Aerosol Optical Depth Relationship Research Articles

Trend relationship between mountain normalized difference vegetation index (NDVI) and aerosol optical depth (AOD) across two decades: implication for water quality within the Lesotho Highlands, Drakensberg, South Africa

There are growing concerns on contribution of vegetation dynamics to atmospheric turbidity and quality of regional water towers. The study sought to determine the trends in the MODIS/TERRA-derived normalized difference vegetation index (NDVI) and aerosol optical depth (AOD) for Lesotho Highland over 2000-2020. The predictive relationship between the two variables was also examined using regression analysis. Irrespective of yearly AOD patterns, the AOD showed biphasic patterns peaking between mid-winter to early spring (July-October) (highest) and autumn (Feb-April) (next highest), and lowest in the summer (Nov-January). The monthly NDVI was largest in January-March (summer-early fall) with smaller values in winter and spring. This seasonality can be related to the peak of anthropogenic biomass combustion during the winter and strong winds during the spring and early summer. The AOD relationship with NDVI showed quadratic patterns peaking and plunging with changes in season. About 30-80% (R2 = 0.3-0.8%) changes in annual AOD from 2000 to 2020 were explainable by the dynamics of NDVI indicating that increased NDVI contributes to about a 50% decrease in AOD in the Lesotho Highlands. However, an outlier trend was observed in 2007 (R2 = 13%). Incidences of high AOD in months of high NDVI may be indicative of traveling aerosols, i.e., aerosols from non-local sources/activity. On the other hand, high AOD in months of low NDVI implicates local aerosol sources. Trend relationship studies on vegetation loss and AOD in mountain areas of other regions could improve knowledge of contaminant dynamics and risk implications for downstream populations.

A global evaluation of daily to seasonal aerosol and water vapor relationships using a combination of AERONET and NAAPS reanalysis data

Abstract. The co-transport of aerosol particles and water vapor has long been noted in the literature, with a myriad of implications such as air mass characterization, radiative transfer, and data assimilation. Here, the relationship between aerosol optical depth (AOD) and precipitable water vapor (PW) is evaluated to our knowledge for the first time globally, at daily to seasonal levels using approximately 20 years of NASA Aerosol Robotic Network (AERONET) observational data and the 16-year Navy Aerosol Analysis Prediction System (NAAPS) reanalysis v1.0 (NAAPS-RA) model fields. The combination of AERONET observations with small uncertainties and the reanalysis fields with global coverage is used to provide a best estimate of the seasonal AOD and PW relationships, including an evaluation of correlations, slope, and PW probability distributions for identification of statistically significant differences in PW for high-AOD events. The relationships produced from the AERONET and NAAPS-RA datasets were compared against each other and showed consistency, indicating that the NAAPS-RA provides a realistic representation of the AOD and PW relationship. The analysis includes layer AOD and PW relationships for proxies of the planetary boundary layer and the lower, middle, and upper free troposphere. The dominant AOD and PW relationship is positive, supported by both AERONET and model evaluation, which varies in strength by season and location. These relationships were found to be statistically significant and present across the globe, observed on an event-by-event level. Evaluations at individual AERONET sites implicate synoptic-scale transport as a contributing factor in these relationships at daily levels. Negative AOD and PW relationships were identified and predominantly associated with regional dry-season timescales in which biomass burning is the predominant aerosol type. This is not an indication of dry-air association with smoke for an individual event but is a reflection of the overall dry conditions leading to more biomass burning and higher associated AOD values. Stronger correlations between AOD and PW are found when evaluating the data by vertical layers, including the boundary layer and the lower, middle, and upper free troposphere (corresponding to typical water vapor channels), with the largest correlations observed in the free troposphere – indicative of aerosol and water vapor transport events. By evaluating the variability between PW and relative humidity in the NAAPS-RA, hygroscopic growth was found to be a dominant term to (1) amplify positive AOD–PW relationships, particularly in the midlatitudes; (2) diminish negative relationships in dominant biomass burning regions; and (3) lead to statistically insignificant changes in PW for high-AOD events for maritime regions. The importance of hygroscopic growth in these relationships indicates that PW is a useful tracer for AOD or light extinction but not necessarily as strongly for aerosol mass. Synoptic-scale African dust events are an exception where PW is a strong tracer for aerosol transport shown by strong relationships even with hygroscopic effects. Given these results, PW can be exploited in coupled aerosol and meteorology data assimilation for AOD, and the collocation of aerosol and water vapor should be carefully taken into account when conducting particulate matter (PM) retrievals from space and in evaluating radiative impacts of aerosol, with the season and location in mind.

