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This paper studies a tensor factor model that augments samples from multiple classes. The nuisance common patterns shared across classes are characterised by pervasive noises, and the patterns that distinguish different classes are represented by class‐specific components. Additionally, the pervasive component is modelled by the production of a low‐rank tensor latent factor and several factor loading matrices. This augmented tensor factor model can be expanded to a series of matrix variate tensor factor models and estimated using principal component analysis. The ranks of latent factors are estimated using a modified eigen‐ratio method. The proposed estimators have fast convergence rates and enjoy the blessing of dimensionality. The proposed factor model is applied to address the challenge of overlapping issues in image classification through a factor adjustment procedure. The procedure is shown to be powerful through synthetic experiments and an application to COVID‐19 pneumonia diagnosis from frontal chest X‐ray images. [ABSTRACT FROM AUTHOR]

In order to study the ecological and atmospheric recovery of the Hexi Corridor region, this paper analyzes the migration changes of land use characteristics and utilizes multivariate data and BenMAP-CE software to study the pollution characteristics of ozone and its precursors and the impact on human health in the Hexi Corridor region. The results showed that the increase of cultivated land area in the Hexi Corridor mainly originated from grassland. The MDA8-O3 concentrations met the primary and secondary standards of the Ambient Air Quality Standards on 43% and 99% of the days, respectively. NO2 showed a negative weekend effect with O3, and HCHO was opposite to it. Temperature, barometric pressure, and vegetation were highly correlated with O3-NO2-HCHO. Ozone pollution in the study area caused about 60% of all-cause premature deaths due to cardiovascular diseases. The study suggests that controlling exogenous transport in Wuwei City during the high ozone period (except August) is mainly dominated by the west and northwest, and that synergistic management of VOCs and NOx emissions can reduce O3 pollution and, consequently, reduce the risk to human health. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In order to improve air quality, China has implemented a series of the most stringent control measures ever in recent years. Quantitatively analyzing the contribution of emissions to the trend change in air pollutants is an essential scientific basis for verifying the effectiveness of air pollution control. We based our study on the air quality online monitoring data and meteorological element data of 11 cities in the Fenwei Plain from 2018 to 2022. We quantitatively investigated the changing patterns of NO2, O3, PM10, and PM2.5 and their influencing factors in the major cities of the Fenwei Plain by using the KZ filtering and MLR modeling analysis methods. The results show the following: (1) The long-term fractions of NO2, PM10, and PM2.5 in the Fenwei Plain decreased by 10.5, 33.1, and 17.1 μg·m−3, with decreases of 25.8%, 29%, and 28.8%, respectively, from 2018 to 2022. The long-term fractions of O3 showed the characteristics of decreasing and then increasing, with 2020 as the dividing line. (2) The short-term components of NO2, PM10, and PM2.5 contributed the most to the total variance, with the proportion of short-term components ranging from 34.7% to 69.8%, 53% to 73%, and 43% to 58%, respectively. The seasonal components of O3 contributed the most to the total variance, with the proportion of short-term components ranging from 54% to 70.7%. (3) Most cities in the Fenwei Plain had unfavorable meteorological conditions with regard to NO2, PM10, and PM2.5 in 2018–2020 and favorable meteorological conditions in terms of NO2, PM10, and PM2.5 in 2020–2022. O3 showed different characteristics from the other three pollutants. Most cities in the Fenwei Plain had meteorological conditions in 2018–2019 that were unfavorable for improving O3 levels. In 2019–2021, meteorological conditions were favorable for improving O3 levels, while in 2021–2022, meteorological conditions were unfavorable for improving O3 levels. [ABSTRACT FROM AUTHOR]

Climate change and air pollution are one of the global environmental problems. It is significant to grasp the air pollution situation of Western Europe in recent 10 years for its or the global pollution control. Based on the OMI tropospheric nitrogen dioxide (NO2) column density data, the spatial and temporal distribution characteristics, variation trend, transmission path, and influencing factors of NO2 in 15 countries in Western Europe from 2011 to 2022 are discussed in this paper. Meanwhile, the annual average spatial and temporal distribution in 2023 is predicted by the random forest (RF) model. The results showed that (1) the 12-year spatial distribution map showed an increasing trend from southwest to northeast, with the border area of the Netherlands and Germany and Milan as two high-value areas, and the overall trend over time was that the high-concentration area gradually shrank, the low-concentration area gradually expanded, and the annual average concentration gradually decreased. (2) The inter-month trend presents a “U” shape, with the mean NO2 pollution ranking in winter > autumn > spring > summer. (3) Natural factors are one of the reasons affecting NO2; for instance, NO2 pollution has a strong positive correlation with the lifted index, relative humidity, and wind speed and a moderately strong negative correlation with precipitable water and air temperature. (4) Exogenous atmospheric transport is another important factor affecting the change of NO2 pollution in Western Europe. The HYSPLIT model is used to analyze the backward trajectory of Milan, Italy, and Nijmegen, Netherlands, in the four seasons of 2022. Both are mainly influenced by westerly airflows, and therefore, the transport effect in the atmosphere brings air pollutants from westerly regions in the atmosphere. [ABSTRACT FROM AUTHOR]