Developing Web-Based 3D Health-Aware Routing for Pedestrians and Cyclists

As the population of people migrating to cities keeps increasing, concerns have been raised about air quality in cities and how it impacts everyday life. Thus, it is important to demonstrate ways of avoiding polluted areas. The approach described in this paper is intended to draw attention to polluted areas and help pedestrians and cyclists to achieve the lowest possible level of air pollution when planning daily routes. We utilise real-time air quality data which is obtained from monitoring stations across the world. The data consist of the geolocation of monitoring stations as well as index numbers to scale the air quality level in every corresponding monitoring stations. When the air quality level is considered having a moderate health concern for people with respiratory disease, such as asthma, an alternative route that avoid air pollution will be calculated so that pedestrians and cyclists can be informed. The implementation can visualize air quality level in several areas in 3D map as well as informs health-aware route for pedestrian and cyclist. It automatically adjusts the observed air quality areas based on the availability of monitoring stations. The proposed approach results in a prototype of a health-aware 3D navigation system for pedestrian and cyclist.

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aiRe - A web-based R application for simple, accessible and repeatable analysis of urban air quality data

Environmental Modelling & Software ◽

10.1016/j.envsoft.2021.104976 ◽

2021 ◽

Vol 138 ◽

pp. 104976

Author(s):

Juan José Díaz ◽

Ivan Mura ◽

Juan Felipe Franco ◽

Raha Akhavan-Tabatabaei

Keyword(s):

Air Quality ◽

Urban Air Quality ◽

Quality Data ◽

Urban Air ◽

Web Based ◽

Air Quality Data

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A Cluster Analysis of PM2.5 Using CMAQ Model Results for Representativeness of Air Quality Monitoring Networks in Busan, Korea

10.5194/egusphere-egu2020-3189 ◽

2020 ◽

Author(s):

Woo-Sik Jung ◽

Woo-Gon Do

Keyword(s):

Air Pollution ◽

Cluster Analysis ◽

Air Quality ◽

Monitoring Network ◽

Hierarchical Cluster ◽

Quality Monitoring ◽

Quality Data ◽

Air Quality Monitoring ◽

Monitoring Networks ◽

Monitoring Stations

With increasing interest in air pollution, the installation of air quality monitoring networks for regular measurement is considered a very important task in many countries. However, operation of air quality monitoring networks requires much time and money. Therefore, the representativeness of the locations of air quality monitoring networks is an important issue that has been studied by many groups worldwide. Most such studies are based on statistical analysis or the use of geographic information systems (GIS) in existing air quality monitoring network data. These methods are useful for identifying the representativeness of existing measuring networks, but they cannot verify the need to add new monitoring stations. With the development of computer technology, numerical air quality models such as CMAQ have become increasingly important in analyzing and diagnosing air pollution. In this study, PM2.5 distributions in Busan were reproduced with 1-km grid spacing by the CMAQ model. The model results reflected actual PM2.5 changes relatively well. A cluster analysis, which is a statistical method that groups similar objects together, was then applied to the hourly PM2.5 concentration for all grids in the model domain. Similarities and differences between objects can be measured in several ways. K-means clustering uses a non-hierarchical cluster analysis method featuring an advantageously low calculation time for the fast processing of large amounts of data. K-means clustering was highly prevalent in existing studies that grouped air quality data according to the same characteristics. As a result of the cluster analysis, PM2.5 pollution in Busan was successfully divided into groups with the same concentration change characteristics. Finally, the redundancy of the monitoring stations and the need for additional sites were analyzed by comparing the clusters of PM2.5 with the locations of the air quality monitoring networks currently in operation.This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A3B03036152).

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Evidence of air quality data misreporting in China: An impulse indicator saturation model comparison of local government-reported and U.S. embassy-reported PM2.5 concentrations (2015–2017)

PLoS ONE ◽

10.1371/journal.pone.0249063 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0249063

Author(s):

Jesse S. Turiel ◽

Robert K. Kaufmann

Keyword(s):

Air Quality ◽

Local Government ◽

Model Comparison ◽

Quality Data ◽

Random Chance ◽

Pollution Levels ◽

Reported Data ◽

Impulse Indicator Saturation ◽

Air Quality Data ◽

Monitoring Stations

This paper analyzes hourly PM2.5 measurements from government-controlled and U.S. embassy-controlled monitoring stations in five Chinese cities between January 2015 and June 2017. We compare the two datasets with an impulse indicator saturation technique that identifies hours when the relation between Chinese and U.S. reported data diverges in a statistically significant fashion. These temporary divergences, or impulses, are 1) More frequent than expected by random chance; 2) More positive than expected by random chance; and 3) More likely to occur during hours when air pollution concentrations are high. In other words, relative to U.S.-controlled monitoring stations, government-controlled stations systematically under-report pollution levels when local air quality is poor. These results contrast with the findings of other recent studies, which argue that Chinese air quality data misreporting ended after a series of policy reforms beginning in 2012. Our findings provide evidence that local government misreporting did not end after 2012, but instead continued in a different manner. These results suggest that Chinese air quality data, while still useful, should not be taken entirely at face value.

