Increasing the accuracy of nitrogen dioxide (NO2) pollution mapping using geographically weighted regression (GWR) and geostatistics

Nitrogen dioxide (NO₂) is known to act as an environmental trigger for many respiratory illnesses. As a pollutant it is difficult to map accurately, as concentrations can vary greatly over small distances. In this study three geostatistical techniques were compared, producing maps of NO₂ concentrations in the United Kingdom (UK). The primary data source for each technique was NO₂ point data, generated from background automatic monitoring and background diffusion tubes, which are analysed by different laboratories on behalf of local councils and authorities in the UK. The techniques used were simple kriging (SK), ordinary kriging (OK) and simple kriging with a locally varying mean (SKlm). SK and OK make use of the primary variable only. SKlm differs in that it utilises additional data to inform prediction, and hence potentially reduces uncertainty. The secondary data source was oxides of nitrogen (NO_x) derived from dispersion modelling outputs, at 1 km × 1 km resolution for the UK. These data were used to define the locally varying mean in SKlm, using two regression approaches: (i) global regression (GR) and (ii) geographically weighted regression (GWR). Based upon summary statistics and cross-validation prediction errors, SKlm using GWR derived local means produced the most accurate predictions. Therefore, using GWR to inform SKlm was beneficial in this study.     

Street‐level Spatial Interpolation Using Network‐based IDW and Ordinary Kriging

This study proposes network‐based spatial interpolation methods to help predict unknown spatial values along networks more accurately. It expands on two of the commonly used spatial interpolation methods, IDW (inverse distance weighting) and OK (ordinary kriging), and applies them to analyze spatial data observed on a network. The study first provides the methodological framework, and it then examines the validity of the proposed methods by cross‐validating elevations from two contrasting patterns of street network and comparing the MSEs (Mean Squared Errors) of the predicted values measured with the two proposed network‐based methods and their conventional counterparts. The study suggests that both network‐based IDW and network‐based OK are generally more accurate than their existing counterparts, with network‐based OK constantly outperforming the other methods. The network‐based methods also turn out to be more sensitive to the edge effect, and their performance improves after edge correction. Furthermore, the MSEs of standard OK and network‐based OK improve as more sample locations are used, whereas those of standard IDW and network‐based IDW remain stable regardless of the number of sample locations. The two network‐based methods use a similar set of sample locations, and their performance is inherently affected by the difference in their weight distribution among sample locations.     

MODIS和OMI数据评估阅兵期间北京市大气减排成效

卫星观测不仅能反映区域宏观大气污染状况,也能从城市尺度上监测大气污染物的变化。基于以上优势,本文利用MODIS气溶胶光学厚度(AOD)和OMI对流层NO_2垂直柱浓度数据,比较2015年与2012年—2014年以及2015年3个时期(减排前、减排中、减排后)AOD和NO_2柱浓度的变化,定性分析了阅兵期间华北平原地区污染物减排效果,重点定量评估北京市联控减排措施的效果。研究发现2015年减排中华北平原重污染地区AOD和NO_2柱浓度相比于前3年同期有明显降低。定量分析北京市的减排效果得到:2015年减排中较前3年同期而言,AOD降低59%,NO_2柱浓度降低41%;较2015年减排前而言,AOD降低73%,NO_2柱浓度降低30%,去除气象条件影响后,AOD下降43%,NO_2柱浓度下降21%,说明严格的联控减排措施有效地改善了空气质量,气象条件也起到积极的作用。减排措施结束后,AOD和NO_2柱浓度比减排中分别增加159%和71%。研究结果表明,卫星遥感与地基监测评估效果相当,能反映北京地面污染物排放能力;它既能观测区域尺度大气污染变化,又可评估城市尺度大气污染减排。随着卫星技术水平的提高,期望未来卫星遥感可作为一种独立手段来定量评估区域及城市尺度空气质量减排措施的效果。     

Spatial estimation of wind speed: a new integrative model using inverse distance weighting and power law

