NO2 Columns Research Articles

Interpreting summertime hourly variation of NO2 columns with implications for geostationary satellite applications

Abstract. Accurate representation of the hourly variation in the NO2-column-to-surface relationship is essential for interpreting geostationary observations of NO2 columns. Previous research indicated inconsistencies in this hourly variation. This study employs the high-performance configuration of the GEOS-Chem model (GCHP) to analyze daytime hourly NO2 total columns and surface concentrations during summer. We use measurements from globally distributed Pandora sun photometers and aircraft observations over the United States. We correct Pandora total NO2 vertical columns for (1) hourly variations in effective temperature driven by vertically resolved contributions to the total column and (2) changes in local solar time along the Pandora line of sight. These corrections increase the total NO2 columns by 5–6 × 1014 molec. cm−2 at 09:00 and 18:00 across all sites. Fine-scale simulations from GHCP (∼12 km) reduce the normalized bias (NB) against Pandora total NO2 columns from 19 % to 10 % and against aircraft measurements from 25 % to 13 % in Maryland, Texas, and Colorado. Similar reductions are observed in NO2 columns over the eastern US (17 % to 9 %), the western US (22 % to 14 %), Europe (24 % to 15 %), and Asia (29 % to 21 %) when compared to 55 km simulations. Our analysis attributes the weaker hourly variability in the total NO2 column to (1) hourly variations in column effective temperature, (2) local solar time changes along the Pandora line of sight, and (3) differences in hourly NO2 variability from different atmospheric layers, with the lowest 500 m exhibiting greater variability, while the dominant residual column above 500 m exhibits weaker variability.

Soil Emissions of Reactive Oxidized Nitrogen Reduce the Effectiveness of Anthropogenic Source Control in China.

Nitrogen dioxide (NO2) has decreased by ∼33% across over 1200 monitoring sites in China during 2015-2023, following a series of clean air policies. However, most of these sites are located in or near cities, leading to uncertainties in NO2 trends beyond urban regions due to limited observations. Here, we used satellite measurements to examine the differences in NO2 trends between urban and rural China. In urban areas, NO2 columns decreased by 4.0% per annum (a-1) during summer 2011-2023, consistent with bottom-up anthropogenic emission inventory and in situ measurements. In contrast, rural NO2 columns showed a slower than expected reduction (-2.6 to -0.0% a-1) during the same period. Model simulations with updates in the soil reactive oxidized nitrogen (Nr) scheme indicated that increasing soil Nr emissions can be an important factor contributing to the observed slow NO2 decrease in rural areas. This unregulated source increased summertime pollutant levels, partially offsetting the national efforts to mitigate NO2, ozone (O3), and particulate nitrate (NO3-) levels by 20.9%, 15.4%, and 4.7%, respectively, from 2011 to 2020. In the agriculture-intensive North China Plain, the increase in soil Nr emissions offset 46.6% of the NO2 reductions achieved by clean air policies. Our results highlight the increasing significance of soil emissions and the need to control them in future air-quality policies.

Simulated Surface‐Column NO2 Connections for Satellite Applications

AbstractObservations of near‐surface NO2 show a diurnal pattern with midday minima and daily maxima in the morning and evening. These surface cycles are dependent on chemical processing, transport, and emissions. We evaluate these cycles with the United States Environmental Protection Agency (EPA) community multiscale air quality (CMAQ) model data from the EPA Air Quality Time Series (EQUATES) project, compared with ground‐based measurements from the EPA air quality system, two Pandora measurement sites, and satellite data from TROPOMI. We find that the morning vertical column density (VCD) lags surface concentrations by 1 hr on average, where this lag varies with location and day. The peak VCD can also lead the surface maximum concentration, especially in the evening, responding to transport and afternoon compression of the boundary layer. Modeled NO2 VCD is sensitive to column calculation technique. With hourly daytime satellite‐based NO2 observations newly available from the TEMPO instrument, the timing and magnitude of cycles in near‐surface NO2 versus column NO2 will help inform the utilization of hourly satellite data. This work will help inform the timing of surface‐column connections to better interpret new hourly satellite observations for health and air quality applications, including emissions characterization.

A Satellite-Based Indicator for Diagnosing Particulate Nitrate Sensitivity to Precursor Emissions: Application to East Asia, Europe, and North America.

