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 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.

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.

Air-quality modelling in the Lake Baikal region.

In this paper, we assess the status of the air quality in the Lake Baikal region which is strongly influenced by the presence of anthropogenic pollution sources. We combined the local data, with global databases, remote sensing imagery and modelling tools. This approach allows to inventorise the air-polluting sources and to quantify the air-quality concentration levels in the Lake Baikal region to a reasonable level, despite the fact that local data are scarcely available. In the simulations, we focus on the month of July 2003, as for this period, validation data are available for a number of ground-based measurement stations within the Lake Baikal region.

A new method for deriving trace gas emission inventories from satellite observations: The case of SO2 over China.

A method is developed that allows the construction of spatial emission inventories. The method is applied for anthropogenic SO2 over China (0.25°×0.25°). The Enhancement Ratio Method (ERM) allows for the calculation of SO2 emissions using relationships between gridded satellite measurements of SO2 and NO2 at low wind speeds, and satellite-based NOx emission estimates. Here, we derive SO2 emissions for five years (2007-2011). A large decrease of emissions during 2007-2009 and a modest increase between 2010 and 2011 is observed. The evolution of emissions over time calculated here is in general agreement with bottom-up inventories, although differences exist, not only between the current inventory and other inventories but also among the bottom up inventories themselves. The ERM-derived emissions are consistent, spatially and temporally, with existing inventories.