UV Aerosol Index Research Articles

Dataset for Deep Learning-based GEMS Asian Dust Detection

In South Korea, Asian dust frequently occurs during the spring, causing various health issues, including respiratory diseases. Consequently, public awareness and concern about air pollutants have increased, leading to demands for improved air quality and accurate forecasting. To meet these demands, the Ministry of Environment has deployed the Geostationary Environment Monitoring Spectrometer (GEMS) on the GK2B satellite to monitor atmospheric pollutants and climate change-inducing substances in real-time. The current GEMS dust product, generated using thresholds of the UV-aerosol index and visible-aerosol index, has shown limitations in accurately detecting suspended particulate matter. This study aims to develop a comprehensive AI dataset for improving GEMS Asian dust detection. Data were collected from January to May 2021, focusing on dates with significant dust events. Label data were meticulously generated through annotations based on outputs from various satellites and groundbased observations. Subsequent data preprocessing and augmentation techniques, including normalization and cut-mix, were applied to enhance the dataset’s robustness and generalizability. To evaluate the dataset, model training was conducted. The results predicted by the model showed improvements over the detection results of existing algorithms. Future datasets will be developed with improved labeling methods and accuracy verification techniques. These dataset improvements are expected to contribute to the development of deep learning models with superior predictive performance compared to current dust detection algorithms.

Novel Comparison of Pyrocumulonimbus Updrafts to Volcanic Eruptions and Supercell Thunderstorms Using Optical Flow Techniques

AbstractConvective dynamics in a supercell thunderstorm, a volcanic eruption, and two pyrocumulonimbus (pyroCb) events are compared by computing cloud‐top divergence (CTD) with an optical flow technique called Deepflow. Visible 0.64‐μm imagery sequences from Geostationary Operational Environmental Satellites (GOES)‐R series Advanced Baseline Imager (ABI) are used as input into the optical flow algorithm. CTD is computed after post‐processing of the retrieved motions. Analysis is performed on specific image times, as well as the full time series of each case. Multiple CTD‐based parameters, such as the maximum and the two‐dimensional area exceeding a specified CTD threshold, are examined along with the optical flow‐retrieved wind speed. CTD is shown to accurately and quantitatively represent the behavior and magnitude of different deep convective phenomena, including distinguishing between convective pulses within each individual event. CTD captures updraft intensification as well as differences in convective activity between two pyroCb events and individual updraft pulses occurring within a single pyroCb event. Finally, the characteristics of high‐altitude smoke plumes injected by two separate pyroCb pulses are linked to CTD using ultraviolet aerosol index and satellite imagery. Optical flow‐derived parameters can therefore be applied to individual pyroCbs in real‐time, with potential to characterize pyroCb smoke source inputs for downstream smoke modeling applications and to facilitate future tools supporting air quality modeling and firefighting efforts.

Quantitative assessment and driving factors analysis of surface urban heat island of urban agglomerations in China based on GEE.

The urban heat island (UHI) effect generated by the development of high-speed urbanization has become one of the major problems affecting the urban ecological environment. As the main body of urbanization in China, China's urban agglomerations are the core areas of urban heat island effect. The purpose of this study is to study the spatial-temporal characteristics and driving factors of surface urban heat island in 19 urban agglomerations in China, with a view to providing theoretical references for the prevention of urban thermal environmental risks. Based on Google Earth Engine (GEE), this paper estimated the surface urban heat island intensity (SUHII) of 19 urban agglomerations in China from 2003 to 2019 using MODIS land surface temperature (LST) data. Correlation analysis and regression analysis were used to explore the correlation between the change of SUHII and driving factors. Finally, the driving factors of SUHII were detected by the geo-detector model. Results showed that (1) the SUHII of 19 urban agglomerations in arid and semi-arid areas of northwestern China is higher than that in humid areas of eastern and southeastern China. (2) The SUHII of 19 urban agglomerations in China generally shows a decreasing trend, and the spatial variation of the change trend is significant. (3) There are positive correlations between SUHII and reference evapotranspiration (ET0), population density (POP), gross domestic product (GDP), nitrogen dioxide (NO2), ozone (O3), and ultraviolet aerosol index (UVAI); negative correlations with normalized difference vegetation index (NDVI), DEM, sulfur dioxide (SO2), carbon monoxide (CO), and formaldehyde (HCHO); the correlations all pass the significance test of P < 0.05 and are statistically significant. (4) The factor detection results showed that NDVI, land cover type (LC), and UVAI were the main driving factors of SUHII. The interaction detection results showed that the interaction between O3 and UVAI had the most significant impact on SUHII.

