Pattern Of Aerosol Optical Depth Research Articles

Impact of aerosol concentration changes on carbon sequestration potential of rice in a temperate monsoon climate zone during the COVID-19: a case study on the Sanjiang Plain, China.

The emission reduction of atmospheric pollutants during the COVID-19 caused the change in aerosol concentration. However, there is a lack of research on the impact of changes in aerosol concentration on carbon sequestration potential. To reveal the impact mechanism of aerosols on rice carbon sequestration, the spatial differentiation characteristics of aerosol optical depth (AOD), gross primary productivity (GPP), net primary productivity (NPP), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FPAR), and meteorological factors were compared in the Sanjiang Plain. Pearson correlation analysis and geographic detector were used to analyze the main driving factors affecting the spatial heterogeneity of GPP and NPP. The study showed that the spatial distribution pattern of AOD in the rice-growing area during the epidemic was gradually decreasing from northeast to southwest with an overall decrease of 29.76%. Under the synergistic effect of multiple driving factors, both GPP and NPP increased by more than 5.0%, and the carbon sequestration capacity was improved. LAI and FPAR were the main driving factors for the spatial differentiation of rice GPP and NPP during the epidemic, followed by potential evapotranspiration and AOD. All interaction detection results showed a double-factor enhancement, which indicated that the effects of atmospheric environmental changes on rice primary productivity were the synergistic effect result of multiple factors, and AOD was the key factor that indirectly affected rice primary productivity. The synergistic effects between aerosol-radiation-meteorological factor-rice primary productivity in a typical temperate monsoon climate zone suitable for rice growth were studied, and the effects of changes in aerosol concentration on carbon sequestration potential were analyzed. The study can provide important references for the assessment of carbon sequestration potential in this climate zone.

Performance evaluation of MODIS and VIIRS satellite AOD products over the Indian subcontinent

In the present study, the first systematic performance evaluation of aerosol optical depth (AOD) products retrieved using two satellite sensors i.e., Visible Infrared Imaging Radiometer Suite (VIIRS) and Aqua-Moderate-Resolution Imaging Spectroradiometer (MODIS) is carried out over India. We have used ground-based AOD from AERONET at 550 nm wavelength for inter-comparison with MODIS Aqua version C6.1 (C061) Deep Blue (DB) aerosol product and VIIRS/SNPP collection version 1.1 (V1.1) DB aerosol product over the time span of 7-year (2014–2020) observation periods. For validation, the average value of satellite pixels falling within the box of 50 Km x 50 Km keeping the AERONET station at the center is retrieved. The average daily data from the AERONET sun photometer (2014–2019) were obtained within ±15 min of satellite overpass time. Statistical parameters like correlation coefficient (R), RMSE, MAE, and RMB were calculated. The uncertainty of satellite AOD is evaluated using an envelope of Expected Error (EE = ±0.05 + 0.15 AOD for land). Statistical analysis shows that the MODIS AOD product outperforms VIIRS-retrieved AOD. The AOD retrieved from both sensors yields a high correlation (0.86—Jaipur, 0.79—Kanpur, 0.84—Gandhi College, and 0.74—Pune for MODIS and 0.75—Jaipur, 0.77—Kanpur, 0.49—Gandhi College, and 0.86—Pune for VIIRS) and low MAE (0.12—Jaipur, 0.20—Kanpur, 0.15—Gandhi College, and 0.09—Pune for MODIS and 0.13—Jaipur, 0.13—Kanpur, 0.26—Gandhi College, and 0.10—Pune for VIIRS). Other statistical measures such as RMSE, RMB, and P also suggest similar performance. More than 66% of the total data fall within the range of EE for both the satellite products at each station. Spatial comparison exhibits the same AOD pattern seasonally as well as annually having a minimum bias from −0.3 to +0.3 between MODIS and VIIRS. Slight underestimation and overestimation are observed in all the stations by MODIS, whereas VIIRS continuously underestimates AOD with increase in optical depth, suggesting improvements in the aerosol model and surface reflection in retrieval. Overall, the comparison of ground AERONET AOD reveals better accuracy of MODIS AOD with that of VIIRS satellite datasets over India.

