Pollution Source Localization Research Articles

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.

Research on multi-UAV air pollution source localization algorithm based on emotional quantity

With the advancement of industrialization, the problem of atmospheric environmental pollution is becoming more and more prominent. To solve this problem, an unmanned aerial vehicle (UAV) as an airborne platform was used to design an air pollution source localization method based on an anxiety-auction algorithm and verify the feasibility of the algorithm through simulation analysis and indoor source localization experiments. The algorithm innovatively introduces the concept of anxiety in psychology into the traditional auction algorithm. By enabling each drone to make a “rational” auction time decision based on its emotional state, team resources can be conserved, and overall source localization efficiency can be enhanced. Based on different environmental factors and conditions, the number of drones and other multi-perspective comparison analyses with the traditional auction algorithm, the analysis results show that the anxiety-auction algorithm performs better in terms of success rate and distance ratio. This paper also built a set of atmospheric pollutant source localization platforms, consisting of an ultra-wideband (UWB) indoor positioning device, UAV platform, source localization monitoring and control module, and the indoor source localization experiment of atmospheric pollutants based on multiple UAVs was successfully designed and carried out.

Mobile Agents-Based Collaborative Perception for Pollution Source Localization in Complex Environments

In this letter, we present a multiagent collaborative perception scheme for pollution source localization in complex environments. To ensure reliable connectivity, a crowd-sensing model is designed to adaptively adjust the sensing range of the swarm of mobile agents. While by introducing the residual energy of agents, a modified mayfly algorithm (MA) is adopted to guide mobile agents to move toward the pollutant source, which can give attention to both energy efficiency and localization accuracy. Besides, artificial potential field is utilized to construct the repulsive force field of each mobile agent to avoid collisions in the process of moving toward the pollution source. Experimental simulations validate that the proposed method has better performance in localization accuracy, collision avoidance, and energy efficiency.

Behavior Composition for Marine Pollution Source Localization Using a Mobile Sensor Network

Marine pollution, which can cause damage to marine ecosystems, cut fishery production, and even harm human health, has aroused worldwide interest in recent years. Marine pollution reduction operations can stagnate in the case that the source of the pollution is unknown or hidden. In this paper, we present a novel method for marine pollution source localization using a network of mobile sensor nodes, such as autonomous underwater vehicles equipped with chemical sensors. Traditional reactive control methods can respond quickly to the shape dynamics of a chemical plume; however, they can hardly achieve intelligent cooperation unlike deliberative methods. In this study, we present a behavior composition method that attempts to combine the advantages of reactive and deliberative methods. An upwind-customized crossover operation based on the genetic algorithm was formulated as one of the elementary behaviors. The upwind sprint and movement away from the centroid of the sensor nodes were also modeled as another two elementary behaviors. Different sensor nodes are capable of different simultaneous elementary behaviors, enabling behavior composition in the mobile sensor network during plume source localization. The proposed method was evaluated using a widely used filamentous plume simulation platform, which has been used to facilitate field experiments in real marine environments. Simulation results indicate that the proposed method achieved high time-efficiency and localization accuracy during plume source localization in marine environments.

Investigation of Indoor Polycyclic Aromatic Hydrocarbons (PAHs) in Rural Northeast China: Pollution Characteristics, Source Analysis, and Health Assessment

Due to the low winter temperatures in rural areas of Northeast China, biomass fuels are widely used for heating and cooking, resulting in increased concentrations of PAHs in rural indoor areas during the heating period and threatening human health. Therefore, exploring the pollution characteristics, source localization, and risk assessment of indoor PAHs in rural Northeast China is of great significance for improving rural indoor air quality. In this study, PAHs were collected from a residential building in rural Northeast China for one consecutive year (January 2020–December 2020), and their concentrations were determined to explore the distribution patterns and sources of PAHs to further assess the carcinogenic risk of PAHs to humans. The results of the study showed that the average concentration of indoor PAHs in rural areas during the heating period (93.02 ng/m3) was about 1.81 times higher than that of the non-heating period (51.26 ng/m3). The main sources of PAHs were mixed combustion of biomass and coal, motor vehicle emissions, and domestic waste combustion. The level of indoor PAHs pollution has posed a carcinogenic risk to the health of the rural population in the Northeast.