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.

Uncovering the dynamics of atmospheric aerosols in China: A comprehensive analysis of OMI-Retrieved aerosol index data

China faces severe atmospheric problems due to the rapid increase in population and industrialization. Therefore, due to the insufficient number of monitoring stations in such a vast country, remote sensing of an ultraviolet aerosol index (UVAI) provides useful information about absorbing aerosols. This study used the OMI-retrieved UVAI data to investigate the spatial patterns, trends, and periodic nature of UVAI over China from October 2004 to December 2022. Additionally, the correlation and degree of coherence of UVAI with meteorological parameters (temperature & precipitation) and anthropogenic factors (Construction, population, CO2 emission, energy consumption, and primary industry) are also examined. Mean UVAI concentration with an increasing trend of 4.34% per year shows high UVAI values (0.70–1.41) in Xinjiang Uygar followed by Heilongjiang, Jilin, Liaoning, Shandong, Beijing, Hubei, and some northern regions i.e., Gansu, Qinghai, and western Nel Mongol. Seasonally, Xinjiang Uygur experiences high UVAI (<1.52) values during all four seasons with the dominance of both fine and coarse mode particles. Moreover, Hubei, Gansu, Jilin, Shandong, and Xinjiang Uygar experiences high UVAI values i.e. 2.55, 2.05, 1.87, 2.82, 2.27 during 2021 with an increasing trend of 5.50% year−1, 5.43% year−1, 3.82% year−1, 4.73% year−1, and 9.33% year−1 respectively. The HYSPLIT backward trajectories plotted at altitudes of 50m, 100m, and 500m indicate the dominance of desert dust over Jinan, Urumqi, and Lanzhou while anthropogenic pollutants over Wuhan and Changchun. Wavelet coherence (WTC) shows the positive correlation of UVAI with temperature and precipitation. Moreover, the co-variability of absorbing aerosols with anthropogenic and natural factors is also inculcated in this study. Considering the outcomes of this study it will help policymakers to analyze the variability of absorbing aerosols in China and different methods like plantation of more trees, efficient fuel consumption, and promotion of public transport, etc., can be used to reduce air pollution.

Spatial mapping of indoor air quality in a light metro system using the geographic information system method

Abstract It is known that one of the greatest problems of developed countries in the twenty-first century is traffic. For this reason, engineers have searched for alternative solutions to the problem of traffic. One such solution is the construction and utilization of rail systems instead of main roads. From an engineering perspective, rail systems can be divided into three groups: metro, light metro, and tram systems. Light metro systems, which are a form of public transportation, are not directly inside the traffic. Their most important advantages include the fact that they do not release combustion products such as CO, and metro and light metro systems may be considered environmentally friendly based solely on their electricity consumption. In this study, measurements of parameters affecting indoor air quality were made inside light metro cars and in and around light metro stations belonging to the light metro system of the Metropolitan Municipality of Antalya, known as the tourism capital of Turkey. In February and March 2021, when the COVID-19 pandemic was first registered in Turkey, particulate matter (PM), temperature, and relative humidity measurements were made for testing indoor and outside air quality. Moreover, as outside air parameters, outside temperature, outside relative humidity, CO, normalized difference vegetation index, and ultraviolet aerosol index data were obtained from the General Directorate of Meteorology of Turkey. The measurement results were analyzed using the inverse distance weighting method in the geographic information system. Based on the results of the analyses, spatial maps were created for indoor and outside air quality parameters in the light metro system. Using these maps, the effects of passenger density and environmental factors both inside the metro cars and at the metro stations on indoor air quality were identified. In addition, the spread of the SARS-CoV-2 virus in the COVID-19 period was analyzed using spatial maps of the PM0.3 and PM0.5 parameters. It is believed that the results of this study will set an example for further indoor air quality studies worldwide, and this study is unique in that it employed a method that is used particularly in survey and geomatics engineering for analyzing indoor air quality in light metro systems.

