Investigation into the use of satellite data in aiding characterization of particulate air quality in the Atlanta, Georgia metropolitan area

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 < or = 2.5 microm 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 Imaging Spectroradiometer) 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.     

Correlation between PM concentrations and aerosol optical depth in eastern China

Using one year of Aerosol Optical Depth (AOD) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite and particular matter (PM) contents measured at eleven sites located mostly in the eastern China in 2007, the relationship between columnar AOD and hourly and daily average (DA) PM were established. The peak AOD observed from MODIS was generally consistent with the surface PM measurements in eastern China, where Zhengzhou had the maximum annual mean PM₁₀ of 182.1 μg m^?3, while Longfengshan had the minimum annual mean of 38.1 μg m^?3. Ground level observations indicated that PM concentration varies widely across different regions, which was mainly due to the difference in weather conditions and anthropogenic emissions. The coarse particles accounted for the main air pollution in Zhengzhou and Benxi whiles the fine particles, however, were the main constituents in other sites. Results showed that MODIS AOD (averaged over the box of 5 × 5 and 3 × 3 pixels) had a better positive correlation with the coincident hourly average (HA) PM concentration than with DA due to diurnal variation in PM mass measurements. After correcting AOD for relative humidity (RH), the correlation did not improve significantly, suggesting that the RH was not the main factor affecting the correlation of PM with AOD. The statistical regression analysis between MODIS AOD and PM mass suggested that the satellite-derived AOD is a useful tool for mapping PM distribution over large spatial domains.     

Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD

Taking advantage of the continuous spatial coverage, satellite-derived aerosol optical depth (AOD) products have been widely used to assess the spatial and temporal characteristics of fine particulate matter (PM2.5) on the ground and their effects on human health. However, the national-scale ground-level PM2.5 estimation is still very limited because the lack of ground PM2.5 measurements to calibrate the model in China. In this study, a national-scale geographically weighted regression (GWR) model was developed to estimate ground-level PM2.5 concentration based on satellite AODs, newly released national-wide hourly PM2.5 concentrations, and meteorological parameters. The results showed good agreements between satellite-retrieved and ground-observed PM2.5 concentration at 943 stations in China. The overall cross-validation (CV) R ² is 0.76 and root mean squared prediction error (RMSE) is 22.26 μg/m³ for MODIS-derived AOD. The MISR-derived AOD also exhibits comparable performance with a CV R ² and RMSE are 0.81 and 27.46 μg/m³, respectively. Annual PM2.5 concentrations retrieved either by MODIS or MISR AOD indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions especially for the regions without PMs monitoring sites.     

西安市及周边地区MODIS气溶胶光学厚度与PM10浓度关系模型研究

西安是空气污染监控和防治有代表性的西部大型城市。研究了西安市及周边地区上空气溶胶光学厚度与PM10浓度的关系模型。利用2011—2012年MODIS卫星气溶胶光学厚度（AOD）遥感产品，通过数据匹配，利用地面气象观测站点的能见度数据和相对湿度数据对AOD产品进行垂直标高订正和湿度订正，2项订正显著提高了AOD和地面PM10浓度的相关性，相关系数从0．36提高到0．65，按季节分类统计和订正春至冬四季的相关系数分别为0．57、0．71、0．62和0．87，夏季和冬季的订正更为有效，可用性更高，这可能由于受到不同季节气溶胶来源和特征的影响。为研究中国西部大型城市，特别是西安市空气环境监测和区域联防联控提供了一种有效方法。     

The Relation between Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth and PM2.5 over the United States: A Geographical Comparison by U.S. Environmental Protection Agency Regions

Abstract

Aerosol optical depth (AOD) acquired from satellite measurements demonstrates good correlation with particulate matter with diameters less than 2.5 µm (PM_2.5) in some regions of the United States and has been used for monitoring and nowcasting air quality over the United States. This work investigates the relation between Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and PM_2.5 over the 10 U.S. Environmental Protection Agency (EPA)-defined geographic regions in the United States on the basis of a 2-yr (2005–2006) match-up dataset of MODIS AOD and hourly PM_2.5 measurements. The AOD retrievals demonstrate a geographical and seasonal variation in their relation with PM_2.5. Good correlations are mostly observed over the eastern United States in summer and fall. The southeastern United States has the highest correlation coefficients at more than 0.6. The southwestern United States has the lowest correlation coefficient of approximately 0.2. The seasonal regression relations derived for each region are used to estimate the PM_2.5 from AOD retrievals, and it is shown that the estimation using this method is more accurate than that using a fixed ratio between PM_2.5 and AOD. Two versions of AOD from Terra (v4.0.1 and v5.2.6) are also compared in terms of the inversion methods and screening algorithms. The v5.2.6 AOD retrievals demonstrate better correlation with PM_2.5 than v4.0.1 retrievals, but they have much less coverage because of the differences in the cloud-screening algorithm.     

