Analysis of the spatiotemporal changes in global tropospheric ozone concentrations from 1980 to 2020.

Tropospheric ozone affects human health, ecosystems, and climate change. Previous studies on Tropospheric Column Ozone (TCO) have primarily concentrated on specific regions or global geographic divisions. This has led to insufficient exploration of the spatiotemporal characteristics and influencing factors of TCO in global and rational subregions. In this study, TCO is calculated using the Modern Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis data and corrected using satellite data. Cluster analysis is conducted to explore the temporal characteristics of TCO variations in different regions. The results show that the global TCO is basically distributed latitudinally, with higher TCO in the northern hemisphere, which is related to atmospheric circulation, radiation, stratospheric transport, and the distribution of ozone precursors. Between 1980 and 2020, the global average annual TCO showed an increasing trend at 0.09 DU yr-1 due to rising anthropogenic emissions of ozone precursors (NOx at 589547.86 t yr-1 and NMVOC at 1070818.24 t yr-1), increasing tropopause height (-0.10 hPa yr-1), and the enhanced ozone flux at the tropopause (0.22 ppbv m s-2 yr-1). Cluster analysis reveals different trends in TCO changes across regions. The ocean south of 60°S and parts of West Antarctica (Region 2), the region from 30°N to 60°N and the western oceanic region of 30°S (Region 3), and the region from the equator to 60°S and the region north of 60°N (Region 5) exhibit increasing trends (with rates of 0.08 DU yr-1, 0.07 DU yr-1, and 0.11 DU yr-1, respectively), linked to the enhanced ozone flux at the tropopause, the rising tropopause height and increasing ozone p precursors. Conversely, the decreasing TCO trends in the equatorial Pacific (Region 1) and East Antarctica (Region 4) (with rates of -0.01 DU yr-1 and -0.02 DU yr-1) may be related to increased cloudiness and weakened photochemical reactions.

Light-absorbing impurities in glacial environments over western Himalaya from reanalysis data and in situ observations.

In context with the scientific evidence of aerosol deposition induced snow and glacier melt, this paper provides baseline information about the spatiotemporal variability of aerosols and snow-ice chemistry filling the data and knowledge gap over the western Himalaya, India based on recently published paper [1]. A systematic approach was employed that entailed analysis of aerosol variability over four decades using MERRA-2 (Modern-Era Retrospective analysis for Research and Applications) data over five major mountain ranges in the western Himalaya. Further, data about nine physicochemical parameters was generated over three selected glaciers in the study area. HYSPLIT (HYbrid Single Particle Lagrangian Integrated Trajectory) model simulated air mass sources at weekly intervals. This dataset is valuable for future investigations aimed at understanding and characterizing the impacts of light-absorbing impurities on radiative forcing, albedo changes, snow-melt, glacier recession and water quality in the western Himalaya.

Aerosols and black carbon variability using OMI and MERRA-2 and their relationship to near-surface air temperature.

An extinction of incoming solar radiation is taking place by absorption and scattering by dust, water droplets, and gaseous molecules. Such phenomena are responsible for altering meteorological variables. In the present study, temporal analysis of the aerosol optical thickness (AOT) and black carbon (BC) surface mass concentration was undertaken using an ozone monitoring instrument (OMI) and modern-era retrospective analysis for research and applications, version 2 (MERRA-2) satellite from the year 2018 to 2022. The study was mainly focused on the western states of India which are Rajasthan, Gujarat, and Maharashtra. The correlation of AOT and BC surface mass concentration with near-surface temperature (2m above ground level) was analyzed. BC and temperature shows strong negative correlation as BC is known for its absorption of radiation. It accumulates in the atmosphere and contributes to atmospheric warming while simultaneously bringing down the near-surface air temperature due to the reduced sunlight reaching the ground. Also, seasonal analysis was conducted for winter, summer, monsoon, and post-monsoon, which shows the higher values of AOT in monsoon; however, seasonal average BC surface mass concentration was found high in winter in each year for all three states. AERONET data from Jaipur, Rajasthan, and Pune, Maharashtra for the year 2021 was used to further evaluate the AOT generated from OMI. The results demonstrated a significant connection, with R2 values of 0.62 and 0.69, respectively. The temperature retrieved from MERRA-2 was also validated with ground truth data of the Continuous Ambient Air Quality Monitoring Station (CAAQMS) at both stations showing high agreement with R2 > 0.70.

