Synthetic Evaluation Method Research Articles

Shallow Groundwater Quality Assessment and Pollution Source Apportionment: Case Study in Wujiang District, Suzhou City

Groundwater serves as a crucial resource, with its quality significantly impacted by both natural and human-induced factors. In the highly industrialized and urbanized Yangtze River Delta region, the sources of pollutants in shallow groundwater are more complex, making the identification of groundwater pollution sources a challenging task. In this study, 117 wells in Wujiang District of Suzhou City were sampled, and 16 groundwater quality parameters were analyzed. The fuzzy synthetic evaluation method was used to assess the current status of groundwater pollution in the study area; the principal component analysis (PCA) was employed to discern the anthropogenic and natural variables that influence the quality of shallow groundwater; and the absolute principal component scores–multiple linear regression (APCS-MLR) model was applied to quantify the contributions of various origins toward the selected groundwater quality parameters. The results indicate that the main exceeding indicators of groundwater in Wujiang District are I (28%), NH4-N (18%), and Mn (14%); overall, the groundwater quality is relatively good in the region, with localized heavy pollution: class IV and class V water are mainly concentrated in the southwest of Lili Town, the north of Songling Town, and the south of Qidu Town. Through PCA, five factors contributing to the hydrochemical characteristics of groundwater in Wujiang District were identified: water–rock interaction, surface water–groundwater interaction, sewage discharge from the textile industry, urban domestic sewage discharge, and agricultural non-point source pollution. Additionally, the APCS-MLR model determined that the contributions of the three main pollution sources to groundwater contamination are in the following order: sewage discharge from the textile industry (10.63%) > urban domestic sewage discharge (8.69%) > agricultural non-point source pollution (6.26%).

Repetitive assembly basic action detection and standard work measurement based on deep learning

ABSTRACT Researchers extensively use deep learning for assembly task action recognition due to its superior feature representation. However, current methods fail to integrate assembly actions with basic human movements, resulting in poor generalization. Moreover, most research focuses on estimating operation times without computing standard work times. To address this issue, this study uses a deep learning method to detect basic repetitive assembly actions and compute their normalized time. This paper uses the TadTR model to determine each assembly operation’s average observation time and operation category. MS-G3D was then used to recognize the MOD action pairs and obtain the basic MOD actions and PTS times for each operation. The synthetic evaluation method was subsequently employed to obtain the evaluation coefficient, ultimately determining standard working hours.This study shows that the accuracy of the left and right MS-G3D models is 0.8804 and 0.7957, respectively, and the deviation of observation hours is less than 10%. Supplementary experiments further validated the proposed method’s flexibility, showing an observation time deviation of less than 5%. Thus, the standard work time measurement method proposed in this study provides finer-grained recognition of assembly actions and a more objective measure of standard working hours.

Data Driven and Fuzzy Synthetic Evaluation Based Submarine Cable Health State Assessment

—Health state assessment is of great importance to ensure the safe and stable operation of submarine cables. Submarine cables have composite structural characteristics and work in complex and influential environments, which hinders manual inspection, arouses difficulties in maintenance, and causes challenges on health state assessment. To this end, a data driven and fuzzy synthetic evaluation based submarine cable health state assessment method is proposed in this paper. Firstly, following the technical guidelines of submarine cable health state assessment, dynamic submarine cable online monitoring data and static data including periodic preventive test and operation inspection results are integrated to build a three-layer index system. In addition, long and short-term memory (LSTM) neural network based ultra-short-term prediction models are built to obtain the predictions of online monitoring indices. On this basis, together with the static data, a fuzzy synthetic evaluation method is used to establish a two-stage state evaluation process to obtain the multi-period health states of submarine cables. The effectiveness of the proposed method is verified with an actual submarine cable from an offshore platform in an oilfield of China National Offshore Oil Corporation (CNOOC).

Evaluation of county-level integrated health organizations: Combination of the weighting-grey synthetic evaluation method

This paper aims to improve the efficacy assessment of County-level Integrated Health Organizations (CIHOs) in China. By analyzing CIHOs in Fujian Province, an empirical study was done to confirm the efficacy of the combined weighted-grey synthetic evaluation approach. The combined weights of evaluation indicators are calculated using the suggested method, which combines the analytical network process method and the coefficient of variation method. The grey center points of the CIHOs to be evaluated are determined, the whitenization weight functions is constructed, a comprehensive evaluation matrix is established, and then the composite score values are calculated and ranked. A more thorough evaluation of CIHOs can be accomplished scientifically using this comprehensive approach.