Particulate nitrate is a major component of fine particulate matter (PM2.5) and a key target for improving air quality. Its formation is varyingly sensitive to emissions of nitrogen oxides (NOx ≡ NO + NO2), ammonia (NH3), and volatile organic compounds (VOCs). Diagnosing the dominant sensitivity is critical for effective pollution control. Here, we show that satellite observations of the NO2 column and the NH3/NO2 column ratio can effectively diagnose the dominant sensitivity regimes in polluted regions of East Asia, Europe, and North America, in different seasons, though with reduced performance in the summer. We demarcate the different sensitivity regimes using the GEOS-Chem chemical transport model and apply the method to satellite observations from the OMI (NO2) and IASI (NH3) in 2017. We find that the dominant sensitivity regimes vary across regions and remain largely consistent across seasons. Sensitivity to NH3 emissions dominates in the northern North China Plain (NCP), the Yangtze River Delta, South Korea, most of Europe, Los Angeles, and the eastern United States. Sensitivity to NOx emissions dominates in central China, the Po Valley in Italy, the central United States, and the Central Valley in California. Sensitivity to VOCs emissions dominates only in the southern NCP in the winter. These results agree well with those of previous local studies. Our satellite-based indicator provides a simple tool for air quality managers to choose emission control strategies for decreasing PM2.5 nitrate pollution.

Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS

Abstract. In this study, we develop an advanced retrieval algorithm for tropospheric nitrogen dioxide (NO2) from the geostationary satellite instruments and apply it to Geostationary Environment Monitoring Spectrometer (GEMS) observations. Overall, the algorithm follows previous heritage for the polar-orbiting satellites Global Ozone Monitoring Experiment-2 (GOME-2) and Tropospheric Monitoring Instrument (TROPOMI), but several improvements are implemented to account for specific features of geostationary satellites. The DLR GEMS NO2 retrieval employs an extended fitting window compared to the current fitting window used in GEMS operational v2.0 NO2 retrieval, which results in improved spectral fit quality and lower uncertainties. For the stratosphere–troposphere separation in GEMS measurements, two methods are developed and evaluated: (1) STRatospheric Estimation Algorithm from Mainz (STREAM) as used in the DLR TROPOMI NO2 retrieval and adapted to GEMS and (2) estimation of stratospheric NO2 columns from the Copernicus Atmosphere Monitoring Service (CAMS) Integrated Forecast System (IFS) cycle 48R1 model data, which introduce full stratospheric chemistry as it will be used in the operational Sentinel-4 NO2 retrieval. While STREAM provides hourly estimates of stratospheric NO2, it has limitations in describing small-scale variations and exhibits systematic biases near the boundary of the field of view. In this respect, the use of estimated stratospheric NO2 columns from the CAMS forecast model profile demonstrates better applicability by describing not only diurnal variation but also small-scale variations. For the improved air mass factor (AMF) calculation, sensitivity tests are performed using different input data. In our algorithm, cloud fractions retrieved from the Optical Cloud Recognition Algorithm (OCRA) adapted to GEMS level 1 data are applied instead of the GEMS v2.0 cloud fraction. OCRA is used operationally in TROPOMI and Sentinel-4. Compared to the GEMS level 2 cloud fraction which is typically set to around 0.1 for clear-sky scenes, OCRA sets cloud fractions close to or at 0. The OCRA-based cloud corrections result in increased tropospheric AMFs and decreased tropospheric NO2 vertical columns, leading to better agreement with results from existing TROPOMI observations. The effects of surface albedo on GEMS tropospheric NO2 retrievals are assessed by comparing the GEMS v2.0 background surface reflectance (BSR) and TROPOMI Lambertian-equivalent reflectivity (LER) climatology v2.0 product. The differences between the two surface albedo products and their impact on tropospheric AMF are particularly pronounced over snow/ice scenes during winter. A priori NO2 profiles from the CAMS forecast model, applied in the DLR GEMS algorithm, effectively capture variations in NO2 concentrations throughout the day with high spatial resolution and the advanced chemical mechanism, which demonstrates its suitability for geostationary satellite measurements. The retrieved DLR GEMS tropospheric NO2 columns show good capability for capturing hotspot signals at the scale of city clusters and describe spatial gradients from city centres to surrounding areas. Diurnal variations of tropospheric NO2 columns over Asia are well described through hourly sampling of GEMS. Evaluation of DLR GEMS tropospheric NO2 columns against TROPOMI v2.4 and GEMS v2.0 operational products shows overall good agreement. The uncertainty of DLR GEMS tropospheric NO2 vertical columns varies based on observation scenarios. In regions with low pollution levels such as open-ocean and remote rural areas, retrieval uncertainties typically range from 10 % to 50 %, primarily due to uncertainties in slant columns. For heavily polluted regions, uncertainties in tropospheric NO2 columns are mainly driven by errors in tropospheric AMF calculations. Notably, the total uncertainty in GEMS tropospheric NO2 columns is most significant in winter, particularly over heavily polluted regions with low-level clouds below or near the NO2 peak.

