Real-time Pollution Research Articles

Heterocyclic Hemipiperazines: Multistimuli-Responsive Switches and Sensors for Zinc or Cadmium Ions.

Advance in the design of molecular photoswitches - adapters that convert light into changes at molecular level - opens up exciting possibilities in preparing smart polymers, drugs photoactivated inside humans, or light-fueled nanomachines that might in the future operate in our bloodstream. Hemipiperazines are recently reported biocompatible molecular photoswitches based on cyclic dipeptides. Here we report a multistimuli-responsive hemipiperazine-based switch that reacts on light, solvents, acidity, or metal ions. Its photoequilibration is controlled by the intramolecular hydrogen bonding pattern. The compound can be used as a mid-nanomolar photoswitchable fluorescent sensor for zinc and cadmium ions, applicable to monitor environmental pollution in real time.

Unveiling the Nexus: Sulphur Dioxide Exposure, Proximity to Mining, and Respiratory Illnesses in Kankoyo: A Mixed-Methods Investigation.

The emission of sulphur dioxide (SO 2) from mining activities presents significant health hazards, particularly to communities near industrial zones. This mixed-methods study investigates the nexus between (SO 2) exposure and respiratory health in Kankoyo Township, Zambia. Employing community engagement, expert interviews, spatial analysis, and a retrospective examination of 15 years of health and (SO 2) data, the research identified a troubling correlation between (SO 2) exposure and adverse respiratory health effects among the local population. Expert interviews highlighted that respiratory issues constituted approximately 75% of health complications, with a notable reduction in asthma cases following the installation of a monitoring station and upgrades to smelter operations. Spatial analysis demonstrated that (SO 2) levels in Kankoyo exceeded the Zambian Environmental Management Agency (ZEMA) limits by 1713% identifying it as a significant pollution hotspot. Additionally, wind profile analysis indicated frequent low-speed winds from the east-northeast (ENE), contributing to pollutant accumulation. Based on these insights, the study recommends implementing real-time pollution data sharing, affordable air quality sensors, addressing medication shortages, establishing specialized respiratory clinics, launching IT-driven awareness campaigns, and further research into additional pollutants and confounding factors.

Hourly near-ground NO2 concentration retrieval from geostationary satellite observations

Abstract. Nitrogen dioxide (NO2) is an important contributor to the formation of acid rain, photochemical smog and aerosol particles, which seriously endangers public health. At present, remote sensing of polar-orbiting satellites is a conventional means to obtain large-scale NO2 distribution, but it cannot capture the rapid change because of long revisit periods. The Advanced Himawari Imager (AHI) on the Himawari-8 geostationary satellite has the advantage of high time resolution, which makes it possible to realize near-real-time atmospheric monitoring. Here, based on the absorption characteristics of NO2 in infrared radiation, hourly near-surface NO2 concentrations are retrieved based on the brightness temperature from AHI and auxiliary information such as meteorology and aerosol. The results of 10-fold cross-validation show that NO2 estimations from satellite are in good agreement with in-situ measurements, and their determination coefficient (R2) can reach 0.79. Due to different emission and atmospheric diffusion conditions at different time, the model performance presents a diurnal variation of high accuracy in the noon and afternoon but low accuracy in the morning. Based on the retrieval dataset, it is found that high NO2 concentrations are mainly concentrated in the densely populated and industrial areas such as the North China area. In addition, NO2 pollution mainly occurs in autumn and winter, and the average NO2 concentration in winter is about 1.63 times that in summer in 2021. This study provides a new insight for satellite retrieval of NO2, which is of great significance for real-time pollution monitoring and public health protection.

Monitoring and Forecasting the Spread of Industrial Emissions in the Atmosphere in Real Time SUSUPLUME

The "Ecomonitor" software package has been considered, suitable for use in various industrial companies for tracking and forecasting the industrial emissions into the atmosphere, taking into account meteorological conditions in order to determine the transfer of pollutants to the area around the company. The mathematical model SUSUPLUME, operating in "Ecomonitor", has been presented. The main advantages of this parametric model have been indicated, namely the ability to assess air pollution in real time, as well as its application to various substances, including SO2, NO2, NO, CO, CO2 and dust.

