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 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.     

Optimal temporal scale for the correlation of AOD and ground measurements of PM2.5 in a real-time air quality estimation system

Aerosol optical depth (AOD), an indirect estimate of particulate matter using satellite observations, has shown great promise in improving estimates of PM_2.5 (particulate matter with aerodynamic diameter less than or equal to 2.5 μm) surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM_2.5 in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) an approach that integrates satellite measurements with ground measurements in the estimates of pollutants and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect, identifying the optimal temporal scale to correlate AOD with PM_2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale: within the last 30 days, displays the best model performance in a southern multi-state area centered in Mississippi using 2004 and 2005 data sets.     

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing

This two-part paper reports on the development, implementation, and improvement of a version of the Community Multi-Scale Air Quality (CMAQ) model that assimilates real-time remotely-sensed aerosol optical depth (AOD) information and ground-based PM_2.5 monitor data in routine prognostic application. The model is being used by operational air quality forecasters to help guide their daily issuance of state or local-agency-based air quality alerts (e.g. action days, health advisories). Part 1 describes the development and testing of the initial assimilation capability, which was implemented offline in partnership with NASA and the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) Regional Planning Organization (RPO). In the initial effort, MODIS-derived aerosol optical depth (AOD) data are input into a variational data-assimilation scheme using both the traditional Dark Target and relatively new “Deep Blue” retrieval methods. Evaluation of the developmental offline version, reported in Part 1 here, showed sufficient promise to implement the capability within the online, prognostic operational model described in Part 2. In Part 2, the addition of real-time surface PM_2.5 monitoring data to improve the assimilation and an initial evaluation of the prognostic modeling system across the continental United States (CONUS) is presented.

Implications: Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM_2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ride-sharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects.     

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。     

Application of Satellite Remote-Sensing Data for Source Analysis of Fine Particulate Matter Transport Events

Abstract

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 μm (PM_2.5) event may indicate whether transport or local sources appear to be most responsible for high PM_2.5 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.     

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.     

Estimating PM2.5 over southern Sweden using space-borne optical measurements

In the present study Bremen aerosol retrieval (BAER) columnar aerosol optical thickness (AOT) data, according to moderate resolution imaging spectroradiometer (MODIS) and medium resolution imaging sensor (MERIS) level 1 calibrated satellite data, have been compared with AOT data obtained with the MODIS and MERIS retrieval algorithms (NASA and ESA, respectively) and by AErosol RObotic NETwork (AERONET). Relatively good agreement is found between these different instruments and algorithms. The R² and relative RMSD were 0.86 and 31% for MODIS when comparing with AERONET and 0.92 and 21% for MERIS. The aerosols investigated were influenced by low relative humidity. During this period, a relatively large range of aerosol loadings were detected; from continental background aerosol to particles emitted from agricultural fires. In this study, empirical relationships between BAER columnar AOT and ground-measured PM_2.5 have been estimated. Linear relationships, with R² values of 0.58 and 0.59, were obtained according to MERIS and MODIS data, respectively. The slopes of the regression of AOT versus PM_2.5 are lower than previous studies, but this could easily be explained by considering the effect of hygroscopic growth. The present AOT–PM_2.5 relationship has been applied on MERIS full resolution data over the urban area of Stockholm and the results have been compared with particle mass concentrations from dispersion model calculations. It seems that the satellite data with the 300 m resolution can resolve the expected increased concentrations due to emissions along the main highways close to the city. Significant uncertainties in the spatial distribution of PM_2.5 across land/ocean boundaries were particularly evident when analyzing the high resolution satellite data.     

A statistical model for predicting PM2.5 for the western United States

ABSTRACT

A new statistical model for predicting daily ground level fine scale particulate matter (PM_2.5) concentrations at monitoring sites in the western United States was developed and tested operationally during the 2016 and 2017 wildfire seasons. The model is site-specific, using a multiple linear regression schema that relies on the previous day’s PM_2.5 value, along with fire and smoke related variables from satellite observations. Fire variables include fire radiative power (FRP) and the National Fire Danger Rating System Energy Release Component index. Smoke variables, in addition to ground monitored PM_2.5, include aerosol optical depth (AOD) and smoke plume perimeters from the National Oceanic and Atmospheric Administration’s Hazard Mapping System. The overall statistical model was inspired by a similar system developed for British Columbia (BC) by the BC Center for Disease Control, but it has been heavily modified and adapted to work in the United States. On average, our statistical model was able to explain 78% of the variance in daily ground level PM_2.5. A novel method for implementation of this model as an operational forecast system was also developed and was tested and used during the 2016 and 2017 wildfire seasons. This method focused on producing a continuously-updating prediction that incorporated the latest information available throughout the day, including both updated remote sensing data and real-time PM_2.5 observations. The diurnal pattern of performance of this model shows that even a few hours of data early in the morning can substantially improve model performance.

