Grid Sampling Method Research Articles

Thermal Environment Effects of Built-Up Land Expansion in Shijiazhuang

Exploring the thermal environment effects of built-up land expansion can lay a firm foundation for urban planning and design. This study revealed the spatiotemporal dynamic characteristics of built-up land and heat island center points in Shijiazhuang using land-use/land-cover data and land surface temperature (LST) products from 1996 to 2019, and the response mechanism between the percentage of built-up land (PLAND) and LST with the grid sampling method and statistical analysis. Results indicated that heat islands are mainly clustered in the downtown, built-up areas of counties and the Hutuo River Basin. The spatiotemporal shift direction of the center point of the urban heat island (UHI) and built-up land in the whole study area varied due to the eco-environmental transformation of the Hutuo River Basin. In areas far from the Hutuo River Basin, the center points of UHI and built-up land were shifted in a similar direction. There is a remarkable linear correlation between the PLAND and LST, the correlation coefficient of which was higher than 0.7 during the study period. Areas with PLAND > 60% are urban regions with stronger heat island effects, and areas with PLAND < 55% are villages and towns where the temperature raised more slowly.

Comparative studies on the effects of biochar and cow dung amendments on the mobility, bioavailability and toxicity of heavy metals in lead-acid contaminated soil

This study compared the heavy metals remediation potency of biochar and cow dung in soils contaminated by lead-acid batteries. Soil samples were gathered from three different battery charging shops in Benin City at the depth of 0-15cm using systematic grid sampling method. Hardwood was pyrolyzed at 350°C for 80minutes and cow dung was air dried and pound into powder. The soil was characterized using standard methods, and was fractionated using modified European Community Bureau of Reference (BCR) three-step sequential extraction procedure while the concentration of heavy metals was determined using Atomic Absorption Spectrophotometer (AAS). The total metal concentration in the lead-acid contaminated soil was 127.50mg/kg Pb, 0.32mg/kg Cr, 0.03mg/kg Cd, 0.18mg/kg Ni and 135.00mg/kg Fe. The contaminated soil was remediated with biochar and as well with cow dung at 5%, 10%, 15% and 20%. The soil was also amended using biochar-cow dung mixture (1:1, 2:1 and 1:2). The outcome revealed that the metal content in lead-acid contaminated soil remediated with biochar and cow dung were found to generally decrease as the percentage of amenders increases, with biochar being further efficient in lowering the levels of heavy metals in the contaminated soil. However, biochar when mixed with cow dung at 2:1 ratio at 20% amendment gives a better result than when used separately. Pot experiment was designed and the amounts of heavy metals bioavailable to plant were determined. The mobility factors and bioavailability indices of the metals reduced as the percentage of amenders’ increases. The result obtained from this study revealed that although biochar and cow dung were effective amenders, biochar was more potent in lowering the mobility, bioavailability and toxicity of heavy metals in lead-acid contaminated soil. Biochar when mixed with cow dung at 2:1 ratio gave the best result in remediation of lead-acid contaminated soil.

Digital mapping of soil erodibility factor in northwestern Iran using machine learning models.

Understanding the spatial distribution of soil erodibility factor (K-factor) at the district scale is essential for managing water erosion risk. In this research, we performed to predict the low and high classes of K-factor in the northwest of Iran. Based on this, soil sampling was performed at 64 points using the grid sampling method with 1km spacing. To calculate the K-factor, the distribution of particle size and organic carbon (OC) were determined. In addition, 21 terrain attributes were calculated by Digital Elevation Model (DEM) to add value to the soil data. Then, K-factor was modeled using Random Forest (RF) and Artificial Neural Network (ANN) models. In the next step, a non-linear Multiple Logistic Regression (NMLR) was used to obtain low and high classes of K-factor. The results showed that the performance of RF is superior to ANN with a high coefficient of determination [R2 = 0.85] and good accuracy [RMSE = 0.003 (Mg ha h/ha MJ mm)]. Therefore, the RF was employed for predicting the K-factor spatial distribution. Finally, using the NMLR model, the study area was divided into low and high classes of K-factor with good correlation [R2 Cox and Snell = 0.78, R2 Nagelkerke = 0.65]. The areas of these two classes were 60.4% for low class and 39.6% for the high class of K-factor. Based on these results, it was concluded that the resultant map of low and high classes of K-factor could be used by farmers and managers for managing soil water erosion risks in the study area.

