Forecasting hospital discharges for respiratory conditions in Costa Rica using climate and pollution data.

Respiratory diseases represent one of the most significant economic burdens on healthcare systems worldwide. The variation in the increasing number of cases depends greatly on climatic seasonal effects, socioeconomic factors, and pollution. Therefore, understanding these variations and obtaining precise forecasts allows health authorities to make correct decisions regarding the allocation of limited economic and human resources. We aimed to model and forecast weekly hospitalizations due to respiratory conditions in seven regional hospitals in Costa Rica using four statistical learning techniques (Random Forest, XGboost, Facebook's Prophet forecasting model, and an ensemble method combining the above methods), along with 22 climate change indices and aerosol optical depth as an indicator of pollution. Models were trained using data from 2000 to 2018 and were evaluated using data from 2019 as testing data. During the training period, we set up 2-year sliding windows and a 1-year assessment period, along with the grid search method to optimize hyperparameters for each model. The best model for each region was selected using testing data, based on predictive precision and to prevent overfitting. Prediction intervals were then computed using conformal inference. The relative importance of all climatic variables was computed for the best model, and similar patterns in some of the seven regions were observed based on the selected model. Finally, reliable predictions were obtained for each of the seven regional hospitals.

Intensification mechanisms and moisture dynamics of super cyclonic storm 'Amphan' over the Bay of Bengal: Implications for aerosol re-distribution.

The present research investigates the dynamics and underlying causes contributing to the exceptional intensity of Super Cyclonic Storm (SuCS) Amphan (16th to 21st May 2020) over the Bay of Bengal (BoB), as well as its impact on aerosol redistribution along the four cities of eastern coast and north-eastern India. Notably, the SuCS was formed during the first phase of the COVID-19 lockdown in India, giving it a unique aspect of study and analysis. Our analysis based on 30 years of climatology data from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis reveals 'positive' monthly anomalous winds (0.8 to 1.6 m/s) prevailed over the central BoB for May 2020. The present study further found the evolution of 'barrier layer thickness'(BLT) leading up to landfall, noting a thickening trend from 8 to 3 days before landfall, contributing to maintaining warmer sea surface temperatures near the coast. Additionally, utilizing European Centre for Medium-Range Weather Forecasts (ECMWF), reanalysis version-5 (ERA-5) data, a mean positive sea surface temperature (SST) anomaly of 0.8 to 1 °C was observed 'before' cyclone period (10-15 May 2020) near the cyclogenesis point. A detailed examination of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical cross-section plots during the cyclone's intensification stage reveals the presence of high-altitude clouds composed primarily of ice crystals. Further, analysis also indicates that the cyclone transported Sea-salt PM2.5 aerosols from the ocean, dispersing them in the landfall region.The aerosol optical Depth (AOD) data obtained from the National Aeronautics and Space Administration's (NASA) 'Clouds and the Earth's Radiant Energy System (CERES)' mission and MERRA-2 were also analysed, revealing that the cyclone redistributed aerosols over the Bengal basin region (mainly over 'Kolkata') and three other nearby cities along the track of the cyclone (i.e., Bhubaneswar (Odisha) Agartala (Tripura) and Shillong (Meghalaya) respectively).

An improved meteorological variables-based aerosol optical depth estimation method by combining a physical mechanism model with a two-stage model.

A two-stage model integrating a spatiotemporal linear mixed effect (STLME) and a geographic weight regression (GWR) model is proposed to improve the meteorological variables-based aerosol optical depth (AOD) retrieval method (Elterman retrieval model-ERM). The proposed model is referred to as the STG-ERM model. The STG-ERM model is applied over the Beijing-Tianjin-Hebei (BTH) region in China for the years 2019 and 2020. The results show that data coverage increased by 39.0% in 2019 and 40.5% in 2020. Cross-validation of the retrieval results versus multi-angle implementation of atmospheric correction (MAIAC) AOD shows the substantial improvement of the STG-ERM model over earlier meteorological models for AOD estimation, with a determination coefficient (R2) of daily AOD of 0.86, root mean squared prediction error (RMSE) and the relative prediction error (RPE) of 0.10 and 36.14% in 2019 and R2 of 0.86, RMSE of 0.12 and RPE of 37.86% in 2020. The fused annual mean AOD indicates strong spatial variation with high value in south plain and low value in northwestern mountainous areas of the BTH region. The overall spatial seasonal mean AOD ranges from 0.441 to 0.586, demonstrating strongly seasonal variation. The coverage of STG-ERM retrieved AOD, as determined in this exercise by leaving out part of the meteorological data, affects the accuracy of fused AOD. The coverage of the meteorological data has smaller impact on the fused AOD in the districts with low annual mean AOD of less than 0.35 than that in the districts with high annual mean AOD of greater than 0.6. If available, continuous daily meteorological data with high spatiotemporal resolution can improve the model performance and the accuracy of fused AOD. The STG-ERM model may serve as a valuable approach to provide data to fill gaps in satellite-retrieved AOD products.

