Mobility Patterns of Low-Income People Lead to High Exposure to Respiratory Hazards

Motivation:

• Traditional understanding of income segregation of exposure to air pollution is largely based on static coarse-grained residential patterns.
• However, these do not capture the income segregation implied by the rich life activities that happen in places that may relate to work, life and social needs.

Research Question:

• To what extent mobility patterns of income-segregated populations are associated with variant exposure of respiratory hazards?

1 Research Framework:

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People Movements

15 mins

80 mins

9 mins

Mobility Dynamics

PM2.5 Emissions

19 mins

31 mins

72 mins

Q1

Q4

Q2

Q3

7mins

Income Segregation

PM2.5 Emissions

Income Groups:

• Q1 Lowest
• Q2
• Q3
• Q4 Highest

Mobility Factors:

• Relevant sectors;
• Comparative scales.

Inequality Metrics:

• Income segregation at places;
• Time-weighted exposure;
• Dwell time disparity.

Quantifying

Understanding

Observing

1.

2.

3.

2 Results

2.1 Air pollutant (PM 2.5) emissions in the environment

Observations:

• Highly unequal distribution of PM2.5 emissions in urban area.

Figure 1. (a) PM2.5 Emission of facilities in grid map of Harris County; (b) Probability Density Function (PDF) and Complementary Cumulative Density Function (CCDF) for emission of grid cells in Harris County.

(a)

(b)

2 Results

2.2 Income Segregation at Places

• Characterizing income segregation at places:

2 Results

2.2 Income Segregation at Places

Observations:

• The income profiles of most places in Harris County are highly segregated, as of the mobility patterns of people from different income groups.

Figure 2. (a) Experienced Income segregation in grid map of Harris County; (b) Probability Mass Function (PMF) for experienced income segregation of grid cells in Harris County.

(a)

(b)

2 Results

2.3 Segregated Exposure of Air Pollution Due to Mobility

• Characterizing PM2.5 exposure of a neighborhood (CBG):

2 Results

2.3 Segregated Exposure of Air Pollution Due to Mobility

Figure 3. (a) Mean household income of Census Block Groups (CBG) in Harris County; (b) Probability density function (PDF) and complementary cumulative density function (CCDF) of the PM2.5 exposure; (c) Mobility - based exposure to PM2.5 at CBG level; (d) Exposure distribution of four income groups in Harris County. Q1 is the lowest-income group; and Q4 is the highest income group.

(a)

(b)

(c)

(d)

2 Results

2.3 Segregated Exposure of Air Pollution Due to Mobility

Observations:

• Pollutant exposures vary among neighborhoods, which follows a heavy-tailed distribution; that is, a great number of neighborhoods expose to pollutant lightly, while a few neighborhoods expose to pollutant heavily.
• By associating the income quantile and the exposure, we find that low-income people (Q1 and Q2) have a higher exposure to PM2.5 due to their mobility behaviors, while high income people expose significantly less to the pollutant.
• Spatially, people living in neighborhoods on peripheral areas of the county expose more to the pollutant due to their movements.

2 Results

2.4 Relationship between emission sectors and human activities

• Characterizing time spent at specific sectors:

2 Results

2.4 Relationship between emission sectors and human activities

Observations:

• Low-income people spend more time around facilities of PM2.5 emission sources. In particular, the low-income people work or interact more with facilities like mining, oil and gas extraction, wholesale trade and warehousing storage.

21 - Mining, Quarrying, and Oil and Gas Extraction;

42 - Wholesale Trade;

49 – Warehousing Storage and Delivery Services;

2 Results

2.5 Distances to emission sources and activity scales

• Characterizing mobility scales and distance between home and emission locations:

Q3

Q1

Q2

Q4

2 Results

2.5 Distances to emission sources and activity scales

Observations:

• The scales of movements of low-income population are greater than distance from their residential areas to emission source facilities. The large scale movements increase their possibility of exposure to emissions.

y=x

y=x

y=x

y=x

3 Concluding Remarks

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Findings:

• Visits to places are highly income segregated; and PM2.5 emissions are spatially magnitude variant in a county.
• People from a few neighborhoods account for a large proportion of pollutant exposure, due to the mobility activities of the individuals.
• Low-income people spent more time at places around the facilities of emission sources, and their scale of movements are large.
• These mobility patterns of low-income people lead to higher exposure to PM2.5 emissions.

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Limitations:

• Limited cases and samples; may not quantify environmental air quality.