Inferring global surface HCHO concentrations from multisource hyperspectral satellites and their application to HCHO-related global cancer burden estimation

Formaldehyde (HCHO) is a toxic and hazardous air pollutant that widely exists in atmosphere. Insufficient spatial and temporal coverage of surface HCHO measurements is limiting studies on surface HCHO-related air quality management and health risk assessment. This study develops a method to derive global ground-level HCHO concentrations from satellite-based tropospheric HCHO columns using TM5-simulated surface-to-column conversion factor with coarse spatial resolution. The method improves the factor more representative in finer grids by constraining TM5-simulated vertical profile shapes with satellite HCHO columns. The surface HCHO concentrations derived by the Ozone Mapping and Profiler Suite (OMPS) show good correlation with in situ HCHO measurements (R = 0.59) from the U.S. Environmental Protection Agency surface network. We investigated how surface HCHO relates to urbanization and population aggregation over seven regions with high HCHO pollution. The results show urban HCHO increases as a power function with population size in China, India, and West Asia. HCHO concentrations in rural aeras also present strong log-log relationship with population aggregation in China, India, the United States, and Europe. Moreover, OMPS-derived ground-level HCHO concentrations were used to estimate global cancer burden caused by long-term outdoor HCHO exposure. The results show that up to 418188 more people worldwide will develop this cancer during the human life cycle. The global cancer burden is mainly from the South-East Asia region (33.11 %) and the Western Pacific region (22.95 %). This cancer occurrence in India and China is ranked 1st and 2nd in the world due to the large population size and serious HCHO pollution. Besides, global surface HCHO concentrations and cancer burden derived from the Environmental Trace Gases Monitoring Instrument which is China's first hyperspectral space-based spectrometer are found similar patterns with that from OMPS. Our results provide new insight into the impact of population urbanization on HCHO pollution and global outdoor HCHO-caused health risks.

TROPOspheric Monitoring Instrument observations of total column water vapour: Algorithm and validation

In this paper, we present the total column water vapour (TCWV) retrieval for the TROPOspheric Monitoring Instrument (TROPOMI) observations in the visible blue spectral band. The TROPOMI TCWV algorithm is being optimized and validated in the framework of the Sentinel 5 Precursor Product Algorithm Laboratory (S5P-PAL) project from the European Space Agency (ESA). The retrieval was first developed to retrieve TCWV from the Global Ozone Monitoring Experiment 2 (GOME-2). We have optimized the settings of the retrieval to adapt it for TROPOMI observations. The TROPOMI TCWV algorithm follows the typical two step approach, using spectral fit retrieval of slant columns, and conversion of the slant columns to vertical columns using air mass factors (AMFs). An iterative optimization algorithm is developed to dynamically find the optimal a priori water vapour profile for the AMF calculation. Further optimizations on the spectral retrieval, air mass factor calculations as well as a new surface albedo retrieval approach are implemented. The TCWV retrieval algorithm is applied to TROPOMI observations from May 2018 to May 2021. The results are validated by comparing them to ERA5 reanalysis data, GOME-2, MODerate resolution Imaging Spectroradiometer (MODIS) and Special Sensor Microwave Imager Sounder (SSMIS) satellite observations. TCWV derived from TROPOMI observations agree well with the other data sets with Pearson correlation coefficient (R) ranging from 0.96 to 0.99. The mean bias between TROPOMI and ERA5 data is -1.24 kg m^-2 for measurements over land and 0.73 kg m^-2 for measurements over water. The comparison to MODIS observations show similar results with small dry bias of 1.51,kg m^-2 for measurements over land and a small wet bias of 1.25 kg m^-2 for measurements over water. Slightly larger dry bias of 1.98 kg m^-2 for measurements over land and 1.74 kg m^-2 for measurements over water are found when compared to GOME-2 obserations. Compared to SSMIS data over water, TROPOMI observations are bias low by 3.25 kg m^-2. The small discrepancies found between TROPOMI and reference data sets are related to the differences in measurement technique, measurement time, surface albedo issue, as well as cloud and aerosol contamination. This study demonstrates that the algorithm can provide stable and consistent results on a global scale and can be applied to generate operational TCWV products from TROPOMI and the forthcoming Copernicus missions Sentinel-4 and Sentinel-5. We have also demonstrated the capability of retrieving fine scale water vapour structures in a case study over the Amazon. This indicates that the TROPOMI data set is also useful for local and regional climate studies.

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.

