Tropospheric NO2 Column Densities Research Articles

Observing decoupling processes of NO2 pollution and GDP growth based on satellite observations for Los Angeles and Tokyo

Nitrogen dioxide (NO2) pollution is mainly caused by anthropogenic processes such as burning of fossil fuels. Due to its severe impact on health as well as the environment in general, it is important to monitor the amount of NO2 pollution especially in areas with a high population density. Therefore, the aim of this study is to investigate to what extent spatiotemporal fluctuations in the tropospheric NO2 column density can map variations in economic output. To do so, we analyzed satellite based tropospheric NO2 column observations obtained from the ERS-2, ENVISAT, MetOp-A and MetOp-B satellite missions covering the period from 1996 to 2017 for Tokyo, Japan and Los Angeles, United States. Within our studies, a harmonic analysis was carried out in order to exclude meteorological influences. Afterwards, the NO2 time series were further investigated through a wavelet analysis method to characterize the fluctuations, that is the temporal variability of the NO2. These fluctuations have been shown to be a particularly sensitive measure of the change in NO2 pollution. We further use the gross domestic product (GDP) for the metropolitan areas as an indicator for economic performance. The results for the study area of Los Angeles shows a substantial reduction in NO2 variability starting in 2007. The NO2 variability for the period during the global financial crisis (December 2007–April 2012), dropped by around 77% in comparison to the previous period (January 1997–November 2007). However, a second period from May 2012 to December 2016 presents a further reduction of the NO2 variability of around 82% in contrast to the period from 2007 to 2012. Contrary to this, continuous economic growth can be observed during the second period. A similar picture emerges for the metropolitan region of Tokyo. A significant decline in NO2 variability of around 73% can be detected from October 2003 to January 2012. Additionally, a second period with a strong decline in NO2 variability of around 80% can be identified from February 2012 to December 2016. A plunge in GDP during 2008 reflects that year's global financial crisis, but cannot independently explain the following sharp decline in NO2 variability. Those results suggest that Tokyo as well as Los Angeles managed to substantially decouple its NO2 pollution from the economic growth due to strict air quality policies.

Ground-Based MAX-DOAS Observation of Trace Gases from 2019 to 2021 in Huaibei, China

With the spread of the COVID-19 pandemic and the implementation of closure measures in 2020, population mobility and human activities have decreased, which has seriously impacted atmospheric quality. Huaibei City is an important coal and chemical production base in East China, which faces increasing environmental problems. The impact of anthropogenic activities on air quality in this area was investigated by comparing the COVID-19 lockdown in 2020 with the normal situation in 2021. Tropospheric NO2, HCHO and SO2 column densities were observed by ground-based multiple axis differential optical absorption spectroscopy (MAX-DOAS). In situ measurements for PM2.5, NO2, SO2 and O3 were also taken. The observation period was divided into four phases, the pre-lockdown period, phase 1 lockdown, phase 2 lockdown and the post-lockdown period. Ground-based MAX-DOAS results showed that tropospheric NO2, HCHO and SO2 column densities increased by 41, 14 and 14%, respectively, during phase 1 in 2021 vs. 2020. In situ results showed that NO2 and SO2 increased by 59 and 11%, respectively, during phase 1 in 2021 vs. 2020, but PM2.5 and O3 decreased by 15 and 17%, respectively. In the phase 2 period, due to the partial lifting of control measures, the concentration of pollutants did not significantly change. The weekly MAX-DOAS results showed that there was no obvious weekend effect of pollutants in the Huaibei area, and NO2, HCHO and SO2 had obvious diurnal variation characteristics. In addition, the relationship between the column densities and wind speed and direction in 2020 and 2021 was studied. The results showed that, in the absence of traffic control in 2021, elevated sources in the Eastern part of the city emitted large amounts of NO2. The observed ratios of HCHO to NO2 suggested that tropospheric ozone production involved NOX-limited scenarios. The correlation analysis between HCHO and different gases showed that HCHO mainly originated from primary emission sources related to SO2.