Adaptation and application of the fuzzy synthetic evaluation (FSE) method for characterizing the trophic state of tropical semiarid reservoirs.

Eutrophication is a recurrent problem in water bodies, especially in tropical semiarid reservoirs. The trophic state index (TSI) is an important tool for the environmental management of aquatic systems. However, determining the TSI involves uncertainties that can affect decision-making. This study aimed to adapt and apply the fuzzy synthetic evaluation (FSE) to characterize the TSI considering the uncertainties of the reference eutrophication classification system. The Castanhão reservoir, the largest in the State of Ceará, Brazil, was taken as a case study. The results showed that (i) the uncertainty of the trophic classification system can be characterized by the triangular and trapezoidal membership functions; (ii) the result matrix associates the global trophic level with a degree of certainty, providing greater confidence to the decision maker; (iii) the eutrophication index (EI) is not an adequate tool for hierarchizing the trophic degree; and (iv) the membership level of the global trophic state generated by the FSE method is a suitable alternative to the EI. It is concluded that the proposed FSE model can be a useful tool for improving water resources management, especially in drylands.

Hydrogeochemical Characteristics and Groundwater Quality in Phreatic and Confined Aquifers of the Hebei Plain, China

This study aims to investigate hydrogeochemical characteristics and groundwater quality in the Hebei Plain and to discuss factors controlling the groundwater quality. A total of 54 groundwater samples were collected and analyzed for 31 hydrogeochemical parameters, and a fuzzy synthetic evaluation (FSE) method was used for assessing groundwater quality. Results show groundwater total hardness, total dissolved solids (TDS), and major ions excluding K+ in phreatic aquifers higher than that in confined aquifers. From the Piedmont plain to the littoral plain, phreatic aquifers towards the reducing environment, and the enhancement of water–rock interaction, ion exchange process, and evaporation probably resulted in the increase in groundwater TDS, major ions (excluding HCO3− and SO42−), B, and Mn concentrations. Moreover, phreatic groundwater chemistry was mainly controlled by rock weathering changing into evaporite dissolution and seawater intrusion from the Piedmont plain to the littoral plain, according to the Gibbs diagram. The proportion of drinkable groundwater in confined aquifers was 1.6 times that in phreatic aquifers. In phreatic aquifers, the proportion of drinkable groundwater in the Piedmont plain was as high as 68%, but none of the drinkable groundwater occurred in the central and littoral plains. Groundwater quality in phreatic aquifers was mainly controlled by five factors, including the water–rock interaction, the marine geogenic sources, the agricultural pollution, the acidification, and the reductive environment. By contrast, groundwater quality in confined aquifers was mainly controlled by three factors, including the water–rock interaction and redox processes, agricultural pollution, and the input of external water. Therefore, in the Hebei Plain, groundwater in confined aquifers is more suitable for drinking purposes than in phreatic aquifers. Additionally, phreatic groundwater in the Piedmont plain should be protected.

Assessment of corporate social responsibility performance for Vietnamese construction organizations

Purpose Corporate social responsibility (CSR) performance is critical to address construction activities’ environmental and social impacts. This study aims to evaluate the level of CSR implementation across various categories within the context of the Vietnamese construction industry. Design/methodology/approach The literature review and semi-structured interviews were conducted to determine Vietnamese construction organizations’ most commonly implemented CSR activities. A total of 252 valid responses were then obtained through a questionnaire survey. In addition, the fuzzy synthetic evaluation (FSE) method was used to evaluate the extent to which CSR categories have been implemented in construction companies. Findings The findings revealed 31 commonly implemented CSR activities of the Vietnamese construction firms, which were grouped into four categories. Of the four categories, the FSE analysis showed that stakeholder CSR was the most critical category, followed by ethical CSR, philanthropic CSR and environmental CSR. Originality/value This research provides valuable CSR activities to construction companies that intend to develop sustainably. Moreover, the proposed prioritization methodology offers practitioners a reliable and easy-to-use evaluation tool that clearly understands CSR performance within their organizations.

