Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty.

BackgroundEpidemiologic and health impact studies of fine particulate matter with diameter < 2.5 μm (PM2.5) are limited by the lack of monitoring data, especially in developing countries. Satellite observations offer valuable global information about PM2.5 concentrations.ObjectiveIn this study, we developed a technique for estimating surface PM2.5 concentrations from satellite observations.MethodsWe mapped global ground-level PM2.5 concentrations using total column aerosol optical depth (AOD) from the MODIS (Moderate Resolution Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer) satellite instruments and coincident aerosol vertical profiles from the GEOS-Chem global chemical transport model.ResultsWe determined that global estimates of long-term average (1 January 2001 to 31 December 2006) PM2.5 concentrations at approximately 10 km × 10 km resolution indicate a global population-weighted geometric mean PM2.5 concentration of 20 μg/m3. The World Health Organization Air Quality PM2.5 Interim Target-1 (35 μg/m3 annual average) is exceeded over central and eastern Asia for 38% and for 50% of the population, respectively. Annual mean PM2.5 concentrations exceed 80 μg/m3 over eastern China. Our evaluation of the satellite-derived estimate with ground-based in situ measurements indicates significant spatial agreement with North American measurements (r = 0.77; slope = 1.07; n = 1057) and with noncoincident measurements elsewhere (r = 0.83; slope = 0.86; n = 244). The 1 SD of uncertainty in the satellite-derived PM2.5 is 25%, which is inferred from the AOD retrieval and from aerosol vertical profile errors and sampling. The global population-weighted mean uncertainty is 6.7 μg/m3.ConclusionsSatellite-derived total-column AOD, when combined with a chemical transport model, provides estimates of global long-term average PM2.5 concentrations.

Background: More than a decade of satellite observations offers global information about the trend and magnitude of human exposure to fine particulate matter (PM2.5).Objective: In this study, we developed improved global exposure estimates of ambient PM2.5 mass and trend using PM2.5 concentrations inferred from multiple satellite instruments.Methods: We combined three satellite-derived PM2.5 sources to produce global PM2.5 estimates at about 10 km × 10 km from 1998 through 2012. For each source, we related total column retrievals of aerosol optical depth to near-ground PM2.5 using the GEOS–Chem chemical transport model to represent local aerosol optical properties and vertical profiles. We collected 210 global ground-based PM2.5 observations from the literature to evaluate our satellite-based estimates with values measured in areas other than North America and Europe.Results: We estimated that global population-weighted ambient PM2.5 concentrations increased 0.55 μg/m3/year (95% CI: 0.43, 0.67) (2.1%/year; 95% CI: 1.6, 2.6) from 1998 through 2012. Increasing PM2.5 in some developing regions drove this global change, despite decreasing PM2.5 in some developed regions. The estimated proportion of the population of East Asia living above the World Health Organization (WHO) Interim Target-1 of 35 μg/m3 increased from 51% in 1998–2000 to 70% in 2010–2012. In contrast, the North American proportion above the WHO Air Quality Guideline of 10 μg/m3 fell from 62% in 1998–2000 to 19% in 2010–2012. We found significant agreement between satellite-derived estimates and ground-based measurements outside North America and Europe (r = 0.81; n = 210; slope = 0.68). The low bias in satellite-derived estimates suggests that true global concentrations could be even greater.Conclusions: Satellite observations provide insight into global long-term changes in ambient PM2.5 concentrations. Satellite-derived estimates and ground-based PM2.5 observations from this study are available for public use.Citation: van Donkelaar A, Martin RV, Brauer M, Boys BL. 2015. Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environ Health Perspect 123:135–143; http://dx.doi.org/10.1289/ehp.1408646

