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Journal articles on the topic 'Air-mobile'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Timoshek, Alexandra, Douglas Eisinger, Song Bai, and Deb Niemeier. "Mobile Source Air Toxic Emissions." Transportation Research Record: Journal of the Transportation Research Board 2158, no. 1 (January 2010): 77–85. http://dx.doi.org/10.3141/2158-10.

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2

Collet, Susan. "Mobile Source Air Toxic Emissions." Journal of the Air & Waste Management Association 66, no. 2 (January 15, 2016): 97. http://dx.doi.org/10.1080/10962247.2016.1128721.

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3

Goldstein, B. D., and J. Warren. "HEI's mobile air toxics project." Environmental Health Perspectives 101, no. 3 (August 1993): 256–58. http://dx.doi.org/10.1289/ehp.93101256.

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4

Kochneva, O. V., L. V. Podkolzina, A. V. Kozlovich, and D. V. Shabanov. "Mobile robotic air cushion system." IOP Conference Series: Earth and Environmental Science 539 (August 13, 2020): 012119. http://dx.doi.org/10.1088/1755-1315/539/1/012119.

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5

Hoffmann, Gabriele. "Refrigerants for Mobile Air Conditioning." ATZ worldwide 119, no. 1 (January 2017): 16–21. http://dx.doi.org/10.1007/s38311-016-0162-x.

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6

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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7

Cook, C. "Modeling Interference Effects for Land-Mobile and Air-Mobile Communications." IEEE Transactions on Communications 35, no. 2 (1987): 151–65. http://dx.doi.org/10.1109/tcom.1987.1096752.

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8

Erlangga, Yuliar Yasin, and Heri Setiawan. "PERANCANGAN MESIN PENGOLAH AIR BERSIH BERGERAK DENGAN MENGGUNAKAN SISTIM MODULAR UNTUK PENANGGULANGAN KEADAAN DARURAT AIR." Machine : Jurnal Teknik Mesin 4, no. 1 (January 2, 2018): 21–28. http://dx.doi.org/10.33019/jm.v4i1.448.

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ABSTRAK Pengolahan air bersih (Water Treatment) sistem modular/mobile (compact mobile) merupakan pengembangan dari sistem penyaringan air dengan sistem “up flow” yang sudah dikembangkan oleh BPPT dengan penambahan dan perbaikan fungsi proses dalam upaya mengoptimalkan proses pengolahan air baku menjadi air bersih. Pengembangan yang dilakukan pada penelitian ini yaitu dengan mengubah dari instalasi pengolahan yang tetap yang biasanya berbentuk civil work ke dalam sub fungsi-fungsi pengolah yang bersifat modular/mobile (compact mobile) dengan ukuran / dimensi yang tidak terlalu besar (compact design). Keuntungan yang bisa didapatkan dari sistem tersebut terutama dalam segi penanganan selama proses pengolahan dan dalam hal penyediaan suku cadang yang mudah sehingga waktu perbaikan disaat terjadi kerusakan pada mesin pengolahan air bersih ini menjadi sangat singkat dan juga ukuran / dimensi dari perangkat ini yang cukup kecil (compact) sehingga mudah dalam pemindahan antar lokasi untuk keadaan darurat air. Pada akhir penelitian ini setelah melalui proses perancangan menurut VDI 2222 dan sudah dilakukan penilaian berdasarkan beberapa aspek maka terbangun sebuah prototipe modular mobile water treatment berkapasitas pengolahan 1 M² per jam yaitu rancangan nomer 2 dengan nilai 92%. Aspek terbarukan yang dipelajari adalah desain modular mobile water treatment itu sendiri, penentuan dan pemilihan solusi dari sub fungsi bagian serta bagaimana sub-sub fungsi bagian tersebut diikatkan pada rangka sehingga instalasi tersebut menjadi kompak untuk dijadikan sebagai mobile water treatment.

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9

Reilly, Charles D., Stephen G. Waller, William J. Flynn, Miguel A. Montalvo, and Jane B. Ward. "U.S. Air Force Mobile Ophthalmic Surgery Team." Military Medicine 169, no. 12 (December 2004): 952–57. http://dx.doi.org/10.7205/milmed.169.12.952.

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10

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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11

Russell, R. Andrew. "Air vortex ring communication between mobile robots." Robotics and Autonomous Systems 59, no. 2 (February 2011): 65–73. http://dx.doi.org/10.1016/j.robot.2010.11.002.