Variable effects of spatial resolution on modeling of nitrogen oxides

Abstract. The lifetime and concentration of nitrogen oxides (NOx) are susceptible to nonlinear production and loss and to the resolution of a chemical transport model (CTM). This is due to the strong spatial gradients of NOx and the dependence of its own chemical loss on such gradients. In this study, we use the GEOS-Chem CTM in its high-performance implementation (GCHP) to investigate NOx simulations over the eastern United States across a wide range of spatial model resolutions (six different horizontal grids from 13 to 181 km). Following increasing grid size, afternoon surface NOx mixing ratios over July 2015 generally decrease over the Great Lakes region (GL) and increase over the southern states of the US region (SS), yielding regional differences (181 km vs. 13 km) of −16 % (in the GL) to 7 % (in the SS); meanwhile, hydrogen oxide radicals (HOx) increase over both regions, consistent with their different chemical regimes (i.e., NOx-saturated in the GL and NOx-limited in the SS). Nighttime titration of ozone by surface nitric oxide (NO) was found to be more efficient at coarser resolutions, leading to longer NOx lifetimes and higher surface mixing ratios of nitrogen dioxide (NO2) over the GL in January 2015. The tropospheric NO2 column density at typical afternoon satellite overpass time has spatially more coherent negative biases (e.g., −8 % over the GL) at coarser resolutions in July, which reversed the positive biases of surface NOx over the SS. The reduced NOx aloft (>1 km altitude) at coarser resolutions was attributable to the enhanced HOx that intrudes into the upper troposphere. Application of coarse-resolution simulations for interpreting satellite NO2 columns will generally underestimate surface NO2 over the GL and overestimate surface NO2 over the SS in summer, but it will uniformly overestimate NOx emissions over both regions. This study significantly broadens understanding of factors contributing to NOx resolution effects and the role of fine-resolution data in accurately simulating and interpreting NOx and its relevance to air quality.

Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak

Atmospheric pollutant data retrieved through satellite sensors are continually used to assess changes in air quality in the lower atmosphere. During the COVID-19 pandemic, several studies started to use satellite measurements to evaluate changes in air quality in many different regions worldwide. However, although satellite data is continuously validated, it is known that its accuracy may vary between monitored areas, requiring regionalized quality assessments. Thus, this study aimed to evaluate whether satellites could measure changes in the air quality of the state of São Paulo, Brazil, during the COVID-19 outbreak; and to verify the relationship between satellite-based data [Tropospheric NO2 column density and Aerosol Optical Depth (AOD)] and ground-based concentrations [NO2 and particulate material (PM; coarse: PM10 and fine: PM2.5)]. For this purpose, tropospheric NO2 obtained from the TROPOMI sensor and AOD retrieved from MODIS sensor data by using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm were compared with concentrations obtained from 50 automatic ground monitoring stations. The results showed low correlations between PM and AOD. For PM10, most stations showed correlations lower than 0.2, which were not significant. The results for PM2.5 were similar, but some stations showed good correlations for specific periods (before or during the COVID-19 outbreak). Satellite-based Tropospheric NO2 proved to be a good predictor for NO2 concentrations at ground level. Considering all stations with NO2 measurements, correlations >0.6 were observed, reaching 0.8 for specific stations and periods. In general, it was observed that regions with a more industrialized profile had the best correlations, in contrast with rural areas. In addition, it was observed about 57% reductions in tropospheric NO2 throughout the state of São Paulo during the COVID-19 outbreak. Variations in air pollutants were linked to the region economic vocation, since there were reductions in industrialized areas (at least 50% of the industrialized areas showed >20% decrease in NO2) and increases in areas with farming and livestock characteristics (about 70% of those areas showed increase in NO2). Our results demonstrate that Tropospheric NO2 column densities can serve as good predictors of NO2 concentrations at ground level. For MAIAC-AOD, a weak relationship was observed, requiring the evaluation of other possible predictors to describe the relationship with PM. Thus, it is concluded that regionalized assessment of satellite data accuracy is essential for assertive estimates on a regional/local level. Good quality information retrieved at specific polluted areas does not assure a worldwide use of remote sensor data.

