Precipitation is a key aspect of the climate system. In this paper, the dependability of five satellite precipitation products (TRMM [Tropical Rainfall Measuring Mission] 3BV42, PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks] CDR, GSMaP [Global Satellite Mapping of Precipitation] RENALYSIS, CMORPH [Climate Prediction Center’s morphing technique] BLD and CMORPH_RAW) were compared with in situ measurements over China for the period of 2005 to 2013. To completely evaluate these precipitation products, the annual, seasonal and monthly precipitation averages were calculated. Overall, the Huaihe River and Qinlin mountains are shown to have heavy precipitation to the southeast and lighter precipitation to the northwest. The comparison results indicate that Gauge correction (CMORPH_BLD) improves the quality of the original satellite products (CMORPH_RAW), resulting in the higher correlation coefficient (CC), the low relative bias (BIAS) and root mean square error (RMSE). Over China, the GSMaP_RENALYSIS outperforms other products and shows the highest CC (0.91) and lowest RMSE (0.85 mm/day) and all products except for PERSIANN_CDR exhibit underestimation. GSMaP_RENALYSIS gives the highest of probability of detection (81%), critical success index (63%) and lowest false alarm ratio (36%) while TRMM3BV42 gives the highest of frequency bias index (1.00). Over Tibetan Plateau, CMORPH_RAW demonstrates the poorest performance with the biggest BIAS (4.2 mm/month) and lowest CC (0.22) in December 2013. GSMaP_RENALYSIS displays quite consistent with in situ measurements in summer. However, GSMaP_RENALYSIS and CMORPH_RAW underestimate precipitation over South China. CMORPH_BLD and TRMM3BV42 show consistent with high CC (>0.8) but relatively large RMSE in summer.

Particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5) is related to various adverse health effects. Ground measurements can yield highly accurate PM_2.5 concentrations but have certain limitations in the discussion of spatial-temporal variations in PM_2.5. Satellite remote sensing can obtain continuous and long-term coverage data, and many previous studies have demonstrated the relationship between PM_2.5 and AOD (aerosol optical depth) from theoretical analysis and observation. In this study, a new aerosol product with a high spatial-temporal resolution retrieved from the AHI (the Advance Himawari Imager) was obtained using a vertical-humidity correction method to estimate hourly PM_2.5 concentrations in Hebei. The hygroscopic growth factor ( $\mathrm{f}\left(\mathrm{RH}\right)$ ) was fitted at each site (in a total of 137 matched sites). Meanwhile, assuming that there was little change in $\mathrm{f}\left(\mathrm{RH}\right)$ at a certain scale, the nearest $\mathrm{f}\left(\mathrm{RH}\right)$ of each pixel was determined to calculate PM_2.5 concentrations. Compared to the correlation between AOD and PM_2.5, the relationship between the “dry” mass extinction efficiency obtained by vertical-humidity correction and the ground-measured PM_2.5 significantly improved, with r coefficient values increasing from 0.19–0.47 to 0.61–0.76. The satellite-estimated hourly PM_2.5 concentrations were consistent with the ground-measured PM_2.5, with a high r (0.8 ± 0.07) and a low RMSE (root mean square error, 30.4 ± 5.5 μg/m³) values, and the accuracy in the afternoon (13:00–16:00) was higher than that in the morning (09:00–12:00). Meanwhile, in a comparison of the daily average PM_2.5 concentrations of 11 sites from different cities, the r values were approximately 0.91 ± 0.03, and the RMSEs were between 13.94 and 31.44 μg/m³. Lastly, pollution processes were analyzed, and the analysis indicated that the high spatial-temporal resolution of the PM_2.5 data could continuously and intuitively reflect the characteristics of regional pollutants (such as diffusion and accumulation), which is of great significance for the assessment of regional air quality.

In contrast to the threats to urban human health, impacts of air pollutants on the ecosystem photosynthesis seem to be less concerned. The existence of aerosols could promote photosynthesis by increasing the ratio of diffuse to direct solar radiation; on the contrary, ozone (O ₃ ) could inhibit photosynthesis, as it is detrimental to leaf stomata. However, it is unknown whether these two opposite impacts worldwide cancel each other out. In the current mainstream methods, earth system models may show conflicts with in situ experimental results due to their relatively coarse resolution. In virtue of satellite remote sensing and a global eddy covariance (EC) network, we studied ten years of data to explore the impacts of aerosol and O ₃ on photosynthesis by fitting an explainable machine learning model. The impacts of aerosol on gross primary productivity (GPP) were positive in many cases, yet very weak. By means of the nitrogen dioxide (NO ₂ ) to formaldehyde (HCHO) ratio, O ₃ was seen with positive impacts on photosynthesis under the NO _x -sensitive regime, but the apparent positive impacts correlated with the plant phenology. Under the volatile organic compound (VOC)-sensitive regime, the impacts of O ₃ on GPP were not obvious, which was likely due to the prioritized depletion of O ₃ by NO ₂ and VOCs. The impacts of air pollutants depended on many factors and results varied case by case, but the overall net impacts were negative.

