Evaluation and Comparison of Multi-Satellite Aerosol Optical Depth Products over East Asia Ocean

The atmosphere over the ocean is an important research field that involves multiple aspects such as climate change, atmospheric pollution, weather forecasting, and marine ecosystems. It is of great significance for global sustainable development. Satellites provide a wide range of measurements of marine aerosol optical properties and are very important to the study of aerosol characteristics over the ocean. In this study, aerosol optical depth (AOD) data from seventeen AERONET (Aerosol Robotic Network) stations were used as benchmark data to comprehensively evaluate the data accuracy of six aerosol optical thickness products from 2013 to 2020, including MODIS (Moderate-resolution Imaging Spectrometer), VIIRS (Visible Infrared Imaging Radiometer Suite), MISR (Multi-Angle Imaging Spectrometer), OMAERO (OMI/Aura Multi-wavelength algorithm), OMAERUV (OMI/Aura Near UV algorithm), and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) in the East Asian Ocean. In the East Asia Sea, VIIRS AOD products generally have a higher correlation coefficient (R), expected error within ratio (EE within), lower root mean square error (RMSE), and median bias (MB) than MODIS AOD products. The retrieval accuracy of AOD data from VIIRS is the highest in spring. MISR showed a higher EE than other products in the East Asian Ocean but also exhibited systematic underestimation. In most cases, the OMAERUV AOD product data are of better quality than OMAERO, and OMAERO overestimates AOD throughout the year. The CALIPSO AOD product showed an apparent underestimation of the AOD in different seasons (EE Below = 58.98%), but when the AOD range is small (0 < AOD < 0.1), the CALIPSO data accuracy is higher compared with other satellite products under small AOD range. In the South China Sea, VIIRS has higher data accuracy than MISR, while in the Bohai-Yellow Sea, East China Sea, Sea of Japan, and the western Pacific Ocean, MISR has the best data accuracy. MODIS and VIIRS show similar trends in R, EE within, MB, and RMSE under the influence of AOD, Angstrom exponent (AE), and precipitable water. The study on the temporal and spatial distribution of AOD in the East Asian Ocean shows that the annual variation of AOD is different in different sea areas, and the ocean in the coastal area is greatly affected by land-based pollution. In contrast, the AOD values in the offshore areas are lower, and the aerosol type is mainly clean marine type aerosol. These findings can help researchers in the East Asian Ocean choose the most accurate and reliable satellite AOD data product to better study atmospheric aerosols' impact and trends.

Temporal and Spatial Variations of Satellite-Based Aerosol Optical Depths, Angstrom Exponent, Single Scattering Albedo, and Ultraviolet-Aerosol Index over Five Polluted and Less-Polluted Cities of Northern India: Impact of Urbanization and Climate Change

It is widely acknowledged that factors such as population growth, urbanization's quick speed, economic growth, and industrialization all have a role in the atmosphere's rising aerosol concentration. In the current work, we assessed and discussed the findings of a thorough analysis of the temporal and spatial variations of satellite-based aerosol optical parameters such as Aerosol Optical Depth (AOD), Angstrom Exponent (AE), Single Scattering Albedo (SSA), and Ultraviolet-Aerosol Index (UV-AI), and their concentration have been investigated in this study over five polluted and less-polluted cities of northern India during the last decade 2011–2020. The temporal variation of aerosol optical parameters for AOD ranging from 0.2 to 1.8 with decadal mean 0.86 ± 0.36 for Patna region shows high value with a decadal increasing trend over the study area due to rise in aerosols combustion of fossil fuels, huge vehicles traffic, and biomass over the past ten years. The temporal variation of AE ranging from 0.3 to 1.8 with decadal mean 1.72 ± 0.11 for Agra region shows high value as compared to other study areas, which indicates a comparatively higher level of fine-mode aerosols at Agra. The temporal variation of SSA ranging from 0.8 to 0.9 with decadal mean 0.92 ± 0.02 for SSA shows no discernible decadal pattern at any of the locations. The temporal variation of UV-AI ranging from -1.01 to 2.36 with decadal mean 0.59 ± 0.06 for UV-AI demonstrates a rising tendency, with a noticeable rise in Ludhiana, which suggests relative dominance of absorbing dust aerosols over Ludhiana. Further, to understand the impact of emerging activities, analyses were done in seasonality. For this aerosol climatology was derived for different seasons, i.e., Winter, Pre-Monsoon, Monsoon, and Post-Monsoon. High aerosol was observed in Winter for the study areas Patna, Delhi, and Agra which indicated the particles major dominance of burning aerosol from biomass; and the worst in Monsoon and Post-Monsoon for the Tehri Garhwal and Ludhiana study areas which indicated most of the aerosol concentration is removed by rainfall. After that, we analyzed the correlation among all the parameters to better understand the temporal and spatial distribution characteristics of aerosols over the selected region. The value of r for AOD (550 nm) for regions 2 and 1(0.80) shows a strong positive correlation and moderately positive for the regions 3 and 1 (0.64), mostly as a result of mineral dust carried from arid western regions. The value of r for AE (412/470 nm) for region 3 and (0.40) shows a moderately positive correlation, which is the resultant of the dominance of fine-mode aerosol and negative for the regions 5 and 1 (− 0.06). The value of r for SSA (500 nm) for regions 2 and 1 (0.63) shows a moderately positive correlation, which explains the rise in big aerosol particles, which scatters sun energy more efficiently, and the value of r for UV-AI for regions 1 and 2 shows a strong positive correlation (0.77) and moderately positive for the regions 3 and 1 (0.46) which indicates the absorbing aerosols present over the study region.

