Use Of Aerosol Optical Depth Research Articles

Estimation of PM2.5 concentrations in North China with high spatiotemporal resolution using the ERA5 dataset and machine learning models

Previous studies have predominantly focused on developing high spatiotemporal resolution PM2.5 models utilizing moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) products alongside certain meteorological factors. However, MODIS AOD is not continuous, and there is a problem of missing data, which is not conducive to the study of PM2.5 in areas without AOD. This study proposes a new method for estimating PM2.5 concentration in North China, which not only simplifies the complex process associated with the use of AOD, but also partially solves the issue of missing AOD data. This study proposes a machine learning (random forest, RF; back propagation neural network, BPNN) based method for estimating PM2.5 using meteorological parameters obtained from the fifth-generation reanalysis (ERA5) dataset released by the European Centre for Medium-Range Weather Forecasts and considering the effects of land cover type, digital elevation model, vegetation index and population data. The model performed well, with 10-fold cross-validation (coefficient of determination) R2 and (root-mean-square error) RMSE of 0.79/0.95 and 26.10/13.22 µg/m3, respectively, for BPNN and RF. The estimated hourly performance of the RF model in winter (00:00 to 23:00 BST) with an R2 ranging from 0.92 to 0.96, an RMSE of 11.45 to 16.70 µg/m3. RF model with the best performance and ERA5 were selected to build a high-resolution (0.25°×0.25°) hourly PM2.5 map (PMM), and the PMM was compared with CHAP, with R2 and RMSE of 0.75 and 20.62 µg/m3, respectively. This study further investigates the impacts of land cover types, digital elevation model, and land surface characteristics on the spatial distribution of PM2.5 in North China.

Cloud condensation nuclei concentrations derived from the CAMS reanalysis

Abstract. Determining number concentrations of cloud condensation nuclei (CCN) is one of the first steps in the chain in analysis of cloud droplet formation, the direct microphysical link between aerosols and cloud droplets, and a process key for aerosol–cloud interactions (ACI). However, due to sparse coverage of in situ measurements and difficulties associated with retrievals from satellites, a global exploration of their magnitude, source as well as temporal and spatial distribution cannot be easily obtained. Thus, a better representation of CCN numbers is one of the goals for quantifying ACI processes and achieving uncertainty-reduced estimates of their associated radiative forcing. Here, we introduce a new CCN dataset which is derived based on aerosol mass mixing ratios from the latest Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) in a diagnostic model that uses CAMSRA aerosol properties and a simplified kappa-Köhler framework suitable for global models. The emitted aerosols in CAMSRA are not only based on input from emission inventories using aerosol observations, they also have a strong tie to satellite-retrieved aerosol optical depth (AOD) as this is assimilated as a constraining factor in the reanalysis. Furthermore, the reanalysis interpolates for cases of poor or missing retrievals and thus allows for a full spatiotemporal quantification of CCN numbers. The derived CCN dataset captures the general trend and spatial and temporal distribution of total CCN number concentrations and CCN from different aerosol species. A brief evaluation with ground-based in situ measurements demonstrates the improvement of the modelled CCN over the sole use of AOD as a proxy for CCN as the overall correlation coefficient improved from 0.37 to 0.71. However, we find the modelled CCN from CAMSRA to be generally high biased and find a particular erroneous overestimation at one heavily polluted site which emphasises the need for further validation. The CCN dataset (https://doi.org/10.26050/WDCC/QUAERERE_CCNCAMS_v1, Block, 2023), which is now freely available to users, features 3-D CCN number concentrations of global coverage for various supersaturations and aerosol species covering the years 2003–2021 with daily frequency. This dataset is one of its kind as it offers lots of opportunities to be used for evaluation in models and in ACI studies.

Inflammation Describes and Explains the Adverse Effects of Aerosol Optical Depth-Particulate Matter on Cardiovascular Outcomes: A Literature Review Since 2012

