Journal articles: 'Pollutants detection'

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Song, Wei. "Abnormal Concentration Detection Method of Chemical Pollutants Based on Multisensor Fusion." Journal of Sensors 2022 (August 2, 2022): 1–10. http://dx.doi.org/10.1155/2022/2936960.

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China is a big industrial producer, but also a big producer and user of chemical materials. Although the use of chemical materials has improved the level of industrialization, it has also caused harm to the environment and ecosystem. With the acceleration of China’s industrialization, more and more attention has been paid to the problem of chemical pollution. The pollution of water resources in China has seriously damaged the balance of ecological environment and is also an important factor threatening people’s own health. The detection of chemical pollutants in water resources, especially organic pollutants, has a long way to go. To solve this problem, this paper designs a method of chemical pollutant concentration detection based on multisource information fusion and analyzes the performance of the detection system. Firstly, this paper introduces the main types of situations of chemical pollution at present. Secondly, a multisensor fusion model based on BP neural network is established, and the collected chemical pollutant samples were input into the model. Finally, the quantitative and qualitative analysis of the detected pollutant concentration results shows that the proposed method not only has good detection effect of chemical pollutant concentration but also has good practicability. In a word, the proposed method not only has good theoretical significance but also has certain potential application value.

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Dwi Laksana, Saga Dermawan, Sayekti Wahyuningsih, and Ari Handono Ramelan. "MITIGATION EFFORTS FOR QUICK DETECTION OF POLUTAN GAS AT PUTRI CEMPO TPA USING PROTOTYPE ZnOTiO2GO." Azerbaijan Chemical Journal, no. 2 (May 7, 2024): 84–93. http://dx.doi.org/10.32737/0005-2531-2024-2-84-93.

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The impact of the increasing volume of waste in the Putri Cempo TPA causes the emergence of pollutants in the form of liquid, gas or solid. Gas is a dangerous pollutant because it can cause respiratory disease and even death. methane gas (CH4), sulfurdioxide (SO2), ammonia (NH3), and hydrogen sulfide (H2S), so that it can be the cause of respiratory (respiratory)-related diseases cardiovascular, and physiological changes such as pulmonary function and blood pressure. This study tries to identify pollutant gases that arise in the Putri Cempo TPA and the detection method uses ZnO-TiO2-GO Material which previously used an adsorbent solution. The use of this material has the advantages of being practical, safer and easier to recycle. The gas identification carried out includes SO2, O3, NO2, H2S and NH3 gases. ZnO-TiO2 and GO materials were varied into variation A, B, C ,D And E. The results showed that The results show that the composition of TiO2; ZnO Graphene C (45%; 45%, 10%) shows better results for detecting NO2 gas than the p-aminodimethylaniline working solution used for laboratory measurements. The results show that the composition of TiO2; ZnO Graphene E (60%; 30%, 10%) shows the composition's ability to detect 10 times more H2S gas than the p-aminodimethylaniline working solution but the ability to detect NH3 is lower than the p-aminodimethylaniline working solution. composition variations in TiO2; ZnO ; Graphene B (30%; 60%, 10%), and D (80%; 15%, 5%) can detect exposure to SO2 and O3 gases 6 times more than p-aminodimethylaniline solution. From various compositions, it shows that TiO2, ZnO, Graphene materials are more dominant than aminodimethylaniline solution except for the detection of NH3 pollutant. The results of the research recommendations need to be developed prototypes for the detection of the five pollutant gases without having to go through laboratory testing. The development of a prototype of the pollutant gas detection tool can speed up the measurement of gas pollutants. So that it is easier for analysts to measure gas pollutants. It is necessary to develop the effect of the concentration of adsorbed pollutant gas on the composition of materials with various conductivity of material compositions for further research

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Zhang, Jun-Hua, Zi-Tong Zhang, Yang-Jing Ou, Fei Zhang, Jie Meng, Gen Wang, Zhao-Lin Fang, and Yan Li. "Red-emitting GSH-Cu NCs as a triplet induced quenched fluorescent probe for fast detection of thiol pollutants." Nanoscale 12, no. 37 (2020): 19429–37. http://dx.doi.org/10.1039/d0nr04645k.

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Pang, Shaoning, Lei Song, Abdolhossein Sarrafzadeh, Guy Coulson, Ian Longley, and Gustavo Olivares. "Indoor Emission Sources Detection by Pollutants Interaction Analysis." Applied Sciences 11, no. 16 (August 17, 2021): 7542. http://dx.doi.org/10.3390/app11167542.

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This study employs the correlation coefficients technique to support emission sources detection for indoor environments. Unlike existing methods analyzing merely primary pollution, we consider alternatively the secondary pollution (i.e., chemical reactions between pollutants in addition to pollutant level), and calculate intra pollutants correlation coefficients for characterizing and distinguishing emission events. Extensive experiments show that seven major indoor emission sources are identified by the proposed method, including (1) frying canola oil on electric hob, (2) frying olive oil on an electric hob, (3) frying olive oil on a gas hob, (4) spray of household pesticide, (5) lighting a cigarette and allowing it to smoulder, (6) no activities, and (7) venting session. Furthermore, our method improves the detection accuracy by a support vector machine compared to without data filtering and applying typical feature extraction methods such as PCA and LDA.

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Yu, Jie, Yitong Cao, Fei Shi, Jiegen Shi, Dibo Hou, Pingjie Huang, Guangxin Zhang, and Hongjian Zhang. "Detection and Identification of Organic Pollutants in Drinking Water from Fluorescence Spectra Based on Deep Learning Using Convolutional Autoencoder." Water 13, no. 19 (September 25, 2021): 2633. http://dx.doi.org/10.3390/w13192633.

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Three dimensional fluorescence spectroscopy has become increasingly useful in the detection of organic pollutants. However, this approach is limited by decreased accuracy in identifying low concentration pollutants. In this research, a new identification method for organic pollutants in drinking water is accordingly proposed using three-dimensional fluorescence spectroscopy data and a deep learning algorithm. A novel application of a convolutional autoencoder was designed to process high-dimensional fluorescence data and extract multi-scale features from the spectrum of drinking water samples containing organic pollutants. Extreme Gradient Boosting (XGBoost), an implementation of gradient-boosted decision trees, was used to identify the organic pollutants based on the obtained features. Method identification performance was validated on three typical organic pollutants in different concentrations for the scenario of accidental pollution. Results showed that the proposed method achieved increasing accuracy, in the case of both high-(>10 μg/L) and low-(≤10 μg/L) concentration pollutant samples. Compared to traditional spectrum processing techniques, the convolutional autoencoder-based approach enabled obtaining features of enhanced detail from fluorescence spectral data. Moreover, evidence indicated that the proposed method maintained the detection ability in conditions whereby the background water changes. It can effectively reduce the rate of misjudgments associated with the fluctuation of drinking water quality. This study demonstrates the possibility of using deep learning algorithms for spectral processing and contamination detection in drinking water.