A systematic review of green and sustainable chemistry training research with pedagogical content knowledge framework: current trends and future directions

This study reviewed the green and sustainable chemistry education (GSCE) research that provided training at the tertiary level from 2000 to 2024. The Web of Science and ERIC databases were screened using title and abstract review. In total, 49 studies were analysed. The analysis instrument has two main parts, namely, general characteristics of the training, which was formed in light of the GSCE literature (i.e., chemistry sub-disciplines, type of implementation, and context), and analysis of the training through the lens of pedagogical content knowledge (PCK) construct that is the commonly-used framework for the analysis of training regarding orientation to teaching GSCE, learner, curriculum, assessment, and instructional strategies utilised. Results showed that organic chemistry (n = 15) is the most emphasised branch of chemistry in the articles. Regarding the learner component, the studies were inadequate, and very few studies provided information about the misconceptions and difficulties that students may encounter while learning GSC. Regarding the curriculum component, among the green chemistry principles, ‘use of renewable feedstocks’ was the most emphasised, while the least emphasised ones were ‘reduce derivatives’ and ‘real-time pollution prevention’. Fourteen studies used subject-specific teaching strategies (e.g., cooperative teaching and project-based strategies). Although representations are not used in GSCE, most of the studies included laboratory studies (n = 31). Finally, regarding the assessment, very few studies focused on measuring students' skills (laboratory skills, discussion skills, etc.) and affective variables. In light of the findings, GSCE training should get more benefit from the literature on science/chemistry teaching strategies. Moreover, alternative assessment tools (e.g., rubrics and concept maps) should be utilized regarding the instruments utilized to assess the participants' GSC knowledge.

Fluorescence fingerprint as an indicator to identify urban non-point sources in urban river during rainfall period

Nowadays, the urban non-point source (NPS) pollution gradually evolved as the main contributor to urban water contamination since the point source pollution was effectively controlled. It was imperative to perform urban NPS identification in urban river to meet the requirements of precise source governance. In this study, the real-time detection about water quality parameters and fluorescence fingerprints (FFs) was performed for BX River and its outlets during rainfall period. EEM-PARAFAC and component similarity analyses discovered that the pollution encountered by BX River mainly came from road runoff and untreated municipal wastewater (UMWW) overflow. The C1 (tryptophan-like) and C3 (terrestrial humic-like) components located at Ex/Em = ∼230(280)/340 and ∼275/430 nm were both detected in these two kinds of urban NPS. The C2 components of road runoff and UMWW overflow displayed remarkable differences, which located at Ex/Em = 250/385 and 245/365 nm, respectively, thus could be served as indicators for distinguishing them. During rainfall period, the outflow from rainwater outlets (RWOs) constantly showed similar FF features to road runoff, while the FFs of outflow from combined sewer outlets (CSOs) alternated between those of road runoff and UMWW overflow. The FF features of sections in BX River changed in response to the dynamic variations in FFs of the outlets, which revealed real-time pollution causes of BX River. This work not only realized the identification and differentiation of urban NPS, but also elucidated the dynamic variations of pollution characteristics throughout the entire process of “urban NPS-outlets-urban river”, and demonstrated the feasibility of FF technique in quickly diagnosing the pollution causes of urban river during rainfall period, which provided important guidance for urban NPS governance.

Improving Real-Time Pollution Prediction Using Dimensionality Reduction and KNN Algorithms

This study introduces an innovative method that uses machine learning to forecast air quality. It use a large dataset that includes a range of environmental parameters. The methodology incorporates standard scaling, Principal Component Analysis (PCA) for reducing dimensionality, and the K-Nearest Neighbours (KNN) algorithm to improve predicting accuracy. The model demonstrates an impressive overall accuracy of 95%, surpassing conventional approaches. This method provides a strong and effective solution for predicting air quality in real-time, making a substantial contribution to proactive environmental management and safeguarding public health. The findings highlight the capacity of sophisticated machine learning methods to tackle the issues related to urban air pollution.