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12

RABER, LINDA. "Air toxics plan targets smaller, mobile sources." Chemical & Engineering News 76, no. 38 (September 21, 1998): 39–40. http://dx.doi.org/10.1021/cen-v076n038.p039.

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13

Tsin, Pak Keung, Anders Knudby, E. Scott Krayenhoff, Hung Chak Ho, Michael Brauer, and Sarah B. Henderson. "Microscale mobile monitoring of urban air temperature." Urban Climate 18 (December 2016): 58–72. http://dx.doi.org/10.1016/j.uclim.2016.10.001.

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14

Gado, Amr, Yunho Hwang, and Reinhard Radermacher. "Dynamic Behavior of Mobile Air-Conditioning Systems." HVAC&R Research 14, no. 2 (March 1, 2008): 307–21. http://dx.doi.org/10.1080/10789669.2008.10391010.

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15

Sebald, Angela. "CareFlight Air and Mobile Celebrates 30 Years!" Air Medical Journal 33, no. 1 (January 2014): 25–26. http://dx.doi.org/10.1016/j.amj.2013.09.007.

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16

Antipenkov, B. A., A. B. Davydov, A. Sh Kobulashvili, G. A. Perestoronin, and A. A. Fal'chenko. "Turbo-expanders for mobile air separation equipment." Chemical and Petroleum Engineering 21, no. 3 (March 1985): 125–27. http://dx.doi.org/10.1007/bf01154881.

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17

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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18

Nyssan, S. А., Е. P. Makashev, B. S. Daribaev, and S. B. Berkimbaeva. "DEVELOPMENT OF A MOBILE APPLICATION FOR CALCULATING THE TEMPERATURE OF ATMOSPHERIC AIR BY A WET THERMOMETER." BULLETIN Series of Physics & Mathematical Sciences 72, no. 4 (September 29, 2020): 231–36. http://dx.doi.org/10.51889/2020-4.1728-7901.36.

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The research is aimed at developing a mobile application for calculating atmospheric air temperature using a wet thermometer. The use of an improved mobile application is the main tool for users to calculate the atmospheric air temperature using a wet thermometer. The mobile app helps users get results anytime, anywhere. The mobile application is designed for users who want to calculate the temperature of atmospheric air using a wet thermometer. When developing the mobile application, the Xcode platform was used. The mobile app is developed in three main languages. Kazakh-speaking users, as well as Russian-speaking and English-speaking users. The mobile app includes many features such as explanations, calculations, the ability to calculate simultaneously, and the ability to change the language to be understandable to users. The mobile application, the wet thermometer, atmospheric air.

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19

Hormeño-Holgado, Alberto J., Miguel Angel Perez-Martinez, and Vicente J. Clemente-Suárez. "Psychophysiological response of air mobile protection teams in an air accident manoeuvre." Physiology & Behavior 199 (February 2019): 79–83. http://dx.doi.org/10.1016/j.physbeh.2018.11.006.

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20

Schmidt, K. "Air Pollution: No Place like a Mobile Home." Science News 140, no. 16 (October 19, 1991): 247. http://dx.doi.org/10.2307/3975706.

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21

Son, Seonghoon, and Namsuk Cho. "Optimization Models for Deploying Air Defense Mobile Radars." Journal of the Korean Institute of Industrial Engineers 45, no. 3 (June 30, 2019): 225–39. http://dx.doi.org/10.7232/jkiie.2019.45.3.225.

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22

Kim, Young-Baek, Jin-Hee Cho, and Sang-Yong Rhee. "Self-Localization Algorithm for a Mobile Air Cleaner." International Journal of Fuzzy Logic and Intelligent Systems 9, no. 4 (December 1, 2009): 321–26. http://dx.doi.org/10.5391/ijfis.2009.9.4.321.

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23

Marks, Paul. "Turn your mobile phone into an air guitar." New Scientist 200, no. 2681 (November 2008): 26. http://dx.doi.org/10.1016/s0262-4079(08)62829-8.

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24

Claggett, Michael, and Terry L. Miller. "Methodology for Evaluating Mobile Source Air Toxic Emissions." Transportation Research Record: Journal of the Transportation Research Board 1987, no. 1 (January 2006): 32–41. http://dx.doi.org/10.1177/0361198106198700104.