Evaluation and analysis of long-term MODIS MAIAC aerosol products in China.

NASA has released the latest Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) Collection 6 (C6) and Collection 6.1 (C6.1) aerosol optical depth (AOD) products with 1 km spatial resolution. This study validated and compared C6 and C6.1 MAIAC AOD products with AERONET observations in terms of accuracy and stability, and analyzed the spatiotemporal characteristics of AOD at different scales in China. The results show that the overall accuracy of MAIAC products is good, with correlation coefficient (R) > 0.9, mean bias (BIAS) < 0.015, and the fraction within the expected error (EE) > 68 %. However, after the algorithm update, the accuracy of Terra MAIAC aerosol products C6.1 has significantly decreased. The accuracy of the products varies with the region. The accuracy of C6.1 in North China, Central East China, and West China, is comparable to or even exceeds that of C6, but performs poorly in South China. In addition, the stability of the updated C6.1 MAIAC aerosol products has not seen significantly improvement. The metrics of no product can all meet the stability goals of the Global Climate Observing System (GCOS, 0.02 per decade) in China. C6.1 improves the retrieval frequency in many regions and temporarily solves the problem of AOD discontinuity at the boundaries of different aerosol models in C6, but there are some fixed climatological AOD blocks (AOD = 0.014) in the eastern Tibetan Plateau region. Both C6 and C6.1 can capture similar annual variation characteristics of AOD to those observed at the AERONET sites. The study provides possible references for improving the MAIAC algorithm and building long-term stable aerosol records.

Source Identification of Urban dust Heavy Metals Using in Situ and Satellite Data in Three Mega Cities of Iran.

The aim of this study was an integrative assessment of heavy metals associated with urban dust data in Iran (Ahvaz, Isfahan, and Shiraz). Samples of urban dust from four sites (traffic, industrial, residential, and Greenland) were collected, and ten heavy metal concentrations were determined using ICP_MS in each sample. The highest average concentrations of metals were at the traffic site for the Mn, Zn, and Cr metals. The PMF model indicates a higher percentage of Pb participation, which shows the importance of traffic resources. The highest non-carcinogenic risk (HI) was for the Cr and the carcinogenic risk was tolerable. To evaluate aerosol and its effects on urban dust, Aerosol Optical Depth (AOD) data were used during 2003-2023. According to the Mankendall test, the trend of AOD has been increasing in Esfahan (p_value < 0.05) and Shiraz. Although Ahvaz's AOD is about two times greater than other cities, the aerosol trend in Ahvaz is decreasing.

Aerosol in global oceanic regions: Four-decade trends, spatial patterns, and policy implications.

High aerosol loadings are observed not only in megacities on continents but also in oceanic regions like the Bohai Sea. This work provides a comprehensive analysis of the spatial and temporal variations in Aerosol Optical Depth (AOD) across different ocean regions worldwide over the past four decades, using remote sensing reanalysis data. The mean AOD value across all oceanic grids is approximately 0.112, with higher levels recorded in the Central Atlantic (~0.206), followed by the North Indian Ocean (~0.201), and the Western North Pacific (~0.197). A latitudinal analysis reveals that high AOD values are predominantly found in the Northern Hemisphere's oceanic regions, especially between latitudes 0° and 70° N. Except for the Gulf of California and Hudson Bay, AOD values in the other fourteen surveyed inland seas surpass the mean levels found at similar latitudes in oceanic regions. Among which, the Bohai Sea stands out as the most polluted oceanic region with AOD value of 0.35. Over the last four decades, AOD trends have revealed a significant decrease across about 89.5 % of global oceanic grids, while an increase in AOD is observed in low-latitude oceanic areas (30° S-30° N). Investigation into inland seas shows that nearly two-thirds have experienced a declining AOD trend, while sharply upward trends in AOD are primarily found in Asia. The Bohai Sea shows the largest increase in AOD, with an annual growth rate of 1.4 %. The turning-points of the AOD in each inland sea confirm the success of regional emission control policies initiated on the adjacent continents. To improve air quality in inland seas like the Bohai Sea, adjusting industrial layouts, such as relocating heavy industries from the surrounding coastal cities' proximities to areas near open seas, could significantly benefit public health.