Spatial interpolation (SI) is currently one of the most common ways to estimate wind speed (Ws). However, classic SI models either ignore the complex geography [e.g. inverse distance weighting (IDW)], or demand high computational resources (e.g. cokriging). This study aimed to develop a simple yet effective SI model for estimating Ws in Eastern Thrace of Turkey. This new method, named MIDW_(Ws), is a modified IDW through the integration of IDW with wind profile model, power law (PL), representing the influence of land cover and topography on Ws. Terrain features and elevation data of PL were obtained using normalized difference vegetation index (NDVI) and digital elevation model (DEM), respectively. Results showed superior and comparable performance of MIDW_(Ws) to standard IDW and ordinary kriging (OK) across all months of year. Compared to ordinary cokriging (OCK) using DEM as covariate, MIDW_(Ws) generated better results in the arid–semiarid seasons (around summer). Local complex atmospheric conditions during rainy seasons (around winter) may have affected the performance of incorporating PL with MIDW_(Ws). Generally, the proposed MIDW_(Ws) is simpler and easier to implement compared to OCK. For landscape-scale projects, its high computational efficiency and relatively robust performance show potential to deal with large volumes of datasets.     

2005年-2014年京津冀对流层NO2柱浓度时空变化及影响因素

基于臭氧监测仪OMI对流层NO₂柱浓度产品研究了京津冀城市群2005年-2014年NO₂时空变化及影响因素:(1)10年柱浓度年均增长率为3.35%,且年度波动较大。忽略2008年国家奥运会的环境治理所引起的变化,2005年-2011年NO₂呈逐渐升高趋势;2012年-2014年呈逐渐降低趋势,以2014年下降最为显著。(2)呈西北低东南高的趋势。燕山-太行山山系以北的承德和张家口市浓度较低,山系以南主要有北京-天津-唐山与石家庄-邢台-邯郸两个污染中心。(3)京津冀北部三面环山不利于NO₂的扩散,夏季丰富的降水对NO₂具有显著湿沉降作用。(4)通过相关性分析、文献及国家政策印证等方法,发现地区产业及能源结构很大程度上决定了地区的污染来源。北京市10年来第三产业一直处于主导且稳步提高,煤炭消耗量低,但汽车保有量增加了1.5倍,主要来源为机动车尾气排放;天津市第二产业比第三产业比重略高,煤炭消耗量是北京的两倍之余,但汽车保有量仅是北京市的一半,由此可知工业排放和机动车是共同来源;河北省第二产业比重很高,燃煤量占京津冀地区的80.6%,河北省工业排放是NO₂的主要来源,但近几年随着机动车保有量的剧增,其尾气排放分担率不可小觑。     

Effect of Land Use Types on the Spatial Prediction of Soil Nitrogen

Mapping the spatial distribution of soil nutrient contents from sample data has received much attention in the recent decade. Accurately mapping soil nutrients purely based on sample data, however, is difficult due to the sparsity and high cost of samples. Land use types usually influence the contents of soil nutrients at the local level and it is desirable to integrate such information into predictive mapping. The area-and-point kriging (AAPK) method, which was proposed recently, may provide an interpolation technique for such purposes. This study mapped the soil total nitrogen (TN) distribution of Hanchuan County, China, using AAPK with sample data (consisting of 402 points) and land use information. Ordinary kriging (OK) and residual kriging (RK) were compared to evaluate the performance of AAPK. Results showed that: (1) land use types had important impacts on the spatial distribution of soil TN; (2) measured data at 135 validation locations had stronger correlation with the data predicted by AAPK than by RK and OK, and the mean error and root mean square error with AAPK were lower than with RK and OK; and (3) AAPK generated smaller error variances than RK and OK did. This suggests that AAPK represents an effective method for increasing the interpolation accuracy of soil TN. It should be pointed out that some of the land use polygons used in this study are very large and complex, which might impact the effectiveness of AAPK in improving the prediction accuracy. Segmenting them into simple smaller areas might be helpful.     

