Air pollution poses an increasing public health risk in many African cities, where systematic monitoring is limited by the high cost of reference-grade instrumentation. Satellite-based sensors such as TROPOMI on Sentinel-5P provide global coverage of atmospheric pollutants, including nitrogen dioxide (NO2), but their limited vertical resolution complicates the estimation of surface-level concentrations. Palmes diffusion tubes offer a low-cost, low-tech alternative for measuring NO2 at the ground level. Combining satellite observations with data from ground-based diffusion tubes presents a promising approach for generating regional air quality maps, particularly in resource-limited settings. This study reports on the implementation of a pilot network of Palmes tubes in Kumasi, Ghana. To ensure sustainability and scalability, a local air quality laboratory was established to prepare and analyse the tubes using locally sourced materials and equipment. Validation against tubes prepared and analysed by an ac-credited external laboratory demonstrated satisfactory agreement. Measurements from March 2025 revealed a wide range of NO2 concentrations, from an average of 7 mu g m(-3) in residential areas away from traffic and industry to peak values of88 mu g m(-3) at heavily trafficked intersections - substantially exceeding the WHO 2021 guideline limits of 25 mu g m(-3) for 24 h exposure and 10 mu g m(-3) for annual mean concentrations. Averaged satellite data showed the accumulation of NO2 plumes downwind of the city, though the relationship with surface-level measurements remains complex and needs further investigation. Column-to-surface ratios in Kumasi are significantly lower than those typically observed in European cities. Using a parametrization method based on ground-based measurements and TROPOMI retrievals, we find that TROPOMI underestimates tropospheric NO2 column densities by roughly a factor of 2.5 in March 2025. This underestimation is attributed to higher aerosol loading over Ghana, which reduces retrieval sensitivity in the troposphere and increases reliance on unrepresentative a priori profiles.

Urban air pollution poses a significant health risk, with over half the global population living in cities where air quality often exceeds World Health Organization (WHO) guidelines. A comprehensive understanding of local pollution levels is essential for addressing this issue. Recent advancements in low-cost sensors and satellite instruments offer cost-efficient complements to reference stations but integrating these diverse data sources in useful monitoring tools is not straightforward. This study presents the updated Retina v2 algorithm, which generates high-resolution urban air pollution maps by assimilating heterogeneous measurements into a portable urban dispersion model. Tested for NO2 concentrations in Madrid during March 2019, it shows improved speed and accuracy over its predecessor, with the ability to incorporate satellite data. Retina v2 balances performance with modest computational demands, delivering similar or better results compared to complex dispersion models and machine learning approaches requiring extensive datasets. Using only TROPOMI satellite data, citywide NO2 simulations show an RMSE of 19.3 µg m−3, with better results when hourly in-situ measurements were included. Relying on data of a single ground station can introduce biases, which can be mitigated by incorporating satellite data or multiple ground stations. Including more stations improves accuracy, with 24 stations yielding a correlation of 0.90 and an RMSE of 13.0 µg m−3. The benefit of TROPOMI diminishes when data from five or more ground stations is available, but it remains valuable for many cities which have limited monitoring networks.

Since anthropogenic NOx (NOx=NO+NO2) and CO2 are co-emitted species for anthropogenic sources, some studies have used the NOx emissions retrieved from satellite observations to infer the anthropogenic CO2 emissions. However, these studies did not consider the fact that satellites measure total NO2 concentrations and their inferred emissions encompass both biogenic and anthropogenic sources. In this study, we introduce a method to distinguish soil NOx emissions from satellite-based total NOx emissions. The total NOx emissions are derived by the state-of-the-art inverse algorithm DECSO (Daily Emission estimation Constrained by Satellite Observations, Mijling and van der A, 2012; Ding et al., 2017a) from TROPOMI observations. Using the characteristic seasonal cycle of soil emissions we derive these emissions for representative regions with only biogenic emissions, which are then applied to nearby regions according land-use fractions. To evaluate this approach, we compared the deviation between the tropospheric NO2 concentration observed by satellite and two atmospheric composition model simulations: one using the satellite-derived soil NOx emissions and another with the Copernicus Atmosphere Monitoring Service (CAMS) global soil emissions inventory (CAMS-GLOB-SOIL). Once the soil NOx emissions are derived, they can be subtracted from the total emissions to get anthropogenic NOx emissions. Subsequently anthropogenic CO2 emissions can be estimated using known CO2/NOx factors from bottom-up inventories. The annual CO2 emissions derived from DECSO (called DECSO-CO2) in our study area (large part of Europe) is 3.7 Gt in 2019, which is comparable with the 3.2 Gt of the CAMS CO2 inventory (called CAMS-CO2). The DECSO-CO2 and CAMS-CO2 are comparable for most large sources and cities, but the DECSO-CO2 show a larger number of low emission spots than the CAMS-CO2. The results demonstrate the potential for DECSO to expand its application to other regions in the world with less information on anthropogenic CO2 emissions.

