Satellite-derived Aerosol Optical Depth Research Articles

Association between long-term PM2.5 exposure and mortality on Sumatra Island: Indonesian Family Life Survey (IFLS) 2000-2014.

The concentration of PM2.5 (particulate matter with a diameter < 2.5µm) on Sumatra Island has increased, mainly because of forest and peatland fires, transportation, and industry. Biomass burning releases partially burned carbon into the atmosphere, resulting in a smoky haze containing PM2.5. Air quality has deteriorated quickly, and PM2.5 has become a major health hazard in Indonesia. Studies on long-term exposure to PM2.5 have indicated its associations with both morbidity and mortality. Here, we measured long-term (2000-2014) exposure to PM2.5 on the basis of satellite-derived aerosol optical depth measurements (1 × 1 km2) used to predict ground-level PM2.5 concentrations. Additionally, population data on Sumatra Island residents from the fourth wave of the Indonesian Family Life Survey (IFLS) were obtained. We investigated the association between long-term PM2.5 exposure and mortality with a retrospective cohort study design. A total of 2409 subjects aged ≥ 40years participated in the IFLS-3 beginning in November 2000, and we examined mortality outcomes until the IFLS-5 in September 2014. We used Cox regression models to calculate hazard ratios (HRs) of mortality associated with PM2.5 exposure. According to the adjusted model, the mortality HRs per 10µg/m3 increase in PM2.5 concentration were 1.10 (95% CI 1.03, 1.17) for all natural causes, 1.17 (95% CI 1.05, 1.25) for cardiovascular causes, and 1.19 (95% CI 1.04, 1.36) for respiratory causes. Long-term exposure to PM2.5 was associated with all-natural, cardiovascular, and respiratory mortality on Sumatra Island, where PM2.5 levels exceed the WHO and US-EPA air quality standards.

High-Resolution PM10 Estimation Using Satellite Data and Model-Agnostic Meta-Learning

Characterizing the spatial distribution of particles smaller than 10 μm (PM10) is of great importance for air quality management yet is very challenging because of the sparseness of air quality monitoring stations. In this study, we use a model-agnostic meta-learning-trained artificial neural network (MAML-ANN) to estimate the concentrations of PM10 at 60 m × 60 m spatial resolution by combining satellite-derived aerosol optical depth (AOD) with meteorological data. The network is designed to regress from the predictors at a specific time to the ground-level PM10 concentration. We utilize the ANN model to capture the time-specific nonlinearity among aerosols, meteorological conditions, and PM10, and apply MAML to enable the model to learn the nonlinearity across time from only a small number of data samples. MAML is also employed to transfer the knowledge learned from coarse spatial resolution to high spatial resolution. The MAML-ANN model is shown to accurately estimate high-resolution PM10 in Beijing, with coefficient of determination of 0.75. MAML improves the PM10 estimation performance of the ANN model compared with the baseline using pre-trained initial weights. Thus, MAML-ANN has the potential to estimate particulate matter estimation at high spatial resolution over other data-sparse, heavily polluted, and small regions.

Comprehensive Assessment and Analysis of the Current Global Aerosol Optical Depth Products

Aerosol Optical Depth (AOD) is one of the most important optical properties of aerosols that may affect the energy budgets of our Earth–atmosphere system significantly. Currently, while regional and even global AOD knowledge has been given by various satellites or models, these products are still fraught with uncertainties. In this study, one sophisticated satellite-derived AOD product from MODIS (MODerate resolution Imaging Spectral-radiometer) and two state-of-the-art model-based AOD products from CAMS (Copernicus Atmosphere Monitoring Service) and MERRA-2 (Modern-Era Retrospective analysis for Research and Application Version 2), based on AERONET measurements from 2000–2022, analyzed the spatial distribution characteristics of global AOD. Then using the Mann-Kendall (MK) trend test, the AOD changing trends revealed by the three products were also computed and analyzed. The accuracies of these products and the reliabilities of changing trends derived are discussed and concluded finally. Our study demonstrates that MODIS products have wider applicability, matching best with AERONET globally, while CAMS and MERRA-2 products are only reliable in North America, South America, and Europe. Through comparative analysis of the AOD trends, we found that MODIS, CAMS, and MERRA-2 AOD consistently exhibited decreasing trends in eastern Asia, Europe, and eastern North America. On the other hand, different products showed increasing trends in regions like West Asia, South Asia, and South Africa, suggesting their limited reliability. The reliability assessment shows that 41.45% of the areas have consistent trends among the three products, with approximately 3.2% showing significant and consistent results. When using site trend validation, the proportions of sites with consistent trends are highest at 64.56% and 46.84% respectively. The regions with the best reliability of global trend changes are mainly distributed in North America, Europe, Australia, eastern Asia, and Central South America. This study provides new insights for validating aerosol changes using remote sensing and has the potential to enhance future monitoring and evaluation methods of aerosol products.