Long-term quantification of springtime aerosols over Saudi Arabia using multi-satellite remotely sensed data.

A comprehensive analysis of aerosol characteristics over Saudi Arabia from 2005 to 2022, utilizing high-resolution satellite-based observations and reanalysis datasets, examining the distribution of aerosols and their subtypes across the three dimensions (temporal, spatial, and altitude based) for March, April, and May. This study focuses on the analysis of parameters such as aerosol optical depth (AOD), angstrom exponent (AE), absorption aerosol optical depth (AAOD), and Ultraviolet Aerosol Index (UVAI), revealing significant spatial disparities, with elevated aerosol concentrations in the central and eastern regions and comparatively lower concentrations along the western coastal areas. In this study, the spatial patterns and temporal trends are analyzed through trajectory modeling. The study also investigates the composition of aerosols in various Saudi cities. Aerosols prevailing in a dozen Saudi Arabian cities were systematically categorized into six sub-types, considering their particle size and UV-absorbing properties. Notably, two major aerosol sub-types, absorbing coarse (AC) aerosols (UVAI > 0.25, AE < 0.70) treated as mineral dust and absorbing mixed (AM) aerosols (0.70 < AE < 1.25) along with neutral fine (NF) particles (- 0.5 < UVAI < 0.25, AE > 1.25) treated as urban, predominate across the Kingdom of Saudi Arabia.

Detectability of the potential climate change effect on transboundary air pollution pathways in the downwind area of China.

Long-term aerosol optical depth (AOD) datasets focused on the Pacific Ocean in the downwind area of China over a 19-year period from 2003 to 2021 were derived from satellite observations, reanalysis datasets, and numerical simulations. Considering the significant year-to-year changes in the amounts of aerosols transported from China to the Pacific Ocean during this period, we proposed a metric named RAOD. This is defined as the AOD over the ocean relative to that near the eastern coast of China within the same latitude band (25-30°N). RAOD was identified as a valuable metric for quantifying the long-term changes in transboundary air pollution pathways. Our analysis revealed a clear exponential decrease in RAOD values from China toward the Pacific Ocean; this was consistent with the prevailing meteorological conditions observed over the 19-year period. However, the possible long-term changes in RAOD due to climate change were found to be insignificant and were overshadowed by much larger year-to-year variations in the meteorological field. Additionally, significant seasonal variations in the absolute slope of the linear regression between RAOD and longitude were observed, and there correlated with wind patterns in the lower troposphere. Elevated slope values in the spring and winter suggested a west-to-east aerosol transport facilitated by strong winds, whereas the lower slope values in summer and autumn indicated a northward aerosol movement under weaker winds. In recent years, aerosols have become less likely to be transported far eastward from the coast of China. Based on these findings, to enhance the detectability of the climate change impacts on meteorological field affecting transboundary air pollution pathways, the RAOD metric derived using a continued long-term satellite observation of aerosols is proposed.

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.

Characteristics and optical properties of atmospheric aerosols based on long-term AERONET investigations in an urban environment of Pakistan.

Radiative balance, local climate, and human health are all significantly influenced by aerosol. Recent severe air pollution over Lahore, a city in Pakistan calls for more thorough research to determine the negative impacts brought on by too many aerosols. To study regional aerosol characteristics and their differences from various aspects, in-depth and long-term (2007-2020) investigations of the columnar aerosol properties over the urban environment of Lahore were carried out by using AERONET data. The Aerosol Optical Depth (AOD400) and Angstrom Exponent (AE400-870) vary from low values of 0.10 to a maximum value of 4.51 and from 0.03 to 1.81, respectively. The huge differences in the amount of AOD440 as well as AE440-870 show the large fluctuation of aerosol classes because of various sources of their emission. During the autumn and winter seasons, the decreasing trend of the optical parameters of aerosols like Single Scattering Albedo (SSA) and Asymmetry Parameter (ASY) with increasing wavelength from 675 to 1020 nm indicates the dominance of light-absorbing aerosols (biomass burning (BB) and industrial/urban (UI). Due to the long-distance dust movement during spring, summer, and autumn, coarse mode particles predominated in Lahore during the study period. Dust type (DD) aerosols are found to be the dominant one during spring (46.92%), summer (54.31%), and autumn (57.46%) while urban industry (BB/UI) was dominant during the winter season (53.21%). During each season, the clean continental (CC) aerosols are found to be in negligible amounts, indicating terrible air quality in Lahore City. The present research work fills up the study gap in the optical properties of aerosols in Lahore and will help us understand more fully how local aerosol fluctuation affects regional climate change over the urban environment of Lahore.