Can the aerosol pollution extreme events be revealed by global reanalysis products?

Extreme aerosol pollution poses significant risks to the climate, environment, and human health. To investigate the formation and impacts of aerosol pollution extreme events (APEE), the reanalysis product presents meticulous spatiotemporal information on the three-dimensional distribution of aerosols. However, there is a lack of comprehensive evaluation and information regarding the data quality of reanalysis products employed in APEE research, as well as limited understanding of their spatial and temporal distribution, variation, and long-term trends. To address this scientific gap, we conducted a global study for distribution and variation patterns of APEE using two widely-used reanalysis products, MERRA-2 (Modern-Era Retrospective Analysis for Research-2) and CAMS (Copernicus Atmospheric Monitoring Service). The APEE was defined here as a day when the daily aerosol optical depth (AOD) exceeding its 90th percentile for a given station and month. Eleven distinct land regions worldwide were selected for evaluation by comparing both reanalysis products with MODIS satellite products and ground-based observations in terms of frequency, intensity, and temporal trends of APEE. The analysis indicates that MERRA-2 and CAMS exhibit high matching rates (70 % and 80 %, respectively) in terms of occurrence timeline for APEE at monthly and seasonal scales, while also exhibiting strong monthly correlation coefficients (>0.65) with ground-based observations over selected regions. The total AOD (-0.002 âˆ¼ -0.123 decade-1), APEE AOD (-0.004 âˆ¼ -0.293 decade-1), and APEE frequency (-0.264 âˆ¼ -1.769 day month-1 decade-1) of both observations and reanalysis products in most regions showed a decreasing trend with various magnitude, except for some regions such as South Asia where the trend is increasing. Based on the aforementioned evaluation, it is evident that reanalysis products are effective and useful in identifying the temporal trends associated with APEE.

Potential source and health risks of black carbon based on MERRA-2 reanalysis data in a typical industrial city of North China Plain.

Black carbon (BC) significantly affects climate, environmental quality, and human health. This study utilised Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), which can compensate for the shortcomings of ground BC monitoring in spatial-temporal distribution to study the pollution characteristics of BC and potential pollution sources in a typical industrial city (Xinxiang) with serious air pollution in northern China. The results showed that average daily ground observation and MERRA-2 concentration of BC of 7.33 µg m-3 and 9.52 µg m-3. The mean BC concentration derived from MERRA-2 reanalysis data was higher than ground measurement due to resolution limitations and pollution from the northern regions. The reliability of the MERRA-2 data was confirmed through correlation analysis. Consideration of the spatial distribution of BC from MERRA-2 and incorporating the potential source contribution function (PSCF), concentration-weighted trajectory (CWT), and emission inventory, other possible source areas and primary sources of BC in Xinxiang were investigated. The results indicated that implementing transportation and residential emission control measures in Henan Province and its surrounding provinces, such as Hebei Province, will effectively decrease the BC level in Xinxiang City. A passively smoked cigarettes model was used to evaluate the risk of BC exposure. The percentage of lung function decrement (PLFD) was the highest in school-age children, while the impact on lung cancer (LC) health risk was comparatively lower. Notably, the BC health risk in Xinxiang was lower than in most cities across Asia.

Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980-2021).

