Satellite-retrieved Aerosol Optical Depth Research Articles

Aerosol pattern changes over the dead sea from west to east - Using high-resolution satellite data

The area of the Dead Sea, a terminal lake on the border between Israel and Jordan, has gone through extensive environmental changes in recent decades, stemming from several processes including extreme evaporation and land degradation. Our study explored the distribution and the long-term high-resolution climatology of the Dead Sea aerosols, using satellite-retrieved Aerosol Optical Depth (AOD) measurements. We used the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that provides AOD measurements at a 1 km resolution. MAIAC retrievals for the entire study period (2000–2016) have shown a very good agreement (r = 0.8) with the nearby AERONET station. For the first time, the AOD temporal distribution and its variability, on both Dead Sea coasts, were calculated on a monthly/seasonal basis. Three main aerosol pollution patterns (i.e. high-levels of AOD) emerged as follows: western focalized (75 and 59% of the months examined for Aqua and Terra, respectively), eastern focalized (14 and 24%), and similar levels of pollution on both coasts (11 and 17%, respectively). Using the Normalized AOD Difference (NAODD) metric, we studied how the AOD spatial patterns changed and when. Following the negative trends in most of the months, our analysis has demonstrated a shift towards unexplained increasing pollution levels over the Jordanian eastern side. For March–April, this shift had already occurred, and the potential reasons including changes in synoptic regimes are discussed. Local and global implications are of much regional interest because of the Dead Sea deterioration and the need for urgent environmental policy changes.

Spatial characteristics and temporal evolution of the relationship between PM2.5 and aerosol optical depth over the eastern USA during 2003–2017

Abstract Previous research has proposed use of satellite-retrieved aerosol optical depth (AOD) to generate geospatial assessments of near-surface PM2.5 mass concentrations and potentially to provide air quality forecasts. Spatiotemporal variability in PM2.5–AOD relationships over the eastern United States of America are analyzed using surface observations, satellite data, reanalysis data, and WRF-Chem simulations. Three primary metrics are analyzed: eta (η, the ratio of PM2.5 to AOD), the correlation coefficient (ρ) between daily values of PM2.5 to AOD, and hit rate (θ, defined as co-occurrence of high PM2.5 and AOD). It is shown that mean daily η exhibits substantial geospatial variability and a pronounced seasonal cycle. η computed for 301 EPA stations ranges from 21 to 155 μg m−3 and has a domain-wide median value of 70 μg m−3. Larger values of η occur in winter and fall. There is also evidence of diurnal variability in η. Lower values are derived in analyses using AOD from Terra (i.e. the morning overpass) than when AOD from the MODIS instrument onboard Aqua (i.e. the afternoon overpass). The spatial median η from Terra is 70 vs. 93 μg m−3 from Aqua. A majority of sites exhibit statistically significant lower values of η, ρ, and θ during 2013–2017 than 2003–2007, indicating a declining association between AOD and PM2.5. This has implications for the potential to use remotely-sensed AOD to generate geospatial estimates of near-surface PM2.5. The spatial distribution of η across the 301 locations exhibits a negative dependence on planetary boundary layer height and 10 m wind speed and a positive dependence on integrated humidity in lower troposphere, urban fraction, 2 m temperature, and vegetation coverage. Simulations with WRF-Chem indicate model-derived estimates of η, ρ, and θ are highly dependent on the aerosol scheme employed but the model captures some of the spatial variability and the correct dependence of η on meteorological and land use causes of that variability.

Construction of a virtual PM2.5 observation network in China based on high-density surface meteorological observations using the Extreme Gradient Boosting model

