scholarly journals A Spatial Feature Engineering Algorithm for Creating Air Pollution Health Datasets

2020 ◽  
Author(s):  
Raja Sher Afgun Usmani ◽  
Thulasyammal Ramiah Pillai ◽  
Ibrahim Abaker Targio Hashem ◽  
NZ Jhanjhi ◽  
Anum Saeed
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Areas ◽  
Similarity Index ◽  
Health Data ◽  
Feature Engineering ◽  
Spatial Problem ◽  
Coverage Area ◽  
Mortality And Morbidity ◽  
Spatial Feature

Dear Editor: This manuscript is already online in techrxiv (https://doi.org/10.36227/techrxiv.12376427.v1), and the co-authors are not correctly associated with the published preprint so we are submitting this again by associating the co-authors. We have also improved the similarity report, the similarity index is 11% of this submission. Thank you.<br><br>Abstract: Air pollution is one of the significant causes of mortality and morbidity every year. In recent years, many researchers have focused their attention on the associations of air pollution and health. These studies used two types of data in their studies, i.e., air pollution data and health data. Feature engineering is used to create and optimize air quality and health features. In order to merge these datasets residential address, community/county/block/city and hospital/school address are used. Using residence address or any location becomes a spatial problem when the Air Quality Monitoring (AQM) stations are concentrated in urban areas within the regions and an overlap in the AQM stations in urban areas coverage area, which raises the question that how to associate the patients with the relevant AQM station. Also, in most of the studies the distance of patients to the AQM stations is also not taken into account. In this study, we propose a four-part spatial feature engineering algorithm to find the coordinates for health data, calculate distances with AQM stations and associate health records to the nearest AQM station. Hence, removing the limitations of current air pollution health datasets. The proposed algorithm is applied as a case study in Klang Valley, Malaysia. The results show that the proposed algorithm can generate air pollution health dataset efficiently and the algorithm also provides the radius facility to exclude the patients who are situated far away from the stations.

scholarly journals A Spatial Feature Engineering Algorithm for Creating Air Pollution Health Datasets

2020 ◽  
Author(s):  
Raja Sher Afgun Usmani ◽  
Thulasyammal Ramiah Pillai ◽  
Ibrahim Abaker Targio Hashem ◽  
NZ Jhanjhi ◽  
Anum Saeed
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Areas ◽  
Similarity Index ◽  
Health Data ◽  
Feature Engineering ◽  
Spatial Problem ◽  
Coverage Area ◽  
Mortality And Morbidity ◽  
Spatial Feature

Dear Editor: This manuscript is already online in techrxiv (https://doi.org/10.36227/techrxiv.12376427.v1), and the co-authors are not correctly associated with the published preprint so we are submitting this again by associating the co-authors. We have also improved the similarity report, the similarity index is 11% of this submission. Thank you.<br><br>Abstract: Air pollution is one of the significant causes of mortality and morbidity every year. In recent years, many researchers have focused their attention on the associations of air pollution and health. These studies used two types of data in their studies, i.e., air pollution data and health data. Feature engineering is used to create and optimize air quality and health features. In order to merge these datasets residential address, community/county/block/city and hospital/school address are used. Using residence address or any location becomes a spatial problem when the Air Quality Monitoring (AQM) stations are concentrated in urban areas within the regions and an overlap in the AQM stations in urban areas coverage area, which raises the question that how to associate the patients with the relevant AQM station. Also, in most of the studies the distance of patients to the AQM stations is also not taken into account. In this study, we propose a four-part spatial feature engineering algorithm to find the coordinates for health data, calculate distances with AQM stations and associate health records to the nearest AQM station. Hence, removing the limitations of current air pollution health datasets. The proposed algorithm is applied as a case study in Klang Valley, Malaysia. The results show that the proposed algorithm can generate air pollution health dataset efficiently and the algorithm also provides the radius facility to exclude the patients who are situated far away from the stations.

scholarly journals A Spatial Feature Engineering Algorithm for Creating Air Pollution Health Datasets

2020 ◽  
Author(s):  
Raja Sher Afgun Usmani
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Areas ◽  
Health Data ◽  
Feature Engineering ◽  
Spatial Problem ◽  
Coverage Area ◽  
Mortality And Morbidity ◽  
Spatial Feature ◽  
Health Features