In this study, the spatial-temporal trends of PM2.5 pollution were analyzed for subregions in Africa and the entire continent from 1980 to 2021. The distributions and trends of PM2.5 were derived from the monthly concentrations of the aerosol species from MERRA-2 reanalysis datasets comprising of sulphates (SO4), organic carbon (OC), black carbon (BC), Dust2.5 and Sea Salt (SS2.5). The resulting PM2.5 trends were compared with the climate factors, socio-economic indicators, and terrain characteristics. Using the Mann-Kendall (M-K) test, the continent and its subregions showed positive trends in PM2.5 concentrations, except for western and central Africa which exhibited marginal negative trends. The M-K trends also determined Dust2.5 as the dominant contributing aerosol factor responsible for the high PM2.5 concentrations in the northern, western and central regions of Africa, while SO4 and OC were respectively the most significant contributors to PM2.5 in the eastern and southern Africa regions. For the climate factors, the PM2.5 trends were determined to be positively correlated with the wind speed trends, while precipitation and temperature trends exhibited low and sometimes negative correlations with PM2.5. Socio-economically, highly populated, and bare/sparse vegetated areas showed higher PM2.5 concentrations, while vegetated areas tended to have lower PM2.5 concentrations. Topographically, low laying regions were observed to retain the deposited PM2.5 especially in the northern and western regions of Africa. The Air Quality Index (AQI) results showed that 94 % of the continent had an average PM2.5 of 12-35 µg/m3 hence classified as "Moderate" AQI, and the rest of the continent's PM2.5 levels was between 35 and 55 µg/m3 implying AQI classification of "Unhealthy for Sensitive People". Northern and western Africa regions had the highest AQI, while southern Africa had the lowest AQI. The approach and findings in this study can be used to complement the evaluation and management of air quality in Africa.

Impacts of changing climate and topography on snow cover variability of Parvati River Basin, western Himalayas, India.

Snow cover is an essential element of the Himalayan region (Third Pole), and it represents the impacts induced by climate change. Recently, studies have reported significant variations in the Himalayan snow cover area, which may impact the livelihood of a large portion of the global population. Therefore, in this study, efforts were made to estimate the association between key climate stressors (CSs), i.e., temperature and precipitation, topography, and temporal variability of snow cover area (SCA) in the Parvati River basin (PRB) of the Indian western Himalayas. In this regard, the PRB has been classified into different elevation zones, i.e., zone I to zone V, ranging from 1100 to 6200 amsl. The databases such as MODIS, MEERA-2, and ASTER DEM V2 have been used to estimate the changes in the SCA and the CSs with changes in elevation and seasons. The linear regression analysis of the dataset from 2001 to 2017 revealed a significant association and increasing trend in the SCA of zone III. However, a significant association could not be established between the elevation and the SCA for the rest of the zones. A zonal seasonal trend investigation of the SCA observed an increasing trend in zones IV and V during the summer season due to a momentous rise in snowfall and a decline in temperature. The SCA has shown a significant declining trend only during the monsoon season in zones IV and V, which is due to a strong negative relationship between the SCA and the temperature of the region. These results demonstrate the amount of SCA in zones of high elevation of the PRB has been declining at an alarming rate, which could negatively influence glaciers' retreat in the near future. Hence, it can be estimated that the outcomes of the study will act as a base for future studies, regional policy formulation, and climate modelling that can further prevent future drastic or extreme events.

Evaluation of MERRA-2 and CAMS reanalysis for black carbon aerosol in China.