With increasing public concerns on air pollution in China, there is a demand for long-term continuous PM2.5 datasets. However, it was not until the end of 2012 that China established a national PM2.5 observation network. Before that, satellite-retrieved aerosol optical depth (AOD) was frequently used as a primary predictor to estimate surface PM2.5. Nevertheless, satellite-retrieved AOD often encounter incomplete daily coverage due to its sampling frequency and interferences from cloud, which greatly affect the representation of these AOD-based PM2.5. Here, we constructed a virtual ground-based PM2.5 observation network at 1180 meteorological sites across China using the Extreme Gradient Boosting (XGBoost) model with high-density meteorological observations as major predictors. Cross-validation of the XGBoost model showed strong robustness and high accuracy in its estimation of the daily (monthly) PM2.5 across China in 2018, with R2, root-mean-square error (RMSE) and mean absolute error values of 0.79 (0.92), 15.75 μg/m3 (6.75 μg/m3) and 9.89 μg/m3 (4.53 μg/m3), respectively. Meanwhile, we find that surface visibility plays the dominant role in terms of the relative importance of variables in the XGBoost model, accounting for 39.3% of the overall importance.We then use meteorological and PM2.5 data in the year 2017 to assess the predictive capability of the model. Results showed that the XGBoost model is capable to accurately hindcast historical PM2.5 at monthly (R2 = 0.80, RMSE = 14.75 μg/m3), seasonal (R2 = 0.86, RMSE = 12.28 μg/m3), and annual (R2 = 0.81, RMSE = 10.10 μg/m3) mean levels. In general, the newly constructed virtual PM2.5 observation network based on high-density surface meteorological observations using the Extreme Gradient Boosting model shows great potential in reconstructing historical PM2.5 at ~1000 meteorological sites across China. It will be of benefit to filling gaps in AOD-based PM2.5 data, as well as to other environmental studies including epidemiology.

Detection and attribution of aerosol–cloud interactions in large-domain large-eddy simulations with the ICOsahedral Non-hydrostatic model

Abstract. Clouds and aerosols contribute the largest uncertainty to current estimates and interpretations of the Earth’s changing energy budget. Here we use a new-generation large-domain large-eddy model, ICON-LEM (ICOsahedral Non-hydrostatic Large Eddy Model), to simulate the response of clouds to realistic anthropogenic perturbations in aerosols serving as cloud condensation nuclei (CCN). The novelty compared to previous studies is that (i) the LEM is run in weather prediction mode and with fully interactive land surface over a large domain and (ii) a large range of data from various sources are used for the detection and attribution. The aerosol perturbation was chosen as peak-aerosol conditions over Europe in 1985, with more than fivefold more sulfate than in 2013. Observational data from various satellite and ground-based remote sensing instruments are used, aiming at the detection and attribution of this response. The simulation was run for a selected day (2 May 2013) in which a large variety of cloud regimes was present over the selected domain of central Europe. It is first demonstrated that the aerosol fields used in the model are consistent with corresponding satellite aerosol optical depth retrievals for both 1985 (perturbed) and 2013 (reference) conditions. In comparison to retrievals from ground-based lidar for 2013, CCN profiles for the reference conditions were consistent with the observations, while the ones for the 1985 conditions were not. Similarly, the detection and attribution process was successful for droplet number concentrations: the ones simulated for the 2013 conditions were consistent with satellite as well as new ground-based lidar retrievals, while the ones for the 1985 conditions were outside the observational range. For other cloud quantities, including cloud fraction, liquid water path, cloud base altitude and cloud lifetime, the aerosol response was small compared to their natural variability. Also, large uncertainties in satellite and ground-based observations make the detection and attribution difficult for these quantities. An exception to this is the fact that at a large liquid water path value (LWP > 200 g m−2), the control simulation matches the observations, while the perturbed one shows an LWP which is too large. The model simulations allowed for quantifying the radiative forcing due to aerosol–cloud interactions, as well as the adjustments to this forcing. The latter were small compared to the variability and showed overall a small positive radiative effect. The overall effective radiative forcing (ERF) due to aerosol–cloud interactions (ERFaci) in the simulation was dominated thus by the Twomey effect and yielded for this day, region and aerosol perturbation −2.6 W m−2. Using general circulation models to scale this to a global-mean present-day vs. pre-industrial ERFaci yields a global ERFaci of −0.8 W m−2.

