Identifying areas under potential risk of illegal construction and demolition waste dumping using GIS tools

Solid waste detection, monitoring and mapping in remote sensing images: A survey

The detection and characterization of illegal solid waste disposal sites are essential for environmental protection, particularly for mitigating pollution and health hazards. Improperly managed landfills contaminate soil and groundwater via rainwater infiltration, posing threats to both animals and humans. Traditional landfill identification approaches, such as on-site inspections, are time-consuming and expensive. Remote sensing is a cost-effective solution for the identification and monitoring of solid waste disposal sites that enables broad coverage and repeated acquisitions over time. Earth Observation (EO) satellites, equipped with an array of sensors and imaging capabilities, have been providing high-resolution data for several decades. Researchers proposed specialized techniques that leverage remote sensing imagery to perform a range of tasks such as waste site detection, dumping site monitoring, and assessment of suitable locations for new landfills. This review aims to provide a detailed illustration of the most relevant proposals for the detection and monitoring of solid waste sites by describing and comparing the approaches, the implemented techniques, and the employed data. Furthermore, since the data sources are of the utmost importance for developing an effective solid waste detection model, a comprehensive overview of the satellites and publicly available data sets is presented. Finally, this paper identifies the open issues in the state-of-the-art and discusses the relevant research directions for reducing the costs and improving the effectiveness of novel solid waste detection methods.

An ensemble machine learning model to uncover potential sites of hazardous waste illegal dumping based on limited supervision experience

With the soaring generation of hazardous waste (HW) during industrialization and urbanization, HW illegal dumping continues to be an intractable global issue. Particularly in developing regions with lax regulations, it has become a major source of soil and groundwater contamination. One dominant challenge for HW illegal dumping supervision is the invisibility of dumping sites, which makes HW illegal dumping difficult to be found, thereby causing a long-term adverse impact on the environment. How to utilize the limited historic supervision records to screen the potential dumping sites in the whole region is a key challenge to be addressed. In this study, a novel machine learning model based on the positive-unlabeled (PU) learning algorithm was proposed to resolve this problem through the ensemble method which could iteratively mine the features of limited historic cases. Validation of the random forest-based PU model showed that the predicted top 30% of high-risk areas could cover 68.1% of newly reported cases in the studied region, indicating the reliability of the model prediction. This novel framework will also be promising in other environmental management scenarios to deal with numerous unknown samples based on limited prior experience.

Automatic identification of illegal construction and demolition waste landfills: A computer vision approach

Dozens of landslide accidents are reported at construction and demolition waste (CDW) landfills worldwide every year. Those accidents could be avoided via timely inspection in which the identification of illegal CDW landfills at a large scale plays a critical role. Traditional field surveys are time-consuming, labor-intensive, which is not effective in large-scale detection of landfills. To address this issue, a methodology is proposed in this study for the automatic identification of CDW landfills in large-scale areas by utilizing semantic segmentation of remote sensing imagery. Deep learning is employed to achieve automatic identification and a case study is conducted to showcase the models. The results shown that: (1) The model proposed in this study can effectively identify CDW landfills, with an accuracy of 96.30 % and an IoU of 74.60 %. (2) DeepLabV3+ demonstrated superior performance over Pspnet and HRNet, though HRNet approached DeepLabV3+ in performance with appropriate optimizations. (3) Case study results indicate the potential existence of 52 CDW landfills in Shenzhen, includng 4 official landfills and 48 suspected illegal CDW landfills, mainly in Longhua, Guangming, and Baoan districts. The method proposed in this study provides an effective approache to identify large-scale illegal CDW landfills and has great significance for supervising CDW landfills.

Assessing and predicting the illegal dumping risks in relation to road characteristics

Using historical data to assess illegal dumping risks has significant potential to enhance the effectiveness of waste management in low-population density counties where the ability to patrol and regulate illegal dumping is limited. Using big data and geographical analysis to identify high-risk areas plays an important role in improving the effectiveness of supervision related to illegal dumping. However, current methods for classifying risk areas have limited accuracy. Taking an area in South Australia as an example, this study aims to improve the accuracy of classifying risk areas by using geo-information technology and machine learning methods. The results show that combining illegal dumping locations with road characteristics allows the high-risk areas to be refined to road sections. Compared with identifying the whole road or area as a high-risk spot, this result could be beneficial for monitoring illegal dumping in real life. Moreover, this model allows the analysis of factors that affect illegal dumping locations. Results show that the influencing factors for different risk levels of illegal dumping vary significantly. The model developed in this research can effectively distinguish risk levels according to these factors, and the model classification accuracy can reach 85%. In addition, there are priorities amongst these factors. This finding could help environmental authorities to allocate equipment and personnel with consideration of varying level of importance of those factors. This study has both technical contributions to identify high risk areas of illegal dumping, and theoretical implications for its management.