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.     

The impact of using different land cover data on wind-blown desert dust modeling results in the southwestern United States

Olson World Ecosystem (OWE) land cover data based on data sources of the 1970s and 1980s with a 10-min spatial resolution, and up-to-date Moderate Resolution Imaging Spectroradiometer (MODIS) land cover data with a 30-s resolution, were used, respectively, in modeling wind-blown desert dust in the southwest United States. The model using different land cover data sets preformed similarly in modeling meteorological field patterns, vertical profiles and surface wind and temperature, in comparisons against observations. The differences of wind and temperature at a specific time and location can be big. Compared against satellite and ground measurements, modeled dust spatial distributions using MODIS land cover data were considerably better than those using OWE land cover. Site against site comparisons of modeled and observed surface PM2.5 concentration time series showed that model performance improved significantly using MODIS land cover data. Modeled surface PM2.5 contour distributions using MODIS land cover data compared more favorably against observations. The performance statistics for modeled PM2.5 concentrations at 40 surface sites increased from 0.15 using OWE data, to 0.58 using MODIS data. This demonstrates that the survey updates and spatial resolution of land cover data are critical in correctly predicting dust events and dust concentrations. Using land cover data such as MODIS data from satellite remote sensing is promising in improving wind-blown dust modeling and forecasting.     

Particulate air pollution from combustion and construction in coastal and urban areas of China

In China, the areas that are undergoing rapid urban growth are faced with increasingly more complicated air pollution problems. Sources of air pollution need to be identified and their contributions quantified. In this study, PM2.5 (particulate matter with aerodynamic diameters < or =2.5 microm), PM2.5-10 (particulate matter with aerodynamic diameters 2.5-10 microm), organic carbon (OC), and elemental carbon (EC) concentrations were measured from April to July 2009 at four selected areas in Xiamen (the downtown area, an industrial park, a suburb, and one remote site). The contributions of carbonaceous aerosols to PM2.5 and PM2.5-10 were 20-30% and 10-20%, respectively, indicating that finer particles contained more carbonaceous aerosols. The EC concentrations in PM2.5 at the downtown, industrial, suburb, and remote sites were 2.16 +/- 0.61, 2.05 +/- 0.45, 1.69 +/- 0.54, and 0.65 +/- 0.43 microg m-3, respectively, showing a decrease from the urban and industrial hotspots to the surrounding areas. These data show that carbonaceous aerosols emitted from the combustion of fossil fuels in urban and industrial hotspots influence air quality at the regional scale. Higher levels of PM2.5 and PM2.5-10 were observed at the suburb site compared to the urban and industrial sites. Peak EC concentrations in PM2.5 were observed during the morning and evening rush hours. However, peak PM2.5 levels at the suburb site were observed around noon, which coincides with construction work hours, instead of the morning and evening rush hours when emissions from combustion dominated. These findings indicate that both fuel combustion and construction have exacerbated air pollution in coastal and urban areas in China.     