Unmasking the sky: high-resolution PM2.5 prediction in Texas using machine learning techniques.

Although PM2.5 (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies. This study aimed to predict PM2.5 concentrations at a fine spatial scale on a daily basis by using novel machine learning approaches and incorporating satellite-derived Aerosol Optical Depth (AOD) and a variety of weather and land use variables. We compiled a comprehensive dataset in Texas from 2013 to 2017, including ground-level PM2.5 concentrations from regulatory monitors; AOD values at 1-km resolution based on images retrieved from the MODIS satellite; and weather, land-use, population density, among others. We built predictive models for each year separately to estimate PM2.5 concentrations using two machine learning approaches called gradient boosted trees and random forest. We evaluated the model prediction performance using in-sample and out-of-sample validations. Our predictive models demonstrate excellent in-sample model performance, as indicated by high R2 values generated from the gradient boosting models (0.94-0.97) and random forest models (0.81-0.90). However, the out-of-sample R2 values fall within a range of 0.52-0.75 for gradient boosting models and 0.44-0.69 for random forest models. Model performance varies slightly across years. A generally decreasing trend in predicted PM2.5 concentrations over time is observed in Eastern Texas. We utilized machine learning approaches to predict PM2.5 levels in Texas. Both gradient boosting and random forest models perform well. Gradient boosting models perform slightly better than random forest models. Our models showed excellent in-sample prediction performance (R2 > 0.9).

Mass spectrometric analysis of unprecedented high levels of carbonaceous aerosol particles long-range transported from wildfires in the Siberian Arctic

Abstract. Wildfires in Siberia generate large amounts of aerosols, which may be transported over long distances and pose a threat to the sensitive ecosystem of the Arctic. Particulate matter (PM) of aged wildfire plumes originating from Yakutia in August 2021 was collected in Nadym and on Bely Island (both in northwestern Siberia). An advanced analysis of the chemical composition of aerosol particles was conducted through a multi-wavelength thermal–optical carbon analyzer (TOCA) coupled to resonance-enhanced multiphoton ionization time-of-flight mass spectrometry (REMPI-TOFMS) as well as through ultra-high-resolution Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS). In Nadym, concentrations of organic carbon (OC) and elemental carbon (EC) peaked at 100 and 40 µg m−3, respectively, associated with Angström absorption exponents for 405 and 808 nm (AAE405/808) between 1.5 and 3.3. The weekly average on Bely Island peaked at 8.9 µg m−3 of OC and 0.3 µg m−3 of EC and AAE405/808 close to unity. In particular, ambient aerosol in Nadym had a distinct biomass burning profile with pyrolysis products from carbohydrates, such as cellulose and hemicellulose, as well as lignin and resinoic acids. However, temporarily higher concentrations of five- and six-ring polycyclic aromatic hydrocarbons (PAHs), different from the PAH signature of biomass burning, suggest a contribution of regional gas flaring. FT-ICR MS with electrospray ionization (ESI) revealed a complex mixture of highly functionalized compounds, containing up to 20 oxygen atoms, as well as nitrogen- and sulfur-containing moieties. Concentrations of biomass burning markers on Bely Island were substantially lower than in Nadym, flanked by the appearance of unique compounds with higher oxygen content, higher molecular weight, and lower aromaticity. Back-trajectory analysis and satellite-derived aerosol optical depth suggested long-range transport of aerosol from the center of a Yakutian wildfire plume to Nadym and from the plume periphery to Bely Island. Owing to lower aerosol concentrations in the plume periphery than in its center, it is demonstrated how dilution affects the chemical plume composition during atmospheric aging.

Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder

Aerosol Optical Depth (AOD) is a crucial atmospheric parameter in comprehending climate change, air quality, and its impacts on human health. Satellites offer exceptional spatiotemporal AOD data continuity. However, data quality is influenced by various atmospheric, landscape, and instrumental factors, resulting in data gaps. This study presents a new solution to this challenge by providing a long-term, gapless satellite-derived AOD dataset for Texas from 2010 to 2022, utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-angle Implementation of Atmospheric Correction (MAIAC) products. Missing AOD data were reconstructed using a spatiotemporal Long Short-Term Memory (LSTM) convolutional autoencoder. Evaluation against an independent test dataset demonstrated the model’s effectiveness, with an average Root Mean Square Error (RMSE) of 0.017 and an R2 value of 0.941. Validation against the ground-based AERONET dataset indicated satisfactory agreement, with RMSE values ranging from 0.052 to 0.067. The reconstructed AOD data are available at daily, monthly, quarterly, and yearly scales, providing a valuable resource to advance understanding of the Earth’s atmosphere and support decision-making concerning air quality and public health.

A novel algorithm for full-coverage daily aerosol optical depth retrievals using machine learning-based reconstruction technique

The ubiquitous missing values in the satellite-derived aerosol optical depth (AOD) products have always been a challenge for spatial and temporal analysis. To address this concern, we propose a novel data-driven model to attain the full-coverage daily AOD dataset with 0.01° spatial resolution in the Guangdong-Hong Kong-Macao Greater Bay Area (hereafter GBA) from 2010 to 2021. Firstly, the missing values of top-of-atmosphere (TOA) reflectance and surface reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) caused by cloud contamination, were reconstructed using the Data Interpolating Empirical Orthogonal Functions (DINEOF). Subsequently, a new model was developed for the estimation of AOD which integrates the geographical and temporal encodings into the Light Gradient Boosting Machine (LightGBM) with the inputs of reconstructed TOA/surface reflectance and other influencing variables like meteorological and geographical factors. Results showed that the derived gap-free AOD dataset outperforms the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD product and agrees well with the ground-based observations, achieving an index of agreement (IOA) of 0.88, R of 0.84, root mean square error (RMSE) of 0.19 and mean absolute error (MAE) of 0.14. Moreover, the derived AOD dataset presents consistent temporal patterns with in-situ measurements, but with more spatial details than other gapless AOD datasets, i.e., Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Overall, this study has developed a promising meteorological framework for the estimation of full-coverage AOD, which can also be applied over other regions. The derived long-term full-coverage daily AOD dataset can also be used for other applications related to climate change, air quality and ecosystem assessment.

Bridging the Data Gap: Enhancing the Spatiotemporal Accuracy of Hourly PM2.5 Concentration through the Fusion of Satellite-Derived Estimations and Station Observations

Satellite-derived aerosol optical depth (AOD) has been extensively utilized for retrieving ground-level PM2.5 distributions. However, the presence of non-random missing data gaps in AOD poses a challenge to directly obtaining the gap-free AOD-derived PM2.5, thereby impeding accurate exposure risk assessment. Here, this study presents a novel and flexible framework that couples stacking and flexible spatiotemporal data fusion (FSDAF) approaches. By integrating multiple models and data sources, this framework aims to generate hourly (24-h) gap-free PM2.5 estimates for the Beijing–Tianjin–Hebei (BTH) region in 2018. This study effectively reconstructed data at least three times more effectively than the original AOD-derived PM2.5, achieving the Pearson coefficient (r), the coefficient determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) values of 0.91, 0.84, 19.38 µg/m3, and 12.17 µg/m3, respectively, based on entire samples. Such strong predictive performance was also exhibited in spatial-based (r: 0.92–0.93, R2: 0.85–0.87, RMSE: 18.13 µg/m3–20.18 µg/m3, and MAE: 11.21 µg/m3–12.52 µg/m3) and temporal-based (r: 0.91–0.98, R2: 0.82–0.96, RMSE: 3.8 µg/m3–21.89 µg/m3, and MAE: 2.71 µg/m3–14.00 µg/m3) validations, indicating the robustness of this framework. Additionally, this framework enables the assessment of annual and seasonal PM2.5 concentrations and distributions, revealing that higher levels are experienced in the southern region, while lower levels prevail in the northern part. Winter exhibits the most severe levels, followed by spring and autumn, with comparatively lower levels in summer. Notably, the proposed framework effectively mitigates bias in calculating population-weighted exposure risk by filling data gaps with calculated values of 51.04 µg/m3, 54.17 µg/m3, 56.24 µg/m3, and 55.00 µg/m3 in Beijing, Tianjin, Hebei, and the BTH region, respectively.

