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The alpine grassland ecosystem accounts for 53% of the Qinghai-Tibet Plateau (QTP) area, which is an important ecological protection barrier, but fragile and highly vulnerable to climate change. Therefore, continuous monitoring of the aboveground biomass (AGB) of grassland is necessary. Although many studies have mapped the spatial distribution of AGB over the QTP, the results vary widely due to the limited ground samples and mismatches with satellite pixel scales. This paper proposed a new algorithm using unmanned aerial vehicles (UAVs) as a bridge to re-estimate the grassland AGB over 20 the QTP from 2000 to 2019. The innovations were as follows: 1) In the aspect of ground data collection, the spatial scale matching among the traditional ground quadrat sampling, UAV photos, and MODIS pixels was fully considered. From 2015 to 2019, 906 pairs of ground-UAV sample data at the quadrat scale and 2,602 sets of UAV data matching the MODIS pixel scale were collected. A total of more than 37,000 UAV photos were captured at the height of 20 meters. Therefore, the ground validation samples was sufficient and scale matched. 2) In terms of model construction, the traditional quadrat scale 25 (0.25m2) was successfully upscaled to the MODIS pixel scale (6,2500 m2) based on the random forest method and stepwise upscaling scheme. Compared with previous studies, the scale matching of independent and dependent variables was realized, effectively reducing the impact of scale mismatch. At the pixel scale, the AGB value estimated by UAV had a more linear correlation with the MODIS vegetation indices than the traditional sampling method. The multi-year independent crossvalidation results showed that the constructed pixel scale AGB estimation had good robustness, with an average R2 of 0.83 30 and RMSE of 34.13 g/m2. Our dataset provides an important input parameter for a comprehensive understanding of the QTP in the process of global climate change. [ABSTRACT FROM AUTHOR]

Atmospheric ammonia (NH3) plays an important role in the formation of fine particulate matter, leading to severe environmental degradation and human health issues. In this work, ground-based FTIR observations are used to obtain the total columns and vertical profiles of atmospheric NH3 at a measurement site in Hefei, China, from December 2016 to November 2018. The spatial distribution and temporal variation, seasonal trend, emission sources and potential sources of NH3 are analyzed. The time series of ammonia columns show that FTIR observations captured the seasonal cycle of NH3 over the two years of measurement, with a 22.14 % yr-1 annual increase rate over the Hefei site. We used IASI satellite data to compare with the FTIR data, and the correlation coefficients (R) between the two datasets are 0.86 and 0.78 for IASI-A and IASI-B, respectively. The results demonstrate the IASI data are in broad agreement with our FTIR data. To examine the contribution of traffic to NH3 columns, we analyze the relationship of NH3 columns with CO surface concentrations. NH3 columns show high correlation (R = 0.77) with CO concentrations in summer, indicating that the elevated NH3 columns are partly caused by urban emissions from vehicles. Further, high correlation of NH3 columns with air temperature is obvious from their diurnal variation during the observation period. In addition, the clear correlation between NH3 columns and air temperature in spring and autumn over Hefei, suggests that agriculture was indeed the main source of ammonia in spring and autumn. Furthermore, the back trajectories of air masses calculated by the HYSPLIT model confirmed that agriculture was the dominant source of ammonia in spring, autumn and winter, while urban anthropogenic emissions contributed to the high level of NH3 in summer over the Hefei site. The potential source areas influencing the NH3 columns were distributed in the local area of Hefei, the northern part of Anhui province, as well as Shangdong, Jiangsu and Henan provinces. This study helps to identify the emission sources and potential sources that contribute to NH3 columns over Hefei, a highly populated and polluted area. This is the first time that ground-based FTIR remote sensing of NH3 columns and comparison with satellite data are reported in China. [ABSTRACT FROM AUTHOR]

We analyzed seasonality and interannual variability of tropospheric HCN column amounts in densely populated eastern China for the first time. The results were derived from solar absorption spectra recorded with ground-based high spectral resolution Fourier transform infrared (FTIR) spectrometer at Hefei (117°10' E, 31°54' N) between 2015 and 2018. The tropospheric HCN columns over Hefei, China showed significant seasonal variations with three monthly mean peaks throughout the year. The magnitude of the tropospheric HCN column peak in May > September > December. The tropospheric HCN column reached a maximum of (9.8 ± 0.78) x 1015 molecules/cm² in May and a minimum of (7.16 ± 0.75) x 1015 molecules/cm² in November. In most cases, the tropospheric HCN columns at Hefei (32° N) are higher than the FTIR observations at Ny Alesund (79° N), Kiruna (68° N), Bremen (53° N), Jungfraujoch (47° N), Toronto (44° N), Rikubetsu (43° N), Izana (28° N), Mauna Loa (20° N), La Reunion Maido (21° S), Lauder (45° S), and Arrival Heights (78° S) that are affiliated with the Network for Detection of Atmospheric Composition Change (NDACC). Enhancements of the tropospheric HCN columns were observed between September 2015 and July 2016 compared to the counterpart measurements in other years. The magnitude of the enhancement ranges from 5 to 46 % with an average of 22 %. Enhancement of tropospheric HCN (ΔHCN) is correlated with the coincident enhancement of tropospheric CO (ΔCO), indicating that enhancements of tropospheric CO and HCN were due to the same sources. The GEOS-Chem tagged CO simulation, the global fire maps and the PSCFs (Potential Source Contribution Function) calculated using back trajectories revealed that the seasonal maxima in May is largely due to the influence of biomass burning in South Eastern Asia (SEAS) (41 ± 13.1 %), Europe and Boreal Asia (EUBA) (21 ± 9.3 %) and Africa (AF) (22 ± 4.7 %). The seasonal maxima in September is largely due to the influence of biomass burnings in EUBA (38 ± 11.3 %), AF (26 ± 6.7 %), SEAS (14 ± 3.3 %), and Northern America (NA) (13.8 ± 8.4 %). For the seasonal maxima in December, dominant contributions are from AF (36 ± 7.1 %), EUBA (21 ± 5.2 %), and NA (18.7 ± 5.2 %). The tropospheric HCN enhancement between September 2015 and July 2016 at Hefei (32° N) were attributed to an elevated influence of biomass burnings in SEAS, EUBA, and Oceania (OCE) in this period. Particularly, an elevated fire number in OCE in the second half of 2015 dominated the tropospheric HCN enhancement in September-December 2015. An elevated fire number in SEAS in the first half of 2016 dominated the tropospheric HCN enhancement in January-July 2016. [ABSTRACT FROM AUTHOR]