Multi-Angle Implementation Of Atmospheric Correction Research Articles

Season, not lockdown, improved air quality during COVID-19 State of Emergency in Nigeria

Globally, ambient air pollution claims ~9 million lives yearly, prompting researchers to investigate changes in air quality. Of special interest is the impact of COVID-19 lockdown. Many studies reported substantial improvements in air quality during lockdowns compared with pre-lockdown or as compared with baseline values. Since the lockdown period coincided with the onset of the rainy season in some tropical countries such as Nigeria, it is unclear if such improvements can be fully attributed to the lockdown. We investigate whether significant changes in air quality in Nigeria occurred primarily due to statewide COVID-19 lockdown. We applied a neural network approach to derive monthly average ground-level fine aerosol optical depth (AODf) across Nigeria from year 2001–2020, using the Multi-angle Implementation of Atmospheric Correction (MAIAC) AODs from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) satellites, AERONET aerosol optical properties, meteorological and spatial parameters. During the year 2020, we found a 21% or 26% decline in average AODf level across Nigeria during lockdown (April) as compared to pre-lockdown (March), or during the easing phase-1 (May) as compared to lockdown, respectively. Throughout the 20-year period, AODf levels were highest in January and lowest in May or June, but not April. Comparison of AODf levels between 2020 and 2019 shows a small decline (1%) in pollution level in April of 2020 compare to 2019. Using a linear time-lag model to compare changes in AODf levels for similar months from 2002 to 2020, we found no significant difference (Levene's test and ANCOVA; α = 0.05) in the pollution levels by year, which indicates that the lockdown did not significantly improve air quality in Nigeria. Impact analysis using multiple linear regression revealed that favorable meteorological conditions due to seasonal change in temperature, relative humidity, planetary boundary layer height, wind speed and rainfall improved air quality during the lockdown.

PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China

The satellite-retrieved Aerosol Optical Depth (AOD) is widely used to estimate the concentrations and analyze the spatiotemporal pattern of Particulate Matter that is less than or equal to 2.5 microns (PM2.5), also providing a way for the related research of air pollution. Many studies generated PM2.5 concentration networks with resolutions of 3 km or 10 km. However, the relatively coarse resolution of the satellite AOD products make it difficult to determine the fine-scale characteristics of PM2.5 distributions that are important for urban air quality analysis. In addition, the composition and chemical properties of PM2.5 are relatively complex and might be affected by many factors, such as meteorological and land cover type factors. In this paper, an AOD product with a 1 km spatial resolution derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, the PM2.5 measurements from ground sites and the meteorological data as the auxiliary variable, are integrated into the Modified Support Vector Regression (MSVR) model that proposed in this paper to estimate the PM2.5 concentrations and analyze the spatiotemporal pattern of PM2.5. Considering the relatively small dataset and the somewhat complex relationship between the variables, we propose a Modified Support Vector Regression (MSVR) model that based on SVR to fit and estimate the PM2.5 concentrations in Hubei province of China. In this paper, we obtained Cross Correlation Coefficient (R²) of 0.74 for the regression of independent and dependent variables, and the conventional SVR model obtained R² of 0.60 as comparison. We think our MSVR model obtained relatively good performance in spite of many complex factors that might impact the accuracy. We then utilized the optimal MSVR model to perform the PM2.5 estimating, analyze their spatiotemporal patterns, and try to explain the possible reasons for these patterns. The results showed that the PM2.5 estimations retrieved from 1 km MAIAC AOD could reflect more detailed spatial distribution characteristics of PM2.5 and have higher accuracy than that from 3 km MODIS AOD. Therefore, the proposed MSVR model can be a better method for PM2.5 estimating, especially when the dataset is relatively small.

