Genetic algorithm (GA) - Artificial neural network (ANN) modeling for the emission rates of toxic volatile organic compounds (VOCs) emitted from landfill working surface

Bibliometric analysis of research hotspots and trends in the field of volatile organic compound (VOC) emission accounting

Volatile organic compounds (VOCs) have been extensively studied because of their significant roles as precursors of atmospheric ozone and secondary organic aerosol pollution. The research aims to comprehend the current advancements in domestic and international VOC emission accounting. The study utilized the CiteSpace software to represent the pertinent material from Web of Science visually. The hot spots and future development trends of VOC emission calculation are analyzed from the perspectives of thesis subject words, cooperative relationships, co-citation relationships, journals, and core papers. According to the statistics, the approaches most often employed in VOC accounting between 2013 and 2023 are source analysis and emission factor method. Atmospheric environment is the journal with the most publications in the area. The Chinese Academy of Sciences and the University of Colorado System are prominent institutions in VOC emission accounting research, both domestically and internationally. The primary research focuses on the realm of VOC emission accounting clusters, which are "emission factor," "source analysis," "model," "air quality," and "health." A current trend in VOC emission accounting involves the construction of a VOC emission inventory using a novel model that combines emission factors and source analysis. This study reviews the progress made in calculating volatile organic compound (VOC) emissions over the past decade. It aims to provide researchers with a new perspective to promote the development of this field.

Impact of volatile organic compounds in large municipal solid waste landfills on regional environment

Landfill disposal is a major approach of disposing municipal solid waste (MSW) in China. In order to explore the impact of volatile organic compounds (VOCs) generated by landfill on the air quality of regional environment, Jiangcungou landfill in Xi'an and its surrounding area were taken as a research object to analyze the spatial distribution and seasonal variation patterns of non-methane hydrocarbon (NMHC) and VOCs components through seasonal sampling of regional NMHC concentration and VOCs concentration (116 species). CALPUFF model was adopted to analyze the regional dispersion characteristics of NMHC on landfill. In addition, propylene equivalent concentration (PEC) and maximum incremental reactivity (MIR) methods were used to estimate O3 formation potential of the landfill, while fraction aerosol coefficient (FAC) and SOA potential (SOAP) methods were used to estimate SOA formation potential of the landfill. It was indicated that, the component with the highest concentration of VOCs on the working surface and the surrounding area of landfill was p + m-xylene (41.0 μg/m3) and halohydrocarbon (111.2 μg/m3-156.3 μg/m3), respectively. The component with the greatest impact on the surrounding air was acetone, which accounts for 75 %-87 % of the corresponding substance concentration on the landfill. In summer, the surrounding area was affected most by NMHC from landfill, whose emissions contributed 9.5 mg/m3 to the surrounding area. The component making the largest contribution to O3 formation was p + m-xylene (8 %-24 %), while ethylbenzene was the component making the largest contribution to SOA formation (20 %-24 %).

Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction

Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that well-performing ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no one-size-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review analyzed the shortcomings, best practices, and prospects of ANNs for MSW-related trend predictions, supporting the realization of practical applications of ANNs to enhance waste management practices and reduce carbon emissions.

Exorbitant signatures of pesticides and pharmaceuticals in municipal solid wastes (MSWs): Novel insights through risk analysis, dissolution dynamics, and model-based source identification

Studies on the occurrence and fates of emerging organic micropollutants (EOMPs) like pharmaceuticals and pesticides in MSWs are scarce in the literature. Therefore, MSWs were sampled from 20 Indian landfills and characterized for five widely consumed EOMPs (chlorpyrifos, cypermethrin, carbofuran, carbamazepine, and sodium diclofenac), physicochemical, and biological properties. The pesticide (median: 0.17-0.44 mg kg-1) and pharmaceutical (median: 0.20-0.26 mg kg-1) concentrations significantly fluctuated based on landfill localities. Eventually, principal component and multi-factor (MFA) models demonstrated close interactions of EOMPs with biological (microbial biomass and humification rates) and chemical (N, P, K, Ca, S, etc.) properties of MSWs. At the same time, the MFA resolved that EOMPs' fates in MSWs significantly differ from bigger cosmopolitan cities to smaller rural townships. Correspondingly, the concentration-driven ecological risks were high in 15 MSWs with EOMP-toxicity ranks of diclofenac > carbofuran = chlorpyrifos > cypermethrin > carbamazepine. The EOMPs' dissolution dynamics and source apportionments were evaluated using the positive matrix factorization (PMF) model for the first time on experimental data, extracting four anthropogenic sources (households, heterogeneous business centers, agricultural, and open drains). The most significant contribution of EOMPs to MSWs was due to heterogeneous business activity. Notably, the aging of soluble chemical fractions seems to influence the source characteristics of EOMPs strongly.

