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

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

Space-based NO_x emission estimates over remote regions improved in DECSO

We improve the emission estimate algorithm DECSO (Daily Emission estimates Constrained by Satellite Observations) to better detect NOx emissions over remote areas. The new version is referred to as DECSO v5. The error covariance of the sensitivity of NO2 column observations to gridded NOx emissions has been better characterized. This reduces the background noise of emission estimates by a factor of 10. An emission update constraint has been added to avoid unrealistic day-to-day fluctuations of emissions. We estimate total NOx emissions, which include biogenic emissions that often drive the seasonal cycle of the NOx emissions. We demonstrate the improvements implemented in DECSO v5 for the domain of East Asia in the year 2012 and 2013. The emissions derived by DECSO v5 are in good agreement with other inventories like MIX. In addition, the improved algorithm is able to better capture the seasonality of NOx emissions and for the first time it reveals ship tracks near the Chinese coasts that are otherwise hidden by the outflow of NO2 from the Chinese mainland. The precision of monthly emissions derived by DECSO v5 for each grid cell is about 20%.</p

NOx emissions in India derived from OMI satellite observations

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

Evaluation of modeling NO₂ concentrations driven by satellite-derived and bottom-up emission inventories using in situ measurements over China

Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slope= 0.74 and 0.64 for the daily mean and daytime only) and the MIX (slope= 1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10-40 % higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of -30 to 0 % on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15 % in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.Atmospheric Remote Sensin

A multi-model operational forecasting system for air quality in Eastern China

International audienc

Predicting Air Pollution in East Asia

International audienc

Intercomparison of NOx emission inventories over East Asia

We compare nine emission inventories of nitrogen oxides including four satellite-derived NOx inventories and the following bottom-up inventories for East Asia: REAS (Regional Emission inventory in ASia), MEIC (Multi-resolution Emission Inventory for China), CAPSS (Clean Air Policy Support System) and EDGAR (Emissions Database for Global Atmospheric Research). Two of the satellite-derived inventories are estimated by using the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm, which is based on an extended Kalman filter applied to observations from OMI or from GOME-2. The other two are derived with the EnKF algorithm, which is based on an ensemble Kalman filter applied to observations of multiple species using either the chemical transport model CHASER and MIROC-chem. The temporal behaviour and spatial distribution of the inventories are compared on a national and regional scale. A distinction is also made between urban and rural areas. The intercomparison of all inventories shows good agreement in total NOx emissions over mainland China, especially for trends, with an average bias of about 20 % for yearly emissions. All the inventories show the typical emission reduction of 10 % during the Chinese New Year and a peak in December. Satellite-derived approaches using OMI show a summer peak due to strong emissions from soil and biomass burning in this season. Biases in NOx emissions and uncertainties in temporal variability increase quickly when the spatial scale decreases. The analyses of the differences show the importance of using observations from multiple instruments and a high spatial resolution model for the satellite-derived inventories, while for bottom-up inventories, accurate emission factors and activity information are required. The advantage of the satellite-derived approach is that the emissions are soon available after observation, while the strength of the bottom-up inventories is that they include detailed information of emissions for each source category.JRC.C.5-Air and Climat