to explore the capability of the unprecedented high spatial resolution to monitor urban and rural sites from space with relation to the surface measurements. Good agreement between surface concentration and TROPOMI VCD was obtained in Rome (R = 0.64 in 2019, R = 0.77 in 2020) and in rural sites (R = 0.71 in 2019). Inversely, a slight correlation (R = 0.20) was observed in rural areas during the lockdown due to very low levels of NO2. Finally, the TROPOMI VCD showed a sharp decline in NO2, larger in urban (− 43%) than in rural sites (− 17%) as retrieved with the concurrent surface measurements averaging all the traffic and urban background (− 44%) and all the rural background stations (− 20%). These results suggest air pollution improvement in Rome gained from implementing lockdown restrictions.

Impact of COVID-19 Pandemic on Air Pollution in Poland Based on Surface Measurements and Satellite Data

Since its first detection in December 2019 in the Chinese city of Wuhan, the virus has become a global pandemic within just three months. The Polish government declared a state of emergency on March 14, 2020, which was connected with the adoption of a number of measures aimed to prevent the spread of the virus. These restrictive measures have led to improvements in air quality throughout the country. Therefore, evaluation of the reduction in anthropogenic emissions due to COVID-19 and related government measures to constrain its spread is crucial to define its impact on air pollution. During this study, aerosol optical depth (AOD) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used along with tropospheric NO2 and SO2 vertical column densities from TROPOspheric Monitoring Instrument (TROPOMI). The data on ground-based concentrations of pollutants (PM2.5, PM10, NO2 and SO2) obtained from air quality monitoring stations were also considered to assess changes in aerosols and air pollutants connected with the cessation of various kinds of anthropogenic and industrial activities due to preventive measures for COVID-19. In large cities of the country, the concentrations of PM2.5, PM10 and NO2 were reduced in the range from –5.1% to –35.5%, from –8.3% to –33.1% and from –0.4% to –18.8%. In addition, satellite data for NO2 and SO2 also indicate a decrease in concentration across the country by –10.8% and –25.6% during the COVID-19 restrictions period. The lockdown events may play a vital role as a potential solution to reduce air pollution in future as it may not be uncommon for governments to introduce deliberately selective hotspot lockdowns to control pollution levels.

Comparison of Satellite-retrieved NO2 Vertical Column Density with Ground-level NO2 concentration in a provincial scale region

Satellite remote sensing will have certain errors in data retrieval accuracy, which requires further ground observation verification. This paper examines and verifies 2019 Jiangsu provincial Ground-level monitoring data and TROPOMI data to evaluate the applicability of TROPOMI tropospheric NO2 Vertical Column Density (VCD) data and compares the results to OMI data analysis verification results. The results show that the correlation coefficient r between the TROPOMI NO2 data and Ground-level monitoring data at the monthly mean scale is as high as 0.9, and consistent seasonal cyclic changes are observed. The correlation between Ground-level monitoring data and OMI NO2 data is lower than TROPOMI (r=0.78 < 0.9). Compared to OMI satellites, the TROPOMI attains a smaller deviation in tropospheric NO2 monitoring. Jiangsu Province had the highest monthly average concentration in January, which were 38.67 ug/m3 (Ground-level), 17.35 × 1015 molec/cm2 (TROPOMI) and 20.04 × 1015 molec/cm2 (OMI). The lowest concentration in August was 13.39 ug/m3 (Ground-level), 5.2 × 1015 molec/cm2 (TROPOMI) and 7.03 × 1015 molec/cm2 (OMI).