Simulation of aerosol direct radiative forcing with RAMS-CMAQ in East Asia

The air quality modeling system RAMS-CMAQ is developed to assess aerosol direct radiative forcing by linking simulated meteorological parameters and aerosol mass concentration with the aerosol optical properties/radiative transfer module in this study. The module is capable of accounting for important factors that affect aerosol optical properties and radiative effect, such as incident wave length, aerosol size distribution, water uptake, and internal mixture. Subsequently, the modeling system is applied to simulate the temporal and spatial variations in mass burden, optical properties, and direct radiative forcing of diverse aerosols, including sulfate, nitrate, ammonium, black carbon, organic carbon, dust, and sea salt over East Asia throughout 2005. Model performance is fully evaluated using various observational data, including satellite monitoring of MODIS and surface measurements of EANET (Acid Deposition Monitoring Network), AERONET (Aerosol Robotic Network), and CSHNET (Chinese Sun Hazemeter Network). The correlation coefficients of the comparisons of daily average mass concentrations of sulfate, PM2.5, and PM10 between simulations and EANET measurements are 0.70, 0.61, and 0.64, respectively. It is also determined that the modeled aerosol optical depth (AOD) is in congruence with the observed results from the AERONET, the CSHNET, and the MODIS. The model results suggest that the high AOD values ranging from 0.8 to 1.2 are mainly distributed over the Sichuan Basin as well as over central and southeastern China, in East Asia. The aerosol direct radiative forcing patterns generally followed the AOD patterns. The strongest forcing effect ranging from −12 to −8 W m⁻² was mainly distributed over the Sichuan Basin and the eastern China’s coastal regions in the all-sky case at TOA, and the forcing effect ranging from −8 to −4 W m⁻² could be found over entire eastern China, Korea, Japan, East China Sea, and the sea areas of Japan     

Application of satellite remote-sensing data for source analysis of fine particulate matter transport events

Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative imagery and quantitative data, such as aerosol optical depth. Yet there has been limited application of these new datasets in the study of air pollutant sources relevant to public policy. One promising approach to more directly link satellite sensor data to air quality policy is to integrate satellite sensor data with air quality parameters and models. This paper presents a visualization technique to integrate satellite sensor data, ground-based data, and back trajectory analysis relevant to a new rule concerning the transport of particulate matter across state boundaries. Overlaying satellite aerosol optical depth data and back trajectories in the days leading up to a known fine particulate matter with an aerodynamic diameter of <2.5 microm (PM2.5) event may indicate whether transport or local sources appear to be most responsible for high PM2.5 levels in a certain location at a certain time. Events in five cities in the United States are presented as case studies. This type of analysis can be used to help understand the source locations of pollutants during specific events and to support regulatory compliance decisions in cases of long distance transport.     

Improving urban air quality in China: Beijing case study

China is undergoing rapid urbanization because of unprecedented economic growth. As a result, many cities suffer from air pollution. Two-thirds of China's cities have not attained the ambient air quality standards applicable to urban residential areas (Grade II). Particulate matter (PM), rather than sulfur dioxide (SO2), is the major pollutant reflecting the shift from coal burning to mixed source pollution. In 2002, 63.2 and 22.4% of the monitored cities have PM and SO2 concentrations exceeding the Grade II standard, respectively. Nitrogen oxides (NOx) concentration kept a relatively stable level near the Grade II standard in the last decade and had an increasing potential in recent years because of the rapid motorization. In general, the air pollutants emission did not increase as quickly as the economic growth and energy consumption, and air quality in Chinese cities has improved to some extent. Beijing, a typical representative of rapidly developing cities, is an example to illustrate the possible options for urban air pollution control. Beijing's case provides hope that the challenges associated with improving air quality can be met during a period of explosive development and motorization.     

Particulate matter pollution in the coal-producing regions of the Appalachian Mountains: Integrated ground-based measurements and satellite analysis

This study integrates the relationship between measured surface concentrations of particulate matter 10 μm or less in diameter (PM₁₀), satellite-derived aerosol optical depth (AOD), and meteorology in Roda, Virginia, during 2008. A multiple regression model was developed to predict the concentrations of particles 2.5 μm or less in diameter (PM_2.5) at an additional location in the Appalachia region, Bristol, TN. The model was developed by combining AOD retrievals from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor on board the EOS Terra and Aqua Satellites with the surface meteorological observations. The multiple regression model predicted PM_2.5 (r² = 0.62), and the two-variable (AOD-PM_2.5) model predicted PM_2.5 (r² = 0.4). The developed model was validated using particulate matter recordings and meteorology observations from another location in the Appalachia region, Hazard, Kentucky. The model was extrapolated to the Roda, VA, sampling site to predict PM_2.5 mass concentrations. We used 10 km x 10 km resolution MODIS 550 nm AOD to predict ground level PM_2.5. For the relevant period in 2008, in Roda, VA, the predicted PM_2.5 mass concentration is 9.11 ± 5.16 μg m^-3 (mean ± 1SD).

Implications: This is the first study that couples ground-based Particulate Matter measurements with satellite retrievals to predict surface air pollution at Roda, Virginia. Roda is representative of the Appalachian communities that are commonly located in narrow valleys, or “hollows,” where homes are placed directly along the roads in a region of active mountaintop mining operations. Our study suggests that proximity to heavy coal truck traffic subjects these communities to chronic exposure to coal dust and leads us to conclude that there is an urgent need for new regulations to address the primary sources of this particulate matter.     

