Tunyang Geng’s research while affiliated with Northwest Normal University and other places

Ozone is a pollutant that is harmful to human health and the troposphere. As a coal base in China, the study of ozone in the Qin–Jin region provides a scientific basis for pollution control and early warning and is of great practical significance. This paper analyzes the spatial and temporal distribution characteristics of tropospheric ozone in the Qin–Jin region from 2013 to 2022. It predicts the tropospheric ozone seasons in 2023 using a combination of ozone monitoring instruments (OMIs), ground stations, and machine learning. It also estimates the loss of health and economic benefits caused by ozone to humans, discusses the multiple factors affecting ozone changes, and identifies ozone-sensitive pollution control areas. The results showed that ozone in the Qin–Jin region spatially tends to increase from northwest to southeast, the Slope showed that ozone in the study area has a slightly increasing trend (0~0.079), the ozone concentration values are much larger than those in other months during the period of April–September, and there is no weekend effect. The predicted mean ozone values for 2023 are 36.57 DU in spring, 50.88 DU in summer, 34.29 DU in fall, and 30.10 DU in winter. The average values of all-cause mortality and economic losses are estimated to be 4591 and 4214 persons and 43.30 and 51.30 billion yuan in 2019 and 2021 in Shanxi Province, and 2498 and 1535 persons and 23.50 and 18.70 billion yuan in 2019 and 2021 in Shaanxi Province, respectively. Natural factors are positively correlated with ozone in the following order, temperature (TEM) > precipitable water (TPW) > vegetation cover (NDVI) > relative humidity (RH), uplift index (LI) is negatively correlated with ozone, and barometric pressure (PS) is mainly uncorrelated. During the period of high ozone pollution in the Qin–Jin region (April–September), emissions of VOCs accelerated ozone production, and emissions of NOx suppressed ozone production in most areas. The high-value pollution period in the Qin–Jin area is mainly a VOC control area, and the synergistic control of NOx and VOCs is secondary.

In recent years, with the acceleration of industrialization and the expansion of urban scale, air pollution including formaldehyde (HCHO) becomes more and more serious. In order to study HCHO pollution in the Yangtze River Economic Belt (YEB), the temporal and spatial evolution of atmospheric HCHO and its influencing factors were analyzed by using the Ozone Monitoring Instrument (OMI) during 2012-2021. The results showed that the concentration of YEB HCHO column was unevenly distributed, with high values concentrated in Anhui, Jiangsu, Yunnan and Hubei provinces. During the past 10 years, the concentration of YEB HCHO column varied between 10.28 and 17.19×1015molec/cm2, and the lowest concentration of HCHO column was 13.16×1015molec/cm2 in 2015. However, it reached the peak value in 2018 (14.93×1015molec/cm2). In natural sources, normalized vegetation index (NDVI) and leaf area index (LAI) had greater influence on YEB HCHO, and the correlation was -0.91~0.97 and -0.9~0.95, respectively. The positive correlation area between HCHO and Mean annual temperature (MAT) reached 93%. The contribution of high-intensity human activity areas to HCHO cannot be underestimated. Industrial and civil sources have great influence on HCHO. In addition, the potential source of HCHO in Shanghai is affected by local emission sources, trans-regional potential sources, northwest air mass and ocean airflow.

The prediction of air pollutants has always been an issue of great concern to the whole of society. In recent years, the prediction and simulation of air pollutants via machine learning have been widely used. In this study, we collected meteorological data and tropospheric NO2 column concentration data in Beijing, China, between 2012 and 2020, and compared the two methods of time sequence-based and influencing factor-based random forest regression in predicting the tropospheric NO2 column concentration. The results showed that prediction of the tropospheric NO2 column concentration using random forest regression was affected by the changes of human activities, especially emergency events and policy variations. The advantage of time sequence analysis lies in its ability to calculate the distribution of air pollutants with a long-time scale of prediction, but it may produce large errors in numerical value. The advantage of influencing factor prediction lies in its high precision and that it can identify the specific impact of each influencing factor on the NO2 column concentration, but it needs more data and work quantities before it can make a prediction about the future.

The concentration of nitrogen dioxide (NO2) in the air is one of the important indexes for evaluating air quality. At the beginning of 2020, a COVID-19 outbreak suddenly hit Wuhan, China. To effectively control the epidemic, Wuhan was put under a 76-day lockdown, during which we collected tropospheric column amounts in the atmosphere and NO2 concentrations measured at ground monitoring stations, and we reviewed the ground NO2 concentrations in 2019 and the tropospheric NO2 concentrations between 2012 and 2019. Using the random forest (RF) model, we predicted the impact of the tropospheric NO2 concentration during the lockdown period without the occurrence of the COVID-19 epidemic and analyzed the impact of multiple certain meteorological factors on tropospheric and ground NO2 concentrations. The results showed that the tropospheric and ground NO2 concentrations were reduced by 11.04~53.36% and 21.96~65.04%, respectively. The main factors affecting the tropospheric NO2 concentration were wind velocity, land surface temperature, surface lifted index, precipitable water volume and tropospheric relative humanity. The main factors affecting the ground NO2 concentration were tropospheric relative humanity, surface lifted index, land surface temperature and tropospheric temperature. The development of different emission reduction and control measures under different meteorological conditions and the formulation of more refined policies will play positive roles in improving the efficiency of air pollution control.

