Moderate Resolution Imaging Spectroradiometer Aerosol Optical Depth Research Articles

Estimation of PM2.5 concentrations in North China with high spatiotemporal resolution using the ERA5 dataset and machine learning models

Previous studies have predominantly focused on developing high spatiotemporal resolution PM2.5 models utilizing moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) products alongside certain meteorological factors. However, MODIS AOD is not continuous, and there is a problem of missing data, which is not conducive to the study of PM2.5 in areas without AOD. This study proposes a new method for estimating PM2.5 concentration in North China, which not only simplifies the complex process associated with the use of AOD, but also partially solves the issue of missing AOD data. This study proposes a machine learning (random forest, RF; back propagation neural network, BPNN) based method for estimating PM2.5 using meteorological parameters obtained from the fifth-generation reanalysis (ERA5) dataset released by the European Centre for Medium-Range Weather Forecasts and considering the effects of land cover type, digital elevation model, vegetation index and population data. The model performed well, with 10-fold cross-validation (coefficient of determination) R2 and (root-mean-square error) RMSE of 0.79/0.95 and 26.10/13.22 µg/m3, respectively, for BPNN and RF. The estimated hourly performance of the RF model in winter (00:00 to 23:00 BST) with an R2 ranging from 0.92 to 0.96, an RMSE of 11.45 to 16.70 µg/m3. RF model with the best performance and ERA5 were selected to build a high-resolution (0.25°×0.25°) hourly PM2.5 map (PMM), and the PMM was compared with CHAP, with R2 and RMSE of 0.75 and 20.62 µg/m3, respectively. This study further investigates the impacts of land cover types, digital elevation model, and land surface characteristics on the spatial distribution of PM2.5 in North China.

Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa

Abstract. The Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) is a new community modeling infrastructure that enables the study of atmospheric composition and chemistry across all relevant scales. We develop a MUSICAv0 grid with Africa refinement (∼ 28 km × 28 km over Africa). We evaluate the MUSICAv0 simulation for 2017 with in situ observations and compare the model results to satellite products over Africa. A simulation from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a regional model that is widely used in Africa studies, is also included in the analyses as a reference. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Both models underestimate carbon monoxide (CO) compared to in situ observations and satellite CO column retrievals from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument. MUSICAv0 tends to overestimate ozone (O3), likely due to overestimated stratosphere-to-troposphere flux of ozone. Both models significantly underestimate fine particulate matter (PM2.5) at two surface sites in East Africa. The MUSICAv0 simulation agrees better with aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric nitrogen dioxide (NO2) column retrievals from the Ozone Monitoring Instrument (OMI) than WRF-Chem. MUSICAv0 has a consistently lower tropospheric formaldehyde (HCHO) column than OMI retrievals. Based on model–satellite discrepancies between MUSICAv0 and WRF-Chem and MOPITT CO, MODIS AOD, and OMI tropospheric NO2, we find that future field campaign(s) and more in situ observations in the East African region (5∘ S–5∘ N, 30–45∘ E) could substantially improve the predictive skill of atmospheric chemistry model(s). This suggested focus region exhibits the largest model–in situ observation discrepancies, as well as targets for high population density, land cover variability, and anthropogenic pollution sources.

Global Scale Inversions from MOPITT CO and MODIS AOD

Top-down observational constraints on emissions flux estimates from satellite observations of chemical composition are subject to biases and errors stemming from transport, chemistry and prior emissions estimates. In this context, we developed an ensemble data assimilation system to optimize the initial conditions for carbon monoxide (CO) and aerosols, while also quantifying the respective emission fluxes with a distinct attribution of anthropogenic and wildfire sources. We present the separate assimilation of CO profile v9 retrievals from the Measurements of Pollution in the Troposphere (MOPITT) instrument and Aerosol Optical Depth (AOD), collection 6.1, from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. This assimilation system is built on the Data Assimilation Research Testbed (DART) and includes a meteorological ensemble to assimilate weather observations within the online Community Atmosphere Model with Chemistry (CAM-chem). Inversions indicate an underestimation of CO emissions in CAMS-GLOB-ANT_v5.1 in China for 2015 and an overestimation of CO emissions in the Fire INventory from NCAR (FINN) version 2.2, especially in the tropics. These emissions increments are consistent between the MODIS AOD and the MOPITT CO-based inversions. Additional simulations and comparison with in situ observations from the NASA Atmospheric Tomography Mission (ATom) show that biases in hydroxyl radical (OH) chemistry dominate the CO errors.