Fuzzy quantitative risk allocation model (FQRAM) to guide decision-making on risk allocation in Ghanaian public-private partnership (PPP) power projects

PurposePublic-private partnership (PPP) power projects are associated with varying risk factors. This paper aims to develop a fuzzy quantitative risk allocation model (QRAM) to guide decision-making on risk allocation in PPP power projects in Ghana.Design/methodology/approachA total of 67 risk factors and 9 risk allocation criteria were established from literature and ranked in a two-round Delphi survey using questionnaires. The fuzzy synthetic evaluation method was used in developing the risk allocation model.FindingsThe model’s output variable is the risk allocation proportions between the public body and private body based on their capability to manage the risk factors. Out of the 37 critical risk factors, the public sector was allocated 12 risk factors with proportions = 50%, while the private sector was allocated 25 risk factors with proportions = 50%.Originality/valueTo the best of the authors’ knowledge, this research presents the first attempt in Ghana at endeavouring to develop a QRAM for PPP power projects. There is confidence in the model to efficiently allocate risks emanating from PPP power projects.

Depth-profiling analysis of ZnO layers with three morphologies by direct-current glow discharge mass spectrometry

In the fabrication of high-quality materials, information regarding the element concentration and distribution in the depth direction is particularly important. In this study, the potential applicability of direct-current glow discharge mass spectrometry (dc-GD-MS) was explored for depth-profiling analysis of zinc oxide (ZnO) layers on steel substrates. The ZnO layers on steel substrates presented three morphologies of thin films, network textures and nanorods fabricated by magnetron sputtering (ZnO-1/steel), sol–gel method (ZnO-2/steel) and hydrothermal synthesis (ZnO-3/steel), respectively. The discharge conditions were optimized based on dc-GD-MS to obtain the ideal depth resolutions. The results revealed that compared with ZnO layers of ZnO-2/steel and ZnO-3/steel, the dc-GD-MS depth-profiling analysis of ZnO-1/steel presented superior performance with depth resolution of 0.22 μm and a clear interface between ZnO layer and steel substrate. dc-GD-MS depth-profiling analysis suggested that the ZnO layer morphology, thickness and sputtering rate could affect the depth resolution. Furthermore, the fuzzy synthetic evaluation method (FSEM) suggested that the layer morphology had a greater impact on the depth resolutions than thickness and sputtering rate under the optimized discharge conditions. The study presented an efficient and sensitive approach for determining the elemental concentrations and distributions of semi-conducting materials, offering novel insights into material fabrication.

Improving Chuanxiong Rhizoma quality standards using an effect-constituent index based bioassay

Chuanxiong Rhizoma is a traditional Chinese medicine (TCM) that is used to promote blood circulation. We set out to improve Chuanxiong Rhizoma quality standards using a bioassay-based Effect-constituent Index (ECI). We performed high performance liquid chromatography (HPLC) analysis to determine the chemical constituents of 10 Chuanxiong Rhizoma samples from different locations. We then constructed a direct bioassay method to investigate each sample’s antiplatelet aggregation effects. To screen for active ingredients that promote antiplatelet aggregation, we carried out Pearson correlation analyses between biopotency and compounds identified in the HPLC data. We developed an ECI of platelet aggregation inhibition using a multi-indicator synthetic evaluation method based on the integration of biopotency and active constituents. To further assess the biopotency-based Chuanxiong Rhizoma quality evaluation result accuracy, we compared the ECI with the chemical indicator’ method. Eight common chemical fingerprints peaks indicated notable content variation among samples. Biological evaluation showed that all 10 samples could inhibit platelet aggregation, although they had significantly different biological potencies. Using spectrum-effect relationships, we determined that Ligustilide was the significant active constituent responsible for antiplatelet aggregation. Using correlation analysis, we found that ECI correlated with the Chuanxiong Rhizoma extract’s platelet aggregation inhibitory effect. Additionally, ECI proved to be a good indicator of Chuanxiong Rhizoma quality, whereas chemical indicators failed to distinguish and predict the biopotency-based quality grade. This work indicates that ECI is a useful tool for associating sample quality with chemical markers linked to TCM clinical effects. ECI also provides a paradigm for improving the quality control of other TCMs that invigorate blood circulation.