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25

Whitehill, Andrew R., Melissa Lunden, Surender Kaushik, and Paul Solomon. "Uncertainty in collocated mobile measurements of air quality." Atmospheric Environment: X 7 (October 2020): 100080. http://dx.doi.org/10.1016/j.aeaoa.2020.100080.

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26

Zhang, Wen, Longzhuang Li, Ning Zhang, Tao Han, and Shangguang Wang. "Air-Ground Integrated Mobile Edge Networks: A Survey." IEEE Access 8 (2020): 125998–6018. http://dx.doi.org/10.1109/access.2020.3008168.

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27

Progler, M., C. Evci, and M. Umehira. "Air interface access schemes for broadband mobile systems." IEEE Communications Magazine 37, no. 9 (1999): 106–15. http://dx.doi.org/10.1109/35.790969.

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28

Sheng, S. "Mobile television receivers: A free-to-air overview." IEEE Communications Magazine 47, no. 9 (September 2009): 142–49. http://dx.doi.org/10.1109/mcom.2009.5277468.

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29

Groneberg, David A., Cristian Scutaru, Mathias Lauks, Masaya Takemura, Tanja C. Fischer, Silvana Kölzow, Anke van Mark, et al. "Mobile Air Quality Studies (MAQS)-an international project." Journal of Occupational Medicine and Toxicology 5, no. 1 (2010): 8. http://dx.doi.org/10.1186/1745-6673-5-8.

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30

Bogue, Robert. "Mobile LIDAR system detects air pollution hot spots." Sensor Review 26, no. 2 (April 2006): 98–100. http://dx.doi.org/10.1108/02602280610652668.

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31

Robinson, Heather, and Beth Newman-Hill. "CareFlight Air and Mobile Services Celebrates 25 Years." Air Medical Journal 27, no. 5 (September 2008): 224–25. http://dx.doi.org/10.1016/j.amj.2008.05.011.

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32

Yu, Tai Yi, I. Cheng Chang, Mei Yin Hwa, and Li Teh Lu. "Estimation of Air Pollutant Emissions from Mobile Sources with Three Emission Factors Models." Advanced Materials Research 550-553 (July 2012): 2378–81. http://dx.doi.org/10.4028/www.scientific.net/amr.550-553.2378.

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Vehicle emissions from mobile sources are major contributors to air pollution and varied with vehicle types, vehicle styles, traveled miles, temperature, oil types and the methods of operation and management. This study performs three emission factor models, Mobile-Taiwan 2, Mobile6.2 and EFDB to calculate emission factor of mobile sources from year 1986 to 2011. The emissions of primary air pollutants, MIRs and CO2emitted from mobile sources were calculated. The contribution ratios of varied vehicle types for different air pollutants would be compared and analyzed. Estimated emissions from mobile sources were 32.2, 177, 643, 197 and 401 kilotons/y for PM10, NOx, CO, THC and MIR for 2000; 31.3, 115, 305, 114 and 227 kilotons/y for 2011. Emissions of traditional air pollutants presented a decreasing trend because of fourth-stage emission standards for mobiles sources and CO2 revealed an increasing trend. According to presented control technology for greenhouse gases on mobile sources, ratio of emission for year 2011 to 2000 would be 1.38-1.49.

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33

Parés, M. E., D. Garcia, and F. Vázquez-Gallego. "MAPPING AIR QUALITY WITH A MOBILE CROWDSOURCED AIR QUALITY MONITORING SYSTEM (C-AQM)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 685–90. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-685-2020.

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Abstract. World cities are currently facing one of the major crisis of the last century. Some preliminary studies on COVID-19 pandemia have shown that air pollutants may have a strong impact on virus effects. Improved gas sensors and wireless communication systems open the door to the design of new air monitoring systems based on citizen science to better monitor and communicate the air quality levels. In this paper, we present the Crowdsourced Air Quality Monitoring (C-AQM) system, which relies on Air Quality Monitoring reference stations and a cluster of new low-cost and low-energy sensor nodes, in order to improve the resolution of air quality maps. The data collected by the C-AQM system is stored in a time series database and is available both to city council managers for decision making and to citizens for informative purposes. In this paper, we present the main bases of the C-AQM system as well as the measurements validation campaign carried out.

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34

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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35

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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36

Larose, Gaston, and Michel Pigeon. "Béton produit en bétonnière mobile et en usine conventionnelle : comparaison des caractéristiques du réseau de bulles d'air." Canadian Journal of Civil Engineering 15, no. 3 (June 1, 1988): 306–14. http://dx.doi.org/10.1139/l88-046.