Nitrogen oxides in the free troposphere: implications for tropospheric oxidants and the interpretation of satellite NO2 measurements

Abstract. Satellite-based retrievals of tropospheric NO2 columns are widely used to infer NOx (≡ NO + NO2) emissions. These retrievals rely on model information for the vertical distribution of NO2. The free tropospheric background above 2 km is particularly important because the sensitivity of the retrievals increases with altitude. Free tropospheric NOx also has a strong effect on tropospheric OH and ozone concentrations. Here we use observations from three aircraft campaigns (SEAC4RS, DC3, and ATom) and four atmospheric chemistry models (GEOS-Chem, GMI, TM5, and CAMS) to evaluate the model capabilities for simulating NOx in the free troposphere and attribute it to sources. NO2 measurements during the Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS) and Deep Convective Clouds and Chemistry (DC3) campaigns over the southeastern U.S. in summer show increasing concentrations in the upper troposphere above 10 km, which are not replicated by the GEOS-Chem, although the model is consistent with the NO measurements. Using concurrent NO, NO2, and ozone observations from a DC3 flight in a thunderstorm outflow, we show that the NO2 measurements in the upper troposphere are biased high, plausibly due to interference from thermally labile NO2 reservoirs such as peroxynitric acid (HNO4) and methyl peroxy nitrate (MPN). We find that NO2 concentrations calculated from the NO measurements and NO–NO2 photochemical steady state (PSS) are more reliable to evaluate the vertical profiles of NO2 in models. GEOS-Chem reproduces the shape of the PSS-inferred NO2 profiles throughout the troposphere for SEAC4RS and DC3 but overestimates NO2 concentrations by about a factor of 2. The model underestimates MPN and alkyl nitrate concentrations, suggesting missing organic NOx chemistry. On the other hand, the standard GEOS-Chem model underestimates NO observations from the Atmospheric Tomography Mission (ATom) campaigns over the Pacific and Atlantic oceans, indicating a missing NOx source over the oceans. We find that we can account for this missing source by including in the model the photolysis of particulate nitrate on sea salt aerosols at rates inferred from laboratory studies and field observations of nitrous acid (HONO) over the Atlantic. The median PSS-inferred tropospheric NO2 column density for the ATom campaign is 1.7 ± 0.44 × 1014 molec. cm−2, and the NO2 column density simulated by the four models is in the range of 1.4–2.4 × 1014 molec. cm−2, implying that the uncertainty from using modeled NO2 tropospheric columns over clean areas in the retrievals for stratosphere–troposphere separation is about 1 × 1014 molec. cm−2. We find from GEOS-Chem that lightning is the main primary NOx source in the free troposphere over the tropics and southern midlatitudes, but aircraft emissions dominate at northern midlatitudes in winter and in summer over the oceans. Particulate nitrate photolysis increases ozone concentrations by up to 5 ppbv (parts per billion by volume) in the free troposphere in the northern extratropics in the model, which would largely correct the low model bias relative to ozonesonde observations. Global tropospheric OH concentrations increase by 19 %. The contribution of the free tropospheric background to the tropospheric NO2 columns observed by satellites over the contiguous U.S. increases from 25 ± 11 % in winter to 65 ± 9 % in summer, according to the GEOS-Chem vertical profiles. This needs to be accounted for when deriving NOx emissions from satellite NO2 column measurements.

Impacts of Strategic Mobility Restrictions Policies during 2020 COVID-19 Outbreak on Brazil’s Regional Air Quality

With the unexpected arrival of the COVID-19 pandemic, countries worldwide were forced to take measures to curb its transmission. Mobility restrictions policies were the primary preventive measures applied around the globe. In addition to reducing the disease spread, they resulted in air quality changes in urbanized areas. In this sense, this study aimed to investigate how the mobility restrictions imposed by public policies impacted the air quality in Brazil during the COVID-19 2020 outbreak. Therefore, air quality (CO, NO2, PM10, PM2.5, and O3) and urban mobility datasets available in five populous Brazilian states (São Paulo, Rio de Janeiro, Espírito Santo, Paraná, and Rio Grande do Sul) were analyzed. Variations in air pollutant concentrations were verified by comparing the period during mobility restrictions (2020) and the average concentrations found before the restrictions (2015–2019). In addition, spatial assessment of changes was evaluated using tropospheric NO2 column densities from the TROPOMI. Although there was no national regulation regarding mobility restrictions, the results show that the reduction in urban mobility was similar for all the analyzed states. Following the mobility behavior, reducing air pollutant concentrations were significant for the first 30 days of restrictions. During this period, the most substantial reductions were observed for CO in the State of Rio Grande do Sul (–53%), NO2 in Rio de Janeiro (–34%), and PM10 in Espírito Santo (–23%). The exception was observed for O3, which followed the world trend of increasing concentrations (e.g., 40% increase in Paraná). Spatially, it was possible to evidence that large urban centers (with a large vehicle fleet) were the ones that showed the most significant reduction in NO2. However, when analyzing longer periods (over 90 days), there is a trend towards an increase in the concentrations of primary pollutants and a consequent reduction in O3, reflecting the significant increase in mobility rates.