Evaluation of the Critical risk factors in PPP - procured Mass Housing Projects in Abuja Nigeria - A fuzzy synthetic evaluation (FSE) approach

The study accessed the critical risk factors in public-private partnership procured mass housing project (PPP-MHP) delivery in Nigeria. The research design adopts a quantitative approach, using well-structured questionnaires distributed to stakeholders involved in PPP-MHPs i.e. the consultants, in-house professionals, contractors, and the organized private sector (OPS) registered with PPP departments of the Federal Capital Territory Development Authority (FCDA) Abuja, Nigeria. The instrument relates to the background information of respondents and the risk peculiar to PPP-MHP. Sixty-three (63) risk factors were submitted for the respondents to rank on a Likert scale of 7 using Mean Item score (MIS) for risk occurrence and its severity, while the risk significance index (RI) was used to determine the risk impact. Fuzzy Synthetic Evaluation (FSE) method was subsequently applied to determine the risk criticality groups and the overall risk level in the sector. The findings show that thirty-one (31) risk factors were critical in the sector while the financial and micro-economic risk group is contributing most significantly to the overall risk level in PPP-MHPs in Nigeria. The implication for practice is that having known the risk group contributing most significantly to the overall risk level in PPP-MHPs, adequate financial and budgetary allocation should be made available before embarking on a such venture to sustain the scheme in the country. The study is one of the recent research conducted on housing since the procurement option is novel in the sector. The study is of immense value to PPP actors by providing the necessary information required to formulate risk response methods to minimize the impact of the risk factors in PPP MHPs.

A clustering-based energy consumption evaluation method for process industries with multiple energy consumption patterns

ABSTRACT The production systems in process industries are confirmed to be tremendously energy-consuming, and the trust in promoting their energy efficiency has become a concern, with its precondition being to evaluate the real-time energy consumption. A widespread evaluation method is to develop a global model that employs energy audit techniques, whereas they are always carried out with few appreciations of multiple energy consumption patterns, and the utilization of energy consumption auxiliary information. To address the challenge, a two-stage clustering-based-energy consumption evaluation method is proposed for process industries in this study. Specifically, a novel structure of the fuzzy clustering method is designed with a mixture of unsupervised and semi-supervised learning stages that leverages the weighted information to independently address energy consumption patterns. Then energy consumption predictions are estimated for potential energy-optimized control. The key performance indicators of energy consumption are calculated for each pattern, and the final evaluation grade will be achieved through the fuzzy synthetic evaluation method. According to the experiment results, the proposed method delivers better evaluation results against baselines with more accurate clustering; it may provide a new thought for energy consumption evaluation and is confirmed to enable practitioners to acquire the potential benefits in engineering.

Evaluating a driving index of nonresidential green building implementation for sustainable development in developing countries from a Vietnamese perspective

AbstractThe purpose of this study is to evaluate a quantitative driving index for implementing green building in Vietnam as a case study in developing countries. Using literature review and expert interviews, 19 driving forces affecting green building implementation were identified in the Vietnamese context. Via obtained data from a questionnaire survey, four principle factors were grouped from defined drivers. The fuzzy synthetic evaluation method was utilized to develop a driving index (DI) for developing the green building in Vietnam. DI model indicated that environmental impact is the most crucial group for green building implementation in Vietnam. Apart from this, the result of the comparative analysis also indicated that most driving forces for adopting green building in developing countries related to environmental and social problems. These results provide the decision‐makers with a comprehensive tool to assess the expectations for green building implementation in both Vietnam and other developing countries.

Hydrogeochemical Characteristics and Groundwater Quality in a Coastal Urbanized Area, South China: Impact of Land Use