Evaluation of systematic errors for the continuous-wave NO2 differential absorption lidar employing a multimode laser diode

The NO₂-differential absorption lidar (NO₂-DIAL) technique has been of great interest for atmospheric NO₂ profiling. Comprehensive studies on measurement errors in the NO₂-DIAL technique are vital for the accurate retrieval of the NO₂ concentration. This work investigates the systematic errors of the recently developed continuous-wave (CW) NO₂-DIAL technique based on the Scheimpflug principle and a high-power CW multimode laser diode. Systematic errors introduced by various factors, e.g., uncertainty of the NO₂ differential absorption cross-section, differential absorption due to other gases, spectral drifting of the λ_on and λ_off wavelengths, wavelength-dependent extinction and backscattering effect, have been theoretically and experimentally studied for the CW-DIAL technique. By performing real-time spectral monitoring on the emission spectrum of the laser diode, the effect of spectral drifting on the NO₂ differential absorption cross-section is negligible. The temperature-dependent NO₂ absorption cross-section in the region of 220-294 K can be interpolated by employing a linear fitting method based on high-precision absorption spectra at 220, 240, and 294 K. The relative error for the retrieval of the NO₂ concentration is estimated to be less than 0.34% when employing the interpolated spectrum. The primary interference molecule is found to be the glyoxal (CHOCHO), which should be carefully evaluated according to its relative concentration in respect to NO₂. The systematic error introduced by the backscattering effect is subjected to the spatial variation of the aerosol load, while the extinction-induced systematic error is primarily determined by the difference between the aerosol extinction coefficients at λ_on and λ_off wavelengths. A case study has been carried out to demonstrate the evaluation of systematic errors for practical NO₂ monitoring. The comprehensive investigation on systematic errors in this work can be of great value for future NO₂ monitoring using the DIAL technique.

Comparison and Validation of TROPOMI and OMI NO2 Observations over China

The new-generation sensor TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel 5 precursor (S5P) satellite is promising for monitoring air pollutants with greater spatial resolution, especially for China, which suffers from severe pollution. As tropospheric NO2 vertical column densities (VCDs) from TROPOMI have become available since February 2018, this study presents the comparisons of NO2 data measured by TROPOMI and its predecessor Ozone Monitoring Instrument (OMI) over China, together with validation against ground Multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements. At the nationwide scale, we used two different filters performed for the TROPOMI data (named TROPOMI50 and TROPOMI75), and the TROPOMI50 yielded larger values than TROPOMI75. The TROPOMI NO2 datasets from different filters show consistent spatial patterns with OMI, and the correlation coefficient values were both above 0.93. However, linear regression indicates that NO2 loadings in TROPOMI is about 2/3 to 4/5 of those in OMI, which is presumably due to a different cloud mask and uncertainties of air mass factors. The absolute difference is prominent over the high pollution areas such as Jing-Jin-Ji region and during winter and autumn, exceeding 0.6 × 1016 molecules cm−2 (molec cm−2). However, the NO2 concentrations retrieved from TROPOMI50 in the southern China may be somewhat higher than OMI. When it comes to the local-scale Jing-Jin-Ji hotspot, the analysis focuses on a comparison to TROPOMI75. TROPOMI manifests high quality and exhibits a significantly better performance of representing spatial variability. In contrast, OMI shows fewer effective pixels and does a poor job of capturing local details due to its row anomaly and low resolution. The absolute difference between two datasets shows the same seasonal behavior with NO2 variation, which is most striking in the winter (0.31 × 1016 molec cm−2) and is lowest in the summer (0.05 × 1016 molec cm−2). Furthermore, the ground MAX-DOAS instrument in Xianghe station, the representative site in Jing-Jin-Ji, is used to assess the skill of satellite retrievals. It turns out that both OMI and TROPOMI underestimate the observations, ranging from 30% to 50%, with OMI being less biased. In spite of the negative drift, the temporal structures of changes derived from OMI and TROPOMI closely match the ground-based records, since the correlation coefficients are above 0.8 and 0.95 for daily and monthly scales, respectively. Overall, TROPOMI NO2 retrievals are better suited for applications in China as well as the Jing-Jin-Ji hotspot due to its higher spatial resolution, although some improvements are also needed in the near future.