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Badea, Mihaela, Izabela RoxanaVoina, Gheorghe Coman, Lucia Dumitrescu, Camelia Draghici, and Mihaela Idomir. "POLLUTANTS DETECTION USING HAPTEN DERIVATIZATION." Environmental Engineering and Management Journal 4, no. 2 (2005): 223–27. http://dx.doi.org/10.30638/eemj.2005.024.

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Gorshteyn, Alexander Y., Zinoviy Kataenko, Sigurdur Smarason, and Albert Robbat. "Subsurface Detection of Environmental Pollutants." Instrumentation Science & Technology 27, no. 2 (April 1999): 111–21. http://dx.doi.org/10.1080/10739149908085839.

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Kumar, Vinay, Munish Sharma, Sonica Sondhi, Komalpreet Kaur, Deepak Sharma, Shivali Sharma, and Divya Utreja. "Removal of Inorganic Pollutants from Wastewater: Innovative Technologies and Toxicity Assessment." Sustainability 15, no. 23 (November 28, 2023): 16376. http://dx.doi.org/10.3390/su152316376.

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In the past decade, a sudden increase in the number and kind of emerging water contaminants has been observed. The emerging contaminants can be categorized as organic or inorganic. Organic contaminants have been known for years, and techniques for their detection and remediation have been developed. However, inorganic pollutants are much more common. This is because they are detected in very low or negligible concentrations and are equally toxic as organic pollutants at higher concentrations. To boost the research on inorganic pollutant contamination, advancements in detection and quantification techniques are required. The presented paper discusses major inorganic pollutants such as metals and their salts, inorganic fertilizers, sulfides, acids and bases, and ammonia and oxides of nitrogen. In addition, it discusses the inorganic toxicants’ toxicity to organisms and the environment, upgraded quantification methods, and advancements in inorganic toxicant mitigation. Moreover, the major bottlenecks in the quantification and removal of inorganic pollutants are discussed at the end.

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Zhang, Kai, Xinlong Song, Meng Liu, Menghua Chen, Jie Li, and Jinglong Han. "Review on the Use of Magnetic Nanoparticles in the Detection of Environmental Pollutants." Water 15, no. 17 (August 28, 2023): 3077. http://dx.doi.org/10.3390/w15173077.

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Magnetic nanomaterials (MNPs) have been widely used in the detection of pollutants in the environment because of their excellent nano effect and magnetic properties. These intrinsic properties of MNPs have diversified their application in environmental contaminant detection. In this paper, the research status quo of the use of MNPs in detecting organic and inorganic contaminants from wastewater and soil is reviewed. The preparation method and modification technology of magnetic nanoparticles are also described in detail. The application prospect of magnetic nanoparticle composites in the detection of contaminants in water and soil is discussed. Compared with traditional detection methods, MNPs are more accurate and efficient in pollutant enrichment. Moreover, the biological synthesis of MNPs was proven to be eco-friendly and aided in sustainable development. The study shows that MNPs have good application prospects in soil pollution detection, but the mechanism still needs to be investigated to realize their popularization and application.

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Gagliardi, Roberta Valentina, and Claudio Andenna. "Change Points Detection and Trend Analysis to Characterize Changes in Meteorologically Normalized Air Pollutant Concentrations." Atmosphere 13, no. 1 (December 30, 2021): 64. http://dx.doi.org/10.3390/atmos13010064.

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Identifying changes in ambient air pollution levels and establishing causation is a research area of strategic importance to assess the effectiveness of air quality interventions. A major challenge in pursuing these objectives is represented by the confounding effects of the meteorological conditions which easily mask or emphasize changes in pollutants concentrations. In this study, a methodological procedure to analyze changes in pollutants concentrations levels after accounting for changes in meteorology over time was developed. The procedure integrated several statistical tools, such as the change points detection and trend analysis that are applied to the pollutants concentrations meteorologically normalized using a machine learning model. Data of air pollutants and meteorological parameters, collected over the period 2013–2019 in a rural area affected by anthropic emissive sources, were used to test the procedure. The joint analysis of the obtained results with the available metadata allowed providing plausible explanations of the observed air pollutants behavior. Consequently, the procedure appears promising in elucidating those changes in the air pollutant levels not easily identifiable in the original data, supplying valuable information to identify an atmospheric response after an intervention or an unplanned event.

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Tong, Jin, Chengxin Song, Tianjian Tong, Xuanjie Zong, Zhaoyang Liu, Songyang Wang, Lidong Tan, Yinwu Li, and Zhiyong Chang. "Design and Optimization of Electronic Nose Sensor Array for Real-Time and Rapid Detection of Vehicle Exhaust Pollutants." Chemosensors 10, no. 12 (November 22, 2022): 496. http://dx.doi.org/10.3390/chemosensors10120496.

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Traditional vehicle exhaust pollutant detection methods, such as bench test and remote sensing detection, have problems such as large volume, high cost, complex process, long waiting time, etc. In this paper, according to the main components of vehicle exhaust pollutants, an electronic nose with 12 gas sensors was designed independently for real-time and rapid detection of vehicle exhaust pollutants. In order to verify that the designed electronic nose based on machine learning classification method can accurately identify the exhaust pollutants from different engines or different concentration levels from the same engine. After feature extraction of the collected data, Random Forest (RF) was used as the classifier, and the average classification accuracy reached 99.92%. This result proved that the designed electronic nose combined with RF method can accurately and sensitively judge the concentration level of vehicle exhaust pollutants.. Then, in order to enable the electronic nose to be vehicle-mounted and to achieve real-time and rapid detection of vehicle exhaust pollutants. We used Recursive Feature Elimination with Cross Validation (RFECV), Random Forest Feature Selector (RFFS) and Principal Component Analysis (PCA) to optimize the sensor array. The results showed that these methods can effectively simplify the sensor array while ensuring the RF classifier’s classification recognition rate. After using RFECV and RFFS to optimize the sensor array, the RF classifier’s classification recognition rate of the optimized sensor arrays for vehicle exhaust pollutants reached 99.77% and 99.44%, respectively. The numbers of sensors in the optimized sensor arrays were six and eight respectively, which achieved the miniaturization and low-cost of the electronic nose. With the limitation of six sensors, RFECV is the best sensor array optimization method among the three methods.

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Abdel-Razzaq, Faten Abdel-Wahhab, Ayad Abdul Razzak Dhaigham, and Sameer Khudhur Yaseen. "Design and Construction of a Dust Detection System using Infrared Laser: The Case of Dust Storms in Baghdad in the Summer 2022." Iraqi Journal of Physics 21, no. 1 (March 1, 2023): 34–41. http://dx.doi.org/10.30723/ijp.v21i1.1077.

