Aerosol Optical Thickness Data Research Articles

Spatiotemporal aerosol prediction model based on fusion of machine learning and spatial analysis

This study examined long-term aerosol optical thickness (AOT) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify aerosol conditions on the Korean Peninsula. Time-series machine learning (ML) techniques and spatial interpolation methods were used to predict future aerosol trends. This investigation utilized AOT data from Terra MODIS and meteorological data from Automatic Weather System (AWS) in eight selected cities in Korea (Gangneung, Seoul, Busan, Wonju, Naju, Jeonju, Jeju, and Baengyeong) to assess atmospheric aerosols from 2000 to 2021. A machine-learning-based AOT prediction model was developed to forecast future AOT using long-term observations. The accuracy analysis of the AOT prediction results revealed mean absolute error of 0.152 ± 0.15, mean squared error of 0.048 ± 0.016, bias of 0.002 ± 0.011, and root mean squared error of 0.216 ± 0.038, which are deemed satisfactory. By employing spatial interpolation, gridded AOT values within the observation area were generated based on the ML prediction results. This study effectively integrated the ML model with point-measured data and spatial interpolation for an extensive analysis of regional AOT across the Korean Peninsula. These findings have substantial implications for regional air pollution policies because they provide spatiotemporal AOT predictions.

Estimates of PM2.5 Concentration Based on Aerosol Optical Thickness Data Using Ensemble Learning with Support Vector Machine and Decision Tree

Air pollution, particularly fine particulate matter with a diameter of 2.5 micrometers or less (PM2.5), is a significant public health concern in many regions worldwide, including the northeastern region of Thailand. This study investigates the correlation between PM2.5 concentrations and meteorological spatial datasets such as surface relative humidity (SRH), surface wind speed (SPD), visibility (Vis), surface temperature (ST), and aerosol optical thickness (AOT) in the region. GIS techniques and the inverse distance weighting technique were used to create spatial maps of the meteorological datasets and ground station PM2.5 measurements. Pearson correlation analysis was performed to examine the relationship between PM2.5 and the meteorological datasets. Decision tree and support vector machine (SVM) algorithms were employed to estimate PM2.5 concentrations based on the spatial datasets. The results showed that Vis and ST have a moderate positive linear relationship with PM2.5, while AOT has a moderate negative linear relationship. SRH and SPD have weak relationships with PM2.5. The decision tree and SVM algorithms demonstrated a strong positive correlation between estimated and measured PM2.5 concentrations. The study shows that machine learning algorithms can be effective tools for estimating PM2.5 concentration based on AOT data, and feature selection can improve model performance. Ensemble learning could be employed to further improve model performance, particularly in regions with high spatial variability. Overall, the study provides a promising approach for estimating PM2.5 concentration using machine learning algorithms and AOT data.

Screening Approach of the Langley Calibration Station for Sun Photometers in China

A sun photometer is a type of photometer that points at the sun, and it has been playing an increasingly important role in characterizing aerosols across the world. As long as the solar photometer is accurately calibrated, the optical thickness of the aerosol can be obtained from the measured value of this device. When the calibration of a single instrument is not accurate, the inversion quantity varies greatly. The calibration constant of the sun photometer changes during its use process; thus, calibrations are frequently needed in order to ensure the accuracy of the measured value. The calibration constant of the solar photometer is usually determined using the Langley method. Internationally, AERONET has two Langley calibration stations: the Mauna Loa observatory in the United States and the Izaña observatory in Spain. So far, the International Comparison and Calibration System has been established in Beijing, similar to AERONET at GSFC, but the Langley calibration system has not yet been established. Therefore, it is necessary to select a suitable calibration station in China. This paper studies the requirements of the calibration station using the Langley method. We used long-term records of satellite-derived measurements and survey data belonging to the aerosol optical thickness data of SNPP/VIIRS, CERES, MERRA-2, etc., in order to gain a better understanding of whether these stations are suitable for calibration. From the existing astronomical observation stations, meteorological stations, and the Sun–Sky Radiometer Observation Network (SONET) observation stations in China, the qualified stations were selected. According to the statistical data from the Ali observatory, the monthly average of clear sky is 20.21 days, and it is always greater than 15 days. The monthly average of aerosol is not more than 0.15 and is less than 0.3. We believe that the atmosphere above the Ali observatory is stable, and the results show that the Ali observatory has excellent weather conditions. This study can provide a selection of calibration sites for solar photometer calibrations in China that may need to be further characterized and evaluated, and at the same time provide a method to exclude unsuitable calibration sites.

