Pollution Sources Research Articles

Analisa Pemanfaatan Limbah Plastik Sebagai Bahan Baku Pembuatan Paving Block

Plastic waste is most commonly found around us and is a source of pollution in the environment, both on land and in water. paving is formed into a rectangle with a press made manually. This paving can be used as a path in the plantation or at home because it is made from recycled waste that pollutes the environment. Methods used Experimental method. The optimum compressive strength value is obtained in sample 2 of LDPE plastic type and decreases in sample 1 of PET type The decrease in compressive strength value occurs because PET plastic type dries quickly. The optimum compressive strength value in LDPE plastic type of 3.341 MPa can also be caused because it is easy to fuse when heated and dries long so that it can be molded easily. The compressive strength value in the 33% variation decreased because the plastic shreds overlapped with other plastic shreds, reducing its compressive strength. The type of waste plastic pet compressive strength produced is smaller than other types and dries easily so it is difficult to mold, the average weight of 1.089 gr / cm3 is heavier than other types of plastic, for specific gravity in water heavier mixed variation 33% with an average of 0.266 gr / cm3 and for the smallest weight type HDPE with an average of 0, 181 gr/cm3, For all types of testing does not meet the minimum requirements of SNI 03-0691-1996 as a paving block with quality D with the required average compressive strength of 10 Mpa and a minimum compressive strength of 8.5 Mpa, in this test the highest compressive strength is in the type of LDPE sample 2 of 3.341 Mpa and for the lowest in the type of PET sample 1 of 0.248 Mpa.

Multivariate statistical analysis as a tool for monitoring drinking water sources in an Atlantic Rainforest Conservation Unit.

Water quality monitoring is paramount in identifying and mitigating pollution sources, protecting aquatic ecosystems, and ensuring safe water for human and wildlife consumption. This study is aimed at evaluating the quality of drinking water sources in three communities located in a Sustainable Use Conservation Unit in the municipality of Mangaratiba, Rio de Janeiro, Brazil, employing a multivariate statistical analysis. A total of 161 water samples were collected from January to December 2022, encompassing 32 surface water and 129 tap water samples. Physicochemical parameters were determined in situ employing a Horiba U50 multiparameter probe. The samples were stored and transported at 4°C to the laboratory for microbiological analyses concerning total coliforms and Escherichia coli using a commercial enzymatic test. All samples contained coliforms, while E. coli were detected in 87% of the samples. The multivariate analysis indicated that the microbiological water quality in sampling region R2 was influenced by rainy periods and that, in general, the water quality within R3 was the most affected by the transport of solids to the water sources. The statistical methods applied herein aided in characterizing the study areas and detecting points of attention regarding physicochemical and microbiological parameters that significantly influence the water quality of each sampling point. Representative points for each study region were identified and may be employed for future monitoring and prevention actions.

Enhanced Assessment of Water Quality and Pollutant Source Apportionment Using APCS-MLR and PMF Models in the Upper Reaches of the Tarim River

