Industrial Sites Research Articles

A Decade Review of Human Health Risks from Heavy Metal Contamination in Industrial Sites

A Decade Review of Human Health Risks from Heavy Metal Contamination in Industrial Sites

Forgotten Industrial Heritage: The Cement Factory from La Granja d’Escarp

In the municipality of La Granja d’Escarp, for over thirty years, an important natural cement factory was in operation. In 1876, the Girona family, who were businessmen and bankers from Barcelona, opened the factory with modern industrial facilities. It included kilns, mills, and crushers, alongside warehouses, a small railway for internal transportation of the various materials used, and even a housing area for workers. The neighboring Ebro River allowed distribution by river transport at first. Later, with the use of railways, transport to consumption points was possible. This industrial complex became a center of significant importance in Catalonia in the production of cement, which was used for building hydraulic and civil works. During the first decade of the twentieth century, the factory stopped its activity and the facilities were abandoned. Nowadays, this industrial heritage site is in a state of neglect, without any kind of protection or maintenance. In turn, this has caused the collapse of some buildings in recent times and the loss of historical value of the architectural ensemble. We have carried out initial geomatic research, which has highlighted the constructive properties of the kilns. We have also tested five samples from different buildings using XRD and TGA/DSC, which showed the use of lime mortars in their construction. This is the first study to be carried out at this site, with the aim of showing the historical importance of the ensemble. The goal of the study was to highlight the value of this industrial heritage site and illustrate that it was once a pioneer in the production of natural cement and a driving force for Catalonia.

Performance evaluation and life cycle assessment of electrokinetic technology with various types of electrodes for the treatment of Co-contaminated soil in brick industry site

ABSTRACT Lead (Pb), cadmium (Cd), vanadium (V), and sulfates (S) are among the contaminants that can be released into the environment during the brick manufacturing process, making them a potential source of soil contamination. This study aims to examine the efficiency of electrokinetic remediation (EKR) utilizing graphite electrodes (EKR-GR) and stainless steel electrodes (EKR-ST) in soil remediation with a short remediation time. Principal component analysis (PCA) was used to analyze the data, and life cycle assessment (LCA) was used to anticipate the effects on the environment. Different voltage gradients (1.5–7.5 V/cm) and time intervals (1–12 hours) were investigated. In terms of removing pollutants, EKR-GR performed better than EKR-ST. For EKR-GR, the maximum removal efficiencies for Pb (75.4%), Cd (91.3%) and S (59.2%) were obtained at 5 V/cm, 5 V/cm, and 6.25 V/cm, respectively. The removal of V approached 100% at multiple voltage gradients. The negative environmental burdens for EKR-GR were higher than those for EKR-ST for all indicators. Three indicators (terrestrial ecotoxicity, global warming, and ionizing radiation) were linked to most of the negative impacts, with values of 0.0185 kg of 1,4-DCB, 0.00446 kg of CO2 eq, and 0.00212 kBq of Co-60 eq, respectively.

Mercury wet deposition in the urban and industrialised region of Campinas, south-east Brazil

Environmental context Wet deposition is a primary mechanism for transferring atmospheric mercury (Hg) to Earth’s surface. In Brazil, as well as in the rest of the Southern Hemisphere, only limited data on wet Hg deposition are available. This article addresses this gap by providing a comprehensive dataset from nearly 2 years of monitoring, focusing on mercury concentrations in rainwater and wet Hg deposition in Campinas, one of the largest industrial and populated regions in São Paulo state (south-east Brazil). Rationale Data on mercury (Hg) wet deposition in Brazil, as well as in the rest of the Southern Hemisphere, are sparse. To fill this gap, this study presents a comprehensive dataset from a long-term monitoring effort (nearly 2 years) that focuses on Hg concentrations in rainwater and wet Hg deposition in Campinas, one of Brazil’s largest industrial and populated regions located in São Paulo state (south-east Brazil). Methodology A total of 66 rain events were captured from April 2019 to December 2020 using a manually operated rain collector. The total Hg concentration was determined using Cold Vapour Atomic Fluorescence Spectrometry (CVAFS), and wet Hg deposition was calculated. Results The volume-weighted mean (VWM) Hg concentration ranged from 5.59 to 15.3 ng L−1, with the highest value recorded during the driest season; the overall average for the sampling period was 6.42 ng L−1. Annual Hg wet deposition varied from 6.5 to 7.1 µg m−2 year−1. Discussion These results indicate a clear seasonal variation in Hg deposition and suggest that precipitation depth is the primary influencing factor at our study site. Both the VWM and Hg deposition levels were comparable to those observed in many urban and industrial sites in the Northern Hemisphere. More long-term wet deposition monitoring is needed in urban and industrialised areas of the Southern Hemisphere to improve our understanding of the impacts of human activities on the Hg cycle.