An improved deep learning network for AOD retrieving from remote sensing imagery focusing on sub-pixel cloud

ABSTRACT Following the success of MODIS, several widely used algorithms have been developed for different satellite sensors to provide global aerosol optical depth (AOD) products. Despite the progress made in improving the accuracy of satellite-derived AOD products, the presence of sub-pixel clouds and the corresponding cloud shadows still significantly degrade AOD products. This is due to the difficulty in identifying sub-pixel clouds, as they are hardly identified, which inevitably leads to the overestimation of AOD. To overcome these conundrums, we proposed an improved deep learning network for retrieving AOD from remote sensing imagery focusing on sub-pixel clouds especially and we call it the Sub-Pixel AOD network (SPAODnet). Two specific improvements considering sub-pixel clouds have been made; a spatial adaptive bilateral filter is applied to top-of-atmosphere (TOA) reflectance images for removing the noise induced by sub-pixel clouds and the corresponding shadows at the first place and channel attention mechanism is added into the convolutional neural network to further emphasize the relationship between the uncontaminated pixels and the ground measured AOD from AERONET sites. In addition, a compositive loss function, Huber loss, is used to further improve the accuracy of retrieved AOD. The SPAODnet model is trained by using ten AERONET sites within Beijing-Tianjin-Hebei (BTH) region in China, along with their corresponding MODIS images from 2011 to 2020; Subsequently, the trained network is applied over the whole BTH region and the AOD images over the BTH region from 2011 ~ 2020 are retrieved. Based on a comprehensive validation with ground measurements, the MODIS products, and the AOD retrieved from the other neural network, the proposed network does significantly improve the overall accuracy, the spatial resolution, and the spatial coverage of the AOD, especially for cases with sub-pixel clouds and cloud shadows.

PM2.5 Estimation in Day/Night-Time from Himawari-8 Infrared Bands via a Deep Learning Neural Network

Satellite-based PM2.5 estimation is an effective means to achieve large-scale and long-term PM2.5 monitoring and investigation. Currently, most of methods retrieve PM2.5 from satellite-derived aerosol optical depth (AOD) or top-of-atmosphere reflectance (TOAR) during daytime. A few algorithms are also developed to retrieve nighttime PM2.5 from the satellite day–night band and the accuracy is greatly limited by moonlight and artificial light sources. In this study, we utilize the properties of absorption pollutants in infrared spectrum to estimate PM2.5 concentrations from satellite infrared data, thus achieve the PM2.5 estimation in both day and night. Himawari-8 infrared bands data are used for PM2.5 estimation by a specifically designed neural network and loss function. Quantitative results show the satellite derived PM2.5 concentrations correlates with ground-based data well with R2 of 0.79 and RMSE of 15.43 μg · m−3 for hourly PM2.5 estimation. Spatiotemporal distributions of model-estimated PM2.5 over China are also analyzed, and exhibit a highly consistent with ground-based measurements. Dust storms, heavy air pollution and fire smoke events are examined to further demonstrate the efficacy of our model. Our method not only circumvents the intermediate retrievals of AOD, but also enables consistent estimation of PM2.5 concentrations during daytime and nighttime in real-time monitoring.

Application of satellite remote sensing data and random forest approach to estimate ground-level PM2.5 concentration in Northern region of Thailand.

Numerous epidemiological studies have shown that particulate matter with aerodynamic diameter up to 2.5 μm (PM2.5) is associated with many health consequences, where PM2.5 concentration obtained from the monitoring station was normally applied as the exposure level, so that the concentration of PM2.5 in unmonitored areas has not been captured. The satellite-derived aerosol optical depth (AOD) product is then used to spatially predict ground truth of PM2.5 concentration that covers the locations with no air quality monitoring station, but this method has seldom been developed in Thailand. This study aimed at estimating ground-level PM2.5 concentration at 3 km × 3 km spatial resolution over Northern region of Thailand in 2021 using the random forest model integrating the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products from Terra and Aqua satellites, meteorological factors, and land use data. A random forest model contained 100 decision trees was utilized to train the model, and 10-fold cross-validation approach was implemented to validate the model performance. The good consistency between actual (observed) and predicted concentrations of PM2.5 in Northern region of Thailand was observed, where a coefficient of determination (R2) and root mean square error (RMSE) of the model fitting were 0.803 and 14.30 μg/m3, respectively, and those of 10-fold cross-validation approach were 0.796 and 14.64 μg/m3, respectively. The three most important predictors for estimating the ground-level concentrations of PM2.5 in this study were normalized difference vegetation index (NDVI), relative humidity, and number of fire hotspot, respectively. Findings from this study revealed that integrating the MODIS AOD, meteorological variables, and land use data into the random forest model precisely and accurately estimated ground-level PM2.5 concentration over Northern region of Thailand that can be further used to investigate the effects of PM2.5 exposure on health consequences, even in unmonitored locations, in epidemiological studies.