Investigating the performance of satellite-based models in estimating the surface PM2.5 over China

Accurate estimation of surface PM2.5 concentration is critical for the assessment of PM2.5 exposure and associated health impacts. Due to the limited spatial coverage of ground monitoring stations, most studies often use the satellite products to estimate surface PM2.5 concentration by constructing a comprehensive relationship between satellite-retrieved aerosol optical depth (AOD) and ground-based measured PM2.5 concentration with machine learning (ML) technologies. However, uncertainties of ML-based models may lead to considerable biases in PM2.5 estimation, which need carefully examined. Here we evaluate the accuracy of estimated PM2.5 concentration from two popular ML-models (i.e., Random Forest and the BP Neural Network) which were trained and tested using hourly data of satellite-retrieved AOD from HIMAWARI, ground-based measured PM2.5 from China National Environmental Monitoring Center, ERA5 meteorological conditions, and other auxiliary variables for a whole year of 2017 over China. We propose a new validation method considering the spatial pattern of the data during the validation. The results suggest that the traditional validation methods may overestimate the performance of the models on estimating the PM2.5 at the area with sparse in-situ measurements. Moreover, the spatial distribution pattern of the training data will largely affect the evaluation of models performance, which should be carefully considered. For future study, at least a site-specifically validation is needed rather than only using random sampling validation.

Impact of Aerosol Vertical Distribution on Aerosol Optical Depth Retrieval from Passive Satellite Sensors

When retrieving Aerosol Optical Depth (AOD) from passive satellite sensors, the vertical distribution of aerosols usually needs to be assumed, potentially causing uncertainties in the retrievals. In this study, we use the Moderate Resolution Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as examples to investigate the impact of aerosol vertical distribution on AOD retrievals. A series of sensitivity experiments was conducted using radiative transfer models with different aerosol profiles and surface conditions. Assuming a 0.2 AOD, we found that the AOD retrieval error is the most sensitive to the vertical distribution of absorbing aerosols; a −1 km error in aerosol scale height can lead to a ~30% AOD retrieval error. Moreover, for this aerosol type, ignoring the existence of the boundary layer can further result in a ~10% AOD retrieval error. The differences in the vertical distribution of scattering and absorbing aerosols within the same column may also cause −15% (scattering aerosols above absorbing aerosols) to 15% (scattering aerosols below absorbing aerosols) errors. Surface reflectance also plays an important role in affecting the AOD retrieval error, with higher errors over brighter surfaces in general. The physical mechanism associated with the AOD retrieval errors is also discussed. Finally, by replacing the default exponential profile with the observed aerosol vertical profile by a micro-pulse lidar at the Beijing-PKU site in the VIIRS retrieval algorithm, the retrieved AOD shows a much better agreement with surface observations, with the correlation coefficient increased from 0.63 to 0.83 and bias decreased from 0.15 to 0.03. Our study highlights the importance of aerosol vertical profile assumption in satellite AOD retrievals, and indicates that considering more realistic profiles can help reduce the uncertainties.

Dust emission and transport in Northwest China: WRF-Chem simulation and comparisons with multi-sensor observations

In this study, the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) is employed to simulate a dust process in Northwest China during May 2018. The model's ability to simulate the dust process in Northwest China is firstly evaluated using various satellite-retrieved and observational data, including satellite-retrieved aerosol optical depth (AOD), extinction coefficient and dust index, as well as observational wind field, precipitation and particulate matter with particle size below 10 mic (PM10) mass concentration at surface. The four-dimensional assimilation method is also used to optimize meteorological data and effectively improve the simulation of the dust process. The comparisons between the simulations based on five dust emission schemes within WRF-Chem and the observations show that, the Shao01 scheme overall has good performance in simulating the emission flux, the spatial pattern of source region, as well as the spatiotemporal variation of dust mass concentration, during this dust process. In comparison to Shao01, the GOCART AFWA and Shao04 schemes can also produce quite similar spatial pattern of dust source region, but tend to overestimate or underestimate dust emission and mass concentration. The Shao11 scheme fails to simulate the dust process since the importance of the fully disturbed particle size distribution is omitted. It is also noted that the GOCART scheme can well reproduce dust emission processes under weak wind erosion but underestimate dust emission flux under strong wind erosion. In addition, the GOCART scheme has produced some spurious emissions and thus blurred the distribution of dust source region. The Shao01 simulation was found to reasonably reproduce the spatial distribution and magnitude of the satellite-retrieved AOD and vertical profile of extinction coefficient, as well as the PM10 mass concentration at surface. Northwest China is covered by mountains, basins, deserts and other landforms, thus the complex terrain is one of the key factors to influence the dust process over the region. Our study shows that after being emitted, the airborne dust transported toward the east and west. The dust to the east was diffused rapidly, but the portion toward the west was blocked and accumulated at the edges of the mountains and thus produced dust weather characterized by high dust concentration and long lifetime. The dust accumulated at the edges of the mountains could reach an altitude of more than 6 km due to wind and thermal effect, and finally arrive at Tibetan Plateau and eastern China.

