Ground PM2 Research Articles

Direct radiative effect of the Russian wildfires and its impact on air temperature and atmospheric dynamics during August 2010

Abstract. In this study, we investigate the shortwave aerosol direct radiative forcing (ADRF) and its feedback on air temperature and atmospheric dynamics during a major fire event that occurred in Russia during August 2010. The methodology is based on an offline coupling between the CHIMERE chemistry-transport and the Weather Research and Forecasting (WRF) models. First, simulations for the period 5–12 August 2010 have been evaluated by using AERONET (AErosol RObotic NETwork) and satellite measurements of the POLarization and Directionality of the Earth's Reflectance (POLDER) and the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) sensors. During this period, elevated POLDER aerosol optical thickness (AOT) is found over a large part of eastern Europe, with values above 2 (at 550 nm) in the aerosol plume. According to CALIOP observations, particles remain confined to the first five kilometres of the atmospheric layer. Comparisons with satellite measurements show the ability of CHIMERE to reproduce the regional and vertical distribution of aerosols during their transport from the source region. Over Moscow, AERONET measurements indicate an important increase of AOT (340 nm) from 0.7 on 5 August to 2–4 between 6 and 10 August when the aerosol plume was advected over the city. Particles are mainly observed in the fine size mode (radius in the range 0.2–0.4 μm) and are characterized by elevated single-scattering albedo (SSA) (0.95–0.96 between 440 and 1020 nm). Comparisons of simulations with AERONET measurements show that aerosol physical–optical properties (size distribution, AOT, SSA) have been well simulated over Moscow in terms of intensity and/or spectral dependence. Secondly, modelled aerosol optical properties have been used as input in the radiative transfer code of WRF to evaluate their direct radiative impact. Simulations indicate a significant reduction of solar radiation at the ground (up to 80–150 W m−2 in diurnal averages over a large part of eastern Europe due to the presence of the aerosol plume. This ADRF causes an important reduction of the near-surface air temperature between 0.2 and 2.6° on a regional scale. Moscow has been affected by the aerosol plume, especially between 6 and 10 August. During this period, aerosol causes a significant reduction of surface shortwave radiation (up to 70–84 W m−2 in diurnal averages) with a moderate part (20–30%) due to solar absorption within the aerosol layer. The resulting feedbacks lead to a cooling of the air up to 1.6° at the surface and 0.1° at an altitude of 1500–2000 m (in diurnal averages), that contribute to stabilize the atmospheric boundary layer (ABL). Indeed, a reduction of the ABL height of 13 to 65% has been simulated during daytime in presence of aerosols. This decrease is the result of a lower air entrainment as the vertical wind speed in the ABL is shown to be reduced by 5 to 80% (at midday) when the feedback of the ADRF is taken into account. However, the ADRF is shown to have a lower impact on the horizontal wind speed, suggesting that the dilution of particles would be mainly affected by the weakening of the ABL development and associated vertical entrainment. Indeed, CHIMERE simulations driven by the WRF meteorological fields including this ADRF feedback result in a large increase in the modelled near-surface PM10 concentrations (up to 99%). This is due to their lower vertical dilution in the ABL, which tend to reduce model biases with the ground PM10 values observed over Moscow during this specific period.

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.

Estimation of PM10 Concentration using Ground Measurements and Landsat 8 OLI Satellite Image

The aim of this work is to produce an empirical model for the determination of particulate matter (PM10) concentration in the atmosphere using visible bands of Landsat 8 OLI satellite image over Kirkuk city- Iraq. The suggested algorithm is established on the aerosol optical reflectance model. The reflectance model is a function of the optical properties of the atmosphere, which can be related to its concentrations. The concentration of PM10 measurements was collected using Particle Mass Profiler and Counter in a Single Handheld Unit (Aerocet 531) meter simultaneously by the Landsat 8 OLI satellite image date. The PM10 measurement locations were defined by a handheld global positioning system (GPS). The obtained reflectance values for visible bands (Coastal aerosol, Blue, Green and blue bands) of landsat 8 OLI image were correlated with in-suite measured PM10. The feasibility of the proposed algorithms was investigated based on the correlation coefficient (R) and rootmean- square error (RMSE) compared with the PM10 ground measurement data. A choice of our proposed multispectral model was founded on the highest value correlation coefficient (R) and lowest value of the root mean square error (RMSE) with PM10 ground data. The outcomes of this research showed that visible bands of Landsat 8 OLI were capable of calculating PM10 concentration to an acceptable level of accuracy.

Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia

AbstractThis study analyzes the relationship between Aerosol Optical Depth (AOD) obtained from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based PM10 mass concentration distribution over a period of 5 years (2008–2012), and investigates the applicability of satellite AOD data for ground PM10 mapping for the Croatian territory. Many studies have shown that satellite AOD data are correlated to ground-based PM mass concentration. However, the relationship between AOD and PM is not explicit and there are unknowns that cause uncertainties in this relationship.The relationship between MODIS AOD and ground-based PM10 has been studied on the basis of a large data set where daily averaged PM10 data from the 12 air quality stations across Croatia over the 5 year period are correlated with AODs retrieved from MODIS Terra and Aqua. A database was developed to associate coincident MODIS AOD (independent) and PM10 data (dependent variable). Additional tested independent variables (predictors, estimators) included season, cloud fraction, and meteorological parameters — including temperature, air pressure, relative humidity, wind speed, wind direction, as well as planetary boundary layer height — using meteorological data from WRF (Weather Research and Forecast) model.It has been found that 1) a univariate linear regression model fails at explaining the data variability well which suggests nonlinearity of the AOD-PM10 relationship, and 2) explanation of data variability can be improved with multivariate linear modeling and a neural network approach, using additional independent variables.

Analysis of Spatial and Temporal Variability of PM10 Concentrations Using MODIS Aerosol Optical Thickness in the Pearl River Delta Region, China

Characterizing spatial and temporal variations of PM pollution is critical for a thorough understanding of its formation, transport and accumulation in the atmosphere. In this study, Aerosol Optical Thickness (AOT) data retrieved from a Moderate Resolution Imaging Spectroradiometer (MODIS) were used to investigate the spatial and temporal variations of PM10 (particles with aerodynamic diameters of less than 10 μm) pollution in the Pearl River Delta (PRD) region. Seasonal linear regression models between 1-km retrieved MODIS AOT data and ground PM10 measurements were developed for the PRD region with meteorological corrections, and were subjected to a validation against observations from the regional air monitoring network in this region from 2006 to 2008, with an overall error of less than 50%. Consistent with ground observations, the estimated PM10 concentrations from the regression models appeared to be highest in winter, lower in autumn and spring, and lowest in summer. A high PM10 concentration band was detected over the inner part of the PRD region, where heavy industries and dense populations are located. The shape and concentration levels of this band exhibit significant seasonal variations, which shift with synoptic wind direction, indicating different source regions and their contributions to the PM10 pollution in the PRD region. Several discrete “hot spots” were found in the southwest of the PRD region during spring and other seasons, where no ground measurements are available. The reasons for the formation of these hot spots are unclear, and further investigations are needed. Despite the limitations of this work, the results demonstrate the effectiveness of retrieving remote sensing data for characterizing regional aerosol pollution, together with ground measurements. The combination of satellite data and ground monitoring presented in this work can help in better understanding the sources, formation mechanisms and transport process of particulate matters on a regional scale.

