Abstract
This paper presents a new approach to assess the soil quality by aggregate indices using the Relative Soil Quality Index (RSQI) proposed by Ho Ngoc Pham. RSQI is integrated from the individual indices into a simple formula for overall assessment of the soil quality. RSQI is different from other approaches. Particularly, the individual indices and the weighting factors of Pham are calculated from the analytical laboratory data and the environmental standards, respectively, and not self-regulated as in methods of some other authors. In this paper, the authors applied the RSQI to assess the Soil Environmental Quality of rice intensive cultivation areas through a case study in Haiduong province in 2013. The RSQI is calculated for sampling points in 12 districts and simulated the Soil Environmental Quality on GIS map. The results show that the Soil Environmental Quality of rice intensive cultivation areas in Haiduong is predominantly divided into three levels: good, moderate, and poor. According to the report of General Statistics Office for Haiduong province, rice intensive cultivation areas in 2013 achieved a relatively high average rice yield of 5.90 tonnes per hectare; it means actual soil properties are in line with results of the research.
1. Introduction
The assessment of soil degradation in the world is primarily based on single criteria to build the assessment thresholds for each group of total content of bioelements, content of available forms of bioelements, heavy metals, and so forth, in which each parameter in the group of total content of bioelements and the group of content of available forms of bioelements is categorized into three levels: high, medium, and low or rich, moderate, and poor, respectively, to serve for the degradation assessment of agricultural land and forestry. The environmental quality index (EQI) approach to assess air, water, and soil was first mentioned in the work of Ott [1], and afterwards, the application of EQI to assess the soil quality (SQ) is continuously developed and widely used [2–7].
The soil degradation assessment in Vietnam has interested many scientists. Vietnamese scientists have made in-depth studies on the thresholds and the assessing scale for the group of total content of bioelements, content of available forms of bioelements and heavy metals, and so forth. In which, typical studies are of Nguyen [8], Le [9], Tran [10], Nguyen [11], Le and Tran [12], and the National Technical Standards on the soil environment for the heavy metals group [13]. However, the approach to assess soil degradation by aggregate indices in Vietnam is still new. Such approach was first mentioned in the dissertation of Nguyen in order to create an environmental land map at the provincial scale [14]. The author applied the Total Soil Quality Index (TSQI) proposed by Pham [15] to determine the Soil Environmental Quality for agricultural land (rice cultivated areas). Nevertheless, calculating the weighting factors of each group is complicated. Therefore, Pham developed the TSQI into the Relative Soil Quality Index (RSQI) which simplifies the calculation of the weighting factors of total content of bioelements, content of available forms of bioelements, , and heavy metals group in reality [16]. Because of the paper’s scope, the authors only apply RSQI into aggregate assessment of the SQ of rice intensive areas in Haiduong province.
2. Materials and Method
2.1. Materials
(i) The research used the soil sample analysis data for 12 districts with rice intensive cultivation areas in Haiduong province [17].
(ii) Research materials of Vietnamese authors [8–12] and Vietnam’s environmental regulations [13] were used to convert the categorized scale of individual index into the individual assessing scale of SQ which served for the calculation of the SQ assessment by aggregate indices, using RSQI.
2.2. Method
2.2.1. Formula of Relative Soil Quality Index (RSQI)
RSQI is a new approach to assess the SQ by aggregate indices. It is based on the synthesis or integration of individual index of surveyed parameters in order to form a formula which simplifies the SQ assessment at each monitoring point. RSQI proposed by Pham is determined by the following formula [16]:wherewhere is the common sum (sum of separate sums and ); includes of numbers of with values ≤1; includes of numbers of with values >1; is the number of monitored parameters.
Noting. Formula (1) clearly shows that RSQI depends on the relative ratio . The higher the value of the ratio is, the smaller the value of RSQI will be. Thus, the SQ is poorer.
(i) Calculating Individual Index (Subindex) of Each Parameter . To calculate RSQI in formula (1), we first need to calculate individual index as the following:(a) The groups below in Vietnam’s environmental regulation (to the heavy metals group) are There are three cases:(b) The groups in the interval [] in Vietnam’s environmental regulation (group of total content of bioelements, group of content of available forms of bioelements):
(ii) Calculating the Separate Sums , , and the Common Sum Using Formulas (2) to (4). From (1) to (10), is the actual monitoring value of parameter ,, , and are the permitted limit values of parameter , is the number of parameters with (as ), is the number of parameters with , is the number of parameters with .
2.2.2. Calculating the Temporary Weighting Factors and the Final Weighting Factors
is the final weighting factors of the parameter ; accounts for the importance which presents the relation between each parameter ; and is the number of parameters of each examination group. The final weighting factor is determined through the temporary weighting factor as follows.
(a) Groups Below in Environmental Regulation (Heavy Metals Group). is calculated by formula:where is allowance limited value of parameter and is the number of parameters selected by the group for examination.
(b) Groups in the Interval in Environmental Regulations (Group of the Total Content of Bioelements, Group of the Content of Available Forms of Bioelements). Consider parameter groups in the intervals: .
The formula to calculate of parameter for each group is as below:where the environmental regulation value of parameter in the interval is and is the number of parameters of each group.
