scholarly journals Creating individual level air pollution exposures in an anonymised data safe haven: a platform for evaluating impact on educational attainment

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Amy Mizen ◽  
Jane Lyons ◽  
Ruth Doherty ◽  
Damon Berridge ◽  
Paul Wilkinson ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Educational Attainment ◽  
Health Data ◽  
Health Conditions ◽  
Quality Data ◽  
Safe Haven ◽  
Cross Sectional ◽  
Individual Level ◽  
Home And School

Introduction There is a lack of evidence on the adverse effects of air pollution on cognition for people with air quality-related health conditions. We propose that educational attainment, as a proxy for cognition, may increase with improved air quality. This study will explore whether asthma and seasonal allergic rhinitis, when exacerbated by acute exposure to air pollution, is associated with educational attainment. Objectives To describe the preparation of individual and household-level linked environmental and health data for analysis within an anonymised safe haven. Also to introduce our statistical analysis plan for our study: COgnition, Respiratory Tract illness and Effects of eXposure (CORTEX). Methods We imported daily air pollution and aeroallergen data, and individual level education data into the SAIL databank, an anonymised safe haven for person-based records. We linked individual-level education, socioeconomic and health data to air quality data for home and school locations, creating tailored exposures for individuals across a city. We developed daily exposure data for all pupils in repeated cross sectional exam cohorts (2009-2015). Conclusion We have used the SAIL databank, an innovative, data safe haven to create individual-level exposures to air pollution and pollen for multiple daily home and school locations. The analysis platform will allow us to evaluate retrospectively the impact of air quality on attainment for multiple cross-sectional cohorts of pupils. Our methods will allow us to distinguish between the pollution impacts on educational attainment for pupils with and without respiratory health conditions. The results from this study will further our understanding of the effects of air quality and respiratory-related health conditions on cognition. Highlights This city-wide study includes longitudinal routinely-recorded educational attainment data for all pupils taking exams over seven years; High spatial resolution air pollution data were linked within a privacy protected databank to obtain individual exposure at multiple daily locations; This study will use health data linked at the individual level to explore associations between air pollution, related morbidity, and educational attainment.

scholarly journals Are children who are treated for asthma and seasonal allergic rhinitis disadvantaged in their educational attainment when acutely exposed to air pollution and pollen? A feasibility study

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Amy Mizen ◽  
Jane Lyons ◽  
Sarah Rodgers ◽  
Damon Berridge ◽  
Ashley Akbari ◽  
...  
Keyword(s):  
Air Pollution ◽  
Allergic Rhinitis ◽  
Health Status ◽  
Air Quality ◽  
Educational Attainment ◽  
Linear Regression Analysis ◽  
Treatment Seeking ◽  
Exam Performance ◽  
Negatively Associated ◽  
Individual Level

BackgroundThere is a lack of evidence of the adverse effects which air quality has on cognition for people with air quality-related health conditions, these are not widely documented in the literature. Educational attainment, as a proxy for cognition, may increase with improved air quality. ObjectivesPrepare individual and household level linked environmental and health data for analysis within an anonymised safe haven; analyse the linked dataset for our study investigating: Cognition, Respiratory Tract illness and Effects of eXposure (CORTEX). MethodsAnonymised, routinely collected health and education data were linked with high spatial resolution pollution measurements and daily pollen measurements to provide repeated cross-sectional cohorts (2009-2015) on 18,241 pupils across the city of Cardiff, using the SAIL databank. A fully adjusted multilevel linear regression analysis examined associations between health status and/or air quality. Cohort, school and individual level confounders were controlled for. We hope that using individual-level multi-location daily exposure assessment will help to clarify the role of traffic and prevent potential community-level confounding. Combined effects of air quality on variation in educational attainment between those treated for asthma and/or Severe Allergic Rhinitis (SAR), and those not treated, was also investigated. FindingsAsthma was not associated with exam performance (p=0.7). However, SAR was positively associated with exam performance (p<0.001). Exposure to air pollution was negatively associated with educational attainment regardless of health status. ConclusionsIrrespective of health status, air quality was negatively associated with educational attainment. Treatment seeking behaviour may explain the positive association between SAR and educational attainment. For a more accurate reflection of health status, health outcomes not subject to treatment seeking behaviours, such as emergency hospital admission, should be investigated.

scholarly journals Is educational attainment associated with acute exposure to air pollution and pollen, and is it worse for pupils with asthma and seasonal allergic rhinitis?

