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Assessing the impact of land cover on air quality parameters in Jordan: A spatiotemporal study using remote sensing and cloud computing (2019–2022)
International Journal of Applied Earth Observation and Geoinformation ( IF 7.6 ) Pub Date : 2024-11-30 , DOI: 10.1016/j.jag.2024.104293
Khaled Hazaymeh, Murad Al-Jarrah

This study aimed to analyze the spatiotemporal concentration of air pollutants in the tropospheric layer of Jordan, in the Middle East, for 2019–2022. The study utilized remotely sensed data from two satellite systems, Sentinel-5P and Landsat-9, to retrieve information about the concentration of nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) and land use types, respectively. The Google Earth Engine (GEE) platform and JavaScript were used to produce monthly short-term average concentration maps and time series for the three pollutants. Pearson correlation analysis was performed to evaluate the performance of Sentinel-5P data against ground-based monitoring stations in estimating NO2, SO2, and CO concentration at a regional scale. Results revealed a moderate correlation, with r-values of 0.42, 0.43, and 0.40, for NO2, SO2, and CO, respectively. The spatiotemporal analysis showed a higher concentration of SO2 and NO2 in the northern and middle regions of the country, coinciding with the spatial distribution of built-up areas and the main urban centers. On a temporal scale, the highest concentration of the three pollutants was observed in the winter months for all governorates of Jordan. For instance, it was found that the highest value of NO2 was in Balqa Governorate in December 2022, 1.57 * 10^4 mol/m2. The highest average monthly SO2 values were observed in Jerash Governorate in December 2019, 7.36 * 10^4 mol/m2. CO concentrations were mainly concentrated in the western parts of the Jordan rift valley.

中文翻译:


评估约旦土地覆盖对空气质量参数的影响:使用遥感和云计算的时空研究(2019-2022)



本研究旨在分析 2019-2022 年中东约旦对流层空气污染物的时空浓度。该研究利用来自两个卫星系统(Sentinel-5P 和 Landsat-9)的遥感数据,分别检索有关二氧化氮 (NO2)、二氧化硫 (SO2) 和一氧化碳 (CO) 浓度以及土地利用类型的信息。使用 Google Earth Engine (GEE) 平台和 JavaScript 为三种污染物生成月度短期平均浓度图和时间序列。进行 Pearson 相关分析,以评估 Sentinel-5P 数据在区域尺度上估计 NO2、SO2 和 CO 浓度时与地面监测站的性能。结果显示 NO2 、 SO2 和 CO 的 r 值分别为 0.42 、 0.43 和 0.40。时空分析显示,该国北部和中部地区的 SO2 和 NO2 浓度较高,与建成区和主要城市中心的空间分布相吻合。在时间尺度上,约旦所有省份的冬季观察到三种污染物的浓度最高。例如,发现 NO2 的最高值在 2022 年 12 月的巴尔卡省,为 1.57 * 10^4 mol/m2。2019 年 12 月,杰拉什省观察到最高的月平均 SO2 值,为 7.36 * 10^4 mol/m2。CO 浓度主要集中在约旦裂谷西部。
更新日期:2024-11-30
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