Spatial–Temporal Variation and the Influencing Factors of NO2 Column Concentration in the Plateau Mountains of Southwest China

Given the complex terrain and economic development status of Guizhou Province, research on tropospheric NO2 column concentration using satellite remote sensing is still insufficient. Observing the spatial–temporal evolution characteristics of tropospheric NO2 column concentration can ensure the stable development of air quality. Based on the Google Earth Engine (GEE) platform, NO2 column concentration data retrieved from Sentinel-5P TROPOMI were analyzed using spatial autocorrelation, hotspot analysis, and geographic detector methods (Geodetector). The results show that NO2 column concentration in Guizhou Province exhibits seasonal variation, characterized by higher levels in winter and lower levels in summer, with transitional values in spring and autumn. The annual average concentration was highest in 2021 at 3.47 × 10−5 mol/m2 and lowest in 2022 at 2.85 × 10−5 mol/m2. Spatially, NO2 column concentration displays a distribution pattern of “high in the west, low in the east; high in the north, low in the south”, with significant spatial clustering. The distribution of cold and hot spots aligns with areas of high and low values. NO2 column concentration is primarily influenced by socio-economic factors, with the interaction between any two factors enhancing the explanatory power of individual factors on NO2 column concentration.

First top-down diurnal adjustment to NOx emissions inventory in Asia informed by the Geostationary Environment Monitoring Spectrometer (GEMS) tropospheric NO2 columns

Pioneering the use of the Geostationary Environment Monitoring Spectrometer’s (GEMS) observation data in air quality modeling, we adjusted Asia’s NOx emissions inventory by leveraging the instrument’s unprecedented sampling frequency. GEMS tropospheric NO2 columns served as top-down constraints, guiding our Bayesian inversion to constrain NOx emissions in Asia during spring 2022. This enabled the model to better capture the diurnal variation in NOx emissions, such as its morning rush hour peak, particularly when more retrievals were available each day, improving the simulation accuracy to a certain extent. The GEMS-informed adjustment reduced the extent of model underestimation of surface NO2 concentrations from 17.38 to 5.58% in Korea and from 13.05 to 4.54% in China, showing about 9.40% and 5.77% greater improvements, respectively, compared to the adjustment based on the sun-synchronous low earth orbit observation proxy. Our findings highlight the potential of geostationary observation data in refining the diurnal cycle of inventoried NOx emissions, thereby more effectively improving the accuracy of air quality simulations.

Quantifying NO x Emission Sources in Houston, Texas Using Remote Sensing Aircraft Measurements and Source Apportionment Regression Models.

Air quality managers in areas exceeding air pollution standards are motivated to understand where there are further opportunities to reduce NO x emissions to improve ozone and PM2.5 air quality. In this project, we use a combination of aircraft remote sensing (i.e., GCAS), source apportionment models (i.e., CAMx), and regression models to investigate NO x emissions from individual source-sectors in Houston, TX. In prior work, GCAS column NO2 was shown to be close to the "truth" for validating column NO2 in model simulations. Column NO2 from CAMx was substantially low biased compared to Pandora (-20%) and GCAS measurements (-31%), suggesting an underestimate of local NO x emissions. We applied a flux divergence method to the GCAS and CAMx data to distinguish the linear shape of major highways and identify NO2 underestimates at highway locations. Using a multiple linear regression (MLR) model, we isolated on-road, railyard, and "other" NO x emissions as the likeliest cause of this low bias, and simultaneously identified a potential overestimate of shipping NO x emissions. Based on the MLR, we modified on-road and shipping NO x emissions in a new CAMx simulation and increased the background NO2, and better agreement was found with GCAS measurements: bias improved from -31% to -10% and r2 improved from 0.78 to 0.80. This study outlines how remote sensing data, including fine spatial information from newer geostationary instruments, can be used in concert with chemical transport models to provide actionable information for air quality managers to identify further opportunities to reduce NO x emissions.

