scholarly journals EVALUASI TINGKAT POLUTAN DARI KEGIATAN PENGELOLAAN SAMPAH DI RUMAH KOMPOS SRIKANA DAN KEPUTRAN KOTA SURABAYA

2013 ◽  
Vol 11 (2) ◽  
Author(s):  
Irma Nur Asifa ◽  
Imam Thohari ◽  
Waluyo Jati
Keyword(s):  
Air Pollution ◽  
Descriptive Study ◽  
Quality Standard ◽  
Residential Area ◽  
Organic Wastes ◽  
Pollution Monitoring ◽  
Monitoring Station ◽  
Air Pollution Monitoring ◽  
Pollutant Level

Garbage materials are able to contaminate the environment in three ways:physically, chemically, and biologically. The large composition of garbage require managementand processing to reduce garbage load entering the TPA. In relation to this, Dinas Kebersihandan Pertamanan Kota 5urabaya has initiated composting of organic wastes partly to reducetheir volume. This study was aimed at measuring pollutants in compost materials especially interms of CO, 502, and H25 parameters. This activity was carried out in Composting Houses of5rikana and Keputran 5urabaya.This descriptive study was performed by measuring CO, SOb and H25 andlaboratory examination. The population under study was composting houses (18 sites) and thesample size was 2 composting houses.Results showed that pollutant level of CO, 502 and H25 in the air was not exceedingthe quality standard being implemented in East Java. The CO level in 5rikana CompostingHouse was 4.21 «(JgjNm3) and in residential area was 3.37 «(JgjNm3). 502 level in compostinghouse was 12.04 «(Jg/Nm3), while outside of house of 16.71 «(JgjNm3) and in residential areawas 4.16 «(Jg/Nm3). The H25 level in composting house was 9.13 «(Jg/Nm3), outside the housewas 6.54«(JgjNm3) and in residential area was 3.22 «(JgjNm3). The CO level in KeputranComposting House was 9.66 «(Jg/Nm3), in outside of the house was 15,74 (uq/Nrn") and in thesurrounding market was 4.18 «(Jg/Nm3). The 502 level in composting house was 6,88«(Jg/Nm3), outside of compost house 19.38«(Jg/Nm3), and in the surrounding market was 2,76«(Jg/Nm3). H2S level in composting house was 16,14 «(Jg/Nm3), in outside of the house was4,49 «(JgjNm3) and in the surrounding market was 1.97 «(Jg/Nm3). Temperature, humidity, andwind velocity lase have some influence as well but not too significant to increase pollutantlevel.It is suggested to Dinas Kebersihan dan Pertamanan Kota Surabaya to addcomposting house in its list of air pollution monitoring station.

scholarly journals Application of Internet of Things (IoT) on air pollution monitoring database system

2019 ◽  
Vol 125 ◽  
pp. 25005
Author(s):  
Sudarsono ◽  
Muhammad Andang Novianta ◽  
Cyrilla Indri Parwati
Keyword(s):  
Air Pollution ◽  
Internet Of Things ◽  
Structural Information ◽  
Line Graph ◽  
Pollution Index ◽  
Database System ◽  
Pollution Monitoring ◽  
Monitoring Station ◽  
Pollution Level ◽  
Air Pollution Monitoring

In the present work, a database system of air pollution monitoring is developed using Internet of Things (IoT) technology. The system aims to give structural information and trace of air pollution level at particular monitoring station. The particular monitoring location (node) is connected to IoT/M2M server via GSM network using GPRS feature and display on IoT/M2M application in web form. The database on IoT/M2M contains name, description, and location of the monitoring station, Pollution index and the time when the data are taken. On IoT/M2M, the data are displayed either in a color bar graph or a line graph. The color indicated the index value of the pollution. The data can be accessed via internet on isfuonline.info. The system is tested at laboratory environment to detect CO, SO2, NO2, O3, and PM. The test result shows that the system is worked well. Time required to transfer the monitoring data to the IoT server is about 15 minutes. Meanwhile, response time of the system is 30 minutes.

