Satellite-based PM_2.5 concentration estimation is growing as a popular solution to map the PM_2.5 spatial distribution due to the insufficiency of ground-based monitoring stations. However, those applications usually suffer from the simple hypothesis that the influencing factors are linearly correlated with PM_2.5 concentrations, though non-linear mechanisms indeed exist in their interactions. Taking the Beijing-Tianjin-Hebei (BTH) region in China as a case, this study developed a generalized additive modeling (GAM) method for satellite-based PM_2.5 concentration mapping. In this process, the linear and non-linear relationships between PM_2.5 variation and associated contributing factors, such as the aerosol optical depth (AOD), industrial sources, land use type, road network, and meteorological variables, were comprehensively considered. The reliability of the GAM models was validated by comparison with typical linear land use regression (LUR) models. Results show that GAM modeling outperforms LUR modeling at both the annual and seasonal scale, with obvious higher model fitting-based adjusted R² and lower RMSEs. This is confirmed by the cross-validation-based adjusted R² with values of GAM-based spring, summer, autumn, winter, and annual models, which are 0.92, 0.78, 0.87, 0.85, and 0.90, respectively, while those of LUR models are 0.87, 0.71, 0.84, 0.84, and 0.85, respectively. Different to the LUR-based hypothesis of the “straight line” relations, the “smoothed curves” from GAM-based apportionment analysis reveals that factors contributing to PM_2.5 variation are unstable with the alternate linear and non-linear relations. The GAM model-based PM_2.5 concentration surfaces clearly demonstrate their superiority in disclosing the heterogeneous PM_2.5 concentrations to the discrete observations. It can be concluded that satellite-based PM_2.5 concentration mapping could be greatly improved by GAM modeling given its simultaneous considerations of the linear and non-linear influencing mechanisms of PM_2.5.

Due to the frequent urban air pollution episodes worldwide recently, decision-makers and government agencies are struggling for sustainable strategies to optimize urban land use/cover change (LUCC) and improve the air quality. This study, thus, aims to identify the underlying relationships between PM₁₀ concentration variations and LUCC based on the simulated PM₁₀ surfaces in 2006 and 2013 in the Changsha-Zhuzhou-Xiangtan agglomeration (CZT), using a regression modeling approach. LUCC variables and associated landscape indexes are developed and correlated with PM₁₀ concentration variations at grid level. Results reveal that the overall mean PM₁₀ concentrations in the CZT declined from 106.74 μg/m³ to 94.37 μg/m³ between 2006 and 2013. Generally, variations of PM₁₀ concentrations are positively correlated with the increasing built-up area, and negatively correlated with the increase in forests. In newly-developed built-up areas, PM₁₀ concentrations declined with the increment of the landscape shape index and the Shannon diversity index and increased with the growing Aggregation index and Contagion index. In other areas, however, the reverse happens. These results suggest that LUCC caused by urban sprawl might be an important factor for the PM₁₀ concentration variation in the CZT. The influence of the landscape pattern on PM₁₀ concentration may vary in different stages of urban development.

Though land use regression (LUR) models have been widely utilized to simulate air pollution distribution, unclear spatial scale effects of contributing characteristic variables usually make results study-specific. In this study, LUR models for PM_2.5 in Houston Metropolitan Area, US were developed under scales of 100 m, 300 m, 500 m, 800 m, and 1000–5000 m with intervals of 500 m by employing the idea of statistically optimized analysis. Results show that the annual average PM_2.5 concentration in Houston was significantly influenced by area ratios of open space urban and medium intensity urban at a 100 m scale, as well as of high intensity urban at a 500 m scale, whose correlation coefficients valued −0.64, 0.72, and 0.56, respectively. The fitting degree of LUR model at the optimized spatial scale (adj. R² = 0.78) is obviously better than those at any other unified spatial scales (adj. R² ranging from 0.19 to 0.65). Differences of PM_2.5 concentrations produced by LUR models with best-, moderate-, weakest fitting degree, as well as ordinary kriging were evident, while the LUR model achieved the best cross-validation accuracy at the optimized spatial scale. Results suggested that statistical based optimized spatial scales of characteristic variables might possibly ensure the performance of LUR models in mapping PM_2.5 distribution.

