Space borne tropospheric nitrogen dioxide (NO₂) observations from 2005–2020 over the Yangtze River Delta (YRD), China: variabilities, implications, and drivers

Nitrogen dioxide (NO2) is mainly affected by local emission and meteorology rather than long-range transport. Accurate acknowledge of its long-term variabilities and drivers are significant for understanding the evolutions of economic and social development, anthropogenic emission, and the effectiveness of pollution control measures on regional scale. In this study, we quantity the long-term variabilities and the underlying drivers of NO2 from 2005 to 2020 over the Yangtze River Delta (YRD), one of the most densely populated and highly industrialized city clusters in China, using OMI space borne observations and the multiple linear regression (MLR) model. We have compared the space borne tropospheric results to the surface in-situ data, yielding correlation coefficients of 0.8 to 0.9 over all megacities within the YRD. As a result, the tropospheric NO2 column measurements can be used as representatives of near-surface conditions, and we thus only use ground-level meteorological data for MLR regression. The inter-annual variabilities of tropospheric NO2 vertical column densities (VCDs) from 2005 to 2020 over the YRD can be divided into two stages. The first stage was from 2005 to 2011, which showed overall increasing trends with a wide range of (1.91 ± 1.50) to (6.70 ± 0.10) × 1014 molecules/cm2·yr−1 (p < 0.01) over the YRD. The second stage was from 2011 to 2020, which showed over all decreasing trends of (−6.31 ± 0.71) to (−11.01 ± 0.90) × 1014 molecules/cm2·yr−1 (p < 0.01) over each of the megacities. The seasonal cycles of tropospheric NO2 VCDs over the YRD are mainly driven by meteorology (81.01 % – 83.91 %) except during winter when anthropogenic emission contributions are pronounced (16.09 % – 18.99 %). The inter annual variabilities of tropospheric NO2 VCDs are mainly driven by anthropogenic emission (69.18 % – 81.34 %) except for a few years such as 2018 which are partly attributed to meteorology anomalies (39.07 % – 91.51 %). The increasing trends in tropospheric NO2 VCDs from 2005 to 2011 over the YRD are mainly attributed to high energy consumption associated with rapid economic growth which cause significant increases in anthropogenic NO2 emissions. The decreasing trends in tropospheric NO2 VCDs from 2011 to 2020 over the YRD are mainly attributed to the stringent clean air measures which either adjust high energy industrial structure toward low energy industrial structure or directly reduce pollutant emissions from different industrial sectors.

Assessment of the Performance of TROPOMI NO2 and SO2 Data Products in the North China Plain: Comparison, Correction and Application

The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite has been used to detect the atmospheric environment since 2017, and it is of great significance to investigate the accuracy of its products. In this work, we present comparisons between TROPOMI tropospheric NO2 and total SO2 products against ground-based MAX-DOAS at a single site (Xianghe) and OMI products over a seriously polluted region (North China Plain, NCP) in China. The results show that both NO2 and SO2 data from three datasets exhibit a similar tendency and seasonality. In addition, TROPOMI tropospheric NO2 columns are generally underestimated compared with collocated MAX-DOAS and OMI data by about 30–60%. In contrast to NO2, the monthly average SO2 retrieved from TROPOMI is larger than MAX-DOAS and OMI, with a mean bias of 2.41 (153.8%) and 2.17 × 1016 molec cm−2 (120.7%), respectively. All the results demonstrated that the TROPOMI NO2 as well as the SO2 algorithms need to be further improved. Thus, to ensure reliable analysis in NCP area, a correction method has been proposed and applied to TROPOMI Level 3 data. The revised datasets agree reasonably well with OMI observations (R > 0.95 for NO2, and R > 0.85 for SO2) over the NCP region and have smaller mean biases with MAX-DOAS. In the application during COVID-19 pandemic, it showed that the NO2 column in January-April 2020 decreased by almost 25–45% compared to the same period in 2019 due to the lockdown for COVID-19, and there was an apparent rebound of nearly 15–50% during 2021. In contrast, a marginal change of the corresponding SO2 is revealed in the NCP region. It signifies that short-term control measures are expected to have more effects on NO2 reduction than SO2; conversely, we need to recognize that although the COVID-19 lockdown measures improved air quality in the short term, the pollution status will rebound to its previous level once industrial and human activities return to normal.

