Satellite observations of ozone and nitrogen dioxide : from retrievals to emission estimates

In the last decades, measurements of atmospheric composition from satellites have become very important for scientific research as well as applications for monitoring and forecasting the state of the atmosphere. Instruments such as GOME-2, and OMI look at backscattered sunlight in nadir view, measuring the ultraviolet and visible spectrum in high resolution. Launched in a sun-synchronous orbit at ??800 km altitude, they scan the Earth’s surface daily in 14–15 orbits, providing a homogeneous dataset with (almost) daily global coverage. Combining the spectral measurements with radiative transport models, concentrations can be inferred for important trace gases such as ozone (O3) and nitrogen dioxide (NO2). Chemical transport models can be used to calculate the strength and location of the underlying emissions. Long time series of satellite retrievals give insight on how human activity contributes to changes of atmospheric composition, affecting health and climate. Information in the vertical distribution of ozone can be retrieved from the sharp decrease in the ozone absorption cross-section in the ultraviolet spectrum. Chapter 2 deals with the question how the performance of the ozone profile retrieval algorithm (OPERA) can be improved. To produce consistent global datasets, the algorithm needs to have good global performance, while short computation time facilitates the use of the algorithm in near real time applications. Because the retrieval is ill-posed (in the sense that many profiles give similar simulated spectra within the measurement errors), the solution depends on a priori (climatological) ozone profiles. The non-linearity of the problem asks for an iteration scheme to find the best fitting solution numerically. We use the convergence behaviour of the iteration as a diagnostic tool for the ozone profile retrievals from the GOME instrument for February and October 1998. In this way, we reveal several retrieval problems of different origin, and we improve issues related to the Southern Atlantic Anomaly, low cloud fractions e.g. above deserts, and ozone cross sections. The a priori ozone climatology and its associated variability is also an important source for retrieval problems. By using a priori ozone profiles that are selected on the expected total ozone column, retrieval problems due to anomalous ozone distributions (such as in the ozone hole) can be avoided. Applying the algorithm adaptations improve the convergence statistics considerably, not only increasing the number of successful retrievals, but also reducing the average computation time, due to less iteration steps per retrieval. For February 1998, non-convergence was brought down from 10.7% to 2.1%, while the mean number of iteration steps (which dominates the computational time) dropped 26% from 5.11 to 3.79. Total nitrogen dioxide columns can be retrieved from space in the 405–465 nm window, but the NO2 spectrum does not contain any significant height information. Instead, data assimilation techniques can be used to distinguish the tropospheric part from the stratospheric part, which gives valuable information of NO2 in the lowest part of the atmosphere. Here it acts as an air pollutant, often from man-made origin. The case study in Chapter 3 evaluates how NO2 air pollution can be controlled with air quality measures. Due to strong economic growth in the last decades, air pollution in large Chinese megacities has become a serious issue. In reparation for the Olympic Games in Beijing in 2008, extensive air quality measures were taken to improve air quality during the event, affecting traffic, industry and power production. We evaluate the effect of the air quality measures on reducing air pollution, by analysing the tropospheric NO2 retrievals over the greater Beijing area before, during and after the Olympic Games. To compensate for the strong variability due to meteorology, we compare the observations with model simulations from the regional chemistry transport model CHIMERE based on a pre-Olympic emission inventory. The relative change between observation and simulation shows that the measures caused a reduction of tropospheric NO2 column concentrations of approximately 60% above Beijing during the Olympic period. The air quality measures were especially effective in the Beijing area, but also noticeable in surrounding cities of Tianjin (30% reduction) and Shijiazhuang (20% reduction). In the months after the Olympic events, NOx emissions in Beijing show a slow recovery towards pre-Olympic levels. In a next step, we use the difference between NO2 observations and simulations to adjust the emission inventory used by the model. Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Chapter 4 presents a new algorithm specifically designed to use daily satellite observations of column concentrations for fast updates of emission estimates of short-lived atmospheric constituents on a mesoscopic scale (??25??25 km2). The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates of East China, using the CHIMERE model on a 0.25 degree resolution together with tropospheric NO2 column retrievals of the OMI and GOME-2 satellite instruments. Closed loop tests show that the algorithm is capable of reproducing new emission scenarios. Applied with real satellite data, the algorithm is able to detect emerging sources (e.g. new power plants), and improves emission information for areas where proxy data are not or badly known (e.g. shipping emissions). It is shown that chemical transport model runs with the daily updated emission estimates provide better spatial and temporal agreement between observed and simulated NO2 concentrations, which facilitates an improved air quality forecast for East China. Monthly emission estimates give valuable insight in changing biogenic and anthropogenic activity. In Chapter 5, the emission estimation algorithm is used to construct a monthly NOx emission time series for 2007–2010 from tropospheric NO2 observations of GOME-2 over East Asia. Most Chinese provinces show a strong positive trend during this period, related to the country’s economic development. Negative emission trends are found in Japan and South Korea, which can be attributed to a combined effect of local environmental policy and global economic crises. The algorithm is also used to quantify the direct effect of regional NOx emissions on tropospheric NO2 concentrations elsewhere. Due to transport of air pollution, high NOx emissions not only affect local air quality, but also contribute significantly to tropospheric NO2 in remote downwind areas