Evaluating the effects of policies on building construction waste management: a hybrid dynamic approach

The construction industry, as a vital pillar of a country's economy, generates a significant amount of construction waste, which places a tremendous burden on the environment and society. Although previous studies have explored the impact of policies on construction waste management, there is a lack of a simulation model that can be easily used, taking into account the dynamic nature, generality, and practicability of the model. To fill this gap, a hybrid dynamics model of construction waste management system is developed using agent-based modeling, system dynamics, perceived value, and experienced weighted attraction. Based on relevant data from the construction waste industry in Shenzhen, China, the effect of five policies on contractor strategy selection and overall evolution is tested. The results indicate that industry rectification policy and combination policy can effectively promote the resource treatment of construction waste and reduce illegal dumping, pollution to the environment of waste and treatment process, and waste treatment cost. The findings of this research will help not only researchers better analyze the effect of construction waste policies but also policymakers and practitioners in proposing effective construction waste management policies.

Efficient supervision strategy for illegal dumping of construction and demolition waste: A networked game theory decision-making model

While the construction industry has brought substantial economic benefits to society, it has also generated substantial construction and demolition waste (CDW). Illegal dumping, which refers to dumping CDW in an unauthorized non-filling location, has become widespread in many countries and regions. Illegally dumping CDW destroys the environment, causing groundwater pollution and forest fires and causing significant economic impacts. However, there is a lack of research on the decision-making behaviours and logical rules of the main participants, construction contractors and the government in the illegal CDW dumping process. This paper constructs an evolutionary game model on a small-world network considering government supervision to portray the decision-making behaviours of illegal dumping participants and conducts a numerical simulation based on empirical equations to propose an effective supervision strategy for the government to manage illegal CDW dumping efficiently. It is found that the illegal dumping behaviours of contractors are mainly affected by the intensity of government supervision, the cost of fines and the income of illegal dumping; while for government, a supervision strategy is found to be necessary, and a supervision intensity of approximately 0.7 is the optimal supervision probability given supervision efficiency. Notably, under a low-level supervision probability, increasing the penalty alone does not curb illegal dumping, and a certain degree of supervision must be maintained. The results show that in addition to setting fines for illegal dumping, the government must enforce a certain level of supervision and purify the market environment to steadily reduce illegal dumping.

Multicriteria analysis of the environmental and economic performance of circularity strategies for concrete waste recycling in Spain

Construction and demolition waste (CDW) is identified by multiple circular economy (CE) policies as a key sector for implementing circularity strategies due to the high volume of waste produced and the large consumption of raw materials. However, CE is not widely applied in the sector because of the lack of solid estimations on its environmental and economic viability. The main aim of this study was to propose a set of methodological steps to identify the optimal circularity alternatives for CDW products based on a multicriteria analysis of their environmental and economic performance. This methodology is applied to evaluate concrete waste. In specific, high-grade applications of concrete waste were analyzed comprising the processing into recycled coarse aggregates (RCA) for their use in structural and non-structural concrete. Multiple scenarios with different RCA replacements (20%, 30% and 100%) and different types of sorting and recycling (on-site and off-site) were evaluated in accordance with the specific site conditions of the region of Catalonia, Spain. The Life Cycle Analysis methodology (LCA) was used to perform the environmental analysis, while a detailed cost analysis was conducted for the economic aspect. The multicriteria method VIKOR was used for the selection of alternatives considering three different criteria. The results of this study showed environmental and economic advantages of CE scenarios based on the use of RCA over conventional concrete, mainly due to the influence of landfilling and transport distances. RCA produced on-site showed a better performance than RCA from fixed plants.

