Satellite-derived Aerosol Optical Depth Research Articles

Estimating hourly PM2.5 concentrations using Himawari-8 AOD and a DBSCAN-modified deep learning model over the YRDUA, China

In view of the spatial and temporal gaps left by the ground monitoring network for fine particulate matter (PM2.5), remote sensing has been regarded as an effective monitoring alternative to provide data of the PM2.5 distribution at a high resolution. Satellite-derived aerosol optical depth (AOD) is a parameter that has a good correlation with the PM2.5 concentration. In this study, a modified deep learning model was established to estimate hourly PM2.5 concentrations in the Yangtze River Delta Urban Agglomeration (YRDUA) region based on 5-km AOD data from Himawari-8. The model incorporated a density-based spatial clustering of applications-with-noise (DBSCAN) cluster analysis and a deep neural network (DNN) (denoted as DBSCAN–DNN) to construct individualized DNNs for the retrieval of PM2.5. The DBSCAN algorithm was used to identify the outlying datasets by involving the relationship between AOD–NO2 and PM2.5. The cluster analysis divided the inputs into separated clusters with distinct pollution levels, which helped to build a reference for the construction of individualized DNNs. The results showed that the DBSCAN–DNN model could greatly improve the estimation accuracy of the PM2.5 concentration based on identical inputs when compared with the pure DNN model. The 10-fold cross-validation R-value was enhanced by over 30%, with the highest R-value reaching 0.94 when applied to Shanghai dataset of 2018. The root-mean-square prediction error (RMSE) was also reduced by over 30%. In addition, the model performed well in generating hourly spatial estimations, thereby showing more detailed information for the dynamic changes of the PM2.5 concentration during the day. Moreover, according to the comparisons of five regional estimation results, the model was proven to have a good applicability to deal with a differentiated data volume and data complexity. This study not only proposes a new method to achieve PM2.5 estimations with a higher accuracy and spatiotemporal resolution, but also provides a new perspective for exploiting the sophisticated correlations among large environmental datasets by introducing pre-clustering into the deep learning approach.

Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM2.5 Components.

Bayesian additive regression tree (BART) is a recent statistical method that combines ensemble learning and nonparametric regression. BART is constructed under a probabilistic framework that also allows for model-based prediction uncertainty quantification. We evaluated the application of BART in predicting daily concentrations of four fine particulate matter (PM2.5) components (elemental carbon, organic carbon, nitrate, and sulfate) in California during the period 2005 to 2014. We demonstrate in this paper how BART can be tuned to optimize prediction performance and how to evaluate variable importance. Our BART models included, as predictors, a large suite of land-use variables, meteorological conditions, satellite-derived aerosol optical depth parameters, and simulations from a chemical transport model. In cross-validation experiments, BART demonstrated good out-of-sample prediction performance at monitoring locations (R2 from 0.62 to 0.73). More importantly, prediction intervals associated with concentration estimates from BART showed good coverage probability at locations with and without monitoring data. In our case study, major PM2.5 components could be estimated with good accuracy, especially when collocated PM2.5 total mass observations were available. In conclusion, BART is an attractive approach for modeling ambient air pollution levels, especially for its ability to provide uncertainty in estimates that may be useful for subsequent health impact and health effect analyses.

Estimating PM2.5 Concentrations Using Spatially Local Xgboost Based on Full-Covered SARA AOD at the Urban Scale