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The durability of concrete to freeze-thaw cycles is dependent upon the existence of an adequate air-void system. There are very few studies on the air-void system of field concretes. Laboratory tests have proven that the air content measurement on the fresh concrete is not sufficient to judge the aptitude of the air-void system to protect the concrete from frost damage.This paper is a comparison of the air-void systems of field concretes produced in either a conventional plant or a mobile unit the use of which is becoming more and more frequent. The concretes produced in the conventional plant generally had sufficient air-void systems for air contents in the usual range (5–7%). The mobile unit showed that a slightly higher air content (8%) was needed to produce an adequate air-void system. Key words: concrete, mobile concrete-mixer, air-void systems, air-entraining agent, spacing factor, surface area, air content.

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37

Urbanský, Matej, Jaroslav Homišin, Peter Kaššay, and Jozef Krajňák. "Measurement of Air Springs Volume Using Indirect Method in the Design of Selected Pneumatic Devices." Acta Mechanica et Automatica 12, no. 1 (March 1, 2018): 19–22. http://dx.doi.org/10.2478/ama-2018-0003.

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AbstractAt our department, we deal with continuous tuning of torsional oscillating mechanical systems (TOMS) during their operation in terms of torsional oscillation size. Therefore, a new mobile mechanical system was built for purposes of research and presentation of the TOMS continuous tuning using extremal control method, which main advantage is that we do not need to know a mathematical model of the mechanical system. The new mobile device is equipped with a special compressed air distribution system, which important components are air springs. The air springs are modified and used as air pressure tanks with various functions in the mobile device. Therefore, it is important to know the magnitude of the air springs inner volume. This paper deals with determination of air springs volume using indirect method, which is based on the air pressure measurement and also the comparison of obtained results with the results computed from air springs manufacturer data.

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38

Jiang, Yifei, Kun Li, Ricardo Piedrahita, Xiang Yun, Lei Tian, Omkar M. Mansata, Qin Lv, Robert P. Dick, Michael Hannigan, and Li Shang. "User-Centric Indoor Air Quality Monitoring on Mobile Devices." AI Magazine 34, no. 2 (June 21, 2013): 11. http://dx.doi.org/10.1609/aimag.v34i2.2472.

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Since people spend a majority of their time indoors, indoor air quality (IAQ) can have a significant impact on human health, safety, productivity, and comfort. Due to the diversity and dynamics of people's indoor activities, it is important to monitor IAQ for each individual. Most existing air quality sensing systems are stationary or focus on outdoor air quality. In contrast, we propose MAQS, a user-centric mobile sensing system for IAQ monitoring. MAQS users carry portable, indoor location tracking and IAQ sensing devices that provide personalized IAQ information in real time. To improve accuracy and energy efficiency, MAQS incorporates three novel techniques: (1) an accurate temporal n-gram augmented Bayesian room localization method that requires few Wi-Fi fingerprints; (2) an air exchange rate based IAQ sensing method, which measures general IAQ using only CO$_2$ sensors; and (3) a zone-based proximity detection method for collaborative sensing, which saves energy and enables data sharing among users. MAQS has been deployed and evaluated via a real-world user study. This evaluation demonstrates that MAQS supports accurate personalized IAQ monitoring and quantitative analysis with high energy efficiency. We also found that study participants frequently experienced poor IAQ.

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39

Kawamoto, Yoichiro, Gota Ogata, and Zhiwei Shan. "Ejector Energy-Saving Technology for Mobile Air Conditioning Systems." SAE International Journal of Passenger Cars - Mechanical Systems 10, no. 1 (March 28, 2017): 102–10. http://dx.doi.org/10.4271/2017-01-0120.

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40

Bartlett, Jim, and Philip Wolstenholme. "KING COUNTY TO USE MOBILE FOUL AIR TREATMENT UNITS." Proceedings of the Water Environment Federation 2000, no. 3 (January 1, 2000): 1126–35. http://dx.doi.org/10.2175/193864700785303466.

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41

KOZIOŁEK, Sebastian. "AIR-CONDITIONED MOBILE COMMAND STATION POWERED BY PHOTOVOLTAIC CELLS." Scientific Journal of the Military University of Land Forces 164, no. 2 (March 1, 2012): 292–300. http://dx.doi.org/10.5604/01.3001.0002.2833.