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Changes in air-pollution related behaviour measured by Google Trends Search Volume Index in response to reported air quality in Poland

10.21203/rs.3.rs-234681/v1 ◽

2021 ◽

Author(s):

Wojciech Nazar ◽

Katarzyna Plata-Nazar

Keyword(s):

Air Pollution ◽

Air Quality ◽

Winter Season ◽

Google Trends ◽

Volume Index ◽

Quality Data ◽

Coefficient Of Determination ◽

Pollution Levels ◽

Search Volume ◽

Air Quality Data

Abstract Background Decreased air quality is connected to a higher number of hospital admissions and an increase in daily mortality rates. Thus, Poles’ behavioural response to sometimes elevated air pollution levels is vital. The aim of this study was to carry out analysis of changes in air-pollution related information seeking behaviour in response to nationwide reported air quality in Poland. Methods Google Trends Search Volume Index data was used to investigate Poles’ interest in air pollution-related keywords. PM10 and PM2.5 concentrations measured across Poland between 2016 and 2019 were collected from the Chief Inspectorate of Environmental Protection databases. Pearson Product-Moment Correlation and the R2 correlation coefficient of determination were used to measure spatial and seasonal correlations between reported air pollution levels and the popularity of search queries. Results The highest PM10 and PM2.5 concentrations were observed in southern voivodeships and during the winter season. Similar trends were observed for Poles’ interest in air-pollution related keywords. All R2 coefficient of determination values were > 0.5 and all correlations were statistically significant. Conclusion Poland’s air quality does not meet the World Health Organisation guidelines. Also, the air quality is lower in southern Poland and during the winter season. It appears that Poles are aware of this issue and search for daily air quality data in their location. Greater interest in air quality data in Poland strongly correlates with both higher regional and higher seasonal air pollution levels.

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Short-term environmental impact of ambient air quality trends in during the COVID-19 pandemic in India

Annals of Civil and Environmental Engineering ◽

10.29328/journal.acee.1001031 ◽

2021 ◽

Vol 5 (1) ◽

pp. 017-025

Author(s):

Karuppasamy Manikanda Bharath ◽

Natesan Usha ◽

Periyasamy Balamadeswaran ◽

S Srinivasalu

Keyword(s):

Air Quality ◽

Ambient Air ◽

Air Pollutant ◽

Quality Monitoring ◽

Quality Data ◽

Air Quality Monitoring ◽

The North ◽

Air Quality Monitoring Stations ◽

Air Quality Data ◽

Monitoring Stations

The lockdown, implemented in response to the COVID-19 epidemic, restricted the operation of various sectors in the country and its highlights a good environmental outcome. Thus, a comparison of air pollutants in India before and after the imposed lockdown indicated an overall improvement air quality across major Indian cities. This was established by utilizing the Central Pollution Control Board’s database of air quality monitoring station statistics, such as air quality patterns. During the COVID-19 epidemic, India’s pre-to-post nationwide lockdown was examined. The air quality data was collected from 30-12-2019 to 28-04-2020 and synthesized using 231 Automatic air quality monitoring stations in a major Indian metropolis. Specifically, air pollutant concentrations, temperature, and relative humidity variation during COVID-19 pandemic pre-to-post lockdown variation in India were monitored. As an outcome, several cities around the country have reported improved air quality. Generally, the air quality, on a categorical scale was found to be ‘Good’. However, a few cities from the North-eastern part of India were categorized as ‘Moderate/Satisfactory’. Overall, the particulate matters reduction was in around 60% and other gaseous pollutants was in 40% reduction was observed during the lockdown period. The results of this study include an analysis of air quality data derived from continuous air quality monitoring stations from the pre-lockdown to post-lockdown period. Air quality in India improved following the national lockdown, the interpretation of trends for PM 2.5, PM 10, SO2, NO2, and the Air Quality Index has been provided in studies for major cities across India, including Delhi, Gurugram, Noida, Mumbai, Kolkata, Bengaluru, Patna, and others.