Pollution Source Localization in Complex Environments Using A Swarm of Mobile Agents

In this letter, we present a pollution source localization method combining particle swarm optimization (PSO) and artificial potential field (APF). PSO is used to control mobile agents to move toward the pollutant source according to concentration and position information. Meanwhile, to avoid collision during the movement, it is assumed that there exist repulsions around mobile agents and obstacles in monitoring area. APF is used to construct the corresponding repulsive force fields for each mobile agent. Besides, the residual energy is introduced to balance the energy consumption among mobile agents. Once the agent swarm locates the pollutant source, support vector regression is adopted to model the pollution diffusion based on concentration measurements and distribution of mobile agents. Experimental evaluations demonstrate the effectiveness and feasibility of our proposed method.

A Local PSO-Based Algorithm for Cooperative Multi-UAV Pollution Source Localization

A Local PSO-Based Algorithm for Cooperative Multi-UAV Pollution Source Localization

Pollution Source Localization in Wastewater Networks.

In December 2016, the wastewater treatment plant of Baarle-Nassau, Netherlands, failed. The failure was caused by the illegal disposal of high volumes of acidic waste into the sewer network. Repairs cost between 80,000 and 100,000 EUR. A continuous monitoring system of a utility network such as this one would help to determine the causes of such pollution and could mitigate or reduce the impact of these kinds of events in the future. We have designed and tested a data fusion system that transforms the time-series of sensor measurements into an array of source-localized discharge events. The data fusion system performs this transformation as follows. First, the time-series of sensor measurements are resampled and converted to sensor observations in a unified discrete time domain. Second, sensor observations are mapped to pollutant detections that indicate the amount of specific pollutants according to a priori knowledge. Third, pollutant detections are used for inferring the propagation of the discharged pollutant downstream of the sewage network to account for missing sensor observations. Fourth, pollutant detections and inferred sensor observations are clustered to form tracks. Finally, tracks are processed and propagated upstream to form the final list of probable events. A set of experiments was performed using a modified variant of the EPANET Example Network 2. Results of our experiments show that the proposed system can narrow down the source of pollution to seven or fewer nodes, depending on the number of sensors, while processing approximately 100 sensor observations per second. Having considered the results, such a system could provide meaningful information about pollution events in utility networks.

Agent-based autonomous pollution source localization for complex environment

Pollution source localization is important prerequisites for source control, pollution isolation, purification, and emergency evacuation. Most available studies on pollution source localization have not comprehensively investigated practical problems, such as location accuracy, obstacle avoidance, energy saving, and motion control. Besides, some multi-agent olfaction methods result in wasting resources in small detecting area. Aiming at the issues, this study presents a single agent-based autonomous approach to pollution source localization for the complex environment. In our solution, the NEAT algorithm is used to generate different networks with different weights and structures, the neural network is used to control the agent according to the concentration and distance information, and the fitness function is set to choose the optimal neural network with the best performance. In particular, the fitness function is constructed by combining the pollution concentration and the residual energy of the agent to consider both positioning accuracy and energy saving. Comprehensive simulations show that the proposed method can find the optimal neural network under energy constraints and the agent under the control of the optimal neural network can avoid obstacles and accurately locate the pollution source in different complex environments.

The improvement of spatial-temporal resolution of PM2.5 estimation based on micro-air quality sensors by using data fusion technique

With the rapid development of the Internet of things (IoTs) and modern industrial society, forecasting air pollution concentration, e.g., the concentration of PM2.5, is of great significance to protect human health and the environment. Accurate prediction of PM2.5 concentrations is limited by the number and the data quality of air quality monitoring stations. In Taiwan, the spatial and temporal data of PM2.5 concentrations are measured by 77 national air quality monitoring stations (built by Taiwan EPA). However, the national stations are costly and scarce because of the highly precise instrument and their size. Therefore, many places are still out of coverage of the monitoring network. Recently, under the framework of IoTs, there are hundreds of portable air quality sensors called “AirBox” developed jointly by the Taiwan local government and a private company. By virtue of its low price and portability, the AirBox can provide a higher resolution of space-time PM2.5 measurement. However, the spatiotemporal distribution is different between AirBox and EPA stations, and data quality and accuracy of AirBox is poorer than national air quality monitoring stations. Thus, to integrate the heterogeneous PM2.5 data, the data fusion technique should be used before further analysis.In this study, we propose a new data fusion method called multi-sensor space-time data fusion framework. It is based on the Optimum Linear Data Fusion theory and integrating with a multi-time step Kriging method for spatial-temporal estimation. The method is used to do heterogeneous data fusion from different sources and data qualities. It is able to improve the estimation of PM2.5 concentration in space and time. Results have shown that by combining PM2.5 concentration data from 1176 low-cost AirBoxes as additional information in our model, the estimation of spatial-temporal PM2.5 concentration becomes better and more reasonable. The r2 of the validation regression model is 0.89. Under the approach proposed in this study, we made the information of the micro-sensors more reliable and improved the higher spatial-temporal resolution of air quality monitoring. It could provide very useful information for better spatial-temporal data analysis and further environmental management, such as air pollution source localization, health risk assessment, and micro-scale air pollution analysis.