Based on satellite remote sensing data acquired by the ozone monitoring instrument (OMI), this study used pixel space analysis, a coefficient of variation, stability analysis, and an atmospheric transmission model to determine the concentration of tropospheric ozone (O3), NO2, HCHO, and SO2 columns in Lanzhou from 2010 to 2019. A series of analyses were carried out on the temporal and spatial distribution of concentration, influencing factors and atmospheric transmission path. The results show that the air pollutants in this area present multi-dimensional characteristics and have a complex spatial distribution. In terms of inter-annual changes, in addition to the increase in the concentration of the HCHO column, the ozone, NO2, and SO2 column concentrations have all decreased over time. In terms of monthly average changes, these four pollutants reached their maximum values in April, December, June, and January, respectively. These four types of pollution had a strong spatial correlation, among which HCHO and SO2 had a significant positive correlation, with a correlation coefficient of 0.76. Many factors affect the atmospheric pollutant in Lanzhou. Among them, pollutants are closely related to urbanization and to the activities of coal-burning industries. Moreover, temperature, precipitation, and sunshine also have certain effects on air quality. The proliferation of pollutants in Gansu Province was one of the sources of pollutants in Lanzhou, while long-distance transportation in the atmosphere from outside the province (Qinghai, Sichuan, and Shaanxi) also exacerbated the pollution in Lanzhou.

In the Beijing-Tianjin-Hebei (BTH) urban agglomeration, the atmospheric particulate matter (PM) pollution has become exacerbated year by year. In an attempt to understand the current condition of aerosol particulate pollution in the BTH region, the temporal and spatial distribution, future trend changes, and potential source areas of absorbing aerosols in the BTH region from 2005 to 2019 were analyzed based on Ozone Monitoring Instrument/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13 × 24 km V003 (OMAERUV) daily product data; besides, relevant influencing factors were explored in this study. As per the analysis of results, the ultraviolet aerosol index (UVAI) increased by 3.03% in the time series from 2005 to 2019. Spatially, the absorbing aerosol in the North China Plain was always maintained in a high-value area, and the15-year average annual UVAI value has been as high as 0.45. In the monthly time series, a “V” shape started from January. The peak value of seasonal characteristics reached the highest in winter, followed by autumn and spring, and the lowest in summer. The external potential sources in the BTH region are mainly sand and dust sources generated in the northwest, while those of absorbing aerosols are mainly carbon sources in spring, with the lowest external absorbing aerosols in summer. The potential sources of absorbing aerosols in autumn are relatively complicated. The potential sources of absorbing aerosols are mainly sand and dust sources in the north. The time series of absorbing aerosols mainly showed an anti-continuous rise, and 54.23% of absorbing aerosols indicated an upward trend that would occur in the future. The relationship between absorbing aerosols and PM2.5 is mutual conversion. According to the path coefficient, industrial production activities are important sources of atmospheric absorbing aerosols, and precipitation can reduce the content of absorbing aerosols in the atmosphere caused by industrial production.

Jiangsu-Zhejiang-Shanghai (JZH) is an important industrial area and foreign trade base in China. In recent years, due to economic development and urban expansion, the emissions of volatile organic compound (VOC) have been increasing, which exacerbated the problem of formaldehyde (HCHO) pollution. The Ozone Monitoring Instrument (OMI) remote sensing has the characteristics of high spatial coverage and strong time continuity, which can enable long-term serial and large-scale dynamic monitoring of atmospheric pollutants. Based on the inversion data of OMI, the paper analyses the temporal-spatial distribution and change trend of the HCHO in JZH from 2008 to 2019, and discusses the transmission path of HCHO in combination with the backward trajectory. In addition, the degree of contribution of anthropogenic sources and natural sources to HCHO are analysed, which is the local HCHO pollution providing reference opinions on governance and atmospheric energy saving and emission reduction. The results showed that the spatial distribution of HCHO in JZH was uneven and will continue to increase in the future. Different types of artificial sources have different degrees of contribution to the study area. In addition, vegetation, temperature, and precipitation have obvious high correlations with HCHO, indicating that natural factors have an important influence on HCHO.

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.

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.

Based on satellite remote sensing data acquired by the Ozone Monitoring Instrument (OMI), this study used pixel space analysis, a coefficient of variation, stability analysis, and an atmospheric transmission model to determine the concentration of tropospheric ozone (O 3 ), NO 2 , HCHO, and SO 2 columns in Lanzhou from 2010 to 2019. A series of analyses were carried out on the temporal and spatial distribution of concentration, influencing factors and atmospheric transmission path. The results show that the air pollutants in this area present multi-dimensional characteristics and have a complex spatial distribution. In terms of inter-annual changes, in addition to the increase in the concentration of the HCHO column, the ozone, NO 2 , and SO 2 column concentrations have all decreased over time. In terms of monthly average changes, these four pollutants reached their maximum values in April, December, June, and January, respectively. These four types of pollution had a strong spatial correlation, among which HCHO and SO 2 had a significant positive correlation, with a correlation coefficient of 0.76. Many factors affect the Atmospheric Compound Pollution in Lanzhou. Among them, pollutants are closely related to urbanization and to the activities of coal-burning industries. Moreover, temperature, precipitation, and sunshine also have certain effects on air quality. The proliferation of pollutants in Gansu Province was one of the sources of pollutants in Lanzhou, while long-distance transportation in the atmosphere from outside the province (Qinghai, Sichuan, and Shaanxi) also exacerbated the pollution in Lanzhou.