Aerosol radiative forcing of forest fires unprecedented in South Korea (2022) captured by Korean geostationary satellites, GK-2A AMI and GK-2B GEMS

The worst forest fires in Korean history broke out on March 4, 2022 and lasted for ten days. In order to monitor the catastrophic forest fires, Geostationary Korea Multi-Purpose Satellite (GK)-2 A Advanced Meteorological Imager (AMI) and GK-2B Geostationary Environment Monitoring Spectrometer (GEMS) data were used in this study. Aerosol optical depth (AOD) irretrievable for the biomass-burning aerosols produced with water vapor classified as could-contaminated, was reconstructed by ultraviolet aerosol index (UVAI). Afterward, aerosol radiative forcing (ARF) at TOA was finally estimated by the correlation of AOD and surface albedo with ARF. Most of the aerosols drifted toward the East Sea by the prevailing westerly winds, and caused a cooling effect on the atmosphere with a maximum daily average radiative forcing of −69.28 Wm-2. Furthermore, the fire-prone conditions for the unprecedented forest fires were discussed in detail as following aspects; 1) the most severe drought caused by a “triple-dip” La Niña; 2) pressure patterns and topographical features that generate strong winds; 3) coniferous forests prone to fires; and 4) increased human activity following the nationwide COVID-19 vaccination. This study demonstrated that the rapid and effective ARF estimation based on the satellite remote sensing can contribute to a better understanding of ARF in the Earth's radiation budget for the global forest fires that will be more frequent, intense, and longer-lasting due to the human-caused climate and environment changes.

Quantifying climate variation and associated regional air pollution in southern India using Google Earth Engine

Climate change and regional air pollution have had significant proportional coherence and are collectively hazardous for the regional ecosystem. To conduct this present investigation, we obtained high-resolution remotely sensed datasets from 2001 to 2022. To estimate climate variation, we utilized Climate Hazard Group InfraRed Precipitation with Station Data Version 2.0 (CHIRPS) and Moderate Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST). Additionally, we used Sentinel-5P datasets to collect spatio-temporal information for regional CO (Carbon Monoxide), NO2 (Nitrogen Dioxide), SO2 (Sulfur Dioxide), and UV Aerosol index for Coimbatore city. Numerous non-parametric and descriptive statistical applications were then employed to check the spatial integrity of satellite data products and spatio-temporal trends using Google Earth Engine algorithms. The study reveals most of the southern parts of Coimbatore city witnessed increased LST (0.10 °C/year) together with decreased rainfall (21.5 mm/year). Moreover, regional concentration of air pollutants exhibits spatio-temporal variability at annual and seasonal scales, where maximum engrossment is occupied by CO during the pre-monsoon and monsoon season. However, other pollutants are also dominant in the northern parts of the city, whereas NO2 and absorbing Aerosol during pre-monsoon season experienced significant increase throughout the years. Understanding the fluctuations in air pollution levels across different weather situations might help in developing targeted pollution reduction methods.

