Geolocation based air pollution mobile monitoring system

Air pollution has a negative impact on the environment and human health. Meanwhile, the number of conventional air quality monitoring stations is minimal due to high procurement and operational costs. This study proposes a nitrogen dioxide (NO2) pollutant measurement system using the metal oxide sensor (MOX) sensor and cellular network for data transmission in the measurement area. A calibration curve is used to measure NO2 levels based on the sensor's internal resistance changes. Measurement data of NO2 concentration, air temperature, relative humidity, and geospatial information are compiled and sent via global positioning system (GSM), general packet radio service (GPRS) radio communication with transmission intervals of every minute. The database server processes the data and displays it on the web application. System testing results at the Tugu Kujang Bogor at 15:38:00-16:38:00 September 23, 2021, showed that the concentration of NO2 ranged from 0.16-0.52ppm with an average of 270 ppb with an AQI of 133 in the unhealthy category for the sensitive group. The measured NO2 levels are outside the range of the NO2 concentration database in the industrial areas of Bogor and Jakarta for the 2016-2020 period. Therefore, this system provides an excellent opportunity to obtain real-time measurement data in the field.

Section: Index and Concentration Level Of No2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low-cost nitrogen dioxide monitoring station based on metal oxide sensor and cellular network

Purbakawaca¹,

Yuwono²,

Alatas³

et al. 2022

“…The algorithm used the raycasting technique. It resulted in 94% of accuracy, with 50 random points featured in the testing stage [4]. In this current research, the algorithm methods explored include crossing number and winding number, paired with the geofencing technique.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They operate by providing some information of a location to a mobile device and later following the instructions given based on the user's location. In addition to using geolocation, some mobile applications add geofencing to their features [4].…”

Section: Introductionmentioning

confidence: 99%

Smart-geofencing for system of reporting inadequate regional infrastructure using crossing and winding number

Basid

Nugroho

2022

Infrastructure is a major factor in increasing regional productivity and become the center of economic growth. problems arise when infrastructure improvements are only centered on downtown areas. One of the ways to develop regional infrastructure is with media reporting inadequate infrastructure. The use of smartphone technology and geofencing techniques are considered to be helpful in reporting media. Increasingly sophisticated built-in features in smartphones are undeniable proof of the technology’s highly impressive advancement. Mobile applications utilizing location-based services (LBS) assisted by a global positioning systems (GPS) sensor are rapidly becoming popular. One of its most-favorited features is geofencing. The volunteered-geographic-information (VGI)-based reporting system for regional infrastructure damage uses this feature to obtain the data input directly from the users. In this current research, the author uses two methods in geofencing, including crossing number (CN) and winding number (WN). Several previous studies suggest that the combination of these methods might result in faster computation, compared to the raycasting method. By using winding and crossing number methods, 98% of increased accuracy can be obtained, while a prior method could only obtain 96%. An improvement in the system’s speed is also expected.

“…Another successful application was presented in [31], which introduced an internet of things (IoT)-based air pollution monitoring system utilizing the Malaysian air pollution index. Furthermore, Talib and Jasim [32] proposed a mobile and cost-effective air pollution monitoring system using Arduino microcontrollers and gas sensors. Tan et al [33], machine learning methods and a time series regression model were utilized to predict PM10 concentrations in a dusty region of the Tibetan plateau.…”

Section: Introductionmentioning

confidence: 99%

Mathematical and computer modeling of atmospheric air pollutants transformation with input data refinement

Temirbekov,

Malgazhdarov,

Tamabay

et al. 2023

This article addresses the critical issue of harmful impurity dispersion in industrial facility atmospheres, considering point sources and factoring in photochemical changes. Conjugate equations are employed to assimilate pollutant data into the transfer equations' right side. Boundary conditions derive from global models like weather research and forecasting (WRF) and system for integrated modeling of atmospheric composition (SILAM), customized for the unique characteristics of an industrial city's pollutants. To encompass anthropogenic heat sources and surface heterogeneity, the model incorporates differential schemes for the atmospheric boundary layer, transport equations, and impurity transformation equations. Parameters for photochemical transformations, varying with weather and time of day, are derived from Ust-Kamenogorsk. A cloud-based geoinformation system (GIS) is developed for monitoring and forecasting air pollution. It assimilates data sources and accounts for photochemical transformations, enabling visualization of diverse weather and environmental scenarios. The article presents numerical modeling results of impurity spread and transformation influenced by mesometeorological processes, topography, and water resources within a specific city.