Mapping soil nutrients via different covariates combinations: theory and an example from Morocco

BackgroundMapping of soil nutrients using different covariates was carried out in northern Morocco. This study was undertaken in response to the region's urgent requirement for an updated soil map. It aimed to test various covariates combinations for predicting the variability in soil properties using ordinary kriging and kriging with external drift.MethodsA total of 1819 soil samples were collected at a depth of 0–40 cm using the 1-km grid sampling method. Samples were screened for their pH, soil organic matter (SOM), potassium (K2O), and phosphorus (P2O5) using standard laboratory protocols. Terrain attributes (T) computed using a 30-m resolution digital elevation model, bioclimatic data (C), and vegetation indices (V) were used as covariates in the study. Each targeted soil property was modeled using covariates separately and then combined (e.g., pH ~ T, pH ~ C, pH ~ V, and pH ~ T + C + V). k = tenfold cross-validation was applied to examine the performance of each employed model. The statistical parameter RMSE was used to determine the accuracy of different models.ResultsThe pH of the area is slightly above the neutral level with a corresponding 7.82% of SOM, 290.34 ppm of K2O, and 100.86 ppm of P2O5. This was used for all the selected targeted soil properties. As a result, the studied soil properties showed a linear relationship with the selected covariates. pH, SOM, and K2O presented a moderate spatial autocorrelation, while P2O5 revealed a strong autocorrelation. The cross-validation result revealed that soil pH (RMSE = 0.281) and SOM (RMSE = 9.505%) were best predicted by climatic variables. P2O5 (RMSE = 106.511 ppm) produced the best maps with climate, while K2O (RMSE = 209.764 ppm) yielded the best map with terrain attributes.ConclusionsThe findings suggest that a combination of too many environmental covariates might not provide the actual variability of a targeted soil property. This demonstrates that specific covariates with close relationships with certain soil properties might perform better than the compilation of different environmental covariates, introducing errors due to randomness. In brief, the approach of the present study is new and can be inspiring to decision-makers in the region and other world areas as well.

Reliable auxiliary communication of UAV via relay cache optimization

With the continuous reduction of drone costs and the miniaturization of equipment, many new applications have emerged in the civil and commercial fields. The remote sensing technology of Unmanned Aerial Vehicle (UAV) is used to manage and partition farmland accurately. Compared with the traditional field grid sampling method, UAV remote sensing technology can break through operating conditions, continuously collect data at low altitudes, monitor the area more flexibly, and significantly reduce labor and safety costs. Since UAVs can only provide data transmission services to users through wireless backhaul links established with ground base stations, the need to access the network is easy to lead to the disclosure of user privacy. The capacity of wireless backhaul links is limited, limiting the transmission rate of drones and reducing the user’s quality of service. Therefore, we apply edge caching technology to the assisted relay communication network to study the impact of caching technology on the performance of mobile relay systems. In particular, we propose the horizontal position design method in the buffer auxiliary relay single-user system, and the 3D position design method in the buffer auxiliary relay multi-user system. Position system, which can be reached by the maximum system average and the optimal speed, is designed. Besides, with the help of objective function conversion and classic derivation analysis method, the semi-closed expression of the optimal position of the UAV is obtained, and the intersection of the mobile relay end velocity and the user end velocity is used as the initial value. Meanwhile, the solution formula is substituted after continuous iteration so that the best advantage of local velocity can be obtained. In addition, mobile relay system is deployed to establish a two-hop wireless link to achieve reliable communication between Base Station (BS) and users security trust. The simulation experiment proves that compared with other methods, the method proposed in this paper has considerable performance improvement in power convergence, speed, trajectory path loss, and energy cost, which can provide higher-quality communication services for users in the system and better support for the broad application of drones.