Dust and climate interactions in the Middle East: Spatio-temporal analysis of aerosol optical depth and climatic variables.

The Middle East (ME) is grappling with an alarming increase in dust levels, measured as aerosol optical depth (AOD), which poses significant threats to air quality, human health, and ecological stability. This study aimed to investigate correlations between climate and non-climate driving factors and AOD in the ME over the last four-decade (1980-2020), based on analysis of three variables: actual evapotranspiration (AET), potential evapotranspiration (PET), and precipitation (P). A comprehensive analysis is conducted to discern patterns and trends, with a particular focus on regions such as Rub al-Khali, Ad-Dahna, An-Nafud Desert, and southern Iraq, where consistently high dust levels were observed. 77 % of the study area is classified as arid or semi-arid based on the aridity index. Our results indicate an upward trend in dust levels in Iraq, Iran, Yemen, and Saudi Arabia. We noted an increasing AET trend in regions such as the Euphrates and Tigris basin, northern-Iran, and the Nile region, along with rising PET levels in arid and semi-arid zones such as Iran, Iraq, and Syria. Conversely, P showed a notable decrease in northern-Iraq, Syria, southwestern Iran, and southern-Turkey. Comparison of long-term changes (10-year moving averages) of AOD and P showed a consistent increase in AOD with P levels decreasing in all climate regions. The Budyko space analysis indicates shifts in evaporation ratio across different climate classes from 1980 to 2020, with predominant movement patterns towards higher aridity indices in arid and semi-arid regions, while factors beyond long-term aridity changes influence shifts in evaporation ratio across various climatic zones. The Middle East experiences complex and intricate interactions between dust events and their drivers. To address this issue, a comprehensive and multi-system approach is necessary, which considers both climate and non-climate drivers. Moreover, an efficient dust control strategy should include soil and water conservation, advanced monitoring, and public awareness campaigns that involve regional and international collaboration.

Comparative analysis of CAMS aerosol optical depth data and AERONET observations in the Eastern Mediterranean over 19 years.

Aerosol optical depth (AOD) is an essential metric for evaluating the atmospheric aerosol load and its impacts on climate, air quality, and public health. In this study, the AOD data from the Copernicus Atmosphere Monitoring Service (CAMS) were validated against ground-based measurements from the Aerosol Robotic Network (AERONET) throughout the Eastern Mediterranean, a region characterized by diverse aerosol types and sources. A comparative analysis was performed on 3-hourly CAMS AOD values at 550 nm against observations from 20 AERONET stations across Cyprus, Greece, Israel, Egypt, and Turkey from 2003 to 2021. The CAMS AOD data exhibited a good overall agreement with AERONET AOD data, demonstrated by a Pearson correlation coefficient of 0.77, a mean absolute error (MAE) of 0.08, and a root mean square error (RMSE) of 0.11. Nonetheless, spatial and temporal variations were observed in the CAMS AOD data performance, with site-specific correlation coefficients ranging from 0.57 to 0.85, the lowest correlations occurring in Egypt and the highest in Greece. An underestimation of CAMS AOD was noted at inland sites with high AOD levels, while a better agreement was observed at coastal sites with lower AOD levels. The diurnal variation analysis indicated improved CAMS reanalysis performance during the afternoon and evening hours. Seasonally, CAMS reanalysis showed better agreement with AERONET AODs in spring and autumn, with lower correlation coefficients noted in summer and winter. This study marks the first comprehensive validation of CAMS AOD performance in the Eastern Mediterranean, offering significant enhancements for regional air quality and climate modeling, and underscores the essential role of consistent validation in refining aerosol estimations within this complex and dynamic geographic setting.

Improving Dust Aerosol Optical Depth (DAOD) Retrieval from the GEOKOMPSAT-2A (GK-2A) Satellite for Daytime and Nighttime Monitoring.