Trend relationship between mountain normalized difference vegetation index (NDVI) and aerosol optical depth (AOD) across two decades: implication for water quality within the Lesotho Highlands, Drakensberg, South Africa

There are growing concerns on contribution of vegetation dynamics to atmospheric turbidity and quality of regional water towers. The study sought to determine the trends in the MODIS/TERRA-derived normalized difference vegetation index (NDVI) and aerosol optical depth (AOD) for Lesotho Highland over 2000-2020. The predictive relationship between the two variables was also examined using regression analysis. Irrespective of yearly AOD patterns, the AOD showed biphasic patterns peaking between mid-winter to early spring (July-October) (highest) and autumn (Feb-April) (next highest), and lowest in the summer (Nov-January). The monthly NDVI was largest in January-March (summer-early fall) with smaller values in winter and spring. This seasonality can be related to the peak of anthropogenic biomass combustion during the winter and strong winds during the spring and early summer. The AOD relationship with NDVI showed quadratic patterns peaking and plunging with changes in season. About 30-80% (R2 = 0.3-0.8%) changes in annual AOD from 2000 to 2020 were explainable by the dynamics of NDVI indicating that increased NDVI contributes to about a 50% decrease in AOD in the Lesotho Highlands. However, an outlier trend was observed in 2007 (R2 = 13%). Incidences of high AOD in months of high NDVI may be indicative of traveling aerosols, i.e., aerosols from non-local sources/activity. On the other hand, high AOD in months of low NDVI implicates local aerosol sources. Trend relationship studies on vegetation loss and AOD in mountain areas of other regions could improve knowledge of contaminant dynamics and risk implications for downstream populations.

The effects of meteorology and biomass burning on urban air quality: The case of Bangkok

Bangkok, one of the biggest metropolitan cities in Asia, has been experiencing poor (PM10 > 50 μg/m3 annual average) air quality for decades. Major sources of air pollution are traffic emissions, biomass burning, and non-combustible sources. In this study, the impact of meteorology and biomass burning on the concentration of particulate matter was assessed. Data from three monitoring stations and remotely sensed fire count between 2017 and 2020 was used to investigate the association. Moreover, the spatiotemporal pattern of aerosol optical depth (AOD) and fire count derived from remotely sensed data were investigated to observe their association. This study found that temperature, relative humidity, rainfall, wind speed, and wind direction have a significant negative relationship with particulate matter concentration. In contrast, atmospheric pressure and fire count have a significant positive correlation with particulate matter concentration. Moreover, the pollution rose diagram for particulate matter indicated that the northeasterly wind is the prevailing wind direction that carries the most pollutants. Furthermore, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and AOD indicated that biomass burning in neighboring regions significantly (p < 0.05) affected the particulate matter concentration in Bangkok. Multiple linear regression (MLR) analysis established that atmospheric pressure has the highest negative impact on the city air quality, followed by biomass burning, while heavy rainstorms were the most substantial parameter that reduced particulate matter concentration. Taken together, these findings highlight the importance of meteorological factors in understanding air pollution mechanisms in Bangkok. Equally important, the insights gained from this study may assist in addressing the long-range transport of air pollutants from biomass burning, which requires a coordinated engagement with neighboring countries.

Full-coverage 250 m monthly aerosol optical depth dataset (2000–2019) amended with environmental covariates by an ensemble machine learning model over arid and semi-arid areas, NW China

Abstract. Aerosols are complex compounds that greatly affect the global radiation balance and climate system and even human health; in addition, aerosols are currently a large source of uncertainty in the numerical simulation process. The arid and semi-arid areas have fragile ecosystems with abundant dust but lack related high-accuracy aerosol data. To solve these problems, we use the bagging trees ensemble model, based on 1 km aerosol optical depth (AOD) data and multiple environmental covariates, to produce a monthly advanced-performance, full-coverage, and high-resolution (250 m) AOD product (named FEC AOD, fusing environmental covariates AOD) covering the arid and semi-arid areas. Then, based on the FEC AOD products, we analyzed the spatiotemporal AOD pattern and further discussed the interpretation of environmental covariates to AOD. The results show that the bagging trees ensemble model has a good performance, with its verification R2 values always remaining at 0.90 and the R2 being 0.79 for FEC AOD compared with AERONET AOD product. The high-AOD areas are located in the Taklimakan Desert and on the Loess Plateau, and the low-AOD areas are concentrated in southern Qinghai province. The higher the AOD, the stronger the interannual variability. Interestingly, the AOD reflected a dramatic decrease on the Loess Plateau and an evident increase in the south-eastern Taklimakan Desert, while the southern Qinghai province AODs showed almost no significant change between 2000 and 2019. The annual variation characteristics show that the AOD was largest in spring (0.267±0.200) and smallest in autumn (0.147±0.089); the annual AOD variation pattern showed different features, with two peaks in March and August over Gansu province but only one peak in April in other provinces/autonomous regions. Farmlands and construction lands have high AOD levels compared to other land cover types. Meteorological factors demonstrate the maximum interpretation ability of the AODs on all set temporal scales, followed by the terrain factors, while surface properties have the smallest explanatory abilities; the corresponding average contributions are 77.1 %, 59.1 %, and 50.4 %, respectively. The capability of the environmental covariates to explain the AOD varies seasonally in the following sequence: winter (86.6 %) &gt; autumn (80.8 %) &gt; spring (79.9 %) &gt; summer (72.5 %). In this research, we provide a pathbreaking high spatial resolution (250 m) and long time series (2000–2019) FEC AOD dataset covering arid and semi-arid regions to support atmospheric and related studies in northwest China; the full dataset is available at https://doi.org/10.5281/zenodo.5727119 (Chen et al., 2021b).

