The interactions between PM_2.5 and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM_2.5 concentration and meteorological conditions have been mainly confined to a certain city or district, and the correlation over the whole of China remains unclear. Whether spatial and seasonal variations exist deserves further research. In this study, the relationships between PM_2.5 concentration and meteorological factors were investigated in 68 major cities in China for a continuous period of 22 months from February 2013 to November 2014, at season, year, city, and regional scales, and the spatial and seasonal variations were analyzed. The meteorological factors were relative humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS). We found that spatial and seasonal variations of their relationships with PM_2.5 exist. Spatially, RH is positively correlated with PM_2.5 concentration in north China and Urumqi, but the relationship turns to negative in other areas of China. WS is negatively correlated with PM_2.5 everywhere except for Hainan Island. PS has a strong positive relationship with PM_2.5 concentration in northeast China and mid-south China, and in other areas the correlation is weak. Seasonally, the positive correlation between PM_2.5 concentration and RH is stronger in winter and spring. TEM has a negative relationship with PM_2.5 in autumn and the opposite in winter. PS is more positively correlated with PM_2.5 in autumn than in other seasons. Our study investigated the relationships between PM_2.5 and meteorological factors in terms of spatial and seasonal variations, and the conclusions about the relationships between PM_2.5 and meteorological factors are more comprehensive and precise than before. We suggest that the variations could be considered in PM_2.5 concentration prediction and haze control to improve the prediction accuracy and policy efficiency.

In recent years, particulate matter of 2.5 µm or less (PM_2.5) pollution in China has decreased but, at the same time, ozone (O₃) pollution has become increasingly serious. Due to the different research areas and research periods, the existing analyses of the correlation between PM_2.5 and O₃ have reached different conclusions. In order to clarify the relationship between PM_2.5 and O₃, this study selected mainland China as the research area, based on the PM_2.5 and O₃ concentration data of 1458 air quality monitoring stations, and analyzed the correlation between PM_2.5 and O₃ for different time scales and geographic divisions. Moreover, by combining the characteristics of the pollutants, topography, and climatic features of the study area, we attempted to discuss the causes of the spatial and temporal differences of R-PO (the correlation between PM_2.5 and O₃). The study found that: (1) R-PO tends to show a positive correlation in summer and a negative correlation in winter, (2) the correlation coefficient of PM_2.5 and O₃ is lower in the morning and higher in the afternoon, and (3) R-PO also shows significant spatial differences, including north–south differences and coastland–inland differences.

Satellite remote sensing has been increasingly employed for the estimation of ground-level atmospheric PM $_2.5$ . There have been several cross-validation (CV) approaches applied for the validation of satellite-based PM $_2.5$ estimation models. However, these validation approaches often lead to confusion, due to the unclear applicable conditions. For this, we fully analyze and assess the existing validation approaches, and provide suggestions on applicable conditions for them. Furthermore, the existing validation approaches still have limitations to disregard the uneven distribution of ground stations, and tend to overestimate the performance of the PM $_2.5$ estimation models. To this end, a CV-based validation approach considering the uneven spatial distribution of monitoring stations (denoted as SDCV) is proposed. SDCV introduces the spatial distance between validation station and modeling station into the CV process, and evaluates the spatial performance through a strategy of excluding modeling stations within a specific distance. Meanwhile, this approach has designed reasonable evaluation indices for the model validation. Taking China as a case study, the results indicate that SDCV can yield a more complete and effective evaluation for the popular PM $_2.5$ estimation models than the traditional validation approaches.

Nitrogen dioxide (NO₂) is an essential air pollutant related to adverse health effects. A space-time neural network model is developed for the estimation of ground-level NO₂ in this study by integrating ground NO₂ station measurements, satellite NO₂ products, simulation data, and other auxiliary data. Specifically, a geographically and temporally weighted generalized regression neural network (GTW-GRNN) model is used with the advantage to consider the spatiotemporal variations of the relationship between NO₂ and influencing factors in a nonlinear neural network framework. The case study across the Wuhan urban agglomeration (WUA), China, indicates that the GTW-GRNN model outperforms the widely used geographically and temporally weighted regression (GTWR), with the site-based cross-validation R² value increasing by 0.08 (from 0.61 to 0.69). Besides, the comparison between the GTW-GRNN and original global GRNN models shows that considering the spatiotemporal variations in GRNN modeling can boost estimation accuracy. All these results demonstrate that the GTW-GRNN based NO₂ estimation framework will be of great use for remote sensing of ground-level NO₂ concentrations.

Vegetation water content (VWC) is recognized as an important parameter in vegetation growth studies, natural disasters such as forest fires, and drought prediction. Recently, the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as an important technique for monitoring vegetation information. The normalized microwave reflection index (NMRI) was developed to reflect the change of VWC based on this fact. However, NMRI uses local site-based data, and the sparse distribution hinders the application of NMRI. In this study, we obtained a 500 m spatially continuous NMRI product by integrating GNSS-IR site data with other VWC-related products using the point–surface fusion technique. The auxiliary data in the fusion process include the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and precipitation. Meanwhile, the fusion performance of three machine learning methods, i.e., the back-propagation neural network (BPNN), generalized regression neural network (GRNN), and random forest (RF) are compared and analyzed. The machine learning methods achieve satisfactory results, with cross-validation R values of 0.71–0.83 and RMSEs of 0.025–0.037. The results show a clear improvement over the traditional multiple linear regression method, which achieves R (RMSE) values of only about 0.4 (0.045). It indicates that the machine learning methods can better learn the complex nonlinear relationship between NMRI and the input VWC-related index. Among the machine learning methods, the RF model obtained the best results. Long time-series NMRI images with a 500 m spatial resolution in the western part of the continental U.S. were then obtained. The results show that the spatial distribution of the NMRI product is consistent with a drought situation from 2012 to 2014 in the U.S., which verifies the feasibility of analyzing and predicting drought times and distribution ranges by using the 500 m fusion product.

Soil moisture is a key component of the water cycle budget. Sensing soil moisture using microwave sensors onboard satellites is an effective way to retrieve surface soil moisture (SSM) at a global scale, but the retrieval accuracy in some regions is inadequate due to the complicated factors influencing the general retrieval process. On the other hand, monitoring soil moisture directly through in-situ devices is capable of providing high-accuracy SSM measurements, but the distribution of such stations is sparse. Recently, the Global Navigation Satellite System interferometric Reflectometry (GNSS-R) method was used to derive field-scale SSM, which can serve as a supplement to contemporary sparse in-situ soil moisture networks. On this basis, it is of great research significance to explore the fusion of these different kinds of SSM data, so as to improve the present satellite SSM products with regard to their data accuracy. In this paper, a multi-source point-surface fusion method based on the generalized regression neural network (GRNN) model is applied to fuse the Soil Moisture Active Passive (SMAP) Level 3 radiometer SSM daily product with in-situ measured and GNSS-R estimated SSM data from five soil moisture networks in the western continental U.S. The results show that the GRNN model obtains a fairly good performance, with a cross-validation R value of approximately 0.9 and a ubRMSE of 0.044 cm³ cm⁻³. Furthermore, the fused SSM product agrees well with the site-specific SSM data in terms of time and space, which demonstrates that the proposed GRNN model is able to construct the non-linear relationship between the point- and surface-scale SSM.