The adverse effects caused by PM2.5 have drawn extensive concern and it is of great significance to identify its spatial distribution. Satellite-derived aerosol optical depth (AOD) has been widely used for PM2.5 estimation. However, the coarse spatial resolution and the gaps caused by data deficiency impede its better application at the urban scale. Additionally, obtaining accurate results in unsampled spatial areas when PM2.5 ground sites are insufficient and distribute sparsely is also a challenging issue for PM2.5 spatial distribution estimation. This paper aimed to develop a model, i.e., spatially local extreme gradient boosting (SL-XGB), combining the powerful fitting ability of machine learning and optimal bandwidths of local models, to better estimate PM2.5 concentration at the urban scale by using Beijing as the study area. This paper adopted simplified high-resolution MODIS aerosol retrieval algorithm (SARA) AOD at 500 m resolution as the major independent variable, hence, ensuring the estimation can be operated at a fine scale. Moreover, the extreme gradient boosting (XGBoost) model was adopted to fill the gaps in SARA AOD, thus improving its availability. Then, based on full-covered SARA AOD and other multisource data, the SL-XGB model, integrating multiple local XGBoost models and particular optimal bandwidths, was trained to estimate PM2.5 concentration. For comparison, SL-XGB and two other models, XGBoost and geographically weighted regression (GWR), were evaluated by 10-fold cross validation (CV). The sample-based CV results reveal that the SL-XGB performed the best as assessed through R2 (0.88), root mean square error (RMSE = 24.08 μg/m3) and mean prediction error (MPE = 16.90 μg/m3). Additionally, SL-XGB also performed the best in the site-based CV with a R2 of 0.86, a RMSE of 26.15 μg/m3 and a MPE of 17.97 μg/m3, which shows its good spatial generalization ability. These results demonstrate that SL-XGB can better simultaneously handle non-linear and spatial heterogeneity issues despite spatially limited data at the urban scale. As far as the PM2.5 concentration distribution was concerned, it presented a gradient increase in PM2.5 concentrations from the northwest to the southeast in Beijing, with abundant spatial details. Overall, the proposed approach for PM2.5 estimation showed outstanding performance and can support preventive pollution control and mitigation at the urban scale.

Exploring the relationship between high-resolution aerosol optical depth values and ground-level particulate matter concentrations in the Metropolitan Area of São Paulo

The spatiotemporal pattern of particulate matter (PM) concentrations is an important factor in predicting health issues in inhabitants of urban areas. The integration of satellite-derived aerosol optical depth (AOD) data with ground-level PM concentration data, obtained from monitoring networks, has contributed to better characterization of the spatiotemporal variability of aerosols worldwide. However, before using satellite AOD data as a proxy for PM in epidemiological and air quality studies in specific regions, the applicability of that strategy must be evaluated. In this study, we evaluate the use of the high-resolution AOD, derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, as a predictor of surface PM concentrations in the Metropolitan Area of São Paulo (MASP). We found relatively weak or negative correlations between PM concentrations and MAIAC AOD, even after vertical correction by planetary boundary layer height and the hygroscopic growth factor. The weak correlations reported in this study are mainly due to the mismatch between the current MAIAC aerosol model and the properties of local aerosols in the MASP. Our results suggest that sources of aerosol particles in the MASP are quite diverse and that there is therefore no single optical model suitable for use with satellite-derived AOD.

Apparent dust size discrepancy in aerosol reanalysis in north African dust after long-range transport

Abstract. North African dust reaches the southeastern United States every summer. Size-resolved dust mass measurements taken in Miami, Florida, indicate that more than one-half of the surface dust mass concentrations reside in particles with geometric diameters less than 2.1 µm, while vertical profiles of micropulse lidar depolarization ratios show dust reaching above 4 km during pronounced events. These observations are compared to the representation of dust in the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) aerosol reanalysis and closely related Goddard Earth Observing System model version 5 (GEOS-5) Forward Processing (FP) aerosol product, both of which assimilate satellite-derived aerosol optical depths using a similar protocol and inputs. These capture the day-to-day variability in aerosol optical depth well, in a comparison to an independent sun-photometer-derived aerosol optical depth dataset. Most of the modeled dust mass resides in diameters between 2 and 6 µm, in contrast to the measurements. Model-specified mass extinction efficiencies equate light extinction with approximately 3 times as much aerosol mass, in this size range, compared to the measured dust sizes. GEOS-5 FP surface-layer sea salt mass concentrations greatly exceed observed values, despite realistic winds and relative humidities. In combination, these observations help explain why, despite realistic total aerosol optical depths, (1) free-tropospheric model volume extinction coefficients are lower than those retrieved from the micro-pulse lidar, suggesting too-low model dust loadings in the free troposphere, and (2) model dust mass concentrations near the surface can be higher than those measured. The modeled vertical distribution of dust, when captured, is reasonable. Large, aspherical particles exceeding the modeled dust sizes are also occasionally present, but dust particles with diameters exceeding 10 µm contribute little to the measured total dust mass concentrations after such long-range transport. Remaining uncertainties warrant a further integrated assessment to confirm this study's interpretations.