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Iraq suffers from serious pollution with harmful particles that have important direct and indirect effects on human activities and human health. In this research, a system for detecting pollutants in the air was designed and manufactured using infrared laser technology. This system was used to detect the presence of pollutants in the dust storms that swept the city of Baghdad which could have a negative impact on human health and living organisms. The designed detection system based on the use of infrared laser (IR) with a wavelength of 1064 nm was used for the purposes of detecting pollutants based on the scattering of the laser beam from these pollutants. The system was aligned to obtain the best signal for the scattered rays, which were detected using a digital storage oscilloscope. These studies were conducted during the dust storms that occurred intensely in Baghdad in the months (April, May, June and July) of the year 2022. Where multiple samples were taken at different periods in these storms, measurements were also done during the storm. The types of polluting elements found in the dust of the storms are: carbon, bromine, chlorine, hydrocarbons alcohol, ether, hydroxyl group, carbon, C=C (aromatic ring), nitrogen dioxide (aromatic nitro), and carbon monoxide. The value of dust pollutant concentrations is (125.67 mg. m-3).

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Gavrilaș, Simona, Claudiu Ștefan Ursachi, Simona Perța-Crișan, and Florentina-Daniela Munteanu. "Recent Trends in Biosensors for Environmental Quality Monitoring." Sensors 22, no. 4 (February 15, 2022): 1513. http://dx.doi.org/10.3390/s22041513.

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The monitoring of environmental pollution requires fast, reliable, cost-effective and small devices. This need explains the recent trends in the development of biosensing devices for pollutant detection. The present review aims to summarize the newest trends regarding the use of biosensors to detect environmental contaminants. Enzyme, whole cell, antibody, aptamer, and DNA-based biosensors and biomimetic sensors are discussed. We summarize their applicability to the detection of various pollutants and mention their constructive characteristics. Several detection principles are used in biosensor design: amperometry, conductometry, luminescence, etc. They differ in terms of rapidity, sensitivity, profitability, and design. Each one is characterized by specific selectivity and detection limits depending on the sensitive element. Mimetic biosensors are slowly gaining attention from researchers and users due to their advantages compared with classical ones. Further studies are necessary for the development of robust biosensing devices that can successfully be used for the detection of pollutants from complex matrices without prior sample preparation.

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Emara, Ahmed, Amr Yousef, and Khaled Elleithy. "Ultra-Sensitive Si-Based Optical Sensor for Nanoparticle-Size Traditional Water Pollutant Detection." Photonics 9, no. 5 (April 23, 2022): 289. http://dx.doi.org/10.3390/photonics9050289.

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A low-cost Si-based optical nano-sensor that monitors traditional water pollutants is introduced in this paper. The introduced sensor works in the near-infrared region, 900 nm to 2500 nm spectral range. The proposed structure consists of a Si layer with an optimized thickness of 300 nm on the top of the Al layer acting as a back reflector. On the top of the Si layer, the water pollutants are modeled as nanoparticle materials of different sizes. The finite difference time domain method is utilized to optimize the thicknesses of the Si layer by analyzing the optical light absorption considering different Si layer thicknesses and different pollutant nanoparticles’ sizes. Different interpolation techniques, including polynomials with various degrees and locally weighted smoothing quadratic regression, are used to find the best fitting model representing the simulated data points with goodness of fit analysis. Three features are proposed to identify the water pollutant with its size, peak absorption wavelength, relative amplitude, and a full width at half maximum. The device’s performance in detecting six different pollutants, silver, aluminum, copper, chromium, selenium, and ammonia, is evaluated. Sensitivity, a figure of merit, and a quality factor are used to evaluate the proposed sensor. The obtained maximum sensitivity is 11,300 nm/RIU, FOM of 740, and quality factor of 670.

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Li, Zhi, and Mingshan Zhu. "Detection of pollutants in water bodies: electrochemical detection or photo-electrochemical detection?" Chemical Communications 56, no. 93 (2020): 14541–52. http://dx.doi.org/10.1039/d0cc05709f.

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Liu, Junyuan. "Detection, Treatment and Remediation for Organochlorines." MATEC Web of Conferences 386 (2023): 03016. http://dx.doi.org/10.1051/matecconf/202338603016.

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Organochlorine pollutants are a class of compounds formed by combining chlorine atoms with carbon atoms, and common organochlorine pollutants include chloroform and hexachlorocyclohexane. These compounds are persistent and difficult to degrade, so they exist in the environment for a long time. Organochlorine pollutants pose serious hazards to both humans and the environment. As a result, reducing the release of organochlorine pollutants and strengthening monitoring are important measures to protect the environment and human health. Treatment methods for organochlorine pollutants include physical methods such as adsorption and membrane separation, chemical methods such as oxidation, reduction and hydrolysis, and biological treatment methods such as microbial degradation and biosorption. Treatment of organochlorine pollution has great benefits. Treatment of organochlorine pollution can reduce the harm caused by these pollutants to the environment, improve the quality of water and air, and reduce the hazards of human health. This paper will first discuss what organochlorines are, the types of organochlorines, and the hazards of organochlorines, then discuss how to detect organochlorine residues in water, soil, and air, and finally propose specific methods for dealing with organochlorines and remediation programs for organochlorine pollution in water and soil.

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Meulenberg, Eline P., Gijsbert O. H. Peelen, Eddie Lukkien, and Kees Koopal. "Immunochemical detection methods for bioactive pollutants." International Journal of Environmental Analytical Chemistry 85, no. 12-13 (October 15, 2005): 861–70. http://dx.doi.org/10.1080/03067310500158180.

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Sushil, Kumar, Chaudhary Ganga Ram, Chaudhary Savita, and Umar Ahmad. "Lanthanide Oxide Nanoparticles for Environmental Remediation: A Review." MatSci Express 01, no. 01 (March 1, 2024): 03–20. http://dx.doi.org/10.69626/mse.2024.0003.

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The lanthanide oxide nanoparticles were used for the removal of pollutants towards environmental remediation. The enhanced properties and effectiveness of nanotechnology based materials makes them particularly suitable contender in detection of pollutants. The used particles synthesized by facile and easy method with additional standard modification. The origin of pollutants, their toxicity and influential effect of respective pollutants on the environment, humans and ecological system studied in details. The prepared nanoparticles as highly efficient probes, with novel methodology utilized for detection and removal of pollutants. The easy, cost effective and efficient technologies were performed for sensing of these pollutants. This article deals with mainly detection of pollutants present in water, air and soil by using nanomaterial. Further, these nanoparticle is utilized in degradation of various types of pollutants present in the environment.

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Burratti, Luca, Erica Ciotta, Fabio De Matteis, and Paolo Prosposito. "Metal Nanostructures for Environmental Pollutant Detection Based on Fluorescence." Nanomaterials 11, no. 2 (January 21, 2021): 276. http://dx.doi.org/10.3390/nano11020276.

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Heavy metal ions and pesticides are extremely dangerous for human health and environment and an accurate detection is an essential step to monitor their levels in water. The standard and most used methods for detecting these pollutants are sophisticated and expensive analytical techniques. However, recent technological advancements have allowed the development of alternative techniques based on optical properties of noble metal nanomaterials, which provide many advantages such as ultrasensitive detection, fast turnover, simple protocols, in situ sampling, on-site capability and reduced cost. This paper provides a review of the most common photo-physical effects impact on the fluorescence of metal nanomaterials and how these processes can be exploited for the detection of pollutant species. The final aim is to provide readers with an updated guide on fluorescent metallic nano-systems used as optical sensors of heavy metal ions and pesticides in water.