A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing

Abstract. Recent years have seen the increasing inclusion of per-retrieval prognostic (predictive) uncertainty estimates within satellite aerosol optical depth (AOD) data sets, providing users with quantitative tools to assist in the optimal use of these data. Prognostic estimates contrast with diagnostic (i.e. relative to some external truth) ones, which are typically obtained using sensitivity and/or validation analyses. Up to now, however, the quality of these uncertainty estimates has not been routinely assessed. This study presents a review of existing prognostic and diagnostic approaches for quantifying uncertainty in satellite AOD retrievals, and it presents a general framework to evaluate them based on the expected statistical properties of ensembles of estimated uncertainties and actual retrieval errors. It is hoped that this framework will be adopted as a complement to existing AOD validation exercises; it is not restricted to AOD and can in principle be applied to other quantities for which a reference validation data set is available. This framework is then applied to assess the uncertainties provided by several satellite data sets (seven over land, five over water), which draw on methods from the empirical to sensitivity analyses to formal error propagation, at 12 Aerosol Robotic Network (AERONET) sites. The AERONET sites are divided into those for which it is expected that the techniques will perform well and those for which some complexity about the site may provide a more severe test. Overall, all techniques show some skill in that larger estimated uncertainties are generally associated with larger observed errors, although they are sometimes poorly calibrated (i.e. too small or too large in magnitude). No technique uniformly performs best. For powerful formal uncertainty propagation approaches such as optimal estimation, the results illustrate some of the difficulties in appropriate population of the covariance matrices required by the technique. When the data sets are confronted by a situation strongly counter to the retrieval forward model (e.g. potentially mixed land–water surfaces or aerosol optical properties outside the family of assumptions), some algorithms fail to provide a retrieval, while others do but with a quantitatively unreliable uncertainty estimate. The discussion suggests paths forward for the refinement of these techniques.

Spatio-temporal modeling of PM2.5 concentrations with missing data problem: a case study in Beijing, China

ABSTRACTOne of the major challenges in conducting epidemiological studies of air pollution and health is the difficulty of estimating the degree of exposure accurately. Fine particulate matter (PM2.5) concentrations vary in space and time, which are difficult to estimate in rural, suburban and smaller urban areas due to the sparsity of the ground monitoring network. Satellite retrieved aerosol optical depth (AOD) has been increasingly used as a proxy of ground PM2.5 observations, although it suffers from non-trivial missing data problems. To address these issues, we developed a multi-stage statistical model in which daily PM2.5 concentrations can be obtained with complete spatial coverage. The model consists of three stages – an inverse probability weighting scheme to correct non-random missing patterns of AOD values, a spatio-temporal linear mixed effect model to account for the spatially and temporally varying PM2.5-AOD relationships, and a gap-filling model based on the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE). Good model performance was achieved from out-of-sample validation as shown in R2 of 0.93 and root mean square error of 9.64 μg/m3. The results indicated that the multi-stage PM2.5 prediction model proposed in the present study yielded highly accurate predictions, while gaining computational efficiency from the INLA-SPDE.

A Bayesian ensemble approach to combine PM2.5 estimates from statistical models using satellite imagery and numerical model simulation

Ambient fine particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) has been linked to various adverse health outcomes. PM2.5 arises from both natural and anthropogenic sources, and PM2.5 concentrations can vary over space and time. However, the sparsity of existing air quality monitors greatly restricts the spatial-temporal coverage of PM2.5 measurements, potentially limiting the accuracy of PM2.5-related health studies. Various methods exist to address these limitations by supplementing air quality monitoring measurements with additional data. We develop a method to combine PM2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. Our approach differs from previous ensemble approaches in its ability to not only incorporate uncertainties in PM2.5 estimates from individual models but also to provide uncertainties for the resulting ensemble estimates. In an application of estimating daily PM2.5 in the Southeastern US, the ensemble approach outperforms previously developed spatial-temporal statistical models that use either AOD or bias-corrected CTM simulations in cross-validation (CV) analyses. More specifically, in spatially clustered CV experiments, the ensemble approach reduced the AOD-only and CTM-only model's root mean squared error (RMSE) by at least 13%. Similar improvements were seen in R2. The enhanced prediction performance that the ensemble technique provides at fine-scale spatial resolution, as well as the availability of prediction uncertainty, can be further used in health effect analyses of air pollution exposure.