Effects of Anthropogenic Emission Control and Meteorology Changes on the Inter-Annual Variations of PM2.5–AOD Relationship in China

We identified controlling factors of the inter-annual variations of surface PM2.5–aerosol optical depth (AOD) relationship in China from 2006 to 2017 using a nested 3D chemical transport model—GEOS-Chem. We separated the contributions from anthropogenic emission control and meteorological changes by fixing meteorology at the 2009 level and fixing anthropogenic emissions at the 2006 level, respectively. Both observations and model show significant downward trends of PM2.5/AOD ratio (η, p < 0.01) in the North China Plain (NCP), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD) in 2006–2017. The model suggests that the downward trends are mainly attributed to anthropogenic emission control. PM2.5 concentration reduces faster at the surface than aloft due to the closeness of surface PM2.5 to emission sources. The Pearson correlation coefficient of surface PM2.5 and AOD (rPM-AOD) shows strong inter-annual variations (±27%) but no statistically significant trends in the three regions. The inter-annual variations of rPM-AOD are mainly determined by meteorology changes. Except for the well-known effects from relative humidity, planetary boundary layer height and wind speed, we find that temperature, tropopause pressure, surface pressure and atmospheric instability are also important meteorological elements that have a strong correlation with inter-annual variations of rPM-AOD in different seasons. This study suggests that as the PM2.5–AOD relationship weakens with reduction of anthropogenic emissions, validity of future retrieval of surface PM2.5 using satellite AOD should be carefully evaluated.

Relationship between cloud condensation nuclei (CCN) concentration and aerosol optical depth in the Arctic region

To determine the direct and indirect effects of aerosols on climate, it is important to know the spatial and temporal variations in cloud condensation nuclei (CCN) concentrations. Although many types of CCN measurements are available, extensive CCN measurements are challenging because of the complexity and high operating cost, especially in remote areas. As aerosol optical depth (AOD) can be readily observed by remote sensing, many attempts have been made to estimate CCN concentrations from AOD. In this study, the CCN–AOD relationship is parameterized based on CCN ground measurements from the Zeppelin Observatory (78.91° N, 11.89° E, 474 m asl) in the Arctic region. The AOD measurements were obtained from the Ny-Ålesund site (78.923° N, 11.928° E) and Modern-Era Retrospective Analysis for Research and Applications, Version 2 reanalysis. Our results show a CCN–AOD correlation with a coefficient of determination R2 of 0.59. Three additional estimation models for CCN were presented based on the following data: (i) in situ aerosol chemical composition, (ii) in situ aerosol optical properties, and (iii) chemical composition of AOD obtained from reanalysis data. The results from the model using in situ aerosol optical properties reproduced the observed CCN concentration most efficiently, suggesting that the contribution of BC to CCN concentration should be considered along with that of sulfate.

Satellite-Based Mapping of High-Resolution Ground-Level PM2.5 with VIIRS IP AOD in China through Spatially Neural Network Weighted Regression

Satellite-retrieved aerosol optical depth (AOD) data are extensively integrated with ground-level measurements to achieve spatially continuous fine particulate matters (PM2.5). Current satellite-based methods however face challenges in obtaining highly accurate and reasonable PM2.5 distributions due to the inability to handle both spatial non-stationarity and complex non-linearity in the PM2.5–AOD relationship. High-resolution (<1 km) PM2.5 products over the whole of China for fine exposure assessment and health research are also lacking. This study aimed to predict 750 m resolution ground-level PM2.5 in China with the high-resolution Visible Infrared Imaging Radiometer Suite (VIIRS) intermediate product (IP) AOD data using a newly developed geographically neural network weighted regression (GNNWR) model. The performance evaluations demonstrated that GNNWR achieved higher prediction accuracy than the widely used methods with cross-validation and predictive R2 of 0.86 and 0.85. Satellite-derived monthly 750 m resolution PM2.5 data in China were generated with robust prediction accuracy and almost complete coverage. The PM2.5 pollution was found to be greatly improved in 2018 in China with annual mean concentration of 31.07 ± 17.52 µg/m3. Nonetheless, fine-scale PM2.5 exposures at multiple administrative levels suggested that PM2.5 pollution in most urban areas needed further control, especially in southern Hebei Province. This work is the first to evaluate the potential of VIIRS IP AOD in modeling high-resolution PM2.5 over large-scale. The newly satellite-derived PM2.5 data with high spatial resolution and high prediction accuracy at the national scale are valuable to advance environmental and health researches in China.