Long-term (2006-2015) variations and relations of multiple atmospheric pollutants based on multi-remote sensing data over the North China Plain

In this analysis, the Aqua/MODIS aerosol optical thickness (AOD), Aura/OMI tropospheric NO₂ and SO₂ column concentration from 2006 to 2015 were used to statistically analyze the spatial distribution characteristics and variation trends of three polluted parameters from three temporal scales of monthly, seasonal and annual average. The results showed that the minimum values of NO₂ and SO₂ column concentrations both appeared in July and August, and the maximum values appeared in December and January, which was contrary to the variations in AOD. The highly polluted levels were mainly distributed in Shijiazhuang, Xingtai, and Yancheng cities of Hebei Province, and gradually transported to Zhengzhou, Henan Province, north and southwest of Shandong Province, and Tianjin, along the main line of Taiyuan-Linyi, Shanxi Province. AOD and NO₂ had significant differences on the seasonal average scale, whereas SO₂ had little changes. These pollutants had declined year by year since 2011, in the 10-year period, AOD and SO₂ respectively decreased by 17.14% and 10.57%, and only NO₂ rose from 8.69 × 10¹⁵ molecules/cm² in 2006 to 9.10 × 10¹⁵ molecules/cm² in 2015 with the increase rate of 4.79%. Integrated with MODIS-released fire products and the Multi-resolution Emission Inventory for China (MEIC), high AOD values in summer were usually accompanied by frequent biomass burning, and heavy heating demand of coal burning led to largest NO₂ and SO₂ levels in winter. Both inter-annual variations of MEIC NOx and OMI-observed NO₂ responded to emission reductions of vehicle exhaustions positively, but vehicle population in Henan and Shandong provinces need to be further controlled. The significant decline of SO₂ is mainly attributed to the enforcement of de-sulfurization devices in power plants. Our study found that in the treatment of complex atmospheric pollution, in addition to strict control of common sources of emissions from AOD, NO₂ and SO₂, it is also necessary to consider their individual characteristics.

The Spatial–Temporal Variation of Tropospheric NO2 over China during 2005 to 2018

In recent years, new and strict air quality regulations have been implemented in China. Therefore, it is of great significance to evaluate the current air pollution situation and effectiveness of actions. In this study, Ozone Monitoring Instrument (OMI) satellite data were used to detect the spatiotemporal characteristics of tropospheric NO2 columns over China from 2005 to 2018, including spatial distribution, seasonal cycles and long-term trends. The averaged NO2 pollution is higher in southeastern China and lower in the northwest, which are well delineated by the Heihe–Tengchong line. Furthermore, the NO2 loadings are highest in the North China Plain, with vertical column density (VCD) exceeding 13 × 1015 molec cm−2. Regarding the seasonal cycle, the NO2 loadings in eastern China is highest in winter and lowest in summer, while the western region shows the opposite feature. The amplitude of annual range increase gradually from the south to the north. If the entire period of 2005–2018 is taken into account, China has experienced little change in NO2. In fact, however, there appears to be significant trends of an increase followed by a downward tendency, with the turning point in the year 2012. In the former episode of 2005–2012, increasing trends overwhelm nearly the whole nation, especially in the Jing–Jin–Tang region, Shandong Province, and Northern Henan and Southern Hebei combined regions, where the rising rates were as high as 1.0–1.8 × 1015 molec cm−2 year−1. In contrast, the latter episode of 2013–2018 features remarkable declines in NO2 columns over China. Particularly, the regions where the decreased degree was remarkable in 2013–2018 were consistent with the regions where the upward trend was obvious in 2005–2012. Overall, this upward–downward pattern is true for most parts of China. However, some of the largest metropolises, such as Beijing, Shanghai and Guangzhou, witnessed a continuous decrease in the NO2 amounts, indicating earlier and more stringent measures adopted in these areas. Finally, it can be concluded that China’s recent efforts to cut NO2 pollution are successful, especially in mega cities.

Observations of tropospheric aerosols and NO2 in Hong Kong over 5years using ground based MAX-DOAS

In this paper, we present long term observations of atmospheric aerosols and nitrogen dioxide (NO₂) in Hong Kong using a Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instrument. Ground based MAX-DOAS measurements were performed over 5years from December 2010 to November 2015. Vertical distribution profiles of aerosols and NO₂ were derived from MAX-DOAS O₄ and NO₂ observations by applying the optimal estimation method. Retrieved MAX-DOAS measurements of aerosols and NO₂ show good agreement with sun photometer observation of aerosol optical depths (AODs) and long path DOAS measurement of ground level NO₂ mixing ratios. Tropospheric vertical column densities (VCDs) of NO₂ derived from MAX-DOAS measurements are used to validate OMI satellite NO₂ observations. Daily data show reasonably good agreement with each other with Pearson correlation coefficient R=0.7. However, MAX-DOAS NO₂ VCDs are on average higher than OMI observations by a factor of 2. Introducing aerosols in the air mass factor calculation would enhance the OMI VCDs by 7-13%, the remaining discrepancy is mainly due to the differences in spatial coverage between the two instruments. Diurnal variation patterns of aerosols and NO₂ indicated significant contributions from local anthropogenic emissions. Analysis of air mass transport shows that the enhancement of surface aerosols and NO₂ concentrations mainly results from accumulation of local emissions under low wind speed conditions.