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Liao, Junlin. "Relationship Analysis of AQI and Air Pollutants with Association Rule." Highlights in Science, Engineering and Technology 56 (July 14, 2023): 209–15. http://dx.doi.org/10.54097/hset.v56i.10110.

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To analyze the main causes of bad air quality in recent years and the relationships embedded between air pollutants, this paper uses an improved Apriori algorithm to analyze the data mining of the correlations between overall air quality and air pollutants and between air pollutants in Guangzhou city's air quality data from 2014 to 2020.The data are divided according to AQI levels and air pollutant exceedance thresholds, and a Boolean type transaction dataset is constructed. Based on this, support, confidence and boosting thresholds are set to mine the transactional dataset for frequent item sets and obtain strong association rules. The results show that (1) The excess concentrations of PM10, CO, and NO2 play a role in the excess concentrations of PM2.5. (2) In recent years, the main pollutant indicators of air pollution are O3, NO2, PM2.5, PM10, among which O3 has the greatest influence. The strong correlation rules obtained through data mining have some significance to the detection and prevention of air pollution.

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Cardone, Barbara, Ferdinando Di Martino, and Vittorio Miraglia. "A GIS-Based Fuzzy Model to Detect Critical Polluted Urban Areas in Presence of Heatwave Scenarios." Computers 13, no. 6 (June 5, 2024): 143. http://dx.doi.org/10.3390/computers13060143.

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This research presents a new method for detecting urban areas critical for the presence of air pollutants during periods of heatwaves. The proposed method uses a geospatial model based on the construction of Thiessen polygons and a fuzzy model based on assessing, starting from air quality control unit measurement data, how concentrations of air pollutants are distributed in the urban study area during periods of heatwaves and determine the most critical areas as hotspots. The proposed method represents an optimal trade-off between the accuracy of the detection of critical areas and the computational speed; the use of fuzzy techniques for assessing the intensity of concentrations of air pollutants allows evaluators to model the assessments of critical areas more naturally. The method is implemented in a GIS-based platform and has been tested in the city of Bologna, Italy. The resulting criticality maps of PM10, NO2, and PM2.5 pollutants during a heatwave period that occurred from 10 to 14 July 2023 revealed highly critical hotspots with high pollutant concentrations in densely populated areas. This framework provides a portable and easily interpretable decision support tool which allows you to evaluate which urban areas are most affected by air pollution during heatwaves, potentially posing health risks to the exposed population.

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Xiong, Jie, Yongqing Bai, Tianliang Zhao, Yue Zhou, Xiaoyun Sun, Jiaping Xu, Wengang Zhang, Liang Leng, and Guirong Xu. "Synergistic Effect of Atmospheric Boundary Layer and Regional Transport on Aggravating Air Pollution in the Twain-Hu Basin: A Case Study." Remote Sensing 14, no. 20 (October 15, 2022): 5166. http://dx.doi.org/10.3390/rs14205166.

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The impact of structural variations in the atmospheric boundary layer (ABL) during the regional transport of air pollutants on its local pollution changes deserves attention. Based on multi-source ABL detection and numerical simulation of air pollutants over the Twain-Hu Basin (THB) during 4–6 January 2019, the mechanism of the rapid growth of atmospheric pollutant concentrations in Xianning by the synergistic effect of regional transport and ABL evolution is explored, and the main conclusions are obtained as follows. The vertically stratified atmosphere is noticeable at nighttime, and the heavy humidity of near-surface fog within the stable boundary layer (SBL) promoted the generation and cumulative growth of secondary PM2.5 components during the pollution formation stage. The horizontal transport characteristics of atmospheric pollutant concentration peak were observed in the residual layer (RL) of 500–600 m. At the pollution maintenance stage, the convective boundary layer (CBL) developed during the daytime, and northerly wind transported high-concentration pollutants from the north to the THB. Under the combined action of horizontal transport and turbulent mixing, the high-concentration atmospheric pollutants in the mixing layer (ML) from the ground to the 500 m height were mixed uniformly and maintained accumulation growth. The next day, the strong vertical turbulent mixing caused the downward transport of high-concentration pollutants in the RL during nighttime due to the development of the CBL again, resulting in a doubling of near-surface pollutant concentration in a short time. With the development of ABL turbulence, local pollution dissipated rapidly without the continuous input of pollutants from external regions. This study emphasizes the importance of multi-scale processes impact on pollution variation, that is, regional transport of atmospheric pollutants at the CBL development stage for the rapid growth of PM2.5 concentration in the ML.

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Yu, Jie, Xiaoyan Zhang, Dibo Hou, Fang Chen, Tingting Mao, Pingjie Huang, and Guangxin Zhang. "Detection of Water Contamination Events Using Fluorescence Spectroscopy and Alternating Trilinear Decomposition Algorithm." Journal of Spectroscopy 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1485048.

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The method based on conventional index and UV-vision has been widely applied in the field of water quality abnormality detection. This paper presents a qualitative analysis approach to detect the water contamination events with unknown pollutants. Fluorescence spectra were used as water quality monitoring tools, and the detection method of unknown contaminants in water based on alternating trilinear decomposition (ATLD) is proposed to analyze the excitation and emission spectra of the samples. The Delaunay triangulation interpolation method was used to make the pretreatment of three-dimensional fluorescence spectra data, in order to estimate the effect of Rayleigh and Raman scattering; ATLD model was applied to establish the model of normal water sample, and the residual matrix was obtained by subtracting the measured matrix from the model matrix; the residual sum of squares obtained from the residual matrix and threshold was used to make qualitative discrimination of test samples and distinguish drinking water samples and organic pollutant samples. The results of the study indicate that ATLD modeling with three-dimensional fluorescence spectra can provide a tool for detecting unknown organic pollutants in water qualitatively. The method based on fluorescence spectra can be complementary to the method based on conventional index and UV-vision.

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Shahzad, Laila, Hamna Zubair, and Maryam Ali. "Toxic Toys Threaten the Health of the Children: An Appraisal of Potential Literature." Pakistan Journal of Medical and Health Sciences 16, no. 2 (February 26, 2022): 13–16. http://dx.doi.org/10.53350/pjmhs2216213.

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Children are considered highly susceptible due to their exposure to the environmental pollutants from the toys, playing dough and paint colors. Despite of graving nature of problem, few studies are conducted on the associated risk factors and environmental pollutants. The current review put an effort on exploration the studies of environmental pollutants in children play products and its related effects on children’s health. The methods of pollutant’s detection were also discussed. The studies covered showed environmental toxicants including a wide range of heavy metals, volatile organic compounds, nonaromatic hydrocarbons, and Bisphenol A and flame retardants. Few studies has reported Chromium (555±144mg/kg) in Chinese-manufactured play dough, Cadmium (10±1.4 mg/kg) in the toy paints of China, and lead (293.8±184.5 mg/kg) in the toy paint of USA. Therefore, a dire need of research and strong legislation is needed to create awareness to protect the health of children. Keywords: Exposure, environmental pollutants, health effects, risk factors

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Li-Destri, Giovanni, Dario Menta, Carmelo Menta, and Nunzio Tuccitto. "Effect of Unmanned Aerial Vehicles on the Spatial Distribution of Analytes from Point Source." Chemosensors 8, no. 3 (August 25, 2020): 77. http://dx.doi.org/10.3390/chemosensors8030077.