亚热带山地丘陵区气象要素空间化方法分析

空间连续的气象要素是区域环境、生态系统模拟的重要参数,空间插值是实现气象观测站点数据空间化的主要方法。由于不同区域气候要素时空分布的复杂性,研究适用于特定区域的空间化方法具有重要意义。本文以湖南省为研究区,分析长江中游亚热带丘陵区气象要素的空间化方法。基于湖南省及其毗邻地区的46个气象站点14年(2000—2013年)的气象观测数据(月降水量、月均温)和DEM数据,对普通克里金法(OK)、反距离加权法(IDW)、样条法(SPLINE)和基于DEM的多元线性回归插值法(MLR)进行比较分析研究。研究结果表明:1)月降水量和月均温的MRE和RMISE值排序分别为:SPLINEIDWMLROK和SPLINOKIDWMLR;年平均降水量和年均温插值的MRE和RMISE值排序分别为:SPLINEIDWOKMLR和SPLINOKIDWMLR;2)MLR法不仅插值误差小,而且能从细节上反映出气象变化的空间变异性;3)对多变量进行数据探索分析表明,MLR插值法中,经度、坡度与年降水量空间分布存在空间的一致性;纬度、高程和坡度对年均温的空间分布具有很强的解释力。     

利用遥感数据评价燃煤电厂空气质量

卫星观测数据可以评价燃煤电厂的空气质量等级。NO₂、SO₂ 和烟尘是燃煤电厂排放的主要污染物,本文利用卫星遥感观测的NO₂、SO₂和气溶胶光学厚度AOD(Aerosol Optical Depth)开展燃煤电厂空气质量评价。以中国华北地区为实验区,分析对比了3种污染物不同时间分辨率和空间分辨率的污染状况,确定了单因子的5级分级标准,根据燃煤电厂排放污染物的权重不同,提出了评价近地表空气质量状况的模型。本文综合考虑3种污染因子来反映电厂空气质量,有利于提高评价的准确性以及反应信息的全面性。结果表明,该模型能正确反映不同地区电厂的空气质量特点。     

多源卫星遥感的华北平原大气NO2浓度时空变化

为探讨1996年以来华北平原对流层NO_2柱浓度变化的空间特征,基于GOME、SCIAMACHY和OMI卫星传感器的监测数据,以SCIAMACHY的NO_2柱浓度为基准,根据建立的GOME和SCIAMACHY,SCIAMACHY和OMI之间的相关关系,校正GOME和OMI监测的NO_2数据;利用线性正弦曲线模型拟合方法研究1996年—2016年长时间序列华北平原对流层NO_2变化的空间分布特征。结果显示,华北平原对流层NO_2浓度自1996年开始持续上升,到2011年达到最高值,然后呈现下降趋势。该变化趋势主要受经济发展和环保政策的双重影响。1996年—2011年,NO_2柱浓度高值区分布在北京市、天津市、河北唐山市和保定市、山东德州市和济南市、安徽滁州市以及江苏南京市、常州市和无锡市,并且具有较高增长率;2012年—2016年NO_2柱浓度平均值远高于1996年—2011年NO_2柱浓度平均值,高值地区范围扩大,分布在河北南部、河南北部和山东西部,但华北平原地区NO_2柱浓度均呈现负增长,表明"十二五"规划提出的大气环保政策取得了显著成效。同时,对流层NO_2柱浓度可以反映典型环保事件如北京奥运会、国庆阅兵和南京青奥会时期大气环保政策的实施效果。     

Comparison of variability and change rate in tropospheric NO2 column obtained from satellite products across China during 1997–2015

ABSTRACT

Tropospheric NO₂ column (TNC) products retrieved from five satellites including GOME/ERS-2 (H, 1997–2002), SCIAMACHY (S, 2003–2011), OMI (O, 2005–2015), GOME-2/METOP_A (A, 2007–2013) and GOME-2/METOP_B (B, 2013–2015) were compared in terms of their spatiotemporal variability and changes over China. The temporal series of H suggested an increasing trend of TNC from 1997 to 2002, those of S, O and A revealed further increasing trends until the highest level of TNC was reached in 2011, but decreasing trends were detected by those of O and B from 2011 to 2015. Seasonally, TNC was the highest in winter and the lowest in summer. Variability and changes from satellite TNC products are also analyzed in different regions of China. Spatially, it was the highest in North China and the lowest in Tibetan Plateau based on five datasets. Overall, TNCs from A, B and S were higher than that from O; and TNC from S was larger than that from A at the country level. The higher TNC the region has, the larger difference satellite products would show. However, different datasets reached a good agreement in the spatial pattern of trends in TNC with highly significant increasing trends detected in North China.     

