• Advances in environmental research
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• v.1 no.4
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• pp.277-288
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• 2012

This study investigated exposure to air pollutants in rooms in low-income houses at Shomolu (R1), Mafoluku (R2) and Mushin (R3) in Lagos state. The concentrations of most measured exceeded limits of Illinois Department of Public Health (IDPH) for indoor air quality. Air quality index (AQI) in rooms studied was unhealthy for sensitive people in terms of CO, unhealthy in terms of $SO_2$ and very unhealthy in terms of $NO_2$ while moderate air quality was obtained in terms of $PM_{10}$ in most rooms. High concentrations of carbontetrachloride, formaldehyde and xylene measured could have been responsible for some of the health complaints of the occupants. Factor analysis shows that cooking with kerosene, use of gasoline generator and insecticide were the major contributors to indoor air pollution in these rooms. Therefore, there is need to urgently tackle poverty as all affected by these pollutants were poor who live in substandard houses without kitchens.

• Asian Journal of Atmospheric Environment
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• v.11 no.4
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• pp.254-269
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• 2017

The work describes an assessment of the major air pollutants, $NO_2$, $SO_2$, CO, $O_3$, $NH_3$, coarse and fine particulate matter ($PM_{10}$, $PM_{2.5}$) in ambient air in and around a 3 million tonne Petroleum Refinery and the possible impacts on a pristine area marked by the presence of the world-famous Kajiranga National Park, a world heritage site and habitat for the most number of one-horned rhinos in the world. The Refinery is at an aerial distance of 20-25 km from the wildlife habitat. The assessment is based on regular monitoring at four stations around the Refinery and one station near the National Park. Heavy rains during June to August influence the pollutant concentrations while at other times of the year, large traffic volume adds to the pollutant concentrations that peak during November to March, the dry months of the year. Correlation analysis by scatter technique is utilised to obtain the enhancement ratios to predict the variations in the concentrations of the pollutants and their spatial distribution. Computation of air quality index (AQI) indicates that the coarse and the fine particulates in the ambient air could be a major hazard to wildlife in the area.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.607-611
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• 2015

Background: Chronic respiratory disease is an important factor for development of lung cancer. To explore the influence of hazy weather on respiratory diseases and its variation the present study was conducted. Materials and Methods: Data from air pollution surveillance from January to October 2014 and case records of visiting patients in the $263^{th}$ Hospital of Chinese PLA in the corresponding period were collected to analyze the relevance between different degrees of air pollution (hazy weather) and the number of visiting patients in Department of Respiratory Disease. Results: Air quality index (AQI) of hazy weather had significantly positive association with particulate matter 2.5 ($PM_{2.5}$) and the number of patients with 5 kinds of respiratory diseases i and different pollutants had distinct influences on various respiratory diseases. Conclusions: The degree of air pollution in Beijing City is in close association with the number of patients with respiratory diseases, in which $PM_{2.5}$ and $SO_2$ are in more significant influences on all respiratory diseases. This could have essential implications for lung cancer development in China.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.63-69
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• 2020

The rapid development in modern industrialization pollutant the water and atmospheric air across the globe that have a major impact on the human and livings health. In worldwide, every country government increasing the importance to improve the outdoor air pollution monitoring and control to provide quality of life and prevent the citizens and livings life from hazard disease. We proposed the environmental dust level detection method for outdoor facilities using sensor fusion technology to measure precise micro-dust level and monitor in realtime. In this proposed approach use the camera sensor and commercial dust level sensor data to predict the micro-dust level with data fusion method. The camera sensor based dust level detection uses the optical flow based machine learning method to detect the dust level and then fused with commercial dust level sensor data to predict the precise micro-dust level of the outdoor facilities and send the dust level informations to the outdoor air pollution monitoring system. The proposed method implemented on raspberry pi based open-source hardware with Internet-of-Things (IoT) framework and evaluated the performance of the system in realtime. The experimental results confirm that the proposed micro-dust level detection is precise and reliable in sensing the air dust and pollution, which helps to indicate the change in the air pollution more precisely than the commercial sensor based method in some extent.