PM2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India

PM2.5 (particulate matter size less than 2.5 µm, also called Respirable suspended particulate matter (RSPM)) is causing devastating effects on various living entities and is deleterious more than any other pollutants. As ambient air pollution is a scourge to India, in the present research work, PM2.5 is considered and the current study aims to estimate surface level PM2.5 concentrations using satellite-derived aerosol optical depth (AOD) along with meteorological data obtained from reanalysis and in-situ measurements over two different cities of India, namely: Agra, a non-industrial site for a study period of 2011–2015 and Rourkela, a highly industrialized location for 2009–2013, respectively. From the average daily variation of PM2.5, the pollution levels are critical and exceeding the threshold values defined by the pollution control board for most of the days at both the sites. Satellite-observed AOD values were also found to be very high over Agra (average AOD 0.76–0.8) and Rourkela (average AOD 0.4–0.46) during the study period. The annual exceedance factor (AEF) values over Agra and Rourkela were found to be always > 1.5 which indicates the above critical state of pollution. Traditional simple linear regression method (Model I), multiple linear regression (Model II (a–e)), log-linear regression (Model III) and conditional based MLR (Model IV and Model V) methods are applied to estimate the PM2.5 concentrations over Taj for Agra region for a study period of 2011–2015 and Sonaparbat for Rourkela region for a study period of 2009–2013. The models obtained over Taj and Sonaparbat are applied to Rambagh (2011–2015) and Rourkela (2009–2013) sites for validation. The coefficient of determination (R) between observed and estimated values are found to be statistically significant for model II (e) during training and validation at both the sites and model performance is adequate. The Model II (e) can thus be used as a unified explanatory model for the estimation of PM2.5 over these two monitoring stations.

Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5

The integration of satellite-derived aerosol optical depth (AOD) and station-measured PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) provides a promising approach for the monitoring of PM2.5. Previous models have generally only considered either the spatiotemporal heterogeneities of the AOD-PM2.5 relationship or the nonlinear relationship between AOD and PM2.5. In this paper, to simultaneously allow for the nonlinearity and spatiotemporal heterogeneities of the AOD-PM2.5 relationship, the geographically and temporally weighted neural network (GTWNN) model is proposed for the satellite-based estimation of ground-level PM2.5. The GTWNN model represents the nonlinear AOD-PM2.5 relationship via a generalized regression neural network, and is separately established for individual locations and times, to address the spatiotemporal heterogeneities of the AOD-PM2.5 relationship. Meanwhile, a spatiotemporal weighting scheme is incorporated in the GTWNN model to capture the local relations of samples for the training of the AOD-PM2.5 relationship. By the use of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD product, meteorological data, and MODIS normalized difference vegetation index (NDVI) data as input, the GTWNN model was verified using ground station PM2.5 measurements from China in 2015. The GTWNN model achieved sample-based cross-validation (CV) and site-based CV R2 values of 0.80 and 0.79, respectively, and it outperformed the geographically and temporally weighted regression model (CV R2: 0.75 and 0.73) and the daily geographically weighted regression model (CV R2: 0.72 and 0.72). The proposed model implements the combination of geographical law and a neural network, and will be of great use for remote sensing retrieval of environmental variables.

Climatological trends in satellite-derived aerosol optical depth over North India and its relationship with crop residue burning: Rural-urban contrast

Columnar Aerosol Optical Depths (AOD) over an urban area (Chandigarh) and a rural area (Khera, Fatehgarh Sahib district) situated in the Indo-Gangetic Plains (IGP) of India were analysed to study their temporal heterogeneity in terms of interannual, seasonal and monthly variations. Over the last few decades, IGP has become one of the global hotspots of air pollution due to the increased anthropogenic activities such as traffic, industries, agricultural waste burning etc. Level-2 AODs (550 nm) were retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard NASA's Terra and Aqua satellites, for a period of 14 years (2005–2018). The climatological mean Terra-MODIS (Aqua-MODIS) AOD over the urban location was ~0.497 ± 0.238 (0.474 ± 0.228), whereas over the rural location it was 0.542 ± 0.269 (0.534 ± 0.282). Linear trend analysis estimated an increase in annual mean Terra-MODIS (Aqua-MODIS) AOD at a rate of ~0.009 (0.013) per year over the urban site; whereas over the rural location the rate of increase was ~0.003 (0.004) per year. Results show that the observed increase is ~1.49% (2.41%) of climatological mean AOD over the urban location for Terra-MODIS (Aqua-MODIS), whereas, over the rural location, it was ~0.50% (0.67%). Using the HYSPLIT trajectory model, it was concluded that, during post-monsoon, the observed high AODs can be related to massive crop residue burning in the IGP region. These AOD trends can also be used to track the regional anthropogenic air-pollution changes. An empirical relation between AOD and PM10 was established, which can be used to estimate PM10 over the urban and rural areas of IGP (using MODIS AODs), complementing the sparse ground-based monitoring. Further, satellite-based air pollution data can be used for baseline assessment and understanding the impact of control policies such as National Clean Air Programme and to support formulate evidence-based pollution control strategies.

Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM2.5) using satellite data over large regions.

Reconstructing the distribution of fine particulate matter (PM2.5) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM2.5 health impacts. Flexible statistical methods for prediction have demonstrated the integration of satellite observations with other predictors, yet these algorithms are susceptible to overfitting the spatiotemporal structure of the training datasets. We present a new approach for predicting PM2.5 using machine-learning methods and evaluating prediction models for the goal of making predictions where they were not previously available. We apply extreme gradient boosting (XGBoost) modeling to predict daily PM2.5 on a 1×1 km2 resolution for a 13 state region in the Northeastern USA for the years 2000-2015 using satellite-derived aerosol optical depth and implement a recursive feature selection to develop a parsimonious model. We demonstrate excellent predictions of withheld observations but also contrast an RMSE of 3.11 μg/m3 in our spatial cross-validation withholding nearby sites versus an overfit RMSE of 2.10 μg/m3 using a more conventional random ten-fold splitting of the dataset. As the field of exposure science moves forward with the use of advanced machine-learning approaches for spatiotemporal modeling of air pollutants, our results show the importance of addressing data leakage in training, overfitting to spatiotemporal structure, and the impact of the predominance of ground monitoring sites in dense urban sub-networks on model evaluation. The strengths of our resultant modeling approach for exposure in epidemiologic studies of PM2.5 include improved efficiency, parsimony, and interpretability with robust validation while still accommodating complex spatiotemporal relationships.

Estimation of High-Resolution PM2.5 over the Indo-Gangetic Plain by Fusion of Satellite Data, Meteorology, and Land Use Variables.

Very high spatially resolved satellite-derived ground-level concentrations of particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) have multiple potential applications, especially in air quality modeling and epidemiological and climatological research. Satellite-derived aerosol optical depth (AOD) and columnar water vapor (CWV), meteorological parameters, and land use data were used as variables within the framework of a linear mixed effect model (LME) and a random forest (RF) model to predict daily ground-level concentrations of PM2.5 at 1 km × 1 km grid resolution across the Indo-Gangetic Plain (IGP) in South Asia. The RF model exhibited superior performance and higher accuracy compared with the LME model, with better cross-validated explained variance (R2 = 0.87) and lower relative prediction error (RPE = 24.5%). The RF model revealed improved performance metrics for increasing averaging periods, from daily to weekly, monthly, seasonal, and annual means, which supported its use in estimating PM2.5 exposure metrics across the IGP at varying temporal scales (i.e., both short and long terms). The RF-based PM2.5 estimates showed high PM2.5 levels over the middle and lower IGP, with the annual mean exceeding 110 μg/m3. As for seasons, winter was the most polluted season, while monsoon was the cleanest. Spatially, the middle and lower IGP showed poorer air quality compared to the upper IGP. In winter, the middle and lower IGP experienced very poor air quality, with mean PM2.5 concentrations of >170 μg/m3.

Secular Changes in Atmospheric Turbidity over Iraq and a Possible Link to Military Activity

We examine satellite-derived aerosol optical depth (AOD) data during the period 2000–2018 over the Middle East to evaluate the contribution of anthropogenic pollution. We focus on Iraq, where US troops were present for nearly nine years. We begin with a plausibility argument linking anthropogenic influence and AOD signature. We then calculate the percent change in AOD every two years. To pinpoint the causes for changes in AOD on a spatial basis, we distinguish between synoptically “calm” periods and those with vigorous synoptic activity. This was done on high-resolution 10 km AOD retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (Terra satellite). We found spatiotemporal variability in the intensity of the AOD and its standard deviation along the dust-storm corridor during three studied periods: before Operation Iraqi Freedom (OIF) (1 March 2000–19 March 2003), during OIF (20 March 2003–1 September 2010), and Operation New Dawn (OND; 1 September 2010–18 December 2011), and after the US troops’ withdrawal (19 December 2011–31 December 2018). Pixels of military camps and bases, major roads and areas of conflict, and their corresponding AOD values, were selected to study possible effects. We found that winter, with its higher frequency of days with synoptically “calm” conditions compared to spring and summer, was the best season to quantitatively estimate the impact of these ground-based sources. Surprisingly, an anthropogenic impact on the AOD signature was also visible during vigorous synoptic activity. Meteorological conditions that favor detection of these effects using space imagery are discussed, where the effects are more salient than in surrounding regions with similar meteorological conditions. This exceeds expectations when considering synoptic variations alone.