A model framework to reduce bias in ground-level PM2.5 concentrations inferred from satellite-retrieved AOD

We present a new method to infer ground-level fine particulate matter (PM2.5) from satellite remote sensing observations of aerosol optical depth (AOD). The conventional method generally uses a range of modelling approaches to determine PM2.5:AOD relationships that are subsequently used to infer ground-level PM2.5 concentrations from satellite-retrieved AOD. Here, we use a high-resolution atmospheric chemistry simulation to explore how changes in the vertical distribution of aerosol extinction coefficients affects the PM2.5:AOD relationship and how we can use that information to improve the robustness of inferred estimates of ground-level PM2.5 over eastern China. We define a metric, ΓPBLAOD, that describes the fraction of AOD that resides in the planetary boundary layer compared with the total columnar AOD. We determine physically-meaningful PM2.5:AOD relationships using data for which ΓPBLAOD≥50%, a criterion based on sensitivity analyses on data clusters that we identify using a hierarchical clustering method. We use statistical and machine learning methods to develop independent models that describe these PM2.5:AOD relationships, and use a Monte Carlo approach to quantify the improvement after our selection of more physically relevant data records. Benefiting from the improved representativeness of AOD for ground-level PM2.5, our method effectively reduces bias in inferred estimates of ground-level PM2.5 by 10–15% (9–12%) for space-borne sensors passing over in the morning (afternoon). It also captures more variations in ground-level PM2.5 by up to 8% (5%) for space-borne sensors passing over in the morning (afternoon), particularly over areas dominated by natural aerosols such as dust. Accordingly, our method improves the seasonal ground-level PM2.5 maps, e.g. the bias of the autumn (winter) mean of ground-level PM2.5 estimates over Qinghai and Gansu (Shaaxi, Shanxi, and Henan) provinces reduces from −8% to −5% (11%–6%).

Intercomparison between CMIP5 model and MODIS satellite‐retrieved data of aerosol optical depth, cloud fraction, and cloud‐aerosol interactions

AbstractAerosols are a critical component of the Earth's atmosphere and can affect the climate of the Earth through their interactions with solar radiation and clouds. Cloud fraction (CF) and aerosol optical depth (AOD) at 550 nm from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used with analogous cloud and aerosol properties from Historical Phase 5 of the Coupled Model Intercomparison Project (CMIP5) model runs that explicitly include anthropogenic aerosols and parameterized cloud‐aerosol interactions. The models underestimate AOD by approximately 15% and underestimate CF by approximately 10% overall on a global scale. A regional analysis is then used to evaluate model performance in two regions with known biomass burning activity and absorbing aerosol (South America (SAM) and South Africa (SAF)). In SAM, the models overestimate AOD by 4.8% and underestimate CF by 14%. In SAF, the models underestimate AOD by 35% and overestimate CF by 13.4%. Average annual cycles show that the monthly timing of AOD peaks closely match satellite data in both SAM and SAF for all except the Community Atmosphere Model 5 and Geophysical Fluid Dynamics Laboratory (GFDL) models. Monthly timing of CF peaks closely match for all models (except GFDL) for SAM and SAF. Sorting monthly averaged 2° × 2.5° model or MODIS CF as a function of AOD does not result in the previously observed “boomerang”‐shaped CF versus AOD relationship characteristic of regions with absorbing aerosols from biomass burning. Cloud‐aerosol interactions, as observed using daily (or higher) temporal resolution data, are not reproducible at the spatial or temporal resolution provided by the CMIP5 models.