Air Pollution Monitoring Using WSN in Cement Factory

2018 ◽  
Vol 15 (2) ◽  
pp. 616-620 ◽  
Author(s):  
G. Anitha ◽  
V. Vijayakumari ◽  
S. Malathy ◽  
S. Jaipriya
Keyword(s):  
Air Pollution ◽  
Human Population ◽  
Industrial Revolution ◽  
Sensor Nodes ◽  
Pollution Monitoring ◽  
Wireless Sensor ◽  
Air Pollution Monitoring ◽  
The World ◽  
Pollutant Level ◽  
Cement Factories

Industrial revolution has started to rule the world in all aspects. As a result of this, pollutant level of contagious gas in the atmosphere is increasing at an alarming rate. The pollutants in the atmosphere create imbalance in ecosystem which in turn affects the health of human population. Although there existmany methodologies to check the pollutant level in atmosphere, it still remains a challenge for certain cement factories and chemical industries to keep a check on it. Such imbalances can be controlled by using appropriate air pollution monitoring system. OPSIS, Uras26, Magnos27 and CODEL are the methods which exist in cement factories to check the pollutant level during the emission from chimney only. Wireless Sensor Network is a versatile technology that can sense, monitor, measure, and gather information. The decision can be made from the collected information. This paper proposes how sensor nodes are deployed in cement factories at various stages of manufacturing process, how the pollutant is measured and conveyed to authority through a communication medium.

A COMPARATIVE ANALYSIS AND REVIEW OF VARIOUS AIR POLLUTION MONITORING SCHEMES

2016 ◽  
Vol 5 (1) ◽  
pp. 30
Author(s):  
HASAN MOHD. TAHSEENUL ◽  
CHOURASIA VIJAY S. ◽  
ASUTKAR SANJAY M. ◽  
◽  
◽  
...  
Keyword(s):  
Air Pollution ◽  
Comparative Analysis ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring

scholarly journals H2020 project CAPTOR dataset: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network.

Data in Brief ◽  
2021 ◽  
pp. 107127
Author(s):  
Jose M. Barcelo-Ordinas ◽  
Pau Ferrer-Cid ◽  
Jorge Garcia-Vidal ◽  
Mar Viana ◽  
Ana Ripoll
Keyword(s):  
Air Pollution ◽  
Low Cost ◽  
Monitoring Network ◽  
Pollution Monitoring ◽  
Raw Data ◽  
Air Pollution Monitoring

Research on air pollution system based on neural network

2020 ◽  
pp. 1-11
Author(s):  
Zhiqi Jiang ◽  
Xidong Wang
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Image Features ◽  
Air Pollutant ◽  
Pollution Monitoring ◽  
System Model ◽  
Process Data ◽  
Deep Convolutional Neural Networks ◽  
Air Pollution Monitoring ◽  
Neural Network Algorithm

This paper conducts in-depth research and analysis on the commonly used models in the simulation process of air pollutant diffusion. Combining with the actual needs of air pollution, this paper builds an air pollution system model based on neural network based on neural network algorithm, and proposes an image classification method based on deep learning and Gaussian aggregation coding. Moreover, this paper proposes a Gaussian aggregation coding layer to encode image features extracted by deep convolutional neural networks. Learn a fixed-size dictionary to represent the features of the image for final classification. In addition, this paper constructs an air pollution monitoring system based on the actual needs of the air system. Finally, this article designs a controlled experiment to verify the model proposed in this article, uses mathematical statistics to process data, and scientifically analyze the statistical results. The research results show that the model constructed in this paper has a certain effect.

IoT Enabled an Open Framework for Air Pollution Monitoring System

2020 ◽  
Author(s):  
B.H. Sudantha ◽  
Manchanayaka MALSK ◽  
Nilantha Premakumara ◽  
Chamani Shiranthika ◽  
C. Premachandra ◽  
...  
Keyword(s):  
Air Pollution ◽  
Monitoring System ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Open Framework

scholarly journals COVID-19 and Air Pollution: Measuring Pandemic Impact to Air Quality in Five European Countries

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 290
Author(s):  
Akvilė Feiferytė Skirienė ◽  
Žaneta Stasiškienė
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Social Services ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Rapid Spread ◽  
Air Quality Improvement ◽  
Environment Agency ◽  
Pollution Emissions ◽  
Air Pollution Emissions

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.

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