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Air Quality in the Harbin-Changchun Metropolitan Area in Northeast China: Unique Episodes and New Trends

Toxics ◽

10.3390/toxics9120357 ◽

2021 ◽

Vol 9 (12) ◽

pp. 357

Author(s):

Yulong Wang ◽

Youwen Sun ◽

Gerong Zhao ◽

Yuan Cheng

Keyword(s):

Air Quality ◽

Metropolitan Area ◽

Central City ◽

Fine Particulate Matter ◽

Quality Data ◽

Quality Level ◽

Fire Control ◽

Fine Particulate ◽

National Environmental ◽

Air Quality Data

Because of the unique geographical, climate, and anthropogenic emission characteristics, it is meaningful to explore the air pollution in the Harbin-Changchun (HC) metropolitan area. In this study, the Air Quality Index (AQI) and the corresponding major pollutant were investigated for the HC cities, based on the air quality data derived from the China National Environmental Monitoring Center. The number of days with the air quality level of “good” gradually increased during recent years, pointing to an improvement of the air quality in HC. It was also found that ozone, a typical secondary pollutant, exhibited stronger inter-city correlations compared to typical primary pollutants such as carbon monoxide and nitrogen dioxide. In addition, for nearly all the HC cities, the concentrations of fine particulate matter (PM2.5) decreased substantially in 2020 compared to 2015. However, this was not the case for ozone, with the most significant increase of ozone observed for HC’s central city, Harbin. This study highlights the importance of ozone reduction for further improving HC’s air quality, and the importance of agricultural fire control for eliminating heavily-polluted and even off-the-charts PM2.5 episodes.

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Impacts of the COVID-19 lockdown on air quality and its association with human mortality trends in megapolis Mexico City

10.21203/rs.3.rs-90079/v1 ◽

2020 ◽

Author(s):

Gurusamy Kutralam-Muniasamy ◽

Fermín Pérez-Guevara ◽

Priyadarsi D. Roy ◽

I. Elizalde-Martínez ◽

V.C. Shruti

Keyword(s):

Air Pollution ◽

Latin America ◽

Air Quality ◽

Mexico City ◽

Air Pollutants ◽

Mortality Rates ◽

Quality Data ◽

Mortality Trends ◽

Human Mortality ◽

Air Quality Data

Abstract Mexico City is the second most populated city in Latin America, and it went through two partial lockdowns between April 1 and May 31, 2020 for reducing the COVID-19 propagation. The present study assessed air quality and its association with human mortality rates during the lockdown by estimating changes observed in air pollutants (CO, NO2, O3, SO2, PM10 and PM2.5) between the lockdown (April 1 - May 31) and pre-lockdown (January 1 – March 31) periods, as well as by comparing the air quality data of lockdown period with the same interval of previous five-years (2015-2019). Concentrations of NO2 (-29%), SO2 (-55%) and PM10 (-11%) declined and the contents of CO (+1.1%), PM2.5 (+19%) and O3 (+63%) increased during the lockdown compared to the pre-lockdown period. This study also estimated that NO2, SO2, CO, PM10 and PM2.5 reduced by 19-36%, and O3 enhanced by 14% compared to the average of 2015-2019. Reduction in traffic as well as less emission from vehicle exhausts led to remarkable decline in NO2, SO2 and PM10. The significant positive associations of PM2.5, CO and O3 with the numbers of COVID-19 infections and deaths, however, underscored the necessity to enforce air pollution regulations to protect human health in one of the important cities of the northern hemisphere.

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WEB BASED 3D VISUALISATION OF TIME-VARYING AIR QUALITY INFORMATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-267-2018 ◽

2018 ◽

Vol XLII-4 ◽

pp. 267-274

Author(s):

U. Isikdag ◽

K. Sahin

Keyword(s):

Air Pollution ◽

Air Quality ◽

Air Pollutant ◽

Quality Data ◽

Interactive System ◽

Quality Information ◽

Time Varying ◽

Web Based ◽

3D Visualisation ◽

Usability Tests

Abstract. Many countries where the industrial development and production rates are high face many side effects of low air quality and air pollution. There is an evident correlation between the topographic and climatic properties of a location and the air pollution and air quality on that location. As the variation of air quality is dependent on location, air quality information should be acquired, utilised, stored and presented in form of Geo-Information. On the other hand, as this information is related with the health concerns of public, the information should be available publicly, and needs to be presented through an easily accessible medium and through a commonly used interface. Efficient storage of time-varying air quality information when combined with an efficient mechanism of 3D web-based visualisation would help very much in dissemination of air quality information to public. This research is focused on web-based 3D visualisation of time-varying air quality data. A web based interactive system is developed to visualise pollutant levels that were acquired as hourly intervals from more than 100 stations in Turkey between years 2008 and 2017. The research also concentrated on visualisation of geospatial high volume data. In the system, visualisation can be achieved on-demand by querying an air pollutant information database of 10.330.629 records and a city object database with more than 700.000 records. The paper elaborates on the details of this research. Following a background on air quality, air quality models, and Geo-Information visualisation, the system architecture and functionality is presented. The paper concludes with results of usability tests of the system.

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