The Spring Festival Effect: The change in NO2 column concentration in China caused by the migration of human activities

The Spring Festival is the most important holiday in China, and human activity and population mobility may contribute greatly to air quality. According to the satellite-based tropospheric nitrogen dioxide (NO2) column and ground-based observational concentration of NO2 in megacities from 2013 to 2018 around the Spring Festival, we found that NO2 concentration obviously decreases, presenting a “tide phenomenon”, particularly in the megacities, with the tropospheric NO2 column density decreasing by 31.8%–44.5%. The tropospheric NO2 column density in Beijing decreased by 41.6% and rebounded by 22.3% after the festival. Vehicle sources were among the important causes of NOx emissions in the megacities, and traffic intensity decreased significantly during the festival. As the coronavirus disease 2019 (COVID-19) pandemic progresses, the traffic intensity in urban areas is decreasing significantly, with the tropospheric NO2 column density decreasing by 56.2% and rebounding by only 6.8% in 2020, without the “tide phenomenon”.

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.

Influence of mixing height and atmospheric stability conditions on correlation of NO2 columns and surface concentrations in a Mexico‐United States border region

AbstractThe objective was to analyze how representative tropospheric NO2 column densities are of surface NO2 measurements under different atmospheric stability conditions in the air basin of two border cities: Calexico, United States, and Mexicali, Mexico. NO2 columns were measured by the Ozone Monitoring Instrument (OMI) on the NASA Aura satellite. NO2 concentrations and meteorological parameters were also measured on the surface for comparison. Specifically, the correlations between OMI and surface NO2 concentrations under different atmospheric stability conditions according to the Pasquill‐Gifford (P‐G) and Monin‐Obukhov (M‐O) classification schemes were determined for 2017 and 2018. During the passage of the satellite through the study area (11:00–13:00 UTC−8), unstable conditions were documented in both years. Good correlation was found between the surface NO2 and OMI NO2 column observations in the second semester of each year, particularly under unstable conditions as diagnosed by the P‐G and M‐O schemes applied in the first and second year, respectively. However, a weakening of these conditions occurs during the autumn–winter period. In both cases, the highest determination coefficients were found for Calexico, with values of 0.48 and 0.36 in 2017 and 2018, respectively; for Mexicali, the determination coefficients were 0.23 and 0.35, respectively. Under each atmospheric stability scheme, the mechanical and convective turbulence caused a decreasing trend in wind speed and solar radiation over the course of second semester of 2017 and in friction velocity, temperature, and sensible heat flux over the course of the same period for 2018. The negative trend of these parameters during the analyzed time frames helped to reduce the influence of unstable atmospheric conditions, favoring better correlations between satellite and surface NO2 measurements. The methodology applied and results obtained herein can enable us to better understand the representativeness of OMI NO2 data in arid border zones with extreme meteorological conditions.

Deep learning-based downscaling of tropospheric nitrogen dioxide using ground-level and satellite observations

Air quality is one of the major issues within an urban area that affect people's living environment and health conditions. Existing observations are not adequate to provide a spatiotemporally comprehensive air quality information for vulnerable populations to plan ahead. Launched in 2017, TROPOspheric Monitoring Instrument (TROPOMI) provides a high spatial resolution (~5 km) tropospheric air quality measurement that captures the spatial variability of air pollution, but still limited by its daily overpass in the temporal dimension and relatively short historical records. Integrating with the hourly available AirNOW observations by ground-level discrete stations, we proposed and compared two deep learning methods that learn the relationship between the ground-level nitrogen dioxide (NO2) observation from AirNOW and the tropospheric NO2 column density from TROPOMI to downscale the daily NO2 to an hourly resolution. The input predictors include the locations of AirNOW stations, AirNOW NO2 observations, boundary layer height, other meteorological status, elevation, major roads, and power plants. The learned relationship can be used to produce NO2 emission estimates at the sub-urban scale on an hourly basis. The two methods include 1) an integrated method between inverse weighted distance and a feed forward neural network (IDW + DNN), and 2) a deep matrix network (DMN) that maps the discrete AirNOW observations directly to the distribution of TROPOMI observations. We further compared the accuracies of both models using different configurations of input predictors and validated their average Root Mean Squared Error (RMSE), average Mean Absolute Error (MAE) and the spatial distribution of errors. Results show that DMN generates more reliable NO2 estimates and captures a better spatial distribution of NO2 concentrations than the IDW + DNN model.

