MODIS AOD Research Articles

Extended aerosol and surface characterization from S5P/TROPOMI with GRASP algorithm. Part II: Global validation and Intercomparison

This paper is the second part of companion papers describing the development of GRASP approach for aerosol and surface retrieval from Sentinel-5P/TROPOMI. Here we focus on the S5P/TROPOMI GRASP aerosol and surface products global validation and systematic intercomparison with other products from independent instruments and algorithms. Specifically, we have validated the S5P/TROPOMI GRASP, Suomi-NPP/VIIRS DB and MODIS/TERRA DT + DB aerosol products with the ground-based AERONET referenced measurements using the same methodology and intercompare the validation results. In addition, the global pixel-to-pixel intercomparisons of the aerosol products (AOD, fine/coarse mode AOD and SSA) are performed over different surfaces, i.e., ocean and land surface with different NDVIs. Besides, we compared the S5P/TROPOMI GRASP, MODIS MCD43 surface BRDF/albedo as well as OMI, GOME-2 and SCIAMACHY Lambertian-Equivalent Reflectivity (LER) albedo climatology developed by Royal Netherlands Meteorological Institute (KNMI) with the surface reference dataset generated based on the synergetic retrieval of AERONET and S5P/TROPOMI measurements. Finally, the intercomparisons of the surface BRDF and albedo datasets were performed globally at the UV, VIS, NIR and SWIR parts of the spectrum. Overall, generally good agreement was observed between independent aerosol and surface datasets with a high percentage of pixels satisfying the Optimal and Target requirements. We would emphasize two advantages for TROPOMI/GRASP aerosol and surface products: (i) it provides spectral AOD together with detailed aerosol properties, such as fine/coarse mode AOD, spectral AAOD and SSA at UV, VIS, NIR and SWIR wavelengths, which are important for constraining aerosol environmental and climate effects; (ii) the TROPOMI/GRASP aerosol and surface products are globally retrieved simultaneously in a fully consistent manner.

Evaluation of WRF-Chem PM2.5 simulations in Thailand with different anthropogenic and biomass-burning emissions

Thailand experiences severe air quality issues, predominantly due to PM2.5 pollution that surpasses WHO guidelines. The main sources are attributed to energy production, industrial activities, vehicular emissions, agricultural burning, and transboundary transport of pollutants. Understanding the transport and transformation of these pollutants is necessary for addressing air quality issues. The Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) provides information about meteorology, chemical reactions, and transport of trace gases and aerosols. The accuracy of WRF-Chem simulations greatly depends on the choice of anthropogenic and biomass burning emissions inventories. This study provides a detailed evaluation of these inventories to model PM2.5 concentrations in Thailand during both haze and off-haze seasons in 2019. We evaluated WRF-Chem using four anthropogenic emission inventories—CAMS-GLOB-ANT, ECLIPSE, HTAP, and REAS—and four biomass burning emissions inventories—FINN1.5, FINN2.5 MOD, FINN2.5 MODVAR, and QFED—using data from ground-based air quality stations, MODIS AOD, and MOPITT CO satellite data. Our findings suggest CAMS-GLOB-ANT performs optimally for North Thailand, while HTAP and REAS are more effective in Eastern Thailand. For biomass burning, FINN1.5 shows superior performance. The study also highlights the challenge in capturing PM2.5 diurnal variability, particularly due to inaccuracies in simulating the planetary boundary layer height during nighttime in complex terrains. Moreover, our analysis exhibits moderate model performances during the off-haze season while using global and regional anthropogenic emissions in Thailand, emphasizing the need for improving anthropogenic inventories for reliable air quality prediction. For biomass burning emissions, updating emission factors to reflect Thailand's specific vegetation types is recommended to improve WRF-Chem's representation of PM2.5 levels.