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We investigated and overcame the limitations associated with the use of unmanned aerial vehicles (UAVs) in the chemical mapping of pollutants coming from point source, as in the case of leaks’ detection. In particular, by simulating the pollutant spatial distribution in the absence and presence of a flying drone, we demonstrated that turbulent flows generated by UAVs can significantly limit the spatial accuracy of the mapping and the pollutant source detection. Finally, as this effect markedly depends on the proximity of the UAV to the pollutant source, we experimentally demonstrated that it is possible to overcome it by employing a sufficiently long probe equipped with an aspiration apparatus transporting the sample from the ground to the detector-equipped UAV.

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Lionetto, Maria G., Roberto Caricato, and Maria E. Giordano. "Pollution Biomarkers in Environmental and Human Biomonitoring." Open Biomarkers Journal 9, no. 1 (January 31, 2019): 1–9. http://dx.doi.org/10.2174/1875318301909010001.

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Environmental pollutants generate harmful conditions for living organisms, including humans. This accounts for the growing interest to early warning tools for detection of adverse biological responses to pollutants in both humans and wildlife. Molecular and cellular biomarkers of pollution meet this requirement. A pollution biomarker is defined as an alteration in a biological response occurring at molecular, cellular or physiological levels which can be related to exposure to or toxic effects of environmental chemicals.Pollution biomarkers have known a growing development in human and environmental biomonitoring representing a valuable tool for early pollutant exposure detection or early effect assessment (exposure/effect biomarkers).The review discusses the recent developments in the use of pollution biomarker in human and environmental biomonitoring and analyzes future perspectives in the application of this tool such as their potentiality for bridging human and environmental issued studies.

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Kara, Gülnihal, and Mehmet Emin Aydın. "Indoor Air Pollutants and Methods of Detection." European Journal of Basic Medical Sciences 1, no. 1 (December 1, 2011): 39–49. http://dx.doi.org/10.21601/ejbms/9166.

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Shamirzaev, V. T., V. A. Gaisler, and T. S. Shamirzaev. "Luminescence detection of petroleum pollutants in water." Bulletin of the Russian Academy of Sciences: Physics 79, no. 2 (February 2015): 165–68. http://dx.doi.org/10.3103/s1062873815020264.

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van der Meer, Jan Roelof, Davide Merulla, and Siham Beggah. "Synthetic bioreporters for detection of environmental pollutants." New Biotechnology 31 (July 2014): S36—S37. http://dx.doi.org/10.1016/j.nbt.2014.05.1695.

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Akkam, Yazan, Derar Omari, Hassan Alhmoud, Mohammad Alajmi, Nosaibah Akkam, and Islam Aljarrah. "Assessment of Xenoestrogens in Jordanian Water System: Activity and Identification." Toxics 11, no. 1 (January 9, 2023): 63. http://dx.doi.org/10.3390/toxics11010063.

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Sex hormone disruptors (xenoestrogens) are a global concern due to their potential toxicity. However, to date, there has been no study to investigate the presence of xenoestrogen pollutants in the Jordanian water system. Samples in triplicates were collected from six locations in Jordan, including dams, surface water, tap or faucet water, and filtered water (drinking water—local company). Xenoestrogens were then extracted and evaluated with a yeast estrogen screen utilizing Saccharomyces cerevisiae. Later, possible pollutants were mined using ultrahigh-performance liquid chromatography (UPLC) coupled with a Bruker impact II Q-TOF-MS. Possible hits were identified using MetaboScape software (4000 compounds), which includes pesticide, pharmaceutical pollutant, veterinary drug, and toxic compound databases and a special library of 75 possible xenoestrogens. The presence of xenoestrogens in vegetable samples collected from two different locations was also investigated. The total estrogen equivalents according to the YES system were 2.9 ± 1.2, 9.5 ± 5, 2.5 ± 1.5, 1.4 ± 0.9 ng/L for King Talal Dam, As-Samra Wastewater Treatment Plant, King Abdullah Canal, and tap water, respectively. In Almujeb Dam and drinking water, the estrogenic activity was below the detection limit. Numbers of identified xenoestrogens were: As-Samra Wastewater Treatment Plant 27 pollutants, King Talal Dam 20 pollutants, Almujeb Dam 10 pollutants, King Abdullah Canal 16 pollutants, Irbid tap water 32 pollutants, Amman tap water 30 pollutants, drinking water 3 pollutants, and vegetables 7 pollutants. However, a large number of compounds remained unknown. Xenoestrogen pollutants were detected in all tested samples, but the total estrogenic capacities were within the acceptable range. The major source of xenoestrogen pollutants was agricultural resources. Risk evaluations for low xenoestrogen activity should be taken into account, and thorough pesticide monitoring systems and regular inspections should also be established.

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Chen, Xingang, Lei Zhu, Zhipeng Ma, Meilin Wang, Rui Zhao, Yueyue Zou, and Yijie Fan. "Ag Nanoparticles Decorated ZnO Nanorods as Multifunctional SERS Substrates for Ultrasensitive Detection and Catalytic Degradation of Rhodamine B." Nanomaterials 12, no. 14 (July 13, 2022): 2394. http://dx.doi.org/10.3390/nano12142394.

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Industrial wastewater containing large amounts of organic pollutants is a severe threat to the environment and human health. Thus, the rapid detection and removal of these pollutants from wastewater are essential to protect public health and the ecological environment. In this study, a multifunctional and reusable surface-enhanced Raman scattering (SERS) substrate by growing Ag nanoparticles (NPs) on ZnO nanorods (NRs) was produced for detecting and degrading Rhodamine B (RhB) dye. The ZnO/Ag substrate exhibited excellent sensitivity, and the limit of detection (LOD) for RhB was as low as 10−11 M. Furthermore, the SERS substrate could efficiently degrade RhB, with a degradation efficiency of nearly 100% within 150 min. Moreover, it retained good SERS activity after multiple repeated uses. The interaction between Ag NPs, ZnO, and RhB was further investigated, and the mechanism of SERS and photocatalysis was proposed. The as-prepared ZnO/Ag composite structure could be highly applicable as a multifunctional SERS substrate for the rapid detection and photocatalytic degradation of trace amounts of organic pollutants in water.

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Fu, Huiping, Ning Ding, Dan Ma, Qing Xu, Bingyong Lin, Bin Qiu, Zhenyu Lin, and Longhua Guo. "Green Synthesis of Three-Dimensional Au Nanorods@TiO2 Nanocomposites as Self-Cleaning SERS Substrate for Sensitive, Recyclable, and In Situ Sensing Environmental Pollutants." Biosensors 13, no. 1 (December 22, 2022): 7. http://dx.doi.org/10.3390/bios13010007.

