Journal articles: 'Mobile air pollution monitoring'

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Lihui Lv, Lihui Lv, Wenqing Liu Wenqing Liu, Guangqiang Fan Guangqiang Fan, Tianshu Zhang Tianshu Zhang, Yunsheng Dong Yunsheng Dong, Zhenyi Chen Zhenyi Chen, Yang Liu Yang Liu, Haoyun Huang Haoyun Huang, and and Yang Zhou and Yang Zhou. "Application of mobile vehicle lidar for urban air pollution monitoring." Chinese Optics Letters 14, no. 6 (2016): 060101–60106. http://dx.doi.org/10.3788/col201614.060101.

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Adams, Matthew, and Denis Corr. "A Mobile Air Pollution Monitoring Data Set." Data 4, no. 1 (December 22, 2018): 2. http://dx.doi.org/10.3390/data4010002.

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Air pollution was observed in Hamilton, Ontario, Canada using monitors installed in a mobile platform from November 2005 up to November 2016. The dataset is an aggregation of several project specific monitoring days, which attempted to quantify air pollution spatial variation under varying conditions or in specific regions. Pollutants observed included carbon monoxide, nitric oxide, nitrogen dioxide, total nitrogen oxides, ground-level ozone, particulate matter concentrations for size cuts of 10 µm, 2.5 µm and 1 µm, and sulfur dioxide. Observations were collected over 114 days, which occurred in varying seasons and months. During sampling, the mobile platform travelled at an average speed of 27 km/h. The samples were collected as one-minute integrated samples and are prepared as line-segments, which include an offset for instrument response time. Sampling occurred on major freeways, highways, arterial and residential roads. This dataset is shared in hopes of supporting research on how to best utilize air pollution observations obtained with mobile air pollution platforms, which is a growing technique in the field of urban air pollution monitoring. We conclude with limitations in the data capture technique and recommendations for future mobile monitoring studies.

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Talib, Aya Mazin, and Mahdi Nsaif Jasim. "Geolocation based air pollution mobile monitoring system." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (July 1, 2021): 162. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp162-170.

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Air pollution is conducted to harmful substances like solid particles, gases or liquid droplets. More pollutants CO, SO2, NOx, CO2.This research is proposed the design and implementation of mobile, low cost and accurate air pollution monitoring system using Arduino microcontroller and gas sensor like MQ2, MQ131, MQ135, MQ136, DHT22, measuring materials mentioned above, smoke, Acetone, Alcohol, LPG, Toluene, temperature, humidity and GPS sensor”NEO-6M” that track the location of air pollution data, and display the analysis result on ESRI maps. The system also save the results on SQL server DB. The data is classified using data mining algorithms, presenting the result on a map helps governmental organizations, nature guards, and ecologists to analyze data in real time to simplify the decision making process. The proposed system uses J48 pruning tree classifier generated using cross validation of fold (10) with highest accuracy 100%, while IBK ≈99.67, Naïve bays ≈90.89, and SVM ≈81.4. It’s found that the common air quality for Baghdad (study area) is between (“Good”, “Satisfactory”, and “Moderately”) for 1835 records of air samples during (January and February 2021) time period.

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Nance, Earthea. "Monitoring Air Pollution Variability during Disasters." Atmosphere 12, no. 4 (March 25, 2021): 420. http://dx.doi.org/10.3390/atmos12040420.

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National environmental regulations lack short-term standards for variability in fine particulate matter (PM2.5); they depend solely on concentration-based standards. Twenty-five years of research has linked short-term PM2.5, that is, increases of at least 10 μg/m3 that can occur in-between regulatory readings, to increased mortality. Even as new technologies have emerged that could readily monitor short-term PM2.5, such as real-time monitoring and mobile monitoring, their primary application has been for research, not for air quality management. The Gulf oil spill offers a strategic setting in which regulatory monitoring, computer modeling, and stationary monitoring could be directly compared to mobile monitoring. Mobile monitoring was found to best capture the variability of PM2.5 during the disaster. The research also found that each short-term increase (≥10 μg/m3) in fine particulate matter was associated with a statistically significant increase of 0.105 deaths (p < 0.001) in people aged 65 and over, which represents a 0.32% increase. This research contributes to understanding the effects of PM2.5 on mortality during a disaster and provides justification for environmental managers to monitor PM2.5 variability, not only hourly averages of PM2.5 concentration.

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Coutrier, P. I., Saut M. Lubis, and Noegroho Hadi. "Air Quality Monitoring and Strategy in Indonesia." Scientific Contributions Oil and Gas 17, no. 1 (April 4, 2022): 2–11. http://dx.doi.org/10.29017/scog.17.1.884.

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BAPEDAL, The Environment Impact Management Agency is responsible for the air pollution control. In addressing the air pollution BAPEDAL launched the "Blue Sky Program". This program consist of two component, air pollution from the mobile sources and the air pollution from stationary sources.

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Khozouie, Nasim, and Faranak Fotouhi-Ghazvini. "Air pollution monitoring By sensors embedded on mobile phone." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 5 (October 30, 2013): 2628–33. http://dx.doi.org/10.24297/ijct.v11i5.1148.

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Mobile technology has been available for at least a decade and is increasingly being used in developing countries as away of contacting and connecting citizens and helping them to organize for a better life.Mobile phones are not just for phone calls, but they can also be used to collect data in several different formats and send them to a central server. There the data can be aggregated and analyzed, with tables and visualizations automatically generated. What is new is the sheer number of observation points that are potentially available by using mobile phones. With over 4 billion phones in use worldwide, the mobile phone network is emerging as a form of “global brain” with sensors everywhere. In addition, there are companies such as Fourier Systems that provide purpose-built mobile devices that are specifically designed for science experiments in school sand for data logging in any science project.

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Haus, R., K. Schäfer, W. Bautzer, J. Heland, H. Mosebach, H. Bittner, and T. Eisenmann. "Mobile Fourier-transform infrared spectroscopy monitoring of air pollution." Applied Optics 33, no. 24 (August 20, 1994): 5682. http://dx.doi.org/10.1364/ao.33.005682.

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Marć, Mariusz, Bożena Zabiegała, and Jacek Namieśnik. "Mobile Systems (Portable, Handheld, Transportable) for Monitoring Air Pollution." Critical Reviews in Analytical Chemistry 42, no. 1 (January 2012): 2–15. http://dx.doi.org/10.1080/10408347.2011.607079.