Black carbon (BC) constitutes a pivotal component of atmospheric aerosols, significantly impacting regional and global radiation balance, climate, and human health. In this study, we evaluated BC data in two prominent atmospheric composition reanalysis datasets: the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and the Copernicus Atmosphere Monitoring Service (CAMS), and analyzed the causes of their deviations. This assessment is based on observational data collected from 34 monitoring stations across China from 2006 to 2022. Our research reveals a significant and consistent decline in BC concentrations within China, amounting to a reduction exceeding 67.33%. However, both MERRA-2 and CAMS reanalysis data fail to capture this declining trend. The average annual decrease of BC in MERRA-2 from 2006 to 2022 is only 0.06 µg/m3 per year, while the BC concentration in CAMS even increased with an average annual value of 0.014 µg/m3 per year. In 2022, MERRA-2 had overestimated BC concentration by 20% compared to observational data, while CAMS had overestimated it by approximately 66%. In the regional BC concentration analysis, the data quality of the reanalysis data is better in the South China (RM = 0.59, RC = 0.53), followed by the North China (RM = 0.50, RC = 0.42). Reanalysis BC data in Northwest China and the Tibetan Plateau are difficult to use for practical analysis due to their big difference with observation. In a comparison of the anthropogenic BC emissions inventory used in the two atmospheric composition reanalysis datasets with the Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC) emissions inventory, we found that: Despite the significant decline in China's BC emissions, MERRA-2 still relies on the 2006 emissions inventory, while CAMS utilizes emission inventories that even show an increasing trend. These factors will undoubtedly lead to greater deviations between reanalysis and observational data.

Airborne desert dust in the Northern Adriatic area (Croatia): Different sources.

During the implementation of the INTERREG IT-HR project ECOMOBILITY, whose one of the goals was to estimate the impact of ship emissions on air quality in the port city of Rijeka (Croatia) and Venice (Italy), two particular weekly samples were collected in Rijeka, during the first and the thirteen weeks of sampling, i.e. S01 (16.10.-23.10.2018) and S13 (24.04.-30.04.2019.), respectively. Both samples have similarities regarding species characteristic for desert dust contribution, but HYSPLIT analyses excluded Saharan desert to be the source of the S01 sample. Unlike Saharan dust, this sample had a high contribution of fine and ultrafine particles (>50 % and 9.8 %, respectively), as well as secondary inorganic (sulfates, ammonium) and organic (water soluble organic compounds - WSOC) aerosols. Detailed synoptic situation and HYSPLIT backward trajectories pointed out the Syrian Desert as the source of this collected sample. The same source was proved by MERRA-2 reanalysis of the desert dust emission. Although the Saharan dust episodes, mostly in precipitation, are well known in the Northern Adriatic area, this is the first time to indicate Syrian Desert as a source of airborne particulates. This assumption was confirmed with chemical species characteristic for the Syrian Desert, i.e. higher content of potassium from K- feldspar and phosphates.

Influence of pollution control measures on the reduction of black carbon in an urban site of megacity, Tianjin, China based on ground-monitored and MERRA-2 reanalysis data.

The concentration of particulate matter (PM) has been reduced significantly with the implementation of air pollution control plans in Tianjin. However, as an important component of PM that can lead to global warming and adverse health effects, the influence of pollution control measures (PCM) on black carbon (BC) has been less studied. In this study, ten years of BC concentration satellite-based reanalysis data were collected from MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2), and their reliability was verified using ground-monitored BC data. Using the proposed Kolmogorov-Zurbenko and artificial neural network (KZ-ANN) model, the influences of meteorology and emission measures were separated. The results indicated that the overall meteorological conditions were not conducive to BC diffusion, especially in autumn and winter with low temperature, surface solar radiation, boundary layer height, and high atmospheric pressure, all of which increased the BC concentration. This study also found that although a significant reduction in BC emissions was observed in Tianjin (the total emissions of BC in 2020 dropped by 52 % compared with the level in 2013), the change in emission-influenced BC was relatively low (the concentration of emission-influenced BC in 2022 dropped by only 2.39 % compared to that in 2013). The reduction of emission-influenced BC concentration during the air pollution prevention control and action plan (APPC) was higher than the level during of the three-year action plan for winning the blue sky defense war (abbreviated as the Blue Sky Defense War). In addition, the lockdown measures during the Corona Virus Disease 2019 (COVID-19) did not have beneficial effect on the reduction of emission-influenced BC concentration. This phenomenon can be explained by the long-range transport of BC from surrounding areas, which was also proven by the results of the backward trajectory analysis. Therefore, efforts on emissions reduction in Tianjin were diminished. It is necessary to cooperate with the governments in surrounding areas to implement joint BC control measures, especially in autumn and winter.