PM10 data assimilation over south Korea to Asian dust forecasting model with the optimal interpolation method

A data assimilation (DA) system using ground PM10 observation for Asian Dust Aerosol Model version 2 (ADAM2), which is the operational dust forecasting model of Korea Meteorological Administration (KMA), has been developed with the optimal interpolation (OI) method. The observations are provided by the PM10 network operated by KMA. Three DA experiments are performed to simulate a dust event observed in Korea from 1 March to 31 May 2009 with different assimilation cycles of 24 (DA24), 12 (DA12), and 06 hours (DA06). 48-hour forecasts from the adjusted Initial Condition (IC) of dust concentration are compared with control simulation (CTL) and observation from independent stations. It is found that CTL simulates spatial patterns of dust emitted and transported associated with a developing low pressure system over the dust source regions quite well, compared with satellite measurement. However, it appears that there is considerable uncertainty in estimating the concentration of dust. With IC adjustment, the model simulates improved dust concentration, showing considerably reduced RMSE, particularly for the prediction within 12 hours of forecast. At the same time, it is shown that the time interval of DA affects the predictability of ADAM2, so that DA06 appears to have better predictability within a 12-hour simulation, reducing RMSE by 50% compared with CTL. This suggests that assimilating PM10 to the dust prediction model using OI has the potential to predict air quality in Korea when the cycle of assimilation is sufficiently short.

Classification of dust days by satellite remotely sensed aerosol products

Considerable progress in satellite remote sensing (SRS) of dust particles has been seen in the last decade. From an environmental health perspective, such an event detection, after linking it to ground particulate matter (PM) concentrations, can proxy acute exposure to respirable particles of certain properties (i.e. size, composition, and toxicity). Being affected considerably by atmospheric dust, previous studies in the Eastern Mediterranean, and in Israel in particular, have focused on mechanistic and synoptic prediction, classification, and characterization of dust events. In particular, a scheme for identifying dust days (DD) in Israel based on ground PM10 (particulate matter of size smaller than 10 μm) measurements has been suggested, which has been validated by compositional analysis. This scheme requires information regarding ground PM10 levels, which is naturally limited in places with sparse ground-monitoring coverage. In such cases, SRS may be an efficient and cost-effective alternative to ground measurements. This work demonstrates a new model for identifying DD and non-DD (NDD) over Israel based on an integration of aerosol products from different satellite platforms (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)). Analysis of ground-monitoring data from 2007 to 2008 in southern Israel revealed 67 DD, with more than 88% occurring during winter and spring. A Classification and Regression Tree (CART) model that was applied to a database containing ground monitoring (the dependent variable) and SRS aerosol product (the independent variables) records revealed an optimal set of binary variables for the identification of DD. These variables are combinations of the following primary variables: the calendar month, ground-level relative humidity (RH), the aerosol optical depth (AOD) from MODIS, and the aerosol absorbing index (AAI) from OMI. A logistic regression that uses these variables, coded as binary variables, demonstrated 93.2% correct classifications of DD and NDD. Evaluation of the combined CART–logistic regression scheme in an adjacent geographical region (Gush Dan) demonstrated good results. Using SRS aerosol products for DD and NDD, identification may enable us to distinguish between health, ecological, and environmental effects that result from exposure to these distinct particle populations.

Acute health impacts of airborne particles estimated from satellite remote sensing

Satellite-based remote sensing provides a unique opportunity to monitor air quality from space at global, continental, national and regional scales. Most current research focused on developing empirical models using ground measurements of the ambient particulate. However, the application of satellite-based exposure assessment in environmental health is still limited, especially for acute effects, because the development of satellite PM2.5 model depends on the availability of ground measurements. We tested the hypothesis that MODIS AOD (aerosol optical depth) exposure estimates, obtained from NASA satellites, are directly associated with daily health outcomes. Three independent healthcare databases were used: unscheduled outpatient visits, hospital admissions, and mortality collected in Beijing metropolitan area, China during 2006. We use generalized linear models to compare the short-term effects of air pollution assessed by ground monitoring (PM10) with adjustment of absolute humidity (AH) and AH-calibrated AOD. Across all databases we found that both AH-calibrated AOD and PM10 (adjusted by AH) were consistently associated with elevated daily events on the current day and/or lag days for cardiovascular diseases, ischemic heart diseases, and COPD. The relative risks estimated by AH-calibrated AOD and PM10 (adjusted by AH) were similar. Additionally, compared to ground PM10, we found that AH-calibrated AOD had narrower confidence intervals for all models and was more robust in estimating the current day and lag day effects. Our preliminary findings suggested that, with proper adjustment of meteorological factors, satellite AOD can be used directly to estimate the acute health impacts of ambient particles without prior calibrating to the sparse ground monitoring networks.