Land use transformation accompanied with various human activities affects groundwater chemistry and quality globally, especially in coastal urbanized areas because of complex human activities. This study investigated the impact of land use on groundwater chemistry and quality in a coastal alluvial aquifer (CAA) of the Pearl River Delta where urbanization continues. A fuzzy synthetic evaluation method was used to evaluate the groundwater quality. Besides, factors controlling groundwater chemistry and quality in the CAA were discussed by using a principal components analysis (PCA). Nearly 150 groundwater samples were collected. All samples were filtered on-site and stored at 4 °C until the laboratory procedures could be performed. Nineteen chemical parameters including pH, dissolved oxygen, redox potential, total dissolved solids, K+, Na+, Ca2+, Mg2+, NH4+, HCO3−, NO3−, SO42−, Cl−, I−, NO2−, Pb, Mn, Fe, and As were analyzed. Results show that groundwater chemistry in the CAA was dominated by Ca-HCO3 and Ca·Na-HCO3 facies. In addition, groundwater with NO3 facies was also present because of more intensive human activities. In the CAA, 61.8% of groundwaters were fit for drinking, and 10.7% of groundwaters were undrinkable but fit for irrigation, whereas 27.5% of groundwaters were unfit for any purpose. Poor-quality groundwaters in urban and agricultural areas were 1.1–1.2 times those in peri-urban areas, but absent in the remaining area. Groundwater chemistry and quality in the CAA was mainly controlled by five factors according to the PCA. Factor 1 is the release of salt and NH4+ from marine sediments, and the infiltration of domestic and septic sewage. Factor 2 is agricultural activities related to the irrigation of river water, and the use of chemical fertilizers. Factor 3 is the industrial pollution related to heavy metals and acid deposition. Factor 4 is the input of anthropogenic reducing sewage inducing the reductive dissolution of As-loaded Fe minerals and denitrification. Factor 5 is the I− contamination from both of geogenic and anthropogenic sources. Therefore, in order to protect groundwater quality in coastal urbanized areas, repairing old sewer systems in urban areas, building sewer systems in peri-urban areas, limiting sewage irrigation and the amount of chemical fertilizers application in agricultural areas, as well as strengthening the supervision of the industrial exhaust gas discharge in urban and peri-urban areas are recommended.

Assessment of symmetries and asymmetries on barriers to circular economy adoption in the construction industry towards zero waste: A survey of international experts

This study evaluates simultaneously the symmetries and asymmetries on the classification of barriers to circular economy (CE) adoption in the building construction industry (BCI) of developing and developed economies. This is crucial because the vagueness of the impacts of CE barriers in extant studies affects encyclopaedic and specific CE policy formulation. Consequently, feedbacks from 140 CE experts across 39 developing and developed economies were analysed. Fuzzy synthetic evaluation (FSE) method was deployed to objectively determine the significant impacts of the barriers, whereas the Mann-Whitney U test was applied to identify significant differences in experts' opinions between the two economies. The FSE results indicated that organizational, information technology, and infrastructure and logistics barriers are the most critical to global CE adoption. The Mann-Whitney U test reveals a significant difference in the experts’ perspectives between developing and developed economies on regulatory, information technology, and economic and market barriers. Therefore, they are perceived as specific barriers as they impact CE adoption in BCI differently across the two economies. However, infrastructure and logistics, and organizational barriers are classified as general barriers. The findings of this study underscored the contextuality of barriers to CE adoption in BCI and demonstrated the need for generic and specific policy development. Also, the significance indices of the classification of the barriers using FSE method serve as an allocative function that will help policymakers and stakeholders allocate requisite resources to the most profound barriers towards achieving global systemic circularity and zero construction waste.

An indicator framework for assessing the readiness of hospital in the smart healthcare transformation

The technical and management issues that existed in traditional healthcare institutions, such as relatively lagging technologies, untimely information, have received increasing attention. In recent decades, the reconstruction or transformation of smart healthcare system has emerged as a new research trend. Especially with the outbreak of the COVID-19 epidemic, the smart healthcare system realizes the timely allocation of medical resources and sharing of information. Therefore, this paper aims to develop an indicator framework for assessing the hospital’s readiness in transforming towards smart healthcare. First, based on the literature review, an indicator framework of readiness of smart healthcare transformation is developed for smart healthcare transformation, which includes three hierarchies of “ smart facility management (smart FM)”, “medical system (MS)” and “organizational management (OM)”. Secondly, an online questionnaire survey is conducted to test the validity of the indicator framework. Based on factor analysis, the indicator framework is proved to be effective as a whole, and all the 26 indicators are significant. Finally, the readiness of case hospital “A” in the smart healthcare transformation is examined by fuzzy synthetic evaluation method. The findings of this paper provide an assessment tool for medical institutions to self-evaluate their status in the information construction process.