P1272 Spatio-temporal distribution of IBD incidence at a territorial level in northern France using the EPIMAD registry (2000-2017): analysis of environmental determinants suggests influence of agricultural practices.

Abstract Background Crohn’s disease (CD) and ulcerative colitis (UC) are chronic inflammatory bowel diseases (IBDs) with complex and multifactorial etiologies. Genetic factors have been identified but cannot fully account for disease incidence. Spatial heterogeneity in IBD incidence has been reported across various countries and scales, suggesting a significant influence of environmental factors. Leveraging the population-based EPIMAD registry in northern France (6 million inhabitants), this study aimed to analyze the spatial distribution of CD and UC incidence and identify potential environmental risk factors at a territorial level. Methods This ecological study was conducted at the municipal level across 3,041 municipalities within the EPIMAD registry's area for the period 2000-2017. Incident cases included 9,019 CD and 5,789 UC patients. Spatio-temporal age- and sex-standardized incidence ratios were modeled using Bayesian hierarchical models. An inventory of open-access environmental data sources identified 128 datasets, 19 of which were selected to generate 112 indicators at the municipal level. These indicators included contamination levels (air, water, soil), proximity to emission sources (e.g., industrial sites), land use, agricultural practices, natural features, and climate. Ecological regressions were used to explore associations between environmental indicators and incidence. Results Significant spatial disparities in CD and UC incidence were observed, remaining stable over time (Figure 1). For both CD and UC, associations were found with certain types of crops (e.g., sugar beets, barley, wheat, flax, and potatoes) and several metals in soil (e.g., molybdenum, nickel). Associations were more pronounced for CD, including strong links with pesticide use indicators (growth regulators, herbicides, fungicides) and soil contamination by other metals such as arsenic, cadmium, and thallium. For UC, a unique association was observed with soil contamination by polycyclic aromatic hydrocarbons (PAHs), while no significant association with pesticide use was found. Conclusion These findings highlight a relationship between IBD incidence and agricultural practices, providing new insights to the existing literature. Multivariate approaches should be employed to better describe environmental exposure profiles. Additionally, individual-level epidemiological studies are needed to confirm and expand upon these results.

A Sugar Industry Site from the Fatimid to the Ottoman Periods at Tell Umm al-Faraj, Western Galilee (Hebrew, pp. 139–159; English summary, p. 77*)

A Sugar Industry Site from the Fatimid to the Ottoman Periods at Tell Umm al-Faraj, Western Galilee (Hebrew, pp. 139–159; English summary, p. 77*)

A Sugar Industry Site from the Fatimid to the Ottoman Periods at Tell Umm al-Faraj, Western Galilee (Hebrew, pp. 139–159; English summary, p. 77*)

A Sugar Industry Site from the Fatimid to the Ottoman Periods at Tell Umm al-Faraj, Western Galilee (Hebrew, pp. 139–159; English summary, p. 77*)

Multi-platform observations and constraints reveal overlooked urban sources of black carbon in Xuzhou and Dhaka

Here we use multi-waveband single scattering albedo observations from ground-based instruments and satellite to constrain black carbon aerosol's physical properties and loading over Xuzhou, China, and Dhaka, Bangladesh. Our daily high-resolution findings reveal smaller black carbon cores and spatially variable morphology dominate both regions. Column loadings reveal higher black carbon mass in Dhaka, while higher total aerosol mass and number are observed in Xuzhou. These findings reflect differences in emission sources, atmospheric conditions, and regulatory policies. Spatial analysis reveals notable enhanced black carbon along Dhaka’s urban riverbanks (8–9 mg m−2), and over rapidly changing, small industrial sites in China, indicating overlooked sources. Complex daily interactions between wind, accumulation, and dispersion challenge traditional seasonal dynamics. These findings demonstrate high-resolution data can be tailored from available remote sensing platforms, providing nuanced insights into regional air quality, enhancing assessment capabilities and informing targeted mitigation strategies.