The success of remote sensing methodology to accurately estimate ambient particulate matter (PM) on the earth’s surface has resulted in the increased use of aerosol optical depth (AOD) AOD-PM 10 , AOD-PM 2.5 and AOD-PM 1 concentration level fused surfaces in cardiovascular epidemiologic and hypothesis-testing of inflammatory physiologic studies. Uniform space-time AOD-PM concentration level fused surfaces have been used as proxies for ambient PM monitor measurements in economically developed and developing counties as well as in densely populated urban regions with on-the-ground ambient PM monitors and in sparsely populated rural areas without ambient PM air monitors to determine the contribution of PM exposure to the subsequent occurrence of cardiovascular chronic diseases and circulatory-related adverse health outcomes. Since 2012, 45 published studies have evaluated the association between increased AOD-PM concentration level readings and adverse cardiovascular outcomes. Fifteen surveillance studies used inflammation as a descriptive physiologic mechanism and another nine publications mentioned another description. In contrast, another 20 investigations have used the inflammatory physiologic mechanism to explain how AOD-PM exposure changes the cardiovascular system and other organs connected to the cardiovascular system through the circulatory system and one report referred to some other explanation. The full extent of the adverse effects of AOD-PM exposure on the cardiovascular system only becomes evident when cardiovascular and other pathophysiological changes are also considered and evaluated. This review paper aims to demonstrate why AOD-PM and cardiovascular system studies are a new and useful source of information about how ambient PM exposure adversely impacts the cardiovascular system in diverse populations in different countries.

Assessment of aerosols over five cities of Angola based on MERRA–2 reanalysis data

The use of Aerosol Optical Depth (AOD) has been proven as an alternative to the traditional ground-level monitoring of air quality in many countries across the world. Therefore, this study based on MERRA-2 data aims: (i) to characterize the spatiotemporal and component variations of aerosols in the atmosphere over the capital cities (Luanda, Sumbe, Benguela, Huambo and Lubango) of the five most densely populated provinces of Angola from 2010 to 2020; (ii) to analyze the relationship between the monthly mean variation of the total AOD and the meteorological parameters (precipitation, temperature, wind speed, and relative humidity) in those five cities; and (iii) to assess the influence of emissions from the Nyamuragira volcano, located in the Democratic Republic of Congo, on the air quality at the five cities. The most significant contribution to the total AOD was derived from organic carbon, in all the cities, whereby the highest values (0.19–0.23) were in Luanda. Ranges of sulphates across the coastal cities were higher when compared to the interior cities caused by the emissions inventory data. The value of AOD in all the cities increased and reached the peak during the dry season. The HYSPLIT model showed that air masses from Nyamuragira at various heights in November 2011 reached Luanda and Sumbe, and CALIPSO could confirm the existence of volcanic aerosols in this same period. This study allowed to conclude that the variability of AOD loading depends on seasons and regions, thus providing a little more information about the matter.

USING MODIS AEROSOL OPTICAL DEPTH TO PREDICT PM10 OVER AL AIN REGION, UAE

Abstract. PM10 concentrations are essential for assessing air quality in arid areas. They are usually measured at air quality monitoring stations. The limited number of monitoring stations can make difficult to study significantly the spatial variability of PM10 over relatively large areas. This study aimed at evaluating the use of Aerosol Optical Depth derived from satellite data to estimate PM10 concentrations. The continuous coverage offered by remote sensing data helps to address the limitation encountered with the spatial distribution of relevant monitoring stations. In the current study we compared MODIS AOD at 550 nm included in MCD19A2 and we established a regression equation between AOD and PM10. The use of daily AOD at 1 km resolution helped establish regression with acceptable correlation coefficient. The regression equation is then used to create daily maps of estimated PM10 concentrations over the study area and helped assessing their variability.

Estimation of ground-level particulate matter concentrations based on synergistic use of MODIS, MERRA-2 and AERONET AODs over a coastal site in the Eastern Mediterranean

Satellite-derived aerosol optical depth (AOD) products are widely used to estimate the spatial and temporal characteristics of ground-level particulate matters (PM). Satellite-based approaches in the operational use of PM estimation models have limitations such as missing values in satellite-based variables due to the impact of cloud cover, relative humidity, and aerosol vertical distribution. Therefore, the spatial-temporal resolution of the estimation models is forced to be improved by using AOD products having different temporal resolutions from different platforms. In this study, ground-based PM10 concentrations were aimed to be estimated using different gap-filled AOD datasets between 2008 and 2016 over a coastal site in the Eastern Mediterranean. For the estimation of PM10 concentrations, daily AOD data were mainly used from MODIS. Then, two different gap-filled MODIS AOD datasets with both AERONET and MERRA-2 AODs were also examined separately on both multi-annual and multi-seasonal basis since the number of MODIS AOD data was temporally limited in the region. A pattern recognition neural network (PRNN) model was used for the estimation of PM10 concentrations. AOD with several meteorological parameters from an on-site meteorological station and aerosol diagnostic products from MERRA-2 were used as inputs to the estimation model. The most significant variables for the model were identified in 23 independent variables using the multiple linear regression (MLR). The results indicated that the best estimation (R = 0.74) was obtained with the gap-filled AODMODIS+MERRA dataset for the period covering all years whereas the AODMODIS dataset alone showed the poorest performance (R = 0.62). However, the performance of the gap-filled datasets varied with the seasons. For example, the AODMODIS dataset alone showed the best estimation performance (R = 0.67) in the summer whereas the gap-filled AODMODIS+AERONET dataset had the best performance (R = 0.59) in the winter. Overall results suggest that for estimating ground-level PM concentrations, an approach of gap-filled AOD dataset usage for estimation models is useful especially in rainy seasons such as the winter and autumn where MODIS AOD retrievals are limited.