Impact of the COVID-19 pandemic related to lockdown measures on tropospheric NO₂ columns over Île-de-France

The evolution of NO2, considered as a proxy for air pollution, was analyzed to evaluate the impact of the first lockdown (17 March–10 May 2020) over the Île-de-France region (Paris and surroundings). Tropospheric NO2 columns measured by two UV-Visible Système d'Analyse par Observation Zénithale (SAOZ) spectrometers were analyzed to compare the evolution of NO2 between urban and suburban sites during the lockdown. The urban site is the observation platform QualAir (48∘50′ N / 2∘21′ E) at the Sorbonne University Pierre and Marie Curie Campus in the center of Paris. The suburban site is located at Guyancourt (48∘46′N / 2∘03′E), Versailles Saint-Quentin-en-Yvelines University, 24 km southwest of Paris. Tropospheric NO2 columns above Paris and Guyancourt have shown similar values during the whole lockdown period from March to May 2020. A decade of data sets were filtered to consider air masses at both sites with similar meteorological conditions. The median NO2 columns and the surface measurements of Airparif (Air Quality Observatory in Île de France) during the lockdown period in 2020 were compared to the extrapolated values estimated from a linear trend analysis for the 2011–2019 period at each station. Negative NO2 trends of −1.5 Pmolec. cm−2 yr−1 (∼ −6.3 % yr−1) are observed from the columns, and trends of −2.2 µg m−3 yr−1 (∼ −3.6 % yr−1) are observed from the surface concentration. The negative anomaly in tropospheric columns in 2020 attributed to the lockdown (and related emission reductions) was found to be 56 % at Paris and 46 % at Guyancourt, respectively. A similar anomaly was found in the data of surface concentrations, amounting to 53 % and 28 % at the urban and suburban sites, accordingly.

Temporal variation of tropospheric NO2 columns in Vietnam during 2015–2020

Nitrogen dioxide (NO2) in the atmosphere can be measured using the tropospheric NO2 columns, indicating the number of molecules of NO2 in an atmospheric column from the ground surface to the top of the atmosphere above a square centimeter of the surface. In this study, the temporal variations of tropospheric NO2 columns in Vietnam during 2015–2020 were investigated. To do this, data on the columnar NO2 obtained from the Ozone monitoring instrument (OMI) onboard the NASA’s Earth orbiting satellite Aura were used. Consequently, northeastern Vietnam showed the highest values of the tropospheric NO2 columns over the whole study period (2015–2020), suggesting that this area would be a hot spot of NO2 pollution in Vietnam. In addition, the lowest and highest mean levels of columnar NO2 were found in 2020 and 2016, respectively. However, there is no statistical significance among the columnar NO2 in 2015–2020. Regarding the monthly variation, March and April exhibited the highest levels of tropospheric NO2 columns, which would be affected by frequent combustion activities (e.g., post-harvesting combustion) and meteorological conditions, such as lower air temperature. Results of this study can contribute to an understanding of NO2 pollution in Vietnam over long period.

A comparison of the impact of TROPOMI and OMI tropospheric NO₂ on global chemical data assimilation

This study gives a systematic comparison of the Tropospheric Monitoring Instrument (TROPOMI) version 1.2 and Ozone Monitoring Instrument (OMI) QA4ECV tropospheric NO2 column through global chemical data assimilation (DA) integration for the period April−May 2018. DA performance is controlled by measurement sensitivities, retrieval errors, and coverage. The smaller mean relative observation errors by 16 % in TROPOMI than OMI over 60° N−60° S during April−May 2018 led to larger reductions in the global root mean square error (RMSE) against the assimilated NO2 measurements in TROPOMI DA (by 54 %) than in OMI DA (by 38 %). Agreements against the independent surface, aircraft-campaign, and ozonesonde observation data were also improved by TROPOMI DA compared to the control model simulation (by 12−84 % for NO2 and by 7−40 % for ozone), which were more obvious than those by OMI DA for many cases (by 2−70 % for NO2 and by 1−22 % for ozone). The estimated global total NOx emissions were 15 % lower in TROPOMI DA, with 2−23 % smaller regional total emissions, in line with the observed negative bias of the TROPOMI version 1.2 product compared to the OMI QA4ECV product. TROPOMI DA can provide city scale emission estimates, which were within 10 % differences with other high-resolution analyses for several limited areas, while providing a globally consistent analysis. These results demonstrate that TROPOMI DA improves global analyses of NO2 and ozone, which would also benefit studies on detailed spatial and temporal variations in ozone and nitrate aerosols and the evaluation of bottom-up NOx emission inventories.