ESTIMATING AIRBORNE PARTICULATE MATTER IN THE NATIONAL CAPITAL REGION, PHILIPPINES USING MULTIPLE LINEAR REGRESSION AND GRADIENT BOOSTING ALGORITHM ON MODIS MAIAC AEROSOL OPTICAL DEPTH

Abstract. The generation of air quality concentration data is imperative for the health and environment of highly urbanized regions. Through remote sensing, air pollutant concentrations can be obtained over large areas for a long time. In this study, particulate matter (PM2.5 and PM10) concentrations were estimated using satellite-derived Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Optical Depth (AOD) values observed in the National Capital Region (NCR), Philippines. Models were generated using multiple linear regression (MLR) and gradient boosting regression to determine the best models for the whole data from 2017 to 2020, dry season, and wet season with a 70–30 split for the train-test sets. Initial models resulted with the best coefficient of determination R2 values of 2.6% and 1.2% using MLR and 2.0% using gradient boosting regression. The results for PM2.5 and PM10 showed the lowest Root Mean Square Error (RMSE) values of 8.79 μg/m3 and 18.99 μg/m3 using MLR and 8.08 μg/m3 and 16.85 μg/m3 using gradient boosting, respectively. The preliminary results indicate the relatively poor performance of models in estimating particulate matter using satellite-derived AOD images. Improvements in the models will include the integration of more in-situ data from air quality monitoring stations and the addition of additional variables and features such as meteorological parameters and geographical layers.

Aerosol optical depth regime over megacities of the world

Abstract. Currently, 55 % of the world's population resides in urban areas and this number is projected to increase to 70 % by 2050. Urban agglomerations with a population over 10 million, characterized as megacities, are expected to be more than 100 by 2100. Such large concentrations of population could boost creativity and economic progress, but also raises several environmental challenges such as air quality degradation. In this study, we investigate the spatial and temporal variability of urban aerosol state of 81 cities with a population over 5 million, relying on daily satellite-based aerosol optical depth (AOD) retrievals, derived at fine spatial resolution (0.1∘×0.1∘), over an 18-year period spanning from 2003 to 2020. According to our results, the lowest long-term mean AOD values worldwide were found in European and American cities (from 0.08 to 0.20). For almost all African and Asian cities, mean AOD ranged from 0.25 up to 0.90, but a considerable dust aerosol contribution (up to 70 %) was found for some of them with associated mean dust optical depth (DOD) values reaching up to 0.4. Mostly Chinese and Indian cities tend to have higher mean AOD values in the areas surrounding their center, while the opposite was found for most of the cities in the rest of the world. High intraannual AOD variability was revealed for the eastern American cities, while lower values were found in Chinese, eastern Indian and the eastern Mediterranean cities. During the study period, statistically significant negative AOD decadal trends were found for East Asian, European and North American cities, with the greatest decrease of −0.1 to −0.3 per decade recorded for the Chinese cities, in which the maximum mean AODs (0.45–0.91) are observed. In most of the US cities, where low mean AOD &lt;0.17 was recorded, considerable declining AOD trends were found (−30 % to −50 % per decade). For the rest of Asian, African and South American cities, statistically significant AOD increase was found, with the greatest values of +0.07 to +0.16 per decade recorded for Indian cities. In Bengaluru (India), it is reported the lowest mean AOD value (0.2) and the maximum AOD increase (+69 %), which may be partially attributed to the population growth over the study period. The agreement of the satellite-derived AOD trends against those obtained from ground-based AERONET measurements was examined. For ground-based stations within the geographical limits of the contiguous urban area of the examined cities, a 0.93 correlation for the long-term means of AOD was found and ∼75 % of the derived trends agreed in sign. It was found that the spatial homogeneity within the examined satellite domain and the location of the surface station were key factors that determined their agreement. The present study highlights the vital and essential contribution of spaceborne products to monitor aerosol burden over megacities of the planet towards fulfilling the United Nations Sustainable Development Goal of “sustainable cities and communities”, dealing with urban air quality.