JEDI‐Based Three‐Dimensional Ensemble‐Variational Data Assimilation System for Global Aerosol Forecasting at NCEP

AbstractA three‐dimensional ensemble‐variational global aerosol data assimilation system based on the Joint Effort for Data assimilation Integration (JEDI) was developed for the Global Ensemble Forecast System‐Aerosols (GEFS‐Aerosols) coupled with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model at the National Centers for Environmental Prediction. Aerosol mass mixing ratios in GEFS‐Aerosols were selected as control or analysis variables and were adjusted by assimilating 550 nm Aerosol Optical Depth (AOD) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments onboard the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite produced by the National Environmental Satellite, Data, and Information Service (NESDIS) at National Oceanic and Atmospheric Administration (NOAA). The original NOAA/NESDIS S‐NPP VIIRS Level 2.0 550 nm AOD retrievals were converted to JEDI Interface for Observation Data Access format. AOD forward operator and its tangent‐linear and adjoint models were implemented based on GOCART in JEDI Unified Forward Operator. A stochastically perturbed emission (SPE) approach was developed in the Common Community Physics Package‐based GEFS‐Aerosols to account for aerosol emission uncertainty. One‐month retrospective and three‐month near‐real‐time experiments consistently showed improved GEFS‐Aerosols analyses and forecasts from assimilating VIIRS 550 nm AOD retrievals against independent NASA Aqua and Terra Moderate Resolution Imaging Spectroradiometer AOD retrievals, Aerosol Robotic Network AOD, and independent AOD and aerosol analyses from NASA and European Centre for Medium‐Range Weather Forecasts. Through scaling and perturbing aerosol emissions, SPE enhanced ensemble error‐spread consistency and further improved AOD assimilation. The valid‐time‐shifting ensemble approach in a cost‐effective manner of populating background ensembles showed positive impacts on AOD assimilation.

On the relationship between eastern China aerosols and western North Pacific tropical cyclone activity

How aerosols affect the weather and climate system has received increasing attention. This study finds a significant negative correlation between March–May eastern China aerosol optical depth (AOD) and July–November western North Pacific (WNP) tropical cyclone (TC) frequency during 2003–2020. This time period spans when several aerosol reanalyses are available and both Terra and Aqua Moderate Resolution Imaging Spectroradiometer AOD retrievals are assimilated therein. Composite analyses and budget analyses of dynamical genesis potential indices indicate the importance of large-scale environmental factors, especially vertical velocity and vertical wind shear, associated with changes in AOD that in turn modulate changes in WNP TC frequency. Increased eastern China AOD may facilitate negative Pacific meridional mode development via modulation of the westerly jet, which then forces an anticyclonic circulation over the WNP basin. Increased AOD can also directly decrease the inter-hemispheric temperature differential and increases the intra-hemispheric temperature differential between the equator and the mid-latitudes, thus weakening ascending motion and enhancing vertical wind shear over the WNP, especially the southeastern portion of the basin. All of these large-scale environment changes induced by increased eastern China AOD tend to suppress WNP TCs. This study highlights the potential influence of eastern China aerosol loadings on WNP TCs, thus improving our understanding of TC climate variability over the WNP.