Impact of aerosol layering, complex aerosol mixing, and cloud coverage on high-resolution MAIAC aerosol optical depth measurements: Fusion of lidar, AERONET, satellite, and ground-based measurements

Knowledge of the vertical distribution and layering of aerosols and identification of the corresponding aerosol sources are needed to improve our understanding of the spatial and temporal variability of aerosol pollution. To achieve this goal, we combined both passive and active remote-sensing techniques to provide a 3D view of local aerosol levels and regional to long-range pollution transport. We studied aerosol optical depth (AOD) data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm at 1-km spatial resolution along with active multiwavelength polarization lidar observations of vertical aerosol profiles in Haifa, Israel, Aerosol Robotic Network (AERONET) sun photometer observations at the lidar site, and local-network observations of aerosol concentrations (PM2.5). This comprehensive dataset enabled analyzing the performance of the MAIAC AOD retrieval in cases of complex aerosol layering and mixing states which are typical of the Eastern Mediterranean. While satellite-derived and ground-based AOD measurements generally showed good agreement, 35 out of 100 measurements showed low correspondence. Analysis of those cases revealed that overestimation of AOD was mostly related to cloud-contaminated pixels and aerosol water-uptake effects in moist, cloud-free air at cloud level. Furthermore, AOD over- and underestimations were related to the presence of complex aerosol mixture and layering conditions, especially when dust was mixed with aged anthropogenic aerosol pollution and marine aerosols with lofted anthropogenic pollution. In these cases 50–70% of measurements were outside of the expected error limit. Perhaps these conditions are not considered in the MAIAC retrieval. Finally, we investigated the link between AOD spatial variability and the MAIAC AOD bias, and performed a cluster analysis corroborating the strong impact of cloud contamination on MAIAC AOD quality. Our observation-based results raise the importance of carefully analyzing the uncertainties in satellite AOD measurements that are used as an important input variable in numerous health-related exposure studies and climate models.

Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach

PM2.5 is an important atmospheric constituent associated to human health. Therefore, the capability of estimating PM2.5 concentrations at high spatiotemporal resolutions, particularly in places with no ground stations, would be invaluable. Although several studies have involved the estimation of PM2.5, few have estimated PM2.5 concentrations at high spatial resolutions. In this study, we leverage the aerosol optical depth (AOD) and random forest (RF) algorithm to estimate daily 1-km PM2.5 concentrations over Texas from 2014 to 2018. For this purpose, we use collection 6 Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD products from the Moderate Resolution Imaging Spectroradiometer (MODIS). To address the different sources and speciation of PM2.5 over Texas, we use several important control parameters. As a result, the accuracy of RF remains consistent throughout the study area. After estimating ground-level PM2.5 levels, we apply a ten-fold cross-validation approach to obtain a correlation coefficient (R) of 0.83–0.90 and a mean absolute bias (MAB) of 1.47–1.77 μg/m3. Our results show that RF is highly capable of estimating ground-level PM2.5 concentrations. In addition to the RF model, we also compare the capability of commonly used models, including multiple linear regression (MLR) and mixed effects model (MEM), for estimating the PM2.5 concentrations of global regions. Results indicate that RF, compared to the other models, has the highest accuracy, MEM the second-highest, and MLR the third. We also leverage the USEPA Environmental Benefits Mapping and Analysis Program Community Edition (BenMAP-CE) to estimate the impact of changes in PM2.5 levels on the number of respiratory-related premature mortalities in Texas in 2014–2018. Considering 2014 as the baseline year, the BenMAP analyses reveal that PM2.5 reductions could have prevented a large number of premature mortalities, particularly among adults aged 25–99, from 2014 to 2018 in Texas.

Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring

Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring

The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China

Respirable particles with aerodynamic diameters ≤ 10 µm (PM10) have important impacts on the atmospheric environment and human health. Available PM10 datasets have coarse spatial resolutions, limiting their applications, especially at the city level. A tree-based ensemble learning model, which accounts for spatiotemporal information (i.e., space-time extremely randomized trees, denoted as the STET model), is designed to estimate near-surface PM10 concentrations. The 1-km resolution Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product and auxiliary factors, including meteorology, land-use cover, surface elevation, population distribution, and pollutant emissions, are used in the STET model to generate the high-resolution (1 km) and high-quality PM10 dataset for China (i.e., ChinaHighPM10) from 2015 to 2019. The product has an out-of-sample (out-of-station) cross-validation coefficient of determination (CV-R2) of 0.86 (0.82) and a root-mean-square error (RMSE) of 24.28 (27.07) μg/m3, outperforming most widely used models from previous related studies. High levels of PM10 concentration occurred in northwest China (e.g., the Tarim Basin) and the Northern China Plain. Overall, PM10 concentrations had a significant declining trend of 5.81 μg/m3 per year (p < 0.001) over the past five years in China, especially in three key urban agglomerations. The ChinaHighPM10 dataset is potentially useful for future small- and medium-scale air pollution studies by virtue of its higher spatial resolution and overall accuracy.