To evaluate the pollution sources and dynamics of the upper reaches of the Tarim River, 10 typical sampling points were selected, and 23 water quality parameters from 2020 to 2022 were analyzed using one-way analysis of variance, the comprehensive Water Quality Identification Index (WQI), and Principal Component Analysis (PCA). The pollution status, sources, and contribution rates of water quality were investigated using the Absolute Principal Component-Multiple Linear Regression Model (APCS-MLR) and Positive Matrix Factorization (PMF). The results indicated that the water quality parameters of dissolved oxygen (DO), chemical oxygen demand (CODMn), biochemical oxygen demand after 5 days (BOD5), total nitrogen (TN), total phosphorus (TP), fluoride ions (F−), and ammonia-nitrogen (NH3-N) in the upper reaches of the Tarim River exceed standards, with noticeable spatial variations observed for each parameter. The water quality evaluation grades in the upper reaches of Tarim River primarily indicate “moderate” and “good” levels, with DO, TN, NH3-N, and electrical conductivity (EC) being the key parameters influencing variations in water quality. The source analysis results from APCS-MLR and PMF yielded similar outcomes, identifying six potential pollution sources. Among these, soil weathering, livestock and poultry breeding, and agricultural activities exhibited higher contribution rates. Specifically, the contribution rates for these sources according to APCS-MLR were 44.11%, 19.63%, and 11.67%, respectively; while according to PMF they are 24.08%, 17.88%, and 27.54%, respectively. Furthermore, industrial pollution sources contributed at a rate of 6.01% according to APCS-MLR, while urban living sources contributed at a rate of 2.13%. However, based on PMF analysis, the contribution rates for industrial pollution sources increased significantly to 16.71%. Additionally, APCS-MLR identified natural sources as contributing at a rate of 16.45%, whereas PMF suggested that a combination of agricultural activities and natural sources contributed at a lower rate of only 9.52%. In conclusion, the water quality within the upper reaches of the Tarim River is predominantly satisfactory. Nonetheless, localized pollution, primarily attributable to human activities, presents a substantial challenge. These observations provide critical insights into improving and protecting the fragile water quality of the Tarim River.

Sources Affecting Microplastic Contamination in Mountain Lakes in Tatra National Park

The global atmospheric transport of microplastics (MPs) plays a crucial role in the contamination of remote, especially higher-elevation, environments. Precipitation is considered the main source of MP pollution. Meanwhile, plastic waste generated from, for example, tourism activities can be a local source of MP pollution. In this study, we specify which of the mentioned sources of MP, global or local, have a higher impact on the pollution level in the high-elevation oligotrophic lakes of Tatra National Park in Poland. Due to its unique natural value, it is listed by UNESCO as an international biosphere reserve and meets the criteria for Natura 2000 areas. We comprehensively analyzed the morphometric and anthropogenic features of 11 lakes in terms of the contamination level, color, shape, and polymer type of the MPs found in the surface waters. MP fibers were found to be present in all studied lakes, with contamination ranging from 25 to 179 items/m3. Polypropylene, polyethylene terephthalate, and natural or semi-natural cellulose fibers—black or red in color with a length of 0.2–1.0 mm—predominated, which corresponds with other studies conducted on remote mountain ecosystems. We did not find any correlation of the number of MPs with local anthropogenic pressure characteristics. In turn, the significant correlation with lake area, coastline length, lake volume, and catchment area indicated airborne sources, including global transport of MPs to the lakes with reduced water outflow.

Air quality in different urban hotspots in a metropolitan city in India and the environmental implication.

This research study investigates hourly data on concentrations of five major air pollutants such as particulate matter (PM10, PM2.5) and gaseous pollutants (SO2, NO2, CO) measured during 2022 at four hotspot sites (industrial site, traffic site, commercial site, harbour, and one residential site) in Chennai, India. The analysis encompasses temporal variations spanning annual, seasonal, and diurnal variations in the pollutants. Notably, PM10 and CO emerge as the predominant pollutants, with the highest concentrations at industrial and traffic sites (PM10: 67.64 ± 40.77µg/m3, CO: 1.41 ± 0.84mg/m3; traffic site: PM10: 58.67 ± 20.05µg/m3, CO: 0.99 ± 0.57mg/m3). Seasonal dynamics reveal prominent winter spikes in particulate matter (PM10, PM2.5) and carbon monoxide (CO) concentrations, while nitrogen dioxide (NO2) and sulphur dioxide (SO2) levels peak during the summer season, particularly in the harbour area. The proximity to roadways exerts a discernible influence on diurnal patterns, with traffic sites showcasing broader rush hour peaks compared to sharper spikes observed at other sites. Furthermore, distinct bimodal patterns are evident for PM10 and PM2.5 concentrations in residential and harbour areas. A common lognormal distribution pattern is identified across the studied sites, suggesting consistent air quality trends despite contrasting locations. The conditional probability function (CPF) is used in conjunction with local meteorological conditions for identifying key pollution sources in each location. The implementation of polar plots emphasizes industries as principal local sources of pollution, at industrial sites significantly contributing to PM10, SO2, and NO2 concentrations under specific wind conditions. The main objective of the present study is to facilitate a good understanding of pollutant dynamics, pollution sources, and their intricate interplay with meteorological factors, thereby contributing to the formulation and implementation of effective air pollution control and mitigation strategies.