Long-term MAX-DOAS measurements of NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;, HCHO, and aerosols and evaluation of corresponding satellite data products over Mohali in the Indo-Gangetic Plain

Abstract. We present comprehensive long-term ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements of aerosols, nitrogen dioxide (NO2), and formaldehyde (HCHO) from Mohali (30.667∘ N, 76.739∘ E; ∼310 m above mean sea level), located in the densely populated Indo-Gangetic Plain (IGP) of India. We investigate the temporal variation in tropospheric columns, surface volume mixing ratio (VMR), and vertical profiles of aerosols, NO2, and HCHO and identify factors driving their ambient levels and distributions for the period from January 2013 to June 2017. We observed mean aerosol optical depth (AOD) at 360 nm, tropospheric NO2 vertical column density (VCD), and tropospheric HCHO VCD for the measurement period to be 0.63 ± 0.51, (6.7 ± 4.1) × 1015, and (12.1 ± 7.5) × 1015 molecules cm−2, respectively. Concerning the tropospheric NO2 VCDs, Mohali was found to be less polluted than urban and suburban locations of China and western countries, but comparable HCHO VCDs were observed. For the more than 4 years of measurements during which the region around the measurement location underwent significant urban development, we did not observe obvious annual trends in AOD, NO2, and HCHO. High tropospheric NO2 VCDs were observed in periods with enhanced biomass and biofuel combustion (e.g. agricultural residue burning and domestic burning for heating). Highest tropospheric HCHO VCDs were observed in agricultural residue burning periods with favourable meteorological conditions for photochemical formation, which in previous studies have shown an implication for high ambient ozone also over the IGP. Highest AOD is observed in the monsoon season, indicating possible hygroscopic growth of the aerosol particles. Most of the NO2 is located close to the surface, whereas significant HCHO is present at higher altitudes up to 600 m during summer indicating active photochemistry at high altitudes. The vertical distribution of aerosol, NO2, and HCHO follows the change in boundary layer height (BLH), from the ERA5 dataset of European Centre for Medium-Range Weather Forecasts, between summer and winter. However, deep convection during the monsoon transports the pollutants at high altitudes similar to summer despite a shallow ERA5 BLH. Strong gradients in the vertical profiles of HCHO are observed during the months when primary anthropogenic sources dominate the formaldehyde production. High-resolution MODIS AOD measurements correlate well but were systematically higher than MAX-DOAS AODs. The ground-based MAX-DOAS measurements were used to evaluate three NO2 data products and two HCHO data products of the Ozone Monitoring Instrument (OMI) for the first time over India and the IGP. NO2 VCDs from OMI correlate reasonably with MAX-DOAS VCDs but are lower by ∼30 %–50 % due to the difference in vertical sensitivities and the rather large OMI footprint. OMI HCHO VCDs exceed the MAX-DOAS VCDs by up to 30 %. We show that there is significant scope for improvement in the a priori vertical profiles of trace gases, which are used in OMI retrievals. The difference in vertical representativeness was found to be crucial for the observed biases in NO2 and HCHO surface VMR intercomparisons. Using the ratio of NO2 and HCHO VCDs measured from MAX-DOAS, we have found that the peak daytime ozone production regime is sensitive to both NOx and VOCs in winter but strongly sensitive to NOx in other seasons.

Spatiotemporal variations of tropospheric NO2 in Lanzhou for the period 2009–2018 based on satellite remote sensing

Air quality has been associated with human health and with issues related to the climate, water, agriculture, and the economy across the world. NO2 is one of the main pollutants in the atmosphere; therefore, it is essential to analyze the quantitative changes and spatial distribution of NO2. The spatiotemporal changes in the vertical column densities and related influencing factors of tropospheric NO2 over Lanzhou for the period 2009–2018 were analyzed using remote sensing inversion data from the Ozone Monitoring Instrument and methods such as coefficient of variation, trend analysis, Hurst index, and risk analysis of NO2 population exposure. Results indicate that the concentration of the tropospheric NO2 column shows an overall upward trend from 2009 to 2018, and that the four-year mean distribution of seasonal tropospheric NO2 vertical column densities is winter > autumn > spring > summer. A combination of topographic characteristics and an analysis of meteorological factors aid the NO2 column in Lanzhou to be “high in winter and low in summer” and the analysis of socioeconomic factors reveals that car ownership makes a marked contribution to the concentration of the NO2 column in Lanzhou. Furthermore, the coefficient of variation, trend analysis, Hurst index, and NO2 population exposure risk analysis suggests that, in the Lanzhou area, overall NO2 concentrations are relatively stable, with a small fluctuation range and slow growth trend. Future projections show the vertical NO2 column densities in the research area undergoing a downward trend while the population density remains high.