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Al-Ali, A. R., Imran Zualkernan, and Fadi Aloul. "A Mobile GPRS-Sensors Array for Air Pollution Monitoring." IEEE Sensors Journal 10, no. 10 (October 2010): 1666–71. http://dx.doi.org/10.1109/jsen.2010.2045890.

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Shakhov, Vladimir, Andrei Materukhin, Olga Sokolova, and Insoo Koo. "Optimizing Urban Air Pollution Detection Systems." Sensors 22, no. 13 (June 24, 2022): 4767. http://dx.doi.org/10.3390/s22134767.

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Air pollution has become a serious problem in all megacities. It is necessary to continuously monitor the state of the atmosphere, but pollution data received using fixed stations are not sufficient for an accurate assessment of the aerosol pollution level of the air. Mobility in measuring devices can significantly increase the spatiotemporal resolution of the received data. Unfortunately, the quality of readings from mobile, low-cost sensors is significantly inferior to stationary sensors. This makes it necessary to evaluate the various characteristics of monitoring systems depending on the properties of the mobile sensors used. This paper presents an approach in which the time of pollution detection is considered a random variable. To the best of our knowledge, we are the first to deduce the cumulative distribution function of the pollution detection time depending on the features of the monitoring system. The obtained distribution function makes it possible to optimize some characteristics of air pollution detection systems in a smart city.

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Zhang, Shuai, Zhaoming Zhou, Conglei Ye, Jibing Shi, Peng Wang, and Dong Liu. "Analysis of a Pollution Transmission Process in Hefei City Based on Mobile Lidar." EPJ Web of Conferences 237 (2020): 02006. http://dx.doi.org/10.1051/epjconf/202023702006.

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The air pollution has been regional in China with the development of economy. To monitoring the air pollution transmission, a new technique, mobile lidar system (GBQ-S01), was introduced. In this paper, a pollution transmission process happened on October 26th, 2017, was analyzed with the use of mobile lidar, air quality monitoring stations data, and Hysplit backward trajectories. The results showed that the polluted air mass was transferred from northeast under the force of air pressure. Under the influences of air pollution transmission and bad meteorological diffusion conditions, The PM10 quality concentrations in Hefei increased a lot within 5 hours; among all the 10 national air quality monitoring stations, the Luyang District (the northernmost one) and Changjiang Middle Road (the easternmost one) received the most serious impact with PM10 concentration reached up to 252 μg/m3 and 219 μg/m3 at 22:00 (Beijing Time).

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Dhingra, Swati, Rajasekhara Babu Madda, Amir H. Gandomi, Rizwan Patan, and Mahmoud Daneshmand. "Internet of Things Mobile–Air Pollution Monitoring System (IoT-Mobair)." IEEE Internet of Things Journal 6, no. 3 (June 2019): 5577–84. http://dx.doi.org/10.1109/jiot.2019.2903821.

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Tang, Zhi Wei. "Design of Automatic Aerial Air Pollution Monitoring System." Applied Mechanics and Materials 651-653 (September 2014): 571–74. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.571.

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Based on air pollution situation, make the ball with hydrogen floating in the air height of 50 meters as the aerial air pollution automatic detection mobile platform, based on sensor technology, embedded technology, thrust composite applications, GPRS, GPS and other hardware combined with the lower position machine applications、PC software system , conducted the system requirements analysis, software and hardware architecture design and description. The system solves the common aerial equipment’s problem of letting propeller blown air when detecting air parameters, using aerial balloon floating platform for air-parameter sensor automatically detects collection. Achieve operational automation, thus greatly reduce the work intensity of aerial air pollution monitoring and management, reduce the error of manual operation may occur, improve the efficiency of environmental protection, promote informatization construction of environmental protection work. This paper elaborates the overall design process of aerial air pollution automatic monitoring system.

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Brantley, H. L., G. S. W. Hagler, S. Kimbrough, R. W. Williams, S. Mukerjee, and L. M. Neas. "Mobile air monitoring data processing strategies and effects on spatial air pollution trends." Atmospheric Measurement Techniques Discussions 6, no. 6 (December 5, 2013): 10443–80. http://dx.doi.org/10.5194/amtd-6-10443-2013.

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Abstract. The collection of real-time air quality measurements while in motion (i.e., mobile monitoring) is currently conducted worldwide to evaluate in situ emissions, local air quality trends, and air pollutant exposure. This measurement strategy pushes the limits of traditional data analysis with complex second-by-second multipollutant data varying as a function of time and location. Data reduction and filtering techniques are often applied to deduce trends, such as pollutant spatial gradients downwind of a highway. However, rarely do mobile monitoring studies report the sensitivity of their results to the chosen data processing approaches. The study being reported here utilized a large mobile monitoring dataset collected on a roadway network in central North Carolina to explore common data processing strategies including time-alignment, short-term emissions event detection, background estimation, and averaging techniques. One-second time resolution measurements of ultrafine particles ≤ 100 nm in diameter (UFPs), black carbon (BC), particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen dioxide (NO2) were collected on twelve unique driving routes that were repeatedly sampled. Analyses demonstrate that the multiple emissions event detection strategies reported produce generally similar results and that utilizing a median (as opposed to a mean) as a summary statistic may be sufficient to avoid bias in near-source spatial trends. Background levels of the pollutants are shown to vary with time, and the estimated contributions of the background to the mean pollutant concentrations were: BC (6%), PM2.5–10 (12%), UFPs (19%), CO (38%), PM10 (45%), NO2 (51%), PM2.5 (56%), and CO2 (86%). Lastly, while temporal smoothing (e.g., 5 s averages) results in weak pair-wise correlation and the blurring of spatial trends, spatial averaging (e.g., 10 m) is demonstrated to increase correlation and refine spatial trends.

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Brantley, H. L., G. S. W. Hagler, E. S. Kimbrough, R. W. Williams, S. Mukerjee, and L. M. Neas. "Mobile air monitoring data-processing strategies and effects on spatial air pollution trends." Atmospheric Measurement Techniques 7, no. 7 (July 22, 2014): 2169–83. http://dx.doi.org/10.5194/amt-7-2169-2014.