Fast and operational gap filling in satellite-derived aerosol optical depths using statistical techniques

Satellite observations, used worldwide in the atmospheric sciences, are extremely useful for providing aerosol information within a wide spatial range. However, the coverage of aerosol data by satellite observations is sometimes of inferior quality because of the effects of surface reflectivity and clouds. To fill the gaps in aerosol optical depths (AODs) retrieved from geostationary ocean color imager observations, this study applies operational statistical techniques, including radial basis functions (RBFs) with four different weightings (i.e., linear, multiquadric, thin-plate, and inverse), Poisson, and ordinary Kriging. Based on computation time and accuracy of the individual gap-filling techniques, Poisson and the liner RBF are selected as the two best methods and then averaged with weights using one-dimensional weighted average (1D-WAVG) and two-dimensional weighted average (2D-WAVG) root mean square errors. All methods produce reliable results, yielding a correlation coefficient between 0.74 and 0.87 over the entire research domain. Out of the individual techniques, the Poisson, with an initial estimation from a zonal mean of AODs, is the most accurate with the lowest computational costs, even for a large number of missing pixels and most regions, excluding East China (EC). The Poisson’s high bias over EC is compensated in 1D- and 2D-WAVGs by taking more accurate estimations of the linear RBF than those of the Poisson over the region. If we consider 1D- and 2D-WAVGs in our analysis, the highest correlation is obtained from the 2D-WAVG over all regions. Because of its reliability and fast computation time, applying the 2D-WAVG can be a good solution to provide spatial–temporal continuous aerosol information. In addition to air pollution studies, such as real-time air quality predictions, estimation of ground-level particulate matter concentrations, and other applications, the fast and operational gap-filling technique can also be expanded to remote sensing data obtained from satellite observations to provide helpful and useful information for the public.

Full-coverage 1-km estimates and spatiotemporal trends of aerosol optical depth over Taiwan from 2003 to 2019

Satellite-derived aerosol optical depth (AOD) provides an effective way to investigate global and regional variations in atmospheric aerosols. However, due to cloud cover and surface reflectance, AOD datasets derived from satellite instruments generally have non-random missing values, which introduces additional uncertainty into AOD data and limits its downstream. To remedy this problem, this study used a two-stage approach based on spatial interpolation and a random forest model to fill the gaps in data generated by the Multiangle Implementation of Atmospheric Correction (MAIAC) aerosol retrieval algorithm, which provides the best-available AOD product to the global public. The relationship between ground-level fine particulate matter concentrations and satellite AOD was considered in the modeling. By gap-filling daily 1-km MAIAC AOD data from 2003 to 2019 over Taiwan, the two-stage model achieved comparable accuracy (coefficient of determination = 0.52, root-mean-square error = 0.22) against ground-level AOD measurements to the accuracy that has been achieved by previous studies. Furthermore, it improved daily high-spatial-resolution AOD estimates to 100% of spatial coverage. Comparisons between the full-coverage estimates and MAIAC retrievals showed that the MAIAC AOD dataset generally underestimated monthly/seasonal/annual mean AOD values in Taiwan. We also used the long-term estimates of the daily 1-km AOD dataset with full coverage to explore the spatiotemporal trends in AOD in Taiwan. The practical approach developed in this study is suitable for application in long- and short-term studies of air pollution and its effects on public health.

Association of Particulate Matter Exposure With Lung Function and Mortality Among Patients With Fibrotic Interstitial Lung Disease