Satellite-observed relationships between land cover, burned area, and atmospheric composition over the southern Amazon

Abstract. Land surface changes can have substantial impacts on biosphere–atmosphere interactions. In South America, rainforests abundantly emit biogenic volatile organic compounds (BVOCs), which, when coupled with pyrogenic emissions from deforestation fires, can have substantial impacts on regional air quality. We use novel and long-term satellite records of five trace gases, namely isoprene (C5H8), formaldehyde (HCHO), methanol (CH3OH), carbon monoxide (CO), and nitrogen dioxide (NO2), in addition to aerosol optical depth (AOD), vegetation (land cover and leaf area index), and burned area. We characterise the impacts of biogenic and pyrogenic emissions on atmospheric composition for the period 2001 to 2019 in the southern Amazon, a region of substantial deforestation. The seasonal cycle for all of the atmospheric constituents peaks in the dry season (August–October), and the year-to-year variability in CO, HCHO, NO2, and AOD is strongly linked to the burned area. We find a robust relationship between the broadleaf forest cover and total column C5H8 (R2 = 0.59), while the burned area exhibits an approximate fifth root power law relationship with tropospheric column NO2 (R2 = 0.32) in the dry season. Vegetation and burned area together show a relationship with HCHO (R2 = 0.23). Wet-season AOD and CO follow the forest cover distribution. The land surface variables are very weakly correlated with CH3OH, suggesting that other factors drive its spatial distribution. Overall, we provide a detailed observational quantification of biospheric process influences on southern Amazon regional atmospheric composition, which in future studies can be used to help constrain the underpinning processes in Earth system models.

Inversing NOx emissions based on an optimization model that combines a source-receptor relationship correction matrix and monitoring data: A case study in Linyi, China

Inversion based on observational data and the source-receptor relationship (SRR) simulated by an air quality model is an effective means to estimate NOx emissions. However, the SRR bias induced by the inherent uncertainty of the simulated model leads to potential errors in the inversed emissions, but this is seldom considered in NOx inversion. In this study, we constructed an inversion model based on the SRR correction (IMSC) by introducing a correction matrix, combined with joint regularization scheme and genetic algorithm. We innovated the dynamic acquisition method of center-restricted parameters for the correction matrix, combined this with other parts of the IMSC, attaining the multi-month and multi-region pollutant emission inversion. Hypothetical examples demonstrated that the IMSC effectively corrected the SRR and accurately estimated the NOx emissions. The IMSC was used to estimate Linyi county-level NOx emissions for January, April, July and October (typical months) during 2020 and 2021. An inversion model without SRR correction (IMWSC) was developed for comparison with the IMSC. Results showed that the IMSC more accurately, robustly, and reasonably estimated NOx emissions. Compared to the IMWSC, the IMSC improved the mean correlation between NOx emissions and NO2 observational concentrations by 25.0%, enhancing the correlation between NOx emissions and NO2 column concentrations by 111.3%. The similar NOx emission change ratios (σaverage = 5.1%) between the typical months in 2021 and 2020 among the different counties indicated a more robust performance of the IMSC than the IMWSC (σaverage = 55.2%). In addition, the NOx emissions inversed by the IMSC also showed better consistency with social activity levels (i.e., electricity consumption). The typical monthly Linyi's county-level NOx emission characterization was also studied. NOx emissions were lower in April, July, and October 2021 than the same period in 2020 due to COVID-19 and pollution controls. This study provides strategies for swiftly and accurately estimating pollutant emissions.

Assessment of tropospheric NO2 concentrations over greater Doha using Sentinel-5 TROPOspheric monitoring instrument (TROPOMI) satellite data: Temporal analysis, 2018–2023

This study presents a temporal evaluation of the tropospheric NO2 column densities over Greater Doha using TROPOMI satellite data from May 2018 to December 2023, and an assessment of the impact of the preparations and hosting of the FIFA Football World Cup Qatar 2022, on NO2 levels before, during and after the tournament over Greater Doha. Analysis of annual NO2 levels from 2019 to 2023 showed an increase in 2022 compared to that of the previous three years and a clear decrease in 2023 post the completion of the world cup preparations and hosting. Results also showed an increase in NO2 levels during winter compared to that in summer, with wind speed being an important determining factor. Findings showed that Fridays and Saturdays (both constitute the local weekend in Qatar) were 44% and 13% lower than that of the averaged weekdays, respectively. The annual NO2 levels in the post-world cup year of 2023 were found to be 24% lower than that in 2022 and around 16% lower than that of the previous years. NO2 levels during the World Cup tournament (20 Nov to Dec 18, 2022) were found to be higher than that of the same corresponding periods in all other available years including an increase of 27% compared to that in 2023. Wind speed played an important role in determining the NO2 levels during the world cup period and accounted for >96% of their daily variability, indicating that meteorological factors substantially influenced the NO2 column during the event.

