Quantifying Air Pollution Vulnerability and its Distributional Consequences

This paper estimates Vulnerability Index of air pollution in Delhi taking into account exposure, susceptibility and coping capacity of households. A general health production function model and a vulnerability assessment framework are used for this purpose. Data was collected through a survey of sample households located in close vicinity to 10 air pollution monitoring stations in Delhi. The estimated vulnerability index is used to show the effect of household exposure to air pollution. The vulnerability index takes into consideration sample households’ socio-economic status, demographic profile and other characteristics. Result showed that households of lower socio-economic status were the most vulnerable to air pollution and its consequences. The study also quantifies the economic benefits to Delhi households from reduction in air pollution to the standard safety limits of PM10 (100 µg/m3). Estimates show that the total annual economic (health) benefits for a typical household is Rs. 33,978 and for the whole population of Delhi is Rs. 52.4 billion. The study also found that a household of a lower socio-economic status could save much more out of their annual income (4.96 per cent) as compared to a household of a higher socio-economic status (1.97 per cent) from reduced air pollution.

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A COMPARATIVE ANALYSIS AND REVIEW OF VARIOUS AIR POLLUTION MONITORING SCHEMES

i-manager’s Journal on Embedded Systems ◽

10.26634/jes.5.1.8249 ◽

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

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

Data in Brief ◽

10.1016/j.dib.2021.107127 ◽

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

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Research on air pollution system based on neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189464 ◽

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.

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IoT Enabled an Open Framework for Air Pollution Monitoring System

2020 5th International Conference on Information Technology Research (ICITR) ◽

10.1109/icitr51448.2020.9310838 ◽

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

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

Atmosphere ◽

10.3390/atmos12030290 ◽

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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