In silico interactions and deep neural network modeling for toxicity profile of methyl methanesulfonate

In this study, the toxicity induced by the alkylating agent methyl methanesulfonate (MMS) in Allium cepa L. was investigated. For this aim, bulbs were divided into 4 groups as control and application (100, 500 and 4000 µM MMS) and germinated for 72 h at 22-24 °C. At the end of the germination period root tips were collected and made ready for analysis by applying traditional preparation methods. Germination, root elongation, weight, mitotic index (MI) values, micronucleus (MN) and chromosomal abnormality (CAs) numbers, malondialdehyde (MDA) levels, superoxide dismutase (SOD) and catalase (CAT) activities and anatomical structures of bulbs were used as indicators to determine toxicity. Moreover the extent of DNA fragmentation induced by MMS was determined by comet assay. To confirm the DNA fragmentation induced by MMS, the DNA-MMS interaction was examined with molecular docking. Correlation and principal component analyses (PCA) were performed to examine the relationship between all parameters and understand the underlying structure and relationships among these parameters. In the present study, a deep neural network (DNN) with two hidden layers implemented in Matlab has been developed for the comparison of the estimated data with the real data. The effect of MDA levels, SOD and CAT activities at 4 different endpoints resulting from administration of various concentrations of MMS, including MN, MI, CAs and DNA damage, was attempted to be estimated by DNN model. It is assumed that the predicted results are in close agreement with the actual data. The effectiveness of the model was evaluated using 4 different metrics, MAE, MAPE, RMSE and R2, which together show that the model performs commendably. As a result, the highest germination, root elongation, weight gain and MI were measured in the control group. MMS application caused a decrease in all physiological parameters and an increase in cytogenetic (except MI) and biochemical parameters. MMS application caused an increase in antioxidant enzyme levels (SOD and CAT) up to a concentration of 500 µM and a decrease at 4000 µM. MMS application induced different types of CAs and anatomical damages in root meristem cells. The results of the comet assay showed that the severity of DNA fragmentation increased with increasing MMS concentration. Molecular docking analysis showed a strong DNA-MMS interaction. The results of correlation and PCA revealed significant positive and negative interactions between the studied parameters and confirmed the interactions of these parameters with MMS. It has been shown that the DNN model developed in this study is a valuable resource for predicting genotoxicity due to oxidative stress and lipid peroxidation. In addition, this model has the potential to help evaluate the genotoxicity status of various chemical compounds. At the end of the study, it was concluded that MMS strongly supports a versatile toxicity in plant cells and the selected parameters are suitable indicators for determining this toxicity.

A two-staged adsorption/thermal desorption GC/MS online system for monitoring volatile organic compounds

Real-time online monitoring of volatile organic compounds (VOCs) in ambient air is crucial for timely and effective human health protection. Here, we developed an innovative, automated two-staged adsorption/thermal desorption gas chromatography/mass spectrometry (GC/MS) system for real-time online monitoring of 117 regulated volatile organic compounds (VOCs). This system comprised a sampling unit, water management trap, two-staged adsorption/thermal desorption unit, thermoelectric coolers (TECs), and a commercial GC/MS system. By implementing a micro-purge-and-trap (MP & T) step and a two-staged adsorption/thermal desorption unit, the presence of interfering substances was effectively minimized. The utilization of a heart-cutting GC, combined with a single MS detector, facilitated the precise separation and detection of 117 C₂-C₁₂ VOCs, while circumventing the identification and coelution challenges commonly associated with traditional GC-FID or GC-FID/MS methods. The performance of our newly developed online system was meticulously optimized and evaluated using standard gas mixtures. Under optimal conditions, we achieved impressive results, with R² values ≥ 0.9946 for the standard linear curves of all 117 VOCs, demonstrating a precision (RSD) ranging from 0.2% to 6.4%. When applied in the field monitoring, the concentration drifts for 10 ppbv standard gas mixtures were 0.01-5.64% within 24 h. Our study developed a system for online monitoring of 117 atmospheric VOCs with relatively high accuracy and robustness.