Sentinel-5P TROPOMI NO₂ retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data

Nitrogen dioxide (NO2) is one of the main data products measured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite, which combines a high signal-to-noise ratio with daily global coverage and high spatial resolution. TROPOMI provides a valuable source of information to monitor emissions from local sources such as power plants, industry, cities, traffic and ships, and variability of these sources in time. Validation exercises of NO2 version v1.2-v1.3 data, however, have revealed that TROPOMI's tropospheric vertical columns (VCDs) are too low by up to 50 % over highly polluted areas. These findings are mainly attributed to biases in the cloud pressure retrieval, the surface albedo climatology and the low resolution of the a-priori profiles derived from global simulations of the TM5-MP chemistry model. This study describes improvements in the TROPOMI NO2 retrieval leading to version v2.2, operational since 1 July 2021. Compared to v1.x, the main changes are: (1) The NO2-v2.2 data is based on version 2 level-1B (ir)radiance spectra with improved calibration, which results in a small and fairly homogeneous increase of the NO2 slant columns of 3 to 4 %, most of which ends up as a small increase of the stratospheric columns; (2) The cloud pressures are derived with a new version of the FRESCO cloud retrieval already introduced in NO2-v1.4, which lead to a lowering of the cloud pressure, resulting in larger tropospheric NO2 columns over polluted scenes with a small but non-zero cloud coverage; (3) For cloud-free scenes a surface albedo correction is introduced based on the observed reflectance, which also leads to a general increase of the tropospheric NO2 columns over polluted scenes of order 15 %; (4) An outlier removal was implemented in the spectral fit, which increases the number of good quality retrievals over the South-Atlantic Anomaly region and over bright clouds where saturation may occur; (5) Snow-Ice information is now obtained from ECMWF weather data, increasing the number of valid retrievals at high latitudes. On average the NO2-v2.2 data have tropospheric VCDs that are between 10 and 40 % larger than the v1.x data, depending on the level of pollution and season; the largest impact is found at mid- and high-latitudes in wintertime. This has brought these tropospheric NO2 closer to OMI observations. Ground-based validation shows on average an improvement of the negative bias of the stratospheric (from −6 % to −3 %), tropospheric (from −32 % to −23 %) and total (from −12 % to −5 %) columns. For individual measurement stations, however, the picture is more complicated, in particular for the tropospheric and total columns.

An improved TROPOMI tropospheric NO₂ research product over Europe

Launched in October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5 Precursor provides the potential to monitor air quality over point sources across the globe with a spatial resolution as high as 5.5 km × 3.5 km (7 km × 3.5 km before 6 August 2019). The DLR nitrogen dioxide (NO2) retrieval algorithm for the TROPOMI instrument consists of three steps: the spectral fitting of the slant column, the separation of stratospheric and tropospheric contributions, and the conversion of the slant column to a vertical column using an air mass factor (AMF) calculation. In this work, an improved DLR tropospheric NO2 retrieval algorithm from TROPOMI measurements over Europe is presented. The stratospheric estimation is implemented using the STRatospheric Estimation Algorithm from Mainz (STREAM), which was developed as a verification algorithm for TROPOMI and does not require chemistry transport model data as input. A directionally dependent STREAM (DSTREAM) is developed to correct for the dependency of the stratospheric NO2 on the viewing geometry by up to 2×1014 molec./cm2. Applied to synthetic TROPOMI data, the uncertainty in the stratospheric column is 3.5×1014 molec./cm2 in the case of significant tropospheric sources. Applied to actual measurements, the smooth variation of stratospheric NO2 at low latitudes is conserved, and stronger stratospheric variation at higher latitudes is captured. For AMF calculation, the climatological surface albedo data are replaced by geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) obtained directly from TROPOMI measurements with a high spatial resolution. Mesoscale-resolution a priori NO2 profiles are obtained from the regional POLYPHEMUS/DLR chemistry transport model with the TNO-MACC emission inventory. Based on the latest TROPOMI operational cloud parameters, a more realistic cloud treatment is provided by a Clouds-As-Layers (CAL) model, which treats the clouds as uniform layers of water droplets, instead of the Clouds-As-Reflecting-Boundaries (CRB) model, in which clouds are simplified as Lambertian reflectors. For the error analysis, the tropospheric AMF uncertainty, which is the largest source of NO2 uncertainty for polluted scenarios, ranges between 20 % and 50 %, leading to a total uncertainty in the tropospheric NO2 column in the 30 %–60 % range. From a validation performed with ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements, the new DLR tropospheric NO2 data show good correlations for nine European urban/suburban stations, with an average correlation coefficient of 0.78. The implementation of the algorithm improvements leads to a decrease of the relative difference from −55.3 % to −34.7 % on average in comparison with the DLR reference retrieval. When the satellite averaging kernels are used to remove the contribution of a priori profile shape, the relative difference decreases further to ∼ −20 %.