Spatiotemporal Variation and Influencing Factors of AOD in the North China Plain

A better knowledge of the spatial and temporal variation in atmospheric aerosol and its influencing factors is of great significance to controlling atmospheric pollution and improving the atmospheric environment. First, the visible infrared imaging radiometer suite (VIIRS) intermediate product (IP) aerosol optical depth (AOD) data from 2013 to 2019 were used to analyze the temporal and spatial variation in AOD in the North China Plain. Secondly, SO2, NO2, PM2.5, meteorological data, NDVI, DEM, GDP, and POPU were selected as influencing factors, and the linkage models between AOD and its influencing factors were established based on the XGBoost model for each of the five representative cities in the North China Plain to quantitatively estimate and reveal the contribution of various influencing factors behind the temporal and spatial distribution in AOD. The results showed that in terms of spatial distribution, the AOD of the North China Plain was bounded by the Taihang Mountains, showing a pattern of high AOD in the southeast and low AOD in the northwest. In terms of temporal changes, the annual average value of AOD in the five cities showed an overall decreasing trend, and the monthly average value of AOD first increased and then decreased, with the highest value appearing in July and the lowest value in December. In addition, the AOD estimation model established in this paper for the five cities in North China had high accuracy, with R2 ranging from 0.60 to 0.67. Among the factors influencing AOD in the North China Plain, NO2 and SO2 were the most influential factors contributing to AOD in the five cities. In addition, PM2.5 was another important pollutant emission factor. In terms of meteorological factors, temperature (T), relative humidity (RH), wind speed (WS), and wind direction (WD) were the other four important influencing factors. There were both commonalities and differences in the rankings of the contribution and importance of AOD influencing factors in the five representative cities in North China.

Introducing Two Fixed Platforms in the Yellow Sea and East China Sea Supporting Long-Term Satellite Ocean Color Validation: Preliminary Data and Results

Following the Aerosol Robotic Network-Ocean Color (AERONET-OC) network scheme and instrument deployment protocols, two fixed platforms (Muping and Dong’ou) in the Yellow Sea and East China Sea were implemented with the support of the China National Satellite Ocean Application Service. Optical radiometry instruments were established at the two sites to autonomously determine remote sensing reflectance (Rrs) and aerosol optical depth (AOD). Details about location selection, platform design, instrument deployment, and the associated data processing procedure are reported in this study. Rrs and AOD measured by independent instruments at the Muping site were compared and results showed that they were consistent, with a median relative percentage difference (MRPD) &lt; 0.6% for AOD and &lt;10% for Rrs. The spectral feature and temporal pattern of Rrs and AOD at the two sites were examined and compared with data from 14 AERONET-OC sites. Rrs and AOD data measured at the two sites were used to evaluate ocean color operational products of MODIS/Aqua (MODISA), OLCI/Sentinel-3A (OLCI-3A), and OLCI/Sentinel-3B (OLCI-3B). Comparison showed that the three satellite sensor-derived Rrs agreed well with in situ measurements, with an MRPD &lt; 25% for MODISA, &lt;30% for OLCI-3A, and &lt;40% for OLCI-3B, respectively. Large uncertainties were observed in the blue bands for the three satellite sensors, particularly for OLCI-3B at 400 nm. AOD at 865 nm derived from the three satellite sensors also agreed well with in situ measurements, with an MRPD of 28.1% for MODISA, 30.6% for OLCI-3A, and 39.9% for OLCI-3B. Two commonly used atmospheric correction (AC) processors, the ACOLITE and SeaDAS, were also evaluated using in situ measurements at two sites and 20 m-resolution MSI/Sentinel-2A data. Close agreements were achieved for both AC processors, while the SeaDAS performed slightly better than ACOLITE. The optimal band selection in the AC models embedded in two AC processors was a combination of one near-infrared and one short-wave infrared band such as 865 and 1609 nm, shedding light on MSI data applications in the aquatic environment.

