China has experienced rapid economic growth in the past nearly 40 years because of their reform and opening-up policy and became the world’s second largest economy behind the USA in 2010 [1
]. However, the country is still confronted with increasingly serious environmental pollution problems [2
], especially heavy metal pollution in water and soil [5
], which has been identified as the main cause of cancer [9
]. Heavy metals, such as cadmium (Cd), mercury (Hg), arsenic (As), lead (Pb), chromium (Cr), zinc (Zn), copper (Cu), cobalt (Co), and nickel (Ni), can pose a serious hazard to the environment and human health [10
]. Although some of them, such as Zn, Cu, and Co, are essential trace elements in the human body within a certain safety threshold [5
], most of the metals are classified as highly cytotoxic, carcinogenic, and mutagenic by the International Agency for Research on Cancer even at low concentrations [5
]. Previous studies showed that excessive Pb and Hg can permanently damage the nervous system and brain and that the accumulation of Cd and As has toxic effects on the liver, lung, kidney, and skin [12
Farmland soils are the basis of agricultural production, and their environmental condition is closely related to the quality and safety of agricultural products and human health [10
]. Heavy metal pollution in farmlands is an important issue as it is closely linked to the human food chain [5
]. Heavy metal pollution in farmlands cause decreased quality and safety incidents in agricultural products. Many agricultural product incidents, such as the blood Pb contamination in Hunan [17
], Cd pollution in Guangxi [18
], and Cd-tainted rice in Hunan 2013 [5
], have occurred across China due to heavy metal pollution in farmlands, which has recently become a major agro-ecological problem that has attracted public concern. This problem is a serious threat to the sustainable development of modern agriculture and socio-economy, the agro-ecological environment, and the quality and safety of agricultural products.
Currently, the study of heavy metal pollution in farmlands has become the focus of attention of the government and the public. In April 2014, the Ministry of Environmental Protection of China (MEPC) and the Ministry of Land and Resources of China jointly released the results of a national survey on soil pollution and soil quality, which showed that 19.4% of the samples collected from the surveyed farmlands were polluted [20
]. In May 2016, the State Council of China released an action plan for soil pollution control, which provides comprehensive and strategic arrangements for the prevention and control of soil pollution in China [21
]. Subsequently, the MEPC and other ministries and local governments have performed a series of active measures for preventing and controlling soil pollution and achieved some success [21
Meanwhile, heavy metal pollution has also attracted the attention of local and international scholars. Hu et al. [5
] reviewed the current status and related public policies of heavy metal pollution in China and presented a few countermeasures for preventing heavy metal pollution. Ding et al. [6
] investigated historical changes and spatial variations in metal concentrations in Chinese composts by analyzing representative compost samples and published data. Some studies showed that heavy metal pollution in farmland soils is mainly from Cd, Hg, Cu, and Ni and that Cd is the main pollutant in farmland soils in China, with the highest pollution rate among these pollutants of 7.75% [22
]. Si et al. [14
] indicated that long-term irrigation with polluted Yellow River water leads to metal accumulation in local farmland soils and spring wheat. Wan et al. [15
] proposed a remediation method for contaminated soil by planting mulberry trees instead of rice based on their risk assessment. Tang et al. [23
] proposed a novel ecological hydraulic remediation technique for soils contaminated by heavy metals that integrates the advantages of chemical elution, solidification, phytoremediation, and field management.
Numerous methods can be used to assess heavy metal pollution with regard to risk assessment [24
]. Some common methods include the single-factor pollution index [24
], Nemerow pollution index (NPI) [25
], enrichment factor (EF) [24
], potential ecological risk index (PERI) [26
], geoaccumulation index (Igeo) [24
], and contamination security index (CSI) [24
]. Cai et al. [26
] compared the abovementioned methods and discussed their use in the assessment of soils in relation to heavy metal pollution using empirical and real-world data. A comprehensive assessment of heavy metal pollution in the topsoil of a historical urban park was conducted using the NPI, EF, Igeo, PERI, and CSI pollution indices [24
]. Xiao et al. [25
] used the Igeo, PERI, hazard index, and carcinogenic risk index to assess and determine the human health risk of heavy metals in urban soils from a steel industrial district in China. Qu et al. [7
] presented a health risk assessment procedure for heavy metal pollution using the Monte Carlo simulation technique that they based on the U.S. Environmental Protection Agency model and concluded that Pb poses a significant cumulative non-carcinogenic risk to workers [7
]. Li et al. [19
] used spatial statistical methods to examine the underlying socioeconomic and physical factors behind water pollution. Potential health risk expressed as a hazard quotient was used to assess the environmental impact and site-specific health risks of chromium (Cr) via direct and indirect exposure assessment methods [29
]. In the study of Chen et al. [27
], several pollution indicators were used to evaluate pollution levels, and Monte Carlo simulation was used to analyze the uncertainty of the health risk model. The principles of these methods differ, and each method has its own strengths and weaknesses [26
In addition, many scholars have applied geographic information system (GIS) technology for analyzing and visualizing spatial data on heavy metal pollution [30
]. For example, Carr et al. [30
] created spatial distribution maps, 3D images, and interpretive hazard maps for heavy metal pollution using GIS techniques. Shan et al. [32
] used GIS to display principal component analysis results spatially to investigate the influence of land use on heavy metal accumulation. Amous and Hassan used GIS techniques for evaluating heavy metal risk in water [33
]. The abovementioned studies are important in controlling heavy metal pollution in farmland soils.