How to Measure the Amount of Aerosol Optical Thickness in the Atmosphere in a Simple Way: A Calitoo Handheld Sun-Photometer Measurement

To have useful information about the effects of atmospheric particles on visibility degradation, extinction of solar radiation, climate changes, and tropospheric corrections in remote sensing, aerosol optical thickness (AOT) plays a crucial role. So, the measurement of AOT is an essential task for atmospheric physicists. In this regard, this article presents a systematic way in a simple form using a Calitoo handheld sun-photometer (SPM) to study atmospheric AOT. Zanjan city is selected for a period of six months as an example due to existing reference data for comparison. Hence, we use a schedule distributed in seven times, including 8:00, 9:30, 10:45, solar noon, 13:45, 15:30, and 17:00 in local time. The used wavelengths for recording are 465, 540, and 619 nm. Our data analysis shows that the mean value for AOT at those wavelengths are 0.25, 0.21, and 0.20, respectively. Also, the mean Ångström exponent (AE) extracted from the AOTs at 465, and 619 nm is 0.80. Accordingly, AE versus AOT plot demonstrates that most of the particles in Zanjan’s atmosphere are classified as urban industrial (∼64%) and dust plus mixed (∼36%) particles. In addition, to have a metric, a comparison between the AOT extracted from simultaneous measurements of Calitoo handheld and Cimel automatic SPMs is employed that indicates a strong correlation (r = 0.99) between them. Furthermore, OMI, Aqua MODIS, and Terra MODIS AOT products are compared with the Calitoo SPM AOT data. The results reveal that there is a better relationship between MODIS and Calitoo data than OMI. This study leads us to conclude that it can open new insights by simple instruments to study the atmospheres that are unknown to scientists due to the limitations of geographical stations.

Insights on the distribution and environmental implications of the radio-isotope 235U in surface soils and glaciers of the Tibetan Plateau

Atom ratio between 235U and 238U is often used as an indicator of U contamination as the isotopic signature of products generated by the nuclear and military industry significantly vary from the natural isotopic ratio of U. In this study, surface soils and glaciers samples were collected in the Tibetan Plateau (TP) and its surrounding areas and analyzed for U isotopic composition. Results show that the 235U/238U atom ratios in the surface soils of the TP ranges from 0.007122 to 0.007615, with an average value of 0.007378 ± 0.00011; while in the snow/ice dust from high-altitude glaciers it ranges from 0.007254 to 0.007687, with an average value of 0.007345 ± 0.000128. These ratios are slightly higher than the typical crustal value, indicating that the TP was affected by an anthropogenic input of 235U, especially in its northeast and southwest sectors. The variability of our results suggests that the spatial distribution of this contamination is not uniform, pointing to differences in the potential sources and transmission paths of radioactive particles. Combining the knowledge of past tests and activities conducted in the geographic areas around the TP with the knowledge of prevailing winds, we hypothesize that the observed 235U contamination in the TP surface soils and glaciers may have originated mainly from the previous nuclear related activities in surrounding areas (e.g., north Gobi Desert and South Asia). In addition, the horizontal and vertical wind field around the Tibetan Plateau, as well as the atmospheric aerosol optical thickness data also demonstrated the possible transport paths of the radionuclides, that is, originated from in northern Gobi desert and South Asia and reached the TP crossing the Himalayas.

A physical knowledge-based machine learning method for near-real-time dust aerosol properties retrieval from the Himawari-8 satellite data