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In this work, a simple, low-cost, green, and mild method for the preparation of three-dimensional nanocomposite materials of gold nanorods (Au NRs)@TiO2 is reported. The surface of Au NRs was coated with TiO2 in situ reduction at room temperature without a complicated operation. The synthetic Au NRs@TiO2 nanocomposites were used as surface-enhanced Raman spectroscopy (SERS) active substrates for the reusable and sensitive detection of environmental pollutants. The results showed that the pollutants on Au NRs@TiO2 nanocomposites have higher SERS activity and reproducibility than those on the Au NR substrate without the presence of TiO2. Moreover, the SERS substrate can be readily recycled by UV-assisted self-cleaning to remove residual analyte molecules. Malachite green (MG) and crystal violet (CV) were used as examples to demonstrate the feasibility of the proposed sensor for the sensitive detection of environmental pollutants. The results showed that the limit of detections (LODs) were 0.75 μg/L and 0.50 μg/L for MG and CV, respectively, with the recoveries ranging from 86.67% to 91.20% and 83.70% to 89.00%. Meanwhile, the SERS substrate can be easily regenerated by UV light irradiation. Our investigation revealed that within three cycles, the Au NRs@TiO2 substrates still maintained the high SERS enhancement effect that they showed when first used for SERS detection. These results indicated that the method can be used to detect MG and CV in really complex samples. Due to the high sensitivity, reusability, and portability and the rapid detection property of the proposed sensor, it can have potential applications in the on-site detection of environmental pollutants in a complex sample matrix.

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Barbillon, Grégory, and Hélène Cheap-Charpentier. "Advances in Surface-Enhanced Raman Scattering Sensors of Pollutants in Water Treatment." Nanomaterials 13, no. 17 (August 25, 2023): 2417. http://dx.doi.org/10.3390/nano13172417.

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Water scarcity is a world issue, and a solution to address it is the use of treated wastewater. Indeed, in these wastewaters, pollutants such as pharmaceuticals, pesticides, herbicides, and heavy ions can be present at high concentrations. Thus, several analytical techniques were initiated throughout recent years for the detection and quantification of pollutants in different types of water. Among them, the surface-enhanced Raman scattering (SERS) technique was examined due to its high sensitivity and its ability to provide details on the molecular structure. Herein, we summarize the most recent advances (2021–2023) on SERS sensors of pollutants in water treatment. In this context, we present the results obtained with the SERS sensors in terms of detection limits serving as assessment of SERS performances of these sensors for the detection of various pollutants.

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Sentic, Milica, Ivana Trajkovic, Dragan Manojlovic, Dalibor Stankovic, Maria Vesna Nikolic, Neso Sojic, and Jasmina Vidic. "Luminescent Metal–Organic Frameworks for Electrochemiluminescent Detection of Water Pollutants." Materials 16, no. 23 (December 4, 2023): 7502. http://dx.doi.org/10.3390/ma16237502.

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The modern lifestyle has increased our utilization of pollutants such as heavy metals, aromatic compounds, and contaminants that are of rising concern, involving pharmaceutical and personal products and other materials that may have an important environmental impact. In particular, the ultimate results of the intense use of highly stable materials, such as heavy metals and chemical restudies, are that they turn into waste materials, which, when discharged, accumulate in environmental water bodies. In this context, the present review presents the application of metal–organic frameworks (MOFs) in electrochemiluminescent (ECL) sensing for water pollutant detection. MOF composites applied as innovative luminophore or luminophore carriers, materials for electrode modification, and the enhancement of co-reaction in ECL sensors have enabled the sensitive monitoring of some of the most common contaminants of emerging concern such as heavy metals, volatile organic compounds, pharmaceuticals, industrial chemicals, and cyanotoxins. Moreover, we provide future trends and prospects associated with ECL MOF composites for environmental sensing.

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Chen, Bin, Darui Gao, Junhai Yang, and Zongshuai Li. "Design of an active detection system for ice and snow pollutants and freezing temperature on runway." Measurement Science and Technology 34, no. 10 (June 29, 2023): 105102. http://dx.doi.org/10.1088/1361-6501/acdff4.

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Abstract This paper studied the problem of ice and snow pollutants identification and freezing temperature detection under winter runway operation, and designed an active detection system for runway ice and snow pollutants and freezing temperature. The temperature change trend during the liquid freezing process was researched, and the freezing temperature detection model based on sequence segmented linear fitting and inflection point identification was proposed in combination with active cooling technology. The differences in dielectric properties of runway snow and ice cover are studied, and a multi-frequency detection-based forked-finger planar capacitive sensor was identified for snow and ice pollutants detection considering practical application scenarios. A finite element simulation model of the forked-finger planar capacitive sensor was established, to optimize the structural parameters of the sensor and verify the feasibility of the capacitor sensor. Finally, the detection device and control system were designed and fabricated, and built an experimental platform for system test. The field experimental results showed that the system has good reliability and stability, and the error of freezing temperature detection model is less than 0.3 °C. At the same time, it can identify three types of pollutants, including water, ice and ice–water mixture, with an accuracy of 89%.

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Wang, Binhui, Da Huang, and Zuquan Weng. "Recent Advances in Polymer-Based Biosensors for Food Safety Detection." Polymers 15, no. 15 (July 30, 2023): 3253. http://dx.doi.org/10.3390/polym15153253.

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The excessive use of pesticides and drugs, coupled with environmental pollution, has resulted in the persistence of contaminants on food. These pollutants tend to accumulate in humans through the food chain, posing a significant threat to human health. Therefore, it is crucial to develop rapid, low-cost, portable, and on-site biosensors for detecting food contaminants. Among various biosensors, polymer-based biosensors have emerged as promising probes for detection of food contaminants in recent years, due to their various functions such as target binding, enrichment, and simple signal reading. This paper aims to discuss the characteristics of five types of food pollutants—heavy metals, pesticide residues, pathogenic bacteria, allergens, and antibiotics—and their adverse effects on human health. Additionally, this paper focuses on the principle of polymer-based biosensors and their latest applications in detecting these five types of food contaminants in actual food samples. Furthermore, this review briefly examines the future prospects and challenges of biosensors for food safety detection. The insights provided in this review will facilitate the development of biosensors for food safety detection.

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Sun, Xubo, and Jing Wei. "Inorganic Comparison of Inorganic Element Detection Technology in Soil." Frontiers in Sustainable Development 3, no. 12 (December 22, 2023): 69–72. http://dx.doi.org/10.54691/dh84bm90.

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Soil pollutants include inorganic substances (heavy metals, acid, salt, etc.), organic matter, chemical fertilizers, pesticides (pesticides, sterilizers and herbicides), radioactive substances, parasites, pathogenic bacteria, and viruses. New pollutants (such as veterinary drugs, antibiotics, bromide flame retardants, all -fluoride compounds, etc.) have also become research hotspots in the soil.