Assessing heavy metal contamination in agricultural soils: a new GIS-based Probabilistic Pollution Index (PPI) – case study: Guarda Region, Portugal

ABSTRACT Soil contamination by heavy metals is a global agricultural problem, as these elements can be absorbed by plants and transferred to humans through the food pathway. Current contamination assessment methods rely on in situ sample collection, which restricts the evaluation process due to the number of samples that can be analysed and the associated costs. This study addresses the need for a more efficient and cost-effective approach to identifying areas at risk of heavy metal contamination without the logistical constraints of physical sampling. To meet this challenge, we developed a new Probabilistic Pollution Index (PPI), calculated by integrating GIS tools with an 8-parameter probability-risk matrix to identify agricultural areas potentially contaminated by heavy metals. The factors considered included roads, industrial sites, pH levels, soil organic matter content, terrain slope, soil texture, mining areas, and drainage. Each parameter was classified and reclassified to produce a contamination risk map, categorizing each pixel into five levels of risk. To test the PPI, data analysis, parameter classification, and reclassification were applied in the district of Guarda, Portugal. The PPI map revealed that the central portion of the Guarda municipality, along with specific zones in Celorico da Beira, Sabugal, Mêda, and Pinhel, exhibited a high-probability risk of contamination. Given the agricultural expanse within each municipality, enhanced monitoring of heavy metal levels was recommended for Mêda, Pinhel, and Vila Nova de Foz Côa. This index provides a scalable, cost-effective, and easily replicable tool for identifying potential contamination hotspots and the sources and processes of contamination. Designed as a first-line method for large-scale assessments, it supports governmental decision-making and facilitates targeted risk mitigation strategies through a low-cost approach.

Analysis of COVID-19 Lockdown to Understand Air Pollution Processes and Their Impacts on Health: A Case Study in the Western Balkans

The effect of COVID-19 lockdown (LD) on many ambient air pollutants (NO, NO2, PM2.5, PM10, O3 and SO2) was assessed for the first time in the Western Balkans with an innovative approach that evaluates a variety of factors including the stringency of the LD measures, the type of location, the pollution sources, the correlation with traffic fluxes and the meteorology. To that end, observations from 10 urban sites were compared with historical time series. The time window 1 February–30 May 2020 was classified in sub-periods on the basis of the stringency of the circulation restrictions. NO2 and O3 are the pollutants most affected by restrictions to population circulation due to lockdown during the first phase of the COVID-19 pandemic, and are well correlated with traffic fluxes. A reduction in fine particulate matter (PM2.5 and PM10) concentrations is observed in all sites only during the full LD periods, while the relation between SO2 average and maximum hourly concentrations and LD periods in industrial and traffic sites vary from site to site. The reduction in NO2 concentrations during the LD resulted in a reduction in mortality associated with air pollution in the largest cities, while the interpretation of the changes in O3 and particulate matter is less clear.

Pollution of the Main Components of the Natural Environment of the Most Developed Part of the Southwestern Coast of Lake Baikal (within the Baikal Municipal Formation)

The data of landscape-geochemical studies of the main components of the environment (snow, surface water, soils and vegetation) near the sedimentation ponds of the industrial site of the former Baikal pulp and paper mill, Baikalsk, at the mouth of the Kharlakta and Babkha rivers on the southwestern coast of Lake Baikal are presented. Increased content of heavy (HM) and alkaline-earth metals, SO42–, NO3–, NH4+, PO43– was detected in the snow near the Baikalsk town, exceeding background readings. Concentrations of F–, NO3–, NH4+, Pb, Mo, Mn, Zn, Sr and V in surface waters of local lake shore sites exceed sanitary and hygienic norms. Soil contamination with HM in the recreational and industrial zone located on the shore of Lake Baikal has been revealed. Correlations between the concentrations of macro- and microelements in soils and their indicators such as the content of organic carbon and physical clay fraction have been established, which indicates the presence of biogeochemical barriers on which Na, Ni, Cu, Zn, Sr, Co accumulate in values above the background readings and MAC.