Environmental management: a country-level evaluation of atmospheric particulate matter removal by the forests of India.

Particulate matter (PM) is a critical air pollutant, responsible for an array of ailments leading to premature mortality worldwide. Nature-based solutions for mitigation of PM and especially role of forests in mitigating PM from an ecosystem perspective are less explored. Forests provide a natural pollution abatement strategy by providing a surface area for the deposition of PM. Depending on their structure and composition, forests have varying capacities for PM adsorption, which is again less explored. Hence, in the present study, we evaluate the removal capacity of PM by the forest-type groups of India. Deposition flux and total PM removal across sixteen forest types were estimated based on the 2019 dataset of PM using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data. Externality values and PM removal costs by industrial equipment were used for associating an economic value to the air pollution abatement service by forests. The total PM2.5 removal by forests in 2019 was estimated to be 1361.28 tons and PM10 was estimated to be 303,658.27 tons. Deposition of PM was found to be high in littoral and swamp forests, tropical semi-evergreen forests, tropical moist deciduous forests, and sub-tropical pine forests. Tropical dry deciduous forests had the highest net weight % removal of PM with 39% removal for PM2.5 and 39% removal for PM10. The air pollution abatement service by forests for PM removal was 188 M US dollars (USD) with externality-based removal service by forests of 2009 M USD. The net PM removed by all forests of India was estimated to be approximately worth ₹ 470-648 Crore (59-81 million dollars) for PM2.5 and worth ₹56,746-1,22,617 Crore (7093-15,327 million dollars) for PM10 based on valuation using value transfer method. The study concludes that forests can be a significant contributor to PM reduction at a global level. Especially for India's National Clean Air Programme and further research and policy considerations, the findings would be extremely useful.

Meteorology-driven PM2.5 interannual variability over East Asia.

Atmospheric fine particulate matter (PM2.5) is a human health risk factor, but its ambient concentration depends on both precursor emissions and meteorology. While emission reductions are used to set PM2.5-related health policies, the effect of meteorology is often overlooked. To explore this aspect, we examined PM2.5 interannual variability (IAV) associated with meteorological parameters using the long-term simulation from the Community Earth System Model (CESM1), a global climate-chemistry model, with fixed emissions. The results are subsequently contrasted with the MERRA-2 reanalysis dataset, which inherently considers emission and meteorology effects. Over continental East Asia, the CESM1 domain-average PM2.5 IAV is 6.7 %, mainly attributed to humidity, precipitation, and ventilation variation. The grid-cell PM2.5 IAVs over southern East China are larger, up to 12 % due to the more substantial influence of El Niño-induced meteorological anomalies. Under such climate extreme, sub-regional PM2.5 concentration may occasionally exceed WHO air quality guideline levels despite the compliance of the long-term mean. The simulated PM2.5 IAV over continental East Asia is ~25 % of that derived from the MERRA-2 data, which highlights the influence of both emission and meteorology-driven variations and trends inherent in the latter. Although emission-driven variability is significant to PM2.5 IAV, in remote areas downwind of major source regions in East Asia, North America, and Western Europe, the MERRA-2 data revealed that meteorological variations contributed more to PM2.5 IAV than emission variations. Thus, when setting policies for complying with the WHO PM2.5-related air quality guideline levels, the highest annual PM2.5 associated with climate extremes should be considered instead of that based on average climate conditions.

Formation of an unprecedented yellow snow episode in Xinjiang on December 1, 2018.