The influence and contribution of fine mode particles to aerosol optical properties during haze events at the foothills of Himalaya-Karakorum region

In the last decade, the regional haze is considered as one of the most devastating weathers which causes serious impacts on air quality, human health, and transportation over the Indo-Gangetic Plain, particularly the eastern part of Pakistan. In this paper for the first time, the long-term characterization of aerosols optical properties during haze events were carried out using ground-based Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite over Lahore and Faisalabad for the period of 2009–2018. Firstly, the MODIS Aerosol Optical Depth (AOD) was correlated with AERONET AOD and reported a high degree of R-squared correlation of value 0.78 between two datasets for the months of October, November and December during the study period (2009–2018). After validation of data, the daily averaged AOD and Angstrom Exponent (AE) values exceeding 1 were selected as heavy haze events. Then the aerosol optical properties such as AOD, AE, Volume Size Distribution (VSD), and Fine Mode Fraction (FMF) of AOD were retrieved from AERONET and MODIS for further analysis. The distribution of AOD and AE showed that most of the values were greater than 1.3 and 1.1, respectively, during haze events over both study sites. The daily averaged profile of aerosol VSD exhibited a bimodal lognormal distribution having dominant fine mode peaks at radius between 0.19 and 0.25 μm as compared to coarse modes peaks. Furthermore, the contribution of fine-mode AOD to the total AOD was quite higher (i.e., 86–98%) during haze events showing the greater contribution of fine mode aerosol particles. The correlation of AE with AOD and FMF revealed that the pronounced aerosol types were fine mode aerosol particles originated due to biomass burning and urban/industrial emissions during haze events. These types of fine mode aerosol particles were also confirmed with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) subtype measurements. Furthermore, the back trajectories revealed that most air masses were received from the local/regional aerosol sources, showing the contribution of fine mode aerosol particles to haze events whilst some of the trajectories were from Afghanistan, Iran, and India showing long range transportation of air masses to the receptor sites.

The Impact of Long-Range Transport of Biomass Burning Emissions in Southeast Asia on Southern China

The long-range transport of biomass burning pollutants from Southeast Asia has a significant impact on air quality in China. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) fire data and aerosol optical depth (AOD) products and the Tropospheric Monitoring Instrument (TROPOMI) carbon monoxide (CO) data were used to analyze the impact of air pollution caused by biomass burning in Southeast Asia on southern China. Results showed that Yunnan, Guangdong and Guangxi were deeply affected by biomass burning emissions from March to April during 2016–2020. Comparing the data for fires on the Indochinese Peninsula and southern provinces of China, it is obvious that the contribution of pollutants emitted by local biomass burning in China to air pollution is only a small possibility. The distribution of CO showed that the overall emissions increased greatly from March to April, and there was an obvious transmission process. In addition, the MODIS AOD in areas close to the national boundary of China is at a high level (>0.6), and the AOD in the southwest of Guangxi province and the southeast of Yunnan Province is above 0.8. Combined with a typical air pollution event in southern China, the UVAI combined with wind direction and other meteorological data showed that the pollutants were transferred from the Indochinese Peninsula to southern China under the southwest monsoon. The PM2.5 data from ground-based measurements and backward tracking were used to verify the pollutant source of the pollution event, and it was concluded that the degree of pollution in Yunnan, Guangxi and Guangdong provinces was related to the distance from the Indochinese Peninsula. Results indicate that it is necessary to carry out in-depth research on the impact of cross-border air pollution transport on domestic air quality as soon as possible and to actively cooperate with foreign countries to carry out pollution source research and control.

Long-term variations and comparison of aerosol optical properties based on MODIS and ground-based data in Thailand

Comparison and analysis of Moderate Resolution Imaging Spectroradiometer (MODIS), Aerosol Robotic Network (AERONET), and SKYNET aerosol optical depth (AOD) products were investigated at four AERONET and two SKYNET stations in Thailand from 2006 to 2020. AERONET Level 2.0 and MODIS Deep Blue (DB) Collection 6.1 data were applied. Aqua and Terra MODIS AODs (satellite AODs) were compared to AERONET and SKYNET AODs (ground based AODs). Ground-based and satellite AODs were in good agreement, giving a high correlation (R2 = 0.75) in Ubon Ratchathani, associated with biomass burning. The long-term trend in AOD was analyzed. Both satellite and ground based AODs demonstrate increasing trends in AOD over Thailand associated with the urbanized area and traffic congestion. Aerosol particles are categorized into dust, sea salt, and biomass burning, containing fine and coarse modes. A variation between satellite data measurements depended on surface reflectances, such as the large water body surrounding (Songkhla) and the impact of surface albedo (Chiang Mai). Higher AOD values were observed between February to April. In addition, meteorological parameters are directly related to aerosol concentrations, while no significant connection could be developed in the observed values between Angstrom exponent (AE) and Meteorological parameters. Meteorological parameters and temperature inversion more influenced AOD concentrations in February. Nevertheless, the hypothesis showed 95% confidence interval of AODs retrieved from satellite data fell within the confidence range. Therefore, satellite aerosol products are sufficiently accurate for estimating aerosol climatology.

