Improving spatial and temporal variation of ammonia emissions for the Netherlands using livestock housing information and a Sentinel-2-derived crop map

IF 3.8 Q2 ENVIRONMENTAL SCIENCES Atmospheric Environment: X Pub Date : 2023-01-01 DOI:10.1016/j.aeaoa.2023.100207
Xinrui Ge , Martijn Schaap , Wim de Vries
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Abstract

Ammonia emissions to the atmosphere have a range of negative impacts on environmental quality, human health, and biodiversity. Despite the considerable efforts in quantifying spatially explicit ammonia emissions, there are significant uncertainties in ammonia emission estimates at regional scales. We aimed to improve the modeling of atmospheric ammonia emission variability in space and time across the Netherlands by updating an agricultural ammonia emission model with a newly derived high-resolution crop map and a livestock housing location database of the Netherlands. To generate a crop map of 12 agricultural land cover classes, we applied random forest classification to the multi-temporal multispectral observations of surface reflectance and vegetation indices derived from Sentinel-2. The crop statistics were used to calculate ammonia emission distribution based on nitrogen demand (manure and mineral fertilizer needed) of different crop types using the INTEGRATOR model. Next, the crop map was utilized to spatially allocate the ammonia emissions to a high-resolution grid across the Netherlands. In addition, ammonia emissions from livestock housing systems were introduced as point sources using location data from the Geographic Information Agricultural Business system. The temporal emission variability was updated using a recently developed TIMELINES module. After the spatial and temporal distribution of ammonia emission was obtained with the crop map and housing information, it was imported into the chemistry transport model LOTOS-EUROS to model ammonia surface concentration for validation with in situ measurements.

The performed crop classification has an average accuracy score of 0.73. The derived crop map was compared with Dutch national statistics, and the results showed that the absolute median of the relative difference between Sentinel-2 derived crop areas and national statistical information is around 5%. The newly modeled ammonia monthly surface concentrations compared better with in situ measurements in terms of the magnitude and temporal variability than those derived from the original emission distribution, indicating that the temporal distribution of ammonia emissions was improved. The comparison of modeled and measured annual averaged surface concentrations illustrated that the spatial distribution of ammonia emission was also improved. All model performance indicators significantly improved, and the performance of the updated model was more stable and robust. The improvement was more evident at the stations where livestock housing is the main emission source. This study illustrates that apart from a satellite-derived crop map, information on the locations of animal housing systems also plays an essential role in better estimates of the spatial and temporal distribution of ammonia emissions. It can be worthwhile to extrapolate the method to other regions in Europe and elsewhere.

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利用牲畜饲养信息和Sentinel-2衍生的作物地图改善荷兰氨排放的空间和时间变化
大气中的氨排放对环境质量、人类健康和生物多样性有一系列负面影响。尽管在量化空间明确的氨排放方面做出了相当大的努力,但在区域尺度上的氨排放估计仍存在很大的不确定性。我们的目标是通过使用新获得的荷兰高分辨率作物地图和牲畜饲养位置数据库更新农业氨排放模型,改进荷兰各地大气氨排放时空变化的建模。为了生成12个农业土地覆盖类别的作物地图,我们将随机森林分类应用于Sentinel-2得出的地表反射率和植被指数的多时相多光谱观测。作物统计数据用于使用INTEGRATOR模型根据不同作物类型的氮需求(所需肥料和矿物肥料)计算氨排放分布。接下来,利用作物地图将氨排放量空间分配到荷兰各地的高分辨率网格中。此外,利用地理信息农业商业系统的位置数据,引入了牲畜饲养系统的氨排放作为点源。使用最近开发的TIMELINES模块更新了时间排放可变性。在利用作物图和住房信息获得氨排放的空间和时间分布后,将其导入化学传输模型LOTOS-EUROS,以模拟氨表面浓度,并通过现场测量进行验证。所执行的作物分类的平均准确度得分为0.73。将衍生的作物地图与荷兰国家统计数据进行了比较,结果显示,Sentinel-2衍生的作物面积与国家统计信息之间的相对差异的绝对中值约为5%。与原始排放分布相比,新建模的氨月表面浓度在幅度和时间变异性方面与现场测量结果相比更好,表明氨排放的时间分布得到了改善。模拟和测量的年平均表面浓度的比较表明,氨排放的空间分布也得到了改善。所有模型的性能指标都显著提高,更新后的模型性能更加稳定和稳健。在牲畜饲养是主要排放源的监测站,这种改善更为明显。这项研究表明,除了卫星绘制的作物地图外,动物饲养系统的位置信息在更好地估计氨排放的空间和时间分布方面也发挥着重要作用。将该方法推广到欧洲其他地区和其他地方是值得的。
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来源期刊
Atmospheric Environment: X
Atmospheric Environment: X Environmental Science-Environmental Science (all)
CiteScore
8.00
自引率
0.00%
发文量
47
审稿时长
12 weeks
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