An assessment of air quality indicators Sentinel-5P TROPOMI data were used to examine the NO2 SO2 O3 AEROSOL levels in Uttar Pradesh before, after, and during the COVID 19 phase

Significantly affecting human activities and economic sectors were the COVID-19 pandemic and related lockdowns. Nevertheless, despite these difficulties, the environment saw some improvement, especially in terms of the air quality. A dearth of thorough research comparing air quality indicators before, during, and after the epidemic has been noted despite the many studies that have emphasized the decrease in pollutants across India during the early phases of the pandemic. In-depth analyses of four important air quality indicators nitrogen dioxide (NO2), sulphur dioxide (SO2), ultraviolet aerosol index (UVAI) and ozone (O2) are conducted in this work. We examined the patterns on the Google Earth Engine (GEE) platform, specifically for the state of Uttar Pradesh in India, utilizing data from the TROPOMI (Tropospheric Monitoring Instrument) during a five-year period, focusing on the same three-month timeline each year. For several atmospheric variables (NO2,SO2,O2, Aerosol,), the given data from 2019 to 2023 includes monthly averages and standard deviations. The overall trend from 2019 to 2023 shows varying quantities of pollutants, with some, like NO2, rebounding after an initial decrease and others, like aerosols, exhibiting considerable annual changes, underscoring the dynamic character of environmental circumstances over time.

Identifying the natural and anthropogenic drivers of absorbing aerosols using OMI data and HYSPLIT model over South Asia

Aerosols absorption contributes significantly to the total radiative effects of aerosols and so an important component of radiative forcing estimates. Therefore, this study explores the spatiotemporal distribution of ultraviolet aerosol index (UVAI), future trends, potential sources of absorbing aerosols and their relationship with temperature, wind speed, precipitation and total ozone column using Ozone Monitoring Instrument retrieved UVAI and HYSPLIT model over South Asia during October 2004 to March 2022. The mean UVAI within the ranges of 0.56–1.62 are observed over Eastern and Southern Pakistan and Northern India associated with dust and biomass burning aerosols. The interannual variations in UVAI show that the values of UVAI increases from 1.73 to 3.11 during 2018–2021 over the Indo-Gangetic Plain. Contrary to this, UVAI < 0 is observed along the Karakorum and Himalaya range during 2005–2021 indicating presence of non-absorbing aerosols. The interaannual variations in UVAI reveal highest UVAI of 0.64 in December followed by 0.51 in July over South Asia. Seasonally, UVAI shows increasing trend at the rate of 0.9064 DJF−1, 0.3810 JJA−1, 0.2707 SON−1 and 0.0774 MAM−1 over South Asia. A positive correlation of 0.56 is observed between UVAI and wind speed followed by over Southern Pakistan followed 0.43 between UVAI and total ozone column Southern Pakistan and India. The UVAI shows increasing trend at the rate of 0.1409, 0.1124, 0.1224, 0.1015, 0.1242 and 0.2054 per year over Lahore, Karachi, Kanpur, New-Delhi, Varanasi, and Dhaka with maximum UVAI of 5.55, 4.47, 4.51, 4.99, 4.61 and 4.65 respectively during the study period. The anthropogenic productivity analysis reveals that primary industry and secondary industry contributes in lowering UVAI values whereas tertiary industry, energy consumption and gross domestic products increase aerosols loading in South Asia. Moreover, HYSPLIT cluster analysis further reveals the localized and trans-boundary sources of absorbing aerosols over the selected cities.

Remotely sensed and surface measurement- derived mass-conserving inversion of daily NOx emissions and inferred combustion technologies in energy-rich northern China

