Pubu Ciren

Comparison of GOES and MODIS Aerosol Optical Depth (AOD) to Aerosol Robotic Network (AERONET) AOD and IMPROVE PM2.5 Mass at Bondville, Illinois

Abstract Collocated Interagency Monitoring of Protected Visual Environments (IMPROVE) particulate matter (PM) less than 2.5 μm in aerodynamic diameter (PM2.5) chemically speciated data, mass of PM less than 10 μm in aerodynamic diameter (PM10), and Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and size distribution at Bondville, IL, were compared with satellite-derived AOD. This was done to evaluate the quality of the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) AOD data and their potential to predict surface PM2.5 concentrations. MODIS AOD correlated better to ERONET AOD (r = 0.835) than did GOES AOD (r = 0.523). MODIS and GOES AOD compared better to AERONET AOD when the particle size distribution was dominated by fine mode. For all three AOD methods, correlation between AOD and PM2.5 concentration was highest in autumn and lowest in winter. The AERONET AOD-PM2.5 relationship was strongest with moderate relative humidity (RH). At low RH, AOD attributable to coarse mass degrades the relationship; at high RH, added AOD from water growth appears to mask the relationship. For locations such as many in the central and western United States with substantial coarse mass, coarse mass contributions to AOD may make predictions of PM2.5 from AOD data problematic. Seasonal and diurnal variations in particle size distributions, RH, and seasonal changes in boundary layer height need to be accounted for to use satellite AOD to predict surface PM2.5.

Air Quality Forecast Verification Using Satellite Data

Abstract NOAA’s operational geostationary satellite retrievals of aerosol optical depths (AODs) were used to verify National Weather Service developmental (research mode) particulate matter (PM2.5) predictions tested during the summer 2004 International Consortium for Atmospheric Research on Transport and Transformation/New England Air Quality Study (ICARTT/NEAQS) field campaign. The forecast period included long-range transport of smoke from fires burning in Canada and Alaska and a regional-scale sulfate event over the Gulf of Mexico and the eastern United States. Over the 30-day time period for which daytime hourly forecasts were compared with observations, the categorical (exceedance defined as AOD > 0.55) forecast accuracy was between 0% and 20%. Hourly normalized mean bias (forecasts − observations) ranged between −50% and +50% with forecasts being positively biased when observed AODs were small and negatively biased when observed AODs were high. Normalized mean errors are between 50% and 100% with t...

Dust aerosol index (DAI) algorithm for MODIS

A dust aerosol index (DAI) algorithm based on measurements in deep blue (412 nm), blue (440 nm), and shortwave IR (2130 nm) wavelengths using Moderate Resolution Imaging Spectroradiometer (MODIS) observations has been developed. Contrary to some dust detection algorithms that use measurements at thermal IR bands, this algorithm takes advantage of the spectral dependence of Rayleigh scattering, surface reflectance, and dust absorption to detect airborne dust. The DAI images generated by this algorithm agree qualitatively with the location and extent of dust observed in MODIS true color images. Quantitatively, the dust index generated for hundreds of dust outbreaks observed between 2006 and 2013 were compared to Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) Vertical Feature Mask (VFM) product and the detections are found to be accurate at 70% over land and 82% over ocean. The Probability of Correct Detection (POCD) is 80% over land and 76% over ocean. The dust detections with DAI-based dust identification algorithm were also compared to 5 years of Aerosol Robotic Network (AERONET) observations for 13 stations with a wide range of geographical coverage. The average detection accuracy is ~70%, whereas the POCD is ~67%. The performance of DAI-based dust detection against AERONET is slightly weaker than that against CALIOP VFM because of the limited number of matchups for some stations. For stations close to source region or coastal and island stations, the accuracy and POCD can be as high as ~85% and ~89%, respectively.

Assessment of human health impact from exposure to multiple air pollutants in China based on satellite observations

Abstract Assessment of human health impact caused by air pollution is crucial for evaluating environmental hazards. In this paper, concentrations of six air pollutants (PM 10 , PM 2.5 , NO 2 , SO 2 , O 3 , and CO) were first derived from satellite observations, and then the overall human health risks in China caused by multiple air pollutants were assessed using an aggregated health risks index. Unlike traditional approach for human health risks assessment, which relied on the in-situ air pollution measurements, the spatial distribution of aggregated human health risks in China were obtained using satellite observations in this research. It was indicated that the remote sensing data have advantages over in-situ data in accessing human health impact caused by air pollution.