Assessment of spatio-temporal trends of satellite-based aerosol optical depth using Mann–Kendall test and Sen’s slope estimator model

Aerosols are an inextricably linked component of the atmosphere. Nowadays the study of aerosols has attracted the attention of the world community due to the increasing concerns over air pollution and climate change. Aerosol optical depth (AOD) is the measure of aerosols distributed within the atmospheric column from the Earth's surface to the top of the atmosphere. This study was conducted to examine the trend in AOD between latitudes 22° and 24.62° N, and longitudes 83.26° and 87.01° E, covering the entire part of the Indian state of Jharkhand. Mann–Kendall (MK) trend test and Sen’s slope estimator model were used to examining the trend over 18 years (period: 2000–2017) AOD data obtained from satellite-based sensor namely MODIS. The highest AOD was observed in the north-eastern part, while the lowest was observed in the state’s southwestern part. The mean relative percentage change (RPC) analysis showed that the AOD increased from 20 to 60%. Jharkhand State comprises various sub-regions; all the sub-regions, including major cities, have shown a remarkable positive trend. In particular, Dhanbad, Sahibganj, Chaibasa, Jamshedpur, Ranchi, and Hazaribagh demonstrate statistically significant positive trends (99% confidence level). It was observed that the highest positive trend (0.1228) and the lowest negative trend (−0.02587) were in Sahibganj and Gumla districts, respectively. This study revealed a statistically robust significant correlated pattern of AOD with the variability of meteorological factors.

Accuracy, stability, and continuity of AVHRR, SeaWiFS, MODIS, and VIIRS deep blue long-term land aerosol retrieval in Asia

The deep blue (DB) aerosol algorithm applied to four satellite instruments, AVHRR, SeaWiFS, MODIS, and VIIRS, produced a long-term aerosol data set since 1989. This study first evaluated and compared the accuracy, stability, and continuity of four DB aerosol optical depth (AOD) products in Asia using AErosol RObotic NETwork measurements. Then, the regional AOD spatial distributions, coverages, and series trends are analyzed. The results show that VIIRS DB has the highest accuracy and stability, with an expected error (EE, ±(0.05 + 20%)) of 76.59% and stability of approximately 0.027 per decade. The performance of MODIS DB is slightly worse than that of VIIRS. However, their AOD pattern, coverage, and trend are comparable. The performance of AVHRR (EE = 58.10%) and the stability of SeaWiFS (0.093 per decade) are less good. Therefore, SeaWiFS DB data should be used with caution for trend analysis. The AOD accuracy and coverage together determine the AOD pattern and the continuity of multi-sensor data. In addition to consistent algorithm accuracy, it is necessary to consider the influences in sensor sampling and inappropriate-pixel screening schemes in the joint multi-sensor analysis. Encouragingly, although multiple DB products have different AOD averages of regional series, their changing trends are consistent. Error analysis shows that the AOD bias characteristic is different in different surface conditions. This indicates that the surface reflectance estimated by the DB algorithm using different techniques is divergent, which may be the direction for the improvement of the algorithm.

Erratic Asian summer monsoon 2020: COVID-19 lockdown initiatives possible cause for these episodes?