Comparison of interpolation models for estimating heavy metals in soils under various spatial characteristics and sampling methods

Heavy metal pollution in soils has become increasingly challenging, especially in developing countries. Estimating the spatial distribution of heavy metals in soils is essential to preventing their build‐up. This article aims to identify the effects of spatial scales, spatial autocorrelation, sampling methods, and proportion on interpolation models in estimating the distribution of heavy metals in soils. Six interpolation models (area‐and‐point kriging, AAPK; inverse distance weighting, IDW; local polynomial interpolation, LP; ordinary kriging, OK; simple kriging, SK; and thin plate spline, TPS), three sampling methods (random, stratified, and systematic sampling), and five sampling proportions (1, 5, 10, 15, and 20%) are considered in this study using sets of simulated data, and the real situation was tested for verification. The results show that, in general, with the increase of spatial autocorrelation or the sampling percentage, the accuracy and stability of different interpolation models gradually increase; however, the various interpolation models have their own specific characteristics and application conditions. The best application conditions of the interpolation models compared with other models under the same situation are summarized and explained in theory. These conclusions have implications for future work.     

Evaluation of Air Pollution Trend Using GIS and RS Applications in South West of Iran

In this paper, remote sensing and GIS have been used to assess the status of NO₂ at the south west of Iran. 221 data about concentration of NO₂ was extracted from Ozone Monitoring Instrument. Ordinary kriging and inverse distance weighting interpolation methods was used to interpolate data. Results showed that ordinary kriging method using cross-validation have had less error. North east of the study area has the highest concentration of NO₂ (329 molecule/cm²) and the concentration of NO₂ decreases from north east to South west of the study area. On the other hand, data trend results showed that the data seems to exhibit a fairly strong trend in the east west direction and a weaker one in the north–south direction.     

Understanding satellite-derived bathymetry using Sentinel 2 imagery and spatial prediction models

ABSTRACT

Optical satellite data is an efficient and complementary method to hydrographic surveys for deriving bathymetry in shallow coastal waters. Empirical approaches (in particular, the models of Stumpf and Lyzenga) provide a practical methodology to derive bathymetric information from remote sensing. Recent studies, however, have focused on enhancing the performance of such empirical approaches by extending them via spatial information. In this study, the relationship between multibeam depth and Sentinel-2 image bands was analyzed in an optically complex environment using the spatial predictor of kriging with an external drift (KED), where its external drift component was estimated: a) by a ratio of log-transformed bands based on Stumpf’s model (KED_S) and b) by a log-linear transform based on Lyzenga’s model (KED_L). Through the calibration of KED models, the study objectives were: 1) to better understand the empirical relationship between Sentinel-2 multispectral satellite reflectance and depth, 2) to test the robustness of KED to derive bathymetry in a multitemporal series of Sentinel-2 images and multibeam data, and 3) to compare the performance of KED against the existing non-spatial models described by Stumpf et al. and Lyzenga. Results showed that KED could improve prediction accuracy with a decrease in RMSE of 89% and 88%, and an increase in R² of 27% and 14%, over the Stumpf and Lyzenga models, respectively. The decrease in RMSE provides a worthwhile improvement in accuracy, where results showed effective prediction of depth up to 6 m. However, the presence of higher concentrations of suspended materials, especially river plumes, can reduce this threshold to 4 m. As would be expected, prediction accuracy could be improved through the removal of outliers, which were mainly located in the channel of the river, areas influenced by the river plume, abrupt topography, but also very shallow areas close to the shoreline. These areas have been identified as conflictive zones where satellite-derived bathymetry can be compromised.     

Estimation of transit trip production using Factorial Kriging with External Drift: an aggregated data case study

Studies in transportation planning routinely use data in which location attributes are an important source of information. Thus, using spatial attributes in urban travel forecasting models seems reasonable. The main objective of this paper is to estimate transit trip production using Factorial Kriging with External Drift (FKED) through an aggregated data case study of Traffic Analysis Zones in São Paulo city, Brazil. The method consists of a sequential application of Principal Components Analysis (PCA) and Kriging with External Drift (KED). The traditional Linear Regression (LR) model was adopted with the aim of validating the proposed method. The results show that PCA summarizes and combines 23 socioeconomic variables using 4 components. The first component is introduced in KED, as secondary information, to estimate transit trip production by public transport in geographic coordinates where there is no prior knowledge of the values. Cross-validation for the FKED model presented high values of the correlation coefficient between estimated and observed values. Moreover, low error values were observed. The accuracy of the LR model was similar to FKED. However, the proposed method is able to map the transit trip production in several geographical coordinates of non-sampled values.     