The seasonal, interannual, and long-term variations in biomass burning activity and related emissions are not well studied over South Asia. In this regard, active fire location retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), the retrievals of aerosol optical depth (AOD) from MODIS Terra, and tropospheric column NO2 from Ozone Monitoring Instrument (OMI) are used to understand the effects of biomass burning on the tropospheric pollution loadings over South Asia during 2003-2013. Biomass burning emission estimates from Global Fire Emission Database (GFED) and Global Fire Assimilation System (GFAS) are also used to quantify uncertainties and regional discrepancies in the emissions of carbon monoxide (CO), nitrogen oxide (NOx), and black carbon (BC) due to biomass burning in South Asia. In the Asian continent, the frequency of fire activity is highest over Southeast Asia, followed by South Asia and East Asia. The biomass burning activity in South Asia shows a distinct seasonal cycle that peaks during February-May with some differences among four (north, central, northeast, and south) regions in India. The annual biomass burning activity in north, central, and south regions shows an increasing tendency, particularly after 2008, while a decrease is seen in northeast region during 2003-2013. The increase in fire counts over the north and central regions contributes 24 % of the net enhancement in fire counts over South Asia. MODIS AOD and OMI tropospheric column NO2 retrievals are classified into high and low fire activity periods and show that biomass burning leads to significant enhancement in tropospheric pollution loading over both the cropland and forest regions. The enhancement is much higher (110-176 %) over the forest region compared to the cropland (34-62 %) region. Further efforts are required to understand the implications of biomass burning on the regional air quality and climate of South Asia.

A global aerosol data assimilation (DA) system based on the ensemble-variational (EnVar) application in the Joint Efforts for Data assimilation Integration (JEDI) was recently developed for the Global Ensemble Forecast System - Aerosols (GEFS-Aerosols) in operations at NOAA/NWS/NCEP. The aerosol optical depth (AOD) retrievals at 550 nm are assimilated to improve the GEFS-Aerosols initial conditions and its subsequent forecasts. To account for aerosol emission uncertainty in the ensemble forecasts and thus enhance AOD assimilation, a stochastically-perturbed emission (SPE) approach was implemented in the Common Community Physics Package (CCPP)-based GEFS-Aerosols. The performance of this JEDI-based EnVar aerosol DA system has been evaluated using the CCPP-based GEFS-Aerosols in the near-real time (NRT) experiments at NOAA/OAR/GSL and the global aerosol reanalysis products that&amp;#160; assimilate 550 nm AOD retrievals from the the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments and the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, respectively. The NRT experiment results are displayed on the GSL website (https://ruc.noaa.gov/projects/nrt/Aerosol-DA/). Both the NRT experiment results and global aerosol reanalyses demonstrate that compared to the six-hour forecasts without AOD assimilation, the analyses and subsequent six-hour forecasts resulting from AOD assimilation show significantly improved agreement with AOD retrievals from VIIRS, MODIS and the Aerosol Robotic NETwork (AERONET), and AOD analyses/reanalyses from NASA and ECMWF. Although AOD retrievals, due to their column-integral nature, provide limited information regarding aerosol compositions and vertical profiles, AOD assimilation in our experiments generally contributes to improved aerosol analyses and forecasts verified against those from NASA and ECMWF. &amp;#160;One of the ongoing Unified Forecast System (UFS)-Research to Operations (R2O) efforts aims to integrate and improve aerosol prediction within UFS (hereafter referred to as UFS-Aerosols). UFS-Aerosols will eventually replace the standalone GEFS-Aerosols for operations at NOAA/NWS/NCEP. Compared to GEFS-Aerosols, UFS-Aerosols is coupled with NASA's second-generation Goddard Chemistry Aerosol Radiation and Transport (GOCART) model including additional nitrate aerosol species, adopts improved biomass burning and dust emissions, and allows for aerosol-radiation interactions. Motivated by the promising results of assimilating AOD for GEFS-Aerosols and to advance aerosol assimilation and prediction in UFS, we are extending and improving this JEDI-based EnVar aerosol DA system for UFS-Aerosols. It requires further development of AOD forward operator, its tangent linear and adjoint models in JEDI&amp;#8217;s Unified Forward Operator (UFO) to accommodate additional nitrate aerosol species in UFS-Aerosols. Enhancements to this DA system for UFS-Aerosols include implementing SPE within UFS-Aerosols to improve background ensemble and implementing assimilation of log-transformed AOD within JEDI to better satisfy the Gaussian assumptions in the DA update. To evaluate these new developments for UFS-Aerosols, cycled DA experiments will be performed to assimilate 550 nm AOD retrievals from VIIRS instruments on board NOAA&amp;#8217;s satellites, and verified against various aerosol observations and reanalyses. Results will be presented.