Development and application of an analytical framework for mapping probable illegal dumping sites using nighttime light imagery and various remote sensing indices

Illegal dump sites (IDS) pose significant risks to human and the environment and are a pressing issue worldwide. Due to their secretive nature, the detection of IDS is costly and ineffective. In this study, an analytical framework was developed to detect probable IDSs in rural and remote areas using nighttime light (NTL) as a proxy for populated areas. An IDS probability map is produced by aggregation of Landsat-8 and Suomi NPP satellite imagery, multiple-criteria decision-making analysis, and classification tools. Six variables are considered, including modified soil adjusted index, land surface temperature, NTL, highway length, railway length, and the number of landfills. Vulnerability of the inhabitants on reserve lands was assessed using three sample regions. The method appears effective in reducing potential IDSs. Only about 7% of the 31,285 km² study area are identified as probable IDS, being classified as "very high" and "high". Landfills without permit are found more effective in lowering IDS occurrence. Spatial distributions of reserve lands and the maturity of highways network nearby may be more important than the length of railways when assessing the inhabitant vulnerability due to IDS. Highway length is the most decisive factor on IDS probability among all classes, with membership grades ranging from 0.99 to 0.55. Land surface temperature appears less effective for the identification of smaller scale IDS. NTL is more prominent on IDS probability in the "very high" class, with a membership grade of 0.80. The finding suggests that populated areas represented by NTL is a priori of IDS.

Construction and Demolition Waste Management Research: A Science Mapping Analysis

Construction and demolition waste treatment has become an increasingly pressing economic, social, and environmental concern across the world. This study employs a science mapping approach to provide a thorough and systematic examination of the literature on waste management research. This study identifies the most significant journals, authors, publications, keywords, and active countries using bibliometric and scientometric analysis. The search retrieved 895 publications from the Scopus database between 2001 and 2021. The findings reveal that the annual number of publications has risen from less than 15 in 2006 to more than 100 in 2020 and 2021. The results declare that the papers originated in 80 countries and were published in 213 journals. Review, urbanization, resource recovery, waste recycling, and environmental assessment are the top five keywords. Estimation and quantification, comprehensive analysis and assessment, environmental impacts, performance and behavior tests, management plan, diversion practices, and emerging technologies are the key emerging research topics. To identify research gaps and propose a framework for future research studies, an in-depth qualitative analysis is performed. This study serves as a multi-disciplinary reference for researchers and practitioners to relate current study areas to future trends by presenting a broad picture of the latest research in this field.

Construction and demolition waste management in Australia: A mini-review

Construction and demolition activities generate huge quantities of waste with substantial impacts on environment. This mini-review article covers the literatures relating to construction and demolition waste management practice in Australia. The Scopus search engine was used in literature search and 26 journal articles relating to construction and demolition waste management in Australia were targeted for analysis. Additionally, various government acts, regulations, policies, and strategy documents were collected and analyzed. This review indicated that the inconsistencies in legislation and landfill levies across states and territories contributed to the cross-jurisdiction waste movement. Given the stakeholders' attitude and project life cycle, this review reported that the design phase had the greatest potential to minimize waste and that the role of designers had been highlighted in various empirical studies. This review provides practitioners and academics with an understanding of the current construction and demolition waste management research in Australia and recommends directions for future research.

Building Information Modeling (BIM) for Construction and Demolition Waste Management in Australia: A Research Agenda

Construction and demolition waste (C&DW) contribute to approximately 30% of the total waste generation worldwide, by which heterogeneous ecological impacts, such as resource depletion, global warming, and land degradation, are engendered. Despite ongoing research efforts to minimize construction waste via the Building Information Modeling (BIM)-aided design, there is a paucity of research on integrating BIM in demolition waste management (DWM). This study investigates prominent barriers and future research directions toward the wider adoption of BIM in C&DWM by conducting a systematic literature review. First, this study identifies the barriers that hinder the implementation of C&DWM in Australia; then, it explores the benefits and challenges of leveraging BIM applications for C&DWM. The findings suggest that, for existing buildings without up-to-date design drawings, it is imperative to improve the accuracy of data capturing and object recognition techniques to overcome the bottlenecks of BIM-DWM integration. Moreover, the development of regional-oriented material banks and their harmonization with life cycle assessment databases can extend the potential of BIM-based sustainability analysis, making it applicable to the DWM domain. This study proposes a research agenda on tackling these challenges to realize BIM’s full potential in facilitating DWM.