A comparative time series analysis and modeling of aerosols in the contiguous United States and China.

Long-term trend analysis and modeling of aerosol distribution is of paramount importance to study radiative forcing, climate change, and human health. Previous studies on spatiotemporal trend analysis have not fully considered the impact of spatial and temporal gaps of satellite-retrieved aerosol optical depth (AOD) on precise aerosol characterization. In addition, very few studies analyzed inter-country aerosol variations, trends, driving forces, and predictions at the regional level, which is important to draw lessons from the experiences of one another. This study is focused on comparative time series analyses and modeling of aerosols over the contiguous United States (U.S.) and China during 2003-2015 using MODIS Collection 6 retrievals. An econometric model, namely autoregressive integrated moving average (ARIMA), is employed to reproduce and predict AOD variability over U.S. and China. Results show that high AOD values are observed in the eastern part of U.S. and China. Temporal variations indicate that AODs reach their peak values in summer for both countries. A sustained negative AOD trend is present throughout the U.S. while a distinct spatial variation of AOD trend is exhibited in China. The large differences in variations and trends are closely linked to the energy strategies, economic and urban development, and lifestyle activities of these two countries. Time series modeling reveals that reasonably good performances are found in most parts of these two countries. In particular, the model replicates AOD time series that has clear seasonal variations with much more accuracy. The results suggest that areas most suitable for applying the model for prediction are those with high AOD quality, high completeness of AOD data, and low-AOD values. Overall, the satisfactory predicted results indicate the applicability and feasibility of the ARIMA modeling technique for accurately extracting AOD profiles, predicting future AOD values as well as extrapolating missed AOD values at the regional scale. The retrieved and predicted AOD values may serve as reliable estimates for air quality and epidemiological studies.

Estimating Daily PM2.5 Concentrations in Beijing Using 750-M VIIRS IP AOD Retrievals and a Nested Spatiotemporal Statistical Model

Satellite-retrieved aerosol optical depth (AOD) data have been widely used to predict PM2.5 concentrations. Most of their spatial resolutions (~1 km or greater), however, are too coarse to support PM2.5-related studies at fine scales (e.g., urban-scale PM2.5 exposure assessments). Space-time regression models have been widely developed and applied to predict PM2.5 concentrations from satellite-retrieved AOD. Their accuracies, however, are not satisfactory particularly on days that lack a model dataset. The present study aimed to evaluate the effectiveness of recent high-resolution (i.e., ~750 m at nadir) AOD obtained from the Visible Infrared Imaging Radiometer Suite instrument (VIIRS) Intermediate Product (IP) in estimating PM2.5 concentrations with a newly developed nested spatiotemporal statistical model. The nested spatiotemporal statistical model consisted of two parts: a nested time fixed effects regression (TFER) model and a series of geographically weighted regression (GWR) models. The TFER model, containing daily, weekly, or monthly intercepts, used the VIIRS IP AOD as the main predictor alongside several auxiliary variables to predict daily PM2.5 concentrations. Meanwhile, the series of GWR models used the VIIRS IP AOD as the independent variable to correct residuals from the first-stage nested TFER model. The average spatiotemporal coverage of the VIIRS IP AOD was approximately 16.12%. The sample-based ten-fold cross validation goodness of fit (R2) for the first-stage TFER models with daily, weekly, and monthly intercepts were 0.81, 0.66, and 0.45, respectively. The second-stage GWR models further captured the spatial heterogeneities of the PM2.5-AOD relationships. The nested spatiotemporal statistical model produced more daily PM2.5 estimates and improved the accuracies of summer, autumn, and annual PM2.5 estimates. This study contributes to the knowledge of how well VIIRS IP AOD can predict PM2.5 concentrations at urban scales and offers strategies for improving the coverage and accuracy of daily PM2.5 estimates on days that lack a model dataset.