Estimation of long-term population exposure to PM2.5 for dense urban areas using 1-km MODIS data

Abstract. Aerosol optical depth (AOD) retrievals from geostationary satellites have high temporal resolution compared to the polar orbiting satellites and thus enable us to monitor aerosol motion. However, current Geostationary Operational Environmental Satellites (GOES) have only one visible channel for retrieving aerosols and hence the retrieval accuracy is lower than those from the multichannel polar-orbiting satellite instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). The operational GOES AOD retrieval algorithm (GOES Aerosol/Smoke Product, GASP) uses 28-day composite images from the visible channel to derive surface reflectance, which can produce large uncertainties. In this work, we develop a new AOD retrieval algorithm for the GOES imager by applying a modified Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. The algorithm assumes the surface Bidirectional Reflectance Distribution Function (BRDF) in the channel 1 of GOES is proportional to seasonal average MODIS BRDF in the 2.1 μm channel. The ratios between them are derived through time series analysis of the GOES visible channel images. The results of AOD and surface reflectance retrievals are evaluated through comparisons against those from Aerosol Robotic Network (AERONET), GASP, and MODIS. The AOD retrievals from the new algorithm demonstrate good agreement with AERONET retrievals at several sites across the US with correlation coefficients ranges from 0.71 to 0.85 at five out of six sites. At the two western sites Railroad Valley and UCSB, the MAIAC AOD retrievals have correlations of 0.8 and 0.85 with AERONET AOD, and are more accurate than GASP retrievals, which have correlations of 0.7 and 0.74 with AERONET AOD. At the three eastern sites, the correlations with AERONET AOD are from 0.71 to 0.81, comparable to the GASP retrievals. In the western US where surface reflectance is higher than 0.15, the new algorithm also produces larger AOD retrieval coverage than both GASP and MODIS.

The Advanced Geosynchronous Radiation Imager (AGRI) is one of the main imaging sensors onboard the Fengyun-4A (FY-4A) satellite. Because of its high observation frequency, AGRI is suitable for continuous monitoring of atmospheric aerosols. In this study, we propose an aerosol optical depth (AOD) retrieval algorithm called the multichannel (MC) algorithm, which uses four channels (0.65, 0.83, 1.61, and <inline-formula> <tex-math notation="LaTeX">$2.25~\mu \text{m}$ </tex-math></inline-formula>) of AGRI. The algorithm assumes that the ratios between surface reflectance of different channels remain unchanged within two weeks, and the ratios are calculated by using Moderate-Resolution Imaging Spectroradiometer (MODIS)-combined AOD data to perform atmospheric correction on AGRI data under low pollution conditions (AOD at 550 nm less than 0.5). Since this algorithm is not developed for specific surface types, AOD retrieval can be achieved over both dark targets and bright surfaces. This algorithm has been applied to aerosol retrieval in South Asia. The accuracy assessment of the AGRI AOD dataset in 2019 and 2020 using the ground-based data from 11 aerosol robotic network (AERONET) sites shows that the AGRI AOD dataset has a high accuracy, and the statistical parameters of AGRI AOD dataset are slightly better than those of MODIS-combined AOD dataset. The root-mean-square error (RMSE), mean absolute error (MAE), relative mean bias (RMB), and percentage of data with errors within the expected error <inline-formula> <tex-math notation="LaTeX">$\pm (0.05+0.15 \times {{\text {AOD}}}_{{\text {AERONET}}})$ </tex-math></inline-formula> (EE15) of AGRI AOD dataset are 0.16, 0.12, 0.23, and 63.71&#x0025;, respectively. The RMSE, MAE, RMB, and EE15 of MODIS-combined AOD dataset are 0.18, 0.13, 0.24, and 61.06&#x0025;, respectively.