A practical kinetic model for polyvinyl chloride polymerization in batch reactors

Vinyl chloride monomer plays a significant contribution to the chemical industry, serving as a fundamental component for the synthesis of polyvinyl chloride (PVC). This material is distinguished as one of the most extensively manufactured polymers worldwide, finding wide application due to its physical properties, providing good durability, affordable cost, and ease of handling. Although certain mathematical models can be found in literature, implementing the multiphase model with kinetic and diffusive effects demands a considerable set of adjustable parameters, making it challenging to reproduce in industrial sites. In contrast, this study describes the dynamic process of vinyl chloride (VC) polymerization by suspension in a batch reactor to produce PVC. The proposed approach considers a simplified yet robust model that requires fewer adjustable parameters, enabling easier implementation and corroborating with experimental data from the literature. By solving systems of algebraic and differential equations, the proposed model accurately reproduces experimental data for the conversion of VC to PVC, specifically for the initiators PW-40 and AIBN. Additionally, the model delivers superior results compared to those obtained using Aspen® Plus PVC module, a commercial platform widely employed in the industry for polymerization process modeling. These improvements are achieved without introducing excessive complexity, making the model a practical and efficient tool for industrial applications, such as the modeling and optimization of PVC reactors.

Adaptive Reuse of Industrial Heritage Sites for Sustainable Urban Development

The research aims at future urban transformation and development by rediscovering the industrial heritage in the 798 Art Zone, Beijing and the M50 Creative Plaza, Shanghai. In the assessment of the criteria, benefits and success factors of adaptive reuse projects, the research employs the use of both quantitative (questionnaires) and qualitative (case studies) methods. The following points have been identified such as government incentives, local engagement, and retaining the appearance of industrial structures. Drawing from industrial heritage architecture, the study reveals the economic, environmental, and social benefits of retrofitting industrial structures, increased economy revitalisation and cultural identity, and the mitigation of new construction. However, the research found some drawbacks of the study such as the absence of unified conceptual frameworks that cover all four dimensions of sustainability and the uncertainty associated with technical and regulatory factors. This work, therefore, points out that, while adaptive reuse projects are critical to the augmentation of urban regeneration, there exist hurdles that require deliberate effort to be surmounted. Based on these findings, suggestions are given for improvement to policies for government, political parties, urban designers and developers to improve the comprehensiveness of pro-historical building reuse policies. This work finally suggests a framework for further research on any adaptive reuse projects to inform future sustainable design efforts that take into consideration the exploration of resources of the culture and ecosystems.

Enhancing Autonomous Vehicle Navigation in Complex Environment With Semantic Proto‐Reinforcement Learning

ABSTRACTDespite great progress in autonomous vehicle (AV) navigation, the technical challenges within this space are still considerable when it comes to successful integration of AVs into complex real‐world environments. To tackle these challenges, this paper presents a new semantic proto‐reinforcement learning (SP‐RL) method for dynamic path planning and real‐time obstacle avoidance of an autonomous car that can be adapted to various weather conditions in localization‐deficient environments, while predicting the human intentions of different shapes on road. This approach seeks to improve navigation capability of AV in dynamic and unstructured environments, as well as to address real‐time detection and avoidance response for obstacles more promptly while being able to adapt its decision‐making system based on weather condition by using the semantic graph network (SGN) within segmentation process therefore enhanced version configured with prototype‐based reinforcement learning (PRL). This innovation is new competitive edge compared previous existing approaches. Dynamic SGN is used to segment challenging 3D and free space environments, so that the AV can comprehend highly unintuitive areas like parking lots, construction site conditionals, or off‐road scenarios. At the same time, PRL is used to help real‐time decision‐making so the AV can quickly and precisely respond unexpected obstacles or changing environments. This approach is confirmed to be effective by extensive testing in the CARLA simulation environment, showing substantial improvement of AV navigation capability. This work demonstrates an exciting step towards solving the most fundamental problems faced by autonomous vehicles and could help to ensure that future AV systems are safer, more robust, and adaptable than current ones. It is appliable for urban areas which represent high volumes of pedestrians and vehicles, industrial sites with changing conditions that may be unpredictable at times or the challenging off‐road areas in rural geographies where typical terrains are rough, uneven, rugged yet not well‐structured. The model is robust to diverse weather conditions that make AV operations more reliable and safer. The model was tested on root mean square error (RMSE), computational time, no crash, obstacles avoidance, and success rate obtaining overall 98%.

