Impacts of Air Quality
How does air quality impact various civic and urban factors in the city of Pittsburgh
How does air quality impact various civic and urban factors in the city of Pittsburgh
Location - Pittsburgh, PA
Instructor - Prof. Suzi Li
Instructor - Prof. Suzi Li
Link to the story map - https://storymaps.arcgis.com/stories/c6fac679ac304116ae896815037083f5
Abstract
Fresh, not toxic air is one of the basic human necessities. Air quality and pollution has been a major public health concern around the world, and the city of Pittsburgh is no exception to this. The Steel City, due to its history of industrial nature has always been a victim to air quality challenges. The quality of air we breathe is affected by various socio environmental factors, such as traffic, industrial activities, urbanization and lifestyle. In this small project, I aim to investigate the impact of some of these factors on air quality and viz a viz the impact of air quality on various urban factors. How health factors, population density, income, presence or absence of vegetation and tree cover could be associated with air quality. Additional we show how optimum number of air quality measuring sensor locations can affect the accuracy of air quality readings for the entire city by interpolating the air quality data from Environmental Protection Agency (EPA). Using spatial analysis tools and data visualization techniques, we analyze the spatial patterns and trends in air quality and these socio-environmental factors. The findings with help to shed light on the complex interactions between human activities, urban development, and air quality in Pittsburgh, and provide insights for policymakers, urban planners, and the public to make informed decisions to improve air quality.
Fresh, not toxic air is one of the basic human necessities. Air quality and pollution has been a major public health concern around the world, and the city of Pittsburgh is no exception to this. The Steel City, due to its history of industrial nature has always been a victim to air quality challenges. The quality of air we breathe is affected by various socio environmental factors, such as traffic, industrial activities, urbanization and lifestyle. In this small project, I aim to investigate the impact of some of these factors on air quality and viz a viz the impact of air quality on various urban factors. How health factors, population density, income, presence or absence of vegetation and tree cover could be associated with air quality. Additional we show how optimum number of air quality measuring sensor locations can affect the accuracy of air quality readings for the entire city by interpolating the air quality data from Environmental Protection Agency (EPA). Using spatial analysis tools and data visualization techniques, we analyze the spatial patterns and trends in air quality and these socio-environmental factors. The findings with help to shed light on the complex interactions between human activities, urban development, and air quality in Pittsburgh, and provide insights for policymakers, urban planners, and the public to make informed decisions to improve air quality.
Introduction
The air we breathe is essential to our health and well-being. However, air pollution is a major concern, especially in cities with a history of industrialization like Pittsburgh. The purpose of this project is to analyze the factors that contribute to poor air quality in Pittsburgh and their impact on the health of the population. In this project, we use a combination of data sources, including open data from the US Environmental Protection Agency (EPA), to explore the various factors that affect air quality.
Although several factors are known to contribute to poor air quality, including emissions from industry, transportation, and residential heating and cooling, the relationships between these factors and air quality are complex. While the correlations between air quality and various factors are significant, the lack of data in some areas shows non-significance. Therefore, it is important to analyze the data thoroughly to understand the relationships between air quality and these contributing factors.
Through this project, we aim to provide policymakers and the public with information to improve air quality and ultimately improve the health of the population in Pittsburgh.
The air we breathe is essential to our health and well-being. However, air pollution is a major concern, especially in cities with a history of industrialization like Pittsburgh. The purpose of this project is to analyze the factors that contribute to poor air quality in Pittsburgh and their impact on the health of the population. In this project, we use a combination of data sources, including open data from the US Environmental Protection Agency (EPA), to explore the various factors that affect air quality.
Although several factors are known to contribute to poor air quality, including emissions from industry, transportation, and residential heating and cooling, the relationships between these factors and air quality are complex. While the correlations between air quality and various factors are significant, the lack of data in some areas shows non-significance. Therefore, it is important to analyze the data thoroughly to understand the relationships between air quality and these contributing factors.
Through this project, we aim to provide policymakers and the public with information to improve air quality and ultimately improve the health of the population in Pittsburgh.
Purpose Statement
The purpose of this project is to explore the relationship between socio-environmental factors and air quality in the city of Pittsburgh for which air quality has always been a challenge.
Bad air quality has significant health impacts which I aim to examine in the project. Studying the impacts on various urban and socio environmental factors on air quality, I aim to identify the key drivers of air pollution in urban areas. Checking how air quality is affected by presence of vegetation, trees and traffic will help determine key themes to curb the issue.