Air pollution is one of the significant causes of mortality and morbidity every year. In recent years, many researchers have focused their attention on the associations of air pollution and health. These studies used two types of data in their studies, i.e., air pollution data and health data. Feature engineering is used to create and optimize air quality and health features. In order to merge these datasets residential address, community/county/block/city and hospital/school address are used. Using residence address or any location becomes a spatial problem when the Air Quality Monitoring (AQM) stations are concentrated in urban areas within the regions and an overlap in the AQM stations in urban areas coverage area, which raises the question that how to associate the patients with the relevant AQM station. Also, in most of the studies the distance of patients to the AQM stations is also not taken into account. In this study, we propose a four-part spatial feature engineering algorithm to find the coordinates for health data, calculate distances with AQM stations and associate health records to the nearest AQM station. Hence, removing the limitations of current air pollution health datasets. The proposed algorithm is applied as a case study in Klang Valley, Malaysia. The results show that the proposed algorithm can generate air pollution health dataset efficiently and the algorithm also provides the radius facility to exclude the patients who are situated far away from the stations.

Improving the Protection of Children Against Air Pollution Threats in Romania � The RokidAIR Project Approach and Future Perspectives

2017 ◽  
Vol 68 (4) ◽  
pp. 841-846
Author(s):  
Hai-Ying Liu ◽  
Daniel Dunea ◽  
Mihaela Oprea ◽  
Tom Savu ◽  
Stefania Iordache
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Health Effects ◽  
Urban Areas ◽  
Behavioural Change ◽  
Health Assessment ◽  
Warning System ◽  
Personal Exposure ◽  
Mobile Technologies ◽  
Quality Maps

This paper presents the approach used to develop the information chain required to reach the objectives of the EEA Grants� RokidAIR project in two Romanian cities i.e., Targoviste and Ploiesti. It describes the PM2.5 monitoring infrastructure and architecture to the web-based GIS platform, the early warning system and the decision support system, and finally, the linking of air pollution to health effects in children. In addition, it shows the analysis performance of the designed system to process the collected time series from various data sources using the benzene concentrations monitored in Ploiesti. Moreover, this paper suggests that biomarkers, mobile technologies, and Citizens� Observatories are potential perspectives to improve data coverage by the provision of near-real-time air quality maps, and provide personal exposure and health assessment results, enabling the citizens� engagement and behavioural change. This paper also addresses new fields in nature-based solutions to improve air quality, and studies on air pollution and its mental health effects in the urban areas of Romania.

scholarly journals Morpho-Physio-Biochemical Attributes of Roadside Trees as Potential Tools for Biomonitoring of Air Quality and Environmental Health in Urban Areas

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 236
Author(s):  
Ha Na You ◽  
Myeong Ja Kwak ◽  
Sun Mi Je ◽  
Jong Kyu Lee ◽  
Yea Ji Lim ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Health ◽  
Urban Areas ◽  
Biochemical Parameters ◽  
Lipid Peroxide ◽  
Platanus Occidentalis ◽  
Physiological Variables ◽  
Sources Of Pollution ◽  
Species Specific

Environmental pollution is an important issue in metropolitan areas, and roadside trees are directly affected by various sources of pollution to which they exhibit numerous responses. The aim of the present study was to identify morpho-physio-biochemical attributes of maidenhair tree (Ginkgo biloba L.) and American sycamore (Platanus occidentalis L.) growing under two different air quality conditions (roadside with high air pollution, RH and roadside with low air pollution, RL) and to assess the possibility of using their physiological and biochemical parameters as biomonitoring tools in urban areas. The results showed that the photosynthetic rate, photosynthetic nitrogen-use efficiencies, and photochromic contents were generally low in RH in both G. biloba and P. occidentalis. However, water-use efficiency and leaf temperature showed high values in RH trees. Among biochemical parameters, in G. biloba, the lipid peroxide content was higher in RH than in RL trees, but in P. occidentalis, this content was lower in RH than in RL trees. In both species, physiological activities were low in trees planted in areas with high levels of air pollution, whereas their biochemical and morphological variables showed different responses to air pollution. Thus, we concluded that it is possible to determine species-specific physiological variables affected by regional differences of air pollution in urban areas, and these findings may be helpful for monitoring air quality and environmental health using trees.

scholarly journals Contribution of Local and Transboundary Air Pollution to the Urban Air Quality of Fukuoka, Japan

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 431
Author(s):  
Ayako Yoshino ◽  
Akinori Takami ◽  
Keiichiro Hara ◽  
Chiharu Nishita-Hara ◽  
Masahiko Hayashi ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Areas ◽  
Air Pollutants ◽  
Regional Scale ◽  
Gaseous Species ◽  
Transboundary Air Pollution ◽  
Local Air Pollution ◽  
The City