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Abstract. The collection of real-time air quality measurements while in motion (i.e., mobile monitoring) is currently conducted worldwide to evaluate in situ emissions, local air quality trends, and air pollutant exposure. This measurement strategy pushes the limits of traditional data analysis with complex second-by-second multipollutant data varying as a function of time and location. Data reduction and filtering techniques are often applied to deduce trends, such as pollutant spatial gradients downwind of a highway. However, rarely do mobile monitoring studies report the sensitivity of their results to the chosen data-processing approaches. The study being reported here utilized 40 h (> 140 000 observations) of mobile monitoring data collected on a roadway network in central North Carolina to explore common data-processing strategies including local emission plume detection, background estimation, and averaging techniques for spatial trend analyses. One-second time resolution measurements of ultrafine particles (UFPs), black carbon (BC), particulate matter (PM), carbon monoxide (CO), and nitrogen dioxide (NO2) were collected on 12 unique driving routes that were each sampled repeatedly. The route with the highest number of repetitions was used to compare local exhaust plume detection and averaging methods. Analyses demonstrate that the multiple local exhaust plume detection strategies reported produce generally similar results and that utilizing a median of measurements taken within a specified route segment (as opposed to a mean) may be sufficient to avoid bias in near-source spatial trends. A time-series-based method of estimating background concentrations was shown to produce similar but slightly lower estimates than a location-based method. For the complete data set the estimated contributions of the background to the mean pollutant concentrations were as follows: BC (15%), UFPs (26%), CO (41%), PM2.5-10 (45%), NO2 (57%), PM10 (60%), PM2.5 (68%). Lastly, while temporal smoothing (e.g., 5 s averages) results in weak pair-wise correlation and the blurring of spatial trends, spatial averaging (e.g., 10 m) is demonstrated to increase correlation and refine spatial trends.

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Yu, Haomin, Qingyong Li, Yangli-ao Geng, Yingjun Zhang, and Zhi Wei. "AirNet: A Calibration Model for Low-Cost Air Monitoring Sensors Using Dual Sequence Encoder Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 1129–36. http://dx.doi.org/10.1609/aaai.v34i01.5464.

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Air pollution monitoring has attracted much attention in recent years. However, accurate and high-resolution monitoring of atmospheric pollution remains challenging. There are two types of devices for air pollution monitoring, i.e., static stations and mobile stations. Static stations can provide accurate pollution measurements but their spatial distribution is sparse because of their high expense. In contrast, mobile stations offer an effective solution for dense placement by utilizing low-cost air monitoring sensors, whereas their measurements are less accurate. In this work, we propose a data-driven model based on deep neural networks, referred to as AirNet, for calibrating low-cost air monitoring sensors. Unlike traditional methods, which treat the calibration task as a point-to-point regression problem, we model it as a sequence-to-point mapping problem by introducing historical data sequences from both a mobile station (to be calibrated) and the referred static station. Specifically, AirNet first extracts an observation trend feature of the mobile station and a reference trend feature of the static station via dual encoder neural networks. Then, a social-based guidance mechanism is designed to select periodic and adjacent features. Finally, the features are fused and fed into a decoder to obtain a calibrated measurement. We evaluate the proposed method on two real-world datasets and compare it with six baselines. The experimental results demonstrate that our method yields the best performance.

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Wang, L., and Y. Huang. "MOBILE ATMOSPHERIC SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W2 (November 16, 2017): 217–21. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w2-217-2017.

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Atmospheric quality dramatically deteriorates over the past decades around themetropolitan areas of China. Due to the coal combustion, industrial air pollution, vehicle waste emission, etc., the public health suffers from exposure to such air pollution as fine particles of particulates, sulfur and carbon dioxide, etc. Many meteorological stations have been built to monitor the condition of air quality over the city. However, they are installed at fixed sites and cover quite a small region. The monitoring results of these stations usually do NOT coincide with the public perception of the air quality. This paper is motivated to mimic the human breathing along the citys transportation network by the mobile sensing vehicle of atmospheric quality. To obtain the quantitative perception of air quality, the Environmental Monitoring Vehicle of Wuhan University (EMV-WHU) has been developed to automatically collect the data of air pollutants. The EMV-WHU is equipped with GPS/IMU, sensors of PM2.5, carbon dioxide, anemometer, temperature, humidity, noise, and illumination, as well as the visual and infrared camera. All the devices and sensors are well collaborated with the customized synchronization mechanism. Each sort of atmospheric data is accompanied with the uniform spatial and temporal label of high precision. Different spatial and data-mining techniques, such as spatial correlation analysis, logistic regression, spatial clustering, are employed to provide the periodic report of the roadside air quality. With the EMV-WHU, constant collection of the atmospheric data along the Luoyu Road of Wuhan city has been conducted at the daily peak and non-peak time for half a year. Experimental results demonstrated that the EMV is very efficient and accurate for the perception of air quality. Comparative findings with the meteorological stations also show the intelligence of big data analysis and mining of all sorts of EMV measurement of air quality. It is promising for the aerial and emergent air quality monitoring over the sky of big cities, if EMV-WHU be miniaturized for the unmanned aerial vehicles(UAV) in the future.

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Qin, Xuening, Tien Huu Do, Jelle Hofman, Esther Rodrigo Bonet, Valerio Panzica La Manna, Nikos Deligiannis, and Wilfried Philips. "Fine-Grained Urban Air Quality Mapping from Sparse Mobile Air Pollution Measurements and Dense Traffic Density." Remote Sensing 14, no. 11 (May 30, 2022): 2613. http://dx.doi.org/10.3390/rs14112613.

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Urban air quality mapping has been widely applied in urban planning, air pollution control and personal air pollution exposure assessment. Urban air quality maps are traditionally derived using measurements from fixed monitoring stations. Due to high cost, these stations are generally sparsely deployed in a few representative locations, leading to a highly generalized air quality map. In addition, urban air quality varies rapidly over short distances (<1 km) and is influenced by meteorological conditions, road network and traffic flow. These variations are not well represented in coarse-grained air quality maps generated by conventional fixed-site monitoring methods but have important implications for characterizing heterogeneous personal air pollution exposures and identifying localized air pollution hotspots. Therefore, fine-grained urban air quality mapping is indispensable. In this context, supplementary low-cost mobile sensors make mobile air quality monitoring a promising alternative. Using sparse air quality measurements collected by mobile sensors and various contextual factors, especially traffic flow, we propose a context-aware locally adapted deep forest (CLADF) model to infer the distribution of NO2 by 100 m and 1 h resolution for fine-grained air quality mapping. The CLADF model exploits deep forest to construct a local model for each cluster consisting of nearest neighbor measurements in contextual feature space, and considers traffic flow as an important contextual feature. Extensive validation experiments were conducted using mobile NO2 measurements collected by 17 postal vans equipped with low-cost sensors operating in Antwerp, Belgium. The experimental results demonstrate that the CLADF model achieves the lowest RMSE as well as advances in accuracy and correlation, compared with various benchmark models, including random forest, deep forest, extreme gradient boosting and support vector regression.