Ambient Aerosol Is Physically Larger on Cloudy Days in Bondville, Illinois

Particle chemical composition affects aerosol optical and physical properties in ways important for the fate, transport, and impact of atmospheric particulate matter. For example, hygroscopic constituents take up water to increase the physical size of a particle, which can alter the extinction properties and atmospheric lifetime. At the collocated AERosol RObotic NETwork (AERONET) and Interagency Monitoring of PROtected Visual Environments (IMPROVE) network monitoring stations in rural Bondville, Illinois, we employ a novel cloudiness determination method to compare measured aerosol physicochemical properties on predominantly cloudy and clear sky days from 2010 to 2019. On cloudy days, aerosol optical depth (AOD) is significantly higher than on clear sky days in all seasons. Measured Ångström exponents are significantly smaller on cloudy days, indicating physically larger average particle size for the sampled populations in all seasons except winter. Mass concentrations of fine particulate matter that include estimates of aerosol liquid water (ALW) are higher on cloudy days in all seasons but winter. More ALW on cloudy days is consistent with larger particle sizes inferred from Ångström exponent measurements. Aerosol chemical composition that affects hygroscopicity plays a determining impact on cloudy versus clear sky differences in AOD, Ångström exponents, and ALW. This work highlights the need for simultaneous collocated, high-time-resolution measurements of both aerosol chemical and physical properties, in particular at cloudy times when quantitative understanding of tropospheric composition is most uncertain.

Impact Assessment of Aerosol Optical Depth on Rainfall in Indian Rural Areas

Aerosol significantly influences the life cycle of clouds and their formation. Many studies reported worldwide on anthropogenic aerosols and their impact on clouds and their optical properties. Atmospheric remote sensing provides the best way to estimate indirectly air quality surveillance and management in megacities of developing countries like India where many cities have elevated concentration profiles of air pollutants with inadequate coverage of spatial and temporal monitoring. The results of the study highlighted the impact on rainfall patterns due to aerosol optical depth (AOD) and fine particulate matter (PM2.5) for a total of 7 years (2015–2021) over five different Indian rural sites by using MODerate Resolution Imaging Spectroradiometer (MODIS). The AOD (550 nm) and PM2.5 were retrieved from the MODIS sensor Terra satellites and the MEERA 2 model, respectively. Also, we have analyzed in this study the relationship of AOD (550 nm) with PM2.5 and meteorological variables (temperature relative humidity and precipitation) over Indian rural sites during 2015–2021. The maximum concentration of AOD (550 nm) has been measured for Gandhi college (2.94 ± 0.44) and minimum for ARM college (0.01 ± 0.28), while the maximum concentration of PM2.5 has been measured for ARM College 296.37 (µg m⁻³) and minimum for Karunya University 0.02 (µg m⁻³). Also, the relation between AOD (550 nm) with total precipitation is measured positively for all locations except Gandhi college whereby PM2.5 associated with total precipitation is measured negatively for all locations except ARM college. Finally, the relationship between PM2.5 and AOD (550 nm) is measured positively in all selected locations except Singhad Institute. The maximum rainfall has been observed for monsoon months (June–August) and post-monsoon months (October) for all locations during the study period. The maximum total precipitation has been measured for Singhad 11,674.7 (mm) and the minimum for Karunya University 4563.41 (mm). However, the results of the study indicated that there was no direct trend observed in AOD in five different selected rural Indian sites.

Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty

Annual global satellite-based estimates of fine particulate matter (PM2.5) are widely relied upon for air-quality assessment. Here, we develop and apply a methodology for monthly estimates and uncertainties during the period 1998-2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management. Many densely populated regions have their highest PM2.5 concentrations in winter, exceeding summertime concentrations by factors of 1.5-3.0 over Eastern Europe, Western Europe, South Asia, and East Asia. In South Asia, in January, regional population-weighted monthly mean PM2.5 concentrations exceed 90 μg/m3, with local concentrations of approximately 200 μg/m3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM2.5 concentrations have decreased over the period 2010-2019 by 1.6-2.6 μg/m3/year, with decreases beginning 2-3 years earlier in summer than in winter. We find evidence that global-monitored locations tend to be in cleaner regions than global mean PM2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellite-derived PM2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM2.5 estimates provide precise regional-scale representation, with residual uncertainty inversely proportional to the sample size.

Characteristics of aerosol optical depth dynamics and their causes over typical cities along the 21st Century Maritime Silk Road

Atmospheric aerosols, i.e., suspension of liquid and/or solid particles in air, have serious impacts on human health. Exploring the variation and patterns of regional atmospheric aerosols is of great significance to monitor and evaluate atmosphere quality, especially in urban areas with large population. Here, with nine typical pivotal cities along the 21st Century Maritime Silk Road through Southeast Asia, South Asia to West Asia as case studies, based on MCD19A2 550 nm AOD products, combined meteorological factors, land use data, and nighttime light data, we analyzed the spatio-temporal distribution, variation features, influencing and/or driving factors of aerosols in developed urban areas over Asia. The results showed that the descending sequence of the annual aerosol optical depth (AOD) of the nine cities was Karachi, Doha, Chittagong, Bangkok, Colombo, Ho Chi Minh, Singapore, Gwadar, and Yangon during 2013-2018. Due to the influence of regional climate system and atmospheric aerosol types, the time series of annual, seasonal, and monthly AODs were significantly different. The high values of AODs in most cities were mainly located in the urban center or rapid socio-economic (e.g., industrial and agricultural) development regions. The effects of different meteorological factors on the AODs varied in different cities. The rainfall, relative humidity, and wind speed had great impacts on AODs in Ho Chi Minh, Bangkok, Singapore, and Yangon. Temperature, relative humidity, and wind speed had close correlations with AODs in Chittagong, Colombo, Karachi, and Gwadar of South Asia and Doha in West Asia. The urban area's AOD was influenced by the combined and synergistic effects of socio-economy, urbanization, and meteorological factors, with that in Karachi being the most significant.

