Inverse Distance Weighting Interpolation Research Articles

Near-real-time prompt assessment for regional earthquake-induced landslides using recorded ground motions

Earthquake-induced landslides (EQILs) can cause severe economic losses and casualties. The prompt assessment of EQILs can provide an important reference for emergency rescue. Based on recorded ground motions and the Newmark sliding block method, a near-real-time prediction method for EQILs is proposed, and the corresponding prompt assessment system is developed. First, the seismic network is used to obtain the ground motion records near the epicenter immediately after an earthquake. The inverse distance-weighted interpolation and the continuous wavelet transform method are then adopted to generate the ground motion input field based on recorded ground motions. Subsequently, the slope of the target region is determined based on a global digital elevation model. Further, the mapping relationship between the Global Lithological Map database and the engineering lithology classification is established to determine the geological parameters for the target region. Finally, the predicted EQIL results are obtained using the Newmark sliding block method. The commonly used Newmark displacement models and the Newmark sliding block method are compared in this work to demonstrate the necessity of time-history analysis. The proposed method is validated using data from the 2011 Tōhoku earthquake and the 2016 Kumamoto earthquake. The 2018 Hokkaido earthquake is used as a case study to illustrate the detailed procedures and advantages of the proposed method. With high efficiency and convenience, the characteristics of the ground motion can be fully considered using the proposed method, which provides an important reference for post-earthquake emergency rescue.

An Adaptive Inverse-Distance Weighting Interpolation Method Considering Spatial Differentiation in 3D Geological Modeling

The inverse-distance weighting interpolation is widely used in 3D geological modeling and directly affects the accuracy of models. With the development of “smart” or “intelligent” geology, classical inverse-distance weighting interpolation cannot meet the accuracy, reliability, and efficiency requirements of large-scale 3D geological models in these fields. Although the improved inverse-distance weighting interpolation can basically meet the requirements of accuracy and reliability, it cannot meet the requirements of efficiency at the same time. In response to these limitations, the adaptive inverse-distance weighting interpolation method based on geological attribute spatial differentiation and geological attribute feature adaptation was proposed. This method takes into account the spatial differentiation of geological attributes to improve the accuracy and considers the first-order neighborhood selection strategy to adaptively improve efficiency to meet above requirements of large-scale geological modeling. The proposed method was applied to an area in eastern China, and the results of the proposed method, compared to the results of classical inverse-distance weighting interpolation and improved inverse-distance weighting interpolation, suggest that the problems encountered above in large-scale geological modeling can be solved with the proposed method. The method can provide effective support for large-scale 3D geological modeling in smart geology.

Spatio-temporal analysis of meteorological and hydrological droughts in the Euphrates Basin, Turkey

Abstract In this study, the aim was to measure changes in the spatio-temporal distribution of a potential drought hazard area and determine the risk status of various meteorological and hydrological droughts by using the kriging, radial basis function (RBF), and inverse distance weighting (IDW) interpolation methods. With that goal, in monthly, three-month, and 12-month time periods drought indices were calculated. Spatio-temporal distributions of the droughts were determined with each drought index for the years in which the most severe droughts were experienced. According to the results, the basin is under risk of meteorological drought due to the occurrence of severe and extreme droughts in most of the area, and especially in the north, during the monthly and three-month time periods. During the 12-month period, it was found that most of the basin is under risk of hydrological drought due to the occurrence of severe and extreme droughts, especially in the southern parts. The most effective interpolation method for the prediction of meteorological and hydrological droughts was determined as kriging according to the results of the cross-validation test. It was concluded that a drought management plan should be made, and early warnings and precautions should be applied in the study area.