西安市及周边地区MODIS气溶胶光学厚度与PM10浓度关系模型研究简

西安是空气污染监控和防治有代表性的西部大型城市。研究了西安市及周边地区上空气溶胶光学厚度与PM₁₀浓度的关系模型。利用2011—2012年MODIS卫星气溶胶光学厚度（AOD）遥感产品，通过数据匹配，利用地面气象观测站点的能见度数据和相对湿度数据对AOD产品进行垂直标高订正和湿度订正，2项订正显著提高了AOD和地面PM₁₀浓度的相关性，相关系数从0.36提高到0.65，按季节分类统计和订正春至冬四季的相关系数分别为0.57、0.71、0.62和0.87，夏季和冬季的订正更为有效，可用性更高，这可能由于受到不同季节气溶胶来源和特征的影响。为研究中国西部大型城市，特别是西安市空气环境监测和区域联防联控提供了一种有效方法。     

PM2.5 assessment in 21 European study centers of ECRHS II: Method and first winter results

The follow-up of a cohort of adults from 29 European centers of the former European Community Respiratory Health Survey (ECRHS) I (1989-1992) will examine the long-term effects of exposure to ambient air pollution on the incidence, course, and prognosis of respiratory diseases, in particular asthma and decline in lung function. The purpose of this article is to describe the methodology and the European-wide quality control program for the collection of particles with 50% cut-off size of 2.5 microm aerodynamic diameter (PM2.5) in the ECRHS II and to present the PM2.5 results from the winter period 2000-2001. Because PM2.5 is not routinely monitored in Europe, we measured PM2.5 mass concentrations in 21 participating centers to estimate background exposure in these cities. A standardized protocol was developed using identical equipment in each center (U.S. Environmental Protection Agency Well Impactor Ninety-Six [WINS] and PQ167 from BGI, Inc.). Filters were weighed in a single central laboratory. Sampling was conducted for 7 days per month for a year. Winter mean PM2.5 mass concentrations (November 2000-February 2001) varied substantially, with Iceland reporting the lowest value (5 microg/m3) and northern Italy the highest (69 microg/m3). A standardized procedure appropriate for PM2.5 exposure assessment in a multicenter study was developed. We expect ECRHS II to have sufficient variation in exposure to assess long-term effects of air pollution in this cohort. Any bias caused by variation in the characteristics of the chosen monitoring location (e.g., proximity to traffic sources) will be addressed in later analyses. Given the homogenous spatial distribution of PM2.5, however, concentrations measured near traffic are not expected to differ substantially from those measured at urban background sites.     

Health Effects of Air Quality Regulations in Delhi, India

This, the first systematic study, quantifies the health effects of air quality regulations in Delhi, which adopted radical measures to improve air quality, including, for example, the conversion of all commercial vehicles to compressed natural gas (CNG), and the closure of polluting industries in residential areas from 2000 to 2002. Air pollution data, collected at 113 sites (spread across Delhi and its neighboring areas) from July-December 2003, were used to compute exposure at the place of residence of 3,989 subjects. A socio-economic and respiratory health survey was administered in 1,576 households. This survey collected time-use, residence histories, demographic information, and direct measurements of lung function with subjects. The optimal interpolation methods were used to link air pollution and respiratory health data at the place of their residence. Resident histories, in combination with secondary data, were used to impute cumulative exposure prior to the air-quality interventions, and the effects of recent air quality measures on lung function were then evaluated. Three important findings emerge from the analysis. First, the interventions were associated with a significant improvement in respiratory health. Second, the effect of these interventions varied significantly by gender and income. Third, consistent with a causal interpretation of these results, effects were the strongest among those individuals who spend a disproportionate share of their time out-of-doors.     