Monitoring dust aerosol properties is critical for the studies of radiative transfer budget, climate change, and air quality. Aerosol optical thickness (AOT) and effective radius (Reff) are two main parameters describing the optical and microphysical properties of airborne dust aerosol. Satellite remote sensing provides an opportunity for estimating the two parameters in spatial coverage and continuously. To take the merits of machine learning algorithms and also utilize the physical knowledge discovered in the conventional retrieval algorithms, a physical-based machine learning method was proposed and applied on the Himawari-8 geostationary satellite for robust retrieval of dust aerosol properties. The main concepts of this study comprise i) constructing the model input data by extracting highly informative features from the Himawari observations according to physical knowledge and ii) exploiting the utility of six state-of-the-art machine learning algorithms in dust aerosol retrieval. The algorithms include artificial neural network (ANN), extreme boost gradient tree (XGBoost), extra tree (ET), random forest (RF), support vector regression (SVR), and kernel Ridge regression (Ridge). The ground-truth AOT and Reff data from AERONET stations were supplied as output labels. The cross-validation technique was adopted for model training and the results show that the ANN model is superior to the other machine learning models for both AOT and Reff estimation, which exhibits the lowest mean absolute error (MAE = 0.0292 and 0.0981) and the highest correlation coefficient (r = 0.98 and 0.84). When validated on an independent dataset, the ANN model achieved the lowest MAE (0.0334 and 0.1487), and the highest r (0.94 and 0.63). More importantly, when compared against representative physical-based algorithms, the developed ANN model still retains the best performance. Furthermore, the ANN model shows an overall better performance than other machine learning models and also the JAXA Himawari-8 Level-2 AOT product, with examples exhibited in three dust storm events and for continuous monitoring of one of the dust storm events. Additionally, feature importance analysis implies that the important features of dust aerosol identified by the ANN model are consistent with that in physical model-based algorithms. In summary, this study shows great potential for generating near-real-time products of dust aerosol properties from Himawari satellite data. These products can provide a scientific basis for climate and meteorological study regarding severe dust storms.

OPTIMAL INTERPOLATION OF AERONET RADIOMETRIC NETWORK OBSERVATIONS FOR THE EVALUATION OF THE AEROSOL OPTICAL DEPTH DISTRIBUTION IN THE EASTERN EUROPEAN REGION

The application of the optimal interpolation method for the assimilation of the observational data of aerosol optical thickness (AOT), which are sparse in space and time, in the chemical transport model is herein validated. Assuming the negligible error of AOT observations in the AERONET ground-based radiometric network, spatial and temporal correlation functions of the errors in the results of the AOT simulated by the GEOS-Chem chemical transport model are obtained. The optimal interpolation method is applied to the AERONET data using the GEOS-Chem results as a background field for the Eastern European region for 2015–2016. It is shown that the use of the optimal interpolation method permits to reduce the root-mean-square error of the AOT estimation by more than a third in places where there are no AERONET stations in comparison with the model results.

A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem v4.0: design, development, and application of assimilating Himawari-8 aerosol observations

Abstract. This paper presents a three-dimensional variational (3DVAR) data assimilation (DA) system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, developed for the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) within the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. For computational efficiency, 32 model variables in the MOSAIC_4bin scheme are lumped into 20 aerosol state variables that are representative of mass concentrations in the DA system. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was employed, which was obtained by simplifying the optical module in WRF-Chem. The tangent linear (TL) and adjoint (AD) operators were then established and passed the TL/AD sensitivity test. The Himawari-8 derived AOT data were assimilated to validate the system and investigate the effects of assimilation on both AOT and PM2.5 simulations. Two comparative experiments were performed with a cycle of 24 h from 23 to 29 November 2018, during which a heavy air pollution event occurred in northern China. The DA performances of the model simulation were evaluated against independent aerosol observations, including the Aerosol Robotic Network (AERONET) AOT and surface PM2.5 measurements. The results show that Himawari-8 AOT assimilation can significantly improve model AOT analyses and forecasts. Generally, the control experiments without assimilation seriously underestimated AOTs compared with observed values and were therefore unable to describe real aerosol pollution. The analysis fields closer to observations improved AOT simulations, indicating that the system successfully assimilated AOT observations into the model. In terms of statistical metrics, assimilating Himawari-8 AOTs only limitedly improved PM2.5 analyses in the inner simulation domain (D02); however, the positive effect can last for over 24 h. Assimilation effectively enlarged the underestimated PM2.5 concentrations to be closer to the real distribution in northern China, which is of great value for studying heavy air pollution events.

Spatial and Temporal Variations of Aerosol Optical Thickness over the China Seas from Himawari-8

Six years of hourly aerosol optical thickness (AOT) data retrieved from Himawari-8 were used to investigate the spatial and temporal variations, especially diurnal variations, of aerosols over the China Seas. First, the Himawari-8 AOT data were consistent with the AERONET measurements over most of the China Seas, except for some coastal regions. The spatial feature showed that AOT over high latitude seas was generally larger than over low latitude seas, and it is distributed in strips along the coastline and decreases gradually with increasing distance from the coastline. AOT undergoes diurnal variation as it decreases from 9:00 a.m. local time, reaching a minimum at noon, and then begins to increase in the afternoon. The percentage daily departure of AOT over the East China Seas generally ranged ±20%, increasing sharply in the afternoon; however, over the northern part of the South China Sea, daily departure reached a maximum of >40% at 4:00 p.m. The monthly variation in AOT showed a pronounced annual cycle. Seasonal variations of the spatial pattern showed that the largest AOT was usually observed in spring and varies in other seasons for different seas.