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

Satellite-based PM2.5 monitoring has the potential to complement ground PM2.5 monitoring networks, especially for regions with sparsely distributed monitors. Satellite remote sensing provides data on aerosol optical depth (AOD), which reflects particle abundance in the atmospheric column. Thus AOD has been used in statistical models to predict ground-level PM2.5 concentrations. However, previous studies have shown that AOD may not be a strong predictor of PM2.5 ground levels. Another shortcoming of remote sensing is the large number of non-retrieval days (i.e., days without satellite data available) due to clouds and snow- and ice-cover.In this paper we propose statistical approaches to overcome these two shortcomings, thereby making satellite imagery a viable method to estimate PM2.5 concentrations. First, we render AOD a robust predictor of PM2.5 mass concentration by introducing an AOD daily calibration approach through the use of mixed effects model. Second, we develop models that combine AOD and ground monitoring data to predict PM2.5 concentrations during non-retrieval days. A key feature of this approach is that we develop these prediction models separately for groups of days defined by the observed amount of spatial heterogeneity in concentrations across the study region. Subsequently, these methodologies were applied to examine the spatial and temporal patterns of daily PM2.5 concentrations for both retrieval days (i.e., days with satellite data available) and non-retrieval days in the New England region of the United States during the period 2000–2008. Overall, for the years 2000–2008, our statistical models predicted surface PM2.5 concentrations with reasonably high R2 (0.83) and low percent mean relative error (3.5%). Also the spatial distribution of the estimated PM2.5 levels in the study domain clearly exhibited densely populated and high traffic areas. The method we have developed demonstrates that remote sensing can have a tremendous impact on the fields of environmental monitoring and human exposure assessment.

Gravity-Current Driven Transport of Haze from North China Plain to Northeast China in Winter 2010-Part I: Observations

Haze exerts a large effect on visibility reduction and has serious impacts on air quality and human health. Understanding the sources and transport of haze is of importance to improve the regional air quality and evaluate its health effects. In this study, we investigated a typical haze episode that occurred in northeast (NE) China during 4-6 November 2010 by analyzing the ground PM10 measurements from 11 monitoring sites, aerosol Lidar observations, synoptic charts, MODIS satellite imageries, and back trajectories. Our analyses suggest that the regional haze formed in the North China Plain (NCP) under stagnant conditions can be transported to NE China in ∼1-3 days across Bohai Bay and Liaodong Bay - a typical transport pathway associated with the topography of northern China. The haze episode appeared to evolve progressively from southwest to northeast in the region of NE China, in agreement with the appearance of PM10 peak values, wind patterns, MODIS images and the back trajectories of air masses. Due to the haze impact, NE China showed significantly elevated particulate matter pollution by a factor of ∼4-6 with the peak concentrations reaching ∼410 μg m-3. The results together indicate that the regional transport from the NCP has a significant contribution to the PM pollution in NE China, thus efforts to control the source emissions over the NCP would be effective to improve the air quality in NE China.

SATELLITE-BASED PM2.5 EXPOSURE ASSESSMENT AND ITS IMPLICATIONS FOR EPIDEMIOLOGICAL STUDIES

Background and Aims: Satellite- based PM2.5 air quality monitoring data has been used to complement ground PM2.5 monitoring networks, especially for regions with sparsely distributed PM2.5 monitors...

Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements

Land use regression (LUR) models provide good estimates of spatially resolved long-term exposures, but are poor at capturing short term exposures. Satellite-derived Aerosol Optical Depth (AOD) measurements have the potential to provide spatio-temporally resolved predictions of both long and short term exposures, but previous studies have generally showed relatively low predictive power. Our objective was to extend our previous work on day-specific calibrations of AOD data using ground PM2.5 measurements by incorporating commonly used LUR variables and meteorological variables, thus benefiting from both the spatial resolution from the LUR models and the spatio-temporal resolution from the satellite models. Later we use spatial smoothing to predict PM2.5 concentrations for day/locations with missing AOD measures. We used mixed models with random slopes for day to calibrate AOD data for 2000–2008 across New-England with monitored PM2.5 measurements. We then used a generalized additive mixed model with spatial smoothing to estimate PM2.5 in location–day pairs with missing AOD, using regional measured PM2.5, AOD values in neighboring cells, and land use. Finally, local (100 m) land use terms were used to model the difference between grid cell prediction and monitored value to capture very local traffic particles. Out-of-sample ten-fold cross-validation was used to quantify the accuracy of our predictions. For days with available AOD data we found high out-of-sample R2 (mean out-of-sample R2 = 0.830, year to year variation 0.725–0.904). For days without AOD values, our model performance was also excellent (mean out-of-sample R2 = 0.810, year to year variation 0.692–0.887). Importantly, these R2 are for daily, rather than monthly or yearly, values. Our model allows one to assess short term and long-term human exposures in order to investigate both the acute and chronic effects of ambient particles, respectively.

Important parameters for smoke plume rise simulation with Daysmoke

Daysmoke is a local smoke transport model and has been used to provide smoke plume rise information. It includes a large number of parameters describing the dynamic and stochastic processes of particle upward movement, fallout, fluctuation, and burn emissions. This study identifies the important parameters for Daysmoke simulations of plume rise and seeks to understand their impacts on regional air quality simulations with the Community Multiscale Air Quality (CMAQ) model. The Fourier Amplitude Sensitivity Test (FAST) was first applied to Daysmoke simulations of prescribed burning in the southeastern U.S. It is shown that, for the specified value ranges of 15 parameters, entrainment coefficient and number of updraft cores are the most important for determining smoke plume rise. Initial plume temperature anomaly, diameter of flaming area, and thermal stability also contribute to a certain extent. CMAQ simulations were then conducted for a couple of different updraft core numbers. The simulated ground PM2.5 concentration is much closer to the measurements with multiple updraft cores than single core. The results from this study therefore suggest that simulations of Daysmoke and CMAQ could be improved by a better understanding of plume structure to aid in specifying the number of smoke updraft cores.

Comparison of summer and winter California central valley aerosol distributions from lidar and MODIS measurements

Aerosol distributions from two aircraft lidar campaigns conducted in the California Central Valley are compared in order to identify seasonal variations. Aircraft lidar flights were conducted in June 2003 and February 2007. While the ground PM 2.5 (particulate matter with diameter ≤ 2.5 μm) concentration was highest in the winter, the aerosol optical depth (AOD) measured from the MODIS and lidar instruments was highest in the summer. A multiyear seasonal comparison shows that PM 2.5 in the winter can exceed summer PM 2.5 by 68%, while summer AOD from MODIS exceeds winter AOD by 29%. Warmer temperatures and wildfires in the summer produce elevated aerosol layers that are detected by satellite measurements, but not necessarily by surface particulate matter monitors. Temperature inversions, especially during the winter, contribute to higher PM 2.5 measurements at the surface. Measurements of the mixing layer height from lidar instruments provide valuable information needed to understand the correlation between satellite measurements of AOD and in situ measurements of PM 2.5. Lidar measurements also reflect the ammonium nitrate chemistry observed in the San Joaquin Valley, which may explain the discrepancy between the MODIS AOD and PM 2.5 measurements.