Assessment of the impact of NO2 contribution on aerosol-optical-depth measurements at several sites worldwide

Abstract. This work aims at investigating the effect of NO2 absorption on aerosol-optical-depth (AOD) measurements and Ångström exponent (AE) retrievals of sun photometers by the synergistic use of accurate NO2 characterization for optical-depth estimation from co-located ground-based measurements. The analysis was performed for ∼ 7 years (2017–2023) at several sites worldwide for the AOD measurements and AE retrievals by Aerosol Robotic Network (AERONET) sun photometers which use OMI (Ozone Monitoring Instrument) climatology for NO2 representation. The differences in AOD and AE retrievals by NO2 absorption are accounted for using high-frequency columnar NO2 measurements by a co-located Pandora spectroradiometer belonging to the Pandonia Global Network (PGN). NO2 absorption affects the AOD measurements in UV-Vis (visible) range, and we found that the AOD bias is the most affected at 380 nm by NO2 differences, followed by 440, 340, and 500 nm, respectively. AERONET AOD was found to be overestimated in half of the cases, while also underestimated in other cases as an impact of the NO2 difference from “real” (PGN NO2) values. Overestimations or underestimations are relatively low. About one-third of these stations showed a mean difference in NO2 and AOD (at 380 and 440 nm) above 0.5 × 10−4 mol m−2 and 0.002, respectively, which can be considered a systematic contribution to the uncertainties in the AOD measurements that are reported to be of the order of 0.01. However, under extreme NO2 loading scenarios (i.e. 10 % highest differences) at highly urbanized/industrialized locations, even higher AOD differences were observed that were at the limit of or higher than the reported 0.01 uncertainty in the AOD measurement. PGN NO2-based sensitivity analysis of AOD difference suggested that for PGN NO2 varying between 2 × 10−4 and 8 × 10−4 mol m−2, the median AOD differences were found to rise above 0.01 (even above 0.02) with the increase in NO2 threshold (i.e. the lower limit from 2 × 10−4 to 8 × 10−4 mol m−2). The AOD-derivative product, AE, was also affected by the NO2 correction (discrepancies between the AERONET OMI climatological representation of NO2 values and the real PGN NO2 measurements) on the spectral AOD. Normalized frequency distribution of AE (at 440–870 and 340–440 nm wavelength pair) was found to be narrower for a broader AOD distribution for some stations, and vice versa for other stations, and a higher relative error at the shorter wavelength (among the wavelength pairs used for AE estimation) led to a shift in the peak of the AE difference distribution towards a higher positive value, while a higher relative error at a lower wavelength shifted the AE difference distribution to a negative value for the AOD overestimation case, and vice versa for the AOD underestimation case. For rural locations, the mean NO2 differences were found to be mostly below 0.50 × 10−4 mol m−2, with the corresponding AOD differences being below 0.002, and in extreme NO2 loading scenarios, it went above this value and reached above 1.00 × 10−4 mol m−2 for some stations, leading to higher AOD differences but below 0.005. Finally, AOD and AE trends were calculated based on the original AERONET AOD (based on AERONET OMI climatological NO2), and its comparison with the mean differences in the AERONET and PGN NO2-corrected AOD was indicative of how NO2 correction could potentially affect realistic AOD trends.