[Temporal and Spatial Distribution Characteristics of Aerosol Optical Properties in Urban Agglomerations on the North Slope of the Tianshan Mountains]

In order to explore the temporal and spatial distribution characteristics of atmospheric aerosol optical depth (AOD) in the urban agglomeration on the northern slope of the Tianshan Mountains, the temporal and spatial distribution characteristics and trends of changes in the AOD in the study area from 2000 to 2019 were analyzed by MODIS aerosol products(MCD19-A2). For 2016-2019, when the AOD was relatively stable, the parameters such as the AOD and Ångström wavelength index (α) were analyzed using multi-band sun photometer ground-based remote sensing technology. The results showed that ① the spatial distribution of AOD in the study area was consistent with the topography, and high values were mainly distributed in the low altitude area. The spatial distribution of AOD in the four seasons showed a strong seasonal change from spring (0.15±0.03) > autumn (0.14±0.03) > summer (0.14±0.02). ② In terms of time, the annual average AOD value of the study area was 0.12 from 2000 to 2019 with an annual growth rate of 1.03%, thereby showing an overall increasing trend. The annual variation in the monthly mean value of AOD was bimodal; the first and second peaks were in May and November. The main reason for the increase in AOD was the release and transmission of dust from natural sources and heating. ③ Under the influence of dust weather, the AOD changed sharply in spring, and the size and change range of aerosol particles were larger than those in summer. The high value of AOD in the study area was mainly affected by coarse mode particles. The moisture absorption growth of fine mode particles caused a fluctuation in the AOD, but it was not the cause of the high value of AOD.

The Climate Response to Emissions Reductions Due to COVID-19: Initial Results From CovidMIP

Many nations responded to the corona virus disease-2019 (COVID-19) pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO2, other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. 12 models performed multiple initial-condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020-2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID-19-related emission reductions on near-term climate.

A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain

Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM_2.5) levels across Great Britain between 2008–2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM_2.5 series using co-located PM₁₀ measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospheric reanalysis models. Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM_2.5. Stage-4 applies Stage-3 models to estimate daily PM_2.5 concentrations over a 1 km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R² of 0.767 and minimal bias. The model performed better over the temporal scale when compared to the spatial component, but both presented good accuracy with an R² of 0.795 and 0.658, respectively. These findings indicate that direct satellite observations must be integrated with other satellite-based products and geospatial variables to derive reliable estimates of air pollution exposure. The high spatio-temporal resolution and the relatively high precision allow these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM_2.5.

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 (PM_2.5) 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 PM_2.5 components could be estimated with good accuracy, especially when collocated PM_2.5 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.

[Aerosol Optical Properties over the Ebinur Region]

The key to understanding the transport and deposition process of salt dust to Ebinur Lake involves the quantitative evaluation of the aerosol concentration and characteristics in Jinghe County. Based on the data of the CE-318 sun photometer station in Jinghe County during 2019, the characteristics of the aerosol optical depth (AOD) and Angström exponent (α) were analysed. The results showed that the daily variation of the AOD in Jinghe County was a single peak curve that increased or decreased monotonously in the early/late peak period and peaked at 12:00-14:00, which was opposite to the trend of the α. There were obvious seasonal differences in the aerosol concentration and dominant mode. The seasonal AOD was ranked as:spring (0.403±0.282) > summer (0.222±0.135) > autumn (0.218±0.112), whereas α was ranked as:summer (1.339±0.446) > autumn (1.116±0.278) > spring (0.914±0.269). During the spring, the range of the change in the AOD was more intense, the aerosol particle size was larger than that during the summer and autumn, and the range of the variation in the particle size was larger. There was a negative correlation between the AOD and α. During the spring and summer, the aerosol particle size varied over a wide range, and the composition was more complex. With the decrease of the α, the AOD tended to increase; during the autumn, the dominant aerosol mode (mainly fine particles) stabilized, and the AOD exhibited no obvious change with the α. From spring to autumn, aerosol gradually transited from a coarse to fine mode. Compared with the summer, local aerosols were more sensitive to the changes of the wind speed, wind direction, and relative humidity during the spring. The primary reason for the increase of the AOD during the spring was the main wind direction and the dust input brought by gale weather. Influenced by the soluble salt ions in the dust, the aerosol particles were able to undergo hygroscopic growth, but this was not the main reason for the high AOD. Temperature was not the internal factor for the change in the local aerosols; however, it was directly proportional to the diffusion ability of aerosol particles. Overall, the AOD of Jinghe County was primarily affected by dust aerosols. The increases in the amounts of small particles and aerosol moisture absorption were not the main reasons for the increase of the AOD in this area.

Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling

Aerosols have adverse health effects and play a significant role in the climate as well. The Multiangle Implementation of Atmospheric Correction (MAIAC) provides Aerosol Optical Depth (AOD) at high temporal (daily) and spatial (1 km) resolution, making it particularly useful to infer and characterize spatiotemporal variability of aerosols at a fine spatial scale for exposure assessment and health studies. However, clouds and conditions of high surface reflectance result in a significant proportion of missing MAIAC AOD. To fill these gaps, we present an imputation approach using deep learning with downscaling. Using a baseline autoencoder, we leverage residual connections in deep neural networks to boost learning and parameter sharing to reduce overfitting, and conduct bagging to reduce error variance in the imputations. Downscaled through a similar auto-encoder based deep residual network, Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) GMI Replay Simulation (M2GMI) data were introduced to the network as an important gap-filling feature that varies in space to be used for missingness imputations. Imputing weekly MAIAC AOD from 2000 to 2016 over California, a state with considerable geographic heterogeneity, our full (non-full) residual network achieved mean R2 = 0.94 (0.86) [RMSE = 0.007 (0.01)] in an independent test, showing considerably better performance than a regular neural network or non-linear generalized additive model (mean R2 = 0.78-0.81; mean RMSE = 0.013-0.015). The adjusted imputed as well as combined imputed and observed MAIAC AOD showed strong correlation with Aerosol Robotic Network (AERONET) AOD (R = 0.83; R2 = 0.69, RMSE = 0.04). Our results show that we can generate reliable imputations of missing AOD through a deep learning approach, having important downstream air quality modeling applications.

[Spatial-temporal Distribution of Aerosol Optical Depth and Its Main Influence Types in China During 1990-2017]

In order to accurately understand the optical characteristics of aerosols in China, based on Mann-Kendall(MK) and Sen's slope trend analysis methods, the spatiotemporal variations of aerosol optical depth(AOD) derived from MERRA-2 reanalysis datasets were estimated in China for the period of 1990-2017. The results showed that ① for the interannual scale, there was a significant increasing trend in the annual mean AOD in China during 1990-2017. Besides, high aerosol loadings were observed in spring and summer, and the seasonal difference between the eastern and western regions was large. This was mainly due to the topographic and meteorological factors. ② At the spatial scale, the annual mean AOD values increased from the northwest to the southeast, with characteristically high AOD values occurring in Sichuan Pendi and the Tarim and Turpan basins and low values in the Qinghai-Xizang plateau region. Similarly, the AOD MK value and Sen's slope value showed significant decreasing trends from the southeast to the northwest, which was closely related to climate change and the human activity intensity. ③ In regard to black carbon aerosol, dust aerosol, organic carbon aerosol, sea salt aerosol, and SO₄ aerosol, dust and SO₄ aerosols were affected by the air humidity and human activity intensity, which have obvious regional differences in China.

[Column-integrated Aerosol Optical Properties Determined Using Ground-based Sun Photometry Measurements in the Hangzhou Region]

To investigate the optical properties of aerosols in the Hangzhou region (Hangzhou, Tonglu, Jiande, and Chun'an), the aerosol optical depth (AOD), Ångström exponent (AE), single scattering albedo (SSA), and aerosol size distribution (ASD) were measured using CIMEL sun-photometers in 2012. The results showed that the annual average values of AOD_440nm in Hangzhou, Tonglu, Jiande, and Chun'an were 0.94±0.16, 0.84±0.17, 0.82±0.22, and 0.71±0.20, respectively. The values generally decreased from the northeast to the southwest, and represented one of highest AOD districts in the Yangtze River Delta, China. The annual average values of AE_440-870nm were 1.24±0.12, 1.19±0.17, 1.06±0.04, and 1.04±0.10, respectively, indicating that particles with small average effective radii were predominant. The relatively lower AE values in March and April were generally attributed to the long-range transport of dust aerosols from Northwest China. Obvious diurnal variations in the AOD were found in Hangzhou, Tonglu, and Jiande, but not in Chun'an. An average fine-mode effective radius of~0.15 μm was observed in spring, autumn, and winter, while a value of~0.25 μm was observed in summer, in conjunction with aerosol hygroscopic growth. An average coarse-mode effective radius of~2.94 μm was observed in summer, autumn, and winter, which was higher than the value in spring. The annual average values of SSA_440nm were 0.91±0.01, 0.92±0.03, 0.92±0.02, 0.93±0.02, respectively, indicating that the particles had relatively strong to moderate absorption. Characterization of the aerosol types showed the predominance of biomass burning and urban industrial type aerosols in Hangzhou, while mixed type aerosols were observed in Tonglu, Jiande, and Chun'an.