A Synthesis of Express Analytic Hierarchy Process (EAHP) and Partial Least Squares-Structural Equations Modeling (PLS-SEM) for Sustainable Construction and Demolition Waste Management Assessment: The Case of Malaysia

Construction and demolition waste (CDW), as the main consequence of construction and demolition (C&D) activities, has severely affected our sustainability needs. However, construction and demolition waste management (CDWM) lacks the integration of sustainability concepts. Thus, there is a great need to include sustainability dimensions in CDWM to reach sustainable construction and demolition waste management (SCDWM). This study aims at empirically investigating SCDWM by analyzing the impacts of factors that contribute to sustainability aspects of CDWM on waste management hierarchy (WMH), including reduce, reuse, recycle, and disposal strategies. According to the literature, 26 factors were initially identified and grouped under four categories, namely environmental, economic, social, and administrative, that contribute to sustainability aspects of CDWM. Subsequently, a pilot test was performed to assess the significance and applicability of these factors in the Malaysian construction industry by implementing the express analytic hierarchy process (EAHP). Then, a questionnaire survey was performed to collect data from 132 construction companies involved in CDWM. Partial least squares-structural equation modeling (PLS-SEM) was used to test the hypothetical relationships by applying SmartPLS software. Results demonstrated that the economic aspect of CDWM (main category) and “public environment contamination due to illegal waste dumping” (sub-category) were the most influential factor in SCDWM in Malaysia.

Status quo of illegal dumping research: Way forward

Due to the rapid social and economic development, the past decades have witnessed the improvement of human being's quality of life and the speedy development of the construction industry. Meanwhile, the illegal dumping of solid waste has presented a significant issue. By using the method of systematic review, this study critically examined the literature related to illegal dumping that were published since 1990, and analyzed the current status and future trends of related research. Results show that the current studies on illegal dumping mainly focus on four perspectives: environmental science and toxicology, economics, management, and the use of emerging technologies. This critical review revealed that although the issue of illegal dumping has been widely recognized in recent years, some questions remain unanswered. Therefore, a future research agenda is proposed. These include: (1) Identifying the migration of pollutants in the food chain during the illegal dumping; (2) Implementing targeted treatment of illegal dumping pollutants; (3) Improving the stakeholder decision analysis model; (4) Expanding the scope of research on stakeholders of illegal dumping; (5) Formulating an unified evaluation standard for the related costs of illegal dumping; (6) Strengthening the evaluation of the interaction effects of influencing factors; (7) Comparing the effects of different types of factors; (8) the exploration of other influencing factors; (9) Analyzing illegal dumping by combining big data with the amount of solid waste; (10) Combining with monitoring to analyze the illegal dumping of household waste.

A text mining-based thematic model for analyzing construction and demolition waste management studies

Over the years, numerous studies have been conducted to investigate construction and demolition waste (CDW) management problems. However, the massive amount of literature brings challenges to scholars because it is difficult and time-consuming to manually identify research emphasis from the literature. Therefore, a method that can informationize literature collection and automatically detect insights from the identified literature is worthy of exploration. This paper attempts to present a comprehensive thematic model by combining Latent Dirichlet Allocation, word2vec, and community detection algorithm on python to detect insights from CDW management literature. Based on the database of Web of Science, 641 articles published between 2000 and 2019 are retrieved and used as the sample for analysis. The comprehensive thematic results reveal a four-domain knowledge map in CDW management research, which covers (1) introducing current situation of CDW management, (2) quantifying CDW generation, (3) assessing CDW and by-products, and (4) facilitating waste diversion. Future research directions in CDW management research have also been discussed. The results prove that the comprehensive thematic model is useful in mining insights from CDW management literature.

