Aerosol optical depth (AOD) is a key parameter that reflects the characteristics of aerosols, and is of great help in predicting the concentration of pollutants in the atmosphere. At present, remote sensing inversion has become an important method for obtaining the AOD on a large scale. However, AOD data acquired by satellites are often missing, and this has gradually become a popular topic. In recent years, a large number of AOD recovery algorithms have been proposed. Many AOD recovery methods are not application-oriented. These methods focus mainly on to the accuracy of AOD recovery and neglect the AOD recovery ratio. As a result, the AOD recovery accuracy and recovery ratio cannot be balanced. To solve these problems, a two-step model (TWS) that combines multisource AOD data and AOD spatiotemporal relationships is proposed. We used the light gradient boosting (LightGBM) model under the framework of the gradient boosting machine (GBM) to fit the multisource AOD data to fill in the missing AOD between data sources. Spatial interpolation and spatiotemporal interpolation methods are limited by buffer factors. We recovered the missing AOD in a moving window. We used TWS to recover AOD from Terra Satellite’s 2018 AOD product (MOD AOD). The results show that the MOD AOD, after a 3 × 3 moving window TWS recovery, was closely related to the AOD of the Aerosol Robotic Network (AERONET) (R = 0.87, RMSE = 0.23). In addition, the MOD AOD missing rate after a 3 × 3 window TWS recovery was greatly reduced (from 0.88 to 0.1). In addition, the spatial distribution characteristics of the monthly and annual averages of the recovered MOD AOD were consistent with the original MOD AOD. The results show that TWS is reliable. This study provides a new method for the restoration of MOD AOD, and is of great significance for studying the spatial distribution of atmospheric pollutants.

In global greening, biogenic volatile organic compound (BVOC) emissions and their influencing factors have been considered due to their significant roles in the biosphere and atmosphere. Many studies have reported relationships of BVOC emissions with environmental factors and plant ecophysiology. However, the direct and indirect effects of environmental factors on BVOC emissions remain unclear, and the causal relationships between plant ecophysiology and BVOC emissions are ambiguous. We measured the isoprene and monoterpene emissions from dominant greening plants using a dynamic enclosure system and quantified the interactions of environment–-plant and ecophysiology–BVOC emissions using a path analysis model. We found that isoprene emission was directly affected by photosynthetic rate, and indirectly affected by photosynthetically active radiation and air temperature (Tair). Monoterpene emissions were directly affected by atmospheric pressure, relative air humidity and specific leaf weight, and indirectly affected by Tair.

Urbanization transforms landscapes from natural ecosystems to configurations of impervious surfaces and green spaces, leading to urban heat island effects that impact health and ecosystem sustainability. This study in Xiamen City, China, categorizes urban areas into functional zones, employs Random Forest and Stepwise Regression models to assess thermal differences, and proposes optimization measures for the building–green space landscape. The optimization involves altering the characterization of the building–green space landscape pattern. Results indicate: (1) due to the spatial heterogeneity of the building–green space landscape pattern in different functional zones, the surface temperature also shows strong spatial heterogeneity in different functional zones; (2) different optimization measures for the building–green space pattern are needed for different functional zones; taking the urban residential zone as an example, the Normalized Difference Vegetation Index (NDVI) in the hot spot area can be adjusted according to the value range of the cold spot area; (3) considering the solar radiation process, Sun View Factor (S_unVF) plays an important role in indicating the change in surface temperature in the commercial service area, and as S_unVF increases, the surface temperature of the functional zone tends to rise. This research offers insights into urban thermal environment improvement and landscape pattern optimization.