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Zhang, Kai, Meng Liu, Xinlong Song, and Dongyu Wang. "Application of Luminescent Bacteria Bioassay in the Detection of Pollutants in Soil." Sustainability 15, no. 9 (April 28, 2023): 7351. http://dx.doi.org/10.3390/su15097351.

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The luminescent bacteria bioassay has been commonly used in the detection of environmental pollutants. Compared with traditional chemical and other biological detection methods, the luminescent bacteria bioassay has many demonstrated advantages such as a sensitive response, low cost, high efficiency, and environmental friendliness. The traditional luminescent bacteria bioassay has poor reproducibility and cannot achieve undisturbed soil testing, and the use of leach liquor also affects the results. This paper reviews the research progress and existing issues for the traditional luminescent bacteria bioassay used in the detection of soil pollutants. The luminescence mechanisms and detection principles of three commonly used luminescent bacteria, i.e., Vibrio fischeri, Photobacterium phosphoreum, and Vibrio qinghaiensis, are discussed and compared. In addition, two new luminescent bacteria bioassays are introduced to detect soil pollutants. One method is based on recombinant luminescent bacteria obtained with a gene-modification technique. This method can realize specific detection and enhance sensitivity, but it still cannot achieve undisturbed soil detection. The other method involves using magnetic nanoparticle (MNP)-based biosensors made from luminescent bacteria and MNPs. It can realize the accurate detection of the biological toxicity of the combined pollutants in soil without disturbing the soil’s integrity. This study shows that MNP-based biosensors have good application prospects in soil pollution detection, but the mechanism behind their utility still needs to be investigated to realize their popularization and application.

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Chachuła, Krystian, Tomasz Michał Słojewski, and Robert Nowak. "Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks." Sensors 22, no. 1 (January 5, 2022): 387. http://dx.doi.org/10.3390/s22010387.

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Illegal discharges of pollutants into sewage networks are a growing problem in large European cities. Such events often require restarting wastewater treatment plants, which cost up to a hundred thousand Euros. A system for localization and quantification of pollutants in utility networks could discourage such behavior and indicate a culprit if it happens. We propose an enhanced algorithm for multisensor data fusion for the detection, localization, and quantification of pollutants in wastewater networks. The algorithm processes data from multiple heterogeneous sensors in real-time, producing current estimates of network state and alarms if one or many sensors detect pollutants. Our algorithm models the network as a directed acyclic graph, uses adaptive peak detection, estimates the amount of specific compounds, and tracks the pollutant using a Kalman filter. We performed numerical experiments for several real and artificial sewage networks, and measured the quality of discharge event reconstruction. We report the correctness and performance of our system. We also propose a method to assess the importance of specific sensor locations. The experiments show that the algorithm’s success rate is equal to sensor coverage of the network. Moreover, the median distance between nodes pointed out by the fusion algorithm and nodes where the discharge was introduced equals zero when more than half of the network nodes contain sensors. The system can process around 5000 measurements per second, using 1 MiB of memory per 4600 measurements plus a constant of 97 MiB, and it can process 20 tracks per second, using 1.3 MiB of memory per 100 tracks.

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Mu, Yunyun, Miao Liu, Jiajun Li, and Xinping Zhang. "Multifold Enhanced Raman Detection of Organic Molecules as Environmental Water Pollutants." Biosensors 13, no. 1 (December 21, 2022): 4. http://dx.doi.org/10.3390/bios13010004.

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Organic molecules, including the benzene series, have been identified as pollutants in environmental water. Due to their very low solubility, they have very small concentrations in water, and they are difficult to be detected by conventional techniques. In particular, there is a lack of real-time, accurate, and rapid detection methods for such molecules in water. However, they are detrimental to human health in many aspects. Toluene has been an important indicator of such environmental pollution detections. In this work, we propose a 3D SERS scheme consisting of a hollow fiber that is coated on the inner wall with densely arranged silver nanoparticles, which supplies multifold Raman enhancement by the plasmonic microcavity. Strong confinement of excitation laser energy and strongly enhanced Raman signals with the bidirectional collection are utilized to achieve high-sensitivity detection of toluene molecules in water. Raman signal with a reasonable signal-to-noise ratio has been measured for a concentration of 0.53 mg/L, indicating a detection limit even lower than this value for such a Raman spectroscopic technique. The corresponding enhancement factor is higher than 6 × 103 with respect to the available systems. Thus, this device not only enables direct trace detection and real-time monitoring of the water-polluting status by organic molecules but also supplies a practical approach for biological sensing.

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He, Xiaoyu, Xiaojian Xu, and Zheng Zheng. "Optimal Band Analysis of a Space-Based Multispectral Sensor for Urban Air Pollutant Detection." Atmosphere 10, no. 10 (October 19, 2019): 631. http://dx.doi.org/10.3390/atmos10100631.

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Air pollution continues to attract more and more public attention. Space-based infrared sensors provide a measure to monitor air quality in large areas. In this paper, a band selection procedure of space-based infrared sensors is proposed for urban air pollutant detection, in which observation geometry, ground and atmosphere radiant characteristics, and sensor system noise are integrated. The physics-based atmospheric radiative transfer model is reviewed and used to calculate total spectral radiance at the sensor aperture. Spectral filters with different central wavelength and bandwidth are designed to calculate contrasts in various bands, which can be presented as a two-dimensional matrix. Minimal available bandwidth and signal-to-noise ratio threshold are set to characterize the impacts of the sensor system. In this way, the band with higher contrast is assumed to have better detection performance. The proposed procedure is implemented to analyze an optimal band for detecting four types of gaseous pollutants and discriminating aerosol particle pollution to demonstrate usefulness. Simulation results show that narrower bands tend to achieve better performance while the optimal band is related to the available minimal bandwidth and pollutant density. In addition, the bands that are near optimal can achieve similar performance.

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Majder-Łopatka, Małgorzata, Wioletta Rogula-Kozłowska, and Wiktor Wąsik. "The application of stand-off infrared detection to identify air pollutants." E3S Web of Conferences 44 (2018): 00104. http://dx.doi.org/10.1051/e3sconf/20184400104.

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Fourier-transform infrared spectroscopy (FT-IR) enables the passive detection and identification of chemical agent clouds from distances. It can be used for the monitoring of large spaces and protection of industrial installations outside buildings. The aim of the study was show the possibilities and limitations of passive FT-IR. In studies done by the Faculty of Fire Safety Engineering at the Main School of Fire Service were used spectrometer RAPID (Remote Air Pollution Infrared Detector), Bruker Daltonics Company. Hazardous substances such as acetone, methanol, ammonia was identified on-line from several observation points at 25 m, 50 m and 75 m. The tests were carried out at various meteorological conditions. The results indicate that remote infrared detection allows detecting hazardous chemical agents in the atmosphere from a distance. Nevertheless, meteorological and terrain conditions have a big impact on the measurement results. During measurements, the detector indicated the presence of other substances. Moreover, it was found that under cloudy and windy weather, remote detection is not possible.