Effect of Transportation Operation on Air Quality in China Based on MODIS AOD during the Epidemic

With the rapid growth of automobile numbers and the increased traffic congestion, traffic has increasingly significant effects on regional air quality and regional sustainable development in China. This study tried to quantify the effect of transportation operation on regional air quality based on MODIS AOD. This paper analyzed the space-time characteristics of air quality and traffic during the epidemic by series analysis and kernel density analysis, and quantified the relationship between air quality and traffic through a Geographically Weighted Regression (GWR) model. The main research conclusions are as follows: The epidemic has a great impact on traffic and regional air quality. PM2.5 and NO2 had the same trend with traffic congestion delay index (CDI), but they were not as obvious as CDI. Both cities with traffic congestion and cities with the worst air quality showed strong spatial dependence. The concentration areas of high AOD value in the east areas of the Hu line were consistent with the two gathering centers formed by cities with traffic congestion in space, and also consistent with the gathering center of cities with poor air quality. The concentration area of AOD decline was consistent with the gathering center formed by cities with the worst air quality. AOD had a strong positive correlation with road network density, and its GWR correlation coefficient was 0.68, then These provinces suitable for GWR or not suitable were divided. This study has a great significance for the transportation planning, regional planning, air quality control strategies and regional sustainable development, etc.

A deep insight into state-level aerosol pollution in India: Long-term (2005–2019) characteristics, source apportionment, and future projection (2023)

The present study provides a national scenario of aerosol pollution with the long-term (2005–2019) trend, source apportionment, and future scenario (2023) for each of the Indian states. We used MODIS AOD and FRP, differential AODs from MERRA-2, and trace gases (NO2, SO2) data from OMI. Almost all the states of IGP fall under the red zone (“highly vulnerable”; AOD >0.5) whereas central, western, and a few south-Indian states fall under the orange zone (“vulnerable”; 0.4 < AOD >0.5). The most alarming feature is that most of the southern Indian states exhibit a shift from blue/green (less vulnerable/safe; AOD <0.4) to vulnerable zones in 2023 as observed using the auto-regressive integrated moving average (ARIMA) model. Principal component analysis (PCA) revealed that the coal-fired thermal power plant (TPP), vehicular, solid fuel/waste, and biomass burning are the major sources of aerosols for the vulnerable states at present and in the future. We estimated and proposed the TPP capacity (GW) that needs to be reduced to bring down the AOD to move the vulnerable zones to less vulnerable and safe zones. The present study would complement and strengthen the ongoing national missions to combat air pollution in India.

A Suitable Model for Spatiotemporal Particulate Matter Concentration Prediction in Rural and Urban Landscapes, Thailand

Spatiotemporal particulate matter (PM) concentration prediction using MODIS AOD with significant PM factors in rural and urban landscapes in Thailand is necessary for public health and has been complicated by the limitations of PM monitoring stations. The research objectives were (1) to identify significant factors affecting PM10 concentrations in rural landscapes and PM2.5 in urban landscapes; (2) to predict spatiotemporal PM10 and PM2.5 concentrations using geographically weighted regression (GWR) and mixed-effect model (MEM), and (3) to evaluate a suitable spatiotemporal model for PM10 and PM2.5 concentration prediction and validation. The research methodology consisted of four stages: data collection and preparation, the identification of significant spatiotemporal factors affecting PM concentrations, the prediction of spatiotemporal PM concentrations, and a suitable spatiotemporal model for PM concentration prediction and validation. As a result, the predicted PM10 concentrations using the GWR model varied from 50.53 to 85.79 µg/m3 and from 36.92 to 51.32 µg/m3 in winter and summer, while the predicted PM10 concentrations using the MEM model varied from 50.68 to 84.59 µg/m3 and from 37.08 to 50.81 µg/m3 in both seasons. Likewise, the PM2.5 concentration prediction using the GWR model varied from 25.33 to 44.37 µg/m3 and from 16.69 to 24.04 µg/m3 in winter and summer, and the PM2.5 concentration prediction using the MEM model varied from 25.45 to 44.36 µg/m3 and from 16.68 and 23.75 µg/m3 during the two seasons. Meanwhile, according to Thailand and U.S. EPA standards, the monthly air quality index (AQI) classifications of the GWR and MEM were similar. Nevertheless, the derived average corrected Akaike Information Criterion (AICc) values of the GWR model for PM10 and PM2.5 predictions during both seasons were lower than that of the MEM model. Therefore, the GWR model was chosen as a suitable model for spatiotemporal PM10 and PM2.5 concentration predictions. Furthermore, the result of spatial correlation analysis for GWR model validation based on a new dataset provided average correlation coefficient values for PM10 and PM2.5 concentration predictions with a higher than the expected value of 0.5. Subsequently, the GWR model with significant monthly and seasonal factors could predict spatiotemporal PM 10 and PM2.5 concentrations in rural and urban landscapes in Thailand.