In this study, Aqua-Moderate Resolution Imaging Spectroradiometer (MODIS) Collection (C6) and C6.1 Dark Target aerosol optical depth (AOD) retrievals at 3 km (DT3K) and 10 km (DT10K), Deep Blue AOD retrievals at 10 km (DB10K), and combined DT and DB (DTB) AOD retrievals at 10 km resolutions were validated from 2002 to 2014 against ground-based sunphotometer AOD measurements obtained from the Chinese aerosol remote sensing network (CARSNET). The CARSNET AOD data were obtained for sites at Mt. Waliguan (MW), Lanzhou (LZ), Ulate (UL), and Zhengzhou (ZZ) located in the Yellow River basin (YERB) region, China. Errors and agreement between satellite and ground data were reported using Pearson’s correlation (R) and relative mean bias (RMB). Results showed that the DT3K C6.1 highest quality flag (QF = 3) AOD retrievals were well correlated with the sunphotometer AOD data, with an R of 0.82 and an RMB of 1.01. Overestimation and underestimation in DT AOD retrievals were observed for AOD &gt; 1.1 and AOD &lt; 1.1, respectively. A significant underestimation of 37% in DB10K AOD retrievals was observed across all the sites except ZZ, which was indicated by a low-value RMB (0.63). Spatial distribution maps showed high AOD values (&gt;0.8) over the lower part of the YERB and low AOD values (&lt;0.4) across the upstream part of the YERB. This might be due to a large number of aerosol emissions over the lower developed areas and a scarcity of aerosols over the upstream mountain areas. Overall, this study supports the use of DT10K C6.1 AOD retrievals over the western semi-arid and arid regions of the YERB and DTB10K AOD retrievals over the north-central water system and eastern plain regions of the YERB.

We interpret in situ and satellite observations with a chemical transport model (GEOS-Chem, downscaled to 0.1° × 0.1°) to understand global trends in population-weighted mean chemical composition of fine particulate matter (PM2.5). Trends in observed and simulated population-weighted mean PM2.5 composition over 1989-2013 are highly consistent for PM2.5 (-2.4 vs -2.4%/yr), secondary inorganic aerosols (-4.3 vs -4.1%/yr), organic aerosols (OA, -3.6 vs -3.0%/yr) and black carbon (-4.3 vs -3.9%/yr) over North America, as well as for sulfate (-4.7 vs -5.8%/yr) over Europe. Simulated trends over 1998-2013 also have overlapping 95% confidence intervals with satellite-derived trends in population-weighted mean PM2.5 for 20 of 21 global regions. Over 1989-2013, most (79%) of the simulated increase in global population-weighted mean PM2.5 of 0.28 μg m-3yr-1 is explained by significantly (p < 0.05) increasing OA (0.10 μg m-3yr-1), nitrate (0.05 μg m-3yr-1), sulfate (0.04 μg m-3yr-1), and ammonium (0.03 μg m-3yr-1). These four components predominantly drive trends in population-weighted mean PM2.5 over populous regions of South Asia (0.94 μg m-3yr-1), East Asia (0.66 μg m-3yr-1), Western Europe (-0.47 μg m-3yr-1), and North America (-0.32 μg m-3yr-1). Trends in area-weighted mean and population-weighted mean PM2.5 composition differ significantly.