Abstract. This work presents a new model-free inversion estimation framework (MFIEF) using daily TROPOspheric Monitoring Instrument (TROPOMI) NO2 columns and observed fluxes from the continuous emission monitoring system (CEMS) to quantify 3 years of daily scale emissions of NOx at 0.05∘×0.05∘ over Shanxi Province, a major world-wide energy-producing and energy-consuming region. The NOx emissions, day-to-day variability, and uncertainty on a climatological basis are computed to be 1.86, 1.03, and 1.05 Tg yr−1 respectively. The highest emissions are concentrated in the lower Fen River valley, which accounts for 25 % of the area, 53 % of the NOx emissions, and 72 % of CEMS sources. Two major forcing factors (10th to 90th percentile) are horizontal transport distance per day (63–508 km) and lifetime of NOx (7.1–18.1 h). Both of these values are consistent with NOx emissions to both the surface layer and the free troposphere. The third forcing factor, the ratio of NOx/NO2, on a pixel-to-pixel basis, is demonstrated to correlate with the combustion temperature and energy efficiency of large energy consuming sources. Specifically, thermal power plants, cement, and iron and steel companies have a relatively high NOx/NO2 ratio, while coking, industrial boilers, and aluminum oxide factories show a relatively lower ratio. Variance maximization is applied to daily TROPOMI NO2 columns, which facilitates identification of three orthogonal and statistically significant modes of variability, and successfully attributes them both spatially and temporally to (a) this work's computed emissions, (b) remotely sensed TROPOMI ultraviolet aerosol index (UVAI), and (c) computed transport based on TROPOMI NO2.

Ozone Monitoring Instrument (OMI) UV aerosol index data analysis over the Arctic region for future data assimilation and climate forcing applications

Abstract. Due to a lack of high-latitude ground-based and satellite-based data from traditional passive- and active-based measurements, the impact of aerosol particles on the Arctic region is one of the least understood factors contributing to recent Arctic sea ice changes. In this study, we investigated the feasibility of using the ultraviolet (UV) aerosol index (AI) parameter from the Ozone Monitoring Instrument (OMI), a semi-quantitative aerosol parameter, for quantifying spatiotemporal changes in UV-absorbing aerosols over the Arctic region. We found that OMI AI data are affected by an additional row anomaly that is unflagged by the OMI quality control flag and are systematically biased as functions of observing conditions, such as azimuth angle, and certain surface types over the Arctic region, resulting in an anomalous “ring” of climatologically high AI centered at about 70∘ N, surrounding an area of low AI over the pole. Two methods were developed in this study for quality-assuring the Arctic AI data. Using quality-controlled OMI AI data from 2005 through 2020, we found decreases in UV-absorbing aerosols in the spring months (April and May) over much of the Arctic region and increases in UV-absorbing aerosols in the summer months (June, July, and August) over northern Russia and northern Canada. Additionally, we found significant increases in the frequency and size of UV-absorbing aerosol events across the Arctic and high-Arctic (north of 80∘ N) regions for the latter half of the study period (2014–2020), driven primarily by a significant increase in boreal biomass-burning plume coverage.

Variations in the aerosol index and its relationship with meteorological parameters over Pakistan using remote sensing.

Particulate pollution has become a major issue in developing countries including Pakistan. Aerosols are causing severe impacts on climate and human health. To understand the effects of aerosols on the environment and human health, we must first understand their optical and physical properties. In this paper, we used ozone monitoring instrument (OMI) retrieved ultraviolet aerosol index (UVAI) to analyze spatial and temporal distribution, annual and seasonal trends of absorbing aerosols, and their relationship with meteorological parameters (e.g., temperature, relative humidity, and wind speed) over Pakistan from October 2004 to December 2021. Significant spatiotemporal changes in UVAI values were found with high values in southern and central regions and low values in northern regions of Pakistan. The mean UVAI over Pakistan showed an increasing trend of 2.89% year-1. Seasonally, UVAI increases at the rate of 3.97% winter-1, 3.24% autumn-1, 0.81% summer-1, and 0.71% spring-1. A strong positive correlation of UVAI with precipitation and temperature (~ 0.6) is observed in the central and southern regions of Pakistan. A negative and positive correlation of -0.3223 and 0.4284 of UVAI with CO2 emissions and primary industry is observed in Pakistan, respectively. We also found potential sources of aerosols over major cities of Pakistan using the Hybrid Single Particle Langrangian Integrated Trajectory (HYSPLIT) model. It determines that the dominant aerosols over Karachi are natural aerosols like sea salt and dust particles and anthropogenic aerosols are dominant over Lahore. Moreover, the natural and anthropogenic factors influencing absorbing aerosols are also discussed herein. Considering the outcomes of this study different methods would be used to reduce the concentration of particulate pollution like afforestation, efficient fuel energy consumption, promotion of public transport networks, etc.

