Full Length Research Paper Water quality assessment of a wastewater treatment wastewater to protect public health and to meet water quality criteria for the aquatic environment and for water recycling and reuse (Agyemang, ). treatment plant ranged from 47 to 50°C and with a mean of °C. The effluent temperature ranged from 28 to In general, water for irrigation should have a pH b etween and Water with pH below is termed "acidic" and water with pH above is termed "basic"; pH is "neutral". Sometimes the term "alkaline" is used instead of "basic" and often "alkaline" is confused with "alkalinity" Jun 19, · Soil pH is a key factor that controls soil nutrient availability, soil microbial activities, and crop growth and development. However, studies on the soil pH variations of cultivated lands in different horizons at the regional scale remain limited. In this work, soil samples were collected from three soil horizons (A, B, and C) at sites over the hilly region of Chongqing, southwestern
For more information about PLOS Subject Areas, click here. Soil pH is a key factor that controls soil nutrient availability, soil microbial activities, and crop growth and development. However, studies on the soil pH variations of cultivated lands in different horizons at the regional scale remain limited. In this work, soil samples were collected from three soil horizons A, B, and C at sites over ph in aquatic plants research paper hilly region of Chongqing, southwestern China.
Six topographic indicators, four climate parameters, and parent material were considered. Classification and regression trees CARTs were applied to investigate the relationships between soil pH and the variables in the A, B, and C horizons. Model performances were evaluated by root mean square error RMSErelative root mean square error RRMSEand coefficient of determination R 2, ph in aquatic plants research paper.
Results showed that soil pH increased obviously from the A to C horizons. Soil pH was predicted well by the forcing factors with the CART models in all horizons.
RMSE, RRMSE, and R 2 varied between 0. The relative importance of the studied variables to soil pH differed with the horizons. Annual temperature range ATRterrain wetness index TWIand Melton ruggedness number were critical factors that controlled soil pH variability in the A horizon. Parent material, precipitation of warmest quarter PWQATR, and TWI were important variables in the B horizon.
Parent material, PWQ, ATR, and precipitation were key factors in the C horizon. The results are expected to provide valuable information for designing appropriate measurements for agricultural practices and preventing soil acidification.
Citation: Zhang Y-Y, Wu W, Liu H Factors affecting variations of soil pH in different horizons in hilly regions. PLoS ONE 14 6 : e Editor: João Canário, Universidade de Lisboa Instituto Superior Tecnico, PORTUGAL. Received: February 24, ; Accepted: June 4, ; Published: June 19, Copyright: © Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution Licenseph in aquatic plants research paper, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files. Competing interests: The authors have declared that no competing interests exist. The relationship between soil properties and soil-forming factors is an issue that has been studied all over the world [ 2 — 7 ]. Soil pH is a key index of soil properties, which was considered as one of the main variables influencing other soil properties [ 9 ]. Studies have shown that soil pH can influence crop yields, soil nutrient release, and soil microbial activity to a large extent [ 1011 ].
If farmland soil is too acidic or too alkaline, then land production will be limited [ 12 ]. Soil pH is an important regulator of soil and is inevitably controlled by different soil-forming factors [ 13 — 16 ].
Previous studies have reported that factors associated with the variations in soil pH differ with locations and scales [ 131517 — 19 ]. At the global scale, soils collected from different climates have distinct soil pH. Soils from arid climates are commonly alkaline with a high soil pH. By contrast, soils from humid climates are commonly acidic with a low soil pH [ 20 ]. Precipitation and potential evapotranspiration control soil pH variations at the global scale [ 13 ].
Additionally, the effects of climate factors on soil pH variations are observed ph in aquatic plants research paper regional scales [ 20 — 22 ]. For example, Cheng et al. Chytrý et al. Meanwhile, the relationships between soil pH and terrain indicators are site-dependent. For example, Moore et al. Chen et al. Li et al. Others also reported that soil pH variations are influenced by parent materials. For example, ph in aquatic plants research paper, soils developed from Triassic sandstones and Quaternary sands have significantly different pH values [ 23 ].
Reuter et al. Fabian et al. However, studies on the effects of environmental forcing factors on soil pH in sub-soils remain limited. A lot of statistical models have been applied to investigate the relationships between the environmental forcing factors and soil pH [ 25 — 28 ph in aquatic plants research paper. Among them, the classification and regression tree CART is a non-parametric decision tree algorithm that is easy to build and explain [ 29 ].
CART could help users make a decision among several choices. CART does not need any model assumptions and could automatically address categorical and continuous variables, ph in aquatic plants research paper. Thus, it has been widely applied to explore non-linear relationships between independent and dependent variables [ 29 — 31 ].
One interesting outcome of CART is the relative importance of independent predictors to the target. Furthermore, cross-validation complemented with CART could effectively avoid overfitting [ 29 ], ph in aquatic plants research paper.
In consideration of the advantages of CART and the lack of studies on the impact of environmental forcing factors on soil pH in sub-soils, we attempted to 1 examine the spatial variability of soil pH in different horizons and 2 investigate the relationships between environmental forcing factors climate, parent material, and topography and soil pH in surface and sub-soils.