How emissions uncertainty influences the distribution and radiative impacts of smoke from fires in North America

Abstract. Fires and the aerosols that they emit impact air quality, health, and climate, but the abundance and properties of carbonaceous aerosol (both black carbon and organic carbon) from biomass burning (BB) remain uncertain and poorly constrained. We aim to explore the uncertainties associated with fire emissions and their air quality and radiative impacts from underlying dry matter consumed and emissions factors. To investigate this, we compare model simulations from a global chemical transport model, GEOS-Chem, driven by a variety of fire emission inventories with surface and airborne observations of black carbon (BC) and organic aerosol (OA) concentrations and satellite-derived aerosol optical depth (AOD). We focus on two fire-detection-based and/or burned-area-based (FD-BA) inventories using burned area and active fire counts, respectively, i.e., the Global Fire Emissions Database version 4 (GFED4s) with small fires and the Fire INventory from NCAR version 1.5 (FINN1.5), and two fire radiative power (FRP)-based approaches, i.e., the Quick Fire Emission Dataset version 2.4 (QFED2.4) and the Global Fire Assimilation System version 1.2 (GFAS1.2). We show that, across the inventories, emissions of BB aerosol (BBA) differ by a factor of 4 to 7 over North America and that dry matter differences, not emissions factors, drive this spread. We find that simulations driven by QFED2.4 generally overestimate BC and, to a lesser extent, OA concentrations observations from two fire-influenced aircraft campaigns in North America (ARCTAS and DC3) and from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network, while simulations driven by FINN1.5 substantially underestimate concentrations. The GFED4s and GFAS1.2-driven simulations provide the best agreement with OA and BC mass concentrations at the surface (IMPROVE), BC observed aloft (DC3 and ARCTAS), and AOD observed by MODIS over North America. We also show that a sensitivity simulation including an enhanced source of secondary organic aerosol (SOA) from fires, based on the NOAA Fire Lab 2016 experiments, produces substantial additional OA; however, the spread in the primary emissions estimates implies that this magnitude of SOA can be neither confirmed nor ruled out when comparing the simulations against the observations explored here. Given the substantial uncertainty in fire emissions, as represented by these four emission inventories, we find a sizeable range in 2012 annual BBA PM2.5 population-weighted exposure over Canada and the contiguous US (0.5 to 1.6 µg m−3). We also show that the range in the estimated global direct radiative effect of carbonaceous aerosol from fires (−0.11 to −0.048 W m−2) is large and comparable to the direct radiative forcing of OA (−0.09 W m−2) estimated in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). Our analysis suggests that fire emissions uncertainty challenges our ability to accurately characterize the impact of smoke on air quality and climate.

Estimation of Aerosol-Corrected Surface Solar Irradiance at Local Incidence Angle over Different Physiographic Subdivisions of India and Adjoining Areas Using MODIS and SRTM Data

AbstractSurface solar irradiance is considered as an important component of the surface radiation budget and constitutes one of the essential climate variables. In the present study, clear-sky instantaneous solar irradiance was estimated over 15 physiographic regions of India during January. Dewpoint temperature profiles were extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the Aqua satellite to calculate actual vapor pressure following Clausius–Clapeyron equation, which was further used in the Zillman parameterization for solar irradiance. The effect of terrain slope and aspect on direct radiation was taken into account by modifying the parameterized incoming shortwave flux by introducing the local incidence angle. A significant positive correlation was found between terrain-corrected MODIS irradiance and measured radiation data but the RMSE was very high (187 W m−2). Further, the effect of aerosol extinction was introduced by multiplying the terrain-corrected flux by a transmission factor obtained from satellite-derived aerosol optical depth and Ångström exponent. Due to the inclusion of the aerosol transmittance, the correlation was significantly improved (R2 = 0.84) and RMSE was reduced (31 W m−2). Further the effect of surface orientation on surface irradiance was evaluated on six hilly subdivisions. A large variation in the flux (135 to 161 W m−2) was noticed among different aspect classes. The variability was highest in the Eastern Himalayas subdivision (>250 W m−2) and was a minimum in the Eastern Hills subdivision (<28 W m−2). In absence of ground radiation data in hills, Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), was used for validation of model output but it performed poorly and got saturated at higher surface irradiance values.

Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models

Satellite-derived aerosol optical depth (AOD) products are one of main predictors to estimate ground-level particulate matter (PM10 and PM2.5) concentrations. Since AOD products, however, are only provided under high-quality conditions, missing values usually exist in areas such as clouds, cloud shadows, and bright surfaces. In this study, spatially continuous AOD and subsequent PM10 and PM2.5 concentrations were estimated over East Asia using satellite- and model-based data and auxiliary data in a Random Forest (RF) approach. Data collected from the Geostationary Ocean Color Imager (GOCI; 8 times per day) in 2016 were used to develop AOD and PM models. Three schemes (i.e. G1, A1, and A2) were proposed for AOD modeling according to target AOD data (GOCI AOD and AERONET AOD) and the existence of satellite-derived AOD. The A2 scheme showed the best performance (validation R2 of 0.74 and prediction R2 of 0.73 when GOCI AOD did not exist) and the resultant AOD was used to estimate spatially continuous PM concentrations. The PM models with location information produced successful estimation results with R2 of 0.88 and 0.90, and rRMSE of 26.9 and 27.2% for PM10 and PM2.5, respectively. The spatial distribution maps of PM well captured the seasonal and spatial characteristics of PM reported in the literature, which implies the proposed approaches can be adopted for an operational estimation of spatially continuous AOD and PMs under all sky conditions.

Contribution of Satellite-Derived Aerosol Optical Depth PM2.5 Bayesian Concentration Surfaces to Respiratory-Cardiovascular Chronic Disease Hospitalizations in Baltimore, Maryland

The fine particulate matter baseline (PMB), which includes PM2.5 monitor readings fused with Community Multiscale Air Quality (CMAQ) model predictions, using the Hierarchical Bayesian Model (HBM), is less accurate in rural areas without monitors. To address this issue, an upgraded HBM was used to form four experimental aerosol optical depth (AOD)-PM2.5 concentration surfaces. A case-crossover design and conditional logistic regression evaluated the contribution of the AOD-PM2.5 surfaces and PMB to four respiratory-cardiovascular hospital events in all 99 12 km2 CMAQ grids, and in grids with and without ambient air monitors. For all four health outcomes, only two AOD-PM2.5 surfaces, one not kriged (PMC) and the other kriged (PMCK), had significantly higher Odds Ratios (ORs) on lag days 0, 1, and 01 than PMB in all grids, and in grids without monitors. In grids with monitors, emergency department (ED) asthma PMCK on lag days 0, 1 and 01 and inpatient (IP) heart failure (HF) PMCK ORs on lag days 01 were significantly higher than PMB ORs. Warm season ORs were significantly higher than cold season ORs. Independent confirmation of these results should include AOD-PM2.5 concentration surfaces with greater temporal-spatial resolution, now easily available from geostationary satellites, such as GOES-16 and GOES-17.

Association of Exposure to Ambient Air Pollution With Thyroid Function During Pregnancy