Assessing the algal population dynamics using multiple machine learning approaches: Application to Macao reservoirs

The quality of reservoir water is important to the health and wellbeing of human and animals. Eutrophication is one of the most serious problems threatening the safety of reservoir water resource. Machine learning (ML) approaches are effective tools to understand and evaluate various environmental processes of concern, such as eutrophication. However, limited studies have compared the performances of different ML models to reveal algal dynamics using time-series data of redundant variables. In this study, the water quality data from two reservoirs in Macao were analyzed by adopting various ML approaches, including stepwise multiple linear regression (LR), principal component (PC)-LR, PC-artificial neuron network (ANN) and genetic algorithm (GA)-ANN-connective weight (CW) models. The influence of water quality parameters on algal growth and proliferation in two reservoirs was systematically investigated. The GA-ANN-CW model demonstrated the best performance in reducing the size of data and interpreting the algal population dynamics data, which displayed higher R-squared, lower mean absolute percentage error and lower root mean squared error values. Moreover, the variable contribution based on ML approaches suggest that water quality parameters, such as silica, phosphorus, nitrogen, and suspended solid have a direct impact on algal metabolisms in two reservoirs' water systems. This study can expand our capacity in adopting ML models in predicting algal population dynamics based on time-series data of redundant variables.

Emission of volatile organic compounds from landfill working surfaces: Formation potential of ozone and secondary organic aerosols

The working surface of landfills is an important source of volatile organic compounds (VOCs), which have received increasing attention because of their role in potentially forming ozone and secondary organic aerosols (SOAs). In this study, 99 monitoring datasets on VOC emissions from a landfill working surface were obtained in 9 months and used to evaluate their ozone formation potential (OFP) and SOA formation potential (SOAFP) from a statistical perspective and compared using various methods. December was found to have the highest total OFP and SOAFP caused by VOC emissions from the landfill working surface. Both the propylene equivalent concentration (PEC) and maximum incremental reactivity (MIR) methods indicated that oxygenated compounds, especially ethanol, contributed the most to the OFP, accounting for 16.1 %-95.4 % and 44.9 %-98.6 % of the total OFP during the entire observation period, respectively. The fraction aerosol coefficient (FAC) method highlighted the effect of aromatic hydrocarbons which contributed to over 97 % of the total SOAFP. In contrast, the SOA potential (SOAP) method indicated that both aromatic hydrocarbons and oxygenated compounds play important roles, contributing 26.6 %-93.9 % and 21.6 %-73.4 % of the total SOAFP, respectively. Based on their mechanisms and comprehensiveness, PEC and SOAP methods are considered more appropriate for evaluating the OFP and SOAFP of VOCs released from landfill working surfaces. The annual total OFP and SOAFP of VOCs from landfill working surfaces of China in 2020 were thus estimated as 1.5 × 10⁴ t and 135 t, respectively, with high variations among different regions along with the population, waste management system, and the amount of landfilled waste. This study provides a comprehensive understanding of the potential impacts and evaluation methods of local waste landfills in the atmospheric environment from a statistical perspective.

Health risk assessment of volatile organic compounds (VOCs) emitted from landfill working surface via dispersion simulation enhanced by probability analysis

The assessment of the health risks of volatile organic compounds (VOCs) emitted from landfills via dispersion model is crucial but also challenging because of remarkable variations in their emissions and meteorological conditions. This study used a probabilistic approach for the assessment of the health risks of typical VOCs by combining artificial neural network models for emission rates and a numerical dispersion model enhanced by probability analysis. A total of 8753 rounds of simulation were performed with distributions of waste compositions and the valid hourly meteorological conditions for 1 year. The concentration distributions and ranges of the typical health-risky VOCs after dispersion were analyzed with 95% probability. The individual and cumulative non-carcinogenic risks of the typical VOCs were acceptable with all values less than 1 in the whole study domain. For individual carcinogenic risks, only ethylbenzene, benzene, chloroform, and 1, 2-dichloroethane at extreme concentrations showed minor or moderate risks with a probability of 0.1%-1% and an impact distance of 650-3000 m at specific directions. The cumulative carcinogenic risks were also acceptable at 95% probability in the whole study domain, but exceeded 1 × 10^-6 or even 1 × 10^-4 at some extreme conditions, especially within the landfill area. The vertical patterns of the health risks with height initially increased, and then decreased rapidly, and the peak values were observed around the height of the emission source. The dispersion simulation and health risk assessment of the typical health-risky VOCs enhanced by Monte Carlo can accurately reflect their probabilistic dispersion patterns and health risks to surrounding residents from both spatial and temporal dimensions. With this approach, this study can provide important scientific basis and technical support for the health risk assessment and management of landfills.