Impact of diurnal variability and meteorological factors on the PM2.5 - AOD relationship: Implications for PM2.5 remote sensing

PM2.5 retrieval from space is still challenging due to the elusive relationship between PM2.5 and aerosol optical depth (AOD), which is further complicated by meteorological factors. In this work, we investigated the diurnal cycle of PM2.5 in China, using ground-based PM measurements obtained at 226 sites of China Atmosphere Watch Network during the period of January 2013 to December 2015. Results showed that nearly half of the sites witnessed a PM2.5 maximum in the morning, in contrast to the least frequent occurrence (5%) in the afternoon when strong solar radiation received at the surface results in rapid vertical diffusion of aerosols and thus lower mass concentration. PM2.5 tends to peak equally in the morning and evening in North China Plain (NCP) with an amplitude of nearly twice or three times that in the Pearl River Delta (PRD), whereas the morning PM2.5 peak dominates in Yangtze River Delta (YRD) with a magnitude lying between those of NCP and PRD. The gridded correlation maps reveal varying correlations around each PM2.5 site, depending on the locations and seasons. Concerning the impact of aerosol diurnal variation on the correlation, the averaging schemes of PM2.5 using 3-h, 5-h, and 24-h time windows tend to have larger R biases, compared with the scheme of 1-h time window, indicating diurnal variation of aerosols plays a significant role in the establishment of explicit correlation between PM2.5 and AOD. In addition, high cloud fraction and relative humidity tend to weaken the correlation, regardless of geographical location. Therefore, the impact of meteorology could be one of the most plausible alternatives in explaining the varying R values observed, due to its non-negligible effect on MODIS AOD retrievals. Our findings have implications for PM2.5 remote sensing, as long as the aerosol diurnal cycle, along with meteorology, are explicitly considered in the future.

A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD

Regional haze episodes have occurred frequently in eastern China over the past decades. As a critical indicator to evaluate air quality, the mass concentration of ambient fine particulate matters smaller than 2.5 μm in aerodynamic diameter (PM2.5) is involved in many studies. To overcome the limitations of ground measurements on PM2.5 concentration, which is featured in disperse representation and coarse coverage, many statistical models were developed to depict the relationship between ground-level PM2.5 and satellite-derived aerosol optical depth (AOD). However, the current satellite-derived AOD products and statistical models on PM2.5–AOD are insufficient to investigate PM2.5 characteristics at the urban scale, in that spatial resolution is crucial to identify the relationship between PM2.5 and anthropogenic activities. This paper presents a geographically and temporally weighted regression (GTWR) model to generate ground-level PM2.5 concentrations from satellite-derived 500 m AOD. The GTWR model incorporates the SARA (simplified high resolution MODIS aerosol retrieval algorithm) AOD product with meteorological variables, including planetary boundary layer height (PBLH), relative humidity (RH), wind speed (WS), and temperature (TEMP) extracted from WRF (weather research and forecasting) assimilation to depict the spatio-temporal dynamics in the PM2.5–AOD relationship. The estimated ground-level PM2.5 concentration has 500 m resolution at the MODIS satellite’s overpass moments twice a day, which can be used for air quality monitoring and haze tracking at the urban and regional scale. To test the performance of the GTWR model, a case study was carried out in a region covering the adjacent parts of Jiangsu, Shandong, Henan, and Anhui provinces in central China. A cross validation was done to evaluate the performance of the GTWR model. Compared with OLS, GWR, and TWR models, the GTWR model obtained the highest value of coefficient of determination (R2) and the lowest values of mean absolute difference (MAD), root mean square error (RMSE), and mean absolute percentage error (MAPE).

Impact of data quality and surface-to-column representativeness on the PM<sub>2.5</sub> / satellite AOD relationship for the contiguous United States

Abstract. Satellite-derived aerosol optical depth (AOD) observations have been used to estimate particulate matter smaller than 2.5 μm (PM2.5). However, such a relationship could be affected by the representativeness of satellite-derived AOD to surface aerosol particle mass concentration and satellite AOD data quality. Using purely measurement-based methods, we have explored the impacts of data quality and representativeness on the AOD-inferred PM2.5 / AOD relationship for the contiguous United States (CONUS). This is done through temporally and spatially collocated data sets of PM2.5 and AOD retrievals from Aqua/Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). These analyses show that improving data quality of satellite AOD, such as done with data assimilation-grade retrievals, increases their correlation with PM2.5. However, overall correlation is relatively low across the CONUS. Also, integrated extinction observed within 500 m above ground level (a.g.l.), as measured by CALIOP, is not well representative of the total column AOD. Surface aerosol in the eastern CONUS is better correlated with total column AOD than in the western CONUS. The best correlation values are found for estimated dry mass CALIOP extinction at 200–300 m a.g.l. and PM2.5, but additional work is needed to address the ability of using actively sensed AOD as a proxy for PM2.5 concentrations.