Air Quality Interpretation of Four Geographically Distinct Hot Spot States of India-An Appraisal Using Aerosol Optical Depth and Particulate Matter

The application of Moderate Resolution Imaging Spectroradiometer-Aerosol Optical Depth (MODIS-AOD) for Air Quality (AQ) assessment has contributed significantly by making use of a spatiotemporal relationship between ground-level particulate matter and aerosol optical density at both city and state levels. Traditional ground-based site-specific particulate matter monitoring method has found a profound gap in their spatial coverage. Such challenging situations prevail over the progressive development of the combined usage of ground monitoring technique along with the MODIS-AOD product (with 10 km spatial resolution). This was achieved and derived by assembling the satellite sensor, Terra as a tool for the regional-based specific studies for monitoring airborne particles. Their analytical linear association was evaluated and explored their feasibility for monitoring either Respirable Suspended organic Matter (RSPM) or AQI in spatiotemporally distinct geographical sites situated in four States of India. Examined the interrelationship between RSPM-AOD against meteorological drivers through Principal Component Analysis (PCA) and Quantitative Risk Assessment (QRA) of the focussed four zones. Elevated Air Pollution was noticed in the urban and industrial cities of Pune, Kochi, and Chennai. They were flanked by various industrial clusters and the substantial personal handling of vehicles. A region-wise RSPM-AOD correlation was recorded for the categorized areas: WS1, WS2, SW, and ES. Each zone was subsequently judged and a noteworthy association was linked between the ground-monitored RSPM data and the retrieved AOD output. This means of records could represent the aerosol component; consequently, infer their sources of origin and in later days could come up with reasonable emission control channels. States of India Maharashtra, Karnataka, Kerala, and Tamil Nadu, are categorized as WS1, WS2, SW, and ES respectively.

Retrieval of Atmospheric Aerosol Optical Depth From AVHRR Over Land With Global Coverage Using Machine Learning Method

Aerosols play an important role in global climate change, which requires long-term data records. Advanced very high-resolution radiometer (AVHRR) provides continuous observations for up to 40 years since 1979, which makes it worthwhile to retrieve aerosol optical depth (AOD) from AVHRR over land. A novel algorithm for retrieving AOD from AVHRR is developed based on the machine learning (ML) method. The AVHRR observations from pathfinder atmospheres–extended (PATMOS-x) Level-2 dataset and corresponding AOD products ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.55~\mu \mathrm {m}$ </tex-math></inline-formula> ) from moderate resolution imaging spectroradiometer (MODIS) in 2014 are used as training data. And AOD products in three years (2015, 2006, and 1998) named AVHRR XGB-AOD were generated for evaluation. Comparisons show that the AVHRR XGB-AOD is consistent with the MODIS AOD with correlation coefficients greater than 0.80 and RMSE less than 0.18 for most months in 2015 and 2006. The temporal and spatial characteristics from AVHRR XGB-AOD are similar to those from the MODIS AOD, but those from the previous AVHRR AOD with deep blue (DB) algorithm are significantly different. Validation with AERONET indicates that more than 68% of the matchups fall within expected error [EE, ±( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.05\,\,\pm \,\,0.25\times {\mathrm {AOD}}_{\mathrm {AERONET}}\mathrm {)] }$ </tex-math></inline-formula> in 2015 and 2006, while the fraction is 66% in 1998. Compared to the DB algorithm, the ML-based algorithm performs better in high-AOD conditions over vegetated regions, such as in Southeast Asia, where the DB algorithm significantly underestimates. In low-AOD conditions, the ML-based algorithm performs better over western North America and Australia, where the aerosol composition varies greatly.