Quantifying the Impact of the Covid-19 Lockdown Measures on Nitrogen Dioxide Levels throughout Europe

In this paper, the effect of the lockdown measures on nitrogen dioxide (NO2) in Europe is analysed by a statistical model approach based on a generalised additive model (GAM). The GAM is designed to find relationships between various meteorological parameters and temporal metrics (day of week, season, etc.) on the one hand and the level of pollutants on the other. The model is first trained on measurement data from almost 2000 monitoring stations during 2015–2019 and then applied to the same stations in 2020, providing predictions of expected concentrations in the absence of a lockdown. The difference between the modelled levels and the actual measurements from 2020 is used to calculate the impact of the lockdown measures adjusted for confounding effects, such as meteorology and temporal trends. The study is focused on April 2020, the month with the strongest reductions in NO2, as well as on the gradual recovery until the end of July. Significant differences between the countries are identified, with the largest NO2 reductions in Spain, France, Italy, Great Britain and Portugal and the smallest in eastern countries (Poland and Hungary). The model is found to perform best for urban and suburban sites. A comparison between the found relative changes in urban surface NO2 data during the lockdown and the corresponding changes in tropospheric vertical NO2 column density as observed by the TROPOMI instrument on Sentinel-5P revealed good agreement despite substantial differences in the observing method.

Atmospheric Pollutants Assessment during the COVID-19 Lockdown Using Remote Sensing and Ground-based Measurements in Buenos Aires, Argentina

The COVID-19 outbreak measures of lockdown have generated exceptional urban behavior conditions allowing the analysis of a unique scenario. We examined the atmospheric emissions in Buenos Aires, Argentina, based on urban and industrial continuous monitoring of NO2, PM10 and PM2.5, and NO2 (TROPOMI/Sentinel-5p) and AOD (MAIAC/MODIS, Aqua) satellite products, in addition with meteorological data. We considered the time intervals before and after the announcement of lockdown, including the same periods for 2019. The results showed that NO2 and PM10 concentrations fell by ~30% and 44%, respectively, at urban stations during lockdown compared to 2019. An increase in PM at the industrial station (64% PM2.5 and 8% PM10) could be due to the contribution of industrial sources other than vehicle traffic. Also, we observed a reduction of the tropospheric NO2 column density mean by 54% at urban stations, and AOD values decreased between 38% and 66% during 2020. Concerning the spatial distribution, the tropospheric NO2 column showed a significant reduction of NO2 for the monthly mean in the metropolitan area at lockdown. Similarly, the AOD highest values had a greater extension for 2020 during the pre-lockdown monthly period. After the strict lockdown, concentration values increased steadily, particularly in ground-based measurements. Therefore, we were able to demonstrate the complementarity of ground-based and satellite data measurements of NO2 and aerosol to identify the effects of lockdown measures on the spatial and temporal variability of pollutants.

Surface and satellite observations of air pollution in India during COVID-19 lockdown: Implication to air quality

The strict nationwide lockdown imposed in India starting from 25th March 2020 to prevent the spread of COVID-19 disease reduced the mobility and interrupted several important anthropogenic emission sources thereby creating a temporary air quality improvement. This study conducts a multi-scale (national-regional-city), multi-species, and multi-platform analysis of air pollutants and meteorological data by synergizing surface and satellite observations. Our analysis suggests a significant reduction in surface measurements of nitrogen dioxide (NO2) (46–61 %) and fine particulate matter (PM2.5) (42–60 %) during the lockdown period that are also corroborated by the reduction in satellite observed aerosol optical depth (AOD) (3–56 %) and tropospheric NO2 column density (25–50 %) data over multiple cities. Other species, namely coarse particulate matter (PM10) (24–62 %), ozone (22–56 %) also showed a substantial reduction whereas carbon monoxide (16–46 %), exhibited a moderate decline. In contrast, sulfur dioxide (SO2) levels did not show any defined reduction trend but rather increased in Mumbai, Bengaluru, and Kolkata. The temporary air quality improvement achieved by the painful natural experiment of this pandemic has helped demonstrate the importance of reducing emissions from other sectors along with transportation and industry to achieve the national air quality targets in the future.