Satellite-based PM2.5 estimation directly from reflectance at the top of the atmosphere using a machine learning algorithm

Atmospheric particulate matter (PM) that have particle diameter less than 2.5 μm (PM2.5) are hazardous to public health whose concentration has been either measured on the ground or inferred from satellite-retrieved aerosol optical depth (AOD). The latter is subject to numerous sources of errors, making the satellite retrievals of PM2.5 highly uncertain. This study developed an ensemble machine-learning (ML) algorithm for estimating PM2.5 concentration directly from Advanced Himawari Imager satellite measured top-of-the-atmosphere (TOA) reflectances in 2016 integrated with meteorological parameters. The algorithm is demonstrated to perform well across China with high accuracies at different temporal scales. The model has an overall cross-validation coefficient of determination (R2) of 0.86 and a root-mean-square error (RMSE) of 17.3 μg m−3 for hourly PM2.5 concentration estimation. Such accuracies of the estimation on PM2.5 concentration by using TOA reflectance directly are comparable with those of the common methods on estimating PM2.5 concentration by using satellite-derived AODs, but the former has a relatively stronger predictive power relating to spatial-temporal coverages than the latter. Annual and seasonal variations of PM2.5 concentration over three major the developed regions in China are estimated using the model and analyzed. The relatively stronger predictive ability of developed model in this study may help provide information about the diurnal cycle of PM2.5 concentrations as well as aid in monitoring the processes of regional pollution episodes and the evolution of PM2.5 concentration.

Assessment of satellite-estimated near-surface sulfate and nitrate concentrations and their precursor emissions over China from 2006 to 2014

China is the largest anthropogenic aerosol-generating country worldwide; however, few studies have analyzed the PM2.5 chemical components and their underlying precursor emissions over long periods and across the national domain. First, global 3-D tropospheric chemistry and transport model (GEOS-Chem)-integrated satellite-retrieved aerosol optical depth (AOD) and vertical profiles were used to estimate near-surface sulfate and nitrate levels at 10-km resolution over China from 2006 to 2014. Ground measurement validation of our satellite model yielded correlation coefficients (r) of 0.7 and 0.73 and normalized mean bias (NMB) values of −37.96% and − 32.73% for sulfate and nitrate, respectively. Second, analyses of the spatiotemporal distributions of sulfate and nitrate as well as the vertical density Ozone Monitoring Instrument (OMI)-measured SO2 (PBL_SO2) and NO2 (TVCD_NO2) indicated that the highest nitrate and sulfate levels occurred in the North China Plain (~25 μg/m3) and Sichuan Basin (SCB) (~30 μg/m3), respectively. The long-term variations in the estimated components and precursor gases indicated that the large sulfate decline was positively correlated with the SO2 emission reduction due to the mandatory desulfurization implemented in 2007. The annual growth rate of sulfate relative to the national mean was −6.19%/yr, and the concentration decreased by 17.10% from 2011 to 2014. Energy consumption increases and a lack of control measures for NO2 resulted in persistent increases in NO2 emissions and nitrate concentrations from 2006 to 2010, particularly in the SCB. With energy consumption structure advancements, reductions in NO2 emissions and corresponding nitrate levels over three typical regions were prominent after 2012. Third, the estimated national-scale uncertainties of satellite datasets at 0.1° × 0.1° were 26.88% for sulfate and 25.55% for nitrate. Differences in the spatial distributions and temporal trends between our estimated components and precursor gases were mainly attributed to the dataset accuracy, the data pre-processing strategy, inconsistent column density and near-surface mass concentration, meteorological variables and complex chemical reactions.