Implications: Wildfire smoke events produce significant air quality impacts across the western United States each year impacting millions. We present and evaluate a statistical model for making updating predictions of fine particulate (PM_2.5) levels during smoke events. These predictions run hourly and are being used by smoke incident specialists assigned to wildfire operations, and may be of interest to public health officials, air quality regulators, and the public. Predictions based on this model will be available on the web for the 2019 western U.S. wildfire season this summer.     

Impact of the mixing boundary layer on the relationship between PM2.5 and aerosol optical thickness

The purpose of this paper is to study the relationship between columnar aerosol optical thickness and ground-level aerosol mass. A set of Sun photometer, elastic backscattering lidar and TEOM measurements were acquired during April 2007 in Lille, France. The PM2.5 in the mixed boundary layer is estimated using the lidar signal, aerosol optical thickness, or columnar integrated Sun photometer size distribution and compared to the ground-level station measurements. The lidar signal recorded in the lowest level (240 m) is well correlated to the PM2.5 (R² = 0.84). We also show that the correlation between AOT-derived and measured PM2.5 is significantly improved when considering the mixed boundary layer height derived from the lidar. The use of the Sun photometer aerosol fine fraction volume does not improve the correlation.     

Assessment of air quality benefits from national air pollution control policies in China. Part II: Evaluation of air quality predictions and air quality benefits assessment

Following the meteorological evaluation in Part I, this Part II paper presents the statistical evaluation of air quality predictions by the U.S. Environmental Protection Agency (U.S. EPA)’s Community Multi-Scale Air Quality (Models-3/CMAQ) model for the four simulated months in the base year 2005. The surface predictions were evaluated using the Air Pollution Index (API) data published by the China Ministry of Environmental Protection (MEP) for 31 capital cities and daily fine particulate matter (PM_2.5, particles with aerodiameter less than or equal to 2.5 μm) observations of an individual site in Tsinghua University (THU). To overcome the shortage in surface observations, satellite data are used to assess the column predictions including tropospheric nitrogen dioxide (NO₂) column abundance and aerosol optical depth (AOD). The result shows that CMAQ gives reasonably good predictions for the air quality.The air quality improvement that would result from the targeted sulfur dioxide (SO₂) and nitrogen oxides (NO_x) emission controls in China were assessed for the objective year 2010. The results show that the emission controls can lead to significant air quality benefits. SO₂ concentrations in highly polluted areas of East China in 2010 are estimated to be decreased by 30–60% compared to the levels in the 2010 Business-As-Usual (BAU) case. The annual PM_2.5 can also decline by 3–15 μg m^?3 (4–25%) due to the lower SO₂ and sulfate concentrations. If similar controls are implemented for NO_x emissions, NO_x concentrations are estimated to decrease by 30–60% as compared with the 2010 BAU scenario. The annual mean PM_2.5 concentrations will also decline by 2–14 μg m^?3 (3–12%). In addition, the number of ozone (O₃) non-attainment areas in the northern China is projected to be much lower, with the maximum 1-h average O₃ concentrations in the summer reduced by 8–30 ppb.     

An empirical relationship between PM(2.5) and aerosol optical depth in Delhi Metropolitan

Atmospheric remote sensing offers a unique opportunity to compute indirect estimates of air quality, which are critically important for the management and surveillance of air quality in megacities of developing countries, particularly in India and China, which have experienced elevated concentration of air pollution but lack adequate spatial-temporal coverage of air pollution monitoring. This article examines the relationship between aerosol optical depth (AOD) estimated from satellite data at 5 km spatial resolution and the mass of fine particles ≤2.5 μm in aerodynamic diameter (PM(2.5)) monitored on the ground in Delhi Metropolitan where a series of environmental laws have been instituted in recent years.PM(2.5) monitored at 113 sites were collocated by time and space with the AOD computed using the data from Moderate Resolution Imaging Spectroradiometer (MODIS onboard the Terra satellite). MODIS data were acquired from NASA's Goddard Space Flight Center Earth Sciences Distributed Active Archive Center (DAAC). Our analysis shows a significant positive association between AOD and PM(2.5). After controlling for weather conditions, a 1% change in AOD explains 0.52±0.202% and 0.39±0.15% change in PM(2.5) monitored within ±45 and 150 min intervals of AOD data. This relationship will be used to estimate air quality surface for previous years, which will allow us to examine the time-space dynamics of air pollution in Delhi following recent air quality regulations, and to assess exposure to air pollution before and after the regulations and its impact on health.     