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Amy Mizen ◽  
Jane Lyons ◽  
Ashley Akbari ◽  
Damon Berridge ◽  
David Carruthers ◽  
...  
Keyword(s):  
Air Pollution ◽  
Allergic Rhinitis ◽  
Health Status ◽  
Air Quality ◽  
Educational Attainment ◽  
School Level ◽  
Health Conditions ◽  
Treatment Seeking ◽  
Exam Performance ◽  
Negatively Associated

IntroductionThere is a lack of evidence of the adverse effects of air pollution and pollen on cognition for people with air quality related health conditions. This study explored the effects of air quality and respiratory health conditions on educational attainment for 18,241 pupils across the city of Cardiff, United Kingdom. Objectives and ApproachAnonymised, routinely collected health and education data were linked at the household and school level with modelled high spatial resolution pollution data, and daily pollen measurements using the Secure Anonymised Information Linkage (SAIL) databank. This created 7 repeated cross-sectional cohorts (2009-2015). Multilevel linear regression analysis examined whether exam performance was associated with health status and/or air quality levels averaged at school and home locations during revision and examination periods. We also investigated the combined effects of air quality and associations with educational attainment for pupils who were treated for asthma and/or Severe Allergic Rhinitis (SAR), and those who were not. ResultsThe cohort contained 9337 males and 8904 female pupils. There were 871 treated for asthma, 2091 for SAR, and 634 treated for both. Asthma was not associated with exam performance (p=0.700). However, SAR was positively associated with exam performance (p 2) was negatively associated with educational attainment (p = 0.002). Other indicators of air quality (pollutants: Ozone, Particulate Matter - PM2.5, and pollen) were not associated with educational attainment (p> 0.05). Exposure to NO2 was negatively associated with educational attainment irrespective of treatment for asthma or SAR. There was no combined effect of air quality on the variation in educational attainment between those who are treated for asthma and/or SAR and those who were not. Conclusion/ImplicationsIrrespective of health status, exposure to NO2 was negatively associated with educational attainment. Treatment seeking behaviour may be a possible explanation for the positive association between SAR and educational attainment. For a more accurate reflection of health status, health outcomes not subject to treatment seeking behaviour should be investigated.

scholarly journals Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

2021 ◽  
Author(s):  
Shwet Ketu ◽  
Pramod Kumar Mishra
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Classification Algorithm ◽  
Quality Data ◽  
Pollution Level ◽  
Classification Problems ◽  
Chi Square ◽  
The Impact

AbstractIn the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation.

scholarly journals Analyzing Individual-Level Secondary Data with Instrumental Variable Methods Is Useful for Studying the Effects of Air Pollution on Dementia

2021 ◽  
Vol 79 (1) ◽  
pp. 15-23
Author(s):  
Kelly C. Bishop ◽  
Sehba Husain-Krautter ◽  
Jonathan D. Ketcham ◽  
Nicolai V. Kuminoff ◽  
Corbett Schimming
Keyword(s):  
Air Pollution ◽  
Instrumental Variable ◽  
Secondary Data ◽  
Quality Data ◽  
Long Term Effects ◽  
Individual Level ◽  
Medicare Claims Data ◽  
Advance Knowledge ◽  
Air Quality Data

We hypothesize that analyzing individual-level secondary data with instrumental variable (IV) methods can advance knowledge of the long-term effects of air pollution on dementia. We discuss issues in measurement using secondary data and how IV estimation can overcome biases due to measurement error and unmeasured variables. We link air-quality data from the Environmental Protection Agency’s monitors with Medicare claims data to illustrate the use of secondary data to document associations. Additionally, we describe results from a previous study that uses an IV for pollution and finds that PM2.5’s effects on dementia are larger than non-causal associations.