Evaluation of Environmental Risk from Heavy Metals in Sediments in Port Areas

This research aims to evaluate the concentration and distribution of heavy metals in sediment in the Port area of ​​PT Pelindo Dumai Branch, which is one of the main ports in Riau Province, Indonesia. Heavy metals such as cadmium, copper, lead, mercury and zinc are known to have significant toxic effects on maritime ecosystems and can accumulate in the food chain, potentially endangering aquatic life and human health. The research methodology involves taking sediment samples from various points around the port area, which are then analyzed using spectroscopic techniques to determine heavy metal concentrations. The results showed that the concentration of heavy metals in sediments varied, with zinc having the highest concentration (average 0.019724 mg/L), followed by copper, lead, cadmium, and mercury. This variation indicates the existence of diverse sources of pollution and uneven distribution in the port area. Although cadmium and mercury concentrations are relatively low, they still raise concerns due to their high toxicity and potential accumulation in the food chain. These results emphasize the need for more intensive environmental monitoring and targeted risk management to reduce the negative impacts of heavy metals on maritime ecosystems and human health. This research provides an important scientific basis for mitigation efforts in port areas, which are crucial to ensuring the sustainability of ecosystems and the well-being of communities that depend on marine resources.

Assessment of exurban expansion on water quality in Rock Creek of the Yellowstone River in southcentral Montana, USA

Exurban expansion can degrade significantly stream water quality. This study was conducted on Rock Creek of the Clark’s Fork of the Yellowstone River in southcentral Montana, USA. The impact from recent exurban expansion was evaluated by a baseline study measuring total suspended solids (TSS), total phosphorus (TP), total nitrogen (TN) and nitrites (NO2) and nitrates (NO3) together. This was part of a larger study by Montana Department of Environmental Quality to assess water quality, identify sources of pollution and develop total maximum daily loads (TMDL). Water samples were measured for these analytes at nine (2022) and 11 (2023) locations with different land-uses over seven periods (both years). Seasonal spikes, corresponding to runoff (2022, late May), occurred in TSS, TP and TN. Data from 2023 showed similar but reduced levels occurring in June for TSS and TP. Total suspended solids, TP, NO2 and NO3 concentrations increased by location year over year, 2022 to 2023. The high relative concentrations of NO2 and NO3 in Rock Creek are primarily associated with exurban expansion, a significant threat to stream quality and wildlife habitat in Montana. The results will assist county planners in land use decisions.

Prevalence of Sick Building Syndrome Symptoms in Residents During the COVID-19 Pandemic—A Case Study of Suzhou City, China