Example. There are 2 parameters () given in , . The environmental regulation values of [] and [] are () and (), respectively. According to (12), we calculate
(c) Calculate the Final Weighting Factor of Parameter . The final weighting factor of each parameter of each group is identified by the following formula:where is the number of parameters selected by the group for examination.
2.2.3. Hierarchy for Assessing SQ of RSQI Index [16]
See Table 1.
2.2.4. Converting Hierarchy for Assessing Criterion to Hierarchy for Assessing SQ
To apply (7)–(10) formulas, first, levels and hierarchy for assessing criterion need to be converted to levels and hierarchy for assessing soil quality (SQ) for each individual criterion.
The conversion Tables 2, 3, and 4 are based on the application of Vietnam research materials about criterion for assessing soil groups.
3. Results and Discussion
3.1. Results
3.1.1. Hierarchy for Assessing SQ of RSQI
The hierarchical scale for aggregate assessing soil quality of RSQI corresponding to parameters in Table 1 is shown in Table 5.
3.1.2. Calculating the Temporary Weighting Factors and the Final Weighting Factors
(i) Calculating the Temporary Weighting Factors is as follows: The group of heavy metals (formula (11)): The group of the total content of bioelements (formula (12)): The group of the content of available forms of bioelements (formula (12)):
(ii) Calculating the Final Weighting Factors (formula (14)) is as follows:The final weighting factors of other parameters of total content of bioelements group and content of available forms of bioelements group are calculated, respectively, and results are shown in Table 6.
3.1.3. Calculating , , , , , and RSQI
Based on the research materials mentioned in Section 2.1, this research calculated individual parameter using formulas (7)–(10), calculated and using formulas (2)–(4), and calculated the RSQI index using formula (1) for soil samples.
Because of the large sample size of the rice intensive cultivation areas surveyed in Haiduong including relatively high, medium, and low plains, we present how to calculate individual index , the separate sums , , and , and the common sum ( parameters) in order to determine the RSQI of a particular soil sample S1 (Table 7). Thus, only result of other samples is shown in Table 8:
3.1.4. Creating the Soil Environmental Quality Map
From the Table 8, GIS technology with the spatial interpolation is applied to develop a simulated map of the SEQ assessment at the research area (Figure 1).
3.2. Discussion
(i) From Table 8, the SQ of rice intensive cultivation areas in Haiduong is good (nondegraded soil), moderate (soil starting degradation), and poor (degraded).
(ii) From the SQ map (Figure 1), incorporation with digital land use map (Haiduong DoNRE, 2013 [18]) will calculate the area of rice intensive cultivation for 12 districts in hectare that consists of 3 groups: good (nondegraded), moderate (starting degradation), and poor (degradation) Soil Environmental Quality. Particularly, the nondegraded area of the province is 25,106.85 ha (36.29%), the area which starts to degrade is 28,821.69 ha (41.66%), and the degraded area is 15,254.33 ha (22.05%). To districts in the provinces with the moderate and poor soil quality, the soil with moderate and poor quality needs to be monitored and fertilized properly.
(iii) According to the General Statistics Office (2013) [19], rice intensive cultivation areas in Haiduong province 2013 reached a relatively high average yield of 5.90 tonnes/ha. Because the RSQI approach shows results of the soil with good quality (nondegraded), the soil with moderate quality (start degraded), and the soil with poor quality (degraded), in which the degraded soil area accounts for only 22.05%, the soil quality of rice intensive cultivation areas is considered fairly good. Therefore, results of the research are in line with the relatively high yield in reality.
4. Conclusion
The research used the soil sample analysis data for twelve districts in Haiduong province to calculate individual indices for 10 parameters. The selected basic parameters are Cd, Cu, and Pb; , SOM, , and ; , , and . The separate sum is integrated from parameters group with whereas the separate sum is integrated from parameters group with . The common sum equals plus . Using these sums, we calculated the RSQI values for 36 soil samples (16 samples in relatively high plains; 7 samples in medium plains; and 13 samples in low plains).
The results of calculations show that the soil sample with good quality () accounts for %; the proportions of soil sample with medium quality () and poor quality () are equal; both of them account for %.
Based on the hierarchical scale of soil quality of RSQI, the soil quality of rice intensive cultivation areas in Haiduong is predominantly divided into three levels: good (nondegraded soil), moderate (soil starting degradation), and poor (degraded soil), corresponding to 3 levels which were assessed by individual index.
The RSQI values are simulated on digital land use map by GIS technology. Each area has the same level described by the same colour on the map. Based on this map, calculating the area of each level (good, moderate, and poor) in hectare, the nondegraded area of the province is 25,106.85 ha (36.29%), the area which starts to degrade is 28,821.69 ha (41.66%), and the degraded area is 15,254.33 ha (22.05%). The map which is developed from the aggregate SQ assessment approach by using RSQI provides local managers an overview of the level of soil degradation before promptly taking appropriate measures to prevent or reduce pollution.
In summary, our results are consistent with the reality. Therefore, the calculation method using individual indices and the aggregate index RSQI has a scientific basis and high accuracy; the method could be applied in warning service and environmental management at the provincial scale.
Disclosure
The content of this paper is one of the results of theses at level VNU, code QMT.12.01.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
The authors hereby express sincere thanks to the Ministry of Natural Resources and Environment and Vietnam National University for funding the subject.