Estimating PM2.5 Concentrations in Contiguous Eastern Coastal Zone of China Using MODIS AOD and a Two-Stage Random Forest Model

Abstract The coarse Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product (spatial resolution: 3 km) retrieved by the dark-target algorithm always generates the missing values when being adopted to estimate the ground-level PM2.5 concentrations. In this study, we developed a two-stage random forest using MODIS 3-km AOD to obtain the PM2.5 concentrations with full coverage in a contiguous coastal developed region, i.e., Yangtze River delta–Fujian–Pearl River delta (YRD–FJ–PRD) region of China. A first-stage random forest–integrated six meteorological fields was employed to predict the missing values of AOD product, and the combined AOD (i.e., random forest–derived AOD and MODIS 3-km AOD) incorporated with other ancillary variables were developed for predicting PM2.5 concentrations within a second-stage random forest model. The results showed that the first-stage random forest could explain 94% of the AOD variability over YRD–FJ–PRD region, and we achieved a site-based cross validation (CV) R2 of 0.87 and a time-based CV R2 of 0.85. The full-coverage PM2.5 concentrations illustrated a spatial pattern with annual-mean PM2.5 of 46, 40, and 35 μg m−3 in YRD, PRD, and FJ, respectively, sharing the same trend with previous studies. Our results indicated that the proposed two-stage random forest model could be effectively used for PM2.5 estimation in different areas.

Long-term trends in air quality in major cities in the UK and India: a view from space

Abstract. Air quality networks in cities can be costly and inconsistent and typically monitor a few pollutants. Space-based instruments provide global coverage spanning more than a decade to determine trends in air quality, augmenting surface networks. Here we target cities in the UK (London and Birmingham) and India (Delhi and Kanpur) and use observations of nitrogen dioxide (NO2) from the Ozone Monitoring Instrument (OMI), ammonia (NH3) from the Infrared Atmospheric Sounding Interferometer (IASI), formaldehyde (HCHO) from OMI as a proxy for non-methane volatile organic compounds (NMVOCs), and aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) for PM2.5. We assess the skill of these products at reproducing monthly variability in surface concentrations of air pollutants where available. We find temporal consistency between column and surface NO2 in cities in the UK and India (R = 0.5–0.7) and NH3 at two of three rural sites in the UK (R = 0.5–0.7) but not between AOD and surface PM2.5 (R &lt; 0.4). MODIS AOD is consistent with AERONET at sites in the UK and India (R ≥ 0.8) and reproduces a significant decline in surface PM2.5 in London (2.7 % a−1) and Birmingham (3.7 % a−1) since 2009. We derive long-term trends in the four cities for 2005–2018 from OMI and MODIS and for 2008–2018 from IASI. Trends of all pollutants are positive in Delhi, suggesting no air quality improvements there, despite the roll-out of controls on industrial and transport sectors. Kanpur, identified by the WHO as the most polluted city in the world in 2018, experiences a significant and substantial (3.1 % a−1) increase in PM2.5. The decline of NO2, NH3, and PM2.5 in London and Birmingham is likely due in large part to emissions controls on vehicles. Trends are significant only for NO2 and PM2.5. Reactive NMVOCs decline in Birmingham, but the trend is not significant. There is a recent (2012–2018) steep (&gt; 9 % a−1) increase in reactive NMVOCs in London. The cause for this rapid increase is uncertain but may reflect the increased contribution of oxygenated volatile organic compounds (VOCs) from household products, the food and beverage industry, and domestic wood burning, with implications for the formation of ozone in a VOC-limited city.