1986 APCA Government Agencies Directory

Carbonaceous species (organic carbon [OC] and elemental carbon [EC]) and inorganic ions of particulate matter less than 2.5 μm (PM_2.5) were measured to investigate the chemical characteristics of long-range-transported (LTP) PM_2.5 at Gosan, Jeju Island, in Korea in the spring and fall of 2008–2012 (excluding 2010). On average, the non-sea-salt (nss) sulfate (4.2 µg/m³) was the most dominant species in the spring, followed by OC (2.6 µg/m³), nitrate (2.1 µg/m³), ammonium (1.7 µg/m³), and EC (0.6 µg/m³). In the fall, the nss-sulfate (4.7 µg/m³) was also the most dominant species, followed by OC (4.0 µg/m³), ammonium (1.7 µg/m³), nitrate (1.1 µg/m³), and EC (0.7 µg/m³). Both sulfate and OC were higher in the fall than in the spring, possibly due to more common northwest air masses (i.e., coming from China and Korea polluted areas) and more frequent biomass burnings in the fall. There was no clear difference in the EC between the spring and fall. The correlation between OC and EC was not strong; thus, the OC measured at Gosan was likely transported across a long distance and was not necessarily produced in a manner similar to the EC. Distinct types of LTP events (i.e., sulfate-dominant LTP versus OC-dominant LTP) were observed. In the sulfate-dominant LTP events, air masses directly arrived at Gosan without passing over the Korean Peninsula from the industrial area of China within 48 hr. During these events, the aerosol optical depth (AOD) increased to 1.63. Ionic balance data suggest that the long-range transported aerosols are acidic. In the OC-dominant LTP event, a higher residence time of air masses in Korea was observed (the air masses departing from the mainland of China arrived at the sampling site after passing Korea within 60–80 hr).

Implications:?In Northeast Asia, various natural and anthropogenic sources contribute to the complex chemical components and affect local/regional air quality and climate change. Chemical characteristics of long-range-transported (LTP) PM_2.5 were investigated during spring and fall of 2008, 2009, 2011, and 2012. Based on air mass types, sulfate-dominant LTP and OC-dominant LTP were observed. A long-term variation and chemical characteristics of PM_2.5 along with air mass and satellite data are required to better understand long-range-transported aerosols.     

A European aerosol phenomenology – 3: Physical and chemical characteristics of particulate matter from 60 rural,urban, and kerbside sites across Europe

This paper synthesizes data on aerosol (particulate matter, PM) physical and chemical characteristics, which were obtained over the past decade in aerosol research and monitoring activities at more than 60 natural background, rural, near-city, urban, and kerbside sites across Europe. The data include simultaneously measured PM₁₀ and/or PM_2.5 mass on the one hand, and aerosol particle number concentrations or PM chemistry on the other hand. The aerosol data presented in our previous works (Van Dingenen et al., 2004, Putaud et al., 2004) were updated and merged to those collected in the framework of the EU supported European Cooperation in the field of Scientific and Technical action COST633 (Particulate matter: Properties related to health effects). A number of conclusions from our previous studies were confirmed. There is no single ratio between PM_2.5 and PM₁₀ mass concentrations valid for all sites, although fairly constant ratios ranging from 0.5 to 0.9 are observed at most individual sites. There is no general correlation between PM mass and particle number concentrations, although particle number concentrations increase with PM_2.5 levels at most sites. The main constituents of both PM₁₀ and PM_2.5 are generally organic matter, sulfate and nitrate. Mineral dust can also be a major constituent of PM₁₀ at kerbside sites and in Southern Europe. There is a clear decreasing gradient in SO₄^2? and NO₃^? contribution to PM₁₀ when moving from rural to urban to kerbside sites. In contrast, the total carbon/PM₁₀ ratio increases from rural to kerbside sites. Some new conclusions were also drawn from this work: the ratio between ultrafine particle and total particle number concentration decreases with PM_2.5 concentration at all sites but one, and significant gradients in PM chemistry are observed when moving from Northwestern, to Southern to Central Europe. Compiling an even larger number of data sets would have further increased the significance of our conclusions, but collecting all the aerosol data sets obtained also through research projects remains a tedious task.     