Effects of local meteorology and aerosols on ozone and nitrogen dioxide retrievals from OMI and pandora spectrometers in Maryland, USA during DISCOVER-AQ 2011

An analysis is presented for both ground- and satellite-based retrievals of total column ozone and nitrogen dioxide levels from the Washington, D.C., and Baltimore, Maryland, metropolitan area during the NASA-sponsored July 2011 campaign of Deriving Information on Surface COnditions from Column and VERtically Resolved Observations Relevant to Air Quality (DISCOVER-AQ). Satellite retrievals of total column ozone and nitrogen dioxide from the Ozone Monitoring Instrument (OMI) on the Aura satellite are used, while Pandora spectrometers provide total column ozone and nitrogen dioxide amounts from the ground. We found that OMI and Pandora agree well (residuals within ±25 % for nitrogen dioxide, and ±4.5 % for ozone) for a majority of coincident observations during July 2011. Comparisons with surface nitrogen dioxide from a Teledyne API 200 EU NO_x Analyzer showed nitrogen dioxide diurnal variability that was consistent with measurements by Pandora. However, the wide OMI field of view, clouds, and aerosols affected retrievals on certain days, resulting in differences between Pandora and OMI of up to ±65 % for total column nitrogen dioxide, and ±23 % for total column ozone. As expected, significant cloud cover (cloud fraction >0.2) was the most important parameter affecting comparisons of ozone retrievals; however, small, passing cumulus clouds that do not coincide with a high (>0.2) cloud fraction, or low aerosol layers which cause significant backscatter near the ground affected the comparisons of total column nitrogen dioxide retrievals. Our results will impact post-processing satellite retrieval algorithms and quality control procedures.

Influence of coal-based thermal power plants on the spatial-temporal variability of tropospheric NO2 column over India

The oxides of nitrogen--NO(x) (NO and NO(2))--are an important constituent of the troposphere. The availability of relatively higher spatial (0.25° grid) and temporal (daily) resolution data from ozone monitoring instrument (OMI) onboard Aura helps us to better differentiate between the point sources such as thermal power plants from large cities and rural areas compared to previous sensors. The annual and seasonal (summer and winter) distributions shows very high mean tropospheric NO(2) in specific pockets over India especially over the Indo-Gangetic plains (up to 14.2 × 10(15) molecules/cm(2)). These pockets correspond with the known locations of major thermal power plants. The tropospheric NO(2) over India show a large seasonal variability that is also observed in the ground NO(2) data. The multiple regression analysis show that the influence of a unit of power plant (in gigawatts) over tropospheric NO(2) (×10(15) molecules/cm(2)) is around ten times compared to a unit of population (in millions) over India. The OMI data show that the NO(2) increases by 0.794 ± 0.12 (×10(15) molecules/cm(2); annual) per GW compared to a previous estimate of 0.014 (×10(15) molecules/cm(2)) over India. The increase of tropospheric NO(2) per gigawatt is found to be 1.088 ± 0.18, 0.898 ± 0.14, and 0.395 ± 0.13 (×10(15) molecules/cm(2)) during winter, summer, and monsoon seasons, respectively. The strong seasonal variation is attributed to the enhancement or suppression of NO(2) due to various controlling factors which is discussed here. The recent increasing trend (2005-2007) over rural thermal power plants pockets like Agori and Korba is due to recent large capacity additions in these regions.

NOx emission reduction and its effects on ozone during the 2008 Olympic Games

We applied a daily assimilated inversion method to estimate NO(x) (NO + NO(2)) emissions for June-September 2007 and 2008 on the basis of the Aura Ozone Monitoring Instrument (OMI) observations of nitrogen dioxide (NO(2)) and model simulations using the Regional chEmistry and trAnsport Model (REAM). This method allows for estimating emission changes with a finer temporal resolution than previous studies and shows that the progression of the emission reduction corresponds roughly to the scheduled implementation of emission controls over Beijing. OMI column NO(2) reductions are approximately 45%, 33%, and 14% over urban Beijing, rural Beijing, and the Huabei Plain, respectively, while the corresponding anthropogenic NO(x) emission reductions are only 28%, 24%, and 6%, during the full emission control period (July 20-Sep 20, 2008). Meteorological changes from summer 2007 to 2008 are the main factor contributing to the column NO(2) decreases not accounted for by the emission reduction. The surface ozone changes due to NO(x) emission reduction are negligible using a standard VOC emission inventory. When using enhanced VOC (particularly aromatics) emissions derived from in situ observations, urban Beijing shifted O(3) production from the VOC-limited regime toward the NO(x)-limited regime resulting in a more substantial ozone decrease (up to 10 ppbv).