Impact of the Grid Resolution and Deterministic Interpolation of Precipitation on Rainfall-Runoff Modeling in a Sparsely Gauged Mountainous Catchment

Precipitation is a key variable in the hydrological cycle and essential input data in rainfall-runoff modeling. Rain gauge data are considered as one of the best data sources of precipitation but before further use, the data must be spatially interpolated. The process of interpolation is particularly challenging over mountainous areas due to complex orography and a usually sparse network of rain gauges. This paper investigates two deterministic interpolation methods (inverse distance weighting (IDW), and first-degree polynomial) and their impact on the outputs of semi-distributed rainfall-runoff modeling in a mountainous catchment. The performed analysis considers the aspect of interpolation grid size, which is often neglected in other than fully-distributed modeling. The impact of the inverse distance power (IDP) value in the IDW interpolation was also analyzed. It has been found that the best simulation results were obtained using a grid size smaller or equal to 750 m and the first-degree polynomial as an interpolation method. The results indicate that the IDP value in the IDW method has more impact on the simulation results than the grid size. Evaluation of the results was done using the Kling-Gupta efficiency (KGE), which is considered to be an alternative to the Nash-Sutcliffe efficiency (NSE). It was found that KGE generally tends to provide higher and less varied values than NSE which makes it less useful for the evaluation of the results.

Growth, proportion, and distribution pattern of longleaf pine across southeastern forests and disturbance types: A change assessment for the period 1997-2018.

The long-term decline of longleaf pine-dominated forests has received considerable attention among land managers and conservation professionals in the last few decades. The objective of this study was to investigate the change in and the variation of the proportion, density, growth, and dominance of longleaf pine across the longleaf pine ecosystems for the 1997-2018 period. We used two sets of measurements of 1,432 plots from the Forest Inventory and Analysis (FIA) dataset covering the entire current longleaf pine range. The relationship between disturbances and longleaf pine basal area ratio and basal area growth were analyzed using linear mixed modeling. Change detection maps were produced using the Inverse Distance Weighted (IDW) interpolation method. The total basal area and aboveground biomass per hectare increased in 64% and 72%, but decreased in 30% and 28% of the study area, respectively, between the first and last inventory intervals. Species richness and diversity generally decreased across the studied plots. Longleaf pine tree density and importance value percent increased during the period. However, longleaf basal area ratio and aboveground biomass ratio in the stands decreased on average by 5% during the period, although these ratios increased in some locations in southwest Georgia and near the west coast of Florida. The longleaf pine basal area ratio and aboveground biomass ratio decreased equally in 37%, and increased in 19% and 21% of the study area, respectively. There was about 79% variation in the ratio of longleaf pine basal area among plots. When compared to the natural control of no disturbance, fire disturbance was significantly associated with greater longleaf pine basal area ratio and basal area growth. Understanding the change in growth and distribution patterns of longleaf pine across its range over time is vital to restore these critical ecosystems.

Study on the impact of calculation radius on IDW gravity modelling

Gravity modelling is a necessary process of applying gravity data to practice, and a vital research area in resource exploring and geodesy. Inverse distance weighted (IDW) interpolation is a widely used algorithm in gravity modelling and many other griding procedures. Based on the gravity anomaly data acquired from the airborne gravimetry over a region in the Northwest of China, IDW method is analysed and implemented with varying calculation radius in gravity modelling. The results show that over the study area, with a proper calculation radius, the IDW can give a more precise prediction than quadric surfaces fitting (QSF) method. The distances between sampled points and unsampled points are the only information taken into consideration in the IDW interpolation, so the analysis of the influence by changing the calculation radius of the unsampled points in this test provides a new research direction of error eliminating when using IDW.