Quarantine policies during the coronavirus disease 2019 (COVID-19) pandemic prolonged time spent at home, leading to an unintended occurrence of sick building syndrome (SBS) symptoms. The aim of this study was to investigate the prevalence of SBS symptoms among residents in Suzhou, China, during the COVID-19 pandemic. A questionnaire survey was conducted online from February to May 2022. A total of 442 adults were included in this study. The prevalence of SBS symptoms was much higher during the COVID-19 pandemic, and the proportions of general, mucosal, and skin symptoms were 59.3%, 45.5% and 49.1%, respectively. The influences of building characteristics, occupant lifestyle, indoor environment and perception of indoor environment were analysed using a multivariate logistic regression model. Neighbourhood pollutant sources, older residences, non-ownership and humidity indicators were significant risk factors for SBS symptoms. Frequent use of air fresheners was significantly associated with general (adjusted odds ratio (AOR): 4.9, 95% confidence interval (CI: 2.4–10.0), mucosal (AOR: 5.3, 95% CI: 2.4–11.5), and skin symptoms (AOR: 4.6, 95% CI: 1.6–13.1), while the use of disinfectants was significantly correlated with skin symptoms (AOR: 4.0, 95% CI: 1.5–10.7). Residents’ perception of an uncomfortable indoor environment was a significant predictor of general (AOR: 2.2, 95% CI: 1.2–4.0) and mucosal symptoms (AOR: 3.1, 95% CI: 1.6–6.0). The use of air cleaners can reduce the prevalence of general symptoms. An important finding of this study is that the psychological impact of staying at home for a long time was associated with general symptoms (AOR: 1.3, 95% CI: 1.1–1.7), which means that attention should also be paid to the mental health of residents during the COVID-19 pandemic. Although the quarantine period has now ended, our results can still serve as a reference for the impact of the indoor environment on the health of people with SBS, especially for those who stay at home for a long time, such as the elderly.

Comparison of Biohydrogen Production by Tetraselmis subcordiformis During Cultivation Using Soil-Less Agricultural Wastewater and Effluent from Microbial Fuel Cells

The development and implementation of innovative production technologies have a direct influence on the creation of new sources of pollution and types of waste. An example of this is the wastewater from soil-less agriculture and the effluent from microbial fuel cells. An important topic is the development and application of methods for their neutralisation that take into account the assumptions of global environmental policy. The aim of the present study was to determine the possibilities of utilising this type of pollution in the process of autotrophic cultivation of the biohydrogen-producing microalgae Tetraselmis subcordiformis. The highest biomass concentration of 3030 ± 183 mgVS/L and 67.9 ± 3.5 mg chl-a/L was observed when the culture medium was wastewater from soil-less agriculture. The growth rate in the logarithmic growth phase was 270 ± 16 mgVS/L-day and 5.95 ± 0.24 mg chl-a/L-day. In the same scenario, the highest total H2 production of 161 ± 8 mL was also achieved, with an observed H2 production rate of 4.67 ± 0.23 mL/h. Significantly lower effects in terms of biomass production of T. subcordiformis and H2 yield were observed when fermented dairy wastewater from the anode chamber of the microbial fuel cell was added to the culture medium.

Assessment of Climate Change Impacts on Hydrology Using an Integrated Water Quality Index

Traditional Water Quality Indices (WQIs) often fail to capture the significant impact of flow velocity on water quality, especially under varying hydrological conditions. In this study, an Integrated Water Quality Index (IWQI) was developed by combining water quality parameters and flow rate, providing a more comprehensive assessment under various flow conditions. Compared to traditional indices, the IWQI showed slightly lower correlations in individual parameter performance, but it performed well in evaluating water quality changes associated with flow variations. Parameters such as Total Phosphorus (TP), Total Coliforms (TC), and Fecal Coliforms (FC), which are prevalent pollutants in the Cheongmi River, significantly influenced IWQI scores. River water quality was evaluated using input data simulated under a climate change scenario. When precipitation was abundant, the IWQI score remained relatively stable even with reduced flow rates. However, during periods of insufficient rainfall, water quality deteriorated sharply. While general water quality parameters exhibited approximately a 10% change as flow decreased, TC and FC showed rapid deterioration, with change rates ranging from 20% to 60%. These findings underscore the importance of managing TC and FC, particularly when insufficient rainfall is predicted, as they are major sources of pollution.