[Applicability of MODIS C006 Aerosol Products in a Typical Environmental Area of the Beijing-Tianjin-Hebei Region]

By fitting with the aerosol optical depth (AOD) from AERONET ground observations at sites in Beijing, Xianghe, and Xinglong with different environmental backgrounds, MODIS C051 Dark Target (DT C051), C006 Dark Target (DT C006), C006 Deep Blue (DB C006), and C006 Deep Blue/Dark Target merged AOD products were compared and evaluated to understand their applicability in the Beijing-Tianjin-Hebei region. The main conclusions are as follows:① The comparison of the C051 and C006 algorithms shows that the accuracy of the AOD at the Beijing and Xianghe sites notably improved, while an improvement was not observed at the Xinglong site; the DB C006 AOD is closest to the AERONET AOD at the Beijing site and the DT C006 AOD is closest to the AERONET AOD at the Xianghe site; the combined C006 AOD is closest to the AERONET AOD at the Xinglong site. ② The inversion error of the MODIS DT C006 at the Beijing site is caused by the improper selection of the aerosol model and surface reflectance; the inversion error of the MODIS DB C006 is mainly due to surface reflectance in spring and the aerosol model in winter. ③ Compared with the DT C051 AOD, the effective data coverage of the DT C006 is reduced, but that of DB C006 and the combined C006 increased; the combined C006 AOD data have the largest coverage. The results show that the application of the combined AOD product is best for the Beijing-Tianjin-Hebei region.

A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US

There has been growing interest in extending the coverage of ground particulate matter with aerodynamic diameter ≤ 2.5 μm (PM_2.5) monitoring networks based on satellite remote sensing data. With broad spatial and temporal coverage, a satellite-based monitoring network has a strong potential to complement the ground monitor system in terms of the spatiotemporal availability of the air quality data. However, most existing calibration models focus on a relatively small spatial domain and cannot be generalized to a national study. In this paper, we proposed a statistically reliable and interpretable national modeling framework based on Bayesian downscaling methods to be applied to the calibration of the daily ground PM_2.5 concentrations across the conterminous United States using satellite-retrieved aerosol optical depth (AOD) and other ancillary predictors in 2011. Our approach flexibly models the PM_2.5 versus AOD and the potential related geographical factors varying across the climate regions and yields spatial- and temporal-specific parameters to enhance model interpretability. Moreover, our model accurately predicted the national PM_2.5 with an R² at 70% and generated reliable annual and seasonal PM_2.5 concentration maps with its SD. Overall, this modeling framework can be applied to national-scale PM_2.5 exposure assessments and can also quantify the prediction errors.

Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA

Satellite-derived estimates of aerosol optical depth (AOD) are key predictors in particulate air pollution models. The multi-step retrieval algorithms that estimate AOD also produce quality control variables but these have not been systematically used to address the measurement error in AOD. We compare three machine-learning methods: random forests, gradient boosting, and extreme gradient boosting (XGBoost) to characterize and correct measurement error in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 × 1 km AOD product for Aqua and Terra satellites across the Northeastern/Mid-Atlantic USA versus collocated measures from 79 ground-based AERONET stations over 14 years. Models included 52 quality control, land use, meteorology, and spatially-derived features. Variable importance measures suggest relative azimuth, AOD uncertainty, and the AOD difference in 30-210 km moving windows are among the most important features for predicting measurement error. XGBoost outperformed the other machine-learning approaches, decreasing the root mean squared error in withheld testing data by 43% and 44% for Aqua and Terra. After correction using XGBoost, the correlation of collocated AOD and daily PM2.5 monitors across the region increased by 10 and 9 percentage points for Aqua and Terra. We demonstrate how machine learning with quality control and spatial features substantially improves satellite-derived AOD products for air pollution modeling.

Quantifying the low bias of CALIPSO's column aerosol optical depth due to undetected aerosol layers

The CALIOP data processing scheme only retrieves extinction profiles in those portions of the return signal where cloud or aerosol layers have been identified by the CALIOP layer detection scheme. In this study we use two years of CALIOP and MODIS data to quantify the aerosol optical depth of undetected weakly backscattering layers. Aerosol extinction and column-averaged lidar ratio is retrieved from CALIOP Level 1B (Version 4) profile using MODIS AOD as a constraint over oceans from March 2013 to February 2015. To quantify the undetected layer AOD (ULA), an unconstrained retrieval is applied globally using a lidar ratio of 28.75 sr estimated from constrained retrievals during the daytime over the ocean. We find a global mean ULA of 0.031 ± 0.052. There is no significant difference in ULA between land and ocean. However, the fraction of undetected aerosol layers rises considerably during daytime, when the large amount of solar background noise lowers the signal to noise ratio (SNR). For this reason, there is a difference in ULA between day (0.036 ± 0.066) and night (0.025 ± 0.021). ULA is larger in the northern hemisphere and relatively larger at high latitudes. Large ULA for the Polar Regions is strongly related to the cases where the CALIOP Level 2 Product reports zero AOD. This study provides an estimate of the complement of AOD that is not detected by lidar, and bounds the CALIOP AOD uncertainty to provide corrections for science studies that employ the CALIOP Level 2 AOD.

Fine particulate matter (PM2.5) association with peripheral artery disease admissions in northeastern United States

BACKGROUND

Current evidence, on the association of PM2.5 and peripheral artery disease (PAD) is very sparse.

METHODS

We use novel PM2.5 prediction models to investigate associations between chronic and acute PM2.5 exposures and hospital PAD admissions across the northeast USA. Poisson regression analysis was preformed where daily admission counts in each zip code are regressed against both chronic and acute PM2.5 exposure, temperature, socio-economic characteristics and time to control for seasonal patterns.

RESULTS

Positive significant associations were observed between both chronic and acute exposure to PM2.5 and PAD hospitalizations. Every 10-μg/m(3) increase in acute PM2.5 exposure was associated with a 0.26 % increase in admissions (CI = 0.08 - 0.45 %) and every 10-μg/m(3) increase in chronic PM 2.5 exposure was associated with a 4.4 % increase in admissions (CI = 3.50 - 5.35 %).