Remote sensing of air pollution due to forest fires and dust storm over Balochistan (Pakistan)

Forest fires and dust storms, inevitable and unprecedented in their intensity and extent, are a major source of atmospheric pollutants. The implementation of both systematic surveillance and lowering air pollution options from forest fires and dust storms remain largely unrecognized in many parts of the world. Therefore, this study examines the air pollution due to forest fires and dust storms over Pakistan using remote sensing techniques. High aerosol optical depth (AOD) (∼1.5) and ultraviolet aerosol index (UVAI) (∼4.50) values were observed in central, eastern, and southeastern Pakistan during May 18–25 indicating the dominance of absorbing aerosols (i.e., dust, smoke, and mixed-type aerosols). Mixed, dust, smoke, and non-smoke fine-dominated aerosols were observed on May 16 while dust, smoke, and mixed-type aerosols were found on May 23. Contrary to this, dust aerosols were found in abundance over southwestern Pakistan on May 16 and 22, and central and Southern Pakistan on May 23. Moreover, high concentrations of NO2, CO, and HCHO were found in central, eastern, and northeastern Pakistan during forest fire breakouts. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model forward trajectories and concentration weighted trajectory (CWT) analysis also showed that the air masses from northwestern Pakistan carried away atmospheric pollutants from the forest fire region to eastern, northeastern, and southern Pakistan. These findings can be useful for both air quality and forest management in Pakistan.

Dust Detection Over East Asia From Multispectral and Multi‐Temporal Himawari‐8/AHI Thermal Infrared Observations

AbstractThe frequent outbreak of dust storms every spring is one of the extreme weathers in East Asia. Geostationary meteorological satellite thermal infrared (TIR) imagery provides observations for high‐frequency, all‐day, large‐scale monitoring of dust sources and transport. This study proposes an integrated method to detect dust over East Asia by using the multispectral and multi‐temporal Himawari‐8/Advanced Himawari Imager (AHI) TIR observations, based on a Feedforward Neural Network (FNN) model and the Robust Principal Component Analysis based Anomalous Dust Detection (ADD) method. The FNN model is trained to map the multispectral Brightness Temperatures (BTs) to the three category probabilities (i.e., cloud, dust, and clear sky), and the ADD method calculates the dust score by normalizing multi‐temporal BT difference anomalous variation. The Integrated Dust Index (IDI) reflects the quantified dust confidence of the AHI pixel, which is defined as the sum of the dust probability and the dust score. The IDI was evaluated using 2 months of the Sentinel‐5P TROPOMI Ultraviolet Aerosol Index and achieved a total dust detection accuracy above 90%. A comparison with Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation nighttime observations shows that the IDI can also detect dust at night. The times series IDI of the massive 14–16 March 2021 dust storm show its capabilities to trace dust sources and monitor dust transport.

Influence of the Long-Range Transport of Siberian Biomass Burnings on Air Quality in Northeast China in June 2017.