Assessment of ecosystem health status using benthic diversity as tools in coastal sediments off the South West Coast, India

The southwest coast of India is densely populated and experiences a wide range of natural and anthropogenic pressures. The diversity of macro and meiobenthos was documented in order to assess the ecosystem health of an anthropogenically impacted coastal ecosystem for the first time along the southwest coast of Tamil Nadu. Offshore sediment and water samples (n=63) were collected from 14 sampling stations using the grid sampling method. Water and sediment samples were analysed for environmental variables such as pH, temperature, electrical conductivity (EC), total dissolved solids (TDS), salinity, sulphate, dissolved oxygen (DO), carbohydrate (CHO), protein, lipid, labile organic matter (LOM), total organic carbon (Corg), total organic matter (TOM) and sediment texture. Benthic macro and meiobenthos were isolated and identified using standard identification keys. A total of 5 phyla of macrobenthos with 29 species and 5 phyla of meiobenthos with 60 species were identified in the study area. Relative abundance analysis showed that the largest numbers of species belonged to Polychaeta for macrobenthos and foraminifera for meiobenthos. Redundancy analysis indicated that sediment carbohydrate content was the highest determining factor for explaining variation in the diversity of benthos. AMBI & M-AMBI results revealed that among the 14 stations, 2 were categorized as “poor” and 9 as “bad”. Multiple anthropogenic activities in the coastal environment leading to increased organic matter input have resulted in a decline in the ecosystem health of this region, which warrants site-specific management to control organic pollution.

西藏东部高寒草甸苔藓植物群落数量分类与排序

为探讨高寒草甸苔藓植物群落分布格局及其与环境因子之间的关系, 按网格法在西藏东部的林芝市和昌都市设置了28个高寒草甸样地, 采用双向指示种分析(TWINSPAN)、除趋势对应分析(DCA)和典范对应分析(CCA)的方法对群落进行数量分类和排序。结果显示, 共记录有苔藓植物19科30属60种, 均为藓类植物；其中, 丛藓科和真藓科的物种数占总种数的55%。群落优势种为北地对齿藓(Didymodon fallax)、垂枝藓(Rhytidium rugosum)、山羽藓(Abietinella abietina)、厚角绢藓(Entodon concinnus)、尖叶对齿藓芒尖变种(Didymodon constrictus var. flexicuspis)和短叶对齿藓(Didymodon tectorus)。根据TWINSPAN分析结果可将西藏东部高寒草甸苔藓植物群落划分为9个群丛, 分别为沙氏真藓(Bryum sauteri)群丛、细枝羽藓(Thuidium delicatulum)+狭网真藓(Bryum algovicum)+羽枝青藓(Brachythecium plumosum)群丛、尖叶对齿藓芒尖变种群丛、山羽藓+厚角绢藓+垂枝藓群丛、扭口藓(Barbula unguiculata)+垂枝藓+山羽藓群丛、垂枝藓+曲尾藓(Dicranum scoparium)群丛、厚角绢藓群丛、北地对齿藓群丛、短叶对齿藓群丛, 分类结果反映了群丛类型与环境之间的关系。CCA分析结果表明, 影响西藏东部高寒草甸苔藓植物群落分布的首要环境因子是纬度, 其次是归一化植被指数(NDVI), 再次是黏土比例, 海拔和太阳辐射也有一定影响。;To explore the distribution pattern of bryophyte communities in the alpine meadow and their relationships with environmental factors in eastern Tibet, 28 sample plots were set up in Nyingchi and Changdu based on the grid sampling method. Two-way indicator species analysis (TWINSPAN), detrended correspondence analysis (DCA) and canonical correspondence analysis (CCA) were applied to the quantitative classification and ordination of bryophyte communities. The results showed that 60 species of bryophytes in 30 genera and 19 families were recorded, which all belonged to mosses. The number of species belonging to Pottiaceae and Bryaceae accounted for 55% of the total species richness. The dominant species of the communities were Didymodon fallax, Rhytidium rugosum, Abietinella abietina, Entodon concinnus, Didymodon constrictus var. flexicuspis, and Didymodon tectorus. According to the TWINSPAN analysis, bryophyte communities were classified into nine associations (i.e. Bryum sauteri association, Thuidium delicatulum + Bryum algovicum + Brachythecium plumosum association, Didymodon constrictus var. flexicuspis association, Abietinella abietina+Entodon concinnus+Rhytidium rugosum association, Barbula unguiculata+Rhytidium rugosum+Abietinella abietina association, Rhytidium rugosum+Dicranum scoparium, Entodon concinnus association, Didymodon fallax association, Didymodon tectorus association), which reflected the relationships between vegetation types and the environmental factors. CCA analysis revealed that latitude was the predominant environmental factor, followed by Normalized Vegetation Index (NDVI), percentage of soil clay, influencing the distribution pattern of bryophyte communities in the alpine meadow of eastern Tibet. In addition, altitude and solar radiation were the other two environmental factors which significantly affected the distribution pattern of bryophyte communities.