Spatiotemporal Distributions and Variations in Summertime Ozone Photochemical Production Regimes over Shanxi

Satellite-based formaldehyde（HCHO）columns and tropospheric nitrogen dioxide columns were observed using the Ozone Monitoring Instrument（OMI），and groundbased observations of ozone（O3）for May-August from 2013 to 2022 were connected to calculate the threshold values of the HCHO to NO2 ratio（FNR）in Shanxi Province. Then，the spatiotemporal distributions and variations in summertime ozone photochemical production regimes were analyzed. The results showed that：① The volatile organic compound（VOC） -sensitive regime area（FNR < 2.3）was obviously reduced，while the VOCs-NOx transitional regime（FNR between 2.3-4.1）area increased in the early years and then decreased， and NO x -sensitive regime area expanded significantly in summer from 2013 to 2022 over Shanxi Province. ② The increased summertime FNR during 2013 to 2019 was associated with the co-effect of increased HCHO columns and decreased tropospheric NO2 columns. The Shanxi Province was generally under an NOx regime since 2016，which reflected the remarkable effect of NO x emission reductions；however，there was a shift from a VOC-sensitive regime to a VOCs-NOx transitional regime，in which O3 pollution aggravation was widespread under the background of decreased NOx emissions. The decrease in O3 concentration during 2020 to 2022 followed the synergistical declines in HCHO columns and tropospheric NO2 columns. ③ The O3 weekend effects were reversed in Linfen and Yuncheng but were persistent in the other nine cities. Satellite-based weekend HCHO and NO2 levels were higher than those on weekdays in some cities of Shanxi Province，indicating that the O3 weekend effect was not only dependent on the changes of precursors emissions but was also closely related to O3 photochemical production sensitivity. The results indicated the necessity of simultaneous controls in NOx emissions and VOCs emissions for ozone abatement plans over Shanxi Province. In addition，Taiyuan，Yangquan，Yuncheng，and Jincheng should continue to promote reduction in NOx emissions.

High resolution mapping of nitrogen dioxide and particulate matter in Great Britain (2003–2021) with multi-stage data reconstruction and ensemble machine learning methods

In this contribution, we applied a multi-stage machine learning (ML) framework to map daily values of nitrogen dioxide (NO2) and particulate matter (PM10 and PM2.5) at a 1 km2 resolution over Great Britain for the period 2003–2021. The process combined ground monitoring observations, satellite-derived products, climate reanalyses and chemical transport model datasets, and traffic and land-use data. Each feature was harmonized to 1 km resolution and extracted at monitoring sites. Models used single and ensemble-based algorithms featuring random forests (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), as well as lasso and ridge regression. The various stages focused on augmenting PM2.5 using co-occurring PM10 values, gap-filling aerosol optical depth and columnar NO2 data obtained from satellite instruments, and finally the training of an ensemble model and the prediction of daily values across the whole geographical domain (2003–2021). Results show a good ensemble model performance, calculated through a ten-fold monitor-based cross-validation procedure, with an average R2 of 0.690 (range 0.611–0.792) for NO2, 0.704 (0.609–0.786) for PM10, and 0.802 (0.746–0.888) for PM2.5. Reconstructed pollution levels decreased markedly within the study period, with a stronger reduction in the latter eight years. The pollutants exhibited different spatial patterns, while NO2 rose in close proximity to high-traffic areas, PM demonstrated variation at a larger scale. The resulting 1 km2 spatially resolved daily datasets allow for linkage with health data across Great Britain over nearly two decades, thus contributing to extensive, extended, and detailed research on the long-and short-term health effects of air pollution.

National, satellite-based land-use regression models for estimating long-term annual NO2 exposure across India

In India, scarcity of ground-based measurements of nitrogen dioxide (NO2) is a major challenge for estimating long-term exposure and associated health impacts. This study aimed to develop and validate a national-scale annual NO2 exposure model for India for 2019 and determine if model cross-validation predictive ability was improved by including non-continuous (manual) measurements along with reference-grade, continuous measurements.We used a supervised forward-addition linear regression method to fit land use regression (LUR) models developed with up to 804 Central Pollution Control Board ground monitoring stations (n = 157 continuous, n = 647 manual) and 209 spatial predictor variables, including satellite-based tropospheric NO2 columns. Two models were developed: one using continuous sites only and one using continuous and manual sites, with standard diagnostics and cross-validation (CV) methods. We also assessed if the kriging of final model residuals reduced spatial autocorrelation and improved model CV results. LUR coefficients for the best-performing model were applied to predictors for 2015–2021 and gridded at 100 m to estimate population-weighted exposure.The continuous sites-only model and combined continuous and manual sites models had CV-R2 values of 0.59 (root-mean-square error [RMSE]: 9.4 μg/m3) and 0.54 (RMSE: 8.3 μg/m3), respectively, and both included the satellite NO2 predictor. Kriging residuals increased the CV-R2 of the combined model to 0.70 (RMSE: 7.2 μg/m3) but offered no improvement for the continuous site model. National population-weighted average NO2 was 22.1 μg/m3 in 2019. We estimated over 92% of the Indian population was exposed to annual NO2 exceeding the WHO air quality guideline (10 μg/m3). In Delhi, Mumbai, and Kolkata, an estimated 45%, 100%, and 100% of the population, respectively, experienced annual NO2 levels that surpassed Indian standards (40 μg/m3). To our knowledge, this is the first such long-term NO2 LUR model specific to India, and predictions are available to interested researchers.

