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>

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 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 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.

An Environmental Justice Analysis of British Air Quality

2003 ◽  
Vol 35 (5) ◽  
pp. 909-929 ◽  
Author(s):  
Gordon Mitchell ◽  
Danny Dorling
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Justice ◽  
The Elderly ◽  
Local Communities ◽  
Car Ownership ◽  
National Study ◽  
Environmental Injustice ◽  
The Usa ◽  
History Of

This paper presents the results of the first national study of air quality in Britain to consider the implications of its distribution across over ten thousand local communities in terms of potential environmental injustice. We consider the recent history of the environmental justice debate in Britain, Europe, and the USA and, in the light of this, estimate how one aspect of air pollution, nitrogen dioxide (NO2) levels, affects different population groups differentially across Britain. We also estimate the extent to which people living in each community in Britain contribute towards this pollution, with the aid of information on the characteristics of the vehicles they own. We find that, although community NO x emission and ambient NO2 concentration are strongly related, the communities that have access to fewest cars tend to suffer from the highest levels of air pollution, whereas those in which car ownership is greatest enjoy the cleanest air. Pollution is most concentrated in areas where young children and their parents are more likely to live and least concentrated in areas to which the elderly tend to migrate. Those communities that are most polluted and which also emit the least pollution tend to be amongst the poorest in Britain. There is therefore evidence of environmental injustice in the distribution and production of poor air quality in Britain. However, the spatial distribution of those who produce and receive most of that pollution have to be considered simultaneously to see this injustice clearly.

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 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.

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