Soil Suitability Assessment for Harumanis Mango Cultivation in UiTM Arau, Perlis

Perlis is one of the largest mango producers in Malaysia cultivating Sala, Harumanis, Thong Dam, Thong Dam Burma, and Melele. Among those types, Harumanis is the most popular and loved by many because of its aroma, texture, and sweetness. The production of quality Harumanis mango depends on the intensive care that is needed by the plant itself such as soil suitability, and climatic factors; such as weather and temperature. The soil suitability is an important role as a nutrient source by plant in maximizing plant growth. The extremely hot and prolonged weather conditions in December 2015 due to El Nino phenomenon are influencing a production of Harumanis products in Perlis on 2016. This phenomenon is effect on a soil moisture content rate for the plant growth. The aim of this study is to determine the soil suitability for the Harumanis mango cultivation in UiTM Arau, Perlis using Analytic Hierarchy Process (AHP) method. Three objectives were performed to achieve the aims which are identifying bulk density, soil pH, and organic matter content value using Inverse Distance Weight (IDW) interpolation method, determining criteria weight of parameter using AHP method, and determining soil suitability for Harumanis mango cultivation in UiTM Arau, Perlis. IDW interpolation method was done by using ArcGIS 10.5, and this method used for assuming value of the whole study area that unmeasured. AHP also applied to determine the soil suitability area for Harumanis mango cultivation. The highest weight value is representing criterion that have relative important and tendency in determining the suitability Harumanis mango cultivation. The results of this study show that 40,551 m2 represent 65% of the total area of the study area is highly suitable soil for Harumanis cultivation. The percentage value is obtained based on a combination of weight criteria between bulk density, soil pH, and organic matter content, where the weight value of bulk density has the relative importance and the highest tendency to determine the soil suitability for Harumanis mango cultivation in UiTM Arau, Perlis.

Application of GIS and RS in real time crop monitoring and yield forecasting: a case study of cotton fields in low and high productive farmlands

Badland reclamation and low productive farmlands always have been one of the most detrimental effects on the national economy, typically in agricultural sector of Uzbekistan. Nonetheless, such kind of lands has been used extensively for major crops like cotton and winter wheat. However, it is difficult to assessing real productivity of them. Advanced technologies as GIS and RS are vital tool for geospatially analysing and making decisions on this type of fields. This research was carried out for real-time crop monitoring and yield forecasting in case of low productive (3.5 ha) and high productive (8.3 ha) cotton areas of Jarkurgan district (Surkhandarya region, Uzbekistan) based on geospatial analyses of multi-temporal satellite images, condition of groundwater, soil salinity, and ground truth data. For monitoring vegetation phenology of cotton and forecasting its harvest, False Colour, NDVI (Normalized Difference Vegetation Index) and SI (Salinity Index) analyses of areas were carried out by using 6 temporal windows of multi-temporal Sentinel 2 from April through August 2019. Besides, groundwater condition data which was obtained from observation wells these located in massives consists of both cotton fields was analysed by IDW (Inverse Distance Weighting) interpolation algorithm method to determine groundwater’s effect to vegetation development and yield.