CONCLUSIONS

The study supports the hypothesis that acute and chronic exposure to PM2.5 can increase the risk of PAD.

Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model

The real-time estimation of ambient particulate matter with diameter no greater than 2.5 μm (PM2.5) is currently quite limited in China. A semi-physical geographically weighted regression (GWR) model was adopted to estimate PM2.5 mass concentrations at national scale using the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth product fused by the Dark Target (DT) and Deep Blue (DB) algorithms, combined with meteorological parameters. The fitting results could explain over 80% of the variability in the corresponding PM2.5 mass concentrations, and the estimation tends to overestimate when measurement is low and tends to underestimate when measurement is high. Based on World Health Organization standards, results indicate that most regions in China suffered severe PM2.5 pollution during winter. Seasonal average mass concentrations of PM2.5 predicted by the model indicate that residential regions, namely Jing-Jin-Ji Region and Central China, were faced with challenge from fine particles. Moreover, estimation deviation caused primarily by the spatially uneven distribution of monitoring sites and the changes of elevation in a relatively small region has been discussed. In summary, real-time PM2.5 was estimated effectively by the satellite-based semi-physical GWR model, and the results could provide reasonable references for assessing health impacts and offer guidance on air quality management in China.

Identifying errors in dust models from data assimilation

Airborne mineral dust is an important component of the Earth system and is increasingly predicted prognostically in weather and climate models. The recent development of data assimilation for remotely sensed aerosol optical depths (AODs) into models offers a new opportunity to better understand the characteristics and sources of model error. Here we examine assimilation increments from Moderate Resolution Imaging Spectroradiometer AODs over northern Africa in the Met Office global forecast model. The model underpredicts (overpredicts) dust in light (strong) winds, consistent with (submesoscale) mesoscale processes lifting dust in reality but being missed by the model. Dust is overpredicted in the Sahara and underpredicted in the Sahel. Using observations of lighting and rain, we show that haboobs (cold pool outflows from moist convection) are an important dust source in reality but are badly handled by the model's convection scheme. The approach shows promise to serve as a useful framework for future model development.

Small global effect on terrestrial net primary production due to increased fossil fuel aerosol emissions from East Asia since the turn of the century

The global terrestrial carbon sink has increased since the start of this century at a time of growing carbon emissions from fossil fuel burning. Here we test the hypothesis that increases in atmospheric aerosols from fossil fuel burning enhanced the diffuse light fraction and the efficiency of plant carbon uptake. Using a combination of models, we estimate that at global scale changes in light regimes from fossil fuel aerosol emissions had only a small negative effect on the increase in terrestrial net primary production over the period 1998-2010. Hereby, the substantial increases in fossil fuel aerosol emissions and plant carbon uptake over East Asia were effectively canceled by opposing trends across Europe and North America. This suggests that if the recent increase in the land carbon sink would be causally linked to fossil fuel emissions, it is unlikely via the effect of aerosols but due to other factors such as nitrogen deposition or nitrogen-carbon interactions.

Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States

A number of models have been developed to estimate PM2.5 exposure, including satellite-based aerosol optical depth (AOD) models, land-use regression, or chemical transport model simulation, all with both strengths and weaknesses. Variables like normalized difference vegetation index (NDVI), surface reflectance, absorbing aerosol index, and meteoroidal fields are also informative about PM2.5 concentrations. Our objective is to establish a hybrid model which incorporates multiple approaches and input variables to improve model performance. To account for complex atmospheric mechanisms, we used a neural network for its capacity to model nonlinearity and interactions. We used convolutional layers, which aggregate neighboring information, into a neural network to account for spatial and temporal autocorrelation. We trained the neural network for the continental United States from 2000 to 2012 and tested it with left out monitors. Ten-fold cross-validation revealed a good model performance with a total R(2) of 0.84 on the left out monitors. Regional R(2) could be even higher for the Eastern and Central United States. Model performance was still good at low PM2.5 concentrations. Then, we used the trained neural network to make daily predictions of PM2.5 at 1 km × 1 km grid cells. This model allows epidemiologists to access PM2.5 exposure in both the short-term and the long-term.

Satellite-based PM concentrations and their application to COPD in Cleveland, OH

A hybrid approach is proposed to estimate exposure to fine particulate matter (PM(2.5)) at a given location and time. This approach builds on satellite-based aerosol optical depth (AOD), air pollution data from sparsely distributed Environmental Protection Agency (EPA) sites and local time-space Kriging, an optimal interpolation technique. Given the daily global coverage of AOD data, we can develop daily estimate of air quality at any given location and time. This can assure unprecedented spatial coverage, needed for air quality surveillance and management and epidemiological studies. In this paper, we developed an empirical relationship between the 2 km AOD and PM(2.5) data from EPA sites. Extrapolating this relationship to the study domain resulted in 2.3 million predictions of PM(2.5) between 2000 and 2009 in Cleveland Metropolitan Statistical Area (MSA). We have developed local time-space Kriging to compute exposure at a given location and time using the predicted PM(2.5). Daily estimates of PM(2.5) were developed for Cleveland MSA between 2000 and 2009 at 2.5 km spatial resolution; 1.7 million (∼79.8%) of 2.13 million predictions required for multiyear and geographic domain were robust. In the epidemiological application of the hybrid approach, admissions for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) was examined with respect to time-space lagged PM(2.5) exposure. Our analysis suggests that the risk of AECOPD increases 2.3% with a unit increase in PM(2.5) exposure within 9 days and 0.05° (∼5 km) distance lags. In the aggregated analysis, the exposed groups (who experienced exposure to PM(2.5) >15.4 μg/m(3)) were 54% more likely to be admitted for AECOPD than the reference group. The hybrid approach offers greater spatiotemporal coverage and reliable characterization of ambient concentration than conventional in situ monitoring-based approaches. Thus, this approach can potentially reduce exposure misclassification errors in the conventional air pollution epidemiology studies.