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Wang, Xu, Jing Gao, and Yuefeng Zhao. "Pollution Monitoring Based on Vehicle-borne Particulate Quantum Lidar." EPJ Web of Conferences 237 (2020): 03023. http://dx.doi.org/10.1051/epjconf/202023703023.

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From February 7 to 10, 2018, due to unfavorable meteorological conditions, a process of air pollution occurred in Hefei and its surrounding areas, and moderate to severe pollution occurred in the municipal districts. Aiming at the pollution process in Hefei City, under the leadership of Hefei Environmental Protection Bureau and with the support of Hefei Environmental Protection Sub-bureau, four fixed observation points were selected to carry out all-weather environmental network monitoring pilot projects in Hefei municipal jurisdiction area. At the same time, a "mobile monitoring vehicle of atmospheric environmental pollution" was arranged to conduct all-weather walking observation to real-time monitor the spatial distribution and three-dimensional space of pollutants in Hefei urban area、inter-transport and space subsidence and diffusion. The RaySound Series Portable high-energy high-frequency lidar was used in the environmental networking monitoring in Hefei. The air quality in the observation area was evaluated comprehensively by using the mode of fixed vertical monitoring, plane scanning monitoring and mobile walking monitoring.

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Del Sarto, Simone, Maria Giovanna Ranalli, David Cappelletti, Beatrice Moroni, Stefano Crocchianti, and Silvia Castellini. "Modelling spatio-temporal air pollution data from a mobile monitoring station." Journal of Statistical Computation and Simulation 86, no. 13 (March 29, 2016): 2546–59. http://dx.doi.org/10.1080/00949655.2016.1167895.

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Deshmukh, Parikshit, Sue Kimbrough, Stephen Krabbe, Russell Logan, Vlad Isakov, and Richard Baldauf. "Identifying air pollution source impacts in urban communities using mobile monitoring." Science of The Total Environment 715 (May 2020): 136979. http://dx.doi.org/10.1016/j.scitotenv.2020.136979.

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Bales, Elizabeth, Nima Nikzad, Nichole Quick, Celal Ziftci, Kevin Patrick, and William G. Griswold. "Personal pollution monitoring: mobile real-time air quality in daily life." Personal and Ubiquitous Computing 23, no. 2 (March 28, 2019): 309–28. http://dx.doi.org/10.1007/s00779-019-01206-3.

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D M, Divya. "IoT Based Air Quality Monitoring System." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 15, 2021): 402–6. http://dx.doi.org/10.22214/ijraset.2021.37337.

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In both created and non-industrial nations, proceeded with openness to helpless air quality is a significant general wellbeing hazard. Poisons that add to helpless air quality are thought to cause over 2.5 million unexpected losses every year all throughout the planet. To monitor things, In this undertaking, we will make an IOT-based Air Pollution Monitoring System in which we will display the quality of air in the mobile application utilizing a GSM and will set off a caution when the air quality drops under a particular range, for example when there is an enough measure of gases which are hurtful like CO2, smoke, liquor, benzene, and NH3 present noticeable all around. It will show the air quality in the proportion of PPM on the LCD and on the versatile application with the goal that we can without much of a stretch screen it. We can use our mobile app to display the contamination level in this IoT project.

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Li, Jason Jingshi, Boi Faltings, Olga Saukh, David Hasenfratz, and Jan Beutel. "Sensing the Air We Breathe — The OpenSense Zurich Dataset." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 323–25. http://dx.doi.org/10.1609/aaai.v26i1.8163.

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Monitoring and managing urban air pollution is a significant challenge for the sustainability of our environment. We quickly survey the air pollution modeling problem,introduce a new dataset of mobile air quality measurements in Zurich, and discuss the challenges of making sense of these data.

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Yang, Fenhuan, Junke Zhang, Yang Xing, Jieqing He, Kai Zhang, Dane Westerdahl, and Zhi Ning. "Deployment of Mobile Air Sensing Network for Urban Air Pollution Monitoring in Hong Kong." Proceedings 1, no. 8 (December 14, 2017): 775. http://dx.doi.org/10.3390/proceedings1080775.

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Taştan, Mehmet, and Hayrettin Gökozan. "Real-Time Monitoring of Indoor Air Quality with Internet of Things-Based E-Nose." Applied Sciences 9, no. 16 (August 20, 2019): 3435. http://dx.doi.org/10.3390/app9163435.

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Today, air pollution is the biggest environmental health problem in the world. Air pollution leads to adverse effects on human health, climate and ecosystems. Air is contaminated by toxic gases released by industry, vehicle emissions and the increased concentration of harmful gases and particulate matter in the atmosphere. Air pollution can cause many serious health problems such as respiratory, cardiovascular and skin diseases in humans. Nowadays, where air pollution has become the largest environmental health risk, the interest in monitoring air quality is increasing. Recently, mobile technologies, especially the Internet of Things, data and machine learning technologies have a positive impact on the way we manage our health. With the production of IoT-based portable air quality measuring devices and their widespread use, people can monitor the air quality in their living areas instantly. In this study, e-nose, a real-time mobile air quality monitoring system with various air parameters such as CO2, CO, PM10, NO2 temperature and humidity, is proposed. The proposed e-nose is produced with an open source, low cost, easy installation and do-it-yourself approach. The air quality data measured by the GP2Y1010AU, MH-Z14, MICS-4514 and DHT22 sensor array can be monitored via the 32-bit ESP32 Wi-Fi controller and the mobile interface developed by the Blynk IoT platform, and the received data are recorded in a cloud server. Following evaluation of results obtained from the indoor measurements, it was shown that a decrease of indoor air quality was influenced by the number of people in the house and natural emissions due to activities such as sleeping, cleaning and cooking. However, it is observed that even daily manual natural ventilation has a significant improving effect on air quality.