Nutritive Value of Four Lucerne Cultivars Planted in Two Soil Types at Bathurst Research Station, Eastern Cape Province, South Africa

Medicago sativa (lucerne) is a widely used perennial fodder crop and ranked amongst the highly nutritive fodders globally. This study assessed the nutritive value (i.e. CP % and TDN %) of four lucerne cultivars under two soil types at Bathurst Research Station. A random grid (quadrant) sampling method was used for sampling and samples were submitted to the laboratory for analysis to determine forage quality. Data collection was carried out by separating harvested biomass for each cultivar using clean packs, weighed and dried at 70˚C for 48 hours then crude protein (CP %) and Total Digestible Nutrients (TDN %) were determined. The results of the study showed that soil type (site) had a significant (P < 0.05) effect on the overall quality of dry matter produced. In soil 1 (S1) the overall CP % content was 11.48% while it was 19.03% in soil 2 (S2). Cultivar 3 (KKS 9911) was the least nutritive cultivar in site 1, while the same cultivar was the most nutritive cultivar in site 2 pertaining CP content. Soil type also significantly affected (P < 0.05) the overall total digestible nutrient (TDN %) content of different lucerne cultivars. In S1, C4 (WL 525) had the highest (P < 0.05) TDN content in comparison to the rest of the cultivars while in the same soil type C1 (SA Std.) had the least TDN content. The four tested lucerne cultivars generally produced highly nutritive lucerne in S2 while they produced less nutritive lucerne in relation to both CP and TDN content in S1.

Productivity Evaluation of Four &amp;lt;i&amp;gt;Medicago sativa&amp;lt;/i&amp;gt; Cultivars under Two Water Regimes (Irrigated and Non-Irrigated) and Two Soil Types at Bathurst Research Station in the Eastern Cape Province, South Africa

Medicago sativa (lucerne) is a perennial and drought tolerant fodder crop which is widely used as feed for livestock in South Africa. This study evaluated four lucerne cultivars under two water regimes and also determined the effect of soil type on lucerne biomass production. To determine dry matter production per cultivar per treatment, a random grid (quadrant) sampling method was used and all biomass within that radius was cut to 5 cm above the ground level in each plot. All biomass sampling was done just before regrowth commences. Biomass data collection on four lucerne cultivars belonging to different dormancy groupings (WL 711, WL 525 HQ, KKS 9911 and SA Standard) were collected seasonally under different soil types on both irrigated and non-irrigated plots and analysed. Soil type (site) had a significant (P < 0.05) effect on the overall quantity of dry matter produced as more dry matter was produced in site 2 (S2) in comparison to site 1 (S2). Different levels of water application (moisture supplementation) also affected the quantity of dry matter produced in each soil type. The once-a-month irrigation treatment (Ir1) led to the production of superior dry matter yield on SI even though the effect was not significant. In S2 twice a month irrigation treatment (Ir2) produced the highest dry matter, however, this effect was significant (P < 0.05) only in comparison with zero irrigation (Ir0) treatment. Cultivar 3 (KKS 9911) was the least productive cultivar in S1, while the same cultivar was the least productive cultivar in S2. These findings show that both soil type and irrigation levels had a significant (P < 0.05) effect on the total dry matter production of the tested Lucerne cultivars belonging to different dormancy groupings.

Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan.

Soil salinity is the most common land degradation agent that impairs soil functions, ecosystem services and negatively affects agricultural production in arid and semi-arid regions of the world. Therefore, reliable methods are needed to estimate spatial distribution of soil salinity for the management, remediation, monitoring and utilization of saline soils. This study investigated the potential of Landsat 8 OLI satellite data and vegetation, soil salinity and moisture indices in estimating surface salinity of 1014.6 ha agricultural land located in Dushak, Turkmenistan. Linear regression model was developed between land measurements and remotely sensed indicators. A systematic regular grid-sampling method was used to collect 50 soil samples from 0–20 cm depth. Sixteen indices were extracted from Landsat-8 OLI satellite images. Simple and multivariate regression models were developed between the measured electrical conductivity values and the remotely sensed indicators. The highest correlation between remote sensing indicators and soil EC values in determining soil salinity was calculated in SAVI index (r = 0.54). The reliability indicated by R2 value (0.29) of regression model developed with the SAVI index was low. Therefore, new model was developed by selecting the indicators that can be included in the multiple regression model from the remote sensing indicators. A significant (r = 0.74) correlation was obtained between the multivariate regression model and soil EC values, and salinity was successfully mapped at a moderate level (R2: 0.55). The classification of the salinity map showed that 21.71% of the field was non-saline, 29.78% slightly saline, 31.40% moderately saline, 15.25% strongly saline and 1.44% very strongly. The results revealed that multivariate regression models with the help of Landsat 8 OLI satellite images and indices obtained from the images can be used for modeling and mapping soil salinity of small-scale lands.

Short-term impacts of winter bale grazing beef cows on forage production and soil nutrient status in the eastern Canadian Prairies

The impact of winter bale grazing on forage productivity and nutrient cycling was determined near Winnipeg, MB, Canada. Variable distribution of nutrients and forage yield within bale-grazed plots on a perennial grass–legume forage field was identified using an intensive grid sampling method. In bale-grazed plots, mean dry matter (DM) yield of forage decreased 68% in year 1 following bale grazing, with no difference in DM yield in year 2 following bale grazing, relative to an untreated control. Decreased yield was attributed to the large mass of waste feed and feces (21% of feed delivered) that remained at the centre of each bale-grazed plot, where each bale was placed. Concentrations of crude protein, total digestible nutrients, phosphorus (P), and potassium in forage increased in the first growing season following winter bale grazing compared with the control, particularly at the centre of each bale-grazed plot. Concentrations of residual nitrate-nitrogen and Olsen P in soil were 15 and 2.5 times greater, respectively, at the centre of the bale-grazed plots compared with untreated control plots. Dispersion of waste feed packs when bale grazing in subhumid climates on clay soils is recommended to minimize smothering and encourage rapid decomposition of waste feed and feces. Long-term studies are needed to determine the potential benefits and risks of bale grazing to forage productivity and soil nutrient status in the eastern Canadian Prairies.

Robust design optimization considering inlet flow angle variations of a turbine cascade

The stochastic variation of inlet flow is one common uncertainty in turbomachines, the performance impact of which requires to be quantified and taken into account in the design optimization of turbomachinery blades. The paper introduces the robust aerodynamic design optimization improving the mean performance and the robustness of a turbine cascade at off-design condition. The consistent-refinement of the grids used for solving the Euler equations is checked and the employed numerical scheme is verified and validated. Performance impact of the inlet flow angle variations is statistically evaluated by using a non-intrusive polynomial chaos with an adaptive sparse grid sampling method. The camber of the turbine blade is redesigned with the enforced blade thickness distribution to minimize the statistical mean and standard deviation (Stdv) of total pressure loss coefficient. The results of both the deterministic and robust aerodynamic design optimization are presented and compared in detail. The effects of camber redesign on reducing the statistical mean and Stdv of total pressure loss coefficient are discussed. Moreover, the statistical flow solutions are illustrated to demonstrate the influence mechanisms of flow variations in the blade passage on the improvements of aerodynamic robustness.

