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

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.

scholarly journals Environmental justice and air quality in Santiago de Chile

2015 ◽  
Vol 17 (3) ◽  
pp. 337-350 ◽  
Author(s):  
Richelle Rose Perez
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Justice ◽  
Fine Particles ◽  
Metropolitan Region ◽  
Quality Data ◽  
Policy Makers ◽  
Pollution Levels ◽  
Spatial Differences ◽  
Socioeconomic Data

<p><strong>Objective </strong>The metropolitan region in Santiago, Chile has an air quality problem.  However, the larger issue may lie in the inequities created by the distribution of the air pollution.</p><p><strong>Methods </strong>To assess the inequities created by the spatial differences in air pollution, the author analyzed fine particle pollution levels for 2008-2011 at monitoring stations throughout the region. The author also compared air quality data with socioeconomic data.</p><p><strong>Results </strong>The areas of the Santiago metropolitan region with the worst air quality have lower socioeconomic levels. Pollution in these areas reaches levels higher than the current Chilean 24 hour standard for fine particles. These areas also have longer time periods of unhealthy air and 21 % more days with unhealthy levels of air pollution.</p><p><strong>Discussion </strong>The differences in exposure to pollution create an inequality and environmental injustice among the socioeconomic groups in the metropolitan region. Chilean policymakers have the regulatory tools needed to improve environmental justice. However, they need to improve the implementation of these tools in order to achieve that goal: Chilean policy makers should consider local sources of air pollution in the most polluted municipalities; Government decision makers should make extra efforts to listen to the community and improve access to environmental information; Environmental justice advocates should involve stakeholders from the social justice movement and other related areas; Policy makers should track progress towards environmental justice by evaluating differences in health outcomes related to differential exposure to air pollution in different parts of the Santiago metropolitan area.</p>

Canadian Air Quality Forecasting and Information Systems

2020 ◽  
Author(s):  
Radenko Pavlovic ◽  
Jacinthe Racine ◽  
Marika Egyed ◽  
Serge Lamy ◽  
Pierre Boucher
Keyword(s):  
Climate Change ◽  
Air Pollution ◽  
Information Systems ◽  
Air Quality ◽  
Spatial Scales ◽  
Atmospheric Composition ◽  
Mitigation Measures ◽  
Quality Data ◽  
Quality Program ◽  
Air Quality Forecasting

&lt;p&gt;&lt;strong&gt;Canadian Air Quality Forecasting and Information Systems&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Environment and Climate Change Canada (ECCC) has been in charge of the national air quality program for more than 20 years. As of today, air pollution remains one of the most important environmental risk factors to health, in addition to hazardous effects on climate change, ecosystems, properties, and food and water chain.&lt;/p&gt;&lt;p&gt;Currently, Canadian air quality forecasting and information systems with observational and modeling components are a key element for policy and mitigation measures, which are used to reduce the negative impacts of air pollution. The operational ECCC&amp;#8217;s air quality program provides immediate adaptive measures based on early warning services. In addition to this operational service, the air quality scenario and policy modelling is essential for implementing cost-effective emission reduction strategies and local planning to ensure compliance with air quality standards.&lt;/p&gt;&lt;p&gt;Canadian air quality forecasting and information systems also enable access to air quality data at different temporal and spatial scales. This is done through coordination of national activities to facilitate seamless provision of atmospheric composition information at various scales. This work will present Canadian air quality forecasting and information systems, components, collaboration, application and data streaming, as an example that can be helpful in building the WMO GAFIS initiative.&lt;/p&gt;

scholarly journals Urban Air Quality Guidance Based on Measures Categorization in Road Transport

2021 ◽  
Vol 7 (2) ◽  
pp. 253-267
Author(s):  
Beti Angelevska ◽  
Vaska Atanasova ◽  
Igor Andreevski
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Areas ◽  
Road Transport ◽  
Urban Air Quality ◽  
Mitigation Measures ◽  
Annual Reports ◽  
Urban Air ◽  
Air Pollution Control ◽  
Pollution Levels

Air pollution is a cause for serious concerns in urban areas in Republic of North Macedonia. Intensive development of road transport increases the main air pollutants’ concentrations - particulate matter and nitrogen dioxide, whose monitored values are continuously exceeding the limit. The main disadvantage of the national plans and annual reports is the absence of comprehensive and categorized list of reduction/mitigation measures for road transport impacts on air quality. Analyzing the current air pollution problem and road transport contribution this paper provides the needed and detailed categorization of short-to-long term reduction/mitigation measures consisting of five subcategories. Based on measure categorization, a guiding frame for urban air quality is designed, intended for further support and assistance for local authorities in the process of air pollution control. Designed with integrated activities, the air quality guidance enables them to select suitable measures to manage road transport pollution and to evaluate their effects estimating the changes in air pollution levels. Hence, the guidance can be used for thorough planning of air quality issues caused by road transport and for policy making. Contributing for urban air quality improvement the guidance is a first step towards the implementation of air pollution management in urban areas. Doi: 10.28991/cej-2021-03091651 Full Text: PDF

scholarly journals COLLABORATIVE AIR QUALITY MAPPING OF DIFFERENT METROPOLITAN CITIES OF INDIA