A Satellite-Based High-Resolution (1-km) Ambient PM2.5 Database for India over Two Decades (2000–2019): Applications for Air Quality Management

Fine particulate matter (PM2.5) is a major criteria pollutant affecting the environment, health and climate. In India where ground-based measurements of PM2.5 is scarce, it is important to have a long-term database at a high spatial resolution for an efficient air quality management plan. Here we develop and present a high-resolution (1-km) ambient PM2.5 database spanning two decades (2000–2019) for India. We convert aerosol optical depth from Moderate Resolution Imaging Spectroradiometer (MODIS) retrieved by Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm to surface PM2.5 using a dynamic scaling factor from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data. The satellite-derived daily (24-h average) and annual PM2.5 show a R2 of 0.8 and 0.97 and root mean square error of 25.7 and 7.2 μg/m3, respectively against surface measurements from the Central Pollution Control Board India network. Population-weighted 20-year averaged PM2.5 over India is 57.3 μg/m3 (5–95 percentile ranges: 16.8–86.9) with a larger increase observed in the present decade (2010–2019) than in the previous decade (2000 to 2009). Poor air quality across the urban–rural transact suggests that this is a regional scale problem, a fact that is often neglected. The database is freely disseminated through a web portal ‘satellite-based application for air quality monitoring and management at a national scale’ (SAANS) for air quality management, epidemiological research and mass awareness.

Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications

Exposure to fine particulate matter (PM2.5) can significantly harm human health and increase the risk of death. Satellite remote sensing allows for generating spatially continuous PM2.5 data, but current datasets have overall low accuracies with coarse spatial resolutions limited by data sources and models. Air pollution levels in China have experienced dramatic changes over the past couple of decades. However, country-wide ground-based PM2.5 records only date back to 2013. To reveal the spatiotemporal variations of PM2.5, long-term and high-spatial-resolution aerosol optical depths, generated by the Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle implementation of Atmospheric Correction (MAIAC) algorithm, were employed to estimate PM2.5 concentrations at a 1 km resolution using our proposed Space-Time Extra-Trees (STET) model. Our model can capture well variations in PM2.5 concentrations at different spatiotemporal scales, with higher accuracies (i.e., cross-validation coefficient of determination, CV-R2 = 0.86–0.90) and stronger predictive powers (i.e., R2 = 0.80–0.82) than previously reported. The resulting PM2.5 dataset for China (i.e., ChinaHighPM2.5) provides the longest record (i.e., 2000 to 2018) at a high spatial resolution of 1 km, enabling the study of PM2.5 variation patterns at different scales. In most places, PM2.5 concentrations showed increasing trends around 2007 and remained high until 2013, after which they declined substantially, thanks to a series of government actions combating air pollution in China. While nationwide PM2.5 concentrations have decreased by 0.89 μg/m3/yr (p < 0.001) during the last two decades, the reduction has accelerated to 4.08 μg/m3/yr (p < 0.001) over the last six years, indicating a significant improvement in air quality. Large improvements occurred in the Pearl and Yangtze River Deltas, while the most polluted region remained the North China Plain, especially in winter. The ChinaHighPM2.5 dataset will enable more insightful analyses regarding the causes and attribution of pollution over medium- or small-scale areas.