ИЗМЕНЧИВОСТЬ АЭРОЗОЛЬНОЙ ОПТИЧЕСКОЙ ТОЛЩИНЫ В ГОРНЫХ И ПРЕДГОРНЫХ РАЙОНАХ СЕВЕРНОЙ ОСЕТИИ ПО ДАННЫМ СПУТНИКОВЫХ ИЗМЕРЕНИЙ

Atmospheric aerosol is one of the indicators of air quality that affects the environmental situation. The aerosol optical thickness (AOT) of the atmosphere is studied in the mountainous, foothill, and plain regions of Republic of North Ossetia-Alania (Russian Federation) based on satellite data EOS Terra, Aqua. Several geographical locations at different heights were selected for the analysis. Daily series of AOT, air temperature and precipitation data were obtained on period over 20 years for each point. The results of statistical analysis of long-term values of AOT, temperature and precipitation are shown. There is a statistically significant relationship between the averaged values, and there is also a dependence on the height of the location and the proximity of mountains. Long-term average dependencies provide a basis for predicting the AOT value based on the measured temperature at height of 2 m for territories located in a complex landscape at different altitudes.

Superior PM2.5 Estimation by Integrating Aerosol Fine Mode Data from the Himawari-8 Satellite in Deep and Classical Machine Learning Models

Artificial intelligence is widely applied to estimate ground-level fine particulate matter (PM2.5) from satellite data by constructing the relationship between the aerosol optical thickness (AOT) and the surface PM2.5 concentration. However, aerosol size properties, such as the fine mode fraction (FMF), are rarely considered in satellite-based PM2.5 modeling, especially in machine learning models. This study investigated the linear and non-linear relationships between fine mode AOT (fAOT) and PM2.5 over five AERONET stations in China (Beijing, Baotou, Taihu, Xianghe, and Xuzhou) using AERONET fAOT and 5-year (2015–2019) ground-level PM2.5 data. Results showed that the fAOT separated by the FMF (fAOT = AOT × FMF) had significant linear and non-linear relationships with surface PM2.5. Then, the Himawari-8 V3.0 and V2.1 FMF and AOT (FMF&AOT-PM2.5) data were tested as input to a deep learning model and four classical machine learning models. The results showed that FMF&AOT-PM2.5 performed better than AOT (AOT-PM2.5) in modelling PM2.5 estimations. The FMF was then applied in satellite-based PM2.5 retrieval over China during 2020, and FMF&AOT-PM2.5 was found to have a better agreement with ground-level PM2.5 than AOT-PM2.5 on dust and haze days. The better linear correlation between PM2.5 and fAOT on both haze and dust days (dust days: R = 0.82; haze days: R = 0.56) compared to AOT (dust days: R = 0.72; haze days: R = 0.52) partly contributed to the superior accuracy of FMF&AOT-PM2.5. This study demonstrates the importance of including the FMF to improve PM2.5 estimations and emphasizes the need for a more accurate FMF product that enables superior PM2.5 retrieval.

The delayed effect of wildfire season particulate matter on subsequent influenza season in a mountain west region of the USA

Particularly in rural settings, there has been little research regarding the health impacts of fine particulate matter (PM2.5) during the wildfire season smoke exposure period on respiratory diseases, such as influenza, and their associated outbreaks months later. We examined the delayed effects of PM2.5 concentrations for the short-lag (1–4 weeks prior) and the long-lag (during the prior wildfire season months) on the following winter influenza season in Montana, a mountainous state in the western United States. We created gridded maps of surface PM2.5 for the state of Montana from 2009 to 2018 using spatial regression models fit with station observations and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness data. We used a seasonal quasi-Poisson model with generalized estimating equations to estimate weekly, county-specific, influenza counts for Montana, associated with delayed PM2.5 concentration periods (short-lag and long-lag effects), adjusted for temperature and seasonal trend. We did not detect an acute, short-lag PM2.5 effect nor short-lag temperature effect on influenza in Montana. Higher daily average PM2.5 concentrations during the wildfire season was positively associated with increased influenza in the following winter influenza season (expected 16% or 22% increase in influenza rate per 1 μg/m3 increase in average daily summer PM2.5 based on two analyses, p = 0.04 or 0.008). This is one of the first observations of a relationship between PM2.5 during wildfire season and influenza months later.