Impact of Covid-19 lockdown on air quality in the Poland, Eastern Europe

The first case of COVID-19 in Poland was registered on 4 March 2020. Governmental measures significantly restricted social and economic activities. This study investigates the impact on air quality resulting from the preventive measures taken by the government to manage Covid-19. The study was carried out with use of aerosol optical depth (AOD) retrieved from Moderate Resolution Imaging Spectrometer (MODIS) satellite and tropospheric column NO2 observed by Ozone Monitoring Instrument (OMI). Concentrations of atmospheric pollutants (PM2.5, PM10, NO2 and SO2) retrieved from ground-based air quality stations, located in five large cities of the country, were also used for quantitative assessment of air quality change. Ground-based and satellite data demonstrated the reduction of pollutants in the period of lockdown as compared to the same periods in 2018 and 2019. In particular, AOD data shows reductions of aerosol concentrations in the air column in April and May of approximately by −23% and −18% as compared to 2018–2019. The greatest contraction was for PM2.5 in April and May with reductions of −11.1% to −26.4% and from −8.7 to −21.1% respectively. For PM10, the reductions were from −8.6% to −33.9% and from −8.5% to −31.5% as compared to the same months in 2019. The results showed that restrictions imposed to prevent the spread of COVID-19 significantly improved Poland's air quality.

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

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

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

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

Can satellite-derived aerosol optical depth quantify the surface aerosol radiative forcing?

Aerosols play an important role in the climate of the Earth through aerosol radiative forcing (ARF). Nowadays, aerosol particles are detected, quantified and monitored by remote sensing techniques using low Earth orbit (LEO) and geostationary (GEO) satellites. In the present article, the use of satellite-derived AOD (aerosol optical depth) products is investigated in order to quantify on a daily basis the ARF at the surface level (SARF). By daily basis we mean that an average SARF value is computed every day based upon the available AOD satellite measurements for each station. In the first part of the study, the performance of four state-of-art different AOD products (MODIS-DT, MODIS-DB, MISR, and SEVIRI) is assessed through comparison against ground-based AOD measurements from 24 AERONET stations located in Europe and Africa during a 6-month period. While all AOD products are found to be comparable in terms of measured value (RMSE of 0.1 for low and average AOD values), a higher number of AOD estimates is made available by GEO satellites due to their enhanced frequency of scan. Experiments show a general lower agreement of AOD estimates over the African sites (RMSE of 0.2), which show the highest aerosol concentrations along with the occurrence of dust aerosols, coarse particles, and bright surfaces. In the second part of this study, the lessons learned about the confidence in aerosol burden derived from satellites are used to estimate SARF under clear sky conditions. While the use of AOD products issued from GEO observations like SEVIRI brings improvement in the SARF estimates with regard to LEO-based AOD products, the resulting absolute bias (13W/m2 in average when AERONET AOD is used as reference) is judged to be still high in comparison with the average values of SARF found in this study (from −25W/m2 to −43W/m2) and also in the literature (from −10W/m2 to −47W/m2).

Modeling and evaluation of the global sea-salt aerosol distribution: sensitivity to size-resolved and sea-surface temperature dependent emission schemes

Abstract. One of the major sources of uncertainty in model estimates of the global sea-salt aerosol distribution is the emission parameterization. We evaluate a new sea-salt aerosol life cycle module coupled to the online multiscale chemical transport model NMMB/BSC-CTM. We compare 5 yr global simulations using five state-of-the-art sea-salt open-ocean emission schemes with monthly averaged coarse aerosol optical depth (AOD) from selected AERONET sun photometers, surface concentration measurements from the University of Miami's Ocean Aerosol Network, and measurements from two NOAA/PMEL cruises (AEROINDOEX and ACE1). Model results are highly sensitive to the introduction of sea-surface-temperature (SST)-dependent emissions and to the accounting of spume particles production. Emission ranges from 3888 Tg yr−1 to 8114 Tg yr−1, lifetime varies between 7.3 h and 11.3 h, and the average column mass load is between 5.0 Tg and 7.2 Tg. Coarse AOD is reproduced with an overall correlation of around 0.5 and with normalized biases ranging from +8.8% to +38.8%. Surface concentration is simulated with normalized biases ranging from −9.5% to +28% and the overall correlation is around 0.5. Our results indicate that SST-dependent emission schemes improve the overall model performance in reproducing surface concentrations. On the other hand, they lead to an overestimation of the coarse AOD at tropical latitudes, although it may be affected by uncertainties in the comparison due to the use of all-sky model AOD, the treatment of water uptake, deposition and optical properties in the model and/or an inaccurate size distribution at emission.

