Evaluation of soil texture classification from orthodox interpolation and machine learning techniques.

The current investigation examines the effectiveness of various approaches in predicting the soil texture class (clay, silt, and sand contents) of the Rawalpindi district, Punjab province, Pakistan. The employed techniques included artificial neural networks (ANNs), kriging, co-kriging, and inverse distance weighting (IDW). A total of 44 soil specimens from depths of 10-15 cm were gathered, and then the hydrometer method was adopted to measure their texture. The map of soil grain sets was formulated in the ArcGIS environment, utilizing distinct interpolation approaches. The MATLAB software was used to evaluate soil texture. The gradient fraction, latitude and longitude, elevation, and soil texture fragments of points were proposed to an ANN. Several statistical values, such as correlation coefficient (R), geometric mean error ratios (GMER), and root mean square error (RMSE), were utilized to evaluate the precision of the intended techniques. In assessing grain size and spatial dissemination of clay, silt, and sand, the effectiveness and precision of ANN were superior compared to kriging, co-kriging, and inverse distance weighting. Still, less than a 50% correlation was observed using the ANN. In this examination, the IDW had inferior precision compared to the other approaches. The results demonstrated that the practices produced acceptable results and can be used for future research. Soil texture is among the most central variables that can manipulate agriculture plans. The prepared maps exhibiting the soil texture groups are imperative for crop yield and pastoral scheduling.

Human Arsenic exposure via dust across the different ecological zones of Pakistan.

The present study aims to assess the arsenic (As) levels into dust samples and its implications for human health, of four ecological zones of Pakistan, which included northern frozen mountains (FMZ), lower Himalyian wet mountains (WMZ), alluvial riverine plains (ARZ), and low lying agricultural areas (LLZ). Human nail samples (N=180) of general population were also collected from the similar areas and all the samples were analysed by using ICP-MS. In general the higher levels (p<0.05) in paired dust and human nail samples were observed from ARZ and LLZ than those of other mountainous areas (i.e., WMZ and FMZ), respectively. Current results suggested that elevated As concentrations were associated to both natural, (e.g. geogenic influences) and anthropogenic sources. Linear regression model values indicated that As levels into dust samples were associated with altitude (r(2)=0.23), soil carbonate carbon density (SCC; r(2)=0.33), and population density (PD; r(2)=0.25). The relationship of paired dust and nail samples was also investigated and associations were found for As-nail and soil organic carbon density (SOC; r(2)=0.49) and SCC (r(2)=0.19) in each studied zone, evidencing the dust exposure as an important source of arsenic contamination in Pakistan. Risk estimation reflected higher hazard index (HI) values of non-carcinogenic risk (HI>1) for children populations in all areas (except FMZ), and for adults in LLZ (0.74) and ARZ (0.55), suggesting that caution should be paid about the dust exposure. Similarly, carcinogenic risk assessment also highlighted potential threats to the residents of LLZ and ARZ, as in few cases (5-10%) the values exceeded the range of US-EPA threshold limits (10(-6)-10(-4)).

Trace metals in Ganges soft-shell turtle (Aspideretes gangeticus) from two barrage: Baloki and Rasul, Pakistan.

The concentration of nine metals was measured in liver, kidney, heart, muscle, plastron, and carapace of Aspideretes gangeticus from Rasul and Baloki barrages, Pakistan. The results indicated that metal concentration were significant different among tissues of Ganges soft-shell turtles. However, higher concentrations of Co (5.12 µg/g) and Ni (1.67 µg/g) in liver, Cd (0.41 µg/g) in heart, Fe (267.45 µg/g), Cd (2.12 µg/g) and Mn (2.47 µg/g) in kidney, Cd (0.23 µg/g), Cu (2.57 µg/g), Fe (370.25 µg/g), Mn (5.56 µg/g), and Pb (8.23 µg/g) in muscle of A. gangeticus were recorded at Baloki barrage than Rasul barrage. Whereas mean concentrations of Pb (3.33 µg/g) in liver, Co (1.63 µg/g), Cu (11.32 µg/g), Pb (4.8 µg/g) and Zn (144.69 µg/g) in heart, Co (4.12 µg/g) in muscle, Ni (1.31 µg/g), Pb (2.18 µg/g), and Zn (9.78 µg/g) in carapace were recorded higher at Rasul barrage than Baloki barrage. The metals followed the trend Fe>Zn>Ni>Cu>Mn>Pb>Cr>Co>Cd. Metals of toxicological concern such as Cr, Pb, and Cd were at that level which can cause harmful effects to turtles. The results provide baseline data of heavy metals on freshwater turtle species of Pakistan.