Study on the aerosol optical properties and their relationship with aerosol chemical compositions over three regional background stations in China

The special and temporal characteristics of aerosol optical depth (AOD) and Angstrom wavelength exponent (Alpha) and their relationship with aerosol chemical compositions were analyzed by using the data of CE318 sun-photometer and aerosol sampling instruments at Lin'an, Shangdianzi and Longfengshan regional atmospheric background stations. Having the highest AOD among the three stations, Lin'an shows two peaks in a year. The AOD at Shangdianzi station shows a single annual peak with an obvious seasonal variation. The AOD at Longfengshan station has obvious seasonal variation which peaks in spring. The Alpha analysis suggests that the aerosol sizes in Lin'an, Longfengshan and Shangdianzi change from fine to coarse categories. The relationship between the aerosol optical depths of the Lin'an and Longfengshan stations and their chemical compositions is not significant, which suggests that there is not a simple linear relationship between column aerosol optical depth and the near surface chemical compositions of atmospheric aerosols. The aerosol optical depth may be affected by the chemical composition, the particle size and the shape of aerosol as well as the water vapor in the atmosphere.     

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     

Aerosol optical and physical properties during winter monsoon pollution transport in an urban environment

We analysed aerosol optical and physical properties in an urban environment (Kolkata) during winter monsoon pollution transport from nearby and far-off regions. Prevailing meteorological conditions, viz. low temperature and wind speed, and a strong downdraft of air mass, indicated weak dispersion and inhibition of vertical mixing of aerosols. Spectral features of WinMon aerosol optical depth (AOD) showed larger variability (0.68–1.13) in monthly mean AOD at short-wavelength (SW) channels (0.34–0.5 μm) compared to that (0.28–0.37) at long-wavelength (LW) channels (0.87–1.02 μm), thereby indicating sensitivity of WinMon AOD to fine aerosol constituents and the predominant contribution from fine aerosol constituents to WinMon AOD. WinMon AOD at 0.5 μm (AOD _{0. 5}) and Angstrom parameter ( α) were 0.68–0.82 and 1.14–1.32, respectively, with their highest value in December. Consistent with inference from spectral features of AOD, surface aerosol loading was primarily constituted of fine aerosols (size 0.23–3 μm) which was 60–70 % of aerosol 10- μm (size 0.23–10 μm) concentration. Three distinct modes of aerosol distribution were obtained, with the highest WinMon concentration at a mass median diameter (MMD) of 0.3 μm during December, thereby indicating characteristics of primary contribution related to anthropogenic pollutants that were inferred to be mostly due to contribution from air mass originating in nearby region having predominant emissions from biofuel and fossil fuel combustion. A relatively higher contribution from aerosols in the upper atmospheric layers than at the surface to WinMon AOD was inferred during February compared to other months and was attributed to predominant contribution from open burning emissions arising from nearby and far-off regions. A comparison of ground-based measurements with Moderate Resolution Imaging Spectroradiometer (MODIS) data showed an underestimation of MODIS AOD and α values for most of the days. Discrepancy in relative distribution of fine and coarse mode of MODIS AOD was also inferred.     

Sources and characteristics of carbonaceous aerosol in two largest cities in Pearl River Delta Region,China

PM_2.5 samples were collected at five sites in Guangzhou and Hong Kong, Pearl River Delta Region (PRDR), China in both summer and winter during 2004–2005. Elemental carbon (EC) and organic carbon (OC) in these samples were measured. The OC and EC concentrations ranked in the order of urban Guangzhou > urban Hong Kong > background Hong Kong. Total carbonaceous aerosol (TCA) contributed less to PM_2.5 in urban Guangzhou (32–35%) than that in urban Hong Kong (43–57%). The reason may be that, as an major industrial city in South China, Guangzhou would receive large amount of inorganic aerosol from all kinds of industries, however, as a trade center and seaport, urban Hong Kong would mainly receive organic aerosol and EC from container vessels and heavy-duty diesel trucks. At Hong Kong background site Hok Tsui, relatively lower contribution of TCA to PM_2.5 may result from contributions of marine inorganic aerosol and inland China pollutant. Strong correlation (R²=0.76–0.83) between OC and EC indicates minor fluctuation of emission and the secondary organic aerosol (SOA) formation in urban Guangzhou. Weak correlation between OC and EC in Hong Kong can be related to the impact of the long-range transported aerosol from inland China. Averagely, secondary OC (SOC) concentrations were 3.8–5.9 and 10.2–12.8 μg m⁻³, respectively, accounting for 21–32% and 36–42% of OC in summer and winter in Guangzhou. The average values of 4.2–6.8% for SOA/ PM_2.5 indicate that SOA was minor component in PM_2.5 in Guangzhou.     

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.     

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.