Aerosol optical depth assimilation for a size-resolved sectional model: impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale analyses and forecasts

Abstract. An aerosol optical depth (AOD) three-dimensional variational data assimilation technique is developed for the Gridpoint Statistical Interpolation (GSI) system for which WRF-Chem forecasts are performed with a detailed sectional model, the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). Within GSI, forward AOD and adjoint sensitivities are performed using Mie computations from the WRF-Chem optical properties module, providing consistency with the forecast. GSI tools such as recursive filters and weak constraints are used to provide correlation within aerosol size bins and upper and lower bounds for the optimization. The system is used to perform assimilation experiments with fine vertical structure and no data thinning or re-gridding on a 12 km horizontal grid over the region of California, USA, where improvements on analyses and forecasts is demonstrated. A first set of simulations was performed, comparing the assimilation impacts of using the operational MODIS (Moderate Resolution Imaging Spectroradiometer) dark target retrievals to those using observationally constrained ones, i.e., calibrated with AERONET (Aerosol RObotic NETwork) data. It was found that using the observationally constrained retrievals produced the best results when evaluated against ground based monitors, with the error in PM2.5 predictions reduced at over 90% of the stations and AOD errors reduced at 100% of the monitors, along with larger overall error reductions when grouping all sites. A second set of experiments reveals that the use of fine mode fraction AOD and ocean multi-wavelength retrievals can improve the representation of the aerosol size distribution, while assimilating only 550 nm AOD retrievals produces no or at times degraded impact. While assimilation of multi-wavelength AOD shows positive impacts on all analyses performed, future work is needed to generate observationally constrained multi-wavelength retrievals, which when assimilated will generate size distributions more consistent with AERONET data and will provide better aerosol estimates.

Inferring wavelength dependence of AOD and Ångström exponent over a sub-tropical station in South Africa using AERONET data: Influence of meteorology, long-range transport and curvature effect

Aerosol optical properties over a southern sub-tropical site Skukuza, South Africa were studied to determine the variability of the aerosol characteristics using CIMEL Sunphotometer data as part of the AErosol RObotic NETwork (AERONET) from December 2005 to November 2006. Aerosol optical depth (AOD), Ångström exponent (α), and columnar water vapor (CWV) data were collected, analyzed, and compiled. Participating in this network provided a unique opportunity for understanding the sources of aerosols affecting the atmosphere of South Africa (SA) and the regional radiation budget. The meteorological patterns significantly (p<0.05) influenced the amount and size distribution of the aerosols. Results showed that seasonal variation of AOD at 500nm (AOD500) over the observation site were characterized by low values (0.10–0.13) in autumn, moderate values (0.14–0.16) in summer and winter seasons, and high to very high values (0.18–0.40) during the spring, with an overall mean value of 0.18±0.12. Ångström exponent α440–870, varied from 0.5 to 2.89, with significant (p<0.0001) seasonal variability. CWV showed a strong annual cycle with maximum values in the summer and autumn seasons. The relationship between AOD, Ångström exponent (α), and CWV showed a strong dependence (p<0.0001) of α on AOD and CWV, while there was no significant correlation between AOD and CWV. Investigation of the adequacy of the simple use of the spectral AOD and Ångström exponent data was used in deriving the curvature (a2) showed to obtain information for determining the aerosol-particle size. The negative a2 values are characterized by aerosol-size dominated by fine-mode (0.1–1μm), while the positive curvatures indicate abundance of coarse particles (>1μm). Trajectory cluster analyses revealed that the air masses during the autumn and winter seasons have longer advection pathways, passing over the ocean and continent. This is reflected in the aerosol properties that are derived from the ocean, desert, and anthropogenic activities that include biomass burning and industrial pollution.