Air pollutants interact with estrogen nuclear receptors, but their effect on thyroid signaling is less clear. Thyroid function is of particular importance for pregnant women because of the thyroid's role in fetal brain development. To determine the short-term association of exposure to air pollution in the first trimester with thyroid function throughout pregnancy. In this cohort study, 9931 pregnant women from 4 European cohorts (the Amsterdam Born Children and Their Development Study, the Generation R Study, Infancia y Medio Ambiente, and Rhea) and 1 US cohort (Project Viva) with data on air pollution exposure and thyroid function during pregnancy were included. The recruitment period for the Amsterdam Born Children and Their Development Study was January 2003 to March 2004; for Generation R, April 2002 to January 2006; for Infancia y Medio Ambiente, November 2003 to January 2008; for Rhea, February 2007 to February 2008; and for Project Viva, April 1999 to November 2002. Statistical analyses were conducted from January 2018 to April 2019. Residential air pollution concentrations (ie, nitrogen oxide and particulate matter [PM]) during the first trimester of pregnancy were estimated using land-use regression and satellite-derived aerosol optical depth models. Free thyroxine, thyrotropin, and thyroid peroxidase antibody levels were measured across gestation. Hypothyroxinemia was defined as free thyroxine below the fifth percentile of the cohort distribution with normal thyrotropin levels, following the American Thyroid Association guidelines. Among 9931 participants, the mean (SD) age was 31.2 (4.8) years, 4853 (48.9%) had more than secondary educational levels, 5616 (56.6%) were nulliparous, 404 (4.2%) had hypothyroxinemia, and 506 (6.7%) tested positive for thyroid peroxidase antibodies. Concentrations of nitrogen dioxide and PM with an aerodynamic diameter of 2.5 μm or less (PM2.5) were lower and had less variation in women in the US cohort than those in European cohorts. No associations of nitrogen oxide with thyroid function were found. Higher exposures to PM2.5 were associated with higher odds of hypothyroxinemia in pregnant women (odds ratio per 5-μg/m3 change, 1.21; 95% CI, 1.00-1.47). Although exposure to PM with an aerodynamic diameter of 10 μm or less was not significantly associated with hypothyroxinemia, the coefficient was similar to that for the association of PM2.5 with hypothyroxinemia (odds ratio per 10-μg/m3 change, 1.18; 95% CI, 0.93-1.48). Absorbances of PM2.5 and PM with aerodynamic diameter from 2.5 to 10 μg and were not associated with hypothyroxinemia. There was substantial heterogeneity among cohorts with respect to thyroid peroxidase antibodies (P for heterogeneity, <.001), showing associations of nitrogen oxide and PM with thyroid autoimmunity only in the women in the Generation R Study. The findings of this study suggest that first-trimester exposures to PM2.5 were associated with mild thyroid dysfunction throughout pregnancy. The association of PM2.5 exposure with thyroid function during pregnancy is of global health importance because air pollution exposure is widespread and hypothyroxinemia may adversely influence the brain development of offspring.

Air pollution exposure and respiratory symptoms between 12 and 24 months

PDS 75: Microbiome, infections and respiratory effects, Johan Friso Foyer, Floor 1, August 27, 2019, 1:30 PM - 3:00 PM Background/Aim: The association between exposure to air pollution in the first years of life and childhood health needs to be further investigated. We aimed to evaluate the effects of early postnatal exposure to PM10 and PM2.5 on different respiratory outcomes occurring between 12 and 24 months of age. Methods: The data come from the Piccolipiù birth cohort that enrolled mother-infant pairs at delivery in Turin, Trieste, Viareggio, Florence and Rome from 2011 to 2015. Childhood respiratory outcomes, including upper, lower respiratory infections and wheezing or asthma, were assessed through a questionnaire completed at 24 months. Monthly estimates of PM10 and PM2.5 were attributed at the residential address of each subject by use of a machine learning spatiotemporal model combining PM measurements, satellite-derived aerosol optical depth (AOD) data, land-use and meteorology. We defined trimester windows of postnatal exposure, up to one year of age, and applied multivariate logistic regression and unconstrained distributed lag models to identify sensitive windows of exposure in relation to the outcomes of interest. Results: Exposure and outcome data are available for 2.136 (78%) of the 2.751 subjects who completed the 24-month questionnaire. The mean value for PM10 and PM2.5 over the entire study period was 27µg/m3 and 18µg/m3 respectively. For a 10 µg/m3 increase in PM10 and PM2.5 between the 6th and the 9th month of life we found a slightly increase of lower respiratory infections (OR = 1.14; 95%CI: 0.84–1.53 and OR=1.26; 95%CI: 0.92–1.73 respectively). We did not observe any association between the two pollutants and the other outcomes analyzed. Conclusions: Our findings did not show a clear association between PM10, PM2.5 exposure and respiratory outcomes in children at 2 years of age. This topic should be further investigated given the important public health implications in promoting children’s health.

Impacts of AOD Correction and Spatial Scale on the Correlation between High-Resolution AOD from Gaofen-1 Satellite and In Situ PM2.5 Measurements in Shenzhen City, China

Satellite-derived aerosol optical depth (AOD) is widely used to estimate surface PM2.5 concentrations. Most AOD products have relatively low spatial resolutions (i.e., ≥1 km). Consequently, insufficient research exists on the relationship between high-resolution (i.e., &lt;1 km) AOD and PM2.5 concentrations. Taking Shenzhen City, China as the study area, we derived AOD at the 16-m spatial resolution for the period 2015–2017 based on Gaofen-1 (GF-1) satellite images and the Dark Target (DT) algorithm. Then, we extracted AOD at spatial scales ranging from 40 m to 5000 m and applied vertical and humidity corrections. We analyzed the correlation between AOD and PM2.5 concentrations, and the impacts of AOD correction and spatial scale on the correlation. It was found that the DT-derived GF-1 AOD at different spatial scales had statistically significant correlations with surface PM2.5 concentrations, and the AOD corrections strengthened the correlations. The correlation coefficients (R) between AOD at different spatial scales and PM2.5 concentrations were 0.234–0.329 and 0.340–0.423 before and after AOD corrections, respectively. In spring, summer, autumn, and winter, PM2.5 concentrations had the best correlations with humidity-corrected AOD, uncorrected AOD, vertical and humidity-corrected AOD, and uncorrected AOD, respectively, indicating a distinct seasonal variation of the aerosol characteristics. At spatial scales of 1–5 km, AOD at finer spatial scales generally had higher correlations with PM2.5 concentrations. However, at spatial scales &lt;1 km, the correlations fluctuated irregularly, which could be attributed to scale mismatches between AOD and PM2.5 measurements. Thus, 1 km appears to be the optimum spatial scale for DT-derived AOD to maximize the correlation with PM2.5 concentrations. It is also recommended to aggregate very high-resolution DT-derived AOD to an appropriate medium resolution (e.g., 1 km) before matching them with in situ PM2.5 measurements in regional air pollution studies.

Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China

Satellite-derived aerosol optical depths (AODs) have been widely used to estimate surface fine particulate matter (PM2.5) concentrations over areas that do not have PM2.5 monitoring sites. To date, most studies have focused on estimating daily PM2.5 concentrations using polar-orbiting satellite data (e.g., from the Moderate Resolution Imaging Spectroradiometer), which are inadequate for understanding the evolution of PM2.5 distributions. This study estimates hourly PM2.5 concentrations from Himawari AOD and meteorological parameters using an ensemble learning model. We analyzed the spatial agglomeration patterns of the estimated PM2.5 concentrations over central East China. The estimated PM2.5 concentrations agree well with ground-based data with an overall cross-validated coefficient of determination of 0.86 and a root-mean-square error of 17.3 μg m−3. Satellite-estimated PM2.5 concentrations over central East China display a north-to-south decreasing gradient with the highest concentration in winter and the lowest concentration in summer. Diurnally, concentrations are higher in the morning and lower in the afternoon. PM2.5 concentrations exhibit a significant spatial agglomeration effect in central East China. The errors in AOD do not necessarily affect the retrieval accuracy of PM2.5 proportionally, especially if the error is systematic. High-frequency spatiotemporal PM2.5 variations can improve our understanding of the formation and transportation processes of regional pollution episodes.

PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data

In recent years, air pollution has become an important public health concern. The high concentration of fine particulate matter with diameter less than 2.5 µm (PM2.5) is known to be associated with lung cancer, cardiovascular disease, respiratory disease, and metabolic disease. Predicting PM2.5 concentrations can help governments warn people at high risk, thus mitigating the complications. Although attempts have been made to predict PM2.5 concentrations, the factors influencing PM2.5 prediction have not been investigated. In this work, we study feature importance for PM2.5 prediction in Tehran’s urban area, implementing random forest, extreme gradient boosting, and deep learning machine learning (ML) approaches. We use 23 features, including satellite and meteorological data, ground-measured PM2.5, and geographical data, in the modeling. The best model performance obtained was R2 = 0.81 (R = 0.9), MAE = 9.93 µg/m3, and RMSE = 13.58 µg/m3 using the XGBoost approach, incorporating elimination of unimportant features. However, all three ML methods performed similarly and R2 varied from 0.63 to 0.67, when Aerosol Optical Depth (AOD) at 3 km resolution was included, and 0.77 to 0.81, when AOD at 3 km resolution was excluded. Contrary to the PM2.5 lag data, satellite-derived AODs did not improve model performance.