Additionally, highlight how the lack of air quality sensors and the interpolation of data by the Environmental Protection Agency (EPA) can affect the accuracy of air quality readings. Using spatial analysis and data visualization techniques to analyze the spatial patterns and trends in air quality and socio-environmental factors, and to communicate our findings to a wider audience.
The story map is intended for policymakers, urban planners, public health officials, researchers, and the general public who are interested in understanding the complex interplay between human activities, urban development, and air quality in Pittsburgh. Through this story map, I hope to raise awareness about the importance of addressing socio-environmental factors to improve air quality, and to advocate for the need for more accurate and comprehensive air quality monitoring in the city.
The purpose of this project is to explore the relationship between socio-environmental factors and air quality in the city of Pittsburgh for which air quality has always been a challenge.
Bad air quality has significant health impacts which I aim to examine in the project. Studying the impacts on various urban and socio environmental factors on air quality, I aim to identify the key drivers of air pollution in urban areas. Checking how air quality is affected by presence of vegetation, trees and traffic will help determine key themes to curb the issue.
Additionally, highlight how the lack of air quality sensors and the interpolation of data by the Environmental Protection Agency (EPA) can affect the accuracy of air quality readings. Using spatial analysis and data visualization techniques to analyze the spatial patterns and trends in air quality and socio-environmental factors, and to communicate our findings to a wider audience.
The story map is intended for policymakers, urban planners, public health officials, researchers, and the general public who are interested in understanding the complex interplay between human activities, urban development, and air quality in Pittsburgh. Through this story map, I hope to raise awareness about the importance of addressing socio-environmental factors to improve air quality, and to advocate for the need for more accurate and comprehensive air quality monitoring in the city.
Work Flow Diagram
Air Quality Map of Pittsburgh
PM2.5 refers to particles that are 2.5 micrometers or less in diameter. They are considered as one of the most dangerous forms of air pollution present, due largely to their minute size. They are formed from several different materials, with a majority of them being highly hazardous to human health, as well as the environment and ecosystems. Some of these materials include metals, sulfates, soot, water vapor, and silica dust or gravel. Due to its inherently dangerous property, PM2.5 is used as a prominent measure of air pollution.
The air quality in Pittsburgh is affected by several factors, including emissions from industry, transportation, and residential heating and cooling. These emissions can contain pollutants such as particulate matter, ozone, and nitrogen oxides, which can harm human health and the environment. The map shows areas with high levels of pollutants (PM 2.5) and provides insight into the areas most affected by poor air quality.
The air quality in Pittsburgh is affected by several factors, including emissions from industry, transportation, and residential heating and cooling. These emissions can contain pollutants such as particulate matter, ozone, and nitrogen oxides, which can harm human health and the environment. The map shows areas with high levels of pollutants (PM 2.5) and provides insight into the areas most affected by poor air quality.
Factors Affecting Air Quality
Traffic counts Map
Traffic is another major contributor to poor air quality, as vehicles release pollutants such as nitrogen oxides, carbon monoxide and particulate matter which are the prime measures in understanding air quality. In Pittsburgh, high levels of traffic can be attributed to the city's geography and transportation infrastructure.
Population Density Map
Population density is another factor that can contribute to poor air quality. As more people live and work in an area, there is a greater demand for transportation and energy use, which can lead to higher levels of pollution. To understand the relationship between population density and air quality in Pittsburgh, we used Census Bureau data.
Annual Median House Hold Income
Median income can also be a factor in air quality, as individuals with lower incomes may be more likely to live in areas with poor air quality due to factors such as housing affordability and transportation access, density of houses and hence the density of traffic. To understand the relationship between median income and air quality in Pittsburgh, we used Census Bureau data.
Tree Density Map
Trees and vegetation can help improve air quality by absorbing pollutants and producing oxygen. However, urban areas often have less vegetation cover than rural areas, which can lead to poorer air quality. To understand the relationship between tree count, vegetation cover, and air quality in Pittsburgh, we used Tree count data and Landsat Imagery.
Quantitatively Analyzing the co-relation between these factors and Air Quality
Mean PM 2.5 Levels = 1.065 + 0.00018*(Tree Count) + 0.00*(Traffic Count) - 0.00*(Median Household Income) - 0.000022*(Total Population) - 0.038
Here, Air Quality (AQ) is predicted by a multiple linear regression model with five predictor variables: Tree Count, Traffic Count, Median Household Income, Total Population, and a constant term.