‘Archeology’ of Hidden Values of Underutilized Historic Industrial Sites in Context of Urban Regeneration and Nature-Based Solutions

Today, cities and their heritage function under so-called BANI (Brittle, Anxious, Nonlinear, and Incomprehensible) conditions. Climate change, military conflicts, and the urge to comply with sustainability requirements and to implement green transformation and urban regeneration cause a threat to rapid decision making, which may result in the loss of more subtle, intangible values in the urban environment. Such loss may particularly affect industrial heritage with its contested values and identities. This encourages searching for approaches to capture, analyze, and employ these invisible values for future developments. The methods of research used in this study include qualitative analysis of the literature, the theoretical conceptualization and development of a methodology to capture and record these invisible and intangible values, and the application of the developed methodology to the case study of a historical industrial site in Kaunas (Lithuania). The main findings of this research include a theoretical framework for the analysis of hidden values of historic industrial sites revolving around the concepts of place identity, spirit of place, symbolic potential of place, embedded values, and time depth and its application to the case study of the historic industrial site in Kaunas. This research has demonstrated that historic industrial sites existing in urban settings contain a multiplicity of aspects and meanings, which are not visible at first glance and require thorough qualitative analysis. These invisible aspects constitute important resources for the future development of the site and can contribute to the identity and local character of regeneration efforts and nature-based solutions.

Amalur EIS: a system for calculating the environmental impacts of industrial sites from E-PRTR records

This article presents Amalur EIS (https://www.amalur-eis.eus/), an Environmental Information System that estimates environmental impacts using data sourced from the European Pollutant Release and Transfer Register database (E-PRTR). The system uses data on the releases into land, air and water of 31,556 European industrial facilities for the period 2007–2021. Amalur EIS calculates environmental impacts of industrial releases using 31 life cycle impact assessment methods (LCIA) and covering 78 of the 91 pollutants regulated by the PRTR Protocol. The system has been constructed using a two-layer software infrastructure: (i) a data layer supported by a relational database built in Postgres and (ii) a presentation layer built in Tableau, so it provides user-friendly access to the information. For an illustrative analysis of the tool, the EF 3.0 LCIA method recommended by the European Commission was used, including normalisation and weighting steps for a better comparison. The analysis concludes that the climate change impact category contributes the most (68.6%) to the total impacts, while the largest contributor from an economic activity perspective is the energy sector (59.5%). Geographically, both elements coincide in the German regions of Düsseldorf, Köln and Brandenburg, resulting in the concentration of the largest impacts at the European regional level. In fact, Germany is the country with the highest impact (20.3% of total). Beyond this analysis, Amalur EIS is poised to be a valuable tool for tracking the transition towards sustainability, particularly in Europe.

Soil quality assessment in agricultural lands of an industrializing region of India.