Transboundary air pollution (TAP) and local air pollution (LAP) influence the air quality of urban areas. Fukuoka, located on the west side of Japan and affected by TAP from the Asian continent, is a unique example for understanding the contribution of LAP and TAP. Gaseous species and particulate matter (PM) were measured for approximately three weeks in Fukuoka in the winter of 2018. We classified two distinctive periods, LAP and TAP, based on wind speed. The classification was supported by variations in the concentration of gaseous species and by backward trajectories. Most air pollutants, including NOx and PM, were high in the LAP period and low in the TAP period. However, ozone was the exception. Therefore, our findings suggest that reducing local emissions is necessary. Ozone was higher in the TAP period, and the variation in ozone concentration was relatively small, indicating that ozone was produced outside of the city and transported to Fukuoka. Thus, air pollutants must also be reduced at a regional scale, including in China.

scholarly journals Healthier breath for all and everywhere – Urban air quality monitoring experience

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
J Gajic ◽  
D Dimovski ◽  
B Vukajlovic ◽  
M Jevtic
Keyword(s):  
Public Health ◽  
Air Pollution ◽  
Air Quality ◽  
Health Risk ◽  
Urban Health ◽  
Urban Areas ◽  
Danube River ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Residential Areas

Abstract Issue/problem Increasing attention is being paid to air pollution as one of the greatest threats to public and urban health. The WHO’s Urban Health Initiative points out the importance of collecting data and mapping the present state of air quality in urban areas. For citizens, such engagement is enabled by the appearance of personal air quality measurement devices that use crowd-sourcing to make measurement results publicly accessible in real time. Description of the problem As a way of contributing to air pollution monitoring in their town, three PhD Public health students conducted over 40 measurements between the start of June and end of August 2018 on various locations in the city of Novi Sad, Serbia. Measurements were performed using AirBeam personal air quality monitoring devices and their results presented as μg/m3 of Particulate Matter 2.5 (PM2.5) and automatically uploaded to the internet using the Air-casting app. Results Measurements conducted in public transportation vehicles returned the rather high average value of 40 μg/m3, where coffee shops and restaurants scored an even higher value of 48,67 μg/m3. The lowest average air pollution levels were registered near the Danube river bank (5.67) and in the parks (6), while the sites near crossroads or in the street showed average air pollution of 8.33 μg/m3. Residential areas where smoking is present during the day reported 2.5 times higher PM2.5 values than those without smokers (33.8 and 12.78 μg/m3). Lessons Bearing in mind that the air quality is considered as a serious health risk in urban areas, results of this pilot investigation suggest potential health risk for citizens living in urban areas. The negative effects of combustion and smoking on air quality are strongly highlighted, as well as the positive impact of green areas and parks near residential areas. Key messages Air pollution exposure as a serious health risk in urban areas. Crowdsourcing as a way of air quality monitoring has great potential for contributing to public health.

scholarly journals Ambient air quality in an urban area and its effects on plants and human beings: a case study of Tiruchirappalli, India

1970 ◽  
Vol 6 (2) ◽  
pp. 13-19 ◽  
Author(s):  
Sirajuddin M Horaginamani ◽  
M Ravichandran
Keyword(s):  
Air Pollution ◽  
Developing Countries ◽  
Air Quality ◽  
Human Health ◽  
Urban Area ◽  
Urban Areas ◽  
Ambient Air ◽  
Control Measures ◽  
Human Beings ◽  
Ambient Air Quality

Though water and land pollution is very dangerous, air pollution has its own peculiarities, due to its transboundary dispersion of pollutants over the entire world. In any well planned urban set up, industrial pollution takes a back seat and vehicular emissions take precedence as the major cause of urban air pollution. Air pollution is one of the serious problems faced by the people globally, especially in urban areas of developing countries like India. All these in turn lead to an increase in the air pollution levels and have adverse effects on the health of people and plants. Western countries have conducted several studies in this area, but there are only a few studies in developing countries like India. A study on ambient air quality in Tiruchirappalli urban area and its possible effects selected plants and human health has been undertaken, which may be helpful to bring out possible control measures. Keywords: ambient air quality; respiratory disorders; APTI; human health DOI: 10.3126/kuset.v6i2.4007Kathmandu University Journal of Science, Engineering and Technology Vol.6. No II, November, 2010, pp.13-19

scholarly journals Source influence on emission pathways and ambient PM<sub>2.5</sub> pollution over India (2015–2050)