Continuous spatial knowledge is required to control the regional ozone pollution. Measurements from ground-level sites are beneficial to this goal, but their number is limited due to the huge expenses of site establishment, operation, and maintenance. Remote sensing seems a promising data source, but its application is challenged by bad weather conditions. Always covered by thick clouds, Chongqing, a populated industrial city in west China, is facing serious ozone pollution, but relevant studies here are relatively insufficient. Another alternative is estimating ozone by models. Well-performed models degrade in Chongqing partially due to the very complex terrain. Modeled hourly ozone does not agree with ground-level measurements. Therefore, an optimization approach is proposed to improve model estimates for such regions. This approach integrates the ground-level information (e.g., measured ozone and meteorology) through the employment of ResNet (Residual Network). ResNet overcomes the notorious vanishing gradient issue in classic neural networks, and the ability of learning complex systems is largely boosted. Ozone distribution is like a gray image that varies every second, which is not the case usually learned by ResNet. A color-image alike data structure is raised to address this “nonstill image” problem; according to the Taylor Expansion, polynomials can describe a complex system, and the errors are acceptable. To facilitate the usage in business operations, this approach is designed to be robust, inexpensive, and easy to use. The scheme of control site selection is discussed in detail. In cross-validations, this approach performs well, averaged $R^{2}$ is higher than 0.9 and the error is less than $5 ~\mu \text {g/m}^{3}$ .

High levels of surface ozone (O ₃ ) pollution threaten human and environmental health. Chongqing, a mountainous municipality located in southwest China, is exposed to serious O ₃ pollution and requires more studies. Due to its complex terrain and always foggy weather, it is difficult to maintain many in situ sites in Chongqing, and chemical transportation model (CTM) simulations are also challenged. The recently launched (in 2017) Sentinel-5p satellite provides O ₃ columns with advanced spatiotemporal resolution. Without the dependence on CTMs, we linked O ₃ columns and surface monitoring data from 2019 to 2021 in virtue of a deep forest machine learning model. Compared with another widely used machine learning model and previous studies, our results showed great advantages in estimating surface O ₃ on a daily scale. Validated against in situ sites in Chongqing, averaged $R^{2}$ of cross validations reached 0.9, while the root-mean-squared error (RMSE) and mean bias error (MBE) were 13.57 and $0.37~\mu \text {g}/\text {m}^{3}$ , respectively. We found out that the model performance is associated with the relative height difference between training sites and the test site. The model performed stably when the height difference was lower than 200 m, but obvious performance degradation was seen when the height difference is exceeding 400 m.

Air pollution is becoming increasingly serious with rapid economic development in China, and the primary pollutant has converted from PM₁₀ to PM_2.5, which is associated with more adverse impacts on human health. Satellite remote sensing, with help of its quantitative observations over large spatial and temporal extent, has become a significant supplement to the in situ measurements. This study exploits the aerosol optical depth (AOD) product retrieved from the Medium Resolution Spectrum Imager (MERSI) onboard Fengyun-3C satellite to estimate PM_2.5 estimation over the major urban areas of Chongqing, a metropolitan city in the Southwestern China. A semi-empirical model and the linear mixed effect (LME) model are combined based on in situ observations from local air quality and meteorological networks from May 2014 to May 2015. This combined model is able to explain about 90% of the variation of the estimated PM_2.5, and performs better than LME model by achieving higher correlation and smaller deviations between the satellites estimated PM_2.5 and in situ measurements. Benefiting from the high resolution of 1 km × 1 km, MERSI AOD achieves much more detailed spatial distribution of ground-level PM_2.5 over the major urban areas of Chongqing, compared to most of concurrent satellite products, such as the MODIS L2 AOD. According to the estimation, PM_2.5 concentration is higher in cold seasons than in warm seasons in Chongqing. Peak levels of PM_2.5 is found in Yuzhong District, the center of Chongqing urban area, while the concentration gradually decreases in surrounding areas, indicating that air pollution in Chongqing is highly contributed by local anthropogenic emissions.