Supply chain risk factor assessment of Indian pharmaceutical industry for performance improvement

PurposeThe purpose of the present work is to improve the industry performance by identifying and quantifying the risks faced by the Indian pharmaceutical industry (IPI). The risk values for the prominent risks and overall industry are determined based on the four risk parameters, which would help determine the most contributive risks for mitigation.Design/methodology/approachAn extensive literature survey was done to identify the risks, which were also validated by industry experts. The finalized risks were then evaluated using the fuzzy synthetic evaluation (FSE) method, which is the most suitable approach for the risk assessment with parameters having a set of different risk levels.FindingsThe three most contributive sub-risks are counterfeit drugs, demand fluctuations and loss of customers due to partners' poor service performance, while the main risks obtained are demand, financial and logistics. Also, the overall risk value indicates that the industry faces medium to high risk.Practical implicationsThe study identifies the critical risks which need to be mitigated for an efficient industry. The industry is most vulnerable to the demand risk category. Therefore, the managers should minimize this risk by mitigating its sub-risks, like demand fluctuations, bullwhip effect, etc. Another critical sub-risk, the counterfeit risk, should be managed by adopting advanced technologies like blockchain, artificial intelligence, etc.Originality/valueThere is insufficient literature focusing on risk quantification. Therefore, this work addresses this gap and obtains the industry's most critical risks. It also discusses suitable mitigation strategies for better industry performance.

Resilient reverse logistics with blockchain technology in sustainable food supply chain management during COVID‐19

AbstractCOVID‐19, which is a global problem, affects the all supply chains throughout the world. One of the supply chains most affected by COVID‐19 is food supply chains. Since the sustainable food supply chain processes are complex and vulnerable in terms of product variety, it has been negatively affected by the operational effects of COVID‐19. While the problems experienced in the supply chain processes and raw material constraints caused stops in production, the importance of new business models and production approaches came to the fore. One of the issues of increasing importance is the adoption of reverse logistics activities in sustainable food supply chains and increasing the resilience of food supply chains by integrating blockchain technology into processes. However, adapting blockchain technology to increase the resilience of reverse logistics activities in the food supply chain has advantages as well as risks that need to be considered. Therefore, it is aimed to determine these risks by using fuzzy synthetic evaluation method for eliminating the risks of blockchain adaptation for flexible reverse logistics in food supply chains to increase resiliency. The novelty of this study is that besides discussing about the benefits of BC‐T, it is to identify the risks it can create, to eliminate these risks and to guide the establishment of resilience in reverse logistics activities of SFSCs. According to results, the risks with the highest value among the subrisks are determined as data security risks. Data management risks are calculated as the risk with the highest value.

Bond Performance of Hook-End Steel Fiber to the Mortars Cured for 360 Days

The reliable bond of steel fibers to concrete matrix is fundamental to ensure they work together under internal and external actions throughout their service life. Due to the lack of research on the long-term bond property, this paper conducted an experimental study on the bond of hook-end steel fiber in the manufactured sand mortars with different water to binder ratios cured for different ages from 7 days to 360 days. The characteristic pull-out load-slip curves are measured, and bond performance indexes at different curing ages are analyzed by using the multi-index synthetical evaluation method. The results show that the bond strengths, the fiber strength use efficiency, the debonding work and the pull-out work increased with the increase in curing age up to to 90 days, which became stable with little variation with the follow-up curing age. The variations are closely associated with the development of mortar strength. Steel fibers embedded in high-strength mortar presented a higher debonding ductility and a lower slipping ductility. In addition, the prediction formulas of long-term debonding strength, bond strength and residual bond strength are proposed.

Odorants in the organophosphorus pesticide contaminated soils：identification, prioritization, and quantification using GC-HRMS based on target and non-target screening

Odorants from the pesticide site remediation were complex and changeable, and the screening of key odorous substances was the prerequisite for the process of odor control. In the study, a novel odorants identification method based on the targeted and non-targeted approach by gas chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometer (GC Orbitrap/MS) was established to investigate the low-concentration odor pollution in three organophosphorus pesticide sites. The comprehensive odor characterization method was developed and applied in the light of olfactory threshold, flavor characteristics, molecular structure, response value, and detection rate. 98 pollutants were screened out using target analysis, and 149 substances were identified with confidence levels of at least 2 based on non-target analysis. Based on the analysis results, 42 odorants were screened out by the synthetic evaluation method for priority control, including phenols, aldehydes, ketones, esters, organic sulfide, amines, nitriles, amide, acylchlorides, ethers, heterocyclic, halogenated benzene and polycyclic aromatic hydrocarbons. The limit of detection (LOD) of 42 odorants was from 0.010 to 0.100 ng L−1, which was lower than other detection methods. This research provided a new technology for identification of odorants in pesticide sites.