THz Gas Sensing Using Terahertz Time-Domain Spectroscopy with Ceramic Architecture

Ceramic architectures based on chemical vapor deposition (CVD) are used to create unique crystal structures, morphologies, and properties. This study proposed room-temperature THz gas sensing using terahertz time-domain spectroscopy (THz-TDS) with ceramic architectures. We synthesized ceramic films on porous glass. Zinc oxide films were created using atmospheric CVD and amorphous carbon nitride films using the dissociative excitation reaction of BrCN with metastable Ar atoms. The transmission method was used in THz-TDS. A stainless hand-made gas cell with a Si window was applied for THz gas sensing. We defined "phase delay" equals VOC sensing response amount of sensing materials at each duration. Ppm-order THz gas sensing was performed.

Parameter Optimization of Catering Oil Droplet Electrostatic Coalescence under Coupling Field with COMSOL Software

At present, the common cooking fume purification devices are mostly based on electrostatic technology. There are few researches on the microscopic process of coalescence and electric field parameters’ optimization. In this paper, COMSOL MultiphysicsTM was used to simulate the electrostatic coalescence of oil droplets in the coupling field of an electric field and flow field. The degree of deformation of oil droplets (D) and the starting coalescence time (tsc) were used to evaluate the coalescence process. The feasibility of the model was verified through experimental results. The effects of voltage, flow speed and oil droplet radius on tsc were investigated, and the parameters were optimized by the response surface method and Matrix correlation analysis. It can be concluded that increasing the voltage, flow speed and oil droplet radius appropriately would be conducive to the coalescence of oil droplets. When the oil droplet radius was in the range of 0–1.5 mm, it promoted the coalescence of oil droplets. The influence of various factors on oil droplet coalescence was flow speed > voltage > oil droplet radius. The optimal result obtained by simulation was that when the radius of the oil droplet was 1.56 mm, the voltage 12 kV and the flow speed 180 mm/ms, the shortest coalescence time of oil droplets was 16.8253 ms.

Prediction of Concrete Compressive Strength Using a Back-Propagation Neural Network Optimized by a Genetic Algorithm and Response Surface Analysis Considering the Appearance of Aggregates and Curing Conditions

In the present research, the authors have attempted to examine the compressive strength of conventional concrete, which is made using different aggregate sizes and geometries considering various curing temperatures. To this end, different aggregate geometries (rounded and angular) were utilized in various aggregate sizes (10, 20, and 30 mm) to prepare 108 rectangular cubic specimens. Then, the curing process was carried out in the vicinity of wind at different temperatures (5 °C < T < 30 °C). Next, the static compression experiments were performed on 28-day concrete specimens. Additionally, each test was repeated three times to check the repeatability of the results. Finally, the mean results were reported as the strength of concrete specimens. Response Surface Analysis (RSA) was utilized to determine the interaction effects of different parameters including the appearance of aggregates (shape and size) and curing temperature on the concrete strength. Afterwards, the optimum values of parameters were reported based on the RSA results to achieve maximum compressive strength. Moreover, to estimate concrete strength, a back-propagation neural network (OBPNN) optimized by a genetic algorithm (GA) was used. The findings of this study indicated that the developed neural network approach is greatly consistent with the experimental ones. Additionally, the compressive strength of concrete can be significantly increased (about 30%) by controlling the curing temperature in the range of 5–15 °C.

An Intelligent Site Selection Model for Hydrogen Refueling Stations Based on Fuzzy Comprehensive Evaluation and Artificial Neural Network—A Case Study of Shanghai

With the gradual popularization of hydrogen fuel cell vehicles (HFCVs), the construction and planning of hydrogen refueling stations (HRSs) are increasingly important. Taking operational HRSs in China’s coastal and major cities as examples, we consider the main factors affecting the site selection of HRSs in China from the three aspects of economy, technology and society to establish a site selection evaluation system for hydrogen refueling stations and determine the weight of each index through the analytic hierarchy process (AHP). Then, combined with fuzzy comprehensive evaluation (FCE) method and artificial neural network model (ANN), FCE method is used to evaluate HRS in operation in China’s coastal areas and major cities, and we used the resulting data obtained from the comprehensive evaluation as the training data to train the neural network. So, an intelligent site selection model for HRSs based on fuzzy comprehensive evaluation and artificial neural network model (FCE-ANN) is proposed. The planned HRSs in Shanghai are evaluated, and an optimal site selection of the HRS is obtained. The results show that the optimal HRSs site selected by the FCE-ANN model is consistent with the site selection obtained by the FCE method, and the accuracy of the FCE-ANN model is verified. The findings of this study may provide some guidelines for policy makers in planning the hydrogen refueling stations.