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A directional polarimetric camera (DPC) is a key payload on board China’s Gaofen 5B (hereafter denoted as GF-5B) satellite, a hyperspectral observation instrument for monitoring aerosols. On the basis of the dark dense vegetation (DDV) algorithm, this study applied DDV algorithm to DPC measurements. First, the reflectance of vegetation in three channels (0.443, 0.49, and 0.675 μm) was analyzed, and inversion channels were identified. Subsequently, the decrease in normalized difference vegetation index associated with various view angles was simulated, and the optimal view angle for extracting dark pixels was determined. Finally, the top-of-atmosphere reflectance at different view angles was simulated to determine the optimal view angle for aerosol inversion. The inversion experiments were conducted by using DPC data collected over North China from November 2021 to January 2022. The results revealed that DDV algorithm could monitor pollution from 30 December 2021 to 4 January 2022, and the inversion results were strongly correlated with Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product and AERONET station data (R > 0.85).

The accurate estimation of ammonia emission is essential for quantifying secondary inorganic aerosol formation and reactive nitrogen deposition. During the last decades, both fertilizer type and the total amount of nitrogen fertilizer in China have changed, while the resulting changes in ammonia emissions and their spatio-temporal variations are unclear. In this study, we compile a long-term (2005–2020) high-resolution ammonia emission inventory for synthetic fertilizer in China with bottom-up method. We parameterized emissions factors (EFs) considering the impacts of soil properties, method of fertilizer application, fertilizer type, crop type, ambient temperature and wind speed. Meanwhile, the monthly nitrogen fertilizer application is calculated by detailed information on crop-specific fertilizer application practices. For the spatial distribution, the ammonia emissions from fertilizer mostly concentrate in eastern and southwestern China, coincident with the high density of agriculture activity and population in these regions. For the seasonal variation, the ammonia emissions from fertilizer application peak in spring and summer because of dense fertilizer application and high ambient temperature. For the long-term trend, we estimate that the emissions from synthetic fertilizer increased from 5.38 Tg in 2005 to 5.53 Tg in 2008 and remained nearly unchanged during 2008–2012, then decreased to 3.96 Tg in 2020. Urea, ammonium bicarbonate (ABC) and nitrogenous compound fertilizer are major fertilizer types used in China. Despite the increased use of nitrogen fertilizer, ammonia emissions remained stable throughout 2008–2012 with the declined use of ABC. This stable period also reflects ammonia emission increases in western China, offsetting the decreases in eastern China. Furthermore, our emission inventory provides a monthly estimation at a spatial resolution of 0.1 degrees, which can be applied to global and regional atmospheric chemistry model simulations.

The long-range transport of biomass burning pollutants from Southeast Asia has a significant impact on air quality in China. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) fire data and aerosol optical depth (AOD) products and the Tropospheric Monitoring Instrument (TROPOMI) carbon monoxide (CO) data were used to analyze the impact of air pollution caused by biomass burning in Southeast Asia on southern China. Results showed that Yunnan, Guangdong and Guangxi were deeply affected by biomass burning emissions from March to April during 2016–2020. Comparing the data for fires on the Indochinese Peninsula and southern provinces of China, it is obvious that the contribution of pollutants emitted by local biomass burning in China to air pollution is only a small possibility. The distribution of CO showed that the overall emissions increased greatly from March to April, and there was an obvious transmission process. In addition, the MODIS AOD in areas close to the national boundary of China is at a high level (>0.6), and the AOD in the southwest of Guangxi province and the southeast of Yunnan Province is above 0.8. Combined with a typical air pollution event in southern China, the UVAI combined with wind direction and other meteorological data showed that the pollutants were transferred from the Indochinese Peninsula to southern China under the southwest monsoon. The PM2.5 data from ground-based measurements and backward tracking were used to verify the pollutant source of the pollution event, and it was concluded that the degree of pollution in Yunnan, Guangxi and Guangdong provinces was related to the distance from the Indochinese Peninsula. Results indicate that it is necessary to carry out in-depth research on the impact of cross-border air pollution transport on domestic air quality as soon as possible and to actively cooperate with foreign countries to carry out pollution source research and control.