We introduce an innovative method to distinguish soil nitrogen oxides (NOx=NO+NO2) emissions from satellite-based total NOx emissions using its seasonal characteristics. To evaluate the approach, we compare the deviation between the tropospheric NO2 concentration observed by satellite and two atmospheric composition model simulations driven by the newly estimated soil NOx emissions and the Copernicus Atmosphere Monitoring Service (CAMS) inventory. The estimated average soil NOx emissions in Europe are 2.5 kg N ha-1 yr-1 in 2019, and the annual soil NOx emissions is approximately 2.5 times larger than that of the CAMS inventory. Our method can easily be extended to other regions at middle or high latitudes with similar seasonal characteristics of soil emissions. The soil emissions are subtracted from the total NOx emissions yielding realistic anthropogenic NOx emissions. We further show this also yields realistic anthropogenic CO2 emissions using known CO2/NOx factors from bottom-up inventories.

In recent years, there has been growing interest in developing air pollution prediction models to reduce exposure measurement error in epidemiologic studies. However, efforts for localized, fine-scale prediction models have been predominantly focused in the United States and Europe. Furthermore, the availability of new satellite instruments such as the TROPOsopheric Monitoring Instrument (TROPOMI) provides novel opportunities for modeling efforts. We estimated daily ground-level nitrogen dioxide (NO2) concentrations in the Mexico City Metropolitan Area at 1-km2 grids from 2005 to 2019 using a four-stage approach. In stage 1 (imputation stage), we imputed missing satellite NO2 column measurements from the Ozone Monitoring Instrument (OMI) and TROPOMI using the random forest (RF) approach. In stage 2 (calibration stage), we calibrated the association of column NO2 to ground-level NO2 using ground monitors and meteorological features using RF and extreme gradient boosting (XGBoost) models. In stage 3 (prediction stage), we predicted the stage 2 model over each 1-km2 grid in our study area, then ensembled the results using a generalized additive model (GAM). In stage 4 (residual stage), we used XGBoost to model the local component at the 200-m2 scale. The cross-validated R2 of the RF and XGBoost models in stage 2 were 0.75 and 0.86 respectively, and 0.87 for the ensembled GAM. Cross-validated rootmean-squared error (RMSE) of the GAM was 3.95 μg/m3. Using novel approaches and newly available remote sensing data, our multi-stage model presented high cross-validated fits and reconstructs fine-scale NO2 estimates for further epidemiologic studies in Mexico City.

<p align="justify"><span lang="en-GB">Nitrogen oxides (NO<sub>x</sub>) emissions play an important role in air quality, the nitrogen cycle, and as precursor for climate gasses. The most important sources of NO<sub>x</sub> emissions are fossil fuel burning (industry and traffic) and the release from soil.</span></p> <p align="justify"><span lang="en-GB">With the inversion algorithm DECSO (Daily Emissions Constrained by Satellite Observations) we derive quantitative NO<sub>x</sub> emissions on a 5 to 20 km resolution from TROPOMI (on Sentinel 5p) observations of NO<sub>2</sub>, taking advantage of the fine spatial resolution (5 x 3.5 km) of the TROPOMI instrument. DECSO is a full inversion algorithm based on data assimilation of satellite observation and the Chemical-transport model CHIMERE. For the data assimilation a Kalman Filter technique is used. For the inversion no apriori information of the NO<sub>x</sub> emissions is needed and for this reason new sources can be detected. In the postprocessing we use the seasonal cycle to distinct between soil emissions (having strong seasonal cycle with summer peak) and anthropogenic emissions (having low variability over the year).</span></p> <p align="justify"><span lang="en-GB">To assess the quality of satellite-derived NO<sub>x</sub> emissions on various scales, i.e. national, regional, city and points-sources, they are compared to various bottom-up inventories. For bottom-up emissions we selected NEC (National Emission Ceilings Directive inventory), LRTAP (LRTAP convention data), the CAMS (Copernicus Atmosphere Monitoring Service) regional anthropogenic emission database and the high-resolution emission inventory HERMES (High-Elective Resolution Modelling Emission System) for Catalonia. Detailed results will be shown, including the spatial and temporal variation per emission category. </span></p>