Assessment of a deep learning model for monitoring atmospheric pollution: Case study in Antwerp, Belgium

Urban areas face a significant health risk due to atmospheric pollution necessitating continuous monitoring to assess and mitigate its impact and achieve a sustainable city. In this study, a deep learning model was developed and trained using Computational Fluid Dynamics (CFD) simulations to predict the dispersion of nitrogen dioxide (NO2) emissions from traffic. The model’s performance was evaluated by comparing it to real-world field measurements conducted in Antwerp, Belgium, throughout 2016. Temporal comparisons with a traffic-influenced air quality station over the entire year yielded an average correlation coefficient (R) of 0.83 and a mean relative error (MRE) of 0.21. Additionally, a spatial evaluation was conducted by comparing the model’s predictions to a measurement campaign involving 73 samplers. The spatial evaluation resulted in an R of 0.72 and a MRE of 0.18 for samplers located near known emission sources. Notably, the deep learning model demonstrated computational efficiency, outperforming CFD simulations by an order of magnitude of 100 to 1000, enabling real-time pollution monitoring and large-scale scenario studies without compromising the consideration of micro-scale effects that are typically overlooked in larger-scale models.

Arduino-Based IoT System for Monitoring Air Pollution

Air pollution is a major global health concern, and there is a growing need for effective monitoring systems. This abstract presents an Arduino-based Internet of Things (IoT) system that can be used to monitor air pollution in real time. The system is low-cost, easy to deploy, and scalable, making it a valuable tool for environmental monitoring. The system incorporates low-cost Arduino microcontrollers, sensors, and wireless communication modules to enable remote data collection, analysis, and visualization. The system can be used to monitor air quality in a variety of settings, including schools, workplaces, and homes. The data collected by the system can be used to identify areas with poor or good air quality. The open-source nature of the Arduino platform facilitates community-driven development and customization, encouraging collaboration and innovation in air pollution monitoring. This paper outlines the procedure for constructing a prototype model for air contamination detection. It also emphasizes of alerting system when pollution levels exceed the threshold, enabling timely action and mitigation of the situation.

Rotation-mode liquid-solid triboelectric nanogenerator for efficient contact-electro-catalysis and adsorption

Contact-electro-catalysis has been shown to be a new method for catalytic degradation of pollutants. However, the catalytic efficiency of contact-electro-catalysis (CEC) realized by ultrasonic method still has great potential for improvement. Here, we propose a wind-driven, Al@Al2O3 and PTFE combined rotational mode liquid-solid triboelectric nanogenerator (TENG) contact-electro-catalytic and adsorbable device. The contact electrification of the liquid-solid interface during mechanical rotation makes the treatment efficiency of organic pollutants much higher than other advanced oxidation processes. Taking crystal violet (CV) as the target pollutant, when the linear speed of the rotator is 1.41 m s-1, the k values for low-concentration and high-concentration crystal violet are 2.24 min-1 and 0.26 min-1, respectively. This is a huge advance in the field of catalysis. In addition, the device can also monitor the treatment progress of organic pollutants in real time.

Air pollution from vehicle-tailpipe emissions and diagnostic approaches through cyber–physical platform - a review

Vehicle tailpipe emission contributes to a large amount of air pollution and results in extreme health risk issues. This issue is increasing gradually due to lack of public awareness, and the downsides of traditional emission testing approaches. The conventional approaches are nonviable and time consuming methods. These approaches have also lack of trained and skilled personnel and lack of monitoring equipment. In these circumstances, the cyber–physical system (CPS) can be utilized to reduce the tailpipe emission problem. CPS can emphasize more on how to discover new knowledge across multiple sensing domain and to make proper use of intelligence. CPS based methods are employed for the regular maintenance of vehicles, easy feedback generation to drivers and transport managers, monitoring real-time pollution footprint of the vehicle, identification of the polluters, predictive maintenance of the engine, performance enhancement of the vehicle, diminution of the pollutant emission. This paper presents an in-depth review of conventional methods for vehicle-tailpipe emission management and the scope of cyber–physical framework for its better treatment.