Assessment of Long-term Fine Particulate Matter Exposure and Its Effect on Cardiovascular Disease in China

Background Investigations on chronic health effects of fine particulate matter (PM2.5) exposure in China are hindered by the lack of continuous ground monitoring. With satellite-driven models to generate spatiotemporally resolved PM2.5 levels, we aimed to estimate long-term PM2.5 concentrations at high spatial resolution (1 km) and assess the risk of cardiovascular diseases associated with long-term exposure to PM2.5 in China. Methods Monthly PM2.5 concentrations in China from 2000 to 2016 were estimated using machine-learning approaches, with the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) as a major predictor. A longitudinal cohort study, the project of Prediction for Atherosclerotic Cardiovascular Disease Risk in China was used, with 116,972 adults included. Follow-up information from 2000 to 2015 was analyzed using a Cox proportional hazards model with time-varying exposures. Results Machine-learning models for PM2.5 estimation performed good with a random cross-validation R2 of 0.93 at the monthly level. For the time period without ground monitoring, prediction R2 were 0.67 at the monthly and 0.80 at the annual levels. For an increase of 10 μg/m3 in annual PM2.5 exposures, the estimated hazard ratios (HRs) for incidence and mortality of cardiovascular disease were 1.251 (95% CI: 1.220-1.283) and 1.164 (95% CI: 1.117-1.213), respectively. Steeper slopes were observed when the concentration-response functions tended to high PM2.5 levels. Conclusions Satellite-based techniques provide a strong support for environmental health studies. Our findings on the chronic health effect of PM2.5 exposure on cardiovascular diseases expand the current knowledge on adverse health effects of severe air pollution and highlight the potential cardiovascular benefits of air quality improvement in highly polluted regions.

Exploring the relationship between high-resolution aerosol optical depth values and ground-level particulate matter concentrations in the Metropolitan Area of São Paulo

The spatiotemporal pattern of particulate matter (PM) concentrations is an important factor in predicting health issues in inhabitants of urban areas. The integration of satellite-derived aerosol optical depth (AOD) data with ground-level PM concentration data, obtained from monitoring networks, has contributed to better characterization of the spatiotemporal variability of aerosols worldwide. However, before using satellite AOD data as a proxy for PM in epidemiological and air quality studies in specific regions, the applicability of that strategy must be evaluated. In this study, we evaluate the use of the high-resolution AOD, derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, as a predictor of surface PM concentrations in the Metropolitan Area of São Paulo (MASP). We found relatively weak or negative correlations between PM concentrations and MAIAC AOD, even after vertical correction by planetary boundary layer height and the hygroscopic growth factor. The weak correlations reported in this study are mainly due to the mismatch between the current MAIAC aerosol model and the properties of local aerosols in the MASP. Our results suggest that sources of aerosol particles in the MASP are quite diverse and that there is therefore no single optical model suitable for use with satellite-derived AOD.

Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM2.5 Levels

The immense problem of missing satellite aerosol retrievals (Aerosol Optical Depth, (AOD)) detrimentally affects the prediction ability of ground-level PM2.5 concentrations and may lead to unavoidable biases. An appropriate missing-imputation method has not been well developed to date. This study developed a two-stage approach (AOD-imputation stage and PM2.5-prediction stage) to predict short-term PM2.5 exposure in mainland China from 2013–2018. At the AOD-imputation stage, geostatistical methods and machine learning (ML) algorithms were examined to interpolate 1 km satellite aerosol retrievals. At the PM2.5-prediction stage, the daily levels of PM2.5 were predicted at a resolution of 1 km, based on interpolated AOD and meteorological data. The statistical performances of the different interpolation methods were comprehensively compared at each stage. The original coverage of retrieved AOD was 15.46% on average. For the AOD-imputation stage, ML methods produced a higher coverage (98.64%) of AOD than geostatistical methods (21.43–87.31%). Among ML algorithms, random forest (RF) or extreme gradient boosted (XG-interpolated) AOD produced better interpolated quality (CV R2 = 0.89 and 0.85) than other algorithms (0.49–0.78), but XGBoost required only 15% of the computing time of RF. For the PM2.5 predicted stage, neither RF-AOD nor XG-AOD could guarantee higher accuracy in PM2.5 estimations (CV R2 = 0.88 (RF or XG-AOD) compared to 0.85 (original)), or more stable spatial and temporal extrapolation (spatial, (temporal) CV R2 = 0.83 (0.83), 0.82 (0.82), and 0.65 (0.61) for RF, XG, and original). For the AOD-imputation stage, the missing-filled efficiency depended more on external information, while the missing-filled accuracy relied more on model structure. For the PM2.5 predicted stage, efficient AOD interpolation (or the ability to eliminate the missing data) was a precondition for the stable spatial and temporal extrapolation, while the quality of interpolated AOD showed less significant improvements. It was found that XG-AOD is a better choice to estimate daily PM2.5 exposure in health assessments.