Background: The association between short-term exposure to air pollution and cognitive and mental health has not been thoroughly investigated so far. Objectives: We conducted a panel study co-designed with citizens to assess whether air pollution can affect attention, perceived stress, mood and sleep quality. Methods: From September 2020 to March 2021, we followed 288 adults (mean age = 37.9 years; standard deviation = 12.1 years) for 14 days in Barcelona, Spain. Two tasks were self-administered daily through a mobile application: the Stroop color-word test to assess attention performance and a set of 0-to-10 rating scale questions to evaluate perceived stress, well-being, energy and sleep quality. From the Stroop test, three outcomes related to selective attention were calculated and z-score-transformed: response time, cognitive throughput and inhibitory control. Air pollution was assessed using the mean nitrogen dioxide (NO2) concentrations (mean of all Barcelona monitoring stations or using location data) 12 and 24 h before the tasks were completed. We applied linear regression with random effects by participant to estimate intra-individual associations, controlling for day of the week and time-varying factors such as alcohol consumption and physical activity. Results: Based on 2,457 repeated attention test performances, an increase of 30 mu g/m(3) exposure to NO2 12 h was associated with lower cognitive throughput (beta =-0.08, 95% CI:-0.15,-0.01) and higher response time (beta = 0.07, 95% CI: 0.01, 0.14) (increase inattentiveness). Moreover, an increase of 30 mu g/m(3) exposure to NO2 12 h was associated with higher self-perceived stress (beta = 0.44, 95% CI: 0.13, 0.77). We did not find statistically significant associations with inhibitory control and subjective well-being. Conclusions: Our findings suggest that short-term exposure to air pollution could have adverse effects on attention performance and perceived stress in adults.

The fast industrialization and urbanization in India lead to serious air quality problems. Using the long record of the satellite observations of NO2 from the Ozone Monitoring Instrument, we have derived monthly NOx emissions over India from 2007 to 2018 by applying an inversion algorithm to the QA4ECV NO2 retrieval product. Our results show that NOx emissions steadily increased by more than 50% from 2007 until 2016. From 2017 onwards, NOx emissions have become stable or are slowly decreasing. Furthermore, our results also reveal a strong seasonal cycle in the emission with the peak in summer, partially caused by biogenic/soil NOx emissions that is one of the main NOx sources in India. The validation of our derived NOx emissions with HTAP, Global power plants database and in-situ observations show that the spatial distribution and quantity of NOx emissions are well captured. The long-term record of NOx emissions captures the trend over India well and illustrates the impact of air quality measures on air pollution in India.

During the COVID‐19 lockdown (24 January–20 March) in China low air pollution levels were reported in the media as a consequence of reduced economic and social activities. Quantification of the pollution reduction is not straightforward due to effects of transport, meteorology, and chemistry. We have analyzed the NOx emission reductions calculated with an inverse algorithm applied to daily NO2 observations from TROPOMI onboard the Copernicus Sentinel‐5P satellite. This method allows the quantification of emission reductions per city and the analysis of emissions of maritime transport and of the energy sector separately. The reductions we found are 20–50% for cities, about 40% for power plants, and 15–40% for maritime transport depending on the region. The reduction in both emissions and concentrations shows a similar timeline consisting of a sharp reduction (34–50%) around the Spring festival and a slow recovery from mid‐February to mid‐March.

Since the last decade, India has encountered severe problems in air quality and became the most air polluted country in the world. With satellite observations, we can monitor the changes of NO2 column concentrations in India. However, the information on emissions is very limited. In this study, we use the KNMI DECSO (Daily emission Estimates constrained by Satellite Observation) algorithm to estimate NOx emissions from OMI observations from 2007 to 2018. The results show that NOx emissions have increased by about 40% in the last 12 years. We compare NOx emissions from DECSO and the HTAP bottom-up NOx emissions with the location and capacity of power plants in India. The comparison between DECSO and HTAP shows that the emissions estimated from satellite are more accurate on spatial and temporal scale. We also run the CHIMERE v2013 model with emissions from DECSO and HTAP respectively and compare the model simulations with NO2 in-situ measurements of the Indian national network. The comparison shows that model simulation with DECSO has lower bias and better correlation with in-situ observations than that with HTAP.