An end-to-end real-time pollutants spilling recognition in wastewater based on the IoT-ready SENSIPLUS platform

The problem of detecting illegal pollutants in wastewater is of fundamental importance for public health and security. The availability of distributed, low–cost and low–power monitoring systems, particularly enforced by IoT communication mechanisms and low-complexity machine learning algorithms, would make it feasible and easy to manage in a widespread manner. Accordingly, an End-to-End IoT-ready node for the sensing, local processing, and transmission of the data collected on the pollutants in the wastewater is presented here. The proposed system, organized in sensing and data processing modules, can recognize and distinguish contaminants from unknown substances typically present in wastewater. This is particularly important in the classification stage since distinguishing between background (not of interest) and foreground (of interest) substances drastically improves the classification performance, especially in terms of false positive rates. The measurement system, i.e., the sensing part, is represented by the so-called Smart Cable Water based on the SENSIPLUS chip, which integrates an array of sensors detecting various water-soluble substances through impedance spectroscopy. The data processing is based on a commercial Micro Control Unit (MCU), including an anomaly detection module, a classification module, and a false positive reduction module, all based on machine learning algorithms that have a computational complexity suitable for low-cost hardware implementation.An extensive experimental campaign on different contaminants has been carried out to train machine-learning algorithms suitable for low-cost and low-power MCU. The corresponding dataset has been made publicly available for download. The obtained results demonstrate an excellent classification ability, achieving an accuracy of more than 95% on average, and are a reliable “proof of concept” of a pervasive IoT system for distributed monitoring.

Research on the Influence of Weather Patterns on Ozone Concentration: A Case Study in Tianjin

Ozone (O3) is an important secondary substance that plays a significant role in atmospheric chemistry and climate change. Although O3 is essential in the stratosphere, it is harmful to human health in the troposphere, where this study was conducted. In recent years, O3 pollution in the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions has deteriorated, which has become an important environmental problem. The generation of O3 is closely related to meteorological factors. In this study, the weather classification method was adopted to study the effect of meteorological conditions on O3 concentration. In the BTH region, Tianjin was selected as the representative city for the research. The real-time pollutants data, meteorological re-analysis data, and meteorological data in 2019 were combined for the analysis. The subjective weather classification method was adopted to investigate the effects of different weather types on O3 concentration. The backward trajectory tracking model was used to explore the characteristics and changes of O3 pollution under two extreme weather types. The results indicate there is a good correlation between O3 concentration and ambient temperature. Under the control of low pressure on the ground and the influence of southwest airflow in the upper air for Tianjin, heavy O3 pollution occurred frequently. The addition of external transport and local generation will cause high O3 values when the weather system is weak. The O3 concentration is closely related to ambient temperature. Continuous high-temperature weather is conducive to the photochemical reaction. The multi-day O3 pollution process would occur when the weather system is robust. The first and second types of extreme weather are more likely to cause persistent O3 pollution processes. Under the premise of stable emission sources, the change in weather patterns was the main reason affecting the O3 concentration. This study aims to improve O3 pollution control and air quality prediction in the BTH region and large cities in China.

Research on the Application of Multimodal-Based Machine Learning Algorithms to Water Quality Classification

With the development of society and the accelerated industrialization, the problem of water pollution has become increasingly prominent. In order to stop the gathering and diffusion of harmful substances in water bodies, leading to further deterioration of water quality and more serious environmental problems, environmental management departments have developed a series of pollutant discharge standards to prevent water pollution in real time. Common testing methods are the colorimetric method and TDS (total dissolved solids) value testing method, which are mostly through water bodies that contain acid, alkali, salt, and other indicators of the concentration test, to produce an assessment of water quality. However, the traditional methods of water quality testing, whether in the measurement time or in the accuracy of the test, are certain defects. In order to be able to quickly detect the concentration of water quality indicators in water bodies, timely response and treatment of highly polluted water bodies are urgently needed. In this paper, we propose a water quality detection classification model based on multimodal machine learning algorithm. Firstly, we preprocessed and analyzed the collected water quality dataset and determined the reasonable and perfect water quality classification influencing factors. Then, we successively built 15 kinds of classification models based on machine learning algorithms for water quality detection. At the same time, we evaluated the performance of each model. From the four evaluation indexes of precision, recall rate, F1 value, and accuracy, respectively, the real value is compared with the predicted value of each model. The experimental results show that sulfate, pH, solids, and hardness are the important influencing factors to perform water quality testing. And the three models XGBoost (Extreme Gradient Boosting), CatBoost (Categorical Boosting), and LGBM (Light Gradient Boosting Machine) have better performances in conducting water quality testing. Finally, we further optimized the classification models based on XGBoost, CatBoost, and LGBM by using two major tools: cross-validation and hyperparameter tuning.