Using a New Top‐Down Constrained Emissions Inventory to Attribute the Previously Unknown Source of Extreme Aerosol Loadings Observed Annually in the Monsoon Asia Free Troposphere

AbstractThe contribution of biomass burning to the total aerosol loading over Monsoon Asia is both significant and continuing to increase. To better match the spatio‐temporal distribution of aerosols and trace gasses observed in the free troposphere, this work applied a 3‐D constrained emission inventory based on top‐down remotely sensed NO2 measurement to investigate the most extreme of the annually occurring biomass burning seasons in Monsoon Asia. In 2016, this constituted an extreme event observed over a 6‐day period covering millions of square kilometers, including over regions that are typically in the rainy phase of the Asian Monsoon. The results are shown to be consistent with respect to Tropical Rainfall Measuring Mission precipitation, AERONET measurements, MODIS AOD, MOPITT CO, and reanalysis meteorology, over both the biomass burning source as well as the millions of square kilometers downwind both to the East and to the Southwest. Reproducing the observed long‐range transport pattern requires the time of biomass burning to be increased, regions not previously identified as burning to be actual source regions, and the emissions of Black Carbon (BC) to be 6.6 to 11.9 times larger than current inventories. The underlying mechanism for this long‐range transport involves a new 3‐D pathway that can occur during the transition from the North phase to the South phase of the Asian Monsoon. The results are also consistent with the new idea that large loadings of BC in the lower free troposphere may significantly affect the meteorological field and the overall vertical distribution of aerosols in the tropical troposphere.

Prediction of Daily PM10 Concentration for Air Korea Stations Using Artificial Intelligence with LDAPS Weather Data, MODIS AOD, and Chinese Air Quality Data

PM (particulate matter) is of interest to everyone because it can have adverse effects on human health by the infiltration from respiratory to internal organs. To date, many studies have made efforts for the prediction of PM10 and PM2.5 concentrations. Unlike previous studies, we conducted the prediction of tomorrow’s PM10 concentration for the Air Korea stations using Chinese PM10 data in addition to the satellite AOD and weather variables. We constructed 230,639 matchups from the raw data over 3 million and built an RF (random forest) model from the matchups to cope with the complexity and nonlinearity. The validation statistics from the blind test showed excellent accuracy with the RMSE (root mean square error) of 9.905 μg/m3 and the CC (correlation coefficient) of 0.918. Moreover, our prediction model showed a stable performance without the dependency on seasons or the degree of PM10 concentration. However, part of coastal areas had a relatively low accuracy, which implies that a dedicated model for coastal areas will be necessary. Additional input variables such as wind direction, precipitation, and air stability should also be incorporated into the prediction model as future work.

New insights into heavy metal elements deposition in the snowpacks of mountain glaciers in the eastern Tibetan Plateau

Atmospheric heavy metals have important environmental and health threats. To investigate atmospheric deposition and contamination of heavy metal elements in the glaciers of the eastern Tibetan Plateau (ETP), we collected the surface snow (cryoconites) samples in the Lenglongling Glacier (LG), the Gannan Snowpack (GS), the Dagu Glacier (DG), the Hailuogou Glacier (HG) and Yulong Snow-mountain Glacier (YG) in summer 2017. Samples were analyzed for concentrations and enrichment factors (EFs) of Al and trace elements (Pb, Co, Cd, Ba, Mn, Ga, Sc, V, Zn, Cr, Ni, Cu, Rb, Sb, Cs, As, Mo, Li) using inductively coupled plasma-mass spectrometry (ICP-MS). Results showed that the concentrations and EFs of heavy metals (e.g. Sb, Cu, Cr, Ni, As, Mo) were generally high value in YG, GS and LG, while were relatively low value in DG and HG, implying that ETP glaciers may have been affected by atmospheric anthropogenic pollutants deposition to varying degrees. Comparing the heavy metal concentrations in the glaciers with those in the precipitation of middle/eastern China cities and also the South Asian cities, we find that the glacial heavy metal concentrations were generally low level, though the anthropogenic pollutants were still significantly enriched. Taking the spatial distribution of As and Ni concentration/EFs in the glaciers and surrounding urban precipitation as an example, we find that the heavy metal pollutants were probably transported to the glaciers through three routes from the surrounding densely populated area of Asia. The MODIS AOD and NCEP/NCAR wind vector also demonstrated that the atmospheric pollutants originated from anthropogenic emissions of urban areas of both South Asia, and northwest and east China, mainly caused by the large scale atmospheric circulation (e.g. the South Asian Monsoon, westerlies and Eastern Asian Summer Monsoon). Therefore, control of these potential pollution emission sources of the surrounding densely populated areas in Asia could be important to ETP glaciers in future perspectives.

Dust concentration over a semi-arid region: Parametric study and establishment of new empirical models

In recent years, the city of Tehran, Iran's capital, has encountered numerous dust events so that the dust concentration of PM10 has reached even more than 800 μg m−3. This emphasizes the importance of the statistical study of dust in Tehran and the development of correlations for estimating dust concentration of PM10. In the present study, by evaluating the data measured during dust observations over the years 2013–2016 in Tehran, new statistical models are established for estimating PM10 concentration in terms of horizontal visibility and MODIS AOD. Firstly, simple nonlinear regression models between dust concentration of PM10 and horizontal visibility as well as MODIS AOD are developed. Afterward, by combining the mentioned parameters with meteorological measurements, including wind speed, relative humidity, and temperature, multivariate nonlinear regression models are obtained. The descriptive statistical parameters and Taylor diagram concept are used in order to compare the performance of various models. Analyzing the developed models indicates that the simple model based on horizontal visibility has the best performance (R = 0.90) and considering meteorological parameters does not have a significant positive impact on the model performance. Furthermore, the estimated dust concentration of PM10 using simple models developed in this study has a better correlation with the measurements in Tehran in comparison with the results obtained by models of other researchers. This demonstrates the importance of developing such models according to the climatic and geological conditions of the investigated region.

Improving the estimating accuracy of extinction coefficient of surface aerosol with a new layer-resolved model in China

The aerosol extinction coefficient was an important factor for air quality. To estimate the aerosol extinction levels, widely used pure statistical models are generally not based on aerosol vertical structures. In this study, we estimated large-scale aerosol extinction coefficients by developing a new layer-resolved model with explicit inference for aerosol vertical distribution. The CALIOP aerosol profile, MODIS AOD and reanalysis boundary layer height data are used. The layer-resolved model was formulated by developing an explicit, steady and straightforward relationship between aerosol within boundary layer and corresponding AOD values. The estimated surface extinction coefficient from this model was compared against the values derived from station visibility observations in China in 2016. The results revealed that our model had outperformed the traditional one-layer model and the simplified two-layer model. Specifically, the numbers of ground stations with an NME value < 0.4 are enhanced by a percentage > 100%, with the NME values significantly decreased from 46%, 48% to 36% and RMSE values from 0.27, 0.25 to 0.21 km−1. Our model is easy for operational implementation thanks to its clear structure and input, and also informative to understand aerosol vertical distributions. Furthermore, this work will also be beneficial to air quality modeling studies to improve accuracy estimating ground-level PM2.5 concentrations.

Hf-Nd-Sr isotopic fingerprinting for aeolian dust deposited on glaciers in the northeastern Tibetan Plateau region

Abstract This study investigated the isotopic compositions of Hf, Nd, and Sr in aeolian dust deposited in several glacier basins of the northeast Tibetan Plateau in order to trace the source and provenance of long-range transported (LRT) dust in the region. Transmission electron microscope (TEM) and energy dispersive X-ray spectrometer (EDX) measurements and dust size distributions both indicated that LRT dust was dominant in glacier cryoconite and snowpacks. Isotopes of Hf-Nd-Sr exhibited a generally heterogeneous geographic distribution that varied from north to south. The northern sites (e.g. Miaoergou Glacier) in the Tianshan Mountains showed much higher eNd and eHf values, whereas the southern sites (e.g. Yuzhufeng Glacier) in the Kunlun Mountains had lower eNd, with the Hf isotope exhibiting two parts: one positive eHf section and one negative eHf section. Lower 87Sr/86Sr ratios were apparent in the Miaoergou Glacier compared to other locations. In general, the Miaoergou Glacier had similar Hf-Nd-Sr isotopic compositions to those of southern Mongolian Gobi and Taklimakan Deserts. By contrast, Qilian Mountain locations exhibited similar Hf, Nd, and Sr isotopic compositions to those of the north Tibetan Plateau soil crust and Chinese Gobi Deserts. However, the Yuzhufeng Glacier, located in the hinterland of the Tibetan Plateau, had LRT dust that was derived predominantly from the north Tibetan Plateau desert and surface crust, but also partly from the Taklimakan desert. Compared with Nd Sr isotopes, Hf Nd isotopes provided new evidence for LRT dust transport. For example, the Yuzhufeng and Miaoergou Glaciers had similar Hf Nd isotopes to those of the Taklimakan Desert and Mongolian Gobi, respectively, implying the potential LRT dust source and atmospheric transport routes. The marked spatial differences in LRT dust source were mainly caused by large-scale atmospheric circulation interactions (e.g. the Mongolian High and Westerlies) during spring and summer. Wind stream analysis and MODIS AOD also demonstrated the potential differences in the sources of dust and the routes taken between northern and southern locations. This work provides new evidence and a more complete view of aeolian transport over the northeast Tibetan Plateau region under the current climate.

Simulation of the Spatio-temporally Resolved PM2.5 Aerosol Mass Concentration over the Inland Plain of the Beijing-Tianjin-Hebei Region

In recent years, haze pollution in China is becoming increasingly serious, especially in the Beijing-Tianjin-Hebei region. In order to identify the temporal and spatial distributional characteristics of PM2.5 aerosol mass concentration in the region, this study selected the inland plain of the Beijing-Tianjin-Hebei region as the research area, and used MODIS AOD as the main predictor in a mixed effects model to establish the daily relationship of AOD-PM2.5 in the study area, from 2013 to 2014. The model was validated by a ten-fold cross validation method. The results showed that the correlation between AOD-PM2.5 can be improved by daily calibration of the mixed effects model (R2=0.78); the cross-validated R2 was 0.70, and RMSE and RPE were 20.80 μg·m-3 and 28.76%, respectively. Considering the importance of unbiased PM2.5 predictions, the correction factors calculated from the surface PM2.5 measurements were applied to correct the biases in the predicted annual average PM2.5 concentrations introduced by non-stochastic missing AOD measurements. The results showed that the annual average concentration of PM2.5 in the study area was higher than 75 μg·m-3, and the spatial distribution of PM2.5 concentration was higher in the southern and western regions, and lower in the northern and eastern regions. These results suggest that the mixed effects model can be used to monitor ground PM2.5, and also provide a scientific basis for the control of atmospheric particulate pollution in the region.

Long term analysis of the columnar and surface aerosol relationship at an urban European coastal site

Simultaneous PM2.5, PM10 and columnar (ground and satellite based) AOD measurements have been analyzed at Burjassot site in the metropolitan area of Valencia (Spain) during the period 2007–2016. The site is representative of a south European city in the Western Mediterranean coastal area, influenced by local urban pollution but also from frequent Saharan dust events. First, multiannual statistics were performed to characterize the main aerosol burden characteristics. The averages and standard deviations resulted 18 ± 9 μg m−3, 25 ± 19 μg m−3, 0.15 ± 0.11, 0.23 ± 0.17, 0.19 ± 0.15 and 1.2 ± 0.3 for PM2.5, PM10, AERONET AOD, MODIS Terra AOD, MODIS Aqua AOD, and AERONET Ångström exponent AE, respectively. The AOD is always referred to 550 nm. PM10 and AOD showed seasonal patterns with maxima in summer and minima in winter. However, PM2.5 and AE did not show such an evident seasonality. The relationship between surface and columnar measurements show a poor correlation (r down to 0.30) for daily values, although the correlation increases to r up to 0.90 for yearly averages. The relationship between PM and AOD becomes more consistent when the databases are binned in intervals of 0.05 AOD. Results for AERONET and MODIS AOD are very similar, although for daily averages is slightly worse for satellite than ground based measurements. In order to explain some seasonality effects found, the mixing layer height has been included in the analysis. Results show that the correlation is maximum when PM2.5 is used and the mixing layer height is greater than 1 000 m (r > 0.90).

Impacts of the July 2012 Siberian fire plume on air quality in the Pacific Northwest

Abstract. Biomass burning emissions emit a significant amount of trace gases and aerosols and can affect atmospheric chemistry and radiative forcing for hundreds or thousands of kilometres downwind. They can also contribute to exceedances of air quality standards and have negative impacts on human health. We present a case study of an intense wildfire plume from Siberia that affected the air quality across the Pacific Northwest on 6–10 July 2012. Using satellite measurements (MODIS True Colour RGB imagery and MODIS AOD), we track the wildfire smoke plume from its origin in Siberia to the Pacific Northwest where subsidence ahead of a subtropical Pacific High made the plume settle over the region. The normalized enhancement ratios of O3 and PM1 relative to CO of 0.26 and 0.08 are consistent with a plume aged 6–10 days. The aerosol mass in the plume was mainly submicron in diameter (PM1 ∕ PM2.5 = 0.96) and the part of the plume sampled at the Whistler High Elevation Monitoring Site (2182 m a.s.l.) was 88 % organic material. Stable atmospheric conditions along the coast limited the initial entrainment of the plume and caused local anthropogenic emissions to build up. A synthesis of air quality from the regional surface monitoring networks describes changes in ambient O3 and PM2.5 during the event and contrasts them to baseline air quality estimates from the AURAMS chemical transport model without wildfire emissions. Overall, the smoke plume contributed significantly to the exceedances in O3 and PM2.5 air quality standards and objectives that occurred at several communities in the region during the event. Peak enhancements in 8 h O3 of 34–44 ppbv and 24 h PM2.5 of 10–32 µg m−3 were attributed to the effects of the smoke plume across the Interior of British Columbia and at the Whistler Peak High Elevation Site. Lesser enhancements of 10–12 ppbv for 8 h O3 and of 4–9 µg m−3 for 24 h PM2.5 occurred across coastal British Columbia and Washington State. The findings suggest that the large air quality impacts seen during this event were a combination of the efficient transport of the plume across the Pacific, favourable entrainment conditions across the BC interior, and the large scale of the Siberian wildfire emissions. A warming climate increases the risk of increased wildfire activity and events of this scale reoccurring under appropriate meteorological conditions.

Quantifying the low bias of CALIPSO's column aerosol optical depth due to undetected aerosol layers.

The CALIOP data processing scheme only retrieves extinction profiles in those portions of the return signal where cloud or aerosol layers have been identified by the CALIOP layer detection scheme. In this study we use two years of CALIOP and MODIS data to quantify the aerosol optical depth of undetected weakly backscattering layers. Aerosol extinction and column-averaged lidar ratio is retrieved from CALIOP Level 1B (Version 4) profile using MODIS AOD as a constraint over oceans from March 2013 to February 2015. To quantify the undetected layer AOD (ULA), an unconstrained retrieval is applied globally using a lidar ratio of 28.75 sr estimated from constrained retrievals during the daytime over the ocean. We find a global mean ULA of 0.031 ± 0.052. There is no significant difference in ULA between land and ocean. However, the fraction of undetected aerosol layers rises considerably during daytime, when the large amount of solar background noise lowers the signal to noise ratio (SNR). For this reason, there is a difference in ULA between day (0.036 ± 0.066) and night (0.025 ± 0.021). ULA is larger in the northern hemisphere and relatively larger at high latitudes. Large ULA for the Polar Regions is strongly related to the cases where the CALIOP Level 2 Product reports zero AOD. This study provides an estimate of the complement of AOD that is not detected by lidar, and bounds the CALIOP AOD uncertainty to provide corrections for science studies that employ the CALIOP Level 2 AOD.

Can MODIS AOD be employed to derive PM2.5 in Beijing-Tianjin-Hebei over China?

The fine particular matter (PM) concentrations in China have increased considerably due to the rapid economic growth and urbanization in the last few decades, especially in the most populated and industrialized regions. Beijing-Tianjin-Hebei is one of the most polluted regions in China, so to monitor the PM2.5 concentrations over this region is quite critical for human health. By making use the new released hourly PM2.5 mass concentration from ground-based observations in Beijing-Tianjin-Hebei over China, and collocated MODIS level 2 AOD data from April 2014 to March 2015, we explored the relation between surface PM2.5 mass concentration and MODIS AOD and possibility to derive the surface PM2.5 from satellite retrieval in the region. Our study show that the relation strongly depend on the seasons due to distinct seasonal characteristics of PM2.5 and AOD, with a relatively better correlation in spring and summertime (correlation coefficient r ranging from 0.52 to 0.79) than autumn and wintertime (r can be low as to 0.23 in site Baoding). Our analysis gave evidence that worse relationship and/or smaller number of sample in wintertime is associated with the significantly high PM2.5 concentration and a lot of missing data occurring in MODIS AOD, implying that current MODIS AOD retrieval scheme does not work very well in highly polluted cases. The derived PM2.5 mass concentration from MODIS AOD in summertime can basically capture the major observed features of the time series and about 20% large bias of the derived values compared to the observation is expected to be reduced if longer time period data is available and used for analysis.

Semi-Physical Estimates of National-Scale PM10 Concentrations in China Using a Satellite-Based Geographically Weighted Regression Model

The estimation of ambient particulate matter with diameter less than 10 µm (PM10) at high spatial resolution is currently quite limited in China. In order to make the distribution of PM10 more accessible to relevant departments and scientific research institutions, a semi-physical geographically weighted regression (GWR) model was established in this study to estimate nationwide mass concentrations of PM10 using easily available MODIS AOD and NCEP Reanalysis meteorological parameters. The results demonstrated that applying physics-based corrections could remarkably improve the quality of the dataset for better model performance with the adjusted R2 between PM10 and AOD increasing from 0.08 to 0.43, and the fitted results explained approximately 81% of the variability in the corresponding PM10 mass concentrations. Annual average PM10 concentrations estimated by the semi-physical GWR model indicated that many residential regions suffer from severe particle pollution. Moreover, the deviation in estimation, which primarily results from the frequent changes in elevation, the spatially heterogeneous distribution of monitoring sites, and the limitations of AOD retrieval algorithm, was acceptable. Therefore, the semi-physical GWR model provides us with an effective and efficient method to estimate PM10 at large scale. The results could offer reasonable estimations of health impacts and provide guidance on emission control strategies in China.

Spatiotemporal variability in dust observed over the Sinkiang and Inner Mongolia regions of Northern China

This study presents a detailed analysis of the spatiotemporal variability in dust observed over Sinkiang and Inner Mongolia in Northern China from 2005 to 2008. The relationships between airborne dust (observed by OMI–AI and MODIS–AOD), the normalized difference vegetation index (NDVI), monthly total precipitation (MTP) and surface wind speed (SWS) are investigated. The results show that the spatial distribution of airborne dust distinctly decreases from west to east across Northern China; this pattern is opposite to that of the NDVI and MTP. Both Sinkiang and Inner Mongolia experience high amounts of airborne dust in spring, with the highest values in April. The two regions also had different dust variabilities. In Sinkiang, three major dust regions were identified, with airborne dust mainly distributed in regions with NDVI values between 0 and 0.1, SWS values between 2 and 5m/s, and a MTP of less than 5mm. In addition, the temporal variation in airborne dust exhibits both positive and negative correlations with the NDVI, MTP and SWS. However, over Inner Mongolia, five high–dust regions were confirmed, with airborne dust generally distributed in the areas with NDVI values between 0 and 0.4, SWS values between 3 and 6m/s, and a MTP of less than 10mm. The time series of airborne dust is negatively correlated with the NDVI and MTP but strongly and positively correlated with the SWS. In addition, the temporal pattern of the AOD in Sinkiang is essentially controlled by dust activity, whereas it may be influenced by anthropogenic emissions in Inner Mongolia. Overall, the spatiotemporal variabilities in dust over Sinkiang and Inner Mongolia are not identical, although both regions are important sources of dust in East Asia.

Comparison and evaluation of dust detection algorithms using MODIS Aqua/Terra Level 1B data and MODIS/OMI dust products in the Middle East

Dust storms have a major impact on air quality, economic loss, and human health over large regions of the Middle East. Because of the broad extent of dust storms and also political–security issues in this region, satellite data are an important source of dust detection and mapping. The aim of this study was to compare and evaluate the performance of five main dust detection algorithms, including Ackerman, Miller, normalized difference dust index (NDDI), Roskovensky and Liou, and thermal-infrared dust index (TDI), using MODIS Level 1B and also MODIS Deep Blue AOD and OMI AI products in two dust events originating from Iraq and Saudi Arabia. Overall, results showed that the performance of the algorithms varied from event to event and it was not possible to use the published dust/no-dust thresholds for the algorithms tested in the study area. The MODIS AOD and OMI AI products were very effective for initial dust detection and the AOD and AI images correlated highly with the dust images at provincial scale (p-value <0.001), but the application of these products was limited at local scale due to their poor spatial resolution. Results also indicated that algorithms based on MODIS thermal infrared (TIR) bands or a combination of TIR and reflectance bands were better indicators of dust than reflectance-based ones. Among the TIR- based algorithms, TDI performed the best over water surfaces and dust sources, and accounted for approximately 93% and 90% of variations in the AOD and OMI AI data.

Estimation of IPAR and Direct Forcing of Aerosol over Dehradun and Surrounding Area (India) Using MODIS Derived Atmospheric Products: A Case Study

Scattering and absorption of solar radiation by anthropogenic aerosols reduce the photosynthetic active radiation (PAR) incident on the Earth surface and the changes incident PAR can cause contradictory responses in terrestrial plants. The reduction of incident PAR is mainly varied according to the regional characteristics and forcing efficiency of aerosols. The MODIS derived atmospheric products are widely used for many such environmental investigations. In this paper a radiative transfer model is developed in cloudless clear sky condition to estimate instantaneous PAR and radiative forcing of aerosol. The estimated instantaneous PAR is validated with the ground measured (AccuPAR model LP-80) incident PAR during 2009. The Mean Absolute Error (MAE) of MODIS based incident PAR simulations was 20.09 w/m2 and Absolute Percentage Difference (APD) was 6.9 % while the MAE of Microtops II based estimations of incident PAR was 7.5 w/m2 and APD was 2.4 % . Inter comparison of estimations incident PAR with sun-photometer and MODIS measurements showed that the MODIS based PAR is over estimated mainly due to the under estimation of MODIS AOD over the study area. The model can generate spatial maps of instantaneous PAR and aerosol forcing efficiency. An Improved PAR map was generated (MAE 7.6 w/m2 and APD 2.5 %) by correcting MODIS AOD based on Microtops II measurements.