CAMS-Net: The Clean Air Monitoring and Solutions Network

2021 ◽  
Author(s):  
Daniel Westervelt ◽  
Celeste McFarlane ◽  
Faye McNeill ◽  
R (Subu) Subramanian ◽  
Mike Giordano ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Low Cost ◽  
Air Monitoring ◽  
Quality Data ◽  
Calibration Methods ◽  
The World ◽  
Clean Air

&lt;p&gt;There is a severe lack of air pollution data around the world. This includes large portions of low- and middle-income countries (LMICs), as well as rural areas of wealthier nations as monitors tend to be located in large metropolises. Low cost sensors (LCS) for measuring air pollution and identifying sources offer a possible path forward to remedy the lack of data, though significant knowledge gaps and caveats remain regarding the accurate application and interpretation of such devices.&lt;/p&gt;&lt;p&gt;The Clean Air Monitoring and Solutions Network (CAMS-Net) establishes an international network of networks that unites scientists, decision-makers, city administrators, citizen groups, the private sector, and other local stakeholders in co-developing new methods and best practices for real-time air quality data collection, data sharing, and solutions for air quality improvements. CAMS-Net brings together at least 32 multidisciplinary member networks from North America, Europe, Africa, and India. The project establishes a mechanism for international collaboration, builds technical capacity, shares knowledge, and trains the next generation of air quality practitioners and advocates, including domestic and international graduate students and postdoctoral researchers.&amp;#160;&lt;/p&gt;&lt;p&gt;Here we present some preliminary research accelerated through the CAMS-Net project. Specifically, we present LCS calibration methodology for several co-locations in LMICs (Accra, Ghana; Kampala, Uganda; Nairobi, Kenya; Addis Ababa, Ethiopia; and Kolkata, India), in which reference BAM-1020 PM2.5 monitors were placed side-by-side with LCS. We demonstrate that both simple multiple linear regression calibration methods for bias-correcting LCS and more complex machine learning methods can reduce bias in LCS to close to zero, while increasing correlation. For example, in Kampala, Raw PurpleAir PM2.5 data are strongly correlated with the BAM-1020 PM2.5 (r&lt;sup&gt;2&lt;/sup&gt; = 0.88), but have a mean bias of approximately 12 &amp;#956;g m&lt;sup&gt;-3&lt;/sup&gt;. Two calibration models, multiple linear regression and a random forest approach, decrease mean bias from 12 &amp;#956;g m&lt;sup&gt;-3 &lt;/sup&gt;to -1.84 &amp;#181;g m&lt;sup&gt;-3&lt;/sup&gt; or less and improve the the r&lt;sup&gt;2&lt;/sup&gt; from 0.88 to 0.96. We find similar performance in several other regions of the world. Location-specific calibration of low-cost sensors is necessary in order to obtain useful data, since sensor performance is closely tied to environmental conditions such as relative humidity. This work is a first step towards developing a database of region-specific correction factors for low cost sensors, which are exploding in popularity globally and have the potential to close the air pollution data gap especially in resource-limited countries.&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Understanding Public Response to Air Quality Using Tweet Analysis

2019 ◽  
Vol 5 (3) ◽  
pp. 205630511986765
Author(s):  
Supraja Gurajala ◽  
Suresh Dhaniyala ◽  
Jeanna N. Matthews
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Machine Learning Algorithms ◽  
Mitigation Measures ◽  
Quality Data ◽  
Public Health Issue ◽  
New Delhi ◽  
Public Response ◽  
Pollution Levels ◽  
Adverse Health Effects

Poor air quality is recognized as a major risk factor for human health globally. Critical to addressing this important public-health issue is the effective dissemination of air quality data, information about adverse health effects, and the necessary mitigation measures. However, recent studies have shown that even when public get data on air quality and understand its importance, people do not necessarily take actions to protect their health or exhibit pro-environmental behaviors to address the problem. Most existing studies on public attitude and response to air quality are based on offline studies, with a limited number of survey participants and over a limited number of geographical locations. For a larger survey size and a wider set of locations, we collected Twitter data for a period of nearly 2 years and analyzed these data for three major cities: Paris, London, and New Delhi. We identify the three hashtags in each city that best correlate the frequency of tweets with local air quality. Using tweets with these hashtags, we determined that people’s response to air quality across all three cities was nearly identical when considering relative changes in air pollution. Using machine-learning algorithms, we determined that health concerns dominated public response when air quality degraded, with the strongest increase in concern being in New Delhi, where pollution levels are the highest among the three cities studied. The public call for political solutions when air quality worsens is consistent with similar findings with offline surveys in other cities. We also conducted an unsupervised learning analysis to extract topics from tweets in Delhi and studied their evolution over time and with changing air quality. Our analysis helped extract relevant words or features associated with different air quality–related topics such as air pollution policy and health. Also, the topic modeling analysis revealed niche topics associated with sporadic air quality events, such as fireworks during festivals and the air quality impact on an outdoor sport event. Our approach shows that a tweet-based analysis can enable social scientists to probe and survey public response to events such as air quality in a timely fashion and help policy makers respond appropriately.