A new Android Application Framework (AAF) for API PM2.5 data management system

Abstract Air Pollution Index (API) is used in Malaysia to measure the ambient air quality and PM2.5 is one of its parameters for ambient air quality monitoring. However, most of the PM2.5 samplers available for research and monitoring by universities and monitoring providers are in manual mode in which the data was logged into the equipment data logger and was manually extracted to the personal computer before analysis. Therefore, this project is aimed to propose an Android Application Framework (AAF) containing IoT devices to monitor the real-time ambient air quality and disseminate computed API information through the social media platform. In this study, mobile apps which are Blynk was used and enabled the user to control remotely desirable outcomes at any time. E-sampler IoT interface, mobile application, data management, data management system, API judgment and information dissemination are proposed to make AAF succeed. As a result, the proposed AAF has been fully utilized in obtaining real-time pollutants data and finally analyzing PM2.5 to obtain real-time API information. IoT device has successfully interface with E-Sampler, to obtain raw data and be able to be downloaded for further analysis. In conclusion, AAF has functioned great and information was disseminated to the public through social media.

ISpray

Despite regulations and policies to improve city-level air quality in the long run, there lack precise control measures to protect critical urban spots from heavy air pollution. In this work, we propose iSpray, the first-of-its-kind data analytics engine for fine-grained PM2.5 and PM10 control at key urban areas via cost-effective water spraying. iSpray combines domain knowledge with machine learning to profile and model how water spraying affects PM25 and PM10 concentrations in time and space. It also utilizes predictions of pollution propagation paths to schedule a minimal number of sprayers to keep the pollution concentrations at key spots under control. In-field evaluations show that compared with scheduling based on real-time pollution concentrations, iSpray reduces the total sprayer switch-on time by 32%, equivalent to 1, 782 m3 water and 18, 262 kWh electricity in our deployment, while decreasing the days of poor air quality at key spots by up to 16%.

Cloud-Based Decision Support System for Air Quality Management

Air quality is important for the protection of human health, the environment and our cultural heritage and it is an issue that will acquire increased significance in the future due to the adverse effects of climate change. Thus, it is important to not simply monitor air quality, but to make information immediately available to those responsible for monitoring the networks, to policy/decision makers, but also to the general population. Moreover, the development of information technologies over the last couple of decades has allowed the proliferation of real-time pollution monitoring. The work presented herein concerns the development of an effective way of monitoring environmental parameters using dedicated software. It offers a complete suite of applications that support environmental data collection management and reporting for air quality and associated meteorology. It combines modern technologies for the proper monitoring of air quality networks, which can consist of one or more measuring stations. Innovatively, it also focuses on how to effectively present the relevant information, utilizing modern technologies, such as cloud and mobile applications, to network engineers, policy/decision managers, and to the general public at large. It also has the capability of notifying appropriate personnel in the event of failures, overruns or abnormal values. The system, in its current configuration, handles information from six networks that include over 55 air pollution monitoring stations that are located throughout Greece. This practical application has shown that the system can achieve high data availability rates, even higher than 99% during the year.