Particulate matter variability in Kathmandu based on in-situ measurements, remote sensing, and reanalysis data

Kathmandu has one of the highest particulate matter air pollution levels in the world. However, few direct measurement data are available for long-term analyses, limiting policy interventions and public health advisories. Remote sensing-based data sets provide an alternative approach to address this issue. In this paper, we present an approach to analyze and understand the diurnal, seasonal, annual, and multi-annual variability of pollution levels based on in situ measurements of particulate matter (PM2.5), remote sensing data based on Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra Aerosol Optical Depth (AOD), and aerosol mass concentration retrievals, as well as AOD and PM2.5 data from Copernicus Atmosphere Monitoring Service (CAMS) and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data sets. The analysis of the direct measurements revealed distinct annual patterns, characterized by winter maxima and summer minima. With the exception of the summer monsoon season, public health guidelines are frequently exceeded significantly throughout the year, particularly in winter. The analysis furthermore pointed toward distinct daily patterns with primary maxima in the mornings, secondary maxima in the late evenings, and minima in the afternoons. The annual pattern of AOD derived from the MODIS data is markedly different from that. Due to the coarse spatial resolution and the fact that MODIS AOD is a column integrated property, it does not reflect the small scale phenomenon of the Kathmandu urban pollution pattern but instead shows a maximum in the spring. The same pattern was observed with the CAMS and MERRA-2 reanalysis AOD data, even though MERRA-2 captures pollution levels during the summer monsoon season very well. The CAMS reanalysis PM2.5 data are generally well-aligned with the near-surface measurement data, even though they overestimate the daily and monthly maxima and do not capture the morning maxima in the diurnal course. Nevertheless, CAMS PM2.5 data can be adjusted via linear regression to reasonably mirror the measurements. It shows that PM2.5 concentrations in Kathmandu have increased significantly in the past decades (almost 2 μg/m3 annually), mainly after the monsoon season from September to February. Our results indicate that around 85% of all winter days in Kathmandu are categorized as “unhealthy” according to the Air Quality Index (AQI).

Spatiotemporal variability of optical properties of aerosols over the Indo-Gangetic Plain during 2011–2015

The present study reports daily, monthly, and seasonal variations of aerosol optical depth (AOD) using aerosol robotic network (AERONET), MICROTOPS-II Sunphotometer, and Moderate Resolution Imaging Spectroradiometer (MODIS) data over three most polluted cities such as Kanpur, Varanasi, and Jaipur in the Indo-Gangetic Plain during 2011–2015. The monthly mean variations of AOD and Angstrom Exponent were also presented and compared with the meteorological parameters for all three cities. The concentrations of AODs in Kanpur and Varanasi were observed higher than in Jaipur throughout the study period. Overall maximum correlation (R2 = 0.74) was found in between AERONET and MODIS AOD in the winter season over Kanpur during 2011–2015, whereas the lowest correlation (R2 = 0.41) was found in between MICROTOPS-II Sunphotometer and MODIS AOD over Varanasi for the same period in the pre-monsoon season. Spectral variations of AODs at five different wavelengths, i.e., 380, 440, 500, 675, and 870 nm, were derived using AERONET for Jaipur and Kanpur and using MICROTOPS-II Sunphotometer for Varanasi during 2011–2015 inferred clearly that increasing wavelength provides decreasing AODs. The Hybrid Single-Particle Lagrangian Integrated Trajectories model was used for the backward trajectories analysis to assess the sources of large aerosol loadings over Jaipur, Kanpur, and Varanasi cities during some typical days.

MODIS aerosol optical depth retrieval based on random forest approach

ABSTRACT Despite significant improvement in Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrieval, high-resolution–high-accuracy AOD retrieval remains a challenging task. This study utilizes machine learning for AOD retrieval of MODIS data. The global long-time-series data of AERONET sites and corresponding MODIS data in time and space were used as sample training data for aerosol retrieval via use of the random forest (RF) approach. The accuracy and stability of retrieval were ensured by processing AERONET site data, performing time–space matching between different data types, and determining related parameters in the RF model. MODIS data use bands 1–7 of the top-of-atmosphere reflectance (TOA) – extending from the visible to near-infrared radiation spectra – along with the corresponding observation geometry data, global land surface satellite (GLASS) albedo dataset, and normalized difference vegetation index (NDVI) data. The proposed method facilitates realizationthe of high-accuracy aerosol retrieval. Furthermore, significant enhancement in the efficiency of aerosol inversion is an added advantage.