Evaluating NOx stack plume emissions using a high-resolution atmospheric chemistry model and satellite-derived NO2 columns

Abstract. This paper presents large-eddy simulations with atmospheric chemistry of four large point sources world-wide, focusing on the evaluation of NOx (NO + NO2) emissions with the TROPOspheric Monitoring Instrument (TROPOMI). We implemented a condensed chemistry scheme to investigate how the emitted NOx (95 % as NO) is converted to NO2 in the plume. To use NOx as a proxy for CO2 emission, information about its atmospheric lifetime and the fraction of NOx present as NO2 is required. We find that the chemical evolution of the plumes depends strongly on the amount of NOx that is emitted, as well as on wind speed and direction. For large NOx emissions, the chemistry is pushed in a high-NOx chemical regime over a length of almost 100 km downwind of the stack location. Other plumes with lower NOx emissions show a fast transition to an intermediate-NOx chemical regime, with short NOx lifetimes. Simulated NO2 columns mostly agree within 20 % with TROPOMI, signalling that the emissions used in the model were approximately correct. However, variability in the simulations is large, making a one-to-one comparison difficult. We find that temporal wind speed variations should be accounted for in emission estimation methods. Moreover, results indicate that common assumptions about the NO2 lifetime (≈ 4 h) and NOx:NO2 ratios (≈ 1.3) in simplified methods that estimate emissions from NO2 satellite data need revision.

Tracing vulnerable communities to ambient air pollution exposure: A geodemographic and remote sensing approach

Most studies analyzing the effects of air pollution on disadvantaged populations use ground air quality measurements. However, ground stations are generally limited, with nearly 40% of countries having no official PM2.5 stations, not allowing air quality analysis for a significantly large share of the world's population. Furthermore, limited studies analyze community data from a geodemographic perspective, in other words, to delineate the sociodemographic profiles and geographically locate the socioeconomic groups more exposed to ambient air pollution. Therefore, a significant question arises: How can we trace vulnerable communities to air pollution in areas lacking air-quality ground data? Here, we propose a novel methodology to respond to this question. We use NO2, SO2, CO, and HCHO tropospheric column air-quality data from Sentinel-5P, a satellite that quantifies concentrations of atmospheric species from space operationally. We integrate them with census and environmental data and apply the local fuzzy geographically weighted clustering spatial machine learning method for segmentation analysis. Our findings for Bali, Indonesia, provide quantitative evidence for the benefits of this methodology in tracing and delineating the profiles of the communities most exposed to air pollution. For example, results show that communities with highly disadvantaged populations, such as unemployed (over 27.8%), low educated (over 27.9%), and children (over 22.1%) (mainly located around Bali's south and north coast touristic areas), exhibit very high values (over the 75th quartile) across the pollutants studied. The proposed method is reproducible easily, quickly, and at low cost, as it is based on freely available satellite data and not on costly ground station measurements. This will hopefully assist decision-makers in tracing the most vulnerable subpopulations, even in areas with inadequate air-quality monitoring networks, thus allowing local governments around the globe (even those that are financially weak) to achieve environmental justice and their sustainable development goals.

Soil Moisture, Soil NOx and Regional Air Quality in the Agricultural Central United States

AbstractAgricultural soils containing nitrogen‐rich fertilizers are a substantial source of reactive nitrogen to the atmosphere with potential to impact air quality. One form of reactive nitrogen, nitrogen oxides (NOx = NO + NO2), are a harmful air pollutant and form secondary pollutants, including particulate matter (PM) and ozone (O3). Soil nitrogen oxide emissions (SNOx) are heavily influenced by environmental conditions, however the understanding of the influence of environmental drivers on the behavior of SNOx is limited. Here, we implement a modified soil moisture‐dependent SNOx parameterization into the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) and investigate the impact on regional air quality in the central U.S. Evaluating against TROPOspheric Monitoring Instrument (TROPOMI) column NO2 observations, WRF‐Chem columns better capture the TROPOMI column magnitudes earlier in the growing season when using the updated SNOx parametrization, with modeled column bias improved to −1.1% over the most heavily fertilized regions. Evaluating against Environmental Protection Agency (EPA) surface NO2 observations, the relationship between surface NO2 and soil moisture is better represented in agriculturally‐dominant regions when using the updated parameterization, with greatest surface NO2 concentrations at moderate soil moisture and lower concentrations at wetter or drier soil conditions. In simulations, these SNOx lead to increased O3 in select urban regions, with more than double the occurrences of O3 exceeding the EPA 8‐hr O3 standard of 70 ppb.