The summer (June through September) monsoon 2020 has been very erratic with episodes of heavy and devastating rains, landslides and catastrophic winds over South Asia (India, Pakistan, Nepal, Bangladesh), East Asia (China, Korea, and Japan), and Southeast Asia (Singapore, Thailand, Vietnam, Laos, Cambodia, Philippines, Indonesia). The withdrawal of the summer monsoon over India was delayed by 2 weeks. The monsoon season over East Asia has been the longest. China recorded a Dam burst in the twentieth century. Furthermore, the Korean Peninsula has experienced back-to-back severe tropical cyclones. Could the lockdown activities initiate to control the COVID-19 spread a possible cause for these major episodes? The strict enforcement of the lockdown regulations has led to a considerable reduction of air pollutants—dust and aerosols throughout the world. A recent study based on satellites and merged products has documented a statistically significant mean reduction of about 20, 8, and 50% in nitrogen dioxide, Aerosol Optical Depth (AOD) and PM2.5 concentrations, respectively over the megacities across the globe. Our analysis reveals a considerable reduction of about 20% in AOD over South as well as over East Asia, more-over East Asia than over South Asia. The reduced aerosols have impacted the strength of the incoming solar radiation as evidenced by enhanced warming, more-over the land than the oceans. The differential warming over the land and the ocean has resulted in the amplification of the meridional ocean-land thermal contrast and strengthening of the monsoon flow. These intense features have supported the surplus transport of moisture from the oceans towards the main lands. Some similarity between the anomalous rainfall pattern and the anomalous AOD pattern is discernable. In particular, the enhancement of rainfall, the reduction in AOD and the surface temperature warming match very well over two regions one over West-Central India and the other over the Yangzte River Valley. Results further reveal that the heavy rains over the Yangzte River Valley could be associated with the preceding reduced aerosols, while the heavy rains over West-Central India could be associated with reduced aerosols and also due to the surface temperature warming.

Multi-spatiotemporal patterns of aerosol optical depth and influencing factors during 2000–2020 from two spatial perspectives: The entire Yellow River Basin region and its urban agglomerations

Analyzing on the patterns and causes of atmosphere pollution in Yellow River Basin region (YRB) is critical for its construction of ecological civilization and high-quality development. Aerosol optical depth (AOD), an important optical property of aerosol that characterizes the attenuation of light by aerosols, is an indicator of regional atmospheric pollution. Based on multi-source data, we investigated spatiotemporal patterns of AOD and its influencing factors over YRB during 2000–2020 from two spatial perspectives: the entire region and its urban agglomerations. The spatial patterns and trends of AOD and its spatial response to several controls was quantitatively analyzed using Theil-Sen Slope, Mann-Kendall test and spatial principal component analysis, and its periodicity and spatial differences was finely described by extreme-point symmetric mode decomposition. The multi-year average AOD are higher in the middle and lower reaches of YRB than that in the upper reaches and significant seasonal differences of AOD spatial pattern are observed in YRB. Spatial patterns of AOD in YRB are mainly regulated by meteorology, topography and socio-economy, various factors and their combination exert different impacts on AOD. Despite the high contribution of meteorological factors in most urban agglomerations, their acting forces on AOD are not consistent. In terms of spatial trend, the areas where AOD decrease (94.54%) are much larger than the areas where AOD increase. For temporal trend, AOD exhibit a nonlinear downward trend throughout YRB, and its periodicity come from interaction of multi-time-scale components: inter-annual oscillations (imf5) and seasonal oscillations: seasonal periodic fluctuation (imf3), dust weather and man-made agricultural activities caused volatility (imf2) and fluctuation caused abrupt event (imf1), seasonal oscillations dominate the AOD changes. Those are of great significance for improving our understanding of the spationtemporal patterns and its controls of AOD on YRB and its urban agglomerations.

Classification of weather clusters over the Middle East associated with high atmospheric dust-AODs in West Iran

Daily aerosol optical depth (AOD) and meteorological fields obtained from Monitoring Atmospheric Composition and Climate (MACC) and ERA-Interim reanalysis, respectively are used to analyze spatial patterns of AOD in the west part of Iran during the period 2003–2012. The areas affected by high daily AODs in southwest and west/northwest parts of Iran are identified using principal component analysis (PCA) and the atmospheric circulation patterns for these high AODs are classified in six clusters for each region, with larger frequencies in spring and summer. Despite their between differences, the weather clusters are mostly associated with a strong westerly/northwesterly flow (Shamal wind) across the Iraqi plains and southwest Iran from May to September, which is triggered by the pressure gradient between the North Africa/Mediterranean high and the monsoon low over southeast Iran. In the cold season, the pressure gradient between the Kurdi cut-off low and the high pressure over North Africa facilitates frontal dust storms that enhance the dust AOD over the west parts of Iran. The Caspian Sea high-pressure system during summer intensifies the easterly-northeasterly winds from the Karakum Desert to northwest Iran, while in winter, the strengthening of the Siberian High and its expansion over the Caspian Sea trigger strong cold northerlies over central/northwest Iran. In addition, the convergence of south/southwesterly winds, as a result of the surface low-pressure in the southern Arabian Peninsula, facilitates pre-frontal dust storms toward southwestern Iran, mostly during March–April. In summer, the gradient zone between the Iraq low-pressure and the Zagros high-pressure enables transport of dust aerosols toward west-northwest Iran. Identification of the weather clusters associated with intense dust storms over the west part of Iran is critical for the prevention of the deleterious effects of dust on climate, ecosystems and human health.

