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Monitoring and obtaining observation data of atmospheric anomia is of great significance for further reducing fine particles pollution. The study uses satellite remotely sensed Infrared Atmospheric Sounding Interferometer (IASI) data to conduct research on the accounting method of atmospheric ammonia emission intensity and total ammonia in the Beijing-Tianjin-Hebei and surrounding areas, providing support for the remote sensing monitoring application of atmospheric ammonia and ammonia reduction.Spatial interpolation was performed on spatially discontinuous data to calculate the annual average emission intensity (YNH3), seasonal average emission intensity (QNH3), and monthly average emission intensity (MNH3) of atmospheric ammonia, and to estimate the total amount of atmospheric ammonia (TNH3). By doing so, the spatial distribution characteristics and temporal variation patterns of atmospheric ammonia in the study area were analyzed, and finally, the influencing factors of atmospheric ammonia emissions were further examined. The results showed that from 2014 to 2022, the daily average emission intensity of atmospheric ammonia in the study area was 7.99kg/km2. The central part of the region was a high value area for YNH3, and the five cities with low column concentrations and low increasing rates were mainly distributed in northern Hebei and southern Henan. The eight cities with low column concentrations but high increasing rates were distributed in southwestern Henan, central Hebei, and Jiaodong Peninsula. The nineteen cities with high column concentrations but low increasing rates and sixteen cities with high column concentrations and high increasing rates were distributed in the central region and the surrounding of the Bohai Sea. Over the past 9 years, both YNH3 and TNH3 have shown an increasing trend, with YNH3 in the study area growing from 5.89kg/km2 to 9.20kg/km2, with a compound annual growth rate of 5.73%; TNH3 increased from 1169kt to 1825kt. In terms of temporal distribution, QNH3 exhibited periodic changes, with summer being the peak season for QNH3 and July being the peak month for MNH3. TNH3 had a high correlation with arable land and population, and their spatial distributions were highly consistent with each other, indicating that farming and population are important influencing factors of ammonia emissions in the study area. Random Forest analysis showed that agricultural and living sources were the most significant reduction factors of ammonia emission. This study demonstrated that remote sensing monitoring of atmospheric ammonia can offer data support for air quality monitoring operations.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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