However, the corresponding information system for heavy metal pollution in farmland soils is outdated, thereby leading to insufficient effective risk assessment or supervision of heavy metal pollution in farmland soils. With the rapid growth of heavy metal pollution monitoring data, an intelligent management information system should be established by using current advanced information technologies to effectively integrate massive amounts of data for data sharing, data mining [35
], and decision support [36
]. In this study, a decision support system based on web-based GIS (WebGIS) for risk assessment of heavy metal pollution in farmland soils is constructed and aims to assess the potential safety risk in farmland soils. Through our system, we can realize convenient data collection, rapid data query, and risk assessment of heavy metal pollution in farmland soils.
2. Framework for Decision Support of Risk Assessment
Decision support theory, methods, and algorithms have been discussed by many researchers in the literature [36
]. In this study, a decision support system for risk assessment of heavy metal pollution in farmland soils (DSS–RAHMP) is proposed (Figure 1
) and can be used for common risk assessment practices for heavy metal pollution in farmland soils.
As shown in Figure 1
, the framework can be divided into four layers from an architectural point of view: (1) infrastructure; (2) business application; (3) presentation; and (4) user layers. The infrastructure layer is used to provide infrastructure support for the risk assessment of heavy metal pollution; this layer includes data storage and web application services, load balance, and virtualization. The business application layer is used to provide users with various application services, such as risk assessment, data query and update, statistical analysis, agricultural survey, and information virtualization. The presentation layer is responsible for formatting and displaying information from the business application layer. The user layer offers various user interfaces, such as desktop computers, laptops, and tablet personal computers (PCs), through which users can access the system.
Moreover, the DSS–RAHMP is composed of: (a) a mobile data acquisition terminal (MDAT); and (b) a web-based information system (WIS). The MDAT is an Android-based portable computerized device that runs a data acquisition system for data acquisition or query. The WIS, which is the core of the DSS–RAHMP, is used for risk assessment, data management, and data query.
2.1. Three-Layered Software Architecture Model
A three-layered software architecture model is used to build the WIS and MDAT, which are both based on a model view controller (MVC) [44
], to make the system open, flexible, scalable and reusable. Design patterns are proposed through the integration of integrated Spring MVC [45
], Hibernate 4.2 [46
], Struts 2.3.1 [47
], SiteMesh 3 [48
], and Maven 3.5.0 [49
]. Figure 2
divides the three-layered software architecture model into four parts: the presentation layer (PL), business logic layer (BLL), data access layer (DAL), and model. The PL provides the user interface of the system and typically uses a controller and Java server pages (JSPs)/Views for browser-based/Android-application-based interactions, respectively; the controller is responsible for forwarding requests, and the JSPs/Views are responsible for displaying the web page to the user. The BLL separates the DAL from the PL and serves as an intermediary for data exchange between the two layers. The BLL implements the business functionality of the system, which is typically composed of services implemented using Java programming language. The DAL is responsible for exposing the data stored in the database to the BLL, thereby isolating the BLL from the details of the specific data storage solution; this isolation can minimize the effect of changes in database provider or schema. The model is an object-oriented entity or a data container that encapsulates and conceals the details of the specific data representation formats. This proposed three-layered software architecture model has high cohesion, reusability, and scalability, low coupling, and easy deployment and maintenance characteristics.
