Incorporating gridded concentration data in air pollution back trajectories analysis for source identification

2021 ◽  
Vol 263 ◽  
pp. 105820
Author(s):  
Otávio Nunes dos Santos ◽  
Leonardo Hoinaski
Keyword(s):  
Air Pollution ◽  
Source Identification ◽  
Concentration Data ◽  
Back Trajectories

Rock Varnish: A Passive Forensic Tool for Monitoring Recent Air Pollution and Source Identification

2011 ◽  
Vol 175 (1) ◽  
pp. 351-359 ◽  
Author(s):  
Kenneth Hoar ◽  
Piotr Nowinski ◽  
Vernon Hodge ◽  
James Cizdziel
Keyword(s):  
Air Pollution ◽  
Source Identification ◽  
Rock Varnish

scholarly journals Impact of household air pollution on human health: source identification and systematic management approach

2019 ◽  
Vol 1 (5) ◽  
Author(s):  
Fahad Ahmed ◽  
Sahadat Hossain ◽  
Shakhaoat Hossain ◽  
Abu Naieum Muhammad Fakhruddin ◽  
Abu Tareq Mohammad Abdullah ◽  
...  
Keyword(s):  
Air Pollution ◽  
Human Health ◽  
Source Identification ◽  
Household Air Pollution ◽  
Management Approach

scholarly journals Fine Particulate Air Pollution, Public Service, and Under-Five Mortality: A Cross-Country Empirical Study

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 271
Author(s):  
Siming Liu ◽  
Qing Wei ◽  
Pierre Failler ◽  
Hong Lan
Keyword(s):  
Air Pollution ◽  
Public Education ◽  
Public Service ◽  
Threshold Model ◽  
Threshold Value ◽  
Developed Countries ◽  
Least Developed Countries ◽  
Concentration Data ◽  
Under Five ◽  
Fine Particulate

The impacts of fine particulate matter (PM2.5) air pollution on health outcomes, especially those of children, have attracted worldwide attention. Based on the PM2.5 concentration data of 94 countries, including the least developed countries estimated by satellite observations in nearly 20 years, this paper investigated the impacts of PM2.5 pollution on under-five mortality rate (U5MR) and analyzed the role of public service in moderating the PM2.5-mortality relationship. Results indicated that PM2.5 pollution had significantly positive influence on U5MR globally. However, the effects of fine particulate pollution on child mortality were heterogeneous in terms of their significance and degrees in countries with different levels of development. A further test based on panel threshold model revealed that public service, measured by public education spending and sanitation service, played a positive moderating role in the PM2.5-mortality relationship. Specifically, when the ratio of public education expenditure in GDP of a country exceeded the first threshold value 3.39% and the second threshold value 5.47%, the magnitude of the impacts of PM2.5 pollution on U5MR significantly decreased accordingly. When the percentage of population with access to improved sanitation facilities in a country was over 41.3%, the health damaging effects were reduced by more than half. This paper fills the current gap of PM2.5 research in least developed countries and provides key policy recommendations.

scholarly journals A Novel Air Quality Early-Warning System Based on Artificial Intelligence

2019 ◽  
Vol 16 (19) ◽  
pp. 3505
Author(s):  
Mo ◽  
Zhang ◽  
Li ◽  
Qu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Model ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Air Pollutant ◽  
Concentration Data ◽  
Whale Optimization ◽  
The World

The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

scholarly journals Examining the Impact of Inter-provincial Migration on Environmental Health in China

2020 ◽  
Author(s):  
Yu-Ou Yang ◽  
Juan-Juan Hou ◽  
Xin-Yu Chen ◽  
Shi-Wei Yu ◽  
Jiu-Tian zhang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Economic Value ◽  
National Level ◽  
Health Benefits ◽  
Economic Benefits ◽  
Concentration Data ◽  
Exposure Scenario ◽  
Population Sampling ◽  
Pm2.5 Exposure ◽  
The Impact