Improved empirical models for estimating surface direct and diffuse solar radiation at monthly and daily level: A case study in North China

Precise knowledge of direct and diffuse solar radiation is important for energy utilization and agricultural activities. However, field measurements in most areas of the world are only for total solar radiation. The satellite-retrieved direct and diffuse solar radiation show poor performance under overcast skies. Therefore, better empirical models are needed to estimate direct and diffuse solar radiation by considering the impact of aerosols over polluted regions. A case study is conducted in North China with the ground-measured solar radiation and satellite-retrieved aerosol optical depth to improve new empirical models at monthly (from 2000 to 2016) and daily (from 2006 to 2009) level. The improved empirical models are validated using the field measurements and compared with the existing models. Results suggest that these models perform well in estimating direct solar radiation at monthly ( R2 = 0.86–0.91, RMSE = 0.76–0.83 MJ/m2) and daily ( R2 = 0.91–0.94, RMSE = 1.51–1.64 MJ/m2) level. The accuracy of estimated monthly ( R2 = 0.95–0.96, RMSE = 0.57–0.65 MJ/m2) and daily ( R2 = 0.91–0.93, RMSE = 1.09–1.15 MJ/m2) diffuse solar radiation, particularly the maximum diffuse solar radiation value, has been improved compared to the existing models. The models presented in this study can be useful in the improvement and evaluation of solar radiation dataset over polluted regions similar to North China.

Associations between Ambient and Indoor PM2.5 Exposure and Cooking Fuel on Insulin Resistance in the PURSE-HIS Cohort

Introduction: Evidence suggests that chronic exposure to ambient PM2.5 is associated with the prevalence and incidence of Diabetes Mellitus. This relationship has not been well explored in India, where there is an increased prevalence of diabetes and elevated exposures to PM2.5. Our study assessed associations between ambient and indoor PM2.5 exposure and cooking fuel type with insulin resistance as measured by the homeostasis model assessment of insulin resistance (HOMA-IR) method, in the Population study of Urban, Rural, Semi-urban Endovascular disease and Holistic Intervention Study (PURSE-HIS) in Tamil Nadu, India.Method: HOMA-IR was assessed in 8080 randomly selected participants. Estimates of ambient PM2.5 were derived using a combination of satellite retrieved aerosol optical depth and chemical transport modelling for 2010. For a subset of the cohort (n = 430), we measured indoor PM2.5, while for the rest of the sample we used cooking fuel as a proxy for indoor PM2.5. Multivariable regression models adjusted for age, BMI, sex, smoking, cooking fuel, socio-economic status. Models were stratified by sex given the difference in residential time-activity reported for females.Result: A 10 µg/m3 increase in ambient PM2.5 was significantly associated with a 0.8 mg/dl (95% CI: 0.5, 1.0) increase in HOMA-IR when adjusted for risk factors and cooking fuel. Cooking fuel type was not independently associated with HOMA-IR, but did have a significant interaction with ambient PM2.5, with higher effect on HOMA-IR in homes using solid fuels when compared to homes using LPG or a mix of fuels. A 10 µg/m3 increase in indoor PM2.5 had a non-significant association with HOMA-IR in female participants (0.05 mg/dl [95%CI: -0.09, 0.19]), but no association in males.Conclusion: Ambient PM2.5 was positively associated with HOMA-IR in both males and females. Indoor PM2.5 and cooking with solid fuel was associated with HOMA-IR in females only.

Natural and anthropogenic contributions to long-term variations of SO2, NO2, CO, and AOD over East China

Concentrations of atmospheric pollutants over East China have varied considerably during the past decades. These variations are partly due to variations of human activities, e.g., increasing energy consumption and implementation of government emission control policies, and partly to natural fluctuations. This study aims to separate the effects of natural processes and anthropogenic activities on the increase/decrease of the concentrations of some of the most important pollutants (SO2, NO2, CO and aerosols) over East China in the last decade. This was achieved by the comparison of the temporal variations in long-term time series of satellite-retrieved aerosol optical depth (AOD) and vertical column densities (VCDs) of SO2, NO2, and CO, with those in model-simulated time series of the natural variations only. The latter were created by the use of the same anthropogenic emissions throughout the whole simulation, while using re-analysis data (MERRA) to describe meteorological processes and natural emissions. Thus, the comparison between observed and simulated temporal variations reveals the effects due to anthropogenic emissions only, assuming that the atmospheric processes affect natural and anthropogenic species in the same way. In the analysis, a Kolmogorov–Zurbenko (KZ) filter is used to extract long-term components from both the observed and simulated data and normalization to the situation at a certain reference point is used to eliminate bias between observations and simulations. By this new method, natural and anthropogenic contributions to long-term variations of trace gases and AOD are quantitatively estimated. The results show that NO2 VCDs increased from 2004 to 2011 by 76% and of the overall increase in this period only 1% ± 1% was attributed to natural factors, 99% ± 1% attributed to anthropogenic factors. AOD increased by 24% between 2001 and 2011 and of the overall increase 24% ± 32% was due to natural factors and 76% ± 32% was due to anthropogenic factors. SO2 VCDs decreased by 15% from 2007 to 2013, natural and anthropogenic factors contributed respectively 16% ± 14% and 84% ± 14% to the overall decrease in this period. CO decreased since 2003 with 13% and of the overall decrease 6% ± 6% was due to natural factors and 94% ± 6% was due to anthropogenic factors.