A Survey on Pollution Monitoring Using Sensor Networks in Environment Protection

Detecting pollution timely and locating the pollution source is of great importance in environmental protection. Considering advantages of the sensor network technology, sensor networks have been adopted in pollution monitoring works. In this paper, a survey on researches of pollution monitoring using sensor networks in environment protection is given. Firstly, sensors and pollution monitoring network systems are studied. Secondly, different pollution detection methods are analyzed and compared. Thirdly, an overview of state-of-art technologies on pollution source localization is given. Finally, challenges on pollution monitoring using sensor networks are presented.

Pollution Source Localization Based on Multi-UAV Cooperative Communication

Harmful gas leakage accidents in chemical plants have occurred from time to time. The application of mobile robots to find odor source has become one of the hottest research topics. Compared to traditional robots, unmanned aerial vehicle (UAV) is more flexible and safer. Therefore, using multi-UAV to solve pollution source tracking is a meaningful study. In this paper, an air pollution source tracking algorithm based on artificial potential field and particle swarm optimization is proposed. The particle swarm optimization algorithm combined with artificial potential field method is used to guide the UAVs to track the plume and avoid the collisions among them. At the same time, adaptive inertia weights are used to help improve the convergence and the searchability of particles. We not only evaluated this algorithm in simulation experiments but also designed a multi-UAV pollution source tracking platform for real-world experiments. The experimental results show that the algorithm can accurately find the pollution source in a short time.

Distributed Water Pollution Source Localization with Mobile UV-Visible Spectrometer Probes in Wireless Sensor Networks.

Pollution accidents that occur in surface waters, especially in drinking water source areas, greatly threaten the urban water supply system. During water pollution source localization, there are complicated pollutant spreading conditions and pollutant concentrations vary in a wide range. This paper provides a scalable total solution, investigating a distributed localization method in wireless sensor networks equipped with mobile ultraviolet-visible (UV-visible) spectrometer probes. A wireless sensor network is defined for water quality monitoring, where unmanned surface vehicles and buoys serve as mobile and stationary nodes, respectively. Both types of nodes carry UV-visible spectrometer probes to acquire in-situ multiple water quality parameter measurements, in which a self-adaptive optical path mechanism is designed to flexibly adjust the measurement range. A novel distributed algorithm, called Dual-PSO, is proposed to search for the water pollution source, where one particle swarm optimization (PSO) procedure computes the water quality multi-parameter measurements on each node, utilizing UV-visible absorption spectra, and another one finds the global solution of the pollution source position, regarding mobile nodes as particles. Besides, this algorithm uses entropy to dynamically recognize the most sensitive parameter during searching. Experimental results demonstrate that online multi-parameter monitoring of a drinking water source area with a wide dynamic range is achieved by this wireless sensor network and water pollution sources are localized efficiently with low-cost mobile node paths.

Real-time localization of pollution source for urban water supply network in emergencies

In recent years, drinking water pollution happens from time to time, severely endangering social stability and residents’ life. By placing sensors in urban water supply pipes to monitor water quality in real time, the negative impacts of pollution accidents can be reduced to a great degree. However, how to localize pollution source in real time according to the detection information from water quality sensors is still a challenging topic. The difficulty is that when the pollution is detected, the pollution information collected by sensors is insufficient to localize the pollution source. This paper mainly studies the real-time localization of the source of paroxysmal pollution when water demand is uncertain. Many previous studies adopted the simulation-optimization method to simulate pollution event in a fixed period of time after it happens, then localize the pollution source reversely using all the simulation data; however, when the pollution source has been detected, real-time localization by simulation-optimization method can shorten the time of simulation and thus deal with the pollution in real time to reduce its harm. This paper first describes the problem of real-time paroxysmal pollution source localization and provides the diagram of simulation-optimization model to the problem; then the design of real-time localization algorithm is proposed with consideration of objective function; at last, experiments are carried out on pipe networks of different scales, and the results show that compared to traditional pollution source localization method, the real-time pollution source localization method can find the true pollution event with less sensor data in a shorter period of time.