Spatial variability of the aerosol optical thickness over Southern Ocean and coastal Antarctica: Comparison with MODIS and MERRA-2 aerosol products

Aerosols scatter or absorb incoming solar radiation and alter the earth's radiation budget. The Southern Ocean provides an excellent environment to study the natural/background aerosols and teleconnection with other regions of the world through long-range transport. Measurements of Aerosol Optical Thickness (AOT) (in the wavelength range 380–1020 nm) have been carried out during a 10th Southern Ocean expedition, between 20o-67oS, during December 2017–February 2018, onboard chartered ice-class vessel SA Agulhas. AOT (500 nm) in the range ~ 0.04–0.17 and an Angstrom parameter (α) (turbidity coefficient (β)) in the range ~0.03–1.29 (0.02–0.05) were observed and values decrease with the increase in latitude towards the Antarctic coast, indicative of a clean environment. AOT versus α scatter plot reveals broadly two classes, fine mode and coarse mode but exhibits different optical properties in the Southern Ocean and Antarctic coast. The HYSPLIT air mass back trajectory analysis shows that for the measurement location between 30o- 60oS, the origin of air masses is from the South Atlantic Ocean, whereas measurement locations between 60o-70oS are influenced by sources from Antarctica with no long-range transport of aerosols from other significant landmasses. The AOT data south of 30o is sparse and ship-based data are the only source to validate satellite and modelled AOT. Comparison of sunphotometer AOT with collection 6.1 MODIS (TERRA) AOT reveals a Mean Bias Error (MBE) in the range −0.01 to −0.1 and Root Mean Square Error (RMSE) in the range 0.02– to 0.1. Similarly, comparison with MERRA-2 reanalysis AOT shows the MBE (RMSE) in the range 0 to −0.11 (0.02–0.11). Both the MODIS (TERRA) and MERRA-2 reanalysis are found to underestimate AOT and show maximum errors (in terms of MBE and RMSE) in the 50oS-60oS latitude band.

Retrieval of Aerosol Optical Thickness in the Arctic Snow-Covered Regions Using Passive Remote Sensing: Impact of Aerosol Typing and Surface Reflection Model

Currently, no aerosol optical thickness (AOT) data set over the Arctic snow/ice-covered regions derived from space-borne passive remote sensing is available. The challenge is to develop an accurate and robust technique to derive AOT above highly variable and bright snow/ice surfaces. To extend data coverage of the eXtensible Bremen Aerosol/cloud and surfacE Retrieval (XBAER) AOT data product in the future, we propose a new algorithm for the retrieval of AOT and surface properties over snow/ice simultaneously. The algorithm utilizes the linear perturbation theory and does not use any simplified atmospheric correction techniques. Key issues like the selection of a proper aerosol type and optimal surface parameterization method for the retrieval of AOT over the Arctic have been investigated. The aerosol type is investigated using the aerosol climatology microphysical properties derived from four Aerosol Robotic Network (AERONET) sites (Barrow, Hornsund, Kangerlussuaq, and Tiksi). The three-parametric Ross-Li linear kernel model is used to describe the snow bidirectional reflectance distribution function (BRDF). The a priori knowledge of wavelength-dependent features of the coefficients in the Ross-Li linear kernel model is derived from Polarization and Directionality of the Earth's Reflectances (POLDER) measurements over the Arctic and utilized as constraints in the retrieval. The studies show that the combination of Ross-Li surface model and weakly absorbing aerosol parameterization provides an optimal way to derive AOT over the Arctic snow/ice-covered regions from passive remote sensing observations. The retrieved AOTs using POLDER show good agreement with AERONET observations.