Particulate matter 2.5 μm or less in diameter (PM2.5) is associated with adverse outcomes for patients with idiopathic pulmonary fibrosis, but its association with other fibrotic interstitial lung diseases (fILDs) and the association of PM2.5 composition with adverse outcomes remain unclear. To investigate the association of PM2.5 exposure with mortality and lung function among patients with fILD. In this multicenter, international, prospective cohort study, patients were enrolled in the Simmons Center for Interstitial Lung Disease Registry at the University of Pittsburgh in Pittsburgh, Pennsylvania; 42 sites of the Pulmonary Fibrosis Foundation Registry; and 8 sites of the Canadian Registry for Pulmonary Fibrosis. A total of 6683 patients with fILD were included (Simmons, 1424; Pulmonary Fibrosis Foundation, 1870; and Canadian Registry for Pulmonary Fibrosis, 3389). Data were analyzed from June 1, 2021, to August 2, 2022. Exposure to PM2.5 and its constituents was estimated with hybrid models, combining satellite-derived aerosol optical depth with chemical transport models and ground-based PM2.5 measurements. Multivariable linear regression was used to test associations of exposures 5 years before enrollment with baseline forced vital capacity and diffusion capacity for carbon monoxide. Multivariable Cox models were used to test associations of exposure in the 5 years before censoring with mortality, and linear mixed models were used to test associations of exposure with a decrease in lung function. Multiconstituent analyses were performed with quantile-based g-computation. Cohort effect estimates were meta-analyzed. Models were adjusted for age, sex, smoking history, race, a socioeconomic variable, and site (only for Pulmonary Fibrosis Foundation and Canadian Registry for Pulmonary Fibrosis cohorts). Median follow-up across the 3 cohorts was 2.9 years (IQR, 1.5-4.5 years), with death for 28% of patients and lung transplant for 10% of patients. Of the 6683 patients in the cohort, 3653 were men (55%), 205 were Black (3.1%), and 5609 were White (84.0%). Median (IQR) age at enrollment across all cohorts was 66 (58-73) years. A PM2.5 exposure of 8 μg/m3 or more was associated with a hazard ratio for mortality of 4.40 (95% CI, 3.51-5.51) in the Simmons cohort, 1.71 (95% CI, 1.32-2.21) in the Pulmonary Fibrosis Foundation cohort, and 1.45 (95% CI, 1.18-1.79) in the Canadian Registry for Pulmonary Fibrosis cohort. Increasing exposure to sulfate, nitrate, and ammonium PM2.5 constituents was associated with increased mortality across all cohorts, and multiconstituent models demonstrated that these constituents tended to be associated with the most adverse outcomes with regard to mortality and baseline lung function. Meta-analyses revealed consistent associations of exposure to sulfate and ammonium with mortality and with the rate of decrease in forced vital capacity and diffusion capacity of carbon monoxide and an association of increasing levels of PM2.5 multiconstituent mixture with all outcomes. This cohort study found that exposure to PM2.5 was associated with baseline severity, disease progression, and mortality among patients with fILD and that sulfate, ammonium, and nitrate constituents were associated with the most harm, highlighting the need for reductions in human-derived sources of pollution.

Land Use and Land Cover Influence on Sentinel-2 Aerosol Optical Depth below City Scales over Beijing

Atmospheric aerosols can impact human health, necessitating the understanding of their distribution determinants, especially in urban areas. The study discusses the relationships between five major land cover types and aerosol optical depth (AOD) within a city combining the high-resolution satellite-derived AOD products (derived from Sentinel-2) and land cover products (60 m and 100 m, respectively) for Beijing and its surroundings from 2017 to 2019. Contribution analysis is performed to quantitatively evaluate the influences of land cover on regional AOD over the study area. Patterns of aerosol distribution remarkably vary in time and space. Statistics of seasonal average AOD peak in spring and then progressively decline from summer through autumn to winter. High AOD values coincide with a low normalized difference vegetation index (NDVI) and a high normalized difference built-up index (NDBI). Urban and built-up land is a major contributor to regional AOD in the study area, especially in spring; forest and grassland always reduce AOD. Anthropogenic activities have a non-negligible influence on AOD and can even reverse the contribution of a land cover type to aerosols. Insights of the study promote the comprehension of the impacts of land cover on aerosols and air pollution and contribute to the planning of land use within a city.

Integrated dataset of the Korean Genome and Epidemiology Study cohort with estimated air pollution data.