2018 ◽  
Vol 18 (11) ◽  
pp. 8017-8039 ◽  
Author(s):  
Chandra Venkataraman ◽  
Michael Brauer ◽  
Kushal Tibrewal ◽  
Pankaj Sadavarte ◽  
Qiao Ma ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Urban Areas ◽  
Energy Demand ◽  
Biomass Combustion ◽  
Anthropogenic Source ◽  
Population Exposure ◽  
Future Economic Development ◽  
Future Air Pollution

Abstract. India is currently experiencing degraded air quality, and future economic development will lead to challenges for air quality management. Scenarios of sectoral emissions of fine particulate matter and its precursors were developed and evaluated for 2015–2050, under specific pathways of diffusion of cleaner and more energy-efficient technologies. The impacts of individual source sectors on PM2.5 concentrations were assessed through systematic simulations of spatially and temporally resolved particulate matter concentrations, using the GEOS-Chem model, followed by population-weighted aggregation to national and state levels. We find that PM2.5 pollution is a pan-India problem, with a regional character, and is not limited to urban areas or megacities. Under present-day emissions, levels in most states exceeded the national PM2.5 annual standard (40 µg m−3). Sources related to human activities were responsible for the largest proportion of the present-day population exposure to PM2.5 in India. About 60 % of India's mean population-weighted PM2.5 concentrations come from anthropogenic source sectors, while the remainder are from other sources, windblown dust and extra-regional sources. Leading contributors are residential biomass combustion, power plant and industrial coal combustion and anthropogenic dust (including coal fly ash, fugitive road dust and waste burning). Transportation, brick production and distributed diesel were other contributors to PM2.5. Future evolution of emissions under regulations set at current levels and promulgated levels caused further deterioration of air quality in 2030 and 2050. Under an ambitious prospective policy scenario, promoting very large shifts away from traditional biomass technologies and coal-based electricity generation, significant reductions in PM2.5 levels are achievable in 2030 and 2050. Effective mitigation of future air pollution in India requires adoption of aggressive prospective regulation, currently not formulated, for a three-pronged switch away from (i) biomass-fuelled traditional technologies, (ii) industrial coal-burning and (iii) open burning of agricultural residue. Future air pollution is dominated by industrial process emissions, reflecting larger expansion in industrial, rather than residential energy demand. However, even under the most active reductions envisioned, the 2050 mean exposure, excluding any impact from windblown mineral dust, is estimated to be nearly 3 times higher than the WHO Air Quality Guideline.

scholarly journals Turbulence-permitting air pollution simulation for the Stuttgart metropolitan area

2020 ◽  
Author(s):  
Thomas Schwitalla ◽  
Hans-Stefan Bauer ◽  
Kirsten Warrach-Sagi ◽  
Thomas Bönisch ◽  
Volker Wulfmeyer
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Metropolitan Area ◽  
Urban Areas ◽  
Single Layer ◽  
Urban Canopy ◽  
Surface Fluxes ◽  
Residential Areas ◽  
Near Surface ◽  
Air Quality Forecast

Abstract. Air pollution is one of the major challenges in urban areas. It can have a major impact on human health and society and is currently a subject of several litigations at European courts. Information on the level of air pollution is based on near surface measurements, which are often irregularly distributed along the main traffic roads and provide almost no information about the residential areas and office districts in the cities. To further enhance the process understanding and give scientific support to decision makers, we developed a prototype for an air quality forecasting system (AQFS) within the EU demonstration project Open Forecast. For AQFS, the Weather Research and Forecasting model together with its coupled chemistry component (WRF-Chem) is applied for the Stuttgart metropolitan area in Germany. Three model domains from 1.25 km down to a turbulence permitting resolution of 50 m were used and a single layer urban canopy model was active in all domains. As demonstration case study the 21 January 2019 was selected which was a heavy polluted day with observed PM10 concentrations exceeding 50 µg m−3. Our results show that the model is capable to reasonably simulate the diurnal cycle of surface fluxes and 2-m temperatures as well as evolution of the stable and shallow boundary layer typically occurring in wintertime in Stuttgart. The simulated fields of particulates with a diameter of less than 10 µm (PM10) and Nitrogen dioxide (NO2) allow a clear statement about the most heavily polluted areas apart from the irregularly distributed measurement sites. Together with information about the vertical distribution of PM10 and NO2 from the model, AQFS will serve as a valuable tool for air quality forecast and has the potential of being applied to other cities around the world.

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