COVID-19's impact on the atmospheric environment in the Southeast Asia region

Since its first appearance in Wuhan, China at the end of 2019, the new coronavirus (COVID-19) has evolved a global pandemic within three months, with more than 4.3 million confirmed cases worldwide until mid-May 2020. As many countries around the world, Malaysia and other southeast Asian (SEA) countries have also enforced lockdown at different degrees to contain the spread of the disease, which has brought some positive effects on natural environment. Therefore, evaluating the reduction in anthropogenic emissions due to COVID-19 and the related governmental measures to restrict its expansion is crucial to assess its impacts on air pollution and economic growth. In this study, we used aerosol optical depth (AOD) observations from Himawari-8 satellite, along with tropospheric NO2 column density from Aura-OMI over SEA, and ground-based pollution measurements at several stations across Malaysia, in order to quantify the changes in aerosol and air pollutants associated with the general shutdown of anthropogenic and industrial activities due to COVID-19. The lockdown has led to a notable decrease in AOD over SEA and in the pollution outflow over the oceanic regions, while a significant decrease (27% - 30%) in tropospheric NO2 was observed over areas not affected by seasonal biomass burning. Especially in Malaysia, PM10, PM2.5, NO2, SO2, and CO concentrations have been decreased by 26–31%, 23–32%, 63–64%, 9–20%, and 25–31%, respectively, in the urban areas during the lockdown phase, compared to the same periods in 2018 and 2019. Notable reductions are also seen at industrial, suburban and rural sites across the country. Quantifying the reductions in major and health harmful air pollutants is crucial for health-related research and for air-quality and climate-change studies.

Transformative Urban Changes of Beijing in the Decade of the 2000s

The rapid economic growth, the exodus from rural to urban areas, and the associated extreme urban development that occurred in China in the decade of the 2000s have severely impacted the environment in Beijing, its vicinity, and beyond. This article presents an innovative approach for assessing mega-urban changes and their impact on the environment based on the use of decadal QuikSCAT (QSCAT) satellite data, acquired globally by the SeaWinds scatterometer over that period. The Dense Sampling Method (DSM) is applied to QSCAT data to obtain reliable annual infrastructure-based urban observations at a posting of ~1 km. The DSM-QSCAT data, along with different DSM-based change indices, were used to delineate the extent of the Beijing infrastructure-based urban area in each year between 2000 and 2009, and assess its development over time, enabling a physical quantification of its urbanization which reflects the implementation of various development policies during the same time period. Eventually, as a proxy for the impact of Beijing urbanization on the environment, the decadal trend of its infrastructure-based urbanization is compared with that of the corresponding tropospheric nitrogen dioxide (NO2) column densities as observed from the Global Ozone Monitoring Experiment (GOME) instrument aboard the second European Remote Sensing satellite (ERS-2) between 2000 and 2002, and from the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY aboard of the ESA’s ENVIronmental SATellite (SCIAMACHY /ENVISAT) between 2003 and 2009. Results reveal a threefold increase of the yearly tropospheric NO2 column density within the Beijing infrastructure-based urban area extent in 2009, which had quadrupled since 2000.

Stratosphere–troposphere separation of nitrogen dioxide columns from the TEMPO geostationary satellite instrument