Interpreting Geostationary Environment Monitoring Spectrometer (GEMS) geostationary satellite observations of the diurnal variation in nitrogen dioxide (NO2) over East Asia

Abstract. Nitrogen oxide radicals (NOx≡NO+NO2) emitted by fuel combustion are important precursors of ozone and particulate matter pollution, and NO2 itself is harmful to public health. The Geostationary Environment Monitoring Spectrometer (GEMS), launched in space in 2020, now provides hourly daytime observations of NO2 columns over East Asia. This diurnal variation offers unique information on the emission and chemistry of NOx, but it needs to be carefully interpreted. Here we investigate the drivers of the diurnal variation in NO2 observed by GEMS during winter and summer over Beijing and Seoul. We place the GEMS observations in the context of ground-based column observations (Pandora instruments) and GEOS-Chem chemical transport model simulations. We find good agreement between the diurnal variations in NO2 columns in GEMS, Pandora, and GEOS-Chem, and we use GEOS-Chem to interpret these variations. NOx emissions are 4 times higher in the daytime than at night, driving an accumulation of NO2 over the course of the day, offset by losses from chemistry and transport (horizontal flux divergence). For the urban core, where the Pandora instruments are located, we find that NO2 in winter increases throughout the day due to high daytime emissions and increasing NO2/NOx ratio from entrainment of ozone, partly balanced by loss from transport and with a negligible role of chemistry. In summer, by contrast, chemical loss combined with transport drives a minimum in the NO2 column at 13:00–14:00 local time (LT). Segregation of the GEMS data by wind speed further demonstrates the effect of transport, with NO2 in winter accumulating throughout the day at low winds but flat at high winds. The effect of transport can be minimized in summer by spatially averaging observations over the broader metropolitan scale, under which conditions the diurnal variation in NO2 reflects a dynamic balance between emission and chemical loss.

An intercomparison of satellite, airborne, and ground-level observations with WRF–CAMx simulations of NO2 columns over Houston, Texas, during the September 2021 TRACER-AQ campaign

Abstract. Nitrogen dioxide (NO2) is a precursor of ozone (O3) and fine particulate matter (PM2.5) – two pollutants that are above regulatory guidelines in many cities. Bringing urban areas into compliance of these regulatory standards motivates an understanding of the distribution and sources of NO2 through observations and simulations. The TRACER-AQ campaign, conducted in Houston, Texas, in September 2021, provided a unique opportunity to compare observed NO2 columns from ground-, airborne-, and satellite-based spectrometers. In this study, we investigate how these observational datasets compare and simulate column NO2 using WRF–CAMx with fine resolution (444 × 444 m2) comparable to the airborne column measurements. We compare WRF-simulated meteorology to ground-level monitors and find good agreement. We find that observations from the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) instrument were strongly correlated (r2 = 0.79) to observations from Pandora spectrometers with a slight high bias (normalized mean bias (NMB) = 3.4 %). Remote sensing observations from the TROPOspheric Monitoring Instrument (TROPOMI) were generally well correlated with Pandora observations (r2 = 0.73) with a negative bias (NMB = −22.8 %). We intercompare different versions of TROPOMI data and find similar correlations across three versions but slightly different biases (from −22.8 % in v2.4.0 to −18.2 % in the NASA MINDS product). Compared with Pandora observations, the WRF–CAMx simulation had reduced correlation (r2 = 0.34) and a low bias (−21.2 %) over the entire study region. We find particularly poor agreement between simulated NO2 columns and GCAS-observed NO2 columns in downtown Houston, an area of high population and roadway densities. These findings point to a potential underestimate of NOx emissions (NOx = NO + NO2) from sources associated with the urban core of Houston, such as mobile sources, in the WRF–CAMx simulation driven by the Texas state inventory, and further investigation is recommended.