Inequalities in occupational exposures among people using popular commute modes

Several recent studies have looked into the differences in air qualities inside popular commute modes. The impact of daily commuting patterns and work-related trips on inhalation doses, however, are not investigated. The purpose of this study is to quantify the variation in air pollutants within popular commute modes in Mumbai, India, and to estimate the variation in exposure as a result of occupational or work-related trips across different sub-groups. Real-time pollutants, both gaseous and particulate matters (PM), were measured on a pre-defined route during rush and non-rush hours on buses, cars, auto-rickshaws, sub-urban trains, and motorbikes through several trips (N = 98). Household surveys were conducted to estimate the exposures of different occupational subgroups (cab-driver, auto-rickshaw drivers, delivery persons) and people commuting to their offices daily. Participants (N = 800) from various socioeconomic backgrounds in the city were asked about their job categories, work-activity patterns, and work-related commute trips. Mass concentrations of particles in different size ranges (PM1, PM2.5, and PM10) were substantially higher (p < 0.05) inside auto-rickshaws (44.6 μg/m3, 84.7 μg/m3, and 138.3 μg/m3) compared to other modes. Inside cars, gaseous pollutants such as carbon monoxide (CO) and total volatile organic compounds (TVOC) were significantly higher (p < 0.05). Although both gaseous and particulate concentrations were lower (p < 0.05) inside buses, bus-commuters were found to be highly exposed to the pollutants due to the extended trip time (∼1.2 times longer than other modes) and driving conditions. Office commuters inhale a large fraction of their daily doses (25–30%) during their work-related travel. Occupational sub-groups, on the other hand, inhale ∼90% of the pollutants during their work. In a day, an auto-rickshaw driver inhales 10–15% more (p < 0.05) pollutants than cab driver or delivery personnel. Therefore, this study highlights the inequalities in occupational exposure as a combined effect of in-cabin air qualities and commute patterns due to occupational obligations.

Economic policy uncertainty and industrial pollution: The role of environmental supervision by local governments

This paper explores the effect of economic policy uncertainty (EPU) on industrial pollution using China's real-time pollution data and a novel news-based EPU index constructed by Davis, Liu, and Sheng (2019). Empirical results suggest that EPU can significantly promote sulfur dioxide (SO2) readings in cities experiencing higher ex-ante fiscal pressure, and this effect is more profound during the daytime. Given that SO2 is the main contributor to industrial pollution, and local governments have less incentive to monitor illegal pollutant discharges during the nighttime period, our findings indicate that rising EPU will harm the environment by motivating local authorities to reduce environmental supervision and thus increase industrial pollution. We also find that promoting regional innovation and strengthening external environmental regulations can mitigate the pollution effect of economic policy uncertainty.

A data calibration method for micro air quality detectors based on a LASSO regression and NARX neural network combined model

Studies have shown that there is a certain correlation between air pollution and various human diseases, especially lung diseases, so it is very meaningful to monitor the concentration of pollutants in the air. Compared with the national air quality monitoring station (national control point), the micro air quality detector has the advantage that it can monitor the concentration of pollutants in real time and grid, but its measurement accuracy needs to be improved. This paper proposes a model combining the least absolute selection and shrinkage operator (LASSO) regression and nonlinear autoregressive models with exogenous inputs (NARX) to calibrate the data measured by the micro air quality detector. Before establishing the LASSO-NARX model, correlation analysis is used to test whether the correlation between the concentration of air pollutants and its influencing factors is significant, and to find out the main factors that affect the concentration of pollutants. Due to the multicollinearity between various influencing factors, LASSO regression is used to further screen the influencing factors and give the quantitative relationship between the pollutant concentration and various influencing factors. In order to improve the prediction accuracy of pollutant concentration, the predicted value of each pollutant concentration in the LASSO regression model and the measurement data of the micro air quality detector are used as input variables, and the LASSO-NARX model is constructed using the NARX neural network. Several indicators such as goodness of fit, root mean square error, mean absolute error and relative mean absolute percent error are used to compare various air quality models. The results show that the prediction results of the LASSO-NARX model are not only better than the LASSO model alone and the NARX model alone, but also better than the commonly used multilayer perceptron and radial basis function neural network. Using this model to calibrate the measurement data of the micro air quality detector can increase the accuracy by 61.3–91.7%.