2.2. Data Acquisition and Management
2.2.1. Data Acquisition
A data acquisition system for heavy metal pollution in farmlands running on an MDAT, that is, a smartphone application (Figure 3
), is developed using the Android platform with Java programming language to collect data quickly and conveniently [50
]. Android Software Development Kit 23.0.0 and Android Studio 2.3, which is the official integrated development environment for the Android platform, are utilized in the development of this smartphone application. Figure 3
shows the four functional modules of the data acquisition system: pollution source tagging, data collection monitoring, questionnaire survey, and system settings. The data acquisition system can exchange data with the WIS using web service. Data acquisition personnel can easily record sampling information on a mobile phone and report data in real time with this data acquisition system.
In addition, quick response (QR) code, radio frequency identification (RFID), near-field communication (NFC), and 4G mobile technologies are integrated in the data acquisition system. The ID of a sample can be quickly obtained by the data acquisition system by scanning QR codes or RFID tags. Thus, this identifier is valuable in improving the efficiency of data acquisition.
2.2.2. Data Management
As the core of the DSS–RAHMP, the WIS is responsible for data processing and storage. All data uploaded by the MDAT can be processed by the WIS. The WIS is developed in Java programming language [50
] and is based on a three-layered software architecture model that runs on Tomcat 7.0 web server and supports Java Development Kit 1.7 or later. In addition, the WebGIS technology, that is, Baidu Maps, is utilized for displaying the monitoring data dynamically and visually in the WIS.
The main functional modules of the WIS are risk assessment, data management monitoring, pollution source management, agricultural survey, statistical analysis, and system settings. In the data management monitoring module, we can create new sampling tasks, assign them to the data acquisition personnel, and audit the monitoring data uploaded by the MDATs. In the pollution source management module, we can visually add pollution sources and monitoring points to the map. Meanwhile, many data mining methods are integrated into the statistical analysis module, and various data reports from multiple dimensions can be generated. The agricultural survey module can be used for various surveys, such as those for heavy metal pollution investigation and the intention of farmers to plant crops. Finally, the risk assessment of heavy metal pollution in farmland soils is conducted in the risk assessment module, which is based on our proposed soil–crop collaborative model (Section 3
2.3. Key Technologies
GIS is a computing system that can store, manipulate, analyze, and display spatial and geographic data [30
]. GIS technology can support rapid spatial orientation, location retrieval, and information visualization in the risk assessment process of heavy metal pollution in farmlands. WebGIS is the junction of web technology and GIS, thereby improving and extending the functionality of GIS. WebGIS is a distributed system that provides geographic information services that are based on Internet or intranet platforms and allows users to access geographic data and processing services only with web browsers. Thus, WebGIS has strong interactivity and dynamics. Compared with traditional GIS software, WebGIS has lower application threshold, wider application range, more timely data update, lower construction cost, and higher security. Many large Internet companies, such as Google, Baidu, and Tencent, recently developed online maps that are based on WebGIS technology for public use. In this study, Baidu Maps is used for visually displaying the pollution risk level and the detail of each monitoring point on the map.
2.3.2. QR Code and RFID
QR code [51
] is the trademark for a type of 2D barcode. RFID [52
] uses electromagnetic fields for automatically identifying and tracking tags attached to objects. In this study, QR code and RFID technologies are used for sample identification by scanning barcodes or reading RFID tags with smartphones. An RFID or a QR code tag, shown in Figure 4
a, is attached to a sample bag. The QR code tag can be scanned by a smartphone camera, and the RFID tag can be read by the built-in NFC module of a smartphone.
To ensure the authenticity and normality of the collected samples, a unified sample coding method that can generate a unique ID for each sample is designed. The ID code of each sample consists of 20 digits, which is divided into three parts, project information, regional code, and serial number, which is further subdivided into eight parts (Figure 4
b). When assigning a sampling task, the system automatically generates and prints the QR code tags for the samples to be collected. When collecting the soil or crop samples, the data acquisition personnel can quickly enter the sample code, which can automatically associate with and bind monitoring points by scanning the QR code or RFID tag embedded on the sample bag using a smartphone. This type of collection method considerably improves the efficiency of data acquisition and reduces workload. Thus, this method can be considered a highly efficient solution.
2.3.3. Web Service
Web service is the service provided by an information system to another [54
] by communicating with each other via the Internet or intranet, thereby describing a standardized way of integrating web-based interfaces using extensible markup language, simple object access protocol, web services description language, and universal description, discovery, and integration open standards. In this paper, web service technology is used for data exchange between the WIS and the MDAT equipment or between the WIS and other external information systems. Thus, this decision support system has good openness and high scalability.