&amp;lt;p&amp;gt;Aerosol optical depth (AOD) is an important factor to estimate the effect of aerosol on light, and an accurate retrieval of it can make great contribution to monitor atmosphere. Therefore, retrieval of AOD has been a frontier topic and attracted much attention from researchers at home and abroad. However, the spatial resolution of AOD, based on Moderate-resolution Imaging Spectroradiometer (MODIS), is low, and hard to meet the needs of regional air quality fine monitoring. In 2018, China launched Gaofen-6 satellite, which set up a network with Gaofen-1 enabling two-day revisited observations in China's land area, improving the scale and timeliness of remote sensing data acquisition and making up for the shortcomings of lacking multi-spectral satellite with medium and high spatial resolution. Along with advancement of the Earth Observation System and the launch of high-resolution satellites, it is of profound significance to give full play to the active role of high-scoring satellites, in monitoring atmospheric environmental elements such as atmospheric aerosols and particulate matter concentrations, and achieve high-resolution retrieval of AOD through Gaofen satellites.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this paper the data of Gaofen-6 and Gaofen-1 was used to retrieve the AOD. based on the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm. This algorithm can retrieve the surface reflectance and AOD synchronously through constructing a closed equation based on double star observations. It can be applied to various types of surface reflectance which extends the coverage of the retrieval of AOD inversion effectively. Experimental data includes the satellite data of New South Wales and eastern Queensland on November 21, 2019, which have been suffered from unprecedented large-scale forest fires for over 2 months. The retrieval of AOD during the time with the satellite data is benefit for the prevention and monitoring of forest fire. The experimental results are compared with the AERONET ground observation data for preliminary validation. The correlation coefficient is about 0.7. The experimental results show that the method have higher accuracy, and further validation work is continuing.&amp;lt;/p&amp;gt;

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

&amp;lt;p&amp;gt;Aerosols attract great attention as having critical influence on the Earth&amp;amp;#8217;s energy budget and human health. Geostationary satellites like Himawari-8 process advantages on temporal resolution that allows rapidly changing weather phenomena tracking and aerosol monitoring. This work aims at providing a novel error analysis for the Advanced Himawari Imager (AHI) aerosol optical depth (AOD) retrieval from the aspect of aerosol model and sun position combing with the high quality ground-based observation in Wuhan, central China. Three-year co-located AOD dataset from AHI and sun-photometer are used. AHI underestimates AOD in all the seasons. Aerosol size distributions and phase functions are discussed as parts of aerosol model to explain the underestimation of AOD. AHI sets a low fine-mode particle median radius comparing with the in-site measurement in Wuhan that increases backscattering, and finally leads to the underestimation of AOD. Sun position also affects AHI AOD retrieval, and we use solar zenith angle (SZA) and scattering angle to represent sun position. Geostationary satellites get fixed satellite position for one site that provides convenience to the discussion. SZA influences AOD retrieval mainly through the length of transfer path and higher percent of samples within expected error often appears at low SZAs. Scattering angle also has obvious influence on AOD retrieval through the simulation of phase function and causes the difference of correlation performance between AHI and sun-photometer in aspect of SZA in morning and afternoon. Finally, we applied the dark target method to retrieve AHI AOD. The comparison of AODs reveals that the retrieval method of AHI performs better in Wuhan. The better performance of AHI AOD may be due to high aerosol loading and lack of enough prior information of aerosol properties in Wuhan. Our work could also be performed on other areas or other geostationary satellites, and help us to further understand the controlling factors that affect AOD retrieval accuracy, then contribute to better AOD retrieval.&amp;lt;/p&amp;gt;