The coefficients for each predictor variable indicate their effect on the Air Quality. A positive coefficient for Tree Count suggests that increasing the number of trees is associated with a slight increase in Air Quality. However, the coefficient is quite small at 0.00018, so the effect is likely to
be negligible. The coefficient for Traffic Count is also positive, indicating that more traffic is associated with a slight increase in Air Quality. However, it has a coefficient of 0.00, suggesting that it has no significant effect on Air Quality.
The negative coefficient for Median Household Income suggests that higher income is associated with lower Air Quality. This is because higher-income areas tend to have fewer industrial facilities and more green spaces. The coefficient for Total Population is negative as well, suggesting that increasing the population is associated with a slight decrease in Air Quality.
Overall, this equation suggests that increasing the number of trees and reducing the population density may have a small positive effect on Air Quality, while higher median income is associated with better Air Quality. However, the coefficients for these predictor variables are small, so the effect may be negligible.
The coefficients for each predictor variable indicate their effect on the Air Quality. A positive coefficient for Tree Count suggests that increasing the number of trees is associated with a slight increase in Air Quality. However, the coefficient is quite small at 0.00018, so the effect is likely to
be negligible. The coefficient for Traffic Count is also positive, indicating that more traffic is associated with a slight increase in Air Quality. However, it has a coefficient of 0.00, suggesting that it has no significant effect on Air Quality.
The negative coefficient for Median Household Income suggests that higher income is associated with lower Air Quality. This is because higher-income areas tend to have fewer industrial facilities and more green spaces. The coefficient for Total Population is negative as well, suggesting that increasing the population is associated with a slight decrease in Air Quality.
Overall, this equation suggests that increasing the number of trees and reducing the population density may have a small positive effect on Air Quality, while higher median income is associated with better Air Quality. However, the coefficients for these predictor variables are small, so the effect may be negligible.
Health Impacts of Air Quality
Air pollution has a significant impact on the health of individuals, particularly on those who have pre-existing health conditions like asthma. Asthma is a chronic respiratory disease that affects millions of people worldwide. Exposure to poor air quality can trigger asthma attacks and make it harder for individuals to breathe.
Since, we are examining the impact of Air quality on respiratory health by correlating it with asthma rates, it is important to look at another significant factors that also affects asthma to give a more just co-relation.
Smoking: One of the leading causes of respiratory disorders, so examining smoking rates.
Traffic Count: Emissions from vehicles can affect respiratory health of individuals.
Median Income: Income levels can determine the access to healthcare and other services.
Access to Healthcare.
Since, we are examining the impact of Air quality on respiratory health by correlating it with asthma rates, it is important to look at another significant factors that also affects asthma to give a more just co-relation.
Smoking: One of the leading causes of respiratory disorders, so examining smoking rates.
Traffic Count: Emissions from vehicles can affect respiratory health of individuals.
Median Income: Income levels can determine the access to healthcare and other services.
Access to Healthcare.
Quantitatively Analyzing the health impact of Air Quality
Asthma Rates = 5.70 + 0.027*(Smoking Rate) + 0.50*(Access to Healthcare) - 0.13*(Mean PM 2.5 Levels) - 0.000002*(Median Income) - 0.00*(Traffic Count) - 0.59
The coefficient of -0.13 for Mean PM 2.5 Levels suggests that for every unit increase in Mean PM 2.5 Levels, there is a 0.13 unit decrease in Asthma Rates, assuming all other independent variables are held constant. This negative relationship suggests that higher levels of PM 2.5 air pollution are associated with lower rates of asthma, which is unexpected and should be further investigated.
The coefficient of 0.027 for Smoking Rate suggests that for every 1% increase in Smoking Rate, there is a 0.027 unit increase in Asthma Rates, assuming all other independent variables are held constant. The coefficient of 0.50 for Access to Healthcare suggests that for every unit increase in Access to Healthcare, there is a 0.50 unit increase in Asthma Rates, assuming all other independent variables are held constant. The coefficient of -0.000002 for Median Income suggests higher income is associated with lower rates of asthma. But since the value of coefficient is low the effect is negligible.
Overall, this regression model suggests that Smoking Rate, Access to Healthcare, Mean PM 2.5 Levels, and Median Income are important factors to consider when examining Asthma Rates.
The coefficient of 0.027 for Smoking Rate suggests that for every 1% increase in Smoking Rate, there is a 0.027 unit increase in Asthma Rates, assuming all other independent variables are held constant. The coefficient of 0.50 for Access to Healthcare suggests that for every unit increase in Access to Healthcare, there is a 0.50 unit increase in Asthma Rates, assuming all other independent variables are held constant. The coefficient of -0.000002 for Median Income suggests higher income is associated with lower rates of asthma. But since the value of coefficient is low the effect is negligible.