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Rashevskiy, N. M., N. V. Sadovnikova, Т. V. Yereshchenko, and M. A. Кulikov. "FORMULATION OF THE DECISION-MAKING PROBLEM FOR MANAGEMENT OF MOBILE AIR QUALITY MONITORING STATIONS." Engineering and Construction Bulletin of the Caspian Region 112 (2021): 28–33. http://dx.doi.org/10.52684/2312-3702-2021-36-2-28-33.

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The problems of air pollution and air quality monitoring are considered. The study is aimed at substantiating the method of forming a plan for observing atmospheric air pollution using mobile laboratories. A feature of the proposed method is the use of a decision support system for the rational arrangement and operation of laboratories. combined sanitary and hygienic criterion selected to assess of pollution is calculated. The quantitative characteristics of land plots for different urban planning zones are estimated. The implementation of the decision-making problem using the network analysis method is considered. In the course of the study, the parameters of the urban and natural environments that affect the assessment of the atmospheric air quality were studied, a network structure of the mutual influence of these parameters was formed.

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Kristyawan, I. P. A., Wiharja, A. Shoiful, P. A. Hendrayanto, A. D. Santoso, and N. Suwedi. "The air quality index based on measurements of mobile air quality monitoring station at the waste-to-energy incineration plant PLTSa Bantargebang." IOP Conference Series: Earth and Environmental Science 926, no. 1 (November 1, 2021): 012015. http://dx.doi.org/10.1088/1755-1315/926/1/012015.

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Abstract Ambient air quality monitoring at waste-to-energy incineration pilot plant PLTSa Bantargebang is performed using a mobile monitoring station. The mobile monitoring station is equipped with meteorological and emission (CO, O3, NO2, PM10, PM2.5, and SO2) measurement. The monitoring was performed for 24 hour with 1 minute intervals. The emission measurement data was analyzed using Indonesian Air pollution standard index regulation (PermenLHK P.14/2020). The CO, O3, NO2, PM10, and SO2 index were in good category (1-50), while the PM2.5 index was classified as moderate (65.992). The results show that the air quality at PLTSa Bantargebang is still acceptable for human health.

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Evagelopoulos, V., N. Charisiou, and G. Evagelopoulos. "Smart air monitoring for indoor public spaces using mobile applications." IOP Conference Series: Earth and Environmental Science 899, no. 1 (November 1, 2021): 012006. http://dx.doi.org/10.1088/1755-1315/899/1/012006.

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Abstract As people spend approximately 90% of their time indoors, monitoring the quality of indoor air is crucial in protecting public health. In recent years, technologies such as Internet of Things (IoT) and cloud computing have introduced new measurement capabilities in a variety of environments. Low-cost sensor technology can significantly help in the field of air pollution monitoring, providing data on air quality levels and indoor air emissions. The work presented herein focuses on a cloud computing server able to analyse data in real time and present the results obtained with visual effects which illustrates the prevailing indoor air conditions, making data easier to understand and more interesting to the user. In addition, the server can alert mobile application users or facility managers when air quality is poor so that remedial action can be undertaken immediately.

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Garcia, D., F. Vázquez-Gallego, and M. E. Parés. "ON THE ORGANIZATION AND VALIDATION OF A PILOT TEST OF A MOBILE CROWDSOURCED AIR QUALITY MONITORING SYSTEM." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2021 (June 30, 2021): 361–66. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2021-361-2021.

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Abstract. The development of new tools that allow continuous monitoring of air quality is essential for the study of actions, in order to improve the levels of pollutants in the air that are harmful to the health of citizens. Cardiovascular and respiratory diseases have been identified as risk factors for death in patients with COVID-19; at the same time, exposure to air pollution is associated with these diseases. In this article, we present the pilot tests of the Crowdsourced Air Quality Monitoring (C-AQM) system, which allows the generation of reliable air pollution maps, using data provided by low-cost sensor nodes. The results verify that the system is correct after performing a data calibration; an improvement in NO2 pollution has been observed on weekends, as well as a situation of less air pollution by NO2 between the first and second pandemic waves in Spain.

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Kovalov, Oleksandr, Vitaliy Sobyna, Dmitry Sokolov, Serhii Harbuz, Serhii Vasyliev, and Volodymyr Kokhanenko. "METHOD OF ORGANIZATION OF ATMOSPHERIC AIR MONITORING." Technogenic and Ecological Safety, no. 9(1/2021) (April 21, 2021): 16–25. http://dx.doi.org/10.52363/2522-1892.2021.1.3.

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The paper proposes the creation of a network of fully automatic monitoring stations for air pollution on the basis of networks of 3G / 4G base stations of mobile operators of Ukraine, which will provide data on concentrations of pollutants subject to mandatory real-time control at a specific point in space. with known coordinates. Substantiation of the choice and adaptation of the mathematical model for calculating the distribution of impurities of pollutants in the atmosphere (the necessary component of the proposed method) taking into account the engineering and technical means of automated measurements. A method for predicting the level of pollution and its distribution taking into account meteorological conditions based on the adaptation of the OND-86 model, as well as its supplementation by calculations based on the nonstationary Gaussian model, has been developed. The method differs from the existing ones by estimating the contribution of each source using the results of operational control, which allows to create automated air quality assurance systems.

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Gryech, Ihsane, Yassine Ben-Aboud, Bassma Guermah, Nada Sbihi, Mounir Ghogho, and Abdellatif Kobbane. "MoreAir: A Low-Cost Urban Air Pollution Monitoring System." Sensors 20, no. 4 (February 13, 2020): 998. http://dx.doi.org/10.3390/s20040998.

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MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is based on mobile and nomadic sensors as well as on medical data collected at a children’s hospital, used to identify urban areas of high prevalence of respiratory diseases. Another key feature is the use of machine learning to perform prediction. In this paper, Moroccan cities are taken as case studies. Using the agile deployment strategy of MoreAir, it is shown that in many Moroccan neighborhoods, road traffic has a smaller impact on the concentrations of particulate matters (PM) than other sources, such as public baths, public ovens, open-air street food vendors and thrift shops. A geographical information system has been developed to provide real-time information to the citizens about the air quality in different neighborhoods and thus raise awareness about urban pollution.

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Zajusz-Zubek, Elwira, and Zygmunt Korban. "Analysis of Air Pollution Based on the Measurement Results from a Mobile Laboratory for the Measurement of Air Pollution." International Journal of Environmental Research and Public Health 19, no. 20 (October 18, 2022): 13474. http://dx.doi.org/10.3390/ijerph192013474.