Aerosol emissions from human activities are extensive and changing rapidly over Asia. Model simulations and satellite observations indicate a dipole pattern in aerosol emissions and loading between South Asia and East Asia, two of the most heavily polluted regions of the world. We examine the previously unexplored diverging trends in the existing dipole pattern of aerosols between East and South Asia using the high quality, two-decade long ground-based time series of observations of aerosol properties from the Aerosol Robotic Network (AERONET), from satellites (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)), and from model simulations (Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The data cover the period since 2001 for Kanpur (South Asia) and Beijing (East Asia), two locations taken as being broadly representative of the respective regions. Since 2010 a dipole in aerosol optical depth (AOD) is maintained, but the trend is reversed—the decrease in AOD over Beijing (East Asia) is rapid since 2010, being 17% less in current decade compared to first decade of twenty-first century, while the AOD over South Asia increased by 12% during the same period. Furthermore, we find that the aerosol composition is also changing over time. The single scattering albedo (SSA), a measure of aerosol’s absorption capacity and related to aerosol composition, is slightly higher over Beijing than Kanpur, and has increased from 0.91 in 2002 to 0.93 in 2017 over Beijing and from 0.89 to 0.92 during the same period over Kanpur, confirming that aerosols in this region have on an average become more scattering in nature. These changes have led to a notable decrease in aerosol-induced atmospheric heating rate (HR) over both regions between the two decades, decreasing considerably more over East Asia (− 31%) than over South Asia (− 9%). The annual mean HR is lower now, it is still large (≥ 0.6 K per day), which has significant climate implications. The seasonal trends in AOD, SSA and HR are more pronounced than their respective annual trends over both regions. The seasonal trends are caused mainly by the increase/decrease in anthropogenic aerosol emissions (sulfate, black carbon and organic carbon) while the natural aerosols (dust and sea salt) did not change significantly over South and East Asia during the last two decades. The MERRA-2 model is able to simulate the observed trends in AODs well but not the magnitude, while it also did not simulate the SSA values or trends well. These robust findings based on observations of key aerosol parameters and previously unrecognized diverging trends over South and East Asia need to be accounted for in current state-of-the-art climate models to ensure accurate quantification of the complex and evolving impact of aerosols on the regional climate over Asia.

Lip and oral cavity cancer (LOC) is a major public health challenge in Asia. Nevertheless, a critical gap remains in understanding the epidemiological burden of LOC among young people (15-44 years) in the region. This study aims to analyze the burden and risk factors of LOC in this age group across the four Global Burden of Disease (GBD) Asian regions from 1990 to 2021 and projects trends to 2030. Data on the incidence, deaths, disability-adjusted life years (DALYs), and risk factors of LOC from 1990 to 2021 were obtained from the GBD 2021 study for East, South, Southeast Asia, and High-income Asia Pacific. This study assessed the LOC burden among young people (15-44 years) through age- and sex-stratified analyses, evaluated temporal trends via joinpoint regression, examined risk factor contributions, and projected trends to 2030 using the Nordpred age-period-cohort model. From 1990 to 2021, the age-standardized incidence rate (ASIR) increased across all subregions, with the largest rise in East Asia. In contrast, age-standardized mortality rate (ASMR) and age-standardized DALYs rate declined everywhere except South Asia. In 2021, South Asia bore the heaviest LOC burden among young people in the four Asian subregions. India reported the highest incident cases, deaths, and DALYs in 2021, and Pakistan had the highest ASR for all three metrics. Taiwan (Province of China) showed the largest increase in ASRs over the period. In 2021, smoking had the highest contribution in East Asia, alcohol use in High-income Asia Pacific, and chewing tobacco in South Asia. Projections to 2030 indicate rising ASIR in East, South, and Southeast Asia but declines in High-income Asia Pacific; decreasing ASMR everywhere except South Asia; and increasing age-standardized DALYs rate in East and South Asia but decreases elsewhere. LOC imposes a substantial and growing burden on young people in South, East, and Southeast Asia, marked by rising ASIR since 1990 and projected increases through 2030. South Asia faces the most urgent challenge with concurrent rises in incidence, mortality, and DALYs-most notably in the 20-24 age group. Region-specific interventions targeting predominant risk factors are critically needed.

A surface reflectance correction model to improve the retrieval of MISR aerosol optical depth supported by MODIS data