Biomass burning (BB) emits a large volume of trace gases and aerosols into the atmosphere, which can significantly affect the earth's radiative balance and climate and has negative impacts on air quality and even human health. In late June 2017, an intense BB case, dominated by forest and savanna fires, occurred in Siberia, and it affected the air quality of Northeast China through long-range transport. Here, multisatellite remote-sensing products and ground-based PM2.5 measurements are used to evaluate the influence of the Siberian smoky plume on Northeast China. The results show that the BB was intense at the early stage when the daily fire count and average fire radiative power exceeded 300 and 200 MW, respectively. The maximum daily fire count reached 1350 in Siberia, and the peak value of instantaneous fire radiative power was as high as 3091.5 MW. High concentrations of CO and aerosols were emitted into the atmosphere by the BB in Siberia. The maximum daily mean values of the CO column concentration and aerosol optical depth (AOD) increased by 3 × 1017 molec·cm2 and 0.5 compared with that during the initial BB stage. In addition, the BB released a large number of absorptive aerosols into the atmosphere, and the UV aerosol index (UVAI) increased by five times at the peak of the event in Siberia. Under the appropriate synoptic conditions and, combined with pyroconvection, the smoky plume was lifted into the upper air and transported to Northeast China, affecting the air quality of Northeast China. The daily mean values of CO concentration, AOD, and UVAI in Northeast China increased by 6 × 1017 molec·cm2, 0.5, and 1.4, respectively, after being affected. Moreover, the concentration of the surface PM2.5 in Northeast China approximately doubled after being affected by the plume. The results of this study indicate that the air quality of Northeast China can be significantly affected by Siberian BBs under favorable conditions.

Investigation of Antalya forest fire's impact on air quality by satellite images using Google earth engine

Several fires occur in Türkiye every year for various reasons. Some of these fires are extinguished within a short period of time. But some of them get bigger due to various factors. Prolonged fires harm the biological ecosystem and nature to a great extent. Gases released into the atmosphere due to the fires negatively affect the life of living things.In this study, the number of fires in the Antalya-Manavgat region in 2021 was determined. For this aim, BAI (Burned Area Index), NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index) plant indexes were used to determine the amount of fire by using high resolution satellite images. In addition, the carbon monoxide (CO) and ultraviolet aerosol index (UVAI) values during the fire were investigated using satellite images. The precipitation and temperature values of the region between the years 2015–2021 were discussed. The emitted gases were correlated with the national air quality station data and when the destroyed area after the fire was determined by satellite, it was determined that the CO gas released in the fire produced meaningful results with the national air quality station.

Temporal and Spatial Variations of Satellite-Based Aerosol Optical Depths, Angstrom Exponent, Single Scattering Albedo, and Ultraviolet-Aerosol Index over Five Polluted and Less-Polluted Cities of Northern India: Impact of Urbanization and Climate Change

It is widely acknowledged that factors such as population growth, urbanization's quick speed, economic growth, and industrialization all have a role in the atmosphere's rising aerosol concentration. In the current work, we assessed and discussed the findings of a thorough analysis of the temporal and spatial variations of satellite-based aerosol optical parameters such as Aerosol Optical Depth (AOD), Angstrom Exponent (AE), Single Scattering Albedo (SSA), and Ultraviolet-Aerosol Index (UV-AI), and their concentration have been investigated in this study over five polluted and less-polluted cities of northern India during the last decade 2011–2020. The temporal variation of aerosol optical parameters for AOD ranging from 0.2 to 1.8 with decadal mean 0.86 ± 0.36 for Patna region shows high value with a decadal increasing trend over the study area due to rise in aerosols combustion of fossil fuels, huge vehicles traffic, and biomass over the past ten years. The temporal variation of AE ranging from 0.3 to 1.8 with decadal mean 1.72 ± 0.11 for Agra region shows high value as compared to other study areas, which indicates a comparatively higher level of fine-mode aerosols at Agra. The temporal variation of SSA ranging from 0.8 to 0.9 with decadal mean 0.92 ± 0.02 for SSA shows no discernible decadal pattern at any of the locations. The temporal variation of UV-AI ranging from -1.01 to 2.36 with decadal mean 0.59 ± 0.06 for UV-AI demonstrates a rising tendency, with a noticeable rise in Ludhiana, which suggests relative dominance of absorbing dust aerosols over Ludhiana. Further, to understand the impact of emerging activities, analyses were done in seasonality. For this aerosol climatology was derived for different seasons, i.e., Winter, Pre-Monsoon, Monsoon, and Post-Monsoon. High aerosol was observed in Winter for the study areas Patna, Delhi, and Agra which indicated the particles major dominance of burning aerosol from biomass; and the worst in Monsoon and Post-Monsoon for the Tehri Garhwal and Ludhiana study areas which indicated most of the aerosol concentration is removed by rainfall. After that, we analyzed the correlation among all the parameters to better understand the temporal and spatial distribution characteristics of aerosols over the selected region. The value of r for AOD (550 nm) for regions 2 and 1(0.80) shows a strong positive correlation and moderately positive for the regions 3 and 1 (0.64), mostly as a result of mineral dust carried from arid western regions. The value of r for AE (412/470 nm) for region 3 and (0.40) shows a moderately positive correlation, which is the resultant of the dominance of fine-mode aerosol and negative for the regions 5 and 1 (− 0.06). The value of r for SSA (500 nm) for regions 2 and 1 (0.63) shows a moderately positive correlation, which explains the rise in big aerosol particles, which scatters sun energy more efficiently, and the value of r for UV-AI for regions 1 and 2 shows a strong positive correlation (0.77) and moderately positive for the regions 3 and 1 (0.46) which indicates the absorbing aerosols present over the study region.