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Wang, Chenyong, Chao Yang, Hongpeng Zhang, Shengzhao Wang, Zhaoxu Yang, Jingguo Fu, and Yuqing Sun. "Marine-Hydraulic-Oil-Particle Contaminant Identification Study Based on OpenCV." Journal of Marine Science and Engineering 10, no. 11 (November 21, 2022): 1789. http://dx.doi.org/10.3390/jmse10111789.

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Particulate pollutants mixed in hydraulic oil will lead to the failure of the marine hydraulic system. Nowadays, the current identification methods of particulate pollutants in oil make it challenging to obtain the specific parameters of pollutants. For this reason, this paper proposes a recognition method of marine-hydraulic-oil-particle pollutants based on OpenCV. The image of particles in the marine hydraulic oil was preprocessed by OpenCV software and using the Canny operator edge detection algorithm to extract the contour of particle pollutants to obtain their area and perimeter. The recognition accuracy reached 95%. Using the Douglas–Peucker algorithm for fit polygons, then image moments to obtain the angle-distance waveform of particulate pollutants, the shape of marine-hydraulic-oil particulate pollutants was successfully identified. The designed method has the advantages of fast calculation efficiency, high accuracy, and real-time detection of various parameters of particulate pollutants in marine hydraulic oil. It has great significance for the fault diagnosis of hydraulic systems and prolonging the working life of hydraulic equipment. This research provides a new idea for the condition monitoring and fault diagnosis of ships and offshore engineering equipment.

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Stanciu, Gabriela, and Magdalena Mititelu. "DETECTION OF SOME POLLUTANTS FROM BLACK SEA MUSSELS." Environmental Engineering and Management Journal 3, no. 4 (2004): 701–6. http://dx.doi.org/10.30638/eemj.2004.069.

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郭, 满姣. "Detection and Protection of Radioactive Pollutants in Groundwater." Advances in Environmental Protection 09, no. 06 (2019): 842–47. http://dx.doi.org/10.12677/aep.2019.96110.

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Lima, G. R., M. S. Sthel, J. R. Tavares, D. U. Schramm, M. G. da Silva, and H. Vargas. "Detection of gaseous pollutants by VNG-Powered vehicles." Procedia Environmental Sciences 4 (2011): 61–70. http://dx.doi.org/10.1016/j.proenv.2011.03.008.

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Sá, Acelino C. de, Simone C. Barbosa, Paulo A. Raymundo-Pereira, Deivy Wilson, Flávio M. Shimizu, Maria Raposo, and Osvaldo N. Oliveira. "Flexible Carbon Electrodes for Electrochemical Detection of Bisphenol-A, Hydroquinone and Catechol in Water Samples." Chemosensors 8, no. 4 (October 17, 2020): 103. http://dx.doi.org/10.3390/chemosensors8040103.

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The detection of pollutant traces in the public water supply and aquifers is essential for the safety of the population. In this article, we demonstrate that a simple electrochemical procedure in acidic solution can be employed for enhancing the sensitivity of flexible screen-printed carbon electrodes (SPEs) to detect bisphenol-A (BPA), hydroquinone, and catechol, simultaneously. The SPEs were pretreated electrochemically in a H2SO4 solution, which did not affect their morphology, yielding high current signals with well separated oxidation peaks. The sensitivity values were 0.28, 0.230, and 0.056 µA L µmol−1 with detection limits of 0.12, 0.82, and 0.95 µmol L−1 for hydroquinone, catechol, and BPA, respectively. The sensors were reproducible and selective for detecting BPA in plastic cups, and with adequate specificity not to be affected by interferents from water samples. The simple, inexpensive, and flexible SPE may thus be used to detect emerging pollutants and monitor the water quality.

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Ansari, Saniya. "Water Pollution Management and detection techniques: a Review." International Journal of Computer Communication and Informatics 3, no. 1 (May 30, 2021): 66–76. http://dx.doi.org/10.34256/ijcci2116.

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Now a day’s water pollution became a national and global issue not only in India but over the whole world. Like India other countries are also facing the same problem of water pollution due to agricultural waste, industrial waste, sewage waste and so on. In this paper it has been explained clearly with the help of considerable number of references. It gives the information about the pollutants which pollutes the water. Sensors have a ability to Control and monitor the quality of water as well as able to detect the contaminants added due to human generating activities. Sensors can be used for the selection and identification of the techniques which will be suitable for performing the given task that is identification of pollutants present in water. Various pollutants found in water are pesticides, harmful chemicals, heavy metals, nutrients, etc. We will also going to discuss about the different techniques used for water pollution detections. Tethered.

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Zhang, Feng Ying, and Yun Hua Gao. "Study on Green Gas Data Detection System." Advanced Materials Research 722 (July 2013): 322–27. http://dx.doi.org/10.4028/www.scientific.net/amr.722.322.

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This paper describes a wireless gas data detection system, based on CC1010 MCU as the core controller design measurement circuit of indoor air quality. IF in the air pollutants such as smokes and doors, TGS gas sensor sensitive to perceived pollutants transferred to the receiving device through the wireless function after CC1010 MCU treatment analysis. The system has the function of wireless data transmission, small size, high sensitivity, good stability, low cost, long life and other advantages of the peripheral circuits required little devices can be widely used in enclosed spaces such as indoor, automobile air pollution detection.

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Di Tinno, Alessio, Rocco Cancelliere, Pietro Mantegazza, Antonino Cataldo, Alesia Paddubskaya, Luigi Ferrigno, Polina Kuzhir, et al. "Sensitive Detection of Industrial Pollutants Using Modified Electrochemical Platforms." Nanomaterials 12, no. 10 (May 23, 2022): 1779. http://dx.doi.org/10.3390/nano12101779.

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Water pollution is nowadays a global problem and the effective detection of pollutants is of fundamental importance. Herein, a facile, efficient, robust, and rapid (response time < 2 min) method for the determination of important quinone-based industrial pollutants such as hydroquinone and benzoquinone is reported. The recognition method is based on the use of screen-printed electrodes as sensing platforms, enhanced with carbon-based nanomaterials. The enhancement is achieved by modifying the working electrode of such platforms through highly sensitive membranes made of Single- or Multi-Walled Carbon Nanotubes (SWNTs and MWNTs) or by graphene nanoplatelets. The modified sensing platforms are first carefully morphologically and electrochemically characterized, whereupon they are tested in the detection of different pollutants (i.e., hydroquinone and benzoquinone) in water solution, by using both cyclic and square-wave voltammetry. In particular, the sensors based on film-deposited nanomaterials show good sensitivity with a limit of detection in the nanomolar range (0.04 and 0.07 μM for SWNT- and MWNT-modified SPEs, respectively) and a linear working range of 10 to 1000 ppb under optimal conditions. The results highlight the improved performance of these novel sensing platforms and the large-scale applicability of this method for other analytes (i.e., toxins, pollutants).

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Journal articles on the topic 'Pollutants detection'

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