围垦对辽东湾北部滨海湿地土壤重金属含量的影响及生态风险评价

PDF HTML阅读 XML下载 导出引用 引用提醒 围垦对辽东湾北部滨海湿地土壤重金属含量的影响及生态风险评价 DOI: 10.5846/stxb201908061654 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 辽宁省自然科学基金项目（2020-BS-183）；辽宁省教育厅人文社科研究项目（WQ2019020）；辽宁省社会科学规划基金项（L19CJY005）；辽宁省省社科联 2021年度辽宁省经济社会发展研究课题（2021lslybkt-055） Effect of reclamation on soil heavy metal content of coastal wetland and ecological risk assessment in Northern Liaodong Bay, China Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:围垦历史与围垦方式被认为是人类对滨海湿地生态系统干扰的主要因素。选取辽东湾北部区作为研究区，采集129个表层土壤样品（0-20 cm），测定土壤理化性质（盐分、pH、土壤颗粒组成、土壤有机碳、硝态氮、速效磷）和重金属元素（Fe、Mn、Cu、Cr、Cd、Ni、Zn、Pb）含量；运用多元统计和反距离权重插值方法揭示研究区土壤重金属元素的来源与空间分布特征，最后利用Hakanson潜在生态风险指数法对重金属的潜在生态风险进行评价。结果表明：①6种重金属表现为彼此的显著正相关（P<0.01）。除Pb外，Cd、Cr、Cu、Ni和Zn的浓度与土壤有机碳、黏粒、粉粒含量呈极显著正相关（P<0.01），而与砂含量呈极显著负相关（P<0.01）。②化工、造纸、石油开采及加工等工业活动可能引起Cr、Cu、Ni和Zn的富集，持续的农业施肥和农药使用可能会增加土壤中Cd、Pb的含量。③围垦初期（1990s），土壤侵蚀和淋洗脱盐对土壤重金属分布具有显著影响。随着开垦年限的延长（1960s），持续的农业施肥、工业活动（如造纸、石油开采）和城市扩建，都大大增加了土壤重金属含量的累积。④不同的围垦方式影响下，除Fe标准化的Cd和Pb在水产养殖区有较高的浓度外，Fe标准化的Cr、Ni、Cu和Zn的浓度均在自然区域（滩地和芦苇）高于人类活动区域（水产养殖、耕地、建设用地）。⑤综合潜在生态风险指数平均值为325.69，表现为极强的生态风险程度，其中Cd表现为极高生态风险的采样点比例为46.51%，是造成区域重金属污染的主要贡献元素，特别是在水产养殖和农业地区。 Abstract:The history and methods of reclamation are considered as the main factors that drive human to disturb the coastal wetland ecosystems. In this study, a total of 129 samples were collected from the topsoil (0-20 cm) in Northern Liaodong Bay, and were determined for their physical and chemical properties (including salinity, pH, soil particle composition, SOC, NO3-N, and AP) and heavy metal levels (including Fe, Mn, Cu, Cr, Cd, Ni, Zn, and Pb). The heavy metals in soils were studied for sources and spatial distribution by using multivariate analysis and Inverse Distance Weight (IDW) interpolation method, and were evaluated for the potential ecological risk by using Hakanson's method. The results showed that: ①The levels of six heavy metals were significantly positively correlated (P<0.01) with each another. The Cd, Cr, Cu, Ni and Zn (excluding Pb) concentrations were significantly positively correlated (P<0.01) with the SOC, clay, and silt content, but were significantly negatively correlated (P<0.01) with the sand content. ②The levels of Cr, Cu, Ni and Zn in soils might be influenced by industrial activities (chemical industry, paper, oil exploitation and processing). Continuous use of fertilizers and pesticides might increase the levels of Cd and Pb in soils. ③In the early stage of reclamation (1990s), soil erosion, leaching and desalination had significant effects on the distribution of heavy metals in soils. Until the 1960s, continuous agricultural fertilization, industrial activities (such as paper making, and petroleum exploitation), and urban expansion had greatly increased the levels of heavy metals in soils. ④Under the influence of different reclamation methods, except that the levels of Fe standardized Cd and Pb were higher in the aquaculture areas, while the levels of Cr, Ni, Cu and Zn were higher in natural areas (marsh and reeds) than in human areas (aquaculture areas, cultivated land and construction land). ⑤The average value of the comprehensive potential ecological risk index was 325.69, showing a high level of risk. The Cd accounted for 46.51% as the highest ecological risk element in samples, which was the major contributor to heavy metal pollution, especially in aquaculture and agricultural areas. 参考文献 相似文献 引证文献

Efficacy of hydrological investigation in Karumeniyar river basin, Southern Tamil Nadu, India using vertical electrical sounding technique: A case study

In the Karumeniyar river basin, the groundwater demand increases for irrigation, industrial and larger per capita needs, and the recent year's groundwater table is drastically falling due to both natural and anthropogenic activities. In this context, a study on geophysical vertical electrical sounding (VES) using Schlumberger configuration has been carried out across 72 locations in the Karumeniyar river basin to demarcate the subsurface geoelectrical parameters and to identify the groundwater potential zone along with aquifer protective capacity. The acquired data were inverted using the 1D (resistivity variation with respect to depth) inversion approach IPI2Win demonstrated the presence of three to six subsurface geoelectrical layers in the study area with H type sounding curve being dominant. Furthermore, the deciphered result from VES is cross-validated with lithology data of four wells in the study area. Based on the interpreted results the parameters such as longitudinal conductance, overburden thickness, reflection coefficient and basement resistivity were calculated. It revealed that 36 VES location signifies good to moderate aquifer protective capacity. According to the reflection coefficient value and overburden thickness, the basin was divided into four distinct groundwater potential zones as high (42%), medium (38%), low (15%) and very low (5%). The inverse Distance Weighting (IDW) interpolation method is adopted to generate the spatial distribution maps in ArcGIS environment. The findings of the present study provide the vital geo-database for groundwater potential zones in the Karumeniyar river basin and have important implications for designing, intendance and management of sustainable groundwater resources.• Vertical Electrical Sounding method is a noninvasive, low cost and effective method for locating groundwater potential zone.• It measures the vertical wise variation of subsurface resistivity distribution based on surface measurement.• This technique provides a quantitative evaluation of different subsurface layers.