Satellite validation strategy assessments based on the AROMAT campaigns

Abstract. The Airborne ROmanian Measurements of Aerosols and Trace gases (AROMAT) campaigns took place in Romania in September 2014 and August 2015. They focused on two sites: the Bucharest urban area and large power plants in the Jiu Valley. The main objectives of the campaigns were to test recently developed airborne observation systems dedicated to air quality studies and to verify their applicability for the validation of space-borne atmospheric missions such as the TROPOspheric Monitoring Instrument (TROPOMI)/Sentinel-5 Precursor (S5P). We present the AROMAT campaigns from the perspective of findings related to the validation of tropospheric NO2, SO2, and H2CO. We also quantify the emissions of NOx and SO2 at both measurement sites. We show that tropospheric NO2 vertical column density (VCD) measurements using airborne mapping instruments are well suited for satellite validation in principle. The signal-to-noise ratio of the airborne NO2 measurements is an order of magnitude higher than its space-borne counterpart when the airborne measurements are averaged at the TROPOMI pixel scale. However, we show that the temporal variation of the NO2 VCDs during a flight might be a significant source of comparison error. Considering the random error of the TROPOMI tropospheric NO2 VCD (σ), the dynamic range of the NO2 VCDs field extends from detection limit up to 37 σ (2.6×1016 molec. cm−2) and 29 σ (2×1016 molec. cm−2) for Bucharest and the Jiu Valley, respectively. For both areas, we simulate validation exercises applied to the TROPOMI tropospheric NO2 product. These simulations indicate that a comparison error budget closely matching the TROPOMI optimal target accuracy of 25 % can be obtained by adding NO2 and aerosol profile information to the airborne mapping observations, which constrains the investigated accuracy to within 28 %. In addition to NO2, our study also addresses the measurements of SO2 emissions from power plants in the Jiu Valley and an urban hotspot of H2CO in the centre of Bucharest. For these two species, we conclude that the best validation strategy would consist of deploying ground-based measurement systems at well-identified locations.

Impacts of COVID-19 lockdown, Spring Festival and meteorology on the NO2 variations in early 2020 over China based on in-situ observations, satellite retrievals and model simulations

The lockdown measures due to COVID-19 affected the industry, transportation and other human activities within China in early 2020, and subsequently the emissions of air pollutants. The decrease of atmospheric NO2 due to the COVID-19 lockdown and other factors were quantitively analyzed based on the surface concentrations by in-situ observations, the tropospheric vertical column densities (VCDs) by different satellite retrievals including OMI and TROPOMI, and the model simulations by GEOS-Chem. The results indicated that due to the COVID-19 lockdown, the surface NO2 concentrations decreased by 42% ± 8% and 26% ± 9% over China in February and March 2020, respectively. The tropospheric NO2 VCDs based on both OMI and high quality (quality assurance value (QA) ≥ 0.75) TROPOMI showed similar results as the surface NO2 concentrations. The daily variations of atmospheric NO2 during the first quarter (Q1) of 2020 were not only affected by the COVID-19 lockdown, but also by the Spring Festival (SF) holiday (January 24–30, 2020) as well as the meteorology changes due to seasonal transition. The SF holiday effect resulted in a NO2 reduction from 8 days before SF to 21 days after it (i.e. January 17 - February 15), with a maximum of 37%. From the 6 days after SF (January 31) to the end of March, the COVID-19 lockdown played an important role in the NO2 reduction, with a maximum of 51%. The meteorology changes due to seasonal transition resulted in a nearly linear decreasing trend of 25% and 40% reduction over the 90 days for the NO2 concentrations and VCDs, respectively. Comparisons between different datasets indicated that medium quality (QA ≥ 0.5) TROPOMI retrievals might suffer large biases in some periods, and thus attention must be paid when they are used for analyses, data assimilations and emission inversions.