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).

Incorrect Asian aerosols affecting the attribution and projection of regional climate change in CMIP6 models

Anthropogenic aerosol (AA) forcing has been shown as a critical driver of climate change over Asia since the mid-20th century. Here we show that almost all Coupled Model Intercomparison Project Phase 6 (CMIP6) models fail to capture the observed dipole pattern of aerosol optical depth (AOD) trends over Asia during 2006–2014, last decade of CMIP6 historical simulation, due to an opposite trend over eastern China compared with observations. The incorrect AOD trend over China is attributed to problematic AA emissions adopted by CMIP6. There are obvious differences in simulated regional aerosol radiative forcing and temperature responses over Asia when using two different emissions inventories (one adopted by CMIP6; the other from Peking university, a more trustworthy inventory) to driving a global aerosol-climate model separately. We further show that some widely adopted CMIP6 pathways (after 2015) also significantly underestimate the more recent decline in AA emissions over China. These flaws may bring about errors to the CMIP6-based regional climate attribution over Asia for the last two decades and projection for the next few decades, previously anticipated to inform a wide range of impact analysis.

Characteristics of aerosol chemical components and their impacts on direct radiative forcing at urban and suburban locations in Southeast Texas

The monthly/seasonal characteristics of the concentration and aerosol optical depth (AOD) of four aerosol components (water-soluble, insoluble, black carbon (BC), and sea-salt) and their direct radiative forcing (DRF) were investigated at three environmental locations in Southeast Texas. We used fine particulate matter (PM2.5) samples measured at one urban residential (Aldine (AD)) and two suburban (Deer Park (DP) and West Liberty (WL)) sites located around Houston during 2016–2017, and performed model-based analysis using the mass concentrations of the four aerosol components to evaluate their impact on the DRF. Overall, the concentrations, AODs, and DRFs of all four aerosol components at AD were higher than those at DP and WL during the study period. In particular, the water-soluble component was the most dominant contributor, except for absorbing BC. The monthly AOD patterns of the four individual aerosol components (especially, water-soluble and BC) at the three sites were found to have strong associations with their concentrations and/or relative humidity (RH). The DRFs at the top of the atmosphere (DRFTOA) and surface level (DRFSFC) for most of the aerosol components were found to be highest in winter 2017 (AD), spring 2016 and winter 2017 (DP), and winter 2016 and fall 2017 (WL). The exceptions were sea-salt and insoluble components, which showed a peak in summer 2016 and no distinct monthly variation, respectively. Uncertainties in the DRFs of the four target aerosol components calculated using in-situ RH measurements were found to be less than 20%, with the exception of the water-soluble component at WL (24%). A sensitivity test showed that the DRFs of the aerosol components were slightly and significantly influenced by changes in AOD and single scattering albedo, respectively; additionally, sensitively changed with RH.

Characterizing the long‐term dynamics of aerosol optical depth in the Yangtze River Middle‐Reach urban agglomeration, China

AbstractAlong with the rapid urbanization and economic development, the Yangtze River Middle‐Reach urban agglomeration (YRMR) has suffered from increasingly serious environmental problems. Characterizing the long‐term dynamics of aerosol optical depth (AOD) in YRMR is of important significance for scientific control of aerosol pollutants over this region. Previously, numerous researches have analysed the temporal and spatial characteristics of AOD in different regions through statistical values of AOD time series or using trend analysis methods based on linear regression models. It is challenging to approximate nonlinear trends and identify change points of AOD time series. This paper proposed a new methodology for investigating the dynamics of AOD over the YRMR region on a monthly basis by using the Terra MODIS Collection 6.1 Level 2 aerosol products (MOD04_L2) during 2001–2017. A three‐dimensional spatio‐temporal interpolation method was used to generate gap‐filled monthly AOD maps, and then a generic Bayesian time series decomposition algorithm was applied to analyse the trends and change points of monthly AOD time series. The results showed that the areas with high AOD values were mainly located in the core cities and the connecting belts. Strong positive AOD anomalies had been observed during 2006–2011, whereas the positive anomalies were typically intermingled with negative anomalies and it indicated that the evolution patterns of AOD for different regions were different and influenced by fluctuations. Trend analysis of the monthly mean AOD values indicated that AOD in YRMR increased first, then fluctuated, and finally decreased. Although it was difficult to separate the increasing and fluctuating periods from each other due to the fluctuation, the decreasing trend after 2011 was significant, and a significant drop of AOD had been observed since 2014. Long‐term AOD variations were closely related to national policies, and the results could provide useful references for regional environmental assessment and decision‐making.