On 1 December 2018, a heavy yellow snow fell in Urumqi (87°37'E, 43°47'N) - the largest city of northwest China's Xinjiang province, which was the first case that the yellow snow has been observed in winter. The air parcel trajectories obtained from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the dust surface mass concentration from Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) were adopted to identify the potential sources and transport paths of pollutants responsible for this yellow snow episode. The meteorological situation and the European Center for Medium-Range Weather Forecasts (ECMWF) forecast products have been utilized to analyze the supportive meteorological conditions. The results showed that the heavy snow in Urumqi was contaminated by the yellow dust originated in Karamay of Xinjiang province. The strong surface winds in Karamay lifted large amounts of dust into the atmosphere. Then the airborne dusts were transported to Urumqi rapidly by strong low-level winds, where precipitation in connection with the upper trough and the cold front lead to the yellow snow episode. This study can provide important scientific significance for predicting this kind of event (yellow snow).

Evaluating Data Product Exposure Metrics for Use in Epidemiologic Studies of Dust Storms.

Dust storms are increasing in frequency and correlate with adverse health outcomes but remain understudied in the United States (U.S.), partially due to the limited spatio-temporal coverage, resolution, and accuracy of current data sets. In this work, dust-related metrics from four public areal data products were compared to a monitor-based "gold standard" dust data set. The data products included the National Weather Service (NWS) storm event database, the Modern-Era Retrospective analysis for Research and Applications-Version 2, the EPA's Air QUAlity TimE Series (EQUATES) Project using the Community Multiscale Air Quality Modeling System (CMAQ), and the Copernicus Atmosphere Monitoring Service global reanalysis product. California, Nevada, Utah, and Arizona, which account for most dust storms reported in the U.S., were examined. Dichotomous and continuous metrics based on reported dust storms, particulate matter concentrations (PM10 and PM2.5), and aerosol-type variables were extracted or derived from the data products. Associations between these metrics and a validated dust storm detection method utilizing Interagency Monitoring of Protected Visual Environments monitors were estimated via quasi-binomial regression. In general, metrics from CAMS yielded the strongest associations with the "gold standard," followed by the NWS storm database metric. Dust aerosol (0.9-20 µm) mixing ratio, vertically integrated mass of dust aerosol (9-20 µm), and dust aerosol optical depth at 550 nm from CAMS generated the highest standardized odds ratios among all metrics. Future work will apply machine-learning methods to the best-performing metrics to create a public dust storm database suitable for long-term epidemiologic studies.

Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM2.5 concentrations over India.

The spatial coverage of PM2.5 monitoring is non-uniform across India due to the limited number of ground monitoring stations. Alternatively, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), is an atmospheric reanalysis data used for estimating PM2.5. MERRA-2 does not explicitly measure PM2.5 but rather follows an empirical model. MERRA-2 data were spatiotemporally collocated with ground observation for validation across India. Significant underestimation in MERRA-2 prediction of PM2.5 was observed over many monitoring stations ranging from -20 to 60 µg m-3. The utility of Machine Learning (ML) models to overcome this challenge was assessed. MERRA-2 aerosol and meteorological parameters were the input features used to train and test the individual ML models and compare them with the stacking technique. Initially, with 10% of randomly selected data, individual model performance was assessed to identify the best model. XGBoost (XGB) was the best model (r2 = 0.73) compared to Random Forest (RF) and LightGBM (LGBM). Stacking was then applied by keeping XGB as a meta-regressor. Stacked model results (r2 = 0.77) outperformed the best standalone estimate of XGB. Stacking technique was used to predict hourly and daily PM2.5 in different regions across India and each monitoring station. The eastern region exhibited the best hourly prediction (r2 = 0.80) and substantial reduction in Mean Bias (MB = -0.03 µg m-3), followed by the northern region (r2 = 0.63 and MB = -0.10 µg m-3), which showed better output due to the frequent observation of PM2.5 >100 µg m-3. Due to sparse data availability to train the ML models, the lowest performance was for the central region (r2 = 0.46 and MB = -0.60 µg m-3). Overall, India's PM2.5 prediction was good on an hourly basis compared to a daily basis using the ML stacking technique.

Changing patterns in the highly contributing aerosol types/species across the globe in the past two decades.