Soil degradation due to industrialization is a growing global concern, emphasizing the importance of evaluating soil quality near industrial zones to ensure food security, environmental sustainability, and public health. This study compares soil quality across five industrial sites, including foundries, electroplating, paper mills, textile mills, and quarries and cement industries, in the Coimbatore district. Soil samples were collected via a purposive sampling approach from nearby agricultural fields via a 500 m2 grid. The soil quality was assessed by including all the soil parameters as soil quality indicators via principal component analysis (PCA) and correlation studies with a minimum data set followed by linear scoring and the weighted additive method. The soil quality was ranked as follows: reference soils (0.752) > foundry industrial sites (0.591) > quarry and cement industrial sites (0.554) > paper mill sites (0.552) > textile and dyeing industrial sites (0.500) > electroroplating industrial sites (0.482). The findings reveal that industrial activities significantly compromise soil quality in adjacent agricultural areas, with varying levels of impact.

A deep learning method based on multi-scale fusion for noise-resistant coal-gangue recognition

Coal-gangue recognition technology plays an important role in the intelligent realization of integrated working faces and coal quality improvement. However, the existing methods are easily affected by high dust, noise, and other disturbances, resulting in unstable recognition results that make it difficult to meet the needs of industrial applications. To realize accurate recognition of coal-gangue in noisy environments, this paper proposes an end-to-end multi-scale feature fusion convolutional neural network (MCNN-BILSTM) based gangue recognition method, which can automatically learn and fuse complementary information from multiple signal components of vibration signals. It combines traditional filtering methods and the idea of multi-scale learning, which can expand the breadth and depth of the feature learning process. the breadth and depth of the feature learning process. Moreover, to strengthen the expression of key features, a feature weighting method based on the attention mechanism is combined to give adaptive weights to different features. Finally, the experimental platform of a tail beam of coal-gangue impact hydraulic support is built, and several comparative experiments are carried out. The comprehensive comparison experiments show that the method shows strong adaptability, robustness, and noise resistance under various complex noise environments, and is suitable for complex practical industrial sites.

Human health risk assessment from potentially toxic elements in the soils of Sudan: A meta-analysis.

Potentially toxic elements (PTEs) in soils threaten human health through several exposure pathways. However, health risks posed by PTEs in soils in developing countries have not yet been comprehensively investigated. Thus, such countries lack important information that is needed to implement sustainable solutions. In this study, we assessed the human health risks for 10 PTEs, including arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), mercury (Hg), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn), in Sudan focusing on soils affected by anthropogenic activities, such as industrial processes, gold mining, tannery, waste dumping, traffic (affecting roadsides), urban/rural agriculture, river, and coastal sediment land uses (LUs). For this purpose, we did a meta-analysis using 3430 PTE concentrations reported from 981 anthropogenically-affected soils in 76 publications between 1996 and 2024. Ingestion was the most common exposure pathway for both carcinogenic and non-carcinogenic PTEs. Hg and Mn were the main non-carcinogenic PTEs leading to adverse health effects in children in industrially-affected soils and coastal sediments, with average hazard quotient (HQ) and hazard index (HI) values >1. Cr and Ni caused the highest carcinogenic risk to children in all anthropogenically-affected soils as indicated by average incremental lifetime cancer risk values (ILCR >1×10-4). Further major cancer risks for children were caused by As in urban agricultural and industrial LUs, Cd in >70% of all LUs, and Pb at industrial and waste dump sites. Adults were under high cancer risks due to Cd in waste dump (ILCR=9.09×10-4) and Cr and Ni (ILCR >1×10-4) in >75% of all LUs. Cd contributed >50% and >70% to ILCR for children and adults along roadsides, respectively. Our findings can guide future research on the links between soil PTEs and human health risks in developing countries. We recommend establishing effective management strategies to reduce risks to human health based on the presence of PTEs in Sudanese soils.

Patrimônio industrial, identidade e memória: o caso do Vale do Ruhr

Abstract This article aims to describe and analyze the process of preservation and repurposing of industrial heritage in the Ruhr Valley based on field research conducted in the region between October 2023 and July 2024. To achieve this, we will begin by contextualizing the industrialization and deindustrialization process that has been occurring over the last century and a half in the Ruhr Valley. We will demonstrate its connections with the urbanization of the area and the construction of a local collective identity grounded on the memory of the mining and industrial past. Then, we will explore the political and economic processes that led to the formation of the Ruhr Industrial Heritage Route, a collection of industrial sites of historical interest now protected and repurposed.