Mapping noise and characteristics of transit-oriented developments (TODs): A Sunbelt region case study

While scholars have copiously explored different aspects of Transit-Oriented Developments (TODs) over the last decades, the literature falls short on examining their noise implications. This study examines the planning, transportation, and environmental implications of noise in the Dallas-Fort Worth metropolitan area by performing geostatistical analysis for various mapping. These maps include a broad range of TODs’ characteristics and noise implications.Using ArcGIS tools applications into technological platforms, the data comprises sound samples through the grid sampling method. The findings confirm that TODs are 11.5 dB(A) noisier compared to non-TODs (although causes for the uncertainty aspects including the microphone position, using a type 2 SPL meter, or many unexplained variables, etc. might exist). This is mainly caused by mixed-land use, neighborhood services, and density features of TODs. These findings apply to the study areas. The study findings call for collaboration among urban planners, transportation planners, environmental planners, and noise controlling engineers to delve deeper into various planning, policy, and acoustic solutions.

Comparative analysis of soil-sampling methods used in precision agriculture

The aim of this study was to compare three different soil-sampling methods used in Precision Agriculture and their environmental impact in the agricultural production environment. The sampling methods used were: management zones by elevation (MZA), grid sampling (GS), and sampling oriented by apparent soil electrical conductivity (OS). It was tested in three different fields. When the recommendations were compared, a significant difference among the suggested dosages was observed, indicating the need to improve the soil-sampling techniques, since there were doubts about input deficits or overdoses, regardless of the technology studied. The GS method was the most environmentally viable alternative for P compared to other methods and the OS presented as the better option for K and N. However, the use of soil sensors has been shown to be a viable technology that needs further improvement in order to improve productivity and, hence, economic and environmental gains.

Spatial heterogeneity of soil water physical properties in coal gangue pile in arid desert area

Based on grid sample method (20 m×20 m), spatial heterogeneity and distribution of soil water physical properties from 0 to 5 cm of the coal gangue pile in arid desert area were explored by using classical statistics and geostatistics methods. The results showed that the variation of soil bulk density, capillary porosity, capillary maximum moisture capacity, total porosity and saturated moisture contents were weak, while water content showed a moderate variation. The best fitting model of soil bulk density was Gaussian model, and exponential model was the best fitting model for other indices. The C0/(C0+C) values of soil bulk density and water content were low and had strong spatial autocorrelation. The capillary porosity, capillary maximum moisture capacity, total porosity and soil saturated moisture showed moderate spatial autocorrelation. Soil bulk density was negatively correlated with capillary porosity, capillary maximum moisture capacity, total porosity and water content, whereas there was no significant correlation between soil moisture content and other indices. Soil capillary porosity, capillary maximum moisture capacity, total porosity, and saturated water content showed a significant synergistic effect between each other. On Kriging contour maps, capillary porosity, capillary maximum moisture capacity, total porosity and saturated moisture had a similar spatial pattern, with high values on the middle and the left side of the lower slope, whereas soil bulk density showed an opposite pattern. Soil water content was mainly affected by the slope position and increased from the upper slope to the lower slope. Our results suggested that land preparation measures should be taken to loosen the soil in root area over the coal gangue pile in arid desert area during vegetation restoration. Moreover, irrigation amount should be properly increased on the upper slope during the initial stage of vegetation restoration, which could improve soil moisture status in the overlying soil area of coal gangue and create uniform and suitable soil water physical conditions for vegetation restoration.