With the rapidly changing aerosol emissions due to the increase in urbanization, energy consumption, population density, and industrialization in the past two decades across the globe, there is an evolution of different chemical properties of aerosols that are yet not quantified properly. Therefore, a rigorous attempt is made in this study to obtain the long-term changing patterns in the contribution of different aerosol types/species, to the total aerosol loading. This study is carried out only over those regions exhibiting either increasing or decreasing trends in the aerosol optical depth (AOD) parameter on a global scale. Applying the multivariate linear regression trend analysis on Modern-Era Retrospective Analysis for Research and Application version 2 (MERRA-2) aerosol species dataset obtained between 2001 and 2020, we found that despite the overall statistically significant decrease in total columnar AOD trend values over North-Eastern America, and Eastern and Central China regions, an increase in the dust and organic carbon aerosols is observed, respectively. As the uneven vertical distribution of aerosols can alter the direct radiative effects, the extinction profiles of different aerosol types obtained using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) dataset between 2006 and 2020, are further partitioned, for the first time, based on their presence in different altitudes (i.e., within the atmospheric boundary layer and free-troposphere) as well as measurement timing (i.e., daytime and night-time) regimes. The detailed analysis showed that there exists an overall higher contribution of aerosols persisting in the free troposphere region which in turn can have a long-term effect on climate due to their higher residence time, particularly absorbing aerosols. As the trends are mostly associated with the changes in energy use, regional regulatory policies, and/or changing background meteorology conditions, therefore this study also elaborates on the effectiveness of these factors with the changes obtained in different aerosol species/types over the region.

Evaluation of MERRA-2 data for aerosols patterns over the Kingdom of Saudi Arabia.

Aerosol is one of the major climate-forcing parameters which affect the Kingdom of Saudi Arabia in particular. The most relevant consideration that characterizes the aerosol properties and distribution is the Aerosol Optical Depth (AOD). In this study Modern Era Retrospective Analysis for Research and Applications (MERRA-2) AOD product from the year 1980-2021 is used to investigate aerosols pattern over the Kingdom of Saudi Arabia. The validation of the MERRA-2 AOD product is made by using AOD data retrieved from Aerosol Robotic Network (AERONET) stations located at Solar Village (SV) and at King Abdullah University of Science and Technology (KAUST). Various statistical analyses are performed to test the reliability of MERRA-2 data in the study region. The results of the statistical analysis indicate that MERRA-2 is highly correlated with both AERONET stations data. Thus, annual and seasonal aerosol climatology maps based on 41 years of MERRA-2 data are prepared and analyzed over the study region. The annual and seasonal aerosol climatology analysis of MERRA-2 data shows high density of AOD at southern and eastern regions while the low density emerges over the western and northern regions of the country during the study period. The results of the study are very encouraging, which increases our confidence level to use historical MERRA-2 AOD product to improve the knowledge on aerosols distribution over the region in future.

Long-term spatiotemporal variation in atmospheric aerosol properties over Türkiye based on MERRA-2 reanalysis data: aerosol classification based on city type.

Due to their complex aerosol characteristics, it is crucial to analyze the trends and properties of atmospheric aerosols over the eastern Mediterranean countries. This study comprehensively evaluates Aerosol Optical Depth (AOD) and Angström Exponent (AE) trends and aerosol classification over Türkiye, using the MERRA-2 reanalysis data from 1980 to 2019. The spatial distributions of AOD and AE were determined across various temporal scales, including multiannual, 5-year intervals, seasonal, and monthly periods. The analysis of the spatial distribution of AOD values revealed that the mean values in the northwestern areas, ranging from 0.20 to 0.25, were comparatively higher than those observed in the eastern regions, which ranged from 0.10 to 0.15. Between 1980 and 1994, the AOD values gradually increased, followed by a subsequent decline from 1995 to 2019. Based on 5-year intervals between 1980 and 2019, the coastal regions exhibited higher AOD values than the inland areas. Specifically, higher AOD values were noted between May and August, whereas lower values were observed during autumn and winter. Additionally, higher AE values were detected over the northwestern region, while the southeastern region had the lowest AE values, particularly during spring, attributed to the frequent occurrence of dust transport events in this area. The AOD and AE values were also examined in different city types, using the population thresholds of the European Commission. The global city category consisting only of Istanbul showed the highest AOD values across all seasons, while the category of very small cities, which includes 12 cities, had the lowest AOD values. Furthermore, this study investigated the contributions of dominant aerosol categories across various city types based on multiannual and seasonal variations of AOD and AE. The results showed that mixed and continental aerosols had higher portions across all city types. However, biomass burning/industrial and mixed aerosol categories were more prominent in global and large cities. Overall, this study provides a comprehensive overview of the atmospheric aerosol properties in Türkiye and can serve as a useful guide for researchers intending to conduct future studies utilizing AOD and AE data obtained through MERRA-2 aerosol diagnosis.