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One of the most important effects of the smog phenomenon is the presence of high concentrations of substances hazardous to human life and health in the air. Environmental monitoring, including the monitoring of substances hazardous to human life or health, is an element of preventive measures that allow to identify current hazards and to define future actions aimed to improve (protect) the state of the environment. The article presents the results of measurements of the concentration of PM10 and PM2.5 as well as SO2, NO, NOx and O3 based on a mobile laboratory located on the campus of the Silesian University of Technology. By treating the following weeks as “objects”, points in the multidimensional space (the concentrations of PM10 and PM2.5 as well as SO2, NO, NOx and O3 were the measures/describing features), similarities between them were determined, and then they were grouped into the “summer period” (from 01/04/2020 to 30/09/2020) and “winter period” (from 01/01/2020 to 31/03/2020 and from 01/10/2020 to 31/12/2020). The article aimed to determine a linear ordering of weeks divided into the “summer period” and the “winter period”. The software MaCzek v. 3.0 (an application working in Windows) was used in the computing layer.

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Wang, Chao, Hongli Liu, Jiawen Ji, and Yangshuang Wu. "A Design of Indoor Air-Quality Monitoring System." Journal of Physics: Conference Series 2366, no. 1 (November 1, 2022): 012011. http://dx.doi.org/10.1088/1742-6596/2366/1/012011.

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Abstract With the deepening of people’s understanding of the harm of air pollution, indoor air quality is getting more and more attention. This paper designs an indoor air quality monitoring system based on sensor technology and Internet of things technology, the system function includes air quality detection, real-time data display, server transfer, remote display throught Wechat mini programe and others, the system can detect six important environmental parameters and allows users to view the data on mobile phone. Based on the data collected, this paper also designs an air quality evaluation algorithm.

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Actkinson, Blake, Katherine Ensor, and Robert J. Griffin. "SIBaR: a new method for background quantification and removal from mobile air pollution measurements." Atmospheric Measurement Techniques 14, no. 8 (August 26, 2021): 5809–21. http://dx.doi.org/10.5194/amt-14-5809-2021.

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Abstract. Mobile monitoring is becoming increasingly popular for characterizing air pollution on fine spatial scales. In identifying local source contributions to measured pollutant concentrations, the detection and quantification of background are key steps in many mobile monitoring studies, but the methodology to do so requires further development to improve replicability. Here we discuss a new method for quantifying and removing background in mobile monitoring studies, State-Informed Background Removal (SIBaR). The method employs hidden Markov models (HMMs), a popular modeling technique that detects regime changes in time series. We discuss the development of SIBaR and assess its performance on an external dataset. We find 83 % agreement between the predictions made by SIBaR and the predetermined allocation of background and non-background data points. We then assess its application to a dataset collected in Houston by mapping the fraction of points designated as background and comparing source contributions to those derived using other published background detection and removal techniques. The presented results suggest that the SIBaR-modeled source contributions contain source influences left undetected by other techniques, but that they are prone to unrealistic source contribution estimates when they extrapolate. Results suggest that SIBaR could serve as a framework for improved background quantification and removal in future mobile monitoring studies while ensuring that cases of extrapolation are appropriately addressed.

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Northeim, Kari, and Joseph R. Oppong. "Mapping Health Fragility and Vulnerability in Air Pollution–Monitoring Networks in Dallas–Fort Worth." International Journal of Environmental Research and Public Health 20, no. 3 (January 18, 2023): 1807. http://dx.doi.org/10.3390/ijerph20031807.

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Environmental air pollution remains a major contributor to negative health outcomes and mortality, but the relationship between socially vulnerable populations and air pollution is not well understood. Although air pollution potentially affects everyone, the combination of underlying health, socioeconomic, and demographic factors exacerbate the impact for socially vulnerable population groups, and the United States Clean Air Act (CAA) describes an obligation to protect these populations. This paper seeks to understand how air pollution monitor placement strategies and policy may neglect social vulnerabilities and therefore potentially underestimate exposure burdens in vulnerable populations. Multivariate logistic regression models were used to assess the association between being in an ozone-monitored area or not on 15 vulnerability indicators. It was found that the odds of not being in an ozone-monitored area (not covered, outside) increased for the predictor mobile homes (OR = 4.831, 95% CI [2.500–9.338] and OR = 8.066, 95% CI [4.390–14.820] for the 10 and 20 km spatial units, respectively) and decreased for the predictor multiunit structures (OR = 0.281, 95% CI [0.281–0.548] and OR = 0.130, 95% CI [0.037, 0.457] for the 10 and 20 km spatial units, respectively) and the predictor speaks English “less than well” (OR = 0.521, 95% CI [0.292–0.931] for 10 km). These results indicate that existing pollution sensor coverage may neglect areas with concentrations of highly vulnerable populations in mobile homes, and future monitoring placement policy decisions must work to address this imbalance.

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Pandya, Sharnil, Hemant Ghayvat, Anirban Sur, Muhammad Awais, Ketan Kotecha, Santosh Saxena, Nandita Jassal, and Gayatri Pingale. "Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living." Sensors 20, no. 18 (September 22, 2020): 5448. http://dx.doi.org/10.3390/s20185448.

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Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.

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Li, Mingxiao, Song Gao, Feng Lu, Huan Tong, and Hengcai Zhang. "Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data." International Journal of Environmental Research and Public Health 16, no. 22 (November 15, 2019): 4522. http://dx.doi.org/10.3390/ijerph16224522.

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The spatiotemporal variability in air pollutant concentrations raises challenges in linking air pollution exposure to individual health outcomes. Thus, understanding the spatiotemporal patterns of human mobility plays an important role in air pollution epidemiology and health studies. With the advantages of massive users, wide spatial coverage and passive acquisition capability, mobile phone data have become an emerging data source for compiling exposure estimates. However, compared with air pollution monitoring data, the temporal granularity of mobile phone data is not high enough, which limits the performance of individual exposure estimation. To mitigate this problem, we present a novel method of estimating dynamic individual air pollution exposure levels using trajectories reconstructed from mobile phone data. Using the city of Shanghai as a case study, we compared three different types of exposure estimates using (1) reconstructed mobile phone trajectories, (2) recorded mobile phone trajectories, and (3) residential locations. The results demonstrate the necessity of trajectory reconstruction in exposure and health risk assessment. Additionally, we measure the potential health effects of air pollution from both individual and geographical perspectives. This helped reveal the temporal variations in individual exposures and the spatial distribution of residential areas with high exposure levels. The proposed method allows us to perform large-area and long-term exposure estimations for a large number of residents at a high spatiotemporal resolution, which helps support policy-driven environmental actions and reduce potential health risks.