Acute health impacts of airborne particles estimated from satellite remote sensing

Satellite-based remote sensing provides a unique opportunity to monitor air quality from space at global, continental, national and regional scales. Most current research focused on developing empirical models using ground measurements of the ambient particulate. However, the application of satellite-based exposure assessment in environmental health is still limited, especially for acute effects, because the development of satellite PM(2.5) model depends on the availability of ground measurements. We tested the hypothesis that MODIS AOD (aerosol optical depth) exposure estimates, obtained from NASA satellites, are directly associated with daily health outcomes. Three independent healthcare databases were used: unscheduled outpatient visits, hospital admissions, and mortality collected in Beijing metropolitan area, China during 2006. We use generalized linear models to compare the short-term effects of air pollution assessed by ground monitoring (PM(10)) with adjustment of absolute humidity (AH) and AH-calibrated AOD. Across all databases we found that both AH-calibrated AOD and PM(10) (adjusted by AH) were consistently associated with elevated daily events on the current day and/or lag days for cardiovascular diseases, ischemic heart diseases, and COPD. The relative risks estimated by AH-calibrated AOD and PM(10) (adjusted by AH) were similar. Additionally, compared to ground PM(10), we found that AH-calibrated AOD had narrower confidence intervals for all models and was more robust in estimating the current day and lag day effects. Our preliminary findings suggested that, with proper adjustment of meteorological factors, satellite AOD can be used directly to estimate the acute health impacts of ambient particles without prior calibrating to the sparse ground monitoring networks.

Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states

Satellite-derived aerosol optical depth (AOD) measurements have the potential to provide spatiotemporally resolved predictions of both long and short-term exposures, but previous studies have generally shown moderate predictive power and lacked detailed high spatio- temporal resolution predictions across large domains. We aimed at extending our previous work by validating our model in another region with different geographical and metrological characteristics, and incorporating fine scale land use regression and nonrandom missingness to better predict PM(2.5) concentrations for days with or without satellite AOD measures. We start by calibrating AOD data for 2000-2008 across the Mid-Atlantic. We used mixed models regressing PM(2.5) measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We used inverse probability weighting to account for nonrandom missingness of AOD, nested regions within days to capture spatial variation in the daily calibration, and introduced a penalization method that reduces the dimensionality of the large number of spatial and temporal predictors without selecting different predictors in different locations. We then take advantage of the association between grid-cell specific AOD values and PM(2.5) monitoring data, together with associations between AOD values in neighboring grid cells to develop grid cell predictions when AOD is missing. Finally to get local predictions (at the resolution of 50 m), we regressed the residuals from the predictions for each monitor from these previous steps against the local land use variables specific for each monitor. "Out-of-sample" 10-fold cross-validation was used to quantify the accuracy of our predictions at each step. For all days without AOD values, model performance was excellent (mean "out-of-sample" R(2) = 0.81, year-to-year variation 0.79-0.84). Upon removal of outliers in the PM(2.5) monitoring data, the results of the cross validation procedure was even better (overall mean "out of sample"R(2) of 0.85). Further, cross validation results revealed no bias in the predicted concentrations (Slope of observed vs predicted = 0.97-1.01). Our model allows one to reliably assess short-term and long-term human exposures in order to investigate both the acute and effects of ambient particles, respectively.

Use of satellite-based aerosol optical depth and spatial clustering to predict ambient PM2.5 concentrations

Satellite-based PM(2.5) monitoring has the potential to complement ground PM(2.5) monitoring networks, especially for regions with sparsely distributed monitors. Satellite remote sensing provides data on aerosol optical depth (AOD), which reflects particle abundance in the atmospheric column. Thus AOD has been used in statistical models to predict ground-level PM(2.5) concentrations. However, previous studies have shown that AOD may not be a strong predictor of PM(2.5) ground levels. Another shortcoming of remote sensing is the large number of non-retrieval days (i.e., days without satellite data available) due to clouds and snow- and ice-cover. In this paper we propose statistical approaches to overcome these two shortcomings, thereby making satellite imagery a viable method to estimate PM(2.5) concentrations. First, we render AOD a robust predictor of PM(2.5) mass concentration by introducing an AOD daily calibration approach through the use of mixed effects model. Second, we develop models that combine AOD and ground monitoring data to predict PM(2.5) concentrations during non-retrieval days. A key feature of this approach is that we develop these prediction models separately for groups of days defined by the observed amount of spatial heterogeneity in concentrations across the study region. Subsequently, these methodologies were applied to examine the spatial and temporal patterns of daily PM(2.5) concentrations for both retrieval days (i.e., days with satellite data available) and non-retrieval days in the New England region of the United States during the period 2000-2008. Overall, for the years 2000-2008, our statistical models predicted surface PM(2.5) concentrations with reasonably high R(2) (0.83) and low percent mean relative error (3.5%). Also the spatial distribution of the estimated PM(2.5) levels in the study domain clearly exhibited densely populated and high traffic areas. The method we have developed demonstrates that remote sensing can have a tremendous impact on the fields of environmental monitoring and human exposure assessment.

