Analysis of atmospheric ozone in Fenwei Plain based on remote sensing monitoring.

This study uses the daily product data of the concentration of ozone in the atmospheric column (ozone column concentration) collected by the Aura satellite's Ozone Monitoring Instrument (OMI), to evaluate the ozone pollution status of the Fenwei Plain in east-central China, by employing pixel-based spatial analysis, an θslope trend index, a Hurst index, and grey correlation. The following results were found. (1) The spatial distribution of ozone in the atmosphere of the Fenwei Plain was higher in the north and lower in the south, with high values appearing in Jinzhong, Lvliang, and other cities. (2) The changes in ozone column concentration periodically and seasonally in the Fenwei Plain. Seasonally, the ozone column concentration was highest in spring, followed by summer, winter, and autumn. (3) The pixel-based trend change of the ozone slope (θslope) indicated that the ozone concentration in the region was in a downward trend, while the long-term correlation of the time series trend Hurst index found that the region should expect to see a weak rebound in the ozone column concentration in the future, so that routine monitoring should be strengthened. (4) The present study on the factors influencing the ozone concentration found that the concentration is relatively closely related to temperature, air pressure, humidity, grain sowing area, highway mileage, and secondary industry.

Analysis of atmospheric SO2 in Sichuan-Chongqing region based on OMI data.

The Sichuan-Chongqing region is the leader and growth pole of economic development in western China. With the rapid development of economy and unique geographical environment, high concentration of sulfur dioxide air pollution has existed for a long time in Sichuan-Chongqing area. Based on 10 years of remote sensing data, this paper studies the temporal and spatial distribution characteristics, stability, and influencing factors of sulfur dioxide in this area. Based on potential sources, the impact of surrounding areas on sulfur dioxide in Sichuan and Chongqing is analyzed. The results shows that the spatial distribution of sulfur dioxide in the Sichuan-Chongqing region is higher in the southeast and lower in the west. The Midwest region has low fluctuation and good stability. The time distribution shows obvious seasonal regularity. The concentration of sulfur dioxide is affected by socio-economic factors and natural factors. In this study, it is found that the distribution of sulfur dioxide is closely related to PM2.5, which provides an important reference for the comprehensive management of air pollution. The OMI data effectively reflects the distribution and change of atmospheric sulfur dioxide in the Sichuan-Chongqing region, and provides certain ideas for air pollution control in the Sichuan-Chongqing region and other regions in China.

Absorbable aerosols based on OMI data: a case study in three provinces of Northeast China.

In order to assess the status of aerosol pollution in three selected Northeast Provinces of China, Ozone Monitoring Instrument/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13 × 24km V003 (OMAERUV) daily product data was used to evaluate (1) the ultraviolet aerosol index (UVAI) temporal and spatial distribution of the three Northeast Provinces from 2009 to 2018; (2) the potential pollution source areas of provincial capital cities; and (3) future trend changes. Furthermore, the influencing factors were also analyzed and are discussed herein. The results show that the UVAI in the Northeast Provinces exhibit an overall increasing trend, with an average annual increase rate of 2.99%. Seasonally, the UVAI increasing trend in winter is higher than in spring which in turn is higher than autumn. And summer has the least increasing trend. In addition, the external source of absorbent aerosol transmission is mainly in the southwest. Moreover, the overall UVAI remains relatively constant in the central part of the region, and increases slightly and significantly in the south and north directions. In general, spring, autumn, and winter all exhibit increasing trends in varying degrees. The difference between the forecasted and actual UVAI values in the Northeast Provinces does not exceed 10%; thus, the forecasting reliability is good. Also, UVAI has different degrees of correlation with natural factors, such as precipitation and temperature. With respect to social factors, UVAI and population density (a social factor) are positively correlated in 98.2% of the study area, demonstrating that there is a strong positive correlation between UVAI and smoke and dust emissions.

[Analysis of Ultraviolet Aerosol Index in Guangdong-Hong Kong-Macao Greater Bay Area].

This study examines the current aerosol pollution scenario in the Guangdong-Hong Kong-Macao (GHM) Greater Bay Area, based on OMAERUV daily data products, to define the spatial and temporal distribution from 2008 to 2019 and predict variation trends of the ultraviolet aerosol index (UVAI). Changes and potential source areas were analyzed, and their influencing factors were identified. The annual time series of UVAI in the GHM Greater Bay Area showed a downward trend, with an average annual decrease of 2.3%; the monthly time series showed an inverted "V" shape beginning in spring, with the highest seasonal UVAI occurring in spring, followed by winter and autumn. Summer exhibited the lowest UVAI; spatially, the central region has consistently exhibited high values, with an average annual UVAI of 0.35 calculated over 12 years. The distribution of UVAI in the time series is mainly sustainable, and 82.69% of the area will show a downward trend in the future. The main potential sources from beyond the study area are carbon sources and marine biological sources produced by eastern industry; the main potential sources of UVAI are carbon and biomass sources in spring, biomass aerosol sources in summer, and carbon sources in autumn. It accounts for the largest proportion, and aerosol sources of sand and dust in winter have increased. Through correlation analysis, aerosols and PM2.5 were found to be interdependent. Industrial production activities are an important contributor of atmospheric aerosols, and precipitation can reduce the atmosphere. Due to the aerosol content produced by industrial production, secondary industrial activities will accelerate the formation of aerosols when the temperature rises.

Study on the variation characteristics of tropospheric ozone in Northeast China.

The goal of this study was to understand the current status of ozone pollution in Northeast China and to distinguish the main influencing factors of ozone, in order to provide a scientific basis for the future prevention and control of ozone in this region. In this study, the Ozone Monitoring Instrument data product was used as the source data, and the pixel space analysis method and grey correlation analysis method were utilized to examine the tropospheric ozone column concentration in Northeast China from 2010 to 2018. The results revealed that the ozone column concentration in Northeast China was relatively high compared to other parts of the country. The high-value areas were primarily distributed in Tahe County, Heihe City, Hegang City, and Qiqihar City. The temporal variation of the ozone column concentration in Northeast China exhibited an obvious periodicity of 9 years, and its interannual change displayed a downward trend. The maximum value occurred in 2010, and the minimum value was found in 2016. The seasonal changes manifested a relative trend of spring > winter > summer > autumn, which was contrary to the research results for other parts of China, which showed a summer maximum. Among the influencing factors, atmospheric pressure, relative humidity, sunshine hours, GDP, and primary and secondary industries exhibited the closest relationships with ozone. The high concentration of ozone in Northeast China was determined to result from the superimposed effects of long-distance atmospheric transport and anthropogenic emissions.