The work was conducted in a hilly region of southwestern China. The length between east and west is km, and the breadth between south and north is km. The elevation varies between and m, and the slope is between 0. The climate is subtropical monsoon humid climate with a mean annual temperate of The parent materials are Silurian marlite, Triassic limestone, Permian limestone, Ordovician limestone, Cambrian limestone, and Jurassic limestone.
The study was conducted in the hilly region of the study area, where tobacco is planted in April and harvested in August. A total of soil profile samples were collected from the upland in September—November and The study did not involve any protected area, private land, endangered or protected species. Up to three layers A: surface soil layer, B: subsoil layer, C: substrate layer were identified for each profile. At each site, a pit with 1 m width × 2 m length × 1. For each horizon, g of thoroughly mixed soil was used for chemical analyses at each site.
Finally, and soil samples were used for the A, B, and C layers, respectively. All soil samples were air-dried and passed through a 2 mm soil sieve prior to data analyses. Soil pH was determined in a soil-to-water suspension of ratio of Soil-forming factors, namely, topography, ph in aquatic plants research paper, climate, and parent material, were considered in this work. Given that the cultivation system and crop species at each site were similar, the organisms that were also a soil-forming factor were not used in this study.
By reviewing published papers and many previous experiments, six topographic variables and four climate parameters were used Table 1. The topographic variables were channel network, valley depth, Melton ruggedness number MNRelevation, ph in aquatic plants research paper, and TWI; they were derived from a 90 m×90 m grid Digital Elevation Model DEM by the software System for Automated Geoscientific Analyses V.
The climate parameters were annual mean temperature, annual precipitation, precipitation of warmest quarter PWQand annual temperature range ATR between the warmest and coldest month Table 1. They were derived from the WorldClim Database. Parent material is a vital factor for soil pH, as noted during the sampling. Classification and regression tree CART is a non-parametric decision method that was ph in aquatic plants research paper by Breiman et al.
The model follows the recursive partitioning rules to generate a classification categorical or regression numeric tree depending on the response variable.
Regression tree was used in the present work. This technique does not need any assumption on model and data distribution. One outcome of the CART models is the relative importance of each variable to the system [ 32 — 35 ].
In the present study, CART was applied to investigate the relative importance of the factors that control the variations in soil pH in different horizons. The following equation was used to obtain the relative importance of variable x j [ 29 ]: 1 where Δs j,k is the reduction in the mean squared error S if node k were split by variable x j.
The appropriate parameters for CART were obtained after several experiments. The minimum numbers of child nodes and parent nodes were 1 and 2, respectively. The maximum tree depth was 5. Tenfold cross-validation was used in the present study to avoid overfitting. Detailed information about CART ph in aquatic plants research paper be found in Breiman et al. In assessing the ph in aquatic plants research paper of the model, three indices, namely, root mean square error RMSEph in aquatic plants research paper, relative root mean square error RRMSEand coefficient of determination R 2were used in the current study.
The models with the lowest values of RMSE and highest values of R 2 showed superior performance. Pearson correlation coefficients were calculated to determine the correlations between the soil pH and soil-forming factors in different horizons and the correlations among soil-forming factors.
A one-way analysis of variance ANOVA with Games Howell was applied to test the differences in soil pH among the parent materials and horizons. The statistical analyses were conducted using Microsoft Excel and SPSS V. The descriptive statistics of soil pH in different horizons, climate factors, and terrain indicators are listed in Table 2. The minimum and mean values of soil pH increased from the A to C horizons.
Among the climate indicators, the CVs showed that temperature, ATR, and PWQ had low variability and that precipitation had medium variability across the area. The differences in the soil pH among the parent materials in the horizons were tested by ANOVA, and the results are shown in Fig 2.
Obviously, soil pH increased from the A to C horizons for each parent material. Furthermore, significant differences in soil pH were observed among the parent materials. Soils developed from Cambrian, Jurassic, Ordovician, and Triassic limestones had higher soil pH than did those from Permian limestone and Silurian marlite for each horizon, ph in aquatic plants research paper. The Pearson correlations between soil pH and soil-forming factors are listed in Table 3.
Effect of pH temperature and dissolved o2 in water ecosystem
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Full Length Research Paper Water quality assessment of a wastewater treatment wastewater to protect public health and to meet water quality criteria for the aquatic environment and for water recycling and reuse (Agyemang, ). treatment plant ranged from 47 to 50°C and with a mean of °C. The effluent temperature ranged from 28 to May 02, · Irrigated water at pH prevented buds unfolding into flowers, but the pH treatment was opposite. This phenomenon was inconsistent with the former conclusion that the highest stress was caused by pH treatment, and the specific reason needed further study. In plants irrigated with water at pH , flower quality of P. lactiflora had changed. Its flower diameter and flower fresh weight were reduced, and flower Cited by: 24 Control plants were spray ed with distilled water at pH P. frutescens turned very sensit ive to t he SAR in terms of plant growth (to tal leaf area
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