In many cities around the world people are exposed to elevated levels of air pollution. Often local air quality is not well known due to the sparseness of official monitoring networks or unrealistic assumptions being made in urban-air-quality models. Low-cost sensor technology, which has become available in recent years, has the potential to provide complementary information. Unfortunately, an integrated interpretation of urban air pollution based on different sources is not straightforward because of the localized nature of air pollution and the large uncertainties associated with measurements of low-cost sensors. This study presents a practical approach to producing high-spatiotemporal-resolution maps of urban air pollution capable of assimilating air quality data from heterogeneous data streams. It offers a two-step solution: (1) building a versatile air quality model, driven by an open-source atmospheric-dispersion model and emission proxies from open-data sources, and (2) a practical spatial-interpolation scheme, capable of assimilating observations with different accuracies. The methodology, called Retina, has been applied and evaluated for nitrogen dioxide (NO2) in Amsterdam, the Netherlands, during the summer of 2016. The assimilation of reference measurements results in hourly maps with a typical accuracy (defined as the ratio between the root mean square error and the mean of the observations) of 39 % within 2 km of an observation location and 53 % at larger distances. When low-cost measurements of the Urban AirQ campaign are included, the maps reveal more detailed concentration patterns in areas which are undersampled by the official network. It is shown that during the summer holiday period, NO2 concentrations drop about 10 %. The reduction is less in the historic city centre, while strongest reductions are found around the access ways to the tunnel connecting the northern and the southern part of the city, which was closed for maintenance. The changing concentration patterns indicate how traffic flow is redirected to other main roads. Overall, it is shown that Retina can be applied for an enhanced understanding of reference measurements and as a framework to integrate low-cost measurements next to reference measurements in order to get better localized information in urban areas.

During the COVID-19 lockdown in China low air pollution levels were reported as a consequence of the reduced economic and social activities. Quantification of the pollution reduction is not straigh...

During the COVID-19 lockdown (24 January-20 March) in China low air pollution levels were reported in the media as a consequence of reduced economic and social activities Quantification of the pollution reduction is not straightforward due to effects of transport, meteorology, and chemistry We have analyzed the NOx emission reductions calculated with an inverse algorithm applied to daily NO2 observations from TROPOMI onboard the Copernicus Sentinel-5P satellite This method allows the quantification of emission reductions per city and the analysis of emissions of maritime transport and of the energy sector separately The reductions we found are 20-50% for cities, about 40% for power plants, and 15-40% for maritime transport depending on the region The reduction in both emissions and concentrations shows a similar timeline consisting of a sharp reduction (34-50%) around the Spring festival and a slow recovery from mid-February to mid-March

An operational multimodel forecasting system for air quality has been developed to provide air quality services for urban areas of China. The initial forecasting system included seven state-of-the-art computational models developed and executed in Europe and China (CHIMERE, IFS, EMEP MSC-W, WRF-Chem-MPIM, WRF-Chem-SMS, LOTOS-EUROS, and SILAMtest). Several other models joined the prediction system recently, but are not considered in the present analysis. In addition to the individual models, a simple multimodel ensemble was constructed by deriving statistical quantities such as the median and the mean of the predicted concentrations. The prediction system provides daily forecasts and observational data of surface ozone, nitrogen dioxides, and particulate matter for the 37 largest urban agglomerations in China (population higher than 3 million in 2010). These individual forecasts as well as the multimodel ensemble predictions for the next 72 h are displayed as hourly outputs on a publicly accessible web site (http://www.marcopolo-panda.eu, last access: 27 March 2019). In this paper, the performance of the prediction system (individual models and the multimodel ensemble) for the first operational year (April 2016 until June 2017) has been analyzed through statistical indicators using the surface observational data reported at Chinese national monitoring stations. This evaluation aims to investigate (a) the seasonal behavior, (b) the geographical distribution, and (c) diurnal variations of the ensemble and model skills. Statistical indicators show that the ensemble product usually provides the best performance compared to the individual model forecasts. The ensemble product is robust even if occasionally some individual model results are missing. Overall, and in spite of some discrepancies, the air quality forecasting system is well suited for the prediction of air pollution events and has the ability to provide warning alerts (binary prediction) of air pollution events if bias corrections are applied to improve the ozone predictions.

Abstract. An operational multi-model forecasting system for air quality including 9 different chemical transport models has been developed and is providing daily forecasts of ozone, nitrogen oxides, and particulate matter for the 37 largest urban areas of China (population higher than 3 million in 2010). These individual forecasts as well as the mean and median concentrations for the next 3 days are displayed on a publicly accessible web site (http://www.marcopolo-panda.eu). The paper describes the forecasting system and shows some selected illustrative examples of air quality predictions. It presents an inter-comparison of the different forecasts performed during a given period of time (1–15 March 2017), and highlights recurrent differences between the model output as well as systematic biases that appear in the median concentration values. Pathways to improve the forecasts by the multi-model system are suggested.

ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTToward a Unified Terminology of Processing Levels for Low-Cost Air-Quality SensorsPhilipp Schneider*Philipp SchneiderNILU - Norwegian Institute for Air Research, PO Box 100, Kjeller, Norway*E-mail: [email protected]More by Philipp Schneiderhttp://orcid.org/0000-0001-5686-8683, Alena BartonovaAlena BartonovaNILU - Norwegian Institute for Air Research, PO Box 100, Kjeller, NorwayMore by Alena Bartonova, Nuria CastellNuria CastellNILU - Norwegian Institute for Air Research, PO Box 100, Kjeller, NorwayMore by Nuria Castell, Franck R. DaugeFranck R. DaugeNILU - Norwegian Institute for Air Research, PO Box 100, Kjeller, NorwayMore by Franck R. Dauge, Michel GerbolesMichel GerbolesEuropean Commission − Joint Research Centre, Ispra, ItalyMore by Michel Gerboles, Gayle S.W. HaglerGayle S.W. HaglerOffice of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina United StatesMore by Gayle S.W. Hagler, Christoph HüglinChristoph HüglinEmpa, Swiss Federal Laboratories for Materials Science and Technology, Duebendorf, SwitzerlandMore by Christoph Hüglin, Roderic L. JonesRoderic L. JonesDepartment of Chemistry, University of Cambridge, Cambridge, United KingdomMore by Roderic L. Jones, Sean KhanSean KhanUnited Nations Environment Programme, Science Division, Global Environment Monitoring Unit, Nairobi, KenyaMore by Sean Khan, Alastair C. LewisAlastair C. LewisNational Centre for Atmospheric Science, University of York, Heslington, York YO105DD, United KingdomMore by Alastair C. Lewis, Bas MijlingBas MijlingRoyal Netherlands Meteorological Institute (KNMI), De Bilt, The NetherlandsMore by Bas Mijling, Michael MüllerMichael MüllerEmpa, Swiss Federal Laboratories for Materials Science and Technology, Duebendorf, SwitzerlandMore by Michael Müller, Michele PenzaMichele PenzaItalian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Brindisi Research Center, Brindisi, ItalyMore by Michele Penza, Laurent SpinelleLaurent SpinelleFrench National Institute for Industrial Environment and Risks (INERIS), 60550 Verneuil-en-Halatte, FranceMore by Laurent Spinelle, Brian StaceyBrian StaceyRicardo Energy & Environment, Gemini Building, Fermi Avenue, Harwell, Oxon OX11 0QR, United KingdomMore by Brian Stacey, Matthias VogtMatthias VogtNILU - Norwegian Institute for Air Research, PO Box 100, Kjeller, NorwayMore by Matthias Vogt, Joost WesselingJoost WesselingNational Institute for Public Health and the Environment, Bilthoven, NetherlandsMore by Joost Wesseling, and Ronald W. WilliamsRonald W. WilliamsOffice of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina United StatesMore by Ronald W. WilliamsCite this: Environ. Sci. Technol. 2019, 53, 15, 8485–8487Publication Date (Web):July 29, 2019Publication History Received3 July 2019Published online29 July 2019Published inissue 6 August 2019https://pubs.acs.org/doi/10.1021/acs.est.9b03950https://doi.org/10.1021/acs.est.9b03950newsACS PublicationsCopyright © 2019 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views5054Altmetric-Citations24LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (743 KB) Get e-AlertscloseSUBJECTS:Calibration,Environmental chemistry,Manufacturing,Particulate matter,Sensors Get e-Alerts

AIRBUS presents a global monitoring service on atmospheric composition and emission allocation. Air composition and meteorological data from satellites and local sensors are combined into a chemical transport model to build up or validate trace gas and particulate matter emission sources and their impact on air quality. The service is using the AIRBUS' developed OMI and TROPOMI satellite sensors to be able to quickly disclose new regions around the globe at low cost, as has been done for eastern Asia and the Indian continent. This yields a database of up-to-date emission sources at a spatial resolution of about 3.5 km (and in the future 1 km) and provides daily observation data on regional and transboundary transport of pollutants. Target constituents are NO2, particulate matter, CH4, (tropospheric) ozone and SO2. The full blown service adds data from several measurement systems (on ground, aircraft, high-altitude solar powered drones or pseudo satelites (HAPS) and nanosatellites) and various data like land-use and traffic information to achieve street level spatial resolution while maintaining accuracy and validation status. The resulting database of emission sources has the same street level spatial resolution and is intended to serve local issues. It has presently been built for several EU cities. This advanced and cost-competitive Air Quality Monitoring service is used for awareness building, policy development and policy evaluation and enforcement. The intent is to realize a commercial global service, based on local cooperation. The paper will describe the service and status.