Fine particle (PM2.5) measurement methodology, quality assurance procedures, and pilot results of the EXPOLIS study

EXPOLIS is a European multicenter (Athens, Basel, Grenoble, Helsinki, Milan, and Prague) air pollution exposure study. It is the first international, population-based, large-scale study, where personal exposures to PM2.5 aerosol particles (together with volatile organic compounds and carbon monoxide) are being monitored. EXPOLIS is performed in six different centers across Europe, the sampled aerosol concentrations vary greatly, and the microenvironmental samples are not collected with the same equipment as the personal samples. Therefore careful equipment selection, methods development and testing, and thorough quality assurance and quality control (QA & QC) procedures are essential for producing reliable and comparable PM2.5 data. This paper introduces the equipment, the laboratory test results, the pilot results, the standard operating procedures, and the QA & QC procedures of EXPOLIS. Test results show good comparability and repeatability between personal and microenvironmental monitors for PM2.5 at different concentration levels measured across Europe in EXPOLIS centers.     

Estimating fine particulate matter component concentrations and size distributions using satellite-retrieved fractional aerosol optical depth: part 1--method development

We develop a method that uses both the total column aerosol optical depth (AOD) and the fractional AOD values for different aerosol types, derived from Multiangle Imaging SpectroRadiometer (MISR) aerosol data, to estimate ground-level concentrations of fine particulate matter (PM2.5) mass and its major constituents in eastern and western United States. Compared with previous research on linking column AOD with ground-level PM2.5, this method treats various MISR aerosol components as individual predictor variables. Therefore, the contributions of different particle types to PM2.5 concentrations can be estimated. When AOD is greater than 0.15, MISR is able to distinguish dust from non-dust particles with an uncertainty level of approximately 4%, and light-absorbing from non-light-absorbing particles with an uncertainty level of approximately 20%. Further analysis shows that MISR Version 17 aerosol microphysical properties have good sensitivity and internal consistency among different mixture classes. The retrieval uncertainty of individual fractional AODs ranges between 5 and 11% in the eastern United States, and between 11 and 31% in the west for non-dust aerosol components. These results provide confidence that the fractional AOD models with their inherent flexibility can make more accurate predictions of the concentrations of PM2.5 and its constituents.     

Statistical characterization of atmospheric PM10 and PM2.5 concentrations at a non-impacted suburban site of Istanbul, Turkey

Inhalable particulate matter (PM10) has been monitored at several stations by Istanbul Municipality. On the other hand, information about fine fraction aerosols (PM2.5) in Istanbul atmosphere was not reported. In this study, 86 daily aerosol samples were collected between July 2002 and July 2003. The PM10 annual arithmetic mean value of 47.1 microg m(-3), was lower than the Turkish air quality standard of 60 microg m(-3). On the other hand, this value was found higher than the annual European Union air quality PM(10) standard of 40 microg m(-3). Furthermore, the annual mean concentration of PM2.5 20.8 microg m(-3) was found higher than The United States EPA standard of 15 microg m(-3). The statistics and relationships of fine, coarse, and inhalable particles were studied. Cyclic behavior of the monthly average concentrations of PM10 and PM2.5 data were investigated. Several frequency distribution functions were used to fit the measured data. According to Chi-squared and Kolmogorov-Smirnov tests, the frequency distributions of PM2.5 and PM10 data were found to fit Log-logistic functions.     

Improving Urban Air Quality in China: Beijing Case Study

Abstract

China is undergoing rapid urbanization because of unprecedented economic growth. As a result, many cities suffer from air pollution. Two-thirds of China’s cities have not attained the ambient air quality standards applicable to urban residential areas (Grade II). Particulate matter (PM), rather than sulfur dioxide (SO₂), is the major pollutant reflecting the shift from coal burning to mixed source pollution. In 2002, 63.2 and 22.4% of the monitored cities have PM and SO₂ concentrations exceeding the Grade II standard, respectively. Nitrogen oxides (NO_x) concentration kept a relatively stable level near the Grade II standard in the last decade and had an increasing potential in recent years because of the rapid motorization. In general, the air pollutants emission did not increase as quickly as the economic growth and energy consumption, and air quality in Chinese cities has improved to some extent. Beijing, a typical representative of rapidly developing cities, is an example to illustrate the possible options for urban air pollution control. Beijing’s case provides hope that the challenges associated with improving air quality can be met during a period of explosive development and motorization.     

Predicting regional space–time variation of PM2.5 with land-use regression model and MODIS data

Purpose  

Existing land-use regression (LUR) models use land use/cover, population, and traffic information to predict long-term intra-urban variation of air pollution. These models are limited to explaining spatial variation of air pollutants, and few of them are capable of addressing temporal variability. This article proposes a space–time LUR model at a regional scale by incorporating aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS).