Aerosol Optical Depth Products Research Articles

Assessment of natural and anthropogenic aerosol air pollution in the Middle East using MERRA-2, CAMS data assimilation products, and high-resolution WRF-Chem model simulations

Abstract. Modern-Era Retrospective analysis for Research and Applications v.2 (MERRA-2), Copernicus Atmosphere Monitoring Service Operational Analysis (CAMS-OA), and a high-resolution regional Weather Research and Forecasting model coupled with chemistry (WRF-Chem) were used to evaluate natural and anthropogenic particulate matter (PM) air pollution in the Middle East (ME) during 2015–2016. Two Moderate Resolution Imaging Spectrometer (MODIS) retrievals – combined product Deep Blue and Deep Target (MODIS-DB&DT) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) – and Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) observations as well as in situ PM measurements for 2016 were used for validation of the WRF-Chem output and both assimilation products. MERRA-2 and CAMS-OA assimilate AOD observations. WRF-Chem is a free-running model, but dust emission in WRF-Chem is tuned to fit AOD and aerosol volume size distributions obtained from AERONET. MERRA-2 was used to construct WRF-Chem initial and boundary conditions both for meteorology and chemical and aerosol species. SO2 emissions in WRF-Chem are based on the novel OMI-HTAP SO2 emission dataset. The correlation with the AERONET AOD is highest for MERRA-2 (0.72–0.91), MAIAC (0.63–0.96), and CAMS-OA (0.65–0.87), followed by MODIS-DB&DT (0.56–0.84) and WRF-Chem (0.43–0.85). However, CAMS-OA has a relatively high positive mean bias with respect to AERONET AOD. The spatial distributions of seasonally averaged AODs from WRF-Chem, assimilation products, and MAIAC are well correlated with MODIS-DB&DT AOD product. MAIAC has the highest correlation (R=0.8), followed by MERRA-2 (R=0.66), CAMS-OA (R=0.65), and WRF-Chem (R=0.61). WRF-Chem, MERRA-2, and MAIAC underestimate and CAMS-OA overestimates MODIS-DB&DT AOD. The simulated and observed PM concentrations might differ by a factor of 2 because it is more challenging for the model and the assimilation products to reproduce PM concentration measured within the city. Although aerosol fields in WRF-Chem and assimilation products are entirely consistent, WRF-Chem is preferable for analysis of regional air quality over the ME due to its higher spatial resolution and better SO2 emissions. The WRF-Chem’s PM background concentrations exceed the World Health Organization (WHO) guidelines over the entire ME. Mineral dust is the major contributor to PM (≈75 %–95 %) compared to other aerosol types. Near and downwind from the SO2 emission sources, nondust aerosols (primarily sulfate) contribute up to 30 % to PM2.5. The contribution of sea salt to PM in coastal regions can reach 5 %. The contributions of organic matter, black carbon and organic carbon to PM over the Middle East are insignificant. In the major cities over the Arabian Peninsula, the 90th percentile of PM10 and PM2.5 (particles with diameters less than 10 and 2.5 µm, respectively) daily mean surface concentrations exceed the corresponding Kingdom of Saudi Arabia air quality limits. The contribution of the nondust component to PM2.5 is <25 %, which limits the emission control effect on air quality. The mitigation of the dust effect on air quality requires the development of environment-based approaches like growing tree belts around the cities and enhancing in-city vegetation cover. The WRF-Chem configuration presented in this study could be a prototype of a future air quality forecast system that warns the population against air pollution hazards.

Retrieval of Aerosol Optical Depth from the Himawari-8 Advanced Himawari Imager data: Application over Beijing in the summer of 2016

Abstract Himawari-8, a new generation geostationary satellite, with the Advanced Himawari Imager (AHI) providing Full Disk observation with high temporal resolution (10 min) exhibits prominent advantage in monitoring aerosols over East Asia region. The AHI has 16 channels from 0.46 to 13.3 to capture visible and infrared spectral data. In this paper, we developed a regional algorithm to retrieve the Aerosol Optical Depth (AOD) over Beijing area based on the AHI visible and short-wave infrared (SWIR) data from June to August 2016. The AHI surface reflectance at 0.46, 0.64, 0.86 and 2.3 μm channel was obtained using the lowest gas-corrected reflectance based on the three-month top of the atmosphere (TOA) data. The algorithm employed superposing technique and linear regression to iteratively train the AHI surface reflectance, and built optimal surface reflectance relationships between visible and SWIR bands with three Normalized Difference Vegetation Index (NDVI) classifications over Beijing area. Based on these surface reflectance relationships, the AHI AOD data were retrieved through the radiation transmission function. The AOD products from the AErosol RObotic NETwork (AERONET) ground measurement, the MODerate resolution Imaging Spectroradiometer (MODIS) and the Japan Aerospace Exploration Agency (JAXA) official AHI were used to evaluate the performance of our AOD retrievals. At diurnal scale, more than 66% of AHI AOD retrievals fell within the EE in the afternoon, which indicated that these retrievals could capture the regional aerosol variability over Beijing area. In contrast, the AHI JAXA AOD product significantly overestimated aerosol loadings. The low AOD biases (13:00–16:00) under low aerosol conditions from our retrieval algorithm demonstrated that the AHI surface reflectance assumptions are available. However, AHI retrievals in XiangHe overestimated AOD under high aerosol conditions, which indicated that the urban aerosol model is inappropriate over this site. In addition, our AHI AOD algorithm was only applied over Beijing area in the summer of 2016. The retrieval time-independence in this algorithm has not been estimated in fall and winter when NDVI is lower. Future study will investigate this surface algorithm at larger temporal and spatial scales.

ESTIMATING PM2.5 CONCENTRATIONS IN BRITISH COLUMBIA, CANADA DURING WILDFIRE SEASON USING SATELLITE OPTICAL DATA

Abstract. British Columba, Canada experienced its record-breaking wildfire season in 2017. The wildfire smoke is one of the main sources of fine particles with diameters smaller than 2.5 μm (PM2.5). The rising level of PM2.5 concentrations during the wildfire season would considerable increase the risk of premature death, especially for people with weak immune systems. In this study, the satellite optical data collected from 3 km MODIS aerosol optical depth (AOD) products were adopted to estimate PM2.5 concentration levels derived from wildfires in British Columbia, Canada from July to September 2017. The satellite optical data were combined with ground station measurements, meteorological and supplementary data to estimate PM2.5 concentrations using the geographically weighted regression (GWR) model. Our results demonstrated that PM2.5 concentrations were the highest in July and August based on the estimation results of seasonal and monthly GWR models. It indicated that the application feasibility of MODIS AOD products in predicting PM2.5 concentrations during the wildfire season in British Columbia.

VERIFICATION OF MCD19A2 DATA AND STUDY OF AEROSOL CHARACTERISTICS IN BEIJING-TIANJIN-HEBEI REGION

Abstract. The atmospheric aerosol pollution in the Beijing-Tianjin-Hebei(BTH) region of China is particularly serious. To quickly and accurately evaluate the aerosol pollution in BTH region using the remote sensing method – this article verified the accuracy of the global 1km resolution MCD19A2 aerosol optical depth (AOD) product to clarify its reliability in BTH region, moreover, MCD19A2 was used to study the characteristics of aerosols and its correlation with air quality index (AQI). First, the AOD spatial distribution in BTH region from 2014 to 2018 was obtained through the processes of MCD19A2 such as format conversion, mosaic. Then the AOD results were verified with AERONET measured data through accurate space-time matching, proving the high accuracy and reliability of MCD19A2 (R = 0.9504, MAE = 0.0863, RMSE = 0.1243, EE = 82.26%). Besides, based on the AOD results at different scales, the spatiotemporal characteristics of aerosols in BTH region were analyzed from the aspects of spatial distribution, annual variation, and seasonal variation. Finally, the study shows that MCD19A2 AOD and AQI have a high correlation and similar spatiotemporal characteristics, proving the direct indication of MCD19A2 on air pollution. This article plays an important role in the research of aerosol characteristics, air pollution monitoring and so on.

Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5

The integration of satellite-derived aerosol optical depth (AOD) and station-measured PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) provides a promising approach for the monitoring of PM2.5. Previous models have generally only considered either the spatiotemporal heterogeneities of the AOD-PM2.5 relationship or the nonlinear relationship between AOD and PM2.5. In this paper, to simultaneously allow for the nonlinearity and spatiotemporal heterogeneities of the AOD-PM2.5 relationship, the geographically and temporally weighted neural network (GTWNN) model is proposed for the satellite-based estimation of ground-level PM2.5. The GTWNN model represents the nonlinear AOD-PM2.5 relationship via a generalized regression neural network, and is separately established for individual locations and times, to address the spatiotemporal heterogeneities of the AOD-PM2.5 relationship. Meanwhile, a spatiotemporal weighting scheme is incorporated in the GTWNN model to capture the local relations of samples for the training of the AOD-PM2.5 relationship. By the use of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD product, meteorological data, and MODIS normalized difference vegetation index (NDVI) data as input, the GTWNN model was verified using ground station PM2.5 measurements from China in 2015. The GTWNN model achieved sample-based cross-validation (CV) and site-based CV R2 values of 0.80 and 0.79, respectively, and it outperformed the geographically and temporally weighted regression model (CV R2: 0.75 and 0.73) and the daily geographically weighted regression model (CV R2: 0.72 and 0.72). The proposed model implements the combination of geographical law and a neural network, and will be of great use for remote sensing retrieval of environmental variables.

Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 1. Bias Correction With Surface Observations in Nonrural Areas.

This work is the first of a two-part study that aims to develop a computationally efficient bias correction framework to improve surface PM2.5 forecasts in the United States. Here, an ensemble-based Kalman filter (KF) technique is developed primarily for nonrural areas with approximately 500 surface observation sites for PM2.5 and applied to three (GEOS-Chem, WRF-Chem, and WRF-CMAQ) chemical transport model (CTM) hindcast outputs for June 2012. While all CTMs underestimate daily surface PM2.5 mass concentration by 20-50%, KF correction is effective for improving each CTM forecast. Subsequently, two ensemble methods are formulated: (1) the arithmetic mean ensemble (AME) that equally weights each model and (2) the optimized ensemble (OPE) that calculates the individual model weights by minimizing the least-square errors. While the OPE shows superior performance than the AME, the combination of either the AME or the OPE with a KF performs better than the OPE alone, indicating the effectiveness of the KF technique. Overall, the combination of a KF with the OPE shows the best results. Lastly, the Successive Correction Method (SCM) was applied to spread the bias correction from model grids with surface PM2.5 observations to the grids lacking ground observations by using a radius of influence of 125 km derived from surface observations, which further improves the forecast of surface PM2.5 at the national scale. Our findings provide the foundation for the second part of this study that uses satellite-based aerosol optical depth (AOD) products to further improve the forecast of surface PM2.5 in rural areas by performing statistical analysis of model output.

Estimating PM2.5 with high-resolution 1-km AOD data and an improved machine learning model over Shenzhen, China

Studies on fine particulate matter with an aerodynamic diameter of 2.5 μm or smaller (PM2.5) are closely related to the atmospheric environment and human activities but are often limited by ground-level in situ observations. Satellite remote sensing techniques have been widely used to estimate the PM2.5 concentration over large areas where ground-monitoring sites are unavailable. However, satellite-retrieved aerosol optical depth (AOD) products usually feature a coarse resolution, which is insufficient for the estimation of the urban-scale PM2.5 concentration. We developed a new improved random forest (IRF) model based on machine learning and a newly released AOD product with a high resolution of 1-km, which could more effectively and accurately estimate the PM2.5 concentration over Shenzhen in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China. Daily PM2.5 concentrations from 2016 to 2018 were estimated from ground-level PM2.5 and meteorological variable data. The popular linear regression model, geographically and temporally weighted regression (GTWR) model and random forest (RF) model without spatiotemporal information were employed for comparison and validation purposes through the 10-fold cross-validation (CV) approach. The IRF model attained an overall R2 value of 0.915 and a root mean square error (RMSE) value of 3.66 μg m−3. This suggests that the IRF model can estimate the urban PM2.5 concentration with a high spatial resolution at the daily, seasonal and annual scales, and the improved machine learning method is better than the linear model proposed by previous studies in terms of the estimation accuracy of the PM2.5 concentration. Generally, the IRF model coupled with AOD data with a 1-km resolution can significantly improve the calculation accuracy of the atmospheric PM2.5 concentration over coastal urban areas in the future.

Assessment of the Representativeness of MODIS Aerosol Optical Depth Products at Different Temporal Scales Using Global AERONET Measurements

Assessments of long-term changes of air quality and global radiative forcing at a large scale heavily rely on satellite aerosol optical depth (AOD) datasets, particularly their temporal binning products. Although some attempts focusing on the validation of long-term satellite AOD have been conducted, there is still a lack of comprehensive quantification and understanding of the representativeness of satellite AOD at different temporal binning scales. Here, we evaluated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products at various temporal scales by comparing the MODIS AOD datasets from both the Terra and Aqua satellites with the entire global AErosol RObotic NETwork (AERONET) observation archive between 2000 and 2017. The uncertainty levels of the MODIS hourly and daily AOD products were similarly high, indicating that MODIS AOD retrievals could be used to represent daily aerosol conditions. The MODIS data showed the reduced quality when integrated from the daily to monthly scale, where the relative mean bias (RMB) changed from 1.09 to 1.21 for MODIS Terra and from 1.04 to 1.17 for MODIS Aqua, respectively. The limitation of valid data availability within a month appeared to be the primary reason for the increased uncertainties in the monthly binning products, and the monthly data associated uncertainties could be reduced when the number of valid AOD retrievals reached 15 times in one month. At all three temporal scales, the uncertainty levels of satellite AOD products decreased with increasing AOD values. The results of this study could provide crucial information for satellite AOD users to better understand the reliability of different temporal AOD binning products and associated uncertainties in their derived long-term trends.

Effect of lockdown due to SARS COVID-19 on aerosol optical depth (AOD) over urban and mining regions in India

The Severe Acute Respiratory Syndrome-COronaVIrus Diseases 2019 (SARS-COVID-19) pandemic has posed a serious threat to human health (death) and substantial economic losses across the globe. It was however presumed that extreme preventive measures of entire lockdown in India might have reduced the air pollution level and therefore decreased the aerosol optical depth (AOD). The Moderate Resolution Imaging Spectroradiometer (MODIS)-based Multi-angle Implementation of Atmospheric Correction (MAIAC) daily AOD product was deployed to investigate the change in AOD level during lockdown phases across the Indian Territory as compared to the long-term mean AOD level (2000–2019) of the same periods. The key findings of the study revealed that AOD level over the Indian Territory is greatly reduced (~45%) during the lockdown periods as compared to the long-term mean AOD level (2000–2019). Furthermore, a noteworthy negative AOD anomaly (~6 to 37%) was observed across the four metropolitan cities in India during the entire lockdown period (25th March to 15th May 2020). However, coal mining regions of the various coalfields in India showed a positive anomaly (~+11 to 40%) during the lockdown periods due to ongoing mining operations. In a nutshell, the study results indicated a huge drop in the AOD level over Indian Territory during lockdown periods. It is expected that the pandemic can influence some policy decisions to propose air pollution control methods. Lockdown events possibly may play a crucial role as a potential solution for air pollution abatement in the future. It may not be uncommon in future when the governments may implement deliberately selective lockdowns at pollution hotspots to control the pollution level.

Advances in the estimation of high Spatio-temporal resolution pan-African top-down biomass burning emissions made using geostationary fire radiative power (FRP) and MAIAC aerosol optical depth (AOD) data

We provide major updates to the ‘top down’ Fire Radiative Energy Emissions (FREM) approach to biomass burning emissions calculations, bypassing the estimation of fuel consumption that is a major source of uncertainty in widely used ‘bottom up’ approaches. The FREM approach links satellite observations of fire radiative power (FRP) to emission rates of total particulate matter (TPM) via spatially varying smoke emissions coefficients (g.MJ−1) – each derived from matchups of FRP and smoke plume aerosol optical depth (AOD). In the original FREMv1 approach, FRP data came from the geostationary Meteosat satellite and AOD data from the 10 km spatial resolution MODIS MOD04 aerosol product. However, the latter often performs quite poorly close to biomass burning sources due to its large 10 km pixels, bias at high MODIS view zenith angles, and saturation and/or removal of areas of high AOD - limitations introducing bias and uncertainty into the final FREM-derived smoke emissions estimates. We address each of these issues through a series of significant methodological and input data improvements, including exploitation of the 1 km MODIS MAIAC AOD product that performs far better close to fire sources. We use our FREMv2 methodology to generate a new pan-African fire emissions inventory for TPM and the carbonaceous gases CO2, CO and CH4, and our annual mean TPM emissions are within 11% of those of the MODIS-based FEER top-down approach, but significantly higher than those of GFASv1.2 and GFEDv4.1s (by 114% and 69% respectively) - agreeing with independent assessments that aerosol emissions of GFASv1.2 require upscaling by a factor of 2 to 3.4 to deliver matching magnitudes between modelled and observed AODs. From our carbonaceous emissions totals we map dry matter consumed (DMC) across Africa, and dividing this by the FireCCISFD11 20 m burned area product we provide one of the first data-driven pan-African maps of fuel consumption per unit area (kg.m−2) which in many areas is higher than in GFEDv4.1s. Our estimates represent the highest spatio-temporal resolution biomass burning emissions data yet available over Africa, and significantly advance the aim of a pan-tropical and mid-latitude inventory based on FRP from the global geostationary satellite network (Meteosat, Meteosat IOD, GOES and Himawari).

Estimating PM2.5 concentrations in Yangtze River Delta region of China using random forest model and the Top-of-Atmosphere reflectance

Previous studies that have used remote sensing data to estimate the PM2.5 concentrations mainly focused on the retrieval of aerosol optical depth (AOD) with moderate-to-low spatial resolution. However, the complex process of retrieving AOD from satellite Top-of-Atmosphere (TOA) reflectance always generates the missingness of AOD values due to the limitation of AOD retrieval algorithms. This study validated the possibility of using satellite TOA reflectance for estimating PM2.5 concentrations, rather than using conventional AOD products retrieved from remote sensing imageries. Given that the TOA-PM2.5 relationship cannot be accurately expressed by simple linear correlation, we developed a random forest model that integrated satellite TOA reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B product, meteorological fields and land-use variables to estimate the ground-level PM2.5 concentrations. The highly-polluted Yangtze River Delta (YRD) region of eastern China was employed as our study case. The results showed that our model performed well with a site-based and a time-based CV R2 of 0.92 and 0.88, respectively. The derived annual and seasonal distributions of PM2.5 concentrations exhibited high PM2.5 values in northern YRD region (i.e., Jiangsu province) and relatively low values in southern region (i.e., Zhejiang province), which shared a similar distribution trend with the observed PM2.5 concentrations. This study demonstrated the outstanding performance of random forest model using satellite TOA reflectance, and also provided an effective method for remotely sensed PM2.5 estimation in regions where AOD retrievals are unavailable.

Potential Approach for Single-Peak Extinction Fitting of Aerosol Profiles Based on In Situ Measurements for the Improvement of Surface PM2.5 Retrieval from Satellite AOD Product

The vertical distribution of aerosols is important for accurate surface PM2.5 retrieval and initial modeling forecasts of air pollution, but the observation of aerosol profiles on the regional scale is usually limited. Therefore, in this study, an approach to aerosol extinction profile fitting is proposed to improve surface PM2.5 retrieval from satellite observations. Owing to the high similarity of the single-peak extinction profile in the distribution pattern, the log-normal distribution is explored for the fitting model based on a decadal dataset (3248 in total) from Micro Pulse LiDAR (MPL) measurements. The logarithmic mean, standard deviation, and the height of peak extinction near-surface (Mode) are manually derived as the references for model construction. Considering the seasonal impacts on the planetary boundary layer height (PBLH), Mode, and the height of the surface layer, the extinction profile is then constructed in terms of the planetary boundary layer height (PBLH) and the total column aerosol optical depth (AOD). A comparison between fitted profiles and in situ measurements showed a high level of consistency in terms of the correlation coefficient (0.8973) and root-mean-square error (0.0415). The satellite AOD is subsequently applied for three-dimensional aerosol extinction, and the good agreement of the extinction coefficient with the PM2.5 within the surface layer indicates the good performance of the proposed fitting approach and the potential of satellite observations for providing accurate PM2.5 data on a regional scale.

Long-term AOD trend assessment over the Eastern Mediterranean region: A comparative study including a new merged aerosol product

Abstract This study conducts a comprehensive estimation of aerosol optical depth (AOD) trend and a classification of types of aerosols over the Eastern Mediterranean (EM) region using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) AOD and Angstrom exponent (AE) during 1980–2018 and MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6.1/Terra and Aqua products during 2001–2018 and 2003–2018, respectively. Seven countries and sixteen cities were selected for the analysis. Evaluation of MODIS and MERRA-2 AOD with nine stations from the Aerosol Robotic Network (AERONET) AOD indicated spatial agreement. A new merged AOD product of MERRA-2 and MODIS (MMM) was generated. The performance of MMM not only indicated a better agreement than MODIS and MERRA-2, but also showed an efficient estimation compared to AERONET. Overall, MERRA-2 was able to quantitatively reproduce seasonal and annual AOD trends on regional scale compared to MODIS and MMM though slight differences were present in some areas. High AOD loading was mainly detected in the areas of natural mineral dust during spring and summer. Regional aerosol loading was relatively low in winter and autumn. Our results indicated that the AOD had an upward trend in the EM region during 1998–2018. Furthermore, AOD trend shifted from upward to downward around 2006 over all the countries. Our analysis revealed a dominance of clean maritime (42.95%) over the eastern part and the mixed type aerosols (34.93 and 49.13%) over the northern and southern parts. This suggests that the EM region is influenced by a variety of aerosols.

Spatio-temporal assessment of Aerosol Optical Depth from Himawari-8 satellite data over Malaysia

Aerosols, particulate matters and air pollution are significant atmospheric issues in the Southeast Asia (SEA) countries particularly Malaysia. Despite their importance, data to investigate their spatio-temporal variation, estimating their abundance and modelling the trend are not widely available due to financial constraints and technical expertise. Remote sensing data are alternative solution to the limited data available from ground stations. One of the latest remotely sensed Aerosol Optical depth (AOD) data available is from Himawari-8 satellite that provides high temporal resolution data (every 10 minutes) at moderate spatial resolution of 5 km. Nevertheless, the robustness of these data for the tropical region has not yet been validated yet. This study aimed at assessing the performance of Himawari-8 AOD L2 product by (i) comparing its absolute AOD values against AOD retrieved from AERONET stations and (ii) analysing the spatial and temporal patterns of the AOD data. The AERONET data were averaged for ±5 min of the Himawari-8 overpass and matched to each other using statistical measures i.e. Correlation, Root mean square error (RMSE), Relative bias (RB) and Mean Absolute Error (MAE). Preliminary results of the analysis provided a coefficient of determination (R2) value = 0.31, RMSE = 0.16, and MAE = 0.15. The Himawari-8 AOD product is found to be overestimated the AOD values by 24.19%. Nevertheless, Himawari-8 AOD L2 product is able to exhibit similar spatial and temporal patterns compared to other remotely sensed AOD data such as that of MODIS. The high temporal resolution of Himawari data will facilitate frequent monitoring of air pollution in this region.

Global Distribution of Column Satellite Aerosol Optical Depth to Surface PM2.5 Relationships

Using a combined Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) mid-visible aerosol optical depth (AOD) product at 0.1 × 0.1-degree spatial resolution and collocated surface PM2.5 (particulate matter with aerodynamic diameter smaller than 2.5 μm) monitors, we provide a global five-year (2015–2019) assessment of the spatial and seasonal AOD–PM2.5 relationships of slope, intercepts, and correlation coefficients. Only data from ground monitors accessible through an open air-quality portal that are available to the worldwide community for air quality research and decision making are used in this study. These statistics that are reported 1 × 1-degree resolution are important since satellite AOD is often used in conjunction with spatially limited surface PM2.5 monitors to estimate global distributions of surface particulate matter concentrations. Results indicate that more than 3000 ground monitors are now available for PM2.5 studies. While there is a large spread in correlation coefficients between AOD and PM2.5, globally, averaged over all seasons, the correlation coefficient is 0.55 with a unit AOD producing 54 μgm−3 of PM2.5 (Slope) with an intercept of 8 μgm−3. While the number of surface PM2.5 measurements has increased by a factor of 10 over the last decade, a concerted effort is still needed to continue to increase these monitors in areas that have no surface monitors, especially in large population centers that will further leverage the strengths of satellite data.

Spatial and Temporal Characteristics of AOD and Angström Exponent in the Yangtze River Delta Based on MODIS_C061

Considering the Yangtze River Delta as the research region, the applicability of the Terra-MODIS C061 deep blue algorithm (DB) AOD products was evaluated using Aerosol Robotic Network (AERONET) ground-based observations. The results demonstrated that the correlation between Terra-MODIS C061 deep blue algorithm (DB) aerosol optical depth (AOD) and AERONET AOD was high (0.95). Characteristics of spatial distribution and temporal variation of AOD and Angström exponent (AE) from 2000 to 2018 in the study area were analyzed using MOD04_L2 products from 2000 to 2018. The results showed that the AOD in the Yangtze River Delta was distributed as "eastern and northern plains high and southern and western mountains low". The AE showed a "northern low and south high" pattern. In terms of temporal distribution, from 2003 to 2007, the annual average AOD increased significantly, with a growth rate of 23%. After 2011, the AOD showed a downward trend. From 2001 to 2003, the annual average of AE rapidly increased, while after 2012, the AE decreased gradually. The AOD showed obvious seasonal changes in the Yangtze River Delta region, with high values in summer and low values in winter. The highest AOD was observed in June (0.84) in all monthly averages, while the lowest was observed in August (0.40). The seasonal average AE was high in autumn and low in spring. The highest AE of 1.47 was observed in September in all monthly averages, and the lowest of 1.08 was observed in March. Aerosol types in the Yangtze River Delta region were investigated according to the relationship between AOD and AE. The results suggested that the urban industrial aerosol was the main aerosol type in the region, followed by mixed type and clean continental aerosols.

Improvement of snow/haze confusion data gaps in MODIS Dark Target aerosol retrievals in East China

The MODerate resolution Imaging Spectroradiometer (MODIS) is one of the most widely used meteorological remote sensing instruments. Its Dark Target aerosol optical depth (AOD) product has been widely used in environment and meteorology researches, such as model evaluation and data assimilation. However, this product has a low coverage under conditions of heavy haze in China. This is because the haze can be misidentified as snow under some circumstances by the algorithm and therefore rejected, leading to large-scale data omission. In the most polluted regions, misidentified snow cover exceeded 8%. Regarding this issue, a new method combining the snow mask derived from the MODIS cloud mask product and Fisher discrimination analysis was developed to give a more accurate identification of snow and ice cover. Applying this new method increases the AOD data coverage significantly. Comparisons with AOD values from ground-based observations showed that the newly produced data under haze conditions had a similar accuracy with the original data in the MODIS AOD product. Because the newly supplemented data are more distributed at the seriously polluted regions, the average AOD increased significantly after data filling in many regions. In winter, AOD in the most severely polluted regions (average air quality index >130) increased by 0.2–0.3 after improvement, about 30–50% of the original value. In 49 haze cases with large-scale pollution, the increase reached 0.3–0.6, about 50%–70% of the original value. During the haze episodes, the data omission led to an underestimation of the regional average AOD by 19–40%. The improvement in AOD coverage helps to provide a better reflection of the air pollution condition in East China through the perspective of AOD.

Human activities and the natural environment have induced changes in the PM2.5 concentrations in Yunnan Province, China, over the past 19 years

Fine particulate matter (PM2.5) concentrations exhibit distinct spatiotemporal heterogeneity, mainly due to the natural environment and human activities. Yunnan Province of China was selected as the research area, and a real-time measured PM2.5 concentration dataset was acquired from 41 monitoring stations in 16 major cities from February 2013 to December 2018. Aerosol optical depth (AOD) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and data on four meteorological variables from 2000 to 2018 were employed. A novel hybrid model was constructed to estimate the historical missing PM2.5 values from 2000 to 2012, calculate the missing PM2.5 concentrations from 2012 to 2014 in some major cities, and analyze the driving factors of the PM2.5 concentration changes and causes of key pollution events in Yunnan Province over the past 19 years. The temporal analysis results indicate that the annual mean PM2.5 concentration in Yunnan Province exhibited three stages: continuous stability, a rapid increase and a rapid decrease. The year 2013 was an important breakpoint in the trend of the concentration change. The spatial analysis results reveal that the annual mean PM2.5 concentration in the north was lower than that in the south, and there was a significant difference between the east and the west. In addition, springtime biomass burning in Southeast Asia was found to be the main cause of PM2.5 pollution in Yunnan Province in spring.

Mapping PM2.5 concentration at a sub-km level resolution: A dual-scale retrieval approach

Satellite-based retrieval has become a popular PM2.5 monitoring method. To improve the retrieval performance, multiple variables are usually introduced as auxiliary variables, in addition to aerosol optical depth (AOD). The different kinds of variables are usually at different resolutions, varying from sub-kilometer to dozens of kilometers. Generally speaking, when undertaking the retrieval, the variables at different resolutions are resampled to the same resolution as the AOD product to ensure scale consistency (single-scale retrieval). However, a drawback of doing this is that the information contained within the different resolutions (scales) is discarded. To fully utilize the information contained in the different scales, a dual-scale retrieval approach is proposed in this paper. In the first stage, the variables which influence PM2.5 concentration at a large scale are used for PM2.5 retrieval at a coarse resolution. Then, in the second stage, the variables which affect PM2.5 distribution at a finer scale are used for the further PM2.5 retrieval at a high resolution (sub-km level resolution), with the retrieved low-resolution PM2.5 from the first stage also acting as input. In this study, four different regression models were adopted to test the performance of the dual-scale retrieval approach at both daily and annual scales: multiple linear regression (MLR), geographically weighted regression (GWR), random forest (RF), and the generalized regression neural network (GRNN). Compared with the traditional single-scale retrieval approach, the proposed dual-scale retrieval approach can achieve PM2.5 mapping at a finer resolution and with a higher accuracy. Dual-scale retrieval can utilize the information contained in different scales, thus achieving an improvement in both resolution and retrieval accuracy. The proposed approach has the potential to be used for the generation of quantitative remote sensing products in various fields, and will promote the quality improvement of these quantitative remote sensing products.

Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China

Reliable aerosol optical depth (AOD) data with high spatial and temporal resolutions are needed for research on air pollution in China. AOD products from the Advanced Himawari Imager (AHI) onboard the geostationary Himawari-8 satellite and reanalysis datasets make it possible to capture diurnal variations of aerosol loadings. However, due to the different retrieval methods, their applicability may vary with different space and time. Thus, in this study, taking the measured AOD at the Aerosol Robotic NETwork (AERONET) stations as the gold standard, the performance of the latest AHI hourly AOD product (i.e., L3 AOD) was evaluated and then compared with that of two reanalysis AOD datasets offered by Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS), respectively, covering from July 2015 to December 2017 over China. For all the matchups, AHI AOD shows the highest robustness with a high correlation (R) of 0.82, low root-mean-square error (RMSE) of 0.23, and moderate mean absolute relative error (MARE) of 0.56. Although MERRA-2 and CAMS products both have lower R values (0.74, 0.72) and higher RMSE (0.28, 0.26), the former is slightly better than the latter. Accuracy of AOD products could be mainly affected by the pollution level and less affected by particle size distribution. Comparisons among these AOD products imply that AHI AOD is more reliable in regions with high pollution levels, such as central and eastern China, while in the northern and western part, MERRA-2 AOD seems more satisfying. The performance of all the three AOD products presents a significant diurnal variety, as indicated by the highest accuracy in the morning for AHI and at noon for reanalysis data. Moreover, due to various pollution distribution patterns and meteorological conditions, there are distinct seasonal characteristics in the performance of AOD products for different regions.