Assessment of human health impact from PM10 exposure in China based on satellite observations

Abstract. Assessment of human health impact from the exposure to PM10 air pollution is crucial for evaluating environmental damage. We established an empirical model to estimate ground PM10 mass concentration from satellite-derived aerosol optical depth and adopted the dose-response model to evaluate the annual average human health risks and losses related to PM10 exposure over China from 2010 to 2014. Unlike the traditional human health assessment methods, which relied on the in situ PM10 concentration measurements and statistical population data issued by administrative district, the approach proposed in this study obtained the spatial distribution of human health risks in China by analyzing the distribution of PM10 concentration estimated from satellite observations and population distribution based on the relationship to the spatial distribution of land-use type. It was found that the long-term satellite observations have advantages over the ground-based observations in estimating human health impact from PM10 exposure.

Building visual green index: A measure of visual green spaces for urban building

Abstract In the context of rapid urbanization, balancing the development and conservation of green space is a challenging task for urban planning and urban administrative management. Accurate measurement of green spaces in urban areas is an important source of information when evaluating the effect of urban development on green space. In this study, we used multispectral remote sensing images to extract urban buildings and green areas and developed a Building Visual Greenness Index (BVGI) to estimate the green space views from buildings. Instead of considering a single value for the entire building, BVGI values for each floor above the second floor were calculated to take into account of the dependence of BVGI on the building height. The results show that BVGI can reflect the actual green space view for residents on each floor, and BVGI is a useful index for green space measurement at a community scale. Furthermore, BVGI may provide a virtual visualization index of green space in urban planning and replace the traditional green indexes at a community scale for urban administrators.

An assessment of air-quality monitoring station locations based on satellite observations

ABSTRACT Optimization of the locations of air quality monitoring stations has great importance in providing high-quality data for regional air pollution monitoring. To assess the representativeness of the locations of the current air quality monitoring stations, we propose a new method based on satellite observations by applying the stratified sampling approach. Unlike the traditional method, which relies on the simulated spatial distribution of air pollutants from dispersion models, we obtained the sampling population through observations from remote sensing. As a first step, the spatial distribution of aggregated air quality was obtained based on ground concentrations of particulate matter (aerodynamic diameters of less than 10 μm, PM10), fine particulate matter (aerodynamic diameters of less than 2.5 μm, PM2.5), nitrogen dioxide (NO2), and sulphur dioxide (SO2) derived from satellite observations. Second, the representativeness of locations of air quality monitoring stations was assessed using the stratified sampling method. The results demonstrated that air quality monitoring stations in Beijing-Tianjin-Hebei were clustered in areas with heavily polluted air, whereas the number of air quality monitoring stations was insufficient in areas with higher air quality. After optimization, the minimum relative error was only 6.77%. It is indicated that combing remote-sensing data with the stratified sampling approach has great potential in assessing the spatial representativeness of air quality monitoring stations.

An Evaluation of the GOES-R ABI Aerosol Retrieval Algorithm

A multi-channel aerosol retrieval algorithm has been developed for the Advanced Baseline Imager (ABI) onboard the next generation US Geostationary Operational Environmental Satellite (GOES-R series). Testing and evaluation of this algorithm is carried out with model simulated and satellite-observed multi-spectral reflectance. Sensitivity tests with synthetic reflectance show that ABI retrieved aerosol optical depth at 0.55 mum (tau0.55 mum) is expected to change by 5% and 9% (14% and 24%) on average over water (land) corresponding to 3% and 5% uncertainties in calibration. Model identification is expected to have a 50%-60% success rate for a 3%-5% calibration uncertainty. Performance of ABI aerosol retrieval is evaluated with MODIS observations. Compared with AERONET ground measurements, on average, retrieved tau0.55 mum differs from AERONET measurements by -0.02plusmn0.05 over water and 0.05plusmn0.17 over land. The effect of sensor polarization sensitivity on ABI aerosol retrievals is also estimated as a bias of -0.01 over water and -0.05 over land for the retrieved tau0.55 mum.