Satellite-informed smart sensor placement framework for near-optimal PM monitoring in urban areas.

Air pollution is a global public health concern, particularly due to PM, which can cause respiratory and cardiovascular diseases. Accurate placement of monitoring sensors is essential to effectively monitor and mitigate PM effects. However, the complex nature of air pollution, including factors like traffic density, population density, and weather conditions, poses challenges for sensor placement.

Interpretable attention-based multi-encoder transformer based QSPR model for assessing toxicity and environmental impact of chemicals.

The rising demand from consumer goods and pharmaceutical industry is driving a fast expansion of newly developed chemicals. The conventional toxicity testing of unknown chemicals is expensive, time-consuming, and raises ethical concerns. The quantitative structure-property relationship (QSPR) is an efficient computational method because it saves time, resources, and animal experimentation.

Soft sensor for predicting indoor PM concentration in subway with adaptive boosting deep learning model.

Public health depends on indoor air quality (IAQ), hence soft measurement techniques must be implemented in the subway environment for more precise and reliable monitoring of indoor particulate matter concentration levels. Adaptive boosting (AdaBoost), an ensemble learning technique, is simple to code and less prone to overfitting. Compared to a single model, it is better able to take into consideration the intricate elements included in air quality data.

Sludge bulking monitoring in industrial wastewater treatment plants through graphical methods: A dynamic graph embedding and Bayesian networks approach.

Sludge bulking is a prevalent issue in wastewater treatment plants (WWTPs) that negatively impacts effluent quality by hindering the normal functioning of treatment processes. To tackle this problem, we propose a novel graph-based monitoring framework that employs advanced graph-based techniques to detect and diagnose sludge bulking events. The proposed framework utilizes historical datasets under normal operating conditions to extract pertinent features and causal relationships between process variables.

Smart solutions for urban health risk assessment: A PM monitoring system incorporating spatiotemporal long-short term graph convolutional network.

Current spatial-temporal early warning systems aim to predict outdoor air quality in urban areas either at short or long temporal horizons. These systems implemented architectures without considering the geographical distribution of each air quality monitoring station, increasing the uncertainty of the forecasting framework. This study developed an integrated spatiotemporal forecasting architecture incorporating an extensive air quality PM monitoring network and simultaneously forecasts PM concentrations at all locations, allowing the monitoring of the health risk associated with exposure to these levels.

Rethinking of the future sustainable paradigm roadmap for plastic waste management: A multi-nation scale outlook compendium.

The myriad consumption of plastic regularly, environmental impact and health disquietude of humans are at high risk. Along the line, international cooperation on a global scale is epitomized to mitigate the environmental threats from plastic usage, not limited to implementing international cooperation strategies and policies. Here, this study aims to provide explicit insight into possible cooperation strategies between countries on the post-treatment and management of plastic.

Data-Driven Prediction of Janus/Core-Shell Morphology in Polymer Particles: A Machine-Learning Approach.

The majority of research on Janus particles prepared by solvent evaporation-induced phase separation technique uses models based on interfacial tension or free energy to predict Janus/core-shell morphology. Data-driven predictions, in contrast, utilize multiple samples to identify patterns and outliers. Using machine-learning algorithms and explainable artificial intelligence (XAI) analysis, we developed a model based on a 200-instance data set to predict particle morphology.

Fine Particulate Matter (PM) Sources and Its Individual Contribution Estimation Using a Positive Matrix Factorization Model.

The effective management and regulation of fine particulate matter (PM) is essential in the Republic of Korea, where PM concentrations are very high. To do this, however, it is necessary to identify sources of PM pollution and determine the contribution of each source using an acceptance model that includes variability in the chemical composition and physicochemical properties of PM, which change according to its spatiotemporal characteristics. In this study, PM was measured using PMS-104 instruments at two monitoring stations in Bucheon City, Gyeonggi Province, from 22 April to 3 July 2020; the PM chemical composition was also analyzed.

Non-Gaussian multivariate statistical monitoring of spatio-temporal wind speed frequencies to improve wind power quality in South Korea.