Comparison of MODIS- and CALIPSO-Derived Temporal Aerosol Optical Depth over Yellow River Basin (China) from 2007 to 2015

In this study, Collection 6.1 (C6.1) of different aerosol optical depth (AOD) products of different spatial resolutions were used from the aqua moderate resolution imaging spectroradiometer (MODIS) including dark target (DT), deep blue (DB), deep blue (DB), and DT-DB (DTB). These products were compared with cloud-aerosol lidar, and infrared pathfinder satellite observation (CALIPSO) AOD retrievals over the Yellow River Basin (YERB), China from 2003 to 2017. The YERB was divided into three sub-regions, namely YERB1 (the mountainous terrain in the upper reaches of the YERB), YERB2 (the Loess Plateau region in the middle reaches of the YERB), and YERB3 (the plain region downstream of the YERB). Errors and agreement between MODIS and CALIPSO data were reported using Pearson’s correlation (R) and relative mean bias (RMB). Results showed that the CALIPSO whole layers AOD (AODS) were better matched with MODIS AOD than the CALIPSO lowest layer AOD (AOD1). The time series of AOD shows higher values in spring and summer, and a small difference in AOD products was observed in autumn. The overall average value of CALIPSO AOD and MODIS AOD both fitted the order: YERB3 > YERB2 > YERB1. The CALIPSO AOD retrievals have the best consistency with the DTB10K and the lowest consistency with DT3K. Overall, the regional distributions of the CALIPSO AOD and MODIS AOD are significantly different over the YERB, and the difference is closely related to the season, region, and topography. This study can help researchers understand the difference of aerosol temporal and spatial distribution utilizing different satellite products over YERB, and also can provide data and technical support for the government in atmospheric environmental governance over YERB.

Climatological trends in satellite-derived aerosol optical depth over North India and its relationship with crop residue burning: Rural-urban contrast

Columnar Aerosol Optical Depths (AOD) over an urban area (Chandigarh) and a rural area (Khera, Fatehgarh Sahib district) situated in the Indo-Gangetic Plains (IGP) of India were analysed to study their temporal heterogeneity in terms of interannual, seasonal and monthly variations. Over the last few decades, IGP has become one of the global hotspots of air pollution due to the increased anthropogenic activities such as traffic, industries, agricultural waste burning etc. Level-2 AODs (550 nm) were retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard NASA's Terra and Aqua satellites, for a period of 14 years (2005–2018). The climatological mean Terra-MODIS (Aqua-MODIS) AOD over the urban location was ~0.497 ± 0.238 (0.474 ± 0.228), whereas over the rural location it was 0.542 ± 0.269 (0.534 ± 0.282). Linear trend analysis estimated an increase in annual mean Terra-MODIS (Aqua-MODIS) AOD at a rate of ~0.009 (0.013) per year over the urban site; whereas over the rural location the rate of increase was ~0.003 (0.004) per year. Results show that the observed increase is ~1.49% (2.41%) of climatological mean AOD over the urban location for Terra-MODIS (Aqua-MODIS), whereas, over the rural location, it was ~0.50% (0.67%). Using the HYSPLIT trajectory model, it was concluded that, during post-monsoon, the observed high AODs can be related to massive crop residue burning in the IGP region. These AOD trends can also be used to track the regional anthropogenic air-pollution changes. An empirical relation between AOD and PM10 was established, which can be used to estimate PM10 over the urban and rural areas of IGP (using MODIS AODs), complementing the sparse ground-based monitoring. Further, satellite-based air pollution data can be used for baseline assessment and understanding the impact of control policies such as National Clean Air Programme and to support formulate evidence-based pollution control strategies.

