[Variation Characteristics and Source Analysis of Atmospheric Ozone Pollution in Guanzhong Region].

Guanzhong urban agglomeration has a good development foundation and great development potential, and it has a unique strategic position in the national all-round opening up pattern. In recent years, the problem of near-surface ozone （O3） in the Guanzhong Region has become increasingly prominent, which has become a bottleneck affecting the continuous improvement of air quality. In order to effectively prevent and control O3 pollution, this study analyzed the characteristics of annual, monthly, and daily changes in O3 concentration in the Guanzhong Region based on the environmental monitoring data from 2018 to 2021. A geo-detector was used to study the driving factors of the spatial differentiation of O3 concentration, and the sources of O3 were analyzed using a backward trajectory model and emission inventory construction. The results showed that the daily and monthly variation in O3 concentration in the Guanzhong Region were unimodal. The daily maximum value appeared at 15：00, the minimum value appeared at 07：00, the peak value of the monthly average appeared in June, and the valley value appeared in December. The O3 concentration was highest in summer, followed by that in spring, and the lowest in winter. The days of O3 exceeding the standard showed mainly mild pollution, and moderate and above pollution showed a trend of decreasing first and then increasing. The O3 concentration in the Guanzhong Region was mainly closely related to precursors and meteorological factors, and the explanatory power of the interaction of each factor was significantly greater than that of any single factor. The regional transport of O3 concentration in the Guanzhong Region was mainly affected by easterly airflow, followed by the northwest direction, with the potential source areas located mainly in Henan Province and Hubei Province. The main local sources of volatile organic compounds （VOCs） were solvent use sources, process sources, and mobile sources, and the main emission sources of nitrogen oxides （NOx） were mobile sources and industrial production combustion sources. The research results have a guiding significance for O3 joint prevention and control in the Guanzhong Region.

Risk assessment and validation of farmland abandonment based on time series change detection.

Farmland abandonment, a widespread phenomenon during land-use transition, leads to a cycling or vanishing evolution of farmland resources. As urbanization advances, an increasing number of agricultural laborers migrate from rural to urban areas, causing ongoing farmland abandonment. However, in contrast to the abandoned information extraction and driving mechanisms revelation, the potential risk of farmland abandonment has received insufficient attention. This study took Yangtze River Economic Belt of China as study area, selected multiple aspects to construct a risk assessment system for farmland abandonment, and applied time series change detection to verify the results. The results showed that (1) farmland abandonment risk, with a regional average value of 0.0978, has strong spatial heterogeneity, with high values clustering in Yunnan-Guizhou and Sichuan-Chongqing mountainous areas and low values distributed in the midstream and downstream plains and the Sichuan Basin. (2) The proportion of farmland area gradually decreased as the risk grade increased. Farmland, with low abandonment risk, occupied an area of 204,837 km2, constituting the highest percentage of 35.18% among the overall farmland, and was mainly distributed in the provinces of Jiangsu and Anhui. The area of farmland with high risk was 16,458 km2, only accounting for 2.83%, the majority of which was clustered in Sichuan and Yunnan provinces. (3) The Normalized Difference Vegetation Index (NDVI) time series change detection validated the reliability of the risk assessment system. Samples of farmland having low abandonment risk indeed had the lowest abandonment rate of 10%, and those which indicated high risk had the highest abandonment rate of 32%. We propose differentiated managements for farmland resources with high and low abandonment risk from the perspective of sustainable use. This study provides a more reasonable and scientific system for farmland abandonment risk assessment and helps to fill the research gap.