Revisiting the levels of Aerosol Optical Depth in south-southeast Asia, Europe and USA amid the COVID-19 pandemic using satellite observations

The countries around the world are dealing with air quality issues for decades due to their mode of production and energy usages. The outbreak of COVID-19 as a pandemic and consequent global economic shutdown, for the first time, provided a base for the real-time experiment of the effect of reduced emissions across the globe in abetting the air pollution issue. The present study dealt with the changes in Aerosol Optical Depth (AOD), a marker of air pollution, because of global economic shutdown due to the coronavirus pandemic. The study considered the countries in south and south-east Asia (SSEA), Europe and the USA for their extended period of lockdown due to coronavirus pandemic. Daily Aerosol Optical Depth (AOD) from Moderate-resolution imaging spectroradiometer (MODIS) and tropospheric column density of NO2 and SO2 from Ozone monitoring instrument (OMI) sensors, including meteorological data such as wind speed (WS) and relative humidity (RH) were analyzed during the pre-lockdown (2017–2019) and lockdown periods (2020). The average AOD, NO2 and SO2 during the lockdown period were statistically compared with their pre-lockdown average using Wilcoxon-signed-paired-rank test. The accuracy of the MODIS-derived AOD, including the changing pattern of AOD due to lockdown was estimated using AERONET data. The weekly anomaly of AOD, NO2 and SO2 was used for analyzing the space-time variation of aerosol load as restrictions were imposed by the concerned countries at the different points of time. Additionally, a random forest-based regression (RF) model was used to examine the effects of meteorological and emission parameters on the spatial variation of AOD. A significant reduction of AOD (−20%) was obtained for majority of the areas in SSEA, Europe and USA during the lockdown period. Yet, the clusters of increased AOD (30–60%) was obtained in the south-east part of SSEA, the western part of Europe and US regions. NO2 reductions were measured up to 20–40%, while SO2 emission increased up to 30% for a majority of areas in these regions. A notable space-time variation was observed in weekly anomaly. We found the evidence of the formation of new particles for causing high AOD under high RH and low WS, aided by the downward vertical wind flow. The RF model showed a distinguishable relative importance of emission and meteorological factors among these regions to account for the spatial variability of AOD. Our findings suggest that the continued lockdown might provide a temporary solution to air pollution; however, to combat persistent air quality issues, it needs switching over to the cleaner mode of production and energy. The findings of this study, thus, advocated for alternative energy policy at the global scale.

A synergy of linear model and wavelet analysis towards space-time characterization of aerosol optical depth (AOD) during pre-monsoon season (2007–2016) over Indian sub-continent

The pre-monsoon aerosol concentration plays a significant role in modifying precipitation amount over the Indian sub-continent. A large variety of aerosol from different sources produces a complex radiative and climate response through the interaction with the hydrometeorological parameters. In this study, we analyzed the space-time dynamics of aerosol optical depth (AOD) in relation to the meteorological and surface parameters over Indian sub-continent during pre-monsoon season from 2007 to 2016. The level-3 daily aerosol products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) were used. The IMD gridded rainfall and temperature, ECMWF derived RH and wind velocity, and GLADS derived soil moisture data were also used at daily time scale. For the space-time model, the Mann-Kendall trend test and a pixel-based multiple linear regression were used, while, wavelet transformation was used on these daily observations for analyzing periodicities of AOD. The time-series average shows moderate to high AOD (0.4–0.8), including a consistent positive anomaly in the Indo-Gangetic basin (IGP) in north India. A significant inter-annual variation is also observed both in MODIS and MISR datasets. The trend statistics shows an increasing trend of aerosol concentration in the eastern and southern India. The linear regression shows a variable response of AOD with changing magnitude of meteorological factors. However, a substantial spatial coverage of significantly decreased AOD is observed with increasing soil moisture content (β < −0.04). The wavelet analysis manifests the abundances of 32–128 days of cycle with a periodic interjection of 8–32 days of cycle suggesting occurrence of fine and coarse mode aerosols events, respectively. The coherency of time series AOD and other covariates shows varying leading and lagging dynamics in these two principal periodicities. The findings, however, evidenced a notable difference in the space-time patterns of AOD in MODIS and MISR datasets. The analyzed AOD cycles are coincided with the Madden-Julian-Oscillation (MJO) that recurs every 30–60 days interval. The findings also support the theoretical proposition of elevated heat pump theory (EHP) driven by fine mode aerosols for occurring pre-monsoon and monsoon precipitation over the Indian sub-continent. The analyzed periodicity of AOD can provide useful insights in studying the short/long term variability of precipitation over polluted environments during the pre-monsoon season.