The Relationship Between MAIAC Smoke Plume Heights and Surface PM

AbstractBiomass burning is a source of fine particulate matter (PM2.5) air pollution, which adversely impacts human health. However, quantifying the health effects from biomass burning PM2.5 is difficult. Monitoring networks generally lack the spatial density needed to capture the heterogeneity of biomass burning smoke. Satellite aerosol optical depth (AOD) can be used to fill spatial gaps but does not distinguish surface‐level aerosols. Plume height (PH) observations may provide constraints on the vertical distribution of smoke and its impact on surface concentrations. We assessed PH characteristics from Multi‐Angle Implementation of Atmospheric Correction (MAIAC) and evaluated its correlation with colocated PM2.5 and AOD measurements. PH is generally highest over the western United States. The ratio PM2.5:AOD generally decreases with increasing PH:PBLH (planetary boundary layer height), showing that PH has the potential to refine surface PM2.5 estimates for collections of smoke events.

Spatiotemporal PM2.5 variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region

The economy has developed rapidly in China during recent decades, especially in the Beijing-Tianjin-Hebei (BTH) region. Environmental problems have thus become increasingly prominent, particularly the presence of fine particulate matter with aerodynamic diameters ≤2.5 μm (PM2.5). High-quality and high-resolution PM2.5 data is urgently needed. Therefore, based on the newly released Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth products, a high-quality PM2.5 data set with a high spatial resolution of 1 km is first reconstructed covering 2000 to 2018 in the BTH region using the linear mixed effect (LME) model. This model shows an excellent performance with a high cross-validation coefficient of determination (R2) of 0.85, a small root mean square error of 21.49 μg/m3, and a small mean absolute error of 15.26 μg/m3 from 2013 to 2018. It also has strong predictive power in estimating historical PM2.5 concentrations, with a monthly R2 equal to 0.72. There was a significant decreasing trend (i.e., −1.53 μg/m3, p < 0.01) in PM2.5 concentrations during the last two decades, and the largest downward trend (i.e., −6.83 μg/m3, p < 0.01) occurred from 2013 to 2018. In addition, the response of PM2.5 to the industrial structure is also examined using the vector autoregression model. In general, both the secondary industry and tertiary industry show significant influences and can contribute approximately 3.8% and 9.8% to the PM2.5 pollution in the BTH region, respectively. This suggests that further industrial structural adjustment, e.g., clean energy production, or low-carbon technology development, is required for the future prevention and control of air pollution and the sustainable development of the economy.

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&amp;amp;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&amp;amp;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&amp;amp;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&amp;amp;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 &lt;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.

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.

Aerosol pattern changes over the dead sea from west to east - Using high-resolution satellite data

The area of the Dead Sea, a terminal lake on the border between Israel and Jordan, has gone through extensive environmental changes in recent decades, stemming from several processes including extreme evaporation and land degradation. Our study explored the distribution and the long-term high-resolution climatology of the Dead Sea aerosols, using satellite-retrieved Aerosol Optical Depth (AOD) measurements. We used the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that provides AOD measurements at a 1 km resolution. MAIAC retrievals for the entire study period (2000–2016) have shown a very good agreement (r = 0.8) with the nearby AERONET station. For the first time, the AOD temporal distribution and its variability, on both Dead Sea coasts, were calculated on a monthly/seasonal basis. Three main aerosol pollution patterns (i.e. high-levels of AOD) emerged as follows: western focalized (75 and 59% of the months examined for Aqua and Terra, respectively), eastern focalized (14 and 24%), and similar levels of pollution on both coasts (11 and 17%, respectively). Using the Normalized AOD Difference (NAODD) metric, we studied how the AOD spatial patterns changed and when. Following the negative trends in most of the months, our analysis has demonstrated a shift towards unexplained increasing pollution levels over the Jordanian eastern side. For March–April, this shift had already occurred, and the potential reasons including changes in synoptic regimes are discussed. Local and global implications are of much regional interest because of the Dead Sea deterioration and the need for urgent environmental policy changes.