Overall, this regression model suggests that Smoking Rate, Access to Healthcare, Mean PM 2.5 Levels, and Median Income are important factors to consider when examining Asthma Rates.
Geographically Weighted Regression of Asthma Rates with Air Quality (PM 2.5 Levels) as a coefficient
GWR analysis is important as it provides a more nuanced understanding of the relationship between Asthma Rates and Mean PM 2.5 Levels by accounting for spatial variability and potentially revealing local patterns that may be masked in a general regression analysis. This would allow for the identification of areas where the relationship between Asthma Rates and Mean PM 2.5 Levels is stronger or weaker than the general regression coefficient estimated in the original regression equation.
Here it shows how the coefficient of -0.13 for Mean PM 2.5 Values is going to affect areas like Downtown, Lawrenceville, Shady side more than areas like Squirrel Hill, Wilkinsburg, etc.
Here it shows how the coefficient of -0.13 for Mean PM 2.5 Values is going to affect areas like Downtown, Lawrenceville, Shady side more than areas like Squirrel Hill, Wilkinsburg, etc.
Discrepancies in Open Data on Air Quality
The interpolation map of AQS data of PM 2.5 levels from EPA shows the estimated levels of fine particulate matter (PM 2.5) in the air across Pittsburgh based on a limited number of monitoring stations. While the map provides a useful representation of the spatial distribution of PM 2.5 levels in the city, it is important to note that there are only a small number of sensors being used to collect data.
Due to the limited number of sensors, the interpolation map may not accurately reflect the true PM 2.5 levels in certain areas of the city. There may be localized areas with high levels of pollution that are not detected by the current sensor network.
This highlights the importance of having a comprehensive and well-distributed sensor network in order to obtain accurate and representative data on air quality. Without a sufficient number of sensors, the data may not provide a complete picture of the true levels of air pollution in the city. It is therefore important to continue to invest in the development and deployment of air quality sensors to improve the accuracy and reliability of air quality data for the public and policymakers.
Due to the limited number of sensors, the interpolation map may not accurately reflect the true PM 2.5 levels in certain areas of the city. There may be localized areas with high levels of pollution that are not detected by the current sensor network.
This highlights the importance of having a comprehensive and well-distributed sensor network in order to obtain accurate and representative data on air quality. Without a sufficient number of sensors, the data may not provide a complete picture of the true levels of air pollution in the city. It is therefore important to continue to invest in the development and deployment of air quality sensors to improve the accuracy and reliability of air quality data for the public and policymakers.
Conclusion
The air quality in Pittsburgh is affected by several factors, including population density, tree count and vegetation cover, traffic, and median income. The regression analyses conducted have provided insights into the relationships between these factors and air quality.
The regression analyses showed that there were statistically significant correlations between air quality and some of these factors, such as tree count and median income. However, though some correlations are principally significant, like Impact of Traffic, Tree cover, vegetation on air quality the analysis shows them as insignificant due to lack of sufficient data.
In conclusion, it is clear that improving air quality in Pittsburgh requires a multi-faceted approach, involving policies and actions targeting various factors. Policymakers and the public must work together to reduce emissions from industry, transportation, and residential heating and cooling. Efforts should also be made to increase access to healthcare and improve overall living conditions, especially in areas with high asthma rates.
The air quality in Pittsburgh is affected by several factors, including population density, tree count and vegetation cover, traffic, and median income. The regression analyses conducted have provided insights into the relationships between these factors and air quality.
The regression analyses showed that there were statistically significant correlations between air quality and some of these factors, such as tree count and median income. However, though some correlations are principally significant, like Impact of Traffic, Tree cover, vegetation on air quality the analysis shows them as insignificant due to lack of sufficient data.
In conclusion, it is clear that improving air quality in Pittsburgh requires a multi-faceted approach, involving policies and actions targeting various factors. Policymakers and the public must work together to reduce emissions from industry, transportation, and residential heating and cooling. Efforts should also be made to increase access to healthcare and improve overall living conditions, especially in areas with high asthma rates.
Future Scope
In future work, it would be useful to incorporate more data sources and conduct more detailed analyses to gain further insights into the factors affecting air quality in Pittsburgh. However, the findings presented in this story map provide a strong foundation for further research and action.
Overall, the story map highlights the need for more comprehensive and accurate data collection on air quality in Pittsburgh in order to make informed decisions for policy and public health initiatives. It is imperative to prioritize efforts to improve air quality in the city, especially in areas with historically poor air quality, to ensure the health and well-being of its residents.