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Wallace, Julie, Denis Corr, Patrick Deluca, Pavlos Kanaroglou, and Brian McCarry. "Mobile monitoring of air pollution in cities: the case of Hamilton, Ontario, Canada." Journal of Environmental Monitoring 11, no. 5 (2009): 998. http://dx.doi.org/10.1039/b818477a.

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Wolf, J. P. "3-D Monitoring of Air Pollution Using Mobile “All-solid-state” Lidar System." Optics and Photonics News 6, no. 1 (January 1, 1995): 27. http://dx.doi.org/10.1364/opn.6.1.000027.

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Van den Bossche, Joris, Jan Theunis, Bart Elen, Jan Peters, Dick Botteldooren, and Bernard De Baets. "Opportunistic mobile air pollution monitoring: A case study with city wardens in Antwerp." Atmospheric Environment 141 (September 2016): 408–21. http://dx.doi.org/10.1016/j.atmosenv.2016.06.063.

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Shakya, Kabindra M., Peleg Kremer, Kate Henderson, Meghan McMahon, Richard E. Peltier, Samantha Bromberg, and Justin Stewart. "Mobile monitoring of air and noise pollution in Philadelphia neighborhoods during summer 2017." Environmental Pollution 255 (December 2019): 113195. http://dx.doi.org/10.1016/j.envpol.2019.113195.

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Senarathna, Mahesh, Sajith Priyankara, Rohan Jayaratne, Rohan Weerasooriya, Lidia Morawska, and Gayan Bowatte. "Measuring TrafficRelated Air Pollution Using Smart Sensors In Sri Lanka: Before And During A New Traffic Plan." GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY 15, no. 3 (October 4, 2022): 27–36. http://dx.doi.org/10.24057/2071-9388-2022-011.

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Motor vehicle emissions are the primary air pollution source in cities worldwide. Changes in traffic flow in a city can drastically change overall levels of air pollution. The level of air pollution may vary significantly in some street segments compared to others, and a small number of stationary ambient air pollution monitors may not capture this variation. This study aimed to evaluate air pollution before and during a new traffic plan established in March 2019 in the city of Kandy, Sri Lanka, using smart sensor technology. Street level air pollution data (PM2.5 and NO2 ) was acquired using a mobile air quality sensor unit before and during the implementation of the new traffic plan. The sensor unit was mounted on a police traffic motorcycle that travelled through the city four times per day. Air pollution in selected road segments was compared before and during the new traffic plan, and the trends at different times of the day were compared using data from a stationary smart sensor. Both PM2.5 and NO2 levels were well above the World Health Organization (WHO) 24-hour guidelines during the monitoring period, regardless of the traffic plan period. Most of the road segments had comparatively higher air pollution levels during compared to before the new traffic plan. For any given time (morning, midday, afternoon, evening), day of the week, and period (before or during the new traffic plan), the highest PM2.5 and NO2 concentrations were observed at the road segment from Girls High School to Kandy Railway Station. The mobile air pollution monitoring data provided evidence that the mean concentration of PM2.5 during the new traffic plan (116.7 µg m-3) was significantly higher than before the new traffic plan (92.3 µg m-3) (p < 0.007). Increasing spatial coverage can provide much better information on human exposure to air pollutants, which is essential to control traffic related air pollution. Before implementing a new traffic plan, careful planning and improvement of road network infrastructure could reduce air pollution in urban areas.

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Wu, Edward Ming-Yang, and Shu-Lung Kuo. "Characteristics of Photochemical Reactions with VOCs Using Multivariate Statistical Techniques on Data from Photochemical Assessment Monitoring Stations." Atmosphere 13, no. 9 (September 13, 2022): 1489. http://dx.doi.org/10.3390/atmos13091489.

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This study assesses the concentrations of the 54 ozone precursors (all being volatile organic compounds (VOCs)) detected at the four photochemical assessment monitoring stations that are part of the air quality monitoring network in the Kaohsiung-Pingtung area in Taiwan. Factor and cluster analyses of the multivariate statistical analysis are performed to explore the interrelationship among the 10 VOCs of relatively higher concentrations selected from the 54 ozone precursors to identify significant factors affecting ozone pollution levels in the study area. Moreover, the multivariate statistical analysis can faithfully reflect why the study area has been affected by photochemical pollution. First, results of the factor analysis suggest that the factors affecting how photochemical reactions occur in the study area can be divided into the following: “pollution from mobile sources”, “pollution from stationary sources”, and “pollution from energy sources”. Among them, mobile sources have the greatest impact on photochemical pollution levels. Second, the impacts of photochemical pollution on air quality in the study area can be classified into four clusters via cluster analysis. Each cluster represents how the 10 VOCs affect air quality, with different characteristics, and how they contribute to photochemical pollution in the study area. If there are more types and samples of photochemical pollutants when performing a multivariate statistical analysis, the analysis results will be more stable. This study adopts data on VOC monitoring over a period of nearly two years, which can effectively improve the validity and reliability of the factor analysis results, while helping environmental agencies review the effectiveness of air quality management in the future and serving as reference for the effectiveness of reducing photochemical pollution in the atmosphere.

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Fattoruso, Grazia, Domenico Toscano, Antonella Cornelio, Saverio De Vito, Fabio Murena, Massimiliano Fabbricino, and Girolamo Di Francia. "Using Mobile Monitoring and Atmospheric Dispersion Modeling for Capturing High Spatial Air Pollutant Variability in Cities." Atmosphere 13, no. 11 (November 20, 2022): 1933. http://dx.doi.org/10.3390/atmos13111933.