Short-term NO2 exposure and cognitive and mental health : A panel study based on a citizen science project in Barcelona, Spain

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 μg/m3 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 μg/m3 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

Supporting Earth-Observation Calibration and Validation: A new generation of tools for crowdsourcing and citizen science

Citizens are providing vast amounts of georeferenced data in the form of in situ data collections as well as interpretations and digitization of Earth-observation (EO) data sets. These new data streams have considerable potential for supporting the calibration and validation of current and future products derived from EO. We provide a general introduction to this growing area of interest and review existing crowdsourcing and citizen science (CS) initiatives of relevance to EO. We then draw upon our own experiences to provide case studies that highlight different types of data collection and citizen engagement and discuss the various barriers to adoption. Finally, we highlight opportunities for how citizens can become part of an integrated EO monitoring system in the framework of the European Union (EU) space program, including Copernicus and other monitoring initiatives

Predicting fine-scale daily NO2 over Mexico city using an ensemble modeling approach

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 root-mean-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

Predicting fine-scale daily NO₂ over Mexico city using an ensemble modeling approach

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 root-mean-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

Ensemble forecasts of air quality in eastern China – Part 1: Model description and implementation of the MarcoPolo–Panda prediction system, version 1

An operational multi-model forecasting system for air quality including nine different chemical transport models has been developed and provides 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 website (http://www.marcopolo-panda.eu, last access: 7 December 2018). The paper describes the forecasting system and shows some selected illustrative examples of air quality predictions. It presents an intercomparison 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.</p

Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1

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&thinsp;million in 2010). These individual forecasts as well as the multimodel ensemble predictions for the next 72&thinsp;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.</p

Regional nitrogen oxides emission trends in East Asia observed from space

Due to changing economic activity, emissions of air pollutants in East Asia are changing rapidly in space and time. Monthly emission estimates of nitrogen oxides derived from satellite observations provide valuable insight into the evolution of anthropogenic activity on a regional scale. We present the first results of a new emission estimation algorithm, specifically designed to use daily satellite observations of column concentrations for fast updates of emissions of short-lived atmospheric constituents on a mesoscopic scale (~ 0.25° × 0.25°). The algorithm is used to construct a monthly NO_x emission time series for the period 2007–2011 from tropospheric NO₂ observations of GOME-2 for East Chinese provinces and surrounding countries. The new emission estimates correspond well with the bottom-up inventory of EDGAR v4.2, but are smaller than the inventories of INTEX-B and MEIC. They reveal a strong positive trend during 2007–2011 for almost all Chinese provinces, related to the country's economic development. We find a 41% increment of NO_x emissions in East China during this period, which shows the need to update emission inventories in this region on a regular basis. Negative emission trends are found in Japan and South Korea, which can be attributed to a combined effect of local environmental policy and global economic crises. Analysis of seasonal variation distinguishes between regions with dominant anthropogenic or biogenic emissions. For regions with a mixed anthropogenic and biogenic signature, the opposite seasonality can be used for an estimation of the separate emission contributions. Finally, the non-local concentration/emission relationships calculated by the algorithm are used to quantify the direct effect of regional NO_x emissions on tropospheric NO₂ concentrations outside the region. For regions such as North Korea and the Beijing municipality, a substantial part of the tropospheric NO₂ originates from emissions elsewhere