California’s COVID-19 economic shutdown reveals the fingerprint of systemic environmental racism

Racial and ethnic minorities in the United States often experience higher-than-average exposures to air pollution. However, the relative contribution of embedded institutional biases to these disparities can be difficult to disentangle from physical environmental drivers, socioeconomic status, and cultural or other factors that are correlated with exposures under status quo conditions. Over the spring and summer of 2020, rapid and sweeping COVID-19 shelter-in-place orders around the world created large perturbations to local and regional economic activity that resulted in observable changes in air pollution concentrations, compositions, and distributions. Here, we use the pandemic-related emergency order and subsequent economic slowdown to causally estimate pollution exposure disparities in California. Using both public ground-based sensor data and a citizen-science network of monitors for respirable particulate matter (PM2.5), along with satellite records of nitrogen dioxide (NO2), we show that the initial sheltering-in-place period produced disproportionate air pollution reduction benefits for Asian, Hispanic/Latinx, and low- income communities. By linking these pollution data with weather, geographic, socioeconomic, and mobility data in difference-in-differences models, we demonstrate that these disparate pollution reductions cannot be explained by environmental conditions, geography, income, or local economic activity and are instead driven by non-local activity. This study thus provides causally-identified evidence of systemic racial and ethnic bias in pollution control under business-as-usual conditions.

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Effect of COVID-19 response policies on walking behavior in US cities

10.1101/2020.12.07.20245282 ◽

2020 ◽

Author(s):

Ruth F. Hunter ◽

Leandro Garcia ◽

Thiago Herick de Sa ◽

Belen Zapata-Diomedi ◽

Christopher Millet ◽

...

Keyword(s):

Low Income ◽

Public Transportation ◽

The United States ◽

Potential Health ◽

Daily Lives ◽

Walking Behavior ◽

Social Patterns ◽

Mobility Data ◽

Response Measures ◽

Spatio Temporal

ABSTRACTThe COVID-19 pandemic has caused mass disruption to our daily lives. Mobility restrictions implemented to reduce the spread of COVID-19 have impacted walking behavior, but the magnitude and spatio-temporal aspects of these changes have yet to be explored. Walking is the most common form of physical activity and non-motorized transport, and so has an important role in our health and economy. Understanding how COVID-19 response measures have affected walking behavior of populations and distinct subgroups is paramount to help devise strategies to prevent the potential health and societal impacts of declining walking levels. In this study, we integrated mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 metropolitan areas in the United States (US). The data covers the period from mid-February 2020 (pre-lockdown) to late June 2020 (easing of lockdown restrictions). We detected when users were walking, measured distance walked and time of the walk, and classified each walk as recreational or utilitarian. Our results revealed dramatic declines in walking, especially utilitarian walking, while recreational walking has recovered and even surpassed the levels before the pandemic. However, our findings demonstrated important social patterns, widening existing inequalities in walking behavior across socio-demographic groups. COVID-19 response measures had a larger impact on walking behavior for those from low-income areas, of low education, and high use of public transportation. Provision of equal opportunities to support walking could be key to opening up our society and the economy.

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Public mobility data enables COVID-19 forecasting and management at local and global scales

Scientific Reports ◽

10.1038/s41598-021-92892-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Cornelia Ilin ◽

Sébastien Annan-Phan ◽

Xiao Hui Tai ◽

Shikhar Mehra ◽

Solomon Hsiang ◽

...

Keyword(s):

Economic Activity ◽

Statistical Models ◽

Human Mobility ◽

The United States ◽

Disease Dynamics ◽

National Data ◽

Machine Learning Methods ◽

Mobility Data ◽

The World ◽

Pharmaceutical Interventions

AbstractPolicymakers everywhere are working to determine the set of restrictions that will effectively contain the spread of COVID-19 without excessively stifling economic activity. We show that publicly available data on human mobility—collected by Google, Facebook, and other providers—can be used to evaluate the effectiveness of non-pharmaceutical interventions (NPIs) and forecast the spread of COVID-19. This approach uses simple and transparent statistical models to estimate the effect of NPIs on mobility, and basic machine learning methods to generate 10-day forecasts of COVID-19 cases. An advantage of the approach is that it involves minimal assumptions about disease dynamics, and requires only publicly-available data. We evaluate this approach using local and regional data from China, France, Italy, South Korea, and the United States, as well as national data from 80 countries around the world. We find that NPIs are associated with significant reductions in human mobility, and that changes in mobility can be used to forecast COVID-19 infections.

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Validating and Refining EPA’s Traffic Exposure Screening Measure

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16010003 ◽

2018 ◽

Vol 16 (1) ◽

pp. 3 ◽

Cited By ~ 2

Author(s):

Dana Rowangould ◽

Greg Rowangould ◽

Elena Craft ◽

Deb Niemeier

Keyword(s):

Air Pollution ◽

Low Income ◽

Local Level ◽

Critical Role ◽

The United States ◽

Air Pollutant ◽

Screening Tools ◽

Dispersion Modeling ◽

Pollution Indicator ◽

Pollution Exposure

Exposure to high air pollutant concentrations results in significant health risks. Many communities of color and low-income communities face disproportionately higher levels of air pollution exposure. Environmental justice (EJ) screening tools play a critical role in focusing early attention on areas with a high likelihood of disparate health impacts. In 2015, the United States Environmental Protection Agency (US EPA) released EJScreen, a screening tool with indicators of a range of pollution burdens across the US. However, little is known about the accuracy of the screening estimates of pollution exposure. This study compares EJScreen’s traffic proximity air quality metric to dispersion modeling results. Using the area around the Houston Ship Channel, we conduct fine-grained air pollution dispersion modeling to evaluate how closely EJScreen’s indicator approximates estimated roadway air pollution concentrations. We find low correlation between modeled concentrations and the EJScreen roadway air pollution indicator. We extend EJScreen’s roadway air pollution screening method in three ways: (1) using a smaller unit of analysis, (2) accounting for the length of each road segment, and (3) accounting for wind direction. Using the Houston region, we use two of the methods and show that the proposed extensions provide a more accurate transportation air pollution screening assessment at the regional and local level.