Stakeholder-Associated Factors Influencing Construction and Demolition Waste Management: A Systematic Review

Construction and demolition (C&D) activities generate a large amount of waste and have significant impacts on the environment. Thus, it is necessary to implement C&D waste management (WM), which requires the involvement of stakeholders and is influenced by a variety of factors. This study aims to undertake a systematic review of the stakeholder-associated factors influencing C&D WM. The Scopus search engine was used in a literature search, and two rounds of screening were performed. Only journal articles or reviews that were published in English after 2000 were used in this study. A total of 106 journal articles were reviewed. The review identified 35 stakeholder-associated factors influencing C&D WM and categorized them into six groups: regulatory environment, government and public supervision, advances in technologies, recycling market, knowledge, awareness, attitude, and behaviour of stakeholders, and project-specific factors. All the 35 factors are discussed in detail with considerations into relevant stakeholders. Although there have been studies focused on the factors influencing C&D WM, few have attempted to take stakeholders’ perspectives into consideration. This study expands the C&D WM literature by mapping the influential factors with relevant stakeholders and enables the practitioners to clearly understand their roles and responsibilities and make better informed decisions in the C&D WM process.

Machine Learning Comparison and Parameter Setting Methods for the Detection of Dump Sites for Construction and Demolition Waste Using the Google Earth Engine

Machine learning has been successfully used for object recognition within images. Due to the complexity of the spectrum and texture of construction and demolition waste (C&DW), it is difficult to construct an automatic identification method for C&DW based on machine learning and remote sensing data sources. Machine learning includes many types of algorithms; however, different algorithms and parameters have different identification effects on C&DW. Exploring the optimal method for automatic remote sensing identification of C&DW is an important approach for the intelligent supervision of C&DW. This study investigates the megacity of Beijing, which is facing high risk of C&DW pollution. To improve the classification accuracy of C&DW, buildings, vegetation, water, and crops were selected as comparative training samples based on the Google Earth Engine (GEE), and Sentinel-2 was used as the data source. Three classification methods of typical machine learning algorithms (classification and regression trees (CART), random forest (RF), and support vector machine (SVM)) were selected to classify the C&DW from remote sensing images. Using empirical methods, the experimental trial method, and the grid search method, the optimal parameterization scheme of the three classification methods was studied to determine the optimal method of remote sensing identification of C&DW based on machine learning. Through accuracy evaluation and ground verification, the overall recognition accuracies of CART, RF, and SVM for C&DW were 73.12%, 98.05%, and 85.62%, respectively, under the optimal parameterization scheme determined in this study. Among these algorithms, RF was a better C&DW identification method than were CART and SVM when the number of decision trees was 50. This study explores the robust machine learning method for automatic remote sensing identification of C&DW and provides a scientific basis for intelligent supervision and resource utilization of C&DW.

Sorting of suitable areas for disposal of construction and demolition waste using GIS and ELECTRE TRI

Inadequate construction and demolition waste disposal create severe environmental impacts in cities when it occurs in an uncontrolled manner. For waste with the possibility of recycling, the appropriate destination would be recycling plants or landfills. In the investigative process, no studies were found that considered the sorting procedure by compensatory and non-compensatory multi-criteria analysis integrated to the Geographic Information System to choose suitable landfills or recycling plants in cities. This study aims to fill this gap with a structured methodology for mapping suitable sites to receive inert waste from small and medium cities. For this, it was considered the identification of relevant criteria and weights definition by Analytic Hierarchy Process method and after performing a sorting procedure by ArcGIS 10.0 software (compensatory aspect) and ELECTRE TRI method (non-compensatory aspect). And then, an aggregate analysis in order to support the decision-making was also developed. An experimental study was carried out with the application of this methodology in a city of the south western of Parana, Brazil, allowing the analysis of criteria such as land use, soil type and slope, as well as distances to urban area, education and health institutions, roads and highways and hydrography network. We combined these data in a multi-criteria analysis to provide an aptitude mapping to identify suitable landfill areas with 5 km², and sorted as low, medium and high aptitude classes by ArcGIS software and ELECTRE TRI method. With all that, it shows to be efficient, providing relevant practical and theoretical implications to this theme.