Estimation of Free-Product PCE Distribution in Thick Multilayered Aquifers as Possible Long-Term Pollution Sources for Shallow and Deep Groundwaters, Using High-Precision Numerical Simulations

Estimation of Free-Product PCE Distribution in Thick Multilayered Aquifers as Possible Long-Term Pollution Sources for Shallow and Deep Groundwaters, Using High-Precision Numerical Simulations

Quantitative Tracking of Seasonal River Pollution Sources and Integration of Sustainable Development Goals in Hilly Regions

Quantitative Tracking of Seasonal River Pollution Sources and Integration of Sustainable Development Goals in Hilly Regions

How to use efficiently airborne criteria pollutants and radon-222 in source apportionment: A self-organizing maps approach

Pollutant source apportionment represents one of the fundamental activities in environmental science. Several efficient chemometric tools are available to the scope, mostly based on multivariate techniques and usually applied to aerosol chemical speciation data. In the present work, an alternative source profiling method is proposed, based on the self-organizing maps (SOM) algorithm. Moreover, the dataset used includes typical criteria pollutants and physical parameters related to airborne particulate matter widely used as a complement of aerosol source apportionment and largely available at a higher time resolution than bulk aerosol samplings, allowing the information on the dynamic behavior of the local airshed to be extended. In this work, data was collected at a coastal location in NW Italy, between January and July 2012. Hourly concentrations of typical gaseous pollutants (SO2, NO, NO2, benzene, toluene, (m-p)-xylene, o-xylene), black-carbon and particle number concentrations by an optical particle sizer (OPS) were collected. The dataset was integrated with radon-222 activity concentration and meteorological parameters to enrich and refine the information obtained by SOM computation as well as to improve the air pollution source localization. Despite the lower specificity of criteria pollutants, the approach developed was capable of revealing distinct pollution sources such as the urban background traffic, the coal-fired power plant active at the time of the study, and the harbor, in agreement with previous PM-based source apportionment studies carried out locally, while enlightening peculiar dynamical conditions detectable at the sub-daily time scale. The application of the SOM algorithm, with the integration of meteorological parameters and atmospheric radon, proved to be very efficient in unveiling the air pollution sources.

The Attachment of Juvenile Mussels via Byssus Weakened by Contaminated Polyethylene Fibers

In the process of mussel farming, the rope for attachment is indispensable, as it provides a stable attachment environment for mussel seedlings, directly affecting their survival rate and growth quality. The objective of this study is to examine the contamination of ropes, composed of polyethylene fibers, by heavy metals and polycyclic aromatic hydrocarbons (PAHs) after three years of deployment and to assess its influence on the attachment and locomotion behaviors of juvenile mussels. Utilizing a laboratory simulation of the seedling wrapping process, a comparative analysis was conducted to evaluate the number of juvenile mussels attached and their movement distances when exposed to contaminated old ropes versus uncontaminated new ropes. The findings indicated that the old ropes markedly diminished the attachment rate of juvenile mussels and heightened their movement distances. In particular, juvenile mussels utilizing old ropes exhibited a final attachment rate of 15.0% and an average movement distance of 0.86 cm, whereas those using new ropes achieved a final attachment rate of 96.7% with an average movement distance of 0.26 cm. Further inspection found that heavy metals and PAHs were present in the old rope, among which the concentrations of Zn (17.127 μg/g) and Pb (22.905 μg/g) in heavy metals were high, and the concentrations of Phe (5.53 μg/kg), Fla (6.35 μg/kg), and Pyr (5.17 μg/kg) in PAHs exceeded the detection limits, which were the main source of pollution. This research underscores the potential risk that heavy metal and PAHs contamination pose to the health of juvenile mussels and the profitability of aquaculture, emphasizing the critical need for the regular replacement of clean ropes.