The Advanced Meteorological Image (AMI) onboard GEOKOMPSAT 2A (GK-2A) enables the retrieval of dust aerosol optical depth (DAOD) from geostationary satellites using infrared (IR) channels. IR observations allow the retrieval of DAOD and the dust layer altitude (24 h) over surface properties, particularly over deserts. In this study, dust events in northeast Asia from 2020 to 2021 were investigated using five GK-2A thermal IR bands (8.7, 10.5, 11.4, 12.3, and 13.3 µm). For the dust cloud, the brightness temperature differences (BTDs) of 10.5 and 12.3 µm were consistently negative, while the BTD of 8.7 and 10.5 µm varied based on the dust intensity. This study exploited these optical properties to develop a physical approach for DAOD lookup tables (LUTs) using IR channels to retrieve the DAOD. To this end, the characteristics of thermal radiation transfer were simulated using the forward model; dust aerosols were explained by BTD (10.5, 12.3 µm)-an intrinsic characteristic of dust aerosol. The DAOD and dust properties were gained from a brightness temperature (BT) of 10.5 µm and BTD of 10.5, 12.3 µm. Additionally, the cumulative distribution function (CDF) was employed to strengthen the continuity of 24-h DAOD. The CDF was applied to the algorithm by calculating the conversion value coefficient for the DAOD error correction of the IR, with daytime visible aerosol optical depth as the true value. The results show that the DAOD product can be successfully applied during the daytime and nighttime to continuously monitor the flow of yellow dust from the GK-2A satellite in northeast Asia. In particular, the validation results for IR DAOD were similar to the active satellite product (CALIPSO/CALIOP) results, which exhibited a tendency similar to that for IR DAOD at night.

Integrating satellite and model data to explore spatial-temporal changes in aerosol optical properties and their meteorological relationships in northwest India.

This study aims to analyze the temporal and spatial distribution of Aerosol Optical Properties across Northwest India using aerosol data from MODIS (Moderate Resolution Imaging Spectroradiometer) and OMI (Ozone Monitoring Instrument) sensors from 2003 to 2022. Therefore, this study investigated the decadal, interannual, and seasonal changes in aerosol optical properties, vegetation index, and meteorological parameters in the northwest Indian region (8 boxes). Using GIOVANNI (Goddard Earth Sciences Data and Information Services Center (GES DISC) Online Visualization and Analysis Infrastructure), we retrieved daily and monthly Aqua and Terra MODIS products of aerosol optical depth (AOD), Angstrom exponent (AE), normalized difference vegetation index (NDVI), and OMI aerosol index (AI) to examine the spatiotemporal variations by using statistical approaches. The results demonstrated that the decadal averages of aerosol properties showed values of AOD 0.35 (Aqua) and 0.34 (Terra) and AE 1.20 (Aqua) and 1.10 (Terra) with the highest levels during the post-monsoon. Notably, the mean interannual concentrations of AOD and NDVI consistently surpass 0.3, and AE and AI exceed 1 in most locations, underscoring the persistence of high aerosol loading. Also, the study revealed a negative decadal change in AOD of about -8.24 %, while AE, AI, and NDVI showed positive decadal changes of about 9.24 %, 15.09 %, and 12.67 %, respectively. In addition, aerosol optical properties and local meteorology strongly correlated (-0.8 to +0.8). Principal Component Analysis (PCA) identifies meteorological parameters as significant drivers, with the first three components explaining over 70 % of the variation in aerosol optical properties. The NOAA HYSPLIT trajectory model suggests that the long-distance dust transport from the Arabian Peninsula frequently penetrates Gujarat province and then to northwest India. The results contributed to air quality management strategies and provided valuable insights into regional climate and air quality with the influence of meteorology.

Health effects of exposure to urban ambient particulate matter: A spatial-statistical study on 3rd-trimester pregnant women.

