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.

The pursuit of higher-resolution and more reliable spatial distribution simulation results for air pollutants is important to human health and environmental safety. However, the lack of high-resolution remote sensing retrieval parameters for gaseous pollutants (sulfur dioxide and ozone) limits the simulation effect to a 1 km resolution. To address this issue, we sequentially generated and optimized the spatial distributions of near-surface PM_2.5, SO₂, and ozone at a 1 km resolution in China through two approaches. First, we employed spatial sampling, random ID, and parameter convolution methods to jointly optimize a tree-based machine-learning gradient-boosting framework, LightGBM, and improve the performance of spatial air pollutant simulations. Second, we simulated PM_2.5, used the simulated PM_2.5 result to simulate SO₂, and then used the simulated SO₂ to simulate ozone. We improved the stability of 1 km-resolution SO₂ and ozone products through the proposed sequence of multiple-pollutant simulations. The cross-validation (CV) of the random sample yielded an R² of 0.90 and an RMSE of 9.62 µg∙m⁻³ for PM_2.5, an R² of 0.92 and an RMSE of 3.9 µg∙m⁻³ for SO₂, and an R² of 0.94 and an RMSE of 5.9 µg∙m⁻³ for ozone, which are values better than those in previous related studies. In addition, we tested the reliability of PM_2.5, SO₂, and ozone products in China through spatial distribution reliability analysis and parameter importance reliability analysis. The PM_2.5, SO₂, and ozone simulation models and multiple-air-pollutant (MuAP) products generated by the two optimization methods proposed in this study are of great value for long-term, large-scale, and regional-scale air pollution monitoring and predictions, as well as population health assessments.

The spatiotemporal distribution pattern of the aerosol optical depth (AOD) is influenced by many environmental factors, such as meteorological condition changes, atmospheric pollution, and topographic changes. Understanding the relationship between the vegetation land cover and the AOD would favor the improvement of forest ecosystem services. This quantitative research integrated remote sensing and ground survey data and used spatial statistical methods to explore the drivers that influence the AOD of the exurban national forest park and analyze the differences between various forest types. The driver analysis was carried out in the hot (Z ≥ 1.64) and cold (Z ≤ −1.64) spots of AOD in 2010 and 2017. Our results showed that (1) the forest type was proved to be the main factor contributing to the AOD pattern and (2) from 2010 to 2017, the average growth rate of broad-leaved forest, coniferous forest, bamboo, and shrub in hot spots was significantly higher than that in cold spots, while there was no significant difference in the mixed forest. The average growth rate of biomass densities of bamboo, coniferous forest, and mixed forest were higher than that of the shrub and broad-leaved forest. These findings provided the guidance for the rational allocation of tree species to increase the biomass and improve the ecosystem service values of forest parks.

This study proposes a random forest–random pixel ID (RF–RID) method, which could reduce local anomalies in the simulation of NO₂ spatial distribution and significantly improve prediction accuracy in rural areas. First, the 470 nm MAIAC AOD and OMI NO₂ total and tropospheric vertical column were packed using the two-step method (TWS). Second, using RID, the filled data and auxiliary variables were combined with random forest (RF) to build an RF–RID model to predict the 1 km/d NO₂ spatial distribution in southwestern Fujian (SWFJ) in 2018. The results show that the RF–RID achieves enhanced performance in the CV of the observed sample (R = 0.9117, RMSE = 3.895). Meanwhile, RF–RID has a higher correlation with the road length (RL) in remote areas, and the proposed method solves the issue related to strips or patches of NO₂ spatial distribution. This model offers insights into the related research on air pollutants in large areas.