Spatial detrending revisited: Modelling local trend patterns in NO2-concentration in Belgium and Germany

Behm, Svenia; Haupt, Harry; Schmid, Angelika

doi:10.1016/j.spasta.2018.04.004

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Compared with previous studies [5,19,41], this study reveals the temporal and spatial evolution characteristics of soil AVK in the marginal zone of large cities based on continuous, high-density soil sampling data, and discusses the underlying reasons for this evolutionary feature from the micro perspective of households, according to long-term sequence statistics and households' survey data.…”

Section: Discussionmentioning

confidence: 85%

“…One is on the spatial variation of soil physical properties and soil salinity from the perspective of natural science [12][13][14]. In past years, geostatistical methods have been increasingly used to analyze the spatial variation of soil nutrients [15][16][17], for example, soil organic matter [18], NO −3 -N [19], available phosphorus [5], AVK [20,21], nugget effect and correlation degree [22], coefficient of variation [23]. At the same time, studies on the influencing factors of soil fertility have gained much attention.…”

Section: Introductionmentioning

confidence: 99%

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A Spatial-Temporal Analysis of the Effects of Households’ Land-use Behaviors on Soil Available Potassium in Cropland: A Case Study from Urban Peripheral Region in Northeast China

Liu

Sun

Luo

et al. 2020

Land

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Available potassium (AVK) in the soil of cropland is one of the most important factors determining soil quality and agricultural productivity. Thus, it is crucial to understand the variation of AVK and its influencing factors for sustaining soil fertility and mitigating land degradation. Farm households are the ultimate land users, and their land-use behaviors inevitably play an important role in the variation of AVK. This paper, therefore, aims to explore the effects of households’ land-use behaviors on soil AVK from spatial and temporal perspectives. Taking an urban peripheral region in Northeast China as the study area, we firstly use geostatistics (Kriging interpolation) and GIS tools to map out the spatial AVK distributions in 1980, 2000, and 2010, based on soil sampling data points, and then assess the impacts of land-use behaviors on AVK using econometric models. The results show that, although the AVK content in the study area has a largely downward trend over the 30 years, there are distinct trends in different stages. The disparity of trends can be attributed to the changes in households’ land-use behaviors over time. The spatial variation of AVK is also substantial and intriguing: the closer to the urban area, the greater the decline of soil AVK content, while the farther away from the urban area, the greater the rise of soil AVK content. This spatial disparity can too be largely explained by the obvious differences in households’ land-use behaviors in various regions.

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Section: Discussionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

A Spatial-Temporal Analysis of the Effects of Households’ Land-use Behaviors on Soil Available Potassium in Cropland: A Case Study from Urban Peripheral Region in Northeast China

Liu

Sun

Luo

et al. 2020

Land

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“…In the following, we carry out a review of the most significant methods within the state-of-the-art that make short-term forecasts of pollutants in the air. The short-term forecasting of NO 2 concentration has attracted the attention of the scientific community [14,15]. Statistical models have been used widely for prediction tasks.…”

Section: Using Machine-learning To Predict Pollutant Concentrationsmentioning

confidence: 99%

Short-Term Air Pollution Forecasting Using Embeddings in Neural Networks

et al. 2023

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Air quality is a highly relevant issue for any developed economy. The high incidence of pollution levels and their impact on human health has attracted the attention of the machine-learning scientific community. We present a study using several machine-learning methods to forecast NO2 concentration using historical pollution data and meteorological variables and apply them to the city of Erfurt, Germany. We propose modelling the time dependency using embedding variables, which enable the model to learn the implicit behaviour of traffic and offers the possibility to elaborate on local events. In addition, the model uses seven meteorological features to forecast the NO2 concentration for the next hours. The forecasting model also uses the seasonality of the pollution levels. Our experimental study shows that promising forecasts can be achieved, especially for holidays and similar occasions which lead to shifts in usual seasonality patterns. While the MAE values of the compared models range from 4.3 to 15, our model achieves values of 4.4 to 7.4 and thus outperforms the others in almost every instance. Those forecasts again can for example be used to regulate sources of pollutants such as, e.g., traffic.

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Spatiotemporal forecast with local temporal drift applied to weather patterns in Patagonia

2020

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Spatial detrending revisited: Modelling local trend patterns in NO2-concentration in Belgium and Germany

Cited by 5 publications

References 23 publications

A Spatial-Temporal Analysis of the Effects of Households’ Land-use Behaviors on Soil Available Potassium in Cropland: A Case Study from Urban Peripheral Region in Northeast China

A Spatial-Temporal Analysis of the Effects of Households’ Land-use Behaviors on Soil Available Potassium in Cropland: A Case Study from Urban Peripheral Region in Northeast China

Short-Term Air Pollution Forecasting Using Embeddings in Neural Networks

Spatiotemporal forecast with local temporal drift applied to weather patterns in Patagonia

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