A Characteristic Analysis of Various Air Pollutants and Their Correlation with O3 in the Jiangsu, Shandong, Henan, and Anhui Provinces of China

Using a series of characteristic analyses of HCHO, SO2, NO2, O3, and absorptive aerosols (ultraviolet aerosol index—UVAI) in the atmosphere of the study area from 2011 to 2021, this paper reviews the pollutants present in Jiangsu, Shandong, Henan, and Anhui provinces, which are the key regions for air pollution control in China. Furthermore, the correlation between various air pollutants and O3 is also examined. The results obtained show the following: (1) Within the research areas, temporal pollutant variations can be observed from 2011 to 2021. NO2 and HCHO only modestly decrease, SO2 sharply declines, while UVAI remains stable. During the study period, HCHO and O3 first increase and subsequently decline, whereas the other three pollutants exhibit the opposite behavior. (2) Among the examined pollutants, SO2 is the most unstable. (3) Our research discovered that the atmospheric transport paths at high-value points significantly vary between seasons using the public backward trajectory model. (4) Through the correlation analysis we performed, this research reveals the different degrees of spatial correlations between O3 and other pollutants. (5) Using the FNR index method, the sensitivity of O3 to its precursors (NOx and VOCs) is investigated. Based on the results, we provide some suggestions concerning the primary control pollutants and relative control strategies in different seasons and regions.

Aerosol Analysis of China’s Fenwei Plain from 2012 to 2020 Based on OMI Satellite Data

The Fenwei Plain plays an essential role for China’s three-year action plan to protect the air environment. At present, the high-value area and maximum value of atmospheric aerosol have been effectively controlled, but the governance situation is not stable. Therefore, based on the daily ultraviolet aerosol index (UVAI) data retrieved by Ozone Monitoring Instrument (OMI) from 2012 to 2020, combined with precipitation and temperature and air pressure and lifting index data, this paper analyzes the spatiotemporal distribution characteristics and some influencing factors of UVAI in the Fenwei Plain. The results show that the overall trend of the annual average UVAI value of the Fenwei Plain in 9 years showed two “peaks” in 2013 and 2018, respectively. The high UVAI values are mainly concentrated in the southwest and central areas of the Fenwei Plain. In the study area, UVAI was highest in winter, followed by autumn and spring, and lowest in summer. There were significant negative correlations between precipitation and UVAI and between temperature and UVAI. There were significant positive correlations between air pressure and UVAI and between lifting index and UVAI. According to the backward trajectory clustering results, during the autumn and winter seasons in this area, due to the sand and dust brought by the northwest and the input of aerosols in the coal-producing area and coal-fired heating area, the UVAI value of this time period is higher.