Spatial Heterogeneity Analysis of Soil Heavy Metals in Chongqing City Based on Different Interpolation Methods

Heavy metal pollution in urban soil is an important indicator of environmental pollution, which is of great significance to the sustainable development of cities. Choosing the best interpolation method can accurately reflect the distribution characteristics and pollution characteristics of heavy metals in soil, which is conducive to effective management and implementation of protection strategies. In this study, the grid sampling with a depth of 40cm was carried out in the whole study area based on the principle of uniform sampling, and the characteristics of As, Cu and Mn elements in the soil of the main urban area of Chongqing were investigated. The interpolation accuracy and difference of results of four interpolation methods, namely ordinary Kriging (OK), inverse distance weighting (IDW), local polynomial (LPI) and radial basis function (RBF), were analyzed and compared. The results showed that the average values of As (5.802 mg kg-1), Cu (23.992 mg kg-1) and Mn (573.316 mg kg-1) in the soil of the study area were lower than the background values of heavy metals in Chongqing. Coefficient of variation showed that As (55.71%), Cu (35.73%) and Mn (32.21%) all belonged to moderate variation. The parameters of semi-variance function theory model show that Cu element belongs to moderate spatial correlation, and As and Mn element have strong spatial correlation. The spatial distribution of the three elements was further predicted by using OK method, IDW method, LPI method and RBF method, which showed that LPI and OK method had strong smoothing effect and could not reflect the information of local point source pollution, while the interpolation results of IDW method and RBF method greatly retained the maximum and minimum information of element content, which reflected the necessity of using different methods when studying the spatial distribution of soil properties.

Pedogenic characteristics of soil in Melur block, Madurai district, Tamil Nadu in India: A case study

Soil is an important source of human life and agricultural production. Studying on the pedon and its site characteristics pave the way for understanding the nature of soils and its utility. A study on pedological characterization of soils in Melur block, Madurai District (Tamil Nadu), was carried out during 2019-2020 using grid sampling with village map/cadastral maps. Soil mapping unit-based soil samples were collected in Chunampoor, Thuvarangulam, Poonjuthi and Veppapadupu and pedons were characterized as per the standard procedure. The results showed that soils were moderately deep to very deep in nature, ranging from 2.5 YR 3/6 to 10YR 4/6. The soil texture varied from sandy clay loam to sandy clay with weak to moderate sub-angular blocky structure. The consistency of soil varied from slightly hard to very hard when dry, very friable to firm when moist, slightly sticky to very sticky and slightly plastic to very plastic in wet condition. The crops viz., paddy, sugarcane, banana, groundnut and vegetables were very suitable for such type of soil of the Madurai district.

A New Upper Bound for Sampling Numbers

We provide a new upper bound for sampling numbers $$(g_n)_{n\in \mathbb {N}}$$ ( g n ) n ∈ N associated with the compact embedding of a separable reproducing kernel Hilbert space into the space of square integrable functions. There are universal constants $$C,c>0$$ C , c > 0 (which are specified in the paper) such that $$\begin{aligned} g^2_n \le \frac{C\log (n)}{n}\sum \limits _{k\ge \lfloor cn \rfloor } \sigma _k^2,\quad n\ge 2, \end{aligned}$$ g n 2 ≤ C log ( n ) n ∑ k ≥ ⌊ c n ⌋ σ k 2 , n ≥ 2 , where $$(\sigma _k)_{k\in \mathbb {N}}$$ ( σ k ) k ∈ N is the sequence of singular numbers (approximation numbers) of the Hilbert–Schmidt embedding $$\mathrm {Id}:H(K) \rightarrow L_2(D,\varrho _D)$$ Id : H ( K ) → L 2 ( D , ϱ D ) . The algorithm which realizes the bound is a least squares algorithm based on a specific set of sampling nodes. These are constructed out of a random draw in combination with a down-sampling procedure coming from the celebrated proof of Weaver’s conjecture, which was shown to be equivalent to the Kadison–Singer problem. Our result is non-constructive since we only show the existence of a linear sampling operator realizing the above bound. The general result can for instance be applied to the well-known situation of $$H^s_{\text {mix}}(\mathbb {T}^d)$$ H mix s ( T d ) in $$L_2(\mathbb {T}^d)$$ L 2 ( T d ) with $$s>1/2$$ s > 1 / 2 . We obtain the asymptotic bound $$\begin{aligned} g_n \le C_{s,d}n^{-s}\log (n)^{(d-1)s+1/2}, \end{aligned}$$ g n ≤ C s , d n - s log ( n ) ( d - 1 ) s + 1 / 2 , which improves on very recent results by shortening the gap between upper and lower bound to $$\sqrt{\log (n)}$$ log ( n ) . The result implies that for dimensions $$d>2$$ d > 2 any sparse grid sampling recovery method does not perform asymptotically optimal.