Particulate Pollution in Korhogo and Abidjan (Cote d’Ivoire) during the Dry Season

ABSTRACT Particulate pollution in West African cities seriously impacts public human health. Assessing the levels of pollution to which populations are exposed is problematic, as to date very few countries in Africa have an air quality monitoring network in place. However, given the specific anthropogenic sources present in West African countries and the increase in their projected emissions in the coming years if no regulations are put in place, solutions must be found. This study evaluates with a simple existing methodology particulate air pollution in two West African cities (Korhogo and Abidjan in Cote d'Ivoire) which have different population practices and local meteorology. From two measurement campaigns at about ten sites during the dry season and using the inverse distance-weighted interpolation method, maps of spatial variation in PM10 and PM2.5 concentrations are obtained at the scale of each of these cities, which provide a snapshot of the concentration levels to which populations are actually exposed. The results of this study showed that populations in low- and middle-income areas are exposed to higher concentrations than those in high-income areas. The analysis of the relative contribution of fine particles to coarse particles shows that PM2.5 contributes to PM10 at less than 40% in Korhogo but more than 50% in Abidjan. Daily average concentrations of PM2.5 are all above WHO standards, and are 2 to 8 times higher in Korhogo, while in Abidjan they are up to 4 times higher. The daily profiles of the mean hourly concentrations of the sites are similar across all sites in Korhogo, while in Abidjan, that of some sites show some differences. This study, by the methodology used, offers an opportunity for different research teams to assess at lower cost the population's exposure levels to urban particulate pollution.

Actualize The Coastal Ecosystem Resilience : Determining The Location Of Artficial Reef

Oceanographic parameters that affect coral reef growth are clarity, temperature, salinity and water currents taken at 20 stations using AAQ Rinko 1183 and FLOWATCH FL-03 current meter. The data processing using the IDW (Inverse Distance Weighted) interpolation method and the determination of the suitability area through scoring and weighting. The identification value consist of: (a) Clarity ranges from 2 - 6 meters, (b) Water turbidity ranges from 0.3 - 4.32 NTU, (c) Bottom substrate type is fine black sand, (d) Current velocity ranges from 0.1 m/s - 0.4 m/s, and (e) TSS ranges from 35.6 - 351 mg / L. The suitability of placing artificial reefs is classified at the appropriate level (S2), as a support for the survival of fishermen and local communities in Damas Beach, Trenggalek Regency

Mapping Atmospheric Corrosivity in Shandong

Air pollution can significantly accelerate the process of material corrosion, which may cause significant economic losses and serious safety incidents. Atmospheric corrosion maps provide atmospheric corrosivity in a given geographic scope, which can guide the designers to select the most suitable anti-corrosion materials for outdoor projects, also provide useful information for maintenance. This article investigated mapping of atmospheric corrosivity in Shandong Province, China. In order to obtain atmospheric corrosivity data, 100 exposure corrosion test sites were established in Shandong according to International Standard Organization (ISO) 8565. Hot-dip galvanized steel samples were exposed for 1 year in the test sites. Taking the results of exposure corrosion test as the data, inverse distance weighting (IDW) and ordinary kriging (OK) interpolation algorithm were used to estimate the atmospheric corrosivity of Shandong Province according to ISO 9223. The validity of OK and IDW was compared in developing atmospheric corrosion maps of Shandong Province on a 1 × 1 km resolution. The cross-validation results showed that OK interpolation algorithm with Gaussian semivariogram model get the best result in the prediction of corrosion rate. When the corrosion category was used as the criterion, the IDW interpolation algorithm of power 4 performed best, predicted results of 74 sites (n = 100) were consistent with observed. However, high mean relative errors (MRE more than 37%) and relatively low correlation (R2 about 24%) indicated that the prediction results of the two interpolation algorithms had a large error, which was caused by the low data density and the complicated corrosive factors of the atmosphere.