The spatial and temporal variability of renewable energy resources, particularly wind energy, should be statistically evaluated to achieve sustainable economic development to mitigate climate change. In this study, a non-Gaussian multivariate statistical monitoring approach is proposed to investigate the wind speed frequencies across different regions of South Korea. Anemometer data were first collected in 11 different provinces of South Korea with hourly resolution for one year.

A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties.

A mature and hybrid machine-learning model is verified by mature empirical analysis to measure county-level COVID-19 vulnerability and track the impact of the imposition of pandemic control policies in the U.S. A total of 30 county-level social, economic, and medical variables and a timeline of the imposed policies constitutes a COVID-19 database.

Missing data imputation and sensor self-validation towards a sustainable operation of wastewater treatment plants via deep variational residual autoencoders.

Missing data imputation and automatic fault detection of wastewater treatment plant (WWTP) sensors are crucial for energy conservation and environmental protection. Given the dynamic and non-linear characteristics of WWTP measurements, the conventional diagnosis models are inefficient and ignore potential valuable features in the offline modeling phase, leading to false alarms and inaccurate imputations. In this study, an inclusive framework for missing data imputation and sensor self-validation based on integrating variational autoencoders (VAE) with a deep residual network structure (ResNet-VAE) is proposed.

Transfer learning driven sequential forecasting and ventilation control of PM associated health risk levels in underground public facilities.

Particulate matter with aerodynamic diameter less than 2.5 µm (PM) has become a major public concern in closed indoor environments, such as subway stations. Forecasting platform PM concentrations is significant in developing early warning systems, and regulating ventilation systems to ensure commuter health.

An autonomous operational trajectory searching system for an economic and environmental membrane bioreactor plant using deep reinforcement learning.

Optimal operation of membrane bioreactor (MBR) plants is crucial to save operational costs while satisfying legal effluent discharge requirements. The aeration process of MBR plants tends to use excessive energy for supplying air to micro-organisms. In the present study, a novel optimal aeration system is proposed for dynamic and robust optimization.

Multivariate statistical monitoring of subway indoor air quality using dynamic concurrent partial least squares.

To maintain the health level of indoor air quality (IAQ) in subway stations, the data-driven multivariate statistical method concurrent partial least squares (CPLS) has been successfully applied for output-relevant and input-relevant sensor faults detection. To cope with the dynamic problem of IAQ data, the augmented matrices are applied to CPLS (DCPLS) to achieve the better performance. DCPLS method simultaneously decomposes the input and output data spaces into five subspaces for comprehensive monitoring: a joint input-output subspace, an output principal subspace, an output-residual subspace, an input-principal subspace, and an input-residual subspace.

Population response modeling and habitat suitability of Cobitis choii fish species in South Korea for climate change adaptation.

Endangered species ecosystems require appropriate monitoring for assessing population growth related to the emerging pollutants in their habitat conditions. The response of population growth of Cobitis choii, an endangered fish species, under the exposure to emerging pollutants present in the Geum River Basin of South Korea was studied. Toxicity models of concentration addition (CA), independent action (IA), and concentration addition-independent action (CAIA) were implemented utilizing the concentration of a set of 25 chemicals recorded in the study area.

Deep learning driven QSAR model for environmental toxicology: Effects of endocrine disrupting chemicals on human health.

Over 80,000 endocrine-disrupting chemicals (EDCs) are considered emerging contaminants (ECs), which are of great concern due to their effects on human health. Quantitative structure-activity relationship (QSAR) models are a promising alternative to in vitro methods to predict the toxicological effects of chemicals on human health. In this study, we assessed a deep-learning based QSAR (DL-QSAR) model to predict the qualitative and the quantitative effects of EDCs on the human endocrine system, and especially sex-hormone binding globulin (SHBG) and estrogen receptor (ER).

A real time QSAR-driven toxicity evaluation and monitoring of iron containing fine particulate matters in indoor subway stations.

A fine particulate matter less than 2.5 µm (PM) in the underground subway system are the cause of many diseases. The iron containing PMs frequently confront in underground stations, which ultimately have an impact on the health of living beings especially in children.