An evaluation of long-term gridded datasets of total columnar ozone retrieved from MERRA-2 and AIRS over the Indian region.

Accurately determining the spatiotemporal variability of ozone on a regional to intercontinental scale is essential for air quality studies. In the present study, a first systematic evaluation and analysis of long-term (2009-2020) gridded datasets (0.5° × 0.625°) of total columnar ozone (TCO) retrieved from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2TCO) is evaluated for the Indian region. The MERRA-2TCO is first validated with observations (IMDTCO) and then further compared with the Atmospheric Infrared Sounder (AIRSTCO) satellite datasets. For an in-depth comparison and statistical analysis, the dataset has been segregated into seven distinct regions, i.e., Western Himalaya (WH), North East (NE), North Central (NC), North West (NW), West Peninsula India (WPI), East Peninsula India (EPI), and South Peninsula India (SPI). Descriptive statistics (NMSE, FB, R, FA2, and d) reveals a significant correlation of MERRA-2TCO against IMDTCO for Delhi with NMSE (0.0013), FB (- 0.029) and Varanasi NMSE (0.0008), FB (- 0.014). The results of simple linear regression analysis show an increasing TCO trend value of 0.31% and 0.44% per decade in both the cities, respectively. A comparison of MERRA-2TCO with AIRSTCO shows a significant correlation of 0.62-0.87 in different regions of India. Furthermore, in support of Brewer's circulation pattern, an increasing shift of columnar ozone from low (SPI) to high (WH) latitudinal regions is observed. Our results show that the MERRA-2 ozone dataset can be effectively used for ozone air quality studies over India and this analysis may strengthen the need for independent, high-quality, and consistent ozone measurements with small uncertainties.

Full Coverage Hourly PM2.5 Concentrations' Estimation Using Himawari-8 and MERRA-2 AODs in China.

(1) Background: Recognising the full spatial and temporal distribution of PM2.5 is important in order to understand the formation, evolution and impact of pollutants. The high temporal resolution satellite, Himawari-8, providing an hourly AOD dataset, has been used to predict real-time hourly PM2.5 concentrations in China in previous studies. However, the low observation frequency of the AOD due to long-term cloud/snow cover or high surface reflectance may produce high uncertainty in characterizing diurnal variation in PM2.5. (2) Methods: We fill the missing Himawari-8 AOD with MERRA-2 AOD, and drive the random forest model with the gap-filled AOD (AODH+M) and Himawari-8 AOD (AODH) to estimate hourly PM2.5 concentrations, respectively. Then we compare AODH+M-derived PM2.5 with AODH-derived PM2.5 in detail. (3) Results: Overall, the non-random missing information of the Himawari-8 AOD will bring large biases to the diurnal variations in regions with both a high polluted level and a low polluted level. (4) Conclusions: Filling the gap with the MERRA-2 AOD can provide reliable, full spatial and temporal PM2.5 predictions, and greatly reduce errors in PM2.5 estimation. This is very useful for dynamic monitoring of the evolution of PM2.5 in China.