Spatiotemporal regionalization of atmospheric dust based on multivariate analysis of MACC model over Iran

Extraordinary dry conditions on the desert and arid areas in Iran, along with strong influences from surrounding dust sources in the Middle East and Arabia, have exposed the country to substantial amounts of dust aerosols with serious impacts on environment, health and socio-economic aspects. Considering this threat, daily aerosol optical depth (AOD) data over Iran obtained from the Monitoring Atmospheric Composition and Climate (MACC) reanalysis during 2003–2012 were used to analyze the spatial and temporal patterns of total AOD (TAOD) and dust AOD (DAOD). This study identifies the areas mostly affected by TAOD and DAOD in terms of daily average values and the areas associated with distinct temporal patterns, utilizing S-mode and T-mode principal component analysis (PCA). The analysis reveals six distinct sub-regions in southeast, west-northwest, northeast, east, central and southwest of Iran, which are among the major centers influenced by TAOD and DAOD extremes. The main findings of the T-mode results, consistent with TAOD and DAOD S-mode patterns, are (i) the maximum dust loading over the eastern, southeastern and southwestern subregions in March and May, (ii) dust loading with peaks in November over the eastern and northeastern sub-regions; (iii) maximum number of dusty days in July and August over southwest, (iv) maximum dust in July over southeastern and Central Iran, (v) a dust pattern with identical extent over the west and northwest sub-region in March and October and, (vi) a prevailing dust loading over the dry areas (Lut desert, Sistan plain and southwest edge of the territory) in October. The spatiotemporal evolution of dust is a function of the growth and expansion of dust extremes originated from various sources in Iran and the Middle East. The PCA methodology, through simplification of aerosol distribution to simpler structures, was also incorporated to provide a geographical interpretation of the highly sensitive territory of Iran.

Inter-annual and seasonal variations in optical and physical characteristics of columnar aerosols over the Pokhara Valley in the Himalayan foothills

ABSTRACT This study reports comprehensive analysis of seasonal and inter-annual variations of aerosol optical and physical properties over the Pokhara Valley in the foothills of central Himalayas in Nepal utilizing the high-quality multi-year columnar aerosol data observed recently from January 2010 to December 2017. The influence of forest fires and agro-residue fires on aerosol properties is also investigated. Seasonal mean aerosol optical depth (AOD) over Pokhara is ≥0.3 during the year, highest in pre-monsoon and lowest in monsoon season. AOD exhibits a consistent seasonal cycle every year with a peak in pre-monsoon. Fine mode fraction contributing to AOD and Angstrom exponent (α) corroborate seasonal pattern of AODs. AODs show good correlation with fire counts in the Pokhara Valley and surrounding region. The aerosol volume size distributions are bimodal. The peaks in volume size distribution and volume concentrations obtained in 2016 pre-monsoon are the highest during the 8-year observation period which coincided with peaks in the occurrence of highest number of fires in the Pokhara Valley and surrounding region. Effective radius of coarse mode aerosols is an order of magnitude larger than fine mode aerosols. The analysis of optical and physical properties of aerosols over the Pokhara Valley suggest that the aerosols observed over the Himalayan foothills are of urban/industrial, biomass burning and dust origin with proportional contributions varying in different seasons. The study provides observational constraints on aerosol physical and optical properties that can serve as inputs for model simulation of aerosol physiochemical processes and quantification of impacts on air quality, climate and sensitive ecosystems in this data-sparse region.