Abstract. Separating the stratospheric and tropospheric contributions in satellite retrievals of atmospheric NO2 column abundance is a crucial step in the interpretation and application of the satellite observations. A variety of stratosphere–troposphere separation algorithms have been developed for sun-synchronous instruments in low Earth orbit (LEO) that benefit from global coverage, including broad clean regions with negligible tropospheric NO2 compared to stratospheric NO2. These global sun-synchronous algorithms need to be evaluated and refined for forthcoming geostationary instruments focused on continental regions, which lack this global context and require hourly estimates of the stratospheric column. Here we develop and assess a spatial filtering algorithm for the upcoming TEMPO geostationary instrument that will target North America. Developments include using independent satellite observations to identify likely locations of tropospheric enhancements, using independent LEO observations for spatial context, consideration of diurnally varying partial fields of regard, and a filter based on stratospheric to tropospheric air mass factor ratios. We test the algorithm with LEO observations from the OMI instrument with an afternoon overpass, and from the GOME-2 instrument with a morning overpass. We compare our TEMPO field of regard algorithm against an identical global algorithm to investigate the penalty resulting from the limited spatial coverage in geostationary orbit, and find excellent agreement in the estimated mean daily tropospheric NO2 column densities (R2=0.999, slope=1.009 for July and R2=0.998, slope=0.999 for January). The algorithm performs well even when only small parts of the continent are observed by TEMPO. The algorithm is challenged the most by east coast morning retrievals in the wintertime (e.g., R2=0.995, slope=1.038 at 14:00 UTC). We find independent global LEO observations (corrected for time of day) provide important context near the field-of-regard edges. We also test the performance of the TEMPO algorithm without these supporting global observations. Most of the continent is unaffected (R2=0.924 and slope=0.973 for July and R2=0.996 and slope=1.008 for January), with 90 % of the pixels having differences of less than ±0.2×1015 molecules cm−2 between the TEMPO tropospheric NO2 column density and the global algorithm. For near-real-time retrieval, even a climatological estimate of the stratospheric NO2 surrounding the field of regard would improve this agreement. In general, the additional penalty of a limited field of regard from TEMPO introduces no more error than normally expected in most global stratosphere–troposphere separation algorithms. Overall, we conclude that hourly near-real-time stratosphere–troposphere separation for the retrieval of NO2 tropospheric column densities by the TEMPO geostationary instrument is both feasible and robust, regardless of the diurnally varying limited field of regard.

Improved Satellite Retrieval of Tropospheric NO2 Column Density via Updating of Air Mass Factor (AMF): Case Study of Southern China

Improving air quality and reducing human exposure to unhealthy levels of airborne chemicals are important global missions, particularly in China. Satellite remote sensing offers a powerful tool to examine regional trends in NO2, thus providing a direct measure of key parameters that strongly affect surface air quality. To accurately resolve spatial gradients in NO2 concentration using satellite observations and thus understand local and regional aspects of air quality, a priori input data at sufficiently high spatial and temporal resolution to account for pixel-to-pixel variability in the characteristics of the land and atmosphere are required. In this paper, we adapt the Berkeley High Resolution product (BEHR-HK) and meteorological outputs from the Weather Research and Forecasting (WRF) model to describe column NO2 in southern China. The BEHR approach is particularly useful for places with large spatial variabilities and terrain height differences such as China. There are two major objectives and goals: (1) developing new BEHR-HK v3.0C product for retrieving tropospheric NO2 vertical column density (TVCD) within part of southern China, for four months of 2015, based upon satellite datasets from Ozone Monitoring Instrument (OMI); and (2) evaluating BEHR-HK v3.0C retrieval result through validation, by comparing with MAX-DOAS tropospheric column measurements conducted in Guangzhou. Results show that all BEHR-HK retrieval algorithms (with R-value of 0.9839 for v3.0C) are of higher consistency with MAX-DOAS measurements than OMI-NASA retrieval (with R-value of 0.7644). This opens new windows into research questions that require high spatial resolution, for example retrieving NO2 vertical column and ground pollutant concentration in China and other countries.

Spatial and temporal distribution of NO2 and SO2 in Inner Mongolia urban agglomeration obtained from satellite remote sensing and ground observations

Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are important pollution gases which can affect air quality, human health and even climate change. Based on the combination of tropospheric NO2 and SO2 column density products derived from OMI satellite with the ground station observations, this study analyzed the spatial-temporal distribution of NO2 and SO2 amount in Inner Mongolia urban agglomerations. It shows that NO2 increased continually from 2005 to 2011 at a rate of 14.3% per year and then decreased from 2011 to 2016 at a rate of −8.1% per year. SO2 increased from 2005 to 2007 with a rate 9.7% per year. While with a peak value in 2011, SO2 generally showed a decreasing trend of −1.6% per year from 2007 to 2016. With regard to the spatial pattern, the highest levels of NO2 occur in Hohhot and Baotou, followed by Wuhai and Ordos, the least in Bayannur. Compared with NO2, the spatial distribution of SO2 is slightly different. The pollution of SO2 is the most serious in Wuhai, followed by Hohhot and Baotou, and the lightest in Ordos and Bayannur. The diurnal variations of NO2 and SO2 are basically the same, which decrease from 0:00 to 6:00, then increase to a peak value at 8:00, and decrease from 8:00 to 15:00. The diurnal variation of NO2 and SO2 is highly related to the diurnal variation of both anthropogenic emission and boundary layer height. Differently, the long-term spatial-temporal distribution of NO2 and SO2 are more closely related to human activities.