Satellite mapping of PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; episodes in the wintertime San Joaquin Valley: a “static” model using column water vapor

Abstract. The use of satellite aerosol optical thickness (AOT) from imaging spectrometers has been successful in quantifying and mapping high-PM2.5 (particulate matter with a mass &lt;2.5 µm diameter) episodes for pollution abatement and health studies. However, some regions have high PM2.5 but poor estimation success. The challenges in using AOT from imaging spectrometers to characterize PM2.5 worldwide was especially evident in the wintertime San Joaquin Valley (SJV). The SJV's attendant difficulties of high-albedo surfaces and very shallow, variable vertical mixing also occur in other significantly polluted regions around the world. We report on more accurate PM2.5 maps (where cloudiness permits) for the whole winter period in the SJV (19 November 2012–18 February 2013). Intensive measurements by including NASA aircraft were made for several weeks in that winter, the DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) California mission. We found success with a relatively simple method based on calibration and checking with surface monitors and a characterization of vertical mixing, and incorporating specific understanding of the region's climatology. We estimate PM2.5 to within ∼7 µg m−3 root mean square error (RMSE) and with R values of ∼0.9, based on remotely sensed multi-angle implementation of atmospheric correction (MAIAC) observations, and certain further work will improve that accuracy. Mapping is at 1 km resolution. This allows a time sequence of mapped aerosols at 1 km for cloud-free days. We describe our technique as a “static estimation.” Estimation procedures like this one, not dependent on well-mapped source strengths or on transport error, should help full source-driven simulations by deconstructing processes. They also provide a rapid method to create a long-term climatology. Essential features of the technique are (a) daily calibration of the AOT to PM2.5 using available surface monitors, and (b) characterization of mixed layer dilution using column water vapor (CWV, otherwise “precipitable water”). We noted that on multi-day timescales both water vapor and particles share near-surface sources and both fall to very low values with altitude; indeed, both are largely removed by precipitation. The existence of layers of H2O or aerosol not within the mixed layer adds complexity, but mixed-effects statistical regression captures essential proportionality of PM2.5 and the ratio variable (AOT ∕ CWV). Accuracy is much higher than previous statistical models and can be extended to the whole Aqua satellite data record. The maps and time series we show suggest a repeated pattern for large valleys like the SJV – progressive stabilization of the mixing height after frontal passages: PM2.5 is somewhat more determined by day-by-day changes in mixing than it is by the progressive accumulation of pollutants (revealed as increasing AOT).

Influence of Spatial Resolution and Retrieval Frequency on Applicability of Satellite-Predicted PM2.5 in Northern China

Satellite aerosol optical depth (AOD) products have been widely used in estimating fine particulate matter (PM2.5) concentrations near the surface at a regional scale, and perform well compared with ground measurements. However, the influence of limitations such as retrieval frequency and the spatial resolution of satellite AODs on the applicability of predicted PM2.5 values has been rarely considered. With three widely used MODIS AOD products, including Multi-Angle Implementation of Atmospheric Correction (MAIAC), Deep Blue (DB) and Dark Target (DT), here we evaluate the influence of their spatial resolution and sampling frequency by estimating daily PM2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region of northern China during 2017 utilizing a mixed effects model. The daily concentrations of PM2.5 derived from MAIAC, DB and DT AOD all have high correlations (R2: 0.78, 0.8, and 0.78) with the observed values, but the predicted annual PM2.5 exhibits a distinct spatial distribution. DT estimation obviously underestimates annual PM2.5 in polluted areas due to lower sampling of heavy pollution events. By contrast, the retrieval frequency (~40-60%) of MAIAC and DB AOD can represent well annual PM2.5 in nearly all 83 sites tested. However, MAIAC and DB-derived PM2.5 have a larger bias compared with observed values than DT, indicating that the large spatial variation of aerosol properties can exert an influence on the reliability of the statistical AOD-PM2.5 relationship. Also, there is notable difference between MAIAC and DB PM2.5 due to their different cloud screening methods. The MAIAC PM2.5 with high spatial resolution at 1 km can capture much finer hotpots than DB and DT at 10 km. Our results suggest that it is crucial to consider the applicability of satellite-predicted PM2.5 values derived from different aerosol products according to the specific requirements besides modeling the AOD-PM2.5 relationship.