Non-invasive biomarkers for investigating urban metal exposure in neotropical bats

In urban centers, sewage treatment plants (STPs) serve as foraging and shelter areas for bats; however, they are sources of persistent pollutants that affect the health of these animals. This study aimed to investigate the impact of pollutants from an STP on the health of different species of neotropical bats from different guilds using non-invasive biomarkers. A conservation unit, the Silvania National Forest (SNF), was used as a reference area for comparison purposes. Blood, buccal mucosa, and fur samples were obtained for comet assay, micronucleus test, leukocyte profile, and metal concentration analysis in fur. Our results demonstrated that bats collected at the STP show a higher frequency of genotoxic damage, nuclear abnormalities, and an inflammatory response linked to infection than bats from the SNF. Regarding guilds, frugivores and nectarivores showed more pronounced responses to damage, but insectivores bats also showed relevant responses. While STPs are considered a source of pollutants, other urban sources of contamination likely contributed to these results. Still we encourage further studies using other non-invasive biomarkers, detection analysis of different pollutants in biological matrices, and the use of other wildlife species inserted in urban centers.

A predictive model for the estimation of industrial [formula omitted] emissions for IoT-based devices

The paper is devoted to solving the problem, associated with increasing the forecasting accuracy of pollutant concentration level based on PM2.5 dust. The LANN model (LANN – Lagrange powered Artificial Neuron Network), proposed in the paper, allows you to take into account the mutual influence of pollution sources on the concentration level at monitoring points, which, on the one hand, allows to manage the volume of emissions from industrial enterprises in such a way as to avoid penalties and, on the other hand, allows the regulatory agencies to determine points for environmental control, at which permissible standards will be most likely exceeded. The suggested solution makes it possible to use low-cost IoT-based air quality monitoring devices, which were primarily considered not applicable due to their low accuracy and exposure to the influence of short-term stochastic factors; besides, no models were available, which would be capable to provide the necessary accuracy and forecast horizon on the base of IoT data. At the same time, the obtained model has low requirements for computing resources compared to dispersion models and is dependent on the accuracy of weather information; besides, the model allows you to take into account trends and obtain better forecast values in terms of accuracy than the models, that use regression methods, neural network models and ML models, stochastic models and their ensembles. The application of the model improves well-known pollutant dispersion estimation calculation techniques through the use of measurement data flow and dynamic adaptation to changing conditions. The model was trained on the dataset over the period 2022–2023 collected from 5 monitoring devices, which were installed in the catchment area of the group, incl. 13 industrial points of PM2.5 pollutant sources.

Analytical Solutions for Axisymmetric Diffusion of Organic Pollutants in a Circular‐Shaped Cutoff Wall System

ABSTRACTThrough the variable substitution and separated variable methods, this study develops a two‐dimensional (2‐D) axisymmetric diffusion analytical solution for organic pollutants in a circular‐shaped cutoff wall (CCW) system for the first time, which can more precisely and reasonably simulate the diffusion behaviors in “circular‐shaped” vertical barriers. Then, the proposed analytical solution's reasonableness is verified by comparing it with an existing analytical solution and a corresponding finite‐difference solution. Meanwhile, the comparison suggests that this solution will degrade to a 2‐D diffusion analytical solution when the pollution source radius is large enough. Furthermore, the presented analytical solution can also be simplified to a one‐dimensional axisymmetric diffusion analytical solution, or to the axisymmetric diffusion analytical solutions in a single‐layered medium. These exact analytical solutions can not only be applied to study axisymmetric diffusion behaviors under specific scenarios, but also be used to validate other complex numerical models. Last, a case study is conducted to investigate the impacts of pollution source concentration distribution, CCW horizontal thickness, and defined equivalent diffusion coefficient on the barrier performance. Overall, the proposed analytical solutions and obtained diffusion laws in this study can provide guidance for the service effect assessment and the engineering design of cutoff walls.