Zonation of coastal sediments based on the effective properties on the accumulation of heavy metals using the IDW and kriging method (case study: SW Iran)

Abstract

This study investigated the effects of some physicochemical properties of sediments on the accumulation of heavy metals in portions of the Musa creek coasts (Jafari and Petrochemical creeks). Effective properties such as pH, EC, texture, G_S, γ_d, n, CaCO₃ and OM were determined. All variables showed a normal distribution and general trends of NW–SE and NE–SW. After detrending the variables, ordinary kriging was used for modelling. The C₀/σ₂, C₀/σ_2, and search radius criteria were used to select the best semivariogram. All the variables displayed a spatial structure with different intensities. The IDW method was also used for estimation. The cross-validation showed that the results of both IDW and kriging methods are almost similar. Distribution of the sand particle, G_S, n and OM decreases with distance from the waterways, whilst clay–silt deposits. In the center of the studied area, CaCO₃ has the highest value and EC has the lowest value.     

Non-stationary Approaches for Mapping Terrain and Assessing Prediction Uncertainty

It is well known that terrain may vary markedly over small areas and that statistics used to characterise spatial variation in terrain may be valid only over small areas. In geostatistical terminology, a non-stationary approach may be considered more appropriate than a stationary approach. In many applications, local variation is not accounted for sufficiently. This paper assesses potential benefits in using non-stationary geostatistical approaches for interpolation and for the assessment of uncertainty in predictions with implications for sampling design. Two main non-stationary approaches are employed in this paper dealing with (1) change in the mean and (2) change in the variogram across the region of interest. The relevant approaches are (1) kriging with a trend model (KT) using the variogram of residuals from local drift and (2) locally-adaptive variogram KT, both applied to a sampled photogrammetrically derived digital terrain model (DTM). The fractal dimension estimated locally from the double-log variogram is also mapped to illustrate how spatial variation changes across the data set. It is demonstrated that estimation of the variogram of residuals from local drift is worthwhile in this case for the characterisation of spatial variation. In addition, KT is shown to be useful for the assessment of uncertainty in predictions. This is shown to be true even when the sample grid is dense as is usually the case for remotely-sensed data. In addition, both ordinary kriging (OK) and KT are shown to provide more accurate predictions than inverse distance weighted (IDW) interpolation, used for comparative purposes.     

Spatiotemporal mixed effects modeling for the estimation of PM2.5 from MODIS AOD over the Indian subcontinent

ABSTRACT

The physical processes associated with the constituents of the troposphere, such as aerosols have an immediate impact on human health. This study employs a novel method to calibrate Aerosol Optical Depth (AOD) obtained from the MODerate resolution Imaging Spectrometer (MODIS – Terra satellite) for estimating surface PM_2.5 concentration. The Combined Deep Blue Deep Target daily product from the MODIS AOD data acquired across the Indian Subcontinent was used as input, and the daily averaged PM_2.5pollution level data obtained from 33 monitoring stations spread across the country was used for calibration. Mixed Effect Models (MEM) is a linear model to deal with non-independent data from multiple levels or hierarchy using fixed and random effects of dependent parameters. MEM was applied to the dataset obtained for the period from January to August 2017. The MEM considers a fixed and random component, where the random components model the daily variations of the AOD – PM_2.5 relationships, site-specific adjustment parameters, temporal (meteorological) variables such as temperature, and spatial variables such as the percentage of agricultural area, forest cover, barren land and road density with the resolution of 10 km × 10 km. Estimation accuracy was improved from an R² value of 0.66 from our earlier study (when PM_2.5 was modeled against only AOD and site-specific parameters) toR² value of 0.75 upon the inclusion of spatiotemporal (meteorological) variables with increased % within Expected Error from 18% to 35%, reduced Mean Bias Error from 3.22 to 0.11 and reduced RMSE from 29.11 to 20.09. We also found that spline interpolation performed better than IDW and Kriging inefficiently estimating the PM_2.5 concentrations wherever there were missing AOD data. The estimated minimum PM_2.5 is 93 ± 25μg/m³ which itself is in the upper limit of the hazardous level while the maximum is estimated as 170 ± 70μg/m³. The study has thus made it possible to determine the daily spatial variations of PM_2.5 concentrations across the Indian subcontinent utilizing satellite-based AOD data.     