Pollution source localization in an urban water supply network based on dynamic water demand.

Urban water supply networks are susceptible to intentional, accidental chemical, and biological pollution, which pose a threat to the health of consumers. In recent years, drinking-water pollution incidents have occurred frequently, seriously endangering social stability and security. The real-time monitoring for water quality can be effectively implemented by placing sensors in the water supply network. However, locating the source of pollution through the data detection obtained by water quality sensors is a challenging problem. The difficulty lies in the limited number of sensors, large number of water supply network nodes, and dynamic user demand for water, which leads the pollution source localization problem to an uncertainty, large-scale, and dynamic optimization problem. In this paper, we mainly study the dynamics of the pollution source localization problem. Previous studies of pollution source localization assume that hydraulic inputs (e.g., water demand of consumers) are known. However, because of the inherent variability of urban water demand, the problem is essentially a fluctuating dynamic problem of consumer's water demand. In this paper, the water demand is considered to be stochastic in nature and can be described using Gaussian model or autoregressive model. On this basis, an optimization algorithm is proposed based on these two dynamic water demand change models to locate the pollution source. The objective of the proposed algorithm is to find the locations and concentrations of pollution sources that meet the minimum between the analogue and detection values of the sensor. Simulation experiments were conducted using two different sizes of urban water supply network data, and the experimental results were compared with those of the standard genetic algorithm.

Distributed Gaussian mixture model-based particle filter method for chemical pollution source localization with sensor network

Chemical pollution source localization with statistical estimation algorithm in sensor networks, which was also known as source parameters estimation, has an important significance in fields such as pollution environmental monitoring and control. In this paper, a distributed Gaussian mixture dispersion model based particle filter method was proposed for the chemical pollution source localization problem. At the same time, we designed a composite information objective function for sensor scheduling scheme, which comprised of information utility measurement and energy consumption measurement. At last, in order to balance the source localization accuracy and energy consumption, a dynamical sensor radius adjusting method was given for sensor nodes scheduling. Simulation and experiment results show that the proposed method could determine the position of chemical pollution source, compared to UKF, the distributed Gaussian mixture particle filter method was suggested because it could get a significant reduction in the required numbers of sensor nodes and less energy to achieve the desired performance with less time.

A Study on Water Pollution Source Localization in Sensor Networks

The water pollution source localization is of great significance to water environment protection. In this paper, a study on water pollution source localization is presented. Firstly, the source detection is discussed. Then, the coarse localization methods and the localization methods based on diffusion models are introduced and analyzed, respectively. In addition, the localization method based on the contour is proposed. The detection and localization methods are compared in experiments finally. The results show that the detection method using hypotheses testing is more stable. The performance of the coarse localization algorithm depends on the nodes density. The localization based on the diffusion model can yield precise localization results; however, the results are not stable. The localization method based on the contour is better than the other two localization methods when the concentration contours are axisymmetric. Thus, in the water pollution source localization, the detection using hypotheses testing is more preferable in the source detection step. If concentration contours are axisymmetric, the localization method based on the contour is the first option. And, in case the nodes are dense and there is no explicit diffusion model, the coarse localization algorithm can be used, or else the localization based on diffusion models is a good choice.

Simulation of Outdoor Near-Surface Airflows Based on a Wind Tunnel with Multiple Actively-Controlled Fans

In this paper, multiple actively-controlled fans generate outdoor near-surface airflow in a wind tunnel with once-through openings. The wind tunnel system consists of 6 groups of fans installed on rotatable plates in a rectangular inlet. We use a stochastic strategy to control the fans and the rotatable plates to regulate wind speed and direction that fluctuate according to the pattern of outdoor near-surface airflow. We utilize the statistics and multi-scale methods to analyze the effectiveness of the strategy for simulating the outdoor near-surface airflow. We provide comparison studies on the multi-scale entropy of wind speed, wind stability, and the standard deviation of directions between out-door wind and the tunnel generated wind. Results show that a flow field akin to the near-surface airflow in outdoor environments can be produced by the wind tunnel using the stochastic control strategy, which can be considered as a reliable experiment environment for gas pollution source localization research in outdoor near-surface breeze conditions.