scholarly journals The Relationship between Land Cover and Sociodemographic Factors

Urban Science ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 68
Author(s):  
Daniel L. Mendoza
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Form ◽  
Capita Income ◽  
Sociodemographic Factors ◽  
Tree Cover ◽  
Quality Data ◽  
Minority Populations ◽  
Per Capita Income ◽  
Per Capita

Multiple social and environmental justice concerns are linked to the urban form such as the distribution of socioeconomic class populations, healthcare spending, air pollution exposure, and human mobility. Because of this, the implications of the relationships between built urban form, sociodemographic factors, and air quality warrant analysis at a high spatial resolution. This study used 1m resolved LiDAR data to characterize land use in Salt Lake County, Utah, and associate it with sociodemographic and air quality data at the census block group and zip code levels. We found that increasing tree cover was associated with higher per capita income and lower minority populations while increasing built cover was linked to lower per capita income and higher minority populations. Air quality showed less strong correlations, however, decreased non-irrigated cover, increased built cover, and higher amounts of households living under poverty were related to higher long-term PM2.5 exposure. Due to regional air pollution concerns, several policy efforts have been undertaken to improve air quality and reduce negative health outcomes in Utah which are being informed by regulatory and research-grade air quality sensors.

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

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

&lt;p&gt;&lt;strong&gt;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.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A3B03036152).&lt;/strong&gt;&lt;/p&gt;

scholarly journals Air Pollution and Lung Capacity of People Living around the Cement Industry

2016 ◽  
Vol 2 (2) ◽  
pp. 76-83
Author(s):  
Erwin Azizi Jayadipraja ◽  
Anwar Daud ◽  
Alimuddin Hamzah Assegaf ◽  
Maming
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Ambient Air ◽  
Quality Standard ◽  
Cement Industry ◽  
Anthropogenic Sources ◽  
Dust Particles ◽  
Ambient Air Quality ◽  
Cross Sectional ◽  
Lung Capacity

Backgrounds: A cement industry is one of anthropogenic sources of air pollution. In polluting the air, the industry creates some dust particles, nitrogen oxide (NO2), sulfur oxide (SO2), and carbon monoxide (CO).Research Purpose: The research aims at finding out the ambient air quality around a cement industry and relating it with the lung capacity of people living around the area.Methodology: This research uses cross sectional studies by measuring the ambient air quality in the morning, noon, and evening in four different settlements within 3 km from the cement industry. The measurement is then correlated with the FEV1 and FVC of lung capacity of people living around the area.Result: Of all four locations, three have ambient air quality (PM2.5 = 109.47 µg/Nm3, TSP = 454.7 µg/Nm3) that surpass the quality standard (PM2.5 = 65 µg/Nm3, TSP = 230 µg/Nm3). Of 241 respondents, the average level of FVC and FEV1 is respectively 1.9352 liter (SD: 0.45578) and 1.7486 liter (SD: 0.43874). Furthermore, the level of PM2.5 in the morning and at noon is respectively p=0.009 and p=0.003; the level of TSP in the morning and at noon is respectively p=0.003 and p=0.01; the level of NO2 in the morning is p=0.006; the level of SO2 in the morning, at noon and in the evening is respectively p=0.000, p=0.022, and p=0.000; and the level of CO in the morning, at noon and in the evening is respectively p=0.003, p=0.015, and p=0.024. Those levels are associated with the level of respondents’ FEV1. Moreover, the level of TSP in the morning is p=0.024; the level of SO2 in the morning and in the evening is p=0.007. These levels relate to the level of respondents’ FVC.Keywords: FVC, FEV1, CO, NO2, SO2, TSP, PM2.5, cement industry. 

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.

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