G0分布下基于白化滤波的极化SAR图像CFAR检测

在已有的极化合成孔径雷达(PolSAR)图像恒虚警(CFAR)检测方法中,存在着高分辨下杂波模型适用性差的难题。为解决此问题,提出了一种G_0分布下虚警概率具有闭合解析表达形式的CFAR检测方法,并定义虚警损失率(CFAR Loss, C_L)参数用以量化评估CFAR检测方法的恒虚警保持效果。首先,在乘积模型框架下,引入了逆Gamma纹理变量假设,推导出了多视极化白化滤波(MPWF)检测量的概率密度函数(PDF)。然后,对MPWF检测量的概率密度函数积分得到了虚警概率关于CFAR检测阈值的解析表达式,并设计了相应的CFAR检测流程。最后,采用仿真数据和AIRSAR实测数据对已有方法和新方法进行了算法运行时间、检测量拟合性能及目标检测性能对比。实验结果表明,方法运行时间比已有方法缩短3至30倍,具有良好的实时性;日本玉野地区的AIRSAR实测数据结果表明G_0分布对高分辨不均匀海区具有良好的拟合性能,且新方法在G_0分布和非G_0分布海区均能有效检测出目标,鲁棒性较强,相比其他检测方法品质因数(FoM)平均高出15.78%;C_L分析结果表明新方法具有良好的恒虚警保持性能,同时指出杂波对数累积量散点距离G_0分布曲线越近,新方法的恒虚警保持效果越好。     

Local interpolation of coseismic displacements measured by InSAR

Coseismic displacements play a significant role in characterizing earthquake causative faults and understanding earthquake dynamics. They are typically measured from InSAR using pre- and post-earthquake images. The displacement map produced by InSAR may contain missing coseismic values due to the decorrelation of ASAR images. This study focused on interpolating missing values in the coseismic displacement map of the 2003 Bam earthquake using geostatistics with the aim of running a slip distribution model. The gaps were grouped into 23 patches. Variograms of the patches showed that the displacement data were spatially correlated. The variogram prepared for ordinary kriging (OK) indicated the presence of a trend and thus justified the use of universal kriging (UK). Accuracy assessment was performed in 3 ways. First, 11 patches of equal size and with an equal number of missing values generated artificially, were kriged and validated. Second, the four selected patches results were validated after shifting them to new locations without missing values and comparing them with the observed values. Finally, cross validation was performed for both types of patch at the original and shifted locations. UK results were better than OK in terms of kriging variance, mean error (ME) and root mean square error (RMSE). For both OK and UK, only 4 out of 23 patches (1, 5, 11 and 21) showed ME and RMSE values that were substantially larger than for the other patches. The accuracy assessment results were found to be satisfactory with ME and RMSE values close to zero. InSAR data inversion demonstrated the usefulness of interpolation of the missing coseismic values by improving a slip distribution model. It is therefore concluded that kriging serves as an effective tool for interpolating the missing values on a coseismic displacement map.     

基于指示和多元地质统计学的空间预测方法(英文)

There are various occasions where simple, ordinary, and universal kriging techniques may find themselves incapa- ble of performing spatial prediction directly or efficiently. One type of application concerns quantification of cumulative distribution function （CDF） or probability of occurrences of categorical variables over space. The other is related to optimal use of co-variation inherent to multiple regionalized variables as well as spatial correlation in spatial prediction. This paper extends geostatistics from the realm of kriging with uni-variate and continuous regionalized variables to the territory of indicator and multivariate kriging, where it is of ultimate importance to perform non-parametric estimation of probability distributions and spatial prediction based on co-regionalization and multiple data sources, respectively.