A nonparametric approach to filling gaps in satellite-retrieved aerosol optical depth for estimating ambient PM2.5 levels

Satellite-retrieved aerosol optical depth (AOD) is commonly used to estimate ambient levels of fine particulate matter (PM2.5), though it is important to mitigate the estimation bias of PM2.5 due to gaps in satellite-retrieved AOD. A nonparametric approach with two random-forest submodels is proposed to estimate PM2.5 levels by filling gaps in satellite-retrieved AOD. This novel approach was employed to estimate the spatiotemporal distribution of daily PM2.5 levels during 2013–2015 in the Sichuan Basin of Southwest China, where the coverage rate of composite AOD retrieved by the Terra and Aqua satellites was only 11.7%. Based on the retrieved AOD and various covariates (including meteorological conditions and land use types), the first random-forest submodel (named AOD-submodel) was trained to fill the gaps in the AOD dataset, giving a cross-validation R2 of 0.95. Subsequently, the second random-forest submodel (named PM2.5-submodel) was trained to estimate the PM2.5 levels for unmonitored areas/days based on the gap-filled AOD, ground-monitored PM2.5 levels, and the covariates, and achieved a cross-validation R2 of 0.86. By comparing the complete and incomplete (i.e., without the days when AOD data were missing) estimates, we found that the monthly PM2.5 levels could be overestimated by 34.6% if the PM2.5 values coincident with AOD gaps were not considered. The newly developed approach is valuable for deriving the complete spatiotemporal distribution of daily PM2.5 from incomplete remote-sensing data, which is essential for air quality management and human exposure assessment.

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 (PM2.5) 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 PM2.5 concentrations across the conterminous United States using satellite-retrieved aerosol optical depth (AOD) and other ancillary predictors in 2011. Our approach flexibly models the PM2.5 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 PM2.5 with an R2 at 70% and generated reliable annual and seasonal PM2.5 concentration maps with its SD. Overall, this modeling framework can be applied to national-scale PM2.5 exposure assessments and can also quantify the prediction errors.

Observations of the Interaction and Transport of Fine Mode Aerosols with Cloud and/or Fog in Northeast Asia from Aerosol Robotic Network (AERONET) and Satellite Remote Sensing.

Analysis of sun photometer measured and satellite retrieved aerosol optical depth (AOD) data has shown that major aerosol pollution events with very high fine mode AOD (>1.0 in mid-visible) in the China/Korea/Japan region are often observed to be associated with significant cloud cover. This makes remote sensing of these events difficult even for high temporal resolution sun photometer measurements. Possible physical mechanisms for these events that have high AOD include a combination of aerosol humidification, cloud processing, and meteorological co-variation with atmospheric stability and convergence. The new development of Aerosol Robotic network (AERONET) Version 3 Level 2 AOD with improved cloud screening algorithms now allow for unprecedented ability to monitor these extreme fine mode pollution events. Further, the Spectral Deconvolution Algorithm (SDA) applied to Level 1 data (L1; no cloud screening) provides an even more comprehensive assessment of fine mode AOD than L2 in current and previous data versions. Studying the 2012 winter-summer period, comparisons of AERONET L1 SDA daily average fine mode AOD data showed that Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing of AOD often did not retrieve and/or identify some of the highest fine mode AOD events in this region. Also, compared to models that include data assimilation of satellite retrieved AOD, the L1 SDA fine mode AOD was significantly higher in magnitude, particularly for the highest AOD events that were often associated with significant cloudiness.