CONIC: Contour Optimized Non-Iterative Clustering Superpixel Segmentation

Superpixels group perceptually similar pixels into homogeneous sub-regions that act as meaningful features for advanced tasks. However, there is still a contradiction between color homogeneity and shape regularity in existing algorithms, which hinders their performance in further processing. In this work, a novel Contour Optimized Non-Iterative Clustering (CONIC) method is presented. It incorporates contour prior into the non-iterative clustering framework, aiming to provide a balanced trade-off between segmentation accuracy and visual uniformity. After the conventional grid sampling initialization, a regional inter-seed correlation is first established by the joint color-spatial-contour distance. It then guides a global redistribution of all seeds to modify the number and positions iteratively. This is done to avoid clustering falling into the local optimum and achieve the exact number of user-expectation. During the clustering process, an improved feature distance is elaborated to measure the color similarity that considers contour constraint and prevents the boundary pixels from being wrongly assigned. Consequently, superpixels acquire better visual quality and their boundaries are more consistent with the object contours. Experimental results show that CONIC performs as well as or even better than the state-of-the-art superpixel segmentation algorithms, in terms of both efficiency and segmentation effects.

Estimating Pollution Loads in Snow Removed from a Port Facility: Snow Pile Sampling Strategies

Choosing the appropriate sampling strategy is significant while estimating the pollutant loads in a snow pile and assessing environmental impacts of dumping snow into water bodies. This paper compares different snow pile sampling strategies, looking for the most efficient way to estimate the pollutant loads in a snow pile. For this purpose, 177 snow samples were collected from nine snow piles (average pile area − 30 m2, height − 2 m) during four sampling occasions at Frihamnen, Ports of Stockholm’s port area. The measured concentrations of TSS, LOI, pH, conductivity, and heavy metals (Zn, Cu, Cd, Cr, Pb, and V) in the collected samples indicated that pollutants are not uniformly distributed in the snow piles. Pollutant loads calculated from different sampling strategies were compared against the load calculated using all samples collected for each pile (best estimate of mass load, BEML). The results/study showed that systematic grid sampling is the best choice when the objective of sampling is to estimate the pollutant loads accurately. Estimating pollutant loads from single snow column samples (collected at a point from the snow pile through the entire depth of the pile) produced up to 400% variation from BEML, whereas samples composed by mixing volume-proportional subsamples from all samples (horizontal composite samples) produced only up to 50% variation. Around nine samples were required to estimate the pollutant loads within 50% deviation from BEML for the studied snow piles. Converting pollutant concentrations in snow to equivalent concentrations in snowmelt and comparing it with available guideline values for receiving water, Zn was identified as the critical pollutant.

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.

Geospatial assessment of wetland soils for rice production in Ajibode using geospatial techniques

Wetlands played an important role in human development and nature nutrient store for rice cultivation. Spatial techniques have gained importance in monitoring wetland changes. The study aimed to assess wetland soils for rice production using spatial techniques. The area was sample using stratified grid sampling. Nutrient availability and rice suitability were assessed in ArcGIS 10.6 environment. The soil was characterized into Eutric fluvaquent (Soil Survey Staff, 2010) and correlated as fluvisols in the World Reference Base system. The results of the land cover changes showed that built-up, waterbody, and farmland have increased by 39, 18, and 29%, respectively, and 13% decrease was observed in vegetation. The study concluded that soils of the studied area varied from marginally (75%), not suitable (20%), and permanently not suitable (5%) for rice production. Therefore, without proper assessment and management of these studied soils, rice production will continue to be futile.

KOMBINASI GEOLISTRIK DAN GRID SAMPLING UNTUK MENGIDENTIFIKASI ADANYA KONTAMINASI HYDROCARBON PADA TANAH

Hydrocarbon pada permukaan tanah dapat dilihat secara visual namun hal ini tidak dapat menghasilkan nilai kuantitatif dan kualitatif yang akurat dalam menentukan berapa jumlah volume dari tanah yang terkontaminasi tersebut. Oleh karena itu dibutuhkan studi yang dapat menjelaskan secara kuantitatif dan kualitatif pada tanah terkontaminasi sehingga memudahkan dalam penanganan dan pengelolaannya. Geolistrik merupakan salah satu metode kuantitatif yang dapat digunakan dalam menentukan kondisi permukaan tanah. Secara kualitatif, komposisi tanah dapat dibuktikan dengan sampling laboratorium untuk menentukan apakah termasuk ke dalam kategori limbah bahan berbahaya dan beracun (LB3) atau tidak sesuai dengan lampiran V yang terdapat pada Peraturan Pemerintah No.101 tahun 2014. Studi ini fokus pada tahapan untuk mengidentifikasi adanya hydrocarbon pada tanah menggunakan kombinasi geolistrik dan grid sampling sehingga mendapatkan volume yang lebih akurat besaran tanah terindikasi mengalami kontaminasi. Sedangkan batasan studi ini adalah dilakukan pada bekas fasilitas B-mediasi yang diduga terkontaminasi. Berdasarkan studi geolistrik yang telah dilakukan, volume permukaan tanah yang diduga terkontaminasi diperoleh sebesar 689 m3 (setara 1.381 ton). Sementara ketika menggunakan kombinasi data geolistrik dan grid sampling pada area diduga tersebut, didapat volume yang terindikasi terkontaminasi sebesar 594 m3 (setara 1.190 ton). Hasil ini dapat disimpulkan bahwa dengan menggunakan kombinasi metode geolistrik dan grid sampling dalam mengidentifikasi adanya kontaminasi pada permukaan tanah dapat menjelaskan secara kuantitatif dan kualitatif volume tanah yang terkontaminasi dengan keakuratan sebesar 115%.