Sequential prediction of quantitative health risk assessment for the fine particulate matter in an underground facility using deep recurrent neural networks.

Particulate matter with aerodynamic diameter less than 2.5 µm (PM) in indoor public spaces such as subway stations, has represented a major public health concern; however, forecasting future sequences of quantitative health risk is an effective method for protecting commuters' health, and an important tool for developing early warning systems. Despite the existence of several predicting methods, some tend to fail to forecast long-term dependencies in an effective way.

An insight into nanocellulose as soft condensed matter: Challenge and future prospective toward environmental sustainability.

Nanocellulose, a structural polysaccharide that has caught tremendous interests nowadays due to its renewability, inherent biocompatibility and biodegradability, abundance in resource, and environmental friendly nature. They are promising green nanomaterials derived from cellulosic biomass that can be disintegrated into cellulose nanofibrils (CNF) or cellulose nanocrystals (CNC), relying on their sensitivity to hydrolysis at the axial spacing of disordered domains. Owing to their unique mesoscopic characteristics at nanoscale, nanocellulose has been widely researched and incorporated as a reinforcement material in composite materials.

Modeling of subway indoor air quality using Gaussian process regression.

Soft sensor modeling of indoor air quality (IAQ) in subway stations is essential for public health. Gaussian process regression (GPR), as an efficient nonlinear modeling method, can effectively interpret the complicated features of industrial data by using composite covariance functions derived from base kernels. In this work, an accurate GPR soft sensor with the sum of squared-exponential covariance function and periodic covariance function is proposed to capture the time varying and periodic characteristics in the subway IAQ data.

Quantitative structure-property relationship (QSPR) models for predicting the physicochemical properties of polychlorinated biphenyls (PCBs) using deep belief network.

Octanol/water partition coefficient (log P), octanol/air partition coefficient (log K) and bioconcentration factor (log BCF) are important physiochemical properties of organic substances. Quantitative structure-property relationship (QSPR) models are a promising alternative method of reducing and replacing experimental steps in determination of log P, log K and log BCF. In the current study, we propose a new QSPR model based on a deep belief network (DBN) to predict the physicochemical properties of polychlorinated biphenyls (PCBs).

A systematic approach of removal mechanisms, control and optimization of silver nanoparticle in wastewater treatment plants.

The release of silver nanoparticles (AgNPs) to wastewater caused by over-generation and poor treatment of the remaining nanomaterial has raised the interest of researchers. AgNPs can have a negative impact on watersheds and generate degradation of the effluent quality of wastewater treatment plants (WWTPs). The aim of this research is to design and analyze an integrated model system for the removal of AgNPs with high effluent quality in WWTPs using a systematic approach of removal mechanisms modeling, optimization, and control of the removal of silver nanoparticles.

Assessment of water quality monitoring for the optimal sensor placement in lake Yahuarcocha using pattern recognition techniques and geographical information systems.

Pollution and the eutrophication process are increasing in lake Yahuarcocha and constant water quality monitoring is essential for a better understanding of the patterns occurring in this ecosystem. In this study, key sensor locations were determined using spatial and temporal analyses combined with geographical information systems (GIS) to assess the influence of weather features, anthropogenic activities, and other non-point pollution sources. A water quality monitoring network was established to obtain data on 14 physicochemical and microbiological parameters at each of seven sample sites over a period of 13 months.

Quantitative assessment of human health risks induced by vehicle exhaust polycyclic aromatic hydrocarbons at Zhengzhou via multimedia fugacity models with cancer risk assessment.

Traffic-related pollution released a large amount of atmospheric polycyclic aromatic hydrocarbons (PAHs) which have severely influenced environmental safety and human health until now. However, the important issue of polycyclic aromatic hydrocarbon (PAH) emission from vehicle exhaust in urban populated areas has not been sufficiently investigated yet. This study focused on environmental behavior of vehicle exhaust PAHs (VEPAHs) and resultant health risk on local residents in urban populated areas.