Investigation into the Use of Satellite Data in Aiding Characterization of Particulate Air Quality in the Atlanta, Georgia Metropolitan Area

ABSTRACT Poor air quality episodes occur often in metropolitan Atlanta, GA. The primary focus of this research is to assess the capability of satellites as a tool in characterizing air quality in Atlanta. Results indicate that intracity PM2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) concentrations show similar patterns as other U.S. urban areas, with the highest concentrations occurring within the city. PM2.5 and MODIS (Moderate Resolution ImagingSpectroradiometer) aerosol optical depth (AOD) have higher values in the summer than spring, yet MODIS AOD doubles in the summer unlike PM2.5. Most (80%) of the Ozone Monitoring Instrument aerosol index (AI) is below 0.5 with little differences between spring and summer. Using this value as a constraint of the carbonaceous aerosol signal in the urban area, aerosol transport events such as wildfire smoke associated with higher positive AI values can be identified. The results indicate that MODIS AOD is well correlated with PM2.5 on a yearly and seasonal basis with correlation coefficients as high as 0.8 for Terra and 0.7 for Aqua. A possible alternative view of the PM2.5 and AOD relationship is seen through the use of AOD thresholds. These probabilistic thresholds provide a means to describe the air quality index (AQI) through the use of multiyear AOD records for a specific area. The National Ambient Air Quality Standards (NAAQS) are used to classify the AOD into different AQI codes and probabilistically determine thresholds of AOD that represent most of a specific AQI category. For example, 80% of cases of moderate AQI days have AOD values between 0.5 and 0.6. The development of AOD thresholds provides a useful tool for evaluating air quality from the use of satellites in regions where there are sparse ground-based measurements of PM2.5. IMPLICATIONS Satellite aerosol products can be used successfully as a tool for characterizing air quality on an urban scale. Statistical analysis of multiyear satellite data can yield a useful and easily understandable way of describing air quality through satellite-derived AQI. In areas without many monitoring sites of PM2.5, this approach could be useful to those air quality forecasters. Additionally, the use of satellite data thresholds could increase satellite utility beyond that of qualifying events for exclusion using the U.S. Environmental Protection Agency's Exceptional Event Rule in determination of attainment of the NAAQS.

Atmospheric correction to IRS-P6 AWiFS data and its validation with ground measurements: A study over the semi-arid region

In this study, we have implemented a fast atmospheric correction algorithm to IRS-P6 advanced wide field sensor (AWiFS) satellite data for retrieving surface reflectance under different atmospheric and surface conditions. The algorithm is based on MODIS climatology products and simplified use of Second Simulation of Satellite Signal in Solar Spectrum (6S) radiative transfer code. The algorithm requires information on aerosol optical depth (AOD) for correcting the satellite dataset. The atmospheric correction algorithm has been tested for IRS-P6 AWiFS False colour composites covering the International Crops Research Institute for the Semi-Arid Tropics Farm, Patancheru, Hyderabad, India, under varying atmospheric conditions. Ground measurements of surface reflectance representing different land use/land cover, i.e. red soil, chick pea, groundnut and pigeon pea crops were conducted to validate the algorithm. Terra MODIS AOD550 validated with Microtops-II sun photometer–derived AOD500 over the urban region of Hyderabad exhibited very good correlation of ∼0.92, suggesting possible use of satellite-derived AOD for atmospheric correction.

AOD–PM 2.5 Association: Paciorek and Liu Respond

AOD–PM _2.5 Association: Paciorek and Liu Respond

Limitations of Remotely Sensed Aerosol as a Spatial Proxy for Fine Particulate Matter

BackgroundRecent research highlights the promise of remotely sensed aerosol optical depth (AOD) as a proxy for ground-level particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5). 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 PM2.5, it is tempting to interpret the spatial patterns in AOD as reflecting patterns in PM2.5.ObjectivesWe evaluated the degree to which AOD can help predict long-term average PM2.5 concentrations for use in chronic health studies.MethodsWe calculated correlations of AOD and PM2.5 at various temporal aggregations in the eastern United States in 2004 and used statistical models to assess the relationship between AOD and PM2.5 and the potential for improving predictions of PM2.5 in a subregion, the mid-Atlantic.ResultsWe found only limited spatial associations of AOD from three satellite retrievals with daily and yearly PM2.5. The statistical modeling shows that monthly average AOD poorly reflects spatial patterns in PM2.5 because of systematic, spatially correlated discrepancies between AOD and PM2.5. Furthermore, when we included AOD as a predictor of monthly PM2.5 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.ConclusionsThese results suggest caution in using spatial variation in currently available AOD to stand in for spatial variation in ground-level PM2.5 in epidemiologic analyses and indicate that when PM2.5 monitoring is available, careful statistical modeling outperforms the use of AOD.