Heavy metal pollution in farmland soils is a major concern in China. Heavy metal pollution in agricultural soil has adverse effects on soil ecosystem and causes potential food safety and health risks. Thus, a risk-oriented decision support system should be constructed for monitoring and assessing heavy metal pollution in farmland soils. In this study, the DSS–RAHMP is proposed and presented, and several techniques, namely, WebGIS, QR code, and RFID, are introduced in this framework. Then, a soil–crop collaborative risk assessment model, which considers the effect of heavy metal concentrations in soil and crops, is proposed for the risk assessment of heavy metal pollution in farmland soils. A soil sample and corresponding crop sample is collected from each of the 945 monitoring points in the studied area. Results show that Cd is the most important pollutant in farmland soils and has the highest unqualified rate of 98.31% in the soil samples and 60.11% in the rice samples. More than 70.9% of the farmland soils are slightly polluted or above in the studied area. Many factors, including natural and man-made factors, may cause heavy metal pollution in farmland soils. Heavy metal enrichment in soil can be the result of natural processes, such as the weathering of heavy metal-rich parent rocks and interactions between soil components, such as organic matter or clay content [24
]. Furthermore, increasing industrialization and human activities greatly affect heavy metal pollution in soils [5
]. Users can easily access the results of the risk assessment of the study area in many ways. For instance, the results are visually displayed on the WIS in the DSS–RAHMP, which provides different user interfaces. Our proposed system has been running steadily for over a year. The findings show that our proposed system framework and model can be applied for assessing the pollution of heavy metals in farmland soils.
Recently, many studies have focused on the assessment of heavy metal pollution [7
]. Nevertheless, most of these works utilized theoretical or empirical methods in their study of soil heavy metal pollution. Only a few reports presented the construction of decision support systems for risk assessment for heavy metal pollution in farmland soils. Some studies explored information systems for heavy metal pollution in water and air [60
]; however, most of them only described system designs or functions and did not present any implementation and application. Moreover, few successful implementations using information systems for risk assessment and management decision-making related to heavy metal pollution in farmland soils are reported in the Chinese industry. One reason for this deficiency is that the research on management and decision support systems for heavy metal pollution in farmlands in China is still in the exploratory stage. Another reason is that the existing information systems in this area are not powerful enough to meet the actual management needs for addressing heavy metal pollution in farmland soils. Our proposed system, which is composed of an MDAT and a WIS, can realize rapid collection and integration management of farmland soil monitoring data by integrating technologies such as WebGIS, QR code, RFID, and web services as bases. The analytical findings and their visual presentation from our proposed system can provide technical support for monitoring and supervising heavy metal pollution in farmland soils. Users can query the risk assessment results and historical sampling data of each monitoring point in real time. Few supervisors can monitor the dynamic changes of all monitoring points in a county. Traditionally, such work is impossible without the support of an information system. The proposed system effectively solves problems such as backward monitoring means, low informatization level, high cost, and low efficiency of management in the supervision of heavy metal pollution in farmland soils. Hence, the proposed system can help improve the level of automation and intelligence of the supervision of heavy metal pollution in farmland soils. The system is employed in several county-level cities in China for managing heavy metal pollution in farmlands, with its functions, including risk assessment, data acquisition, statistical analysis, and agricultural survey. The practical applications of the system are beneficial for the improvement of farmland soil environment management and heavy metal pollution prevention and control. Consequently, this study offers scientific and practical implications through the proposed WebGIS-based risk assessment system for heavy metal pollution in farmland soils. Our proposed soil–crop collaborative risk assessment model can objectively evaluate the heavy metal pollution in farmland soils in comparison with the traditional assessment models considering only soil samples. In addition, our research work may provide valuable references for governmental agencies to initiate and adjust relevant policies for preventing and controlling heavy metal pollution in farmland soils in China.
However, we should emphasize that this research has some limitations. The study is conducted from a technical point of view and focused on developing a risk assessment system for soil heavy metal pollution. Non-technical factors, such as political, socioeconomic, and legal issues [19
], which may also lead to heavy metal pollution in farmland soils [59
], were not considered in the risk assessment. Moreover, uncertainties may exist in risk assessment due to the low optimization of the layout of monitoring points and random sampling errors. In addition, the functional modules of our system need to be further extended and optimized.
For future research, we will concentrate on the following: (1) improving the risk assessment method (e.g., considering non-technical factors) to make it highly scientific and objective and upgrading the information system to satisfy the needs of users and support massive and complex datasets; (2) creating an early warning model for heavy metal pollution in farmland soils; and (3) creating a crop-planting adjustment model for seriously polluted farmland soils.