The best-fitting distribution of water balance and the spatiotemporal characteristics of drought in Guizhou Province, China

Drought is one of the most severe natural hazards with a significant impact on water resources. Droughts affect the sustainability of water resources and may result in environmental degradation of a region. The appropriate drought index is playing an important role on drought assessment. This paper addresses the best-fitting distribution of monthly water balance (WB), and the spatiotemporal characteristics of drought in Guizhou Province, China, during 1960–2017. The best-fitting distribution of monthly WB calculated using three potential evapotranspiration (PET) estimation methods was determined based on the goodness-of-fit test. The Standardized Precipitation Evapotranspiration Index (SPEI) at various timescales was computed based on the optimal distribution. The modified Mann-Kendall method, run theory, and inverse distance weight (IDW) interpolation method were used to investigate spatiotemporal evolution of drought. Over result revealed that the common log-logistic distribution was not the most suitable distribution for WB in Guizhou Province and the gen. extreme value distribution is optimum fit for the WB series calculated based on different methods for 76.47% of the stations. Moreover, different calculation methods seem to have little influence on the fitting results. The drought shows an upward trend in spring and autumn and a downward trend in summer and winter during 1960–2017; the increasing magnitude of summer and autumn drought was greater in the northeastern and northwestern Guizhou Province. The annual droughts are significantly increasing (p = 0.05) at Anshun and Panxian. The drought frequency showed a decreasing trend from light drought to severe drought in Guizhou Province, and the drought intensity was relatively more severe during 2010–2017 than other periods. These findings are helpful for water resource management in Guizhou Province and indicate that we should pay more attention to the drought mitigation of the higher-elevation regions.

Occurrence, density, and distribution of longleaf pine regeneration in southeastern forests: An assessment by forest type, disturbance and site quality

Regenerating longleaf pine is essential for the long-term sustainability of longleaf pine forest ecosystems and the important services those forests provide, particularly for endangered organisms. The objective of this study was to investigate the occurrence, density, and distribution of longleaf regeneration across a range of site quality, forest type, and disturbance regimes within the longleaf pine range from 1995 to 2019. We used 6,128 plots from multiple inventory cycles of the USDA Forest Service Forest Inventory and Analysis (FIA) dataset covering the entire longleaf pine range. The methods of analysis included logistic regression and the Inverse Distance Weighted (IDW) interpolation method. Two sets of measurements of 2,092 plots were used for change detection analysis. Longleaf regeneration was abundant in longleaf and loblolly pine-dominated stands. For a one-unit increase in the basal area ratio of longleaf pine trees in the plots, the probability of occurrence of longleaf pine seedlings and saplings increased up to 239% and 362%, respectively. The density and occurrence of longleaf regeneration were significantly greater on low-quality sites than on high-quality sites. Longleaf seedlings occurred with less frequency on disturbed sites, particularly by insects, diseases, and animals, than on undisturbed sites. Similarly, longleaf saplings were found with less frequency on sites disturbed by harvesting, weather, insects, diseases, and animals. We found a declining geographic range of longleaf pine trees, saplings, and seedlings during the 1995–2019 period. The density and ratio of longleaf seedlings decreased in about 25.2% and 14.0%, and increased in around 13.5% and 6.2% of the study area, respectively. Likewise, the density and ratio of longleaf saplings decreased in nearly 36.1% and 24.8% and increased in approximately 14.4% and 11.5% of the study area, respectively. Understanding the regeneration dynamics of longleaf pine is essential to restore and effectively regenerate longleaf pine forest ecosystems.