Using new satellite based exposure methods to study the association between pregnancy PM₂.₅ exposure, premature birth and birth weight in Massachusetts

BACKGROUND

Adverse birth outcomes such as low birth weight and premature birth have been previously linked with exposure to ambient air pollution. Most studies relied on a limited number of monitors in the region of interest, which can introduce exposure error or restrict the analysis to persons living near a monitor, which reduces sample size and generalizability and may create selection bias.

METHODS

We evaluated the relationship between premature birth and birth weight with exposure to ambient particulate matter (PM₂.₅) levels during pregnancy in Massachusetts for a 9-year period (2000-2008). Building on a novel method we developed for predicting daily PM₂.₅ at the spatial resolution of a 10x10 km grid across New-England, we estimated the average exposure during 30 and 90 days prior to birth as well as the full pregnancy period for each mother. We used linear and logistic mixed models to estimate the association between PM₂.₅ exposure and birth weight (among full term births) and PM₂.₅ exposure and preterm birth adjusting for infant sex, maternal age, maternal race, mean income, maternal education level, prenatal care, gestational age, maternal smoking, percent of open space near mothers residence, average traffic density and mothers health.

RESULTS

Birth weight was negatively associated with PM₂.₅ across all tested periods. For example, a 10 μg/m³ increase of PM₂.₅ exposure during the entire pregnancy was significantly associated with a decrease of 13.80 g [95% confidence interval (CI) = -21.10, -6.05] in birth weight after controlling for other factors, including traffic exposure. The odds ratio for a premature birth was 1.06 (95% confidence interval (CI) = 1.01-1.13) for each 10 μg/m3 increase of PM₂.₅ exposure during the entire pregnancy period.

CONCLUSIONS

The presented study suggests that exposure to PM₂.₅ during the last month of pregnancy contributes to risks for lower birth weight and preterm birth in infants.

Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application

BACKGROUND

Epidemiologic and health impact studies of fine particulate matter with diameter < 2.5 microm (PM2.5) are limited by the lack of monitoring data, especially in developing countries. Satellite observations offer valuable global information about PM2.5 concentrations.

OBJECTIVE

In this study, we developed a technique for estimating surface PM2.5 concentrations from satellite observations.

METHODS

We mapped global ground-level PM2.5 concentrations using total column aerosol optical depth (AOD) from the MODIS (Moderate Resolution Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer) satellite instruments and coincident aerosol vertical profiles from the GEOS-Chem global chemical transport model.

RESULTS

We determined that global estimates of long-term average (1 January 2001 to 31 December 2006) PM2.5 concentrations at approximately 10 km x 10 km resolution indicate a global population-weighted geometric mean PM2.5 concentration of 20 microg/m3. The World Health Organization Air Quality PM2.5 Interim Target-1 (35 microg/m3 annual average) is exceeded over central and eastern Asia for 38% and for 50% of the population, respectively. Annual mean PM2.5 concentrations exceed 80 microg/m3 over eastern China. Our evaluation of the satellite-derived estimate with ground-based in situ measurements indicates significant spatial agreement with North American measurements (r = 0.77; slope = 1.07; n = 1057) and with noncoincident measurements elsewhere (r = 0.83; slope = 0.86; n = 244). The 1 SD of uncertainty in the satellite-derived PM2.5 is 25%, which is inferred from the AOD retrieval and from aerosol vertical profile errors and sampling. The global population-weighted mean uncertainty is 6.7 microg/m3.

CONCLUSIONS

Satellite-derived total-column AOD, when combined with a chemical transport model, provides estimates of global long-term average PM2.5 concentrations.

Limitations of remotely sensed aerosol as a spatial proxy for fine particulate matter

BACKGROUND

Recent research highlights the promise of remotely sensed aerosol optical depth (AOD) as a proxy for ground-level particulate matter with aerodynamic diameter <or= 2.5 microm (PM(2.5)). Particular interest lies in estimating spatial heterogeneity using AOD, with important application to estimating pollution exposure for public health purposes. Given the correlations reported between AOD and PM(2.5), it is tempting to interpret the spatial patterns in AOD as reflecting patterns in PM(2.5).

OBJECTIVES

We evaluated the degree to which AOD can help predict long-term average PM(2.5) concentrations for use in chronic health studies.

METHODS

We calculated correlations of AOD and PM(2.5) at various temporal aggregations in the eastern United States in 2004 and used statistical models to assess the relationship between AOD and PM(2.5) and the potential for improving predictions of PM(2.5) in a subregion, the mid-Atlantic.

RESULTS

We found only limited spatial associations of AOD from three satellite retrievals with daily and yearly PM(2.5). The statistical modeling shows that monthly average AOD poorly reflects spatial patterns in PM(2.5) because of systematic, spatially correlated discrepancies between AOD and PM(2.5). Furthermore, when we included AOD as a predictor of monthly PM(2.5) in a statistical prediction model, AOD provided little additional information in a model that already accounts for land use, emission sources, meteorology, and regional variability.

CONCLUSIONS

These results suggest caution in using spatial variation in currently available AOD to stand in for spatial variation in ground-level PM(2.5) in epidemiologic analyses and indicate that when PM(2.5) monitoring is available, careful statistical modeling outperforms the use of AOD.