Synergy of Satellite‐ and Ground‐Based Aerosol Optical Depth Measurements Using an Ensemble Kalman Filter Approach

AbstractSatellite‐ and ground‐based remote sensing are two widely used techniques to measure aerosol properties. However, neither is perfect in that satellite retrievals suffer from various sources of uncertainties, and ground observations have limited spatial coverage. In this study, focusing on improving estimates of aerosol information on large scale, we develop a data synergy technique based on the ensemble Kalman filter (EnKF) to effectively combine these two types of measurements and yield a monthly mean aerosol optical depth (AOD) product with global coverage and improved accuracy. We first construct a 474‐member ensemble using 11 monthly mean AOD data sets to represent the variability of the AOD field. Then Moderate Resolution Imaging Spectroradiometer AOD retrievals are selected as the background field into which ground‐based measurements from 135 Aerosol Robotic Network sites are assimilated using the EnKF. Compared with satellite data, the bias and root‐mean‐square errors of the combined field are greatly reduced, and correlation coefficients are greatly improved. Moreover, cross validation shows that at locations where surface observations were not assimilated, the reduction in root‐mean‐square error and bias and the increase in correlation can still reach ~20%. Locations where the spatial representativeness of AOD is large or the site density is high are where the greatest changes are typically found. This study shows that the EnKF technique effectively extends the information obtained at surface sites to a larger area, paving the way for combining information from different types of measurements to yield better estimates of aerosol properties as well as their space‐time variability.

Dust Lidar Ratios Retrieved from the CALIOP Measurements Using the MODIS AOD as a Constraint

Lidar ratio for dust aerosols is retrieved from a synergetic use of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) Total Attenuated Backscatter coefficients and the Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depths (AODs) for 5 years from 2007 to 2011. MODIS AODs from the Dark Target (DT) algorithm over ocean and from the Deep Blue (DB) algorithm over land are used as a constraint for the retrieval. The dust lidar ratio is retrieved larger over land (46.6 ± 36.3 sr) than ocean (39.5 ± 16.8 sr) and shows distinct regional variation. Lidar ratio for Saharan dust (49.5 ± 36.8 sr) is larger than Arabian dust (42.5 ± 26.2 sr). Lidar ratios for dust aerosols transported to Mediterranean Sea (44.4 ± 15.9 sr), Mid Atlantic (40.3 ± 12.4 sr), and Arabian Sea (37.5 ± 12.1 sr) show lower values relative to their source regions. Retrieved dust lidar ratios for Taklamakan and Gobi Deserts region (35.0 ± 31.1 sr) and Australia (35.4 ± 34.4 sr) are slightly lower than the above-mentioned regions. AOD comparison between CALIOP and MODIS shows that the CALIOP AOD is biased low. When including clear air AOD for CALIOP, however, AODs from two sensors become more comparable.

LONG TEMPORAL ANALYSIS OF THE MODIS AEROSOL PRODUCT OVER HUAIHAI ECONOMIC REGION IN CHINA

Abstract. The Huaihai Economic Region (HER, 32.5–36.5°N, 114–121°E) with a land area of 178,000 km2, which contains 20 cities, is one of the earliest regional economic cooperative organizations in China. The huge population (100 million, about 7% of the China’s population), local heavy industries, chemical enterprises and vehicle emissions have resulted in serious air pollution. In this paper, a long-term aerosol optical depth (AOD) data set from 2000 to 2018 over HER with 10 km spatial resolution has been produced and analyzed from the latest version of MODIS (MODerate resolution Imaging Spectroradiometer) aerosol products. Validation results show that MODIS AOD has a strong correlation relationship (r = 0.94, slope = 0.92) with Aeronet Robotic Network (AERONET) over HER. The seasonal average AOD maps indicate that the high AOD values mainly occurred with a banding distribution from southeastern to northwestern HER, with AODs larger in summer and spring than in fall and winter. Temporally, the monthly average AOD has increased since 2000 and reached the highest level in 2011 (+0.02/year); then it has obviously declined from 2012 to 2018 (−0.05/year), owing to strict air pollution control implemented since 2012. In general, the annual average AOD over HER is 0.71 for the past nearly 20 years, and in 2018, the annual average AOD firstly lower than that in 2000. The study suggests that there has been a slightly improvement in air quality over HER and long-term and sustainable efforts should be made.