Intra-block spatial and temporal variability of plant water status and its effect on grape and wine parameters

Improving wine composition is a critical factor for the wine industry. Phenolic compounds play an important role in wine composition contributing to its organoleptic characteristics. Although several factors can influence the phenolic concentration, plant water status in particular has shown to have a direct impact on the phenolic compounds. It is however complex to quantitate water deficit by plant water status measurements as they depend on the specific site (topography, viticultural management practices and soil characteristics) creating variable values within the vineyard block. This study focused on analysing the effect of natural spatial and temporal variability of plant water status on grape and wine parameters. A field experiment was done in a commercial Cabernet Sauvignon block to monitor the temporal and spatial intra-block variability of plant water status using a grid sample method. Soil analysis and topography were included in the evaluation. Each target vine was assessed for yield, ripeness as well as standard juice parameters. Micro-vinification was done for each target vine and the concentration of anthocyanins and tannins analysed. The results showed that the spatial and temporal variability was evident along the season. Plant water status influenced changes in the concentration of phenolic compounds and grape parameters. The vines in the stressed class were associated with changes in soil texture and topography. These plants presented a moderate increase (6.7%) in sugar content; a significant increase in anthocyanins (22.2%) and tannins (27.5%); and a strong reduction in yield (53.2%) when compared with the non-stressed classes. The results of this study may help to understand and quantify how spatial variability is naturally distributed and its effect on grape and wine parameters.

How does mixed-use urbanization affect noise? Empirical research on transit-oriented developments (TODs)

Over the last decades, Transit-Oriented Developments (TODs) have stimulated various debates in several contexts including environmental and transportation planning. Scholars have explored the TODs’ broad spatial and socioeconomic characteristics; however, very few studies (if at all) have conducted their attributes including mixedland use in regards to noise, particularly when they require such examination. This paper aims to explain and explore TODs’ implications associated with noise by comparing them with non-TODs in the Dallas-Fort Worth metroplex area. The data include sound pressure levels (SPLs) and various built environment features through TOD stations and surrounding areas compared with non-TOD stations by performing the grid sampling method. Using a unique two-level hierarchical linear model (HLM), the study examines the impacts of TODs on noise both at micro and macro levels. The findings show that with their features TODs generate higher sound levels with almost 9.0 dB(A) difference. The study provides insights into the relationship between noise, land use, and transportation by discussing their practice and policy implications.

Comparison of the Aerosol Photometer and Light Scattering Airborne Particle Counter (LSAPC) Methods for Installed Filter System Leak Testing

Abstract The installed filter system leak test method for cleanrooms and clean zones is described in ISO 14644-3, Cleanrooms and associated controlled environments—Part 3: Test method. Portions of the methods in ISO 14644-3 were adapted from IEST-RP-CC034.4[Schaumburg, Illinois, US: Institute of Environmental Science and Technology], which provides a set of recommended procedures for leak testing HEPA and ULPA filters in situ (in the field) with the aerosol photometer test method and the light scattering airborne particle counter (LSAPC) test method. Leak testing is performed to confirm that the final high efficiency air filter system is properly installed by verifying the absence of bypass leakage in the installation, and that the filters are free of defects (small holes and other damage in the filter medium, frame, seal and leaks in the filter bank framework). This study was conducted to compare the aerosol photometer and LSAPC methods. Testing in the study consisted of creating artificial leaks in the filter system, measuring the upstream and downstream concentrations with the aerosol photometer and with the LSAPC, and comparing the filter leak penetration results. Comparison testing was applied to the procedure for the installed terminal panel filter system leakage scan test (stationary measurement) and the procedure for evaluating overall leakage of high-airflow box-type filters mounted in a duct or air-handling unit (AHU) (overall leakage test). It was found that the aerosol photometer and the LSAPC gave similar results for filter leakage within experimental error. The comparison of the leakage scan tests (stationary measurement) showed that the penetration calculated for the channel ≥ 0.3 μm of the LSAPC with an air flow rate of 1cu ft/min (CFM) (28.3 l/min) was very similar to the penetration measured with the aerosol photometer for a leakage level &amp;gt;0.01 % of the upstream concentration. The comparison result of the overall leakage test showed that overall total penetration values, obtained after dilution of artificial calibrated leaks in the filter media, were identical, within the measurement uncertainty, for particles ≥0.3 μm whether the sampling rate of the LSAPC was 1 CFM (28.3 l/min) or 50 l/min. Several recommendations are provided. In particular, for filters mounted in a duct or AHU where the filter function is critical, the recommended method is the leakage scan test method, using a grid sampling method in a plane downstream of the filter (as agreed between customer and supplier).