PM 10 remote sensing from geostationary SEVIRI and polar-orbiting MODIS sensors over the complex terrain of the European Alpine region

The subject of this study is to investigate the capability of spaceborne remote sensing data to predict ground concentrations of PM 10 over the European Alpine region using satellite derived Aerosol Optical Depth (AOD) from the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI) and the polar-orbiting MODerate resolution Imaging Spectroradiometer (MODIS). The spatial and temporal resolutions of these aerosol products (10 km and 2 measurements per day for MODIS, ∼ 25 km and observation intervals of 15 min for SEVIRI) permit an evaluation of PM estimation from space at different spatial and temporal scales. Different empirical linear relationships between coincident AOD and PM 10 observations are evaluated at 13 ground-based PM measurement sites, with the assumption that aerosols are vertically homogeneously distributed below the planetary Boundary Layer Height (BLH). The BLH and Relative Humidity (RH) variability are assessed, as well as their impact on the parameterization. The BLH has a strong influence on the correlation of daily and hourly time series, whilst RH effects are less clear and smaller in magnitude. Despite its lower spatial resolution and AOD accuracy, SEVIRI shows higher correlations than MODIS ( r SEV ∼ 0.7, r MOD ∼ 0.6) with regard to daily averaged PM 10. Advantages from MODIS arise only at hourly time scales in mountainous locations but lower correlations were found for both sensors at this time scale ( r∼ 0.45). Moreover, the fraction of days in 2008 with at least one satellite observation was 27% for SEVIRI and 17% for MODIS. These results suggest that the frequency of observations plays an important role in PM monitoring, while higher spatial resolution does not generally improve the PM estimation. Ground-based Sun Photometer (SP) measurements are used to validate the satellite-based AOD in the study region and to discuss the impact of aerosols' micro-physical properties in the empirical models. A lower error limit of 30 to 60% in the PM 10 assessment from space is estimated in the study area as a result of AOD uncertainties, variability of aerosols properties and the heterogeneity of ground measurement sites. It is concluded that SEVIRI has a similar capacity to map PM as sensors on board polar-orbiting platforms, with the advantage of a higher number of observations. However, the accuracy represents a serious limitation to the applicability of satellites for ground PM mapping, especially in mountainous areas.

Keeping an Eye on PM 2.5 : Satellite Data Reveal Global Picture of Particulate Pollution

Vol. 118, No. 6 NewsOpen AccessKeeping an Eye on PM2.5: Satellite Data Reveal Global Picture of Particulate Pollution Angela Spivey Angela Spivey Search for more papers by this author Published:1 June 2010https://doi.org/10.1289/ehp.118-a259aCited by:1AboutSectionsPDF ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InReddit Scientists are using satellite observations to provide estimates of air quality with increasing reliability. For the first time researchers have now used satellite data to provide long-term air quality estimates of fine particulate matter (PM2.5) that span the globe [EHP118:847–855; van Donkelaar et al.]. The study showed that many developing countries have high long-term levels of PM2.5, which is produced by sources such as forest fires, coal-fired power plants, vehicles, and industrial facilities. These particles pose a health concern because of their ability to penetrate deep into the lungs.The study used satellite data gathered over 6 years, remarkably providing some of the first long-term measurements of air quality for many regions where ground-level sampling stations are few or nonexistent. The scientists combined data gathered from two different NASA satellite instruments with different capabilities—MODIS (Moderate Resolution Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer)—to generate a more accurate estimate of PM2.5. The satellite data yield a measurement called aerosol optical depth (AOD), which relates to the total amount of aerosol particles in the air between the ground and the satellite. The scientists combined AOD from the two satellites, then applied a chemical-transport model that integrated details about atmospheric structure and chemistry. The authors validated this approach by comparing their estimates to those taken from actual sampling performed at the ground level and found a statistically significant level of agreement.The estimates showed that 80% of the global population lives in places where concentrations of PM2.5 exceed the World Health Organization (WHO) air quality guideline of 10 μg/m3. The WHO has set an interim target of 35 μg/m3, which is exceeded over central and eastern Asia for 38% and 50% of the population, respectively. (The WHO guideline sets an ultimate goal for national air standards, whereas the interim target is proposed as an incremental reduction that could achieve significant, though not optimal, reductions in pollution-related health effects.) Eastern China showed a very high level of pollution—an estimated annual average of more than 80 μg/m3.The authors state that the methods described and validated by the study could be applied to studies of health effects from exposure to air pollution around the world. This is particularly true for areas where ground-based sampling is lacking, many of which are sites of rapid urbanization, where large populations are exposed to high levels of air pollution. They note that additional work is needed to address issues that may limit the accuracy of the satellite-based estimates, such as non-uniform satellite sampling and the satellites’ inability to retrieve AOD under cloudy conditions.FiguresReferencesRelatedDetailsCited By Chu Y, Liu Y, Li X, Liu Z, Lu H, Lu Y, Mao Z, Chen X, Li N, Ren M, Liu F, Tian L, Zhu Z and Xiang H (2016) A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth, Atmosphere, 10.3390/atmos7100129, 7:10, (129) Vol. 118, No. 6 June 2010Metrics About Article Metrics Publication History Originally published1 June 2010Published in print1 June 2010 Financial disclosuresPDF download License information EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted. Note to readers with disabilities EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact [email protected]. Our staff will work with you to assess and meet your accessibility needs within 3 working days.