Pregnant women are highly vulnerable to environmental stressors such as ambient particulate matter (PM). Particularly during their 3rd trimester, their bodies undergo significant oxidative stresses. To further consolidate this dialogue into practice, the current study evaluated healthy pregnant women (n = 150 housewives; 18-40 years old; gestation age >36 weeks) from the highly polluted city of Yazd, Iran, from September to November 2021. The aerosol optical depth (AOD) data retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) were employed as influencing variables and validated using field-collected PM10 data (r = 0.62, p-value <0.01). The links between blood platelet count, enzymes (SGOT, SGPT, LDH, bilirubin), metabolic products (urea and acid uric) and different combinations of AOD data were assessed using the Generalized Additive Model. The results showed a high temporal variability in AOD (0.94 ± 0.51) but a spatially stable distribution pattern. The mean AOD during the 3rd trimester, followed by that of the three-month peak, were identified as the most significant non-linear predictors, while the mean AOD during the 1st trimester and throughout the entire pregnancy showed no significant associations with any of the biomarkers. Considering the associations found between AOD variables and maternal oxidative stresses, urgent planning is required to improve the urban air quality for sensitive subpopulations.

Spatio-temporal correspondence of aerosol optical depth between CMIP6 simulations and MODIS retrievals over India.

In the present work, the aerosol optical depth (AOD) at 550 nm of the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra satellite was utilised to evaluate the AOD simulations of newly emerged general circulation models (GCMs) of coupled model intercomparison project-phase 6 (CMIP6) over the Indian landmass. Further, the AOD from the CMIP6 models has been compared with its previous generation models from CMIP5 to examine the extent of uncertainties in AOD with reference to the MODIS AOD datasets. The evolution of aerosols over India using the different shared socioeconomic pathways (SSPs) has also been studied till the year 2050. The results show that the CMIP5 and CMIP6 models underestimated the mean annual AOD of the Indian region as a whole. A multi-model mean (MMM) of thirteen GCMs from CMIP6 showed an underestimation of AOD by 40 to 60% over the Indo-Gangetic plains, while an overestimation of 60 to 80% in AOD was observed over the Peninsular and Central Indian regions in comparison with MODIS for the study period of 2001 to 2014. In future simulations, the pathway SSP370 has shown a significant increasing trend of AOD whereas SSP126 and SSP585 have shown significant decreasing trends of AOD by the year 2050. In the future, the changes in the AOD will mainly be contributed by the anthropogenic aerosols (AOA, BC, and Sulphates) emissions in all SSPs. The large bias of MMM with the MODIS requires further research in terms of analysing the accuracy of emission datasets that have been used to simulate the AODs by the CMIP6 models over the Indian region.

Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments.

Aerosol Optical Depth (AOD) data derived from satellites is crucial for estimating spatially-resolved PM concentrations, but existing AOD data over land remain affected by several limitations (e.g., data gaps, coarser resolution, higher uncertainty or lack of size fraction data), which weakens the AOD-PM relationship. We developed a 0.1° resolution daily AOD data set over Europe over the period 2003-2020, based on two-stage Quantile Machine Learning (QML) frameworks. Our approach first fills gaps in satellite AOD data and then constructs three components' models to obtain reliable full-coverage AOD along with Fine-mode AOD (fAOD) and Coarse-mode AOD (cAOD). These models are based on AERONET (AErosol RObotic NETwork) observations, Gap-filled satellite AOD, climate and atmospheric composition reanalyses. Our QML AOD products exhibit better quality with an out-of-sample R2 equal to 0.68 for AOD, 0.66 for fAOD and 0.65 for cAOD, which is 23-92 %, 11-13 % and 115-132 % higher than the corresponding satellite or reanalysis products, respectively. Over 91.6 %, 81.6 %, and 88.9 % of QML AOD, fAOD and cAOD predictions fall within ±20 % Expected Error (EE) envelopes, respectively. Previous studies reported that a weak satellite AOD-PM correlation across Europe (Pearson correlation coefficient (PCC) around 0.1). Our QML products exhibit higher correlations with ground-level PMs, particularly when broadly matched by size: AOD with PM10, fAOD with PM2.5, cAOD with PM coarse (R = 0.41, 0.45 and 0.26, respectively). Different AOD fractions more effectively distinct PM size fractions, than total AOD. Our QML aerosol dataset and models pioneer full-coverage, daily high-resolution monitoring of fine-mode and coarse-mode aerosols, effectively addressing existing AOD challenges for further PMs exposures' estimations. This dataset opens avenues for more in-depth exploration of the impacts of aerosols on human health, climate, visibility, and biogeochemical processes, offering valuable insights for air quality management and environmental health risk assessment.

Influence of industrial sustainability transition on air quality in a typical resource-exhausted city.