Quantitative assessment of PM2.5-related human health impacts at the provincial level in China and analysis of its heterogeneity affected by economic structural transformation

Rapid economic development has led to massive fossil energy consumption and emissions of air pollutants such as PM2.5, which have severely impacted human health and the environment. By uncovering the primary regions and pivotal sectors of PM2.5-related human health impacts (PM2.5-HHI) and evaluating the influence of economic structural factors on them, we can facilitate a more targeted strategy for managing PM2.5 pollution sources. This study employs a structural decomposition analysis method based on input–output analysis to evaluate the impact of China’s provincial economic structural transformation and changes in final demand on PM2.5-HHI in the years 2012, 2015, and 2017. Results indicated that PM2.5-HHI is primarily concentrated in economically developed provinces (e.g., Shandong and Guangdong), which is compared to Shanghai, Heilongjiang, Liaoning, and Hebei experienced negative growth in PM2.5-HHI during 2007–2017. The production-based PM2.5-HHI is primarily driven by energy-intensive sectors such as the production and distribution of electric power and heat power. By contrast, the building sector is key to driving consumption-based PM2.5-HHI. An increasing number of regions are reducing PM2.5-HHI by implementing production structure changes. Moreover, the driving effect of production structure changes on PM2.5-HHI growth is strengthening in Beijing and Tianjin. Changes in the final demand structure mainly led to the growth of PM2.5-HHI in areas with higher economic development levels, such as Beijing and Shandong, but this driving effect is weakening. The final demand–driven PM2.5-HHI shows an evolutionary trend of an increasing share driven by fixed capital formation and exports and a decreasing share driven by household consumption. Changes in emission intensity play a key role in decreasing PM2.5-HHI in each region. Alternatively, changes in the structure of emission sources have a relatively minor impact on PM2.5-HHI. To mitigate PM2.5-HHI, regional economic and resource endowment advantages should be used to promote regional coordinated development and strengthen green production-process innovation in energy-intensive industries. Meanwhcile, it is necessary to optimize urban construction planning and improve the energy efficiency of buildings.

A Stackelberg game framework for promoting battery-powered construction machinery through government subsidies

Diesel-powered construction machinery is a major source of harmful pollutants and carbon emissions. Technological advancements have made battery-powered construction machines (BPCMs) increasingly viable in operations. However, the high purchase prices of BPCMs represent a huge obstacle for contractors to embrace these low-emission alternatives. To facilitate the sustainable development of the construction industry, it is imperative for the government to implement effective subsidy allocation policies to promote the electrification of construction machinery. This paper proposes a Stackelberg game framework for optimal subsidy allocation. In the proposed framework, the government decides the subsidy amount offered to each type of BPCMs to minimize both pollutant emissions and carbon emissions. The contractors can then observe the government’s decision and make their optimal decisions regarding the purchase, operation, and replacement of construction machines accordingly to minimize their total costs. Contractors’ decisions in turn influence the government’s decision. Such an intricate framework has many appealing properties, which are analyzed in depth to provide useful managerial insights. Additionally, the design of effective subsidy policies by the government depends on a precise prediction of the contractors’ demand for construction machinery. To this end, a random forest machine learning model is developed. Real data were collected for model construction and testing. Statistical results and industrial comments show the high quality of our predictions. Overall, this paper is expected to reduce both pollutant and carbon emissions from construction machinery, thereby facilitating the development of green construction.

Electro-coagulation technique using iron [Fe] and aluminium [Al] for microplastics removal from fashion industry wastewater, Thailand

The textile sector is considered as the 3rd largest source of water pollution and land degradation during 2020. of the world’s water pollution is linked with textile production and utilisation. Textile washing releases 14 million tons of microplastics, according to European Environmental Agency estimates. Wastewater Treatment Plant [WWTP] has declared everyday normal releases of more than 4 million MP particles because of its tiny size (<5mm) and low thickness (<1.2 g/cm3). Electrochemistry for the removal of tinny pollutants is recognised as an efficient treatment mechanism. The main aim of this research paper is to identify the efficiency of electro-coagulation technology using Fe and Al as anode and cathode in microplastic removal from Thailand’s textile industries. Results show the maximum 100% microplastic removal efficiency with pH 10 at a current density of 30 A/m2 within 60 minutes of the current supply. This paper helps to understand the role of electro-coagulation in Thailand textile wastewater plants and adopt the best available technique for microplastic removal.