A METHOD FOR EXTRACTING SUBSTATION EQUIPMENT BASED ON UAV LASER SCANNING POINT CLOUDS

Abstract. Smart grid construction puts higher demands on the construction of 3D models of substations. However, duo to the complex and diverse structures of substation facilities, it is still a challenge to extract the fine three-dimensional structure of the substation facilities from the massive laser point clouds. To solve this problem, this paper proposes a method for extracting substation equipment from laser scanning point clouds. Firstly, in order to improve the processing efficiency and reduce the noises, the regular voxel grid sampling method is used to down-sample the input point cloud. Furthermore, the multi-scale morphological filtering algorithm is used to segment the point cloud into ground points and non-ground points. Based on the non-ground point cloud data, the substation region is extracted using plane detection in point clouds. Then, for the filtered substation point cloud data, a three-dimensional polygon prism segmentation algorithm based on point dimension feature is proposed to extract the substation equipment. Finally, the substation LiDAR point cloud data collected by the UAV laser scanning system is used to verify the algorithm, and the qualitative and quantitative comparison analysis between the detected results and the manually extracted results are carried out. The experimental results show that the proposed method can accurately extract the substation equipment structure from the laser point cloud data. The results are consistent with the manually extracted results, which demonstrate the great potential of the proposed method in substation extraction and power system 3D modelling applications.

Machine learning-based source identification and spatial prediction of heavy metals in soil in a rapid urbanization area, eastern China

Accelerated urbanization has resulted in the accumulation of considerable amounts of heavy metals (HMs) in urban soils. It is important to identify correlations between the urbanization process and HM accumulation in the soil and predict the spatial distribution of soil HMs based on variables related to urban expansion, so that strategies for urban soil management can be created. However, accurate predictions of urban soil HMs based on predictors associated with urbanization are still lacking. In this study, 251 topsoil samples (0–20 cm) were collected using the grid-sampling method (2 km × 2 km) in a rapid urbanization area (Hefei City, China). The concentrations of As, Zn, Pb, Hg, Ni, Cu, Cr, and Cd in the soil, as well as some attributes of soil that were impacted by urbanization were determined. The areas of different land use types in a specific grid, urbanization history, and soil properties of the site were used as predictors. The overall distribution of soil HMs were then predicted using random forest (RF), artificial neural network (ANN), and support vector machine (SVM) models. The results showed that the concentrations of As, Zn, Pb, Hg, Cu, and Cd increased significantly with an increase in urbanization history. However, the highest concentrations of Ni and Cr were observed in soils between the 2nd and 3rd ring road. According to the RF model, soil CaO, OM, sulfur, phosphorus, and surrounded built-up area were identified as the most important factors for soil Zn, Pb, Cu, and Cd, indicating a predominant anthropogenic control of these HMs. The level of Hg in the soil was also likely related to human emissions because of the importance of urbanization history and the surrounded constructing area (CA) in governing the spatial distribution of Hg. The influence of Fe2O3, Al2O3, and SiO2 on soil As, Ni, and Cr indicates their primary origin from natural processes. In comparison, the SVM and RF model revealed higher R2 and lower error indices than those of the ANN model, suggesting that SVM and RF have the ability to predict urban soil HMs satisfactorily. By using independent predictors for soil HM prediction, ANN, RF, and SVM also produced significant predictions. Furthermore, the performance of the ANN, RF, SVM models were expected to be improved by introducing variables that can reflect the sources, transport, and retention of HMs in urban soils.