This study explored the spatio-temporal patterns of meteorological drought change and the mechanisms of drought occurrence in Yulin City of the northern Shaanxi by using Standardized Precipitation Index (SPI), Empirical Orthogonal Function (EOF) analysis and composite analysis based on the meteorological observation data and NCEP/NCAR reanalysis data from 1961 to 2015. The main findings of the research are as follows: (1) In the annual and seasonal drought series, there is a non-significant trend toward drought in summer, while there are non-significant trends toward wetness for the other series. Overall, the frequency of drought is low in the southeast and high in the west and the north of the study area. (2) EOF1 is characterized by a uniform pattern in the whole region, i.e., there is a feature of consistent drought or flood in Yulin City. EOF2, EOF3 and EOF4 mainly indicate opposite characteristics of the changes of floods and droughts in the eastern/western parts and the southeast/other parts in the study area. (3) In the summer of the typical drought (flood) years, the study area is controlled by the northwest airflow behind the trough (zonal airflow at the bottom of low-pressure trough), and the meridional circulation (zonal circulation) is distributed in the mid-latitudes, which is conducive to the intrusion of cold air into the south (north) of China. The cold and warm air intersection area is to the south (to the north). The water vapor flux is weak (strong) and the water vapor divergence (convergence) prohibits (enhances) the precipitation process in the study area.

The stationarity assumption of hydrological processes has long been compromised by human disturbances in river basins. The traditional hydrological extreme-value analysis method, i.e., “extreme value theory” which assumes stationarity of the time series, needs to be amended in order to adapt to these changes. In this paper, taking the East River basin, south China as a case study, a framework was put forward for selection of a suitable distribution curve for non-stationary flood series by using the time-varying moments (TVM). Data used for this study are the annual maximum daily flow of 1954–2009 at the Longchuan, Heyuan and Boluo Stations in the study basin. Five types of distribution curves and eight kinds of trend models, for a combination of 40 models, were evaluated and compared. The results showed that the flood series and optimal distribution curves in the East River basin have been significantly impacted by a continuously changing environment. With the increase of the degree of human influence, the thinner tails of distributions are more suitable for fitting the observed flow data, and the trend models are changed from CP (mean and standard deviation fitted by parabolic trend model) to CL (mean and standard deviation fitted by linear trend model) from upstream to downstream of the catchment. The design flood flow corresponding to a return period of more than 10 years at the Longchuan, Heyuan and Boluo Stations was overestimated by more than 28.36%, 53.24% and 26.06%, respectively if the non-stationarity of series is not considered and the traditional method is still used for calculation. The study reveals that in a changing environment, more advanced statistical methods that explicitly account for the non-stationarity of extreme flood characteristics are required.

The effect of vegetation on temperature is an emerging topic in the climate science community. Existing studies have mostly examined the effects of vegetation on daytime temperature (Tmax), whereas this study investigates the effects on nighttime temperature (Tmin). Ground measurements from 53 sites across northeastern China (NEC) from 1982 to 2006 show that early summer (June) Tmax and Tmin increased at mean rates of approximately 0.61 °C/10 year and 0.67 °C/10 year, respectively. Over the same period, the satellite-based Normalized Difference Vegetation Index (NDVI) decreased by approximately 0.10 (accounting for 18% of the climatological NDVI for 1982–1991). It is highlighted that a larger increase in Tmax (Tmin) co-occurred spatially with a larger (smaller) decrease in NDVI. Deriving from such spatial co-occurrences, we found that the spatial variability of changes in Tmax (i.e., ΔTmax) is negatively correlated with the spatial variability of changes in NDVI (i.e., ΔNDVI), while the spatial variability of changes in Tmin (i.e., ΔTmin) is positively correlated (r2 = 0.10; p < 0.05) with that of ΔNDVI. Similarly, we detected significant positive correlations between the spatial variability of ΔNDVI and the change in surface latent heat flux (r2 = 0.16; p < 0.01) and in surface air specific humidity (r2 = 0.28; p < 0.001). These findings on the spatial co-occurrences suggest that the vegetation growth intensifies the atmospheric water vapor through evapotranspiration, which enhances the atmospheric downward longwave radiation and strengthens the greenhouse warming effects at night. Thereby, the positive correlation between ΔNDVI and ΔTmin is better understood. These results indicate that vegetation growth may not only exert effects on daytime temperature but also exert warming effects on nighttime temperature by increasing atmospheric water vapor and thus intensifying the local greenhouse effect. This study presents new observation evidence of the effects of vegetation on local temperature.