Multi-sensor temporal assessment of tropospheric nitrogen dioxide column densities over Pakistan.

Spatial and temporal distributions of tropospheric NO2 vertical column densities over Pakistan during the period 2002-2014 are discussed. Data products from three satellite instruments SCIAMACHY, OMI, and GOME-2 are used to prepare a database of tropospheric NO2 column densities over Pakistan and temporal evolution is also determined. Plausible NO2 sources in Pakistan are also discussed. The results show a large NO2 growth over all provinces and the major cities of Pakistan except the megacity of Karachi. Decline in industrial activities due to energy crises, worsening law and order situation, terrorist attacks, and political instability was explored as the main factor for lower NO2 VCDs over Karachi City. The overall increase can be attributed to the anthropogenic emissions over the areas with high population, traffic density, and industrial activities. Source identification revealed that use of fossil fuels by various sectors including power generation, vehicles, and residential sectors along with agriculture fires are among significant sources of NO2 emissions in Pakistan. Existing emission inventories such as EDGARv4.2 and MACCity largely underestimate the true anthropogenic NOx emissions in Pakistan. This study may provide vital information to policy makers and regulatory authorities in developing countries, including Pakistan, in order to devise effective air pollution abatement policies.

The contribution of soil biogenic NO and HONO emissions from a managed hyperarid ecosystem to the regional NO&amp;lt;sub&amp;gt;&amp;lt;i&amp;gt;x&amp;lt;/i&amp;gt;&amp;lt;/sub&amp;gt; emissions during growing season

Abstract. A study was carried out to understand the contributions of soil biogenic NO emissions from managed (fertilized and irrigated) hyperarid ecosystems in NW China to the regional NOx emissions during the growing season. Soil biogenic net potential NO fluxes were quantified by laboratory incubation of soil samples from the three dominating ecosystems (desert, cotton, and grape fields). Regional biogenic NO emissions were calculated bottom-up hourly for the entire growing season (April–September 2010) by considering corresponding land use, hourly data of soil temperature, gravimetric soil moisture, and fertilizer enhancement factors. The regional HONO emissions were estimated using the ratio of the optimum condition ((FN,opt(HONO) to FN,opt (NO)). Regional anthropogenic NOx emissions were calculated bottom-up from annual statistical data provided by regional and local government bureaus which have been downscaled to monthly value. Regional top-down emission estimates of NOx were derived on the monthly basis from satellite observations (OMI) of tropospheric vertical NO2 column densities and prescribed values of the tropospheric NOx lifetime. In order to compare the top-down and bottom-up emission estimates, all emission estimates were expressed in terms of mass of atomic nitrogen. Consequently, monthly top-down NOx emissions (total) were compared with monthly bottom-up NOx emissions (biogenic + anthropogenic) for the time of the satellite overpass (around 13:00 LT) with the consideration of the diurnal cycle of bottom-up estimates. Annual variation in total Tohsun Oasis NOx emissions is characterized by a strong peak in winter (December–February) and a secondary peak in summer (June–August). During summer, soil biogenic emissions were from equal to double that of related anthropogenic emissions, and grape soils were the main contributor to soil biogenic emissions, followed by cotton soils, while emissions from the desert were negligible. The top-down and bottom-up emission estimates were shown to be useful methods to estimate the monthly/seasonal cycle of the total regional NOx emissions. The resulting total NOx emissions show a strong peak in winter and a secondary peak in summer, and the second maximum in summer was only found if the soil emissions were taken into account, which provides confidence in both completely independent methods. Despite the regional character of these findings, particularly the second maximum in summer provides substantial evidence to hypothesize that biogenic emissions from soils of managed drylands (irrigated and fertilized) in the growing period may be much more important contributors to regional NOx budgets of dryland regions than thought before.