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 87-94
Author(s):  
R. Dubey ◽  
S. Bharadwaj ◽  
M. I. Zafar ◽  
S. Biswas
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Models ◽  
Dispersion Model ◽  
Time Of Day ◽  
Quality Data ◽  
Pollution Levels ◽  
Health Related ◽  
Monitoring And Forecasting ◽  
Central Pollution Control Board

Abstract. Environmental pollution has become extremely serious as a result of today's technological advancements all over the world. One of the most important environmental and public health risks is air pollution. The exponential growth of population, vehicular density on highways, urbanization, and other factors are rising air pollution in cities, necessitating techniques for monitoring and forecasting air quality or determining its health consequences. Various experiments are being conducted on city air quality and its distribution through the built climate. The amount of emissions in the air varies according to the time of day as depicted it is merely high in morning time between 9 to 10 am and between 5 to 6 pm in all cities. These collected data are also characterized as peak hour, average hour, and off-peak hour. It also varies geographically and during special occasions. Since computing and showcasing of air pollution levels require terrain data, air quality data from the open sources i.e. CPCB (central pollution control board, India), and air pollution prediction models. Acculumating the data of the air pollution parameter from the open sources of cities based on typically very crowded, averagely crowded, and thinly crowded areas across the city and then mapping it on ArcGIS. The data monitoring has been done for the whole year merely main emphasizes has been done on the three seasons autumn, winter, and summer (January, May, and August). Also, in winter the value of having pollutants is high due to winter inversion and in the morning also the value is higher, and in monsoon, due to precipitation, it decreases. The dispersion model help in considering the wind speed and direction, the computed data from each source location reaching out to the monitoring sensing station from the comparatively adding to the value of pollutant. With the help of questionnaires, computed out to the result that people residing or having the workplace near to the busy crossing are more promising to have the health-related issue like chocking, respiratory diseases. Men are merely more affected by this between the age of 37 to 63 years.

scholarly journals Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 251
Author(s):  
Evangelos Bagkis ◽  
Theodosios Kassandros ◽  
Marinos Karteris ◽  
Apostolos Karteris ◽  
Kostas Karatzas
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Urban Areas ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Quality Monitoring ◽  
Reference Station ◽  
Air Quality Monitoring ◽  
Pollution Levels

Air quality (AQ) in urban areas is deteriorating, thus having negative effects on people’s everyday lives. Official air quality monitoring stations provide the most reliable information, but do not always depict air pollution levels at scales reflecting human activities. They also have a high cost and therefore are limited in number. This issue can be addressed by deploying low cost AQ monitoring devices (LCAQMD), though their measurements are of far lower quality. In this paper we study the correlation of air pollution levels reported by such a device and by a reference station for particulate matter, ozone and nitrogen dioxide in Thessaloniki, Greece. On this basis, a corrective factor is modeled via seven machine learning algorithms in order to improve the quality of measurements for the LCAQMD against reference stations, thus leading to its on-field computational improvement. We show that our computational intelligence approach can improve the performance of such a device for PM10 under operational conditions.

scholarly journals Air Quality Index Prediction using Machine Learning Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 7489-7492
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Learning Algorithms ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Pollution Monitoring ◽  
Quality Data ◽  
Support Vector

— The global environment is presently facing a key issue of air pollution. The four air pollutants which are becoming a concerning intimidation to human health are respirble particulate matter, nitrogen oxide, particle matter, and sulfur dioxide. A vast amount of air quality data is collected in different monitoring stations throughout the world. The collected data can be analyzed to forecast the air quality index (AQI) of future. This paper proposes machine learning algorithms such as random forest, support vector machine, self adaptive resource allocation to predict the future AQI. Tamil Nadu Pollution Control Board (TNPCN) deployed air pollution monitoring station in five regions. Air pollutant of PM10, PM2.5, SO2 and NO2 are monitord and AQI is calculated.. The data collected from January 2019 to November 2019 by TNPCN and also AQI of previous five years were used This system attempts to predict the level of pollutant PM,SO2,NO2 in the air to detect the AQI.

scholarly journals Changes in Air Pollution-Related Behaviour Measured by Google Trends Search Volume Index in Response to Reported Air Quality in Poland

2021 ◽  
Vol 18 (21) ◽  
pp. 11709
Author(s):  
Wojciech Nazar ◽  
Katarzyna Plata-Nazar
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Information Seeking ◽  
Google Trends ◽  
Volume Index ◽  
Quality Data ◽  
Pollution Levels ◽  
Search Volume ◽  
Pm10 And Pm2.5 ◽  
Information Seeking Behaviour

Decreased air quality is connected to an increase in daily mortality rates. Thus, people’s behavioural response to sometimes elevated air pollution levels is vital. We aimed to analyse spatial and seasonal changes in air pollution-related information-seeking behaviour in response to nationwide reported air quality in Poland. 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 as well as locations of monitoring stations were collected from the Chief Inspectorate of Environmental Protection databases. Pearson Product-Moment Correlation Coefficients were used to measure the strength of spatial and seasonal relationships between reported air pollution levels and the popularity of search queries. 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. Greater interest in air quality data in Poland strongly correlates with both higher regional and higher seasonal air pollution levels. It appears that Poles are socially aware of this issue and that their intensification of the information-seeking behaviour seems to indicate a relevant ad hoc response to variable threat severity levels.

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.

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;

Sign in / Sign up

Export Citation Format

Share Document