As an extension of the traditional Land Use Regression (LUR) modelling, the generalized additive model (GAM) was developed in recent years to explore the non-linear relationships between PM_2.5 concentrations and the factors impacting it. However, these studies did not consider the loss of information regarding predictor variables. To address this challenge, a generalized additive model combining principal component analysis (PCA–GAM) was proposed to estimate PM_2.5 concentrations in this study. The reliability of PCA–GAM for estimating PM_2.5 concentrations was tested in the Beijing-Tianjin-Hebei (BTH) region over a one-year period as a case study. The results showed that PCA–GAM outperforms traditional LUR modelling with relatively higher adjusted R² (0.94) and lower RMSE (4.08 µg/m³). The CV-adjusted R² (0.92) is high and close to the model-adjusted R², proving the robustness of the PCA–GAM model. The PCA–GAM model enhances PM_2.5 estimate accuracy by improving the usage of the effective predictor variables. Therefore, it can be concluded that PCA–GAM is a promising method for air pollution mapping and could be useful for decision makers taking a series of measures to combat air pollution.

Chromium is not only an essential trace element for the growth and development of living organisms; it is also a heavy metal pollutant. Excessive chromium in farmland soil will not only cause harm to crops, but could also constitute a serious threat to human health through the cumulative effect of the food chain. The determination of heavy metals in tailings of farmland soil is an essential means of soil environmental protection and sustainable development. Hyperspectral remote sensing technology has good characteristics, e.g., high speed, macro, and high resolution, etc., and has gradually become a focus of research to determine heavy metal content in soil. However, due to the spectral variation caused by different environmental conditions, the direct application of the indoor spectrum to conduct field surveys is not effective. Soil components are complex, and the effect of linear regression of heavy metal content is not satisfactory. This study builds indoor and outdoor spectral conversion models to eliminate soil spectral differences caused by environmental conditions. Considering the complex effects of soil composition, we introduce a support vector machine model to retrieve chromium content that has advantages in solving problems such as small samples, non-linearity, and a large number of dimensions. Taking a mining area in Hunan, China as a test area, this study retrieved the chromium content in the soil using 12 combination models of three types of spectra (field spectrum, lab spectrum, and direct standardization (DS) spectrum), two regression methods (stepwise regression and support vector machine regression), and two factors (strong correlation factor and principal component factor). The results show that: (1) As far as the spectral types are concerned, the inversion accuracy of each combination of the field spectrum is generally lower than the accuracy of the corresponding combination of other spectral types, indicating that field environmental interference affects the modeling accuracy. Each combination of DS spectra has higher inversion accuracy than the corresponding combination of field spectra, indicating that DS spectra have a certain effect in eliminating soil spectral differences caused by environmental conditions. (2) The inversion accuracy of each spectrum type of SVR_SC (Support Vector Regression_Strong Correlation) is the highest for the combination of regression method and inversion factor. This indicates the feasibility and superiority of inversion of heavy metals in soil by a support vector machine. However, the inversion accuracy of each spectrum type of SVR_PC (Support Vector Regression_Principal Component) is generally lower than that of other combinations, which indicates that, to obtain superior inversion performance of SVR, the selection of characteristic factors is very important. (3) Through principal component regression analysis, it is found that the pre-processed spectrum is more stable for the inversion of Cr concentration. The regression coefficients of the three types of differential spectra are roughly the same. The five statistically significant characteristic bands are mostly around 384–458 nm, 959–993 nm, 1373–1448 nm, 1970–2014 nm, and 2325–2400 nm. The research results provide a useful reference for the large-scale normalization monitoring of chromium-contaminated soil. They also provide theoretical and technical support for soil environmental protection and sustainable development.

Assessments of the burden of air pollution at the national scale over long-term of time in China would help government managers control historic health impacts of air pollution and institute measures to avoid these risks in future. We took a new 0.01° × 0.01° satellite-based PM_2.5 dataset to analyse spatial and temporal trends of PM_2.5 associated premature mortality in China from 1998 to 2017. Results showed that national PM_2.5 related deaths from STK, IHD, COPD, LC and ALRI increased from approximately 0.89 million cases in 1998 to the peak of 1.34 million in 2014 and steady declined to 1.17 million cases in 2017. The health burden exhibited strong spatial variations, with high attributable deaths concentrated in regions such as Shandong, Hebei, Henan, Beijing, Guangdong, et al. Changing trends varied from different provinces such as Shandong increased mostly, and Sichuan showed a significant decreasing trend. Meanwhile, the results also showed that the expand directions of premature mortality was similar to that of population and the changing of absolute number of premature mortality more depended on the growth of PM_2.5 concentration. The findings recommend that government should make better policies to the health risk controlling which take population and pollution into consideration than only focus on pollution concentration.