Combination of data-driven models and interpolation technique to develop of PM10 map for Hanoi, Vietnam

The degradation of air quality is the most concerned issue of our society due to its harmful impacts on human health, especially in cities with rapid urbanization and population growth like Hanoi, the capital of Vietnam. This study aims at developing a new approach that combines data-driven models and interpolation technique to develop the PM10 concentration maps from meteorological factors for the central area of Hanoi. Data-driven models that relate the PM10 concentration with the meteorological factors at the air quality monitoring stations in the study area were developed using the Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) algorithms. Models’ performance comparison showed that ANN models yielded better goodness-of-fit indices than MLR models at all stations in the study area with average coefficient of correlation (r) and Nash–Sutcliffe Efficiency Index (NSE) of 0.51 and 0.34 for the former, and 0.7 and 0.49 for the latter. These indices indicates that the ANN-based data-driven models outperformed the MLR-based models. Thus, the ANN-based models and the Inverse Distance Weighting (IDW) interpolation technique were then combined for mapping the monthly PM10 concentration with a spatial resolution of 1 km from global meteorological data. With this combination, the PM10 concentration maps account for both local PM10 concentration and impacts of spatio-temporal variations of meteorological factors on the PM10 concentration. This study provides a promising method to predict the PM concentration with a high spatio-temporal resolution from meteorological data.

Application of Empirical Orthogonal Function Interpolation to Reconstruct Hourly Fine Particulate Matter Concentration Data in Tianjin, China

Fine particulate matter with diameters less than 2.5 μm (PM2.5) concentration monitoring is closely related to public health, outdoor activities, environmental protection, and other fields. However, the incomplete PM2.5 observation records provided by ground-based PM2.5 concentration monitoring stations pose a challenge to the study of PM2.5 propagation and evolution model. Consequently, PM2.5 concentration data imputation has been widely studied. Based on empirical orthogonal function (EOF), a new spatiotemporal interpolation method, EOF interpolation (EOFI) is introduced in this paper, and then, EOFI is applied to reconstruct the hourly PM2.5 concentration records of two stations in the first half of the year. The main steps of EOFI here are to firstly decompose the spatiotemporal data matrix of the original observation site into mutually orthogonal temporal and spatial modes with EOF method. Secondly, the spatial mode of the missing data station is estimated by inverse distance weighting interpolation of the spatial mode of the observation sites. After that, the records of the missing data station can be reconstructed by multiplying the estimated spatial mode and the corresponding temporal mode. The optimal mode number for EOFI is determined by minimizing the root mean square error (RMSE) between reconstructed records and corresponding valid records. Finally, six evaluation indices (mean absolute error (MAE), RMSE, correlation coefficient (Corr), deviation rate bias, Nash–Sutcliffe efficiency (NSE), and index of agreement (IA)) are calculated. The results show that EOFI performs better than the other three interpolation methods, namely, inverse distance weight interpolation, thin plate spline, and surface spline interpolation. The EOFI has the advantages of less computation, less parameter selection, and ease of implementation, it is an alternative method when the number of observation stations is rare, and the proportion of missing value at some stations is large. Moreover, it can also be applied to other spatiotemporal variables interpolation and imputation.

Hybrid data driven drilling and rate of penetration optimization

Optimizing the drilling process for cost and efficiency requires faster drilling with a higher rate of penetration (ROP). A high ROP usually indicates fast and cost-efficient drilling operations; however, high ROPs may induce unintended events, which may lead to nonproductive time (NPT).In this paper, the proposed hybrid, data-driven optimization system aims to improve the drilling process, maximize the ROP, and minimize NPT. This two-phase system leverages existing drilling data (Phase One) and real-time adjustments (Phase Two) to optimize drilling efficiency through the adjustment of controllable dynamic drilling parameters; weight on bit (WOB), pump flow rate (GPM), and rotary speed (RPM).In the first phase, geological data is queried to generate a model of drilling best practices in a certain field with attention to ROP and NPT. After the top-rated wells are identified, the model recommends adjustments to the controllable dynamic drilling parameters (WOB, GPM, RPM) and produces a cross-plot for each ROP. The optimum ROP parameter for each well is subsequently calculated as a conditioned mean based on proximity to the well as an Inverse Distance Weighting (IDW) interpolation. This phase is completed prior to any drilling activity.The second phase considers the model produced in phase one to run an automated drill-off test. The model directs the driller to deviate for controllable parameters WOB and RPM by a small percentage (0–5%), in a Constrained Random Search (CRS) way. These deviations allow the driller to explore new drilling conditions in small increments; the data is then compiled into a heat-map function of ROP and the model is updated to reflect optimum drilling parameters.The adjustments resulted in faster, safer ROP with a potential 22%+ improvement in efficiency and 15–20% cost-reduction. Lastly, to characterize the relationship between the controllable dynamic parameters (WOB, RPM, GPM) to the ROP, an ANN based empirical relationship was derived.