Public concern about the adverse health effects of air pollution has grown rapidly in Korea, and there has been increasing demand for research on ways to minimize the health effects of air pollution. Integrating large epidemiological data and air pollution exposure levels can provide a data infrastructure for studying ambient air pollution and its health effects. The Korean Genome and Epidemiology Study (KoGES), a large population-based study, has been used in many epidemiological studies of chronic diseases. Therefore, KoGES cohort data were linked to air pollution data as a national resource for air pollution studies. Air pollution data were produced using community multiscale air quality modeling with additional adjustment of monitoring data, satellite-derived aerosol optical depth, normalized difference vegetation index, and meteorological data to increase the accuracy and spatial resolution. The modeled air pollution data were linked to the KoGES cohort based on participants' geocoded residential addresses in grids of 1 km (particulate matter) or 9 km (gaseous air pollutants and meteorological variables). As the integrated data become available to all researchers, this resource is expected to serve as a useful infrastructure for research on the health effects of air pollution.

Estimating ground-level PM2.5 over Bangkok Metropolitan Region in Thailand using aerosol optical depth retrieved by MODIS.

A number of previous studies have shown that statistical model with a combination of satellite-derived aerosol optical depth (AOD) and PM2.5 measured by the monitoring stations could be applied to predict spatial ground-level PM2.5 concentration, but few studies have been conducted in Thailand. This study aimed to estimate ground-level PM2.5 over the Bangkok Metropolitan Region in 2020 using linear regression model that incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD measurements and other air pollutants, as well as various meteorological factors and greenness indicators into the model. The 12-fold cross-validation technique was used to examine the accuracy of model performance. The annual mean (standard deviation) concentration of observed PM2.5 was 22.37 (± 12.55) µg/m3 and the mean (standard deviation) of PM2.5 during summer, winter, and rainy season was 18.36 (± 7.14) µg/m3, 33.60 (± 14.48) µg/m3, and 15.30 (± 4.78) µg/m3, respectively. The cross-validation yielded R2 of 0.48, 0.55, 0.21, and 0.52 with the average of predicted PM2.5 concentration of 22.25 (± 9.97) µg/m3, 21.68 (± 9.14) µg/m3, 29.43 (± 9.45) µg/m3, and 15.74 (± 5.68) µg/m3 for the year round, summer, winter, and rainy season, respectively. We also observed that integrating NO2 and O3 into the regression model improved the prediction accuracy significantly for a year round, summer, winter, and rainy season over the Bangkok Metropolitan Region. In conclusion, estimating ground-level PM2.5 concentration from the MODIS AOD measurement using linear regression model provided the satisfactory model performance when incorporating many possible predictor variables that would affect the association between MODIS AOD and PM2.5 concentration.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11869-022-01238-4.

Lidar-based daytime boundary layer height variation and impact on the regional satellite-based PM2.5 estimate

Satellite-derived aerosol optical depth (AOD) is popularly used to infer ground-level PM2.5 concentration due to its wide coverage. The fact that aerosols are largely confined in the atmospheric boundary layer makes boundary layer height (BLH) an important scale factor for AOD-based PM2.5 estimates. Our recent ground-based lidar observations, nevertheless, indicate that aerosol particles are heterogeneously mixed within the boundary layer, and even frequently reside above BLH, forming the residual layers (RL)-like pattern. To better sort out the underlying mechanism behind the above-mentioned phenomenon and the impact on hourly ground-level PM2.5 estimates from satellite-based AOD, we firstly propose a novel notion of haze layer height (HLH), which is calculated from Micro-Pulse Lidar (MPL) profile. Combined analysis of 3.5-year ground-based lidar profiles, CE-318 AOD, and PM2.5 measurements show that the coefficient of determination (R2) between PM2.5 and AOD normalized with HLH increases from 0.49 to 0.61 for 90% of the dataset. Second, we applied HLH to an Auto-encoder-based Deep Residual Network (ADRN) and tested the effect on satellite AOD-based PM2.5 estimation within a 300 km range surrounding the MPL station. With the aid of the AOD imputation technique, a similar improvement of using HLH instead is found on the regional scale PM2.5 estimation, which can be demonstrated by the comparison with air quality measurements and other machine learning models. The results show that using ADRN with HLH achieves the highest performance (mean R2 = 0.87, RMSE = 10.12 μg/m3) among 4 machine learning models. This new approach, largely combining active and passive remote sensing data through artificial neural networks (CAPTA), shows improved accuracy and coverage of hourly PM2.5 estimation with aerosol vertical information and AOD calculation under clouds. In addition, further analysis showed that the average difference between morning and daily PM2.5 concentration could equate to an accuracy of 0.19–2.57 yrs. in terms of life expectancy, indicating that our new approach to the determination of PM2.5 from space sheds new insight into the assessment of the aerosol impact on public health.