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Air pollution is still one of the biggest environmental threats to human health on a global scale. In urban environments, exposure to air pollution is largely influenced by the activity patterns of the population as well as by the high spatial and temporal variability in air pollutant concentrations. Over the last years, several studies have attempted to better characterize the spatial variations in air pollutant concentrations within a city by deploying dense, fixed as well as mobile, low-cost sensor networks and more recently opportunistic sampling and by improving the spatial resolution of air quality models up to a few meters. The purpose of this work has been to investigate the use of properly designed mobile monitoring campaigns along the streets of an urban neighborhood to assess the capability of an operational air dispersion model as SIRANE at the district scale to capture the local variability of pollutant concentrations. To this end, an IoT ecosystem—MONICA (an Italian acronym for Cooperative Air Quality Monitoring), developed by ENEA, has been used for mobile measurements of CO and NO2 concentration in the urban area of the City of Portici (Naples, Southern Italy). By comparing the mean concentrations of CO and NO2 pollutants measured by MONICA devices and those simulated by SIRANE along the urban streets, the former appeared to exceed the simulated ones by a factor of 3 and 2 for CO and NO2, respectively. Furthermore, for each pollutant, this factor is higher within the street canyons than in open roads. However, the mobile and simulated mean concentration profiles largely adapt, although the simulated profiles appear smoother than the mobile ones. These results can be explained by the uncertainty in the estimation of vehicle emissions in SIRANE as well as the different temporal resolution of measurements of MONICA able to capture local high concentrations.

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Dyvak, M., V. Manzhula, A. Melnyk, and V. Tymchyshyn. "A System for Monitoring Air Pollution by Motor Vehicles Based on an Autonomous Air-Mobile Measuring Complex." Optoelectronic Information-Power Technologies 42, no. 2 (October 26, 2022): 73–83. http://dx.doi.org/10.31649/1681-7893-2021-42-2-73-83.

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The article proposes an approach to constructing a system of complex and uninterrupted monitoring of harmful emissions of motor vehicles into the air. The architecture of the environmental monitoring system for measuring and forecasting the distribution of pollutant concentrations in motor vehicle exhaust gases, among which mainly CO, SO₂, NO₂, and СО₂, is presented. The mobile information and measurement complex Sniffer4D Hyper-local Air Quality Analyzer and a charging station based on solar batteries are used as the hardware. For modeling and forecasting the distribution of concentrations of harmful emissions, mathematical models of the dynamics of the distribution of concentrations of pollutants due to harmful emissions in the exhaust gases of motor vehicles are proposed in the form of differential equations that are analogs of differential equations in partial derivatives, as models of turbulent diffusion and interval models of the distribution of the background level of pollution concentration in the form of nonlinear algebraic equations. Implemented software for data collection, processing (model learning and prediction), and visualization.

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Dyvak, M., V. Manzhula, A. Melnyk, and V. Tymchyshyn. "A System for Monitoring Air Pollution by Motor Vehicles Based on an Autonomous Air-Mobile Measuring Complex." Optoelectronic Information-Power Technologies 42, no. 2 (October 26, 2022): 73–83. http://dx.doi.org/10.31649/1681-7893-2021-41-1-73-83.

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The article proposes an approach to constructing a system of complex and uninterrupted monitoring of harmful emissions of motor vehicles into the air. The architecture of the environmental monitoring system for measuring and forecasting the distribution of pollutant concentrations in motor vehicle exhaust gases, among which mainly CO, SO₂, NO₂, and СО₂, is presented. The mobile information and measurement complex Sniffer4D Hyper-local Air Quality Analyzer and a charging station based on solar batteries are used as the hardware. For modeling and forecasting the distribution of concentrations of harmful emissions, mathematical models of the dynamics of the distribution of concentrations of pollutants due to harmful emissions in the exhaust gases of motor vehicles are proposed in the form of differential equations that are analogs of differential equations in partial derivatives, as models of turbulent diffusion and interval models of the distribution of the background level of pollution concentration in the form of nonlinear algebraic equations. Implemented software for data collection, processing (model learning and prediction), and visualization.

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Adams, Matthew D., and Pavlos S. Kanaroglou. "Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models." Journal of Environmental Management 168 (March 2016): 133–41. http://dx.doi.org/10.1016/j.jenvman.2015.12.012.

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Mallani, Hareetaa. "Smart Air Quality Monitoring and Sensing Device (SAQM Sensing Device)." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 2787–92. http://dx.doi.org/10.22214/ijraset.2021.37857.

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Abstract: Air pollution is the biggest problem of every nation, whether it is developed or developing. Health problems have been growing at faster rate especially in urban areas of developing countries where industrialization and growing number of vehicles leads to release of lot of gaseous pollutants. Harmful effects of pollution include mild allergic reactions such as irritation of the throat, eyes and nose as well as some serious problems like bronchitis, heart diseases, pneumonia, lung and aggravated asthma. According to a survey, due to air pollution 50,000 to 100,000 premature deaths per year occur in the U.S. alone. LPG sensor is added in this system which is used mostly in houses. The system will show temperature and humidity. The system can be installed anywhere but mostly in industries and houses where gases are mostly to be found and gives an alert message when the system crosses threshold limit. The advantages of the detector, have a reliable stability, rapid response recovery and long-life features. It is affordable, userfriendly, low-cost and minimum-power requirement hardware which is appropriate for mobile measurement, as well as comprehensible data collection

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Rybak, V. A., and O. P. Ryabychina. "Hardware System for Environmental Diagnosis of Air Pollution." Journal of the Russian Universities. Radioelectronics 23, no. 3 (July 21, 2020): 93–99. http://dx.doi.org/10.32603/1993-8985-2020-23-3-93-99.

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Introduction. The existing scientific and technical problem was shown that, on the one hand, in accordance with legislation and international obligations (for example, under the Aarhus Convention), the population can request data on the current state of the environment, and on the other, monitoring systems existing today unable to timely ensure their provision. The paper presents the results of studies on selection and justification of sensors for air pollution, pressure, temperature and humidity. Aim. The development of hardware for environmental monitoring of atmospheric air pollution and its testing when choosing the optimal safe route for people to move. Materials and methods. For data transmission, the GSM wireless module was selected; to determine the location - GPRS. Hardware system was based on the Arduino Nano microcomputer, to which these sensors were connected. Studies were conducted in Minsk, Republic of Belarus. Results. The developed hardware combined air pollution, humidity, temperature sensors with GSM and GPRS modules was based on a microcomputer, which allowed it to be used both stationary and with an unmanned aerial vehicle (drone), and to carry out mobile monitoring. The data transmitted by the device were processed in order to build maps of air pollution. For this, sets of points gained by interpolation by the method of linear averaging of neighboring values were plotted on the map. Pollution values were displayed on the map by color coding. Conclusion. The maps thus gained can be used, for example, to select an optimal route for people to move in the city from the point of view of minimizing the adverse effects of pollution on human health and in technological emergencies. At the time of development, the proposed solution has no analogs.

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Journal articles on the topic 'Mobile air pollution monitoring'

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