Analysis of the impact of the forest fires in August 2007 on air quality of Athens using multi-sensor aerosol remote sensing data, meteorology and surface observations

Data from multiple satellite remote sensors are integrated with ground measurements and meteorological data to study the impact of Greek forest fires in August 2007 on the air quality in Athens. Two pollution episodes were identified by ground PM 10 measurements between August 23 and September 4. In the first episode, Evia and Peloponnese fires contributed substantially to the air pollution levels in Athens. In the second episode, transport of industrial pollution from Italy and Western Europe as well as forest fires in Albania contributed substantially to the air pollution levels in Athens. Local air pollution sources also contributed to the observed particle levels during these episodes. Satellite data provide valuable insights into the spatial distribution of particle concentrations, thus they can be used identify pollution sources. In spite of a few weaknesses in current satellite data products identified in this analysis, combining satellite aerosol remote sensing data with trajectory models and ground measurements is a powerful tool to study intensive particle pollution events such as forest fires.

Spatial analysis of MODIS aerosol optical depth, PM2.5, and chronic coronary heart disease

BackgroundNumerous studies have found adverse health effects of acute and chronic exposure to fine particulate matter (PM2.5). Air pollution epidemiological studies relying on ground measurements provided by monitoring networks are often limited by sparse and unbalanced spatial distribution of the monitors. Studies have found correlations between satellite aerosol optical depth (AOD) and PM2.5 in some land regions. Satellite aerosol data may be used to extend the spatial coverage of PM2.5 exposure assessment. This study was to investigate correlation between PM2.5 and AOD in the conterminous USA, to derive a spatially complete PM2.5 surface by merging satellite AOD data and ground measurements based on the potential correlation, and to examine if there is an association of coronary heart disease with PM2.5.ResultsYears 2003 and 2004 daily MODIS (Moderate Resolution Imaging Spectrometer) Level 2 AOD images were collated with US EPA PM2.5 data covering the conterminous USA. Pearson's correlation analysis and geographically weighted regression (GWR) found that the relationship between PM2.5 and AOD is not spatially consistent across the conterminous states. The average correlation is 0.67 in the east and 0.22 in the west. GWR predicts well in the east and poorly in the west. The GWR model was used to derive a PM2.5 grid surface using the mean AOD raster calculated using the daily AOD data (RMSE = 1.67 μg/m3). Fitting of a Bayesian hierarchical model linking PM2.5 with age-race standardized mortality rates (SMRs) of chronic coronary heart disease found that areas with higher values of PM2.5 also show high rates of CCHD mortality: = 0.802, posterior 95% Bayesian credible interval (CI) = (0.386, 1.225).ConclusionThere is a spatial variation of the relationship between PM2.5 and AOD in the conterminous USA. In the eastern USA where AOD correlates well with PM2.5, AOD can be merged with ground PM2.5 data to derive a PM2.5 surface for epidemiological study. The study found that chronic coronary heart disease mortality rate increases with exposure to PM2.5.