Spatiotemporal trends of PM2.5 concentrations in central China from 2003 to 2018 based on MAIAC-derived high-resolution data

Long-term PM2.5 levels with high precision at fine spatiotemporal resolution are essential for quantitatively understanding the health risk of exposure to ambient fine particulate matter (PM2.5) and making effective air pollution control policies. The emerging statistically derived PM2.5 estimations from satellite remote sensing observations of aerosol optical depth (AOD) data are an effective alternative to reconstruct global, long-term, high spatiotemporal resolution PM2.5 information. However, studies on PM2.5 estimation and its application to exposure and health-related studies are limited in China due to the lack of historical in-situ measurements before 2013. In this study, we explored the long-term trends of PM2.5 exposure in central China, a hotspot that has recently been experiencing severe particulate pollution, at the local scale. We first developed a spatiotemporal model incorporating periodical characteristics within the data to estimate daily concentrations of historical PM2.5 at a fine scale of 1 km for 2003–2018. The linear effects of predictors including AOD, meteorological and land-use parameters and the non-linear interaction between AOD and meteorological parameters were considered in the modeling process. The most recently released high-resolution satellite aerosol product, Multi-Angle Implementation of Atmospheric Correction (MAIAC) was used to help to represent the fine-scale particle gradients. Our daily estimates correlated well with in-situ observations (cross-validation R2 = 0.59), achieving precision comparable to previous statistical models. Through linking with gridded demographic data, the population-weighted PM2.5 average during 2003 to 2018 was found to be high (62.23 μg/m3 for the whole domain) with obvious spatial variations and seasonality. An inverse U pattern was seen in the time series, with two inflection points around 2008 and 2015. Our model provides reliable particulate information with high spatial resolution and long-term temporal coverage, which can inform local-scale PM2.5-related epidemiological studies and health-risk assessments for central China.

A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5

Accurate estimation of fine particulate matter with diameter ≤2.5 μm (PM2.5) at a high spatiotemporal resolution is crucial for the evaluation of its health effects. Previous studies face multiple challenges including limited ground measurements and availability of spatiotemporal covariates. Although the multiangle implementation of atmospheric correction (MAIAC) retrieves satellite aerosol optical depth (AOD) at a high spatiotemporal resolution, massive non-random missingness considerably limits its application in PM2.5 estimation. Here, a deep learning approach, i.e., bootstrap aggregating (bagging) of autoencoder-based residual deep networks, was developed to make robust imputation of MAIAC AOD and further estimate PM2.5 at a high spatial (1 km) and temporal (daily) resolution. The base model consisted of autoencoder-based residual networks where residual connections were introduced to improve learning performance. Bagging of residual networks was used to generate ensemble predictions for better accuracy and uncertainty estimates. As a case study, the proposed approach was applied to impute daily satellite AOD and subsequently estimate daily PM2.5 in the Jing-Jin-Ji metropolitan region of China in 2015. The presented approach achieved competitive performance in AOD imputation (mean test R2: 0.96; mean test RMSE: 0.06) and PM2.5 estimation (test R2: 0.90; test RMSE: 22.3 μg/m3). In the additional independent tests using ground AERONET AOD and PM2.5 measurements at the monitoring station of the U.S. Embassy in Beijing, this approach achieved high R2 (0.82–0.97). Compared with the state-of-the-art machine learning method, XGBoost, the proposed approach generated more reasonable spatial variation for predicted PM2.5 surfaces. Publically available covariates used included meteorology, MERRA2 PBLH and AOD, coordinates, and elevation. Other covariates such as cloud fractions or land-use were not used due to unavailability. The results of validation and independent testing demonstrate the usefulness of the proposed approach in exposure assessment of PM2.5 using satellite AOD having massive missing values.