Empirical evidence of a positive climate forcing of aerosols at elevated albedo

We use Aerosol Robotic Network (AERONET) observation data to empirically determine how natural and anthropogenic aerosol categories (i.e. mineral dust, biomass burning, and urban-industrial aerosols) affect light extinction, showing that their radiative forcing varies strongly with the surface albedo. Generally, the radiative forcing depends on the aerosol loading, but the efficiency varies with the aerosol type and aerosol-radiation-surface interactions. Desert dust, biomass burning and urban-industrial aerosols can exhibit dramatic shifts in radiative forcing at the top of the atmosphere, from cooling to warming, at surface albedos from below 0.5 to above 0.75. Based on the linear relationship between the radiative forcing efficiency and surface albedo for aeolian aerosols, using Moderate Resolution Imaging Spectroradiometer (MODIS) AOT (Aerosol Optical Thickness) and surface albedo data, we characterized a large Asian dust event during the spring of 2001, and demonstrate its immense spatially varying radiative forcing, ranging from about −84.0 to +69.3 W/m2. For extensive Russian wildfires during the summer of 2010, strong radiative cooling forcing variability of biomass combustion aerosols is found, ranging from about −86.3 to +3.1 W/m2. For a thick urban-industrial aerosol haze over northern India during the winter of 2017, a large range of about −85.0 to −0.3 W/m2 is found. These wide ranges underscore the need to accurately define aerosol-radiation-surface interactions.

Measurement of Aerosols Optical Thickness of the Atmosphere using the GLOBE Handheld Sun Photometer.

Here, we describe the measurement of aerosol optical thickness using the GLOBE handheld sun photometer. Aerosol optical thickness (AOT) was measured at Xavier University of Louisiana (XULA, 29.96° N, 90.11° W and 3 m above sea level). The measurements were done at two different wavelengths, 505 nm and 625 nm. AOT measurements were done 6 times a day (7 AM, 9 AM, 11 AM, solar noon, 3 PM and 5 PM). The data shown in this paper are the monthly average AOT values taken at solar noon. During each measurement time; at least five values of the sunlight voltage V and the dark voltage Vdark are taken for each channel. The mean for these five measurements is taken as the average for that measurement time. Other meteorological data such as temperature, surface pressure, rainfall and relative humidity are also measured at the same time. The whole protocol is completed within a time span of 10-15 min. The measured AOT values at 505 nm and 625 nm are then used to extrapolate the AOT values for wavelengths 667 nm, 551 nm, 532 nm and 490 nm. The measured and extrapolated AOT values were then compared with values from the nearest AERONET station at Wave CIS site 6 (AERONET, 28.87° N, 90.48° W and 33 m above sea level), which is about 96 km south of XULA. In this study we tracked the annual and daily variations of AOT for a 12 month period from September 2017 to August 2018. We also compared AOT data from two independently calibrated GLOBE handheld sun photometers at the XULA site. The data show that the two instruments are in excellent agreement.

Inversion algorithm for non-spherical dust particle size distributions

Dust particles are the main aerosol component of the atmosphere, and can influence human, environmental, and ecological health. Particle size distribution is an important aerosol micro-physical parameter that denotes the concentration distribution of particles of different radii and can determine the extinction characteristics of these particles. In traditional inversion algorithms, the aerosol is generally assumed to be spherical according to Mie theory, and the relationship between aerosol optical thickness and particle size distribution is described by the Fredholm integral equation of the first kind. For non-spherical dust particles, this spherical assumption is obviously unreasonable and yields unreliable results. Therefore, we developed an algorithm assuming non-spherical particles for inversion of dust particle size distributions. In the case of non-spherical particles, the extinction efficiency factor kernel functions of the ellipsoid were calculated using the anomalous diffraction approximation method, and the kernel function of Mie scattering theory was substituted with these new kernel functions. Moreover, the Phillips–Twomey method was employed to solve the Fredholm integral equation of the first kind using aerosol optical thickness data from a CE-318 sun photometer. To verify the feasibility of the anomalous diffraction approximation method, experiments were carried out under sunny, dusty, windy and hazy weather conditions. These experiments showed that the extinction kernel function for non-spherical particles obtained using the anomalous diffraction approximation method is suitable for inversion of non-spherical dust particle size distributions under different weather conditions in the Yinchuan area.