The industrial transition of resource-exhausted cities is the focus of attention, and air quality has naturally become an important indicator to measure the sustainable development quality. Aerosol optical depth (AOD) is an important parameter to indicate air quality. This paper aimed to study the influence of industrial transition on air quality and provide a list of recommendations and management strategies for sustainable development in resource-exhausted cities. Results showed the secondary industry played important roles for economic development before 2015, however, it decreased after 2014, and the tertiary industry played more and more important roles from 2015. Analyses of the spatial distribution of AOD in each year showed that AOD was relatively higher in urban areas with concentrated population, and the threshold range of AOD value with high area ratio in spatial distribution decreased gradually, which was consistent with the analysis results of time series. Results of correlation analyses indicated that air temperature and land surface temperature were the main natural meteorological factors influencing AOD. Gross population, SO2 emission and the cultivated land area were the main socio-economic factors influencing AOD. It could be concluded that the industrial transition of the city has achieved good results, the economic structure has been gradually optimized and adjusted, and the air quality has gradually improved over industrial sustainability transition. Scientific exploitation, energy conservation, application of clean energy and industrial structure optimization would be effective measures for sustainable development.

Trends and drivers of aerosol vertical distribution over China from 2013 to 2020: Insights from integrated observations and modeling.

Understanding aerosol vertical distribution is of great importance to climate change and atmospheric chemistry, but there is a dearth of systematical analysis for aerosol vertical distribution amid rapid emission decline after 2013 in China. Here, the GEOS-Chem model and multiple-sourced observations were applied to quantify the changes of aerosol vertical distributions in response to clean air actions. In 2013-2020, the MODIS aerosol optical depth (AOD) presented extensive decreasing trends by -7.9 %/yr to -4.2 %/yr in summer and -6.1 %/yr to -5.8 %/yr in winter in polluted regions. Vertically, the aerosol extinction coefficient (AEC) from CALIPSO decreased by -8.0 %/yr to -5.5 %/yr below ~1 km, but the trends weakened significantly with increasing altitude. Compared with available measurements, the model can reasonably reproduce 2013-2020 trends and seasonality in AOD and vertical AEC. Model simulations confirm that emission reduction was the dominant driver of the 2013-2020 decline in AOD, while the effect of meteorology varied seasonally, with contributions ranging from -2 % to 13 % in summer and -67 % to -2 % in winter. Vertical distributions of emission-driven AEC trends strongly depended on emission reductions, local planetary boundary layer height, and relative humidity. For aerosol components, sulfate accounted for ~50 % of the AOD decline during summer, followed by ammonium and organic aerosol, while in winter the contribution of organic aerosol doubled (24 %-35 %), and nitrate exhibited a weak increasing trend. Chemical production and meteorological conditions (e.g., relative humidity) primarily drove the nitrate contribution, but emission reduction and hygroscopicity were decisive for other components. This work provides an integrated observational and modeling effort to better understand rapid changes in aerosol vertical distribution over China.

High aerosol loading over the Bohai Sea: Long-term trend, potential sources, and impacts on surrounding cities.

Air pollution over the oceans has received less attention compared to densely populated urban areas of continents. The Bohai Sea, a semi-enclosed sea in northern China, is surrounded by thirteen industrial cities that have experienced significant improvements in air quality over the past decade. However, the changes in air pollution over the Bohai Sea and its impacts on surrounding cities remain poorly understood. To address this, this study investigated the evolution of air pollution and its chemical composition in the Bohai Sea over four decades, utilizing satellite remote sensing data, reanalysis datasets, emissions inventories, and statistical modeling. Historically, the region has suffered from severe air pollution, resulting from a combination of continental emissions and marine inputs (e.g., sea salt, ports and maritime vessel activities). The aerosol optical depth (AOD) over the sea was higher than the mean levels observed in its surrounding coastal cities. Statistically, 45% of the air masses reaching the Bohai Sea are associated with natural sources (dust- and marine-rich), while the remainder carry anthropogenic pollutants from continental regions. With the exception of Cangzhou city, these coastal cities suffer from air pollutants originating from the Bohai Sea. Cities in the northern region of the sea, spanning from Tianjin to Yingkou, are particularly impacted. The majority of the surrounding cities are affected by a large proportion of anthropogenic aerosol types transported through air masses from the Bohai Sea, including those from biomass burning and industrial activities. These findings emphasize the considerable influence of human-induced sources in the Bohai Sea on neighboring urban areas. Furthermore, being a maritime region, natural sources like sea salt and dust from the sea may also exert a discernible impact on the neighboring environment.