An empirical study on waste generation rates at different stages of construction projects in China

Estimation of construction waste generation is critical to construction waste management decisions. However, current construction waste estimation methods have various limitations (e.g. small samples). To address those limitations, this research conducts an empirical study to evaluate the waste generation rate of different types of waste at different construction stages. In this study, construction waste from 148 new-built residential construction sites in China were sorted and weighted on site and their waste generation rates were estimated separately. The results indicated that the amount of inorganic nonmetallic waste with a generation rate of 16.59 kg m^-2 was the highest among the five types of waste (i.e. inorganic nonmetallic waste, organic waste, metallic waste, composite waste, hazardous waste), while the waste generation rate for the underground construction stage, which was 27.57 kg m^-2, was the highest among the three stages (i.e. underground stage, superstructure stage, finishing stage). Compared with previous data, the new waste generation rate proposed in this research can better estimate the actual waste generation situation in construction sites, which provides reliable information for proper decision-making. Furthermore, based on the result of the empirical study, some recommendations for construction waste reduction are proposed.

Characterizing the implications of waste dumping surrounding the Yangtze River economic belt in China

China has prohibited an extensive list of solid waste from abroad since 2017. While China seeks to move away from being the world's dumping ground, cleaning up its own backyard is proving to be a great challenge. China's Yangtze River economic zone, which covers 11 provinces and accounts for 40% of the country's Gross Domestic Product, has been found to be alarmingly polluted: 74 million metric tons of solid wastes, including industrial solid waste, construction debris, municipal solid waste, and hazardous waste, have been disposed of by dumping. In this study, the statistics and spatial patterns of waste dumping were determined and mapped, and then the subsequent environmental impacts on the local and downstream marine ecosystem were evaluated. The results indicated the largest dumped-waste volume was found in Sichuan province (industrial solid waste) and Hubei province (solid waste mixture). The potential environmental impacts aroused by waste dumping in Hubei, Jiangxi and Sichuan provinces were serious, while the impacts in Yunnan and Zhejiang were slight. It is imperative for the Yangtze River Economic Zone to develop stringent measures for curbing the dumping of solid waste, assessing the implications from existing dumping activities, and enhancing the capacity for responsible waste management.

Estimating the effectiveness of different environmental law enforcement policies on illegal C&D waste dumping in Israel

Illegal dumping of construction and demolition (C&D) waste is a major concern for environmental policy-makers. Three different environmental law enforcement policies, aimed at the reduction of illegal C&D waste dumping, are enforced in Israel. These policies include fines (F), vehicle impoundment (V), and criminal indictment (I) by the court. Although, the scope of illegal C&D waste dumping in Israel appears to decline, little is known which of the above policies has been effective in combating the phenomenon. In an attempt to answer this question, we use data on F-V-I instances, recorded between July 2007 and December 2016, and compare them with monthly changes in the ratio between the amount of waste brought to authorized waste dumping sites and the estimated amount of C&D waste generated in the country each month. As the study shows, only the V-sanction was found to be significantly affecting the ratio (t = 3.083; p < 0.01), while the effect of other policy was found insignificant. We explain low efficiency of other law enforcement policies by relatively small fines imposed on the offenders, long court proceedings, combined with a relatively low chance of being caught. By contrast, the V-sanction may be more effective because it results in immediate and severe economic losses to the offenders, causing C&D waste transporters to haul their load to authorized sites. As we argue, for an environmental enforcement policy to be effective, it should be adequate to the severity of the offense and applied swiftly.

Managerial Areas of Construction and Demolition Waste: A Scientometric Review

In past decades, the massive generation of construction and demolition waste (CDW) was increasingly threatening the public environment and humanity health worldwide. A large amount of research has been devoted to the CDW from difference perspectives. However, few scholars have attempted to summarize and review the extant studies, especially in the managerial areas of CDW (MA-CDW). This paper fills this gap via a systematic and quantitative review in the CDW management field. Employing the scientometric analysis method, a total of 261 articles published from 2006 to 2018 were collected to construct the knowledge map and comprehensive framework for MA-CDW. Results show that the overall evolutionary trend of MA-CDW was from basic management concepts to internal and external challenges analysis, to organizational strategy and innovative management practices. The major MA-CDW knowledge domains were identified and summarized into four pillars, namely: (1) factor and challenge; (2) composition and quantification; (3) assessment and comparison; and (4) technology and method. Based on the trend, knowledge gaps and future research directions were found out and discussed. This study contributes to the existing MA-CDW knowledge by presenting a comprehensive knowledge framework. Furthermore, these findings can provide the researchers and practitioners with an in-depth understanding for the sustainable governance of CDW.