scholarly journals Vehicular Air Purifier – IoT Enabled System with Artificial Intelligence to Prevent Air Pollution

2021 ◽  
Vol 3 (Special Issue 7S) ◽  
pp. 74-78
Author(s):  
Dhanalakshmi M. ◽  
Radha V.
Keyword(s):  
Artificial Intelligence ◽  
Air Pollution ◽  
Air Purifier

Air purifier devices based on adsorbents produced from valorization of different environmental hazardous materials for ammonia gas control

RSC Advances ◽  
2016 ◽  
Vol 6 (62) ◽  
pp. 57284-57292 ◽  
Author(s):  
Elham F. Mohamed ◽  
Sohair A. Sayed Ahmed ◽  
Nasser M. Abdel-Latif ◽  
Asmaa EL-Mekawy
Keyword(s):  
Air Pollution ◽  
Pollution Control ◽  
Hazardous Materials ◽  
Waste Recycling ◽  
Environmental Load ◽  
Air Pollution Control ◽  
Ammonia Gas ◽  
Air Purifier ◽  
Gas Control

The present study aimed to find a beneficial solution for waste recycling and disposal by converting it from an environmental load to products useful for air pollution control.

scholarly journals Research on Quantitative Remote Sensing Monitoring Algorithm of Air Pollution Based on Artificial Intelligence

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Yun Liu ◽  
Yuqin Jing ◽  
Yinan Lu
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Air Pollution ◽  
Transmission Model ◽  
Atmospheric Radiation ◽  
Remote Sensing Monitoring ◽  
Quantitative Remote Sensing ◽  
Monitoring Accuracy ◽  
Radiation Transmission ◽  
Monitoring Algorithm

When the current algorithm is used for quantitative remote sensing monitoring of air pollution, it takes a long time to monitor the air pollution data, and the obtained range coefficient is small. The error between the monitoring result and the actual result is large, and the monitoring efficiency is low, the monitoring range is small, and the monitoring accuracy rate is low. An artificial intelligence-based quantitative monitoring algorithm for air pollution is proposed. The basic theory of atmospheric radiation transmission is analyzed by atmospheric radiation transfer equation, Beer–Bouguer–Lambert law, parallel plane atmospheric radiation theory, atmospheric radiation transmission model, and electromagnetic radiation transmission model. Quantitative remote sensing monitoring of air pollution provides relevant information. The simultaneous equations are constructed on the basis of multiband satellite remote sensing data through pixel information, and the aerosol turbidity of the atmosphere is calculated by the equation decomposition of the pixel information. The quantitative remote sensing monitoring of air pollution is realized according to the calculated aerosol turbidity. The experimental results show that the proposed algorithm has high monitoring efficiency, wide monitoring range, and high monitoring accuracy.

Air pollution and pulmonary health: Effects of short-term indoor air quality intervention with portable air purifier

2016 ◽  
Vol 2016 (1) ◽  
Author(s):  
Ming Kei Chung ◽  
Xiaoxing Cui* ◽  
Lin Fang ◽  
Jianbang Xiang ◽  
Feng Li ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Health Effects ◽  
Indoor Air ◽  
Short Term ◽  
Air Purifier

scholarly journals Indoor Air Quality, Information, and Socioeconomic Status: Evidence from Delhi

2021 ◽  
Vol 111 ◽  
pp. 420-424
Author(s):  
Michael Greenstone ◽  
Kenneth Lee ◽  
Harshil Sahai
Keyword(s):  
Air Pollution ◽  
Socioeconomic Status ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Indoor Air Pollution ◽  
World Health ◽  
Quality Information ◽  
Air Purifier ◽  
Health Organization

In Delhi, one of the world's most polluted cities, there is relatively little information on indoor air pollution and how it varies by socioeconomic status (SES). Using indoor air quality monitors (IAQMs), we find that winter levels of household air pollution exceed World Health Organization standards by more than 20 times in both high-and low-SES households. We then evaluate a field experiment that randomly assigned monthlong IAQM user trials across medium-and high-SES households but suffered from significant survey non-response. Among respondents, IAQMs did not affect take-up of subsidized air purifier rentals or other defensive behavior.

scholarly journals IoT Based Automatic Air Pollution Monitoring and Purification System

2021 ◽  
Vol 9 (9) ◽  
pp. 1378-1383
Author(s):  
Manoj Gurung
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Indoor Air Pollution ◽  
Premature Mortality ◽  
Cost Effective ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Air Purifier

Abstract: Degradation of air quality, like climate change and global warming, has become an all-encompassing existential hazard to humanity and natural life. Exposure to severely polluted air on a regular basis causes pulmonary disorders and contributes to severe allergies and asthma. According to studies, more than 10 million people die each year as a result of irregularities produced directly or indirectly by air pollution. The work of Lelieveld et al. [1] sheds light on the gravity of the problem. It is estimated that by 2050, the worldwide premature mortality from air pollution will exceed 6.6 million fatalities per year (358000 from ozone, the rest from PM 2.5) [1]. As a result, we decided to focus our study on improving indoor air quality. Despite the fact that there are numerous indoor air purifiers on the market, their cost belies their effectiveness, and the effective ones are far too expensive for working-class people to afford [2]. In order to address this issue, we created an automated Internet of Things (IoT) based air filtration system that uses an automated air purifier which is triggered when air quality falls below WHO criteria. Our initiative intends to improve indoor air quality by utilizing the most cost-effective and efficient modules available. Keywords: Indoor Air Pollution, Air Purifier, IAQ, Sharp Dust Sensor GP2Y1010AU0F, IoT, Particulate Matter (PM), HEPA Filter

scholarly journals Air Pollution and Cardiorespiratory Hospitalization, Modeling and Analysis Using Artificial Intelligence Techniques

2021 ◽  
Author(s):  
Raja Sher Afgun Usmani ◽  
Thulasyammal Ramiah Pillai ◽  
Ibrahim Abaker Targio Hashem ◽  
Mohsen Marjani ◽  
Rafiza Shaharudin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Air Pollution ◽  
Short Term Memory ◽  
Short Term ◽  
Human Welfare ◽  
Term Memory ◽  
Modeling And Analysis ◽  
Klang Valley ◽  
Long Short Term Memory ◽  
Better Than

Abstract Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the Artificial Intelligence (AI) techniques. We propose the Enhanced Long Short-Term Memory (ELSTM) model and provide a comparison with other AI techniques, i.e., Long Short-Term Memory (LSTM), Deep Learning (DL), and Vector Autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.

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