Prenatal exposure to ambient air pollution and traffic and indicators of adiposity in early childhood: The Healthy Start Study

Background: Prenatal exposure to ambient air pollution and traffic have been associated with lower birth weight, but relationships with weight and body composition in childhood are unclear. The aim of this study was to examine associations of prenatal exposure to ambient air pollution and traffic with indicators of adiposity in early childhood. Methods: We included 738 participants of the Colorado-based Healthy Start Study whose height, weight, waist circumference and/or fat mass (via air displacement plethysmography, n=652) were measured at age 4-6 years. We estimated residential exposure to ambient concentrations of particulate matter with a diameter of &lt;2.5µm (PM2.5) and ozone (O3) averaged by trimester and throughout pregnancy via inverse distance-weighted interpolation of stationary monitoring data. We assessed the distance to the nearest major roadway and traffic density in multiple buffers surrounding the participants’ homes. Associations of prenatal exposure to air pollution and traffic with overweight (yes/no), waist circumference and fat mass index (fat mass/height2) were assessed by logistic and linear regression, adjusting for potential confounders. Results: Third trimester average 8-hour maximum O3 was associated with a higher odds of being overweight (adjusted odds ratio 1.71 [95%CI 1.12, 2.62] per IQR increase) and higher fat mass index (adjusted difference 0.27kg/m2 [95%CI 0.09, 0.44kg/m2] per IQR increase) at age 4-6 years. We found associations of third trimester PM2.5 with a lower fat mass index (adjusted difference -0.19kg/m2 [95%CI -0.32, -0.07kg/m2] per IQR increase), while first trimester O3 was associated with a lower odds of being overweight and lower fat mass index. Residential proximity to a highway was associated with a higher odds of being overweight and a non-significant higher waist circumference and fat mass index. Conclusion: Higher ambient concentrations of O3 during the third trimester of pregnancy and residential proximity to a highway during pregnancy may contribute to overweight in early childhood.

Land Suitability Assessment for Some Carbohydrate Food Crops at Wetland Area in Arisan Jaya

Priatna SJ, Rahman D, Supriyadi S. 2020. Land suitability assessment for some carbohydrate food crops at wetland area in Arisan Jaya. Jurnal Lahan Suboptimal : Journal of Suboptimal Lands 9(2): 117-126.The nature condition and lack of knowledge about soil characteristics have become a limitation for crops cultivation and development in Arisan Jaya. This study aimed to determine the type of crops that has potential to be developed in site. The assessment was carried out in Arisan Jaya, Pemulutan Barat Sub-District, Ogan Ilir Regency, South Sumatera from April to August 2015. The study method is 1:30.000 semi-detailed survey. The location of the sample was determined by grid method with one sample for every 1.000 meters with 10 sample sites. A disturbed soil sample was taken as deep as 150 cm from the ground surface. Land characteristics data were matched with crops growth requirements based on the suitability classes set for wetland rice, dry land rice, corn, cassava and sweet potato. The distribution of soil properties was known by IDW (Inverse Distance Weighting) interpolation method, which was overlapped to determine the distribution of land suitability classes. Very acidic soil conditions was a major limiting factor for crops cultivation in general (the actual suitability class is Nf). Wetland rice was relatively more suitable to be cultivated than dryland rice in the site. Corn and cassava could be planted as rotational crops before the rainy season or after rice season, although the productivity would not be optimal (S2 potential suitability class). Climatic conditions was another limiting factor for the development of sweet potato at the site (S3 potential suitability class).