Spatial-Temporal Change Evolution of PM2.5 in Typical Regions of China in Recent 20 Years

Two decades of PM2.5 pollution has seriously hindered China's sustainable development. However, relevant research of PM2.5 has been hindered because of the lack of long-term historical monitoring data. Therefore, ground observations of PM2.5 concentration from 2013 to 2016 in four typical regions of China and the MODIS aerosol optical thickness data, boundary layer height, temperature, and other meteorological data from 2000 to 2016 were used as the basic data. A combined simulation model was constructed by combining the two algorithms of backward artificial neural network and support vector regression and obtains the PM2.5 concentration history for the past 20 years using geospatial analysis technology. The results demonstrate that the combination model is better than the single model, with lower error and higher generalization ability. The spatial-temporal analysis results show that the concentration of PM2.5 continued to increase in the Beijing-Tianjin-Hebei region and in the three northeastern provinces of China, the PM2.5 concentration decreased slowly in the Pearl River Delta, the pollution range of PM2.5 in three of the research areas showed an expanding trend, and the PM2.5 concentration and pollution range remained stable in the Yangtze River Delta. In 2012, the concentration of PM2.5 in the four study areas decreased and the pollution range narrowed, but the PM2.5 concentration rose slightly after that decline and the high pollution range narrowed during 2013-2016, which with the country to take PM2.5 regional defense and other governance measures.

Comparison of Seasonal Cycles of Phytoplankton Chlorophyll, Aerosols, Winds and Sea-Surface Temperature off Somalia

In climate research, an important task is to characterise the relationships between Essential Climate Variables (ECVs). Here, satellite-derived data sets have been used to examine the seasonal cycle of phytoplankton (chlorophyll concentration) in the waters off Somalia, and its relationship to aerosols, winds and Sea Surface Temperature (SST). Chlorophyll-a (Chl-a) concentration, Aerosol Optical Thickness (AOT), A° ngstro¨m Exponent (AE), Dust Optical Thickness (DOT), Sea Surface Temperature (SST) and sea-surface wind data for a 16-year period were assembled from various sources. The data were used to explore whether there is evidence in the data to show that dust aerosols enhance Chl-a concentration in the study area. The Cross 11 Correlation Function (CCF) showed highest positive correlation (r2=0.3) in the western Arabian Sea when AOT led Chl-a by 1 to 2 time steps (here, 1 time step is 8 days). A 2o□ 2obox off Somalia was selected for further investigations. The correlations of alongshore wind speed, Ekman Mass Transport (EMT) and SST with Chl-a were higher than that of AOT, for a lag of 8 days. When all four variables were considered together in a multiple linear regression, the increase in r2 associated with the AOT is only about 0.02, a consequence of covariance among AOT, SST, EMT and alongshore wind speed. The AOT data show presence of dust aerosols most frequently during the summer monsoon season (June- September). When the analyses were repeated for the dust aerosol events, the correlations were generally lower, but still significant. Again, the inclusion of DOT in the multiple linear regression increased the correlation coefficient by only 2%, indicating minor enhancement in Chl-a concentration. Interestingly, during summer monsoon season, there is a higher probability of finding more instances of positive changes in Chl-a after one time step, regardless of whether there is dust aerosol or not. On the other hand, during the winter monsoon season (November-December) and rest of the year, the probability of Chl-a enhancement is higher when dust aerosol is present than when dust aerosol is present than when it is absent. The phase relationship in the annual climatologies of Chl-a and AOT

An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness, part 1: Algorithm development

The fine-mode fraction (FMF) can be a useful tool to separate natural aerosols from man-made aerosols and to assist in estimating surface concentrations of particulate matter with a diameter<2.5μm. A LookUp Table-based Spectral Deconvolution Algorithm (LUT-SDA) was developed here for satellite-based applications using data such as MODerate resolution Imaging Spectroradiometer (MODIS) measurements. This method was validated against ground-based FMF retrievals from the Aerosol Robotic Network (AERONET). The LUT-SDA was then applied to two MODIS-retrieved aerosol optical thickness (AOT) products for the period of December 2013 to July 2015: the MODIS Collection 6 (C6) Dark Target (DT) AOT product and the simplified high-resolution MODIS Aerosol Retrieval Algorithm (SARA) AOT product. In comparison with the MODIS C6 FMF product in three study areas (Beijing, Hong Kong, and Osaka), FMFs estimated by the LUT-SDA agreed more closely with those retrieved from the AERONET with a very low bias. Eighty percent of the FMF values fell within the expected error range of ±0.4. The root mean square error (RMSE) was 0.168 with few anomalous values, whereas the RMSE for the MODIS FMF was 0.340 with more anomalous values. The LUT-SDA FMF estimated using SARA AOT data conveys more detailed information on urban pollution than that from MODIS C6 DT AOT data. As a demonstration, the seasonally-averaged spatial distribution of the FMF in Beijing was obtained from the LUT-SDA applied to SARA AOT data and compared with that of the AERONET-retrieved FMF. Their seasonal trends agreed well.