The March 2021 dust storm in China degraded air quality across a wide area of Asia. Atmospheric circulation and meteorological factors play an important role in the occurrence of dust storms. To understand whether decreasing or increasing these factors can mitigate dust storms, this study utilizes remote sensing imagery data from the Himawari-8/-9 satellites to understand spatial and temporal variations in China’s 2016–2023 dust storms. Our findings are as follows: (1) in 2016–2023, dust storms covered northern China, with Xinjiang, Inner Mongolia, Gansu, and Ningxia being high-frequency areas; (2) the origins of the dust storms are northwest of Mongolia and Xinjiang, with upper air masses originating from Siberia and concentrating in central-west Inner Mongolia and northern Gansu; (3) dew point temperature, wind speed, cloud cover, and atmospheric circulation are important determinants of the occurrences of dust storms. Analyzing trends and influential factors of dust storms is important as this provides a scientific basis for decision-making in dust storm management.

Machine learning methods have been recognized as rapid methods for satellite-based aerosol retrievals but have not been widely applied in geostationary satellites. In this study, we developed a wide and deep learning model to retrieve the aerosol optical depth (AOD) using Himawari-8. Compared to traditional deep learning methods, we embedded a “wide” modeling component and tested the proposed model across China using independent training (2016–2018) and test (2019) datasets. The results showed that the “wide” model improves the accuracy and enhances model interpretability. The estimates exhibited better accuracy (R² = 0.81, root-mean-square errors (RMSEs) = 0.19, and within the estimated error (EE) = 63%) than those of the deep-only models (R² = 0.78, RMSE = 0.21, within the EE = 58%). In comparison with extreme gradient boosting (XGBoost) and Himawari-8 V2.1 AOD products, there were also significant improvements. In addition to higher accuracy, the interpretability of the proposed model was superior to that of the deep-only model. Compared with other seasons, higher contributions of spring to the AOD concentrations were interpreted. Based on the application of the wide and deep learning model, the near-real-time variation of the AOD over China could be captured with an ultrafine temporal resolution.

Currently, China’s urbanization has entered a stage of high-quality development, and rapid urban growth has led to a series of environmental pollution issues, with haze pollution caused by delicate particulate matter (PM2.5) increasingly becoming a focal point for scholars. This paper focuses on Xi’an, Wuhan, Taiyuan, and Lanzhou, exploring the relationship between PM2.5 concentrations using methods such as the Pearson correlation coefficient (PCC), dominance analysis (DA), and ordinary least squares regression (OLSR). The results indicate that (1) Xi’an’s built environment is distributed radially, Wuhan is circular, Taiyuan is grid-like, and Lanzhou is strip-shaped; (2) Xi’an, Wuhan, Taiyuan, and Lanzhou exhibited different development patterns between 2014 and 2022, with Xi’an experiencing rapid urban expansion but lagging infrastructure, while Wuhan focused on improving post-urbanization quality. Taiyuan and Lanzhou advanced expansion and infrastructure construction simultaneously. (3) The regression coefficients of PM2.5 concentration concerning factors such as building density, green space density, road density, and water density in Xi’an are relatively high. In contrast, the regression coefficients of urban spatial structure factors in Wuhan, Taiyuan, and Lanzhou show consistency. This study provides a basis for reducing PM2.5 and explores the interaction and contribution relationship between urban spatial structure and PM2.5, offering a new research perspective for promoting urban sustainable development.

Expansion of the entity attribute information of geographic knowledge graphs is essentially the fusion of the Internet’s encyclopedic knowledge. However, it lacks structured attribute information, and synonymy and polysemy always exist. These reduce the quality of the knowledge graph and cause incomplete and inaccurate semantic retrieval. Therefore, we normalize the attributes of a geographic knowledge graph based on optimal granularity clustering and co-occurrence analysis, and use structure and the semantic relation of the entity attributes to identify synonymy and correlation between attributes. Specifically: (1) We design a classification system for geographic attributes, that is, using a community discovery algorithm to classify the attribute names. The optimal clustering granularity is identified by the marker target detection algorithm. (2) We complete the fine-grained identification of attribute relations by analyzing co-occurrence relations of the attributes and rule inference. (3) Finally, the performance of the system is verified by manual discrimination using the case of “landscape, forest, field, lake and grass”. The results show the following: (1) The average precision of spatial relations was 0.974 and the average recall was 0.937; the average precision of data relations was 0.977 and the average recall was 0.998. (2) The average F1 for similarity results is 0.473; the average F1 for co-occurrence analysis results is 0.735; the average F1 for rule-based modification results is 0.934; the results show that the accuracy is greater than 90%. Compared to traditional methods only focusing on similarity, the accuracy of synonymous attribute recognition improves the system and we are capable of identifying near-sense attributes. Integration of our system and attribute normalization can greatly improve both the processing efficiency and accuracy.

This study has analyzed ozone pollution in Tianjin from 2013 to 2022, focusing on the relationships between ozone distribution, meteorological conditions, and precursor substances. A method for identifying high-value areas of ozone precursors using the Ozone Sensitivity Factor (FNR) has been introduced. Results show that the average ozone concentration in Tianjin has been 100.608 µg/m³, with an annual growth rate of 2.84 µg·m⁻³·yr⁻¹. Tianjin has ranked among the top provinces and urban agglomerations in China for both ozone concentration and growth rate. Ozone levels have peaked in summer, followed by spring, autumn, and winter, while the growth rate has been highest in spring. This indicates that ozone pollution extends from summer into spring and autumn. An analysis of six ozone pollution events reveals significant regional transmission impacts from northern Hebei and Inner Mongolia, contributing over 30%, with additional significant contributions from southern and southwestern Hebei and western Shandong. In terms of controlling ozone precursors, high-HCHO-value areas have been identified. The correlation between areas of high HCHO values and ground-level ozone concentrations was 0.56339 during the ozone season and 0.2214 during the non-ozone season, both of which improved identification accuracy to varying degrees, suggesting that targeting precursor emissions in these areas could enhance pollution mitigation efforts.