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The paper presents the design of an air-conditioned mobile command station powered by photovoltaic cells. The mechanical project was created on the basis of a vehicle equipped with a solar panels system to power the air conditioner for the command station. The present project involves the application of the original invention. The paper deals with studies related to the use of a solar energy system to power electrical equipment in special-purpose vehicles. This innovative solution includes the modification of technologies and renewable energy sources mainly used in stationary objects. The research was carried out in collaboration with the automotive industry. The results and analysis were reviewed in order to develop the first prototype and optimize it.

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42

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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43

Febriyanda, Septa, Taufik Hidayat, and Diki Susandi. "SISTEM PENJUALAN ONLINE AIR MINUM ISI ULANG BERBASIS MOBILE." JSiI (Jurnal Sistem Informasi) 7, no. 1 (March 6, 2020): 57. http://dx.doi.org/10.30656/jsii.v7i1.2002.

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Abstrak - Penjualan online atau e-commerce merupakan mekanisme bisnis tersendiri yang sudah cukup dikenal luas. Mekanisme bisnis ini memberikan kesempatan kepada seluruh manusia di muka bumi untuk sama-sama berhasil dalam bisnis di dunia maya. CV. Tirta Lestari merupakan perusahaan yang bergerak dalam bisnis penjualan air minum is ulang. Bisnis yang dilakukan selama ini masih belum memanfaatkan mekanisme penjualan online yang berakibat banyaknya biaya yang dikeluarkan dalam melakukan promosi penjualan kepada konsumen. Tujuan penelitian ini adalah merancang dan membangun aplikasi penjualan air minum isi ulang berbasis mobile yang terhubung langsung ke website secara lengkap dengan informasi air minum beserta harga dan cara pembeliannya sehingga dapat mempermudah konsumen mencari produk air minum isi ulang dan membantu perusahaan dalam melakukan penjualan air minum isi ulang. Metode penelitian yang digunakan dalam penelitian ini adalah metode pengembangan waterfall yang meliputi komunikasi perencanaan, pemodelan, konstruksi, dan deployment. Pemodelan sistem dibuat dengan menggunakan pemodelan UML. Hasil yang dicapai berupa penerapan suatu aplikasi untuk smartphone android yang dapat mencari produk air minum, serta memberikan informasi-informasi yang dibutuhkan untuk pemesanan produk air minum isi ulang. Kata Kunci— Penjualan Online, Android, UML, Waterfall, Air Minum.

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A, de Nazelle, Orjuela J, Avila-Palencia I, Dons E, Int Panis L, Jerrett M, Laeremans M, and Nieuwenhuijsen M. "Opportunities for mobile-phone based air pollution exposure assessment." Environmental Epidemiology 3 (October 2019): 91–92. http://dx.doi.org/10.1097/01.ee9.0000606688.55382.0a.

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Carnie, Steven L., Lorena Del Castillo, and Roger G. Horn. "Mobile Surface Charge Can Immobilize the Air/Water Interface." Langmuir 35, no. 48 (July 16, 2019): 16043–52. http://dx.doi.org/10.1021/acs.langmuir.9b01691.

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Fujita, Eric M., David E. Campbell, W. Patrick Arnott, Ted Johnson, and Will Ollison. "Concentrations of mobile source air pollutants in urban microenvironments." Journal of the Air & Waste Management Association 64, no. 7 (February 18, 2014): 743–58. http://dx.doi.org/10.1080/10962247.2013.872708.

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Barrado, Cristina, Roc Messeguer, Juan Lopez, Enric Pastor, Eduard Santamaria, and Pablo Royo. "Wildfire monitoring using a mixed air-ground mobile network." IEEE Pervasive Computing 9, no. 4 (2010): 24–32. http://dx.doi.org/10.1109/mprv.2010.54.

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Katulski, Ryszard J., Jacek Namieśnik, Jarosław Sadowski, Jacek Stefański, Krystyna Szymańska, and Waldemar Wardencki. "Mobile system for on-road measurements of air pollutants." Review of Scientific Instruments 81, no. 4 (April 2010): 045104. http://dx.doi.org/10.1063/1.3356076.

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Ma, Rui, Ning Liu, Xiangxiang Xu, Yue Wang, Hae Young Noh, Pei Zhang, and Lin Zhang. "Fine-Grained Air Pollution Inference with Mobile Sensing Systems." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, no. 2 (June 15, 2020): 1–21. http://dx.doi.org/10.1145/3397322.

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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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