Estimation of cloud condensation nuclei concentration from aerosol optical quantities: influential factors and uncertainties

Abstract. Large-scale measurements of cloud condensation nuclei (CCN) are difficult to obtain on a routine basis, whereas aerosol optical quantities are more readily available. This study investigates the relationship between CCN and aerosol optical quantities for some distinct aerosol types using extensive observational data collected at multiple Atmospheric Radiation Measurement (ARM) Climate Research Facility (CRF) sites around the world. The influences of relative humidity (RH), aerosol hygroscopicity (fRH) and single scattering albedo (SSA) on the relationship are analyzed. Better relationships are found between aerosol optical depth (AOD) and CCN at the Southern Great Plains (US), Ganges Valley (India) and Black Forest sites (Germany) than those at the Graciosa Island (the Azores) and Niamey (Niger) sites, where sea salt and dust aerosols dominate, respectively. In general, the correlation between AOD and CCN decreases as the wavelength of the AOD measurement increases, suggesting that AOD at a shorter wavelength is a better proxy for CCN. The correlation is significantly improved if aerosol index (AI) is used together with AOD. The highest correlation exists between CCN and aerosol scattering coefficients (σsp) and scattering AI measured in situ. The CCN–AOD (AI) relationship deteriorates with increasing RH. If RH exceeds 75%, the relationship where AOD is used as a proxy for CCN becomes invalid, whereas a tight σsp–CCN relationship exists for dry particles. Aerosol hygroscopicity has a weak impact on the σsp–CCN relationship. Particles with low SSA are generally associated with higher CCN concentrations, suggesting that SSA affects the relationship between CCN concentration and aerosol optical quantities. It may thus be used as a constraint to reduce uncertainties in the relationship. A significant increase in σsp and decrease in CCN with increasing SSA is observed, leading to a significant decrease in their ratio (CCN / σsp) with increasing SSA. Parameterized relationships are developed for estimating CCN, which account for RH, particle size, and SSA.

Assessment of the relationship between satellite AOD and ground PM10 measurement data considering synoptic meteorological patterns and Lidar data

Correlation between satellite aerosol optical depth (AOD) and ground monitoring particulate matter (PM) depends on the meteorology that determines PM optical properties, its dispersion, accumulation and vertical distribution. This study presents a novel approach to analyze PM–AOD relationship considering the totality of meteorological factors expressed as synoptic patterns. Meteorological observations at 07:00 Bangkok time from 9 regional meteorological stations, in dry seasons (November–April) of 11years (2000–2010), were used to categorize governing meteorology over Central Thailand into four categories representing the typical observed synoptic patterns. The MANOVA analysis showed that these patterns were statistically different. PM10 recorded at 22 air quality stations in Bangkok Metropolitan Region were examined which showed the highest levels for the days belonging to pattern 1, followed by pattern 4, both with presence of a high pressure ridge, while the minimum for pattern 2 when thermal lows dominated. Lidar aerosol backscatter profiles recorded at Pimai station were used as indicator of PM vertical distribution that showed similarity within each pattern. R2 between MODIS and Sun photometer AODs at Pimai was above 0.8. Correlation coefficients (R) between MODIS AOD and corresponding 1h PM10 for clear sky days (cloudiness≤3/10) were examined for each pattern in comparison with lump case. Significant improvements were observed for pattern 1, average R across 22 stations was 0.46 for Terra and 0.38 for Aqua AOD compared to lump case with R of 0.34 and 0.31, respectively. Comparable improvement was also observed for pattern 4. For pattern 2, R values were significantly reduced which may be caused by the deeper mixing layers and varying vertical profiles with overall low values of Lidar backscatter coefficients. Improved R values in pattern 1 and 4, which had highest PM10 in BMR, suggested a better potential of using MODIS AOD for PM10 monitoring with synoptic pattern classification.