Abstract Background China has a large volume of inter-provincial migrants, accounting for more than 11% of the total population. The economic benefits of inter-provincial migration have been well studied, whereas the health impacts related to environmental factors are generally ignored. Methods In this study, we use 1% national population sampling survey data from 2015 and daily PM2.5 (particles ≤ 2.5 µm in aerodynamic diameter) concentration data from 360 cities to analyze the health benefits associated with air pollution due to inter-provincial migration. The exposure-response function was used to estimate the economic value of these health benefits via the adjusted-human-capital and cost-of-illness methods. Results Considering a full-exposure scenario, inter-provincial migration resulted in a reduction in the PM2.5 exposure concentration of 3.94 µg/m3 in 2015, corresponding to a reduction of 6114 premature deaths, 233.4 thousand hospitalization cases, and 1.5 million asthma attacks. The corresponding economic value of these health benefits was about 10.44 billion yuan (0.02% of the national GDP in 2015). A protection scenario, assuming that the migrants protected themselves from air pollution, showed very similar results to the full-exposure scenario (PM2.5 exposure reduced by 3.60 µg/m3); hence, personal protection does not reduce significantly the health risks of air pollution. Conclusions At the national level, the labor force obtains both economic and health benefits. However, a high number of migrants flow out of the central region of China result in a labor deficiency and social imbalance. Migration to large cities provides economic benefits at the expense of health. Environmental migration becomes an increasingly important motivation for inter-provincial migration, which places new pressure on policy makers to consider social welfare and environmental protection in the provinces.

scholarly journals Geographically distributed longitudinal nitrogen dioxide and other air pollution sensor measurements in the Avon Longitudinal Study of Parents and Children cohort catchment area

2019 ◽  
Vol 4 ◽  
pp. 162
Author(s):  
Leigh Johnson ◽  
Richard Thomas ◽  
Joshua Vande Hey ◽  
Anna Hansell ◽  
John Gulliver ◽  
...  
Keyword(s):  
Air Pollution ◽  
Longitudinal Study ◽  
Nitrogen Dioxide ◽  
Catchment Area ◽  
Pollution Monitoring ◽  
Sensor Data ◽  
Concentration Data ◽  
Local Authorities ◽  
Parents And Children ◽  
Avon Longitudinal Study

Longitudinal cohort studies provide unique opportunities to investigate the health impact of air pollution. We aimed to enhance the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort study through the systematic collection of routinely monitored air pollution data collected by local authorities and the Department for Environment, Food and Rural Affairs (DEFRA) using a range of sensor technologies. These sensor data are in themselves not well suited for population epidemiology, rather these data are primarily used for validating and calibrating modelled air pollution concentration data over study areas. In this data note we describe the sources of routine air pollution monitoring data and detail data of pollutants including nitrogen dioxide, nitric oxide, nitrogen oxides, particulate matter, benzene and ozone collated from the local authorities that overlap the ALSPAC catchment area (Bristol, North Somerset, South Gloucestershire and part of Bath and North East Somerset).

scholarly journals Evaluation of multivariate time series clustering for imputation of air pollution data

2021 ◽  
Vol 10 (2) ◽  
pp. 265-285
Author(s):  
Wedad Alahamade ◽  
Iain Lake ◽  
Claire E. Reeves ◽  
Beatriz De La Iglesia
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Missing Data ◽  
Multivariate Time Series ◽  
Real Data ◽  
Economic Benefits ◽  
Air Pollutant ◽  
Monitoring Site ◽  
Concentration Data

Abstract. Air pollution is one of the world's leading risk factors for death, with 6.5 million deaths per year worldwide attributed to air-pollution-related diseases. Understanding the behaviour of certain pollutants through air quality assessment can produce improvements in air quality management that will translate to health and economic benefits. However, problems with missing data and uncertainty hinder that assessment. We are motivated by the need to enhance the air pollution data available. We focus on the problem of missing air pollutant concentration data either because a limited set of pollutants is measured at a monitoring site or because an instrument is not operating, so a particular pollutant is not measured for a period of time. In our previous work, we have proposed models which can impute a whole missing time series to enhance air quality monitoring. Some of these models are based on a multivariate time series (MVTS) clustering method. Here, we apply our method to real data and show how different graphical and statistical model evaluation functions enable us to select the imputation model that produces the most plausible imputations. We then compare the Daily Air Quality Index (DAQI) values obtained after imputation with observed values incorporating missing data. Our results show that using an ensemble model that aggregates the spatial similarity obtained by the geographical correlation between monitoring stations and the fused temporal similarity between pollutant concentrations produces very good imputation results. Furthermore, the analysis enhances understanding of the different pollutant behaviours and of the characteristics of different stations according to their environmental type.

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