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Expected Health Effects of Reduced Air Pollution from COVID-19 Social Distancing

Atmosphere ◽

10.3390/atmos12080951 ◽

2021 ◽

Vol 12 (8) ◽

pp. 951

Author(s):

Steve Cicala ◽

Stephen P. Holland ◽

Erin T. Mansur ◽

Nicholas Z. Muller ◽

Andrew J. Yates

Keyword(s):

Air Pollution ◽

Health Effects ◽

Electricity Consumption ◽

The United States ◽

Time Frame ◽

Social Distancing ◽

Mobility Data ◽

Premature Deaths ◽

The Impact ◽

At Home

The COVID-19 pandemic resulted in stay-at-home policies and other social distancing behaviors in the United States in spring of 2020. This paper examines the impact that these actions had on emissions and expected health effects through reduced personal vehicle travel and electricity consumption. Using daily cell phone mobility data for each U.S. county, we find that vehicle travel dropped about 40% by mid-April across the nation. States that imposed stay-at-home policies before March 28 decreased travel slightly more than other states, but travel in all states decreased significantly. Using data on hourly electricity consumption by electricity region (e.g., balancing authority), we find that electricity consumption fell about 6% on average by mid-April with substantial heterogeneity. Given these decreases in travel and electricity use, we estimate the county-level expected improvements in air quality, and, therefore, expected declines in mortality. Overall, we estimate that, for a month of social distancing, the expected premature deaths due to air pollution from personal vehicle travel and electricity consumption declined by approximately 360 deaths, or about 25% of the baseline 1500 deaths. In addition, we estimate that CO2 emissions from these sources fell by 46 million metric tons (a reduction of approximately 19%) over the same time frame.

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Effect of COVID-19 response policies on walking behavior in US cities

Nature Communications ◽

10.1038/s41467-021-23937-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ruth F. Hunter ◽

Leandro Garcia ◽

Thiago Herick de Sa ◽

Belen Zapata-Diomedi ◽

Christopher Millett ◽

...

Keyword(s):

Low Income ◽

Public Transportation ◽

The United States ◽

Walking Distance ◽

Daily Lives ◽

Walking Behavior ◽

Social Patterns ◽

Mobility Data ◽

Level Data ◽

Recreational Walking

AbstractThe COVID-19 pandemic is causing mass disruption to our daily lives. We integrate mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 metropolitan areas in the United States. The data covers the period from mid-February 2020 (pre-lockdown) to late June 2020 (easing of lockdown restrictions). We detect when users were walking, distance walked and time of the walk, and classify each walk as recreational or utilitarian. Our results reveal dramatic declines in walking, particularly utilitarian walking, while recreational walking has recovered and even surpassed pre-pandemic levels. Our findings also demonstrate important social patterns, widening existing inequalities in walking behavior. COVID-19 response measures have a larger impact on walking behavior for those from low-income areas and high use of public transportation. Provision of equal opportunities to support walking is key to opening up our society and economy.

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Demonstrating Nutrient Cost Gradients: A Brooklyn Case Study

International Journal for Vitamin and Nutrition Research ◽

10.1024/0300-9831/a000210 ◽

2014 ◽

Vol 84 (5-6) ◽

pp. 244-251 ◽

Cited By ~ 2

Author(s):

Robert J. Karp ◽

Gary Wong ◽

Marguerite Orsi

Keyword(s):

Low Income ◽

Energy Content ◽

Food Selection ◽

The United States ◽

United States Department ◽

Micronutrient Deficiencies ◽

Caloric Density ◽

Low Income Families ◽

The Cost ◽

The Impact

Abstract. Introduction: Foods dense in micronutrients are generally more expensive than those with higher energy content. These cost-differentials may put low-income families at risk of diminished micronutrient intake. Objectives: We sought to determine differences in the cost for iron, folate, and choline in foods available for purchase in a low-income community when assessed for energy content and serving size. Methods: Sixty-nine foods listed in the menu plans provided by the United States Department of Agriculture (USDA) for low-income families were considered, in 10 domains. The cost and micronutrient content for-energy and per-serving of these foods were determined for the three micronutrients. Exact Kruskal-Wallis tests were used for comparisons of energy costs; Spearman rho tests for comparisons of micronutrient content. Ninety families were interviewed in a pediatric clinic to assess the impact of food cost on food selection. Results: Significant differences between domains were shown for energy density with both cost-for-energy (p < 0.001) and cost-per-serving (p < 0.05) comparisons. All three micronutrient contents were significantly correlated with cost-for-energy (p < 0.01). Both iron and choline contents were significantly correlated with cost-per-serving (p < 0.05). Of the 90 families, 38 (42 %) worried about food costs; 40 (44 %) had chosen foods of high caloric density in response to that fear, and 29 of 40 families experiencing both worry and making such food selection. Conclusion: Adjustments to USDA meal plans using cost-for-energy analysis showed differentials for both energy and micronutrients. These differentials were reduced using cost-per-serving analysis, but were not eliminated. A substantial proportion of low-income families are vulnerable to micronutrient deficiencies.

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