10-year spatial and temporal trends of PM2.5 concentrations in the southeastern US estimated using high-resolution satellite data.

Long-term PM2.5 exposure has been associated with various adverse health outcomes. However, most ground monitors are located in urban areas, leading to a potentially biased representation of true regional PM2.5 levels. To facilitate epidemiological studies, accurate estimates of the spatiotemporally continuous distribution of PM2.5 concentrations are important. Satellite-retrieved aerosol optical depth (AOD) has been increasingly used for PM2.5 concentration estimation due to its comprehensive spatial coverage. Nevertheless, previous studies indicated that an inherent disadvantage of many AOD products is their coarse spatial resolution. For instance, the available spatial resolutions of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) AOD products are 10 and 17.6 km, respectively. In this paper, a new AOD product with 1 km spatial resolution retrieved by the multi-angle implementation of atmospheric correction (MAIAC) algorithm based on MODIS measurements was used. A two-stage model was developed to account for both spatial and temporal variability in the PM2.5–AOD relationship by incorporating the MAIAC AOD, meteorological fields, and land use variables as predictors. Our study area is in the southeastern US centered at the Atlanta metro area, and data from 2001 to 2010 were collected from various sources. The model was fitted annually, and we obtained model fitting R2 ranging from 0.71 to 0.85, mean prediction error (MPE) from 1.73 to 2.50 μg m−3, and root mean squared prediction error (RMSPE) from 2.75 to 4.10 μg m−3. In addition, we found cross-validation R2 ranging from 0.62 to 0.78, MPE from 2.00 to 3.01 μgm−3, and RMSPE from 3.12 to 5.00 μgm−3, indicating a good agreement between the estimated and observed values. Spatial trends showed that high PM2.5 levels occurred in urban areas and along major highways, while low concentrations appeared in rural or mountainous areas. Our time-series analysis showed that, for the 10-year study period, the PM2.5 levels in the southeastern US have decreased by ∼20 %. The annual decrease has been relatively steady from 2001 to 2007 and from 2008 to 2010 while a significant drop occurred between 2007 and 2008. An observed increase in PM2.5 levels in year 2005 is attributed to elevated sulfate concentrations in the study area in warm months of 2005.

Remote sensing of surface visibility from space: A look at the United States East Coast

Measurement of surface visibility is important for the management of air quality, human health, and transportation. Currently, visibility measurements are only available through ground-based instrumentation, such as the Automated Surface Observing System (ASOS), and therefore lack spatial coverage. In analogy to the recent work of using satellite-based aerosol optical depth (AOD) to derive surface dry aerosol mass concentration at continental-to-global scale for cloud-free conditions, this study evaluates the potential of AOD retrieved from the MODerate Resolution Imaging Spectroradiometer (MODIS) for deriving surface visibility. For this purpose of evaluation the truncated (up to ∼16 km or 10 miles) and discrete (at the interval no less than 0.4 km or 1/4 mile) visibility data from hourly operational weather reports are not suitable, and the ASOS-measured one-minute raw surface extinction coefficient (bext) values have to be used. Consequently, a method for quality control on the bext data is first developed to eliminate frequent problems such as extraneous points, poor calibration, and bad formatting, after which reliable bext data are obtained to estimate the surface visibility. Subsequent analysis of the AOD and bext relationship on the East Coast of the United States reveals their average linear correlation coefficient (R) of 0.61 for all twelve (2000–2011) years of data at 32 ASOS stations, with the highest R value in summer and the lowest values in fall and winter. Incorporation of the Goddard Earth Observing System, Version 5 (GEOS-5) modeled vertical profile of aerosols into the derivation of visibility from AOD is evaluated for two methods, one scaling the modeled surface bext with the ratio of MODIS AOD to the modeled AOD, and another scaling the ratio of modeled AOD in the boundary layer to total columnar AOD with the MODIS AOD and assuming well-mixed aerosol extinction in the boundary layer. Analysis with three summers (2003–2004, 2006) of available GEOS-5 data and ASOS data reveals that the second method is superior, and generates a regression model that, after independent evaluation for summer 2005, is found to be statistically robust with R of 0.70 and a mean bias of 0.32 km in derived visibility. This study is among the first to demonstrate the potential of using satellite-based aerosol product over land to operationally derive surface visibility.

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.